Plant Breeding Reviews, Volume 22

PLANT BREEDING REVIEWS Volume 22

edited by

Jules Janick Purdue University

John Wiley & Sons, Inc.

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PLANT BREEDING REVIEWS Volume 22

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Plant Breeding Reviews is sponsored by: American Society for Horticultural Science Crop Science Society of America Society of American Foresters National Council of Commercial Plant Breeders

Editorial Board, Volume 22 G. R. Askew I. L. Goldman M. Gilbert

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PLANT BREEDING REVIEWS Volume 22

edited by

Jules Janick Purdue University

John Wiley & Sons, Inc.

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This book is printed on acid-free paper. Copyright © 2003 by John Wiley & Sons. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey Published simultaneously in Canada No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, e-mail: [email protected]. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002.

Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. Library of Congress Cataloging-in-Publication Data: ISBN 0-471-21541-4 ISSN 0730-2207 Printed in the United States of America 10

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Contents

List of Contributors 1. Dedication: Denton E. Alexander; Teacher, Maize Geneticist, and Breeder

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Robert J. Lambert

2. Estimating and Interpreting Heritability for Plant Breeding: An Update

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James B. Holland, Wyman E. Nyquist, and Cuauhtemoc T. Cervantes-Martínez I. II. III. IV. V.

The Meaning of Heritability Response to Selection Covariances of Relatives Variance Among Selection Units Estimating Heritability as a Function of Variance Components VI. Estimating Heritability from Parent-Offspring Regression VII. Estimating Realized Heritability VIII. Examples of Heritability Estimates Appendices Literature Cited

3. Advanced Statistical Methods for Estimating Genetic Variances in Plants

10 12 21 25 29 65 69 70 102 108

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Shizhong Xu I. Introduction II. Genetic Model III. Least Squares Estimation

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CONTENTS

IV. Maximum Likelihood Analysis V. Bayesian Analysis VI. Discussion and Conclusions Literature Cited

4. Oil Palm Genetic Improvement

138 152 157 161

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A. C. Soh, G. Wong, T. Y. Hor, C. C. Tan, and P. S. Chew I. II. III. IV. V.

Introduction Germplasm Resources Improvement Objectives Breeding Techniques Future Prospects Literature Cited

5. Breeding Wheat for Resistance to Insects

166 169 174 180 205 206

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William A. Berzonsky, Hongjian Ding, Scott D. Haley, Marion O. Harris, Robert J. Lamb, R. I. H. McKenzie, Herbert W. Ohm, Fred L. Patterson, Frank Peairs, David R. Porter, Roger H. Ratcliffe, and Thomas G. Shanower I. II. III. IV. V. VI.

Introduction Wheat Stem Sawfly Wheat Midge Hessian Fly Russian Wheat Aphid Greenbug Literature Cited

6. Peanut Breeding and Genetic Resources

222 225 234 247 260 270 278

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C. Corley Holbrook and H. Thomas Stalker I. II. III. IV. V. VI. VII.

Introduction Evolution and Taxonomy Reproductive Development Cytogenetics and Genomes Genetic Resources Breeding Peanut Summary Literature Cited

298 300 305 307 309 316 338 340

CONTENTS

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7. History and Breeding of Table Beet in the United States

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I. L. Goldman and J. P. Navazio I. II. III. IV. V. VI.

Introduction Crop Origins Horticulture of Table Beet Genetics and Breeding Breeding Methods Future Directions Literature Cited

358 359 361 367 376 384 386

8. Yeast as a Molecular Genetic System for Improvement of Plant Salt Tolerance

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Tracie K. Matsumoto, Ray A. Bressan, P. M. Hasegawa, and José M. Pardo I. II. III. IV.

Introduction Yeast Complementation Orthologous Plant and Yeast Genes Similarity of Cellular Salt Tolerance in Plants and Yeast Literature Cited

390 394 398 415 416

Subject Index

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Cumulative Subject Index

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Cumulative Contributor Index

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Contributors

William A. Berzonsky, Department of Plant Sciences, North Dakota State University, Fargo, ND 58105, [email protected] Ray A. Bressan, Department of Horticulture and Landscape Architecture, Center for Plant Environmental Stress Physiology, Purdue University, West Lafayette, IN 47907, [email protected] Cuauhtemoc T. Cervantes-Martínez, Universidad Autónoma Chapingo, Carretera México-Texcoco, km 38.5, Chapingo, 56230, Mexico, c_cervant@tauras1 .chapingo.mx P. S. Chew, Applied Agricultural Research Sendirian Berhad, 47000 Sungei Buloh, Selangor, Malaysia, [email protected] Hongjian Ding, Department of Plant, Soil and Entomological Sciences, University of Idaho, Moscow, ID 83844, [email protected] I. L. Goldman, Department of Horticulture, 1575 Linden Drive, Madison, WI 53706, [email protected] Scott D. Haley, Soil and Crop Sciences Department, Colorado State University, Fort Collins, CO 80523, [email protected] Marion O. Harris, Department of Entomology, North Dakota State University, Fargo, ND 58105, [email protected] P. M. Hasegawa, Department of Horticulture and Landscape Architecture, Center for Plant Environmental Stress Physiology, Purdue University, West Lafayette, IN 47907, [email protected] C. Corley Holbrook, U.S. Department of Agriculture–ARS, P.O. Box 748, Tifton, GA 31793, [email protected] James B. Holland, U.S. Department of Agriculture–Agriculture Research Service, Plant Science Research Unit, Department of Crop Science, Box 7620, North Carolina State University, Raleigh, NC 27695-7620, [email protected] T. Y. Hor, Applied Agricultural Research Sendirian Berhad, 47000 Sungei Buloh, Selangor, Malaysia, [email protected] Robert J. Lamb, Cereal Research Centre, Agriculture and Agri-Food Canada, Winnipeg, Manitoba R3T 2M9, Canada, [email protected] Robert J. Lambert, Department of Crop Sciences, University of Illinois Urbana–Champaign, Urbana, IL 61801, [email protected] Tracie K. Matsumoto, Department of Horticulture and Landscape Architecture, Center for Plant Environmental Stress Physiology, Purdue University, West Lafayette, IN 47907, [email protected] R. I. H. McKenzie, Cereal Research Centre, Agriculture and Agri-Food Canada, Winnipeg, Manitoba R3T 2M9, Canada J. P. Navazio, SEEDS, 608 West Benton Street, Iowa City, IA 52246, jpnavazio @earthlink.net Wyman E. Nyquist, Department of Agronomy, Purdue University, West Lafayette, IN 47907-1150, [email protected]

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Herbert W. Ohm, Department of Agronomy, Purdue University, West Lafayette, IN 47907, [email protected] José M. Pardo, Instituto de Recursos Naturales y Agrobiologia, Consejo Superior de Investigaciones Cientificas, P.O. Box 1052, Sevilla 41080, Spain Fred L. Patterson, Department of Agronomy, Purdue University, West Lafayette, IN 47907 Frank B. Peairs, Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO 80523, [email protected] David R. Porter, U.S. Department of Agriculture–ARS, Plant Science and Water Conservation Research Laboratory, Stillwater, OK 74075, dporter@pswcrl .ars.usda.gov Roger H. Ratcliffe, U.S. Department of Agriculture–ARS, Department of Entomology, Purdue University, West Lafayette, IN 47907, roger_ratcliffe@entm .purdue.edu Thomas G. Shanower, U.S. Department of Agriculture–ARS, Northern Plains Agricultural Research Laboratory, Sidney, MT 59270, tshanowe@sidney .ars.usda.gov A. C. Soh, Applied Agricultural Research Sendirian Berhad, 47000 Sungei Buloh, Selangor, Malaysia, [email protected] H. Thomas Stalker, Department of Crop Sciences, North Carolina State University, P.O. Box 7620, Raleigh, NC 27695, [email protected] C. C. Tan, Applied Agricultural Research Sendirian Berhad, 47000 Sungei Buloh, Selangor, Malaysia, [email protected] G. Wong, Applied Agricultural Research Sendirian Berhad, 47000 Sungei Buloh, Selangor, Malaysia, [email protected] Shizhong Xu, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, [email protected]

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Denton E. Alexander

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1 Dedication: Denton E. Alexander Teacher, Maize Geneticist, and Breeder Robert J. Lambert Department of Crop Sciences University of Illinois Urbana–Champaign Urbana, Illinois 61801 Denton E. Alexander (Alex) was born on a farm near Potomac, Illinois, on December 18, 1917. He was farm-reared and educated in rural elementary and secondary schools in the area. From 1935 to 1937, Alex attended Illinois State Normal University in Normal, Illinois, receiving an elementary school teacher certificate. He taught in a rural school, near his home, for two years. He attended the University of Illinois Urbana– Champaign from 1939 to 1941 receiving the B.S. degree in Agriculture. During the early months of World War II, Alex was an aircraft engine instructor in the U.S. Army Air Corps (1941–1943). From 1943 to 1947, he was involved with mass spectrographic separation of uranium isotopes at the Manhattan Project, Oak Ridge, Tennessee. He returned to Illinois in 1947 and entered graduate school at the University of Illinois, Urbana–Champaign and received the Ph.D. in 1950. In 1950 to 1951, he served as a postdoctoral Fellow with Marcus M. Rhoades in the Botany Department. He joined the Department of Agronomy faculty at the University of Illinois Urbana–Champaign, as an instructor in 1951 and attained the rank of Professor of Plant Genetics and Breeding in 1963. Alex’s early tenure in the Department of Agronomy was devoted to organizing and teaching the first undergraduate introductory course in genetics in the College of Agriculture. This course was cross-listed with Animal Science, Dairy Science, Horticulture and for a time, Veterinary

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Medicine. He taught more than 5000 undergraduate students from 1951 to 1985. He was particularly insistent that really superior students in his classes obtain advanced degrees in Genetics. Several dozen of these students have had successful commercial and academic careers. The best description of Alex’s teaching abilities comes from one of his peers who said, “Alexander is one of those fortunate individuals who are articulate, have an infectious enthusiasm, and establishes an excellent rapport with students. He justly merits his reputation as an inspiring teacher.” Alex received several awards for his excellence in teaching. In 1964, Alex established the Illinois Corn Breeders School, an outreach program for commercial U.S. Corn Breeders. The objective of the school is to update corn breeders in the latest techniques in corn breeding, biotechnology, and related disciplines. From 1964 to 2001 attendance has varied from about 80 to 150. Alex continues to serve on the advisory committee of the school. The 37th annual session was held in 2001.

CYTOGENETIC RESEARCH Alex’s early research in the Department of Agronomy was strongly influenced by his postdoctoral research with Marcus M. Rhoades. That single year’s work resulted in detailed studies of the frequency of spontaneous haploidy and of the meiotic behavior of chromosomes during microsporogenesis of maize. Barbara McClintock had earlier reported that bridgelike figures occur during haploid microsporogenesis. Alex found many of these “aberrants” in the hundreds of haploid plants he isolated. He and his students found that spontaneous exchanges occur between nonhomologous chromosomes and proposed these facts as evidence that modern maize is a derived alloploid. More recent studies by others support this theory. Immersed in cytogenetic studies, Alex became interested in Rhoades’ elongate (el) gene. Rhoades had found this recessive allele, when homozygous, affected the second meiotic division in such a way that microspores received the unreduced chromosome number (20). This immediately suggested a method to inexpensively “tetraplolidize” maize on a large scale. Alex crossed the el allele into a large array of diploid maize genotypes that included both diploid inbreds and synthetics. Crosses were pollinated by Randolph’s 4n tester to obtain putative tetraploids. These tetraploid kernels were used to form 4n synthetics and 4n inbreds were developed by the backcross method. Six 4n synthetics were developed: R4nA [(2n WF9 × el) WF9]; R4nB (25, 2n inbreds × el); R4nC (11, 2n inbreds × el); R4nD (60, 2n line × el plus crosses of 4n ker-

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nels from each lines × Syn B plus 360 kernels from Syn C); R4n O.P. (56, 2n open pollinated cultivars × el); R4nC-D (mixture of equal quantities of seed from R4n Syn C and R4n Syn D and random mated). Mass selection for increased seed-set was carried out in 4n Syn C, 4n Syn D, 4n Syn C-1, and 4n Syn O.P. and showed an increased seed set from about 50 percent to 60 percent range in five selection cycles. Additional selection for ten cycles resulted in seed-set in the 90 to 95 percent range for these synthetics. Alex’s research on tetraploid maize expanded our knowledge of tetraploid qualitative genetics. The materials served as a basis for the quantitative genetic research by Dr. John Dudley, also of the University of Illinois.

HIGH OIL MAIZE Alex’s most consequential research contribution has been to the improvement of nutritional properties of maize. The Department of Agronomy at the University of Illinois has had a tradition of breeding for enhanced levels of protein and oil in corn, dating back into the nineteenth century. In the 1920s through the 1940s, substantial effort was devoted to breeding for higher levels of both protein and oil. These efforts largely failed, not because higher levels of oil and protein were not reached in commercial hybrid candidates, but because of their inferior performance. The “new” idea that corn grain could be improved nutritionally was intriguing. Failure to produce commercially useful high-oil inbreds, stemmed back to an inferior parent population (i.e., the Illinois High Oil strain). Alex concluded that a wide based population should be recurrently selected for oil content that would serve as source of commercial inbreds. So in 1956, he began selection for increased oil in a 56-cultivar open-pollinated population. The program consisted of cycles of selfing, analysis, and recombination of the highest oil selections. This process was carried out for six cycles with budgets of no more than $500 per year! Extension activities can be a useful effort for researchers. Alex spoke to a group of farmers and businessmen about his high oil research, and complained bitterly about the cumbersome and expensive analytical scheme. Why not analyze single kernels nondestructively and get a single cycle of selection per year instead of the normal two years? A member of the audience, Dr. Stan Watson, came to Alex after the session and suggested that an instrument (wide-line nuclear magnetic resonanceNMR) that Corn Products Company was using at its Argo, Illinois, plant to analyze for water in starch might, just might, do the job. There was a concern that single kernel analysis was beyond the instrument’s ability.

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Alex provided samples and the exploratory run on large samples turned out to be practical. Two months later, Stan and Tom Conway reported single kernels could be accurately analyzed in a minute or two! That time was soon reduced to 30 seconds and ultimately to 2 seconds. Selection for oil immediately became an inexpensive, effective scheme with the development and application of NMR. It permitted inexpensive, precise, non-destructive analysis of oil levels in bulk samples and individual kernels. Evaluation of selection progress over 28 cycles of single kernel selection showed oil concentration increased from 4.5 percent to 22 percent. This same level of increase took about 90 generations in the classical Illinois selection experiment which uses bulk samples. Alex also developed several other high-oil maize synthetics that have received commercial interest. He used these materials to develop high-oil singlecross hybrids to promote commercialization of high-oil corn. In the early 1970s, Alex expanded into research on fatty acids and later on Vitamin E. He and Charles Poneleit demonstrated single gene control of oleic to linoleic transformation in 1965. He was able to isolate the recessive ln1 gene that controls conversion of oleic to linoleic fatty acids in maize. He and several of his students evaluated the genetic variation for alpha and gamma tocopherol in a maize synthetic and isolated two strains contrasting in high alpha and high in gamma tocopherol. Although the University of Illinois has a long history of research on high oil maize, most of the research never was used in the marketplace until about 1990. Alex’s enthusiasm, perseverance, and intellect convinced administrators of the value of high-oil corn in the marketplace. As a result of several discussions with administrators and several commercial companies the university signed the first joint research and market development agreement on high-oil maize in 1990 with PfisterDuPont. This agreement had two components, one involved research on high-oil corn, and the other for Pfister-DuPont to develop a marketing system for the product. Approximately 1.25 million acres of high-oil maize was produced in 2000. The success of this program is due in large part to Alex’s application of “sound science,” enthusiasm, and a conviction that high-oil corn had commercial value. This is a unique trait for a plant breeder.

HONORS Alex has received several honors during his career of teaching and research. Among them are: Phi Kappa Phi, Crop Science Research Award, Fellow American Society of Agronomy and Crops Science Society of America (1970), the first Paul A. Funk award in research from the

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College of Agriculture, University of Illinois (1971), Foreign member, Soviet Academy of Agriculture Sciences (1970), Distinguished Service Award for contributions to maize program, La Molina, Peru (1978), and Honorary member Association of Genetic Societies of Yugoslavia (1981). Alex officially retired from the university in 1989 and has remained a Visiting Professor in corn breeding. He comes to the office every day and still has the same zeal for high-oil corn that he had in 1956. Alex’s long career in corn breeding, genetics, and teaching stimulated his enthusiasm to develop new and challenging ideas that had the potential to help mankind, but also to add to our knowledge of the science of plant breeding. His intellect stimulated new ideas to his colleagues, especially the undergraduate and graduate students he influenced to obtain advanced degrees. Some people are born to lead, and Alex has definitely been a leader in many agricultural endeavors. During one’s lifetime, most scientists do not have an opportunity to be associated with a person of intellect, enthusiasm, compassion, excellent work ethic, and an all-around good fellow. People who have had the opportunity to be associated with Alex are grateful for his effect on their lives. Some maize breeders are “out-front” in terms of their research programs; Alex’s program over the years has been in this category. Alex is an allaround good and delightful fellow. Alex and Betty, after 60 years of marriage, continue to live in Urbana and, as always, enjoy visits from his former graduate students.

PUBLICATIONS Alexander, D. E. 1957. The genetic induction of autotetraploidy: a proposal for its use in corn breeding. Agron. J. 49:1, 40–43. Alexander, D. E. 1958. Report to the government of the Federal Peoples Republic of Yugoslavia on hybrid maize breeding and seed production. FAO of the United Nations Rep. 775:1–6. Alexander, D. E. 1959. Metod Selekcije za Dobijanje ‘Restorer’ Linja. Hibridni Kukuruz Jugoslavije. Godina II (Broj 5):35–37. Alexander, D. E. 1959. Relationship of “T” type sterility and yield in maize (in Serbo, Croatian, and English). Hibridni Kukuruz Jugoslavije 2:51–54. Alexander, D. E. 1960. Razmatranja Selekcije Kukuruza Na Sadrzaj Ulja (Though on Breeding High Oil Corn). Hibridni Kukuruz Jugoslavije. Godina III (Broj 3), 23:26–33. Alexander, D. E. 1960. Performance of genetically induced corn tetraploids. 15th Annual Hybrid Corn Industry Res. Conf. 15:68–78. Am. Seed Trade Assoc., Washington, DC. Alexander, D. E. 1962. Corn as an oil crop. 17th Annual Hybrid Corn Industry Res. Conf. 17:85–91. Am. Seed Trade Assoc., Washington, DC. Alexander, D. E. 1962. Effect of population density on corn yields. Proc. Illinois Fertilizer Conference. 19–20. Alexander, D. E. 1963. The “Lysenko Method” of increasing oil content of the sunflower. Crop Sci. 3:279–280.

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Alexander, D. E. 1963. Spontaneous triploidy and tetraploidy in maize. J. Hered. 54:3. Alexander, D. E. 1964. NMR: a new corn breeding tool. Illinois Res. 6:4–5. Alexander, D. E. 1964. Spontaneous reciprocal translocation during megasporogenesis of maize haploids. Nature 201:737–738. Alexander, D. E. 1964. Illinois and the beginnings of hybrid corn. Illinois Research 6:1–16. Alexander, D. E. 1967. Problems associated with breeding opaque-2 corns, and some proposed solutions. Proc. High Lysine Corn Conf., Purdue Univ. p.143–147. Alexander, D. E. 1970. The modification of protein quality in maize breeding. Acta Agronomica Academiae Sciedntiraum Hungaricae 19:435–445. Alexander, D. E. 1977. High oil corn: status of breeding and utilization. 13th Annual Illinois Corn Breeders School, Univ. of Illinois, Urbana–Champaign, IL. Alexander, D. E. 1982. Genetic engineering in plants. Proc. Univ. of Illinois Pork Industry Conf. p. 86–90. Alexander, D. E. 1982. The use of wide-line NMR in breeding high-oil corn (in Chinese). Agr. Technol. Abroad, Peking, China. Alexander, D. E. 1988. High Oil Corn. Proc. of Annual Corn and Sorghum Indus. Res. Conf. American Seed Trade Assoc., Washington, DC. Alexander, D. E., and J. B. Beckett. 1962. Search for a conversion-type mutator system at the Rf1 locus in maize. Crop Sci. 2:139–140. Alexander, D. E., and J. A. Cavanah. 1963. Survival of tetraploid maize in mixed 2n-4m plantings. Crop Sci. 3:329–331. Alexander, D. E., and R. D. Seif. 1963. Relation of kernel oil content to some agronomic traits in maize. Crop Sci. 3:354–355. Alexander, D. E., and R. G. Creech. 1977. Breeding corn for industrial and nutritional quality in maize. In: G. F. Sprague (ed.), Corn and corn improvement Am. Soc. Agron., Inc., Madison, WI. Alexander, D. E., and J. Spencer. 1982. Registration of South African photoperiod insensitive maize composites I, II, III. Crop Sci. 22:158. Alexander, D. E., L. Sivela, F. I. Collins, and R. C. Rodgers. 1967. Analysis of oil content of maize by wide-line NMR. J. Am. Oil Chem. Soc. 44:555–558. Brown, C. M., D. E. Alexander, and S. G. Carmer. 1966. Variation in oil content and its relation to other characters in oats (Avena sativa L.). Crop Sci. 6:190–191. de la Roche, I. A., D. E. Alexander, and E. J. Weber. 1971. Inheritance of linoleic and oleic acids in Zea mays L. Crop Sci. 11:856–859. de la Roche, I., D. E. Alexander, and E. Weber. 1971. The selective utilization of diglyceride species into maize triglycerides. Lipids 6:537–540. Dudley, J. W., and D. E. Alexander. 1969. Performance of advanced generations of autotetraploid maize (Zea mays L.) synthetics. 9:613–615. Dudley, J. W., R. J. Lambert, and D. E. Alexander. 1974. Seventy generations of selection for oil and protein concentration in the maize kernel. In: J. W. Dudley (ed.), Seventy generations of selection for oil and protein in maize. ASA, CSSA, Madison, WI. Galliher, H. L., D. E. Alexander, and E. J. Weber. 1985. Genetic variability of alphatocopherol and gamma-tocopherol in corn embryos. Crop Sci. 25:547–549. Garwood, D. L., E. J. Weber, R. J. Lambert, and D. E. Alexander. 1970. Effect of different cytoplasms on oil, fatty acids, plant height and ear height in maize (Zea mays L.). Crop Sci. 10:39–41. Han, Y. C., C. M. Parsons, and D. E. Alexander. 1987. Nutritive value of high-oil corn for poultry. Poultry Sci. 66:103–111.

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Hinesly, T. D., K. E. Redborg, E. L. Ziegler, and D. E. Alexander. 1982. Differential accumulations of cadmium and zinc by corn hybrids grown on soil amended with sewage sludge. Agron. J. 74:469–474. Hinesly, T. D., E. L. Ziegler, G. L. Barrett, and D. E. Alexander. 1978. Zinc and Cd accumulation by corn inbreds grown on sludge amended soil. Agron. J. 70:425–428. Lambert, R. J., D. E. Alexander, and R. C. Rodgers. 1967. Effect of kernel position on oil content in corn (Zea mays L.) Crop Sci. 7:143–144. Lambert, R. J., D. E. Alexander, and Z. J. Han. 1998. A high-oil pollinator enhancement of kernel oil and effects on grain yields of maize hybrids. Agron. J. 90:211–215. Lambert, R. J., D. E. Alexander, E. L. Mollring, and B. Wiggans. 1997. Selection for increased oil concentration in maize kernels and associated changes in several kernel traits. Maydica 42:39–43. Levings, C. S., and D. E. Alexander. 1966. Double reduction in autotetraploid maize. Genetics 54(6):1297–1305. Levings, C. S., and D. E. Alexander. 1967. Double reduction in autotetraploid maize. Genetics 54:6:1297–1305. Levings, C. S., J. W. Dudley, and D. E. Alexander. 1967. Inbreeding and crossing in autotetraploid maize. Crop Sci. 7:72–73. Martique, C. A., F. Scheuch, and D. E. Alexander. 1967. Recurrent selection for oil content in maize by nuclear magnetic resonance spectroscopy. Annales Cientificas, La Moline, Peru. Miller, R. L., J. W. Dudley, and D. E. Alexander. 1981. High intensity selection for percent oil in corn. Crop Sci. 21:433–437. Misevicˇ, D., and D. E. Alexander. 1989. Twenty-four cycles of phenotypic recurrent selection for percent oil in maize. I. Per se and test-cross performance. Crop Sci. 29:320–324. Misevicˇ, D., D. E. Alexander, J. Dumanovicˇ, B. Kerecˇki, and S. Ratkovicˇ. 1987. Grain filling and oil accumulation in high-oil and standard maize hybrids. Genetika 19:27–35. Misevicˇ, D., D. E. Alexander, J. Dumanovicˇ, B. Kerecˇki, and S. Ratkovicˇ. 1988. Grain moisture loss rate of high-oil and standard-oil maize hybrids. Agron. J. 80:841–845. Pamin, K., W. A. Compton, C. E. Walker, and D. E. Alexander. 1986. Genetic variation and selection response for oil composition in corn. Crop Sci. 26:279–282. Poneleit, C. G., and D. E. Alexander. 1965. Inheritance of linoleic and oleic acids in maize. Science 147:1585–1586. Scheuch, F., A. Manriquech, and D. E. Alexander. 1967. The modification of endosperm proteins: a new concept in maize breeding. Annales Cientificas, La Moline, Peru. Silvela, L., R. Rodgers, A. Barrena, and D. E. Alexander. 1989. Effect of selection intensity and population size on percent oil in maize, Zea mays L. 1989. Theo. Appl. Genet. 78:298–304. Sprague, G. F., D. E. Alexander, and J. W. Dudley. 1980. Plant breeding and genetic engineering. A perspective. BioScience 30:17–21. Weber, D. F., and D. E. Alexander. 1972. Redundant segments in Zea mays detected by translocations of monoploid origin. Chromosoma 39:27–42. Weber, E., and D. E. Alexander. 1975. Breeding for lipid composition in corn. J. Am. Chem. Soc. 52:370–373. Wilson, C. M., and D. E. Alexander. 1967. Ribonuclease activity in normal and opaque-2 mutant endosperm of maize. Science 155:1575–1576.

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2 Estimating and Interpreting Heritability for Plant Breeding: An Update James B. Holland U.S. Department of Agriculture-Agriculture Research Service Plant Science Research Unit, Department of Crop Science, Box 7620 North Carolina State University Raleigh, North Carolina 27695-7620 Wyman E. Nyquist Department of Agronomy Purdue University West Lafayette, Indiana 47907-1150 Cuauhtemoc T. Cervantes-Martínez Universidad Autónoma Chapingo Carretera México-Texcoco km 38.5 Chapingo, 56230 Mexico I. THE MEANING OF HERITABILITY II. RESPONSE TO SELECTION A. Applications of Heritability Estimates B. Theoretical Basis of Response to Selection C. Reference Populations, Assumptions, and Model Definitions III. COVARIANCES OF RELATIVES A. Covariance of Noninbred Relatives B. Covariance of Inbred Relatives IV. VARIANCE AMONG SELECTION UNITS V. ESTIMATING HERITABILITY AS A FUNCTION OF VARIANCE COMPONENTS A. Estimating Genetic Components of Variance from Replicated Family Evaluations B. Variance Component Estimation Procedures C. Precision of REML-based Heritability Estimators D. Accounting for Unbalanced Data in Formulas for Heritability on a FamilyMean Basis Plant Breeding Reviews, Volume 22, Edited by Jules Janick ISBN 0-471-21541-4 © 2003 John Wiley & Sons, Inc. 9

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VI. ESTIMATING HERITABILITY FROM PARENT-OFFSPRING REGRESSION A. REML Estimates of the Parent-Offspring Regression Coefficient B. Heritability Estimated from Parent-Offspring Regression without Inbreeding C. Heritability Estimated from Parent-Offspring Regression with Inbreeding VII. ESTIMATING REALIZED HERITABILITY VIII. EXAMPLES OF HERITABILITY ESTIMATES A. Broad-Sense Heritability in Clonally Propagated Species B. Heritability Estimated from Half-sib Family Evaluations C. Heritability Estimated from Full-sib Family Evaluations D. Heritability Estimated from NC Design I E. Heritability Estimated from NC Design II F. Heritability Estimated from Testcross Progenies G. Heritability Estimated from Self-fertilized Family Evaluations H. Heritability Corresponding to Selection among Self-fertilized Half-sib and Full-sib Families APPENDICES. SAS CODE FOR ESTIMATING HERITABILITY WITH REML Appendix 1. Estimating Heritability from Multiple Environments, One Replication per Environment Appendix 2. Estimating Heritability from Multiple Environments, Several Replications per Environment Appendix 3. Estimating Heritability in Multiple Populations Grown in a Common Experiment Appendix 4. Estimating Heritability via Parent-Offspring Regression and from Replicated Family Evaluations LITERATURE CITED

I. THE MEANING OF HERITABILITY Heritability was originally defined by Lush as the proportion of phenotypic variance among individuals in a population that is due to heritable genetic effects (Nyquist 1991, p. 248). This definition is now termed “heritability in the narrow sense” and is designated h2 (Nyquist 1991, pp. 248 and 250). Variations on this idea are often also referred to as heritability of one kind or another, such as heritability of family means (h2f ), the proportion of the phenotypic variance of family means that is due to family genetic effects, and “heritability in the broad sense” (H ), the proportion of phenotypic variance that is due to all genetic effects (Nyquist 1991, pp. 239, 312–313; Falconer and Mackay 1996, pp. 123, 232). Whereas Lush’s definition was based on his experience as an animal breeder, in which the basic unit of observation and selection is nearly always the individual animal, plant breeders deal with a great diversity of observational units and mating systems. This complicates both the procedures for estimating heritability and the meaning of heritability itself. As Nyquist (1991, p. 238) observed,

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The plant kingdom presents a great diversity of natural modes of reproduction, varying from reproduction without sexuality (asexual) to reproduction by sexual means, cross-fertilization (allogamous), or self-fertilization (autogamous). Mixtures of these main modes of reproduction also exist. With self-fertilization, inbred populations exist and many unique difficulties arise. . . . Considering the diverse array of plant populations which can arise, many different estimators have been labeled heritability, and sometimes it is not clear what the exact nature of the estimator is or what is being estimated.

Hanson (1963) urged plant breeders to unify their concept of heritability as “the fraction of the selection differential expected to be gained when selection is practiced on a defined reference unit.” Therefore, throughout this review, various heritability estimators are evaluated in terms of response to selection. Heritability has meaning only in reference to defined selection units and response units, and these can vary among breeding schemes. Nyquist (1991) critically reviewed the substantial literature on estimating heritability and predicting response to selection in plant populations, and he clarified many of the issues that affect heritability in plants. Little can be added to his review of the topic except to address some newer methods of heritability estimation that have developed and been used in the last ten years. These newer methods include mixed models analysis of unbalanced data, pedigree analysis, and use of DNA markers to estimate genetic components of variation. Mixed models analysis in general terms has been reviewed thoroughly by McLean et al. (1991), Searle et al. (1992), and Littell et al. (1996), but the use of mixed models analysis for plant breeding applications has not been reviewed. Use of pedigree information to estimate genetic variance components in plant breeding was reviewed by Xu (2003). Ritland (2000) reviewed the use of DNA markers for estimating heritability and other population genetic parameters. Marker-based methods will have the greatest impact on studies of natural populations with unknown pedigree relationships and perhaps on domesticated species whose breeding systems are not easily controlled. Recently, these methods have become practical in part because of advances in computing power that have made powerful but previously computationally unmanageable estimating procedures almost routine. This chapter focuses on placing mixed models analysis procedures in the context of typical plant breeding experiments and provides examples of computing code that can be used to obtain heritability estimates and their standard errors with the commonly used SAS system (see

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Appendices 1 to 4). To place the estimation procedures in context, the interpretation of heritability estimators obtained from different mating schemes and generations is discussed. II. RESPONSE TO SELECTION A. Applications of Heritability Estimates The main purpose of estimating heritability and the genetic parameters that compose the heritability estimate is to compare the expected gains from selection based on alternative selection strategies. One can use heritability estimates to predict gain from selection, for example, based on single, unreplicated plot values, and compare this to gain from selection expected if materials are replicated within and across macroenvironments (Hoi et al. 1999). Heritability estimates are useful for comparing the gain from selection under different experimental designs, and this information—combined with information about the relative costs of additional replications within each macroenvironment, additional years of evaluations, and additional locations for evaluations—can be used to design optimal breeding strategies (Milligan et al. 1990). Where genotype-by-environment (GE) interactions cause significant rank changes among families evaluated in different environments, heritability estimates corresponding to response to selection based on means over all environments can be compared with heritability based on means within subsets of local environments to determine the optimal selection strategy (Atlin et al. 2000). Similarly, heritabilities based on different family structures derived from the same base population can be compared to determine which family structure is best for maximizing genetic gain over units of time (Burton and Carver 1993). Heritability may vary among populations, thus, heritability estimates from different populations can be useful for choosing appropriate base populations in which selection will be most effective (Goodman 1965). Because heritabilities vary among traits within a population, heritability estimates of different traits, in addition to genetic correlation estimates among the traits, can be used to identify indirect selection schemes that may be more effective than direct selection schemes (Diz and Schank 1995; Banziger and Lafitte 1997; Rebetzke et al. 2002). B. Theoretical Basis of Response to Selection An understanding of the response to selection is needed in order to apply Hanson’s (1963) definition of heritability as the fraction of the selection differential expected to be gained when selection is practiced

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on a defined selection unit. One way to conceive of the response to selection is as a response or change in the mean of progeny phenotypic values due to a change in the mean value of selection units brought about by selection. The selection differential referred to by Hanson (1963) is the difference between the mean of selected selection units and the overall mean of the initial population. We introduce the notation S = ms – m0, where S is the selection differential, ms is the mean of the selected selection units, and m0 is the overall initial population mean. From elementary statistics, the expected response in any variable, Y, due to a change in a related variable, X, is given as ∆Y = b(∆X), where b is the coefficient of regression of Y on X, ∆Y is the change in Y, and ∆X is the change in X (Steel et al. 1997). This general formula can be applied to response to selection by considering X as the variable representing selection unit phenotypic values, and Y as the variable representing phenotypic values of random members of the response units. Thus, ∆X is the selection differential, ms – m0, and ∆Y is R, the expected response to selection: m1 – m0, where m1 is the mean (or expected value) of the response unit phenotypes in the first cycle resulting from selection within the initial population. Summarizing, R = SbYX. Therefore, the expected proportion of the selection differential to be achieved as a gain from selection, or heritability, is R/S = h2 = bYX. The generality of this concept of heritability is very useful for plant breeding, because it is applicable to all plant breeding situations, including selection within randomly-mating cross-pollinated populations, as well as selection among self-fertilized lines (with or without subsequent random-mating), selection among clones, and selection among testcross progenies in hybrid crops. The generality of this concept is also a weakness, because it can have many different genetical meanings, depending on the circumstances and type of selection to which it is applied. We agree with Hanson (1963) and Nyquist (1991, p. 313) that the only remedy for this situation and the possible confusion arising from it is that researchers clearly indicate the basis of their heritability estimates— what is the defined reference unit for selection, and to what method of selection does it refer? Furthermore, we suggest that the reference unit for measuring response also be indicated along with heritability estimates, as this also impacts the interpretation of heritability. The application of the heritability formula to specific breeding situations is discussed in Section VIII. To specify an appropriate heritability function for any breeding situation, the coefficient of regression of the value of the response unit on the value of the selection unit is required. Mathematically, the regression coefficient is the covariance of the phenotypes of selection and response units divided by the selection unit

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phenotypic variance (Nyquist 1991, p. 249). Specifying the response unit phenotypic value as Y, the phenotypic value of the selection unit related to the response unit through its female parent as Xf, and the phenotypic value of the selection unit related to the response unit through its male parent as Xm, we obtain: bYXf = Cov(Xf, Y )/Var(Xf), bYXm = Cov(Xm, Y )/Var(Xm). If selection is practiced on selection units related to both female and male parents, the total expected response to selection is the sum of the two expected responses (Nyquist 1991, p. 272): R = bYXfSf + bYXmSm, where Sf and Sm are the selection differentials on female and male sides of the pedigree, respectively. As shown by Nyquist (1991, p. 272), if selection units related to female and male parents have the same expected value and population variance (i.e., no sexual dimorphism), then bYXf = bYXm, Sf = Sm = S, and the total response to selection is: R = [2Cov(Xf, Y )/Var(Xf)]S = [Cov(X, Y )/Var(X)]S where Cov(X, Y ) = 2Cov(Xf, Y ) = Cov(Xf, Y ) + Cov(Xm, Y ). Therefore, the heritability equation that refers to response to selection when selection is practiced on both male and female sides of a pedigree is: h2 = Cov(X, Y )/Var(X).

[1]

To apply this formula to a specific breeding method, the selection and response units must be specified because their relationship determines the numerator of the equation. For example, response units can be related to the selection units as clonal (asexual) offspring, first-generation progeny of random-matings of the selection units, progeny resulting from self-fertilization of the selection units, or they can be indirectly related to the selected units, such as offspring of relatives of the parents (called “recombination units” by Hallauer and Miranda [1988, p. 170]), rather than direct offspring of the selection units actually evaluated. Each of these situations results in unique covariances between selection and response units. Nyquist (1991, pp. 272–277) presented the selection

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pedigree diagrams and covariances between selection units and response units for many commonly used selection schemes. Specification of the selection unit is also necessary because the denominator of the heritability equation is the variance among selection unit phenotypic values. The variance among the selection units depends on whether individuals or families are evaluated. If families are evaluated, the experimental design used to estimate family means, such as the number of replications and environments in which selection units are evaluated, will impact the variance of selection units, which are family mean phenotypic values in this case. C. Reference Populations, Assumptions, and Model Definitions Heritability estimates must refer to a defined population of genotypes (Comstock and Moll 1963; Dudley and Moll 1969). Reference populations are generally assumed to be random-mating populations in HardyWeinberg and gametic phase equilibria, although for self-pollinating crops, sometimes the reference population is taken to be completely inbred genotypes derived from a Hardy-Weinberg and gametic phase equilibria reference population by inbreeding without selection. Diploid inheritance is assumed throughout this chapter. To estimate the heritability of the reference population, individuals or families should be sampled at random for measurement. Also, heritability estimates must refer to a specified population of environments (Comstock and Moll 1963; Dudley and Moll 1969; Nyquist 1991, pp. 239–243). Defining the reference population of environments is often more difficult than defining the reference population of genotypes, and reference populations of environments are rarely explicitly defined by researchers. Generally, however, a reference population of environments is defined geographically. For example, public plant breeders often are assigned to develop improved cultivars for a specific state or province of a country, in which case the reference set of populations that is of interest to such a breeder is their state. In contrast, international agricultural research centers are often explicitly concerned with developing germplasm that is broadly adapted to a loosely-defined ecological zone throughout the world. Their reference set of environments may include, for example, all subtropical zones throughout the world. Having defined the target set of environments, the researcher should attempt to sample test environments at random from this population. This is also difficult, because evaluations are often performed on experimental research stations, limiting the plant

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breeder’s ability to sample target production fields. Similarly, it is rarely feasible for researchers to evaluate material for more than a small number of years, thus limiting the sample of potential climatic variations under which the germplasm of interest can be evaluated. These problems are close to insurmountable, although recent research focused on better defining target production environments may help researchers to better sample the reference population of environments (Gauch and Zobel 1997). We can only emphasize that researchers attempt to sample a range of environments that represent the target production environments for the germplasm, and that at a minimum, this should include a sample of several locations and several years. Defining and adequately sampling the reference population of genotypes and environments is important for estimating heritability because this provides the context to which the heritability estimate refers. The genotypic values of the individuals in the population may depend on the environment or the conditions under which the experiment was performed (Comstock and Moll, 1963). For example, a drought-tolerant genotype of wheat (Triticum aestivum) will most likely be more vigorous under drought conditions compared with a normal genotype, whereas under higher moisture conditions, the normal genotype may be superior. Thus, when the experiment is performed in only a single environment, the estimated genotypic values cannot necessarily be used to make inferences beyond the original environment. The scope of inference of any experiment is an important issue that is often overlooked, but should be as well-defined as possible to avoid any confusion regarding interpretation of the results. The genotypic values refer specifically to the conditions under which the experiment was performed, and it cannot be assumed that the values would be the same in another reference set of environments. Therefore, genetic variance depends on the reference environments as well as the genotypes evaluated. Furthermore, the genetic variance component estimated in the experiment refers only to the population which was sampled for the experiment. A clear definition of the population being sampled is also important for the estimate of genetic variance to have any meaning. One population of any species will not necessarily have the same amount of genetic variation as another population even from the same species, which can be due to many factors, such as selection, mating behavior, random drift, migration, and mutation. Thus, for example, there is no reason to expect that the genotypic variance estimated for a particular trait for one population of alfalfa (Medicago sativa) will have any relevance to another population of alfalfa. Furthermore, the variation observed for any one trait in any population may not hold for another trait in the same

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population. For example, a maize (Zea mays) population that has been under selection for resistance to a particular disease may eventually become fixed for the resistant phenotype, but it may still have genetic variation for other traits, such as yield or flowering time. Heritability estimates must be made from data collected in multiple locations and during multiple years representing the target set of environments or else the estimates will be biased unless genotype-byenvironment interaction is negligible, which is rarely true for quantitative characters of agronomic importance (Nyquist 1991, pp. 239 and 312). This bias arises because the genotype-by-environment interaction variance is confounded with the genotypic variance component if the genotypic variance component is estimated from a single environment or from a sample of multiple locations or from a sample of multiple years only (Nyquist 1991, pp. 288–289). Another bias can arise if researchers ignore the cross-classified nature of years and locations during the statistical analysis of their experiment. For example, if families are evaluated at three locations across three years, the environments can be classified by year and location, leading to variance components estimates for years, locations, year-by-location interaction, families, and family-by-year, family-by-location, and family-by-year-by-location interactions. Or the analysis can proceed by classifying each year and location combination as one of nine environments, leading to variance component estimates of environments, families, and family-byenvironment interaction. The latter choice leads to a simpler statistical model, but also creates bias in the resulting estimate of heritability, because the estimate of family-by-environment interaction variance is smaller than the sum of family-by-year, family-by-location, and familyby-year-by-location variances (Comstock and Moll 1963; Nyquist 1991, pp. 289–290). Throughout this chapter, the model that ignores the crossclassification of families and environments is used only to simplify the presentation of mathematical formulas. This should be avoided if possible in analysis of cross-classified data sets, and formulas for estimating heritability are provided with both approaches to handling environmental classification (Table 2.1, pp. 86-101) at the end of the chapter. Having defined the reference populations of genotypes and environments, we can define the effects of the statistical model that will be used to estimate heritability. First, assume that the genotypes are sampled at random from the reference population, meaning that the genotypic effects (Gj’s) are independent with expected value of zero and a common variance, s G2 . Assume also that the environments are sampled at random from the reference population of environments. Further, distinguish between the effects of macroenvironments (which generally refer to a

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J. HOLLAND, W. NYQUIST, AND C. CERVANTES

combination of a geographical location and unique weather pattern, that is, a single year and location combination) and the effects of microenvironments (which refer to environmental variations within macroenvironments). Therefore, we introduce a term for the effect of macroenvironments, Ei, and a term for the effect of microenvironments, e′ijk. Each is distributed around a mean of zero with variance s 2E for macroenvironments and s 2e′ for microenvironments. We also introduce a term R(i)k for the mean effect of replications (complete blocks) within environments. This leads to a common form of the linear model for data observed on genotypes replicated in multiple blocks within multiple environments on a plot basis: Yijk = m + Ei + R(i)k + Gj + GEij + e′ijk.

[2]

This type of model assumes that genotypes can be replicated; in Section V.A we demonstrate how to generalize the model to nonclonal material. The model also assumes that only one phenotypic value is recorded on each plot. If data are taken on individual plants within each plot, the error variance can be partitioned into variance due to random plot effects and within-plot variance. If not, then plot effects and plant-within-plot effects are confounded in the residual effect, which is denoted as e′ijk in Equation [2], to maintain consistency with Nyquist (1991, p. 258). See Nyquist (1991, pp. 252–259) for details on the definition of residual variances in this model and more complex statistical models. Other than the overall mean effect, m, all effects in this model are random. If selection is based on the mean phenotypic value of genotypes evaluated in multiple replications and macroenvironments (r replications within each of e macroenvironments), then the values of interest are mean values of genotypes: e

X . j. = µ +

e

r

∑ E i ∑ ∑ R(i )k i =1

e

+

i =1 k =1

er

e

+ Gj +

= µ + E .. + R .. + G j + GE .. + e ′. j .

e

r

∑ GE ij ∑ ∑ ε ijk′ i =1

e

+

i =1 k =1

er

. [3]

Similarly, if the genotypes of the next base population are evaluated in replicated, multiple environment trials, their mean phenotypic – values (Y.j.) are the response unit values. We assume that the set of environments in which selection units are evaluated and the set of environ-

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ments in which response units are evaluated are independent samples of the common reference population of environments. In practical terms, this means that the evaluations of selection units are different from those in which the response units are evaluated. To specify this in our statistical notation, we write E i for environments used for selection unit evaluations and E i′ for environments used for response unit evaluations, and similarly, R i(k) and R i′(k′) for block effects sampled for selection and response unit evaluations, respectively. The number of environments sampled for selection unit evaluations is ex and the number of environments sampled for response unit evaluations is ey. The number of complete blocks within each evaluation environment is specified as rx for selection units, and ry for response units. Summarizing this notation, we have: i = 1, ..., ex, and Var(Ei) = s 2E, i¢ = ex + 1, ..., ex + ey, and Var(Ei′) = s 2E, k = 1, ..., rx, and Var(R(i)k) = s 2R, k¢ = rx + 1, ..., rx + ry, and Var(R(i′)k′) = s 2R. Having defined the statistical model, we can expand the heritability equation to include all of the effects that contribute to selection and response unit phenotypes. Assuming that selection units are chosen based on their mean phenotypic values, and response units are evaluated in replicated multiple environment experiments, we are interested in the following regression coefficient:

b XY =

Cov( X . j .,Y . j ′. ) ex

Cov[(µ +

∑ Ei i =1

ex

ex

∑

rx

∑ ∑ R(i )k i =1 k =1

+

ex +ey

(µ +

=

Var( X . j . )

e x rx

ex +ey

i ′=ex +1

ey

Var(µ +

+ Gj +

ex

ex

+

∑

i =1

i =1 k =1

+ G j′ +

rx

∑ E i ∑ ∑ R(i )k +

ex

ex

rx

∑ ∑ ε ijk′ i =1 k =1

),

e x rx

ex +ey

e y ry

ex

i =1

R( i ′ ) k ′

i ′=ex +1 k ′= rx +1

+

∑ GE ij

rx + ry

∑ ∑

Ei′

ex

e x rx

ex +ey

i ′=ex +1

+

ey ex

+ Gj +

ex

i ′=ex +1 k ′= rx +1

e y ry rx

∑ GE ij ∑ ∑ ε ijk′ i =1

ex

+

rx + ry

∑ ∑

GE i ′j ′

i =1 k =1

e x rx

ε i′′j ′k ′ )] .

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J. HOLLAND, W. NYQUIST, AND C. CERVANTES

To evaluate the numerator of this expression, we use the formula for the covariance between two variables, X and Y: Cov(X, Y) = E [(X – mX) (Y – mY)]. Applying this to the numerator of the regression coefficient formula, we obtain: Cov( X . j .,Y . j ′. ) = ex

ex

rx

∑ E i ∑ ∑ R(i )k

E[( i =1 ex ex +ey

i =1 k =1

+

e x rx

ex

i =1

∑ ∑

i ′=ex +1 k ′= rx +1

∑

+ G j′ +

e y ry

i =1 k =1

+

ex

ex +ey

R( i ′ ) k ′

ex +ey

rx

Ei′ ∑ GE ij ∑ ∑ ε ijk′ i ′=∑ e +1

+ Gj +

rx + ry

ex

ey

+

x

ey

+

rx + ry

ex +ey

∑ ∑

GE i ′j ′

i ′=ex +1

)(

e x rx

ε i′′j ′k ′

i ′=ex +1 k ′= rx +1

)].

e y ry

Working out this expectation in detail is tedious, but we observe that it simplifies greatly because the different model effects are independent. For example, the expectations of cross-products between macroenvironment effects of selection units (Ei) and genetic effects of response units (Gj ′) are all zero because the environment and genotype effects are independent. Applying this rule, we obtain: Cov( X . j .,Y . j ′. ) = ex +ey

ex

∑ E[(

∑

Ei

i =1

ex

)(

ex

Ei′

i ′=ex +1

)] + E[(

ey

E[(G j )(G j ′ )] + E[(

∑∑

i =1 k =1

e x rx

∑

GE ij

i =1

)(

ex

)(

ey

R( i ′ ) k ′

i ′=ex +1 k ′= rx +1

)] + E[(

)] +

e y ry ex

GE i ′j ′

i ′=ex +1

rx + ry

∑ ∑

R( i ) k

ex +ey

ex

∑

ex +ey

rx

ex +ey

rx

i =1 k =1

e x rx

rx + ry

∑ ∑

∑ ∑ ε ijk′ )(

i ′=ex +1 k ′= rx +1

e y ry

ε i′′j ′k ′ )].

Next, because we assumed that the environments in which selection and response units were evaluated were independent, their expected cross-product is zero. The same holds true for block effects, genotypeby-environment interaction effects, and error effects. This leaves only the expectation of the cross-product of the selection and response unit genotypic effects, which is not zero, because there is a genetic relationship between them:

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Cov( X . j .,Y . j ′. ) = E[(G j )(G j ′ )]. The cross-product between selection and response genotypic values depends on their genetic relationship, which requires knowledge of their pedigree relationship. Given the pedigree relationship, the value of this cross-product can be determined using the theory of the covariance of relatives (Section III; see also Nyquist 1991, pp. 268–277 and 296–299). It also remains to determine the phenotypic variance among selection units (the denominator of the response equation) in order to fully evaluate the expected value of the heritability estimator. This phenotypic variance is treated in Section IV.

III. COVARIANCES OF RELATIVES A. Covariance of Noninbred Relatives To demonstrate the covariance between genotypic values of relatives, we start by writing the genotypic value (Gij) for an individual as the sum of its additive and dominant effects at the kth locus: Gijk = α ik + α jk + δ ijk , where aki is the additive statistical effect of allele i at locus k in the defined reference populations of genotypes and environments and dkij is the dominance deviation effect due to the allele pair i and j at locus k in the same reference populations (Falconer and Mackay 1996, pp. 112–113; Holland 2001, p. 37). The genotypic variance caused by locus k in a random-mating population is:

σ G2 ( k ) =

m

m

i =1

j =1

m

m

∑ pik (α ik )2 + ∑ p kj (α jk )2 + ∑ ∑ pik p kj (δ ijk )2, i =1 j =1

= 2E[(α ik )2 ] + E[(δ ijk )2 ] = σ A2 ( k ) + σ D2 ( k ) where pki is the frequency of the ith allele at the locus k, s 2A(k) is defined as the additive genetic variance due to locus k, and s 2D(k) is defined as the dominance genetic variance due to locus k. If the population is in gametic phase equilibrium and epistasis is ignored, these terms can be summed over l loci to obtain the total genetic variance as the sum of total additive and total dominance variances:

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J. HOLLAND, W. NYQUIST, AND C. CERVANTES

σ G2 =

l

∑ (σ A2 ( k ) + σ D2 ( k ) ) = σ A2 + σ D2 . k =1

Epistasis can be included by expanding the model to include two-locus and higher-order interactions: Giji ′j ′i ′′j ′′K =

l −1

l

k =1

+

l −2

l −1

l

∑ (α ik + α jk + δ ijk ) + ∑ ∑ l

∑ ∑ ∑

kk ′ kk ′ (αα iikk′ ′ + αδ iikk′j ′′ + δα iji ′ + δδ iji ′j ′ )

k =1 k ′= k +1

(ααα iikk′i ′′′k ′′ + ααδ iikk′i ′′′kj ′′′′ + K) + K,

k =1 k ′= k +1 k ′′= k ′+1

where aakkii ¢′ is the additive-by-additive interaction statistical effect of ¢ allele i at locus k with allele i¢ at locus k ¢, adkk ii′j ′ is the additive-bydominance interaction statistical effect of allele i at locus k with allele ¢ pair i¢ and j ¢ at locus k ¢, ddkk iji′j ′ is the dominance-by-dominance interaction statistical effect of allele pair i and j at locus k with allele pair i¢ and j ¢ at locus k ¢, aaa kkii′i¢k″≤ is the additive-by-additive-by-additive interaction statistical effect of allele i at locus k with allele i¢ at locus k ¢ and allele ¢k ≤ i≤ at locus k ≤, and aadkkii′i″j ″ is the additive-by-additive-by-dominance interaction statistical effect of allele i at locus k with allele i¢ at locus k ¢ and allele pair i≤ and j ≤ at locus k ≤. The two-locus interaction effects are defined explicitly by Lynch and Walsh (1998, pp. 85–86) and Holland (2001). The higher-order effects are defined analogously (Lynch and Walsh 1998, p. 85). Assuming there is no linkage between loci affecting the trait concerned and assuming the population is in gametic phase equilibrium, the genotypic variance including epistatic terms is: 2 2 2 2 σ G2 = σ A2 + σ D2 + σ AA + σ AD + σ DD + σ AAA + K,

where s 2AA is the variance of additive-by-additive epistatic effects, s 2AD is the variance of additive-by-dominance epistatic effects, s 2DD is the variance of dominance-by-dominance epistatic effects, s 2AAA is the variance of additive-by-additive-by-additive epistatic effects, and variance components of higher-order effects are included as desired. Using this genetic model, we can determine the genetic components of variance that comprise the covariance of two individuals from the same family, or between a random individual X in the selection unit and random individual Y in the response unit in the context of selection response. The covariance between the genetic effects for two individuals depends on their pedigree relationship, as this influences the prob-

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ability that two individuals inherited common genetic effects. The probability that a randomly chosen allele from a given locus in the first individual is identical by descent (IBD) to a randomly chosen allele from the same locus in the second individual is equal to the probability that the two individuals share a common additive statistical effect at all loci. This probability is represented by the symbol q (or f, as used by Nyquist 1991 and Falconer and Mackay 1996), the coefficient of coancestry. Identical by descent (IBD) means that the two alleles descend from the same allele in a common progenitor (Falconer and Mackay 1996, p. 58; Lynch and Walsh 1998, p. 132). The probability of allele pairs being IBD equals the probability that two individuals share a common dominance statistical effect at all loci and is represented by the symbol u. It follows from this and from the assumptions of Hardy-Weinberg and gametic phase equilibria and no linkage, that the genetic covariance between individuals X and Y is: 2 Cov(G X , GY ) = 2θσ A2 + uσ D2 + (2θ )2 σ AA 2 2 2 +2θuσ AD + u2σ DD + (2θ )3 σ AAA +K

[4]

A formal derivation of this covariance can be found in Lynch and Walsh (1998, pp. 141–145). If individuals are chosen at random from the population, both q and u for the pair equal zero and their expected genetic covariance is zero. If individuals are related in any way, however, the genetic covariance is greater than zero. Rules for evaluating q and u and examples of their values for commonly encountered pairs of relatives are given by Falconer and Mackay (1996, pp. 85–88; pp. 152–155) and Lynch and Walsh (1998, pp. 133–145). As an example, for an outbred parent and its outbred offspring resulting from a mating with an unrelated individual, q = (1/4) and u = 0, leading to: 2 2 Cov(GP , GO ) = ( 12 )σ A2 + ( 14 )σ AA + ( 81 )σ AAA +K .

In Section V, we demonstrate that the covariance of collateral relatives from a systematic mating structure can be estimated with appropriate experimental designs, and this leads to estimates of the genetic components of variance in the regression of response unit phenotypes on selection unit phenotypes needed for the heritability estimator. To interpret the covariance of collateral relatives in terms of genetic components, we use the method described in this section. For example, for outbred halfsibs, q = (1/8) and u = 0, leading to:

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J. HOLLAND, W. NYQUIST, AND C. CERVANTES 2 2 1 1 Cov(HS) = ( 14 )σ A2 + ( 16 )σ AA + ( 64 )σ AAA +K .

For outbred full-sibs, q = (1/4) and u = (1/4), leading to: 2 2 2 2 1 Cov(FS) = ( 12 )σ A2 + ( 14 )σ D2 + ( 14 )σ AA + ( 81 )σ AD + ( 16 )σ DD + ( 81 )σ AAA +K .

B. Covariance of Inbred Relatives When relatives are inbred, Equation [4] and the equations for population genetic components of variance given previously in Section III.A do not hold. With inbreeding, the allelic effects within a locus become correlated, leading to an additional set of three genetic parameters and three additional probability measures required to describe the genetic variance of the population and the covariance of relatives. The total genetic variance of a population inbred to a degree F, where F is the inbreeding coefficient, is (Nyquist 1991, p. 297):

σ G2 ( F ) = (1 + F )σ A2 + (1 − F )σ D2 + 4FD1 + FD2* 2 + F (1 − F )H * + (1 + F )2 σ AA + K,

where D1 is the covariance between additive effects and their respective homozygous dominance deviation effects, D*2 is the variance of homozygous dominance effects, and H* is the sum of squared inbreeding depression effects (Cockerham 1983). The covariance of relatives X and Y if either are inbred is: * Cov(G X , GY ) = 2θ XY σ A2 + 2δ X˙˙ +Y˙˙σ D2 + 2(γ XY ˙˙ + γ XY˙˙ )D1 + δ XY ˙˙ ˙˙ D2 2 +(∆ X˙˙ ⋅Y˙˙ − FX FY )H * + (2θ XY )2 σ AA + K,

[5]

where qXY is the coefficient of coancestry between X and Y; 2dX¨ +Y¨ is the probability that the allele pair at a locus in X is IBD to the pair at the same locus in Y, and that neither the two alleles at a locus within X nor within Y are IBD (this is equivalent to u if neither X nor Y is inbred); gX¨ Y is the probability that the pair of alleles at a locus within X are IBD to each other and to one of the two alleles at the same locus in Y, and gXY¨ is defined similarly; dX¨ Y¨ is the probability that all four genes at a common locus in X and Y are IBD; and ∆X¨ ◊Y¨ is the probability that the two alleles within a locus in X are IBD and the two alleles of Y are IBD (Cockerham 1971, 1983). If the relatives descended by self-fertilization from a com-

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mon ancestor, these probability parameters become functions of the inbreeding coefficients of the last common ancestor (Ft ), the relative that descended from g generations of self-fertilization (Fg ), and the other relative that descended from g¢ generations of self-fertilization (Fg′ ). In this case, the covariance of relatives is denoted Ctgg ′ : C tgg ′ = (1 + Ft )σ A2 +

(1 − Fg )(1 − Fg ′ ) 1 − Ft

σ D2 + (Fg + Fg ′ + 2Ft )D1

 (Fg − Ft )(Fg ′ − Ft )  * Ft (1 − Fg )(1 − Fg ′ ) * 2 D2 + . +  Ft + H + (1 + Ft )2 σ AA 2(1 − Ft ) 1 − Ft [5b]   Further simplifications occur when there are only two alleles per locus, in which case H* = s 2D; if the two alleles are at equal frequency, then D1 = D*2 = 0 (Cockerham 1983). Cockerham (1983), Nyquist (1991, p. 299), and Gibson (1996) presented tables of the coefficients of the five genetic parameters that describe the covariance of inbred relatives related by some common ancestor. (A typographical error exists in Nyquist (1991), Table 8, p. 299, for t = 2, g = 2, and g′ = 3; change 23/8 to 25/8.) For example, the genetic covariance of an outbred parent (S0 generation) and its progeny resulting from a single generation of self-fertilization (S1 generation) is C001 = s 2A + (1/2)s 2D + (1/2)D1 + s 2AA (Nyquist 1991, p. 299). Note that the F2 population is considered to be equivalent to the noninbred, random-mating population, even though one generation of selfing has already occurred (Nyquist 1991, p. 297), because it is in Hardy-Weinberg equilibrium.

IV. VARIANCE AMONG SELECTION UNITS The variance among selection units is obtained from the general formula for the sample variance of a random variable. The population variance of a random variable X is: Var(X) = E [X – E(X)]2 (Lynch and Walsh 1998, pp. 22–23). The sample variance of X differs slightly because E (X) – is not known, but is estimated as X from the data; therefore the unbiased estimate of Var(X) obtained from a sample of size n is:

σˆ X2 =

n ( n − 1)

n

( X i − X . )2 1 = ( n − 1) n i =1

∑

n

∑ (X i − X . )2 i =1

[6]

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J. HOLLAND, W. NYQUIST, AND C. CERVANTES

(Steel et al. 1997, p. 76). Applying this to the selection units, we are interested in estimating the variance of family mean phenotypic values. Recall from Equation [3] in Section II.C that the mean phenotypic value of a selection unit is: e

X .j . = µ +

e

r

e

∑ Ei ∑ ∑ R(i )k i =1

i =1 k =1

+

e

+ Gj +

er

e

r

∑ GEij ∑ ∑ εijk′ i =1

+

e

i =1 k =1

er

.

The value for the overall mean is obtained using the same model, introducing g as the total number of families or genotype groups evaluated: e

e

XK = µ +

g

r

∑ Ei ∑ ∑ R(i )k ∑ j =1 i =1

+

e

i =1 k =1

+

er

e

Gj +

g

g

∑∑

e

i =1 j =1

r

i =1 j =1 k =1

+

eg

g

∑ ∑ ∑ εijk′

GEij

egr

.

Substituting these terms into Equation [6] gives: e

g

σˆ (2X

.j. )

=(

g

1 1 ) [ X − X K ]2 = ( ) [(G + g − 1 j = 1 .j . g − 1 j =1 j

∑

e

∑

g

r

∑ ∑ εijk′ i =1 k =1

er

)−(

e

g

e

∑ GEij i =1

e g

r

∑ G j ∑ ∑ GEij ∑ ∑ ∑ εijk′ j =1

g

+

i =1 j =1

eg

+

+

i =1 j =1 k =1

egr

)]2.

[7]

Again, since the different model effects are independent, we can ignore the cross-products between different model effects (such as between Gi and GEij). Therefore, the expectation of the formula in Equation [7] simplifies to: g g g e e r   e e r  GEij ∑ ∑ GEij ε ijk ′ ∑ Gj ∑ ∑ ∑ εijk′  ∑ ∑ ∑ g g g  1 j =1 i =1 j =1 i =1 j =1 k =1 E[σˆ X2 ] = E {( ) ∑ (G j − )2 + ∑ ( i = 1 )2 + ∑ ( i = 1 k = 1 )2  } − − .j. g − 1 j = 1 g e eg er egr  j =1 j =1     g g g e e r   e e r  ∑ Gj ∑ GEij ∑ ∑ εijk′  ∑ GEij ∑ ∑ ∑ εijk′ ∑ g g g 1  j =1 i =1 j =1 i =1 j =1 k =1 )2 ] . =( ) E[∑ (G j − )2 ] + E[∑ ( i = 1 )2 ] + E[∑ ( i = 1 k = 1 − − egr g − 1  j =1 g e eg er  j =1 j =1    

[8]

The expectation of the summation involving Gj in Equation [8] simplifies as follows:

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27

g

g

E[

∑ (G j −

∑ Gj

g

j =1

)2 ] = E[

g

j =1

j =1

g

= E[

∑ ([G j −

Gj g

j =1 g

= E[

∑ ({ j =1

=(

g −1 g

1

∑ (G j − g [G1 + G2 + K + G j + K + G g ])2 ]

]−

1 [G1 + G2 + K + G j − 1 + G j + 1 + K + G g ])2 ] g

−G j ′ 2 g −1 }G j )2 ] + E[ ( )] g g j = 1 j ′ = 1, j ′ ≠ j g

g

∑ ∑

g

)2

1

g

g

∑ E[G j2 ] + ( g 2 )∑ E[ ∑ j =1

j =1

G j2′ ]

j ′ = 1, j ′ ≠ j

g −1 2 2 1 =( ) gσ G + ( )g ( g − 1)σ G2 g g2 =(

g 2 − 2g + 1 + g − 1 2 )σ G g

=(

g2 − g 2 )σ G g

= ( g − 1)σ G2 .

Similarly, the expectation of the sum involving GEij in Equation [8] simplifies as follows:

∑ g ∑ [ i =1 e

GEij

E{

g

e

e

−

∑ ∑ GEij i =1 j =1

eg

j =1

]2 } g

=

1 e2

g

E{

∑ [(GE1j + GE2j + K + GEej ) − ( g

=

1 e

2

1 e

2

g

E{

∑ [(GE1j −

j =1

g

j =1

) + (GE2j −

g

E{

e

∑ ∑ [GEij − (

∑ GEij j =1

g

j =1 i =1

)]2

g

=

e e2

g

E{

g

∑ GEij

j =1

g

∑ [GEij − (

j =1

+

j =1

g

g

+K+

g

g

=

j =1

j =1

∑ GE1j

g

∑ GE1j ∑ GE2j

)]2 }.

∑ GE2j j =1

g

∑ GEej j =1

g

)]2 }

g

) + K + (GEej −

∑ GEej j =1

g

)]2 }

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J. HOLLAND, W. NYQUIST, AND C. CERVANTES

This reduces further in the same way that the expectation of the Gj term simplified; without showing the intermediate steps, this equation becomes: g

∑

GEij g e 1 g −1 2 j =1 E[ GEij − ( )]2 = ( g − 1)E[GEij ]2 = ( )σ GE . 2 g e e e j =1

∑

A similar derivation shows the simplification of the term involving error effects in Equation [8] to: e

g

∑ ∑ ε ijk′

∑ ( i =1 ker=1

E[

e

r

−

g

r

∑ ∑ ∑ ε ijk′ i =1 j =1 k =1

j =1

egr

)] = (

g −1 2 )σ ε ′ . er

Putting these simplified forms back into Equation [8], we obtain: g g e   e  Gj GEij  GEij   j =1 i =1 j =1 )2 + E ( i = 1 )2  − g  E (G j − 1 g e eg  E (σˆ X2 ) = ( )  g  e r .j. e r g − 1 j =1    ε ijk ′ ε ijk ′   1 1 1 = = = i j k )2 −  + E ( i = 1 k = 1 er egr   1  ( g − 1) 2 ( g − 1) 2  =( σ GE + σ ε′  )( g − 1)σ G2 + g − 1  e er 

∑

∑

= σ G2 +

∑∑

∑

∑∑

∑∑∑

2 σ2 σ GE + ε′ . e er

[9]

This variance can be estimated easily in practice by dividing the mean square for genotypes from the analysis of variance by the total number of observations per genotype, which is er, because the expectation of the genotypic mean square from this type of experiment is: 2 E ( MSGenotype ) = erσ G2 + rσ GE + σ ε2′

(Nyquist 1991, pp. 256–257; Steel et al. 1997, pp. 379–384). We can use Equation [9] to obtain the phenotypic variance among individual, unreplicated phenotypic values of selection units, by setting e = 1 and r = 1. The resulting phenotypic variance is: s 2P = s 2G + s 2GE + s 2e′.

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The reason that neither the macroenvironmental nor the block variance contributes to this variance is because we defined it to refer to the expected variation among plots within a single replication of a single environment (Nyquist 1991, pp. 245 and 252). If one were to conduct a selection experiment in which different genotypes were grown in different environments or different blocks and then to select on unadjusted phenotypic values, one would have to add the macroenvironmental and block variance components to the phenotypic variance. Since there is no reason for such an inefficient selection procedure to be conducted, we will not consider that case. Instead, this phenotypic variance can be used to obtain a heritability estimator relevant to the response to selection among plants evaluated with a single replication within the target set of environments. Obviously, if one has data from multiple environments and replications, they should be used for selection purposes. Nevertheless, the heritability on a single-plot basis is often of interest because it suggests the magnitude of selection response that can be expected if future cycles of selection were to be conducted with a single replication. If such a heritability estimate is sufficiently high, the breeder may decide that single-replication evaluations are sufficient, and that evaluation resources can be spent elsewhere. From Equation [9], it can be seen that increasing the number of replications within each environment will reduce the error portion of the phenotypic variance of genotypic means, but it will not affect the contribution due to GE variance. To reduce the contribution of GE variance to the phenotypic variance of selection units, the parents should be evaluated in more macroenvironments.

V. ESTIMATING HERITABILITY AS A FUNCTION OF VARIANCE COMPONENTS A. Estimating Genetic Components of Variance from Replicated Family Evaluations To estimate genetic components of variance in species that are not easily clonally propagated, families or lines containing multiple individuals related in some systematic fashion can be developed, and these families can be evaluated in replicated trials. This permits partitioning of the phenotypic variance into a component due to families (which is due to common genetic effects of the members of the same family) and components due to family-by-environment interaction and residual effects. We use the same model as for clonal genotypes in Equation [2], Section II.C, but instead of genotypic and genotype-by-environment

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J. HOLLAND, W. NYQUIST, AND C. CERVANTES

effects (Gj’s and GEij’s) there are family and family-by-environment effects (Fj’s and FEij’s): Yijk = m + Ei + R(i)k + Fj + FEij + e′ijk. It is important to note that the residual term of this model, e′ijk, differs from that used in the model for clonally propagated species (Equation [2]). With clonally propagated species, all of the variation among plots of the same genotype grown in the same replication or incomplete block are due to microenvironmental effects. With heterogeneous families, however, differences among plots of the same family in the same replication or incomplete block may be due to three causes: (1) microenvironmental effects, (2) effects of different samples of genotypes of the same family that occur in different plots, and (3) effects due to interaction between genotypes within a family and the macroenvironment (Nyquist 1991, p. 254). The second of these terms is the within-family genetic variance. After accounting for the other model effects, the value of each plot is the mean or total of within-family genotypic and genotypicby-environmental effects plus the microenvironmental effect of the plot. s2 Therefore, the residual variance component, s e′2 , equals s ε2 + W , where n s2e is the variance due to plot effects; s 2W is the within-plot component of variance that includes within-family genetic variation, within-plot microenvironmental effect variation, and variation due to interactions between the macroenvironment and genotypes within the family; and n is the number of plants per plot. If there are many plants per plot, the within-family genetic component of variance contributes little to the residual variance of this model. See Nyquist (1991, pp. 254–256) for details on models including individual plant observations. The family variance component can be estimated and obviously represents an estimate of some portion of the genetic variance. To determine what genetic components of variance comprise the family variance, one must compute the expected genetic covariance of individuals within the family because the covariance of random individuals within the same family grown in independent environments equals the family variance component. The covariance between two random members of the same family grown in independent environments is the covariance between Yijk and Yi ′j ′k ′, where i ≠ i¢, j = j ¢, and k ≠ k ¢: Cov(Yijk,Yi ′j ′k ′) = Cov(m + Ei + R(i)k + Fj + FEij + e′i jk, m + Ei ′ + R(i ′)k ′ + Fj ′ + FEi ′ j ′ + e′i ′ j ′ k ′) = Cov(Fj, Fj ′) = Cov(Fj , Fj) (because j = j ¢ ) = s 2F .

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This covariance also equals the genetic covariance between random members of the same family, so we can equate the estimable family component of variance to the genetic covariance of relatives within a family, which is interpretable in terms of genetic components of variance (Section III; Cockerham 1963): E[Cov(Yijkl ,Yi ′jk ′l′ )] = E[σˆ F2 ] 2 = 2θ XY σ A2 + uXY σ D2 + (2θ XY )2 σ AA + K,

[10]

where Yijkl and Yi ′jk ′l′ are noninbred members of the same family, and: E[Cov(Yijkl ,Yi ′jk ′l′ ) = E[σˆ F2 ] * = 2θ XY σ A2 + 2δ X˙˙ +Y˙˙σ D2 + 2(γ XY ˙˙ + γ XY˙˙ )D1 + δ XY ˙˙ ˙˙ D2 + 2 (∆ X˙˙ ⋅Y˙˙ − FX FY )H * + (2θ XY )2 σ AA + K,

[11]

(where Yijkl and Yi ′jk ′l ′ are inbred members of the same family). Cockerham (1963) described a method to translate variance components from more complex mating designs into genetic covariances between relatives. Since both the genotypic variance component and phenotypic variance among family means can be estimated from replicated family evaluations, it is possible to estimate heritability of family means (h2f ) as the ratio of the family variance component to the phenotypic variance among family means:

σˆ 2 hˆ 2f = F . σˆ P2

[12]

Interpretation of such estimates may be difficult, however, if the family variance component does not equal the covariance between selection and response units of interest. As an example, full-sib families can be sampled from a randommating population and evaluated in r replications within each of e environments. Based on this, the variance component due to families estimates s 2F = Cov(full sibs) = (1/2)s 2A + (1/4)s 2D +(1/4)s 2AA (from this point forward, we will follow the convention of including the additive-byadditive epistatic variance component of genotypic variance, but assuming that higher-order epistatic terms are negligible). The family variance component estimate divided by the estimator of the variance among fullsib family means provides a heritability estimate of the form:

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J. HOLLAND, W. NYQUIST, AND C. CERVANTES

σˆ 2 hˆ 2f ≈ F = σ P2

1 ˆ2 σ 2 A 1 ˆ2 σ 2 A

+

1 ˆ2 σ 4 D

+

1 ˆ2 σ 4 AA

2 + 14 σˆ D2 + 14 σˆ AA

+

1 ˆ2 σ 2 AE

2 2 + 14 σˆ DE + 14 σˆ AAE

e

σˆ 2 + ε′ er

.

Is this estimator interpretable in terms of response to selection among full-sib families? To determine this, the expected response to selection among full-sib families is calculated as the selection differential times the regression coefficient of random-mated offspring on the full-sib family means representing the maternal and paternal sides of their pedigree. The response units (random-mated offspring) are related to the selection units through untested full-sibs that are used for recombination (Xf1 and Xm1 in Fig. 2.1). (Throughout this review, we assume that selection units and response units are related through untested relatives, as in Fig. 2.1 and 2.2. The same relationships hold when the response unit is a direct relative of one of the tested members of the selection unit if many individuals comprise the selection unit. If a small number of individuals comprise the selection unit, the expected covariance requires adjustment; see Nyquist (1991, pp. 291–293) for details. The expectation of the numerator of the desired regression coefficient is the expected covariance between selection and response units (Fig. 2.1):

Fig. 2.1. Pedigree relationship between members of full-sib family selection units (X ) and outbred progeny (response unit, Y) created from intermating remnant (untested) full sibs of selected families.

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E[Cov(mean of Xf 2...Xfn, Y ) + Cov(mean of Xm2...Xmn, Y)] = E[Cov(mean of Xf 2...Xfn, Y)] + E[Cov(mean of Xm2...Xmn, Y)] = E[Cov(Xf 2, Y)] + E[Cov(Xm2, Y )] = 2E[Cov(Xf 2, Y)] = 2[2qXf 2Y s 2A + uXf 2Y s 2D + (2qXf 2Y)2s 2AA] = 4qXf 2Y s 2A + 2uXf 2Y s 2D + 8(qXf 2Y)2s 2AA. Following Cockerham (1971), the coancestry of Xf 2 and Y is equal to the average coancestry of the parents of Xf 2 and Y: qXf 2Y = q( Xf 2)( Xf 1 Xm1) = ( 12 )(qXf 2 Xf 1 + qXf 2 Xm1 ) = ( 12 )[q(A B)(A B) + q( A B )(C D) ] = ( 12)[( 14 )(q AA + q AB + qBA + qBB ) + ( 14 )(qAC + qAD + qBC + qBD )] = ( 12 )[( 14 )( 12 )(1 + FA ) + ( 14 )(qAB + qBA ) + ( 14 )( 12 )(1 + FB ) + ( 14 )(qAC + qAD + qBC + qBD )]

Fig. 2.2. Pedigree relationships among selection units composed of self-fertilized St:g lines and alternative response units. Response units include “immediate response” units (remnant seed of the same generation line), “permanent response” units (completely inbred progeny of tested line), outbred S0 generation progeny resulting from intermating unrelated selected lines, and inbred St′:g′ lines resulting from self-fertilizing outbred progeny. Each response unit has a corresponding covariance with the selection unit and a corresponding heritability.

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Parents A, B, C, and D are random members of the initial population, so by definition, their coancestries are zero: qAB = qBA = qAC = qAD = qBC = qBD = 0. A, B, C, and D are also noninbred, so FA = FB = 0. Substituting these values into the equation gives: qXf 2Y = ( 12 )[( 14 )( 12 ) + ( 14 )( 12 )] = ( 12 )( 14 ) = ( 81 ) The coefficient of dominance variance in the covariance of Xf 2 and Y is the probability of their having an IBD allele pair. If Xf 2 has alleles a and b at a locus and Y has alleles c and d at the same locus, then Xf 2 and Y have an IBD allele pair if a ≡ c and b ≡ d or if a ≡ d and b ≡ c, that is, if they have the same genotype IBD at the locus. By inspection of Fig. 2.1 it is obvious that Xf 2 and Y cannot have an IBD allele pair, because one allele of Y is received from Xm1, which is unrelated to Xf 2. Formally, uXf 2Y = qAC qBD + qAD qBC = 0. Substituting these values into the equation for the covariance between selection and response units gives (Nyquist 1991, p. 277): E[Cov(mean of Xf 2...Xfn, Y ) + Cov(mean of Xm2...Xmn, Y )] = ( 12 )s 2A + ( 81 )s 2AA + ... . The denominator of the regression coefficient between selection and response units is the variance of family means with the following expectation from Equation [9]: E[σˆ P2 ] = E[σˆ F2 +

2 2 1 2 2 σ AE + 14 σ DE + 14 σ AAE σˆ 2 σ2 σˆ FE 2 + ε ′ ] = 12 σ A2 + 14 σ D2 + 14 σ AA + 2 + ε′ . e er e er

Therefore, the expectation of the regression of response units after the first generation of selection on selection units is: 1 2 σ 2 A

E[bYX ] = 1 2 σ 2 A

+

1 4

σ D2

+

1 4

2 σ AA

+

2 + 81 σ AA

1 2 σ 2 AE

2 2 + 14 σ DE + 14 σ AAE

e

σ2 + ε′ er

.

This regression coefficient differs from the heritability estimate based on the family variance component given previously by the absence of the dominance variance component in the numerator. In order to properly estimate this regression coefficient, one needs to estimate the additive genetic variance separately from the dominance variance, using a mating design experiment (Hallauer and Miranda 1988, pp. 64–83).

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The importance of defining the response unit also can be illustrated with this example. If the response unit is defined as an individual in the gametic phase equilibrium population derived by random mating the first generation population of response units for many generations without selection, the covariance between selection and response units in that gametic phase equilibrium population includes only the additive variance component, and does not include the additiveby-additive epistatic variance component (Nyquist 1991, pp. 250–251; Holland 2001). Therefore, different heritabilities are appropriate for the two different response units, first generation response units and response units after many generations of random-mating without further selection. Nyquist distinguished between these heritabilities with the notation h21 for heritability when individuals in the first generation are the response units and h2• when individuals in the random-mated equilibrium population are the response units. The full-sib family variance component is biased by (1/4)s 2D + (1/8)s 2AA as an estimator of the numerator h2f1 and biased by (1/4)s 2D + (1/4)s 2AA as an estimator of the numerator of h2f •. If selection is conceived of as simply regrowing remnant seed of only the selected full-sib families (without intermating) in an independent sample of environments from the same reference population of environments, the response to this form of “selection” is equal to the selection differential times the heritability estimator based on the family variance component. The relevant covariance in this case is the covariance between the mean of the tested family and an untested full-sib from the same family, which is expected to be (1/2)s 2A + (1/4)s 2D + (1/4)s 2AA. Perhaps this is a trivial form of selection, because it is a single-generation “dead end” that does not permit long-term selection response, but at least this provides an interpretation of the heritability estimator based on the family variance component. With this example in mind, we present heritability estimators based on different experimental and mating designs in Section VIII and Table 2.1 at the end of the chapter, accompanied by an interpretation of each estimator in terms of selection response. We also indicate the bias present in the numerators of these estimators relative to h21. The heritability estimators based on family variance components from outbred clonal families, half-sibs, and families created by testcrosses to an inbred line can all be interpreted directly in terms of response to selection among these family types and measured in corresponding family types developed after intermating the selected parents. In contrast, with full-sib families and self-pollinated lines, the interpretation of heritability estimators based on family variance components is difficult, and such heritability

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estimates have less practical utility, being interpretable only in terms of very limited concepts of selection, unless the genetic variance is fully composed of additive genetic variance. Finally, we caution that aside from the potential biases in estimates of heritability calculated as the ratio of family variance component to phenotypic variance due to nonadditive genetic components of variance, there is also a statistical source of bias in this type of estimator. The difficulty is that the expectation of this ratio does not necessarily equal the ratio of the expectations of family and phenotypic variance components:  σˆ 2  E[σˆ 2 ] F E hˆ 2f = E  F  ≠ . 2 2 ˆ ˆ  σ P  E[σ P ]

( )

Small portions of the variances and covariances of the estimated variance components (see Section V.C.1) contribute to the expectation of such a heritability estimator (see Lynch and Walsh 1998, pp. 808–809 for the expectation of a complex variable such as heritability). Researchers can compute the bias for specific cases of this type of heritability estimator, but we expect such biases to be small. Furthermore, as sample sizes increase, this source of bias will decrease in magnitude. Thus, this heritability estimator is asymptotically unbiased, in that: E ( hˆ 2f 1 ) = h2f 1, as e, r , f → ∞. Specifically, when heritability is estimated as a ratio of linear combinations of variance components using the methods described in Section V.B.1 (when data are balanced) and V.B.4 (in the general case), the estimator is consistent, meaning that:

(

 Pr  hˆ 2f 1 − h2f 1 

)

2

 > ε  → 0 as e, r , f → ∞, for any positive ε , 

or with a slight abuse of notation: hˆ 2f 1 → h2f 1, as e, r , f → ∞. B. Variance Component Estimation Procedures 1. Balanced Data. Traditionally, plant breeders and quantitative geneticists have estimated heritabilities based on variance components esti-

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Table 2.2. ANOVA layout for balanced data from a replicated evaluation of f half-sib families in r complete blocks within each of e environments. Source of Variation

Degrees of Freedom

Expected Mean Square

Environment

e–1

s 2e + fs 2R + rs 2FE + rfs 2E

Rep (Environment)

(r – 1)e

s 2e + fs 2R

Family

f–1

s 2e + rs 2FE + ers 2F

Family × Environment

( f – 1)(e – 1)

s 2e + rs 2FE

Error

( f – 1)(r – 1)e

s 2e

mated from ordinary least squares analysis of variance (ANOVA). Observed mean squares were equated to their expectations (linear functions of variance components) and the variance components were estimated algebraically as functions of mean squares. This estimation method is referred to as the method of moments (Milliken and Johnson 1992, pp. 233–239). Rules for deriving expected mean squares are given by Steel et al. (1997, pp. 379–384) and by Milliken and Johnson (1992, pp. 216–231), and specific ANOVA layouts including expected mean squares for numerous mating and experimental designs are presented by Hallauer and Miranda (1988) and Nyquist (1991). Equations for computing observed mean squares are given in most standard statistics texts, including Steel et al. (1997). To illustrate the method of moments estimation procedure, an ANOVA layout including expected mean squares for an experiment involving f half-sib families evaluated in r replications within each of e environments is presented in Table 2.2. The variance component due to half-sib families is estimated with the following linear function of observed mean squares:

σˆ F2 =

MSFamily − MSFamily × Environment er

.

By Equation [10], the expectation of this family variance component is the covariance between half-sibs, which is equal to one-fourth of the additive genetic variance plus one-sixteenth of the additive-by-additive genetic variance: E[σˆ F2 ] =

1 4

σ A2 +

1 16

2 σ AA .

The variance among half-sib family means can also be estimated as a function of the family mean-square estimate, based on the following expectation:

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 MSFamily  σ 2 + rσ 2 + erσ 2 σ2 σ2 FE F  = ε′ = σ F2 + FE + ε ′ . E[σˆ P2 ] = E  er e er  er  The heritability corresponding to selection among half-sib family means is commonly estimated by the ratio of these two estimators using Equation [12] (Nyquist 1991, p. 278): hˆ 2f 1 =

σˆ F2 MSFamily / er

=

σˆ F2 ≈ 2 ˆ ε2′ ˆ FE σ σ 2 + σˆ F + er e

1 4 1 4

σˆ A2

+

1 16

2 σˆ AA

σˆ A2 + +

1 4

1 16

2 σˆ AA

2 1 ˆ2 + 16 σˆ AE σ AAE σˆ ε2′ + e er

.

In these equations, we have assumed that the data are balanced, that is, that e, r, and f are constant values and no data are missing. In this case, standard errors of the variance component estimates are estimable (Hallauer and Miranda 1988, p. 91), and methods for estimating confidence intervals for heritability estimates based on nested mating designs were developed by Graybill et al. (1956) (for a biased estimator of heritability) and Broemeling (1969) (for an unbiased estimate of narrow-sense heritability). Knapp et al. (1985), Knapp (1986), and Singh et al. (1993) derived exact confidence intervals for heritability on a family-mean basis for some typical plant breeding experiments. Knapp and Bridges (1987; also see Nyquist 1991, p. 311) developed approximate confidence intervals for family-mean based heritabilities estimated from more complicated plant breeding designs, such as perennial crop traits measured over time and factorial mating designs replicated over environments. All of these methods involved functions of mean squares and assumed that data were balanced. 2. Unbalanced Data. Generally, plant breeding experiments are designed as balanced experiments, but often unbalanced data sets arise for unplanned reasons: seeds of particular families may not be sufficient for complete replication, plots may be lost due to planting or harvesting errors, or plots may be discarded due to exceptional stresses. The effects of unbalanced data include changes in the coefficients of variance components in the expected mean squares, loss of independence between mean squares, and unknown distributional properties of variance component estimates. The changes in the coefficients of expected mean squares can be handled using methods given by Milliken and Johnson (1992, pp. 219–231), and correct coefficients of variance components in the expected mean squares can be computed with software packages

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such as SAS (Rawlings 1988, pp. 466–467; SAS Institute 1999). Also, an approximate method to handle missing data in experiments designed to estimate heritability is to predict the missing plots using an analysis of covariance (Steel and Torrie 1980, pp. 426–428; Nyquist 1991, p. 262). Using these methods, variance components and heritability can be estimated using method of moments procedures. The variance-component estimates are unbiased, but no longer are they minimum variance estimators (Shaw 1987; Milliken and Johnson 1992, p. 233). Furthermore, their distributional properties are not known (Shaw 1987; Milliken and Johnson 1992, p. 233), such that estimates of the precision of variance component estimates and heritability estimates are not available. Another approach is to eliminate those families that are missing data, in order to obtain a balanced data set, but this decreases the efficiency of the estimate. Better estimates (those with smaller variances and with known distributional properties) can be obtained using maximum likelihood methods, specifically, restricted maximum likelihood (REML). When data are completely balanced and there are no negative estimates of variance components under the ANOVA method, the ANOVA and REML variance component estimates are identical (Shaw 1987). When there are missing data, resulting in an unbalanced data structure, however, REML estimates of variance components are more desirable because they are consistent estimators, asymptotically normally distributed, and their asymptotic sampling dispersion matrix is known (Shaw 1987; Searle et al. 1992; Dieters et al. 1995). Modern computers and software have made REML-based estimates of variance components relatively easy to obtain. For example, GENSTAT and PROC MIXED of SAS provide robust and convenient methods for conducting REML analysis of many types of mixed model designs (Littell et al. 1996; Payne and Arnold 1998; SAS Institute Inc. 1999). 3. Maximum Likelihood and Restricted Maximum Likelihood Estimation. In this section, the procedures involved in maximum likelihood estimation are described with a minimum of mathematical detail. This process is also described in Milliken and Johnson (1992, pp. 239–242) and Lynch and Walsh (1998, pp. 853–867). The computational procedures are too complex to be performed by hand reasonably, so they can only be effectively implemented using computers. It may be of use to some readers to understand the basic procedure that occurs in the computing process. Maximum likelihood estimation is a general method of estimating any sort of parameters from data. It can be used any time that one can

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write the likelihood function of the parameters to be estimated. In comparison, the method of least squares in ANOVA involves solving for the parameter estimates that fit a given model with the minimum squared deviations. Using these solutions, the probability of the estimated parameter being greater than the parameter under the null hypothesis (generally that the parameters are zero) is computed, and these probabilities are reported as p-values. Maximum likelihood estimation works in a different manner: it begins by postulating the likelihood of different possible parameter values based on the model assumptions, given the observed data. It then chooses the set of parameter estimates with maximum value of the likelihood under those conditions. The likelihood function of the data depends on the assumptions made about how the data are distributed. The likelihood function indicates how likely it is to observe the data given a model and its distribution. The likelihood function is based on the probability density function (PDF) for the model. For example, the PDF for a single observation of a normally distributed random variable y is: e −( y − µ ) /(2σ 2

P ( y | µ, σ 2 ) =

2

)

2πσ 2

(Casella and Berger 1990, p. 103). The equation involves two parameters: the mean (m) and the variance (s 2). The mean indicates where the peak of the distribution is (where a randomly-chosen data point is most likely to be), and the variance indicates how “spread” the distribution is. If the variance is high, it is more likely to observe data points farther from the mean than if the variance were low. So, given a mean and a variance for a normal distribution, and given a value of an observation, the probability of observing the data point is obtained with the PDF. Given a different mean and variance, but the same data point, however, one might calculate a different probability for the data point. There are an infinite number of possible combinations of means and variances that could be used, but they will differ in terms of how likely it was to observe the data point from any of them. The maximum likelihood estimate of the mean and the variance is that pair of estimates that together gives the highest likelihood of observing the data. From among all of the infinite possible combinations of means and variances, we are interested only in the one with the highest likelihood, that is, the maximum likelihood estimate. Obviously, with only a single data point, one cannot estimate both a mean and a variance. So, we need a function that describes the

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likelihood of observing a whole set of data points. This can be derived very simply by remembering that all of the observations from the distribution are drawn randomly and independently (if we do the experiment properly), and, therefore, the joint probability of observing n independent events (i.e., data points) simultaneously is simply the product of the probabilities of observing each event individually: P ( y 1, y 2, K, y n | µ, σ 2 ) =

n

∏ P(y i | µ, σ 2 ). i =1

The likelihood of the parameters given the data is equivalent to the probability of the data given the parameters: L(m,s 2|y1, y2, …, yn) = P(y1, y2, …, yn|m, s 2). The likelihood function is the same as the joint PDF. However, the joint PDF is regarded as a function of the random variables yi, conditional on the parameters, whereas the likelihood function is viewed primarily as a function of the parameters, conditional on the observations yi. Recall that there is an infinite set of means and variances that can be tried by calculating their likelihood. We will describe a combination of mathematical and “searching” methods that can be used to identify the best estimate out of all of the possibilities. As an example, consider a simple two-factor factorial experiment in which a quantitative trait is observed. The linear additive model for the experiment is: Yijk = m + ai + bj + abij + eijk. If both A and B are considered random, the following standard model assumptions about the distribution of random model effects are made: a ~ N(0, s 2a), b ~ N(0, s 2b), ab ~ N(0, s 2ab), e ~ NID(0, s 2e). The likelihood function of the parameters given the data (with n total observations) is based on the known distributional properties of multivariate normally distributed variables. The PDF for this type of model,

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which is an extension of the standard normal distribution, but permitting more than one random effect in the model, is (Lynch and Walsh 1998, pp. 194–195): 2 L(µ, σ α2 , σ β2 , σ αβ , σ ε2 | Y) =

e

− 12 ( Y − m )′ V −1 ( Y − m )

2π n | V |

.

The elements of this equation include the mean and the variance component parameters that we wish to estimate; the observed data, Y, which here is written as a vector of observations y1, y2, ...yn; and the variance-covariance matrix of the observations, V. If we choose a set of values for the parameters of interest (the variance components), we can calculate the likelihood that those parameter values would have produced the data actually observed. The distributional assumptions inherent in the model are used to determine the structure of variance-covariance matrix, V, for the entire data set, Y. V indicates the covariances between every pair of observations in the entire data set. We can determine the expected covariance between any two observations (Yijk and Yi′j ′k ′ ) by expanding their values in terms of the model effects as follows: E[Cov (Yijk, Yi ′j ′k′)] = E[Cov (m + ai + bj + abij + eijk, m + ai ′ + bj ′ + abi ′j ′ + ei ′j ′k′)] = E[Cov (m, m) + Cov (m, ai ′) + Cov (m, bj ′) + Cov (m, abi ′j ′) + Cov (m, ei ′j ′k′) + Cov (ai, m) + Cov (ai, ai ′) + Cov (ai, bj ′) + ... + Cov (bj, bj ′) + ... + Cov (abij, abi ′j ′) + ... + Cov (eijk, ei ′j ′k′)]. The covariance between a constant and anything else is zero, so all of the covariances involving the constant m are zero. All of the expected covariances between different factors, such as Cov(ai, bj′) are also zero because there is no covariance between the level of one factor and the level of another if we have a properly randomized experiment. So the ellipses in this formula includes many of those covariances, which are all zero. The formula then reduces to: E[Cov(Yijk, Yi ′j ′k ′)] = E[Cov (ai, ai ′) + Cov (bj, bj ′) + Cov (abij, abi ′j ′) + Cov (eijk, ei ′j ′k ′)]. If i ≠ i ¢, then the two observations were made on different levels of A, in which case they have expected covariance of zero, because the levels of

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A were drawn at random from the population of A levels. However, if i = i¢, then E[Cov (ai, ai)] = s 2a. This reasoning can be applied to each of the covariances. If the two observations have the same level of B in common, then their covariance includes s 2b. If they have both the same levels of A and B in common, then their covariance includes s 2a, s 2b, and s 2ab. Finally, the expected covariance of any observation with itself includes s 2e along with s 2a, s 2b, and s 2ab. The V matrix includes all of the covariances between each observation and is used in the PDF of the model that includes all of the normally distributed parameters (s 2a, s 2b, s 2ab, and s 2e). Using as an example a twofactor factorial experiment in which each factor has two levels, and with two replications of each treatment combination in a completely randomized design, the following data set and models result: Y111 = m + a1 + b1 + ab11 + e111, Y112 = m + a1 + b1 + ab11 + e112, Y121 = m + a1 + b2 + ab12 + e121, Y122 = m + a1 + b2 + ab12 + e122, Y211 = m + a2 + b1 + ab21 + e211, Y212 = m + a2 + b1 + ab21 + e212, Y221 = m + a2 + b2 + ab22 + e221, Y222 = m + a2 + b2 + ab22 + e222. The covariances among these observations are included in the V matrix, in which the first row and first column correspond to covariances involving observation Y111, the second row and second column correspond to covariances involving Y112, and so forth. Using the notation s 2T = s 2a + s 2b + s 2ab + s 2e, and s 2M = s 2a + s 2b + s 2ab, the V matrix appears as follows: σ 2  T2 σ M σ α2 σ 2 V =  α2 σ β σ 2  β 0 0 

2 σM σ T2 σ α2 σ α2 σ β2 σ β2 0 0

σ α2 σ α2 σ T2 2 σM 0 0 σ β2 σ β2

σ α2 σ α2 2 σM σ T2 0 0 σ β2 σ β2

σ β2 σ β2 0 0 σ T2 2 σM 2 σα σ α2

σ β2 σ β2 0 0 2 σM σ T2 σ α2 σ α2

0 0 σ β2 σ β2 σ α2 σ α2 σ T2 2 σM

0   0  σ β2  σ β2  . σ α2  σ α2  2  σM  σ T2 

[13]

To simplify the search for the parameter estimates, the derivative of the likelihood function can be taken with respect to a parameter. The

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value of that parameter that makes the derivative equal to zero represents the maximum likelihood value of the parameter given the other parameter values. This method is based on the fact that the derivative of any function with respect to a variable equals zero at the point where the variable causes a peak (maximum value) in the function. The natural log of the PDF is usually easier to differentiate than the PDF, and the maximum point of the log of a function is also a maximum point of the function itself. Computation of the likelihoods becomes impractical for very large sample sizes, whereas computation of the log likelihood is much easier. Therefore, in practice, the log of the PDF is differentiated and set to zero to solve for the maximum likelihood value of the variable with respect to which the derivative was taken. The log of the likelihood function equation described above includes some constant, k1, which will “disappear” upon differentiation: 2 log[L(µ, σ α2 , σ β2 , σ αβ , σ ε2 | Y)] = k1 − 12 log| V | − 12 (Y − µ)T V −1 (Y − µ).

The derivative of this log-likelihood function with respect to one of the parameters (e.g., s 2a) is computed, set equal to zero, and the equation is solved for the maximum likelihood value of that parameter. The same is done for the next parameter (e.g., s 2b), and so on. A complication is that the likelihood of any parameter depends on the value of the other parameters in the model, because the V matrix contains all of the different variance component estimates. Thus, an initial set of estimates for all of the parameters is required and the parameter estimation process must be iterated until a stable solution for all parameters is found. The iterative procedure usually, but not always, converges to the best possible solution. This roughly describes the process that the MIXED procedure in SAS uses to obtain REML estimates of variance components. REML is a modification of the general maximum likelihood that produces parameter estimates with smaller bias (Lynch and Walsh 1998, pp. 789–791). REML proceeds by first estimating the fixed effects in the model, then by maximizing the likelihood function of n* residual orthogonal contrasts, where n* is the number of degrees of freedom remaining after fitting the fixed effects in the model (Lynch and Walsh 1998, pp. 789–791). The method of transforming the original data to the n* residual orthogonal contrasts is shown in Lynch and Walsh (1998, p. 790). A simple and very instructive example of the difference between maximum likelihood and REML is given by Steel et al. (1997, p. 411).

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The PROC MIXED output provides a list of the number of iterations it used to find the estimates. If it cannot converge to a solution, it provides a warning statement that the solutions did not converge. If this happens, the estimates are not unique and should be viewed with suspicion. One can try again by starting with new initial parameter estimates. Having estimated the variance components with REML, heritability estimates can be computed as functions of the variance component estimates. 4. The Likelihood Ratio Test. Mixed models analysis does not involve computation of mean squares of random factors; therefore, F-tests for the effects of random model factors are not available with mixed models analysis. Instead, the null hypothesis that a variance component for factor i is equal to zero (H0: s 2i = 0, HA: s 2i > 0) can be tested with a likelihood ratio test. To conduct the likelihood ratio test, one must analyze two models separately. One model, referred to as the full model, contains all the parameters of interest, including the variance component for factor i, s 2i. A second model, called the reduced model, contains all of the same parameters, except for the one whose significance is to be tested, s 2i. The likelihoods or the log likelihoods of the two models are compared using the likelihood ratio (LR) test: LR = 2log

LF L = −2log Red = −2[log(LRed ) − log(L F )], LRed LF

where LF is the likelihood of the maximum likelihood full model, LRed is the likelihood of the maximum likelihood reduced model, and “log” refers to the natural log, as in Section V.B.3 (Steel et al. 1997, pp. 412–413; Lynch and Walsh 1998, pp. 857–858). The LR statistic is distributed as a c 2 with degrees of freedom equal to the number of parameters dropped from the full model to make the reduced model. Typically, each variance component is tested one at a time, leading to LR statistics with one degree of freedom. For example, if there are 100 levels of the random factor i in the experiment and it has 99 degrees of freedom associated with it in the model, the LR test constructed by dropping that factor (and thus the parameter s 2i) from the model has only one degree of freedom. For the test of the null hypothesis that a variance component is zero, the p-value of the LR test should be divided by two (Self and Liang 1987). The example given in Steel et al. (1997, pp. 412–414) demonstrates that the p-value of the LR test is approximately

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twice as large as the exact F-test of the same hypothesis. Likelihoods (and twice the log likelihoods) of models are part of the default output of SAS PROC MIXED, so the LR test is easily accomplished in practice. Cervantes-Martinez et al. (2001) used likelihood ratio tests to test the significance of family and family-by-environment variance components in a plant breeding experiment. 5. Mixed Models Analysis. Heritability estimation via REML will be beneficial when data are unbalanced (Section V.B.2). REML estimation of heritability has an additional advantage in that it can easily be performed in the context of mixed models analysis methods. We suggest that this will prove useful to plant breeders because it facilitates simultaneous estimation of variance components and heritability in different populations included in the same experiment. Mixed models analysis will also permit estimation of variance components and heritability from incomplete block designs (and perhaps spatial analysis methods), leading to improved heritability estimates through better control of experimental error. Also, with mixed models analysis, heritability estimation can be combined with best linear unbiased prediction (White and Hodge 1989). Combined estimation of genetic components of variance from multiple experiments containing different genetic entries with some known genetic relationships may be possible by augmenting the mixed models approach with pedigree analysis methods. Furthermore, mixed models are more appropriate than ANOVA for handling repeated observations on experimental units, as commonly occurs in perennial crops (Nyquist 1991, pp. 260–264; Littell et al. 1996, pp. 87–134). We confine ourselves in this chapter to briefly demonstrating simultaneous estimation of variance components from different populations grown in the same experiment, including incomplete blocks designs. The general mixed model has the form: y = Xb + Zu + e,

[14]

where y is a vector of observed values, X is a design matrix for fixed effects, β is a vector of fixed effects, Z is a design matrix for random effects, u is a vector of random effects, and e is a vector of error effects associated with each observation (White and Hodge 1989, pp. 278–280; Lynch and Walsh 1998, p. 746). Using our previous example from Section V.B.3 of a two-factor factorial experiment with two replications of a completely randomized design and two levels of each random

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factor, the model can be written in mixed model form using the following matrices: yT = [Y111 uT = [a1 eT = [e111

Y112 a2 e112

Y121 b1 e121

Y122 b2 e122

Y211 ab11 e211

Y212 ab12 e212

Y221 ab21 e221

Y222], ab22], e222].

The columns of the Z matrix correspond to the rows of the u matrix (or the columns of the transposed u matrix shown). For each observation, the Z matrix indicates whether or not it is affected by each random model effect in u: 1 0 1 0 1 0 0 0  1 0 1 0 1 0 0 0    1 0 0 1 0 1 0 0  1 0 0 1 0 1 0 0  . Z = 0 1 1 0 0 0 1 0   0 1 1 0 0 0 1 0  0 1 0 1 0 0 0 1 0 1 0 1 0 0 0 1   X is an 8 × 1 column vector of 1’s, β is a 1 × 1 vector containing the only fixed effect in the experiment, m. Putting these matrices together generates the data set of eight observations and eight combinations of model effects already given in Section V.B.3: Y111 = m + a1 + b1 + ab11 + e111, Y112 = m + a1 + b1 + ab11 + e112, Y121 = m + a1 + b2 + ab12 + e121, Y122 = m + a1 + b2 + ab12 + e122, Y211 = m + a2 + b1 + ab21 + e211, Y212 = m + a2 + b1 + ab21 + e212, Y221 = m + a2 + b2 + ab22 + e221, Y222 = m + a2 + b2 + ab22 + e222. The variance-covariance matrix of the observations, V, in this case is the same as in Equation [13], but in the mixed model it is arrived at by equation: V = ZGZT + R, where G is the symmetric matrix that includes the

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variances and covariances between random effects in the model (all offdiagonal elements are zero:   σ2  α 2  σα     2 σβ   2   σβ . G= 2   σ αβ   2 σ αβ     2 σ αβ    2  σ αβ   R is a symmetric matrix that generally contains the error variance along the diagonal (Lynch and Walsh 1998, p. 748). In this example, ZGZT is equal to V given in Equation [13], except that ZGZT lacks the error variance terms, which are instead partitioned into the R matrix. One utility of the mixed model is that it permits estimation of both fixed and random effects (Littel et al. 1996, p. 499): ˆ −1X)− XT V ˆ −1y, βˆ = (XT V ˆ −1 (y − Xbˆ ). uˆ = GZT V The significance of fixed-effect factors is tested with F-tests, whereas the significance of random factors can be tested with likelihood ratio tests (Section V.B.4). Another major benefit of mixed models analysis is its flexibility in modeling the variances and covariances of random and error effects. Pertinent to the estimation of heritability, the form of the G matrix can be modified to fit different variance-covariance structures among the random effects, permitting both the simultaneous fitting of incomplete block effects along with variance component estimation or the estimation of unique variance components for different subsets of genetic entries. We will not explicitly demonstrate the inclusion of incomplete block effects in the model, as it is straightforward to add another random effect to the model and estimate the block effects and variances separately from the genetic components of variance in the G matrix. To separately estimate variance components for different sets of entries in an experiment, one needs to model the G matrix so that it permits dif-

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ferent subsets of levels of the same factor (e.g., the family factor or the line factor in a heritability estimation study) to have unique variances. For example, we assumed in Section V.B.3 that all levels of factor A were drawn from a common reference population and have the same variance: a ~ N(0, s 2a). With mixed models procedures, however, we can estimate separate variance components for factor A for different subsets of entries. Formally, we can allow each group g to have a unique variance for factor A: ag ~ N(0, s 2a(g)), and the estimation of these separate variance components for factor A is accomplished by modifying G. To demonstrate by expanding the previous example of the two-factor factorial experiment (Section V.B.3), we assume that four different levels of factor A were sampled and that potentially levels 1 and 2 have a common variance for factor A (s 2a(1)), whereas levels 3 and 4 were drawn from a population of factor A with a unique variance, (s 2a(2)). If each level of factor A occurs with each of the two levels of factor B and is replicated twice, this experiment would have 16 observations, as follows: Y111 = m + a1 + b1 + ab11 + e111, Y112 = m + a1 + b1 + ab11 + e112, Y121 = m + a1 + b2 + ab12 + e121, Y122 = m + a1 + b2 + ab12 + e122, Y211 = m + a2 + b1 + ab21 + e211, Y212 = m + a2 + b1 + ab21 + e212, Y221 = m + a2 + b2 + ab22 + e221, Y222 = m + a2 + b2 + ab22 + e222, Y311 = m + a3 + b1 + ab31 + e311, ... Y422 = m + a4 + b2 + ab42 + e422. Compared to the model in Section V.B.3, the β vector is unchanged, but the other matrices change. The X matrix is augmented from an 8 × 1 to a 16 × 1 matrix by adding eight more rows of ones. The u vector is augmented from an 8 × 1 to a 14 × 1 vector by adding columns corresponding to two additional factor A main effects and four additional AB interaction effects. The Z matrix is augmented by adding six additional columns corresponding to the additional rows of the u vector, and eight additional rows corresponding to the eight additional observations. The G matrix structure is similar to the previous example, except that it would have dimensions of 14 rows and 14 columns, in which the first two diagonal elements are s 2a(1), the next two diagonal elements are s 2a(2),

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the next two diagonal elements are s 2b, and the final eight diagonal elements are s 2ab. From this structure, ZGZT models the covariance of two observations with the same level of A to include s 2a(1), if these observations have levels 1 or 2 of factor A, or to include s 2a(2), if the observations have levels 3 or 4 of factor A. The e vector increases by eight rows to account for the new observations, and the R matrix is augmented with eight more rows and columns, but has the same diagonal structure as before. Similarly, one could also assume that the subset of entries drawn from the population of levels of A with variance s 2a(1) may also have a unique interaction variance with factor B: s 2ab(1), as opposed to s 2ab(2) for the second subset. These could also be fitted in the model by modifying the G matrix appropriately. Finally, one can also fit unique error variances for different subsets of entries by modifying the R matrix such that one group has error effects drawn from the population with variance s 2e(1), and the other has error effects drawn from the population with variance s 2e(2). The ability to separately model variance components for different sets of treatments in the same experiment has considerable utility for variance component and heritability estimation experiments in plant breeding. For example, if one wants to compare unselected populations to populations derived from one or more generations of selection from it, it is reasonable to want to study the effect of selection on both population means and variances, as well as on heritability. Previously, plant breeders handled this by estimating population means with an ANOVA based on all of the data, and then by estimating variance components separately for subsets of families belonging to different cycles of selection. The latter procedure is inefficient if incomplete block designs were used: generally, if subsets of entries were analyzed for variance components, incomplete block effects were ignored in the analysis because, otherwise, severe data imbalance resulted. Even if incomplete blocks were maintained in the analysis of separate groups, considerable information on both complete and incomplete block effects was lost. The improved precision of estimates of genetic effects permitted with incomplete block designs was thus lost when estimating variance components. With mixed models analysis, however, a single analysis procedure can be used to estimate complete and incomplete block effects (thus improving precision), and to estimate unique variance components for subsets of genetic entries representing unique reference populations. Another advantage is that genetic covariances between related families, lines, or individuals in different generations of inbreeding from the same reference population can be estimated, along with estimation of separate

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genetic variances for each generation. As discussed in Section III.B, the genetic expectations of variance and covariance components under inbreeding are complex, involving more than simply additive and dominance genetic variances (Equation [5]). By testing related lines in different generations of inbreeding and appropriately modeling the genetic variance component within generations and covariances between generations, it may be possible to obtain better estimates of the nonadditive components of variance than have been reported to date (Cornelius and Dudley 1976). Similarly, it is sometimes of interest to compare the genotype-by-environment interactions of different populations grown in the same sets of environments in order to determine if environmental stability differs among the populations (Holland et al. 2000), and this can be accomplished by modeling unique GE interaction variances as well as unique genetic variances for the different populations. Finally, the method is robust for missing data, as already discussed. These new modeling approaches are already available in practice to most plant breeders. For example, all of the analyses described can be performed with SAS PROC MIXED (Littell et al. 1996), with careful use of the “group” and “subject” options available with the “random” and “repeated” statements. SAS code for specific examples of models with multiple genetic variances are provided in Appendices 3 and 4, and SAS code and example data sets are available at www4.ncsu.edu/~jholland/heritability.html. Tests are available in PROC MIXED for hypotheses generated with these new analysis approaches. If separate variance components are estimated for different subsets of families, one can test the hypothesis that the different subsets have a common population variance, by also analyzing the data as if all entries were from a single population, and using the likelihoods of the two models to conduct a likelihood ratio test of the hypothesis (Section V.B.4). 6. Difficulties Remaining with Mixed Models Analysis. At least one major practical and one major theoretical difficulty currently hinder wider adoption of mixed models analysis by plant breeders, at least in the short term. The practical difficulty is that the computer memory required to estimate all of the fixed and random effects and the covariance components can easily exceed the computer memory available to most plant breeders if a complex model and a large data set are analyzed. Each additional variance component parameter to be estimated dramatically increases memory required. For example, we attempted to estimate unique variance components for five separate genetic populations simultaneously from data collected by Cervantes-Martínez et al. (2001). The study included 100 genotypes within each population that

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were randomly assigned to sets, with each set designed as a lattice within each of four environments. Approximately 15 percent of the data points were missing. Due to memory limitations, it was not possible on either a personal computer with 256MB RAM or on a UNIX server with 2GB of RAM to simultaneously estimate with SAS PROC MIXED the five unique genetic variance components, five GE interaction variances, five unique error variances, and common variances due to environments, sets, complete blocks, and incomplete blocks. Therefore, we resorted to analyzing the populations separately. With less complex experimental designs, however, these types of analyses are feasible today (e.g., Appendix 3), and the more memory-intensive computations may be possible in the near future, given the constant and rapid advances in computing processor speed and memory availability. Research on alternative algorithms for finding maximum likelihood solutions for large and unbalanced data sets also presents a way to solve the computational difficulties of REML. When the number of parameters in a model becomes large, PROC MIXED requires large amounts of memory to identify the maximum likelihood solution with REML because it uses a ridge-stabilized Newton-Raphson algorithm to maximize the logarithm of the residual likelihood function (REML) (SAS Institute Inc. 1999). This method finds the optimum solution in fewer iterations compared to other methods (Lindstrom and Bates 1989), but to do so, it requires matrix inversion. Inversion of the large, sparse matrices associated with models with many parameters is the most memory-intensive portion of the algorithm. A less memory-intensive alternative to the Newton-Raphson algorithm is the derivative-free (DF) algorithm (Graser et al. 1987) in which the residual likelihood function is evaluated explicitly, and its maximum with respect to the variance-covariance components is located without matrix inversion. Although the DF algorithm requires less central processing unit (CPU) time per round, it often requires many more rounds of iterations to obtain converged estimates (Boldman and Van Vleck 1991), making it slower to converge. However, it has been found that this procedure is computationally feasible for experiments involving very large data sets (Graser et al. 1987; Meyer 1989, 1997). Another approach is the use of the Takahashi algorithm to invert large, sparse matrices, which removes most of the constraints on algorithms to invert large matrices. In particular, average information (AI) REML is a quasi-Newton algorithm which requires first derivatives of the likelihood, but replaces second derivatives with the average of the observed and expected information to approximate the second derivative matrix of the function evaluated at the optima. This algorithm has been found to be computationally highly advantageous over DF procedures

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(Meyer 1997). The AI REML procedure also produces approximate standard errors of variance component estimates, which are not available with the DF REML procedure. These standard errors are useful for estimating the precision of heritability estimators (Section V.C). Many strategies to reduce the computations in each round of iteration and the number of rounds required to reach convergence have been included in the derivative-free REML (DFREML) (Meyer 1989) and multiple trait derivative-free REML (MTDFREML) (Boldman et al. 1993) programs. MTDFREML software also can implement the AI REML algorithm (Boldman et al. 1993). However, these programs have been designed specifically for applications in animal breeding, and are not available for application in plant breeding yet. A theoretical difficulty also needs to be resolved before mixed models can be implemented widely for plant breeding experiments. Mixed models can handle both fixed effect factors and random effect factors simultaneously, but plant breeders often deal with a situation that is not easily handled in current mixed models. Often, plant breeders randomly sample lines or families from an experimental population to estimate variance components and heritability in the reference population, in which case the families are a random effect. Generally, however, check entries (usually widely accepted cultivars) are also included in the same experiment so that breeders can compare the best lines from the experimental population to the check entry. Obviously, the check entries were not drawn from the same reference population as the experimental lines. Nor can it reasonably be argued that the check entries are random samples from some other reference population; the check entries were chosen specifically because they are superior! Thus, one could argue that the check entries represent a group of fixed effects, whereas the experimental entries represent random effects drawn from a separate, but definable, reference population. It is not obvious how to handle this situation with mixed models analysis. Although one can model separate variance components for experimental entries and check entries, it seems that in theory no variance component can be associated with the check entries. Plant breeders did not face this dilemma previously because ANOVA procedures were used for both estimating family means and variance components, and the same equations were used to estimate the family mean squares from a multiple-environment trial whether families were considered fixed or random. Even the F-test for families was the same whether families were considered fixed or random (but changing environments from fixed to random would cause differences in the F-test for families). Therefore, means for both experimental and check entries and

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standard errors for their comparisons could be estimated from the ANOVA including all entries. Then, after dropping check entries, a new ANOVA could be performed to obtain the mean squares, and, consequently, the variance component, due to random experimental families only. A similar approach could be used with mixed models: all entries could be considered fixed effects for the purposes of making comparisons between experimental and check entries. Then a second analysis could be performed on the experimental entries only, considering them random effects, to estimate the genetic variance component in the reference population. This is an entirely reasonable approach, because in this case, for the purposes of making comparisons between experimental families and checks, one is interested only in the families actually included in the experiment, rather than in making inferences to the reference population from which they were sampled. (White and Hodge 1989, pp. 29 and 64, discuss the reasons that families can be treated as fixed for some purposes and random for other purposes.) However, some efficiency may be lost with incomplete block designs, as previously mentioned. If there were some way to treat only the check entries as fixed effects and the experimental entries as random effects in the same analysis, one could use such an analysis to estimate the variance components of the random entries while obtaining information on incomplete and complete blocks from the check entries, maximizing the precision of the estimates. In such a case, it still seems to make more sense to conduct a second analysis, considering all entries as fixed effects, to make comparisons among the lines included in the experiment. Otherwise, using a mixed models analysis, the random family effects are predicted, rather than estimated (Lynch and Walsh 1998, pp. 748–749), and best linear unbiased predictors (BLUPs) of the random families are used for comparisons (Robinson 1991). Thus, even if possible, this type of analysis would raise the issue of how experimental line BLUPs can be compared to check entry means from the same experiment. In any case, we are not satisfied with considering all entries, including both checks and random samples from experimental populations, to be random effects drawn from a common population, for the purpose of making comparisons among them with BLUPs, as we have done in some cases (Cervantes-Martínez et al. 2001) simply to make papers acceptable to journal editors. We know of no theoretical work or guidance on this subject and suggest that such work would be useful to plant breeders. Finally, we also note that the correct expectations of mean squares in the mixed model remains controversial, and that this can affect the variance component estimates. Statisticians do not agree on whether the

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expected mean square of a random factor should include the variance component of the interaction of that random factor with a different fixed factor. The debate centers on the assumptions of the model used, specifically, whether interaction effects between fixed and random factors must sum to zero across all levels of the fixed factor (Rawlings 1988, pp. 468–469; Steel et al. 1997, p. 411). Unfortunately, the model and consequent expected mean squares used in SAS PROC MIXED differ from those traditionally used by plant breeders (Steel et al. 1997, pp. 379–384). C. Precision of REML-based Heritability Estimators 1. Approximate Standard Errors of REML-based Heritability Estimators. Heritability estimates can be constructed from the REML variance component estimates using Equation [12], but the sampling variance of such a heritability estimate is not immediately obvious. Dickerson (1969) presented an approximate standard error for heritability estimated based on variance component estimates, but it is conservative (Hallauer and Miranda 1988, p. 49) and not generally recommended nowadays (Nyquist 1991, p. 310). The delta method (Lynch and Walsh 1998, p. 807) provides a general method for obtaining approximate standard errors for any statistic based on estimates with an estimated or known sampling variance-covariance matrix. Gordon et al. (1972), Dieters et al. (1995), Singh and Ceccarelli (1995), and Hohls (1996) proposed approximate standard errors for specific heritability estimators using the delta method. The general form of the approximate standard error estimator for variance component-based heritability estimates proposed by Gordon et al. (1972) is appropriate, but the specific formulas provided by them are based on covariance estimates of the estimated variance components that were derived assuming balanced data. Estimates of the covariances between the estimated variance components are provided directly by REML estimation procedures. The use of these estimates is appropriate whether or not the data are balanced when the sample size is large. Hohls (1996) described an appropriate method to obtain approximate standard errors for heritability estimates from a design II experiment, but his derivation of the standard error was incorrect. Dieters et al. (1995) compared two different approximations and an empirical estimate of the variance of heritability estimates, finding that both approximations performed reasonably well compared with the empirical estimate. Singh and Ceccarelli (1995) derived approximate standard errors for heritability estimates based on REML estimates of variance components for single- and multiple-location trials of random genotypes.

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Here, we derive approximate sampling variances and standard errors of heritability estimates based on variance and covariance components estimated by REML. The variance component estimates require the assumption of normally-distributed data, but do not require the assumption of balanced data. The proposed method is robust for missing data, assuming that data are missing at random (Little and Rubin 1987). All components necessary to use the equations are included in the SAS PROC MIXED output, and SAS code to obtain the components and to compute the estimators directly is presented in Appendices 1 to 4. An estimator for narrow-sense heritability for family means (h2f, Section V.A) is:

σˆ 2 hˆ 2f = F , σˆ P2 where s 2F is the family variance component in the reference population, and s 2P– is the phenotypic variance of family mean deviations in the reference population (Equation [12]). The phenotypic variance of family mean deviations is estimated as the sum of the estimates of the genetic variance component and other variance components multiplied by coefficients. The other variance components generally include those corresponding to family-by-environment interaction (FE) (s 2FE), experimental error (s 2e), and within-plot variance (s 2w), if data on individual plants are available. To obtain a general form for the sampling variance of the heritability estimate, the phenotypic variance component will be written as:

σˆ P2 = σˆ F2 + c2σˆ 22 + c3σˆ 32 + K + c k σˆ k2 , where k is the number of variance components contributing to the phenotypic variance of family means; and s 22, s 23, ..., s 2k refer to the (k-1) other variance components whose estimates are multiplied by coefficients c2, c3, ..., ck, respectively, and summed along with s 2F to estimate phenotypic variance of family mean deviations. To estimate the sampling variance of the heritability estimator, estimates of sampling variances of all of the variance component estimates included in the heritability equation are needed. In addition, estimates of the covariances between all of the variance component estimates included in the heritability formula are required. These elements can be written as a k × k variance-covariance matrix, C:

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 Vˆ (σˆ 2 ) F  ˆ (σˆ 2 , σˆ 2 ) C  2 F Cˆ =  M  Cˆ (σˆ F2 , σˆ k2 ) 

57

Cˆ (σˆ F2 , σˆ 22 ) L Cˆ (σˆ F2 , σˆ k2 )  L Cˆ (σˆ 22, σˆ k2 )  Vˆ (σˆ 22 ) , O M M   Cˆ (σˆ 22, σˆ k2 ) L Vˆ (σˆ k2 ) 

where Vˆ (sˆ 2i) refers to the estimated variance of the ith variance component estimate, and Cˆ (sˆ 2i,sˆ 2j ) refers to the estimated covariance between the i th and j th variance component estimates. The variance component estimates computed by REML procedures are asymptotically normally distributed, with variances and covariances given by the C matrix. The approximation to normality improves with larger sample sizes (Searle et al. 1992). The delta method can be used to obtain the approximate variance of a function of asymptotically normally distributed estimators (Lindsey 1996; Lynch and Walsh 1998, p. 807). Therefore, given REML estimates of the variance components and of the elements of the C matrix, and considering the heritability estimate to be a function of the estimators, sˆ 2F, sˆ 22, sˆ 23, …, sˆ 2k, we can apply the delta method to obtain the approximate sampling variance of the heritability estimate. In general, the approximate sampling variance of any estimator, f, that is a function of k moments, m1, m2, ..., mk, is given by: 2

V (φ ) ≈

∑ i

 ∂φ  ∂φ ∂φ C (mi , mi ′ )   V (mi ) +  ∂mi  i ≠ i ′ ∂mi ∂mi ′

∑

[15]

[see, for example, Mode and Robinson (1959) or Bulmer (1985, p. 86)]. This formula is obtained as the first two (lowest order) terms in the Taylor series expansion of f around its true value. Equation [15] can also be written in matrix form as:  ∂φ     ∂m1   ∂φ    V (φ ) =  ∂m2   M     ∂φ   ∂m  k  

T

V (m1 ) C (m1, m2 )  C (m1, m2 ) V (m2 )  M M  C (m1, mk ) C (m2, mk )

L C (m1, mk )  L C (m2, mk )  O M  L V (mk ) 

 ∂φ     ∂m1   ∂φ     ∂m2  .  M     ∂φ   ∂m  k  

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Applying this quadratic form to the heritability estimator, gives:  ∂hˆ 2   f   ∂σˆ F2   ˆ2   ∂hf  2 ˆ ˆ V (hf ) =  ∂σˆ 2   2  M   ˆ2   ∂hf   2  ∂σˆ k 

T

Vˆ (σˆ 2 ) Cˆ (σˆ F2 , σˆ 22 ) F  Cˆ (σˆ F2 , σˆ 22 ) Vˆ (σˆ 22 )  M M  Cˆ (σˆ F2 , σˆ k2 ) Cˆ (σˆ 22, σˆ k2 ) 

L Cˆ (σˆ F2 , σˆ k2 )  L Cˆ (σˆ 22, σˆ k2 )   O M   L Vˆ (σˆ k2 ) 

 ∂hˆ 2   f   ∂σˆ F2   ˆ2   ∂hf   ˆ2 .  ∂σ 2   M   ˆ2   ∂hf   2  ∂σˆ k 

The k × 1 column vector containing the derivatives of the heritability estimator with respect to the different variance components included in the estimator will be referred to as d. In this way, the general formula for the sampling variance for the heritability estimate can be expressed in matrix notation as: ˆˆ Vˆ (hˆ 2f ) = dˆ ′Cd.

[16]

The estimated C matrix can be obtained directly from the output of SAS PROC MIXED (SAS Institute Inc. 1999) and from GENSTAT (Singh and Ceccarelli 1995; Hohls 1996; Payne and Arnold 1998). All that remains, therefore, is to determine the derivatives involved in the d vector to obtain a specific formula for the sampling variance of a particular heritability estimator. The derivative of the heritability estimator with respect to any of the variance components in the equation can be written as:

∂hˆ 2f ∂σˆ i2

(σˆ ) ∂∂σσˆˆ − (σˆ ) ∂∂σσˆˆ = (σˆ ) 2 P

2 F 2 i

2 F

2 P

2

Therefore, the d vector can be simplified to:

2 P 2 i

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 2  σˆ P   2 1  σˆ P  d= 2  σˆ P2    σˆ P2   σˆ 2 − σˆ 2 F  P  2 1  − c2σˆ F = σˆ P2  M  2  − c σˆ k F 

( ) ∂∂σσˆˆ − (σˆ ) ∂∂σσˆˆ ( ) ∂∂σσˆˆ − (σˆ ) ∂∂σσˆˆ

( )

2 F 2 F 2 F 2 2

2 F

2 F

2 P 2 F 2 P 2 2

M

     1 =  σˆ P2    

( )

( ) ∂∂σσˆˆ − (σˆ ) ∂∂σσˆˆ ( ) / σˆ  1 − hˆ  ( ) / σˆ  = 1  −c hˆ . (

) / σˆ

2 F 2 k

2 F

2 P 2 k

2 P

( ) ( ) ( ) ( )

 σˆ 2 (1) − σˆ 2 (1)  F   P   ˆ2 2 ˆ  σ P (0) − σ F (c2 )    M    σˆ 2 (0) − σˆ 2 (c ) F k   P 

( ) ( )

2 f

2 P

2 P

2

59

2

   

2 f

σˆ P2  M  −c hˆ 2   k f

This can be further generalized by specifying the relevant variance components in a matrix called s, and defining the family and phenotypic variances as functions of coefficient vectors λG and λP multiplied by σ: σ 2   F2  σ  σ =  2 ,  M  σ k2   

λG

1    0 =  , M    0

1    c λP =  2  , M    c k 

[17]

[18]

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σ F2

σ P2

T

σ 2   F2  σ 2  T   = λ G σ, M   σ k2    T 1  σ G2     2 c σ  =  2   2  = λ TP σ. M  M     c k  σ k2  1    0 =  M    0

Using these equations, the d vector can be written as: d=

[

]

1 λ G − ( h2f λ P ) . 2 σP

[19]

As an example, we will derive the sampling variance for heritability on an experimental-unit basis (plot basis) estimated from an experiment in which data are taken on a set of random half-sib families grown in randomized complete block trials in multiple environments. In this case, heritability on an experimental-unit basis is estimated by: hˆ 2f =

σˆ F2 σˆ 2 = F, 2 + σˆ ε2′ σˆ P2 σˆ F2 + σˆ FE

where s 2FE is the family-by-environment interaction variance component, and s 2e′ is the experimental error variance component. The variance-covariance matrix of the variance component estimates is: 2 Vˆ (σˆ 2 ) Cˆ (σˆ F2 , σˆ FE ) Cˆ (σˆ F2 , σˆ ε2′ )  F   2 2 2 Cˆ = Cˆ (σˆ F2 , σˆ FE ) Vˆ (σˆ FE ) Cˆ (σˆ FE , σˆ ε2′ ).  ˆ ˆ2 ˆ2  2 ˆ 2 ˆ 2 C (σ F , σ ε ′ ) C (σˆ FE , σˆ ε ′ ) V (σˆ ε ′ ) 

The genetic and phenotypic coefficient vectors are:

[ = [1

λTG = 1

0

λTP

1

] 1] .

0,

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Applying the delta method, we obtain:  ∂hˆ 2   ˆ 2 2   σˆ 2 + σˆ 2   f   σ FE + σˆ ε ′   FE 2 ε ′   ∂σˆ F2   (σˆ 2 )2    σˆ P P  ˆ2      2 2 1  −σˆ F   ∂hf   −σˆ F  = dˆ =  = 2  2 2  2 2     ∂σˆ FE   (σˆ P )  σˆ P  σˆ P  2  ∂hˆ 2   −σˆ 2   −σˆ F  f F       2 2 2  ∂σ ε2′   (σˆ P )    σˆ P    σˆ 2 + σˆ 2 + σˆ 2 − σˆ 2  FE F ε′  F  σˆ P2   1 − hˆ 2  f   2 −σˆ F 1  1  ˆ2  1  − hf  = = = [λ G − (hˆ 2f λ P )]. 2 2  2 2  σˆ  ˆ  σˆ P σˆ P σ P P 2   − hˆ f    −σˆ F2     σˆ P2   ˆˆ Vˆ (hˆ 2f ) = dˆ T Cd =

(σˆ 2 + σˆ 2 )2V (σˆ 2 ) + (σˆ 2 )2[V (σˆ 2 ) + V (σˆ 2 ) + 2C (σˆ 2 , σˆ 2 )] ε′ F F FE ε′ FE ε′  FE . 2 2 + σˆ ε2′ )[C (σˆ F2 , σˆ FE ) + C (σˆ F2 , σˆ ε2′ )] (σˆ P2 )4 −2σˆ F2 (σˆ FE  1

The estimate of heritability on a family-mean basis has the following form: hˆ 2f =

σˆ F2 , 2 ˆ ε2′ ˆ FE σ σ 2 + σˆ F + e er

the C matrix remains unchanged, and the λG and λP vectors are:

[

λTG = 1

0

 0 and λTP = 1 

]

1 e

1 , er 

leading to estimates of the phenotypic variance of family mean deviations and heritability as follows:

σˆ P2 = λTP σˆ ,

λ T σˆ σˆ 2 hˆ 2f = G = F . λTP σˆ σˆ P2

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The d vector is: d=

1 [λ G − (hˆ 2f λ P )], 2 σˆ P

and the variance of the estimate of heritability of family means is obtained with Equation [16]. The standard error of the heritability estimate is the square root of the variance (Equation [16]). For example, the standard error of the heritability estimate on a plot basis in the example given is: s.e.(hˆ 2f ) = 1 (σˆ P2 )2

(σˆ

2 FE

)

2

2 2 2 2 + σˆ ε2′ )[Cˆ (σˆ F2 , σˆ FE + σˆ ε2′ Vˆ (σˆ F2 ) + (σˆ F2 )2[Vˆ (σˆ FE ) + Vˆ (σˆ ε2′ ) + 2Cˆ (σˆ FE , σˆ ε2′ )] − 2σˆ F2 (σˆ FE ) + Cˆ (σˆ F2 , σˆ ε2′ )].

This formula is algebraically equivalent to that given by Singh et al. (1995), who also used the delta method. It is also equivalent to the estimator obtainable from a general form given by Gordon et al. (1972) when data are balanced. Gordon et al. (1972) derived their covariance estimators by assuming balanced data and independent mean squares, but this may not be valid when data are unbalanced. On the other hand, whether or not data are unbalanced, the variances and covariances of the elements of the heritability estimator are given by the C matrix estimated by REML procedures, assuming large sample sizes. We can be certain, therefore, that the variance estimators described here are valid in the case of unbalanced data. 2. Alternative Methods for Estimating Precision of REML-based Heritability Estimates. The approximate standard errors of heritability estimate may not lead to reliable confidence interval estimators, because of the unknown distribution of the heritability estimates (Lynch and Walsh 1998). The delta method approximate standard errors also assume large sample size, and it is not known exactly how large the number of families, environments, or replications an experiment should have to obtain valid estimates of precision of heritability estimates. Dieters et al. (1995) compared estimates of standard errors of heritability for two traits in pine trees (Pinus elliottii) estimated with the delta method to those estimated with a simpler approximation given by Dickerson (1969) (see Nyquist 1991, p. 310) and an empirical estimate. They reported that the delta method approximation of standard error of heritability appeared to be reliable, but seemed slightly less conservative and perhaps more biased than the Dickerson approximation. An alternative to estimating approximate standard errors based on parametric methods is the use of data resampling techniques to obtain

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both standard errors and confidence intervals for heritability estimates (Lynch and Walsh 1998, pp. 569–570). Furthermore, resampling methods to obtain estimates of heritability may be more robust for smaller sample sizes than the delta method estimators. Zhu and Weir (1996) suggested that better estimates of variances of heritability estimates for diallel designs can be obtained with the jackknife method (Miller 1974) than with approximate formulae. This may also be true for other mating designs. Knapp and Bridges (1988) also used jackknife methods for estimating confidence intervals for ratios of variance component estimates, but they noted that the extension of jackknife methods to complex data structures was difficult because the optimal data resampling strategy to use in jackknifing in complex experimental designs is not always clear. For example, if data exist on multiple families evaluated in multiple replications within multiple environments, it is not obvious how to properly resample the factorial data set to simultaneously account for uncertainty in family, FE interaction, and error variances. Further research on the reliability of delta method approximations and resampling methods for estimating the precision of REML-based heritability estimates would be helpful. D. Accounting for Unbalanced Data in Formulas for Heritability on a Family-Mean Basis The estimates of the variance among phenotypic means (sP–2) that serve as the denominators of heritabilities on a family-mean basis in Table 2.1 are correct only if the data are balanced. In the balanced case, the divisor for each variance component comprising sP–2 represents the number of effects corresponding to that variance component included in each family mean. For example, family j might be evaluated in e environments. In this case, each family mean includes an average over e unique FE effects, and the contribution of s 2FE to the variance among family means is reduced by a factor of exactly e. Therefore, the divisor of s 2FE in the formula for the variance among family means is e. As described in Section IV, the phenotypic variance of family means can be obtained as the mean square for families divided by the total number of observations per family (Nyquist 1991, pp. 256–257). The phenotypic variance of family means is also equal to the variance of a family mean plus the variance component due to families. However, this is no longer true when data are unbalanced because the total number of observations per family is not equal among families. Also, the coefficients on the variance components in the expected mean square for families are not integers corresponding to consistent numbers of levels of the design factors as when data are balanced (Section V.B.2).

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Selection among families when data are unbalanced should be based on the least square means of families. Therefore, by analogy to the balanced data situation, when data are unbalanced, one can compute the phenotypic variance of family means as the average variance of the family least square means plus the variance component due to families. Least square means are computed as Lbˆ , where bˆ is the vector of fixed effects (Section V.B.5) (including family effects in this case), and L is the vector of coefficients that define a least square mean, according to rules described by Rawlings (1988, pp. 460–462; see also SAS Institute 1999). Differences among least square means are defined as (Li – Lj)bˆ , where Li and Lj are the vectors defining the two least square means. The variance of a difference between least square means is computed as (Li – Lj) ˆ –1X)–(L – L )′ (SAS Institute 1999). The average variance of com(X′V i j parisons of least square means across all pairs of least square means can be computed and summed with the variance component for families to obtain the phenotypic variance of family means. This factor can then be used as the denominator for heritability formulas on a family-mean basis. Another approach is to simply compute all of the family least square means, then calculate the variance among those means. Finally, one could compute the coefficient for the family variance component in the expected mean square for families given the actual unbalanced design of the experiment (using the random statement in PROC GLM of SAS, for example, SAS Institute 1999), and divide the mean square for families by the coefficient to obtain the variance among family least square means. The mean square may have to be constructed as a linear function of variance-component estimates from PROC MIXED, multiplied by the appropriate coefficients in the expected mean squares, obtained from PROC GLM. We are not certain how to simply relate these expressions to the linear combinations of variance component estimates, as was possible for the balanced data situation (Equation [9]). Further research to clarify this issue is needed. From empirical investigation, we have found that Equation [9] is a good approximation to the empirical variance among family least square means if e is replaced by eh, the harmonic mean of the number of environments per family, and er is replaced by ph, the harmonic mean of the total number of plots in which each family is observed. The harmonic mean of the number of environments per family is: eh =

f f

1 j =1 e j

∑

,

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where ej is the number of environments in which the jth family is tested. The harmonic mean of the number of plots per family is: ph =

f f

∑ j =1

1 ej

∑

rij

=

f f

∑ j =1

, 1 pj

i =1

where rij is the number of replications of family j in environment i, and pj is the total number of replications (plots) of family j across all environments. VI. ESTIMATING HERITABILITY FROM PARENT-OFFSPRING REGRESSION A. REML Estimates of the Parent-Offspring Regression Coefficient REML methods can be used to estimate the parent-offspring covariance and the parent phenotypic variance, leading to a REML-based estimator of heritability. This approach follows that outlined in Section V, and will be the optimal method when data are unbalanced. Furthermore, with mixed models analysis and appropriate experimental design, one can simultaneously estimate heritability from parent-offspring regression and from the ratio of the family variance component to the phenotypic variance of family means. An example of this method is presented in Appendix 3. B. Heritability Estimated from Parent-Offspring Regression without Inbreeding We demonstrated in Section III.A that the genetic covariance between outbred parent and outbred offspring is (1/2)s 2A + (1/4)s 2AA (ignoring higher-order epistatic terms). If parents and progeny are grown in independent environments, then the covariance between their phenotypic values is the genetic covariance (Section II.C). If parents and progeny are grown in the same (or nonindependent) environments, however, the covariance between their phenotypic values will include a portion of the genotype-by-environment variance component, requiring a more complex analysis of covariance to partition the genetic variance component from the genotype-by-environment interaction variance component (Casler 1982; Nyquist 1991, pp. 281–282).

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The expectation of the regression of offspring values on parental values is identical whether a single offspring from each parent or the mean of many offspring are measured. If parents are random-mated via openpollination and the phenotypic value of only one parent of each progeny set is known (corresponding to selection on one parent only), then the regression of offspring on parent phenotypic values is equal to half of the narrow-sense heritability:

[ ]

 Cov( P , O )  E bˆOP = E  =  Var ( P ) 

1 2 σ 2 A

2 + 14 σ AA

σ P2

=

1 2

h12 ,

where h21 = narrow-sense heritability corresponding to selection response in response units that are members of the initial population formed by intermating selected parents (Section V.A; Nyquist 1991, pp. 250–251). This is distinguished from the permanent response to selection, as measured in a population derived from the initial response population but resulting from many generations of random mating (Section V.A). The epistatic variance components do not contribute to the numerator of the permanent response to selection (Nyquist 1991, pp. 250–251; Holland 2001), so narrow-sense heritability corresponding to permanent response to selection has the familiar form: h∞2 =

σ A2 σ P2

(Falconer and Mackay 1996, p. 160; Nyquist 1991, p. 251). Formulas for h2• can be obtained from the formulas for h21 simply by deleting the epistatic components of variance from the numerator. If the phenotypic values of both parents of each progeny group are known (corresponding to selection on both parents), then the regression of offspring values on mean parental values is directly equal to the narrow-sense heritability:  Cov( P , O )  Cov( 21 Pf + 21 Pm , O ) E[bOP ] = E  = = Var[ 21 ( Pf + Pm )]  Var ( P )  2 2 1 2 σ A + 14 σ AA σ A2 + 21 σ AA = h12. = 2 = 2 1 2 σ σP 2 P

1 2

Cov( Pf , O ) + 21 Cov( Pm , O 1 [ Var ( Pf 4

) + Var ( Pm )]

[20]

These regression coefficients provide estimators of narrow-sense heritability in Lush’s original sense because the phenotypic variance in the denominator is the phenotypic variance of individual plants. Because data are taken from individual parental plants, these estimators are use-

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ful only to the extent that individual plant data are useful. Thus, such estimators are probably more useful for traits that are not greatly affected by interplant competition. A major advantage to estimating heritability from parent-offspring regression is that the sample of parents chosen does not have to be a random sample from the reference population, in contrast to all of the heritability estimators based on variance component estimation (Falconer and Mackay 1996, p. 181; Lynch and Walsh 1998, p. 537; Nyquist 1991, p. 281). In some cases, breeders have data only on selected plants and their offspring, and in such a case, the parent-offspring regression method will provide an unbiased estimator of heritability. C. Heritability Estimated from Parent-Offspring Regression with Inbreeding Heritability may be estimated from the regression of self-fertilized offspring phenotypic values on their parental values. For example, parents in a random-mating population may be self-fertilized to form S0:1 families and the regression of S0:1 line means on S0 parents has the following expectation: E[bˆS1,S0 ] =

2 σ A2 + 12 σ D2 + 12 D1 + σ AA C 001 = = h12 Var(S0plants) σ P2

(Nyquist 1991, p. 303). Such heritability estimators are appropriate only for obtaining the expected response to selection conducted in the same parental generation and evaluated in the same offspring generation as used in the estimation experiment. For example, the heritability estimator based on the regression of S0:1 line means on their S0 parents can be used to obtain the expected response to selection among S0 plants as evaluated in S0:1 lines. However, if one is interested in the response to selection among S0 plants as evaluated in highly homozygous lines derived from them (S0:• lines), one requires the numerator C00• (equal to s 2A + D1 + s 2AA) in the heritability estimator. Similarly, if one is interested in the response to selection among S0 plants as measured in outbred progeny developed from intermating the selected S0′s (or the selfed progeny of the S0′s), one requires the narrow-sense heritability estimator given in Equation [20] that does not involve s 2D or D1 in the numerator. More frequently, plant breeders have data on earlier and later generations of inbreeding of lines derived from the same common ancestor. For example, F4 generation lines can be regressed on F3 generation lines derived from the same common F2 ancestor. Using the S-generation notation, this is the regression of S0:2 lines on their S0:1 parents, and the

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relevant covariance is Ctgg ′ = C012 = s 2A + (1/8)s 2D + (5/4)D1 + (3/16)D* 2 + s 2AA. The relevant phenotypic variance in the denominator of the regression coefficient is the phenotypic variance among S0:1 line means, which 2 includes Ctgg = C011 = s 2A + (1/4)s 2D + D1 + (1/8)D* 2 + s AA as the genotypic variance component. In general, the expectation of the regression coefficient is: C tgg ′

E[bˆ OP ] = C tgg +

C tgg E e

σ2 + ε′ er

. [21]

Such heritability estimators strictly refer to response to selection among St:g lines as evaluated in St:g′ offspring lines. It also refers to selection among individual inbred plants as measured in later generation inbred lines by setting t = g. The permanent response to selection evaluated in highly homozygous offspring lines involves the numerator Ctg•. The response to selection evaluated in outbred progeny involves the additive portion of Ctgg′ (which also equals the additive portion of Ctgg) in the numerator. Nyquist (1991) suggested that appropriate estimators of heritability corresponding to the response to selection among S0 plants as measured in S0:1 lines can be obtained by adjusting the regression of St:g lines on St parents for any pair of generations t and g (p. 305). Assuming that the genetic variance is completely additive, the heritability estimator can be obtained from the parent-offspring regression of any pair of inbred generations as: h2 =

bOP 1 + Ft (1 − bOP )

,

where Ft is the inbreeding coefficient in generation t. Gibson (1996) showed that this estimator can be severely biased for particular allele frequencies if nonadditive effects are important or if genotype-byenvironment interactions are important, but in any case, this estimator is closer to the regression of S0:1 lines on S0 parents than is the unadjusted regression coefficient of St:g lines on St parents. Perhaps Nyquist (1991, p. 305) and Gibson (1996) did not emphasize strongly enough that this correction is valid only if the regression estimate to be adjusted involves phenotypic values of individual parents in the St generation. If the parental values instead are phenotypic means of St:g lines, then the phenotypic variance among parental values cannot be adjusted to equal the phenotypic variance among individual S0 plants.

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Holthaus et al. (1996) estimated the heritability of b-glucan content in oat (Avena sativa) using the regression of self-fertilized progeny means on individual single-plant values of highly inbred parents. They then adjusted the estimate to refer to selection among noninbred plants using Smith and Kinman’s (1965) correction of h2 = bOP/(2Ft) = bOP/2 (for homozygous parents with Ft = 1). Nyquist (1991) pointed out that the Smith and Kinman correction factor is incorrect, and the adjusted heritability estimate [1 + Ft (1 – bOP)] should be used instead (p. 305). Therefore, Holthaus et al. (1996) should have made the following adjustment under the assumption of predominant additive genetic variance: h2 = bOP/[1 + Ft(1 – bOP)] = bOP/1.5, resulting in an estimate of h2 = 0.55/1.5 = 0.37, rather than h2 = 0.26.

VII. ESTIMATING REALIZED HERITABILITY Distinct from all other estimation procedures discussed in this chapter, realized heritability estimation relies on determining how much of the selection differential applied in previous generations was achieved as a response in progeny. It is a retrospective analysis, although the estimate can be used to make predictions about future responses to selection in similar populations, at least in the short-term. Realized heritability (h2r) can be estimated by rearranging the response to selection formula and solving for heritability as a ratio of the observed response to selection (R) to the observed selection differential (S): Rˆ hˆ r2 = . Sˆ In order for this estimate to be freed from GE interaction bias, the response to selection should be measured in an independent environment from the selection differential. This formula can be generalized to estimating realized heritability from response to multiple generations of selection by performing standard least squares regression of cumulative response on the cumulative selection differential (Hill 1972; Nyquist 1991, p. 283). Walsh and Lynch (1999) proposed a weighted least squares analysis to account for variation due to genetic drift and for correlations between responses observed in different cycles when estimating realized heritability. If selection differentials and responses are measured from n cycles of recurrent selection, the weighted least squares estimate of realized heritability is: hˆ r2 = (ST V −1S)−1 ST V −1R,

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where S is an n × 1 column vector of cumulative selection differentials, R is an n × 1 column vector of responses, and V is an n × n variancecovariance matrix of the selection response. The elements of V are: 1 1 + ]σ 2 Mi M0 P 1 w ij = [2Fi hr2σ P2 + ]σ 2 , M0 P w ii = [2Fi hr2 +

where Fi is the inbreeding coefficient in cycle i (0 < i < j), Mi is the number of families from cycle i tested in the evaluation trial, and sP2– is the phenotypic variance among family means, and h2r is the heritability estimate itself. One can use the standard least squares regression estimate of realized heritability as an initial estimator to use in the V matrix of the equation, and then use the equation iteratively until converging on a stable solution. The standard error of the realized heritability estimate is the square root of the sampling variance: Var(h2r) = (STV–1S)–1. Holland et al. (2000) used this method to estimate realized heritability from three cycles of recurrent selection for grain yield in oat.

VIII. EXAMPLES OF HERITABILITY ESTIMATES A. Broad-Sense Heritability for Clonally Propagated Species For clonally propagated species, the genotypic content of parents and their offspring are identical, therefore, the expected covariance of parent and offspring phenotypes from independent environments is equal to the total genotypic variance: E[Cov(YP, YO)] = E[Cov(GP, GO)] = s G2 = s 2A + s 2D + s 2AA + s 2AD + s 2DD + ... . It follows from this that the response to selection among clonally propagated individuals or families involves the total genotypic variance. Therefore, we seek heritability estimators of the form sG2 /s 2P, which are referred to as heritability in the broad sense, “H” (Nyquist 1991, p. 239), as they refer to the proportion of phenotypic variance due to total genotypic variance. Such estimators are not relevant to selection response in sexually reproducing populations, but in clonally propagated populations they are useful for predicting response to selection. Broad-sense heritability estimators can vary, depending on the experimental design and on the selection unit, as these will impact the phenotypic variance in the denominator of the heritability function. When data are taken on individual plants within plots in a replicated multipleenvironment trial with cross-classified environments, the model for phenotypic data is:

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Yijklm = m + Li + Yj + LYij + R(ij)k + Gl + GLil + GYjl + GLYijl + eijkl + w(ijkl)m, where Li is the effect of the ith location; Yj is the effect of the jth year; LYij is the effect of the interaction between location i and year j; GLil is the effect of the interaction between genotype l and location i; GYjl is the effect of the interaction between genotype l and year j; GLYijl is the effect of the interaction between genotype l, location i, and year j; and w(ijkl)m is the effect of the mth plant within the ijklth plot. In this case, the phenotypic variance of individual plant deviations from their block mean is: s 2P = s 2G + s 2GL + s 2GY + s 2GLY + s 2e + s 2w, and heritability corresponding to selection among individual plants within a block is: Hˆ =

σˆ G2 2 2 2 + σˆ GY + σˆ GYL + σˆ ε2 + σˆ w2 σˆ G2 + σˆ GL

=

σˆ G2 , σˆ P2

where s 2w is the within-plot variance component (Table 2.1.1.A). If selection is based on plot mean values within one replication, the selection units are averages across the n plants per plot, leading to a heritability estimator of the form: Hˆ f =

σˆ G2 σˆ 2 2 2 2 + σˆ GY + σˆ GYL + σˆ ε2 + w σˆ G2 + σˆ GL n

=

σˆ G2 (Table 2.1.1.B). σˆ P2

If selection is based on family-mean values averaged across years (but from one location), the selection units are averages across n plants per plot, r replications per environment, and y years, leading to a heritability estimator of the form: Hˆ f =

σˆ G2 σˆ 2 σˆ 2 σˆ 2 σˆ 2 2 + GY + GLY + ε + w σˆ G2 + σˆ GL y y yr yrn

=

σˆ G2 (Table 2.1.1.C). σˆ P2

If selection is based on family-mean values averaged across locations within one year, the selection units are averages across n plants per plot, r replications per environment, and l locations, leading to a heritability estimator of the form: Hˆ f =

σˆ G2 σˆ 2 σˆ 2 σˆ 2 σˆ 2 2 + GLY + ε + w σˆ G2 + GL + σˆ GY l l lr lrn

=

σˆ G2 (Table 2.1.1.D). σˆ P2

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Finally, if selection is based on family-mean values averaged across all years and locations sampled, the selection units are averages across n plants per plot, r replications per environment, y years, and l locations, leading to a heritability estimator of the form: Hˆ f =

σˆ 2 σˆ 2 σˆ G2 + GL + GY l y

σˆ G2 σˆ 2 = G (Table 2.1.1.E). σˆ 2 σˆ P2 σˆ 2 σˆ 2 + GLY + ε + w ly lyr lyrn

If a similar evaluation is conducted with cross-classified years and locations, but data are taken on plot totals only, the model for phenotypic data becomes: Yijklm = m + Li + Yj + LYij + R(ij)k + Gl + GLil + GYjl + GLYijl + e′ijkl. If plot means are calculated from the plot totals, then e′ijkl in the formula replaces n

ε ijkl +

∑ w (ijkl)m

m =1

n

in the previous formula for an individual (Section II.C). Family heritabilities on a plot basis and on a family-mean basis are similar to those just presented, with the substitution of s 2e + (s 2w /n) for s 2e′. Also, plot totals themselves can be analyzed (Table 2.1.2.A, B, C, and D; see Nyquist 1991, pp. 259–260). If data are collected on individual plants within plots and the environments are an independent sample of all locations and years (not simply different, random locations within one year or different, random years within one location), or if the cross-classification is ignored (with the introduction of bias as described in Section II.C), the statistical model becomes: Yijkl = m + Ei + R(i)k + Gj + GEij + eijk + w(ijk)l. The phenotypic variance of individual plant deviations from their block mean is: s 2P = s G2 + s2GE + s 2e + s 2w, and heritability corresponding to selection among individual plants within a block is: Hˆ =

σˆ G2 2 + σˆ ε2 + σˆ w2 σˆ G2 + σˆ GE

=

σˆ G2 (Table 2.1.3.A). σˆ P2

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Family heritability corresponding to selection among plot means within one replication is: Hˆ f =

σˆ G2

=

σˆ 2 2 + σˆ ε2 + w σˆ G2 + σˆ GE n

σˆ G2 (Table 2.1.3.B). σˆ P2

Heritability corresponding to selection among family means averaged across all environments is: Hˆ f =

σˆ G2 σˆ 2 σˆ 2 σˆ 2 σˆ G2 + GE + ε + w e er ern

=

σˆ G2 (Table 2.1.3.C). σˆ P2

If data are taken only on plot totals, then the statistical model is modified by substituting e′ijk for n

ε ijk +

∑ w (ijk )l l =1

n

,

and heritabilities on a plot basis and on a family-mean basis follow (Table 2.1.4.A and Table 2.1.4.B). (See the text for Table 2.1.2 for cross-classified environments instead of an independent sample of environments.) These formulas can also be applied to the situation in which genotypes are replicated in multiple locations in a single year, by substituting s 2GL for s 2GE and l for e in the preceding formulas, but this results in a positive bias to the estimate of s 2G and a negative bias to the estimate of genotype-by-environment interaction variance (Nyquist 1991, pp. 288–289). Similarly, these formulas can be applied when genotypes are replicated in multiple years at a single location, by substituting s 2GY for s 2GE and y for e in the preceding formulas, but this also results in a positive bias to the estimate of s G2 and a negative bias to the estimate of genotype-by-environment interaction variance (Nyquist 1991, pp. 288–289). B. Heritability Estimated from Half-sib Family Evaluations Half-sib families can serve as selection units, in which case, remnant seed of selected half-sib families is often used for intermating to form a new population. In this case, the response to selection depends on the regression of random-mated offspring derived from remnant half-sib seed of two selected half-sib families on the selection units. The covariance between selection and response units on either the male or female side of the pedigree equals (1/8)(1 + FP)s 2A + [(1/8)(1 + FP)]2s 2AA. Considering

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that selection occurs on both parents of the offspring, the expected response to selection is twice this value, (1/4)(1 + FP)s 2A + (1/32)(1 + FP)2s 2AA, divided by the phenotypic variance of half-sib family means (Nyquist 1991, pp. 274–275). The variance component due to half-sib families, s 2F, is equal to the covariance among half-sibs, (1/4)(1 + FP)s 2A + [(1/4)(1 + FP)]2s 2AA (Sections III.A and VIII.D). The ratio of the half-sib family variance component to the phenotypic variance of half-sib family means has the expectation:     σˆ F2  E[hˆ 2f 1 ] = E  2 2  σˆ 2 + σˆ FE + σˆ ε ′   F e er    2 1 1 ( FP )σ A2 + 16 (1 + FP )2 σ AA 1 + 4 ≈ . 2 2 2 1 1 2 ( F ) ( F ) σ σ 1 + + 1 + σ P AE P AAE 2 16 1 1 (1 + FP )σ A2 + 16 (1 + FP )2 σ AA + 4 + ε′ 4 e er

In this case, the expected value of the estimator of heritability is almost equal to the true parameter h2f 1, with a small upward bias of (1/32)(1 + FP)2s 2AA in the numerator. Specific formulas for heritability estimates and their standard errors based on evaluation of half-sib families can be obtained by modifying the equations in Section 5 of Table 2.1. C. Heritability Estimated from Full-sib Family Evaluations The estimate of the ratio of the full-sib family variance component to the phenotypic variance among full-sib means was given in Section V.A, and was shown to be different from the desired heritability estimator for selection among full-sib families. The desired heritability function for selection among full-sib families can be estimated only if the additive and dominance genetic variance components can be partitioned. Such partitioning is made possible by mating design experiments, as described in Sections VIII.D and VIII.E. D. Heritability Estimated from NC Design I The NC Design I involves mating a sample of m plants as male parents each to a separate sample of f females, and evaluating the progenies in r replications within each of e environments. This permits the estimation of the variance components due to male parents and due to female parents nested within male parents, which have the following genetic expectations:

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2 2 E (σˆ M ) = Cov(HS) = ( 14 )(1 + FP )σ A2 + [( 14 ) (1 + FP )]2σ AA ,

E (σˆ F2 ( M ) ) = Cov(FS) − Cov(HS) 2 2 1 = (1 + FP )[( 12 )σ A2 + (1 + FP )( 14 )σ D2 + ( 14 )(1 + FP )σ AA − ( 14 )σ A2 − ( 16 )(1 + FP )σ AA ] 3 2 = (1 + FP )[( 14 )σ A2 + (1 + FP )( 14 )σ D2 + ( 16 )(1 + FP )σ AA ],

where FP is the inbreeding coefficient of the plants crossed to form the full- and half-sib families. If one were interested in conducting a recurrent selection program using the data obtained from the nested design, observed means for neither independent half-sib or full-sib families exist. Instead, selection could be conducted on the basis of male group means (the mean of all full-sib families that have a common male parent) or means of nonindependent full-sib families. The heritability of male group means based on plot total data is: 2 σˆ M

hˆ 2f 1 = 2 + σˆ M

σˆ F2 ( M ) f

σˆ 2 σˆ 2 σˆ 2 + ME + F ( M )E + ε ′ e ef erf

(Table 2.1.6.D, Table 2.1.7.C).

The phenotypic variance among nonindependent full-sib family means is obtained by adding the sums of squares for males and females within males to obtain the sum of squares for nonindependent full-sib families. That sum of squares is then divided by the corresponding sum of the degrees of freedom to obtain the mean square for nonindependent full-sib families, with the following expectation: E ( MSnonindep. FS ) = = =

2 2 + erσ F2 ( M ) + erfσ M ( m − 1)(σ ε2′ + rσ F2 ( M )E + rfσ ME ) + m( f − 1)(σ ε2′ + rσ F2 ( M )E + erσ F2 ( M )

( m − 1) + m( f − 1) ( m − 1 + mf −

m )(σ ε2′

+

rσ F2 ( M )E

2 2 + erσ F2 ( M ) ) + ( m − 1)( rfσ ME + erfσ M )

m − 1 + mf − m 2 2 ( mf − 1)(σ ε2′ + rσ F2 ( M )E + erσ F2 ( M ) ) + ( m − 1)( rfσ ME + erfσ M )

= σ ε2′ + rσ F2 ( M )E + erσ F2 ( M ) + = σ ε2′ + rσ F2 ( M )E + erσ F2 ( M ) +

mf − 1 ( m − 1) ( mf − 1)

2 2 ) ( rfσ ME + erfσ M

rf ( m − 1) 2 2 (σ ME + eσ M ). ( mf − 1)

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This shows that there is less variation among nonindependent full-sib family means because a different male parent is not mated to every female. Thus, the heritability for nonindependent full-sib family means is estimated as: (1 + hˆ 2f 1 =

f ( m − 1) ˆ 2 )σ M mf − 1

σˆ 2 σˆ 2 f ( m − 1) ˆ 2 σˆ 2 ( )(σ M + ME ) + σˆ F2 ( M ) + F ( M )E + ε ′ e e er mf − 1

(Table 2.1.6.C, Table 2.1.7.B).

Selection acts on the additive genetic variation in both the male and female components, but not on the dominance variation present in the female component. Hallauer and Miranda (1988, p. 80) suggested the following heritability estimator for the Design I experiment based on noninbred parents (FP = 0) (see Nyquist 1991, pp. 294–295): hˆ 2 = 4σˆ F2 ( M )

2 4σˆ M . 2 4σˆ EF σˆ ε2′ (M ) + + e er

The expectation of the numerator of this estimator is s 2A + (1/4)s 2AA, which makes it appealing as an estimator of narrow-sense heritability, but it is incorrect as an estimator of narrow-sense heritability because the denominator is not equal to the phenotypic variance among individual plants. Therefore, it is not appropriate to predict the expected response to selection among individual plants. Furthermore, the denominator contains more than one times the genetic variance because error variance in this case contains within-family genetic variation divided by the number of plants per plot. This may contribute only a small fraction of the genetic variance if the number of plants per plot, replications, or environments is sufficiently large. This estimator cannot be interpreted in terms of response to selection among individual plants nor among half-sib or fullsib families, so we do not recommend its use (Nyquist 1991, p. 295). E. Heritability Estimated from NC Design II The NC Design II involves a factorial mating each of m plants as males to each of f plants as females and evaluating the mf full-sib families in each of r replications within each of e environments. The variance components due to a common male parent, a common female parent,

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and the interaction between male and female parents have the following expectations: E (sˆ 2M ) = Cov(HS) = ( 14 )(1 + FA )s 2A + [( 14 )(1 + FA )]2 s 2AA E (sˆ 2F ) = Cov(HS) = ( 14 )(1 + FB )σ A2 + [( 14 )(1 + F B)]2 s 2AA , E (sˆ 2MF ) = Cov(FS) − 2Cov(HS) = ( 14 )(1 + FA )(1 + FB )sD2 , where FA is the inbreeding coefficient of the male plants, and FB is the inbreeding coefficient of the female plants crossed to form the evaluated families (Cockerham 1963; Nyquist 1991, pp. 269–270). Heritability corresponding to selection among half-sib families can be estimated with either the male or female half-sib variance component, or the average of the two (Table 2.1, Sections 8 and 9). As in the case of the Design I experiment (Section VIII.D), an estimate of narrow-sense heritability is available if individual plant data are collected (Table 2.1.8.A). Hallauer and Miranda (1988, p. 71) suggested the following heritability estimator for the Design II experiment based on noninbred parents (FP = 0): 2 4σ M

hˆ 2f = 2 4σ M

+

2 4σ FM

2 2 σ2 4σ ME + 4σ FME + + ε′ e er

.

However, this estimator is not interpretable in terms of response to selection among individual plants or among families; therefore, we do not recommend its use. F. Heritability Estimated from Testcross Progenies Testcross progenies that are evaluated in hybrid crops represent a special case. As an example, we consider a random-mating reference population from which individual plants, families, or inbred lines are sampled, and crossed to an unrelated inbred “tester” line to form testcross progenies. The testcross progenies are evaluated phenotypically and superior progenies selected. The plants, families, or lines that were parents of those superior progenies are intermated to form an improved population. New plants, families, or lines are sampled from the improved population, and these are testcrossed to the same tester line. The gain from selection of interest is the difference between the mean of the testcrosses of the improved population and the mean of testcrosses of the original population.

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We define a special genetic model to handle the genetic effects observed in testcross populations. The reference population in this case is not a Hardy-Weinberg random-mating population; instead, it is the nonequilibrium first generation population derived from crossing plants or lines sampled from a Hardy-Weinberg equilibrium population to a common inbred tester line. The single-locus genotypic model for the testcross population is: GjT = mT + aj + aT + djT, where aj is the effect of the jth allele derived from a plant in the experimental population, aT is the effect of the allele from the inbred tester, and djT is the dominance interaction effect of the pair of the jth allele and tester allele. Melchinger (1987) developed theory for a two-locus, twoallele model with linkage and epistasis, but for simplicity, we will ignore epistasis and assume that the single-locus model generalizes to a multilocus model simply by summing over loci. All of the testcross plants inherit one allele in common from the inbred tester line, so the genotypic model simplifies to: GjT = m* + aj*, where m* = mT + aT, and the average effect of the jth allele from the experimental population is confounded with its dominance interaction with the tester allele: aj* = aj + djT. The total genotypic variance in the testcross population is: s 2G = E[GjT – E(GjT)]2 = E[m* + aj* – m*]2 = E[aj*]2 = s 2A(T). The genetic variance is entirely due to the differences in the average effects of alleles from the experimental population in combination with the tester allele, and we term this variance s 2A(T) to indicate that it is an additive genetic variance only in reference to this specific testcross population. Consider now the genotypic effects segregating within the equilibrium population from which plants were sampled to cross to the inbred tester: Gij = m + ai + aj + dij. The mean value of testcross progeny derived from a single plant in the experimental population is: GijT = ( 12 )(GiT + G jT ) = ( 12 )[( m* + ai *) + ( m* + a j*)] = m* + ( 12 )[ ai* + a j*]. The variance component due to testcross families derived from individual plants in the experimental populations is then: E[σˆ F2 ] = Var(GijT ) = E[GijT − E (GijT )]2 = E [ µ* + ( 12 )(α i* + α j*) − µ* ]2 = ( 14 )E [(α i* + α j*)]2 .

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The ai* and aj* effects are uncorrelated if the experimental population is in equilibrium, so this simplifies to: E [sˆ 2F ] = Var(GijT ) = ( 12 )[E (ai*)2 + E (a j*)2 ] = ( 14 )(s 2A(T ) + a2A(T ) ) = ( 12 )s 2A(T ) . The meaning of this is that the genetic component of variance among testcross families is half of the total genetic variance among testcross progeny. We can generalize this model to include inbreeding in the experimental population, in which case the probability that the two alleles from a single individual are IBD is F. The mean of the testcross population is not affected by inbreeding in the experimental population, however. With probability F, the parent of the testcross family is inbred at an arbitrary locus, and the mean genotypic value of the testcross family at that locus is m* + ai*. With probability 1 – F, the parent of the testcross is not inbred and the mean genotypic value of the testcross family at a locus is m* + (1/2)[ai* + aj*]. Therefore, the variance of testcross family mean genotypic values equals: E[sˆ 2F ] = E[Var(GijT )] = FE[ a*i ]2 + (1 − F )E [( 12 )( a*i + a *j )]2 = Fs 2A(T ) + ( 14 )(1 – F )(2)s 2A(T ) = ( 12 )(1 + F )s 2A(T ) . If F = 0, this reduces to E[s 2F] = Var(GTij) = (1/2)s 2A(T ), as already shown. If F = 1, E[s 2F] = Var(GTij) = s 2A(T ). Now consider the response to selection among testcross progeny. The selection unit is a testcross family derived from a single plant in the experimental population. The response unit of interest is the testcross family of a progeny from the mating of the selected plant (or its selfed progeny) from the experimental population to another plant from the experimental population. The genotypic value of the parent plant based on the mean of its testcross progeny (the selection unit) is: GTij = m* + ( 12 )[ai* + aj*]. This parent plant can be inbred to an arbitrary degree, F, which is the probability that ai = aj, and, therefore, that ai* = aj*. Its random-mated progeny will inherit one allele from it (either i or j) with equal probability and one allele from the other parent (arbitrarily named k). Therefore, the

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mean genotypic value of its random-mated progeny in testcross to the same tester (i.e., the mean genotypic value of the response unit to which it is related) is: mean testcross value of progeny of GTij = m* + ( 14 )[ai* + ak* + aj* + ak*] = m* + ( 14 )(ai* + aj*) + ( 12 )(ak*). The covariance between testcross values of parent and offspring is: Cov[GTij, progeny of GTij] = Cov[(m* + ( 12 )[ai* + aj*]), (m* + ( 14 )(ai* + aj*) + ( 12 )(ak*))] = E[( 12 )[ai* + aj*]( 14 )(ai* + aj*) + ( 14 )(ak*)] = ( 81)E[(ai*)(ai*) + (ai*)(aj*) + (aj*)(ai*) + (aj*)(aj*)] (because E[(ai*)(ak*)] = 0) = ( 81)(E [ai*2] + E[aj*2] + 2E[ai*aj*]) = ( 81){s 2A(T) + s 2A(T) + 2FE[ai*2]} (because E[ai*aj*] = E[ai*2] with probability F) = ( 14 )(1 + F)s 2A(T). If F = 0, then Cov[GTij, progeny of GTij] = (1/4)s 2A(T). If selection is practiced on both parents, then the response to selection involves two times the covariance between selection and response units, and this heritability is estimable as the ratio of the genotypic (testcross family) variance component to the phenotypic variance of testcross families: 1 2

R= 1 [1 2

+

S[1 + F ]σ A2 (T )

F ](σ A2 (T )

σ2 σ2 + A(T ) E ) + ε ′ e er

=

Sσ F2

σ P2

.

If inbred lines from the experimental population are used, F refers to the inbreeding coefficient of the last common parent of the inbred progeny. Because the ratio of the family variance component to the phenotypic variance is an appropriate estimator of heritability for testcross proge-

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nies, then the formulas given in Table 2.1, Sections 10 to 13, are appropriate for testcross progenies. G. Heritability Estimated from Self-fertilized Family Evaluations Estimating heritability in self-pollinating species poses an additional complication, because the “response unit” can vary depending on the selection scheme. If the selection units are St:g families, we can envision at least five different kinds of response units in which response to selection could be measured. First, one might measure the response to selection in the remnant seed of the selected lines, that is, an independent sample of plants within the same selected St:g lines. This is represented in Fig. 2.2 as individuals X1n+1 through X1n+z whose last common ancestor was individual B, and we refer to this as immediate response to selection (Cockerham and Matzinger 1985). Second, one might measure the response in highly inbred lines derived from many generations of self-fertilization from the plants that composed the St:g generation selection unit. Such lines would be in the St:• generation and we refer to this as permanent response to selection, because the genotypic constitution of such lines is fixed across any further generations of selfing, in the absence of selection. Permanent response units are illustrated in Fig. 2.2 as individuals Y11 through Y1n whose last common ancestor was also individual B. Third, St:g+i lines derived from i additional generations of self-fertilization from the selection units could also be used as response units. These are not illustrated in Fig. 2.2, but would be intermediate between the immediate and permanent response units. Fourth, unrelated remnant seed of selected lines could be intermated to form a new base population, in which individual noninbred S0 progeny could be used as response units. These are illustrated as individuals Z1 through Zn in Fig. 2.2. Finally, the response unit could be St′:g′ lines derived from plants produced after t′ generations of self-fertilization within the new base population. This could include S0-derived lines (t′ = 0) and inbred lines derived from highly homozygous individuals (t′ → ∞). Such lines are illustrated as individuals V1 through Vn derived from a last common ancestor, W (W = Z2 if t′ = 0), in Fig. 2.2. Each of these situations involves a different covariance between selection and response units, and consequently, a unique heritability to predict the response to selection. Cockerham and Matzinger (1985) developed response equations for selection among inbred lines as measured in their self-fertilized progeny or in outbred progeny resulting from intermating selected lines.

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A commonly used estimate of heritability in self-fertilized species is based on the ratio of the genetic component of variance to the phenotypic variance of line means estimated from replicated evaluations of inbred lines (Equation [12]). Recall that the variance component due to inbred lines (families) is expected to equal the genetic covariance of any two individuals within such lines (Equation [11]). Using Cockerham’s (1983) notation: s 2F = Ctgg′, where t equals the number of selfing generations from which the last common ancestor of the progeny were derived, and g = g¢ equals the number of selfing generations from which the tested plants were derived. For example, the variance component due to S0:1 lines is C011 = s 2A + (1/4)s 2D + D1 + (1/8)D*2 + s 2AA, the variance component due to S0:2 lines is C022 = s 2A + (1/16)s 2D + (3/2)D1 + (9/32)D*2 + s 2AA, and the variance component due to S1:2 lines is C122 = (3/2)s 2A + (1/8)s 2D + (5/2)D1 + (9/16)D*2 + (1/16)H* + (9/4)s 2AA (Cockerham 1983; Nyquist 1991, p. 299). In general, and assuming free recombination between genes, Ctgg′ is given by Equation [5b]. Thus, for example, the common heritability estimator based on the variance among S0:1 lines is expected to equal:   σˆ F2 E[hˆ 2f 1 ] = E  2  2 σˆ FE σˆ 2 + ε′  σˆ F + e er 

 2  σ A2 + 14 σ D2 + D1 + 81 D2* + σ AA ≈ 2  σ ε2′ σ FE 2 1 σ 2 + D + 1 D* + σ 2 + + + σ  1 2 A D AA 8 e er 4 

(Nyquist 1991, Equation [86]). This is strictly correct only for predicting immediate response, that is, growing remnant seed of only the selected lines in new environments (Cockerham and Matzinger 1985; Fig. 2.2). The presence of a subscript 1 on h2f1 throughout in Nyquist (1991) implied intercrossing to obtain the response unit. The symbolism of h2 for selffertilizing populations in Nyquist (1991) was inadequate. Here, we suggest the use of h2fi for immediate response to selection. In general, the covariance between selection unit and immediate response unit is Ctgg, which is the same as the covariance of relatives within the line, and has the same expectation as the variance component due to lines. In this case, the covariance between selection and response units is not doubled because each response unit is related to only one selection unit. Cockerham and Matzinger (1985) refer to response to selection measured in completely homozygous lines developed by selfing without selection from the selected lines as “permanent response” (Fig. 2.2). We suggest the use of h2fp for heritability related to permanent response to selection among self-fertilized families. The covariance between selected family means and the response unit in this case is Ctg• = (1+Ft)s 2A + (1 + 2Ft + Fg)D1 + (1/2)(Ft + Fg)D*2 + (1 + Ft)2s 2AA (Cockerham and

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Matzinger 1985). The difference between the family variance component (Ctgg) and the covariance between selected line means and their homozygous selfed progeny (Ctg•) can be easily observed in the tables given by Cockerham (1983) or Nyquist (1991, p. 299). For example, the variance among S0:1 lines, as already given, is expected to be: E(s 2S0:1) = C011 = s 2A + ( 14 )s 2D + D1 + ( 81 )D*2 + s 2AA, whereas the covariance between S0:1 family means and their S0:• progeny is expected to be: C01• = s 2A + ( 32 )D1 + ( 14 )D*2 + s 2AA. In all such cases, the additive genetic variance component is identical in the family variance component and the covariance between selection lines and progeny homozygous lines. Therefore, if the additive genetic variance is the predominant component of genetic variance in the selection populations, then the heritability estimator based on the variance component due to lines should be adequate to predict the permanent response to selection (without intermating). If dominance variance or any of the other variance or covariance components that involve dominance are important, however, then the heritability estimator based on the family variance component may inaccurately predict the permanent response to selection. The regression coefficient of response units on selection units involves the genetic component D1, which is a covariance component, and may be negative or positive, resulting in an upward or downward bias in the heritability estimate based on the family variance component. In this case, estimating heritability appropriately may require direct estimation of the covariance between a random individual in the selection unit and a random individual in a homozygous progeny line. As the selected lines become more inbred (being derived from more generations of selfing from the last common ancestor), the differences between the family variance component and the covariance of selection units and homozygous progeny decrease. At the extreme, if highly inbred lines are the selection units, then the variance component due to lines (Ct••) is equal to the covariance between the selection lines and their selfed offspring (Ct••), and the expected immediate and permanent responses to selection are equal. Cockerham and Matzinger (1985) also developed equations for the expectations of responses to selection among inbred lines as measured in outbred progenies (Fig. 2.2). They introduced the notation of Ctgg′1 for the covariance between the selection units, which are St:g lines, and

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outbred progeny in the first generation developed from intermating plants from selected lines. They also introduced the notation of Ctgg′• for the covariance between the selection units, which are St:g lines, and outbred progeny in the equilibrium random-mated population developed from intermating plants from selected lines: Ctgg′• = (1/2)[(1 + Ft)s 2A + (Ft + Fg)D1]. The two terms differ only by the inclusion of an epistatic variance component in the response measured in the first randomly mated generation: Ctgg′1 = Ctgg′• + (1/4)(1 + Ft)2s 2AA (see Section V.A). This covariance is doubled in the heritability function that will give the correct expected response to selection among St:g lines as measured in outbred progeny because selection is practiced on both lines giving rise to the outbred progeny: 2Cˆ tgg ′∞ (1 + Ft )σˆ A2 + (Ft + Fg )Dˆ 1 hˆ 2f 1 = . ≈ C tgg E σˆ ε2′ σˆ P2 C tgg + + e er Note that, again, the coefficient of the additive variance in the numerator of this heritability estimator is equal to the coefficient of the additive variance in the covariance of relatives within the line. Thus, if the genetic variance for the trait is predominantly additive, the heritability estimator based on the ratio of the variance component due to lines to the phenotypic variance of line means should be appropriate. Finally, what heritability function is appropriate for selection among St:g lines developed from the initial population, followed by intermating the lines to form a new random-mated population, then deriving St′:g′ lines from the new population and measuring the response in these inbred lines (Fig. 2.2)? The relevant covariance between selection and response units for this situation has not been published to our knowledge, but we can at least state that the additive portion of the covariance will be equal to the additive portion of Ctgg′• as given previously. In summary, in self-pollinated species, a commonly used estimator of heritability is the ratio of the variance component due to inbred lines to the phenotypic variance of inbred line means (Table 2.1, Sections 10 to 13). This estimator is exact only for immediate response to selection, that is, the growing of remnant seed of selected lines in new (independent) environments. This estimator does not exactly provide the expected response to permanent selection (response as measured in highly homozygous progeny developed by self-pollination without selection from the selected lines) unless the selection units are already highly homozygous lines, or unless the genetic variance is completely additive in nature. Nor does this estimator provide the expected response to selection measured

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in outbred progenies developed from intermating selected lines, unless the genetic variance is completely additive. The difficulty that this situation presents is that the heritability function that is easily estimated may not be useful for predicting response to selection in progeny types of interest, whereas the appropriate heritability estimators for outbred progeny cannot be easily obtained except by specialized and complex mating designs or by direct estimation of the relevant covariance. We suggest that the scope of inference for such heritability estimators be limited to immediate response unless sufficient evidence exists to broaden its scope of inference to permanent response or response in outbred progenies. H. Heritability Corresponding to Selection among Self-fertilized Half-sib and Full-sib Families Burton and Carver (1993) derived expectations for response to selection on the basis of self-fertilized progeny of half-sib and full-sib families (HS-S1 and FS-S1 families). Including an additive-by-additive variance component, the response to selection among HS-S1 families is expected to be: R=

S

(

7 16

σ A2 +

7 32

D1 +

49 σ2 512 AA

σ P2

).

The response to selection among FS-S1 families is expected to be: R=

S( 78 σ A2 +

7 16

D1 +

σ P2

49 128

2 ) σ AA

.

Based on estimates of family variance components and phenotypic variances of HS-S1 and S0:1 families in soybean (Glycine max) and FSS1 and S0:1 families in wheat, Burton and Carver (1993) suggested that selection among FS-S1 families would provide optimum response to selection, despite the larger coefficient of additive genetic variance in the numerator of the expected response to selection among S0:1 families. The reason for this was that more seed could be produced of FS-S1 families, permitting larger plot sizes and more replication of FS-S1 than S0:1 families, resulting in a reduction of the phenotypic variance among family means that more than compensated for the reduction in genetic variance in the numerator of the heritability equation. Holland et al. (2000) used FS-S1 families for recurrent selection in oat in order to obtain sufficient seed to conduct replicated trials with three replications at each of five locations and still conduct one cycle of selection per year.

86

+

2 σˆ GL

+

2 σˆ GY

+

σˆ G2 2 σˆ GLY

+

σˆ ε2 +

σˆ w2

=

σˆ G2 σˆ 2P

[

λTG = 1 0

0

0

0

]

0

[

λTP = 1 1

1

1

1

2 2 2 σˆ G2 + σˆ GL + σˆ GY + σˆ GLY + σˆ ε2 +

σˆ G2 n

σˆ w2

= P

σˆ G2 σˆ 2

[

λTG = 1 0

0

0

0

]

0

 λTP = 1 

1

1

1

1

] 1

1  n 

2 σˆ G2 + σˆ GL +

y

2 σˆ GY

+ y

2 σˆ GLY

σˆ G2 + yr

σˆ ε2 + yrn

σˆ w2

= P

σˆ G2 σˆ 2

[

λTG = 1 0

0

0

0

] 0

 λTP = 1 

1

y

1

y

1

yr

1

1   yrn 

Hˆ f =

σˆ ε2

σˆ 2 σˆ 2 σˆ 2 2 σˆ G2 + GL + σˆ GY + GLY + + w l l lr lrn

σˆ G2 = P

σˆ G2 σˆ 2

λTG = 1

[

0

0

0

0

] 0

 λTP = 1 

l

1

1

l

1

lr

1

1   lrn 

D. Heritability of family means, giving the response to selection among family means averaged across locations within one year as measured in independent environments.

Hˆ f =

C. Heritability of family means, giving the response to selection among family means averaged across years within one location as measured in independent environments.

Hˆ f =

B. Family heritability on a plot basis, giving the response to selection among plot means within one replication of one environment as measured in independent environments.

σˆ G2

2:53 PM

Hˆ =

8/20/02

A. Heritability on an individual-plant basis, giving the response to selection among individual plants within one replication of one environment as measured in independent environments (see Nyquist 1991, p. 267).

Table 2.1. Heritability estimators and their biases,† and elements of lG and lP vectors used in estimating variances of heritability estimates for various experimental and mating designs.‡ In each case, the variance components (without coefficients) in the denominator of the heritability estimate are the components of the vector s (described in Section V.C.1), with the first variance component in the denominator being the first row of s, the second variance component in the denominator being the second row of s, and so forth. 1. Broad-sense heritability estimated from data obtained on individual plants of a clonally propagated population from multiple environment evaluations in cross-classified locations and years (Section VIII.A). The estimators are unbiased.

3935 P-02 Page 86

σˆ G2 +

l

2 σˆ GL

+ y

2 σˆ GY

+ ly

2 σˆ GLY

σˆ G2 + lyr

σˆ ε2 + lyrn

σˆ w2

= P

σˆ G2 σˆ 2

[

λTG = 1 0

0

0

0

]

0

 λTP = 1  l

1 y

1

1 ly

1 lyr

1   lyrn 

2 2 2 σˆ G2 + σˆ GL + σˆ GY + σˆ GLY + σˆ ε2′

σˆ G2 =

σˆ G2 σˆ 2P

[

λTG = 1 0

0

0

]

0

[

λTP = 1

1

1

1

] 1

2 σˆ G2 + σˆ GL +

y

σˆ G2 2 σˆ GY + y

2 σˆ GLY

+ yr

σˆ ε2′

= P

σˆ G2 σˆ 2

[

λTG = 1 0

0

0

] 0

 λTP = 1 

1

y

1

y

1

1   yr 

Hˆ f =

σˆ G2 +

l

2 σˆ GL

2 + σˆ GY +

σˆ G2 l

2 σˆ GLY

+ lr

σˆ ε2′

= P

σˆ G2 σˆ 2

λTG = 1

[

0

0

0

] 0

 λTP = 1 

l

1

1

l

1

1  lr 

C. Heritability of family means, giving the response to selection among family means averaged across locations within one year as measured in independent environments.

Hˆ f =

B. Heritability of family means, giving the response to selection among family means averaged across years within one location as measured in independent environments.

Hˆ f =

2:53 PM

A. Family heritability on a plot basis, giving the response to selection among plot values within one replication of one environment as measured in independent environments (see Nyquist 1991, pp. 267–268).

8/20/02

2. Broad-sense heritability estimated from data obtained on total plot values of multiple-plant plots of a clonally propagated population from multiple environment evaluations in cross-classified locations and years (Section VIII.A). The estimators are unbiased.

Hˆ f =

E. Heritability of family means, giving the response to selection among family means averaged across locations and years as measured in independent environments.

3935 P-02 Page 87

87

88

σˆ G2 +

l

2 σˆ GL

+ y

2 σˆ GY

σˆ G2 + ly

2 σˆ GLY

+ lyr

σˆ ε2′

= P

σˆ G2 σˆ 2

[

λTG = 1 0

0

0

]

0

 λTP = 1  l

1 y

1 ly

1

1   lyr 

2 σˆ G2 + σˆ GE + σˆ ε2 + σˆ w2

σˆ G2 =

σˆ G2 σˆ 2P

[

λTG = 1 0

0

]

0

[

λTP = 1 1

1

] 1

2 σˆ G2 + σˆ GE + σˆ ε2 +

σˆ G2 n

σˆ w2

= P

σˆ G2 σˆ 2

[

λTG = 1 0

0

]

0

 λTP = 1 

1

1

1  n 

σˆ 2 σˆ 2 σˆ 2 σˆ G2 + GE + ε + w e er ern

σˆ G2 = P

σˆ G2 σˆ 2

[

λTG = 1 0

0

] 0

 λTP = 1 

e

1

1 er

1   ern 

4. Broad-sense heritability estimated from data obtained on total plot values of multiple-plant plots of a clonally propagated population from multiple independent environment evaluations (Section VIII.A). The estimators are unbiased.

Hˆ f =

C. Heritability of family means, giving the response to selection among family means averaged across environments as measured in independent environments.

Hˆ f =

B. Family heritability on a plot basis, giving the response to selection among plot means within one replication of one environment as measured in independent environments.

Hˆ =

2:53 PM

A. Heritability on an individual-plant basis, giving the response to selection among individual plants within one replication of one environment as measured in independent environments.

8/20/02

3. Broad-sense heritability estimated from data obtained on individual plants of a clonally propagated population from multiple independent environment evaluations (Section VIII.A). The estimators are unbiased.

Hˆ f =

D. Heritability of family means, giving the response to selection among family means averaged across locations and years as measured in independent environments.

3935 P-02 Page 88

σˆ G2 σˆ 2 = G 2 2 σˆ 2P + σˆ GE + σˆ ε ′

[

λTG = 1 0

]

0

[

λTP = 1 1

]

1

σˆ G2 +

e

σˆ G2 2 σˆ GE

σˆ 2 + ε′ er

= P

σˆ G2 σˆ 2

[

λTG = 1 0

]

0

 λTP = 1  e

1

1  er 

1 4

σ 2P

(FP − 1)σ 2AA

σˆ 2F + σˆ 2FE + σˆ ε2 + σˆ w2

Bias =

hˆ12 =

 4  ˆ2  σ F  (1 + FP )  =  4 λTG =   1 + FP

σˆ 2P

 (1 + FP )  2 σˆ 2A +   σˆ AA 4  

0

0

 0 

[

λTP = 1

1

1

] 1

A. Heritability on an individual-plant basis, giving the response to selection among individual plants within one replication of one environment as measured in outbred progeny grown in independent environments.

5. Narrow-sense heritability estimated from a half-sib family experiment with data obtained on individual plants in multiple independent environments (Section VIII.B).

Hˆ f =

2:53 PM

B. Heritability of family means, giving the response to selection among family means averaged across environments as measured in independent environments.

σˆ G2

8/20/02

Hˆ f =

A. Family heritability on a plot basis, giving the response to selection among plot values within one replication of one environment as measured in independent environments.

3935 P-02 Page 89

89

90

σ2 P

(1 + FP )2σ 2AA

=

[ 0

0

1 16 σˆ 2P

(1 + FP )σˆ 2A +

λTG = 1

1 4

]

0

(1 + FP )2σˆ 2AA

 λTG = 1  1

1

1  n 

σˆ 2F

σ 2P

(1 + FP )2σ 2AA

=

1 4

[

λTG = 1 0

1 16 σ 2P

(1 + FP )σˆ 2A +

0

]

0

(1 + FP )2σˆ 2AA

 λTP = 1 

1 e

1 er

1   ern 

A. Heritability on an individual-plant basis, giving the response to selection among individual plants within one replication of one environment as measured in outbred progeny grown in independent environments.

6. Narrow-sense heritability estimated from a Design I experiment with data obtained on individual plants in multiple independent environment evaluations (Section VIII.D).

1 32

σˆ 2 σˆ 2 σˆ 2 σˆ 2F + FE + ε + w e er ern

Bias =

hˆ 2f 1 =

C. Family heritability, giving the response to selection among half-sib family means averaged across environments as measured in outbred progeny grown in independent environments.

1 32

n

σ w2

2:53 PM

Bias =

σ 2F + σ 2FE + σ ε2 +

σ 2F

8/20/02

hˆ 2f 1 =

B. Family heritability on a plot basis (half-sib family, single-plot mean value), giving the response to selection among plot means within one replication of one environment as measured in outbred progeny grown in independent environments.

3935 P-02 Page 90

σ 2P

(FP − 1)σ 2AA  4 λTG =   1 + FP

=

0

0

0

σˆ 2P 0

 0 

[

λTP = 1 1

1

1

1

]

1

(1 +

f (m − 1) ˆ 2 )σ M mf − 1

0

0

0

 0  1

σˆ 2P

1

f (m − 1) 1 ][ (1 + FP )σˆ 2A + mf − 1 4

 f (m − 1) f (m − 1) λTP =  mf − 1  mf − 1

=

[1 +

1

1 16

1  n 

(1 + FP )2σˆ 2AA ]

(1 +

f (m − 1) ˆ 2 )σ M mf − 1

0

0

0

0

 0 

=

[1 +

1

σˆ 2P

e

1

f (m − 1) 1 ][ (1 + FP )σˆ 2A + mf − 1 4

 f (m − 1) f (m − 1) λTP =   mf − 1 e(mf − 1)

σˆ 2 σˆ 2 σˆ 2 f (m − 1) ˆ 2 σˆ 2 (σ M + ME ) + σˆ 2F ( M ) + F ( M ) E + ε + ε mf − 1 e e er ern

 f (m − 1) λTG = 1 + mf − 1 

hˆ 2f 1 =

1   ern 

(1 + FP )2σˆ 2AA ]

er

1

1 16

C. Family heritability, giving the response to selection among nonindependent full-sib family means averaged across environments as measured in outbred progeny in independent environments (the estimator is unbiased relative to hf21).

0

f (m − 1) ˆ 2 σˆ 2 (σ M + σˆ 2ME ) + σˆ 2F ( M ) + σˆ 2F ( M ) E + σˆ ε2 + w mf − 1 n

 f (m − 1) λTG = 1 + mf − 1 

hˆ 2f 1 =

2:53 PM

B. Family heritability on a plot basis (nonindependent full-sib family, single-plot mean value), giving the response to selection among plot means within one replication of one environment as measured in outbred progeny grown in independent environments (the estimator is unbiased relative to hf21).

1 4

σˆ 2M + σˆ 2F ( M ) + σˆ 2ME + σˆ 2F ( M ) E + σˆ ε2 + σˆ w2

 (1 + FP )  2 σˆ 2A +   σˆ AA 4  

8/20/02

Bias =

hˆ 12 =

 4  ˆ2  σ M  (1 + FP ) 

3935 P-02 Page 91

91

92

σ 2P

[

λTG = 1 0

2 σˆ 2 σˆ 2ME σˆ F ( M ) E σˆ 2 + + ε + w e ef erf erfn

(1 + FP )2σ 2AA

+

0

1 4

1 16 σˆ 2P

0

0

]

0

(1 + FP )σˆ 2A +

 λTP = 1 

(1 + FP )2σˆ 2AA

f

1 e

1

1 ef

1 erf

1   erfn 

(1 +

f (m − 1) ˆ 2 )σ M mf − 1

0

0

 0 

1

σˆ 2P

1

f (m − 1) 1 ][ (1 + FP )σˆ 2A + mf − 1 4

 f (m − 1) f (m − 1) λTP =  mf − 1  mf − 1

=

[1 +

1 16

 1 

(1 + FP )2σˆ 2AA ]

B. Family heritability, giving the response to selection among nonindependent full-sib family means averaged across environments as measured in outbred progeny grown in independent environments (the estimator is unbiased relative to hf21).

0

f (m − 1) ˆ 2 (σ M + σˆ 2ME ) + σˆ 2F ( M ) + σˆ 2F ( M ) E + σˆ ε2′ mf − 1

 f (m − 1) λTG = 1 + mf − 1 

hˆ 2f 1 =

A. Family heritability on a plot basis (nonindependent full-sib family, single-plot value), giving the response to selection among plot values within one replication of one environment as measured in outbred progeny grown in independent environments (the estimator is unbiased relative to hf21).

7. Narrow-sense heritability estimated from a Design I experiment with data obtained on total plot values of multiple-plant plots in multiple independent environment evaluations (Section VIII.D).

1 32

f

σˆ 2F ( M )

=

2:53 PM

Bias =

σˆ 2M +

σˆ 2M

8/20/02

hˆ 2f 1 =

D. Family heritability, giving the response to selection among male group means averaged across environments as measured in outbred progeny grown in independent environments.

3935 P-02 Page 92

f (m − 1) ˆ 2 )σ M mf − 1

0

0

 0  1

σˆ 2P

e

1

f (m − 1) 1 ][ (1 + FP )σˆ 2A + mf − 1 4

 f (m − 1) f (m − 1) λTP =   mf − 1 e(mf − 1)

=

[1 + 1 16

1  er 

(1 + FP )2σˆ 2AA ]

+

σ 2P

[

λTG = 1

2 σˆ 2ME σˆ F ( M ) E σˆ ε2′ + + e ef erf

(1 + FP )2σ 2AA

f

σˆ 2F ( M )

σˆ 2M =

0

1 4

0

0

]

0

1 16 σˆ 2P

(1 + FP )σˆ 2A +

 λTP = 1 

(1 + FP )2σˆ 2AA

f

1 e

1

ef

1

1   erf 

σˆ 2M

1 4

σ 2P

(FP − 1)σ 2AA  4 λTG =   1 + FP 0

σˆ 2M + σˆ 2F + σˆ 2MF + σˆ 2ME + σˆ 2FE + σˆ 2MFE + σˆ ε2 + σˆ w2

Bias =

hˆ 12 =

(1 + FP )

4

0

=

0

0

σˆ 2P 0

0

1+ F σˆ 2A +  P  σˆ 2AA  4 

 0 

[

λTP = 1

1

1

1

1

1

1

] 1

A. Heritability on an individual-plant basis, giving the response to selection among individual plants within one replication of one environment as measured in outbred progeny grown in independent environments.

8. Narrow-sense heritability estimated from a Design II experiment with data obtained on individual plants in multiple independent environment evaluations (Section VIII.E).

1 32

σˆ 2M +

Bias =

hˆ 2f 1 =

2:53 PM

C. Family heritability, giving the response to selection among male group means averaged across environments as measured in outbred progeny grown in independent environments.

0

σˆ 2 σˆ 2 f (m − 1) ˆ 2 σˆ 2 (σ M + ME ) + σˆ 2F ( M ) + F ( M ) E + ε ′ mf − 1 e e er

(1 +

8/20/02

 f (m − 1) λTG = 1 + mf − 1 

hˆ 2f 1 =

3935 P-02 Page 93

93

94

[

0

0

0

0

0

]

0

 λTP = 1 

=

1 2

1

1

1

1

1 8 2 σˆ P

(1 + FP )σˆ 2A +

1

1

1  n 

(1 + FP )2σˆ 2AA

[

σˆ 2M + σˆ 2F

0

0

0

0

0

]

0

 λTP = 1 

=

1 2

1

1

1

1

1 8 2 σˆ P

(1 + FP )σˆ 2A +

1

1

1  n 

(1 + FP )2σˆ 2AA

[

2σˆ 2M

0

0

0

0

0

0

]

0

 λTP = 1 

σˆ 2 σˆ 2 σˆ 2 σˆ 2 σˆ 2 σˆ 2M + σˆ 2F + σˆ 2MF + ME + FE + MFE + ε + w e e e er ern

λTG = 2

hˆ 2f 1 = =

1

1 2

1

1 e

1 e

1 8 σˆ 2P

(1 + FP )σˆ 2A +

e

1

er

1

1   ern 

(1 + FP )2σˆ 2AA

D. Family heritability, giving the response to selection among full-sib family means averaged across environments as measured in outbred progeny grown in independent environments (the estimator is unbiased relative to hf21).

1

σˆ 2 σˆ 2M + σˆ 2F + σˆ 2MF + σˆ 2ME + σˆ 2FE + σˆ 2MFE + σˆ ε2 + w n

λTG = 1

hˆ 2f 1 =

C. Alternative estimate of family heritability on a plot basis (full-sib family, single-plot mean value), giving the response to selection among plot means within one replication of one environment as measured in outbred progeny grown in independent environments (the estimator is unbiased relative to hf21).

0

n

σˆ w2

2:53 PM

λTG = 2

σˆ 2M + σˆ 2F + σˆ 2MF + σˆ 2ME + σˆ 2FE + σˆ 2MFE + σˆ ε2 +

2σˆ 2M

8/20/02

hˆ 2f 1 =

B. Family heritability on a plot basis (full-sib family, single-plot mean value), giving the response to selection among plot means within one replication of one environment as measured in outbred progeny grown in independent environments (the estimator is unbiased relative to hf21).

3935 P-02 Page 94

[

0

0

0

0

e 0

+

]

0

e

σˆ 2FE

+ σˆ 2F + e

σˆ 2MFE +

+ ern

σˆ w2

 λTP = 1 

er

σˆ ε2

=

1

1 2

1

1 e

1 e

1 8 2 σˆ P

(1 + FP )σˆ 2A +

e

1 er

1

1   ern 

(1 + FP )2σˆ 2AA

σ 2P

(1 + FP )2σ 2AA

[

λTG = 1 0

0

0

0

σˆ 2M = σˆ 2 σˆ 2 σˆ 2 σˆ 2 + FE + MFE + ε + w ef ef erf erfn 0

1 4

0

]

0

1 16

σˆ P2

(1 + FP )σˆ 2A +

 λTP = 1 

(1 + FP )2σˆ 2AA

f

1

f

1

e

1

1 ef

1 ef

1 erf

1   erfn 

σˆ 2F σˆ 2ME

1 32

σ 2P

(1 + FP )2σ 2AA

[

λTG = 1 0

0

0

0

0

σˆ 2 σˆ 2 σˆ 2 σˆ 2 σˆ 2 σˆ w2 + MFE + ε + σˆ 2F + M + MF + FE + m m e me me mer mern

Bias =

hˆ 2f 1 = =

0

1 4

] 0

m

1

(1 + FP )2σˆ 2AA

 λTP = 1 

1 16 σˆ 2P

(1 + FP )σˆ 2A +

m

1

e

1

me

1

me

1

mer

1

1   mern 

G. Family heritability, giving the response to selection among maternal half-sib family means averaged across environments as measured in outbred progeny grown in independent environments.

1 32

σˆ 2 σˆ 2 σˆ 2 σˆ 2M + F + MF + ME f f e

Bias =

hˆ 2f 1 =

2:53 PM

F. Family heritability, giving the response to selection among paternal half-sib family means averaged across environments as measured in outbred progeny grown in independent environments.

1

σˆ 2M + σˆ 2F + σˆ 2MF +

σˆ 2M 2 σˆ ME

8/20/02

λTG = 1

hˆ 2f 1 =

E. Alternative estimate of family heritability, giving the response to selection among full-sib family means averaged across environments as measured in outbred progeny grown in independent environments (the estimator is unbiased relative to hf21).

3935 P-02 Page 95

95

96

[

0

0

0

0

]

0

[

λTP = 1

1 2

1

1

1

1

1 8 σˆ 2P

(1 + FP )σˆ 2A +

1

]

1

(1 + FP )2σˆ 2AA

[

0

0

0

0

]

0

[

1 2

λTP = 1

σˆ 2M + σˆ 2F = + σˆ 2ME + σˆ 2FE + σˆ 2MFE + σˆ ε2′ 1

1

1

1 8

1

σˆ 2P

(1 + FP )σˆ 2A +

1

]

1

(1 + FP )2σˆ 2AA

[

2σˆ 2M

0

0

0

0

0

]

0

=

 λTP = 1 

σˆ 2 σˆ 2 σˆ 2 σˆ 2 σˆ 2M + σˆ 2F + σˆ 2MF + ME + FE + MFE + ε ′ e e e er

λTG = 2

hˆ 2f 1 =

1 2

1

1

1 8

1 e

1 e

σˆ 2P

(1 + FP )σˆ 2A +

e

1

1  er 

(1 + FP )2σˆ 2AA

C. Family heritability, giving the response to selection among full-sib family means averaged across environments as measured in outbred progeny grown in independent environments (the estimator is unbiased relative to hf21).

1

σˆ 2M + σˆ 2F + σˆ 2MF

λTG = 1

hˆ 2f 1 =

B. Alternative estimate of family heritability on a plot basis (full-sib family, single-plot value), giving the response to selection among plot values within one replication of one environment as measured in outbred progeny grown in independent environments (the estimator is unbiased relative to hf21).

0

=

2:53 PM

λTG = 2

σˆ 2M + σˆ 2F + σˆ 2MF + σˆ 2ME + σˆ 2FE + σˆ 2MFE + σˆ ε2′

2σˆ 2M

8/20/02

hˆ 2f 1 =

A. Family heritability on a plot basis (full-sib family, single-plot value), giving the response to selection among plot values within one replication of one environment as measured in outbred progeny grown in independent environments (the estimator is unbiased relative to hf21).

9. Narrow-sense heritability estimated from a Design II experiment with data obtained on total plot values of multiple-plant plots in multiple independent environment evaluations (Section VIII.E).

3935 P-02 Page 96

[

0

0

0

0

]

0

 λTP = 1  1

1

1 8

1 e

1 e

σˆ 2P

(1 + FP )σˆ 2A +

e

1

1  er 

(1 + FP )2σˆ 2AA

σˆ 2M

σ 2P

(1 + FP )2σ 2AA

[

λTG = 1 0

0

0

=

1 4

0

0

]

0

1 16 σˆ 2P

(1 + FP )σˆ 2A +

 λTP = 1 

(1 + FP )2σˆ 2AA

f

1

f

1

e

1

1 ef

1 ef

1   erf 

σˆ 2F

1 32

σ 2P

(1 + FP )2σ 2AA

[

λTG = 1 0

0

σˆ 2 σˆ 2 σˆ 2 σˆ 2 σˆ 2 σˆ 2 σˆ 2F + M + MF + FE + ME + MFE + ε ′ m m e em em erm

Bias =

hˆ 2f 1 =

0

=

1 4

0

0

] 0

1 16 σˆ 2P

(1 + FP )σˆ 2A +

 λTP = 1 

(1 + FP )2σˆ 2AA

m

1

m

1

e

1

em

1

em

1

1   erm 

F. Family heritability, giving the response to selection among maternal half-sib family means averaged across environments as measured in outbred progeny grown in independent environments.

1 32

σˆ 2 σˆ 2 σˆ 2 σˆ 2 σˆ 2 σˆ 2 σˆ 2M + F + MF + ME + FE + MFE + ε ′ f f e ef ef erf

Bias =

hˆ 2f 1 =

E. Family heritability, giving the response to selection among paternal half-sib family means averaged across environments as measured in outbred progeny grown in independent environments.

1

1 2

2:53 PM

λTG = 1

σˆ 2M + σˆ 2F + σˆ 2MF

σˆ 2M + σˆ 2F = σˆ 2ME σˆ 2FE σˆ 2MFE σˆ ε2′ + + + + e e e er

8/20/02

hˆ 2f 1 =

D. Alternative estimate of family heritability, giving the response to selection among full-sib family means averaged across environments as measured in outbred progeny grown in independent environments (the estimator is unbiased relative to hf21).

3935 P-02 Page 97

97

98

σˆ 2F =

σˆ 2F σˆ 2P

[

λTG = 1 0

0

0

0

]

0

[

λTP = 1 1

1

1

1

σˆ 2F + σˆ 2FL + σˆ 2FY + σˆ 2FLY

σˆ 2F σˆ 2 + σˆ ε2 + w n

= P

σˆ 2F σˆ 2

[

λTG = 1 0

0

0

0

]

0

 λTP = 1 

1

1

1

1

] 1

1  n 

σˆ 2F + σˆ 2FL + y

σˆ 2FY + y

σˆ 2FLY

σˆ 2F + yr

σˆ ε2 + yrn

σˆ w2

= P

σˆ 2F σˆ 2

[

λTG = 1 0

0

0

0

] 0

 λTP = 1 

1

y

1

y

1

1 yr

1   yrn 

hˆ 2f 1 =

σˆ 2F +

l

σˆ 2FL

+ σˆ 2FY + l

σˆ 2FLY

σˆ 2F + lr

σˆ ε2 + lrn

σˆ w2

= P

σˆ 2F σˆ 2

λTG = 1

[

0

0

0

0

] 0

 λTP = 1 

l

1

1

l

1

lr

1

1   lrn 

D. Heritability of family means, giving the response to selection among family means averaged across locations within one year as measured in independent environments.

hˆ 2f 1 =

C. Heritability of family means, giving the response to selection among family means averaged across years within one location as measured in independent environments.

hˆ 2f 1 =

B. Family heritability on a plot basis, giving the response to selection among plot means within one replication of one environment as measured in independent environments.

σˆ 2F + σˆ 2FL + σˆ 2FY + σˆ 2FLY + σˆ ε2 + σˆ w2

2:53 PM

hˆ12 =

8/20/02

A. Heritability on an individual-plant basis, giving the response to selection among individual plants within one replication as measured in independent environments.

10. Narrow-sense heritability estimated from data obtained on individual plants from multiple environment evaluations of families in cross-classified locations and years, giving the response to selection among testcross progenies (Section VIII.F) or “immediate response” to selection among inbred families (Section VIII.G) as measured in independent environments. The estimators are unbiased relative to h21 for these situations, but are biased when applied to selection among inbred families when response units are outbred or evaluated at a different level of inbreeding than the selection units (Section VIII.G).

3935 P-02 Page 98

σˆ 2 σˆ 2 σˆ 2 σˆ 2 σˆ 2 σˆ 2F + FL + FY + FLY + ε + w l y ly lyr lyrn

σˆ 2F = P

σˆ 2F σˆ 2

[

λTG = 1 0

0

0

0

]

0

 λTP = 1  l

1

1 y

1 ly

1 lyr

1   lyrn 

σˆ 2F

σˆ 2F + σˆ 2FL + σˆ 2FY + σˆ 2FLY + σˆ ε2′

=

σˆ 2F σˆ 2P

[

λTG = 1 0

0

0

]

0

[

λTP = 1

1

1

1

] 1

σˆ 2F + σˆ 2FL + y

σˆ 2F 2 σˆ FY + y

σˆ 2FLY + yr

σˆ ε2′

= P

σˆ 2F σˆ 2

[

λTG = 1 0

0

0

] 0

 λTP = 1 

1

y

1

y

1

1   yr 

hˆ 2f 1 =

σˆ 2F +

l

σˆ 2FL

+ σˆ 2FY +

σˆ 2F l

σˆ 2FLY + lr

σˆ ε2′

= P

σˆ 2F σˆ 2

λTG = 1

[

0

0

0

] 0

 λTP = 1 

l

1

1

l

1

1  lr 

C. Heritability of family means, giving the response to selection among family means averaged across locations within one year as measured in independent environments.

hˆ 2f 1 =

B. Heritability of family means, giving the response to selection among family means averaged across years within one location as measured in independent environments.

hˆ 2f 1 =

2:53 PM

A. Family heritability on a plot basis, giving the response to selection among plot values within one replication of one environment as measured in independent environments.

8/20/02

11. Narrow-sense heritability estimated from data obtained on total plot values from multiple environment evaluations of families in cross-classified locations and years, giving the response to selection among testcross progenies (Section VIII.F) or “immediate response” to selection among inbred families (Section VIII.G) as measured in independent environments. The estimators are unbiased relative to h21 for these situations, but are biased when applied to selection among inbred families when response units are outbred or evaluated at a different level of inbreeding than the selection units (Section VIII.G).

hˆ 2f 1 =

E. Heritability of family means, giving the response to selection among family means averaged across locations and years as measured in independent environments.

3935 P-02 Page 99

99

100

σˆ 2F +

l

σˆ 2FL

+ y

σˆ 2FY

σˆ 2F + ly

σˆ 2FLY + lyr

σˆ ε2′

= P

σˆ 2F σˆ 2

[

λTG = 1 0

0

0

]

0

 λTP = 1  l

1

1 y

1 ly

1   lyr 

σˆ 2F

+

+

σˆ 2F

σˆ 2FE

σˆ ε2 +

σˆ w2

=

σˆ 2F σˆ 2P

[

λTG = 1 0

0

]

0

[

λTP = 1 1

1

] 1

σˆ 2F + σˆ 2FE + σˆ ε2 +

σˆ 2F n

σˆ w2

= P

σˆ 2F σˆ 2

[

λTG = 1 0

0

]

0

 λTP = 1 

1

1

1  n 

σˆ 2F +

e

σˆ 2FE

+

σˆ 2F er

σˆ ε2 + ern

σˆ w2

= P

σˆ 2F σˆ 2

[

λTG = 1

0

0

] 0

 λTP = 1 

1 e

1 er

1   ern 

13. Narrow-sense heritability estimated from data obtained on total plot values from multiple independent environment evaluations of families, giving the response to selection among testcross progenies (Section VIII.F) or “immediate response” to selection

hˆ 2f 1 =

C. Heritability of family means, giving the response to selection among family means averaged across environments as measured in independent environments.

hˆ 2f 1 =

B. Family heritability on a plot basis, giving the response to selection among plot means within one replication of one environment as measured in independent environments.

hˆ 12 =

2:53 PM

A. Heritability on an individual-plant basis, giving the response to selection among individual plants within one replication of one environment as measured in independent environments.

8/20/02

12. Narrow-sense heritability estimated from data obtained on individual plants from multiple independent environment evaluations of families, giving the response to selection among testcross progenies (Section VIII.F) or “immediate response” to selection among inbred families (Section VIII.G) as measured in independent environments. The estimators are unbiased relative to h21 for these situations, but are biased when applied to selection among inbred families when response units are outbred or evaluated at a different level of inbreeding than the selection units (Section VIII.G).

hˆ 2f 1 =

D. Heritability of family means, giving the response to selection among family means averaged across locations and years as measured in independent environments.

3935 P-02 Page 100

σˆ 2F σˆ 2 = F 2 2 σˆ 2P + σˆ FE + σˆ ε ′

[

λTG = 1 0

]

0

[

λTP = 1 1

]

1

σˆ 2F σˆ 2 = F σˆ 2 σˆ 2P σˆ 2 σˆ 2F + FE + ε ′ e er

[

λTG = 1 0

]

0

 λTP = 1  1 e

1  er 

†

2 2 2 Bias measured as E(hˆ12) – h 1 or E(hˆf12) – h 1, where h 1 or hf12 is the regression of response units (members of the first generation of the next base population) on selection units. The bias will differ if the response unit is a gametic phase equilibrium population resulting from many generations of random mating (Nyquist 1991, pp. 250–251). ‡ 2 s G is the genotypic variance component, s 2e is the experimental error variance component, s 2e′ is the experimental error variance when 2 data are not available on individual plants within plots, component s 2w is the plant-to-plant-within-plot variance component, s GL is the 2 2 genotype-by-location interaction variance component, s GY is the genotype-by-year interaction variance component, s GLY is the genotypeby-location-by-year interaction variance component, s 2GE is the genotype-by-environment interaction variance component, s 2M is the male (or half-sib family) variance component, s 2F(M) is the female nested within male variance component, s 2A is the additive genetic variance 2 2 component, s ME is the male-by-environment interaction variance component, s F(M)E is the female-within-male-by-environment interaction 2 2 variance component, s F is the family variance component (or the female variance component in Design II experiments), s MF is the male2 by-female interaction variance component, s FE is the family-by-environment interaction variance component (or female-by-environment 2 interaction variance component in Design II experiments), s MFE is the male-by-female-by-environment interaction variance component, 2 2 2 s FL is the family-by-location interaction variance component, s FY is the family-by-year interaction variance component, and s FLY is the family-by-location-by-year interaction variance component, r is the number of replications per environment, e is the number of environments, l is the number of locations, y is the number of years, f is the number of female parents mated to each male parent in Designs I and II, m is the number of male parents mated to each female parent in Design II and the total number of male parents in Design I, and FP is the inbreeding coefficient of parents used in the mating design.

hˆ 2f 1 =

2:53 PM

B. Heritability of family means, giving the response to selection among family means averaged across environments as measured in independent environments.

σˆ 2F

8/20/02

hˆ 2f 1 =

A. Family heritability on a plot basis, giving the response to selection among plot means within one replication of one environment as measured in independent environments.

among inbred families (Section VIII.G) as measured in independent environments. The estimators are unbiased relative to h21 for these situations, but are biased when applied to selection among inbred families when response units are outbred or evaluated at a different level of inbreeding than the selection units (Section VIII.G).

3935 P-02 Page 101

101

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J. HOLLAND, W. NYQUIST, AND C. CERVANTES

APPENDICES SAS CODE FOR ESTIMATING HERITABILITY WITH REML The SAS codes presented in the appendices and sample data sets for which these examples were written are freely available on the Internet at www4.ncsu.edu/~jholland/heritability.html. The symbol H is used for h2f in the codes due to limitations on typeset. Appendix 1. Estimating Heritability from Multiple Environments, One Replication per Environment SAS version 8.0 code for estimating heritability and its standard for a trait measured on one replication at multiple environments is given here. As an example, we assumed that family means were based on data from six independent environments. The equation for family heritability on a plot basis from Table 2.1.13.A and the equation for family heritability on a mean basis from Table 2.1.13.B were used. Heritability on a family-mean basis was approximated by setting e = 6 and r = 1 in this example. proc mixed asycov; class env geno; model trait = ; random env geno; ods listing exclude asycov covparm; ods output asycov = covmat covparms = estmat; proc iml; start seh(V, C, LG, LP, H, SE); Vp = LP`*V; Vg = LG`*V; H = Vg/Vp; d = (1/Vp)*(LG - (LP*H)); VH = d`*C*d; SE = sqrt(VH); finish seh; use estmat; read all into v; use covmat; read all into c; * Note that SAS introduces an extra first column into the matrix which must be removed; C = C(|1:nrow(C), 2:ncol(C)|); *order of variance components in v and c matrices is s 2E, s 2G, residual (=s 2GE); LG = {0, 1, 0}; LP = {0, 1, 1};

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call seh(V, C, LG, LP, print "Heritability on e = 6; LP = 0//1//(1/e); call seh(V, C, LG, LP, print "Heritability on quit; run;

103

H, SE); a Plot Basis", H, SE;

H, SE); a Family-Mean Basis", H, SE;

Appendix 2. Estimating Heritability from Multiple Environments, Several Replications per Environment SAS version 8.0 code for estimating heritability and its standard error for a trait measured on several replications at each of multiple environments is given here. As an example, we assumed that family means were based on data from three replications within each of six independent environments. The equation for family heritability on a plot basis from Table 2.1.13.A and the equation for family heritability on a mean basis from Table 2.1.13.B were used. Heritability on a family-mean basis was approximated by setting e = 6 and r = 3 in this example. proc mixed asycov; class env rep geno; model trait = ; random env rep(env) geno env*geno; ods listing exclude asycov covparm; ods output asycov = covmat covparms = estmat; proc iml; start seh(V, C, LG, LP, H, SE); Vp = LP`*V; Vg = LG`*V; H = Vg/Vp; d = (1/Vp)*(LG - (LP*H)); VH = d`*C*d; SE = sqrt(VH); finish seh; use estmat; read all into v; use covmat; read all into c; * Note that SAS introduces an extra first column into the C matrix which must be removed; C = C(|1:nrow(C), 2:ncol(C)|); *order of variance components in v and c matrices is s 2E, s 2R, s 2G, s 2GE, residual; LG = {0, 0, 1, 0, 0}; LP = {0, 0, 1, 1, 1}; call seh(V, C, LG, LP, H, SE); print "Heritability on a Plot Basis", H, SE;

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J. HOLLAND, W. NYQUIST, AND C. CERVANTES

r = 3; e = 6; LP = 0//0//(1/e)//(1/(e*r)); call seh(V, C, LG, LP, H, SE); print "Heritability on a Family-Mean Basis", H, SE; quit; run;

Appendix 3. Estimating Heritability in Multiple Populations Grown in a Common Experiment SAS version 8.0 code for estimating heritability and its standard error within two different populations for a trait measured on several replications at each of multiple environments is given here. As an example, the treatment design consists of equal number of genotypes from each population randomly assigned to different sets, and the experimental design is a replications-within-sets layout replicated three times within each of four independent environments. The equation for family heritability on a plot basis from Table 2.1.13.A and the equation for family heritability on a mean basis from Table 2.1.13.B were used. Heritability on a family-mean basis was approximated by setting e = 4 and r = 3 in this example. Unique family and family-by-environment interaction variances are estimated for the two populations, but a common error variance is assumed. proc mixed asycov; classes env rep set geno pop; model trait = pop; random env set rep(env*set) pop*env set*env; random geno(set) env*geno(set)/group = pop; lsmeans pop/pdiff; ods output asycov = covmat covparms = estmat; run; proc iml; start seh(V,C,LG,LP,H,SE); Vp = LP`*V; Vg = Lg`*V; H = Vg/Vp; d = (1/Vp)*(LG - (Lp*H)); VH = d`*C*d; SE = sqrt(VH); finish seh;

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105

use estmat; read all into v;use covmat; read all into c; *Note that SAS introduces an extra first column into the C matrix which must be removed; C = C(|1:nrow(C), 2:ncol(C)|); *order of variance components in V and C matrices is s 2Env, s 2Set, s 2Rep, s 2Env*Pop, s 2Env*Set, s 2Geno(Set)Pop1, s 2Geno(Set)Pop2, s 2Env*Geno(Set)Pop1, s 2Env*Geno(Set)Pop2, s 2Error.; *get heritability for first population; *LG and LP vectors for Population 1; LG = {0,0,0,0,0,1,0,0,0,0}; LP = {0,0,0,0,0,1,0,1,0,1}; call seh(V,C,LG,LP,H,SE); print "Heritability on a Plot Basis - Population 1", H, SE; e = 4; r = 3; LP = 0//0//0//0//0//1//0//(1/e)//0//(1/(e*r));print LP; call seh(V,C,LG,LP,H,SE); print "Heritability on a Family-Mean Basis - Population 1", H, SE; *LG and LP vectors for Population 2; LG = {0,0,0,0,0,0,1,0,0,0}; LP = {0,0,0,0,0,0,1,0,1,1}; call seh(V,C,LG,LP,H,SE); print "Heritability on a Plot Basis - Population 2", H, SE; e = 4; r = 3; LP = 0//0//0//0//0//0//1//0//(1/e)//(1/(e*r)); call seh(V,C,LG,LP,H,SE); print "Heritability on a Family-Mean Basis - Population 2", H, SE; quit; run;

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J. HOLLAND, W. NYQUIST, AND C. CERVANTES

Appendix 4. Estimating Heritability via Parent-Offspring Regression and from Replicated Family Evaluations SAS version 8.0 code for estimating heritability and its standard error for a trait measured on parental and offspring genotypes in different environments is given here. This code was used to analyze the following data set: disease ratings were made on 140 F2 (S0) parents (individual plants) in Florida, 1991; disease ratings were made on two replications of 24-plant row plots of all 140 self-fertilized progeny (F2:3 or S0:1) in North Carolina, 1991; disease ratings were made on two replications of 24-plant row plots of a selected group of 52 S0:1 progeny in North Carolina 1994. Holland et al. (1998) used these data to estimate heritability from regression of 140 F2:3 means from the North Carolina, 1991, environment; from the ANOVA of S0:1 families grown in North Carolina, 1991, environment (biased by family-by-environment interactions in numerator); and they estimated repeatability unbiased by FE in the numerator using the selected set of 52 S0:1 lines grown in both North Carolina, 1991 and 1994, environments. Using mixed models approaches, a single estimator of heritability based on the variance of all 140 S0:1 families can be obtained, including the data from some families in 1994 to remove FE bias from the numerator. The equation for family heritability on a plot basis from Table 2.1.13.A and the equation for family heritability on a mean basis from Table 2.1.13.B were used. Heritability on a family-mean basis was approximated by setting e equal to the harmonic mean of the number of environments in which each family was evaluated (1.23) and setting er equal to the harmonic mean of the number of plots in which each family was evaluated (2.42). In the same analysis, the parent-offspring regression analysis can be performed to obtain the heritability estimator with individual S0 plants as the phenotypic variance using Equation [21]. Before implementing the mixed models analysis, ANOVAs were conducted to provide initial estimates of the variance and covariance parameters to promote faster computation and convergence on the maximum likelihood estimates. Initial parameter estimates are specified in the “parms” statement in PROC MIXED. Multiple random statements in PROC MIXED are used to permit modeling the G matrix effects separately for different variance components. Variables coding for the environment (“env”), the block within environment (“rep”), the generation (“gen” = “F2” or “F3”), and the family number code are associated with each observation. proc mixed asycov; class env rep family gen; model rust = ; *the macroenvironment and block within environment effects are treated as random variables each with a single variance component;

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107

random env rep(env) ; *unique family – by – environment interaction variances are modeled for the two different generations by using the group option; random family*env/group = gen; *unique family variances are modeled for the two different generations, and the covariance between families with the same code is modeled by using the subject option and specifying an unstructured covariance matrix and specifying that each family has the same variancecovariance structure; random gen/subject=family type = un; *unique error variances are modeled for the two different generations with the repeated command and group option; repeated /group = gen; * initial values of variance-covariance parameters based on the preliminary ANOVAs are introduced with the parms command - in the order that effects are specified in the random statements (s 2E, s 2R(E), s 2FE(S0), s 2FE(S1), s 2F(S0), sG(S0,S1), s 2F(S1), s 2e(S0), s 2e(S1)). The variance components for GE and Error within the F2 generation (s 2FE(S0) s 2e(S1)) are forced to be zero with the hold option - this is necessary because only one variance component is estimable in the F2 generation, as data were taken on individual plants, so the component s 2F(S0) is actually the phenotypic variance in the S0 generation; parms (0.1451) (0.0057) (0) (0.146) (4.274) (2.5741) (3.0086) (0) (0.7515)/ hold=3,8; ods listing exclude asycov covparm; ods output asycov = covmat covparms = estmat; proc iml; start seh(V, C, LG, LP, H, SE); Vp = LP`*V; Vg = LG`*V; H = Vg/Vp; d = (1/Vp)*(LG - (LP*H)); VH = d`*C*d; SE = sqrt(VH); finish seh; use estmat; read all into v; use covmat; read all into c; * Note that SAS introduces an extra first column into the matrix which must be removed; C = C(|1:nrow(C), 2:ncol(C)|); *Note carefully the order of variance components in v and c matrices:s 2E, s 2R(E), s 2FE(S0), s 2FE(S1), s 2F(S0), sG(S0,S1), s 2F(S1),

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s 2e(S0), s 2e(S1). The vector of variance components estimates that is of interest for estimating heritability based on S0:1 line variances includes only s 2F(S1), s 2FE(S1), and s 2e(S1); these are the 7th, 4th, and 9th components of the V and C matrices, respectively; v = v(|7|)//v(|4|)//v(|9|) c = c(|{7 4 9}, {7 4 9}|); LG = {1, 0, 0}; LP = {1, 1, 1}; call seh(V, C, LG, LP, H, SE); print "Heritability on a Plot Basis", H, SE; *the harmonic mean of the number of plots per S0:1 family is 2.42 and the number of environments in which each family was tested is 1.23; eh = 1.23; ph = 2.42; lp = 1//(1/eh)//(1/ph); call seh(V, C, LG, LP, H, SE); print "Heritability on a Family-Mean Basis", H, SE; *now create a new pair of v and c matrices to estimate heritability from parent offspring regression. In this case the variance components of interest are the 6th and 5th, respectively: sG(S0,S1) and s 2F(S0); use estmat; read all into v; use covmat; read all into c; v = v(|6|)//v(|5|); C = C(|1:nrow(C), 2:ncol(C)|); c = c(|{6 5}, {6 5}|); LG = {1, 0}; LP = {1, 1}; call seh(V, C, LG, LP, H, SE); print "Heritability from regression of S1 offspring on individual parents", H, SE; quit; run;

LITERATURE CITED Atlin, G. N., R. J. Baker, K. B. McRae, and X. Lu. 2000. Selection response in subdivided target regions. Crop Sci. 40:7–13. Banziger, M., and H. R. Lafitte. 1997. Efficiency of secondary traits for improving maize for low-nitrogen target environments. Crop Sci. 37:1110–1117. Boldman, K. G., and L. D. Van Vleck. 1991. Derivative-free restricted maximum likelihood estimation in animal models with a sparse matrix solver. J. Dairy Sci. 74:4337–4343.

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Boldman, K., L. D. Van Vleck, and L. A. Kriese. 1993. A manual for use of MTDFREML. USDA-ARS, Clay Center, NE. Broemeling, L. D. 1969. Confidence intervals for measures of heritability. Biometrics 24:424–427. Bulmer, M. G. 1985. The mathematical theory of quantitative genetics. Oxford Univ. Press, Oxford, UK. Burton, J. W., and B. F. Carver. 1993. Selection among S1 families vs. selfed half-sib or fullsib families in autogamous crops. Crop Sci. 33:21–28. Casella, G., and R. L. Berger. 1990. Statistical inference. Duxbury Press, Belmont, CA. Casler, M. D. 1982. Genotype × environment interaction bias to parent-offspring regression heritability estimates. Crop Sci. 22:540–542. Cervantes-Martínez, C. T., K. J. Frey, P. J. White, D. M. Wesenberg, and J. B. Holland. 2001. Selection for greater b-glucan content in oat grain. Crop Sci. 41:1085–1091. Cockerham, C. C. 1963. Estimation of genetic variances. p. 53–94. In: W. D. Hanson and H. F. Robinson (eds.), Statistical genetics and plant breeding. Publ. 982. Natl. Acad. Sci.Natl. Res. Counc., Washington, DC. Cockerham, C. C. 1971. Higher order probability functions of identity of alleles by descent. Genetics 69:235–246. Cockerham, C. C. 1983. Covariances of relatives from self-fertilization. Crop Sci. 23:1177–1180. Cockerham, C. C., and D. F. Matzinger. 1985. Selection response based on selfed progenies. Crop Sci. 25:483–488. Comstock, R. E., and R. H. Moll. 1963. Genotype-environment interactions. p. 161–196. In: W. D. Hanson and H. F. Robinson (eds.), Statistical genetics and plant breeding. Publ. 982. Natl. Acad. Sci.-Natl. Res. Counc., Washington, DC. Cornelius, P. L., and J. W. Dudley. 1976. Genetic variance and predicted response to selection under selfing and full-sib mating in a maize population. Crop Sci. 16:333–339. Dickerson, G. E. 1969. Techniques for research in quantitative animal genetics. p. 36–79. In: Techniques and procedures in animal science research. Am. Soc. Animal Sci., Albany, NY. Dieters, M. J., T. L. White, R. C. Littell, and G. R. Hodge. 1995. Application of approximate variances of variance components and their ratios in genetic tests. Theor. Appl. Genet. 91:15–24. Diz, D. A., and S. C. Schank. 1995. Heritabilities, genetic parameters, and response to selection in pearl millet × elephantgrass hexaploid hybrids. Crop Sci. 35:95–101. Dudley, J. W., and R. H. Moll. 1969. Interpretation and use of estimates of heritability and genetic variances in plant breeding. Crop Sci. 9:257–262. Falconer, D. S., and T. F. C. Mackay. 1996. Introduction to quantitative genetics, 4th ed. Longman Technical, Essex, UK. Gauch, H. G., and R. W. Zobel. 1997. Identifying mega-environments and targeting genotypes. Crop Sci. 37:311–326. Gibson, P. T. 1996. Correcting for inbreeding in parent-offspring regression estimates of heritability with non-additive and genotype × environment effects present. Crop Sci. 36:594–600. Goodman, M. M. 1965. Estimates of genetic variance in adapted and exotic populations of maize. Crop Sci. 5:87–90. Gordon, I. L., D. E. Byth, and L. N. Balaam. 1972. Variance of heritability ratios estimated from phenotypic variance components. Biometrics 28:401–415.

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Graser H. U., S. P. Smith, and B. Tier. 1987. A derivative-free approach for estimating variance components in animal models by restricted maximum likelihood. J. Anim. Sci. 64:1362–1370. Graybill, F. A., F. Martin, and G. Godfrey. 1956. Confidence intervals for variance ratios specifying genetic heritability. Biometrics 12:99–109. Hallauer, A. R., and J. B. Miranda, Fo. 1988. Quantitative genetics in maize breeding. 2nd Ed. Iowa State Univ. Press, Ames, IA. Hanson, W. D. 1963. Heritability. p. 125–139. In: W. D. Hanson and H. F. Robinson (eds.), Statistical genetics and plant breeding. Publ. 982. Natl. Acad. Sci.-Natl. Res. Counc., Washington, DC. Hill, W. G. 1972. Estimation of realised heritabilities from selection experiments. II. Selection in one direction. Biometrics 28:767–780. Hohls, T. 1996. Setting confidence limits to genetic parameters estimated by restricted maximum likelihood analysis of North Carolina design II experiments. Heredity 77:476–487. Hoi, S.-W., J. B. Holland, and K. J. Frey. 1999. Heritability of lipase activity of oat caryopses. Crop Sci. 39:1055–1059. Hoi, S.-W., J. B. Holland, and E. G. Hammond. 1999. Heritability of lipase activity of oat caryopses. Crop Sci. 39:1055–1059. Holland, J. B. 2001. Epistasis and plant breeding. Plant Breed. Rev. 21:27–92. Holland, J. B., D. V. Uhr, D. Jeffers, and M. M. Goodman. 1998. Inheritance of resistance to southern corn rust in tropical-by-corn belt maize populations. Theor. Appl. Genet. 96:232–241. Holland, J. B., Å. Bjørnstad, K. J. Frey, M. Gullord, D. M. Wesenberg, and T. Buraas. 2000. Recurrent selection in oat for adaptation to diverse environments. Euphytica 113:195–205. Holthaus, J. F., J. B. Holland, P. J. White, and K. J. Frey. 1996. Inheritance of b-glucan content of oat grain. Crop Sci. 36:567–572. Knapp, S. J. 1986. Confidence intervals for heritability for two-factor mating design single environment linear models. Theor. Appl. Genet. 72:587–591. Knapp, S. J., and W. C. Bridges Jr. 1987. Confidence interval estimators for heritability for several mating and experimental designs. Theor. Appl. Genet. 73:759–763. Knapp, S. J., and W. C. Bridges. 1988. Parametric and jackknife confidence interval estimators for two-factor mating design genetic variance ratios. Theor. Appl. Genet. 76:385–392. Knapp, S. J., W. W. Stroup, and W. M. Ross. 1985. Exact confidence intervals for heritability on a progeny mean basis. Crop Sci. 25:192–194. Lindsey, J. K. 1996. Parametric statistical inference. Oxford Univ. Press, Oxford, UK. Lindstrom, M. J. and D. M. Bates. 1989. Newton-Raphson and EM algorithms for linear mixed-effects models for repeated-measures data. J. Am. Stat. Assoc. 83:1014–1022. Littell, R. C., G. A. Milliken, W. A. Stroup, and R. D. Wolfinger. 1996. SAS system for mixed models. SAS Inst. Inc., Cary, NC. Little, R. J. A., and D. R. Rubin. 1987. Statistical analysis with missing data. Wiley, New York. Lynch, M., and B. Walsh. 1998. Genetics and analysis of quantitative traits. Sinauer Associates, Inc., Sunderland, MA. McLean, R. A., W. L. Sanders, and W. W. Stroup. 1991. A unified approach to mixed linear models. Am. Stat. 45:54–64. Melchinger, A. E. 1987. Expectation of means and variances of testcrosses produced from F2 and backcross individuals and their selfed progenies. Heredity 59:105–115. Meyer, K. 1988. DFREML—a set of programs to estimate variance components under an individual animal model. J. Dairy Sci. 71:33.

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Meyer, K. 1989. Restricted maximum likelihood to estimate variance components for animal models with several random effects using a derivative-free algorithm. Genet. Sel. Evol. 21:317–340. Meyer, K. 1997. An “average information” restricted maximum likelihood algorithm for estimating reduced rank genetic covariance matrices or covariance functions for animal models with equal design matrices. Genet. Sel. Evol. 29:97–116. Miller, R. G. 1974. The jacknife—a review. Biometrika 61:1–15. Milligan, S. B., K. A. Gravois, K. P. Bischoff, and F. A. Martin. 1990. Crop effects on broadsense heritabilities and genetic variances of sugarcane yield components. Crop Sci. 30:344–349. Milliken, G. A., and D. E. Johnson. 1992. Analysis of messy data, Volume I: Designed experiments. Chapman & Hall, New York. Mode, C. J., and H. F. Robinson. 1959. Pleiotropism and the genetic variance and covariance. Biometrics 15:518–537. Nyquist, W. E. 1991. Estimation of heritability and prediction of selection response in plant populations. Crit. Rev. Plant Sci. 10:235–322. Payne, R. W., and G. M. Arnold. 1998. GenStat Release 4.1 Procedure Library Manual PL11, Numerical Algorithms Group Ltd., Oxford, UK. Rawlings, J. O. 1988. Applied regression analysis: a research tool. Wadsworth & Brooks, Pacific Grove, CA. Rebetzke, G. J., A. G. Condon, R. A. Richards, and G. D. Farquhar. 2002. Selection for reduced carbon isotope discrimination increases aerial biomass and grain yield of rainfed bread wheat. Crop Sci. 42:739–745. Ritland, K. 2000. Marker-inferred relatedness as a tool for detecting heritability in nature. Mol. Ecol. 9:1195–1204. Robinson, G. K. 1991. That BLUP is a good thing: the estimation of random effects. Stat. Sci. 6:15–51. SAS Institute Inc. 1999. SAS OnlineDoc, Version 8. CD-ROM. SAS Institute, Inc., Cary, NC. Searle, S. R., G. Casella, and C. E. McCulloch. 1992. Variance components. Wiley, New York. Self, S. G., and K. Liang 1987. Asymptotic properties of maximum likelihood estimators and likelihood ratio tests under nonstandard conditions. J. Am. Stat. Assoc. 82:605–610. Shaw, R. G. 1987. Maximum-likelihood approaches applied to quantitative genetics of natural populations. Evolution 41:812–826. Singh, M., and S. Ceccarelli. 1995. Estimation of heritability using variety trials data from incomplete blocks. Theor. Appl. Genet. 90:142–145. Singh, M., S. Ceccarelli, and J. Hamblin. 1993. Estimation of heritability from varietal trials data. Theor. Appl. Genet. 86:437–441. Smith, J. D., and M. L. Kinman. 1965. The use of parent-offspring regression as an estimator of heritability. Crop Sci. 5:595–596. Steel, R. G. D., and J. H. Torrie. 1980. Principles and procedures of statistics: a biometric approach. 2nd ed. McGraw-Hill, New York. Steel, R. G. D., J. H. Torrie, and D. A. Dickey. 1997. Principles and procedures of statistics: a biometric approach. 3rd ed. McGraw-Hill, New York. Walsh, B., and M. Lynch. 1999. Selection and evolution of quantitative traits. http://nitro.biosci.arizona.edu/zbook/volume_2/vol2.html. White, T. L., and G. R. Hodge. 1989. Predicting breeding values with applications in forest tree improvement. Kluwer, Dordrecht. Xu, S. 2003. Advanced statistics in plant breeding. Plant Breed. Rev. 22: In press. Zhu, J., and B. S. Weir. 1996. Mixed model approaches for diallel analysis based on a biomodel. Genet. Res. 68:233–240.

112

Statistical genetic parameters of the two-locus, two-allele model, assuming Hardy-Weinberg and gametic phase equilibria.

2

2

2

2

2

i

j

2

2

= 0,

2

i

j

2

i

j

i =1 j =1

∑ ∑ p p δδ

2

i =1 j =1 k =1 l =1

ijkl

2

B k q l δ kl

k =1 l =1

2

2

i

j

2

2

= 0,

j =1 k =1 l =1

2

ijk

ik

j

2

i =1 k =1

= 0,

k δα ijk

ikl

2

= 0,

i

i =1

2

i

∑ p δδ

i =1 j =1 k =1

2 j

ijkl

∑∑∑ p p q

=0 2

=0

=0

B k δ kl

i k δδ ijkl

∑∑pq

2

k =1

2

∑q

k q l δδ ijkl

= 0,

i =1

2

∑ p αδ i

k =1

2

∑q

=0

= 0,

= 0,

∑∑∑p q

k q l δδ ijkl

= 0,

i =1 j =1

∑ ∑q

2

2

i

k q lαδ ikl

i =1

2

A i ij

∑ p αα

i =1

2

∑pδ

∑ ∑ p p δα

k q l δδ ijkl

= 0,

2

k =1 l =1

2

= 0,

= 0,

∑ ∑q

k αα ik

= 0,

= 0,

k =1

∑q

k δα ijk

∑∑∑∑p p q

2

i =1 j =1 k =1

∑∑∑ p p q

2

i =1 k =1 l =1

2

k =1 l =1

2

=0

∑ ∑q

B kαk

i k q lαδ ikl

∑∑∑p q

2

i =1 k =1

A j ij

i k αα ik

i

∑∑pq

2

i =1 j =1

2

k =1

= 0,

2

i =1

∑∑p p δ

2

∑q

A i

= 0,

i

∑pα

= 0,

k =1

2

k δδ ijkl

= 0,

∑q

k δδ ijkl

=0

2:53 PM

2

Restrictions of genotypic model:

8/20/02

B Model for genotypic value: G ijkl = µ.... + α iA + α jA + α kB + α lB + δ ijA + δ kl + αα ik + αα il + αα jk + αα jl + αδ ikl + αδ jkl + δα ijk + δα ijl + δδ ijkl

Table 2.6.

Errata for Table 2.6 from Holland, J. B. 2001. Epistatsis and Plant Breeding, in Plant Breeding Reviews, Volume 21, edited by Jules Janick. John Wiley & Sons, New York.

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3 Advanced Statistical Methods for Estimating Genetic Variances in Plants Shizhong Xu* Department of Botany and Plant Sciences University of California Riverside, California 92521

I. INTRODUCTION II. GENETIC MODEL A. Inbred Lines B. Outbred Population III. LEAST SQUARES ESTIMATION IV. MAXIMUM LIKELIHOOD ANALYSIS A. Random Model B. Mixed Model and BLUP C. Estimation of Variance Components V. BAYESIAN ANALYSIS VI. DISCUSSION AND CONCLUSIONS LITERATURE CITED

I. INTRODUCTION The main duty of plant breeders is to select the “best” plants to breed. Although the criterion of “best” depends on what the breeders want to improve, it always means the best “genetic quality,” not necessarily the “best” phenotypic value. The selected plants should carry the “best” set of genes that can produce progeny with the best (phenotypic) performance *I thank Drs. Rex Bernardo, James Holland, Jules Janick, William Muir, and Wyman Nyquist for their comments on the manuscript. This research was supported by the National Institutes of Health Grant 5 R01 GM55321 and the U.S. Department of Agriculture National Research Initiative Competitive Grant Programs 00-35300-9245. Plant Breeding Reviews, Volume 22, Edited by Jules Janick ISBN 0-471-21541-4 © 2003 John Wiley & Sons, Inc. 113

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and thus the highest yield or production. Because the phenotypic value of a plant only partially reflects the genetic value, particularly if the heritability is low, other information, for example, pedigrees, should also be used to infer the genetic value of a plant. In addition, information from molecular markers, if available, should also be considered to predict the genetic value. The optimal strategy of prediction is to incorporate all relevant information. This requires accurate estimation of genetic parameters and advanced statistic methodology to combine information from all sources in an optimal way. In this chapter, the concentration will be on statistical methods for estimating genetic variances that only integrate the phenotypic values and pedigree information, excluding molecular marker information. Methods that deal with molecular marker information have been reviewed elsewhere by Jansen (2001). The prerequisite of prediction is to quantify the genetic variance relative to the environmental variance and partition the total genetic variance into variance components, for example, additive, dominance, and epistatic variances. Using clones or plants within an inbred line, one can estimate the environmental variance. Subtracting the environmental variance from the total phenotypic variance (estimated from a random outbred population), we get an estimate of the genetic variance (Kearsey and Pooni 1996). However, to partition the genetic variance components, one needs special mating designs to produce groups of progeny with different genetic compositions. For example, in a hierarchical mating design, a group of unrelated male plants are selected for mating; each male plant is mated with many female plants; and each female plant (mated with only one male plant) produces many progeny. A male family consists of a mixture of full-sib and half-sib families. The genetic variance is actually estimated from the covariance between sibs. Because full-sibs, on average, share half of their genes, the covariance between fullsibs estimates half of the additive genetic variance. Furthermore, full-sibs can share a common genotype at any locus, that is, both alleles are identical by descent (IBD). The probability that full-sibs share a common IBD genotype equals –14 . Therefore, the covariance between fullsibs also includes a quarter of the dominance genetic variance. Half-sibs, however, share only one quarter of their genes and no common IBD genotypes. Therefore, the covariance between half-sibs estimates one quarter of the additive genetic variance and no dominance variance is estimated from the half-sib covariance. The dominance variance component can be estimated from the (weighted) difference between the covariance of full-sibs and the covariance of half-sibs (Falconer and Mackay 1996). Note that the covariance between sibs are measured by the between-group variance if the groups are classified according to sib

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families. Therefore, estimating genetic variance components requires special mating designs so that individual plants can be classified into groups within which all individuals have identical genetic relationship. Fisher (1918) invented the method of analysis of variance (ANOVA) particularly for solving this genetic problem. A thorough review on various designs and methods of estimating heritability can be found in Nyquist (1991). The most well-known mating designs for genetic analysis are the North Carolina (NC) Designs I, II, and III, described by Comstock and Robinson (1952). The theoretical basis of the experiments can be found in Comstock and Robinson (1948). In all designs, the parents are a group of F2 plants derived from selfing a single F1 plant, which itself is derived from the cross of two inbred lines. The statistical analysis of NC Design I is the one-way ANOVA or the nested ANOVA. The statistical analysis for NC design II is the two-way factorial ANOVA. NC Design II provides an explicit estimation of the dominance variance. The last design, NC Design III, also requires the two-way ANOVA, one factor being the F2 male parents and the other being the two inbred lines. The interaction between the line effect and F2 parent effect provides an estimate of the dominance effect. The use of the term NC designs is no longer limited to the cross of two inbred lines. More generally, the parents are random individuals from a noninbred, random mating population in linkage equilibrium (Cockerham 1963). NC Designs I and II were particularly developed for estimation of the average degree of dominance and no attempt was made to estimate the epistatic effect. Stuber et al. (1992) modified NC Design III by selecting the male parents from the F3 generation. Cockerham and Zeng (1996) incorporated molecular markers into the modified Design III to estimate epistatic effects. Cockerham (1954) developed a genotype factorial model to further partition the genetic variance into variance components due to espistasis. He used two loci as an example to demonstrate the factorial model where each locus is treated as a factor and the genotypes of the locus are considered as levels of treatment. Using the genotype factorial model, Cockerham was able to connect various sources of genetic effects with different model effects in the factorial analysis. For example, the linear term of the factorial ANOVA for one locus is the additive effect for the locus, the quadratic term is the dominance effect, the linear-by-linear interaction is the additive-by-additive epistatic effect, linear-by-quadratic interaction is the additive-by-dominance effect, and the quadratic-byquadratic interaction is the dominance-by-dominance effect. Although genotypes of a gene cannot be observed, the genotype factorial model has helped to understand the concept of epistasis and provided the

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theoretical foundation for epistatic mapping currently investigated by many researchers using molecular markers (Kao et al. 1999; Zeng et al. 2000). It is obvious that in the early days, ANOVA seemed to be the only statistical method used by plant breeders for estimating genetic parameters. ANOVA requires plants to be classified into different groups and plants within each group to share the same genetic background so that the genetic relationships among plants within groups are homogeneous. Therefore, only designed mating experiments fulfill these requirements. Estimation of additive and dominance effects bears no problem with the ANOVA, but estimation of epistatic effects requires more complex mating designs, which are hard to handle with the ANOVA. In most instances, the more complex mating designs permitted estimation of all types of digenic epistasis and in a few instances trigenic epistasis, for example, additive-by-additive-by-additive epistasis. The primary objective of the more complex mating designs was to develop additional covariance of relative to permit estimation of additional components of genetic variation (Hallauer and Miranda 1988). One of the first suggestions for estimation of epistatic variances was done by Cockerham (1956); mating designs I and II were used with parents at two different levels of inbreeding, but the progenies evaluated were noninbred in both instances. The procedure suggested by Cockerham (1956) has been used by Eberhart et al. (1966) and Silva and Hallauer (1975) in maize. Rawlings and Cockerham (1962a,b) developed the triallel and quadrallel analyses that provide up to nine covariances of relatives; these analyses permitted F-tests for the presence of epistasis in the analyses of variance and estimation of epistatic components of variance. Wright (1966) used diallel and triallel analyses, which provide nine mean squares, for estimation of epistasis in Krug Hi Synthetic 3 of maize. Chi (1965) used a complex mating design suggested by Kempthorne (1957) that included 11 variances and 55 covariances among relatives to estimate epistasis in an open-pollinated variety Reid Yellow Dent maize. Estimation of epistatic components of variance, however, has not been generally satisfying (Hallauer and Miranda 1988), especially in maize. In some instances negative estimates of epistatic variance components were two times greater than their standard errors. Most of the studies included adequate sampling and testing, but the results of estimation have been disappointing. Hence, either the genetic models used are inadequate, or epistatic variance is small relative to the total genetic variance of maize populations, or both. Although quantitative estimates of epistatic variance in maize populations have not been convincing, reports have indicated that epistatic effects are present in

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quantitative traits. Most of the evidence was obtained by use of mean comparisons. In all instances, qualitative evidence rather than quantitative evidence of epistasis is available. This implies that in most situations, the problem of poor estimation of epistatic variance may come from inadequate genetic models rather than the lack of epistatic effects. Hallauer and Miranda (1988) stated that the inherent correlation of coefficients of epistatic components of variances with those of the additive and dominance variance components is a major problem. Many populations in plant breeding programs are not produced from systematic mating designs. Plants may have heterogeneous genetic relationships. The experimental material may be collected from multiple generations with an irregular mating system. Estimating genetic variances using such pedigree data cannot be done with ANOVA. Instead, a general linear model (GLM) approach should be applied. It is wellknown that an ANOVA model can be expressed by a GLM (Seber 1977). Therefore, GLM is a general approach of genetic analysis for both complicated and simple mating designs. The GLM model for arbitrary pedigrees was introduced by Cockerham (1980). The genetic effects (additive, dominance, and epistatic effects) of founder plants are defined as the parameters (regression coefficient β) in the linear model. The phenotypic values of all plants in the pedigree(s) are the observed y variables. The independent variables, X, are defined as the proportions of genes inherited from the founder alleles for all plants in the pedigrees. The model may be described as y = Xβ + ε. Cockerham (1980) further clarified the difference between a fixed effect model and a random effect model. When the founders are not randomly sampled, the genetic effects (first moments) are considered to be the parameters of interest and the model is a fixed model. Under the fixed effect model, researchers are primarily interested in the genetic differences of the founders under investigation and have no desire to infer the genetic variance of the population from which the founders are sampled. On the other hand, if the founders are randomly sampled from a population, the genetic effects are considered as random variables. In this case, the purpose of the genetic analysis is to infer the genetic variance of the population from which the founders are drawn, leading to a random model. Under the random model framework, if classifiable environmental effects are also included in the model as fixed effects to control the environmental variation, the model becomes a mixed model. The mixed model analysis was first introduced by Henderson (1950). Since then it has been widely used to estimate genetic parameters and predict breeding values in animal breeding (Henderson 1975, 1984). However, introduction to the plant breeding community was only a recent event, due

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to Bernardo (1995). Parameter estimation of the mixed model analysis is primarily accomplished by using the least square (LS) and maximum likelihood (ML) methods. Recently, the Bayesian method has become popular in statistics due to the advent of high-powered computers. Application of the Bayesian statistic to mixed model analysis has been widely accepted (Gianola and Fernando 1986; Wang et al. 1993). In this chapter, the focus is on methods for the most complicated mating designs using the most general linear model with the most advanced Bayesian statistics because ANOVA for the simple mating designs is simply a special situation.

II. GENETIC MODEL A. Inbred Lines Let s be the number of source (inbred) lines initiating the crosses of interest for genetic analysis. The lines are assumed to be unrelated. The matings of the inbred lines and the plants in subsequent generations can be arbitrary. I will use two loci, denoted by a and b, as an example to derive the genic factorial model for a random-mating population. The genotypes of progeny will take various combinations of the alleles of the source populations. Therefore, we only need to define the allelic effects and various allelic interaction effects in the founders. Denote Q ai, i = 1,…,s, as the ith allele of locus a and Q bk, k = 1,…,s, as the kth allele at locus b. Each individual carries two alleles at any locus, one from its male parent (paternal allele) and the other from its female parent (maternal allele). Assume that the two alleles within a locus are arranged in the order as paternal followed by maternal. If an individual carries Q ai at the paternal allele and Q aj at the maternal allele for locus a and Q bk at the paternal and Q bl at the maternal allele for locus b, its genotype can be expressed as Q aiQ aj Q bk Q bl with a genotypic value denoted by Gijkl, which can be described by the following linear model, Gijkl = µ + α am + α af + δ amfa + α bm + α bf + δ bmfb i

j

i j

k

l

k l

+ (αα )amm + (αα )amfb + (αα )afmb + (αα )affb b i k

i l

j k

j l

+ (αδ )ammf + (αδ )afmf + (δα )amfm + (δα )amff b b b b ab ab i k l

+ (δδ )amfmf ab b

i j k l

j k l

i j k

i j l

[1]

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where m = the population mean, amai = the additive effect of allele Q ai at locus a received from the male parent, for i = 1,…,s, aaf j = the additive effect of allele Q aj at locus a received from the female parent, for j = 1,…,s, f dm ai aj = the dominance effect due to interaction between alleles Q ai and Q aj, for i, j = 1,…,s. m a bk = the additive effect of allele Q bk at locus b received from the male parent, for k = 1,…,s, a bf l = the additive effect of allele Q bl at locus b received from the female parent, for l = 1,…,s, dmbk fdl = the dominance effect due to interaction between alleles Q bk and Q bl, m m (aa) ai bk = additive-by-additive epistatic effect between alleles Q ai and Q bk, mm f (ad) ai bk bl = additive-by-dominance epistatic effect among alleles Q ai, Q bk and Q bl,, mf m f (dd) ai aj bk bl = dominance-by-dominance epistatic effect among the four alleles, Q ai, Q aj, Q bk and Q bl,. I adopted these notations from the lecture notes of Quantitative Genetics by Professor W. E. Nyquist at Purdue University. Although the notation seems to be complicated, the genic factorial model is very informative. Excluding the population mean, there are 15 different ordered genetic effects needed to describe the genotypic value of an individual. The model is equivalent to a four-way factorial experiment with s levels for each factor or treatment (see the comparison in Table 3.1). The major complication of the genic factorial model, however, comes from the fact that both the paternal and maternal alleles of the progeny can be traced back to the same set of (s) founder alleles. Therefore, the effects associated with the label “m” are indistinguishable from the corresponding effects labeled “f.” These indistinguishable effects must be combined and estimated together. For example, a mai represents the value of allele Q ai while a faj represents the value of allele Q aj, but both Q ai and Q aj come from the same set of founder alleles, the same factor in terms of an s4 factorial experiment. Therefore, the variance among a mai, i = 1,…,s, in fact reflects the same variance as that among a faj, j = 1,…,s. Therefore, the genic factorial model contains only two sets of main effects, the allelic effects of locus a and the allelic effects of locus b. Similarly, the four sets of additive-by-additive effects are indistinguishable, all reflecting the variance among the interactions between the s alleles

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Table 3.1. Analogy between the genic factorial model for two loci and a four-way factorial experiment each with s levels (s4). Source of Variation

Four-way Factorial

Genic Factorial

Degree of Freedom

Main effect

Ai Bj Ck Dl

am ai afaj ambk afbl

s–1 s–1 s–1 s–1

Two-factor interaction

(AB)ij (CD)kl (AC)ik

dmai faj dmbk fbl (aa)mai mbk

(s – 1)2 (s – 1)2 (s – 1)2

(AD)il (BC)jk (BD)jl

(aa)mai fbl (aa)faj mbk (aa)faj fbl

(s – 1)2 (s – 1)2 (s – 1)2

Three-factor interaction

(ACD)ikl (BCD)jkl (ABC)ijk (ABD)ijl

(ad)mai mbk fbl (ad)faj mbk fbl (da)mai faj mbk (da)mai faj fbl

(s – 1)3 (s – 1)3 (s – 1)3 (s – 1)3

Four-factor interaction

(ABCD)ijkl

(dd)mai fajmbk fbl

(s – 1)4

of locus a and the s alleles of locus b. The two sets of additive-bydominance effects are also combined, so are the two sets of dominanceby-additive effects. Define Aa and Ab as the allelic effects for the two loci, and Da and Db as the corresponding dominance effects for the two loci. Similarly, let (AA)ab be the additive-by-additive effect, (AD)ab the additive-by-dominance effect, (DA)ab the dominance-by-additive effect and (DD)ab the dominance-by-dominance effect. The condensed version of the linear model appears G = m + 2(Aa + Ab) + (Da + Db) + 4(AA)ab + 2[(AD)ab + (DA)ab] + (DD)ab.

[2]

Note that the 15 ordered genetic terms in Equation [1] have been merged into 8 composite unordered terms in Equation [2]. As individual loci are not identifiable (without the use of molecular information), their effects cannot be estimated separately; only the sums of their effects are estimable. Let A = Aa + Ab be the overall allelic effect for the two loci, D = Da + Db be the overall dominance effect, (AA) = (AA)ab, (AD) = (AD)ab + (DA)ab and (DD) = (DD)ab. The above model can be rewritten as G = m + 2A + D + 4(AA) + 2(AD) + (DD)

[3]

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which has been given by Cockerham (1980). The 15 genetic terms in Equation [1] are now merged into 5 terms in Equation [3]. This model holds for more than two loci if higher order interactions (involving genes of three or more loci) are ignored. Another complication of the genic factorial model compared to a usual s4 factorial design is that the genotype of an individual plant is not observable (without using molecular data). This is equivalent to the situation where an observation in an s4 factorial design cannot be uniquely assigned to a particular treatment combination. In the context of linear model, the genic factorial model is a linear model with an uncertain design matrix. Cockerham (1980) replaced the unobserved design matrix by its expectation conditional on the pedigree relationships of the plants. In his notation, the model appears G=µ+

∑ α i Ai + ∑ δ ij Dij + (∑ α i Ai )2 i≤ j

i

+

i

∑ α i Ai )(∑ δ ij Dij ) + (∑ δ ij Dij )2, for i, j = 1, K, s i≤ j

i

i≤ j

[4]

1 where ⁄2ai is the probability that a random gene sampled from the entry comes from the ith inbred line (∑i1⁄2ai = 1) in that ∑i ai = 2 from Equation [4], Ai is the sum of the additive effects for genes in a gamete from the ith source, dij is the probability that one allele of the genotype comes from source i and the other from source j (∑i≤ j dij = 1), Dij is the sum of the dominance effects for genes coming from sources i and j. This model is written in a way as if the epistatic effects are multiplicative, that is, the epistatic effects simply take the products of various allelic and dominance effects. In addition, the coefficients of the epistatic interaction also take the product of the coefficients of the single-locus effects under the assumption of independent segregation between loci. In the general epistatic model, the epistatic terms are further expanded to express their full contents. Cockerham (1980) expanded the additive-by-additive term as

∑ α i Ai )2 = ∑ α i2(AA)ii + 2∑ α iα k (AA)ik

(

i

i

i
where (AA)ik is the additive-by-additive effect. The difference between the general epistatic model and the multiplicative model is that (AA)ik in the general epistatic model is a single effect representing the interaction between the additive effects of loci a and b, while the interaction

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effect in the multiplicative model simply takes Ai × Aj, the product of the additive effects of the two loci. The expansion of the additive-bydominance term appears

∑ α i Ai )(∑ δ ij Dij ) = ∑ ∑ α iδ kl (AD)i ( kl)

(

i≤ j

i

k≤l

i

where (AD)i(kl) is the additive-by-dominance effect. The dominance-bydominance term is expanded as follows,

∑ δ ij Dij )2 = ∑ ∑ δ ijδ kl (DD)(ij )( kl)

(

i≤ j

i≤ j k≤l

where (DD)(ij)(kl) = (DD)(kl)(ij) is the dominance-by-dominance effect. Substituting the above equivalences into Equation [4], we have the following general epistatic model, G=µ+

∑ α i Ai + ∑ δ ij Dij + ∑ α i2(AA)ii + 2∑ α iα k (AA)ik i≤ j

i

+

i
i

∑ ∑ α iδ kl (AD)i ( kl) + ∑ ∑ δ ijδ kl (DD)(ij )( kl) . i

k≤l

i ≤ j k≤l

[5]

One can imagine that (AA)ik is the element of the ith row and the kth column of an s × s symmetrical matrix. We can also imagine that (AD)i(kl) is 1 the element of the ith row and the tth column of an s × –2 s(s + 1) rectangular matrix, where t is a function of k, l, and s, as shown by, t = (k − 1)s + l −

k (k − 1) 2

[6]

for k ≤ l. For example, when s = 4, t can take the following series of values: k l t

1 1 1

1 2 2

1 3 3

1 4 4

2 2 5

2 3 6

2 4 7

3 3 8

3 4 9

4 4 10

Substituting (kl) by t, we rewrite (AD)i(kl) by (AD)it, a typical notation for an element of a two dimension matrix. Let r be a function of i, j and s, as defined by,

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r = (i − 1)s + j −

i(i − 1) 2

123

[7]

for i ≤ j. We can rewrite (DD)(ij)(kl) by (DD)rt, representing the element of 1 1 the rth row and the tth column of a –2 s(s + 1) × –2 s(s + 1) symmetrical matrix. We may also substitute (k ≤ l) by t and (i ≤ j) by r, leading to an alternative expression of model (5) that is ready to be programmed, G=µ+

∑ α i Ai + ∑ δ ij Dij + ∑ α i2(AA)ii + 2∑ α iα k (AA)ik i≤ j

i

+

i
i

∑ ∑ α iδ kl (AD)it + ∑ δ ij2 (DD)(ij )(ij ) + 2∑ δ ijδ kl (DD)( r )(t ) i

k≤l

i≤ j

r
[8]

1

for i, j, k, l = 1,…,s and r, t = 1,…, –2 s(s + 1). Imagine that the dominanceby-dominance effects are stored in an symmetric matrix, called the DD matrix. In Equation [8], the dominance-by-dominance effects have been split into a part consisting of the diagonal elements (DD)(ij)(ij) of the DD matrix and a part consisting of the off-diagonal elements (DD)(r)(t), r ≠ t, of the DD matrix. Under the fixed effect model, the effects are not estimable because the design matrix of the genic factorial model is not of full rank. This can be shown by examining the sum of the coefficients of each set of effects. For example, ∑i ai = 2, ∑i≤j dij = 1, ∑i a2i + 2∑i
∑ α i Ai + ∑ δ ij Dij + 2∑ α iα k (AA)ik i <s

+

i< j

i
∑ ∑ α iδ kl (AD)it + ∑ δ ij2 (DD)(ij )(ij ) + 2∑ δ ijδ kl (DD)( r )(t ) i <s k < l

i< j

r
[9]

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where r and t have been redefined with the pairs corresponding to i = j and k = l not counted, that is, t = (k − 1)s + l −

k (k + 1) 2

[10]

r = (i − 1)s + j −

i(i + 1) 2

[11]

for k < l and

for i < j, respectively. For example, when s = 4, t will take the following series of values, k 1 l 2 t 1

1 3 2

1 4 3

2 3 4

2 4 5

3 4 6

We now use a hybrid population initiated from the cross of two inbred lines to demonstrate the expression of the model. The pedigree is shown in Fig. 3.1 with individuals 1 and 2 as the inbred lines and all other plants are derived from these two lines. Sometimes, each node of the pedigree may represent a group of plants. In that case, a node is better called an entry (Cockerham 1980). Hereafter, I use plant and entry interchangeably. The genetic model for each plant in the pedigree is G = m + {a1A1 + a2A2} + {d11D11 + d12D12 + d22D22} + {a21(AA)11 + a22(AA)22 + 2a1a2(AA)12} + {a1d11(AD)1(11) + a1d12(AD)1(12) + a1d22(AD)1(22) + a2d11(AD)2(11) + a2d12(AD)2(12) + a2d22(AD)2(22)} + {d211(DD)(11)(11) + 2d11d12(DD)(11)(12) + 2d11d22(DD)(11)(22) + d212(DD)(12)(12) + 2d12d22(DD)(12)(22) + d222(DD)(22)(22)}.

[12]

After imposing the restrictions, the model becomes G = m* + a1A1 + d12D12 + 2a1a2(AA)12 + a1d12(AD)1(12) + d212(DD)(12)(12).

[13]

Given values of ai and dij for each particular entry or plant, one can analyze the data to estimate and test the genetic effects. Cockerham gave ai and dij for some special designs. For example, the

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Fig. 3.1

3

1

1

back cross individual 4 in Fig. 3.1 has a1 = –2 , a2 = –2 , d11 = d12 = –2 and d22 = 0. However, a general algorithm has not been available for calculating dij. One objective of this review is to describe such a general algorithm. Although a recursive algorithm can be easily derived for calculating ai, similar recursive algorithm is not available for dij. The Monte Carlo method to calculate both ai and dij is now introduced. Assume that loci a and b are unlinked so that their allelic inheritances from one generation to another are independent. Therefore, only one locus, say a, needs to be considered for deriving ai and dij. Recall that Q ai, i = 1,…,s, denotes the ith allele (inbred line) at locus a in the founders. We can drop the locus identifier and use Qi in place of Q ai. Let z mi be an

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indicator variable defined as zmi= 1 if the individual received Qi through the male parent and zmi = 0, otherwise. The symbol z if is similarly defined, but for the allele received through the female parent. Note that s m s f z = i z i = 1. The values of ai and dij are determined by z mi and i i z if as shown by,

∑

∑

α i = E (z im ) + E (z if )

[14]

E (z m z f ) if i = j i j . δ ij =  m f m f + E (z j z i ) if i ≠ j E (z i z j )

[15]

and

The expectations of the quadratic terms of zmi and z if are functions of the coancestry coefficient between the two parents. Although they can be derived analytically, it is convenient to invoke the Monte Carlo method to evaluate the expectations. A recursive algorithm to simulate zmi and z if for each plant can be used. The algorithm requires that individuals are entered into the pedigree in a chronological order so that the parents must be evaluated before their children. Let us start with an inbred line, say line l. It is immediately known that zml = z lf = 1 and z mi = z if = 0, for i ≠ l. Imagine that each individual requires two s × 1 vectors, one being used to store {zmi }si=1 and the other to store {z if }si=1. After all the inbred lines (founders) have been evaluated, one can start to simulate values for the progeny. For clarity, denote zmi and z if by zmi (j) and z if (j), respectively, for the jth plant, where j now indexes the plant in the pedigree rather than the inbred lines. Let jm and j f denote the male and female parents of j, respectively. Therefore, zmi (j m) and z fi (jm) are the corresponding indicators for j’s male parent and zmi (j f ) and z if ( j f) are the indicators for j’s female parent. Now one can simulate zmi (j) and z if (j) for individual j conditional on the corresponding values of the parents. The recursive algorithm is described as follows, z m ( j ) = u m ( j )z m ( j m ) + [1 − u m ( j )]z f ( j m ) i i i for i = 1, K, s  f f f m f f f z i ( j ) = u ( j )z i ( j ) + [1 − u ( j )]z i ( j )

[16]

where um(j) and uf(j) are two independent Bernoulli random variates 1 each with a success probability of –2 . The process continues from the founders to all the descendents. When all individuals are evaluated, one

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round of sampling is complete and one is ready to proceed to the next round. The algorithm is computationally very fast. With a short period of time, a very large number of replicates, say M, can be simulated. The values of ai and dij can take the mean values of the Monte Carlo samples, that is,

αi ≈

1 M

∑ (zim + zif )

[17]

and 1  δ ij ≈  M 1  M

∑ (zimz jf ) ∑ (zimz jf + z mj zif )

if i = j . if i ≠ j

[18]

In fact, calculation of ai alone can be accomplished through a simple recursive algorithm without invoking the Monte Carlo simulation. This is because ai is a linear function of zmi and z if, and thus E[z m ( j )] = E[u m ( j )]E[z m ( j m )] + E[1 − u m ( j )]E[z f ( j m )] i i i for i = 1, K, s.  f f f m f f f E[z i ( j )] = E[u ( j )]E[z i ( j )] + E[1 − u ( j )]E[z i ( j )] [19] 1

Letting pmi(j) = E[zmi (j)] and noting that E[um (j)] = E[uf(j)] = –2 , produces the following recursive equations,  p m ( j ) = 1 [ p m ( j m ) + p f ( j m )]  i i i 2 for i = 1, K, s  f f m f f 1  pi ( j ) = 2 [ pi ( j ) + pi ( j )]

[20]

Therefore, ai = pmi + p if. With this recursive algorithm, pmi and p if for an arbitrary cross can be calculated. Table 3.2 gives the values of pmi, p if, ai, and dij for individuals in the pedigree shown in Fig. 3.1. Take individual 8 as an example to show the coefficients of the model effects. From Table 3.2, we know that the parents of 8 are 4 and 5. Therefore,  p m (8) = 1 [ p m (4) + p f (4)] = 1 (1 + 1/2) = 3/4  1 1 1 2 2  f m f 1 1  p1 (8) = 2 [ p1 (5) + p1 (5)] = 2 (1/2 + 1/2) = 1/2 leading to a1 = 3/4 + 1/2 = 5/4 and a2 = 1/4 + 1/2 = 3/4. Substituting these values into Equation [8], we have

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G = µ + { 54 A1 +

3 4

25 +{ 16 (AA)11 +

(AA)22 +

9 16

A2 } + { 83 D11 + 12 D12 + 81 D22 }

15 (AA)12 } 8 15 5 5 +{ 32 (AD)1(11) + 8 (AD)1(12) + 32 (AD)1(22) 9 3 3 + 32 (AD)2(11) + 8 (AD)2(12) + 32 (AD)2(22)) } 9 3 +{ 64 (DD )(11)(11) + 83 (DD )(11)(12) + 32 (DD )(11)(22)

+ 14 (DD )(12)(12) + 81 (DD )(12)(22) +

1 64

(DD )(22)(22) }.

After imposing the restrictions, the model becomes G = µ* +

5 4

A1 + 12 D12 +

15 (AA)12 8

+ 85 (AD)1(12) + 14 (DD )(12)(12) . Table 3.2. Proportional contributions (pi) of founder alleles (Q1 and Q2) to male (pmi ) and female (p if ) sides of individual plants (or entries) and the coefficients of additive and dominance effects for inbred founder pedigree. Male Parent

Female Parent

1

—

—

2

—

3

Plant

p1

p2

a1

a2

d11

d12

d22

pm i (1) p fi (1)

1 1

0 0

2

0

1

0

0

—

pm i (2) p fi (2)

0 0

1 1

0

2

0

0

1

1

2

pm i (3) p fi (3)

1 0

0 1

1

1

0

1

0

4

1

3

pm i (4) p fi (4)

1 1/2

0 1/2

3/2

1/2

1/2

1/2

0

5

3

3

pm i (5) p fi (5)

1/2 1/2

1/2 1/2

1

1

1/4

1/2

1/4

6

3

2

pm i (6) p fi (6)

1/2 0

1/2 1

1/2

3/2

0

1/2

1/2

7

1

5

pm i (7) p fi (7)

1 1/2

0 1/2

3/2

1/2

1/2

1/2

0

8

4

5

pm i (8) p fi (8)

3/4 1/2

1/4 1/2

5/4

3/4

3/8

1/2

1/8

9

5

5

pm i (9) p fi (9)

1/2 1/2

1/2 1/2

1

1

3/8

1/4

3/8

10

5

6

pm i (10) p fi (10)

1/2 1/4

1/2 3/4

3/4

5/4

1/8

1/2

3/8

11

5

2

pm i (11) p fi (11)

1/2 0

1/2 1

1/2

3/2

0

1/2

1/2

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Table 3.3 gives the result of a single Monte Carlo simulation. This is one realization of many possible ways of gene flow from the two parents to the descendents. Table 3.4 gives the result based on the average of M = 50000 Monte Carlo samples. The values are almost identical to the true values given in Table 3.2. B. Outbred Populations The model is readily extended to outbred populations. For simplicity, assume that the population under study consists of s founders that are randomly sampled from a large hypothetical reference population. The

Table 3.3. Founder allele inheritance indicators (zi) of founder alleles (Q1 and Q2) on the male (zmi) and female (zfi ) sides of individual plants (or entries), and the coefficients of additive and dominance effects from a single Monte Carlo simulation. Male Parent

Female Parent

1

—

—

2

—

3

Plant

z1

z2

a1

a2

d11

d12

d22

zm i (1) zfi (1)

1 1

0 0

2

0

1

0

0

—

zm i (2) zfi (2)

0 0

1 1

0

2

0

0

1

1

2

zm i (3) zfi (3)

1 0

0 1

1

1

0

1

0

4

1

3

zm i (4) zfi (4)

1 1

0 0

2

0

1

0

0

5

3

3

zm i (5) zfi (5)

0 1

1 0

1

1

0

1

0

6

3

2

zm i (6) zfi (6)

1 0

0 1

1

1

0

1

0

7

1

5

zm i (7) zfi (7)

1 0

0 1

1

1

0

1

0

8

4

5

zm i (8) zfi (8)

1 0

0 1

1

1

0

1

0

9

5

5

zm i (9) zfi (9)

0 1

1 0

1

1

0

1

0

10

5

6

zm i (10) zfi (10)

0 0

1 1

0

2

0

0

1

11

5

2

zm i (11) zfi (11)

1 0

0 1

1

1

0

1

0

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Table 3.4. Proportional contributions (pi) of founder alleles (Q1 and Q2) to male (pmi) and female (p fi ) sides of individual plants (or entries) and the coefficients of additive and dominance effects for inbred founder pedigree calculated from 50000 Monte Carlo simulations. Male Parent

Female Parent

1

—

—

2

—

3

Plant

p1

p2

a1

a2

d11

d12

d22

pm i (1) p fi (1)

1 1

0 0

2

0

1

0

0

—

pm i (2) p fi (2)

0 0

1 1

0

2

0

0

1

1

2

pm i (3) p fi (3)

1 0

0 1

1

1

0

1

0

4

1

3

pm i (4) p fi (4)

1.000 0.498

0.000 0.502

1.498

0.502

0.498 0.502

0.000

5

3

3

pm i (5) p fi (5)

0.499 0.498

0.501 0.502

0.997

1.003

0.247 0.503

0.250

6

3

2

pm i (6) p fi (6)

0.497 0.000

0.503 1.000

0.497

1.503

0.000 0.497

0.503

7

1

5

pm i (7) p fi (7)

1.000 0.499

0.000 0.501

1.500

0.501

0.499 0.501

0.000

8

4

5

pm i (8) p fi (8)

0.747 0.500

0.253 0.500

1.247

0.754

0.372 0.502

0.126

9

5

5

pm i (9) p fi (9)

0.495 0.498

0.505 0.502

0.993

1.007

0.370 0.253

0.377

10

5

6

pm i (10) p fi (10)

0.500 0.247

0.500 0.753

0.747

1.254

0.124 0.499

0.377

11

5

2

pm i (11) p fi (11)

0.498 0.000

0.502 1.000

0.498

1.502

0.000 0.498

0.502

objective of the genetic study is to infer the genetic variance in the reference population using the subpopulation derived from the founders. The model is identical to that of the inbred line crosses except that the total number of alleles to be handled in the founders becomes 2s instead of s. However, the two alleles carried by the same founder always share the same probability in each of the progeny. Therefore, all the effects associated with the first allele of the founder are confounded with those associated with the second allele of the same founder as discussed further in the chapter. The only effects that can be separated from each

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other are the dominance effects due to the two alleles within the same founder. Let us define the two alleles of the ith founder by Qi1 and Qi2, respectively. The dominance effect Dii previously defined in the inbred line situation is now decomposed into Di1i1 + Di2i2 and Di1i2 + Di2i1 where Di1i1 and Di2i2 are the dominance effects due to true homozygosities of the founder alleles and Di1i2 = Di2i1 is due to heterozygosity of the two alleles within the founder. However, Di1i1 and Di2i2 cannot be estimated separately and are thus lumped together and denoted by Di*i* = Di1i1 + Di2i2. Similarly, Di1i2 and Di2i1 are indistinguishable so that Dii* = Di1i2 + Di2i1. Corresponding to the dominance effects, the probability dii can be partitioned into di*i* = di1i1 + di2i2 and dii* = di1i2 + di2i1. As a consequence, the dominance effects in the inbred situation, diiDii, have been replaced by di*i*Di*i* + dii*Dii* in the outbred situation (Cockerham 1980). All other effects associated with these dominance effects should be partitioned accordingly. For example,

α i δ ii (AD)i ( ii ) = α i δ i*i* (AD)i ( i*i*) + α i δ ii* (AD)i ( ii*) and

δ ii2 (DD )( ii )( ii ) = δ i2*i* (DD )( i*i*)( i*i*) + 2δ i*i*δ ii* (DD )( ii*)( i*i*) + δ ii2* (DD )( ii*)( ii*). There is no easy way to calculate di*i* and dii* for an arbitrary pedigree other than invoking the Monte Carlo algorithm. The algorithm is identical to that of the inbred line crosses except that the table is expanded by including all the 2s alleles in the founders (see Table 3.5). Let zmi1(j) or zmi2(j) be the indicator that individual j receives the first or the second allele from founder i through j’s father. Similarly, define z if1(j) and z if 2(j) as the corresponding indicators but through the mother. The recursive process to simulate these indicators remains the same as that described by Equation [16]. The values of di*i* and dii* are now defined as m f m f δ i *i * = E(zi 1 zi 1 ) + E(zi 2 zi 2 )  f f δ ii* = E(zim1 zi 2 ) + E(zim2 zi 1 ).

[21]

Table 3.5 lists the d values calculated from the averages of 50000 Monte Carlo samples.

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Table 3.5. Proportional contributions (pi) of founder alleles (Qi1 and Qi2) to male (p m i ) and female (p fi ) sides of individual plants (or entries) and the coefficients of additive and dominance effects for outbred founder pedigree calculated from 50000 Monte Carlo simulations. Male Female Plant Parent Parent

Q11 Q12 Q21 Q22

d1*1*

d11*

d2*2*

d22*

d12

1

—

—

pm i (1) p fi (1)

1 0

0 1

0 0

0 0

0.000 1.000 0.000 0.000 0.000

2

—

—

pm i (2) p fi (2)

0 0

0 0

1 0

0 1

0.000 0.000 0.000 1.000 0.000

3

1

2

pm i (3) p fi (3)

1/2 1/2 0 0 0.000 0.000 0.000 0.000 1.000 0 0 1/2 1/2

4

1

3

pm i (4) p fi (4)

1/2 1/2 0 0 0.253 0.252 0.000 0.000 0.496 1/4 1/4 1/4 1/4

5

3

3

pm i (5) p fi (5)

1/4 1/4 1/4 1/4 0.254 0.000 0.249 0.000 0.497 1/4 1/4 1/4 1/4

6

3

2

pm i (6) p fi (6)

1/4 1/4 1/4 1/4 0.000 0.000 0.260 0.246 0.494 0 0 1/2 1/2

7

1

5

pm i (7) p fi (7)

1/2 1/2 0 0 0.252 0.246 0.000 0.000 0.502 1/4 1/4 1/4 1/4

8

4

5

pm i (8)

3/8 3/8 1/8 1/8 0.250 0.126 0.126 0.000 0.499

p fi (8)

1/4 1/4 1/4 1/4

pm i (9) p fi (9) pm i (10) p fi (10) pm i (11) p fi (11)

1/4 1/4 1/4 1/4 0.387 0.000 0.368 0.000 0.246 1/4 1/4 1/4 1/4

9

5

5

10

5

6

11

5

2

1/4 1/4 1/4 1/4 0.123 0.000 0.247 0.127 0.503 1/8 1/8 3/8 3/8 1/4 1/4 1/4 1/4 0.000 0.000 0.247 0.251 0.503 0

0

1/2 1/2

III. LEAST SQUARES ESTIMATION Having defined various genetic effects in the founders and the proportions of alleles and allelic combinations received by each plant, we are ready to discuss methods of estimation for genetic effects. Let yj be the phenotypic value of the jth entry for j = 1,…,N, where N is the total number of plants or entries in the pedigree. In line crossing experiments, the jth entry usually represents a group of nj plants from the same family. If the parents of the family are inbred, the progeny will have an identical genotype. Otherwise, members of the same family may have different genotypes, but the expected allele inheritance patterns will be identical. Therefore, different plants within the same entry are usually pooled

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together and yj represents the mean phenotypic value of the jth entry. This is quite different from animal breeding in which traits are often measured on an individual basis. The following linear model is used to describe yj, yj = Gj + ej

[22]

where Gj is the genotypic value of the jth entry and ej is the residual error. The genotypic value Gj has been described earlier as a linear function of all the genetic effects defined in the founders. Let s 2e be the residual variance under the individual plant basis. The residual error ej has a vari1 ance of n—j s 2e if family members have an identical genotype. If there is segregation within family, the residual variance will include an additional variance, which will be discussed later. After imposing the restriction, the linear model in Equation [13] in matrix notation for the N = 11 entries of the pedigree shown in Fig. 3.1 is  y 1  1     y 2  1  y  1  3    y 4  1  y  1  5    y 6  = 1     y 7  1  y 8  1     y 9  1  y  1  10    y 11  1

2.000 0.000 1.000 1.498 0.997 0.497 1.500 1.247 0.993 0.747 0.498

0.000 0.000 1.000 0.502 0.503 0.497 0.501 0.502 0.253 0.499 0.498

0.000 0.000 2.000 1.504 2.000 1.494 1.503 1.880 2.000 1.873 1..496

0.000 0.000 1.000 0.752 0.501 0.247 0.752 0.626 0.251 0.373 0.248

0.000  0.000 1.000   0.252  0.253  0.247   0.251  0.252   0.064  0.249   0.248  

where   β0   µ *      β1   A1   β  D . β =  2  =  12  β3  (AA)12  β  (AD) 1(12)   4  β5  (DD )(12)(12) 

e1    e2  e   β0   3    e 4  β1  e  β   5   2  + e6   β3    β  e7   4  e8  β5    e9  e   10  e11 

[23]

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The design matrix of this linear model is obtained from 50000 Monte Carlo simulations (see Table 3.4). The compact form of the above linear model is y = Xβ + e.

[24]

Assume that the number of plants within each entry is a constant, that is, nj = n for j = 1,…,N. The least square solution of the genetic effects is

βˆ = (X′X)–1X′y.

[25]

The residual variance is estimated by

σˆ e2 =

1 (y − Xβˆ )′ (y − Xβˆ ) N−p

[26]

where p is the number of parameters in the model. In this example, N = 11 and p = 6, so that the error variance estimate has N – p degrees of freedom. If the number of plants per entry nj varies across the pedigree, a weighted least squares approach should be used. Let R be an N × N diag1 onal matrix with the jth diagonal element being n—j . The weighted least square solution is

β = (X′R–1X)–1X′R–1y.

[27]

The corresponding residual variance is estimated by

σˆ e2 =

1 (y − Xβˆ )′R −1 (y − Xβˆ ). N−p

[28]

If we interpret the distribution of y as the conditional distribution given Xβ, we may write Var(y) = Var(e) = Rs 2e from which we can derive the variance-covariance matrix of the estimated parameters, as shown by, Var(βˆ ) = (X ′R −1X)−1 (X ′R −1 Var(y)R −1X)(X ′R −1X)−1 = (X ′R −1X)−1 (X ′R −1RR −1X)(X ′R −1X)−1 σˆ e2 = (X ′R −1X)−1 σˆ e2 .

[29]

Elements of matrix X are functions of a and d, which take the expectations of the z variables described in Equations [14] and [15]. The variances and covariances of these z’s, however, have been ignored in the

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previous models and they will cause inflation of the residual variance covariance structure. Therefore, the residual variance should be modified to reflect the change of the residual variance. Let us define

ω i = z im + z if

[30]

and z m z f i j τ ij =  m f m f z i z j + z j z i

if i = j . if i ≠ j

[31]

By definition, ai = E(wi) and dij = E(tij). For two loci, the actual model is G = m* + E(w1)A1 + E(t12)D12 + 2E(w1)E(w2)(AA)12 + E(w1)E(t12)(AD)1(12) + E(t12)2(DD)(12)(12) = m* + a1A1 + d12D12 + 2a1a2(AA)12 + a1d12(AD)1(12) + d212(DD)(12)(12).

[32]

Note that wi and tij are simply replaced by their expectations (Cockerham 1980). When w and t are replaced by their separate expectations, the variance that is not explained by the expectations will go to the residual variance. The inflated residual variance has a form of Var(e j ) = ( 12 β ′Σ j β + σe

1

1 )σ e2 nj

= R jj σ e2

[33]

1

for Rjj = s—e2 β′∑j β + — nj where β is the vector of genetic effects, as defined earlier, and ∑j = Var(ξj) and

ξj = [1 w1 t12 w1w2 w1t12 t12t12]′. For pedigree data, ξj for entry j and ξk for entry k are correlated with a covariance matrix denoted by ∑jk = Cov(ξj,ξk′ ). As a result, the residuals of different entries are correlated with a covariance of Var(ej,ek) = β′∑jkβ = Rjk s 2e 1

[34]

for Rjk = s—e2 β′∑jk β. When matrix R is defined as a symmetrical matrix with nonzero off-diagonal elements, the method is called a general least squares (GLS) method. With the GLS method, the limitation that family members must have an identical genotype can be relaxed. In other

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words, the observation of the jth entry, yj, can be the mean value of nj full-sibs who are not necessarily of identical genotype. Note that there are no explicit expressions for most elements of matrices ∑j and ∑jk. Therefore, Monte Carlo calculations are required. As we calculate the Monte Carlo expectations of ξj and ξk, the variance-covariance matrices should be recorded. For large pedigrees, combined with a large number of model effects, large memory space is required to store ∑j and ∑jk. Fortunately, plant pedigrees are usually small so that the required memory space is usually manageable with the current computing power. The residual variance matrix Var(e) = Rs 2e is now expressed as a function of the model effects, which are parameters to be estimated. An explicit LS solution is impossible, and thus an iteratively reweighted least squares (IRWLS) method (Xu 1998a,b) is required. With the IRWLS method, initial values of β and s2e are provided by the investigator. These initial values are used to calculate the weights from which a weighted least squares method is used to estimate β and s 2e, which are used in turn to update the weights. The iteration continues until a certain criterion of convergence is reached. The IRWLS method converges very quickly. If a normal distribution for ej is assumed, an appropriate test statistic can be derived for hypothesis testing. Let H0 : Hβ = 0 symbolize the hypothesis to be tested. If the null hypothesis is absence of all the genetic effects, the H matrix for model (23) is 0  0 H = 0  0 0 

1 0 0 0 0

0 1 0 0 0

0 0 1 0 0

0 0 0 1 0

0  0 0 .  0 1 

If the hypothesis is absence of the dominance-by-dominance effect, H is defined as H = [0 0 0 0 0 1]. Let q be the rank of H. The following F-like statistic is used to test H0 : Hβ = 0, F =(

2 2 N − p σˆ e( r ) − σˆ e( f ) )( ) q σˆ e2( f )

[35]

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where s 2e(f ) is the estimated residual variance under the full model and s 2e(r) is the estimated residual variance when a restricted model is fitted (Graybill 1976). The restricted model is solved by minimizing the residual variance under the restriction Hβ = 0. The statistical test with a = 0.05 is as follows: Reject H0 if and only if F satisfies F ≥ F0.05:q,N–p where F0.05:q,N–p is the upper 0.05 probability point of the central F distribution with q and N – p degrees of freedom. 1 2 The F-like test is only valid under the assumption of ej ~ N(0, — nj s e. This assumption, however, can only be achieved approximately. First, ej is not normal, rather, it is likely to be a mixture of many normal variates. This is because the design matrix X has taken a linear function of the probabilities of the founder alleles and allelic combinations rather than a linear function of the indicators of allelic inheritance. Second, the residual variance Var(ej) is actually composed of a pure environmental variance and a partial genetic variance that is not explained by the model. The latter, however, highly depends on the type of cross that an entry belongs to. As a consequence, Var(ej) varies across entries, even if 1 nj does not. Violation of the assumption ej ~ N(0, n– j s e2) will cause the distribution of F to deviate from Fq,N–p under H0. Therefore, the critical value F0.05:q,N–p for significance testing may lead to a type I error that is different from 0.05. Now, to introduce a permutation test for finding the appropriate critical value of the test statistic (Fisher 1935). To find the critical value for hypothesis H0 : bi = 0, we permute elements within the column of matrix X that corresponds to bi and then calculate the F test statistic for the permutated data set. The permutation actually destroys the relationship between the phenotypic value and the coefficient corresponding to the effect being tested. There are N! possible permutations for a pedigree with N entries, each permutation being associated with a different F. After all the possible permutations have been done, we have a sample of F, denoted by F (1),F (2),…,F (N!), forming an empirical distribution. The upper 0.05 percentile of this empirical distribution is the critical value for the F test. For testing two or more effects, we simultaneously permute elements within all the columns of matrix X that correspond to the effects of interest. To test the absence of all genetic effects, that is, β = 0, simply permute vector y and maintain the structure of matrix X. For a large N, the number of possible permutations may be so big, say > 10000, that it is impossible to evaluate all of them. This problem can be accomplished by randomly reshuffling the elements within the columns of interest for a large number of times, say M. We can choose an M as large as the computing time permits, independent of N. The reshuffling process requires Monte Carlo simulations (Churchill and

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Doerge 1994). We first simulate N independent identically distributed uniform random variates designated as vector U and assign a rank order to each element in vector U. Next, we simply sort and associate the row index for the elements for the particular column of X with the rank order of the elements in the U vector. The least squares analysis is only applicable to situations when the genetic effects are treated as fixed effects. Under the fixed effect model, we are interested in estimating and testing the mean effects rather than the variance among the effects. A population initiated from the crosses of a few inbred lines can be described by the fixed model and thus analyzed using the LS method. An outbred population derived from random matings of several noninbred founders can be described by the random effect model. As a consequence, least squares analysis is no longer adequate. Therefore, more advanced statistical methods are needed, such as the maximum likelihood and Bayesian methods.

IV. MAXIMUM LIKELIHOOD ANALYSIS A. Random Model If founders are outbred and they are randomly sampled from a reference population, the purpose of the genetic analysis may be to infer the genetic variance in the reference population using the pedigrees as sampled data. With the random model, the genetic effects of the founders are treated as random variables. When treated as random variables, the genetic effects are no longer parameters of interest, rather, the variance among the genetic effects (genetic variance) becomes the parameter of interest. The random model analysis offers three major advantages over the fixed model. First, a random model can handle more founders than the fixed model, primarily because the number of parameters (variances) does not increase as the number of genetic effects increases. Second, the most confusing part of the fixed model analysis is the restrictions on the defined genetic effects. The genetic effects are not estimable in their original form, and thus restrictions are necessary for parameter estimation. After the restrictions, however, the estimable parameters have lost their original meanings. Sometimes, the estimable parameters are hard to interpret regarding their biological meanings. Under the random model, this problem is irrelevant because we do not estimate the random effects. We only estimate the variance among the random effects. The variance, however, is invariable with regard to the central location of the effects. Plant breeders may be interested in estimating the breeding values of

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several varieties, even if the breeding values were treated as random effects. Under the random model framework, the breeding values in their unrestricted form are always estimable. This is because the coefficient matrix of the normal equations for the random effects is always positive definite due to the fact that the variance components have been added to the diagonals of the coefficient matrix. Therefore, under the random model framework, one can directly handle the original form of the genetic effects without imposing any restrictions. Finally, the random model analysis is a general methodology for genetic analysis, while the fixed model approach appears to be a special case of the random model. If we let the genetic variances to be infinitely large when trying to predict the genetic effects under the random model, the best linear unbiased prediction (BLUP) of the genetic effects is actually identical to the best linear unbiased estimation (BLUE) of the genetic effects under the fixed model. B. Mixed Model and BLUP Nongenetic effects, such as location and year effects, may be treated as fixed effects or random effects. If they are treated as fixed effects, they must be included in the model as covariates so that their effects do not interfere with estimates of the genetic effects. A model including both the fixed and random effects is a mixed model (Henderson 1975). Previously, we used β to represent the genetic effects and X to represent the design matrix for the genetic effects. Under the mixed model framework, we use β to represent the nongenetic fixed effects and X to represent the design matrix for the fixed effects. The genetic effects are now represented by a vector u and its design matrix (proportions of founder genes) by Z. The mixed model is y = Xβ + Zu + e.

[36]

The matrix symbols of the mixed model are now consistent with that commonly seen in the literature. For clarity and convenience, let us define u in an unrestricted form, although a restricted form can be computationally more efficient. Let us further partition u into u ′ = [u 1′

u2

u 3′

u ′4

u 5′ ]

where the five subvectors correspond to the five types of genetic effects: additive, dominance, additive-by-additive, additive-by-dominance, and dominance-by-dominance. For example, the unrestricted form of u1 in

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the earlier example is u′1 = {A1 A2}, while the restricted form is u1 = {A1}. Assume that the reference population is in Hardy-Weinberg and linkage equilibrium so that different types of genetic effects are statistically independent. We can then define     2 Var(u) = Gσ e =     

Iγ A2 0 0 0 0

0   Iγ D2 0 0 0   2 0 Iγ AA 0 0  σ e2 2 0 0 Iγ AD 0   2  0 0 0 Iγ DD  0

0

0

where g A2 = sA2 /s 2e and sA2 is the variance of the additive effects, for example, Var(u1) = IsA2 . Similarly, g D2 = sD2 /s 2e, where sD2 is the variance of the dominance effects, for example, Var(u2) = IsD2 , and so on. The identity matrix in Var(u1) = IsA2 and so on, means that the founders are genetically unrelated. By definition, founders are defined as a group of unrelated individuals who initiated the pedigree under study. If the origins of the founders and their genetic relationships are unknown, they are assumed to be unrelated. Sometimes, the genetic relationships of founders may be known. For example, a pedigree may be initiated by the crosses of two full-sibs. There are two ways to handle such related founders: (1) replace the identity matrix by an appropriate matrix that reflects the genetic relations of the founders, and (2) redefine the founders by treating the ancestors of the related individuals as the new 1 founders. The earlier G notation, G =s—e2 Var(u), has been adopted from Hen1 derson (1975) for consistency with the R notation, R = s—e2 Var(e). If G were defined as G = Var(u), R would have been defined as R = Var(e) to validate the following derivations. For convenience of presentation, numerical subscripts will be used to differentiate different kinds of variance 2 2 2 , s 24 = sAD and s 25 = sDD . components, that is, s 21 = sA2 , s 22 = sD2 , s 23 = sAA The variance-covariance matrix of the residuals under the mixed model is different from that of the fixed model. Recall that Var(ej) = Rjjs 2e for Rjj 1 1 1 2 — = s—e2 β ′∑j β + — nj and Cov(ej, ek) = R jks e for R jk = se2 β ′∑jk β under the fixed model. Under the mixed model, however, R jj = Tr( Σ j G) +

1 nj

[37]

and Rjk = Tr(∑jkG)

[38]

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where Tr() is the symbol for the trace of the matrix inside the parentheses. The expectation and variance-covariance matrix of y conditional on the fixed effects are E(y) = Xβ and Var(y) = V = (ZGZ′ + R)s 2e. Under the mixed model framework, we are interested in estimating the fixed effects β, predicting the random effects u, and estimating the variance compo2 2 nents λ′ = {s 2A sD2 sAA sAD s 2DD} and s 2e. First, estimation and prediction of the fixed and the random effects will be discussed, assuming that the variance components are known. Under normal distributions of y and u, the BLUE of β and the BLUP of u are obtained using the well-known mixed model equations of Henderson (1950):  β  X ′R −1y  X ′R −1X X ′R −1Z   =   . Z ′R −1X Z ′R −1Z + G −1  u  Z ′R −1y 

[39]

The BLUE of β is

βˆ = (X′V–1X)–1X′V–1y

[40]

uˆ = GZ′V–1(y – Xβ).

[41]

and the BLUP of u is

The derivation of the Henderson’s mixed model equations assumes a joint normal distribution of u and e with mean zeros and variancecovariance matrix of u  G 0  2 Var   =  σ e . e  0 R  The mixed model equations are actually found by maximizing the joint density of y and u with respect to β and u. The joint density is p (y, u | b, σ e2, λ) = p (y | u, b, σ e2 )p (u | λ)

[42]

where p(y | u, b, σ e2 ) =

1 1

N (2πσ e2 )2

1

| R |2

 1  exp− (y − Xb − Zu)′R −1 (y − Xb − Zu) 2  2σ e 

[43]

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and p (u | l) =

 1  exp − u ′G −1u  2  2σ e  (2πσ e2 ) | G | 1

1 k 2

1 2

[44]

∑

5

where N is the number of entries in the pedigree, k = k is the i =1 i dimension of vector u and ki is the dimension of ui for i = 1,…,5. Animal breeders commonly treat locations and years as fixed, probably because genotype-by-environment interactions seem to be negligible or at least conceived as being unimportant. In plants, however, locations and years are often regarded as random effects. The reason is that repeating an experiment in locations and years is to draw inference about the genetic performance for the target population from which the locations and years are assumed to be random samples. When the locations and years are treated as random effects, the linear model becomes a random model, unless other fixed effects are included in the model. Under the assumption of random environmental effects, the genotype-byenvironmental effects are also random effects, which should be included in the linear model so that the interaction variance can be estimated. When the environmental effects are treated as random, the model with genotype-by-environmental effects is y = 1m + Xβ + Zu + Wη + e

[45]

where W = X ⊗ Z, the symbol ⊗ denotes the Kronecker product and h is a vector of the genotype-by-environment interaction effects. The expectation of the model is E(y) = 1m and the variance is Var(y) = V = X′Var(β)X + Z′Var(u)Z + W′Var(η)W + Var(e)

[46]

where Var(β) = Es 2e is the variance matrix for the environmental effects, Var(u) = Gs 2e is the variance matrix for the genetic effects, Var(η) = Hs 2e is the variance matrix for the genotype-by-environmental effects, and Var(e) = Rs 2e is the variance matrix for the residuals. Detailed structures of matrices E, G, H, and R depend on the genetic model, the composition of the founders and the design of the experiment. C. Estimation of Variance Components The BLUP estimates require knowledge of the genetic variance components, which are the parameters of interest in quantitative genetics. This

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section discusses methods of variance component estimation. For convenience, we only present methods applied to the mixed model in which the environmental effects are treated as fixed. Numerous methods have been developed for estimation of variance components, for example, MINQUE (Rao 1971), MIVQUE (Laotte 1973), ML (Hartley and Rao 1967), and REML (Patterson and Thompson 1971). Minimum norm quadratic unbiased estimation (MINQUE) and minimum variance quadratic unbiased estimation (MIVQUE) are methods similar to the ANOVA for variance component analysis in that solutions are obtained in one step (iterations not needed) and negative estimates of variance components are allowed. The computational advantage of these methods is no longer important given the available high-powered computers. Maximum likelihood estimation (ML) and restricted maximum likelihood estimation (REML) bear several asymptotic properties, for example, consistency, which are desired for parameter estimation. The drawback of ML and REML is the needed high computational intensity (iterations needed). This disadvantage has become less important due to the high-powered computers. REML differs from ML in that it maximizes the likelihood of linear combinations of the raw data. The linear combinations are found by eliminating the design matrix for the fixed effects so that estimates of variance components are not influenced by estimates of the fixed effects. Technically, REML is a maximum likelihood method that requires the same computational algorithms as ML does. Therefore, only algorithms for the ML method will be discussed in subsequent sections. The ML method requires the likelihood function, which is proportional to the probability density of observed data conditional on parameters. Here, the data are represented by vector y and the parameters represented by vector θ ¢ = {β s 2e λ}. Therefore, the likelihood can be obtained from the joint density of y and u by integrating out the missing values u, that is, L(θ ) =

− 21

∫ p (y, u| β, σ e , λ )du ∝| V | 2

exp{− 12 (y − Xβ )′ V −1 (y − Xβ )}.

[47]

The likelihood is a complicated function of the genetic variances. Analytical solutions are hard to derive or do not exist, and thus we resort to a numerical approach. There are numerous ways to search for the ML solutions, but two of them are commonly used in this context: the simplex method and the expectation-maximization (EM) algorithm. 1. Simplex Method. The simplex method was developed by Nelder and Mead (1965). It is a general searching algorithm for minimizing any

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complicated objective function. Let θ be a k × 1 vector of the unknown parameters and f(θ) is the objective function to be minimized. Note that maximizing a likelihood function is equivalent to minimizing the negative of the likelihood function. Therefore, f (θ) = –L(θ) in this context. A simplex is defined by a set of k + 1 points in a k-dimensional space Rk joined by lines. In the situation of two dimensions, a simplex is a triangle. The basic idea of the simplex method is that an iteration process generates a new simplex by reflecting one vertex in the hyperplane spanned by the other vertices of the simplex. The new vertex is generated from one of the three basic actions: reflection, contraction, and expansion. The simplex adapts itself to the local landscape, and contracts on to the final minimum. The method is shown to be effective and computationally compact. Existing subroutines for the simplex method are available in many standard computer software packages, such as C and FORTRAN. The values of θ must be searched within the space of Rk. The three actions in the simplex method, however, do not necessarily guarantee that all vertices of θ are within Rk. For example, the variance components cannot be negative, but a reflection of θ may contain a negative variance component. The nonnegativity of variance components can be achieved by reparameterization. Instead of directly searching for the variance component, we search for an optimal value of another variable in the real, denoted by j for – ∞ < j < + ∞, and assign ej to the variance component. This will automatically restrict the variance component within (0, + ∞). The simplex method requires the inverse of matrix V, which can be very expensive to compute if the dimension of V is large. However, the following equivalence can be used to solve for V–1 (Henderson 1975) V–1 = R–1 – R–1Z(Z′R–1Z + G–1)–1Z′R–1.

[48]

Although R has the same dimensions as V, it is usually a diagonal matrix. Furthermore, G and Z′R–1Z + G–1 usually have a much smaller dimension than V so that their inverses can be directly computed. 2. EM Algorithm. The mixed model described in the previous section can be formulated as a missing value problem so that the maximum likelihood estimate (MLE) can be found via the EM algorithm (Dempster et al. 1977). Again, the data are represented by y and the missing values (genetic effects) by u. We first treat the initial estimates of the missing values as “data” and then consider the joint density of y and u as the “likelihood function.” This is called the complete data likelihood and

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is given by Equation [42]. We assume that the genetic effects are independent of each other so that the density of u in Equation [44] can be expressed as p (u | λ ) =

5

∏ p(u i | λ ).

[49]

i =1

The density for ui is p (u i | λ ) =

1 (2πσ i2 )

1 k 2 i

 1  exp − u ′i u i  . 2  2σ i 

[50]

We first maximize the complete data log likelihood function (Equation [42]) with respect to each parameter by treating the initial estimates of the missing values as observed data. The solutions of the complete data MLE are 5

β = (X ′R −1X)−1 X ′R −1 (y − ∑ Z i u i )

[51]

i =1

for the fixed effects β and

σ i2 =

1 u i′ u i for i = 1, K, 5 ki

[52]

for the variance of the random effects. The MLE of the residual variance is

σ e2 =

1 e′e N

[53]

where e = y − Xβ −

5

∑ Zi u i . i =1

[54]

We then take expectations with respect to the missing values u, conditional on the parameter values of θ and the data y. However, the parameter values are unknown and they are quantities to be estimated. Therefore, the algorithm requires iterations starting from some arbitrary

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initial values of the parameters. Let θ(0) be the initial values of parameters from which expectations of various quantities are taken with respect to u. The EM algorithm contains two steps, the Expectation (E) step and the Maximization (M) step. The M-step symbolically replaces the equations by the conditional expectations, that is, 5

β (1) = E[(X ′R −1X)−1 X ′R −1 (y − ∑ Z i u i | y, θ (0) ]

[55]

i =1

σ i2(1) =

1 E[u ′i u i | y, θ (0) ], for i = 1, K, 5 ki

[56]

1 E[e′e | y, θ (0) ]. N

[57]

and

σ e2(1) =

The parameter values at the current step, indicated by a superscript (t), are then used again to calculate the expectations, from which new estimates of parameters, indicated by a superscript (t+1), are obtained. Calculation of the expectations is called the E-step. The iteration process continues until a certain criterion of convergence is reached, which concludes the EM algorithm. It is realized that all these quantities can be simplified as linear or quadratic functions of u. The expectation of a quadratic function of a vector can be expressed as a function of the expectation and variance of the vector. Therefore, we will first derive the conditional expectation and variance of ui, which requires the following expectation and variancecovariance matrices: y  Xβ      Eu i  =  0  and Var e   0     

y   V Z i σ i2 Rσ e2      2 2 u i  = Z i′σ i Z ′i Z i σ i 0  .  e  Rσ 2 0 Rσ e2     e 

The theorem of multivariate normal distribution (Giri 1996) gives standard formulas for the expectations and variances of a subvector conditional on the value of another vector. Given this expectation and variance-covariance matrices, the conditional expectation and variance of ui given y and θ are

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E(ui|y, θ) = E(ui) + Cov(ui,y′)Var(y)–1[y – E(y)] = s 2i Z′iV–1(y – Xβ)

[58]

Var(ui|y, θ) = Var(ui) – Cov(ui,y′)Var(y)–1Cov(y,u′i) = s2i θZi – s 4i Z′i V–1Zi = s 2i Z′i (I – s 2i V–1)Zi

[59]

and

respectively. Similarly, the conditional expectation and variance of e are E(e|y, θ) = E(e) + Cov(e,y′)Var(y)–1[y – E(y)] = s2eRV–1(y – Xβ)

[60]

Var(e|y, θ) = Var(e) – Cov(e,y′)Var(y)–1Cov(y,e′) = s2eR – s4eRV–1R = s2eR(I – s2eV–1R).

[61]

and

We now go back to the M-step to explicate the estimated parameters at the (t + 1)th step. For the fixed effects, we have 5

β (t +1) = (X ′R −1X)−1 X ′R −1[y − ∑ Z i E(u i | y, θ (t ) )].

[62]

i =1

The variance components at the (t + 1)th step are (t +1)

σ i2

=

1 {E[u ′i | y, θ (t ) ]E[u i | y, θ (t ) ] + Tr[Var(u i | y, θ (t ) )] } ki

[63]

1 {E[e′ | y, θ (t ) ]E[e | y, θ (t ) ] + Tr[Var(e | y, θ (t ) )] } N

[64]

and

σ e2(t +1) =

where Tr[M] is the trace operator (sum of the diagonal elements) of matrix M. 3. Monte Carlo EM Algorithm. The expectations of the linear and quadratic terms of the missing values u are evaluated explicitly using the theorem of multivariate normal distribution. This explicit evaluation,

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however, requires matrix inversion, which can be cumbersome when the matrix is large. Within the E-step, it is possible to evaluate the expectations numerically using the method of Monte Carlo simulation. Guo and Thompson (1991, 1994) were among the first to use the Monte Carlo EM for variance component analysis. The basic idea of the Monte Carlo EM is to generate, within each M-step, a realized sample of the missing values u from the conditional distribution of u given y and θ (t). Given the sample of u, the expectations of any function of u can simply take the sample mean of the function. Denote u(1), u(2),…,u(M) as a Monte Carlo sample with M realized observations. From this Monte Carlo sample, we update the estimated parameters as follows,

β (t +1) =

1 M

σ i2(t +1) =

M

∑

[65]

∑ k i−1(u i′(m)u(im) ), for i = 1, K, 5

[66]

m =1

1 M

5

∑ Zi u(im) )

(X ′R −1X)−1X ′R −1 (y −

i =1

M

m =1

and

σ e2(t +1) =

1 M

∑ N −1  e ( M

t m

)e(m)  

[67]

∑ Zi u(im) .

[68]

m =1

where e( m) = y − Xβ (t ) −

5

i =1

Guo and Thompson (1991) used the Gibbs sampler to simulate the missing values. The Gibbs sampler requires a standard form of distribution for the variable to be sampled, for example, normal or Poisson distribution (Geman and Geman 1984). The Gibbs sampler is a special case of a general sampling procedure, called the Metropolis-Hastings method (Metropolis et al. 1953; Hastings 1970). Gibbs Sampler. Let us consider sampling of ui. Assume that there are ki entries of the additive effects so that ui = [ui1,ui2,…,uiki ]′. Let p(ui|y,θ (t)) denote the conditional density of ui given y and θ (t). This joint conditional distribution is multivariate normal with expectation of

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E(ui|y,θ (t)) = s 2i (t)Z′iV–1(y – Xβ (t))

[69]

Var(ui|y,θ (t)) = s 2i (t)Z′i (I – s 2i (t)V–1)Zi.

[70]

and variance of

Although ui can be sampled jointly from p(ui|y,θ (t)), the Gibbs sampler actually draws one element of ui at a time from a full conditional distribution. The full conditional distribution of uij given values of the remaining elements is p(uij|y,θ (t), ui(–j) ∝ p(uij,ui(–j)|y,θ (t))

[71]

where ui(–j) = [ui1,…,ui(j–1), ui(j + 1),…,uiki ]′ represent values of the remaining elements of ui. This full conditional distribution is a univariate normal, which has a form identical to the joint conditional distribution p(ui|y,θ (t)) except that ui(–j) in ui = [uij,ui(–j)] are replaced by known values that are either given as initials or simulated in a previous Monte Carlo cycle. The Gibbs sampler proceeds as follows. Given arbitrary starting values,

[

]

(0 ) ′ u (i0) = ui(01) , ui(02) , …, uik , i

generate a random variate ui(1) 1 from (0 )  p ui 1 y, θ (t ) , ui(02) , …, uik ,  i 

followed by ui(1) 2 from (0 )  p ui 2 y, θ (t ) , ui(11) , ui(03) , …, uik ,  1

and ui(1) 3 from (0 )  p ui 3 y, θ (t ) , ui(11) , ui(12) , ui(04) , …, uik ,  i 

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and so on, up to uik(1)i from p uiki y, θ (t ) , ui(11) , ui(12) , …, ui(1( k) −1)  .   i This completes one cycle of the sampling scheme. After many cycles of simulations, say m, we arrive at a joint sample

[

]

( m) ′ u (im) = ui(1m) , ui(2m) , …, uik . i

(t) As m → ∞, u(m) i can be regarded as a random sample from p(ui|y,θ ). Thus, M successive realizations of this cycle can be regarded as M (dependent) samples from the distribution p(ui|y,θ (t)) and used to provide estimates of the fixed effects and variance components at the (t + 1)th iteration of the EM algorithm.

Metropolis-Hastings Algorithm. When the target distribution for sampling does not have a standard form or it is hard to generate random numbers from the target distribution, a general acceptance-rejection method, called the Metropolis-Hastings (M-H) method (Metropolis et al. 1953; Hastings 1970), can be used. With the M-H method, instead of generating ui directly from the target distribution, we generate u(*) i from a (m) proposed distribution whose density is denoted by q(u(*) |u i i ), where ui(m) is the current value of ui (generated at the mth cycle). The proposed density, also called candidate generating density (Chib and Greenberg 1995), is chosen as having a shape similar to the target density, but its realization can be easily generated from the computer. Because u(*) i is not generated from the target distribution, it is not accepted immediately but (*) with a certain probability a(u(m) i , u i ), called the Metropolis-Hastings (*) moving probability. If u i is accepted, the Markov chain moves from m to m + 1 with u(m+1) = u(*) i i , otherwise, the chain still moves but the value (m) of ui remains, that is, u(m+1) = u(m) i i . The M-H moving probability is defined as

α

(

u (im) , u (*) i

)

 p  u (*) y, θ (t )  q  u ( m) u (*)   i    i    i = min  , 1 . ( m) (t ) (*) ( m )  p  u i y, θ  q  u i u i    

[72]

Chib and Greenberg (1995) made several remarks on this algorithm. First, the M-H algorithm is specified by the candidate generating or pro-

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(m) posal density q(u(*) i |u i ) whose selection we discuss in the next section. Second, if a candidate value is rejected, the current value is taken as the (*) next item in the sequence. Third, the calculation of a(u(m) i , u i ) does not (t) require knowledge of the normalizing constant in p(ui|y,θ ) because it appears in both the numerator and the denominator. Fourth, if the candidate generating density is symmetric, an important special case, (m) (m) (*) q(u(*) i |u i ) = q(u i |u i ) and the probability of move reduces to

α

(

u (im) , u (*) i

)

  (*)  (t )   p  u i y, θ   = min  , 1 . ( m) (t )  p  u i y, θ    

[73]

This is the original form of Metropolis algorithm (Metropolis et al. 1953), (t) (m) (t) (*) which says that if p(u(*) i |y,θ ) ≥ p(u i |y,θ ), the chain moves to u i , otherwise, it moves to

u(*) i

with probability given by

( p( u

(*)

p u i y, θ ( m) i

(t )

y, θ

) . In )

(t )

other words, if the jump goes “uphill,” it is always accepted; if “downhill,” it is accepted with a nonzero probability. The following two candidate generating densities are commonly recommended (Chib and Greenberg 1995). One is the random walk gener(m) ating density, u(*) + d, where d is distributed as ki dimensional i = ui uniform, that is, the ith component of d is uniform on the interval (– di,di). Note that di controls the spread along the ith coordinate axis. The magnitude of di also controls the rate of acceptance. If di is chosen too small, the acceptance rate will be high, but it will take longer time for the chain to explore all the sampling space. If di is chosen too large, the acceptance rate will be low so that the successive values will be highly correlated, still undesirable. It is recommended to adjust the magnitude of di experimentally so that an acceptance rate of 40 to 50 percent will be achieved (Chib and Greenberg 1995). The second proposal density is the random (m) walk candidate generating density, u(*) + d with d distributed as i = ui independent normal Nki (0, D) where D = diagonal[d1,d2,…,dki ] and di is a small positive number whose magnitude controls the acceptance rate. The advantage of the M-H sampler over the Gibbs sampler is that matrix inversion has been avoided, leading to easier programming. The disadvantage of it is the longer time for the chain to reach the stationary distribution, due to less or equal to 100 percent acceptance rate in the M-H algorithm. One can show that the Gibbs sampler is a special case of the M-H with the candidate generation density chosen in such a way

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that the acceptance rate is 100 percent. This can only be achieved when the candidate generating density is actually the target density.

V. BAYESIAN ANALYSIS Bayesian analysis is intended to infer the conditional distribution of the parameters given observed data. The conditional distribution is a probability statement about the parameters. Bayesian analysis requires prior information about the parameters and combines the prior information with the likelihood function to generate a posterior distribution. The posterior distribution of the parameters contains all the information needed to estimate the parameters (Gelman et al. 1995). In Bayesian analysis, everything (including a parameter) is treated as a variable. A variable can be classified into one of two classes: observables and unobservables. The observables include data (phenotypic values and pedigrees in the context of plant breeding). The unobservables include parameters (fixed effects β and variance components λ and s2e) and missing values (genetic effects of founders u). Previously, we used θ = {β, λ, s2e} to denote the parameters. We now extend the list of θ so that it also includes the missing values; thus θ = {β, λ, s2e, u} now denotes the unobservables. Let p(θ ) be the prior density (to be specified later) and p(y|θ ) be the likelihood function (probability density of the data given the unobservables). The posterior density of the unobservables is

() ( ) ( p (y) ) α p (y θ )p (θ )

p θy =

p yθ p θ

[74]

where p(y) = ∫ ∫ p(y|θ)p(θ)dθ is a marginal density of y, not a function of θ. Therefore, p(y) can be ignored when finding the maximum p(θ/y). Given the complexity of the likelihood and the prior, the joint posterior probability density may not always have a standard form. In addition, Bayesian inference should be made at the marginal level for each parameter. In other words, parameters are investigated one at a time. When θi (a single parameter in the parameter list) is investigated, all other parameters are treated as missing values, which are eventually integrated out from the joint probability density so that their influence on the estimate of θi is removed. Let us partition θ into θ = [θi θ–i] where θi is a single element of θ and θ–1 is a vector of the remaining elements. The marginal posterior distribution of θi is

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[ ]

p θi y ∝

∫∫ p( y θ i , θ −i )p(θ i , θ−i )dθ−i .

153

[75]

Bayesian inference for θi should be made from this marginal distribution (Eq. 75). Unfortunately, this marginal posterior distribution has no explicit expression. Numerical integration is often prohibited because of the high dimensionality of θ–i. Therefore, we resort to the Markov chain Monte Carlo (MCMC) algorithm to simulate a sample from the joint posterior distribution, p(θ|y). Using a realized sample, we can easily infer the marginal distribution of θi by simply looking at the empirical distribution of θi and ignoring the variation of θ–i. With the MCMC algorithm, we do not directly generate the sample from p(θ|y); rather, we only generate realizations from the conditional posterior distribution for each unobservable, p(θi|θ–i,y). This conditional posterior distribution has an identical form to the joint posterior distribution except that, in the conditional distribution, θ–i is treated as constant and θi as a variable. Start(0) ing from an initial value for θ, denoted by θ (0) = [θ (0) 1 ,…,θ r ], where r is the total number of unobservables, we update one unobservable or a group of unobservables at a time with other unobservables fixed at their initial values. After all the unobservables have been updated, we complete one cycle of the Markov chain; the updated values are denoted by (1) θ (1) = [θ (1) 1 ,…,θ r ]. The chain will grow and eventually reach a stationary distribution. Let M be the length of the chain. Because there is one realization of θ in each cycle of the chain, we will have a realized sample of θ with sample size M, denoted by {θ (1),…,θ (M)}. Discarding data points of the first few thousand cycles (burn-in period) and thereafter saving one realization in every hundred cycles (to reduce the serial correlation between consecutive observations), we get a random sample of θ drawn from p(θ|y). Note that the Bayesian method implemented via the MCMC here is simply an extension of the E-step of the above EM algorithm by extending the list of missing values to include the parameters. The actual form of the likelihood function, p(y|θ) = p(y|β, u, s 2e), has been given in Equation [43]. The following prior density can be chosen  p   5    5 p θ = p β p λ p uλ =  p βi   p σ i2   p  u i σ i2  .  i =1   i =1  [76]   i =1

() ()()( ) ∏ ( ) ∏ ( ) ∏

The prior for the fixed effect p(bi) can take a normal or a uniform distribution. If the variance of the normal prior is infinitely large or the uniform prior is unbounded, the prior is called improper prior. This kind

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of prior means that bi, a priori, can take any value in the real. This is equivalent to no prior information about bi. Improper prior is allowed in Bayesian analysis. However, it should be used with caution because sometimes improper prior can lead to improper posterior (Carlin and Louis 1996). This can happen when the design matrix X is not of full column rank (one of the bi may be a linear combination of the other bi). Therefore, it is recommended to choose a finite variance for a normal prior or a bounded uniform prior. We can take a truncated normal as the prior for s2i, due to the nonnegative nature of s2i. It is also common to take a uniform (0, ∞) as the prior for s2i. One particular prior for s2i may be p(s2i) 1 ∝ s—i2, which is called the vague prior (Press 1989). The prior for s2e is chosen in a similar manor. Finally, the prior density for ui is p  u i σ i2  =

1

(

2πσ i2

)

1 ki 2

 1  exp − u ′i u i  .  2σ i2 

[77]

In fact, this density should not be called the prior density because ui is not a vector of parameters. It is simply the objective probability density for the missing values. I now give a detailed expression of the M-H sampler for each parameter in turn. The fixed effects β can be sampled as a block or separately for each element. For the block sampling, we have θi = β and θ–i = {λ, s2e, u}. Denote the current value of β by β (t). When the random walk candidate generating density is used to generate the candidate vector β (*), the M-H acceptance probability is

(

α β ,β (t )

(*)

)

( ) ( )

 p  y β (*) , u (t ) , σ 2(t )  p β (*)  e     = min  , 1 . (t ) (t ) (t ) 2(t )   p  y β , u , σ e  p β  

[78]

The proposal density q(β (*)|β (t)) and q(β (t)|β (*)) have canceled each other out. If the prior for β is uniform, p(β (*)) and p(β (t)) will also cancel each other out so that the M-H acceptance probability is simply

(

α β ,β (t )

(*)

)

 p  y β (*) , u (t ) , σ 2(t )   e     = min  , 1 . (t ) (t ) 2(t )  p  y β , u , σ e    

[79]

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which is simply the likelihood ratio. Using the random walk proposal density for generating s 2e(*), we get the following M-H acceptance probability,

α

(

σ e2(t ),

σ e2(*)

)

 p  y β (t ), u (t ) , σ 2(*)  e    = min  (t ) (t ) 2(t )  p  y β , u , σ e  

( ) , 1 . p(σ )  

p σ e2(*) 2(t ) e

[80]

If the vague prior is used for s2e, the M-H acceptance probability is simply

α

(

σ e2(t ),

σ e2(*)

)

 p  y β (t ) , u (t ) , σ 2(*)  σ 2(t )  e  e    = min  , 1 . (t ) (t ) 2(t ) 2(*)  p  y β , u , σ e  σ e   

[81]

Using the random walk proposal density for generating s 2i(*), we have the following M-H acceptance probability,

α

(

σ i2(t ),

σ i2(*)

)

( ) ( )

 p  u (t ) σ 2(*)  p σ 2(*)  i   i i   = min  , 1 . (t ) 2(t )  2(t )   p ui σ i  p σ i   

[82]

For the vague prior, the acceptance probability becomes

α

(

σ i2(t ),

σ i2(*)

)

 p  u (t ) σ 2(*)  σ 2(t )    i i  i  = min  , 1 . (t ) 2(t )  2(*)   p ui σ i σ i   

[83]

Finally, sampling ui has been described in the EM section and will be reiterated here for completeness. Again, using the random walk proposal density to generate u(*) conditional on the current value u(t) i i , we have

α

(

u (it ),

u (*) i

)

  (t ) (*) 2(t )   (*) 2(t )    p  y β , u , σ e  p ui σ i   = min  , 1 . 2(t ) (t ) (t ) (t ) 2(t )  p  y β , u , σ e  p  u i σ i    

[84]

Note that the MCMC sampling scheme described earlier is entirely based on the Metropolis-Hastings algorithm.

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Gianola and Foulley (1990) proposed a Bayesian method for variance component analysis where the marginal posterior density of a single variance component is obtained by integrating out all other parameters with the multiple integration conducted via some approximations not through the Monte Carlo method. Alternatively, Wang et al. (1993) proposed a method of variance component estimation that is completely implemented via the Gibbs sampler. With uniform priors for the fixed effects and vague priors for the variance components, Wang et al. (1993) found that the conditional posterior distribution of β is multivariate normal, the conditional posterior distribution of u is also multivariate normal, and each variance component (including s2e) is in the scaled inverted c 2 form. To sample a random number from an inverted c 2 distribution, we first sample a random variable from the c 2 distribution and then simply take the inverse of the variable to get the variable with the inverted c 2 distribution. Unlike MLE, the Bayesian method does not provide a significance test. Therefore, results generated from Bayesian analysis should be interpreted in a different way. The product of the MCMC algorithm is a realized sample of all unknown variables drawn from the joint posterior distribution. Since the sample size can be arbitrarily large, the posterior sample contains all the information needed to infer the statistical properties of the parameters. Therefore, the MCMC algorithm serves as an experiment to generate data. Upon completion of the experiment, we need to summarize the result and draw conclusions. In fact, the statistical properties of parameters are “observed” from the data rather than inferred as in usual data analyses. This is because the sampled data points are directly made on the parameters. The most informative summary statement from the posterior sample is the frequency table for each parameter of interest. The table may be converted into a histogram, a visual representation of the posterior density. The posterior mean, posterior variance, and credibility interval are also easily obtained from the posterior sample. The posterior mean or posterior mode of a parameter may be compared to the point estimate obtained using the maximum likelihood analysis. The 95 percent credibility interval is defined as

(

)

Pr a ≤ θ i ≤ b y =

∫a p (θ i y)dθ i b

= 0.95

[85]

where a and b are found such that b – a is minimum among all other values which satisfy Equation [85]. Note that p(θi|y) is simply obtained from the joint posterior sample by ignoring the variation of θ–i. The Bayesian credibility interval appears similar to, but has a quite differ-

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ent meaning from, the confidence interval in interval estimation. The 95 percent credibility interval is a statement of conditional probability, that is, conditional on observed data, the probability that θi lies between a and b is 0.95. The confidence interval in interval estimation, however, defines an interval based on observed data. Because the interval is a function of data, it varies from one experiment to another. The 95 percent confidence interval is defined in such a way that if the experiment were repeated many times, 95 percent of the times the interval would contain the true parameter value. Since Bayesian inference refers to a statement of conditional probability given data in the current experiment, it never intends to make an inference about the hypothetical future experiments. As a consequence, power analysis is irrelevant to Bayesian statistics. Under the Bayesian framework, every quantity in the linear model is treated as a variable. Therefore, all linear models analyzed with the Bayesian method are, strictly speaking, random models. However, we can still distinguish a fixed model from a random model in a subtle way. If ui is not treated as a vector of missing values but treated as parameters of interest, we should consider p(ui|s2i) truly as a prior density. The parameter in this prior density is s2i, which should be called the hyperparameter. A hyperparameter should be accessed (provided by the researcher) rather than estimated from the data. Therefore, if s2i is treated as a hyperparameter, the model may be referred to as the fixed effect model; if s2i is treated as a parameter and estimated from the data rather than accessed, a priori, the model may be referred to as the random effect model. The difference between the fixed and the random models becomes vague under the Bayesian framework.

VI. DISCUSSION AND CONCLUSIONS The statistical methods reviewed here are based on the two locus epistatic genetic model of Cockerham (1980). Most quantitative traits, however, are considered to have a polygenic background, which can be explained by the so-called infinitesimal model (Bulmer 1985; Falconer and Mackay 1996; Lynch and Walsh 1998). This model assumes that the genetic variation of a quantitative trait is controlled by the segregation of infinitely large number of loci each with an infinitely small effect. How can the two gene models be applied to the infinitesimal model? There are two aspects that need to be considered here. First, Cockerham’s model provides a theoretical foundation for quantitative genetics, especially with epistatic effects. Cockerham adopted the factorial design

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of experiments to the genic factorial analysis. The dominance and additive-by-additive effects in the genic factorial model are nothing but the two-factor interactions in the usual factorial analysis; the additiveby-dominance effects are nothing but the three-factor interactions; and the dominance-by-dominance effects are the four-factor interactions. The epistatic effects are hard to define without invoking the two-gene model. Secondly, the two genes considered by Cockerham actually represent the “average” of infinite pairs of genes. This means that the additive effect contributed from founder i, Ai, is the sum of the additive effects from all loci. As a result, Cockerham (1980) defined the coefficient of the additive effects as a proportion of genes coming from founder i, rather than as an indicator of an allele coming from founder i. Let n be the number of loci and Aij, for j = 1,…,n, be the additive effect for locus j from founder i. Previously, I have defined wi = zim + zif as the allelic inheritance indicator from founder i for a single locus. Let us define wij as the allelic inheritance indicator from founder i for locus j. The additive effect and its coefficient from founder i is actually n

∑ ω ij Aij

=

j =1

∑ ω ij [ n

– – A i + ( Aij − A i )

j =1

=

n

–

n

∑ ω ij A i +∑ ω ij j =1

=[

– ( Aij − A i )

j =1

n

1 n

]

n

–

∑ ω ij ]n A i +∑ ω ij j =1

– ( Aij − A i )

j =1

[86]

– – where Ai is the additive effect averaged – across loci (thus nAi = Ai is the sum of the additive effects) and Aij – Ai is the deviation of the additive effect of locus j from the average effect, indicating the heterogeneity of the effects across loci. It is known that 1 n →∞ n lim

n

∑ ω ij j =1

( )

= E ωi = αi

and

∑ ω ij ( Aij − n

lim

n →∞

j =1

– Ai

) = 0.

The is based on the independence between wij and Aij – second argument – – Ai, and Aij – A of different signs tend to cancel each other out. If the

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second argument is violated, then Cockerham’s model should be revised. However, for the infinitesimal model, all genes have an equal infinitely – small effect so that Aij – Ai = 0, for j = 1,…,n. As a consequence, n

∑ ω ij Aij n →∞ lim

j =1

n

–

∑ ω ij A i n →∞

= lim

j =1

– + lim ω ij (Aij − A i ) = α i Ai . n →∞

[87]

The dominance and epistatic effect terms follow the same argument as the additive effects. Therefore, Cockerham’s model applies to the infinitesimal model. An implicit assumption of the Cockerham model is that loci are genetically unlinked. For a finite number of chromosomes carrying infinite number of loci, most of the gene pairs will be linked. Linkage will cause cosegregation between loci, which eventually will cause correlation between w’s of different loci. Ultimately, 1 n →∞ n lim

n

∑ ω ij j =1

( )

≠ E ωi = αi

and n

–

∑ ω ij (Aij − A i ) ≠ 0 . n →∞ lim

j =1

This is because when loci are linked, the number of “independent” segregations may not increase. So, the segregation of n linked loci may be equivalent to the segregation of m (m << n) independent loci. As n increases, m may not necessarily increase. When m is not sufficiently large, the average w of m independent loci will be different from the expectation of w. Consider an extreme case where there are four chromosomes each carrying an infinite number of loci. If all the four chromosomes are very short so that the recombination between any pair of loci within the same chromosome is negligible, the segregation of all loci carried by the four chromosomes is equivalent to the segregation of four independent loci. No matter how many loci the four chromosomes carry, the average w of four loci will largely deviate from the expectation. It may be possible to take linkage into consideration with some assumptions about the distribution of the chromosome positions of the loci (Zeng 1992). This requires knowledge of the number of chromosomes and the size of each chromosome. Sound statistical methods can be developed if these assumptions hold. However, in most situations,

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these assumptions may not be met. How robust the Cockerham model is to the violation of these assumptions needs further investigation. Fortunately, with abundant molecular markers, loci can be mapped on the chromosomes and even on narrow regions of the chromosomes. These regions are called quantitative trait loci (QTL) (Lander and Botstein 1989). Numerous designs of experiments and statistical methods have been developed for QTL mapping. Cockerham and Zeng (1996) recently incorporated molecular marker information into the Cockerham model. In recent work by Yi and Xu (2000), arbitrarily complicated mating designs have been investigated for QTL mapping. Given the availability of these advanced statistical techniques for QTL mapping in pedigree data, is the Cockerham model still useful for quantitative genetics and plant breeding? The answer is probably Yes. First, again, Cockerham’s model has laid the theoretical foundation for epistatic effects of quantitative traits. For historical reasons and educational purposes, reiterating Cockerham’s model will help to understand the connection between the statistical model and the genetic model. Both statisticians seeking a genetic problem and geneticists seeking a statistical method will benefit from Cockerham’s model. Secondly, some plants in the breeding industry may not have molecular data and genetic maps, although they may be available in the future. In the transition period of time, statistical analyses of quantitative traits without marker information still play an important role. Even for plants with molecular data, before performing QTL mapping, analyses of phenotypic data alone to test the genetic mode of gene actions can still be informative. It is faster and cheaper than collecting molecular data and performing QTL analysis. The results will provide a general guide with regard to what model of a QTL analysis should take. Thirdly, Cockerham’s model is a very general model; almost all designs of experiments, for example, full-sib and half-sib mating designs, are special cases of the general model. The following applications have been reviewed: least squares (LS), the weighted least squares (WLS), the maximum likelihood (ML), and the Bayesian methods to the epistatic genetic model. LS and WLS are used to estimate and test the first moments, that is, the genetic effects. When normal distribution of the residual errors is assumed, estimation of the standard errors for genetic effects is straightforward. ML has been used to estimate the second moments, that is, the variances of the genetic effects, in addition to the first moments. Estimation of the standard errors for variance component estimates is not discussed in this chapter because of its complexity. There are a set of resampling based methods which can be used to infer the standard errors of parameter estimates. These include the bootstrap (Efron 1979; Efron and Gong

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1983) and jackknife (Miller 1974) methods. They are not reviewed in this article because: (1) The resampling based methods requires that the resampled units (plants or group of plants) be sampled independently from an identical distribution, while in complicated pedigrees, each unit may have a different genetic background and consists of different proportion of genes from each founder; it is hard to define a sampling strategy that will provide optimal estimates of the standard errors. (2) The Bayesian method just described provides a posterior distribution for each interested parameter; the posterior distribution contains all the information about the estimated parameter, including mean, mode, and standard deviation, making the resampling based methods unnecessary. Two major components in statistical analyses of plant breeding have been discussed. One is the genetical model defining various genetic effects and their variances. The other is the statistical techniques for parameter estimation (LS, WLS, ML, and Bayesian). I chose the Cockerham’s model as the benchmark for the genetic model. The state-of-theart method is the Bayesian statistic implemented via the MCMC sampling, which I highly recommend to the plant breeding community. The problem is the implementation of the method. It requires an indepth understanding of the model and methods themselves. It also requires a user-friendly computer program to perform the analysis. Such a program has not been available in the well-developed statistical packages, for example, SAS. It is necessary to develop a specialized computer program to implement the method. LITERATURE CITED Bernardo, R. 1995. Genetic models for predicting maize single-cross performance in unbalanced yield trail data. Crop Sci. 35:141–147. Bulmer, M. G. 1985. The mathematical theory of quantitative genetics. Clarendon Press, Oxford. Carlin, B. P., and T. A. Louis. 1996. Bayes and empirical Bayes methods for data analysis. Chapman & Hall, New York. Chi, K. R. 1965. Covariances among relatives in a random mating population of maize. Ph.D dissertation, Iowa State Univ., Ames, IA. Chib, S., and E. Greenberg. 1995. Understanding the Metropolis-Hastings algorithm. Am. Stat. 49:327–335. Churchill, G. A., and R. W. Doerge. 1994. Empirical threshold values for quantitative trait mapping. Genetics 138:963–971. Cockerham, C. C. 1954. An extension of the concept of partitioning hereditary variance for analysis of covariances among relatives when epistasis is present. Genetics 39:859–882. Cockerham, C. C. 1956. Analysis of quantitative gene action. Brookhaven Sym. Biol. 9:53–68.

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Cockerham, C. C. 1963. Estimation of genetic variances. p. 53–99. In: W. D. Hanson, H. F. Robinson (eds.), Statistical genetics and plant breeding. Natl. Acad. Sci.-Natl. Res. Counc. Washington DC. Cockerham, C. C. 1980. Random and fixed effects in plant genetics. Theor. Appl. Genet. 56:119–131. Cockerham, C. C., and Z. B. Zeng. 1996. Design II with markers. Genetics 143:1437–1456. Comstock, R. E., and H. F. Robinson. 1948. The components of genetic variance in populations of biparental progenies and their use in estimating the average degree of dominance. Biometrics 4:254–266. Comstock, R. E., and H. F. Robinson. 1952. Estimation of average dominance of genes. p. 494–517. In: J. W. Gowen (ed.), Heterosis. Iowa State Univ. Press, Ames, IA. Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Royal Stat. Soc. B 39:1–38. Eberhart, S. A., R. H. Moll, H. F. Robinson, and C. C. Cockerham. 1966. Epistatic and other genetic variances in two varieties of maize. Crop Sci. 6:275–280. Efron, B. 1979. Bootstrap methods: Another look at the jackknife. Ann. Stat. 7:1–26. Efron, B., and G. Gong. 1983. A leisurely look at the bootstrap, the jackknife, and crossvalidation. Am. Stat. 37:36–48. Falconer, D. S., and T. F. C. Mackay. 1996. Introduction to quantitative genetics. Longman, Harlow, UK. Fisher, R. A. 1918. The correlation between relatives on the supposition of mendelian inheritance. Trans. Roy. Soc. Edingburgh 52:4399–4433. Fisher, R. A. 1935. The designs of experiments. Oliver & Boyd, London. Gelman, A., J. B. Carlin, H. S. Stern, and D. B. Rubin. 1995. Bayesian data analysis. Chapman & Hall, New York. Geman, S., and D. Geman. 1984. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Patt. Anal. Mach. Intell. 6:721–741. Gianola, D., and R. L. Fernando. 1986. Bayesian methods in animal breeding theory. J. Anim. Sci. 63:217–244. Gianola, D., and J. L. Foulley. 1990. Variance estimation from integrated likelihood (VEIL). Genet. Sel. Evol. 22:403–417. Giri, N. C. 1996. Multivariate statistical analysis. Marcel Dekker, New York. Graybill, F. A. 1976. Theory and application of the linear model. Wadsworth & Brooks/Cole Advanced Books & Software, Pacific Grove, CA. Guo, S. W., and E. A. Thompson. 1991. Monte Carlo estimation of variance component models for large complex pedigrees. IMA J. Math. Appl. Med. Biol. 8:171–189. Guo, S. W., and E. A. Thompson. 1994. Monte Carlo estimation of mixed models for large complex pedigrees. Biometrics 50:417–432. Hallauer, A. R., and J. B. Miranda. 1988. Quantitative genetics in maize breeding. Iowa State Univ. Press, Ames, IA. Hartley, H. O., and J. N. K. Rao. 1967. Maximum-likelihood estimation for the mixed analysis of variance model. Biometrika 54:93–108. Hastings, W. K. 1970. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57:97–109. Henderson, C. R. 1950. Estimation of genetic parameters. Ann. Math. Statist. 21:309–310. Henderson, C. R. 1975. Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423–447. Henderson, C. R. 1984. Application of linear models in animal breeding. University of Guelph, Guelph, Canada. Jansen, R. C. 2001. Quantitative trait loci in inbred lines. p. 567–597. In: D. J. Balding, M. Bishop, C. Cannings (eds.), Handbook of statistical genetics. Wiley, New York.

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Kao, C. H., Z. B. Zeng, and R. Teasdale. 1999. Multiple interval mapping for quantitative trait loci. Genetics 152:1203–1216. Kearsey, M. J., and H. S. Pooni. 1996. Genetical analysis of quantitative traits. Chapman & Hall, London. Kempthorne, O. 1957. An introduction to genetic statistics. Wiley, New York. LaMotte, L. R. 1973. Quadratic estimation of variance components. Biometrics 29:311–330. Lander, E. S., and D. Botstein. 1989. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185–199. Lynch, M., and B. Walsh. 1998. Genetics and anlysis of quantitative traits. Sinauer Associates, Sunderland, MA. Metropolis, N., A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller. 1953. Equations of state calculations by fast computing machines. J. Chem. Phy. 21:1087–1091. Miller, R. G. 1974. The jackknife: a review. Biometrika 61:1–15. Nelder, J. A., and R. Mead, 1965. A simplex method for function minimization. Comput. J. 7:308–313. Nyquist, W. E. 1991. Estimation of heritability and prediction of selection response in plant populations. Crit. Rev. Plant Sci. 10:235–322. Patterson, H. D., and R. Thompson. 1971. Recovery of inter-block information when block sizes are unequal. Biometrics 58:545–554. Press, S. J. 1989. Bayesian statistics: principles, models, and applications. Wiley, New York. Rao, C. R. 1971. Estimation of variance and covariance components-MINQUE theory. J. Mult. Anal. 1:257–275. Rawlings, J., and C. C. Cockerham. 1962a. Analysis of double cross hybrid populations. Biometrics 18:229–244. Rawlings, J., and C. C. Cockerham. 1962b. Triallel analysis. Crop Sci. 2:228–231. Seber, G. A. F. 1977. Linear regression analysis. Wiley, New York. Silva, J. C., and A. R. Hallauer. 1975. Estimation of epistatic variance in Iowa Stiff Stalk Synthetic maize. J. Hered. 66:290–296. Stuber, C. W., S. E. Lincoln, D. W. Wolff, T. Helentjaris, and E. S. Lander. 1992. Identification of genetic factors contributing to heterosis in a hybrid from two elite maize inbred lines using molecular markers. Genetics 132:823–839. Wang, C. S., J. J. Rutledge, and D. Gianola. 1993. Marginal inferences about variance components in a mixed linear model using Gibbs sampling. Genet. Sel. Evol. 21:41–62. Wright, J. A. 1966. Estimation of components of genetic variances in an open-pollinated variety of maize by using single and three-way crosses among random inbred lines. Ph.D Diss., Iowa State Univ., Ames, IA. Xu, S. 1998a. Further investigation of the regression method of mapping quantitative trait loci. Heredity 80:364–373. Xu, S. 1998b. Iteratively reweighted least squares mapping of quantitative trait loci. Behav. Genet. 28:341–355. Yi, N., and S. Xu. 2000. Mixed model analysis of quantitative trait loci. Proc. Nat. Acad. Sci. 97:14542–14547. Zeng, Z. B. 1992. Correcting the bias of Wright’s estimates of the number of genes affecting a quantitative character: a further improved method. Genetics 131:987–1001. Zeng, Z. B., J. Liu, L. F. Stam, C. H. Kao, J. M. Mercer, and C. C. Laurie. 2000. Genetic architecture of a morphological shape difference between two drosophila species. Genetics 154:299–310.

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4 Oil Palm Genetic Improvement A. C. Soh, G. Wong, T. Y. Hor, C. C. Tan and P. S. Chew* Applied Agricultural Research Sendirian Berhad 47000 Sungei Buloh Selangor, Malaysia

I. INTRODUCTION A. Origins B. Crop Status C. Uses II. GERMPLASM RESOURCES A. Genetic Base and Diversity B. Base Broadening C. Genetics and Cytogenetics III. IMPROVEMENT OBJECTIVES A. Yield Potential B. Adaptability C. Oil Quality D. Stress Tolerance IV. BREEDING TECHNIQUES A. Floral Biology B. Breeding Plans and Selection Methods C. In Vitro Methods D. Molecular Breeding V. FUTURE PROSPECTS LITERATURE CITED

*We thank our Company, Applied Agricultural Research Sendirian Berhad and its Principals, Boustead Holdings Berhad and Kuala Lumpur Kepong Berhad, for permission to publish this paper.

Plant Breeding Reviews, Volume 22, Edited by Jules Janick ISBN 0-471-21541-4 © 2003 John Wiley & Sons, Inc. 165

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I. INTRODUCTION A. Origins The African oil palm (Elaeis guineensis Jacq.), which is the oil palm of commerce, is endemic to tropical Africa stretching from the Atlantic coast (Guinea) in the west to Zanzibar and the island of Madagascar in the east, and from the sub-Sahara in the north (Senegal, 16°N) to the south of Angola (15°S) bordering Namibia (Hartley 1988; Latiff 2000). The center of origin and diversity appears to be concentrated in the tropical rain forests of west and central Africa (Angola, Cameroon, Congo, Ghana, Ivory Coast, Nigeria, and Zaire). They occur naturally as semiwild groves in swamps and riverine forest fringe areas in the plains usually close to settlements, but are also found on drier and higher grounds up to the altitude of 1500 m above sea level. The American oil palm (Elaeis oleifera), which also produces an oil in smaller quantities consumed by the natives and is of interest in breeding because of some of its desirable attributes, however, is endemic to tropical Latin America stretching from Mexico in the north to the Amazonas of Bolivia, Brazil, Colombia, and Peru in the south and straddling the Pacific and Atlantic coasts. They also occur naturally in groves in open grasslands and riverine areas and their distribution appears to be associated with indigenous Indian migratory movements (Santos et al. 1986). The distribution of the groves tended to be more discontinuous. The genus Elaeis, which belongs to the Cocoideae subfamily of the Palmaceae, has an American or African center of origin. The two oil palm species were presumed to have diverged when the American and African continents drifted apart in prehistoric times (Zeven 1965). There are two lesser-known species of Elaeis: E. odora (South America) and E. madascariensis (Africa). B. Crop Status Crude palm oil extracted traditionally from the fruit pulp of the semiwild oil palms was the source of dietary fat and certain vitamins (A and E) of the indigenous populations of tropical Africa and America. Interest in the oil palm as a crop arose as a substitute for animal fat in the manufacture of soap, candle wax, and margarine. Plantations were started in the East Indies (Indonesia, Malaysia) by the European colonists to ensure a steady supply of the raw material (Hartley 1988). Two seedlings each from the botanic gardens of Amsterdam and Mauritius, the thick-shelled or dura form (Plate 4.1A) derived presumably from the same palm in West Africa, were taken and grown in the Bogor Botanic Gardens, Java, Indonesia in 1848. With the expanding interest in the

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crop, subsequent hybridizations and selections were made from these four Bogor palms and the progenies distributed to the Deli province in Sumatra and thence Malaysia (Malaya then) to become known as the Deli dura population (Rosenquist 1986). This uniform, high-oil yielding Deli dura was the commercial planting material for the rapidly expanding plantations in the Far East from 1911 till the early 1960s. With the revelation of monogenic inheritance of the shell gene by Beirnaert and Vanderweyen (1941) and that the hybrid progenies of the thick-shelled dura and the shell-less female sterile pisifera are 100 percent thinshelled teneras having thick-mesocarp and thence higher oil yield (Plate 4.1A), the change to the tenera hybrid planting material was very rapid as teneras from West Africa had been imported and bred earlier by researchers in the Far East and they became the source of the pisiferas (Hartley 1988). The oil palm is the second most important oil crop next to soybean and is poised to become the dominant oil crop early in the new decade (Mielke 2000; Yusof and Mohd Arif 2000). Palm oil constitutes 19 percent of the world’s oils and fats production and is the dominant oil of international trade. The oil is produced from about 6 million hectares (ha) of plantations in the countries of the humid tropics, that is, Colombia, Indonesia, Ivory Coast, Malaysia, Nigeria, Thailand, with Malaysia accounting for 48 percent and Indonesia 31 percent of production. With the rapid expansion of the world’s population particularly in the third world countries where dietary fat intake is still very low, per capita oil and fat consumption is likely to increase tremendously and the oil palm being the most productive and profitable oil crop will continue to expand in its cultivation to meet this demand. C. Uses The oil palm fruit bunch produces two types of oil, “palm oil” from the mesocarp (20%) and “palm kernel oil” from the kernel (3%). Crude palm oil extracted from the sterilized mesocarp is refined, bleached from its original orange-red color, and deodorized to give refined palm oil used solely or blended with other oils in cooking oil, salad oil, margarines, and spreads. The refined oil is fractionated to give olein and stearin and with further fractionation gives fatty acids and alcohols, intermediate commodities traded and used in food and oleochemical industries. Palm oil can be used as a biofuel as with other vegetable oils. About 80 percent of palm oil, however, is used in the food industries although its other uses are increasing. Palm kernel oil is a competitor for coconut oil and has more uses in the oleochemical industries. Fig. 4.1 is a summary chart of the fractionated products and their uses.

Fig. 4.1.

Uses of palm oil. Source: Pantzaris (2000).

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Secondary and waste products from the palm oil industry are assuming economic importance prompted largely by health and environmental concerns. Carotene from the crude palm oil can be processed into Vitamin A supplement and a natural dye for snack foods, for example, instant noodles. Likewise, tocopherols and tocotrienols can be extracted from palm oil for industrial Vitamin E production (Jalani et al. 1997; Choo 2000). Kernel cake and sludge cake, wastes from the palm and kernel oil extraction processes, respectively have use as animal feed while the fruit bunch stalk fiber waste can be used directly in the plantation as an organic mulch or processed into an organic compost.

II. GERMPLASM RESOURCES A. Genetic Base and Diversity Expeditions and collections made by early oil palm researchers at the various centers in West Africa, for example, Nigeria (Calabar, Ufuma), Ivory Coast (La Me), Zaire (Sibiti, Yangambi), especially after World War II formed the genetic base and diversity of the respective oil palm breeding programs (Rosenquist 1986; Hartley 1988). There were subsequent genetic material exchanges for breeding purposes. Most of the oil palm breeding populations were descended from one or a few palms and were termed by Rosenquist (1986) as breeding populations of restricted origins. A brief description of some of the major breeding populations follows. 1. Deli. This is the thick-shelled dura variety derived from the original four Bogor palms in Java. Distribution of the subsequent progenies to other countries followed by local selection led to the development of the Elmina, Serdang Avenue, and Ulu Remis Deli dura subpopulations/ selections in Malaysia and the Dabou and La Me dura subpopulations/ selections in Ivory Coast. The Ulu Remis Deli duras were most widely distributed. The rather uniform, high-yielding Deli population led to the speculation of a common progenitor for the four Bogor palms. Deli duras provide the mother palms for almost all major oil palm commercial hybrid seed production programs. The Dumpy and Gunung Melayu palms are short variants of the Deli. 2. AVROS. Seeds from the Djongo (best) palm at Eala Botanical Garden in Zaire were obtained and planted in 1923 by Algemeene Vereniging van Rubberplanters ter Oostkust van Sumatra (AVROS) at Sungai Pancur to

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give rise to the well-known SP540 tenera palm. Crossing with the teneras at Bangun Bandar Experimental Station and subsequent backcrossing to the SP540 selfs resulted in the AVROS population. AVROS pisiferas are noted for their vigorous growth, precocious bearing, thin shell, thick mesocarp and high oil yield conferring attributes. Major commercial hybrid seed production programs in Colombia, Costa Rica, Indonesia, Malaysia, and Papua New Guinea are based on Deli dura × AVROS pisifera lineage. 3. Yangambi. The Institut National pour l’Etude Agronomique du Congo (INEAC) started the breeding program at Yangambi, Zaire, with openpollinated seeds from the Djongo palm and from teneras in Yawenda, N’gazi, and Isangi. Their breeding led to the development of the Yangambi population characterized by its excessive vigor, bigger fruit, and high oil yield conferring attributes. The Yangambi population is featured in many breeding and seed production programs worldwide. 4. La Me. The Institut de Recherches pour les Huiles et Oleagineux (IRHO) developed the La Me population from 21 tenera palms, particularly the Bret 10 palm, grown from seeds collected from the wild groves in Ivory Coast. Pisiferas and teneras derived from the L2T tenera palm are used in breeding and seed production programs in West Africa and Indonesia. La Me teneras and their progenies are characteristically smaller palms with smaller bunches and fruits, but they appear to be more tolerant of suboptimal growing conditions. 5. Binga. This subpopulation is derived essentially from the F2 and F3 Yangambi progenies planted in Binga Plantation, Yangambi, Zaire. Palms Ybi 69MAB and Bg 312/3 are the parent palms of breeding interest. 6. Ekona. The Ekona population is derived from the wild palms in the Ekona area of Cameroon and bred further in the Unilever plantations of Cowan Estate, Ndian Estate, and Lobe Estate. Progenies from palm CAM. 2/2311, noted for its high bunch yield, good oil content and wilt resistance, have been distributed to Costa Rica and Malaysia. 7. Calabar. The breeding populations of the Nigerian Institute for Oil Palm Research (NIFOR) are much broader based with collections from Aba, Calabar, Ufuma, and Umuabi. Progenies from the Calabar selections were most interesting and have been distributed to Costa Rica, Ghana,

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Indonesia, Ivory Coast, and Malaysia. Palm NF 32.3005 was featured in most of the distributions. Despite the reasonably diverse original genetic base, the bulk of the current commercial oil palm hybrid seed production and the breeding programs that support them are still based on the Deli and the Yangambi/AVROS and La Me breeding populations, which can be traced to one or two palms from the natural groves. Because of the high selection intensities practiced and the need to retain the proven pedigrees of the hybrids for commercial reasons, genetic variability has been rapidly diminished as stressed by a number of researchers (Thomas et al. 1969; Ooi et al. 1973; Hardon et al. 1987). Prompted by this and the increasing importance of the crop, Malaysia, with the cooperation of Nigeria under the auspices of the International Board for Plant Genetic Resources, made the first systematic large-scale collections in the semiwild groves in the oil palm belt of Nigeria (Rajanaidu et al. 1979; Obasola et al. 1983) with the objectives of genetic conservation and base broadening. The study on the genetic structure of the natural populations from the Nigerian collection revealed greater variability within families and between families than between populations. This guided the approach made in subsequent collections in Angola, Cameroun, Gambia, Guinea, Madagascar, Sierra Leone, Tanzania, and Zaire (Rajanaidu and Rao 1988). There was also a limited attempt to capture different ecotypes. The collected materials were planted and maintained as living collections in Malaysia with a sample retained by the host country for each collection (Rajanaidu 1990b; Rajanaidu and Jalani 1994) although long term conservation through cryopreservation and in vitro methods has also been attempted (Engelmann and Duval 1986; Paranjothy et al. 1986; Rohani and Paranjothy 1995). Collections have also been made for E. oleifera in South America (Brazil, Colombia, Costa Rica, Honduras, Panama, and Surinam) as well as for other oil bearing palms, for example, Bactris gasipaes (Pejibaye), Jessenia-Oenocarpus, Orbignya martiana (Babassu), which have unusual fatty acid composition and other uses. These could be used in future genetic modification of palm oil. B. Base Broadening Private breeding programs, although obliged to broaden the genetic base of their breeding populations to ensure breeding progress, are generally reluctant to outcross with less advanced breeding materials and thus tend to end up with related materials. High unsaturated fatty acid content,

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large kernel, and dwarf high-yielding lines have been developed from the Nigerian collection and distributed to the private industry breeders. Private breeders are reluctant to exploit them directly in commercial hybrid seed production although they have been urged to do so (Rajanaidu et al. 1999, 2000b), but prefer to introgress the desirable features into their advanced breeding parents to maintain the integrity, uniformity, and consistency of their commercial hybrids (Sharma 1999; Soh et al. 1999). These qualities may not be assured in the commercial hybrids developed directly from the early selections of the semi-wild Nigerian collections. C. Genetics and Cytogenetics Most oil palm breeding programs being commercially oriented cannot afford to undertake basic genetic studies. Thus, such studies tend to be incidental, derived from breeding trials, and seldom meet the basic assumptions required for genetic analyses. 1. Qualitative Traits. The revelation of the simple Mendelian inheritance of the shell thickness trait in the palm fruit in the 1940s by Bernaert and Vanderweyen (1941) provided the basis of modern oil palm breeding and hybrid seed production schemes (Plate 4.1A, Figs. 4.2, 4.3). The dura palm is homozygous dominant for the thick shell trait while the pisifera palm is the shell-less recessive homozygote which is usually female sterile as its pistillate inflorescence tends to abort. The dura × pisifera hybrid is the thin-shelled tenera heterozygote and is the commercial variety. The duras, which are the female parents in hybrid seed production, are usually regenerated (100%) from dura × dura crosses while the pisifera male parents are regenerated from tenera × tenera (25% pisifera segregants) and tenera × pisifera (50% pisifera segregants) crosses. Dura × tenera crosses used in progeny-tests segregate 1:1 for dura and tenera palms. Fertile pisiferas do occur occasionally and fertile pisifera × fertile pisifera crosses produce 100% pisifera progenies. Although the presence of shell is under monogenic control, the thickness of the shell was purported to be modified by minor genes (Van der Vossen 1974). Okwuagwu (1988) and Okwuagwu and Okolo (1992, 1994) postulated maternal inheritance of kernel size with the involvement of a kernel-inhibiting factor. A number of other qualitative traits have also been reported from observations and genetic analyses to be simply inherited (Hartley 1988), for example, crown disease, leaf form (idolatrica = fused pinnae), fruit shape (mantled), and fruit color (nigrescens = black unripe, virescens = green unripe, albescence = whitish unripe). Although the Dumpy trait appeared to be simply inherited, observations on segregating progenies

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did not confirm it (Soh et al. 1981). The mode of inheritance for tolerance to fusarium wilt has yet to be elucidated (De Franqueville and Renard 1990). 2. Quantitative Traits. Estimations of genetic variance and heritability have been attempted by oil palm breeders to provide information to guide them to increased breeding efficiency. This involves information on the existence of sufficient genetic variability, the choice of the population to improve and the adoption of appropriate breeding procedure to obtain the best selection response (Soh and Tan 1983). North Carolina Model 1 (hierarchical) and Model 2 (factorial) mating designs and the parent-offspring method are commonly used to estimate genetic variance and heritability in oil palm. Heritability estimates for various agronomic traits have been compiled by Soh and Tan (1983) and Hardon et al. (1985). With the use of data from breeding experiments derived from selected parents from very restricted populations, some of the assumptions such as random choice of parents, no correlation of genotypes at separate loci, and a definite reference population, necessary for valid quantitative genetic analyses, could not be met and the biases incurred could not be estimated. These estimates merely serve as a guide for selection decisions as any estimate of heritability is unique, that is, dependent on the population and environment sampled and the estimation method used. For selection decisions or for prediction of selection response for a particular population and environment, specific heritability estimate obtained from the population concerned should be used. In lieu of reliable specific heritability estimates or if the responses are for different environments, average heritabilities may be substituted. 3. Cytogenetics. Cytological analyses by Maria et al. (1995, 1998a,b) on the karyotypes of E. guineensis and E. oleifera confirmed that both species have 2n = 32 chromosomes and had similar chromosome arm lengths for the three chromosome groups. These factors might explain the crossability of the two species and the partial fertility of their hybrids (Hardon 1986). Genomic in situ hybridization (GISH), in which total genomic DNA is used as a probe for hybridizing to chromosome spreads, has been used to separate the parental chromosomes in oleifera × guineensis hybrids and would be useful in backcross breeding programs. There are two estimates of the oil palm genome size: Jones et al. (1982) estimated 1900 megabase pairs using Fuelgen microdensitometry, while Rival et al. (1997) estimated 3400 using flow cytometry, the discrepancy attributed to the different methods used.

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III. IMPROVEMENT OBJECTIVES A. Yield Potential The oil palm is the highest yielding oil crop (Corley 1985; Robbelen 1990); its highest annual recorded oil yield of 10 t/ha is two to five times that of annual oilseed crops such as soybean and rapeseed. Yield improvement in the oil palm plantations for the past 50 years has been attributed to 70 percent cultivar improvement and 30 percent improvement in agronomic practices (Davidson 1993). Breeding progress in the Deli dura planting materials was about 15 percent per generation. (A breeding generation in oil palm requires eight to ten years; three years to bearing, four to six years of yield measurement, and one year of hybridization.) With the switch-over to the tenera planting material there has been at least 30 percent improvement in oil yield mainly through better oil content from the thicker mesocarp (Hardon et al. 1987). Since then there have been two generations of improved tenera hybrid planting materials and oil yields have improved from 4.9 t/ha to 8.9 t/ha but this could not be attributed entirely to breeding as the materials were planted at different times and locations and perhaps with different agronomic inputs (Lee and Toh 1992). Current planting materials derived from mixed hybrids thus possess high yield potentials. The theoretical yield potential based on crop physiological computations is 17 t/ha oil from 45 t of fresh fruit bunches (Corley 1985). Oil yields exceeding 12 t/ha, fresh fruit bunch yields of 45 t/ha, and oil content in bunch of 35 percent have been reported in experimental plantings, although not necessarily jointly. Nevertheless, they indicate that the prospect of significant quantum increase in yield potential is rather limited, at least in the near term. For larger quantum yield improvement, drastic changes with the crop architectural design (plant form and planting pattern), cultivation, and harvesting methods would be needed. Increase in crop yield potential in other crops has been achieved through the combination of high biomass production and high harvest index, the former through high density planting (Evans and Fischer 1999). With current genotypes and cultivation system (uneven terrain plantings and high dependence on manual operations particularly for harvesting) plant densities of higher than 138 to 160 palms/ha currently practiced would seem inadvisable. However, proposals and efforts are underway to breed for high efficiency palms with more efficient light capture, higher photosynthetic rate, higher leaf expansion ratio, higher leaf area ratio, better conversion efficiency of energy captured to dry matter, reduced respiratory loss, and improved harvest index (Breure and Corley 1983; Squire 1984; Breure 1985, 1986; Henson 1992; Smith 1993).

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Many of these contentions are based on inferences drawn from measurements made on individual palms in mixed stands and on correlations based on small sample sizes which were prone to auto-correlations or statistical dependencies because most of the physiological parameters were derived variables in which total dry matter or yield constituted a major component (Rajanaidu and Zakri 1988; Chang and Rao 1989). Proof of the value of these physiological or ideotype breeding traits (Rasmusson 1991) would have to be obtained from replications of such attributed palms within plots and between plots in experimental plantings. These are as yet not forthcoming. In cereals, higher yield potential has been achieved through higher biomass and/or better harvest index using dwarfing genes (Peng et al. 1999; Reynolds et al. 1999) while increased yield in maize has been attributed to tolerance to high density planting (Tollenaar and Wu 1999). The Dumpy semi-dwarfing gene(s) in oil palm did not contribute to better harvest index (Soh et al. 1981). Despite this, slight increases in harvest index and planting densities are conceivable with new genotypes in the breeding pipeline (Rajanaidu et al. 2000b). While the concept of genetic yield potential is helpful in achieving the physiological limit, the concept of harvestable yield is important for a perennial tree crop where the actual harvesting (cutting) operation (Plates 4.1B, C) is manual with little prospect of a feasible and cost effective mechanized alternative. Harvestable or recoverable yield relates to ease of harvesting, that is, harvesting at optimal fruit ripeness and minimal loss of ripe fruits. Dwarf palms with longer bunch stalks will facilitate harvesting, manual or mechanical (Soh et al. 1994b). Palms with ripe fruits exhibiting a distinct color change will ensure harvesting at optimum ripeness while those with nonabscising fruit habit will minimize loose ripe fruit loss. Such programs have been proposed using breeding or genetic engineering approaches (Osborne et al. 1992; Rao 1998) as these traits are found in less advanced breeding or germplasm materials. B. Adaptability Much of the reported planting material improvement is likely to have been through adaptability to site limitations. This involves moving the site yield potential (Tinker 1984) toward the genetic yield potential by circumventing site limiting factors, for example, soil, moisture, terrain, rather than through improvement in genetic yield potential per se. However, genotype × environment (G×E) interaction has not been generally considered to be a serious factor (Rosenquist 1982; Cochard et al. 1993). The presence or absence of G×E depends on the specific genotypes and specific environments tested (Corley et al. 1993). The misperception that

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G×E is not important could be attributed to the use of the genetically variable commercial sources of planting materials as the genotypes (Chan et al. 1986; Rajanaidu et al. 1986); the use of related progenies in different environments (Cochard et al. 1993); or the use of related progenies in similar environments (Rosenquist 1982; Lee and Rajanaidu 1999). Results of other experiments and analyses (Obisesan and Fatunla 1983; Obisesan and Parimoo 1985; Ong et al. 1986; Corley et al. 1993) have detected the presence of G×E effects using genetically diverse progenies although their contributions (3 to 4%) to the total experimental variance for yield were still small (Rajanaidu et al. 1993; Rafii et al. 2000). Although purported stable progenies have been identified (Ong et al. 1986; Lee et al. 1988; Rafii et al. 2000), their exploitation as cultivars depends on the ability to reproduce these tenera hybrid progenies in large quantities, as only limited hybrid seeds can be produced from a pair of dura and pisifera parents. Most of these authors also did not indicate the characteristics that conferred stability in the progenies or attempt a biological explanation of the G×E effects (Caligari 1993; Lee and Rajanaidu 1999). Corley et al. (1993), using a simple analytical approach found that progenies with few bigger bunches rather than many smaller bunches tended to yield poorly in stressful environments because the abortion of a single large bunch would result in a considerable loss in yield. Plasticity of bunch weight was also found to vary between progenies. In terms of selection, an attractive approach used in animal breeding to handle G×E effects is to treat a trait in different environments as separate but genetically correlated traits (Falconer and Mackay 1996). They can then be incorporated into a selection index to predict selection response for the trait in the second environment as a correlated response to selection for the trait in the first environment (White and Hodge 1989; Yamada 1993; Soh 1999). With the advent of near true hybrid cultivars from inbred parents or cloned parents and clones, G×E effects with respect to location, spacing, fertilizer, and other factors will likely assume increased importance (Lee and Donough 1993; Corley et al. 1995; Soh et al. 1995) and may warrant consideration in the development of cultivars suited for different situations, in line with the trend toward precision farming or precision plantation practices (Chew 1998). C. Oil Quality The oils produced by the oil palm are versatile in their uses in the food and oleochemical industries. Despite this, palm oil has yet to penetrate the cooking and salad oil markets of the temperate countries. Its large,

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saturated fatty acid component (Table 4.1) makes the oil with a melting point of 36°C (Yusoff 2000) solidify at colder temperatures. Because of this and the fact that it is a palm-derived oil, consumers have incorrectly lumped it with coconut oil as a saturated fat unhealthy for human consumption leading to a predisposition to heart disease. This is despite the fact that palm oil contains about 50 percent unsaturated fatty acids and that the saturated component is mainly palmitic acid, which Khosla and Sundram (1996) contended to be neutral in its cholestrolemic behavior. Palm oil also contains antioxidants in its carotene and tocopherol/ tocotrienol contents which appear to have anticarcinogenic properties. It can also be used directly, that is, without hydrogenation, for margarine without the production of the unhealthy trans fatty acids. Nevertheless, end-user and consumer bias has pressured breeders to breed for a more liquid palm oil. The ideal palm oil should have the following fatty acid composition based on the American Heart Association’s (1990) recommended fat intake: total energy requirement derived from dietary fat intake should not exceed 30 percent; one-third from saturates [lauric (C12:0), myristic (C14:0), palmitic (C16:0), stearic (C18:0)], which are hypercholestrolemic; one-third from polyunsaturate [linoleic

Table 4.1. Variability of fatty acid composition and iodine value in oil palm populations. Content (%)

Fatty acid C14:0 (Myristic) C16:0 (Palmitic) C18:0 (Stearic) C18:1 (Oleic) C18:2 (Linoleic)

a

Nigerian Elaeis guineensis

PORIMa E. guineensis

IRHOb E. guineensis

E. oleifera

E. oleifera × E. guineensis

0.3–3.1

0.9–1.5

0.3–1.6

0.1–0.3

0.1–0.5

37.4–46.6

41.8–46.8

34.7–50.1

14.4–24.2

22.4–44.7

3.8–14.7

4.2–5.1

3.1–8.8

0.6–2.2

1.4–4.9

33.0–55.9

37.3–40.8

32.0–46.0

55.8–67.0

36.9–60.1

5.4–15.8

9.1–11.0

10.0–16.0

6.0–22.5

8.3–16.8

43.8–69.8

51.0–55.3

Iodine value –

67.4–91.9

–

Palm Oil Research Institute of Malaysia. Institut de Recherches pour les Huiles et Oleagineux. Source: Arasu 1985; Rajanaidu 1990a; Rajanaidu et al. 2000b.

b

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(C18:2)], which is hypocholestrolemic; and one-third from monounsaturate [oleic (C18:1)], which is neutral cholestrolemic. In this respect, palm oil is excessive in saturates and deficient in polyunsaturates. Advanced oil palm breeding populations have low genetic variability for unsaturated fatty acid contents, generally expressed as iodine value. The situation is better in the West African semi-wild prospected materials, but the highest values can be found in the American oil palm, E. oleifera, collections. Palm Oil Research Institute (PORIM), currently known as Malaysian Palm Oil Board (MPOB), recommended elevating the iodine value to about 70 by breeding to put palm oil on a competitive basis with olive oil (Task Force 1985). To do this without sacrificing the current progress in yield and agronomic attributes using selections from the collected E. guineensis materials would involve four to five generations of backcross breeding to the advanced breeding materials. With the E. oleifera materials, more backcross generations are envisaged because of interspecific hybrid infertility (Hardon 1986, Sharma 2000a). The same would apply to breeding for higher carotene and tocopherol/tocotrienol contents. Reservations to this approach have been expressed (Hardon and Corley 2000). Because of the long lag breeding time (20 to 30 years), by the time the cultivar is developed, the market may have changed and the perceived premiums disappeared. Genetic and agronomic manipulations are more expedient with annual crops, which can respond readily to market changes. An oil quality genetic improvement program also requires a large separate effort, which may be at the expense of the main yield improvement program through dilution of effort and selection pressure. It may be more expedient to concentrate on yield improvement and achieve higher quality component production from more oil produced and using chemical and bioprocessing technological advances. D. Stress Tolerance Being a profitable and easily grown crop, oil palm plantings have expanded into suboptimal and marginal areas (e.g., dry sandy areas, podsols, peat, highlands). In these areas biotic (disease and pest) and abiotic (water, temperature, nutrient) stresses, which impede or reduce production, are likely to be encountered. 1. Biotic. Basal stem rot caused by the basidiomycete fungus Ganoderma boninense is the only disease warranting consideration in resistance breeding in the Far East. It used to be a malady of older palms planted on former coconut or oil palm areas with high water tables, but

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reports of its occurrence on younger plantings in inland areas have become more frequent (Ariffin 2000). Progress in resistance breeding had been hampered by the lack of an efficient screening technique (Khairudin et al. 1993; Ariffin et al. 1995). No resistant or tolerant genotype has been found to date with the preliminary screening technique developed (Ariffin 2000). In a recent paper (De Franqueville et al. 2001) parental, progeny, and clonal differences in resistance/susceptibility were demonstrated in palms planted on Ganoderma infested soil. Ganoderma disease control is the subject of an international cooperative effort coordinated by CAB International (1998) in which resistance control is an important objective. Because a reasonably efficient nursery screening technique is available, tolerance to Fusarium (Fusarium oxysporum f. sp. elaedis) vascular wilt forms an integral part of the breeding programs in West Africa (De Franqueville and Renard 1990). Lethal bud rot debilitates oil palm in Latin America. Any proposal to breed for tolerance to this disease would be premature as the pathogenic cause of the disease is still in contention (Ariffin 2000). Crown disease, a physiological affliction of young two-to-three-yearold oil palms in the Far East, causes bending and twisting of the young fronds, which can result in loss in early yields when severe and prolonged. The Deli dura material appears to be more susceptible. The disorder is caused by a recessive gene, the expression of which is masked by an epistatic gene conferring incomplete penetrance (Blaak 1970). The disorder can be bred out by discarding families with any susceptible progeny. 2. Abiotic. Oil palms in West Africa experience reasonably long periods of drought and hence West African commercial planting materials tend to be more drought tolerant because they have been bred under such conditions. There are also drought tolerance breeding programs (Houssou et al. 1987; Okwuagwu and Ataga 1999). The semi-wild collections obtained by MPOB from the drier regions of Nigeria would include drought tolerant genotypes. Palms introduced from the Bamenda Highlands of Cameroun (Blaak 1967) are presumed to possess genes for cold tolerance although their likely precocious flowering behavior under lowland conditions have been touted. Manifestations of Mg deficiency occur on oil palms planted in sandy areas in some parts of Papua New Guinea and Indonesia. Tolerance to Mg deficiency is an objective of the breeding program in Papua New Guinea (Breure et al. 1986). Palms planted on deep peat tend to lodge. Dwarf or smaller palms would circumvent this problem. Peat plantings

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are also prone to micronutrient deficiencies, particularly Cu, Zn, and B. As these deficiencies can be corrected easily with micronutrient applications, a breeding approach is unnecessary.

IV. BREEDING TECHNIQUES A. Floral Biology The oil palm is cross-pollinated and monecious bearing a staminate or pistillate inflorescence in each subtending leaf or frond (Plates 4.1D, 4.1E). Inflorescences, which are compound spikes, begin to appear on young palms when they are about 27 months old from seed sowing, which is also the same length of time from inflorescence initiation to its emergence from the sheath in mature palms (Henson 1998). Sex differentiation occurs at the fourteenth month from inflorescence initiation. Stress conditions at this time will induce staminate inflorescence while at the twenty-third month will result in inflorescence abortion and nonappearance of inflorescence on the subtending frond. There should be potentially two to three inflorescences produced per month corresponding to the frond production rate. Pollen viability and stigma receptivity last about five to six days and three to four days in the field, respectively. The main pollination agent is the pollinating weevil, Elaedobius kamerunicus, which occurs naturally in West Africa and Latin America but was only introduced to the Far East in the early 1980s. Controlled pollination for breeding and commercial hybrid seed production involves the following protocol: 1. Pistillate and staminate inflorescences are isolated seven to ten days before receptivity/anthesis using terelene/paper/canvas bags with clear plastic windows. 2. Pollen can be stored as oven-dried pollen in the freezer for about 6 months or as freeze-dried pollen in vacuum-sealed ampoules in the freezer for about 24 months. 3. Controlled pollination is effected by puffing a 1 pollen: 10 to 20 talc mixture through a perforation in the plastic window (Plate 4.1F). 4. The bag is removed after about one month and the fruits left to mature and ripen in about five months. 5. The controlled-pollinated seed from the depulped ripe fruit needs to undergo 40 to 60 days heat treatment at 37° to 38°C and 17 to 19 percent moisture to break its dormancy before it can germinate when remoistened to 21 to 22 percent (Plate 4.2A). These are the requirements for germinating dura seeds. For tenera seeds, the corresponding moisture requirements are 20 to 21 percent and 27 to

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28 percent, respectively. Germination of the shell-less pisifera and thin-shelled tenera seeds is erratic (Arasu 1970) and in vitro (embryo rescue) germination is sometimes needed. B. Breeding Plans and Selection Methods The oil palm being a cross-pollinated crop borrows a number of breeding methodologies developed in maize breeding such as recurrent selection and topcross testing, but being a perennial tree crop also shares many similarities with animal breeding in the selection techniques such as the importance of sire or pisifera testing, simultaneous multiple trait or index selection, and the close temporal and genetic correspondence of commercial hybrid production with each cycle of breeding. 1. Recurrent Selection. Major oil palm breeding programs adopt one of two basic schemes; the modified recurrent selection scheme and the modified reciprocal recurrent selection scheme. Modified Recurrent Selection Scheme. This scheme is practiced by most programs in the Far East (Fig. 4.2). It evolved from the initial use of the

Dura (D)×D Trials

Cycle 0

Tenera (T)×T or T×P(Pisifera) Trials

Dura Selection Based on individual, family, and progenytest performance

Pisifera Selection

D×P Progeny-test

D×P Seed Production

Based on tenera sib, and progenytest performance Tenera Selection

New Introduction

D×D Trials Fig. 4.2.

Cycle 1

Modified recurrent selection scheme.

Based on individual, family and progeny-test performance

T×T or T×P Trials

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Deli dura as the commercial planting material to its subsequent use as a maternal parent in commercial tenera hybrid seed production. Pisiferas, introduced or bred, and being female sterile, are initially selected based on their tenera-sib performance in the tenera × tenera/ pisifera cross or based on proven lineage. They are then progeny-tested (top-crossed) with a sample of phenotypically selected Deli dura mother palms. Proven pisiferas and the phenotypically selected duras are then used in the commercial mixed tenera hybrid seed production. Parents used for further breeding in dura × dura and tenera × tenera/pisifera crosses are also phenotypically selected although progeny-tested parents are also sometimes included. As parent selection often involves family with individual palm selection, this scheme is sometimes referred to as the family and individual palm selection scheme (Rosenquist 1990). But as selected parents are interbred in each cycle, Soh (1990) preferred to refer to it as a form of recurrent selection (Allard 1960). This scheme emphasizes and exploits general combining ability (GCA) effects. The main advantages of this scheme are that more recombinant crosses can be made and tested and in saving time, space, and effort the need of extensive progeny-testings is avoided. The main disadvantage is that the parents chosen for further breeding and the dura mother palms for hybrid seed production usually have not been hybrid progeny-tested. The key assumption that the additive or GCA effects expressed within population crosses (determined by family and individual means or combining ability analyses), will be reflected in the interpopulation hybrid crosses, may be untenable (Soh 1987b, 1999; Soh and Hor 2000). The scheme may also lead to undue prolonged reliance on the Deli as the source of dura mother palms. Modified Reciprocal Recurrent Selection. This scheme (Fig. 4.3) practiced mainly in West Africa, for example, Ivory Coast, Nigeria (Sparnaaij 1969; Meunier and Gascon 1972), and more recently in Indonesia (Lubis et al. 1990) is adapted from the reciprocal recurrent selection method developed in maize breeding (Comstock et al. 1949). It exploits both GCA and specific combining ability (SCA) effects. The choice of this scheme presumably arose from the observations and results that crosses of the Deli duras with African teneras or pisiferas tended to give heterotic yields. This attractive scheme has three apparent advantages: (1) the selfed parents selected for commercial hybrid production and for further breeding are both based on progeny-tests (from the dura and tenera grandparents) of the prospective commercial interpopulation hybrids; (2) the scheme has two distinct phases. The “within hybrid improvement phase” allows

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Dura (D)×D Trials

D×D Selfs/Sibs

Dura Selection Based on progenytest performance

Cycle 0

D×T Progeny-test

D×P(Pisifera) Seed Production

183

Tenera (T)×T Trials

T×T Selfs/Sibs

Tenera/Pisifera Selection Based on progenytest performance

Cycle 1 D×D Selfs/Sibs

D×T Progeny-test

T×T Selfs/Sibs

Fig. 4.3. Modified reciprocal recurrent selection scheme. To sustain genetic variability for longer term improvement, a recombinant phase involving outbred parents is run in parallel in a similar manner.

shorter term commercial exploitation of the best test-cross and its improvement by recurrent selection within the selfs/sibs of the selected parents. The “recombinant phase” involving outcrosses, which is run in parallel similarly, allows accumulation of favorable alleles (additive and nonadditive) and maintenance of genetic variability for sustained longer term improvement; (3) the commercial hybrid can be reproduced using the duras and pisiferas from the selfs/sibs of the parents (which have been planted concurrently as the progeny-tests) once the interpopulation hybrid test results are known (Jacquermard et al. 1981). Its main disadvantage is the large program size requirement. To produce three to four million hybrid seeds from the reproduction of the top 15 percent of the crosses, about 500 crosses and 180 parental selfs have to be planted over 600 ha and evaluated over 15 to 25 years (Soh 1999).

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Breeding Progress. In the reciprocal recurrent selection scheme, the original cross from which subsequent breeding improvements are made can be reproduced to be the standard control cross for measurement of breeding progress achieved. For the first cycle of the reciprocal selection scheme, Gascon et al. (1988) reported a 18 percent oil yield improvement by selecting the top 2.8 percent of the 529 hybrid crosses tested for reproduction as commercial hybrid seeds. From early trial results of the second cycle (within hybrid improvement) hybrids, Nouy et al. (1990) predicted a 10 to 15 percent oil yield increase which was subsequently confirmed (Cochard et al. 1993). Lubis et al. (1990) suggested a 25 percent oil yield increase by selecting the top 8 percent of the 500 hybrids tested in the first cycle of the Indonesian Oil Palm Research Institute’s (IOPRI) reciprocal recurrent selection program. Progeny-test results for the recombinant phase, that is, recombinant parents or wide crosses, are still unavailable. An important consideration for this phase is whether to use the selfs of the recombinant parents for the production of commercial hybrids which would result in higher within hybrid variability or to await another generation of selfs in order to produce more uniform hybrids. In the study of Durand-Gasselin et al. (1999) on 2-, 3-, and 4way hybrid crosses, the increased within cross variability for production traits was minimal but these crosses involved related parents rather than outbred or recombinant parents. Also in this study, as reported earlier by Nouy et al. (1990), breeding progress was achieved mainly through exploitation of GCA effects although SCA effects might have played a small part. It is difficult to estimate the breeding progress made by the modified recurrent selection scheme with confidence due to the lack of common link crosses across experiments and common base reference populations. Nevertheless, some indications can be obtained by piecing together some experimental results reported. Hardon et al. (1987) estimated 15 percent oil yield progress per generation from breeding within the Deli dura but did not translate this in terms of improvement in the corresponding tenera hybrids. Lee et al. (1990) attempted this by comparing the mean performance of the tenera hybrid progenies of third and fourth generation Deli duras when crossed to the same pisiferas but the trials were planted at different periods although at similar localities. A 6.4 percent increase in oil yield was obtained mainly through improved oil content. Similarly, Rajanaidu et al. (1990) reported a 7 percent oil yield difference with tenera hybrid progenies of their first and second generation Elmina Deli duras crossed to similar pisiferas planted in the same trial. Based on the progeny-test results, selecting the best pisifera (top 15%) would give a 12 percent

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improvement (Lee and Yeow 1985; Hardon et al. 1987). Owing to the limited number (less than 10) of pisiferas usually available and tested, such stringent selection would seriously restrict commercial hybrid seed production. The top 30 to 50 percent selection would have been more likely in which case an improvement of around 5 percent would be achievable (Soh 1986). By combining the expected progress made in both the dura and pisifera parents, a gross estimate of the 10 to 15 percent breeding progress made using the modified recurrent selection scheme would not be unreasonable. As evident, it is difficult if not futile to compare objectively the relative efficiencies of the two selection schemes. The choice depends very much on one’s bias, objectives (i.e., short or long term gains), and stage of the breeding program. We prefer a combined approach, modified recurrent selection to develop relatively unimproved populations to reduce the final size of recombinant families to go into the modified reciprocal recurrent selection program. Most mature oil palm breeding programs in the Far East are beginning to adopt the latter technique. 2. Other Breeding Methods Backcross Breeding. This method of breeding has been used in introgression work to improve a particular genotype or population. The Dumpy trait has been introgressed into the AVROS population to reduce height (Soh et al. 1981; Lee and Toh 1992). The backcross approach is also used to introgress the better oil quality traits from the Nigerian guineensis and the American oleifera materials and to restore the yielding ability and fertility of the recurrent advanced breeding parents respectively (Sterling et al. 1988; Le Guen et al. 1991; Soh et al. 1999; Sharma 2000a). Recombinant Inbred Lines. The development of recombinant inbred lines has been proposed and attempted in oil palm (Lawrence 1983; Pooni et al. 1989). This would involve the production of dura inbred line cultivars (as the tenera is a heterozygote and the pisifera homozygote is female sterile) with oil yields superior to the best thin-shelled tenera hybrid, which is unlikely. Other disadvantages would be the possible loss of desirable genes linked to yield in the infertile gametes/zygotes in the inbreeding process, and longer time to produce a commercial variety (Soh 1987b). However, the recombinant inbred line approach combined with single seed descent may be considered for development of inbred parents for hybrid production. This will put oil palm hybrid seed production on similar footing as hybrid maize production in the

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generation of hybrid combinations for testing. The availability of inbred lines particularly isogenic lines will be a boon to basic studies on oil palm genetics (quantitative, molecular, physiological, biochemical) and a host of other fundamental studies and applications. Breeding for Clonal Propagation. This would involve treating the oil palm as a clonally propagated crop such as potato, cassava, rubber, or sugar cane. The breeding approach for a clonally propagated crop starts off by generating a segregating hybrid population with high mean and progeny variability. Single plant selection is followed by cycles of increasing scales of testings of the clonal offsprings, in later stages involving multilocation trials (Simmonds 1979; Tan 1987; Brown et al. 1988; Kawano et al. 1998). Currently, we do not foresee this happening in oil palm as the feasibility of repeated cycles of recloning ramets (clonal offspring) is still uncertain (Soh 1998). There will be further discussions on this subject in a later section. 3. Index and BLUP Selection. In oil palm breeding, as in animal breeding, the choice of breeding parents is crucial. First, the number of parents that can be accommodated in the program is limited because of its perennial tree crop nature. Second, the hybrid production cycle is closely linked to the breeding cycle. Knowledge of the breeding values of the parents and multiple trait selection methods will help in making the choice of the parents. The selection index and best linear unbiased prediction (BLUP) methods derived from application of linear statistical models which are usually associated with animal breeding (Henderson 1984) are now more commonly used in plant breeding (White and Hodge 1989) including oil palm breeding. Selection Index. Multi-trait selection index and index selection incorporating plot and family information were found to be useful in choosing candidate palms (ortets) for cloning (Soh and Chow 1989, 1993; Baudouin et al. 1994; Soh et al. 1994a). The same approach can be used in selecting parents for breeding. BLUP. The combining ability approach has been the usual approach adopted in selecting parents in oil palm breeding (Breure and Konimor 1992; Soh et al. 1999). Many oil palm breeding trials, however, tend to be highly unbalanced in mating as well as experimental designs due to the constraints of the crop. The combining ability approach does not readily allow objective integration and comparison of genetic information from across unbalanced experiments. In this regard, the BLUP

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method developed for cattle breeding, which has similar constraints, confers the following advantages (Soh 1999): (1) it can handle highly unbalanced mating and experimental designs; (2) it can utilize information from a number of experiments even without standard check varieties; (3) it can utilize information from other relatives or sources; (4) it allows a more directed approach in breeding, breeding toward an ideal or optimum genotype and breeding toward a target environment; (5) it allows refinement as better genetic information becomes available; (6) it can handle multiple traits simultaneously. Essentially, the BLUP approach removes environment and replicate effects as “nuisance” fixed effects in order to estimate the random genetic effects, for example, breeding values or additive genotype. Soh (1994) pioneered the use of BLUP in oil palm breeding by estimating the breeding values of pisifera parents from a very unbalanced set of tenera hybrid trials and predicted increased usage of this technique. BLUP has now been used not only to estimate breeding values of parents but also in predicting hybrid performances in oil palm (Purba et al. 2000a) as has been done in maize (Bernado 1994, 1995, 1996) and sugarcane (Chang and Milligan 1992a,b).

C. In Vitro Methods 1. Commercial clonal propagation. Commercial oil palm planting materials being a mixture of hybrids from nonfully inbred parents have considerable between palm genetic variability arising from between and within family genetic variabilities depending on the relatedness and inbred status among dura and pisifera parents. Individuals could vary by more than 30 percent of the mean yield of the hybrids (Hardon et al. 1987; Meunier et al. 1988). Although the differences might not be entirely genetic, nevertheless they provided the rationale and impetus in the 1960s to develop the in vitro propagation of the oil palm, which has no natural means of vegetative propagation. The oil palm, being a monocot and a perennial tree, was considered a recalcitrant species for in vitro propagation. However, with concerted research efforts the first in vitro oil palms were achieved by the mid 1970s (Jones 1974; Rabechault and Martin 1976; Lioret and Ollagnier 1981). Rohani et al. (2000) and Krikorian and Kann (1986) have reviewed the details and historical development of the oil palm tissue culture techniques. However, large-scale commercial propagation and plantings of proven tenera clones have yet to take off despite more than two decades of research and development (R&D) work involving the setting up of more

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than 20 tissue culture laboratories worldwide with expenditures running into tens of millions in clonal propagation for trial and pilot commercial test plantings. For successful large-scale commercial propagation of proven tenera clones, a number of critical issues needs to be resolved including somaclonal variation, cloning efficiency, ortet (parent palm of clone) selection efficiency, feasibility of recloning and the liquid suspension system, clonal field evaluation requirements, and expected genetic progress. Somaclonal Variation. The first issue is the specter of somaclonal variation manifested as abnormal flower and fruit development on otherwise apparently normal palms. In the pistillate flower the vestigial androecium develops into supernumery carpels forming a “mantle” over the developing fruit (Plate 4.2B) similar to the rare genetic mantled or “poissoni” fruit variant found in nature (Hartley 1988). The gynoecium may be fertilized or develop parthenocarpically. The feminized (carpelloid) flowers of the abnormal staminate inflorescence do not bear pollen. Severely mantled fruit bunches tend to be parthenocarpic leading to bunch abortion and palm sterility (Corley et al. 1986; Paranjothy et al. 1990). Palms bearing slightly to moderately mantled bunches can recover to normal bunch bearing (Ho and Tan 1990; Durand-Gasselin et al. 1995; Duval et al. 1997). Susceptibility to the abnormality varies between and within clones and the risk appears to increase with extended culture; the earliest batches of ramets (clonal plants) tended to be mantle free (Paranjothy et al. 1995b). The mantled fruit phenomenon was experienced by all laboratories. As some clones can be severely mantled with susceptibility apparent only at the fruiting stage (two to three years after field planting), confidence in mass propagation was seriously eroded. Speculations were made on the physiological, epigenetic (gene expression) and genetic bases, including the involvement of cytoplasmic or organelle DNA (e.g., mitochondria), of the mantle somaclonal variant (Corley et al. 1986; Soh 1987a; Jones 1995; Paranjothy et al. 1995b). Mantling was found to be apparently sexually transmissible (Rao and Donough 1990) but this still did not rule out the epigenetic basis. Use of fast growing instead of nodular callus (Duval et al. 1988; Marmey et al. 1991) and/or phytohormones such as cytokinins (Agamuthu and Ho 1992; Besse et al. 1992; Jones et al. 1995, Ng et al. 1995) in the proliferation stage was suggested as the culprit, but this soon proved untenable as laboratories that did not use either still produced mantled ramets. As empirical approaches to research the causal factors through protocol manipulations appear cumbersome, if not imprecise, the alter-

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native molecular approach was initiated. Research on the molecular basis of the mantle somaclonal variation using molecular marker techniques, for example, protein, isozymes, restricted fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), differential display, has been contracted out to the laboratories of six international centers of excellence by MPOB besides in-house efforts (Chowdhury 1995; Paranjothy et al. 1995a; Cheah et al. 1999; Sharifah et al. 1999). The CIRAD (Centre de Cooperation Internationale en Recherché Agronomique pour le Development) group in Montpellier, France, has also been involved in similar research (Marmey et al. 1991; Rival et al. 1997, 1998a,b; Jagligot et al. 2000; Morcillo et al. 2000). Preliminary findings implicated an epigenetic basis (also possibly a genetic basis), mediated through methylation perhaps involving retrotransposons, affecting the expression of homeotic (flowering) genes (Rival et al. 1997; Shah et al. 1999; Rajinder et al. 2001). Methylation has been implicated in somaclonal variation of other crops (Phillips et al. 1994; Tsaftaris and Polidorus 2000). The development of a diagnostic tool is the ultimate objective although it looks elusive at the moment as putative molecular markers isolated so far appeared to have specific rather than general applicability on clone or culture basis. Meanwhile, an empirical approach would be to look for genotypes that are amenable to cloning and resistant to somaclonal variation and this could be subsequently followed up with the development of molecular markers or genes for these traits. Many laboratories are now reporting significantly reduced mantling rates in packages of clones planted (Table 4.2) and have attributed them to use of more suitable or improved protocols reinforced by stringent culture selection (Maheran and Abu Zarin 1999; Ho 2000, Soh et al. 2001). Similar experiences have been reported in banana micropropagation (Krikorian 1994). Cloning Efficiency. The second issue is the inefficiency of the cloning process. Although callogenesis rates (100% palm-wise, 17% explantwise), and percent embryogenic palms (80%) appear not to be process limiting, embryogenesis rates on callus cultures varied from 0.2 to 36 percent, averaging 4 percent per ortet (Table 4.3). With about 2000 leaf explants per ortet usually sampled, this translates to 12 leaf explants with embryos obtained per ortet on average with some having one or none and others having more than 120. More importantly is the capability of active proliferation and thus mass propagation of the embryos obtained. Generally about half are capable (Wooi 1995; Soh et al. 2001), while the remaining will either persist in producing shoots at the detriment

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190 Table 4.2.

A. SOH, G. WONG, T. HOR, C. TAN, AND P. CHEW Mantling incidence rates in clones. Mantled Ramets in Clones

Year Cloned

Year Planted

1983/ 1984 1987

1986– 1991 1989– 1992 1992– 1995 1996 1996– 1997 1997– 1998 1998

1987/ 1988 1992 1993 1994 1995

No. of Clones

Clones Mantleda (%)

No. Rametsb Planted

Range (%)

Mean (%)

Ortetc

12

67

7,427

0–35.7

6.2

Embryos (RC)d Seedlings (RC) Ortet Ortet

17

12

6,840

0–72.0

1.1

63

75

80,632

0–35.4

7.6

3 20

67 80

451 9,378

0–3.5 0–9.1

2.0 2.7

Ortet

18

50

11,865

0–3.5

1.4

Ortet

22

32

3,412

0–6.0

1.0

Explant Source

a

A clone with any of its ramets expressing the mantle somaclonal variation is considered mantled. b Ramets = clonal plants or members of a clone. c Ortet = parent tree of clone d Repeated cross of superior family. Source: Soh et al. 2001

of embryo proliferation or fail to develop further. In the final analysis, only about 18 percent of the ortets cultured can provide cultures amenable to mass propagation. Prospects of further improvement in efficiency appear limited although it may be possible by coaxing the cultures through more drastic protocol manipulations. Most laboratories are wary to deviate significantly from their established protocols for fear of Table 4.3.

Oil palm cloning and recloning efficiencies, gelled-culture system.

Basis

Callogenesis % (range)

Embryogenesis % (range)

Ortet (cloning)

Palm Explant

100 17 (12–27)

80 (43–100) 4 (0.2–36)

Ramet (recloning)

Palm Explant

100 16 (11–21)

89 (77–100) 12 (0.2–52)

Explant Sourcea

a

Ortets = parent palms of clones; ramets = clonal plants Source: Soh et al. 2001

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increased risk of somaclonal variation and the additional tedious protocol field-tests encumbered. Ortet Selection Efficiency. Selecting superior palms or ortets for cloning is an inefficient process. In the breeding of many crops (Allard 1960; Simmonds 1979) and in clonally propagated crops (e.g., potato, cassava, rubber) (Tan 1987; Brown et al. 1988; Kawano et al. 1998), single plant selection especially for yield from a mixed seedling-derived population has been inefficient if not futile. Soh (1986) using data from trials of an advanced tenera hybrid (i.e., Deli × AVROS) population estimated that selecting the top 5 percent of the palms for cloning would give clones yielding 10 to 15 percent more than the mean of the hybrids, and that clones yielding more than 30 percent were unlikely because of the low heritability and genetic variability. Also, the better clones could only be identified after clonal testing. If the clones were to be compared to the improved hybrids, which would be available by the time the clonal test results are available, the advantage would be much reduced. Corley et al. (1999) was partial toward the higher estimate citing the higher heritability values obtained by Baudouin and Durand-Gasselin (1991) which were subsequently found, when the palms were older, to correspond to Soh’s estimates (Cochard et al. 1999). The elucidation of the low heritability for oil yield in these advanced tenera hybrid populations implied that the same experiences in other crops mentioned earlier would apply in oil palm (Soh 1998). This has been further supported by the clonal trial results of Donough and Lee (1995), Cochard et al. (1999), and Soh et al. (2001). In our first trial (Table 4.4), although 4 out of 12 clones derived mainly from highly selected ortets of Deli × AVROS lineage exceeded the Deli × AVROS hybrid control in mean oil yield (1992–2000), only two clones exceeded by more than 10 percent (111% and 118%). In our second trial (Table 4.5), which was a trial of clones derived from the embryos of a reproduced superior Deli × (Yanagambi × AVROS) hybrid, that is, each clone represented a random ortet in the superior cross, the mean oil yield (1994–2000) was exceeded only substantially by 3 clones out of 10 (108%, 108%, 113%). Also from the second trial, for oil yield, the genetic variability between palms within the cross, estimated from the between clone variance (genetic coefficient of variation, CVg = 6%), was much smaller than the environmental variability between palms (CVe = 20%). This implied that the genetic differences between palms would have been masked by the environmental effects. These results reaffirmed the inefficiency of ortet (individual palm) selection based on its phenotype and the dependence of good clonal tests to pick out the infrequent outstanding clones especially

192

23.1

20.2 2.4

27.1

27.8 2.6

88

100

92 89 103 77 79 91 98 94 80 75 83 85

30.6 3.3

31.4

33.3 30.5 36.0 26.4 29.2 30.4 33.3 32.4 33.7 27.8 30.9 23.8

98

100

106 97 115 84 93 97 106 103 107 89 98 76

b

Ortet clones = clones derived from selected adult palms. O/B = oil in fresh bunch by weight. c Oil yield = FFB × O/B, mean for period. d D×P (AVROS) = a dura × pisifera hybrid of superior lineage. Source: Soh et al. 2001.

a

21.2 20.6 23.9 17.9 18.3 21.0 22.6 21.8 18.6 17.4 19.2 19.6

26.1 26.5 27.8 25.5 28.0 29.4 31.6 28.4 25.5 27.9 29.4 27.6

(% of (t/ha/yr) control)

1997–2000

24.7 2.2

26.8

26.5 24.9 29.2 21.6 23.0 25.1 27.3 26.4 25.2 22.0 24.3 21.4

(t/ha/yr)

92

100

99 93 109 81 86 94 102 99 94 82 91 80

5.6 1.0

6.3

5.5 5.5 6.6 4.6 5.1 6.2 7.1 6.2 4.7 4.9 5.6 5.4

89

100

87 87 105 73 81 98 113 98 75 78 89 86

8.5 0.9

8.5

8.7 8.1 10.0 6.7 8.2 8.9 10.5 9.2 8.6 7.8 9.1 6.6

100

100

102 95 118 79 96 105 124 108 101 92 107 78

(% of control)

1997–2000

(% of control) (t/ha/yr)

1992–1996

(% of control) (t/ha/yr)

1992–2000

Oil yieldc

6.9 0.6

7.3

6.9 6.6 8.1 5.5 6.4 7.4 8.6 7.5 6.4 6.1 7.1 5.9

95

100

95 90 111 75 88 101 118 103 88 84 97 81

(% of (t/ha/yr) control)

1992–2000

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1/3 2/7 3/12 5/15 5/17 5/14C 6/22 7/26 9/28 10/39 12/84 11/283 D×P (AVROS)d control Mean of clones LSD .05

1992–1996 O/Bb 1992– 1998 (% of (%) (t/ha/yr) control)

Fresh fruit bunch (FFB) yield

Trial BCT4-89—yield performance of ortet clones.a

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Clone

Table 4.4.

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b

O/B = oil in fresh bunch by weight. Oil yield = FFB × O/B, mean for period. Source: Soh et al. 2001

a

E283 E284 E308 E312 E348 E352 E353 E379 E380 E383 Mean LSD .05

105 98 88 99 102 105 101 104 94 104 100

27.9 27.7 27.4 27.6 25.4 28.7 29.0 30.8 28.0 23.5 27.6 1.6

Clone 31.0 25.8 27.1 24.3 29.5 32.0 26.2 26.0 29.3 31.2 28.2 3.3

(t/ha/yr) 110 91 96 86 105 113 93 92 104 111 100

(% of mean)

1997–2000

24.0 21.1 20.7 20.5 23.1 24.5 21.6 21.8 22.2 24.1 22.4 2.7

(t/ha/yr) 107 94 92 92 103 109 96 97 99 108 100

(% of mean)

1994–2000

5.2 4.8 4.3 4.8 4.6 5.3 5.2 5.7 4.6 4.3 4.9 1.0

(t/ha/yr) 108 98 88 98 94 108 106 116 94 88 100

(% of mean)

1994–1996

8.6 7.2 7.4 6.7 7.5 9.2 7.6 8.0 8.2 7.3 7.8 0.9

(t/ha/yr)

110 92 95 86 96 118 97 103 105 94 100

(% of mean)

1997–2000

Oil yieldb

6.7 5.8 5.7 5.7 5.9 7.0 6.3 6.7 6.2 5.7 6.2 0.7

(t/ha/yr)

108 94 92 92 95 113 102 108 100 92 100

(% of mean)

1994–2000

2:54 PM

18.5 17.3 15.5 17.5 18.0 18.5 17.9 18.4 16.6 18.4 17.7 3.0

1994–1996 O/Ba 1994– 1999 (% of (%) (t/ha/yr) mean)

Fresh fruit bunch (FFB) yield

Trial BCT9-91—yield performance of embryo-derived clones of a reproduced superior dura × pisifera (Yanagambi-AVROS) cross.

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Table 4.5.

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when clones tended to exhibit G×E effects (Lee and Donough 1993; Corley et al. 1995; Soh et al. 1995). Alternative Approaches in Clonal Propagation. A number of analyses incorporating secondary trait, plot, and family information in selection indexes had been carried out to improve ortet selection efficiency. Family information was found to be most important (Soh and Chow 1993; Baudouin et al. 1994, Soh et al. 1994a), lending support to two other alternative approaches to oil palm cultivar development using clones proposed earlier by Soh (1986, 1987a). Instead of cloning elite tenera palms which appeared inefficient if not elusive, we could clone a sample of the reproduced seedlings/palms of the best proven cross, or clone one or both parents of the best proven cross which could then be used to produce semiclonal (one clonal parent and one sexual parent) or biclonal (both clonal parents) hybrid seeds. The rationale for the two other alternative approaches were: 1. Both approaches would capture the best family oil yield performance, which would be about 10 to 15 percent above the mean of the families, as the best family would have been selected from statistically designed trials with replicated plots as compared to selecting individual palms in the original elite tenera palm cloning approach. 2. The cloning best family approach would provide a larger clone and explant base to circumvent the ortet cloning inefficiency. In the case of seed embryos and seedlings, the early and the relative ease in cloning would be advantageous. In the clonal hybrid seed approach, risk of clonal abnormality would be minimized as fewer ramets would be needed to serve as clonal parents and could be obtained from the early cultures that are usually less susceptible to mantling. Also deleterious recessive mutations could be masked or self-selected out in the sexual seed production process. Commercial-scale plantings from cloning a sample of the best family approach or from mass selected ortets are increasing because of improved confidence in achieving low mantling rates with the refined laboratory protocols and good yields achievable (Khaw and Ng 1997; Maheran and Abu Zarin 1999; Wong et al. 1997, 1999a). Trials of biclonal and semi-clonal hybrids are giving encouraging results in terms of negligible mantling and good potential yield (Sharma 2000b; Veerappan et al. 2000; C. W. Chin pers. comm. 2001) and commercial clonal hybrid seeds should be available soon. Commercial production of proven elite clones (Plates 4.2C, D), the “holy grail” of oil palm clonal propagation effort, however, has yet to be

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achieved. The two alternative approaches can only recapture the best family not the best individual performance. Second, with the still inefficient cloning technique, to achieve an annual ramet production capacity of half a million, 100 ortets per year would have to be cloned. The availability of sufficient elite ortets would be limiting unless a program to create a constantly available pool of elite ortets from the reproductions of the best families has been planned earlier (Soh 1998). Recloning. As discussed earlier, good clonal tests are required to pick out the outstanding clones. To reproduce proven clones in reasonably large numbers, the feasibility of recloning (from ramets) must be in place as resampling (from ortets) will still be constrained in ramet production by the inefficient technique and limited explant source. The feasibility of clonal reproductions from cryopreserved somatic embryos, the alternative approach, has yet to be proven (Engelmann and Duval 1986; Paranjothy et al. 1986; Dumet et al. 1993, 1994). Initial experiences with recloning were discouraging because of the higher susceptibility to mantling encountered (T. Durand-Gasselin pers. comm. 1999; Wong et al. 1999a), although it appeared to be more amenable giving higher embryogenesis rates (Table 4.3). Subsequent reclonings produced lower mantling rates (Table 4.6). Our latest results from 24 reclones averaged 2 percent mantling with only two clones having relatively higher mantling rates (about 10 percent). A possible explanation of the decreasing mantling susceptibility with the later reclones could be the “wearing-off” of the carry-over epigenetic effects in the older ramets sampled. Recloning now appears feasible. Liquid Suspension Culture. With the feasibility to reclone, the prospect of mass propagation has improved. However, the conventional gelledculture protocol involving proliferating polyembryogenic cultures with asynchronous development on which recloning has been based is still very inefficient in terms of ramet production rates and labor and space utilization (Plate 4.2E). It also offers limited scope for improvement and automation, thus constraining the level of mass propagation. The latest development in the liquid suspension culture technique may solve this problem (De Touche et al. 1991; Duval et al. 1995; Teixeira et al. 1995; Wong et al. 1999b). High efficiency has been achieved in our liquid culture system. About 80 to 90 percent of the embryogenic palm (ortet, ramet) cultures and 60 to 90 percent of their embryogenic callus cultures proliferated in the liquid system (Table 4.7) and most importantly, at much higher rates (six to seven times per monthly subculture) than in the gelled system (two times per bimonthly subculture). High success rates have also been achieved at shoot regeneration of the embryos and

196

1993–1995 1994–1997 1995–1997 1997 1997–1998

3 3 5 4 10

6 8 8 4 24

100 100 100 50 80

Reclonesb of Primary Clones Mantledc (%)

b

Ramets = clonal plants. Reclones = clones obtained from cloning ramets. c Clones with any of its secondary ramets (from recloning) mantled. d Ramets with any of its secondary ramets (from recloning) mantled. Source: Soh et al. 2001.

a

1989 1991 1992 1993 1994

Year Recloned

Year Planted

No. Primary Clones Recloned 100 100 75 50 67

Reclones of Primary Ramets Mantledd (%) 35,330 7,853 1,016 478 2,779

No. of Secondary Ramets Planted

Mean (%) 2.9–14.1 11.3 2.3–9.2 4.9 0–5.2 3.2 0–3.6 1.2 0–12.7 2.1

Range (%)

Mantled Ramets in Reclones

2:54 PM

No. Primary Rametsa Recloned

Mantling incidence rates in reclones.

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Table 4.6.

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Table 4.7. Oil palm cloning efficiencies, liquid suspension system. Cultures developed on gelled medium and proliferated in liquid medium. Shoots converted on gelled medium.

Basis

Embryogenic Callus Proliferation (%)

Shoot Conversion from Embryo (%)

Ortet (cloning)

Embryogenic palms Embryogenic callus

83 63

94 95

Ramet (recloning)

Embryogenic palms Embryogenic callus

93 95

100 100

Explant Sourcea

a

Ortets = parent palms of clones; ramets = clonal plants. Source: Soh et al. 2001

subsequent root induction on gelled medium (Plate 4.2F). Synchronized culture development is the hallmark of the liquid system and this has been largely achieved although further improvement in synchronization of embryo germination is still possible. Predisposition to higher risk of somaclonal variation has been attributed to the liquid culture technique in other crops (George 1993) and also in oil palm (Y. Duval, pers. comm. 1993). The latest results from our liquid-cultured ramet plantings, for both clones and reclones, did not indicate increased risk of mantling. The few mantling incidences observed occurred mostly at much higher levels of ramet production (possible with the liquid culture method) as compared to the experiences with the gelled-culture technique (Tables 4.8, 4.9, 4.10, 4.11). CIRAD has apparently observed similar results on smaller sample test plantings (A. Rival pers. comm. 2001). With the latest advances in the recloning and the liquid suspension culture techniques, the feasibility of commercial mass production of proven elite tenera clones is at hand. Table 4.8.

Mantling incidence rates in clones, liquid suspension system. Normal

Source Clones Reclones Total a

Mantleda

Mixedb

No. clones Planted

No. with Bunches

(No.)

(%)

(No.)

(%)

(No.)

(%)

16 16 32

16 16 32

10 11 21

63 69 66

4 3 7

25 19 22

2 2 4

13 13 13

A clone/reclone with any of its clonal plants mantled is considered mantled. Mixed = clones/reclones having mixture of normal and mantled embryo lines. Source: Soh et al. 2001

b

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198 Table 4.9.

A. SOH, G. WONG, T. HOR, C. TAN, AND P. CHEW Mantling incidence rates in embryo lines, liquid suspension system. Mantleda

Normal Embryo Line

No. Planted

No. with Bunches

(No.)

(%)

(No.)

(%)

Clones Reclones Total

24 23 47

24 23 47

17 17 34

71 74 72

7 6 13

29 26 28

a

A clone/reclone with any of its clonal plants mantled is considered mantled. Source: Soh et al. 2001

Field Evaluation. The oil palm industry is about to enter a new era of large-scale plantings of more genetically uniform materials in the form of clonal hybrids and clones. Nevertheless, there are still issues that need to be addressed and investigated now to ensure successful exploitation of these technological developments and minimize risk of catastrophic setbacks. On the propagation side, there is need to demonstrate that clonal hybrids do not perform poorer than sexual hybrids because of reduction in fitness of the clonal parents (Phillips et al. 1994; Bregitzer and Poulson 1995). This should be repeated with hybrids of recloned parents as there may be need to perpetuate the parents of outstanding hybrids. There is also need to know whether mantled ramet parent or normal ramet parent of mantled clones would transmit the abnormality to their hybrid progenies. It would be useful to demonstrate the heritability of clonability and susceptibility/resistance to clonal abnormality and their repeatability in clones and reclones. It would also be essential to identify the predisposing environmental factors. Solving these issues, perhaps with the Table 4.10.

Mantling incidence rates of ramets, liquid suspension system. Mantling Incidence

Rameta Type Primaryb Secondaryc Total a

Ramets Planted

No. Ramets with Bunches

(No.)

(%)

1536 1758 3294

1249 1394 2643

36 57 93

2.9 4.1 3.5

Ramets = clonal plants. Primary ramets = ramets from cloning. c Secondary ramets = ramets from recloning. Source: Soh et al. 2001 b

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Table 4.11. Ramet production and mantling rates of clones in gelled versus liquid culture systems at various subculture levels. Liquid Suspension System

Gelled-culture System

Clone No. 41 178-46 100-24 154-44 94-195 124-24 180-2 79-24A 121-29A 176-86

Subculture No.a

No. Shoots Obtained

Mantled Ramets (%)

Subculture No.

No. Shoots Obtainableb (million)

Mantled Ramets (%)

11 7, 11, 13 13, 14 9 7 6 6, 8, 9 14, 15 6, 9 9, 14

1120 375 314 290 147 636 169 201 342 1108

73 7 4 0 0 0 0 0 0 0

11 7, 8, 9 3, 15, 16 9, 13 9 1,3 6, 7 13, 17 3, 8 4, 8, 9, 14

3 0.09 0.003 0.02 20.0 0.001 0.01 0.4 6.0 1000.0

93 0 0 9 2 0 0 0 0 0

a

About 40 ramets (clonal plants) were sample-tested per subculture. Extrapolated figures based on proliferation and germination rates of cultures. Source: Soh et al. 2001

b

development and assistance of molecular markers, would facilitate the selection (perhaps also breeding) of amenable and resistant genotypes for cloning. We also need to establish, if feasible, safety limits for mantling risk and reduced fitness for cycles of recloning and for levels of production especially for the liquid culture technique. Molecular markers would have an important role here. Prospects for optimization and automation of the liquid culture protocol are still good and the development of synthetic seed might be more efficient than dealing with regenerated plantlets. On the field testing and planting side, clonal hybrids and clones being genetically more homogeneous and discrete than mixed hybrids, would respond differently to different environmental factors, for example location, planting density, fertilizer requirement, and abiotic (soil, water, mineral) and biotic (pest, disease) stresses, that is, exhibit G×E effects (Corley and Donough 1992; Donough et al. 1996; Smith et al. 1996). Adaptability trials are thus mandatory. For clonal hybrids, the choice of the hybrids should be based on G×E tests of the original sexual parental hybrids. In the case of clones which are more prone to G×E effects and where ortet selection is inefficient, repeat G×E tests may be necessary to pick out the superior clones. In both cases, to reduce the time and effort in G×E testing for a perennial tree crop, the selection of

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a minimum sample of environments that can discriminate the differential responses of the genotypes yet be representative of the major environments where the genotypes are to be grown needs urgent consideration and investigation. This would be especially so for clones to reduce the need for repeat cycle tests and the number of candidate clones in each subsequent cycle. Use of molecular marker and quantitative trait loci (QTL) assisted selection has often been suggested but with a quantitative trait susceptible to G×E effects such as yield, the task would be difficult if not daunting. As is common for successful cultivar development in most crops, there is always the need to produce or reproduce superior families preferably of different genetic origins and in larger numbers to provide the ortets for cloning to feed the subsequent clonal testing and selection program. With successful cloning, large plantings of genetically homogeneous materials of restricted genotypes can be foreseen and this would lead to genetic vulnerability of the crop to epiphytotics, pest outbreaks, inadequate pollination, and moisture and mineral deficiencies. Thus, research is required for the packaging of clones and their field planting arrangements to reduce the genetic vulnerability and to also synchronize the cropping pattern with the future mechanized harvesting, and new crop management systems. Genetic Progress. There have been reports of markedly superior yields of clones over tenera hybrids. Comparisons have sometimes been made against unspecified commercial hybrid materials with the attending errors due to between and within material variabilities and sampling. Even when individual crosses were used, the relative performances of the standard crosses within the respective proprietary commercial hybrid materials were not known. The comparisons of Cochard et al. (1999) were more objectively based having link crosses representing the first generation commercial hybrid material from their reciprocal recurrent selection program. The relative performance of the second generation hybrids with respect to the first generation hybrids was also known. The advantage of cloning improvement over breeding improvement should be based on the comparison between those of the clones and the second generation hybrids as discussed earlier. Soh (1998) suggested use of the reproduced hybrid family of the ortet as the control since the relative performance of the family in the hybrid progeny test, which represents the commercial hybrids, is usually known. This will also enable studies on selection response and selection efficiency to be made. In terms of the relative genetic progress achievable by breeding and cloning, Jacquemard and Durand-Gasselin (1999) based on their reciprocal recurrent selection scheme and their higher heritability estimate

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projected a 10 to 15 percent oil yield advantage of clones over seeds (conventional, clonal) for seven to eight years. The projection by Soh et al. (2000) was more conservative at 5 to 10 percent advantage for about five years with their modified recurrent selection based program (Fig. 4.4). Selection and reproduction of the top tenera hybrids, that is, D×P2 would give about 5 percent oil yield increase from the current hybrids

Fig. 4.4.

Expected breeding and cloning progress for oil yield (OY).

D×P1 = current commercial tenera hybrids; D×P2 = reproduction of top D×P1 hybrids; D×P3 = reproduction of top families of improved second breeding cycle hybrids; Clones1, 2 = clones from selected palms of the best families of the first and second cycle hybrids; Clonal Seeds1,2 = reproductions of the best families of the first and second cycle hybrids using clonal parents; Reclones1 = reproduction of the best Clones1 clones; Assumptions: Breeding progress = 15 percent per generation of eight to ten years; Progress from reproduction of top families = additional 5 percent, obtainable one to two years after progenytest results; Progress from reproduction of best families by clones/clonal seeds = additional 15 percent, obtainable five to six years after clonal test results; Progress reproduction of best clones (i.e., reclones) = additional 10 to 15 percent, obtainable three years after clonal test results.

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(D×P1) within one to two years of obtaining the D×P Progeny Test1 results. Reproduction of the top hybrids (D×P3) from D×P Progeny Test2 results of the next breeding cycle hybrids eight to ten years later would give another 15 percent progress, 10 percent from improved second generation hybrids, and 5 percent more from the top hybrids. Reproduction of the best hybrid family by cloning a sample of the family or from clonal seeds (hybrids from clonal parents), that is, Clones1/Clonal Seeds1 available in five to six years’ time would give 15 percent progress. Similarly, the next generation of clones (Clones2) and clonal seeds (Clonal Seeds2) available by years 12 to 14 would give an additional 15 percent progress. Reproduction of the best clone (Reclones1) in years 13 to 16 from the Clonal Test1 results would give an additional 10 to 15 percent progress from Clones1. Thus on average clones would have an advantage of 5 to 10 percent over improved hybrids for about five years. These were at best rough projections as sufficient data on G×E effects and somaclonal effects on production traits in clones and reclones (Phillips et al. 1994; Bregitzer and Poulson 1995), although reported in other crops, are as yet unavailable in oil palm. Genetic progress also depends on the relative selection pressures placed on the hybrid families and clones for commercial reproduction and the genetic variabilities of the base hybrid populations. 2. Clones for Basic Studies and Other Applications. Besides commercial propagation, clones are useful for basic studies in genetics and physiology and other applications. Clones, which allow replication of individual plant genotypes, have useful application in basic studies on quantitative genetics (e.g., partitioning between genetic and environmental variability between palms, estimation of heritability, and comparison of selection responses to different selection methods) (Soh 1998, Soh et al. 2001). Clones will also be useful for physiological studies on crop yield potential (Corley and Donough 1992; Smith et al. 1996), the physiological bases of G×E responses and for plant developmental studies both at the plant and the in vitro level particularly with the more homogeneous suspension cultures. Genetic transformation depends on a reliable clonal propagation technique (Parveez et al. 1997). Protoplast cultures were attempted to facilitate genetic transformation, but plant regeneration has yet to be achieved (Bass and Hughes 1984; Sambanthamurthi et al. 1996; Budiani et al. 1998). Parveez et al. (1997) successfully used the biolistics approach with polyembryogenic cultures. Anther/microspore culture is useful in initiating haploids for dihaploid hybrid breeding, but dihaploids have yet to be obtained in oil palm (Tirtoboma 1998).

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D. Molecular Breeding 1. Marker-Assisted Selection. Marker-assisted selection has long been a quest of plant breeders (Allard 1960). With the development of molecular techniques, molecular markers closely linked to quantitative trait loci (QTL) controlling desirable traits are being sought. Such markers enable earlier and more precise selection, which is particularly advantageous for perennial tree crop breeding. The development of genetic linkage maps and markers associated with the desirable traits is a prerequisite. A program to map the oil palm genome using RFLP, AFLP, and microsatellite probes has been initiated (Mayes et al. 1997a; Cheah et al. 1999; Cheah 2000). Mapping the shell gene using bulk segregant analysis is the first obvious objective (Mayes et al. 1997b; Moretzsohn et al. 2000). Rajinder and Cheah (1999), using the bulk segregant and pseudotest cross analyses on the selfed progenies of a high iodine value Nigerian breeding parent and a tenera clone and the progenies of an oleifera × guineensis interspecific hybrid, were able to locate the locus for the virescens fruit and also found putative QTL for carotene content and the clonal mantling abnormality. The generation of high-density maps for positional cloning and efforts at rapid gene discovery using expressed sequence tags (EST) are underway. Use of the GISH technique to discriminate the guineensis and oleifera chromosomes in oleifera × guineensis hybrid backcross breeding has been mentioned earlier. Other traits of interest for marker-assisted selection would include dwarfness, disease resistance, and amenability to tissue culture. While molecular marker-assisted selection, which is essentially genotypic selection, is an exciting development for breeding, its success to date has been below expectation even in annual crops. Marker-assisted selection has appeared to be effective in early segregating generations, a combination of marker-assisted selection and phenotypic selection better for subsequent generations, and only phenotypic selection effective in later cycles (Stuber et al. 1999). Likewise, Bernado (2000), in a simulation study to evaluate the impact of genomic knowledge (sequence and function) of the genes controlling a quantitative trait, found that the incorporation of gene information with phenotypic selection using BLUP was most useful in selection of traits with few loci but not for traits with many loci, for example, yield. Lack of well-saturated, uniformlyspaced markers, QTL with small effects generally well distributed throughout the genome, which may be susceptible to G×E effects, level of linkage disequilibria, sample sizes, breakdown of favorable epistatic complexes, and multicollinearity (lack of independence among the factors whose effects are being measured), are some the factors considered

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responsible for the limited success of marker-assisted selection (Stuber et al. 1999; Bernado 2000). Undeniably, marker-assisted selection will be most useful in backcrossing and introgression programs. Molecular markers and probes have also been used to discriminate oil palm breeding populations, newly collected semi-wild populations (Shah et al. 1994; Mayes et al. 1997b; Kulratne et al. 2000; Purba et al 2000b; Rajanaidu et al. 2000a), and clones (Cheah and Wooi 1995). They have also been used to determine the coancestry of parents for quantitative genetic analyses (Purba et al. 2000a). 2. Transgene Technology. Transgenic oil palms containing the Basta resistance gene are available at the nursery seedling stage. The transgenes for high oleic acid production, which is the objective, have yet to be inserted (Parveez et al. 1997). High oleic acid was selected as the transgene of choice because a high oleic palm oil would enable it to compete with the liquid cooking oil market in temperate countries. Furthermore, oleic acid is a good industrial chemical feedstock. The approach (Cheah 2000; Shah et al. 2000) adopted involves down regulating the palmitoyl-ACP thioesterase, by using an antisense gene and up regulating the β-ketoacyl ACP synthase II (KASII) activities, by using a strong promoter, to maximize oleic acid production at the expense of palmitic acid (Fig. 4.5) The thioesterase, KASII, and desaturase genes

Fig. 4.5. Pathway to achieving high oleic oil. Palmitoyl-ACP thioesterase is downregulated using an antisense gene and KAS II is up-regulated using a strong promoter to maximize oleic acid at the expense of palmitic acid. Source: Cheah 2000.

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and the promoters for expression of the genes in the mesocarp tissues have been obtained and transformation will follow.

V. FUTURE PROSPECTS The palm oil industry will continue to grow for the following reasons: (1) growing world population and the still low per capita consumption of oil; (2) high productivity of the crop and low cost of production; (3) versatility of palm oil and palm kernel oil in oleochemical and industrial uses; and (4) being a renewable resource, the edge over mineral oil in the increasingly environmental conscious world. Biofuel from vegetable oil is currently used in the countries of the European Union. Similarly, palm diesel will soon make its scene in the palm oil countries, like Malaysia, for economic and strategic reasons, although crude palm oil has been traditionally used to power vehicles in plantations in West Africa. The big stake in the crop in many emerging economies demands continuing funding for research and development (R&D). The need for increasing investment in R&D is even more crucial for higher cost producers, like Malaysia, because of the tremendous biotechnological advancements made in competing annual oil crops such as rapeseed and soybean and the subsidies provided that erode the competitive edge and profitability of palm oil. Cultivar improvement is strategically an important if not the primary objective of most crop R&D efforts. Oil palm breeders will have to be able to respond readily to the changing market needs. The first need is to improve crop productivity, and precision farming will achieve this objective. Specific cultivars adapted to certain environments and plantation management requirements are primary prerequisites. The production of definite hybrids (single cross, biclonal) and clones is directed toward this end. Cultivars must be managementfriendly in terms of ease of mechanization of harvesting and implementation of other plantation operations. The second need is to extend the versatility of the crop in terms of added value products (Murphy 2000; Murphy and Peterson 2000) especially when its high productivity confers an existing comparative advantage. A good example is the production of polyhydroxy butyrate (PHB) for biodegradable thermoplastic production, a recently initiated collaborative project in Malaysia. Others include: production of β-carotene for Vitamin A and natural dye, tocotrienol (Vitamin E), industrial fatty acids such as petroslenic acid, erucic acid, and ricinolic for specialty plastic and lubricant production. Most of these objectives will have to be achieved via biotechnological

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means. It can be argued that oil palm is not the most appropriate crop for biotechnological genetic modifications because of its perennial nature and the lag time involved (Corley 2000; Hardon and Corley 2000; Tinker 2000). However, the rapid development of biotechnological techniques and tools, for example, microarray or DNA chip technology, bioinformatics, and the revelation of much genetic collinearity or synteny (similarity or identity of genes) between crops suggest the genes, techniques and information developed in the cereal crops can be used in oil palm to reduce the development time. And, if the transgenes have been strategically incorporated into advanced breeding parents or proven clones, the response time in production of new hybrids to meet market changes would be considerably hastened. Indeed, oil palm breeders will have a challenging but interesting time ahead. LITERATURE CITED Agamuthu, P., and C. C. Ho. 1992. Quantification of endogenous cytokinins in the primordial of normal seed derived and mantled tissue-culture derived oil palm. p. 143–150. In: A. Z. Mohamed, H. F. Tung, and H. Nair (eds.), Proc. First Asia-Pacific Conf. Plant Physiology, Kuala Lumpur. Allard, R. W. 1960. Principles of plant breeding. Wiley, New York. American Heart Association. 1990. The cholesterol facts: a summary of the evidence relating dietary fat, serum cholesterol and coronary heart disease. Circulation 8:1721–1733. Arasu, N. T. 1970. A note on the germination of pisifera (shell-less) oil palm seeds. Malay. Agr. J. 47:524. Arasu, N. T. 1985. Genetic variation for fatty acid composition in the oil palm (Elaeis guineensis Jacq) Ph. D. Thesis, Univ. Birmingham, UK. Ariffin, D. 2000. Major diseases of oil palm. p. 596–622. In: Y. Basiron, B. S. Jalani, and K. W. Chan (eds.), Advances in oil palm research, Malaysian Palm Oil Board, Kuala Lumpur. Ariffin, D., A. Idris Seman, and A. Marzuki. 1995. Development of a technique to screen oil palm seedlings for resistance to Ganoderma. p. 132–141. In: Proc. 1995 PORIM National Oil Palm Conference on Technology in Plantation: The way forward. Palm Oil Res. Inst. Malaysia, Kuala Lumpur. Bass, A., and Hughes. 1984. Conditions for isolation and regeneration of viable protoplasts of oil palm (Elaeis guineensis). Plant Cell Rep. 3:169–171. Baudouin, L., and T. Durand-Gasselin. 1991. Genetic transmission of characters linked to oil yields in oil palm by cloning. Results for young palms. Oleagineux 46:313–320. Baudouin, L., J. Meunier, and J. M. Noiret. 1994. Methods used for choosing ortets. Plantations, Recherche Develop. 1:32–37. Beirnaert, A., and T. Vanderweyen. 1941. Contribution a l’etude genetique et biometrique des varieties d’Elaeis guineensis Jacq. Publ. INEAC, Serie Scientifique 27. Bernado, R. 1994. Prediction of maize single-cross performance using RFLPs and information from related hybrids. Crop Sci. 34:20–25. Bernado, R. 1995. Genetic models for predicting single-cross performance in unbalanced yield trial data. Crop Sci. 35:141–147.

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5 Breeding Wheat for Resistance to Insects William A. Berzonsky Department of Plant Sciences North Dakota State University Fargo, North Dakota 58105 Hongjian Ding Department of Plant, Soil and Entomological Sciences University of Idaho Moscow, Idaho 83844 Scott D. Haley Soil and Crop Sciences Department Colorado State University Fort Collins, Colorado 80523 Marion O. Harris Department of Entomology North Dakota State University Fargo, North Dakota 58105 Robert J. Lamb and R. I. H. McKenzie Cereal Research Centre, Agriculture and Agri-Food Canada Winnipeg, Manitoba R3T 2M9, Canada

Herbert W. Ohm and Fred L. Patterson Department of Agronomy Purdue University West Lafayette, Indiana 47907 Frank B. Peairs Department of Bioagricultural Sciences and Pest Management Colorado State University Fort Collins, Colorado 80523 David R. Porter U.S. Department of Agriculture–ARS Plant Science and Water Conservation Research Laboratory Stillwater, Oklahoma 74075 Roger H. Ratcliffe U.S. Department of Agriculture–ARS Department of Entomology Purdue University West Lafayette, Indiana 47907 Thomas G. Shanower U.S. Department of Agriculture–ARS Northern Plains Agricultural Research Laboratory Sidney, Montana 59270

Plant Breeding Reviews, Volume 22, Edited by Jules Janick ISBN 0-471-21541-4 © 2003 John Wiley & Sons, Inc. 221

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I. INTRODUCTION II. WHEAT STEM SAWFLY A. Geographic Distribution and Host Range B. Biology, Damage, and Economic Impact C. Breeding for Host Plant Resistance D. Alternative Control Methods III. WHEAT MIDGE A. Geographic Distribution and Host Range B. Biology, Damage, and Economic Impact C. Breeding for Host Plant Resistance D. Alternative Control Methods IV. HESSIAN FLY A. Geographic Distribution and Host Range B. Biology, Damage, and Economic Impact C. Breeding for Host Plant Resistance D. Alternative Control Methods V. RUSSIAN WHEAT APHID A. Geographic Distribution and Host Range B. Biology, Damage, and Economic Impact C. Breeding for Host Plant Resistance D. Alternative Control Methods VI. GREENBUG A. Geographic Distribution and Host Range B. Biology, Damage, and Economic Impact C. Breeding for Host Plant Resistance D. Alternative Control Methods LITERATURE CITED

I. INTRODUCTION Host-plant resistance plays an important role in the management of the insect pests of wheat (Triticum sp.). The Hessian fly [Mayetiola destructor (Say)] and the Russian wheat aphid [Diuraphis noxia (Mordvilko)] coevolved with wheat in southeast Asia (Briggle et al. 1982; Souza 1998), while the wheat midge [Sitodiplosis mosellana (Géhin)], the greenbug [Schizaphis graminum (Rondani)], and the wheat stem sawfly (Cephus spp.) became pests of wheat as cultivation practices spread into Europe, Asia, and the new world (Hunter 1909; Barnes 1956; Shanower and Hoelmer 2001). Clearly, all five insects have a long history of association with wheat. Reliance on host plant resistance to manage wheat insects can be explained in both an historical and economical context. In the United States, numerous major insect pest outbreaks occurred in wheat during the twentieth century, and throughout this period host plant resistance was a pest management mainstay. Development of wheat resistant to the Hessian fly and the wheat stem sawfly represents some of the earliest

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and most dramatic uses of host-plant resistance. Painter (1951), whose name is most closely associated with host plant resistance to insects, worked extensively with wheat. Wheat is not intensively managed in North America. Consequently, even after the introduction of synthetic insecticides in the 1940s, North American wheat growers continued to depend on host plant resistance to control insects. In 1996, only 3 percent of spring wheat and winter wheat production areas were treated with insecticides in the United States (National Research Council 2000). By contrast, crops such as maize (Zea mays L.), cotton (Gossypium hirsutum L.), tobacco (Nicotiana tabacum), and potato (Solanum tuberosum) received insecticides on 30 percent, 79 percent, 96 percent, and 83 percent of their production areas, respectively. Many of the evaluation procedures and concepts relating to the development of crop resistance to pest species are similar. Painter (1951) described three classes of host plant resistance mechanisms. One class of modalities is antixenosis, sometimes referred to as nonpreference. It encompasses any mechanism that interferes with the ability of the insect to find or colonize a plant. In wheat, an example is the resistance shown by ‘Kahla’ durum wheat (Triticum turgidum var. durum) in association with a reduction in the number of eggs oviposited on wheat spikes by the adult female wheat midge (see Section III). Another class of mechanisms is antibiosis, which encompasses any mechanism that reduces the growth or survival of the feeding stages of the insect. In wheat, examples of antibiosis are death of newly emerged Hessian fly or wheat midge larvae when they first attempt to establish a parasitic relationship with a resistant plant. Because reactions attributed to antibiosis are assumed to be more robust or reliable than those due to antixenosis, breeders and entomologists have typically concentrated more on identifying antibiotic mechanisms. The concern has been that in the absence of preferred genotypes, antixenotic plants can still be colonized and significantly damaged. The third class of resistance mechanisms is tolerance. It describes the ability of the plant to grow and reproduce in spite of supporting what would normally be a damaging insect population. In wheat, an example of such a mechanism is the ability of plants to tiller after seedlings are attacked by Hessian fly larvae. Of the three resistance mechanisms, tolerance is perhaps the most difficult to identify since it requires a more precise quantification of plant responses to the insect, preferably over a range of insect densities. On the other hand, tolerance theoretically places less selection pressure on the insect population relative to antixenosis and antibiosis.

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After developing and releasing a resistant genotype, vigilance is necessary to monitor the evolution of adapted, virulent insect biotypes unaffected by the resistance. Among breeders and entomologists, the term biotype is used to describe a population within an insect species that differs in its ability to use a crop plant (Gallun and Khush 1980). Most commonly, the frequency of different biotypes within a particular region is determined by collecting insects from that region and testing them on a set of plant genotypes (differentials) with different resistance genes (Smith et al. 1994). Some of the best-known examples of insect species with well-defined biotypes are pests of wheat, for example, the Hessian fly and the greenbug. A pest that has been successfully managed for over 50 years in the United States by the deployment of wheat genotypes exhibiting antibiosis, the Hessian fly overcame antibiosis genes H3, H5, and H6 within 15, 9, and 22 years, respectively (Foster et al. 1991). Gould (1998) argued that this adaptation probably would have occurred even more rapidly if a greater percentage of the wheat area in each region had been planted to genotypes carrying a single gene for resistance. The coevolution of host plant resistance genes and avirulence/virulence genes in wheat insect pests is in theory explained by the gene-forgene model, which is identical to the model first proposed for interactions between plant genes and fungal pathogen genes (Flor 1955). For each gene encoding a reaction in the plant, there is a corresponding gene in the insect that confers virulence. An avirulent insect produces a gene product that functions as an elicitor. The elicitor is recognized by a plant receptor that is coded for by a resistance gene. Plant defense mechanisms are triggered after this recognition. Virulent insects do not produce elicitors and therefore are not recognized by the plant. Consequently, in this type of interaction no defense reaction is triggered. This gene-for-gene model has proven useful in explaining the existence of insect biotypes and in designing possible strategies for deploying host genes for insect resistance. However, its practical application remains unproven since pyramiding single host genes for resistance has not yet been shown to unequivocally extend the durability of resistance. This chapter focuses on breeding efforts to develop resistance in wheat to five major insect pests present in North America. A range of breeding methods, from traditional evaluation and selection to marker-assisted selection, has been used to develop host-plant resistance in wheat (Smith et al. 1994). This range is reflected, along with an emphasis on the latest breeding techniques and approaches used to develop host plant resistance. Control methods, other than host plant resistance, are discussed to illustrate the importance of deploying resistant genotypes within the framework of an integrated pest management program.

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II. WHEAT STEM SAWFLY* A. Geographic Distribution and Host Range Wheat stem sawflies are important pests of cereal crops, primarily attacking wheat (Triticum sp.) but also barley (Hordeum sp.), rye (Secale sp.), and triticale [X Triticosecale (Wittmack)], in addition to a number of wild grasses (Morrill 1995). At least seven species of sawfly in two genera (Cephus and Trachelus) are significant wheat pests in different parts of the northern hemisphere (Shanower and Hoelmer 2001). In Europe, including eastern Russia, Cephus pygmaeus (Linnaeus) and Trachelus tabidus (Fabricius) are the most frequently reported pest species. These two species are widespread across North Africa, the Middle East, and extending into central Asia. Trachelus judaicus (Lep.) and T. libanensi (André) are minor wheat pests in the Middle East and Turkey. Cephus cultratus (Eversmann) and C. fumipennis (Eversmann) are found across central Asia and eastward, into China, Mongolia, and southeastern Russia, though their status as pests has not been reported. Among these six species, only C. pygmaeus and T. tabidus have been studied in any detail. Three pest species are present in North America, at least two of which were accidentally introduced from Europe. Cephus pygmaeus and T. tabidus were first discovered in eastern North America in the late 1890s (Shanower and Hoelmer 2001). These species originated in Europe and have apparently not spread west of the Mississippi River. The most economically important species of wheat stem sawfly in North America is Cephus cinctus (Norton). This species is widely distributed across the Northern Great Plains, with the greatest losses occurring in Montana, North and South Dakota, Nebraska, Manitoba, Saskatchewan, and Alberta (Weiss and Morrill 1992). The existence of biotypes of specific wheat stem sawfly populations has not been documented. There have, however, been several reports of population differences or changes that may be indicative of biotype development. For instance, comparing two populations of sawfly, Holmes et al. (1957) observed significant differences in the percentage of infested stems that were tunneled. A population from Lethbridge, Alberta successfully tunneled through the solid-stem durum cultivar ‘Golden Ball’ at a significantly higher rate than a population from Regina, Saskatchewan. These observations were confirmed through reciprocal studies at both locations. Another population-level change, early adult emergence in the spring, has also been documented. Morrill and Kushnak (1996) reported that wheat stem sawfly adapted to the advanced *Coauthored by W. A. Berzonsky and T. G. Shanower.

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growth stage of winter wheat relative to spring wheat, by emerging earlier in the spring. Similarly, Lou et al. (1998) reported significant variation using randomly amplified polymorphic DNA between North Dakota and Montana populations of the wheat stem sawfly. The populations from North Dakota exhibited longer post-diapause development and later emergence than the Montana populations. B. Biology, Damage, and Economic Impact Although different sawfly species attack wheat in different parts of the world, they share many biological and ecological characteristics. Females (Plate 5.1a) insert eggs into the stems of host plants approximately at the boot stage, that is, Feekes growth stage 10. Though several eggs may be laid within a stem, only a single larva (Plate 5.1b) survives to maturity due to cannibalism. As the plant matures, the developing larva feeds within the stem and moves down to the basal portion of the stem and chews a notch around the inside of the stem. The notch weakens the stem, which usually breaks at this point, causing lodging and producing a “stub” that remains anchored to the ground. Just below the notch but above the larva, the stem is plugged with frass and sawdust. The stub serves as an hibernaculum and the sawfly larva undergoes an obligatory diapause during the winter or the dry season. The stub is often covered with debris, soil and/or snow, and is therefore well protected from excessive cold or dry conditions. The diapausing larva is contained within a thin membranous cocoon inside the hibernaculum. Diapause is broken as temperatures, moisture levels, and photoperiods increase. Post-diapause development includes prepupal and pupal stages lasting approximately two weeks depending on ambient temperatures. Adults emerge from the cocoon and usually exit the stub through the plug, which is loosened by moisture. The agriculturally important sawfly species are univoltine, with a short-lived (seven to ten days), apparently nonfeeding, adult stage. Wheat stem sawfly damages wheat in two ways. The first is that cut stems are often lost when they lodge and cannot be harvested. This yield reduction may be as high as the percentage of cut stems in a field, and ranges widely across locations and seasons. Seamans et al. (1944) estimated this loss at between 5 and 10 percent, with severe infestations resulting in 50 percent of stems being cut. Hollow-stem cultivars are more frequently cut than solid-stem cultivars (McNeal et al. 1955). The introduction and use of solid-stem cultivars, beginning in 1946, has reduced but not eliminated this type of damage. Larval feeding within the wheat stem causes the other type of damage. This damage reduces vascular efficiency, resulting in fewer kernels per head and

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lower kernel weight. Several authors (Seamans et al. 1944; Munro et al. 1949; McNeal et al. 1955; Holmes 1977) have reported lower head weight (ranging from 5 to 17%) in sawfly infested stems as compared with noninfested stems. Kernel weight reductions may be even greater under low rainfall conditions. When precipitation was more than 15 percent below normal, McNeal et al. (1955) observed up to 20 percent lower kernel weight in sawfly-infested stems relative to noninfested stems. Sawfly attack may also reduce protein content of the grain, though this impact on grain quality is not observed every year (Holmes 1977). Morrill et al. (1994) found that head weight losses caused by the sawfly ranged from 11 to 18 percent in solid-stem cultivars and 14 to 26 percent in hollowstem cultivars. Losses are difficult to estimate because ovipositing sawflies select genotypes with the largest diameter stems, and these have the highest yield potential (Morrill et al. 2000). Female offspring are more likely to be produced in these largest diameter stems (Morrill et al. 2000; Morrill and Weaver 2000). Annual grain yield losses due to the wheat stem sawfly of more than $25 million have been reported from Montana for several years (Anon. 1997). A resurgence of wheat stem sawfly in Canada produced average economic losses of $27.45 in U.S. dollars per hectare (ha) in 2000 (Beres et al. 2000). Spring wheat was harvested from 6.565 million ha in Alberta and Saskatchewan in 2000. If only 10 percent of the area was infested with sawfly, and verbal reports indicate that a much greater area was infested, then losses exceeded $18 million in these two provinces alone. Annual losses in other U.S. states and Canadian provinces increase the total damage caused by wheat stem sawfly to more than $50 million, with some estimates as high as $144 million. These losses are substantial, but they do not include potential lost revenue to farmers who grow lower-yielding sawfly resistant cultivars or increased costs due to production changes associated with some sawfly cultural controls (e.g., tillage, swathing). C. Breeding for Host Plant Resistance The solid-stem characteristic of wheat, long known to be an effective trait for resistance to sawfly (Kemp 1934), can be thought of as providing an antixenotic and an antibiotic mechanism of resistance to the wheat stem sawfly. Wheat genotypes with solid stems resist the colonization of sawfly larvae compared with hollow-stem genotypes (Holmes and Peterson 1962), and the solid stems affect survival of larvae after feeding has been initiated (Holmes and Peterson 1957; Holmes and Peterson 1962; Holmes 1982). According to Holmes and Peterson (1964), part of the antibiosis attributable to solid-stem relates to pith moisture. The excessive pith moisture at egg-laying in solid-stem wheat genotypes

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tends to inhibit egg-hatching, and the solid pith is also a barrier to sawfly larvae movement, especially as the pith dries with advancing plant maturity. Morrill et al. (2000) confirmed the findings of Wall (1952) who observed that stem diameter is also a factor in antibiosis. They concluded that sawfly fitness—fecundity and longevity—generally increased with host stem diameter. Roberts (1954) concluded that independent of stem solidness, there are other antixenosis and antibiosis factors in wheat, which influence resistance. Presently, because stem solidness is the only well-defined and genetically studied trait, which confers resistance to sawfly (Eckroth and McNeal 1953), few wheat breeders directly select for any other resistance. 1. Resistance Mechanisms and Genetics. Breeders have successfully selected solid-stem genotypes with useful levels of resistance to wheat stem sawfly. Unfortunately, expression of the solid-stem trait is highly influenced by environment, and it is often associated with reduced grain yield relative to hollow-stem. The influence of environment on expression of stem solidness is realized in the variability observed in the resistance of known solid-stem wheat cultivars (Callenbach 1951). Several studies identified light, air temperature, moisture, and plant spacing as factors affecting stem solidness in wheat (Luginbill and McNeal 1958; Holmes et al. 1960). Platt (1941) proposed that light was the most important environmental factor influencing stem solidness. Canadian studies (Roberts and Tyrrell 1961; Holmes 1984) demonstrated that a decrease in days with bright sunshine and an increase in days with measurable amounts of precipitation, decreased the resistance of solid-stem cultivars to the wheat stem sawfly. Low air temperatures and increased plant spacing decreased resistance as well (Platt 1941; Luginbill and McNeal 1958). Adding to the complexity of expression of stem solidness is the fact that resistance depends on the stage of plant development and soil fertilization practices. Roberts (1960) reported that ‘Rescue’ wheat and other cultivars lost their resistance to sawfly egg development sometime between the flag leaf stage and flowering. Holmes and Peterson (1960) attributed differences in sawfly infestation levels to differences in cultivar growth rates. Applications of P fertilizers, either alone or mixed with N at planting, were shown to increase winter wheat sawfly cutting in Montana, while K fertilizers applied along with N and P in various combinations were found to decrease cutting (Luginbill and McNeal 1954). After evaluating selections from a hollow-stem by solid-stem wheat cross, highly significant negative correlations between grain yield and stem solidness were calculated by McNeal et al. (1965). Weiss and Morrill (1992) estimated that the yield advantage for sawfly susceptible cultivars, compared with resistant cultivars in the absence of sawfly

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infestation, ranged from 0 to 1.4 t/ha at two North Dakota locations and from 0 to 0.65 t/ha at two Montana locations. Estimates were made from advanced hard red spring wheat yield trials conducted over approximately a 20-year period from 1970 to 1990. Conversely, the use of solidstem cultivars during years of major sawfly infestations in Montana resulted in a yield advantage at all locations in better than three out of every four years (Weiss and Morrill 1992). Although stem solidness is generally recognized as being detrimental to achieving high grain yield in wheat, it need not necessarily preclude the development of yield-competitive, solid-stem genotypes. McNeal and Berg (1979) observed that the grain yield from a composite solidstem wheat population was equal to or higher than that from a composite hollow-stem population at two Montana locations. In addition, Ernest, a solid-stem wheat cultivar, which performs competitively for yield, was released by North Dakota State University in 1995 (McBride et al. 1996). In replicated yield trials conducted at four locations from 1998 to 2000, the average yield of Ernest in the western portion of North Dakota was 3454 kg/ha; whereas, the average yield of the hollow-stem cultivar, Keene, released by North Dakota State University in 1996, was 3675 kg/ha (Peel 2000). Hayat et al. (1995) crossed three solid-stem cultivars, which spanned 30 years of breeding, with two different hollow-stem cultivars. After calculating correlations between stem solidness and various agronomic characteristics in the progeny, they concluded that there was no undesirable relationship between stem-solidness and grain yield. Furthermore, during the development of more recent solid-stem cultivars, they postulated that breeders had broken linkages between undesirable genes in the original solid-stem source and desirable genes for important agronomic traits. Stem-solidness in wheat is controlled by genes with additive effects. Wallace et al. (1974) made crosses between three hollow-stem and three solid-stem wheat genotypes and found the mean stem-solidness of F2 progeny to be intermediate to the parents. Likewise, McNeal et al. (1971) produced F2 progeny with stem solidness intermediate to hollow and solid-stem parents. Crosses and backcrosses between solid- and hollow-stem genotypes by McKenzie (1965) revealed there were as many as three genes determining stem solidness in the wheat genotypes used, with some having epistatic effects. Hayat et al. (1995) also observed epistatic gene action in the expression of stem solidness. Stem hollowness is dominant, and there may be several genes acting to inhibit pith development in wheat stem internodes (Larsen and McDonald 1962; Lebsock and Koch 1968). Thus, breeders generally attempt three-way crosses, such as hollow stem/solid stem//solid stem, to accumulate genes for stem solidness and produce genotypes with solid stems.

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2. Sources of Host Plant Resistance. Hybrids between plant introductions and solid-stem ‘Rescue’ confirmed different genes for resistance in some introductions as well as genes that might promote stem solidness (McNeal et al. 1966). Breeders may use wheat plant introductions, like those that are maintained in the USDA-ARS Cereal Small Grains Collection, as new sources of stem-solidness and resistance to sawfly. Bruckner et al. (1994) screened at least a portion of the wheat plant introductions in the USDA-ARS Collection for presence of solid-stem. However, existing solid-stem cultivars and germplasm releases often are more useful sources of solid-stem because of their improved agronomic characteristics and adaptation. In 1946, the release of ‘Rescue’ wheat out of Canada made available the first solid-stem cultivar for resistance to wheat stem sawfly (Doane 1984). Since then, both U.S. and Canadian public and private wheat breeding programs in the Upper Great Plains and Canadian Prairie provinces have focused on continuing efforts to develop sawfly resistant cultivars (McCaig and DePauw 1995). A change in the adaptation of the sawfly that led to more infestation of winter wheat (Morrill and Kushnak 1996) also spurred efforts to incorporate solid-stem into winter wheat cultivars (Morrill et al. 1992). Table 5.1 describes some of the solid-stem hexaploid spring and winter wheat cultivars released to date. The Montana Agricultural Experiment Station also made two releases of solid-stemmed spring wheat germplasm (McNeal and Berg 1978; Alexander et al. 1989) and one release of solidstemmed winter wheat germplasm (Bruckner et al. 1993). To expand the genetic base for sawfly resistance and improve yield and quality in sawfly-resistant wheats, BW 597, a sawfly resistant germplasm line, was released by Ag Canada in 1988 (Mundel et al. 1989). Durum wheat might serve as another “gene pool” for transfer of stem solidness to hexaploid wheat. Damania et al. (1997) identified a number of solid-stem durum landraces from Turkey. Unfortunately, not unlike some other traits, it has proven difficult to effectively transfer stem solidness from durum to hexaploid wheat. For example, McNeal (1961) reported no success in transferring the same level of stem solidness and sawfly resistance from ‘Golden Ball’ durum wheat to hollow-stem hexaploid wheat genotypes, and he surmised that genes for solidness in ‘Golden Ball’ were the same as those in hexaploid ‘Rescue’. According to Tsvetkov (1973), among thousands of progeny from durum by T. aestivum crosses, less than 1 percent had solid stems at all internodes, again underscoring the difficulty in transferring the trait from durum wheat. 3. Selection Protocols and Field Methods. The challenge to wheat breeders is to develop genotypes that exhibit stable expression of stem solidness across environments along with optimal grain yields comparable

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Descriptions of solid-stem bread wheat cultivars released since 1946.

Wheat Cultivar

Typea

Year Released

Releasing Agencyb

Rescue Chinook Rego Cypress Sawtana Sawmont Fortuna Tioga Canuck

HRS HRS HRW HRS HRS HRW HRS HRS HRS

1946 1952 1957 1962 1962 1965 1966 1974 1974

AC AC MAES and ARS AC MAES and ARS MAES & ARS MAES and ARS NDAES and ARS AC

Chester

HRS

1976

AC

Lew Leader

HRS HRS

1976 1981

MAES and ARS AC

Glenman Lancer

HRS HRS

1985 1985

MAES AC

Cutless Rambo AC Eatonia

HRS HRS HRS

1986 1986 1993

NDAES WPB AC

Ernest Vanguard Rampart AC Abbey

HRS HRW HRW HRS

1995 1995 1996 1998

NDAES MAES MAES AC

Scholar

HRS

1998

MAES

Sci. Agric. 28:154–161 (1948) — Crop Sci. 6:306 (1966) — — Crop Sci. 6:392 (1966) Crop Sci. 7:110 (1967) — Can. J. Plt. Sci. 55:315–316 (1975) Can. J. Plt. Sci. 56:975–976 (1976) Crop Sci. 17:674 (1977) Crop Sci. 22:1265–1266 & Can. J. Plt. Sci. 62:231–232 (1982) Crop Sci. 25:574–575 (1985) Crop Sci. 27:1093 (1987) & Can. J. Plt. Sci. 66:409–412 (1986) — — Can. J. Plt. Sci. 74:821–823 (1994) — Crop Sci. 37:291 (1997) Crop Sci. 37:1004 (1997) Can. J. Plt. Sci. 80:123–127 (2000) Crop Sci. 40:861–862 (2000)

Conan

HRS

1999

WPB

—

Descriptive Reference(s)c

a

HRS = Hard Red Spring Wheat; HRW = Hard Red Winter Wheat. AC = Agriculture Canada; ARS = Agriculture Research Service (USDA); MAES = Montana Agricultural Experiment Station; NDAES = North Dakota Agricultural Experiment Station; WPB = Western Plant Breeders, Inc. c Descriptions of some cultivars might also be found in Anon. 2000; Glogoza 2000; McBride et al. 1996; Weiss and Morrill 1992. b

to hollow-stem genotypes. Major obstacles to achieving this goal are that the sawfly is difficult to rear in the laboratory and that the establishment of dependable field screening nurseries is problematic. Peterson et al. (1968b) described having to “seed” field screening nurseries with

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infested wheat stubs to provide adequate selection pressure, and a mechanical potato harvester was modified to try and help collect infested stubs (Peterson et al. 1968a). Wallace et al. (1973) calculated a stem solidness index, which consisted of scores ranging from 4 (completely hollow stem) to 20 (completely solid stem), and which included an assessment of all wheat plant internodes. They proposed that wheat breeders should select for an index of at least 14.6 and a sawfly control level of 65 percent to significantly reduce sawfly populations. Stem solidness rating scales and indices used can differ between breeding programs, but a typical scale is represented diagrammatically in Fig. 5.1. Solidness of the lower internodes of wheat is purportedly less influenced by environment compared with upper internodes, and thus, measurements of the solidness of the lower internodes are considered the most important measurements relative to the propensity of stems to be damaged by sawfly (McNeal and Wallace 1967). In deference to the difficulties in screening for actual sawfly cutting and damage under greenhouse and field conditions, breeders generally practice a form of indirect selection for resistance when they select for the solid-stem trait. In practice, resistant genotypes are selected in the absence of sawfly. An intrinsic problem with indirect selection is that other valuable sawfly resistance factors, unrelated to the solid-stem trait, might be overlooked. However, indirect selection does enable selection for resistance outside of major sawfly infestation regions. This is becoming more important because selections must frequently be made in the absence of sawflies when breeding programs service a large production region.

Fig. 5.1 Representative rating scale used by breeders to assess the degree of stemsolidness in wheat (1 = 100% solid pith, 2 = approx. 75% solid pith, 3 = approx. 50% solid pith, 4 = approx. 25% solid pith, and 5 = hollow pith).

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D. Alternative Control Methods Various other control strategies, including cultural control, biological control and pesticides, have been investigated for managing wheat stem sawflies. The most important cultural control practices for reducing wheat stem sawfly impact have been tillage and swathing—cutting wheat early and allowing it to dry on the ground prior to threshing. Tillage has long been suggested as a strategy to control sawflies (Criddle 1915). Both spring and fall tillage, using a variety of implements, have been shown to reduce wheat stem sawfly populations (Criddle 1922; Weiss et al. 1987). The critical factor is separating soil from the base of the stem. Soil attached to the stem provides protection, insulating the sawfly from low temperatures and humidity. Sawfly mortality was substantially higher in uncovered stems than in stems covered with soil (Morrill et al. 1993). There is no evidence that tillage will effectively suppress sawfly populations. Swathing can reduce losses that result from lodging and grain being lost prior to harvest, but it has no effect on the 5 to 17 percent loss in grain weight due to the disruption of grain-fill by larvae feeding in the stems. Tillage and swathing, which require additional field operations, increase production costs and reduce profitability. Tillage also increases soil erosion rates, and runs counter to no-till recommendations. An integrated approach, combining cultural control with the use of solidstem cultivars, is a more recent development in control. Morrill et al. (2001) investigated using strips (10 to 30 m wide) of solid-stem cultivars planted around hollow-stem cultivars and between fallow fields to reduce sawfly damage. A winter wheat cultivar planted in strips around spring types worked best at reducing sawfly damage, and stem-cutting of hollow-stem spring wheat cultivars was reduced by 70 percent when surrounded by solid-stem, winter-type cultivars. Three sawflies, C. cinctus, C. pygmaeus, and T. tabidus, have been targets of biological control programs in Canada and the United States (Shanower and Hoelmer 2001). Several natural enemies were introduced in eastern North America in the 1950s, and because these were successfully established, C. pygmaeus and T. tabidus are no longer considered serious pests of wheat in these areas. A much greater, though unsuccessful, effort was directed at importing and releasing natural enemies against C. cinctus in western North America. This program began in the 1930s and continued through 1955 (Hoelmer and Shanower 2001; Shanower and Hoelmer 2001). There is a renewed interest in biological control of C. cinctus, using natural enemies collected from native grasses and previously unexplored areas, such as Asia. C. cinctus specimens were first recovered from native grasses in 1891, and it was speculated that “the economic importance of this species arises from the fact that it may be expected at any time to abandon its natural food plant in favor

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of the small grains, on which it can doubtless successfully develop” (Ainslic 1920). When C. cintus existed in native grasses, populations were suppressed by several species of parisatoids (Ainslic 1920; Somsen and Luginbill 1956), and by 1980, two species of larval parasitoids also commonly attacked C. cinctus in wheat (Morrill et al. 1998). Insecticides are not widely utilized for sawflies in North America. They are generally ineffective because larvae are well concealed inside the stems, making them relatively inaccessible to insecticides. Furthermore, for this region of the world, insecticides are often too costly for the typical production management practices.

III. WHEAT MIDGE* In Europe, Sitodiplosis mosellana is commonly called the “orange wheat blossom midge” to distinguish it from the yellow wheat blossom midge, Contarinia tritici (Kirby). However, outside Europe, where wheat midges have been introduced, S. mosellana is the main and often only midge that is a pest of wheat. Here we refer to S. mosellana simply as the “wheat midge.” This wheat midge is one of a group of gall midges that are important pests of world crops. Our knowledge of the general biologies of the gall midges and their associations with crops is effectively summarized by Barnes (1956), and more recently by Gagné (1989). Harris and Foster (1999) described the mating biology of the gall midges. Basedow (1977) and Basedow and Schütte (1974) produced an extensive literature review on the biology and impact of wheat midge on wheat in Germany during the 1970s. A. Geographic Distribution and Host Range The wheat midge is an important pest of wheat in parts of North America (Olfert et al. 1985; Lamb et al. 1999), Europe (Oakley et al. 1998), and China (Ni and Ding 1994). In North America, the wheat midge primarily infests wheat with a spring growth habit (seeded in spring and harvested in late summer), but in parts of Europe and China, winter wheat (seeded in late summer and harvested the next summer) predominates and is the primary host. The wheat midge is now a serious pest of wheat in many parts of its range, particularly in western Canada (Olfert et al. 1985; Lamb et al. 1999, 2000b), resulting in widespread applications of insecticide when feasible (Elliott 1988a,b). The first reference to a midge larva in wheat was in 1741 in England (Webster 1891), although the *Coauthored by R. J. Lamb, R. I. H. McKenzie, and H. Ding.

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identity of the midge was not specified (possibly either S. mosellana or C. tritici). The wheat midge was probably carried from Europe to North America and possibly also to Asia on seeds transported to new areas of wheat production. Larvae could be expected to move readily with seed stocks because they do not always drop from the spikes before harvest. Viable mature larvae sometimes can be found in the creases of seeds harvested by a modern combine, and so it is likely that early seed harvesting techniques resulted in seed stocks becoming contaminated as well. Sitodiplosis mosellana probably was introduced to North America soon after European settlement (Barnes 1956), and early reports of damage by wheat midge on the Eastern Seaboard (Felt 1920) and West Coast (Reehar 1945) are consistent with what we know about the pest. Wheat midge also occurred in the center of the continent by the turn of the century, soon after agricultural production of wheat had become widespread in the area (Fletcher 1902). The wheat midge ceased to be a devastating pest in eastern North America after about 1925, although the cause for the decline is unknown. Similarly, the cause for the rise in importance of S. mosellana in the Northern Great Plains of North America is also a mystery. The first report of S. mosellana in China is from 1936, although it is unknown when the wheat midge actually arrived there (Zheng 1965). Occurrence of S. mosellana has not been reported in the southern hemisphere. Although suitable conditions for S. mosellana exist in the wheat producing areas of New Zealand, parts of Australia, and South America, presence of the pest has not been reported there. The wheat midge infests the inflorescences of common wheat, durum wheat (Wright and Doane 1987), and other species in the genus Triticum (Wise et al. 2001). However, other genera in the Gramineae also are hosts for wheat midge, for example, rye (Barnes 1956) and occasionally barley (Reehar 1945; Kurppa 1989). Sitodiplosis mosellana in China occurs on four wild grasses, Roegneria pendulina Nevski, Clinelymus dahuricus (Turckz.) Nevski, C. sibiricus (L.) Nevski, Aneurolepidium dasystachys (Trin.) Nevski (Zheng 1965), and oviposits on Alopecurus myosuroides Hudson, although it is not known whether adults successfully develop on this grass (Barnes 1932). The wheat midge oviposits on and larvae survive in all 17 species in the genus Triticum. Host suitability of these species can be measured by the relative level of infestation, measured as the number of mature larvae developing on a plant when all plants in the test are exposed to the same number of ovipositing females (Wise et al. 2001). The ancestral diploid wild wheats, T. monococcum and T. tauschii, have the lowest infestations among species in the genus, and two hexaploid species, T. sphaerococcum and T. zhukovskyi, are more heavily infested than

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common wheat. In the five lineages recognized in the genus, infestation generally increases in association with domestication. The level of infestation is not related to seed size or the number of seeds in spikes. Larvae more heavily infest wheat species with free-threshing seeds and compact spikes than ancestral wheat species with less compact spikes and glumes. In general, domestication has increased the susceptibility of wheat, possibly because the free-threshing trait affects the suitability of the glume-seed interface for oviposition and establishment of larvae on the seeds (Wise et al. 2001). This generalization is supported by the observation that the first resistant wheat in China, a wheat called “Nanda,” was not free-threshing (Zheng 1965). B. Biology, Damage, and Economic Impact When wheat spikes are emerging, female wheat midges (Plate 5.1c) usually deposit one to two eggs (Mukerji et al. 1988; Smith and Lamb 2001) on the upper, inner surface of a glume (Smith and Lamb 2001). However, eggs can be laid on almost any of the outer parts of a spikelet, including the rachis (Mukerji et al. 1988; Smith and Lamb 2001). Females do not deposit eggs directly on the reproductive tissues of the plant. They produce 60 to 80 eggs (Pivnick and Labbé 1993) over their adult life, which is typically 3 to 5 days, although under ideal conditions in the laboratory they may live up to 12 days. Males typically survive for two to four days (Zheng 1965). When eggs hatch in four to seven days (Mukerji et al. 1988), larvae must crawl 5 to 20 mm from the site of oviposition to the surface of the ovary. Larvae feed there for 10 to 14 days, coinciding with the early stages of development of the wheat seed, from anthesis until the seeds have a biomass of about 9 mg or 37 percent of the biomass of a ripe seed (Lamb et al. 2000b). When larvae reach full-size with a biomass of about 0.2 mg, they molt to the third instar, but they do not shed the secondinstar skin immediately. Presumably, this skin protects against moisture loss (Gagné and Doane 1999). Larvae remain in their partly shed skins for a variable number of days, until a heavy dew or suitable rain triggers their departure (Hinks and Doane 1988). They free themselves of the skin, exit the floret, and drop to the soil where they burrow to a depth of a few cm. In the soil, they form an overwintering cocoon and diapause. The following spring, most emerge from diapause, crawl to just below the soil surface where they form a second cocoon, and pupate (Barnes 1956; Zheng 1965). A few weeks later, adults emerge. Emergence is in June in some areas (Barnes 1956: Zheng 1965) and July in others (Lamb et al. 1999). These emergence times generally coincide with the heading of winter and spring wheat, respectively.

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Wheat midge larvae compete directly with humans for the grain by feeding on the seeds, often destroying them in the process as evidenced by shriveling (Plate 5.1d). Wheat midge can infest seeds in all parts of a spike (Lamb et al. 2000b). Consumption of a similar amount of seed biomass takes place regardless of seed size, consequently, the effect of larval feeding is greater on small seeds compared with large seeds (Lamb et al. 2000b). The degree of damage to the seed is also affected by where a larva feeds, with the severest damage resulting from an attack near the embryo (Zheng 1965). Most infested seeds are attacked by one to three larvae, but 30 or more larvae occasionally are found to have matured on a single seed. Usually the seed is destroyed if four or more larvae are present, although the amount of damage appears to vary greatly from seed to seed. With up to 11 larvae developing on a single seed, competitive interactions among larvae do not affect larval biomass; however, larval survival and body size are probably reduced when unusually high numbers occur on a single seed (Lamb et al. 2000b). An index called the specific impact can be used to measure the efficiency of conversion of wheat seed biomass into larval biomass; the biomass lost by the seed is divided by the biomass gained by the larva (Lamb et al. 2000b). Wheat midge larvae finish feeding when uninfested seeds have attained about one-third of their final biomass. At this time, the specific impact of the wheat midge on common wheat is 8.5 mg of seed biomass lost for each mg of biomass gained when a single larva attacks a seed. The specific impact declines to 4.1 as the density of larvae per seed increases to three or more larvae on a seed. Specific impact rises to 100 mg of seed biomass per mg of larval biomass when seeds of T. aestivum mature (Lamb et al. 2000a). The specific impact for mature seeds of T. monococcum, a wild diploid wheat, is the same as that of common wheat, and up to 140 mg of seed biomass is lost by mature durum wheat seeds. The rise in specific impact from the time larvae finish feeding to when the seeds mature demonstrates that wheat plants do not compensate for wheat midge damage, although only about a third of the development of the seed is complete when feeding stops (Lamb et al. 2000a). In fact, the early damage to the seed appears to prevent subsequent normal seed-fill. No indirect damage to uninfested seeds adjacent to infested seeds has been detected. Thus, the sum total biomass lost by each infested seed estimates the biomass lost by a plant attacked by wheat midge. Midge feeding results in one of the highest specific impact values recorded for insects feeding on wheat or other crop plants (Gavloski and Lamb 2000). The distribution of biomass for individual, infested ripe seeds is bimodal with over 40 percent less than 8 mg when hand harvested (Lamb et al. 2000b). The smaller seeds in this bimodal distribution are usually

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lost when an infested crop is harvested mechanically, resulting in a unimodal distribution for the biomass of damaged seed. The visual characteristics of seed damage are related to the biomass of the seeds, such that biomass loss can be associated with visual damage ratings for market grading (Lamb et al. 2000b). Considering the loss of damaged seed during combine harvesting, the current market price of wheat, and the current cost associated with using insecticides, the economic threshold for Canadian spring wheat is about 4 to 10 percent infested seeds before harvest (Lamb et al. 2000b). Midge damage adversely impacts agronomic performance characteristics such as resistance to premature sprouting, yield (Olfert et al. 1985), seed germination, and early vigor of seedlings (Miller and Halton 1961; Lamb et al. 2000b), but significantly, it also impacts grain quality (Miller and Halton 1961; Dexter et al. 1987; Helenius and Kurppa 1989). Damage to both common and durum wheat can result in changes in seed protein levels and loss of dough strength, although the consequences of these effects may differ because of differences in the product end-uses (Dexter et al. 1987). For durum wheat, the most significant adverse quality effect is the occurrence of black specks in semolina, probably as a result of secondary infection by microorganisms that invade the damaged surface of the seed after midge feeding. The wheat midge may also be a vector for microorganisms that infect wheat seeds (Mongrain et al. 1997). The first severe local outbreak of wheat midge occurred in 1983 on the Manitoba–Saskatchewan border, near the northern limit of agricultural production (Olfert et al. 1985). A more widespread outbreak throughout Manitoba, most of Saskatchewan, northern North Dakota, and western Minnesota began in 1993 and continues today. The current outbreak coincided with a period of relatively high summer precipitation, conditions thought to promote adult midge emergence and longevity. C. Breeding for Host Plant Resistance Although S. mosellana has a long history as a pest of wheat in the northern hemisphere, relatively little has been published on host plant resistance for this insect. When an outbreak of the wheat midge in the Northern Great Plains became widespread in 1993 (Lamb et al. 1999), this pest began to attract the attention of North American entomologists and breeders, who focused on developing host plant resistance. Chinese researchers, however, developed crop resistance for S. mosellana in the 1950s, because it was a serious pest of winter wheat in north-central China (Anon. 1977).

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1. Resistance Mechanisms and Genetics. Some spring wheats exhibit an antixenotic resistance mechanism in response to the wheat midge (Lamb et al. 2000a). Infestations on these lines are less severe because few eggs are laid on them. In the laboratory, egg densities on some of these lines are 10 percent or less compared with a susceptible check cultivar in choice tests, and 20 percent or less in no-choice tests. No line has been identified that completely escapes oviposition. These same wheat lines also deter oviposition in the field, reducing egg densities by at least 50 percent in single-row and multi-row field plots (Lamb et al. 2002c). Other wheat lines have egg densities intermediate between the most deterrent lines and a susceptible wheat. The most deterrent experimental lines show a level of deterrence to oviposition that would be useful in combination with an antibiotic resistance against feeding larvae. A similar oviposition deterrence occurs in durum wheat, but it is rarer than in common wheat. Two related accessions of a durum wheat named ‘Kahla’ show stable, low infestation levels, about 30 percent of infestation levels compared with commercial durum wheat cultivars. This resistance is not associated with any of 12 morphological traits that have been examined for durum wheat spikes (Lamb et al. 2001). Antibiotic resistance against wheat midge has been described by Chinese (Sun et al. 1998) and Canadian (Lamb et al. 2000a) scientists. Advanced breeding lines of spring wheat with antibiosis cause a 58 to 100 percent suppression of larval density and either completely eliminate seed damage, or reduce damage by 70 percent compared with a susceptible wheat (Lamb et al. 2000a). On resistant seeds in the laboratory, larvae do not develop or grow normally and their survival is reduced. The antibiosis is associated with a hypersensitive reaction on the seed surface. The resistance is equally effective in field trials, with at least a twenty-fold difference in the level of infestation between susceptible and resistant wheats. No larvae develop to maturity on some resistant lines. Large plots of one resistant line produced less than 1 percent as many larvae as a typical susceptible wheat, and the larvae that did survive, produced few, small adults. Antibiosis of this type has not been detected in durum wheat (Lamb et al. 2001). The antibiotic mechanism that confers resistance in some of these spring wheat lines is associated with the rapidly induced production of phenolic acids by seeds (Ding et al. 2000). Such a resistance mechanism is valuable because while it disrupts the interaction between the wheat midge and its host, it also allows the grain to be safely consumed by humans. Production of the phenolic acids increases rapidly only when seeds are attacked, returning to the same low levels that occur in susceptible wheats when the seeds ripen (Ding et al. 2000). Chinese winter

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wheats may have the same resistance mechanism because they exhibit elevated levels of phenolic acids ten days after anthesis and after feeding by midge larvae (Sun et al. 1998). Another type of antibiotic resistance that is hypothesized is the prevention of newly hatched larvae from moving to the seed surface from oviposition sites (Ding and Guo 1993). This resistance is associated with a specific type of lemma and palea pubescence. The pubescence can block the larvae from crawling into a floret. In practice, this resistance is difficult to distinguish from deterrence of oviposition because dead, newly hatched larvae, or the remnants of the eggs from which they emerged are unlikely to be detected. This resistance warrants further investigation because the mechanism is clearly different than the induction of phenolic acids. Furthermore, a specific type of pubescence may provide an effective target for visual selection. Some spring wheat lines with resistance, appear to cause reduced hatching of eggs in the laboratory (Lamb et al. 2000a). Up to 80 percent of the eggs fail to hatch on some lines, which could account for a substantial proportion of their resistance. In field evaluations, this antibiosis would be difficult to distinguish from oviposition deterrence and antibiosis against newly hatched larvae, if screening is based on counts of mature larvae or damaged seeds. Further studies are required to determine the contribution of a reduced hatching rate to resistance and to confirm that this phenomenon is not a laboratory artifact. 2. Sources of Host Plant Resistance. The source of antibiotic resistance now being used in Canada to develop host plant resistance was derived from winter wheat cultivars from the United States (Barker and McKenzie 1996). The resistance to wheat midge had not previously been tested in these winter wheats, some of which were released years ago. The resistance was transferred and expressed in spring wheat, and it appears to be simply inherited. Tests on doubled-haploid populations indicate that a single, partially-dominant major gene controls expression of the antibiosis in spring wheats (McKenzie et al. 2002). In the laboratory, the resistance appears to be more highly expressed in a spring than in a winter wheat background (Barker and McKenzie, 1996; Lamb et at. 2000a). The winter wheats used as sources of resistance are: ‘Augusta’, ‘Blueboy’, ‘Caldwell’, ‘Clark’, ‘Florida 302’, ‘Howell’, ‘Knox 62’, ‘Monon’, and ‘Seneca’ (Barker and McKenzie 1996; Lamb et al. 2000a). A spring wheat cultivar with antibiotic resistance has not yet been released and registered for production in Canada, but advanced breeding lines have been developed. A number of spring wheat lines, which exhibit the antibiotic resistance, also inhibit oviposition to varying degrees (Lamb et al. 2000a). The most inhibitory lines were derived from crosses between ‘Augusta’ win-

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ter wheat and spring wheat breeding lines. Deterrence to egg-laying in durum wheat has only been identified in a single cultivar, ‘Kahla’ (Lamb et al. 2001). Some commercial Chinese winter wheat cultivars exhibit varying levels of resistance to wheat midge (Anon. 1977). Whether these represent different sources of resistance than those identified in Canada is unclear. Modern resistant wheats from China are derived from crosses with cultivars first used in the 1950s. Basedow and Schütte (1974) proposed that wheat midge damage was reduced in ‘Florian’, ‘Starke’, and ‘Farino’ relative to other cultivars. Basedow (1977) also recommended ‘Kleiber’, ‘Kolibri’, and ‘Arin’ for resistance, although Wright and Doane (1987) subsequently concluded that ‘Kolibri’ is susceptible under Canadian conditions, and Anderson and Harris (unpublished) concluded that ‘Arin’ is susceptible under laboratory conditions. The spring wheat, WW19052, reported as resistant in Finland (Kurppa 1989), proved susceptible in laboratory tests conducted in Canada (Lamb et al. 2000a). The ancestral cultivated wheats, T. spelta and T. dicoccoides, are promising sources of resistance to wheat midge because they share the A and B genomes of modern wheat cultivars and exhibit relatively low infestations (Wise et al. 2001). One free-threshing accession of T. dicoccum has relatively low levels of infestation and may prove to be a more useful source of resistance. Because so few accessions of the wild wheats have been assessed, the related diploid or polyploid wheats represent a potentially large untapped source of resistance to the wheat midge. 3. Selection Protocols and Field Methods. Screening wheat genotypes for resistance is a critical part of the development of host plant resistance. In Chinese (Ding and Ni 1994) and Canadian breeding programs, screening is mostly conducted in the field, and it involves identifying plants with lower than expected levels of infestation. The primary difficulty in effectively conducting field screenings is the misclassification of genotypes thought to be resistant. Misclassifications often occur because heading of the genotypes to be tested does not always coincide with the flight period of the wheat midge (Basedow 1977; Wright and Doane 1987; Barker and McKenzie 1996). Examples are that late-heading genotypes often escape infestation (Lamb et al. 2001), and genotypes escape infestation if they head during a period of inclement weather (Smith and Lamb 2001). Small research plots, which head at the same time as the majority of locally grown commercial wheat, can also be problematic. In this situation, the commercial wheat can receive most of the eggs, reducing oviposition in the research plots to nondiscriminating numbers (Lamb et al. 1999). The window for infestation is

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narrow because spikes are suitable for infestation for such a relatively short period of time, that is, when spikelets begin to emerge from the flag leaf until about anthesis or flowering (Elliott and Mann 1996). This period ranges from 5 to 7 days for a specific plant up to about 14 days for a field of wheat. The onset of this period is determined by the fact that females will only lay eggs on spikelet surfaces, not on adjacent leaf or stem surfaces. Spikelets are suitable for oviposition as soon as they emerge, so the first emerging spikelets often receive a disproportionate number of eggs (Smith and Lamb 2001). The end of the period is essentially determined by the larva’s inability to establish on seeds that have begun to develop, rather than on female preferences for oviposition. Even though their offspring are unable to establish on seeds that have begun their development (Ding and Lamb 1999), female midges will continue to oviposit on spikes. Escape from infestation because of asynchrony between oviposition and heading is probably the cause of most anecdotal reports of resistance or reduced susceptibility (Wright and Doane 1987; Kurppa 1989). Usually, densities of adult wheat midges have been monitored along with heading dates and infestation levels to minimize the misclassification of escapes as resistant (Barker and McKenzie 1996). Asynchrony can be mitigated using multiple seeding dates for the test lines, followed by tagging spikes from all the test lines to identify the ones that emerge on the same day. This method limits screening to hundreds of lines, rather than thousands. The main objective of screening wheat for resistance is to identify susceptible lines containing easily-visible, mature larvae, which are present in the wheat heads for one to two weeks. In the initial stages of the Canadian breeding program, 12 spikes were collected about two weeks after flowering, usually at the beginning of August. This timing assured that larvae were still present and readily counted when spikes were dissected. The spikes were air-dried and could be stored for weeks without compromising the evaluation procedure. The remnant skin, shed from the second instar larvae, can be used to confirm that wheat midge attacked a seed. If thousands of rows are to be screened, collecting spikes for processing in the laboratory becomes time-consuming. In the Winnipeg breeding program, the screening of segregating lines is now done in the field. An evaluator facilitates counting larvae by threshing wheat spikes into a white tray. Any larvae present in a spike drop into the tray, where they are visible. Up to five spikes are threshed per plot, and the first spike with larvae identifies a genotype as susceptible, even if it is the first spike threshed in that plot. If no larvae are found on five spikes, the genotype is scored as potentially resistant. Using this method, one indi-

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vidual can typically screen 300 to 400 plots per day. This method can identify various resistance mechanisms because a lack of mature larvae would be characteristic of genotypes with a deterrence to oviposition and/or one that prevents egg hatch and larval migration to feeding sites. Seeds with an antibiotic type mechanism of resistance are characterized by a hypersensitive reaction after being fed on by midge larvae (Barker and McKenzie 1996; Lamb et al. 2000a). To effectively select for hypersensitivity, ripe spikes must be harvested and the few affected seeds must be located by a laborious microscopic examination. Screening specifically for reduced oviposition is more difficult than screening for reduced numbers of larvae (Lamb et al. 2001). Egg densities on wheat spikes are highly variable in the laboratory and field, although differences in egg densities from spike to spike are greater in the laboratory (Smith and Lamb 2001). The primary cause of high variability in egg density among spikes is variation in egg group size and the presence of multiple egg groups on a single spike. These factors cannot be controlled because they are caused by oviposition behavior rather than environmental heterogeneity (Smith and Lamb 2001). Comparisons among spikes, which emerge on the same day, can reduce variation in egg density because these spikes are known to be of similar age and are presumably exposed to the same density of ovipositing females. Laboratory screening of resistance to wheat midge is feasible (Lamb et al. 2000a) because the insect can be reared in large numbers and stored during its obligatory diapause as a mature larva. Mature larvae can be collected from infested wheat spikes in commercial fields when they are mature but have not yet dropped to the soil. The larvae can be threshed or dissected out of the spikes and dropped in lots of 200 onto moist soil in a plastic container. After about two weeks at room temperature, the larvae will have burrowed into the soil and the containers can be transferred to a cold room, maintained at a temperature of 0° to 2.5°C. Viable larvae can be maintained under these conditions for at least a year. The obligatory diapause is completed in about four months, at which time the containers can be returned to room temperature (18° to 20°C). After about a month, the adults begin to emerge, and the top twothirds of clear plastic soft drink bottles can be effectively used as emergence cages. With their rounded base removed, the bottles fit snugly into plastic overwintering containers. The mouths of the bottles should be sealed, preferably with cotton, to allow gas exchange. When adults are resting on the sides of the bottles, they can be removed from the overwintering containers, and the bottle bases can be reattached with tape. The sex and number of adults can be determined through the transparent bottles before they are added to cages containing wheat spikes. The adults are delicate and easily injured, but they can be safely transported

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in the bottles. Using methods adapted from Hinks and Doane (1988), it is now possible to rear wheat midges in large numbers in the laboratory. A cage of 50 spikes with 25 plants exposed to 100 females can produce 1000 or more mature larvae. Laboratory reared adults have the advantage of being free of parasites, and they also seem to emerge more reliably and lay more eggs. The rate of parasitism for larvae collected from the field often reaches 50 percent. Two conditions are critical to achieving high oviposition rates in laboratory cages—high humidity (60% or higher relative humidity) (Hinks and Doane 1988), and light above the cage contrasting with darker conditions at the base of the cage. Although difficult to maintain in winter in heated buildings, a high relative humidity assures that females survive for a few days, and the eggs they lay do not dessicate. The contrast in light level between cage base and top seems to cause females to rise to the top of the cage where the spikes are, resulting in greater oviposition. Cages with clear plastic walls cause much mortality because of their high static charge. A cage with a wooden frame, plastic walls for resting insects, and plastic film covering the outside to maintain humidity has been effective. Intact plants can be tested in such cages, but maintaining spikes at one constant height makes this difficult. Excised spikes are particularly effective for studies of oviposition preference, when the spikes are kept in water and tested over 24 to 36 h (Smith and Lamb 2001). Patterns of oviposition across wheat genotypes are similar when females are provided with either excised spikes or whole plants. Although laboratory tests are under more controlled conditions than field tests, they have their limitations. At least two technicians are required to grow the plants, rear the midge, and continuously screen about 150 plants per week for antibiosis. Furthermore, fewer plants can be screened for antixenosis if counting eggs is required because it is slower to count eggs than larvae. The development of new insect biotypes means breeders and entomologists must continually look to develop new resistant cultivars (Gould 1983). Chinese researchers developed winter wheats with resistance to wheat midge in the 1950s (Anon. 1977), but the wheat midge became a problem again in the 1980s. Instead of a breakdown in host plant resistance, this likely was a result of growers shifting their attention to using high-yielding cultivars, which did not have midge resistance. Over the past decade, host plant resistance has been re-emphasized by transferring the necessary genes from previously released to newly released cultivars. Resistant Canadian spring wheats have not yet been produced commercially, and so they have not exerted a selection pressure on wheat midge populations. Nothing is known about the role of North American resistant winter wheats in affecting wheat midge virulence in the late 1800s. At that time,

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at least one farmer from eastern Canada identified his winter wheat as resistant to wheat midge, and a winter wheat called ‘Democrat’ was also thought to be less susceptible than most cultivars grown at the time (Fletcher 1888). It is possible that resistance from these early wheats was incorporated in the first cultivars of winter wheat, which became widely grown in eastern North America. In Canadian spring wheats, the antibiotic-type resistance against larvae will cause high mortality, and therefore a high selection pressure will be exerted on the midge to develop virulence. In large-plot tests, the fitness of wheat midge developing on resistant wheat is probably less than 0.5 percent than that of wheat midge from susceptible wheat (Lamb et al. 2000a). Nevertheless, some larvae survive. An important objective in sustaining resistance to the wheat midge is to effectively manage the use of host plant resistance. Two approaches are being tried. In the first, the antibiotic resistance is being combined with antixenosis against oviposition (Lamb et al. 2000a), based on the assumption that pyramiding two types of resistance will delay the evolution of virulence to either type (Gould 1986). This approach relies on the untested hypothesis that the two resistance mechanisms are independent. Selecting for both types of resistance at the same time is difficult because oviposition is a highly variable process (Smith and Lamb 2001), and antibiotic genotypes cannot be identified if no eggs are laid. Closely-linked classical phenotypic markers and molecular markers would be helpful, and research to identify such markers continues. The second approach applies the principles of population genetics to dilute virulence genes in the pest by maintaining adequate levels of avirulent genes in the midge population (Gould 1986). Deploying susceptible plant genotypes along with resistant ones might allow virulent midges to mate with avirulent ones to produce heterozygotes. Consequently, assuming that the virulence gene is recessive, most midges would be heterozygous for virulence, and virulence genes would be selectively eliminated by resistant plants at each generation. This might be a particularly favorable approach with the wheat midge, since females distribute their eggs in small groups to a number of different plants, and the feeding stage is confined to the plant on which the eggs are deposited. Thus, the midge behavior would assure that virulent and avirulent midges are initially distributed throughout a mixed stand of resistant and susceptible plants. Subsequently, they would primarily be exposed to individual host plant resistance or susceptibility. Preliminary data indicate that a 5 percent mix of susceptible seeds with 95 percent resistant seeds could protect a field from economic damage and generate almost two orders of magnitude more avirulent than virulent midges. A 5 percent mix of susceptible seeds in the resistant seed might be practical for a low-value crop such as wheat. Success of this approach will

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likely be problematic because it will require cooperation between international seed certification agencies and cooperation among growers across international borders. D. Alternative Control Methods Wheat can escape midge infestation if spikes are produced at a time when females are not available or when females are unable to lay eggs. Kurppa (1989) refers to this phenomenon as pseudo-resistance. Nevertheless, asynchrony between the crop and the wheat midge might be effective in crop protection. In western Canada, females are active from the last days of June to the first days of August (Lamb et al. 1999). Winter wheats, which head in June, escape infestation, as do spring wheats, which are seeded early and are expected to head earlier than normal. However, in many seasons the ideal weather conditions necessary for adequate early-seeding of cereal grains do not occur, making this practice infeasible for Northern Great Plains wheat growers. In other regions of the world where the main host is winter wheat (e.g., Europe and China), the flight of the midge coincides with heading of winter wheat in late May and June (Barnes 1956; Ni and Ding 1994). This suggests that the wheat midge might adapt to changes in heading date resulting from altered agronomic practices or the introduction of new, earlier-maturing cultivars. As few as one midge larva per kernel, or approximately 36 per spike, is estimated to adversely impact wheat yield and quality (Olfert et al. 1985). Considered along with weather conditions and the resistance level of the wheat genotype, this level of midge infestation can be used to determine an economic threshold for spraying insecticides. Elliot (1988a) sprayed nine insecticides on wheat at different dates and evaluated their efficacy with respect to midge control. Aerial applications of chlorpyrifos were the most effective, reducing midge infestations of wheat heads by 87 to 95 percent compared with controls. Apparently, the vapor pressure of chlorpyrifos is much higher than the permethrin, deltamethrin, and cypermethrin chemicals. Thus, in the absence of systemic activity, the chlorpyrifos, with a higher vapor pressure, are able to enter the glumes and control midge egg and larval development better than other chemicals. Despite this potential for chemical control, spraying to kill wheat midge egg and larva is not recommended because application of insecticides at heading can also reduce the natural midge egg parasite populations (Elliot 1988a; Floate et al. 1989). Use of insecticides to control the adult midge during flight is also a problem because there is a very short window of application, from the time when half of the wheat heads are emerging to when half are flowering.

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IV. HESSIAN FLY* The Hessian fly is a destructive pest throughout most of the wheatproducing regions of the world. Although widespread outbreaks of the fly have occurred at irregular intervals, sporadic outbreaks that cause extensive local or regional crop losses are more common and are dependent on the presence of biotic and abiotic conditions that favor survival and rapid increase of populations. The population dynamics of the insect and pattern of damage to wheat has favored the use of preventive rather than remedial control methods, with strong emphasis on biological and cultural approaches that are more reliable and economically feasible for wheat growers. Of particular importance has been the growing of resistant wheat cultivars. In this section, we present a brief description of Hessian fly distribution, biology, life history, and the nature of the damage it does to wheat. Our primary purposes are to bring together information on the use of host plant resistance, and to discuss breeding resistant cultivars and how the genetic interaction of the Hessian fly with its wheat host affects the durability of resistance. A. Geographic Distribution and Host Range The Hessian fly has existed in southern Europe for many centuries and probably followed wheat from its original habitat in southwest Asia (Briggle et al. 1982). There are records of the Hessian fly in the former Soviet Union from 1847 onward, especially in the Ukraine and other former Soviet republics. It is also found in parts of Transcaucasia and western and eastern Siberia. Presence of the Hessian fly in Iraq, Syria, Lebanon, and Cyprus has been confirmed, and it is known to occur in Asiatic Turkey. The Hessian fly has been a serious pest of wheat in the North African countries of Morocco, Algeria, and Tunisia since the early 1900s. The insect is now widely distributed throughout most wheatgrowing regions of Europe, North Africa, Asia, and North America. In North America, the Hessian fly occurs in the United States and Canada. It was found in Canada in the late 1800s, and it now occurs from Nova Scotia to British Columbia. In the United States, it was reported from Long Island, New York in the late 1700s, and now is found in all major wheat-growing areas from the Atlantic Coast to the Great Plains. It also occurs in the western United States in parts of California, Idaho, Oregon, Montana, North and South Dakota, and Washington. In the semi-arid regions of the northwestern United States, the Hessian fly is more widely distributed and abundant on irrigated wheat. The wide global distribution of the Hessian fly is evidence that the insect is adaptable to most *Coauthored by H. W. Ohm, F. L. Patterson, and R. H. Ratcliffe.

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environments that are suitable for wheat production. It is known to be present in New Zealand, but not Australia. There is no record, however, of its occurrence in southern and southeast Asia, Mexico, or South America. Although the Hessian fly has a long history of pestilence in North America, with the exception of North Africa, its pest status is not known in most other regions of the world (Ratcliffe and Hatchett 1997). Wheat is the preferred host of the Hessian fly, and the pest is capable of infesting and injuring all classes of wheat. Barley and triticale are other cultivated cereal crops occasionally infested, and wild grasses such as Agropyron repens (L.), A. smithii Rydb., Elmus virginicus L., E. canadensis L., Hordeum pusillum Nutt., and Aegilops sp. may serve as hosts when wheat is not available (Jones 1936). In the southeastern United States, volunteer wheat can act as a reservoir for flies that infest fall-planted small grains, since it often is present when soybeans are double-cropped into wheat stubble using no-till practices (Johnson et al. 1987; Buntin and Raymer 1989). B. Biology, Damage, and Economic Impact The adult Hessian fly is a small midge, dark gray to black, and about 3 mm long with an orange abdomen. The egg is glossy red, cylindrical, and about 0.5 mm long. The first instar larva also is red, but turns white within a few days. The second instar larva is vermiform, white, and about 4 mm long when mature. The puparium, commonly called “flaxseed” because of its resemblance to the seed of flax, is dark brown and 3 to 5 mm long (Plate 5.2a). Gagné and Hatchett (1989) provide a detailed description of larval instars. In winter wheat areas, the typical life cycle of the Hessian fly begins with fall emergence of adults from infested wheat stubble or volunteer wheat. Soon after mating, the female (Plate 5.2b) begins ovipositing on the upper leaf surface of young plants and lays between 200 and 300 eggs. The adults do not feed, are short-lived, and they die within a few days after emergence. The Hessian fly female tends to lay more eggs when humidity is high, and survival of small larvae is enhanced under these conditions, resulting in higher levels of infestation. Eggs hatch in three to ten days depending on temperature. Newly hatched larvae crawl behind the leaf sheaths and migrate to the base of the leaf sheath where they begin to feed. Feeding injury is caused entirely by the larva. On young plants, the larva feeds beneath the leaf sheath at the base of the plant. The feeding mechanism of the larva and how it obtains food from the plant are not fully understood. A study of the larval mouthparts has revealed highly specialized mandibles that are probably used to inject salivary fluids into plants (Hatchett et al. 1990). These secretions are

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believed to contain enzymatic substances that inhibit plant growth and increase cell wall permeability, allowing the larva to suck the juices from the plant. No injury to plant tissue has been observed at feeding sites, although infested plants show a characteristic stunted appearance. On some genotypes, leaves of infested plants also appear more erect and are shorter and darker green than those of plants without the Hessian fly (Gallun 1977). Only the first instar larva is mobile, but both first and second instar larvae feed and grow. The larval developmental time varies, but most larvae are full-grown before the onset of cold weather. When the second instar larva completes growth, the outer skin hardens and forms a protective puparium in which the third instar larva and pupa subsequently develop. The insect overwinters in the puparium. The following spring, the pupa completes development and adults emerge and infest wheat about the time the plants begin to joint. Most of the larvae of the spring generation are found just above the nodes between the leaf sheath and culm. The existence of Hessian fly biotypes capable of infesting resistant wheats has been known for many years. Currently, 16 biotypes designated Great Plains (GP), and A through O have been identified from the field (Ratcliffe and Hatchett 1997). Hessian fly biotypes are identified on the basis of avirulence or virulence of larvae to four wheat differentials carrying resistance genes H3, H5, H6, or the H7H8 combination. The GP biotype is least virulent and cannot survive on wheats having any of these resistance genes. Biotype L is most virulent and can infest all cultivars having the H3, H5, H6 genes, or the H7H8 gene combination. Genetic studies have shown that virulence of the insect against specific resistance genes of wheat is simply inherited and conditioned by single recessive autosomal or sex-linked genes (Hatchett and Gallun 1970; Formusah et al. 1996; Stuart et al. 1998). Thus, the genetic interactions between resistance genes and biotypes are highly specific and exhibit a gene-for-gene relationship. A biotype can only be virulent to a wheat cultivar if it is homozygous for recessive virulence genes at loci corresponding to loci at which the wheat plant has resistance genes. Feeding causes injury that prevents elongation of internodes and transport of nutrients to the developing spike. This injury reduces both quantity and quality of the grain, and much of the grain of broken culms is lost when the crop is harvested. The larvae pass the summer inside the puparia in the dry stubble. In late summer or fall, the larvae pupate, and adults emerge and infest volunteer or early-seeded wheat. In northern winter wheat areas, two generations are normally produced each year; whereas, in southern areas, supplementary broods may develop either before or after the main fall or spring generation (Buntin and Chapin 1990; Lidell and Schuster 1990). In the northern United States

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and southern Canada spring wheat areas, only a single generation is produced in the spring. Damage due to Hessian fly can be diagnosed by examining seedlings for dead or stunted stems. Leaves should be peeled down to their points of attachment with the stem. If they are the cause of dead or stunted stems, the larvae or flaxseed can be located at the feeding site above the point of attachment. In older plants, the immature insect can be found within the shortened internode at the point of infestation, or, in lodged stems, just above the node where the stem broke. The number of stems and leaves are reduced as are foliar and root weights in wheat plants infested with Hessian fly larvae (Wellso et al. 1989). When infestations are severe, stunting of seedlings occurs earlier and many of the young plants die after the larvae have matured. Larval feeding also reduces the winter hardiness of wheat plants that survive the fall infestation. This contributes to further loss during the winter. Heavily infested fields are characterized by areas that have dead plants and thin stands. At lower infestation levels, seedlings may survive and develop new tillers that enable a plant to compensate for stunting of the main stem. In the United States, Hessian fly damage has been estimated at as much as $100 million in a single year (Cartwright and Jones 1953). Packard and Cartwright (1939) reported that 13 percent of the wheat area sown in Indiana in the fall of 1919 was abandoned the following spring because of severe Hessian fly damage. More recently, the Hessian fly caused losses in Georgia estimated at $4 million in 1986 and $28 million in 1989 (Hudson et al. 1988, 1991). Similar losses have been reported in other southern states, such as the losses in Texas in 1984, which were estimated to be $5 million (Hoelscher et al. 1987). C. Breeding for Host Plant Resistance Breeding for Hessian fly resistance in wheat has been one of the most successful efforts in utilizing host plant resistance. Carlson et al. (1978) stated that most wheat cultivars grown in the eastern United States in the previous 50 years had resistance to biotype GP, which occurred widely in the Great Plains. In the 1970s and early 1980s, wheat cultivars released from the Purdue-USDA program at West Lafayette, Indiana occupied as much as 80 percent of the soft red winter wheat area in the United States (Roberts et al. 1988). Hatchett et al. (1987) reported that 60 cultivars with resistance to Hessian fly were released in the United States during the period from 1950 to 1983. 1. Resistance Mechanisms and Genetics. The presence of Hessian fly resistance in wheat cultivars in the field was observed as early as 1782,

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just six years after the insect was reported in the United States (Fitch 1847). Field observations of wheat cultivars with Hessian fly resistance continued in the 1800s (Gaylor 1843; Harmon 1843; Packard 1880); however, the first systematic studies of Hessian fly resistance in wheat cultivars were not conducted until the late 1800s and early 1900s in California (Packard 1928). Wheat cultivars reported as resistant to Hessian fly in field studies were ‘Kanred’, ‘Mediterranean’, ‘Fulcaster’, ‘Prohibition’, ‘Dawson’, and ‘Illini Chief’ (Packard 1928). Hessian fly resistance in deployed wheat genotypes is dominant or partially dominant and conditioned by single, duplicate, or multiple genetic factors. Genes H1, H2, H3, H4, H5, H7, H8, and H12 were identified in common wheat; and genes H6, H9, H10, H11, H14, H15, H16, H17, H18, H19, H20, H28, and H29 were identified in durum wheat. Twenty-nine major wheat resistance genes, designated H1 through H29, have been described from common and durum wheat, wild wheat relatives, and rye. Ratcliffe and Hatchett (1997) listed 27 of the 29 genes, along with their source, chromosome location if known, and selected literature references. Since this 1997 publication however, some genes have been renumbered. Gene H27, for example, was renumbered H29 (Annual Wheat Newsletter, 1998, Vol. 44:439). Triticum tauschii is the D-genome progenitor of common wheat, and some accessions were found to have resistance to Hessian fly. The genetics of resistance of four tauschii accessions, TA1642, TA1644, TA1645, and TA1647, from Iran and their genetic relationship with the H13 gene, also derived from T. tauschii, were investigated by Hatchett and Gill (1983). Results indicated that resistance of the four accessions is conditioned by single, dominant genes, and they segregate independently of the H13 gene. Tests for allelism among genes of the four accessions have indicated that the genes in TA1642 and TA1647 are the same and the genes in TA1644 and TA1645 are the same (Hatchett et al. 1981), but the two genes appear to be linked. Accessions TA1642 and TA1647 originated from the same general area of northeastern Iran; whereas, accession TA1644 originated from northwestern Iran. The collection site of accession TA1645 is unknown. Hard red winter wheat germplasm lines carrying resistance genes H22, H23, H24, and H26 derived from T. tauschii have been released (Gill et al. 1991; Raupp et al. 1993; Cox and Hatchett 1994). The incorporation of genes from T. tauschii into adapted wheat cultivars should provide a broader base of genetic resistance to virulent biotypes of the Hessian fly. Cytogenetic and genetic studies have demonstrated that the greatest number of host plant resistance genes are located on wheat chromosome 5A. With the possible exception of the ‘Kawvale’ and ‘Marquillo’ resistances, which may involve tolerance, the primary mechanism of resistance

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conditioned by these genes is antibiosis, and resistance is expressed as the death of the first instar larvae within two to three days after establishment. The biochemical nature of resistance (antibiosis) in wheat to the Hessian fly has not been determined for any gene. Shukle et al. (1990, 1992) and Grover (1995) reported that their research, although not providing direct information concerning the biochemical basis of resistance in wheat to the Hessian fly, supported the hypothesis that hypersensitivity is the phenotypic basis of resistance in the Hessian fly-wheat interaction. This hypersensitivity involves “recognition” of an avirulence gene product or process. Miller and Swain (1960) and Miller et al. (1960) studied the influence of silica in sheaths of wheat plants and free amino acids, organic acids, and sugars on resistance to Hessian fly. None of these factors were significantly associated with resistance. Other factors for resistance in wheat to the Hessian fly have been reported, but have not been utilized to any extent in breeding programs or cultivar development. These factors include resistance derived from the wheat cultivars ‘Kawvale’ and ‘Marquillo’, which may include tolerance to larval feeding as well as antibiosis (Painter 1951). Painter (1951) reported that Marquillo may have several genetic factors for resistance, which tended to be recessive. Additionally, Maas et al. (1987) showed that the resistance of Marquillo was conditioned by a highly temperature-sensitive, partially dominant gene, which they designated H18. The Hessian fly resistance in Marquillo was transferred from ‘Iumillo’ durum wheat. Subsequently, a second gene for resistance to Hessian fly, in addition to gene H18, was identified in Iumillo, and it is partially effective in the homozygous state at temperatures of 23 and 26°C (Cambron et al. 1995). The genetic basis of the resistance of Kawvale has not been determined. Resistance also has been associated with leaf pubescence, which influences oviposition by Hessian fly females (Roberts et al. 1979). In addition to reduced oviposition, Roberts et al. (1979) found that egg hatch and larval establishment was lower on the cultivar ‘Vel’, which has pubescent leaves compared with ‘Arthur’, which has glabrous leaves. This indicates possible antibiosis as well as antixenosis. The development of virulent biotypes of the Hessian fly poses the greatest threat to the durability of host plant resistance. The strategy for management of resistance genes in breeding programs has been the sequential release of cultivars with different resistance genes. Once a resistance gene begins to lose effectiveness in the field, cultivars with new biotype-specific resistance are released. Although this strategy has effectively used major genes, the growing of wheat cultivars with high levels of resistance exerts strong selection pressure on Hessian fly pop-

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ulations and favors survival and an increase in the frequency of biotypes capable of injuring resistant wheat (Gallun et al. 1961). Ratcliffe et al. (1994, 1996) reported that major shifts in biotype composition and virulence to resistance genes in wheat occurred in Hessian fly populations throughout the eastern United States from the mid-1980s to mid-1990s. With one exception, the gene combination H7H8 in the extreme southeast, virulence to genes H3, H5, H6, and the combination H7H8 is prevalent in Hessian fly populations throughout this region. This greatly reduces the effectiveness of these genes. Biotype L is predominant throughout this region, except in populations from southern Georgia and South Carolina, and northern Florida (Ratcliffe et al. 2000). Hessian fly populations from many areas of the eastern United States also have demonstrated virulence to resistance genes that have not been deployed in wheat cultivars to date. One or more of ten Hessian fly populations collected from Arkansas, Florida, Indiana, Michigan, Pennsylvania, or South Carolina were found to be virulent to host plant resistance genes H9, H10, H11, H12, H13, H14, H15, H18, and H19, which were not as yet deployed (Ratcliffe et al. 1994). Only genes H16 and H17 were resistant to all populations tested. Resistance genes H10, H11, H12, and H19 were considered to be the least effective for use in the eastern United States soft winter wheat region because of their susceptibility or inconsistent response to many of the Hessian fly populations (Ratcliffe et al. 1996). Many of these genes, which are not yet deployed, could still be used selectively in wheat cultivars to be grown in different areas of the eastern United States. In the hard winter wheat region of the Great Plains, many cultivars have both Marquillo and ‘Kawvale’ in their parentages. Only the ‘Marquillo’ and ‘Kawvale’ resistances and the H3 gene have been used in cultivar development. ‘Marquillo’ has been a durable source of resistance in several varieties for more than 30 years. The influence of high-temperature susceptibility of the resistance of ‘Marquillo’ to slowing the development of virulence in the Hessian fly is debatable. Although genotypes having the H3 gene have been developed for the hard winter region, they have not been widely grown. For instance, in 1985 only 15 percent of the Kansas wheat area was planted to cultivars having the H3 gene. Thus, there has been little selection pressure for a biotype virulent to the H3 gene. Other resistance genes are highly effective against Hessian fly in the hard wheat region. But, biotypes virulent to these genes occur at low frequencies, and they are expected to increase in numbers as additional resistant cultivars are grown. Thus, wheat cultivars having different biotype-specific resistances will be needed to protect against the build-up of virulent biotypes. Wheat breeders in the hard wheat region are presently utilizing several different sources of resistance, including the

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Marquillo resistance, H3, H5, H6, H9, H11, and H13 genes, and H21, H22, H23, H24, H25, and H26 genes, to enhance durability of resistance (Ratcliffe and Hatchett 1997). Resistant soft white and hard red spring wheat cultivars grown in the U.S. Pacific Northwest employ the H3 resistance gene (Pike et al. 1993; Guy et al. 1998), and possibly H5 and H6 genes (Ratcliffe et al. 2000). Virulence to H3 and H6 resistance genes was reported in populations from Idaho and Washington (Ratcliffe et al. 2000). The optimum use of present and future Hessian fly-resistance genes or gene combinations in wheat breeding programs is dependent on gene deployment strategies that improve the durability of resistance in cultivars. Gene combinations involving different mechanisms or exhibiting differences in expression, especially in response to abiotic conditions (temperature-sensitivity), may provide effective suppression of the Hessian fly while also slowing the selection for virulence in field populations. No experimental evidence is available to indicate which deployment strategy would maximize the durability of Hessian fly host plant resistance, although genetic models that describe the interaction of resistance genes in wheat and virulence genes in the insect have been developed (Gould, 1986). The pyramiding of genes that confer biotypespecific resistance has become increasingly attractive as a breeding strategy now that DNA-based marker-assisted selection is feasible (Dweikat et al. 1997). Dweikat et al. (1994, 1997) identified DNA (RAPD) markers linked to Hessian fly resistance genes H3, H5, H6, H9, H10, H11, H12, H13, H14, H16, or H17 either by utilizing denaturing gradient gel electrophoresis or agarose gel electrophoresis. Several of the DNA markers were used to determine the presence/absence of specific Hessian fly resistance genes in wheat lines that have one or possibly multiple genes for resistance (Dweikat et al. 1997). Seo et al. (1997) developed a RAPD marker amplified by primer OPE-13 that cosegregated with a translocation containing H21 from rye. The main strategies for breeding and releasing resistant wheats include (1) using cultivars with single gene resistance and deploying them when necessary or redeploying them after several years; (2) deploying single cultivars with two or more effective gene resistances; (3) deploying cultivars with two or more genes, but also including a portion of susceptible plants to slow virulence change in the Hessian fly population; and (4) producing commercial hybrid wheat cultivars, which are homogeneous for resistance, since partial dominance occurs in a heterozygote. Important considerations in choosing a breeding strategy include the potential adaptation of Hessian fly to an area and the risk of loss to damage by the insect. Environmental conditions and cropping patterns may

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keep the insect population survival low, moderate, or high. Recent emphasis on reduced tillage for control of soil erosion favors increased survival of the Hessian fly. Also, double-cropping of soybean (Glycine max (L.) Merr. planted into wheat stubble favor survival of the Hessian fly on volunteer wheat plants in the soybean crop. Another important consideration is the time required to develop a new cultivar or hybrid. In a seed market supplied by several breeding programs, there often is an advantage to the program that most rapidly brings new, superior products to the market. Consideration must also be given to choosing a breeding strategy that is compatible with other important goals for improvement of cultivars. Usually, a breeding program will have additional goals of equal or greater importance that will influence breeding strategies for resistance to Hessian fly. Finally, it is necessary to consider support for the breeding program in monitoring Hessian fly population changes, developing single gene resistance lines with DNA markers, and conducting basic studies addressing the nature of virulence in the insect and resistance in the host. Cox and Hatchett (1986), from simulation genetic models, concluded that deployment of single gene resistance was adequate for controlling damage from the Hessian fly in the central Great Plains area of the United States where Hessian fly populations generally are low. They reasoned against pyramiding genes for resistance because of the greater time required to develop a cultivar and because it would rapidly deplete genetic resources. In the past, genes H3, H18 and the resistance of Kawvale have been deployed alone. Single genes for resistance may be introduced in original crosses, and their presence can be verified by seedling tests, field evaluations, or by DNA markers, if available. With these procedures, little is added to the time required to develop a cultivar. If, on the other hand, a single gene is to be added by backcrossing, additional time will be required. In areas where Hessian fly populations presently are low, a critical concern is whether reduced tillage and changes in cropping patterns will favor survival of larger populations of Hessian fly in the future. Pike et al. (1993) and Ratcliffe et al. (2000) noted that damage caused by Hessian fly in northern Idaho appeared to be increasing, in part because of increased adoption of conservation tillage practices in the region. Such practices result in more residue and infested stubble being left on the soil surface and greater potential for survival of Hessian fly puparia. Foster et al. (1991) reported the successful redeployment of gene H6 in the eastern United States. The Hessian fly population may or may not retain virulence to a gene withdrawn from deployment, but this will likely depend on each specific case. Since the Hessian fly has only four chromosomes, virulence to a previously deployed gene may be retained in

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the population because of linkage to a population fitness gene or to a currently required virulence gene. There are strategies, which might work better for areas where moderate size Hessian fly populations exist, such as in the U.S. Midwest. Indiana is representative of areas with two generations of Hessian fly per year, one fall and one spring in most years. Single gene resistances have been deployed since 1955, and losses from Hessian fly infestation have been low. In the 33-year period from 1956 to 1988, losses of 0.5 percent to 2 percent occurred in 12 years, 3 percent to 5 percent in one year, and 11 percent to 15 percent in one year (Patterson et al. 1990). Most farmers plant after the Hessian fly-free date in the fall to reduce damage from Hessian fly and some diseases. We believe that Hessian fly populations will increase in the region because of reduced tillage and doublecropping of soybean after wheat in the same year. We also believe that for the immediate future there will be enough susceptible cultivars grown to stabilize an avirulent Hessian fly population without providing a susceptible component within any new cultivar. However, pyramiding two genes for resistance to Hessian fly should extend the period of effectiveness of resistance, as suggested by Gould (1986) from simulation studies. DNA markers could be used to identify both or just one gene during the selection process. Given DNA markers for two genes, resistance could be incorporated in the original crosses and identified in selections by using the markers. This would not delay normal cultivar development, but it would require larger populations of selected lines and involve the added cost of extracting DNA and evaluating lines for the presence of markers. A second approach would be to identify potential cultivars in early yield trials and start separate “progressive” backcross programs to add the two genes for resistance. In progressive backcrossing, the recurrent parent used would periodically change to the most recent selection of the potential cultivar, which would possess improved agronomic traits. After several backcrosses the two lines, resistant to Hessian fly, would be intercrossed and selfed. Progenies would be tested for a homozygous response to the DNA markers. Any resistant line could be selected and its seed increased to produce a new cultivar. The development of cultivars with pyramided resistance would be somewhat delayed, primarily at the stage of seed increase, compared with developing lines with single gene resistance. In the absence of two DNA markers, the gene without a marker could be added to the potential cultivar and identified by seedling tests each generation. This line would become the recurrent backcross parent in order to add the gene that was identified by using a marker. After selfing, the line is homozygous for the marker, and seed could be increased for its release as a cultivar. Notably, following this breeding method does not produce a susceptible version of the cultivar.

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The southeastern United States is representative of an area where high populations and several generations of Hessian fly may occur in the fall and spring. Therefore, later planting to escape fall infestation is not effective. In this area, host plant resistance is so important that resistant cultivars, if available, would dominate the area, and susceptible cultivars would not likely be planted to stabilize avirulence in the Hessian fly population. Gould (1986) suggested that 20 to 50 percent of the plants of any one genotype should be susceptible in order to stabilize the avirulence of the Hessian fly population. Resistance derived from two pyramided genes would be very valuable for extending the duration of host plant resistance. If susceptible and resistant plants are to be represented in a single, new cultivar, then three near-isogenic genotypes need to be developed. With the aid of DNA markers for resistance, the resistant genotypes could be developed by progressive backcrossing, while a susceptible genotype is maintained during development. Final designation of the new release as a cultivar or a blend would depend on state and federal seed laws. If DNA markers could be used to identify one gene and not the other, the resistant line without a marker would be used as the recurrent parent in the final stages when the gene with a marker is incorporated. However, in the absence of markers, this would delay the release of a new cultivar a few generations because of the additional backcrosses necessary to assure the recovery of the gene in a homozygous state. In areas where high Hessian fly populations exist, it is important that host plant genes for resistance to Hessian fly are not deployed singly, because this may allow virulence to develop to each resistance gene, thus nullifying any durable protection possibly provided by pyramiding genes. 2. Sources of Host Plant Resistance. The largest number of Hessian fly resistance genes have been identified from common and durum wheat, but relatively few have been utilized in breeding programs. Although virulence to H13 has been reported from Hessian fly populations in the eastern United States (Ratcliffe et al. 1994, 1996), the soft red winter wheat germplasm line ‘Molly’ and cultivar INW9811, which carry H13, demonstrated resistance against a broad range of fly populations from the eastern United States (Ratcliffe et al. 2000). Because host plant resistance can be rendered ineffective by new Hessian fly virulent biotypes, additional sources of resistance are continually sought in wheats and their relatives. Among wheat relatives, the wild Triticum species have been a principal source of new genes for resistance to Hessian fly. These species are part of the wild wheat collection maintained by the Kansas State University Wheat Genetics Resource Center, including 1695 accessions of diploid (2n = 14), T. boeoticum (AA), T. urartu (AA), T. tauschii (DD), and tetraploid (2n =

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28), T. araraticum (AAGG), T. dicoccoides (AABB) species. Results of evaluating a portion of these accessions have been reported (Gill et al. 1987; Raupp et al. 1993; Cox and Hatchett 1994). Genes H13, H22, H23, H24, and H26 were derived from T. tauschii. Resistance also was identified in accessions of T. boeoticum and T. araraticum (Gill et al. 1987; Raupp et al. 1993; Cox and Hatchett 1994). All resistance was expressed as larval antibiosis. These results indicated that T. boeoticum, T. tauschii, and T. araraticum may have a considerable amount of useful genetic variation for resistance to Hessian fly. Sharma et al. (1992) evaluated 41 accessions of primitive, cultivated and wild wheats, 16 accessions of Aegilops species, and 20 accessions/cultivars of Agropyron species for resistance to biotype L of Hessian fly. Three T. monococcum accessions, 13 Aegilops accessions, and 13 of the Agropyron accessions/cultivars were found to be homogeneously resistant. Antibiosis was operative in some cases, but in others there appeared to be physical barriers to colonization due to the presence of leaf or ligule pubescence. Some alien species, distantly related to wheat, have resistance to Hessian fly, and many wheat-alien species translocations have been made, but few have resulted in transfer of resistance. Friebe et al. (1996) studied 58 such translocations. Most were whole arm translocations with breakpoints in the centromere regions. Two were intercalary translocations, containing the H25 gene from rye and the Lr19 gene from wheatgrass. The majority of translocations resulting from irradiation treatments were between nonhomologous chromosomes, and they are genetically unbalanced. Translocations produced by inducing homoeologous pairing were of the compensating type and have greater genetic stability. Hessian fly resistance genes H21 and H25 from rye have been transferred to wheat and are located to wheat-rye chromosome translocations (Friebe et al. 1990, 1991a; Hatchett et al. 1993). Gene H21 was transferred from ‘Chaupon’ rye to wheat and is located to a 2BS-2RL wheat-rye chromosome translocation (Friebe et al. 1990), while gene H25 from ‘Balbo’ rye was transferred to wheat and is located to the 6RL arm of wheat-rye chromosome translocations (Friebe et al. 1991a). As with resistance genes from T. tauschii, the incorporation of genes from rye into adapted wheat cultivars should provide a broader base of genetic resistance to virulent biotypes of the Hessian fly. At present, genes H21 and H25 have been incorporated into wheat germplasm lines, but not cultivars. Gene H27 was identified as part of a wheat chromosome-4 translocation involving the Mv genome of Aegilops ventricosa Tausch (Delibes et al. 1997). 3. Selection Protocols and Field Methods. The relative ease of rearing and maintaining Hessian fly colonies in the laboratory makes it possible to evaluate wheat sources for resistance under greenhouse and growth chamber conditions (Cartwright and LaHue 1944). This enables

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testing that is not often possible in the field, because the greenhouse and growth chamber light, temperature, and humidity conditions are more uniform. Field evaluations of wheat selections for Hessian fly resistance can frequently be inconclusive because of insufficient infestation levels, delays in seeding, inadequate seed germination, and the effect of early freezes that extend evaluations into the winter season (Foster et al. 1988). Difficulty in differentiating between resistant and susceptible plants because of genotype by environment interactions may overburden a breeding program with lines susceptible to the fly and seriously retard breeding progress. Therefore, the method of Cartwright and LaHue (1944), in which selections are grown and tested in greenhouse flats, has been widely adapted for screening wheat for Hessian fly resistance. Successful application of this approach however, requires maintenance of large bulk populations of pure biotypes or field populations of Hessian fly in order to adequately evaluate wheat selections. Methods for collecting, purifying (by biotype), and increasing flies in the greenhouse were described by Sosa (1978), and the method for growing, infesting, and evaluating wheat seedlings for Hessian fly resistance was described by Foster et al. (1988). D. Alternative Control Methods Many biotic and abiotic factors regulate the abundance and destructiveness of the Hessian fly. When these factors favor the insect’s survival and development, populations may increase rapidly from one generation to the next. Consequently, when or where economic infestations may appear is not always predictable. For this reason, control methods for the Hessian fly are mostly preventive rather than remedial. The most important methods, in addition to growing resistant cultivars, are delayed seeding of winter wheat to escape fall infestation, and destroying volunteer wheat. Delayed fall planting, or planting after the “fly free” date, is used to escape the peak emergence of adults. Plantings are adjusted according to the average daily temperature for the specific wheat growing area. Dates range from mid-September in the upper midwestern United States and northeast to late October in the southeast. However, in the southeastern United States, late planting is a poor strategy for managing the Hessian fly because oviposition and larval survival can occur through the winter (Buntin and Chapin 1990). Throughout much of the United States however, the combined practices of growing Hessian flyresistant wheat cultivars, delayed fall seeding, and good management of volunteer wheat are effective in reducing infestations in winter wheat. A number of parasitoids attack the Hessian fly (Krombein et al. 1979; Wellso et al. 1988), but there has been no adequate assessment of their impact on suppressing fly populations. The level of parasitism is often

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very high at the end of the season, and parasitoids contribute to the reduction of the population for the next season. However, they offer no protection for the current crop, which likely has been damaged by the time the parasitoids become a factor. Although several major parasitoids have been studied (Wellso et al. 1988; Schuster and Lidell 1990; Pike et al. 1993), no concerted effort has been made to apply biological control measures in management of the Hessian fly. Chemical control of the Hessian fly is seldom used. Research in Georgia demonstrated that resistant genotypes were as effective in controlling Hessian fly damage as systemic insecticides applied at seeding (Buntin and Chapin 1990). The interaction between wheat and the Hessian fly is a well-defined example of a gene-for-gene interaction between a plant host and insect pest. Approximately 70 years of research has lead to the identification and phenotypic characterization of 29 resistance genes from common and durum wheats, wild wheat relatives, and rye, and several of the corresponding avirulence genes in the Hessian fly. The success that has been achieved in breeding wheat for Hessian fly resistance is underscored by the fact that the fly has been successfully managed in many areas of the United States after resistant cultivars have been grown for several years. Although many of the resistance genes described to date have been transferred to common wheat, relatively few have been utilized in cultivar development. Some genes, such as the H7H8 combination, have been deployed as natural gene combinations in wheat cultivars; however, most have been released individually. Due to the current abundance of available resistance genes, we now can consider various strategies for pyramiding genes in wheat cultivars in order to possibly maximize durability of Hessian fly resistance in the field. Strategies under consideration focus on first determining which genes to combine, which to release individually, and which to deploy in specific geographical regions. The pyramiding of genes that confer biotypespecific resistance has become increasingly attractive as a breeding strategy with the advent of DNA-based, marker-assisted selection. Newly emerging biotechnological advances may provide increasingly efficient procedures for pyramiding resistance genes.

V. RUSSIAN WHEAT APHID* A. Geographic Distribution and Host Range The Russian wheat aphid has been recognized as a sporadic pest of small grains since the turn of the century. In recent years, it has spread beyond its native range to become an important pest in several parts of *Coauthored by F. B. Peairs and S. D. Haley.

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the world, including South Africa and the western United States. The development and deployment of host plant resistance has contributed significantly to the cost-effective and environmentally sound management of this pest. The geographic distribution of Russian wheat aphid has been reviewed by Halbert and Stoetzel (1998). The center of origin is considered to be the region between the Caucasus Mountains and the Tian Shan (Mountains of Heaven) in central Asia. The aphid is common in China in areas bordering the Tian Shan, but it is not considered to be an important pest in that country. Russian wheat aphid is also found in southern and central Europe, the Middle East and North Africa, but again it is not considered to be a damaging pest in these regions. Historical records are sketchy, so it is difficult to determine how long the aphid has been in these areas. However, the first recorded outbreak occurred at the turn of the century in Moldova and the Ukraine. Also, wheat genotypes resistant to Russian wheat aphid are common in southwest Asia (Souza 1998), which indicates that the aphid has been present there for some time. Russian wheat aphid was first reported from South Africa in 1978, where it has become a significant pest of wheat in the low-rainfall production areas of that country as well as in Lesotho (Walters 1984). It was first observed in the western hemisphere in 1980 in central Mexico (Gilchrist et al. 1984). It is well established in this area and can be a pest of wheat and barley in dry seasons and in irrigated winter plantings. Oversummering hosts include cool season grasses, such as crested and intermediate wheat grasses (Agropyron sp.). Russian wheat aphid attacks wheat, barley, triticale, and rye. Oat (Avena sativa L.) can be infested as well, but usually little damage is observed. Other grass species, mostly cool season types, also can serve as Russian wheat aphid hosts (Armstrong et al. 1991). The first observation of the aphid in the United States was in 1986 in Bailey County, Texas. The aphid spread rapidly from there and by 1988, it had been reported from all states to the north and west of Texas, as well as three Canadian provinces (Morrison et al. 1988). In 1987, Russian wheat aphid was found in Chile. However, it has not been a problem there, perhaps due to effective biological controls (Zuñiga 1990). In 1991, Argentina reported the occurrence of the aphid in the Mendoza province (Ortego 1994; Reed and Kindler 1994). Thus, Russian wheat aphid is present in all significant wheat producing areas except Australia, where it is expected to do well should it become established (Hughes and Maywald 1990). B. Biology, Damage, and Economic Impact Russian wheat aphid is holocyclic in its native range, meaning that both parthenogenetic and sexual reproduction occurs. Aphids are

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parthenogenetic during the spring and summer, giving birth to live young. In the fall, males and egg-laying females are produced, mating occurs, and overwintering eggs are laid on wheat and other grass hosts (Poprawski et al. 1992). In the Americas and South Africa, the aphid is anholocyclic, meaning that sexual reproduction is not known to occur. A partial exception to this is in the U.S. Pacific Northwest. There, the egg-laying form exists, but there are no males and no eggs are produced (Kiriac et al. 1990). Three morphological types of Russian wheat aphid are commonly found in North America—wingless immature females, wingless mature females, and mature winged females (Plate 5.2c). The wingless types cause most of the damage to wheat, while the winged adults enable populations to disperse to other wheat fields and to other hosts. Winter wheat is infested in the fall and Russian wheat aphid overwinters on wheat if conditions are favorable. Russian wheat aphid population densities start to increase at spring regrowth. Peak activity usually occurs just prior to heading, and dispersal flights start as early as April, resulting in further infestations of winter wheat and spring grains. Flights peak in late June and early July as the winter wheat crop matures and aphids move to oversummering hosts. The cycle is completed when Russian wheat aphid disperses to newly planted winter wheat in September and October (Peairs 1991). Russian wheat aphid can be found in winter wheat, usually on the younger leaves, from emergence in the fall to grain-ripening. Aphid feeding prevents young leaves from unrolling, and colonies are found within the tubes formed by these tightly curled leaves. Leaves infested by Russian wheat aphid have long white, purple, or yellowish streaks. Under some conditions, infested wheat tillers have a purplish color. Heavily infested plants are stunted and some may appear prostrate or flattened. After flowering, some heads are twisted or distorted and have a bleached appearance. Heads often have a “fish hook” shape caused by awns trapped by tightly curled flag leaves. At this time, most Russian wheat aphids are found feeding on the stem within the flag leaf sheath or on developing kernels. There may be poorly formed or blank grains, and the entire spike sometimes is killed (Peairs 1998a). Chlorophyll content of Russian wheat aphid infested leaves is reduced. Also, chlorophyll fluorescence is lowered in susceptible wheat and barley plants, because aphid feeding inhibits photochemical efficiency in photosystem II (Miller et al. 1994; Burd and Elliott 1996). A decline in photosynthetic rates in susceptible plants has been attributed to an inhibition of electron transport (Haile et al. 1999). Yield losses to Russian wheat aphid vary with the growth stage at infestation, duration of infestation, incidence of infestation, crop con-

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dition at infestation, and other factors. Unlike other cereal aphids, losses are only partially related to aphid density. However, there is more of a relationship between losses and the proportion of infested tillers (Archer and Bynum 1992; Archer et al. 1998a,b). Affected winter wheat yield components are number of spikes, seeds per spike, and seed weight. Spring infestations also result in reduced protein content and dough mix time (Girma et al. 1993). Spray recommendations and decisions to treat are based on an assumption that a 0.5 percent yield loss can be expected for every 1 percent of the tillers that are infested (Legg et al. 1991). The economic impact of the Russian wheat aphid on small grain production in the western United States was assessed for the crop years 1987 to 1993 (Morrison and Peairs 1998). Direct losses were measured as increased production costs due to the need for insecticide treatments and as production losses due to aphid damage. These totaled $432.5 million over the seven-year period. Indirect losses due to Russian wheat aphid infestation, that is, losses sustained by a regional economy due to reduced yields, also were estimated (Amosson 1992). These totaled $460.5 million over the same period, raising the overall seven-year economic impact to $893 million (Morrison and Peairs 1998). Losses were not estimated for 1986, the first year of infestation, or from 1994 onward, although some damage has been sustained, particularly in Colorado and neighboring states.

C. Breeding for Host Plant Resistance 1. Resistance Mechanisms and Genetics. Development of wheat cultivars with host plant resistance to the Russian wheat aphid is unquestionably the most efficient and cost-effective form of control available. Seven resistance genes have been characterized and given Dn (dominant) or dn (recessive) gene symbol designations (McIntosh et al. 1998, 1999). These resistance genes include: Dn1 (Du Toit 1989), Dn2 (Du Toit 1989); dn3 (Nkongolo et al. 1991a) from goatgrass, T. tauschii, Dn4 (Saidi and Quick 1996), Dn5 (Marais and Du Toit 1993), Dn6 (Saidi and Quick 1996), and Dn7 (Marais et al. 1994) from rye. Two other resistance genes identified among pure-line selections from the heterogeneous accession PI 294994 (Elsidaig and Zwer 1993) were recently given the provisional gene symbol designations Dn8 and Dn9 (Liu et al. 2001). Of the genes with Dn symbol designations, Dn4 has been used most widely in both germplasm (Lanning et al. 1993; Quick et al. 1996b) and cultivar (Quick et al. 1996a; Quick et al. 2001a,b,c,d) development efforts. Resistance in wheat germplasm accessions is generally qualitatively inherited and attributed to at most two genes in a particular pure-line

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accession (Du Toit 1989; Nkongolo et al. 1991a, 1991b; Dong and Quick 1995; Saidi and Quick 1996; Dong et al. 1997; Ehdaie and Baker, 1999). Empirical evidence from several years of greenhouse-based screening in our breeding program (unpublished data) suggests; however, that environment or background genetic effects (e.g., modifier genes), or both, may alter expression of major resistance genes. Souza (1998) summarized reports of Russian wheat aphid resistance, and he categorized various small grains as having mechanisms consistent with antixenosis, antibiosis, and/or tolerance. Of the 34 reports he cited, 10 were representative of one category of resistance, 20 were representative of a combination of two, and 4 were representative of all three categories. Conflicting results were common, for example, PI 137739 was categorized as having antibiosis alone, antibiosis and antixenosis, antibiosis plus tolerance, or all three categories (SchroederTeeter et al. 1994). Various biochemical changes may explain expression of resistance to Russian wheat aphid in small grains. Infested ‘Tugela-DN’ plants had higher levels of free amino acids, especially proline, and a higher total phenolic content than infested plants of its susceptible, near isogenic counterpart, ‘Tugela’ (van der Westhuizen and Pretorius 1995). Studies of stylet exudates revealed that Russian wheat aphid feeding on susceptible ‘Arapahoe’ resulted in increased concentrations of essential amino acids, while feeding on resistant ‘Halt’ plants did not (Telang et al. 1999). Unidentified proteins have been associated with resistance to Russian wheat aphid, including a protein complex (23 kD) in PI366450 barley (Miller et al. 1994), a 24 kD complex in PI 140207 wheat (Porter and Webster 2000), 3 polypeptides (32 to 35 kD) in PI 137739 wheat (Rafi et al. 1996), a 100 kD nuclear polypeptide, and a 56 kD organelleencoded polypeptide in PI 137739, and several proteins (15.5 to 17, 18.5 to 19.5, 22 to 24, and 28 to 33 kD) in resistant (Dn1) South African wheat cultivars (van der Westhuizen and Pretorius 1996). Elevated beta1,3-glucanase activity and peroxidase and chitinase levels have been demonstrated in infested Dn1-resistant wheat cultivars (van der Westhuizen et al. 1998). Ni and Quisenberry (1997) found the PI 137739 and PI 225245 accessions to be more antixenotic than three other wheats. PI 137739 had longer trichomes than other lines in this study. A wax removal study (Ni et al. 1998) confirmed the limited role that epicuticular waxes play in Russian wheat aphid host preference. 2. Sources of Host Plant Resistance. Effective levels of resistance have been identified in common wheat and its relatives, and several different sources are currently in use by breeding programs. Souza (1998) recently provided a comprehensive review of sources of resistance to Russian

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wheat aphid. Various research groups have screened wheat germplasm accessions in attempts to identify effective resistance to Russian wheat aphid (Du Toit 1987, 1988; Harvey and Martin 1990; Zemetra et al. 1990; Quick et al. 1991; Smith et al. 1991; Souza et al. 1991; Porter et al. 1993a). In addition to these more targeted germplasm screens, over 40,000 wheat accessions from the National Plant Germplasm Collection of the USDA-ARS have been systematically evaluated for seedling reaction to the Russian wheat aphid (USDA-ARS-NGRP 2001). Of the nearly 300 common wheat accessions that have shown resistant or moderatelyresistant reactions, more than 70 percent originate from the primary area of origin of the Russian wheat aphid, Afghanistan, Iran, and the southern states of the former Soviet Union (USDA-ARS-NGRP 2001). Several other resistance sources have been identified and have proven quite useful for both germplasm and cultivar development. These resistance sources include: PI 149898 (Baker et al. 1994); PI 220127 (Martin and Harvey 1997), PI 220350 (Martin and Harvey 1997), PI 222668 (Haley et al. 2002), and ‘Yilmaz 10’ (Martin and Harvey 1997). Other resistance sources are currently being used by breeding programs in affected regions to extend the range of diversity of available resistance genes. With any uncharacterized resistance source, detailed inheritance and allelism studies are required to provide basic information and foster efficient transfer of the resistance to adapted germplasm. 3. Selection Protocols and Field Methods. General methods for evaluating and categorizing resistance to cereal aphids have been reviewed (Smith et al. 1994). Plant damage ratings usually include measures of chlorosis, leaf rolling, and stunting (Burd et al. 1993). Robinson and Burnett (1992) reviewed greenhouse rearing and field infestation techniques. Schotzko and Smith (1991) and Robinson (1993) emphasized the importance of preconditioning aphids prior to categorizing host plants. BosquePerez and Schotzko (2000) showed that both seedling growth stage and initial infestation level influenced subsequent plant damage. Tolmay et al. (1999) compared available methods and recommended a combination of tests that resulted in complete categorization in six weeks. Identification and subsequent introgression of Russian wheat aphid resistance into adapted breeding lines is usually based on seedling screening procedures in greenhouse environments (Webster et al. 1987; Nkongolo et al. 1991b; Quick et al. 1991). Procedures involve rearing test plants (with resistant and susceptible checks) in soil flats or pots and infesting young seedlings with aphids seven to ten days after planting and prior to full expansion of the first leaf. Russian wheat aphids are applied with a paintbrush to test plants, or via infested leaf tissue segments, following sorting with sieves to eliminate early instar aphids.

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Ratings typically may be done two to three weeks after infestation when symptoms on susceptible checks become apparent. Resistance is distinguished from susceptibility according to standard ratings for the presence of chlorotic spots (or general chlorosis), degree of leaf rolling, and stunting. In general, no replication is required in greenhouse testing, thus enabling efficient evaluation of several thousand genotypes in a single winter screening cycle. Field screening has also been used to confirm resistance identified in greenhouse tests and evaluate the effectiveness of such resistance at adult plant growth stages. Because environmental conditions in the field may not be conducive to aphid development and reproduction, field screening is generally used to complement large-scale seedling screening in the greenhouse. To minimize the potential for resistant-biotype development, gene pyramiding may be desirable. However, due to the lack of a differential host plant series, identification of multiple resistance genes in a single breeding line is generally not feasible without making time-consuming test-crosses. Because a differential series is not available and testcrossing may not be practical in large-scale breeding programs, the identification and use of markers for resistance genes is a requirement for effective pyramiding. Marker-assisted selection (MAS) for Russian wheat aphid resistance may also accelerate backcrossing of resistance into susceptible genotypes and benefit selection when reliable greenhouse screening is not possible (e.g., during warm summer months). Molecular markers linked with several different Russian wheat aphid resistance genes have been identified. Of the genes with Dn symbol designation, markers linked with Dn1 (Liu et al. 2001), Dn2 (Ma et al. 1998; Myburg et al. 1998; Anderson et al. 2000; Liu et al. 2001), Dn4 (Ma et al. 1998; Liu et al. 2001), Dn5 (Venter and Botha 2000; Liu et al. 2001), Dn6 (Liu et al. 2001), and Dn7 (Anderson et al. 2000) have been identified. Markers linked to genes isolated from sub-accessions of PI 294994 (provisionally designated Dn8 and Dn9) have also been identified (Liu et al. 2001), as have other genes whose allelic relationship with known genes has not been clarified (Linscott et al. 2000; Liu et al. 2001). Research is underway to explore the utility of these markers for MAS in wheat breeding programs. D. Alternative Control Methods Much research has gone into developing management approaches, other than the use of host plant resistance, for Russian wheat aphid. These can be classified as cultural, biological, and chemical control approaches. The integrated pest management (IPM) philosophy promotes the use of

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a combination of management practices that result in cost-effective and environmentally sound control of the Russian wheat aphid. Cultural controls for Russian wheat aphid have been reviewed by Peairs (1998b). Sanitation involves the removal of volunteer crop plants, crop residues, weeds and other potential sources of infestation or reinfestation. The most important of these is volunteer wheat and barley, which also serve as important hosts for many other pests and pathogens. Managing volunteer plants to have a two to three week volunteer period is a common recommendation for Russian wheat aphid and other cereal pests (Stuckey et al. 1989). Grazing wheat is a common production practice in the southern plains of the United States. Grazing reduces pest insect densities through ingestions, trampling, and competition. This practice can reduce Russian wheat aphid densities by about 66 percent, although late season densities will be similar in grazed and ungrazed areas (Walker and Peairs 1998). Proper fertilization is thought to result in vigorous plants better able to withstand Russian wheat aphid and other stresses. For example, nitrogen fertilization ameliorated aphid injury, resulting in relatively less yield loss than in plants receiving inadequate nitrogen (Riedell 1990). Seeding dates also can be modified to reduce Russian wheat aphid densities. Generally, early planting dates are recommended for spring grains, while recommendations for fall seeding are more sitespecific. For example, late planting was favorable in Alberta, Canada (Butts 1992), while early and intermediate dates had relatively fewer aphids in southeast Colorado (Walker et al. 1990). These cultural practices target the pest, but no practices have been developed that favor natural enemies. However, crop diversification has been proposed as a strategy to reduce the barriers to effective biological control imposed by the wheat-fallow system (Holtzer et al. 1996). Several subsets of biological control of Russian wheat aphid are recognized. Classical biological control, employed against exotic pests, involves the collection of the pest’s natural enemies in its native habitat and releasing them against the pest within its invaded territory. Conservation biological control involves steps to avoid disrupting naturally occurring biological control. Augmentative biological control is the addition of natural enemies to the crop environment with the goal of supplementing existing natural enemy populations, and inundative biological control is the release of sufficient natural enemies to provide immediate control of a pest infestation. Biological control of Russian wheat aphid has been reviewed by Hopper et al. (1998) and Prokrym et al. (1998). Most efforts employed classical biological control. At least 29 species of insects and 6 species of fungi were observed attacking

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Russian wheat aphid in its native range. These were collected, multiplied, and released in infested areas of the United States. Of these, four species of parasitic wasps appear to have become established in limited areas. Conservation biological control has been practiced indirectly, in that efforts have been made to minimize insecticide use through the development and application of economic injury levels and thresholds. Holtzer et al. (1996) have proposed crop diversification as a means to enhance naturally occurring biological control. Augmentative biological control was ineffective in one evaluation under commercial conditions in Colorado (Randolph et al. 2002), while inundative biological control has not been attempted against Russian wheat aphid because of the prohibitive costs usually associated with this approach. Insecticides recommended for control of Russian wheat aphid vary by region and will change over time. Seed treatments and soil treatments at planting with granular or liquid systemic insecticides can control Russian wheat aphid for a substantial period of time if adequate soil moisture is available. Such preventive treatments are recommended only if the risk of infestation is great, for example, when planting early in the fall or late in the spring or planting near volunteer wheat or barley plants. Fall and spring infestations may be treated with foliar insecticides applied with either aerial or ground equipment. Fall infestations are generally easier to control than spring infestations because of smaller plant size. Effective products include some pyrethroids and systemic organophosphates. Treatments generally provide three weeks of control. Both cool temperatures and drought stress can interfere with the plant’s ability to absorb systemic insecticides. A contact insecticide alone or a contact/systemic tank mix is recommended for crops under stress. Insecticide applications generally have been cost-effective in the United States, but they also have not completely prevented yield losses (Donohue et al. 1998; Peairs 1998a). Optimum pest management of Russian wheat aphid will vary with location. Biological control has developed as the core strategy in Chile (Zuñiga 1990) and the U.S. Pacific Northwest (Pike et al. 1997), supplemented by other control approaches as needed. Areas with the most consistent Russian wheat aphid infestations, Colorado and parts of surrounding states in the United States (Elliott et al. 1998) and South Africa (Tolmay and Prinsloo 1994), have adopted host plant resistance as a core strategy. The Dn4 host plant resistance gene in winter wheat has proven to be an effective management approach, as measured by the need for insecticide treatment (Table 5.2). While the benefit of resistance provided by Dn4 differs with genetic background, insecticide treatment could not be justified even under severe infestation conditions. One

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Table 5.2. Estimated need for insecticide treatment in seven Colorado winter wheat cultivars, based on yield loss under artificial infestation (Peairs, unpublished), (‘Prairie Red’, ‘Prowers’, and ‘Yumar’ are Dn4 resistant backcross versions of ‘TAM 107’, ‘Lamar’, and ‘Yuma’, respectively. ‘Halt’, also Dn4 resistant, was the first Russian wheat aphid resistant wheat released in Colorado.) Wheat Cultivar Halte Tam 107 Prairie Red Lamar Prowers Yuma Yumar

Reductiona

Lost Valueb

Worth Spraying?c

Savingsd

% 5.0 28.9 10.7 47.9 26.7 47.7 11.9

$ per ha 2.32 13.44 4.97 22.27 12.41 22.18 5.53

Yes or No No Yes No Yes Yes Yes No

$ per ha 10.80 — 8.84 — — — 17.39

a

Compares the average yields of severely infested plots to average yields of plots completely protected by insecticides (without regard to cost). b Value of the lost yield, using the Colorado ten-year average yield and price (2.2 t/ha and $127.86 per t). c Determination of sufficient yield loss to justify spraying, assuming a $4.86 per ha cost. d Savings due to using a resistant cultivar, calculated by subtracting the lost value (column 3) of the resistant cultivar from the lost value of the susceptible version of the same cultivar. e The reduction for ‘Halt’ is estimated from several experiments, because it wasn’t directly included in the studies summarized here. The savings for ‘Halt’ were calculated using ‘TAM 107’ as the susceptible comparison.

exception to this was observed with ‘Prowers’ winter wheat (Quick et al. 2001b), which was shown to be heterogeneous for resistance. In greenhouse tests, ‘Prowers 99’ winter wheat (Quick et al. 2001d), a composite selection from Prowers for improved resistance, has shown a similar level of resistance to ‘Prairie Red’ (Quick et al. 2001a) and ‘Yumar’ (Quick et al. 2001c), although this assessment has not been tested under severe field infestation conditions. While it seems that growers could rely solely on host plant resistance for Russian wheat aphid management, we think it prudent to supplement resistant cultivars with other low-cost control approaches. These would include, where feasible, grazing, late planting, crop diversification, conservation biological control, proper fertilization, and other practices that result in a healthy vigorous crop. Our concern is that reliance on host plant resistance has led to the development of insect biotypes many times in the past, including biotypes of other cereal aphids (Porter et al. 1997). Biotypes of Russian wheat aphid do exist in the Old World (Puterka et al. 1992; Basky et al. 2001). While there is no evidence for

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biotype development in entirely asexual Russian wheat aphid populations, neither is there any evidence that the Russian wheat aphid population currently in North America is incapable of reverting to intermittent sexual reproduction. VI. GREENBUG* A. Geographic Distribution and Host Range The greenbug is a serious perennial aphid pest of wheat with a worldwide distribution. It also is a pest of sorghum (Sorghum bicolor L.), and was first reported in North America on oat in Virginia in 1882 (Hunter 1909). The current distribution of greenbug now includes most of the wheat producing areas in North America (Webster and Phillips 1912; Wadley 1931; Leonard 1968). A 1993 survey revealed that over 3.2 million ha (41%) of dryland and 0.5 million ha (93%) of irrigated wheat in the western United States were infested with greenbug (Webster and Amosson 1995). In central Oklahoma, greenbug outbreaks generally occurred following a year of above-normal precipitation during the spring and summer; abovenormal temperature during the winter, spring, and fall; and below-normal temperature during the summer (Rogers et al. 1972). B. Biology, Damage, and Economic Impact The greenbug is a phloem-feeding arthropod pest of wheat. It is a small (approximately 3 mm long) light-green aphid (Plate 5.2d) that feeds on sieve tube sap through a soda-straw shaped proboscis called a stylet. After probing the wheat leaf surface and inserting the stylet, the greenbug secrets watery saliva that contains proteins, which aid in tissue penetration and provides defensive detoxification during feeding (Miles 1999). The aphid intermittently injects and sucks back the watery saliva, presumably to assess the tissue type while probing. The stylet is directed through the maze of intercellular and occasionally the intracellular spaces to the phloem, which is the ultimate feeding site (Chatters and Schlehuber 1951). Once settled into the ingestion phase of feeding, the aphid feeds passively on sap flowing into the stylet due to turgor pressure in the sieve tubes (Miles 1999). In the 1950s, when greenbug resistant wheat began to be developed, biotypes were identified as differing in their abilities to damage resistant plants. Greenbug biotypes are genetically distinct populations, and each biotype is a phenotypic expression of an indefinite number of *Coauthored by D. R. Porter.

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genotypes (Puterka and Peters 1990). Eleven greenbug biotypes (designated A through K) have been identified using multiple wheat and sorghum differentials. The first report on greenbug biotypes was by Wood (1961), who described biotype B as being able to damage ‘Dickinson Selection 28A’ (DS 28A) wheat. This wheat germplasm line was resistant to the population of greenbugs existing in the field at that time. Consequently, the field population of greenbugs to which ‘DS 28A’ wheat was resistant was designated biotype A. Harvey and Hackerott (1969) described greenbugs that caused severe damage to sorghum during the 1968 crop season as a new biotype, “C.” Biotype C greenbugs damaged seedlings of ‘Piper’ sudan grass (Sorghum sudanense Piper Stapf), which were highly resistant to biotype B. One biotype that was not classified using differentials was biotype D. Instead, biotype D was described by Teetes et al. (1975) and Peters et al. (1975) based on resistance to organophosphorous insecticides and not on its ability to damage previously resistant plants. As such, resistance to biotype D is not included as an objective when developing resistant wheat. Porter et al. (1982) reported that a field collection of greenbugs made in 1979 damaged biotype C-resistant ‘Amigo’ wheat, and therefore, they designated these greenbugs biotype E. An isolate of greenbugs collected in Ohio was designated biotype F because of its ability to damage Canada bluegrass (Poa compressa L.) resistant to biotypes B, C, and E (Kindler and Spomer 1986). Biotype F greenbugs appeared to be similar to biotype A because of their avirulence on DS 28A wheat. Puterka et al. (1988) cultured greenbug isolates collected from Oklahoma and Texas in 1987; tested them on resistant barley, wheat, and sorghum; and compared reactions with those of biotypes E and F. The Oklahoma isolate, designated biotype G, damaged all known resistance sources of wheat. The Texas isolate, biotype H, damaged ‘Post’ barley, which was resistant to all previous biotypes. Harvey et al. (1991) reported greenbugs damaging biotype E-resistant sorghum hybrids in Kansas during August 1990. Greenhouse evaluations using field-collected greenbugs confirmed that a virulent isolate, designated biotype I, damaged all biotype E-resistant sorghums, except a plant introduction (PI 266965). Biotype I caused reactions similar to biotype E on wheat and other small grains. Beregovoy and Peters (1995) described a greenbug isolate collected from wheat in Idaho as biotype J based on its avirulence to all barley and wheat resistance sources tested, including the susceptible wheat control ‘Triumph 64’. Since biotype J is avirulent to wheat, it is not a target when developing greenbug resistant wheat. Harvey et al. (1997) designated a greenbug isolate biotype K that was able to damage PI 550610, a biotype I-resistant sorghum plant introduction. This isolate was originally collected from wheat in southwestern Kansas in 1992 and identified as

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biotype I. It was used to evaluate sorghum resistance until the spring of 1995, when personnel at NC+ Hybrids at Hastings, Nebraska, observed sorghum lines previously resistant to biotype I were now susceptible to this isolate. Greenbug samples taken from sorghum and wheat fields throughout Kansas and Oklahoma revealed that biotype I was the dominant biotype on both crops, followed by E and K (Kindler et al. 2001). Gradual shifts in the prevailing biotype (i.e., from biotype C to E, then E to I) have occurred over the years. Biotype E can still be found in the field, along with biotype K, but at much lower frequency than biotype I (Kindler et al. 2001). Biotype G is also very prevalent throughout the southern Great Plains on noncultivated grasses (Anstead 2000). Greenbug feeding activity results in distinguishable leaf damage. Symptoms are characterized by yellow-red necrotic lesions at the feeding site surrounded by a larger area of chlorosis that eventually leads to general necrosis of the leaf. Greenbugs feeding on winter wheat seedlings during the fall cause irreversible damage. Population densities of 30 aphids per culm, which fed for seven days, caused reductions of at least 40 percent in grain weight (Kieckhefer and Kantack 1988). In addition to leaf damage, greenbug feeding reduces root length and dry weight (Burton 1986; Riedell and Kieckhefer 1995). Thus, greenbug infestations of winter wheat at the seedling stage may adversely impact a plant’s ability to withstand environmental stresses, such as drought and heat during plant development. This impact can be especially significant during grain-fill. Ryan et al. (1987) showed that greenbug feeding on susceptible wheat diminished photosynthetic capacity, which exacerbated drought stress effects on the plant. In addition to direct plant damage caused by feeding, the greenbug is also a vector of viral diseases, notably barley yellow dwarf virus. The first confirmed widespread barley yellow dwarf epidemic in winter wheat, which occurred during the 1977 to 1978 growing season in southern Idaho, was transmitted by greenbug (Forster 1990). When greenbug populations are very low, annual losses reach approximately $1.6 million (Webster and Amosson 1995). However, when populations are high, such as in 1976, annual losses exceed this estimate. In 1976, losses due to greenbug damage were estimated at $80 million in the state of Oklahoma alone (Starks and Burton 1977a). C. Breeding for Host Plant Resistance 1. Resistance Mechanisms and Genetics. Efforts to develop greenbugresistant wheat began in the 1950s with the identification of greenbug-

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resistant ‘DS 28A’ selected from durum wheat (Dahms et al. 1955). Evaluation of segregating populations of ‘DS 28A’ indicated that a single recessive gene controlled resistance to greenbug (Daniels and Porter 1958; Curtis et al. 1960). Host plant resistance mechanisms have been ascribed to tolerance to greenbug toxins (Curtis et al. 1960) and reduction in greenbug reproduction (Painter and Peters 1956). DS 28A was resistant to greenbugs in the field at the time (Curtis et al. 1960), but later was shown to be susceptible to biotype B (Wood 1961) and biotypes C and E (Porter et al. 1982). Kindler and Spomer (1986) reported DS 28A was resistant to biotype F. These relationships are presented in Table 5.3. Curtis et al. (1960) assigned the gene symbol gb to the single recessive gene for resistance in DS 28A. In Argentina, Arriaga (1956) reported the development of ‘Insave F.A.’ rye, which was highly resistant to the greenbug. The resistance in ‘Insave F.A.’ is reportedly controlled by a single, dominant gene, which was designated Rpv (Arriaga and Re 1963). Wood et al. (1969) reported Insave F.A. rye to be resistant to the newly detected biotype C. In 1966, Insave F.A. rye was crossed with ‘Chinese Spring’ wheat to transfer greenbug resistance to wheat. Resistant wheat-rye hybrid selections were treated with colchicine to produce Gaucho, an octoploid triticale resistant to biotype C (Sebesta et al. 1994, 1996). Gaucho was used in additional crosses to wheat, and with x-ray irradiation treatments, the greenbug resistance gene was transferred to wheat through production of a chromosome translocation (Sebesta and Wood 1978). The resultant greenbug resistant wheat genotype, ‘Amigo’, was released in 1976 and registered in 1995 (Sebesta et al. 1995). Greenbug resistance in Amigo is controlled by a gene located to a T1AL.1RS chromosome translocation (Hollenhorst and Joppa 1983). This single, dominant gene was designated Gb2 (Tyler Table 5.3.

Greenbug resistance genes and biotype interactions in wheat. Genotype Reactiona Greenbug Biotype

Genotype

Gene Designation

Origin

B

C

E

F

G

H

I

K

DS 28A Amigo Largo CI 17959 CI 17882 GRS 1201

gb1 Gb2 Gb3 Gb4 Gb5 Gb6

T. turgidum durum S. cereale T. tauschii T. tauschii T. speltoides S. cereale

S R S S S R

S R R R R R

S S R R R R

R S S S S S

S S S S S R

S S R S S S

S S R R R R

S S R R R R

a

R = resistant, and S = susceptible.

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et al. 1987). Amigo wheat is resistant to biotypes B (Sebesta and Wood 1978) and C, but not E (Porter et al. 1982). In addition to greenbug resistance, Amigo was resistant to several important wheat diseases (Sebesta et al. 1995). The source of greenbug resistance in Amigo was transferred to the wheat cultivar ‘TAM 107’, which was released to growers in August 1984 (Porter et al. 1987). Amigo was also used in the development of ‘Century’ wheat, released in 1986 (Smith et al. 1989) and ‘TAM 110’, released in 1996 (Lazar et al. 1997). Joppa et al. (1980) identified a gene for resistance to biotype C in a plant introduction (PI 268210) and transferred this resistance to the wheat germplasm line ‘Largo’ (Joppa and Williams 1982). Joppa et al. (1980) reported that a single, dominant gene controlled tolerance to biotype C. Porter et al. (1982) reported that Largo was resistant to biotypes C and E. Two additional sources of resistance to biotype E, wheat germplasm lines CI 17959 and CI 17882, were reported by Martin et al. (1982) and Tyler et al. (1985), respectively. Tyler et al. (1987) determined that the greenbug resistances discovered in CI 17959 and CI 17882 were controlled by single, dominant genes. Friebe et al. (1991b) located the resistance gene in CI 17882, Gb5, to chromosome 7S from Aegilops speltoides [now Triticum speltoides (Tausch) Gren.]. Chromosome 7S had substituted for wheat chromosome 7A in this germplasm line. Given the regular detection of new, damaging biotypes in greenbug populations, a question arises as to what is the best strategy to use in deploying resistance genes to maximize their durability. Porter et al. (1997) examined this question by chronicling the history of greenbug biotype reports, the history of breeding wheat for resistance, and the relationship between wheat resistance and greenbug biotypes. They reported that there is a popular misconception about the role that greenbug resistance genes in wheat play in the development of new biotypes. That misconception is based on the theory that the widespread use of a resistant cultivar with a single, major antibiosis gene will enhance the selection of new, virulent insect biotypes (Smith 1989). Their analysis concluded that although there has been a steady stream of reports of new, virulent greenbug biotypes over the years, the deployment of resistance genes in wheat had nothing to do with selecting for these virulent greenbug subpopulations. None of the biotypes reported could have been affected by greenbug resistance in wheat because there was never a wheat cultivar in field production that was resistant to the greenbug biotype prevalent at the time (Porter et al. 1997). The release of TAM 110 in 1996 marked the first time that a wheat cultivar had been in production that is resistant to the predominant greenbug biotypes (i.e., biotypes E, I, and K). Pyramiding resistance genes is a breeding strategy that, in theory, increases the longevity of resistance (Nelson 1972), and some greenbug

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resistance gene pyramiding has occurred in wheat. For example, Amigo and Largo resistances were combined to produce the germplasm lines, TXGH10563B and TXGH13622 (Porter et al. 1989), and TAM 110 (Lazar et al. 1997). The gene pyramiding theory for prolonging greenbug resistance was evaluated by Porter et al. (2000). Wheat genotypes containing Gb2, Gb3, and Gb6 alone and in the combinations Gb2/GB3, Gb2/Gb6, and Gb3/Gb6 were tested for effectiveness against greenbug biotypes E, F, G, H, and I. Results of these tests showed that pyramiding provided no additional protection over that conferred by the single resistance genes. Based on the results of these tests, Porter et al. (2000) concluded that the sequential release of single resistance genes, combined with monitoring of greenbug population biotypes, was the most effective gene deployment strategy for greenbug resistance in wheat. Biotype G of greenbug has not been reported to be a problem in wheat fields. Given the biotypes now prevalent in wheat fields (biotypes E, I, and K), incorporation of Gb3, Gb4, Gb5, or Gb6 in newer cultivars should provide adequate protection from greenbug attack. Should biotype G become established in wheat fields, ‘GRS 1201’ (Gb6) is the only known source of resistance available. Another consideration when deciding which source of resistance to use and how to deploy it is the overall relative effectiveness of a gene or genes. While all genes are effective in providing host plant protection against greenbug feeding, there are differences in the type and level of resistance. Webster and Porter (2000) compared the level of biotype E resistance in Largo with GRS 1201 using a combination of plant and aphid measurements (that is, antibiosis, antixenosis, and tolerance). They found that GRS 1201 had a higher overall level of resistance than Largo. 2. Sources of Host Plant Resistance. Tyler et al. (1987) reviewed the status of greenbug biotypes and sources of resistance in wheat. They assigned designations for 5 genes (gb1, Gb2, Gb3, Gb4, and Gb5) for the sources of resistance in the wheat germplasms DS 28A, Amigo, Largo, CI 17959, and CI 17882. Since that time, one additional source of resistance has been reported in wheat. Porter et al. (1991) reported identification of greenbug biotype G resistance in a wheat-rye translocation germplasm (GRS-1201). GRS 1201 has a single, dominant gene (Gb6) located to the rye arm (1RS) of the wheat-rye translocation chromosome T1AL.1RS. It is resistant to biotypes B, C, E, G, and I (Porter et al. 1994), and it was formally released in 1992 (Porter et al. 1993b). ‘GRS 1204,’ a similar source of resistance to biotype G, was shown to have two nonhomoeologous wheat-rye chromosome translocations, T2AS-1RS.1RL and T2AL.2AS1RS, which were produced using radiation (Friebe et al. 1995). Since Gb6 is located on a nonhomoeologous chromosome translocation in GRS

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1204, GRS 1201 would be a better choice for transferring greenbug resistance to wheat (Friebe et al. 1995). Another source of biotype G resistance involving a wheat-rye translocation chromosome was reported in the germplasm line TX85C5820-5 (Tuleen et al. 1992). Early attempts to find greenbug resistance genes were made by screening collections of hexaploid wheats. But, greenbug resistance genes were difficult to locate within hexaploid wheat collections. This lack of success, changed the breeding focus to moving resistance genes into wheat from related species. Table 5.3 shows the sources of the six resistance genes and the reactions of all sources of resistance in wheat to greenbug biotypes as reported by Harvey et al. (1991), Porter et al. (1994), and Harvey et al. (1997). 3. Selection Protocols and Field Methods. An efficient mass screening technique for identifying and evaluating greenbug resistance in wheat has been developed and refined over the years (Starks and Burton 1977b). This technique relies on greenhouse evaluations of seedling plants using greenbugs from cultures reared specifically for testing. Rearing large numbers of aphids for testing is relatively simple, but it is labor and space-intensive. Typically, about 30 seeds of a susceptible wheat or barley cultivar are planted into 20 cm pots of a commercially available potting soil and enclosed with a cylindrical plastic cage (approximately 35 cm tall) that is vented on the sides and top. The plants are caged to keep greenbugs confined and prevent contamination from other insects. About 14 days after planting, aphids are introduced into the cages to reproduce and increase in number. After about two to three weeks of aphid population development, the heavily infested leaves of the culture plants are then used for infesting wheat seedlings to be tested. Aphid-infested leaves are also used to infest newly emerging seedlings in additional culture pots to maintain adequate aphid populations. Wheat genotypes to be tested are planted in either hills or rows in metal flats (51 × 35 × 9 cm) containing a soil mix. To screen large numbers of individual genotypes in search of new sources of resistance, five seeds per entry are planted in hills spaced 4.5 cm apart. With this spacing, each metal flat can accommodate 60 hills (that is, 60 wheat entries). For a more detailed test of individual genotypes, 30 seeds of each entry are planted in a row (10 rows per flat). This allows for a better assessment of the level of resistance and also enables one to observe possible segregation. Standard procedure is to include known greenbug susceptible and resistant wheat checks in each flat. Seedlings are infested about three days after emergence by placing aphid-infested leaves from the culture plants between the rows of

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seedlings. Greenbugs move from the detached leaves and onto the seedlings, where they quickly become established and begin feeding. Ten to 14 days after infestation (when susceptible check plants are dead) plants are rated for resistance using a 1 to 9 scale, where 1 = no damage and 9 = dead plant (Starks et al. 1983). At the end of the test, remaining greenbugs can be killed with an insecticide and resistant seedlings can be transplanted to pots and grown for seed increase. Burton et al. (1991) provides an excellent overview of the entire testing and selection process typically employed for developing aphid-resistant wheat germplasm. Greenbug resistance screening can also be accomplished in the field, but this is not a recommended approach. Ensuring uniform aphid infestation across field tests can be difficult. Furthermore, aphids are exposed to a highly variable environment that may reduce their population levels and consequently, reduce the selection pressure needed to assess host plant resistance. The use of molecular markers for developing greenbug resistance has been limited. The Gb5 resistance gene on chromosome 7S of T. speltoides was transferred to an interstitial chromosome segment of 7AL in ‘Pavon’ wheat with the aid of the ph1b mutation, which promotes higher than normal homoeologous chromosome pairing (Dubcovsky et al. 1998). Gb5, located on the resultant 7AS.7AL-7S#1L.7AL chromosome, provides resistance to greenbug biotypes C, E, I, and K (Table 5.3). In the presence of the wild-type Ph1 locus, chromosome segment 7S#1 does not recombine with wheat chromosome 7A. Therefore, only one of several RFLP markers associated with 7S#1 is needed to track Gb5 (Dubcovsky et al. 1998). A wheat germplasm line (designated UCRBW98-2) carrying the 7AS·7AL-7S#1L·7AL with Gb5 was released for breeding purposes (Lukaszewski et al. 2000). Markers have also been identified for resistance genes Gb2 and Gb6, which are located to the 1RS arm of the wheat-rye translocation chromosome T1AL.1RS (Graybosch et al. 1999). In the presence of the wild-type Ph1 locus, 1RS is inherited as a nonrecombined block of genes. Therefore, the secalin proteins and ryespecific PCR markers associated with 1RS (Graybosch et al. 1999) can also be used to track Gb2 and Gb6. D. Alternative Control Methods Greenbugs have a number of natural enemies (predators and parasitoids) that contribute to control by suppressing populations during the growing season. Small parasitic wasps lay eggs inside the body of the aphid causing the aphid to swell and turn a tan color as the immature wasp feeds inside. This swollen, tan-colored aphid is called a “mummy.” Important predators of greenbugs include lady beetles, lacewing larvae,

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and hover fly larvae (Royer et al. 1997b). Another interesting greenbug control method being evaluated is the use of entomopathogenic fungi. Feng et al. (1990) reported pathogenicity of an aphid-derived isolate of Beauveria bassiana and Verticillium lecanii on greenbugs and five other aphid species. While these fungi may be able to kill greenbugs, the environmental conditions needed for them to be effective (that is, 23 to 25°C, and 92 to 100% relative humidity) may limit their efficacy across all wheat production regions. Although not necessarily economical, the most common method used to control greenbugs in wheat has been the application of chemical insecticides. Treatment thresholds vary by plant growth stage (e.g., two to four aphids per tiller at the seedling stage, two to eight aphids per tiller when plants are 7.6 to 15.2 cm in height, and eight to twenty aphids per tiller when the plants are 15.2 to 40.6 cm inches in height) and crop condition (Royer et al. 1997b). Chemicals listed for use against greenbugs in wheat include chlorpyrifos, dimethoate, disulfoton, imidacloprid, malathion, methyl parathion, parathion, and a mix of parathion and methyl parathion (Royer et al. 1997a). Unfortunately, in addition to the extra costs associated with the use of insecticides and their potential for contributing to environmental contamination, the greenbug has demonstrated an ability to develop resistance to insecticides. Resistance to several organophosphorous insecticides, including disulfoton and dimethoate, has been reported in Texas and Oklahoma (Peters et al. 1975; Teetes et al. 1975). Sloderbeck et al. (1991) reported that greenbugs collected from a sorghum field in Kansas, which had received two aerial applications of parathion, showed resistance to parathion and chlorpyrifos-methyl. The resistance to parathion is due to the combined effects of metabolic detoxification and target site insensitivity (Siegfried and Ono 1993). Surveys indicate that a low level of insecticide resistance persists in the absence of selection pressure in greenbug populations over large areas of Kansas and Oklahoma (Wilde et al. 2001).

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Smith, C. M., D. Schotzko, R. S. Zemetra, E. J. Souza, and S. Schroeder-Teeter. 1991. Identification of Russian wheat aphid (Hemiptera: Aphididae) resistance in wheat. J. Econ. Entomol. 84:328–332. Smith, E. L., R. C. Sharma, O. G. Merkle, E. E. Sebesta, R. L. Burton, J. A. Webster, R. M. Hunger, D. C. Abbott, B. F. Carver, and G. H. Morgan. 1989. Registration of Century wheat. Crop Sci. 29:1093–1094. Smith, M. A. H., and R. J. Lamb. 2001. Factors influencing oviposition by Sitodiplosis mosellana (Diptera: Cecidomyiidae) on wheat spikes (Gramineae). Can. Entomol. 133:533–548. Somsen, H. W., and P. Luginbill. 1956. A parasite of the wheat stem sawfly. USDA Tech. Bul. No. 1153. Sosa, O., Jr. 1978. Biotype L, ninth biotype of the Hessian fly. J. Econ. Entomol. 71:458–460. Souza, E. J. 1998. Host plant resistance to the Russian wheat aphid (Homoptera: Aphididae) in wheat and barley. p. 122–147. In: S. S. Quisenberry and F. B. Peairs (eds.), A response model for an introduced pest—the Russian wheat aphid. Thomas Say Publ. Entomol., Entomol. Soc. Am., Lanham, MD. Souza, E., C. M. Smith, D. J. Schotzko, and R. S. Zemetra. 1991. Greenhouse evaluation of red winter wheats for resistance to the Russian wheat aphid (Diuraphis noxia, Mordvilko). Euphytica 57:221–225. Starks, K. J., and R. L. Burton. 1977a. Preventing greenbug outbreaks. USDA Leafl. 309. Starks, K. J., and R. L. Burton. 1977b. Greenbugs: determining biotypes, culturing, and screening for plant resistance with notes on rearing parasitoids. USDA Tech. Bul. 1556. Starks, K. J., R. L. Burton, and O. G. Merkle. 1983. Greenbugs (Homoptera: Aphididae) plant resistance in small grains and sorghum to biotype E. J. Econ. Entomol. 76:877–880. Stuart, J. J., S. J. Schulte, P. S. Hall, and K. M. Mayer. 1998. Genetic mapping of Hessian fly avirulence gene vH6 using bulked segregant analysis. Genome 41:702–708. Stuckey, R., J. Nelson, G. Cuperus, E. Oelke, and H. Bahn. 1989. Wheat pest management: a guide to profitable and environmentally sound production, pesticide safety, and use. Oklahoma State Univ./Cooperative Extension Service/Wheat Industry Resource Committee. Sun, S., H. Ni, H. Ding, Z. Qu, and S. Zhang. 1998. Studies on the mechanism of biochemical resistance of wheat to wheat midge (in Chinese with English abstract). Sci. Agr. Sinica 31:24–29. Teetes, G. L., C. A. Schaefer, J. R. Gipson, R. C. McIntyre, and E. E. Latham. 1975. Greenbug resistance to organophosphorous insecticides on the Texas High Plains. J. Econ. Entomol. 68:214–216. Telang, A., J. Sandstrom, E. Dyreson, and N. A. Moran. 1999. Feeding damage by results in a nutritionally enhanced phloem diet. Entomologia Experimentalis et Applicata 91:403–412. Tolmay, V., and G. Prinsloo. 1994. Russian wheat aphid (Diuraphis noxia) in South Africa. p. 182–184. In: F. Peairs, M. Kroening, and C. Simmons. (eds), Proc. 6th Russian Wheat Aphid Workshop, Fort Collins, CO. Tolmay, V. L., M. C. Van der Westhuizen, and C. S. Van Deventer. 1999. A six week screening method for mechanisms of host plant resistance to Diuraphis noxia in wheat accessions. Euphytica 107:79–89. Tsvetkov, S. 1973. An attempt for transferring stem solidity of T. durum Desf. to T. aestivum L. with the aim of developing new forms of winter common wheat with solid stem, resistant to Cephus pygmaeus L. Wheat Inf. Serv. 37:1–4.

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Tuleen, N. A., K. B. Porter, and G. L. Peterson. 1992. Registration of TX85C5820-5 greenbug-resistant wheat germplasm. Crop Sci. 32:289. Tyler, J. M., J. A. Webster, and O. G. Merkle. 1987. Designations for genes in wheat germplasm conferring greenbug resistance. Crop Sci. 27:526–527. Tyler, J. M., J. A. Webster, and E. L. Smith. 1985. Biotype E greenbug resistance in wheat streak mosaic virus-resistant wheat germplasm lines. Crop Sci. 25:686–688. USDA-ARS-National Genetic Resources Program. 2001. Germplasm Resources Information Network–(GRIN). National Germplasm Resources Laboratory, Beltsville, MD. [Online] Available: http://www.ars-grin.gov/cgi-bin/npgs/html/desc.pl?65047. van der Westhuizen, A. J., and Z. Pretorius. 1995. Biochemical and physiological responses of resistant and susceptible wheat to Russian wheat aphid infestation. Cereal Res. Commun. 23:305–313. van der Westhuizen, A. J., and Z. Pretorius. 1996. Protein composition of wheat apoplastic fluid and resistance to the Russian wheat aphid. Austral. J. Plant Physiol. 23:645–648. van der Westhuizen, A. J., X. M. Qian, and A. M. Botha. 1998. b-1,3-glucanases in wheat and resistance to the Russian wheat aphid. Physiol. Plant. 103:125–131. Venter, E., and A.-M. Botha. 2000. Development of markers linked to Diuraphis noxia resistance in wheat using a novel PCR-RFLP approach. Theor. Appl. Genet. 100:965–970. Wadley, F. M. 1931. Ecology of Toxoptera graminum, especially as to factors affecting importance in the northern United States. Ann. Entomol. Soc. Am. 24:325–395. Walker, C. B., and F. B. Peairs. 1998. Influence of grazing on Russian wheat aphid (Homoptera: Aphididae) infestations in winter wheat. p. 297–303. In: S. S. Quisenberry and F. B. Peairs (eds.), A response model for an introduced pest—the Russian wheat aphid. Thomas Say Publ. Entomol., Entomol. Soc. Am., Lanham, MD. Walker, C. B., F. B. Peairs, and D. Harn. 1990. The effect of planting date in southeast on Russian wheat aphid infestations in winter wheat. p. 54–62. In: G. Johnson (ed.), Proc. 4th Russian Wheat Aphid Workshop, October 10–12. Ext. Serv., Montana State Univ., Bozeman, MT. Wall, A. 1952. The diameter of the wheat stem in relation to the length and sex of emerging sawfly. Sci. Agr. 32:272–277. Wallace, L. E., F. H. McNeal, and M. A. Berg. 1973. Minimum stem solidness required in wheat for resistance to the wheat stem sawfly. J. Econ. Entomol. 66:1121–1123. Wallace, L. E., F. H. McNeal, and M. A. Berg. 1974. Resistance to both Oulema melanopus and Cephus cinctus in pubescent-leaved and solid-stemmed wheat selections. J. Econ. Entomol. 67:105–107. Walters, M. C. 1984. Progress in Russian wheat aphid (Diuraphis noxia Mordw.) research in the Republic of South Africa. Tech.l Commun. 191, Dept. Agr., Republic of South Africa. Webster, F. M. 1891. The wheat midge, Diplosis tritici, Kirby. Bul. Ohio Agr. Expt. Sta., Ser. 2, 4:99–114. Webster, F. M., and W. J. Phillips. 1912. The spring grain-aphis or greenbug USDA Bur. Entomol. Bul. 110. Webster, J. A., and S. Amosson. 1995. Economic impact of the greenbug in the western United States: 1992–1993. Great Plains Agr. Council Publ. 155. Webster, J. A., and D. R. Porter. 2000. Plant resistance components of two greenbug (Homoptera: Aphididae) resistant wheats. J. Econ. Entomol. 93:1000–1004. Webster, J. A., K. J. Starks, and R. L. Burton. 1987. Plant resistance studies with Diuraphis noxia (Homoptera: Aphididae), a new United States wheat pest. J. Econ. Entomol. 80:944–949.

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Weiss, M. J., and W. L. Morrill. 1992. Wheat stem sawfly (Hymenoptera: Cephidae) revisited. Amer. Entomol. 38:241–245. Weiss, M. J., Morrill, W. L., and L. L. Reitz. 1987. Influence of planting date and spring tillage on the wheat stem sawfly. Montana Agr. Expt. Sta. Agr. Res. 4:2–5. Wellso, S. G., R. P. Hoxie, and C. R. Olien. 1989. Effects of Hessian fly (Diptera: Cecidomyiidae) larvae and plant age on growth and soluble carbohydrates of Winoka winter wheat. Environ. Entomol. 18:1095–1100. Wellso, S. G., B. Chen, J. E. Foster, P. L. Taylor, and R. P. Hoxie. 1988. Selected bibliography of North American parasitoids of the Hessian fly. Purdue Agr. Ext. Serv. Bul. 533, West Lafayette, IN. Wilde, G. E., R. A. Shufran, S. D. Kindler, H. L. Brooks, and P. E. Sloderbeck. 2001. Distribution and abundance of insecticide resistant greenbugs (Homoptera: Aphididae) and validation of a bioassay to assess resistance. J. Econ. Entomol. 94:547–551. Wise, I. L., R. J. Lamb, and M. A. H. Smith. 2001. Domestication of wheats (Gramineae) and their susceptibility to herbivory by Sitodiplosis mosellana (Diptera: Cecidomyiidae). Can. Entomol. 133:255–267. Wood, E. A., Jr. 1961. Biological studies of a new greenbug biotype. J. Econ. Entomol. 54:1171–1173. Wood, E. A., Jr., H. L. Chada, and P. N. Saxena. 1969. Reaction of small grains and grain sorghum to three greenbug biotypes. Oklahoma. Ag. Expt. Sta. Prog. Rep. P-618., Stillwater, OK. Wright A. T., and J. F. Doane. 1987. Wheat midge infestation of spring cereals in northwestern Saskatchewan. Can. J. Plant Sci. 67:117–120. Zemetra, R. S., D. Schotzko, C. M. Smith, and E. J. Souza. 1990. Seedling resistance to the Russian wheat aphid in white wheat germplasm. Cereal Res. Commun. 18:223–227. Zheng, S. 1965. Wheat midge (in Chinese). Agr. Press, Beijing, China. Zuñiga, E. 1990. Biological control of cereal aphids in the southern cone of South America. p. 362–367. In: P. A. Burnett (ed.), World perspective on barley yellow dwarf. CIMMYT, Mexico.

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6 Peanut Breeding and Genetic Resources C. Corley Holbrook U.S. Department of Agriculture–ARS P.O. Box 748 Tifton, Georgia 31793 H. Thomas Stalker Department of Crop Sciences North Carolina State University P.O. Box 7620 Raleigh, North Carolina 27695

I. II. III. IV. V.

INTRODUCTION EVOLUTION AND TAXONOMY REPRODUCTIVE DEVELOPMENT CYTOGENETICS AND GENOMES GENETIC RESOURCES A. Collections B. Germplasm Exchange C. Core Collections VI. BREEDING PEANUT A. Conventional Breeding Methods B. Marker-Assisted Selection C. Interspecific Hybridization D. Transgenic Technology VII. SUMMARY LITERATURE CITED

Plant Breeding Reviews, Volume 22, Edited by Jules Janick ISBN 0-471-21541-4 © 2003 John Wiley & Sons, Inc. 297

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I. INTRODUCTION Peanut is widely used as an oilseed crop around the world and as a direct source of human food in the United States. Several species of peanut have been cultivated for their edible seeds, but only Arachis hypogaea L. has been domesticated and widely distributed. Production in the United States is completely mechanized, but in many other regions the seeds are planted and harvested by hand. In the United States, approximately 70 percent of the peanuts are runners (small-seeded types of var. hypogaea), 20 percent are virginias (large-seeded types of var. hypogaea), 10 percent are spanish (var. vulgaris), and less than 1 percent are valencia (var. fastigiata) market types (Knauft and Gorbet 1989). Peruvian and aequatoriana types are produced in only a few countries in Central and South America. In the United States, production is controlled by a federal price support system that controls the quantity and guarantees a minimum price to the producer. Historically, it has been to the producer’s advantage to maximize yields by having large amounts of inputs during each crop year. Thus, in the United States, plant breeders have concentrated efforts on maximizing yields under the constraints of market acceptability. Large amounts of pesticides are applied to the crop and in recent years, breeding for insect and disease resistance has become a priority. In other regions of the world, especially in drier areas, there are few inputs used for subsistence production systems. Yields are restricted in most areas worldwide by diseases, especially leaf spots (Shokes and Culbreath 1997) and rust (Subrahmanyam 1997), and yield per hectare averages less than half of the United States production. Incorporating biotic stress tolerance is an objective of breeding efforts in areas where applying pesticides and fungicides is not economical. However, incorporating disease resistance or tolerance tends to decrease yield potential. The crop improvement situation has dramatically changed within the past few years in the United States as restrictions for pesticide applications have been imposed and with the occurrence of diseases (e.g., tomato spotted wilt virus) for which no chemical controls are available. Thus, much of the plant breeding efforts are being redirected from only developing cultivars with high yields to ones that are also incorporating resistance genes to plant and seed pathogens. Consumer concerns about food quality of peanut has become increasingly important. Because peanuts are susceptible to Aspergillus infection which results in aflatoxin production, all seed lots are tested at buying points and by processors to eliminate toxins from the food chain. Peanuts also have proteins that result in allergic reactions in about 0.6

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percent of the population (Li et al. 2000). Trace amounts of peanut protein can lead to fatal anaphylactic reactions in individuals allergic to peanuts, and this is a great concern for the industry. In the United States, many peanuts are dry roasted, which apparently increases the allergic properties of the proteins (Maleki et al. 2000). Refined peanut oil does not contain protein and thus the oil is allergen free. However, when the seed is cold pressed, as is done in many parts of the world, proteins remain in the oil used for cooking and allergic reactions can occur. There are four market types in the United States (runner, virginia, spanish, and valencia), and during the 1970s and the 1980s, three cultivars [‘Florunner’ (Norden et al. 1969), ‘Florigiant’ (Carver 1969), and ‘Star’ (Simpson 1972)] dominated production. The large-seeded virginia types are predominately grown in the Virginia–North Carolina area, but during the past few years there also have been significant hectarages in western Texas. The runner market type is grown predominately in the Southeast and Southwest, and spanish types are grown in Texas and Oklahoma, although their importance has greatly diminished during recent years. Valencia market types are mostly produced in New Mexico for the in-shell market, but to a lesser extent in other regions for the fresh market or boiling trade. Virginia types are consumed as in-shell or roasted products, whereas most of the runner types are used for peanut butter. In most other countries, peanuts are crushed for oil, but in the United States only seed lots deemed unsuitable for human consumption are used for oil stocks. These seed lots usually are high in aflatoxins, which are eliminated during oil extraction and purification, and there is a significant price reduction to producers. Uniformity of commercial product has been promoted by the industry, so peanut breeding programs have selected new cultivars that closely match previous ones in size and quality traits. Cultivars that have had wide distribution are commonly used as parents in hybridization programs, and thus the genetic base of peanut cultivars has historically been very limited. However, since the late 1980s, a large number of diverse cultivars have been released by private and public plant breeding programs, and consequently the genetic base of commercially produced germplasm is much broader at the present time. Parental selection is an important consideration in plant breeding, and with uniformity requirements imposed by the peanut industry, the genetic base of peanut will continue to be relatively narrow in the future. The genetics of peanut was reviewed by Wynne and Coffelt (1982), Murthy and Reddy (1993), and Knauft and Wynne (1995). Both qualitative and quantitative variations are abundant in the domesticated peanut, but the inheritance of only a few traits has been documented.

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Reciprocal cross differences have also been reviewed by these authors for yield and other agronomic traits, which result from cytoplasmic and paternal inheritance. Because of the excellent reviews already in print about breeding methodologies for peanut, this chapter will concentrate on summarizing germplasm issues, interspecific hybridization, as well as breeding efforts with A. hypogaea and related species since the reviews published by Knauft and Ozias-Akins (1995) and Knauft and Wynne (1995).

II. EVOLUTION AND TAXONOMY Species in the genus Arachis are distinguished from most other plants by flowering above ground, but producing fruits below the soil surface. Peanut is a member of the Fabaceae, tribe Aeschynomeneae, subtribe Stylosanthinae in the genus Arachis. Only A. hypogaea has been domesticated, although several species have been cultivated for their seed (A. villosulicarpa Hoehne and A. stenosperma Krapov. and W. C. Gregory) or forage (A. pintoi Krapov. and W. C. Gregory and A. glabrata Benth). The domesticated species was described by Linneaus in 1753 as Arachis (from the Greek “arachos,” meaning weed) and hypogaea (meaning underground chamber). Arachis hypogaea is believed to have originated in the southern Bolivia to northern Argentina region of South America, and in this region many types are found with primitive plant, pod, and seed characteristics. The species is an annual herb with two subspecies that are primarily distinguished by branching pattern and distribution of vegetative and reproductive nodes along the mainstem and lateral branches. Subspecies hypogaea has two botanical varieties (hypogaea and hirsuta), and subsp. fastigiata has four botanical varieties (fastigiata, vulgaris, peruviana, and aequatoriana) (Krapovickas and Gregory 1994) (Table 6.1). Isleib and Wynne (1983) grouped lines using principal component analyses and found that most morphological differences are observed between subspecies. Six A. hypogaea centers of diversity have evolved in South America, including the geographic regions of (1) Guarani (Paraguay-Paraná), (2) upper Amazon and west coast of Peru, (3) Goiás and Minas Gerais region of Brazil, (4) Rondonia and northwest Mato Grosso regions of Brazil, (5) southwest Amazon region in Bolivia, and (6) northeastern Brazil. An important center of diversity also exists in Africa where a large amount of variation was likely generated through hybridization and selection in different African environments (Wynne and Coffelt 1982). Although A. hypogaea is believed to have originated east of the Andes Mountains, the oldest archeological findings are in

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Peru, dated ca. 1500 BCE (Banks 1987; Banks et al. 1993) where peanut predates the remains of maize (Zea mays L.) in the region of the Casma Valley. This Peruvian site may be the oldest simply because of good preservation conditions of pods in the dry climate, or there could have been a secondary domestication event; although recent molecular data indicates a single origin of A. hypogaea (Kochert et al. 1996).

Table 6.1.

Arachis hypogaea subspecific and varietal classification.a

Botanical Variety

Market Type

hypogaea

hirsuta

Location Bolivia, Amazon

Traits No flowers on the mainstem; alternating pairs of floral and reproductive nodes on lateral branches; branches short; relatively few trichomes

Virginia

Large seeds; less hairy

Runner

Small seeds; less hairy

Peruvian runner

Peru

fastigiata

More hairy Flowers on the mainstem; sequential pairs of floral and vegetative axes on branches

Valencia

Brazil— Guaranian Goias Minas Gerais Paraguay Peru Uruguay

Little branched; curved branches

peruviana

Peru, N. W. Bolivia

Less hairy, deep pod reticulation

aequatoriana

Ecuador

Very hairy, deep pod reticulation; purple stems, more branched, erect

vulgaris

Spanish

a

Brazil— Guaranian Goias Minas Gerais Paraguay Uruguay

After Stalker and Simpson (1995).

More branched; upright branches

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In addition to the domesticated species, 68 wild species have been described (Krapovickas and Gregory 1994), and several additional ones have been collected but are without descriptors (Valls 2000). Arachis species are native to a large region of South America extending from the foothills of the Andes to the Atlantic and from the northern shores of Brazil to about 34°S in Uruguay. Valls et al. (1985) reported that species distributions are nearly continuous, and there is an extensive amount of distributional overlap among taxa in different sections of the genus. The greatest amounts of variation are found in Brazil where species of Arachis originally evolved (Gregory et al. 1980). The genus has been divided into nine sections (Krapovickas and Gregory 1994) (Table 6.2), which consists of diploid (2n = 2x = 20), tetraploid (2n = 4x = 40) and Table 6.2. Arachis species identities (type holotype unless other wise designated) (from Krapovickas and Gregory 1994; Stalker and Simpson 1995).

Section and Species Section Arachis batizocoi Krapov. & W. C. Gregory benensis Krapov., W. C. Gregory & C. E. Simpson cardenasii Krapov. & W. C. Gregory correntina (Burkart) Krapov. & W. C. Gregory cruziana Krapov., W. C. Gregory & C. E. Simpson decora Krapov., W. C. Gregory & Valls diogoi Hoehne duranensis Krapov. & W. C. Gregory glandulifera Stalker helodes Martius ex Krapov. & Rigoni herzogii Krapov., W. C. Gregory and C. E. Simpson hoehnei Krapov. & W. C. Gregory hypogaeaa,b L. ipaensis Krapov. & W. C. Gregory kempff-mercadoi Krapov., W. C. Gregory & C. E. Simpson kuhlmannii Krapov. & W. C. Gregory magna Krapov., W. C. Gregory & C. E. Simpson

Status

Collectord

Collection No.

– sp. nov.

K KGSPSc

9505 35005

sp. nov. com. nov. sp. nov.

KSSc Clos

36015 5930

KSSc

36024

sp. nov. – sp. nov. – – sp. nov.

VSW Diogo K St Manso KSSc

9955 317 8010 90-40 588 36030

sp. nov. – sp. nov.

KG Linn. KMrFr

30006 9091 19455

sp. nov. sp. nov. sp. nov.

KGPBSSc KG KGSSc

30085 30034 30097

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Section and Species

Status

303

Collectord

Collection No.

Section Arachis (cont.) microsperma Krapov., W. C. Gregory & Valls monticola Krapov. & Rigoni palustris Krapov., W. C. Gregory & Valls praecox Krapov., W. C. Gregory & Valls simpsonii Krapov. & W. C. Gregory stenosperma Krapov. & W. C. Gregory trinitensis Krapov. & W. C. Gregory valida Krapov. & W. C. Gregory villosa Benth. williamsii Krapov. & W. C. Gregory

sp. nov. – sp. nov. sp. nov. sp. nov. sp. nov. sp. nov. sp. nov. – sp. nov.

VKRSv K VKRSv VS KSSc HLK Wi KG Tweedi WiCl

7681 8012 6536 6416 36009 410 866 30011 1837 1118

Section Caulorrhizae pintoi Krapov. & W. C. Gregory repens Handro

sp. nov. –

GK Otero

12787 2999

Section Erectoides archeri Krapov. & W. C. Gregory benthamii Handro brevipetiolata Krapov. & W. C. Gregory cryptopotamica Krapov. & W. C. Gregory douradiana Krapov. & W. C. Gregory gracilis Krapov. & W. C. Gregory hatschbachii Krapov. & W. C. Gregory hermannii Krapov. & W. C. Gregory major Krapov. & W. C. Gregory martii Handro oteroi Krapov. & W. C. Gregory paraguariensis ssp. paraguariensis Chodat & Hassl. ssp. capibarensis Krapov. & W. C. Gregory stenophylla Krapov. & W. C. Gregory

sp. nov. – sp. nov. sp. nov. sp. nov. sp. nov. sp. nov. sp. nov. sp. nov. sp. nov. sp. nov.

KCr Handro GKP KG GK GKP GKP GKP Otero Otero Otero

34340 682 10138 30026 10556 9788 9848 9841 423 174 194

– ssp. nov. sp. nov.

Hassler HLKHe KHe

6358 565 572

Section Extranervosae burchellii Krapov. & W. C. Gregory lutescens Krapov. & Rigoni macedoi Krapov. & W. C. Gregory marginata Gardner pietrarellii Krapov. & W. C. Gregory prostrata Benth. retusa Krapov., W. C. Gregory & Valls setinervosa Krapov. & W. C. Gregory villosulicarpa Hoehne

sp. nov. – sp. nov. – sp. nov. – sp. nov. sp. nov. –

Irwin et al. Stephens GKP Gardner GKP Pohl VPtSv Eiten & Eiten Gehrt

21163 255 10127 3103 9923 1836 12883 9904 SP47535 (continued)

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304 Table 6.2.

C. HOLBROOK AND H. STALKER (continued)

Section and Species Section Heteranthae dardani Krapov. & W. C. Gregory giacomettii Krapov., W. C. Gregory, Valls & C. E. Simpson pusilla Benth. sylvestris (A. Chev.) A. Chev. Section Procumbentes appressipila Krapov. & W. C. Gregory chiquitana Krapov., W. C. Gregory & C. E. Simpson kretschmeri Krapov. & W. C. Gregory lignosab (Chodat and Hassl.) Krapov. & W. C. Gregory matiensis Krapov., W. C. Gregory & C. E. Simpson rigonii Krapov. & W. C. Gregory subcoriacea Krapov. & W. C. Gregory vallsii Krapov. & W. C. Gregory

Status

Collectord

Collection No.

sp. nov. sp. nov.

GK VPzV1W

12946 13202

– –

Blanchet Chevalier

2669 486

sp.nov. sp. nov.

GKP KSSc

9990 36027

sp. nov. com. nov. sp. nov.

KrRa Hassler

2273 7476

KSSc

36014

– sp. nov. sp. nov.

K KG VRGeSv

9459 30037 7635

–

Archer

4439

Section Rhizomatosae Ser. Prorhizomatosae burkartii Handro Ser. Rhizomatosae glabrata var. glabrata Benth. var. hagenbeckiic Benth. (Harms ex. Kuntze) F. J. Herm. pseudovillosac (Chodat & Hassl.) Krapov. & W. C. Gregory

–

Riedel

1837

– com. nov.

Hagenbeck Hassler

2255 5069

Section Trierectoides guaranitica Chodat & Hassl. tuberosa Bong. ex Benth

– –

Hassler Riedel

4975 605

Section Triseminatae triseminata Krapov. & W. C. Gregory

sp. nov.

GK

12881

a

See Table 1. Type Lectotype. c Type Lecto holotype. d Collectors: B = Banks, Cl = Claure, Cr =Cristobal, Fr = Fernandez, G = Gregory, Ge = Gerin, H = Hammons, He = Hemsy, K = Krapovickas, Kr = Kretchmere, L = Langford, Mr = Mroginski, P = Pietrarelli, Pt = Pittman, R = Rao, Ra = Raymon, S = Simpson, Sc = Schinini, St = Stalker, Sv = Silva, V = Valls, Ve = Veiga, Vl = Valente, W = Werneck, and Wi = Williams. Others = as listed. b

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aneuploid species (2n = 2x = 18). Diversity apparently occurred in the genus as species became separated into major drainage systems, and species of sections Erectoides, Extranervosae, and the diploids of section Rhizomatosae are believed to be most ancient. The center of genetic variation for the genus is the Mato Grosso region of Brazil to eastern Bolivia (Stalker et al. 1994). However, when specifically comparing A. hypogaea to other species, the greatest probability of finding unique genes is in the north-central, northeast, south, and southeast regions of Brazil. These are areas where species distantly related to the domesticated peanut are found and ones that are cross incompatible with A. hypogaea. More than 1300 accessions of Arachis species have been collected (Stalker and Simpson 1995), and importantly, many of these are cross compatible with the domesticated species. Arachis villosulicarpa, which grows in the northeastern region of Mato Grosso, Brazil and A. stenosperma, which grows in central and southeast Brazil have also been grown for their seeds (Gregory et al. 1973; Simpson et al. 1993b). In the case of A. stenosperma, seeds were apparently carried either by the native people or missionaries from central Brazil to the southeast because plants of the species are found at abandoned missionary sites. Several species have been used for forages, including A. glabrata which has several cultivars under cultivation (Prine et al. 1986, 1990). Unfortunately, this species produces few seeds, and propagation is entirely through rhizomes. Arachis pintoi is cultivated as a forage in South America and Australia (Asakawa and Ramirez-R 1989; Cameron et al. 1989). This species produces large numbers of seeds and is relatively easy to establish under field conditions. Numerous other species have been used as ornamentals, including A. repens Handro, which is used extensively as a roadside and landscape plant in Central and South America (Stalker and Simpson 1995). Large plant breeding efforts have been undertaken since the mid-1950s to characterize species for agronomically useful traits (Lynch and Mack 1995; Stalker and Simpson 1995), and many accessions have been identified with high levels of disease and insect resistances.

III. REPRODUCTIVE DEVELOPMENT The morphology, anatomy, and reproductive development of peanut has been described numerous times (Gregory et al. 1973; Rao 1988; Moss and Rao 1995) and will only be briefly described here. Inflorescences are borne in the axils of leaves on both primary and secondary branches.

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They are simple or compound and each has up to five flowers. Only one flower per inflorescence usually opens on any given day. Flowers are papillionaceous and sessile, but appear to be stalked because of an elongated tubular hypanthium or calyx tube. Styles are contained within the calyx tube, and both the style and calyx tube rapidly elongate during the 12 to 24 h prior to anthesis and can reach a length of 5 cm or more. The hypanthium is attached to the base of the ovary, which is superior. The corolla consists of a standard, wing and keel petals, and the calyx has five sepals that are borne on the distal end of the elongated hypanthium. The standard ranges in color from deep orange to light yellow, and in rare cases it may be white. A central crescent area exists on the face of the standard that is usually darker in color, or in some cases a different color than the remainder of the standard (Moss and Rao 1995). Wings are usually the same color as the standard. Flowers contain ten androecium, with five anthers being elongated and the remaining five being more globular and small. One or more anthers is usually sterile and difficult to observe. Maeda (1972) observed that sterility is more common in spanish and valencia types than in virginia types. Microsporogenesis in peanut was described by Xi (1991). Pollen is usually mature 6 to 8 h prior to anthesis (Pattee et al. 1991) and, at anthesis the pollen is two-celled and each cell has two nuclei. Stigmas are generally as long as or slightly shorter than the anthers. Both the stigma and anthers are enclosed by the keel, which induces selffertilization. However, bees may be needed to trip flowers in some species, and these insects also carry pollen to other plants resulting in up to 8 percent outcrossing (Knauft et al. 1992). Pollination occurs at approximately the same time as anthesis, which occurs a few hours after sunrise (Pattee et al. 1991). The stigmatic surface contains enzymes that promote pollen adhesion (Lu et al. 1990), and within 8 h after anthesis these enzymes apparently degrade. Thus, the most successful crossing in artificial hybridization programs occurs during the early morning hours. In contrast to the large stigmatic surface of A. hypogaea and other annual species in the genus, perennials have small stigmas which are surrounded by hairs, that greatly limit the number of pollen grains that can adhere to the receptive surface (Lu et al. 1990). This may account for annuals being much better seed parents than perennials in crossing programs, as well as generally producing greater numbers of seeds in germplasm nurseries. The ovary of peanut is unilocular and usually has one to three ovules. The embryo sac has a prominent egg, and starch grains surround the endosperm nucleus. After fertilization, the starch grains disappear, and a multicellular proembryo develops (Pattee and Stalker 1991). The

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flower petals then wither within 24 hours, but the hypanthium and style usually remain attached to the base of the ovary for up to five days (Pattee and Mohapatra 1986). The proembryo divides three to four times (resulting in an 8 to 16 nucleate egg) and then becomes quiescent at the time when a meristem located adjacent to the basal ovule becomes active. A carpophore (or gynophore, but commonly called a “peg”) begins to elongate with positive geotropism into the soil (Zamski and Ziv 1976). After the peg enters the soil it becomes diageotropic (i.e., begins to grow horizontally), ceases to elongate, and at the same time it swells, and the embryos resume cell division. Pods usually develop in the absence of light (Ziv 1981), but aerial pods can occur. In A. hypogaea, pod development generally begins 16 to 17 days after pollination, but in other species the process may be delayed until 23 to 25 days (Halward and Stalker 1987a). Pegs of the domesticated species are relatively short and do not break easily, but pegs of Arachis species may be a meter or more in length and are fragile. The seed has two cotyledons, a hypocotyl, epicotyl, and radicle. The cotyledons comprise nearly 96 percent of the seed weight and are the major storage tissue for the developing seedlings (Moss and Rao 1995). The seeds contain about 20 percent carbohydrates, 45 percent oil (Ahmed and Young 1982), and 25 percent protein (Amaya et al. 1977); but a considerable range in oil and protein percentage exists among genotypes. Most peanuts are deficient in lysine and tryptophane, and allergens are associated with seed storage proteins (Burks et al. 1998).

IV. CYTOGENETICS AND GENOMES Both diploid (2n = 2x = 20) and tetraploid (2n = 4x = 40) species have evolved in the genus, and three species have been identified with 18 chromosomes, including A. decora Krapov., W. C. Gregory and Valls, A. palustris Krapov., W. C. Gregory and Valls, and A. praecox Krapov., W. C. Gregory and Valls (Lavia 1996, 1998; Krapovickas and Lavia 2000). This is surprising since trisomics are rather common in shriveled seeds of A. hypogaea, but monosomics are extremely rare (Stalker 1985b). Tetraploids are found in section Arachis, including A. hypogaea and A. monticola Krapov. and Rigoni, and in sections Extranervosae and Rhizomatosae. Polyploidy is believed to have originated independently in different sections (Smartt and Stalker 1982), and diploids are likely to be more ancient than tetraploids. The chromosomes of Arachis species are small, ranging from 1.4 to 3.9 µm in length, and several species have been karyotyped (Stalker and Dalmacio 1981; Singh and Moss 1982;

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Stalker 1985a, 1991; Lavia 1998). Species in section Arachis generally have metacentric chromosomes, with the exception of A. glandulifera Stalker, which is highly asymmetrical (Stalker 1991). Six chromosomes appear to have little karyotypic change, whereas the nucleolus organizer region is found in different positions on the seventh chromosome, and there have been many structural changes in the other three chromosomes of Arachis species (Murty et al. 1985; Bharathi et al. 1983; Kirti et al. 1983; Jahnavi and Murty 1985). Based on their observations, species in sections Erectoides, Extranervosae, and Triseminatae are more ancient than species in sections Arachis and Rhizomatosae. Section Arachis is important because the domesticated peanut belongs in the group and introgression from closely related species should be easier than from more distantly related species. Three genomes have been defined in section Arachis, including the A genome found in most species; the B genome species as represented by A. batizocoi Krapov. and W. C. Gregory, A. ipaensis Krapov. and W. C. Gregory, A. cruziana Krapov., W. C. Gregory and C. E. Simpson (Burow et al. 1997b), and likely A. williamsii Krapov. and W. C. Gregory (Lavia 1996; Tallury et al. 2001), A. hoehnei Krapov. and W. C. Gregory, and A. magna Krapov., W. C. Gregory & C. E. Simpson (S. P. Tallury, pers. comm. 2001); and the D genome is represented by A. glandulifera (Stalker 1991). Translocations appear to be common in A. batizocoi (Stalker et al. 1991a), and the nucleolar organizer chromosome is karyotypically different in botanical varieties of A. hypogaea (Stalker and Dalmacio 1986). Other genomes have been proposed in sections Ambinervosae (Am) [Ambinervosae was changed to Heteranthae by (Krapovickas and Gregory 1994)], Erectoides (E), Caulorhizae (C), Extranervosae (Ex), and Triseminalae (T) (Smartt and Stalker 1982). Tetraploids in section Rhizomatosae appear to have similarities to the genomes of sections Erectoides and Arachis (Stalker 1981b, 1985a). Arachis hypogaea is believed to be an allotetraploid species, in large part because of karyotypic differences in chromosomes where only one distinctively small chromosome pair is observed and the diploidlike chromosome pairing during meiosis. Smartt and Gregory (1967) concluded that A. hypogaea has A and B genomes. Kochert et al. (1991) supported this conclusion because RFLP analyses showed that most gel lanes had two bands in A. hypogaea and only one band in diploids. Molecular data indicates that the domesticated peanut had a single-event origin and that introgression from related species has been extremely limited (Kochert et al. 1996). Only one additional tetraploid species belongs to section Arachis (A. monticola) which Stalker and Simpson

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(1995) concluded is a weedy derivative of A. hypogaea rather than its progenitor. Hybridization of species between different sections of Arachis is possible, but the F1s are completely sterile (Gregory and Gregory 1979). Using amphiploids of species in closely related sections to produce hybrids can enhance cross compatibility, and chromosome homologies can be detected; but Stalker (1981a) reported that progenies of section Erectoides × Arachis hybrids were sterile and fertility restoration was impossible. Hybridization of species within a section produce results ranging from progenies with complete sterility to ones having a high level of fertility. In section Arachis, hybrids between A-genome species produce progenies with relatively high fertility levels and 10 bivalents (Stalker and Moss 1987; Stalker et al. 1991b). Hybrids between Agenome and either B- or D-genome species have four to eight univalents and plants are sterile (Stalker et al. 1991b). V. GENETIC RESOURCES A. Collections The domesticated peanut was carried by the Spaniards to Malaysia, China, Indonesia, and Madagascar (Krapovickas 1969). The peanut also moved eastward to Europe and then to Africa. Peanuts were likely introduced into North America from Brazil by way of slave ships that were resupplied in northeastern Brazil to complete the voyage (Stalker and Simpson 1995). The most common method of collecting peanut has been to gather samples in small markets in the primary and secondary centers of diversity in South America. A large amount of diversity exists in these markets because local farmers grow an array of genotypes. Samples are typically separated based on pod and seed characteristics (Stalker and Simpson 1995). Many markets have yielded up to 30 accessions, and Stalker and Simpson (1995) noted one market in Brazil that yielded 86 distinct lines. A greater amount of agronomic data can be obtained when collections are directly made from farmer’s fields. However, travel to individual locations is very time-consuming and in many cases, travel to remote areas is difficult because of poor or nonexisting roads. Limited seed supply that is being saved for the next growing season is also problematic because farmers must retain ample seed stocks for planting during the following year. Sampling strategies for peanut have been summarized by Simpson (1985), who concluded that a 1 kg sample of

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seed is sufficient when the seeds are visually homogeneous for adequate division between the donor country and the participants on a collection trip, but larger samples are needed if variation exists. Because of the large seed size of peanut, handling large samples on germplasm expeditions is difficult. From 1959 to date, there have been more than 40 collection trips in South America for A. hypogaea and related peanut species (Stalker and Simpson 1995). In addition, J. Smartt introduced a large number of A. hypogaea accessions into the United States from Africa during the 1960s and a large number of accessions were also introduced from Israel in the 1970s by Dr. A. Ashri. The USDA germplasm collection numbers over 8000 accessions of A. hypogaea (Holbrook 2001) and about 800 accessions of Arachis species (Stalker and Simpson 1995). Large Arachis species collections are also maintained at Texas A&M, N. C. State University, the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) in India, and at the National Center of Genetic Resources (CENARGEN) in Brazil. The largest collection of domesticated peanut is at ICRISAT, where there are 14,310 accessions from 92 countries and 413 accessions of Arachis species (Upadhyaya et al. 2001a). Descriptor lists have been published for A. hypogaea by International Board for Plant Genetic Resources (IBPGR) and ICRISAT (1992) and the USDA (Pittman 1995). Simpson et al. (1992) applied 53 of the descriptors to 2000 lines and observed a large amount of variation in pod and seed characters. Although the plant descriptions outline the basic structure of variation in A. hypogaea, many intermediates exist, and the taxonomy of the cultivated peanut is not always clear. A large number of accessions in the ICRISAT collection have been evaluated for morphological traits, water use efficiency, and reactions to many disease and insect pests of peanut (Upadhyaya et al. 2001a). Accessions in the U.S. collection have not been evaluated as extensively as the ones at ICRISAT. Williams (2001) discussed using the geographic information system (GIS) to more effectively study, locate, and conserve Arachis genetic resources. Based on existing germplasm collections and geographical distribution of genetic diversity, Williams (2001) concluded that additional collection of wild Arachis species is warranted in eastern Bolivia and northwestern Paraguay. Several areas within primary and secondary centers of diversity that warrant further collection of the cultivated species were also listed. Preservation of accessions in the A. hypogaea collection is generally straightforward and regeneration decisions are based on the total number and age of seeds available in storage, and the number of requests made by the user community. Both the USDA peanut curator and plant breed-

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ers from private industry, state-funded universities, and the USDA have taken a vested interest in the germplasm collection and have cooperated to assure adequate seed reproduction. Sanders et al. (1982) indicated that the sum of temperature (F) plus relative humidity should be less than 100 to have optimal seed storage. Under ideal storage conditions, peanuts remain viable for 15 or more years, and A. J. Norden (pers. comm. 1990) stored seeds for 20 years with good germination percentages when seeds were stored at 10°C and 45 percent relative humidity. Wild Arachis accessions are more difficult to maintain than ones of the domesticated peanut. Species of Arachis occupy a wide range of habitats in South America, and accessions can be lost before adequate growing conditions under cultivation can be discovered, especially for species that produce few seeds. Both annual and perennial species exist in nature, and the annuals generally produce greater numbers of seeds than perennials. Of the more than 1300 Arachis wild species accessions that have been collected, about 800 remain in germplasm nurseries (Stalker and Simpson 1995). Arachis marginata Gardner is an example of a species that is very difficult to maintain in the United States because its plants grow very slowly and are weak. The Triseminales species A. tuberosa Benth. and A. guaranitica Chodat and Hass1. enter permanent dormancy when seeds are dried, but seeds have been kept viable for several years in moist sphagnum moss. Under long day conditions, many species flower profusely but do not produce pegs; whereas under short day conditions, many species have a very high reproductive efficiency but do not produce many flowers (Stalker and Wynne 1983). At the autumnal and vernal equinoxes, many species produce sufficient numbers of flowers and pegs to greatly increase seed numbers of plants derived from either self-pollination or interspecific hybrids (Stalker unpublished data). More than 25 percent of the species accessions currently in germplasm nurseries (especially the Rhizomatosae species) produce very few seeds and are maintained as vegetative materials in greenhouses. Many perennial species will not produce seeds when grown under greenhouse conditions and only a few seeds when propagated in the field. Stalker and Simpson (1995) indicated that 23 percent of the Arachis collection have fewer than 50 seeds in storage at any of the sites where peanuts are maintained. Because peanuts produce pegs, either placing pots close to each other in the greenhouse or failing to isolate plots in the field can result in seed mixtures. Several curators only propagate the wild species collection under greenhouse conditions, but this results in few seeds being produced and restricts evaluations for agronomically useful traits. At North Carolina State University, plots are grown in blocks that are 5 to 10 m

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apart in all directions, and accessions of cross-incompatible species are grown adjacent to each other. This greatly reduces outcrossing, but very low levels of cross-contamination have been found between plots containing the same species even when they were separated by incompatible accessions. Species of sections Arachis, Erectoides, and Procumbentes appear to outcross more than species in other sections of the genus. B. Germplasm Exchange Germplasm exchange has become more difficult during recent years as countries have imposed more strict quarantine policies. Diseases such as rosette virus are found only in Africa, and other production areas have attempted to avoid introducing this virus. A one- to two-year observation period under greenhouse conditions is generally imposed to restrict introductions of unwanted diseases. The USDA Plant Introduction Station at Griffin, Georgia has routinely screened introductions for peanut stripe Potyvirus (PStV) using bioassays since the mid-1980s. Property right issues have not greatly affected the movement of peanut germplasm within the United States, but since the Convention on Biological Diversity in 1993, international seed exchange has become significantly more tedious and restricted (Williams and Williams 2001). Germplasm obtained prior to 1993 at the CGIAR Centers, including ICRISAT which has a mandate to preserve Arachis genetic resources, is freely available; but germplasm obtained since then is subject to the terms of the Convention on Biological Diversity. Fortunately, a memorandum of understanding has been signed by the USDA and ICRISAT to facilitate exchange of germplasm (Shands and Bertram 2000). Both institutions have agreed to forego claims of ownership and intellectual property rights on exchanged germplasm. The same policy applies to germplasm forwarded to state or private institutions when it is passed through the USDA (Williams and Williams 2001). In addition to restricting exchange of germplasm already in collections, field collections have also been hindered since implementation of the Convention on Biological Diversity. Leaders of many countries wish to have monetary compensation for germplasm and they do not recognize advantages of in-kind benefits, such as storage and maintenance, sharing herbarium and seed collections, joint publication, and research (Williams and Williams 2001). Williams and Williams (2001) reported making special arrangements with the governments of Ecuador and Paraguay so they could collect peanut in these countries. Host scientists were actively involved in collection trips, and the benefits to those countries were

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clearly established prior to collecting seeds. For example, in Ecuador, rather than immediately sending seeds to the United States after collecting samples, they planned a seed-increase nursery in the host country. They also arranged for the materials to be characterized in Ecuador under contract to the USDA and both countries then had access to the data set prior to the germplasm being exported. For a collection expedition in Paraguay, the USDA agreed to store germplasm at the United States National Seed Storage Lab because there are no genebank facilities in that country. Their efforts should serve as a model for collection of peanut and other crop species in these and other countries. C. Core Collections The U.S. A. hypogaea collection contains a great amount of genetic diversity, but the germplasm has not been extensively used in cultivar development (Knauft and Gorbet 1989). Ironically, the size of the collection has limited its use because of the costs associated with screening all accessions for traits that could be used in cultivar development. A core collection (Frankel 1984; Frankel and Brown 1984) was developed for the U.S. A. hypogaea germplasm collection by Holbrook et al. (1993). Data on peanut in the Germplasm Resource Information Network (GRIN) were used for selecting entries, and included country of origin and measurements on plant type, pod type, seed size, testa color, number of seeds per pod, and average seed weight. Available information on accessions varied from only the country of origin to information for all seven variables. The U.S. germplasm collection was first stratified by country of origin and then divided into nine sets based on the amount of additional information available for accessions and on the number of accessions per country of origin. This procedure resulted in the selection of 831 accessions from the U.S. A. hypogaea germplasm collection. Accessions included in the core collection are noted in GRIN. To maximize the usefulness of the peanut core collection, the relationship between the individual accessions and the clustering procedure used in its development is available to users ([email protected]). The first format lists accessions in numerical order so that the cluster designation for individual accessions can be rapidly determined. The second file lists accessions by clusters so that all accessions within a cluster can be rapidly determined. The core collection approach to germplasm evaluation is a two-stage approach. The first stage involves examining all accessions in the core collection for a desired characteristic. This information is then used to

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determine which clusters of accessions in the entire germplasm collection should be examined during the second stage of screening. Theoretically, the probability of finding additional accessions with a desired characteristic would be highest in these clusters. Holbrook and Anderson (1995) used data on resistance to late leaf spot [Cercosporidium personatum (Berk. & M. A. Curtis)] that was available for the entire peanut germplasm collection to retrospectively determine how effective the use of this core collection would have been for identifying sources of resistance in the entire collection. Disease ratings for the core accession(s) representing each cluster were defined as the indicator value for that cluster. Data were first examined to determine how many leaf spot resistant accessions would have been identified by examining the core collection. Data were also examined to determine how many leaf spot resistant accessions would have been identified by examining all accessions from clusters having a resistant indicator value. The use of a two-stage screening approach on this peanut core collection would have resulted in the identification of 61 leaf spot resistant accessions. This approach would have required screening 27 percent of the entire collection and would have identified 54 percent of the resistant accessions in the entire collection. In addition, the best four (and eight of the best ten) sources of resistance in the entire collection would have been identified. Holbrook et al. (2000c) also evaluated the effectiveness of a two-stage core screening approach in identifying resistance to the peanut root-knot nematode [Meloidogyne arenaria (Neal) Chitwood race 1] in the U.S. germplasm collection of peanut. Accessions from 30 clusters having resistant indicator values and from four clusters having susceptible indicator values were tested for resistance in greenhouse trials. The efficiency of identifying resistance to the peanut root-knot nematode in clusters having resistant indicator values was significantly better than the success rate in clusters having susceptible indicator values. These results demonstrated that the use of a two-stage screening approach with a core collection could improve the efficiency of identifying valuable genes in germplasm collections. A major benefit of having a peanut core collection has been a significant increase in peanut germplasm evaluation work (Holbrook 1999). In the United States, peanut is a regional crop with relatively few individuals involved in breeding and genetic research. The development of a peanut core collection has allowed germplasm evaluations that require fewer resources. Anderson et al. (1996a) screened the peanut core collection for resistance to tomato spotted wilt virus (TSWV), which is among the greatest yield-reducing viruses affecting peanut. The peanut

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core collection provided a logical subset of peanut germplasm that could be rapidly screened and they identified 55 accessions with resistance. Isleib et al. (1995a) screened the peanut core collection for resistance to cylindrocladium black rot (CBR) initiated by Cylindrocladium parasiticum Crous, Wingefield, et Alfenas and to early leaf spot (Cercospora arachidicola Hori). In a greenhouse screening trial, 11 core accessions had greater resistance to CBR than NC 3033, the resistant check. In field trials, 12 early maturing core accessions had a level of resistance to early leaf spot similar to that of GP-NC 343, the resistant check. Twelve medium or late maturing lines had less defoliation than the resistant check. Because of the high costs associated with evaluating peanut for preharvest aflatoxin contamination (PAC), it is not feasible to examine the entire collection for resistance to PAC. Use of the peanut core collection has resulted in the first report of field resistance to PAC in the U.S. peanut germplasm collection (Holbrook et al. 1998c). Fourteen accessions averaged 70 percent reduction in PAC and six of these accessions exhibited a 90 percent reduction in PAC in multiple years of testing. The development of the peanut core collection was essential for conducting this research. The most agronomically acceptable portion of the core collection also has been evaluated for resistance to Rhizoctonia limb rot (Rhizoctonia solani Kuhn, AG-4) (Franke et al. 1999). This subset of the core collection consisted of 66 accessions having a spreading or spreading-bunch growth habit. Six core accessions had a high level of resistance to Rhizoctonia limb rot. The peanut core collection has provided a logical subset of the entire germplasm collection that can be extensively examined for traits other than disease resistance. Holbrook and Anderson (1993) measured plant descriptor information for all accessions in the core collection. Using data from eight above ground and nine below ground descriptors, Holbrook (1997) concluded that additional peanut accessions should be collected from Columbia, Venezuela, Uruguay, and possibly Bolivia to increase variation in the entire collection. Accessions in the core collection also have been used to identify genetic variation for oil content (Holbrook et al. 1998a) and fatty acid composition (Hammond et al. 1997). A second core collection has been developed to represent the A. hypogaea germplasm maintained by ICRISAT (Upadhyaya et al. 2001b). This core collection was developed from a total of 14,310 accessions using an approach slightly different from that used by Holbrook et al. (1993). The ICRISAT peanut collection was first stratified by botanical variety within subspecies, and then stratified by country of origin.

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Accessions of the same botanical variety but from small and adjacent countries with similar agro-climates were grouped together. This resulted in 75 groups, and accessions within each group were then clustered using multivariate statistical analysis. Approximately 10 percent of the accessions from each cluster were randomly sampled resulting in a core collection consisting of 1704 entries.

VI. BREEDING PEANUT A. Conventional Breeding Methods Standard breeding methods for self-pollinated crops have been used to develop peanut cultivars and several reviews have been published (Isleib and Wynne 1992; Isleib et al. 1994; Knauft and Ozias-Akins 1995; Knauft and Wynne 1995). Although mutation breeding methodologies were used extensively in the late 1950s to early 1970s, the materials produced in the United States were not widely utilized by producers. Thus, mutation breeding is little used by the present-day peanut breeding community. Mass selection also is used infrequently in peanut because of negative correlations between disease resistance and yield (Knauft and Wynne 1995). Pedigree breeding is commonly used in peanut, and backcross methodologies are becoming more frequent as useful qualitatively inherited traits are identified. Use of single seed descent methods has greatly increased in recent years. Isleib et al. (1994) indicated that there are advantages to this method to save space and resources. Recurrent selection has received little attention in peanut breeding because of efforts needed to make a large number of crosses, but Halward et al. (1991) concluded that selection progress for multiple traits could be made in an A. hypogaea × A. cardenasii Krapov. & W. C. Gregory interspecific hybrid. Production of F1 hybrids is not a viable option in peanut because of the difficulties encountered in making a large number of crosses. There does not appear to be a large advantage to early generation testing in peanut, in large part because most breeding efforts with the crop are for quantitatively inherited traits. Genotype × environment interactions are widespread in peanut, and multi-year and multi-location testing is necessary prior to cultivar release. These interactions were reviewed by Knauft and Wynne (1995), so the following discussion will focus on research activity since their review. Since 1995, there have been many investigations related to breeding peanut with resistance to the peanut root-knot nematode, TSWV, PAC, and fungal pathogens. Research has also been published

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related to improving roasted peanut flavor and oil quality and drought tolerance. Some of the aforementioned traits are conditioned by one or two major genes, and many peanut breeding programs are using backcrossing methods to incorporate these traits into cultivars. Isleib (1999) examined the factors affecting the probability of recovering progeny homozygous for more desirable alleles than either parent. Probabilities for various combinations of these factors were presented and can be used by plant breeders to assist in choosing selection procedures that will maximize the chance of recovering desirable plants from biparental populations. Isleib (1997) compared the cost-effectiveness of different breeding procedures when the relative costs of crossing, selfing, and evaluation of progeny are known. Reducing input costs associated with pest management is becoming increasingly important in the United States due to changes in the federal peanut program that have significantly reduced commodity support prices (Jordan et al. 1999). Growing peanut cultivars with disease resistance will cut costs of production and allow United States production to become more competitive with world market prices. Although several programs during the 1980s had been initiated to breed for resistance to diseases, few disease-resistant cultivars had been released before 1990 because of the short duration of these efforts (Wynne et al. 1991b). However, many sources of disease resistance were identified in the germplasm collection and incorporated into peanut breeding programs. The use of this germplasm is just beginning to have significant economic impacts on United States peanut farmers. Isleib et al. (2001) summarized the use of genetic resources in peanut cultivar development and estimated that resistant cultivars have had an economic impact of more than $200 million annually for U.S. peanut producers. The largest positive impact on peanut production has come through development of cultivars with resistance to Sclerotinia blight, root-knot nematode, and TSWV. 1. Resistance to Nematodes. The peanut root-knot nematode causes significant economic losses in many peanut production areas of the world. Chemicals for control of this pest are becoming increasingly limited, and the development of peanut cultivars with resistance is desirable. Holbrook and Noe (1992) evaluated 1321 A. hypogaea plant introductions for resistance to M. arenaria and identified 17 accessions that supported fewer egg masses and seven genotypes that supported less egg production per gram of fresh root weight compared with the susceptible cultivar, ‘Florunner’. Holbrook et al. (1996) evaluated an additional 1000

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plant introductions and identified eight accessions that had significantly fewer egg masses than ‘Florunner’. Although none of the eight had significantly higher levels of resistance than those reported by Holbrook and Noe (1992), two exhibited significantly higher yields than other entries when grown in soil heavily infested with M. arenaria. Holbrook et al. (2000b) tested 741 accessions from the U.S. peanut core collection for resistance to M. arenaria. Thirty-six core accessions showed a reduction in root galling, egg-mass ratings, egg count per root system, and egg count per gram of root in comparison to the susceptible check, ‘Florunner’. Holbrook et al. (2000c) evaluated accessions from 30 clusters having resistant indicator accessions. This second stage screening identified 259 accessions that had reduced egg-mass production and 28 that had greatly reduced numbers of egg masses. Relatively large numbers of resistant accessions were from China and Japan compared with the percentage of the germplasm collection that originated from these countries. Researchers have identified many sources of resistance in the U.S. germplasm collection from various geographical origins. Resistant peanut breeding lines that produce greater yield than susceptible cultivars when grown in soil heavily infested with M. arenaria have also been identified (Holbrook et al. 1998b). However, only moderate levels of resistance have been observed originating from A. hypogaea. Timper et al. (2000) examined the expression of different mechanisms of resistance in six moderately resistant genotypes from various geographical locations. If different mechanisms were involved, then it should be possible to combine the mechanisms to improve the level and durability of the resistance. The primary expression of resistance in these six genotypes was similar and appears to be a reduction in the percentage of second-stage juveniles (J2) that establish a functional feeding site. Very high levels of resistance to M. arenaria exist in Arachis species (Baltensperger et al. 1986; Nelson et al. 1989; Holbrook and Noe 1990), and resistance has been introgressed into A. hypogaea. Stalker et al. (1995b) introgressed nematode resistance into A. hypogaea (2n = 4x = 40) from A. cardenasii (2n = 2x = 20). Garcia et al. (1996) reported that this resistance was conditioned by two dominant genes where one gene (Mag) inhibited root galling and another gene (Mae) inhibited egg production by M. arenaria. Resistance to M. arenaria also has been introgressed into A. hypogaea by using a complex interspecific hybrid [released as TxAG-6 by Simpson et al. (1993a)] from the three nematode resistant species, A. batizocoi, A. cardenasii, and A. diogoi Hoehne (Simpson 1991). TxAG-7 was derived from the first backcross generation of A. hypogaea cv. ‘Florunner’ × TxAG-6 (Simpson et al. 1993a).

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Resistance to M. arenaria in A. cardenasii was reported to completely inhibit nematode development and was accompanied by an apparent necrotic, hypersensitive host reaction (Nelson et al. 1990). The resistance of A. batizocoi caused a reduction in the total number of invading nematodes that reached maturity and produced eggs, and increased the time required for M. arenaria to complete its life cycle. No hypersensitive reaction was observed in A. batizocoi (Nelson et al. 1990). The resistance of TxAG-7 was similar to that of A. cardenasii, except that no host-cell necrosis characteristic of a hypersensitive reaction was associated with invading J2s (Starr et al. 1990). In addition to resistance to M. arenaria, TxAG-6 and TxAG-7 also have resistance to M. javanica and an undescribed Meloidogyne sp. (Abdel-Momen et al. 1998). A backcrossing program was used to introgress the root-knot nematode resistance from TxAG-7 into peanut breeding populations (Starr et al. 1995). This work resulted in the release of ‘COAN’, the first peanut cultivar with resistance to M. arenaria (Simpson and Starr 2001). This resistance is conditioned by a single dominant gene. The yield potential of ‘COAN’ is slightly less than that of its recurrent parent, ‘Florunner’ (Starr et al. 1998), but Church et al. (2000) observed significantly higher yield potential in nematode resistant breeding lines that resulted from two additional backcross generations. However, ‘COAN’ is extremely susceptible to TSWV (Holbrook et al. 2000d). Breeding for nematode resistance represents the first practical use of marker-assisted selection (MAS) in peanut. Burow et al. (1996) identified three RAPD markers (RKN 229, RKN 410, and RKN 440) linked to M. arenaria resistance in several breeding populations derived from TxAG-7 in the fifth backcross generation. The resistance in each of the populations appeared to have been derived from A. cardenasii and was most likely due to a single gene. Subsequent studies (Choi et al. 1999) confirmed that the resistance in some of these populations was conferred by a single dominant gene from A. cardenasii. However, data from other populations indicated the possibility of a second gene for resistance. Choi et al. (1999) also evaluated the utility of three RFLP probes for use in selecting individuals homozygous for resistance in a segregating population. Resistant and susceptible alleles for RFLP loci R2430E and R2545E were relatively easy to score and both were sufficiently close to the resistance allele to be used with a high degree of confidence. The third loci, S1137E is more distant from the resistant gene and is less reliable as a selectable marker. 2. Resistance to Soilborne Diseases. The most important soilborne diseases of peanut in the United States are white mold (Sclerotium rolfsii

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Sacc.), Sclerotinia blight (Sclerotinia minor Jagger), and Cylindrocladium black rot (CBR) (C. parasiticum). White mold is found throughout the major peanut growing areas of the United States and causes the greatest yield losses of all diseases (Backman and Brenneman 1997). Genetic variation for resistance to white mold exists in A. hypogaea, and sources of resistance have been identified (Branch and Csinos 1987; Smith et al. 1989; Grichar and Smith 1992). Shokes et al. (1996) found that field screening was more consistent than greenhouse tests for evaluating genotype responses to white mold. The nonuniform spatial distribution of natural inoculum can be a problem for field evaluations (Shew et al. 1984) and sterilized oat seed inoculated with S. rolfsii has been used to increase pathogen populations and improve the uniformity of fungal distribution (Shew et al. 1987; Brenneman et al. 1990; Shokes et al. 1996). However, even with uniform inoculum distribution, individual plants may escape the disease. Shokes et al. (1996) developed an inoculation method, termed the ‘agar disk technique’, which can be used to inoculate individual plants to prevent escapes. This technique has been used to identify resistant breeding lines (Shokes et al. 1998). Several cultivars with moderate resistance to white mold are available to producers. ‘Southern Runner’ (Gorbet et al. 1987), initially released as a cultivar with partial resistance to late leaf spot, was also found to have moderate resistance to white mold (Brenneman et al. 1990; Grichar and Smith 1992; Branch and Brenneman 1993). Gorbet et al. (1997) also observed some resistance to white mold in ‘Toalson’ (Simpson et al. 1979), ‘Pronto’ (Banks and Kirby 1983), ‘Georgia Browne’ (Branch 1994), and ‘Sunbelt Runner’ (Mixon 1982). ‘Tamrun 96’ (Smith et al. 1998) has shown superior resistance to white mold when compared to other commonly grown cultivars (Besler et al. 1997, 2001). Genetic variation for resistance to CBR has been observed in A. hypogaea and in general, spanish cultivars are most resistant, valencia cultivars are the most susceptible, and virginia cultivars are moderately susceptible (Phipps and Beute 1997). However, CBR-resistant lines of each type have been described and the partially resistant virginia-type cultivars, ‘NC 8C’ (Wynne and Beute 1983), ‘NC 10C’ (Wynne et al. 1991a), and ‘NC 12C’ (Isleib et al. 1997) have been released. The earlier cultivars had seed characteristics that were only marginally acceptable to manufacturers, and the newer release has more acceptable seed traits and higher yields. The inheritance of resistance is complex (Green et al. 1983), and the resistance appears to delay the onset of epidemics rather than the rate of disease progress (Culbreath et al. 1991). Recently,

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Kucharek et al. (2000) observed partial resistance to CBR in cultivars and a breeding line that also has resistance to tomato spotted wilt virus. Sclerotinia blight is the most important soilborne disease of peanut in Virginia and Oklahoma (Porter and Melouk 1997). Sources of resistance to S. minor have been identified (Coffelt and Porter 1982; Melouk et al. 1989; Akem et al. 1992; Porter et al. 1992), and several moderately resistant cultivars and germplasm lines have been released (Coffelt et al. 1982, 1994; Smith et al. 1990, 1991; Kirby et al. 1998). Peanut cultivars with spanish ancestry appear to be more resistant to Sclerotinia blight than cultivars or breeding lines from valencia or virginia backgrounds (Akem et al. 1992). Wildman et al. (1992) reported that at least two loci are involved in the inheritance of resistance. Screening techniques for identifying resistance have been developed (Brenneman et al. 1988; Melouk et al. 1992) that rely on rate of lesion growth and development. The age and/or developmental stage of the lateral limbs of plants have a marked effect on lesion development (Brenneman et al. 1988), and the younger and more succulent tissues are more susceptible to the disease. Melouk et al. (1992) developed a detached shoot technique that can be used to assess resistance to Sclerotinia blight in peanut genotypes. Goldman et al. (1995) reported that significant progress had been made in developing runner-type peanut breeding lines with resistance to Sclerotinia blight using field and greenhouse screening tools. Lines derived from interspecific crosses between A. hypogaea and A. cardenasii appear to be highly resistant in field experiments (J. Bailey, pers. comm. 2001), and the material should lead to highly resistant cultivars. 3. Resistance to Foliar Diseases. Early leaf spot (C. arachidicola) and late leaf spot (C. personatum) are the most damaging diseases of peanut worldwide (Shokes and Culbreath 1997). Sources of resistance to both early and late leaf spot have been identified in A. hypogaea (Chiteka et al. 1988a,b; Anderson et al. 1993), and used to develop breeding lines with resistance (Gorbet et al. 1982; Melouk et al. 1984; Wells et al. 1994; Xue and Holbrook 1998, 1999a,b; Branch and Fletcher 2001). Holbrook and Isleib (2001) observed that resistance to late leaf spot was more frequent than expected in A. hypogaea accessions which originated in Bolivia, and this country was also a valuable source of origin for resistance to early leaf spot. Although just one leaf spot pathogen usually predominates in a production region, both leaf spot species are generally found in a single field. Shifts in leaf spot species also have been observed over a period of years in the Virginia–Carolina production region. Although commonly used fungicides control both C. arachidicola and

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C. personatum, cultivars with resistance to both pathogens will be needed to completely suppress the two leaf spot diseases. Inheritance of resistance to leaf spots is complex (Kornegay et al. 1980; Anderson et al. 1986, 1991; Green and Wynne 1987; Iroume and Knauft 1987a,b; Jogloy et al. 1987), and several components contribute to resistance, including initial infection, lesion size, sporulation, and defoliation (Green and Wynne 1986; Chiteka et al. 1988a,b; Anderson et al. 1993; Waliyar et al. 1993, 1995). Resistance to leaf spot in peanut has generally been associated with late maturity (Norden et al. 1982; Miller et al. 1990). However, Branch and Culbreath (1995) documented a breeding line that had early maturity and tolerance to leaf spot. Aquino et al. (1995) found that latent period and maximum percentage of lesions that sporulated were the components of resistance most highly correlated with late leaf spot disease development. They suggested that using either of these two components to evaluate breeding populations may facilitate more rapid selection of lines with improved levels of rate-reducing resistance. Until the release of ‘Southern Runner’ in 1984, no commercial cultivars were available with meaningful resistance to late leaf spot. The level of resistance in ‘Southern Runner’ is moderate, and fungicide applications are still needed to obtain optimum yields. Moderate levels of resistance are also available in the cultivars ‘Florida MDR 98’ and ‘C-99R’ (Gorbet et al. 1999). Very high levels of resistance or immunity to the leaf spot diseases occur in related wild species of peanut (Stalker and Moss 1987; Stalker and Simpson 1995). Programs are ongoing to introgress this resistance into A. hypogaea (Stalker and Beute 1993). Ouedraogo et al. (1994) reported that resistance to both C. arachidicola and C. personatum was introgressed from wild species into productive runner-type breeding lines. Although the resistance was equal to or better than that of ‘Southern Runner’, it was less than the level of resistance observed in the wild species. Stalker and Mozingo (2001) reported associations of RAPD markers with resistance to C. arachidicola in an interspecific hybrid with A. cardenasii in the pedigree. Breeding progress has been made to suppress both early and late leaf spots, but combining high levels of resistance into high-yielding cultivars with acceptable market traits continues to be very difficult. 4. Resistance to Tomato Spotted Wilt Virus. Since 1985, TSWV has become a major disease problem in the peanut producing areas of the United States. The disease is now common in most peanut-growing areas, including Georgia, Florida, Alabama, Texas, and North Carolina,

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and it has become the most important disease problem for many peanut growers (Culbreath et al. 1997a). Symptoms vary from severe stunting and distortion of peanut vines to elaborate concentric ring spots on individual leaflets, and in some cases, death of the entire plant. TSWV is vectored primarily by the thrip species Frankliniella fusca (Hinds) (tobacco thrips) and F. occidentalis (Pergande) (western flower thrips) in the United States, with tobacco thrips being the most important vector for secondary spread of TSWV (Lynch and Mack 1995). Because disease transmission is by intracellular feeders, there is no measure to cure the plant once it has become infected (Lynch and Mack 1995). Thrips concentrate in leaf terminals and flowers, which are areas that are protected from insecticide sprays (Wightman and Ranga Rao 1994). Small numbers of thrips can result in high rates of pathogen spread (Ullman et al. 1997), and because inoculation can occur quickly, pesticides do not kill the vector before they can transmit the virus. Because chemical controls are unavailable for the disease per se, the most important factor in management of spotted wilt is cultivar selection (Culbreath et al. 1999a). Before the emergence of TSWV, ‘Florunner’ was the dominant cultivar in the Southeast peanut production area. However, ‘Florunner’ is highly susceptible to TSWV and yield is dramatically reduced (Culbreath et al. 1992a). Largely because of spotted wilt, there have been dramatic shifts in cultivars grown in Georgia, Florida, and Alabama (Culbreath et al. 2000) and ‘Florunner’ is no longer produced in this region. ‘Southern Runner’ was the first peanut cultivar observed with moderate levels of resistance to TSWV (Culbreath et al. 1992b, 1994, 1996). Intensive screening of cultivars and breeding lines has resulted in the release of several additional TSWV resistant cultivars, including ‘Georgia Browne’, ‘Georgia Green’ (Branch 1996), ‘C-99R’, and ‘Florida MDR 98’, all of which have a level of TSWV resistance similar to ‘Southern Runner’ (Culbreath et al. 1994, 1996, 1997b). Lyerly (2000) inoculated ‘Georgia Green’ and several Arachis species in greenhouse tests and observed that the cultivar was highly susceptible. She concluded that field resistance in ‘Georgia Green’ does not result from genetic resistance to the virus, but from a combination of other factors that suppress infection. Accessions of two diploid species (A. diogoi and A. correntina (Burkart) Krapov. and W. C. Gregory) were highly resistant to TSWV infection in greenhouse inoculation experiments (Lyerly 2000). Unfortunately, none of the cultivars released to date has a high level of resistance to TSWV, and all available cultivars may suffer significant damage during extreme epidemics. The genetic mechanism responsible for resistance has not been elucidated. At the time TSWV emerged as a problem in the Southeast, PI 203396 was an ancestor to many breeding

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populations due to its resistance to late leaf spot. Fortunately, PI 203396 also has resistance to TSWV, and was one parent of the TSWV-resistant runner-type cultivar ‘Southern Runner’. PI 203396 is also in the pedigrees of the TSWV-resistant cultivars ‘Georgia Green’, ‘Florida MDR98’, and ‘C-99R’. Other sources of resistance to TSWV are available in the U.S. A. hypogaea germplasm collection (Anderson et al. 1996a) as well as late generation breeding lines that have resistance equal to or better than currently available cultivars (Culbreath et al. 1999b). Genetic engineering may also result in improved resistance because the nucleocapsid protein gene of TSWV has been inserted into peanut using microprojectile bombardment (Yang et al. 1998; Magbanua et al. 2000) and Agrobacterium-mediated transformation (Li et al. 1997). Progenies from transformation have shown protection against TSWV in the field (Li et al. 1997; Magbanua et al. 2000). However, this type of single gene resistance may be short-lived because there appears to be a highly heterogeneous natural viral population available for adaptation (Qiu and Moyer 1999). TSWV has a tripartite genome organization that allows potential exchange of information between related viral genomes (Qiu et al. 1998), and Qiu and Moyer (1999) demonstrated that genome reassortment is a genetic mechanism employed by this virus to adapt to resistant plants. Because there is great interest by the peanut industry for production of cultivars with a high oleic acid concentration, the TSWV reactions to this germplasm deserves attention. The first peanut cultivars containing high oleic acid were extremely susceptible to TSWV (Culbreath et al. 1997b). Moderate levels of resistance to TSWV have been reported in the mid-oleic cultivars ‘Florida MDR-98’ (Culbreath et al. 1997b) and ‘ViruGard’ (Culbreath et al. 2000). High oleic breeding lines recently have been produced with acceptable levels of resistance to TSWV (Culbreath et al. 1999b). 5. Resistance to Preharvest Aflatoxin Contamination. Aspergillus flavus and A. parasiticus Speare can colonize seed of several agricultural crops including peanut (CAST 1989). This can result in the contamination of the edible yield from these crops with the toxic fungal metabolite aflatoxin. Elimination or suppression of preharvest aflatoxin contamination (PAC) in the food chain is one of the most serious challenges facing the U.S. peanut industry. Lamb and Sternitzke (2001) calculated that aflatoxin costs the farmer, buying point, and sheller segments of the southeast U.S. peanut industry over $25 million annually. Peanut genotypes with some resistance to invasion by A. flavus have been reported (Mehan et al. 1991; Cole et al. 1995). These genotypes were identified by screen-

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ing germplasm using in vitro colonization by A. flavus of rehydrated sound mature kernels. Promising correlations between field resistance and in vitro resistance have been observed in Africa (Zambettakis et al. 1981; Waliyar et al. 1994) and India (Mehan et al. 1986, 1987). However, this in vitro screening has produced inconsistent results when compared to a natural field situation in the United States where Anderson et al. (1995) and Blankenship et al. (1985) did not observe significant levels of preharvest aflatoxin resistance in genotypes previously reported with in vitro resistance; and Kisyombe et al. (1985) observed significant field resistance in only one of 14 in vitro resistant selections. These initial screening efforts for resistance to preharvest infection and aflatoxin contamination were hampered by a lack of basic knowledge about the fungus and by technological limitations in measuring resistant reactions. Since these early efforts, much information has been gathered about the influence of environmental stress on aflatoxin contamination in peanuts. Detailed studies on the effect of temperature and moisture stress on colonization of kernels and aflatoxin contamination of peanuts have been conducted (Cole et al. 1982; Hill et al. 1983; Sanders et al. 1985). Technological advances also have provided great improvement in detection and measuring techniques for assessing aflatoxin contamination (Wilson 1989) which can more accurately measure resistance in a field or greenhouse environment (Holbrook et al. 1994; Anderson et al. 1996b; Holbrook et al. 1998c). Holbrook et al. (1994) developed a large-scale field screening technique to directly measure field resistance to PAC that uses subsurface irrigation in a desert environment to allow an extended period of drought stress in the pod zone while keeping the plant alive. Without subsurface irrigation, peanut plants died and their seeds rapidly dehydrated in the soil before contamination could occur. Sanders et al. (1993) also observed high levels of aflatoxin contamination when peanuts in the pod zone were artificially stressed with heat and drought while keeping plants nonstressed by providing root zone irrigation. Large-scale field screening for resistance to PAC was also enhanced by the development of a consistently successful and reproducible field inoculation technique (Will et al. 1994). Artificial inoculation helps to insure uniform testing conditions, which reduces variation due to escapes that often masks genetic differences. Anderson et al. (1996b) developed a screening technique that can be used in greenhouse facilities where high amounts of preharvest aflatoxin accumulation were produced by completely isolating the pod zone and restricting moisture to the root zone. Because aflatoxin concentrations are higher when the plant is stressed, development of cultivars with reduced aflatoxin contamination

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when grown under heat- and/or drought-stressed conditions would be a valuable tool in alleviating this problem. Screening of the U.S. peanut core collection (Holbrook et al. 1993) resulted in the identification of 15 accessions that showed low levels of aflatoxin contamination in multiple environments (Holbrook et al. 1998c). These accessions exhibited a 70 to 90 percent reduction in aflatoxin contamination in comparison to susceptible accessions in at least three environments. Development of resistant cultivars could be accelerated if an effective trait for indirect selection can be identified. Holbrook et al. (1997) conducted a study to determine if resistance to other fungi could be used as an indirect selection tool for resistance to colonization of peanut by A. flavus and/or aflatoxin contamination. Genotypes with resistance to late leaf spot and/or white mold were evaluated, but none exhibited less A. flavus colonization or aflatoxin contamination than ‘Florunner’. These results indicate that the mechanisms of resistance to these two fungi did not effect colonization by A. flavus or aflatoxin production. Drought tolerance is another characteristic that has the potential to serve as an indirect selection tool for resistance to PAC. Holbrook et al. (2000a) evaluated the resistance to PAC in genotypes that had varying levels of drought tolerance (Rucker et al. 1995) and concluded that tolerant genotypes also had greatly reduced aflatoxin contamination. Significant positive correlations were observed between aflatoxin contamination and leaf temperature and between aflatoxin contamination and visual stress ratings. A significant negative correlation was also observed between aflatoxin contamination and yield under drought stressed conditions. Leaf temperature, visual stress ratings, and yield are all less variable and relatively inexpensive to measure compared to the amount of aflatoxin in seed samples. A similar relationship between drought tolerance and reduced aflatoxin contamination was observed in the drought tolerant cultivar ‘Streeton’ in Australia (Cruickshank et al. 2000). This cultivar has up to 40 percent lower aflatoxin levels during years of high aflatoxin incidence in comparison to other cultivars. Physiological studies (Nageswara Rao et al. 2000b) have shown that the lower aflatoxin incidence is associated with better root water uptake resulting in better maintenance of crop water status during severe end-of-season drought. Several in vitro studies have indicated that fatty acid composition could either directly or indirectly affect aflatoxin contamination (Fabbri et al. 1983; Passi et al. 1984; Doehlert et al. 1993; Burow et al. 1997a). Holbrook et al. (2000e) evaluated the effect of altered fatty acid composition on PAC in peanut, but observed no measurable effect of reduced

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linoleic acid composition on PAC. They concluded that the products of the lipoxygenase pathway that have been shown to affect aflatoxin biosynthesis in vitro may not be present in sufficient quantities to effect aflatoxin contamination of developing peanut seed. 6. Improved Drought Tolerance. The peanut plant is highly droughttolerant and is grown in many areas of the world where most other food legumes will not produce a crop. However, insufficient water at the time of flowering and fruiting will significantly reduce yield. Further, aflatoxin contamination is mainly a problem in peanut that has been subjected to heat and drought stresses late in the growing season (Sanders et al. 1985). The issues surrounding agricultural water use are increasing in importance, and the development of cultivars with improved drought tolerance should help alleviate these concerns. Drought tolerance may be enhanced by improvements in soil water extraction capability (Wright and Nageswara Rao 1994), or improvements in water use efficiency, or both (Hebbar et al. 1994). Rucker et al. (1995) evaluated drought tolerance characteristics of 19 peanut genotypes which differed in the size of their root systems. Under drought-stressed field conditions, these genotypes differed in canopy temperature and visual stress ratings, two potential measures of drought tolerance. Wright et al. (1994) demonstrated genetic differences in peanut for transpiration efficiency which is defined as g of dry matter produced per kg of water transpired. Measurements of transpiration and/or root biomass are difficult and therefore are not practical for use in large scale breeding efforts for improved drought tolerance. Farquhar et al. (1982) proposed that the transpiration efficiency of a genotype could be estimated by measuring the carbon isotope discrimination (∆) in leaves. Transpiration efficiency in peanut genotypes is negatively correlated (r ranging from –0.88 to –0.92, P < 0.01) with ∆ (Hubick et al. 1986, 1988; Wright et al. 1988, 1994). While measurement of ∆ is rapid, it is an expensive measurement and may not be applicable to large segregating breeding populations. Specific leaf area (ratio of leaf area to leaf dry weight) also is highly correlated with ∆ in peanut (Nageswara Rao and Wright 1994). Specific leaf area can be easily and inexpensively measured, and it is being used in a large-scale screening program for improved drought resistance in Australia (Nageswara Rao et al. 2000a). This research group has demonstrated progress from using physiological traits to indirectly select for high-pod yield of peanut under water-limited conditions. After two cycles of selection, they selected progenies that yield 30 percent more than their parents under drought-stressed conditions (Nageswara Rao et

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al. 2000b). However, care must be taken when using specific leaf area as a selection criteria since it is significantly influenced by time of sampling and leaf age (Wright and Hammer 1994; Nageswara Rao et al. 1995). Wright et al. (1996) observed variation in the strength of correlations between specific leaf area and ∆ in a range of peanut genotypes and environments. Nageswara Rao et al. (2001) evaluated the use of a hand-held portable SPAD chlorophyll meter for rapidly assessing drought tolerance in peanut. They observed a significant correlation (r = 0.77, P < 0.01) between the chlorophyll meter reading and specific leaf area and suggested that this meter could be used as a rapid and reliable measure to identify genotypes with low SLA, and hence high transpiration efficiency in peanut. 7. Improved Oil Quality. Properties of peanut oil are determined by the fatty acid composition. Two fatty acids, oleic (O) and linoleic (L), comprise over 80 percent of the oil content of peanut. Standard peanut cultivars average 55 percent oleic acid and 25 percent linoleic acid (Knauft et al. 1993). Linoleic acid is less saturated and less stable than oleic acid, and the oxidative stability and shelf life of peanut and peanut products can be enhanced by increasing the O/L ratio. Norden et al. (1987) examined the fatty acid composition of 494 genotypes and identified two breeding lines with 80 percent oleic acid and 2 percent linoleic acid. This was a major deviation from previously known levels of fatty acid composition in peanut. Moore and Knauft (1989) found that inheritance of the high-oleate trait was controlled by duplicate recessive genes, ol1 and ol2. F435 differed at both loci from a virginia-type line but at only one locus from a runner line. Knauft et al. (1993) reported monogenic inheritance in crosses of the runner market-type cultivars and breeding lines. A cross with a virginia market-type segregated in a 15:1 ratio typical of recessive digenic inheritance. The authors concluded that one of the recessive alleles occurs with high frequency in peanut breeding populations in the United States whereas the other allele is rarer. Isleib et al. (1996) examined five different cultivars of virginia-type peanut cultivars and found that four were either Ol1Ol1ol2ol2 or ol1ol1Ol2Ol2 and one was Ol1Ol1Ol2Ol2. When only one gene transfer is required, Isleib et al. (1998) were able to identify heterozygotes based on linoleate levels. This will allow breeders to identify carriers of the recessive allele in successive cycles of backcrossing without intervening generations of selfing and decrease the time required to achieve the desired number of backcrosses. Lopez et al. (2001) examined the inheritance of high oleic acid in six

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spanish market-type peanut cultivars. Segregation patterns indicated that two major genes were involved. However, the presence of lowintermediate O/L ratio genotypes indicated that other genetic modifiers might be involved in the expression of the O/L ratio in these genotypes. Isleib et al. (1998) also observed an effect of other loci on fatty acid concentrations. Studies of peanut lines without the high oleic characteristic have indicated that oleic content can be influenced by additive gene effects (Mercer et al. 1990), and by additive × additive epistasis (Upadhyaya and Nigam 1999). Gorbet and Knauft (1997) found that ‘SunOleic 95R’, a high oleic runner cultivar, had a much longer shelf life than the traditional runner-type peanut cultivars. Peanuts with high levels of oleic acid also show some promise for beneficial health effects in humans and animals that consume them. 8. Improved Flavor. Flavor of roasted peanuts is an essential characteristic influencing consumer acceptance, and enhancement of roasted peanut flavor is an important objective of the peanut industry. Only recently has it been recognized that the roasted peanut attribute is an inherited trait (Sanders et al. 1995). Through the research of Pattee and coworkers, several roasted peanut quality sensory attributes have been shown to be heritable (Pattee and Giesbrecht 1990; Pattee et al. 1993, 1995a, 1998; Isleib et al. 1995b). Isleib et al. (2000) examined the genotypic variation in roasted peanut flavor quality in breeding lines and cultivars developed since 1930. They concluded that the negative influence of commonly used ancestors in virginia-type cultivars has resulted in trends toward poorer roasted peanut flavor (reduced intensity of the roasted peanut and sweet attributes and increased intensity of the bitter attribute). Conversely, runner and spanish types tend to give better roasted peanut flavors. After estimating the ancestral contribution to roasted peanut flavor for 128 cultivars and breeding lines, Isleib et al. (1995b) concluded that the parental line ‘Jenkins Jumbo’ (Hammons and Norden 1979) was the single most important ancestor in virginia-type peanuts, and this ancestor exerted a highly negative effect on flavor. ‘Jenkins Jumbo’ was initially used as a source of large pod and seed size without the knowledge that it would have a deleterious effect on flavor. Isleib et al. (1995a) also reported that at least one parent commonly used as a source of disease resistance (PI 203396) can reduce the roasted flavor of its progeny. Pattee et al. (2001) reported that the three peanut cultivars with resistance to CBR have a negative effect on flavor in progenies. They urged caution when incorporating exotic germplasm into breeding populations, and suggested

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flavor evaluations as soon as practical in the breeding process. Over the same time period, runner-type cultivars have increased slightly in average sweetness; however, there has been an increase in the variance of roasted peanut intensity in breeding populations since 1980. Best linear unbiased prediction (BLUP) is a method for predicting the breeding value of a parent based on the performance of its relatives (Henderson 1975). Pattee et al. (2001) concluded that BLUPs of breeding value can be used to predict cross means, but segregation within crosses provides additional opportunity for progress from selection. BLUPs of breeding values were superior to midparent values in predicting cross means. These authors identified large-seeded lines with superior flavor, indicating that it should be possible to improve flavor in the virginia market-type without sacrificing large seed size. Pattee and Knauft (1995) evaluated four high oleic acid breeding lines and observed no changes in roasted peanut attribute intensity in comparison to ‘Florunner’. Pattee et al. (2001) observed that high-oleic cultivars and breeding lines derived by backcrossing with ‘Sunrunner’ (Norden et al. 1985) had a high positive breeding value for the roasted peanut attribute. It is not clear if this is a real genetic effect or an artifact of the sensory evaluation since the protocol requires a storage period during which some oxidation of linoleic acid in the ‘Sunrunner’ seeds may occur that could produce off-favors. Cultural preferences should influence breeding decisions about flavor. Local consumers in some areas of Mexico have a distinct preference for the flavor characteristics of A. hypogaea spp. hypogaea var. hirsuta (Becker 1993). However, these hirsuta accessions did not have a higher intensity of the roasted peanut attributes that are favored in the United States (Pattee et al. 1995b). Some of the hirsuta landraces did have a higher intensity of sweetness that may account for their preference in some parts of Mexico. 9. Breeding for Allergen Resistance. Peanut allergens cause severe reactions in approximately 0.6 percent of the population in the United States, and exposure can be fatal even with exposure to trace amounts of peanut protein. Thus, allergens are of great concern to the peanut scientific community and a highly desirable objective would be to develop nonallergenic peanut cultivars. Several proteins related to seed storage protein complex are responsible for causing allergic reactions, with Ara h 1 and Ara h 2 being the major contributors. Although serology of the peanut allergy appears to be different in regions of the world, Koppelman et al. (2001) did not find differences among genotypes. Thus, traditional breeding methodologies will not solve the problem until variation

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is identified in the species. Research efforts are being placed into developing vaccines to peanut allergens as opposed to developing plant breeding programs. B. Marker-Assisted Selection Isozyme analyses of cultivated peanut have shown little variation (Grieshammer and Wynne 1990), but polymorphisms are somewhat more frequent between interspecific hybrids (Lacks and Stalker 1993). Isozyme studies of species in section Erectoides showed a large amount of variation, and species appeared to associate with members of other sections. These observations were clarified when Krapovickas and Gregory (1994) divided the Erectoides into three sections (Erectoides, Trierectoides, and Procumbentes). Analyses of seed storage proteins have shown that variation exists among species of section Arachis (Singh et al. 1991a; Bianchi-Hall et al. 1993, 1994), but proteins are not useful for descriptions at the species level. Restriction fragment length polymorphisms (RFLP) represented the first marker system that has a sufficiently large number of polymorphisms that could be used to create linkage maps and to implement indirect selection strategies. In A. hypogaea, little molecular variation has been detected by using RFLP technologies (Kochert et al. 1991). However, significant amounts of variation for RFLP (Kochert et al. 1991; Paik-Ro et al. 1992) and polymerase chain reaction (PCR) (Halward et al. 1992, 1993) have been observed among Arachis species. Accessions in section Arachis, representing taxa that will hybridize with A. hypogaea, have been analyzed using RFLPs and then multivariate analysis has been used to group accessions into clusters (Kochert et al. 1991) that corresponded closely with morphological traits (Stalker 1990). Tetraploids were clearly separated from diploids in both investigations. Stalker et al. (1995a) utilized RFLPs to examine genetic diversity among 18 accessions of A. duranensis Krapov. & W. C. Gregory, and they found more variation between than within accessions; and individual accessions could be identified. Kochert et al. (1996) concluded that the cultivated peanut originated from a cross between A. duranensis and A. ipaensis. Chloroplast analysis indicated that A. duranensis was the female progenitor of the cross (Kochert et al. 1996). An RFLP map was developed for peanut by analyzing an F2 population from the diploid (2n = 2x = 20) interspecific cross A. stenosperma (acc. HLK 410) and A. cardenasii (acc. GKP 10017). The linkage map covered 1063 cM with 117 markers in 11 linkage groups. Fifteen unassociated markers were also reported (Halward et al. 1993). A second

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molecular map of peanut has been created by using a tetraploid cross of cultivar ‘Florunner’ × 4x {A. batizocoi (acc. GKP 9484) × [A. cardenasii (acc. GKP 10017) × A. diogoi (acc. GKP 10602)]} (M. D. Burow, pers. comm.) where more than 380 RFLP markers have been mapped. Most markers had disomic inheritance, with the exception of one linkage group that may be polysomic. Further, the R239 marker for nematode resistance maps to the same linkage group on both the maps produced by Halward et al. (1993) and Burow, Patterson, and Simpson (M. D. Burow, pers. comm.). Garcia (1995) used RAPDs to add markers to the map and found coliniarity between RAPDs and RFLPs. Garcia et al. (1996) used RAPD and sequence characterized amplified regions (SCARs) technologies to map two dominant genes conferring resistance to the peanut root-knot nematode M. arenaria race 1. In their study, the cultivated peanut was crossed with a tetraploid breeding line that had a resistant species [A. cardenasii (GKP 10017)] in the pedigree. Bulked segregant analysis was used to find RAPD markers common to both resistant progeny and the resistant Arachis species. One marker (Z3/265) was closely linked with M. arenaria resistance and subsequently mapped to a linkage group on a backcross map in an area known to contain A. cardenasii introgression. This fragment was cloned to make SCAR and RFLP probes, and linkages confirmed (Garcia et al. 1996). Burow et al. (1996) also linked RFLP markers to genes conditioning M. arenaria resistance in the tetraploid cross of cultivar ‘Florunner’ × 4x [A. batizocoi (A. cardenasii × A. diogoi)], but it is not known whether the genes identified in the two crosses are the same. In an investigation to link molecular markers with resistance to C. arachidicola, Stalker and Mozingo (2001) reported associations of RAPDs with a gene conferring resistance to sporulation, lesion diameter, defoliation, and overall rating in an interspecific hybrid with A. cardenasii in the pedigree. A marker also was associated with resistance to southern corn rootworm damage. The resistance genes likely originated from the wild species parent. In addition, they associated markers with Cylindrocladium black rot resistance and sporulation to C. arachidicola in a cross between cultivar ‘NC 7’ (Wynne et al. 1979) and PI 109839. This represented the first report of molecular markers being associated with resistance genes in an A. hypogaea × A. hypogaea cross. He and Prakash (1997) were the first investigators to report applications of amplified fragment length polymorphisms (AFLP) technologies in peanut. They used 28 primer pairs to generate 111 AFLP markers in A. hypogaea. They reported a greater amount of variation using this technology than with any other molecular marker technique. However, other studies conducted with cultivated peanut have shown less varia-

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tion than reported by He and Prakash using this technology (Mila and Stalker, unpublished data). Simple sequence repeats (SSR) markers are highly variable, codominant, easily detected from relatively little amounts of DNA after PCR amplification, and reportedly more variable than other marker systems. Hopkins et al. (1999) reported six polymorphic SSRs in A. hypogaea with the number of fragments amplified per SSR ranging from 2 to 14. Newer technologies commonly used in other legumes such as Medicago species have not been published in the peanut literature, in large part because few programs are currently working in the area of molecular marker expression using Arachis species. C. Interspecific Hybridization Creating hybrids among Arachis species is difficult, but the high levels of resistance to the many diseases and insect pests that plague peanut have generated much interest in recovering interspecific hybrids. Although transformation technologies are alternatives for utilizing resistant species that will not hybridize with A. hypogaea, genes or gene complexes that confer high levels of disease or insect resistance have not been isolated in peanut. Until agronomically useful genes are isolated and shown to be stably expressed by transformation technologies, interspecific hybridization is the most promising method to introgress genes from related Arachis species that are not present in the cultivated peanut. The most comprehensive crossing study in peanut was completed by Gregory and Gregory (1979), who set up a diallel crossing program with 100 accession of Arachis species. Their work indicates that crosses between species of different sections result in very few hybrids, and all progenies are sterile. Hybrids within sectional groups are easier to produce and fertility levels are generally higher, although a considerable amount of sterility can still occur (Stalker et al. 1991b). Bridge crosses have also been attempted with species in sections Rhizomatosae and Erectoides, but Stalker (1985a) concluded that the germplasm outside section Arachis is inaccessible to the domesticated peanut through sexual hybridization. Restricted fertilization is not believed to be the cause of hybridization failures in most interspecific peanut crosses (Sastri and Moss 1982). However, application of gibberellic acid or mentor pollen to the stigma at the time of pollination may increase the frequency of pegging (Sastri and Moss 1982; Stalker et al. 1987). Using annual species as female parents versus perennial species usually results in a higher success rate, which may be related to perennials having smaller stigmas and being

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surrounded by a protective ring of hairs (Lu et al. 1990). Because of the apparent immunity of species in section Rhizomatosae to leaf spots and many viruses, research programs in the United States prior to 1980 (as well as much of the work at ICRISAT since that time) have concentrated on introgressing genes from A. glabrata into A. hypogaea. However, only species in section Arachis are readily available for gene introgression, so this part of the chapter will concentrate on interspecific hybrids with species in the primary (A. hypogaea and A. monticola) and secondary (diploid species in section Arachis) gene pools. Arachis hypogaea × A. monticola hybrids are relatively easy to produce and the taxa represent one biological species. Most of the diseases and insects that are problematic in the domesticated species are also a problem with A. monticola, and considering that A. monticola has fragile pegs and one-seeded pods, it has generally not been considered as a good parent for crop improvement. Further, hybrids many times have prolonged dormancy (Stalker and Simpson 1995), which hinders breeding progress. However, two cultivars have been released with A. monticola in their pedigree (Hammons 1970; Simpson and Smith 1975), but neither has been produced on large areas. Diploid species of section Arachis have greater potential for cultivar improvement than A. monticola because many accessions have very high levels of resistance to many economically important insect pests (Stalker and Campbell 1983; Lynch and Mack 1995) and diseases (Stalker and Moss 1987; Stalker and Simpson 1995). Several methods have been attempted to introgress germplasm into A. hypogaea from diploid species with each having advantages and disadvantages; but none of them lead to quick introgression of desired genes because of sterility in progenies and limited genetic recombination. However, several species contain traits not found at high levels in A. hypogaea [e.g., TSWV resistance in A. diogoi and A. correntina accessions (Lyerly 2000)], and the efforts involved in producing interspecific hybrids can be rewarding. Introgression of useful genes into A. hypogaea is not a trivial exercise even when cross-compatible species are used because sterility barriers are present due to different ploidy levels, genomic incompatibilities, and cryptic genetic differences. Constraints to obtaining hybrids may occur at the time of fertilization, during early cell division of the embryo, because the embryo does not reinitiate growth after a quiescent phase during peg elongation, or during later embryo development. Even when hybrids are obtained, genetic recombination is often restricted, and desired genes are not incorporated into the A. hypogaea genome. Thus, simply obtaining fertile and stable 40-chromosome progenies from inter-

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specific hybridization does not guarantee gene incorporation into the desired genome. Introgression of useful traits in peanut is a two-step process where a trait is first incorporated into the A. hypogaea genome (which also results in progenies with many unfavorable traits), and then a plant breeding program is initiated to enhance yield and quality traits while at the same time selecting for the desired trait. Direct crosses with A. hypogaea have been obtained with both A and B genome species of section Arachis. Krapovickas and Rigoini (1951) produced the first interspecific hybrid with A. villosa var. correntina, and since then many other interspecific combinations have been successful (Stalker and Moss 1987; Stalker and Simpson 1995). Although most of the diploid species hybridize relatively easily with the domesticated peanut, several interspecific hybrids are difficult to obtain (Stalker et al. 1991b). When the domesticated species is used as the female parent, hybridization is usually more successful. Direct hybridization between A. hypogaea and diploids results in sterile triploid hybrids. Fertility can be restored at the hexaploid level after colchicine treating vegetative cuttings (Singh et al. 1991b), or sometimes by simply propagating plants under field conditions for prolonged periods of time in a frost-free environment (Singh and Moss 1984). Hexaploids are expected to have 30 bivalents, but many plants are cytologically unstable and have up to 30 univalents (Company et al. 1982). Thus, most hexaploids produce very few seeds. Hexaploid × diploid (and reciprocal) crosses abort, so a one-step program to lower the chromosome number to the tetraploid level is not possible (Halward and Stalker 1987b). Reducing the chromosome number to 2n = 40 has been accomplished by several methods, including self-pollination and subsequent chromosome loss (Stalker 1992) and by backcrossing with A. hypogaea. When hexaploids are backcrossed with the tetraploid A. hypogaea, pentaploids result that are mostly sterile and produce few flowers, and continued backcrossing is not a practical breeding strategy. Unexpectedly, some pentaploids have 25 bivalents (Company et al. 1982) which indicates that there is more genomic similarity between A. hypogaea and related diploid species than the commonly designated A and B genomic designations would indicate. When pentaploid plants are allowed to self-pollinate, a few will produce viable aneuploid progenies and a second generation of selfing will usually result in tetraploid progenies from which fertile lines can be selected. Garcia (1995) investigated introgression of genes from diploid Arachis species to A. hypogaea by crossing the cultivated peanut with several diploid species, restoring fertility after colchicine treating F1s to produce fertile hexaploids, and then selfing hexaploids and backcrossing with a

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recurrent parent at each respective generation after selfing. Garcia found that increasing numbers of molecular markers were lost during each selfing generation, which likely resulted from meiotic irregularities and subsequent random loss of genes. Garcia et al. (1995) also analyzed a tetraploid population derived by selfing hexaploids and reported introgression of A. cardenasii (A genome species) genes into A. hypogaea in 10 of the 11 linkage groups on a molecular map. The authors concluded that this was evidence that the genomes of A. hypogaea are similar and that the species is not a true allopolyploid. Progenies selected from the highly diverse A. hypogaea × A. cardenasii population were highly resistant to early leaf spot caused by C. arachidicola, late leaf spot caused by C. personatum, peanut root-knot nematode, southern corn rootworm (Diabrotica undecimpunctata howardi Barber), and potato leafhopper (Empoasca fabae Harris). Several germplasm lines have been released from these progenies (Stalker and Beute 1993; Moss et al. 1997; Stalker et al. 2002a,b; Stalker and Lynch 2002). A second method to produce tetraploid interspecific hybrids is to produce autotetraploids or amphiploids of Arachis species prior to crossing with A. hypogaea. Polyploids are relatively easy to produce in peanut by colchicine-treating germinating seeds, but cytological identification of polyploid branches is necessary to confirm chromosome numbers of reproductive tissues. Autotetraploids have been produced with at least eight diploid species of section Arachis (Singh 1986a), but they are generally weak plants and do not survive for more than one growing season. Thus, crossing programs between autotetraploids and A. hypogaea need to be conducted as soon as plants are cytologically identified. Germplasm lines have not been released from this cytological pathway in peanut. Amphidiploids can also be produced between two or more diploid species before crossing with A. hypogaea. Gardner and Stalker (1983) created amphidiploids of hybrids between A-genome diploids and observed high bivalent association in the F1 hybrids with A. hypogaea. When amphidiploids are created by crossing A. batizocoi (B genome) with A-genome species, there are usually a greater numbers of bivalents in the polyploids. Although advantages exist for using A. batizocoi as a parent in crosses to enhance fertility restoration, this species is susceptible to late leaf spot and other diseases, which may result in unfavorable traits being introduced into breeding lines. Rust-resistant hybrids have been selected from progenies of A. hypogaea × amphiploid (A. batizocoi × A. duranensis) and (A. correntina × A. batizocoi) (Singh 1986b). Simpson et al. (1993a) released a germplasm line derived from a 4x [A. batizocoi (A. cardenasii × A. diogoi)] cross as well as its backcross with

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‘Florunner’. Both germplasm lines were highly resistant to the root-knot nematode, M. arenaria. Simpson and Starr (2001) also released the cultivar ‘COAN’, a backcross of this amphiploid to the cultivar ‘Florunner’ that is resistant to M. arenaria and M. javanica. D. Transgenic Technology The only viable alternative for accessing genes in the tertiary gene pool (or ones outside the genus) is to insert genes using transformation techniques. Utilizing this technology first depends on having a reliable tissue culture system to regenerate plants. In peanut, research was conducted for several decades by many investigators to develop reliable plant regeneration systems. The first successful in vitro system for regeneration in peanut was with deembryonated cotyledons (Illingsworth 1968). Although regeneration is highly influenced by genotype, media, light, temperature, and growth regulators, shoots have now been obtained from peanut using several explants and dedifferentiated callus cultures (Ozias-Akins and Gill 2001). Young leaflets can be used for regeneration, but only cells surrounding the central vein will produce callus that is competent for plant regeneration (Cheng et al. 1992; Utomo et al. 1996; Akasaka et al. 2000). These types of cells are susceptible to Agrobacterium infection but to date, only the genotype ‘New Mexico Valencia A’ has been shown to be capable of regeneration after Agrobacterium infection (Cheng et al. 1994, 1996). Protoplasts have only been regenerated from cells derived from immature cotyledons (Li et al. 1995). OziasAkins and Gill (2001) indicated that stable transformation using other genotypes has been incompletely documented, and they concluded that Agrobacterium transformation has restricted value for improving cultivated peanut. Many tissue types can now be regenerated in vitro, including hypocotyls, immature leaflets, leaf sections, cotyledons, and epicotyls. Because actively dividing cells are required to integrate foreign DNA into tissues, embryogenic cultures that divide rapidly are highly desirable, and cotyledons or immature embryos are good sources of callus for transformation in peanut (Ozias-Akins et al. 1992, 1993; Baker and Wetzstein 1995). Because researchers have been interested in developing cultivars adapted to specific geographic regions, many genotypes have been tested, and Ozias-Akins and Gill (2001) summarized the tissue culture protocols and genotypes used in peanut. Transformation techniques can be applied to primary cultures if shoot primordia or somatic embryos are formed directly from an explant, from dedifferentiated callus cultures, or repetitive tissue cultures. Suspension cultures are more

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difficult to use in peanut than cultures on semi-solid medium because recovery of fertile plants is difficult and genotype dependent (OziasAkins and Gill 2001). Tissue electroporation has not been reported in A. hypogaea (OziasAkins and Gill 2001), and microprojectile bombardment has been the most consistent and successful transformation technique in the species. Although transient expression can be used to monitor gene incorporation, it does not always correlate with gene transfer (Altpeter et al. 1996). Thus, selectable markers are usually used to identify transformed cells or tissues. Hygromycin phosphotransferase and neomycin phosphotransferase II are the most frequently used for antibiotic selection in peanut. Gene promoters are the most critical element for obtaining gene expression in peanut, and several work in peanut, including ones derived from monocots and dicots (Ozias-Akins and Gill 2001). The CaMV35S promoter is the most commonly used promotor in peanut and the only one used to control the hph gene (Ozias-Akins and Gill 2001). Genes have been inserted into A. hypogaea for virus (Brar et al. 1994; Li et al. 1997; Yang et al. 1998; Magbanua et al. 2000; Sharma and Anjaiah 2000) and lesser cornstalk borer resistances (Singsit et al. 1997). Durable resistance to tomato spotted wilt viruses is yet to be achieved (Li et al. 1997; Magbanua et al. 2000), but introduction of the crystaline proteins from Bacillus thuringensis appear to be stable (Ozias-Akins and Gill 2001). The later transformation system was developed more to reduce aflatoxin contamination in peanut than to inhibit the lesser cornstalk borer. Many potentially important traits could be incorporated into the cultivated genome, and transformation technologies will become increasingly important for peanut breeding as genes are isolated with agronomic potential. Especially important will be genes conditioning disease resistance and seed quality, however, no transgenic peanuts are currently in the marketplace.

VII. SUMMARY A large effort has been made to collect and preserve genetic resources in both A. hypogaea and related Arachis species. Although additional collecting is needed in South and Central America, the germplasm collection is large and represents a significant part of the variation in the genus. International germplasm exchange has become problematic during recent years, not because of individuals in the scientific community, but because of political constraints on collection and distribution of seeds. Creating a peanut core collection has greatly enhanced evaluation

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research in peanut during recent years, and sources of resistance to many pests of peanut have been identified. Most major peanut-producing states employ peanut breeders but to date, cultivars originating from Florida, Georgia, North Carolina, and Texas have dominated the peanut hectarage. Only one private company and one U.S. Department of Agriculture scientist are involved in peanut cultivar development. Plant breeding efforts in peanut have shifted emphasis during recent years from mostly selecting for increased yields to also selecting high-yielding cultivars with greater resistance to biotic stresses and enhanced quality traits, especially for improving oil profiles and flavor characteristics. The prevalence of foliar and soilborne diseases continues to keep peanut yields well below their potential levels, and until diseases such as TSWV, sclerotinia blight, and white mold can be suppressed, average yields will likely not increase. If the federal peanut support program changes to further reduce or eliminate price supports for peanut, then there will be greatly increased emphasis on lowering production costs while increasing biotic stress resistances in future cultivars. Cultivars with multiple sources of high levels of pest resistance will be needed in the future, an endeavor that has been very difficult for this crop species. In addition, breeding for drought resistance is a high priority set by the producer who wishes to maximize yields and for the manufacturer who needs a product free from aflatoxins. Although cultivar development has traditionally emphasized improvement of A. hypogaea genotypes through pedigree selection, interspecific hybridization has received much attention since the 1960s in large part because of the high levels of pest resistances identified in wild peanut species. Genetic resistance to several of the most important pathogens of peanut, including TSWV, have only been found in species related to A. hypogaea. The first peanut cultivar with a diploid wild species in its pedigree was recently released for commercial production, and others should follow within a few years. Molecular technologies are being developed for peanut, but little detectable molecular variation exists in A. hypogaea. However, a large amount of molecular variation is present in species of Arachis, and marker-assisted selection has great potential for enhancing introgression of useful traits from related species to cultivars. Transgenic technologies have been developed to insert foreign genes into the cultivated peanut, and the critical component needed for cultivar development is identification of agronomically useful genes. Cultivars developed with transgenic technologies have not been released for production, but several programs are working toward this goal. Regardless of the methodologies used in the future, critical needs are to lower production costs by selecting for abi-

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otic stress resistances, to select genotypes that will increase consumer safety by lowering toxin levels in the seed, and to create higher consumer demands for peanut products through enhanced quality factors.

LITERATURE CITED Abdel-Momen, S. M., C. E. Simpson, and J. L. Starr. 1998. Resistance of interspecific Arachis breeding lines to Meloidogyne javanica and an undescribed Meloidogyne species. J. Nematol. 30:341–346. Ahmed, E. H., and C. T. Young. 1982. Composition, nutrition, and flavor of peanuts. p. 655–688. In H. E. Pattee and C. T. Young (eds.), Peanut science and technology. Am. Peanut Res. Educ. Soc. Yoakum, TX. Akasaka, Y., H. Daimin, and M. Mii. 2000. Improved plant regeneration from cultured leaf segments in peanut (Arachis hypogaea L.) by limited exposure to thidiazuron. Plant Sci. 156:169–175. Akem, C. N., H. A. Melouk, and O. D. Smith. 1992. Field evaluation of peanut genotypes for resistance to sclerotinia blight. Crop Protect. 11:345–348. Altpeter, F., V. Vasil, V. Srivastava, E. Stoger, and I. K. Vasil. 1996. Accelerated production of transgenic wheat (Triticum aestivum L.) plants. Plant Cell Rep. 16:12–17. Amaya, F., C. T. Young, R. O. Hammons, and G. Martin. 1977. The tryptophan content of the U.S. commercial and some South American wild genotypes of the genus Arachis: a survey. Oleágineux 32:225–229. Anderson, W. F., C. C. Holbrook, and T. B. Brenneman. 1993. Resistance to Cercosporidium personatum within peanut germplasm. Peanut Sci. 20:53–57. Anderson, W. F., C. C. Holbrook, and A. K. Culbreath. 1996a. Screening the core collection for resistance to tomato spotted wilt virus. Peanut Sci. 23:57–61. Anderson, W. F., C. C. Holbrook, and D. M. Wilson. 1996b. Development of greenhouse screening for resistance to Aspergillus parasiticus infection and preharvest aflatoxin contamination in peanut. Mycopathologia 135:115–118. Anderson, W. F., C. C. Holbrook, and J. C. Wynne. 1991. Heritability and early-generation selection for resistance to early and late leafspot in peanut. Crop Sci. 31:588–593. Anderson, W. F., C. C. Holbrook, D. M. Wilson, and M. E. Matheron. 1995. Evaluation of preharvest aflatoxin contamination in some potentially resistant peanut genotypes. Peanut Sci. 22:29–32. Anderson, W. F., J. C. Wynne, C. C. Green, and M. K. Beute. 1986. Combining ability and heritability of resistance to early and late leaf spot of peanut. Peanut Sci. 13:10–14. Aquino, V. M., F. M. Shokes, D. W. Gorbet, and F. W. Nutter. 1995. Late leaf-spot progression on peanut as affected by components of partial resistance. Plant Dis. 79:74–78. Asakawa, N. M., and C. A. Ramirez-R. 1989. Metodologia para la inoculacion y siembra de Arachis pintoi. Past. Trop. 11:24–26. Backman, P. A., and T. B. Brenneman. 1997. Stem rot. p. 36–37. In: N. Kokalis-Burelle, D. M. Porter, R. Rodriguez-Kabana, D. H. Smith, and P. Subrahmanyam (eds.), Compendium of peanut diseases. 2nd ed. APS Press, Am. Phytopath. Soc., St. Paul, MN. Baker, C. M., and H. Y. Wetzstein. 1995. Repetitive somatic embryogenesis in peanut cotyledon cultures by continual exposure to 2,4-D. Plant Cell Tiss. Org. Cult. 40: 249–254.

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Singh, A. K., H. T. Stalker, and J. P. Moss. 1991b. Cytogenetics and use of alien genetic variation in groundnut improvement. p. 65–77. In: T. Tsuchiya and P. K. Gupta (eds.), Chromosome engineering in plants: genetics, breeding and evolution. Part B. Elsevier Scientific Publ., Amsterdam, The Netherlands. Singh, A. K., S. S. Krishnan, M. H. Mengesha, and C. D. Ramaiah. 1991a. Polygenetic relationships in section Arachis based on seed protein profiles. Theor. Appl. Genet. 82:593–597. Singsit, C., M. J. Adang, R. E. Lynch, W. F. Anderson, A. Wang, G. Cardineau, and P. Ozias-Akins. 1997. Expression of a Bacillus thuringiensis cryIA(c) gene in transgenic peanut plants and its efficacy against lesser cornstalk borer. Transgen. Res. 6:169–176. Smartt, J., and W. C. Gregory. 1967. Interspecific cross-compatibility between the cultivated peanut and Arachis hypogaea L. and its behavior in backcrosses. Oleágineux 22:455–459. Smartt, J., and H. T. Stalker. 1982. Speciation and cytogenetics in Arachis. p. 21–49. In: H. E. Pattee and C. E. Young, (eds.), Peanut science and technology. Am. Peanut Res. Ed. Soc., Yoakum, TX. Smith, O. D., T. E. Boswell, W. J. Grichar, and C. E. Simpson. 1989. Reaction of select peanut (Arachis hypogaea L.) lines to southern stem rot and pythium pod rot under varied disease pressure. Peanut Sci. 16:9–14. Smith, O. D., C. E. Simpson, M. C. Black, and B. A. Besler. 1998. Registration of ‘Tamrun 96’ peanut. Crop Sci. 38:1403. Smith, O. D., C. E. Simpson, W. J. Grichar, and H. A. Melouk. 1991. Registration of Tamspan 90 peanut. Crop Sci. 31:1711. Smith, O. D., S. M. Aguirre, T. E. Boswell, W. J. Grichar, H. A. Melouk, and C. E. Simpson. 1990. Registration of TxAG-4 and TxAG-5 peanut germplasm. Crop Sci. 30:429. Stalker, H. T. 1981a. Hybrids in the genus Arachis between sections Erectoides and Arachis. Crop Sci. 21:359–362. Stalker, H. T. 1981b. Cytogenetics of Arachis. p. 22–29. In: J. C. Wynne and T. A. Coffelt (eds.), Proc. peanut breeding symp. Am. Peanut Res. Educ. Soc., Richmond, VA. Dept. Crop Science, NCSU, Res. Rep. 80. Stalker, H. T. 1985a. Cytotaxonomy of Arachis. p. 65–79. In: J. P. Moss (ed.), Proc. Intern. Workshop on Cytogenetics of Arachis. Int. Crops Res. Inst. Semi-Arid Tropics, Patancheru, A. P., India. Stalker, H. T. 1985b. Groundnut cytogenetics at North Carolina State University. p. 119–123. In: J. P. Moss (ed.), Proc. Intern. Workshop on Cytogenetics of Arachis. Int. Crops Res. Inst. Semi-Arid Tropics. Patancheru, A. P., India. Stalker, H. T. 1991. A new species in section Arachis of peanuts with a D genome. Am. J. Bot. 78:630–637. Stalker, H. T. 1990. A morphological appraisal of wild species in section Arachis of peanuts. Peanut Sci. 17:117–122. Stalker, H. T. 1992. Utilizing Arachis Germplasm Resources. p. 281–295. In: Groundnut—a global perspective. Proc. Intern. Workshop. November 25–29, 1991, Int. Crops Res. Inst. Semi-Arid Tropics. Patancheru, A. P., India. Stalker, H. T., and M. K. Beute. 1993. Registration of four interspecific peanut germplasm lines resistant to Cercospora arachidicola. Crop Sci. 33:1117. Stalker, H. T., and W. V. Campbell. 1983. Resistance of wild species of peanuts to an insect complex. Peanut Sci. 10:30–33. Stalker, H. T., and R. D. Dalmacio. 1981. Chromosomes of Arachis species, section Arachis (Leguminosae). J. Hered. 72:403–408.

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Stalker, H. T., and R. D. Dalmacio. 1986. Karyotype analysis and relationships among varieties of Arachis hypogaea L. Cytologia 58:617–629. Stalker, H. T., and R. L. Lynch. 2002. Registration of four insect-resistant peanut germplasm lines. Crop Sci. 42:313–314. Stalker, H. T., and J. P. Moss. 1987. Speciation, cytogenetics, and utilization of Arachis species. Adv. Agronomy. 41:1–40. Stalker, H. T., and L. G. Mozingo. 2001. Molecular genetics of Arachis and marker assisted selection. Peanut Sci. 28:117–123. Stalker, H. T., and C. E. Simpson. 1995. Genetic resources in Arachis. p. 14–53. In: H. E. Pattee and H. T. Stalker (eds.), Advances in peanut science. Am. Peanut Res. Educ. Soc., Stillwater, OK. Stalker, H. T. and J. C. Wynne. 1983. Photoperiod response of peanut species. Peanut Sci. 10:59–62. Stalker, H. T., J. S. Dhesi, and D. Parry. 1991a. An analysis of the B genome species Arachis batizocoi. Plant Syst. Evol. 174:159–169. Stalker, H. T., G. D. Kochert, and J. S. Dhesi. 1995a. Variation within the species A. duranensis, a possible progenitor of the cultivated peanut. Genome 38:1201–1212. Stalker, H. T., M. H. Seitz, and P. Reece. 1987. Effect of gibberellic acid on pegging and seed set of Arachis species. Peanut Sci. 14:17–21. Stalker, H. T., M. K. Beute, B. B. Shew, and K. R. Barker. 2002a. Registration of two rootknot nematode-resistant peanut germplasm lines. Crop Sci. 42:312–313. Stalker, H. T., M. K. Beute, B. B. Shew and T. G. Isleib. 2002b. Registration of five leafspotresistant peanut germplasm lines. Crop Sci. 42:314–316. Stalker, H. T., J. S. Dhesi, D. Parry, and J. H. Hahn. 1991b. Cytological and interfertility relationships of Arachis section Arachis. Am. J. Bot. 78:238–246. Stalker, H. T., T. G. Phillips, J. P. Murphy, and T. M. Jones. 1994. Diversity of isozyme patterns in Arachis species. Theor. Appl. Genet. 87:746–755. Stalker, H. T., B. B. Shew, M. K. Beute, K. R. Barker, C. C. Holbrook, J. P. Noe, and G. A. Kochert. 1995b. Meloidogyne arenaria resistance in advanced-generation Arachis hypogaea × A. cardenasii hybrids. Proc. Am. Peanut Res. Educ. Soc. 27:241 (abstr.). Starr, J. L., G. L. Schuster, and C. E. Simpson. 1990. Characterization of the resistance to Meloidogyne arenaria in an interspecific Arachis spp. hybrid. Peanut Sci. 17:106–108. Starr, J. L., C. E. Simpson, and T. A. Lee, Jr. 1995. Resistance to Meloidogyne arenaria in advanced generation breeding lines of peanut. Peanut Sci. 22:59–61. Starr, J. L., C. E. Simpson, and T. A. Lee, Jr. 1998. Yield of peanut genotypes resistant to root-knot nematodes. Peanut Sci. 25:119–123. Subrahmanyam, P. 1997. Rust. p. 31–33. In: N. Kokalis-Burelle, D. M. Porter, R. RodriguezKabana, D. H. Smith, and P. Subrahmanyam (eds.), Compendium of peanut diseases. 2nd ed. APS Press, Am. Phytopath. Soc., St. Paul, MN. Tallury, S. P., S. R. Mila, S. C. Copeland, and H. T. Stalker. 2001. Genome donors of Arachis hypogaea L. Am. Peanut Res. Educ. Soc. 33:60. Timper, P., C. C. Holbrook, and H. Q. Xue. 2000. Expression of nematode resistance in plant introductions of Arachis hypogaea. Peanut Sci. 27:78–82. Ullman, D. E., J. L. Sherwood, and T. L. German. 1997. Thrips as vectors of plant pathogens. p. 539–566. In: T. Lewis (ed.), Thrips as crop pests. CAB Int., New York. Upadhyaya, H. D., M. E. Ferguson, and P. J. Bramel. 2001a. Status of the Arachis germplasm collection at ICRISAT. Peanut Sci. 28:89–96. Upadhyaya, H. D., and S. N. Nigam. 1999. Detection of epistasis for protein and oil contents and oil quality parameters in peanut. Crop Sci. 39:115–118.

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Upadhyaya, H. D., R. Ortiz, P. J. Bramel, and S. Singh. 2001b. Development of a groundnut core collection using taxonomical, geographical and morphological descriptors. Gene. Res. Crop Evol. (In Press). Utomo, S. D., A. K. Weissinger, and T. G. Isleib. 1996. High efficiency peanut regeneration using a nonimbibed immature leaflet culture method. Peanut Sci. 23:71–75. Valls, J. F. M. 2000. Recent advances in the characterization of wild Arachis germplasm in Brazil. Proc. Am. Peanut Res. Educ. Soc. 32:47 (abstr.). Valls, J. F. M., V. Ramanatha Rao, C. E. Simpson, and A. Krapovickas. 1985. Current status of collection and conservation of South American groundnut germplasm with emphasis on wild species of Arachis. p. 15–35. In: J. P. Moss (ed.), Proc. Intern. Workshop on Cytogenetics of Arachis. October 31–November 2, 1983. Int. Crops Res. Inst. Semi-Arid Tropics, Patancheru, A. P. India. Waliyar, F., H. Hassan, S. Bonkoungou, and J. P. Bosc. 1994. Sources of resistance to Aspergillus flavus and aflatoxin contamination in groundnut genotypes in West Africa. Plant Dis. 78:704–708. Waliyar, F., D. McDonald, P. V. Subba Rao, and P. M. Reddy. 1993. Components of resistance to an Indian Source of Cersospora arachidicola in selected peanut lines. Peanut Sci. 20:93–96. Waliyar, F., B. B. Shew, R. Shidahmed, and M. K. Beute. 1995. Effect of host resistance on germination of Cercospora arachidicola on peanut leaf surfaces. Peanut Sci. 22: 154–157. Wells, M. A., W. J. Grichar, O. D. Smith, and D. H. Smith. 1994. Response of selected peanut germplasm lines to leafspot and southern stem rot. Oleágineux 49:21–26. Wildman, L. G., O. D. Smith, C. E. Simpson, and R. A. Taber. 1992. Inheritance of resistance to Sclerotinia minor in selected Spanish peanut crosses. Peanut Sci. 19:31–35. Will, M. E., C. C. Holbrook, and D. M. Wilson. 1994. Evaluation of field inoculation techniques for screening peanut genotypes for reaction to preharvest A. flavus group infection and aflatoxin contamination. Peanut Sci. 21:122–125. Williams, D. E. 2001. New directions for collecting and conserving peanut genetic diversity. Peanut Sci. 28:135–140. Williams, K. A., and D. E. Williams. 2001. Evolving political issues affecting international exchange of Arachis genetic resources. Peanut Sci. 28:131–135. Wilson, D. M. 1989. Analytical methods for aflatoxin in corn and peanuts. Arch. Environ. Contam. Toxicol. 18:308–314. Wright, G. C., and G. L. Hammer. 1994. Distribution of nitrogen and radiation use efficiency in peanut canopies. Aust. J. Agric. Res. 45:565–574. Wright, G. C., K. T. Hubrick, and G. D. Farquhar. 1988. Discrimination in carbon isotopes of leaves correlates with water-use efficiency of field-grown peanut cultivars. Aust. J. Plant Physiol. 15:815–825. Wright, G. C., and R. C. Nageswara Rao. 1994. Groundnut water relations. p. 281–335. In: J. Smartt (ed.), The groundnut crop. A scientific basis for improvement. Chapman and Hall, London. Wright, G. C., R. C. Nageswara Rao, and M. S. Basu. 1996. A physiological approach to the understanding of genotype by environment interactions—a case study on improvement of drought adaptation in peanut. p. 365–381. In: M. Cooper and G. L. Hammer (eds.), Plant adaptation and crop improvement. CAB Int., Wallingford, UK. Wright, G. C., R. C. Nageswara Rao, and G. D. Farquhar. 1994. Water-use efficiency and carbon isotope discrimination in peanut under water deficit conditions. Crop Sci. 34:92–97.

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7 History and Breeding of Table Beet in the United States I. L. Goldman Department of Horticulture 1575 Linden Drive Madison, Wisconsin 53706 J. P. Navazio SEEDS 608 West Benton Street Iowa City, IA 52246

I. INTRODUCTION II. CROP ORIGINS A. Taxonomy of Beta B. Domestication of the Beta Complex III. HORTICULTURE OF TABLE BEET A. Vegetative and Reproductive Biology B. Uses of Table Beet C. Seed Production IV. GENETICS AND BREEDING A. Qualitative Genetics B. Quantitative Genetics C. Color Genetics and Breeding D. Pests and Diseases V. BREEDING METHODS A. Founding Table Beet Populations in the United States B. Hybrid Table Beet C. Techniques in Table Beet Breeding D. Commercial Cultivar Development VI. FUTURE DIRECTIONS LITERATURE CITED

Plant Breeding Reviews, Volume 22, Edited by Jules Janick ISBN 0-471-21541-4 © 2003 John Wiley & Sons, Inc. 357

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I. INTRODUCTION Table beet (Beta vulgaris subsp. vulgaris L., Chenopodiaceae) is a member of a complex of crop plants from the genus Beta. This complex includes members domesticated for their use as root crops (mangel, table beet), leaf and petiole crops (leaf beet, swiss chard, table beet), animal feed (fodder beet), and a source of sucrose (sugar beet). The development of these diverse crops from a common B. vulgaris progenitor illustrates the power of artificial selection and human creativity in crop evolution. The table beet, with its pigmented, swollen root, is one of the two current vegetable members of the B. vulgaris complex. The table beet has been cultivated for thousands of years as both a root and leaf vegetable crop. Its origins as a cultivated crop trace back to the development of a leaf vegetable by the Romans from wild species of Beta growing in the Mediterranean region. During this period, or perhaps as this leaf crop moved northward in Europe, selection for swollen-rooted forms resulted in the development of the modern table beet, which is consumed primarily for its swollen, fleshy root and hypocotyl, and secondarily for its leaf blades and petioles. Table beet is grown and consumed worldwide as both a fresh and processed vegetable, but is most popular in Eastern Europe, Asia, the Mediterranean region, and the United States. Compared to most United States vegetable crops, the area of table beet grown each year is low, and generally this does not exceed 3200 ha in the United States, nearly half of which is in Wisconsin (Anon. 2000). This area does not include baby leaf production in the western United States, which has gained in prominence in the past decade. However, it is unlikely baby leaf production in the United States exceeds 200 ha per year. The two most recognizable characteristics of the table beet are (1) the presence of betalain pigments, which can color roots and foliage red, pink, orange, yellow, and (2) the earthy flavor of the root. Although table beet cultivars were introduced into the United States with the early settlers in the seventeenth century, little improvement other than mass selection and a few attempts at inbreeding were conducted in the United States until the second half of the twentieth century. The only cultivars available then were open-pollinated, and three key founding populations, which were also cultivars (‘Egyptian’, ‘Detroit’, and ‘Long Dark Blood’), were the principle germplasm. Beginning in the 1950s, the Owen cytoplasm (O type cytoplasm, which confers male sterility, discovered by the geneticist F. V. Owen), the SF allele, (which conditions self-compatibility), and the b allele (annual growth habit) were transferred from sugar beet to table beet and

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the modern inbred-hybrid method of breeding table beet was initiated. Today, F1 hybrids are produced on significant area in Europe, Asia, and the United States, although the majority of production is still with openpollinated cultivars. Productivity, uniformity, maturity, and multiple disease resistance are key performance traits in table beet breeding. Uses for the crop have expanded to include “baby leaf” salad greens and natural colors for the food industry. Quality traits, including root color, root shape, root flavor, leaf and petiole phenotype have become important selection criteria. Gene transfer from sugar beet and related Beta species could improve future table beet breeding efforts; there is much opportunity for germplasm enhancement using these source materials. F1 hybrid breeding programs should continue to expand the diversity and productivity of table beet hybrids, but selection and maintenance of new open-pollinated cultivars will remain an important objective for table beet breeders.

II. CROP ORIGINS A. Taxonomy of Beta The table beet is variously known as the garden beet, beetroot, and red beet (Ford-Lloyd 1995). Many authors’ subspecies designations refer to table beet as subspecies crassa. Most modern taxonomic treatments of the genus Beta consider table beet to be subspecies vulgaris (Ford-Lloyd 1995). Beta is an old-world genus. Species of Beta occur in maritime regions of the Mediterranean and Black Seas (Ford-Lloyd 1995). The genus Beta is comprised of four sections that contain a total of 12 species. Sections include Beta, Patellares, Corolinae, and Nanae. Section Beta has the widest distribution and extends into China. Patellares is found in the South West, particularly in the Canary Islands. Corolinae is found in eastern Europe and the Near East, while Nanae is found only in the mountains of Greece, and Corolinae is found primarily in the Canary Islands. Section Beta includes six subspecies within B. vulgaris: vulgaris (sugar beet, table beet, fodder beet), cicla (swiss chard, leaf beet, spinach beet), maritima (wild sea beet), adanensis, trojana, and macrocarpa. The section also includes the species B. patula and B. atriplicifolia (Ford-Lloyd 1995). Swiss chard, table beet, fodder beet, sugar beet, and certain wild species (such as Beta maritima) are diploid with a chromosome constitution of 2n = 2x = 18 and freely intercrossable, although with some difficulty depending on the species. Several important genes have been

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exchanged among these cultivated types for breeding purposes (Goldman 1996b). In particular, genes from sugar beet have been used to improve table beet. These genes will be discussed in more detail in Section V. The wild Beta species have been used to improve cultivated Beta crops. Gaskill (1954) reported viable hybrids from crosses of swiss chard with both Beta procumbens and Beta webbiana. Both species may contain useful genes for crop improvement, including disease and pest resistance. Interestingly, neither species hybridized successfully with sugar beet, but both were successful in matings with swiss chard. In Gaskill’s program, swiss chard served as a useful bridge species in introducing these desirable traits to sugar beet. Beta procumbens has been a source of root-knot nematode resistance genes for sugar beet. The Hs1pro-1 gene carried on chromosome 1 of B. procumbens was transferred to sugar beet via interspecific hybridization and backcrossing (Jung and Wricke 1987). Recently, Cai et al. (1997) used the technique of positional cloning to clone a nematode resistance gene from B. procumbens and transfer it to sugar beet. B. Domestication of the Beta Complex The progenitor of the table beet was originally selected for its use as a leaf vegetable in the Mediterranean region and then later for use as a fresh or stored root vegetable (Campbell 1976). Early European herbals clearly point toward distinct uses for the leaf portion and the swollen red hypocotyl and root (Pink 1993). Use of B. vulgaris as a leaf crop probably included the leaf beet, a vegetable form of beet grouped in the vulgaris subspecies. The leaf beet has fleshy petioles, similar in thickness to asparagus, although it does not possess a swollen root. As the crop moved into northern Europe, farmers would have faced a shorter growing season and a colder winter. These conditions may have resulted in the transition toward a biennial life cycle by creating selection pressure toward a swollen hypocotyl/root as an over-wintering propagule (Ford-Lloyd 1995). Alternatively, selection for a swollenrooted form may have taken place in the Mediterranean region prior to its movement into northern Europe. Some authors have suggested that swollen roots, which indicate the storage of carbohydrates for energy production during reproductive growth, may have been selected from leafy beets cultivated in Assyrian, Greek, and Roman gardens (FordLloyd and Williams 1975; Williams and Ford-Lloyd 1974). These leaf beets may have resembled the swiss chard of today. There is no mention of a swollen-rooted beet in historical documents until the sixteenth century, but at this time a profusion of citations cov-

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ering a variety of forms occurred in European herbals (Ford-Lloyd 1995). As the pigmented, swollen-rooted forms were introduced, hybridization between the swollen-rooted and leaf forms was widespread, resulting in an array of phenotypes. An example of this hybridization is the description of a mangel, a swollen-rooted form of B. vulgaris, that arose from crosses between a red table beet and white leaf beet described in 1787 by the Abbe de Commerell (Ford-Lloyd 1995). Clearly, intercrossing among cultivated B. vulgaris crops has played a role in the modern evolution of these subspecies. In the eighteenth century, the use of beet root was expanded to include animal feed. The fodder beet, as it came to be known, soon became an important component of European agriculture and served as the progenitor of the sugar beet (Pink 1993). Fodder beet possessed edible roots and leaves, making it an excellent forage crop. Various names exist for the common fodder beet, including forage beet, mangels, mangolds, and mangel-wurzels. All of these possess very large swollen roots of various shapes and colors and were developed for animal feed (Ford-Lloyd 1995). The sugar beet, a close relative of table beet, is designated as the same subspecies as table beet and is a crop of modern origin. Sugar beet was developed from a fodder beet population known as “White Silesian” (Fischer 1989) during a search for alternative sources of sucrose when France was unable to obtain sugarcane sugar due to a British blockade during the Napoleonic wars (Winner 1993). This work was based on the discovery of a sweet syrup from B. vulgaris by Olivier de Serres that was later identified as sucrose by Marggraf during the eighteenth century and discovered to be identical to the sugar from sugarcane (Winner 1993). An alternative explanation for the development of a sugar source from B. vulgaris is that France wished to possess a domestic sugar industry that would hinder Britain’s dominance of the sugar trade. Selection for high sucrose was practiced by a number of breeders during the eighteenth and nineteenth centuries, including one of Marggraf’s students named F. K. Archard, and their methodologies were among the first to describe mass selection in a scientific manner (Goldman 2000).

III. HORTICULTURE OF TABLE BEET A. Vegetative and Reproductive Biology The swollen nature of the hypocotyl and storage root of the table beet is due to the presence of supernumerary cambia, which form very early in plant development. Prior to supernumerary cambial formation, a

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cylindrical vascular cambium forms in cells located between the primary xylem and primary phloem. This vascular cambium eventually encircles the primary xylem and then gives rise to the supernumerary cambia (Benjamin et al., 1997). The signal responsible for triggering this unique development is presently unknown, although hormones such as auxins and cytokinins may play a role. The presence of most or all of the annular zones of development is possible at a very early stage of vegetable growth because supernumerary cambia form early in development (Benjamin et al. 1997). After initiation of the vascular cambium, cell division, expansion, and storage of carbohydrate take place. Hole et al. (1984) suggested that the storage of carbohydrate and the subsequent formation of a swollen taproot must proceed by a second signal that is independent of the formation of the vascular cambium. Table beet plants grown at densities greater than 500 plants/m2 do not form a swollen taproot, but do contain vascular cambium. Table beet, like sugar beet, contains sucrose as its primary storage carbohydrate. The sucrose concentration of sugarbeet is determined to a large degree by the ratio of large and small cells, which comprise the parenchymatous and vascular zones of each ring, respectively. Growth is attributed to expansion of the large cells in the parenchymatous zone; thus, there is a tradeoff between root weight and sugar concentration (Benjamin et al. 1997). Levels of sucrose are much higher in sugarbeet than table beet cultivars, but it is likely that sucrose accumulation follows a similar pattern in both crops. The tradeoff between root weight and sugar concentration has received little attention in table beet breeding. Consumer preferences for small-diameter “baby” beets, which are typically roots of less than 2.5 cm in diameter, may suggest the tradeoff has been considered by producers due to the impact it may have on consumer preference. Table beet has a biennial life cycle and requires specific environmental stimuli to promote a switch from vegetative growth to reproductive growth. Table beet is an obligate long-day plant. Vernalization, the exposure of the plant to a relatively short period of temperatures slightly above freezing, has the effect of increasing the competence of leaves to produce a flowering stimulus (Benjamin et al. 1997). Perception of the stimulus occurs in the apical meristem. While little work on the vernalization requirement in table beet has been conducted, some work has been reported in sugar beet. The minimum and maximum temperatures for vernalization in sugar beet were 0° and 15°C, respectively, with the fastest flowering response occurring at 12°C (Benjamin et al. 1997). Bolting, or the premature appearance of a flower stalk during the vegetative growth stage, is detrimental to crop production. Bolt-

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ing is a fairly common occurrence in early-planted table beet crops, particularly in temperate environments. Significant yield losses can be expected where bolting has occurred, and selection against bolting is routinely practiced. Jaggard et al. (1983) reported that 50 percent of field-grown sugar beet plants bolted when temperatures were less than 12°C for 60 days during vegetative growth. Threshold temperature levels for bolting in table beet have not been reported. During a standard breeding cycle, vernalization typically takes place for 12 weeks at temperatures of approximately 2 to 5°C. B. Uses of Table Beet 1. Beet Leaf for Salad Mix. The traditional use of the table beet as a cooked root vegetable is no longer the only way consumers eat this crop. In recent years, there has been an increase in production of table beets for the use of their leaves in premixed bagged salads. Salad mix ingredients must have a shelf life of at least one week and should add a desirable feature to the mix such as leaf shape, color, or appearance (smooth or crinkled). Beet leaves have excellent shelf life and their brilliant red petioles which contrast with the bright green young leaves is very attractive. The size of the beet tops used for the salad mix market ranges from 6.0 to 9.0 cm in length and a width of 3.5 to 5.0 cm. To attain this size in production the period from sowing to harvest rarely exceeds 40 days depending on the season, and can be as low as 28 to 30 days from sowing in the warmer months of production. To achieve the best yields many salad mix growers use precision planters, planting multiple rows on beds that can range anywhere from 66 to 91 cm wide for many smaller market farmers and 101 to 203 cm wide for large-scale production in California and Arizona. Production on 203 cm wide beds can use up to 5 million seeds/ha with up to 24 rows on top of each bed. At this planting density, there is approximately 4.0 to 5.0 cm between beet plants in all directions, thus forming a “blanket” of plants on top of the bed. Usually these plantings are harvested only once, as subsequent regrowth of the crop produces many “half leaves” which occur due to the cutting of the leaves in the apical growing point at the time of the initial harvest. Large-scale leaf harvest for “baby leaf” production is usually accomplished with a mobile harvester machine that has a cutting bar attachment that operates like a band saw. The crop is cut for harvest within 3.0 cm of the surface of beds previously leveled using laser technology. Smaller scale harvest of the “baby leaf” crop is often accomplished with

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a cutting bar that is either swung manually on a scythe with an attached hamper or is accomplished manually with knives. Many market farmers harvesting for high value specialty markets will plant baby leaf crops in more traditional rows at high densities for hand clipping. Depending on the season, the use of beet leaves for salad mix is sometimes supplanted by the use of swiss chard leaves. In California’s Salinas Valley and numerous coastal vegetable growing regions, where damage by the pea leaf miner can lower leaf quality in warmer months, ‘Rhubarb’ swiss chard leaves exhibit less damage from the stings of the pea leaf miner (Liriomyza huidobrensis) than do beet. In winter and early spring production (November through April sowings) in this area, hybrids are preferred for baby leaf production because of their seedling vigor and ability to maintain a consistent green leaf color during cool weather (10°–20°C). 2. Fresh Market Beets. Table beet production for the U.S. fresh market is predominated by the beet roots with attached tops in “bunches” of five to seven roots held together with twist ties. Tops or leaves must also be between 25 and 35 cm tall to meet market standards for bunched beets. This market demands beets with leaf tissue that remains green with red pigmentation only in the petioles and veins of the leaf blade. Many of the cultivars currently used for bunching and the fresh market have been selected for greener tops than cultivars used a generation ago for the fresh market. These cultivars include topcross hybrids like ‘Avenger’, ‘Pacemaker III’, ‘Red Ace’, ‘Solo’, and ‘Warrior’ as well as openpollinated standards ‘Early Wonder’, ‘Early Wonder Tall Top’, and ‘Greentop Bunching’. Increased acceptance of European cultivars in North American fresh markets has occurred over the last decade with ‘Pronto’ and ‘Red Cloud F1’, but the tops are not tall or robust enough for many growers. Specialty fresh market cultivars for the expanding produce sector exemplified by farmer’s markets and Community Supported Agriculture (CSA) farms include the yellow-fleshed ‘Burpee’s Golden Beet’ and the Italian variety ‘Chioggia’ which has the striking visual affect of alternating red and white cambial layers in the vascular tissue. More detail about the biosynthesis of betalain pigments in these tissues is presented in Section IV. 3. Beets for Processing. Current production practices for canned beets have not changed significantly in several decades. While most modern beet cultivars are rated at between 45 and 60 days to maturity, the majority of beets grown for processing are grown over a 90 to 120 day season. In order to keep the roots from becoming oversized over such a long sea-

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son, the standard planting regimen is single rows with 54 to 72 plants/m of row (1.25–1.6 cm between plants). Through crowding of the plants at this density, it is possible to slow plant growth, thereby enabling the grower to have more flexibility in timing the harvest. Roots of North American table beet cultivars used at these densities are able to push against one another while growing outward from the center of the row and still attain an optimal size for processing without becoming oversized. Many European cultivars that have been evaluated under these planting densities develop flattened sides and have, therefore, never had a significant share of the North American processing market. The optimal size for processing beets is 2.5 to 4.0 cm for the “baby whole” beet pack and 4.0 to 7.0 cm for sliced beets. Popular table beet cultivars for processing include ‘Red Ace’ F1, ‘Ruby Queen’, and ‘Detroit Dark Red’. ‘Red Ace’ is used in all major processed beet growing regions due to its exceptional seedling vigor, bolting tolerance, interior quality, mild flavor, disease resistance, and strong petioles for mechanical harvest. ‘Ruby Queen,’ an open-pollinated cultivar, is used predominantly in the Upper Midwest and New York State growing regions, where its orange-red interior color is preferred by the consumers of those areas. In contrast, the beet processing industry of Oregon’s Willamette Valley only accepts the more purple-red color of the open-pollinated ‘Detroit Dark Red’ cultivar types. C. Seed Production Production of beet seed for commercial purposes is usually accomplished over the course of two seasons in a climate with mild winters (average seasonal low –8°C) and cool dry summers (average seasonal high 24°C). In North America, the “root-to-seed” method predominates. The majority of table beet seed production is conducted in the Puget Sound region of Washington, where significant damage to the roots can occur if they are left in the field over winter. Stock seed is planted into a “plantbed” in late June or early July at densities approximating those of fresh market production. While plants are vigorously growing, between 6 and 10 weeks after planting, the crop can be rogued for off-types based on the foliage. By early October, when crop growth has slowed and roots have attained an adequate size, the crop is topped by mechanically cutting off the leaves and all but 2 to 5 cm of the petioles to prepare the crop for storage. Within a week to 10 days of topping plants, roots are lifted and piled into “windrows,” with each windrow representing four to six rows of beet roots (sometimes these windrows are pushed into a very shallow “pit” that is only 15 cm deep

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and 1 m wide). Enough soil is mounded over the roots to cover them with 30 cm of soil on all open sides. This is adequate thermal insulation to protect the roots from freezing damage in areas where annual low temperatures do not exceed –10°C and where the duration of cold periods near 0°C are not longer than 48 to 72 h. The roots are unearthed between the middle of March and early April. Roots for seed production are known as “stecklings.” Stockseed roots are graded for shape, prominence of taproot, absence of disease, and trueness-to-type. As it is important to improve and maintain consistent internal color, all stecklings used for stockseed production should be visually inspected for acceptable pigment levels. This is accomplished in two ways, both involving cutting the roots. The first method involves –1 to 15 –1 of the root mass, diagonally, off the side cutting approximately 10 of the root. The second method requires slicing the root diagonally, starting at the center of the apical growing point, thereby cutting the root into two halves that are approximately the same size. Both methods allow the breeder to see both the overall intensity of color as well as detecting the presence of “zoning,” which is the differential coloring between cambial rings. While a number of producers will apply a fungicide drench to the stecklings at this point, especially to the cut surface of the root, there are some workers who believe that any moisture that is introduced will only hasten decay. For this reason, several breeding programs forgo the application of fungicides at this point and simply allow the roots to air dry. Roots are then stored in clean, dry wood shavings until planting. At the time of replanting the stecklings are mechanically dropped into furrows. The roots are placed upright and soil is firmly placed around each steckling. Rows are 76 to 91 cm apart and stecklings are 40 to 66 cm apart within the rows. An early irrigation, if necessary, should be made available after transplanting as adventitious roots that emerge in the second year are sensitive to drought during establishment. Isolation of beet seed fields by at least 1.2 km from other beet seed production fields or at least 2.4 to 8.3 km from swiss chard, mangel, or sugar beet seed fields is very important since B. vulgaris is a wind-pollinated species and its pollen can travel for 20 km (Poole 1937). When producing stock seed, fields should also be planted at a minimum of 3 to 8 km apart. Timely cultivation and hoeing to eliminate weeds while the plants are relatively small is important. Certain weeds, if allowed to go to seed, will cause problems during the milling and cleaning of the beet seed. Harvest of beet seed is initiated when the seed balls at the base of each branch are mature and brown. It is not possible to combine standing beet seed in the field as it matures unevenly on the stalk. Plants are cut near

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the base or pulled by hand and laid in windrows to cure during a dry period in late summer. When the seed and plants are sufficiently dry they can be fed through a combine.

IV. GENETICS AND BREEDING The table beet is dwarfed by the sugar beet from an economic point of view, and as such fewer workers have been committed to its improvement, thus there are relatively few genetic investigations focusing on table beet. Some of the key genes involved in table beet improvement are described in greater detail in Section V. In addition, most of the Mendelian genes influencing color in table beet are presented in Section IV. A. Qualitative Genetics Owen and Ryser (1942) reviewed ten Mendelian factors in B. vulgaris. These included the B allele, which induces bolting (flower stalk formation) under long photoperiods. The B allele was first described by Munerati (1931). The homozygous recessive condition (bb) causes plants to remain vegetative under long photoperiods and is therefore required for table beet cultivars. The B allele can be used successfully to enhance the efficiency of table beet improvement, as discussed in later sections. The bl allele was described by Munerati and Costa (1930) to describe black root, or the corky appearance of tissue on the exterior surface of the root. This character has long been considered to be unstable in various ways, including the appearance of corky mosaics on roots (Owen and Ryser 1942). It is quite possible that the bl allele is incompletely penetrant and/or exhibits variable expressivity. It is noteworthy that several modern table beet cultivars, including the F1 hybrid ‘Big Red’, exhibit corky exteriors, particularly in the hypocotyl region. This may be due to the action of the bl allele, although no such studies have been conducted. A second gene, known as ru (russet root), also causes a corky appearance of the root exterior, but it is less pronounced than the corkiness caused by the bl allele and may in fact be the cause of the phenotype of modern cultivars such as ‘Big Red’. Watson and Goldman (1997) described a different gene bl conditioning a blotchy phenotype (irregular sectors of red and white root color) in table beet. Segregation of bl was found to be consistent with a single recessive gene in four BC1 and two F2 populations; however, some evidence (P < 0.10 and P < 0.08) for departures from a single gene model was observed when blbl plants were used as females. In these cases, an

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excess of red-rooted progeny was present in blbl × BlBl matings. Austin and Goldman (2001) found transmission ratio distortion at the bl locus when blbl plants are used as both females and males in matings with wild type plants, but the degree of distortion was greater when blbl plants are used as females. The Cl allele interacts with the R locus and causes the formation of colored leaves, most noticeably on the underside of the leaf. When R- is present, leaves are red, and when rr is present, leaves are yellow. However, the Cl allele also causes smaller than normal plants and its action appears to be highly variable. In many cases, Cl causes the appearance of blotches of pigment. Cl is linked to Y, R, and B (Owen and Ryser 1942). The Cl allele is similar to the Cv allele, which causes the formation of pronounced pigmentation near the veins of the leaf. (The C and v in this symbol stand for colored veins.) Like Cl, homozygous dominant Cv plants have red coloration when R- is present and yellow coloration when rr is present. Expression is variable and this allele is incompletely dominant. The coloration is also associated with a glossy appearance on the upper side of the leaf, along with an inward rolling of the leaf margins. The midrib is free of coloration, and is more prominently contrasted with venation coloration in field grown plants. The Cv allele is also linked to Y, R, and B (Owen and Ryser 1942). Extensive segregation analyses carried out by Owen and Ryser (1942) demonstrated a number of these loci are linked in a complex. These include Y, R, Cl, Cv, and B. Locus intervals and linkage distances were reported as Y-R (8 cM), R-Cl (4 cM), C-Cv (0 cM), and Cv-B (12 cM). Abegg (1936) estimated the linkage between R and B at 15.5 cM. Goldman (1998) described the gene fasciated flower stalk (ffs). The fasciated character arose spontaneously in the inbred line W411. Its primary characteristics are a flattened flower stem with petioles coalesced into a twisted, ribbonlike appearance. Variable expression of the fasciated trait was noted in progenies produced from a variety of matings, however all plants carrying this recessive allele in the homozygous condition display a distinct flattening of the flower stem at the stem-hypocotyl junction. Fasciation due to ffs could be useful in seed production, as fasciated plants tend to exhibit heavy seed set with seed maturity occurring in a synchronous fashion. It is presently not known whether plants homozygous for ffs exhibit increased seed set under field conditions. B. Quantitative Genetics Watson and Gabelman (1984) conducted perhaps the only study designed to estimate general and specific combining ability (GCA and SCA, respectively) effects for any trait in table beet. They evaluated

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genetic variation in betalain pigment and sucrose concentration using a diallel mating scheme with nine inbred lines. They found significant GCA effects for both betacyanin and betaxanthin concentration, as well as for sucrose concentration. Variation in pigment concentration, but not sucrose concentration, could be explained by SCA effects. Large genotype × environment interactions were observed, particularly for GCA × years and SCA × years, however these were primarily due to differences in the magnitude of the variances across years. Little evaluation of genotype × environment interaction has been conducted in table beet. Grzebelus (1997) found moderate levels of genotype × environment interaction for nitrate content of table beet roots, with several populations exhibiting excellent stability across various environments. In his efforts to reduce nitrate content of table beet roots, he identified the cultivar Okragly Ciemnoczerwony as a promising starting point for breeding, as it accumulated low concentrations of nitrate across many environments. Wang and Goldman (1996) found large genotype × environment interactions for folic acid concentrations in open-pollinated populations of table beet, and smaller genotype × environment interactions for F1 hybrids. C. Color Genetics and Breeding The primary pigments in table beet are the betalains, a unique class of alkaloid pigments found primarily in the Caryophyllales and some fungi (Clement et al. 1992). Betalain pigments are comprised of the red-violet betacyanins and the yellow betaxanthins. Both are derived from betalamic acid following the cleavage of L-DOPA between the 4- and 5positions, and differ from one another by conjugation of a substituted aromatic nucleus in the 1,7-diazaheptamethinium chromophore (Fischer and Dreiding 1972; Impelizzeri and Piatelli 1972; Clement et al. 1992). Interestingly, L-DOPA and similar compounds have been associated with pathogen-induced, but not mechanically-induced, beet root tissue damage at the site of injury (J. Halloin, unpublished). The cleavage of LDOPA results in two intermediates, 4,5-secodopa and cyclodopa glucoside. The former intermediate is converted into betalamic acid, which in turn condenses with cyclodopa glucoside to form both betacyanin and betaxanthin. Glycosylation occurs both before and after the condensation reaction (Sciuto et al. 1972), and both pigment molecules contain glucose residues. Betalain pigments extracted from red beet roots provide a natural alternative to synthetic red dyes. Betalains have been successfully used in commercial food coloring operations for a number of years (von Elbe et al. 1974), and continue to be an important source of red color in the

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food industry, particularly in Europe. Red beet dye use is increasing in a number of products; including cosmetics, candy, ice cream, meat products, yogurt, and powdered drink mixes. The main limitation of beet concentrates in the food industry is the relatively low concentration of betacyanin (0.10–0.18% of fresh weight) in beet root juice. Juice derived from beet roots contains sucrose in high concentration, therefore, diluting the commercial product (concentrated juice) substantially. Low concentrations of pigment necessitate the addition of large quantities of commercial product to foods to obtain sufficient coloring. Concentrating betacyanin in the root juice is a time- and energy-consuming process for the food processor, often representing the most costly step in the extraction of betalains. Since sugar concentrations can be 80 to 200 times greater than pigment concentrations in the beet root, the commercial product would also be significantly enhanced by lowering sugar levels in the beet root through selection. Breeding red beet for increased betalain concentration has lowered the cost of red beet dye threefold to a point where it is now only approximately three times as expensive as the coal-tar derived compounds in commercial use. Further cost reductions seem likely through breeding, particularly if a better understanding of pigment genetics was available. The presence of various alleles at two linked loci (R and Y) condition production of betalain pigment in the beet plant (Keller 1936). Sugar beet workers also use the designation G as a synonym for the Y locus (LindeLaursen 1972). Wolyn and Gabelman (1990a) demonstrated that three alleles at the R locus determine the ratio of betacyanin to betaxanthin in the beet root and shoot. Incomplete dominance for pigment ratio in Rt and R genotypes was observed. Color patterning in the beet plant is affected by these R locus alleles as well as alleles at the Y locus. Red roots are observed only in the presence of dominant alleles at the R and Y loci, while white roots are conditioned by recessive alleles at both loci. A yy condition coupled with rr, which is characteristic of most sugar beet cultivars, produces no betacyanin and produces betaxanthin only in the hypocotyls (Table 7.1). A third locus, P, appears to be required for pigment formation in Beta. Linde-Laursen (1972) suggested the P allele is indispensable for color formation and demonstrated its close linkage with the R and Y loci. White-rooted beet plants likely carry the p allele in a homozygous recessive fashion along with dominant alleles at the R and Y loci. Watson and Goldman (1997) described a mutant phenotype characterized by irregular sectors of blotchy red and white root color. The blotchy mutant was used in crosses with nonblotchy inbred lines to characterize its inheritance. Segregation data in backcross and F2 gen-

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7. HISTORY AND BREEDING OF TABLE BEET IN THE UNITED STATES Table 7.1.

Genotypes at the R and Y loci affecting color phenotypes in red beet.

Genotype R-, rr Rrr RhRh phRp RtRt Rtrr

371

YYyy yy Y– – YrYr-

Phenotype Red roots, hypocotyls, petioles Yellow roots, hypocotyls, petioles White roots, red hypocotyls Yellow hypocotyl, white root Red hypocotyl, yellow roots Pink roots, hypocotyls, petioles Striped petiole Red roots, green tops Yellow roots, green tops

erations revealed a recessive gene controls the blotchy phenotype. Watson and Goldman proposed the symbol bl to describe the genetic control of this blotchy phenotype. Additional genetic analysis was conducted in populations segregating for bl, rr, and yy. These data reaffirmed the relatively close linkage (ca 8.5 cm) between R and Y (Goldman and Austin 2000). Since the bl gene appears to disrupt pigment synthesis, additional investigation of this locus may shed light on color development in Beta. It is likely that additional loci play a significant role in betalain pigment formation in Beta, thus a molecular mapping approach may yield useful information regarding genome distribution of pigment biosynthesis quality trait loci. Despite the importance of these simply-inherited genes in pigment synthesis, several investigations suggest additional modifying loci play a role in the quantity of betalain pigment synthesized in the beet root (Watson and Gabelman 1984; Wolyn and Gabelman 1990b). Betalain pigment concentration behaves like a quantitative trait in that it (1) interacts with the environment; (2) exhibits continuous variation in phentoype; and (3) cannot be explained qualitatively via the segregation of one or two defined genes. In addition, populations of beet plants carrying dominant R and Y alleles can be improved for pigment concentration. Watson and Gabelman (1984) observed small genotypic and phenotypic correlations between pigment concentration and sucrose content in red beet, suggesting that selection for high pigment and low sucrose was a feasible breeding goal. Wolyn and Gabelman (1990b) confirmed this prediction by showing that betalain pigment responds to selection in a quantitative fashion in two beet populations. The nature of the additional genes is unknown, but betalain synthesis has been reported to be influenced by light, phytochrome, and cytokinin (Goodwin and Mercer 1983). Betalain

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pigment production is influenced by both light and temperature, and Wolyn and Gabelman (1986) showed that both late planting and late harvesting of table beet plants results in enhanced levels of pigment. Similar conclusions were made by Magruder et al. (1940) in comparisons of spring and fall sown table beet plants (Fig. 7.1). In 1978, a half-sib family recurrent selection program was initiated to increase pigment levels and decrease sugar (total dissolved solids) levels in red beet. This program was initiated by W. H. Gabelman and continued after his retirement by I. L. Goldman and D. N. Breitbach. Divergent selection for both high and low solids was practiced in conjunction with selection for high pigment. Table beet is particularly amenable to half-sib family recurrent selection because self-incompatibility facilitates crosspollination in a crossing block. Thus, pollen from the population at large will pollinate and fertilize individual female plants and the progeny from each becomes a half-sib family. Results from the first three cycles of selec-

Crosby Egyptian

Flat Egyptian

Early Wonder, Ohio Canner

Detroit Dark Red

Long Dark Blood

Morse Detroit Fig. 7.1. 1940.)

Founding populations of table beet in the U.S. (Adapted from Magruder et al.

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tion demonstrated that pigment synthesis was highly responsive to selection (Wolyn and Gabelman 1990b). Pigment levels increased an average of 45 percent in three cycles. Simultaneous selection for high or low solids was statistically ineffective for the first three cycles. Over eight cycles, total pigment increased approximately 200 percent in both populations (Goldman et al. 1996). Selection for low solids was ineffective, while only a mild response was detected from selection for high solids. Since betalain pigments are formed following glycosylation of cyclodopa and betalamic acid, sugar molecules are associated with pigment biosynthesis. Simultaneous selection for high pigment and low solids may be metabolically incompatible (Goldman et al. 1996). At present, more than 12 cycles of recurrent selection for increased betalain pigment concentration have been conducted in several table beet populations at the University of Wisconsin. D. Pests and Diseases The primary pathogens of table beet in all major production areas in North America are the fungi that cause: (1) seedling “damping-off” disease known as black root (Aphanomyces cochliodes, Phoma betae, Pythium spp., and Rhizoctonia solani); (2) the foliar diseases Cercospora leaf spot (Cercospora beticola) and downy mildew (Peronospora farinosa); and (3) the mature-root rots Phoma heart-rot (Phoma betae) and Rhizoctonia root rot (R. solani). Another principle malady in many table beet production areas is boron-deficiency. Symptoms of this deficiency include a necrotic chapping of the upper surface of the leaf blade and of the concave surface of the petiole. Later, the new apical leaf growth fails to develop properly and finally the cambial tissue in the root’s interior will collapse and turn black. Proper monitoring and supplementing of the soil for adequate levels of boron will rectify this abiotic disease. Walker et al. (1945) discovered differential cultivar susceptibility to boron deficiency among table beet cultivars. While several prominent viral diseases like beet western yellows, curly top, and rhizomania are known to occur in table beet, they are all more problematic in sugar beet and in regions where sugar beets are produced. Therefore, these will not be discussed here. Similarly, many of the insect pests that attack B. vulgaris are most prevalent in sugar beet production areas. A major exception to this is the leaf miner, Liriomyza huidobrensis, found in the Salinas Valley and in coastal vegetable growing regions of California. The mines left by the feeding of the larval stage of other North American species of leaf miners are one of the destructive manifestations of their infestation. However, L. huidobrensis does

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its worst damage with puncture wounds left in the leaf by feeding and egg-laying activities of the adults. These wounds are a serious problem in the production of “baby leaf” beets for the salad mix industry as there can often be hundreds of puncture wounds per leaf in spring and early summer production, and these render the leaves unmarketable. The complex of unrelated fungi that can cause damping-off symptoms in table beet collectively cause a disease referred to as black root. These organisms can attack the beet seedlings at either a pre- or postemergence stage of growth and it is not uncommon for more than one species to infect hosts at the same time. During the preemergence stage of growth, several species of Pythium are the most likely fungi to affect the seedling health, especially under cool, wet conditions. Pythium ssp. rarely infect beet seedlings after the emergence of the first set of true leaves. Symptoms include a soft decay of both the taproot and hypocotyl (Walker 1952). Unlike Pythium, R. solani can cause dry localized lesions with clearly delineated margins when responsible for damping-off. These symptoms occur at or near the time of emergence. Phoma betae is frequently seed borne and can cause lesions to form at or near the point at which the beet root is above the soil surface. These lesions are almost black in appearance and have the potential to girdle the plant. The black root organism to attack last is A. cochlioides, which is related to the water molds and requires free soil water to reproduce. Infection can be swift in excessively wet fields during warm spring weather. Plants infected with Aphanomyces often have shrunken, brown hypocotyls and may be diseased up to the cotyledons with gradual wilting and yellowing of the leaves resulting. If the beet plant survives, the fleshy root can be severely scarred and unmarketable (Sherf and MacNab 1986). Foliar diseases are important to the commercial grower of table beet for at least two reasons. First, the presence of disease on leaf surfaces cuts down on the amount of photosynthate that is manufactured with subsequent reduction in root yield and quality, specifically in the amount of sugar produced. Secondly, the disease symptom spots of the Cercospora pathogen or the mycelium of downy mildew detract from the visual appeal of fresh market bunched beets and reduces the shelf life of greens. Cercospora leaf spot (C. beticola) is the most common foliar disease of all B. vulgaris crops and is prevalent in at least three of the major production areas for table beet (New York, Oregon, and Wisconsin). The fungal pathogen infects older leaves first during periods of warm temperatures and high relative humidity. The symptoms first appear as small circular brown spots with darker borders that are distinct. As the spots widen to 3 mm or more, they turn gray. Upon subsequent growth, the tissue within the spots gets thin, eventually ripping and thus form-

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ing a hole. Under severe disease pressure, numerous holes can cover the leaf, causing the leaf senescence and death. Peronospora farinosa f. sp. betae causes downy mildew in table beet. While this fungus may attack any aboveground parts of beet and related crops, it is most commonly observed near the crown of the plant in the youngest, apical foliar growth from mid-season until the end of the season. These young leaves may become completely covered by a whitish fungal growth that becomes violet-gray with age. All subsequent leaf initiation is affected and under cool, humid conditions the mycellium can spread up the petioles to older leaves. This causes a reduction in root yield and reduces the number of marketable plants as bunched beets. This pathogen also has the ability to overwinter in the crown of beet stecklings held for seed production. In the past few years, downy mildew has become an increasingly troublesome disease in the primary seed production area of North America, Washington State’s Skagit Valley. The disease may appear first on foliar regrowth, then on emerging floral shoots, and subsequently onto the entire inflorescence. Seed yields can be severely limited. While complete resistance to downy mildew has not been found, there exists moderate to strong levels of resistance in many North American open-pollinated beet cultivars that have had the benefit of susceptible roots being rogued out of production lots for many years. While P. betae can be a deadly pathogen at any stage of beet development, it is perhaps most destructive when it attacks mature roots. This disease is known as Phoma heart-rot. Often, beet plants are infected by P. betae at the seedling stage and the fungus lies dormant in the crown of the plant until the plant experiences some type of stress or damage due to other pathogens, cultivation, or boron deficiency. When the common symptom of internal canker occurs in beet roots due to boron deficiency, the secondary invasion of P. betae heart-rot is usually not far behind. After the normal vagaries of harvest and preparation for long-term root storage, another organism Phoma betae can become active on any damaged surfaces of the beet, especially where the petioles have been severed. This can be serious during storage of stecklings for seed production. Rhizoctonia root rot, also known as dry rot canker or pocket rot, is considered the most destructive root disease of mature beet roots in North America (Whitney and Duffus 1986). While it is important as one of the organisms responsible for the seedling black rot complex, Rhizoctonia solani is ubiquitous in many agricultural soils and can often cause economically devastating symptoms on table beets from midseason through harvest. Symptoms include a dry rot that can occur on the surface of the root or crown. While this brown, spongy decay is superficial at first, it can progress as the root grows, forming a pocket or canker with a distinct margin between the healthy and diseased tissue. These fissures render the

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roots unusable for either fresh market or processing. Resistance to this fungus is quantitatively inherited and selection in sugar beet has resulted in germplasm with increased levels of resistance (Hecker and Ruppel 1986). V. BREEDING METHODS A. Founding Table Beet Populations in the United States The primary table beet populations (Fig. 7.1) used in U.S. table beet production during the 1930s were described by Magruder et al. (1940). Somewhat surprisingly, they are rather few in number, suggesting that much of the U.S. table beet germplasm has a rather narrow pedigree. These same populations formed the basis for modern table beet breeding in the United States, although in several cases, other germplasm, such as sugar beet, was introduced to improve table beet. Among the key populations available in the first half of the twentieth century, the most prominent were ‘Flat Egyptian’, ‘Crosby Egyptian’, ‘Light Red Crosby’, ‘Early Wonder’, ‘Detroit Dark Red’, ‘Morse Detroit’, ‘Ohio Canner’, and ‘Long Dark Red’. These populations comprised as much as 95 percent of the U.S. beet area in 1940 (Magruder et al. 1940). Several of these populations appear to be derivatives of each other; for example, there does not appear to be a significant difference between ‘Ohio Canner’ and ‘Early Wonder’, or between ‘Crosby Egyptian’ and ‘Light Red Crosby’ (Magruder et al. 1940). Thus, of these seven populations, only ‘Long Dark Red’, ‘Flat Egyptian’, ‘Crosby Egyptian’, ‘Early Wonder’, and ‘Detroit Dark Red’ appear to be significantly separated from each other by origin. Among these, there are three clearly identifiable groups. These are the “Egyptian” group, which is comprised of ‘Flat Egyptian’, ‘Crosby Egyptian’, ‘Light Red Crosby’, and ‘Early Wonder’; the “Detroit” group, which is comprised of ‘Detroit Dark Red’ and ‘Ohio Canner’; and the “Long” group, which is comprised of ‘Long Dark Red’ and ‘Cylindra’. Each of the populations comprising these groups will be discussed in some detail in the following sections, and for the purpose of this chapter are considered “founding” populations for U.S. table beet breeding. 1. ‘Flat Egyptian’. This population was introduced to the United States from Germany in 1868 by the Ernst Benary company. It was listed in seed catalogs as ‘Extra Early Egyptian’ and ‘Dark Red Egyptian’. The primary characteristics of this population were its earliness, small foliage, and flat root shape. ‘Flat Egyptian’ was popular as an early-maturing cultivar for northern climates and for the production of small-sized roots for canning. Root shape is the flattest among the founding populations.

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2. ‘Crosby Egyptian’. Developed by Josiah Crosby of Arlington, Massachusetts, from a population of ‘Flat Egyptian’, this population was first listed in seed catalogs in 1885. The primary characteristics of ‘Crosby Egyptian’ were increased depth of the root and lessening of the rough root exterior. Thus, this population was much rounder and smoother than the ‘Flat Egyptian’ population from which it was derived. 3. ‘Light Red Crosby’. This population was first widely described in 1904 by D. M. Ferry and Company as a vermilion or light-red colored table beet, although it was developed approximately ten years earlier by W. W. Tracy of the same company and first listed in 1896 in their wholesale catalog. This population eventually became known as ‘Ferry’s Crosby’, due to confusion generated from various other ‘Crosby’ populations. The root and exterior skin color was lighter in color than the original ‘Crosby’. 4. ‘Early Wonder’. This population was first listed in 1911 by the F. H. Woodruff and Sons and S. D. Woodruff and Sons catalogs. ‘Early Wonder’ was also listed in 1914 as the “Arlington strain of Crosby’s Egyptian beet,” and thus it is clearly a derivative of that population. The primary selection criterion used in development of this population was a root that was rounder in shape than ‘Crosby’s Egyptian’. The resulting ‘Early Wonder’ combined the early maturity of the original ‘Flat Egyptian’ with a very round root. This population has been and continues to be of great importance for fresh market table beet production, where its robust foliage is suitable for bunching. 5. ‘Detroit Dark Red’. This population was originally selected from a population known as ‘Early Blood Turnip’ by a man named Reeves of Port Hope, Ontario, Canada. In 1892, this cultivar was listed as ‘Detroit Dark Red Turnip’ beet by D. M. Ferry and Company. This population is perhaps the most important and widely-adapted variety. ‘Detroit Dark Red’ is considered a multipurpose cultivar and has been used for fresh market, processing, and market garden production. Roots are smooth and round and foliage is very dark green. ‘Detroit Dark Red’ has many synonyms that have been widely distributed throughout the world, most of which include the word “Detroit” in their names, such as ‘Detroit Blood’, ‘Detroit Early Dark Red’, and ‘Early Detroit Dark Red’. The popular cultivar ‘Morse Detroit’ was selected from ‘Detroit Dark Red’ and offered first by C. C. Morse and Company in 1928 as ‘Morse’s Improved Detroit’. 6. ‘Ohio Canner’. This population was developed at the Ohio Agricultural Experiment Station and released in 1932. Its skin, flesh, and foliage

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color is very similar to ‘Detroit Dark Red’, however its primary attribute was reduced amount of differential coloring between cambial rings (zoning). This made ‘Ohio Canner’ an excellent choice for canning, because light-colored rings were not present in canned product. 7. ‘Long Dark Blood’. This is perhaps the oldest founding population of table beet in the United States. Magruder et al. (1940) suggest it may have been introduced by early settlers from France, where it was a very popular cultivar. ‘Long Dark Blood’, as the name suggests, is a longrooted type with cylindrically-shaped roots. Maturity is later than for most other founding populations. The long, slender roots grow in part aboveground, and in some cases at slight angles to the belowground portion of the root. This population has many synonyms, most of which include the words “Blood” and “Long.” ‘Long Dark Blood’ was a primary founding population for cylindrical table beet germplasm in Europe and the United States. B. Hybrid Table Beet The table beet breeding program at the University of Wisconsin–Madison, initiated by W. H. Gabelman in 1949, was geared toward assessing the feasibility of F1 hybrid beet. Gabelman had received his Ph.D. from Yale University under the direction of D. F. Jones. Several decades earlier, Jones had developed the double-cross method for producing F1 hybrid corn seed, thereby setting the stage for the feasible economic development of that industry (Wallace and Brown 1988). Gabelman was influenced by Jones and began to apply the inbred-hybrid breeding method to crosspollinated vegetable crops such as carrot, table beet, and onion (Goldman 1996a,b,c). This method was first developed by H. A. Jones for onion, and was later adopted by breeders of many crops to produce F1 hybrids (Goldman et al. 2000). Gabelman’s program for table beet was creative and novel. Documentation of his strategy, which originated in the 1950s, can be found in an unpublished manuscript, entitled Table Beet Breeding written by W. H. Gabelman, F. A. Bliss, and R. L. Engle in 1963. Gabelman’s approach required an understanding of male sterility, inbreeding potential, and seed production characteristics. He had to introgress the sterility from a related crop and overcome the difficulties of identifying maintainer lines in a crop species that was selfincompatible; both of which were formidable challenges. During the first several decades, the primary breeding objectives of his table beet program focused on sterile and maintainer lines for the production of hybrids, disease resistance, round to globe shaped roots, improved color and sweetness, multigerm and monogerm seed, and enhancement of combining ability.

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In a creative application of using one crop to enhance another, Gabelman took advantage of useful genes found in sugar beet to improve table beet germplasm. Table beet flowers are perfect and wind pollinated. Cross-pollination is obligate due to a self-incompatibility system with possibly four loci (Lundqvist et al. 1973). However, a dominant gene for self-fertility, SF (Savitsky 1954), overrides this system and allows for selfpollination. Gabelman knew that the development of table beet inbreds would be difficult if self-incompatibility was present, and for this reason sought to use the SF allele in his breeding program. Inbreeding depression is one consequence of using SF in a breeding program, but a number of inbred lines have been successfully developed. Sugar beet breeders may use SF in conjunction with a Mendelian recessive for male sterility (aa) in their inbred-hybrid breeding programs. The first gene to be introduced to table beet from sugar beet was the SF allele. The source of this allele was a sugar beet breeding line obtained from V. F. and H. Savitsky, emigres from Russia working with F. V. Owen in the USDA-ARS in Fort Collins, Colorado. The SF allele allowed for inbreeding individual plants, a technique that was not previously possible in beet because of its self-incompatibility. Inbreeding not only enabled the development of more homogenous populations that ultimately resulted in uniform inbred lines, but it made possible the maintenance of sterile inbred lines by allowing self-pollination of maintainer genotypes. In practice, the abundance of pollen present in plants carrying SF made it difficult for foreign pollen to successfully pollinate and fertilize these plants. The value of this allele in maintaining fertile inbred maintainer lines without contamination from foreign windblown pollen was an important factor in table beet seed production in the Pacific Northwest (W. H. Gabelman, pers. comm.). Cytoplasmic male sterility (CMS) and the restoration of fertility was described in sugar beet by F. V. Owen (1945). A sterile cytoplasm will result in a male sterile plant if alleles at both the x and z loci are homozygous recessive. Dominant alleles at either or both loci will result in male fertility. Maintainer lines, referred to as “B” lines by many vegetable breeders and “O-types” by sugar beet breeders, possess normal, fertile cytoplasm and homozygous recessive alleles at the x and z loci. Sterility in the Owen cytoplasm is usually due to disruptions in the mitochondrial genome, however the precise nature of these mutations in beet is presently unknown. The discovery of a CMS system paved the way for the development of both F1 sugar beet and table beet hybrids. Gabelman introduced the x and z alleles, conditioning sterility at the nuclear restorer locus (in homozygous recessive condition) from sugar beet breeding lines obtained from F. V. Owen into table beet germplasm. These alleles in combination with the sterile cytoplasm obtained from

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Owen and the SF allele obtained from Savitsky allowed for the development of male sterile breeding lines and their maintainer lines. Stein and Gabelman (1959) reported that the male sterility in table beet lines carrying the sterile cytoplasm from Owen possessed pigmented anthers. Sterile lines from sugar beet do not possess pigmented anthers, however sugar beet also lacks the dominant R allele conditioning pigmentation in many parts of the beet plant. Stein and Gabelman (1959) suggested that sterility either causes or enhances red pigmentation, however the mechanism for this is presently unknown. Since this work was completed, others have described at least three distinct color types of sterility in table beet anthers, including red, pink, and brown (Goldman 1996b). The B allele conditioning annual flowering habit was also obtained from sugar beet breeding material from V. F. Savitsky. Application of the B allele to sugar beet breeding has been discussed by Bosemark (1993). In general, the B allele allows for efficient development of sterile inbred lines since spring-sown plants carrying Bb flower during the growing season in the field. A cross of the constitution SxxSFSFBb × N–sFsFbb will give rise to 50 percent annual (Bb) progeny which, because they are flowering, can be classified for sterility in the field. These annual sterile plants can then be decapitated, vernalized, and reflowered in winter in the greenhouse nursery, assuring continuous inbreeding of the sterile line with its maintainer line. Wild-type Beta possesses a trait known as multigerm seed, whereby each aggregate fruit may contain from one to five seeds, compressed into a single “seed ball.” The seed ball is actually a lignified flower carcass with a corky appearance. Multigerm seed will obviously make precision seeding more difficult, as population density will be determined by the number of successful plants from each seed ball. The monogerm character, conditioned by recessive alleles at the m locus, was first identified as a mutant in a commercial sugar beet field (Savitsky 1950). Many, if not most, sugar beet cultivars carry the monogerm trait. While monogerm table beet cultivars are still not widely accepted, their appearance in the marketplace is beginning to be of greater importance. Gabelman also incorporated the monogerm character into table beet germplasm, resulting in the development of the first table beet inbred lines carrying this trait (Goldman 1996b). The original sugar beet × table beet crosses required approximately ten generations of backcrossing and selection before commercially-acceptable round, red roots were recovered. Recent molecular analyses of these materials suggest that the Wisconsin inbred lines have retained their intermediacy between sugar beet and table beet at the DNA level (Wang and Goldman 1999).

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The inbred lines released from Gabelman’s program have been used in the production of hybrid beet seed throughout the world. In particular, the widespread distribution of sterile inbred lines has facilitated the development of hybrid beet in many countries. Many of the inbred lines released by Gabelman have also proved an important source of genes for high quality, uniform beet roots for fresh market and processing. C. Techniques in Table Beet Breeding The development of table beet inbred lines followed a pattern similar to that used in development of onion and carrot inbred lines, both of which have been recently reviewed (Goldman et al. 2000; Simon 2000). The inbred-hybrid method of breeding, which was developed in maize during the early decades of the twentieth century, set the pattern for breeding techniques in many crops. The heterosis of F1 hybrids offered a number of advantages over open-pollinated crops in certain situations. Three of the primary advantages of table beet hybrids were early season vigor, rapid growth rate, and uniformity. H. A. Jones, a pioneer in onion breeding, developed methods for an inbred-hybrid breeding program in onion. Like onion, table beet hybrids are developed using cytoplasmic-genic sterility (CMS) systems, and so the methods established by Jones influenced modern table beet breeding. This approach was based on the sterile “O” type cytoplasm received from F. V. Owen and maintainer inbred lines extracted from openpollinated cultivars. Crossing of this sterile table beet line with fertile plants from open-pollinated cultivars revealed the presence of maintainer genotypes (Bliss and Gabelman 1965), although in certain matings it appeared that a single nuclear restorer locus was present. The first four maintainer lines developed were designated W32, W162, W163, and W187. Once sterile and maintainer pairs were available, the sterile lines could be used as females in various hybrid combinations with restorer lines, populations, or maintainer lines used as the pollen parent. Such early hybrids were promising, although they did not result in superior horticultural performance over open-pollinated cultivars. It was not until the second cycle of inbred lines was developed, including W218, W260, and W279, that heterosis for a variety of horticultural characteristics was apparent in various hybrid combinations (Goldman 1996b). Table beet inbred lines have been released as A and B pairs, where the A line refers to the sterile phenotype with genotype Sxxzz, and the B line refers to the maintainer of sterility (fertile) phenotype with genotype Nxxzz. All inbred lines released publicly are biennial and thus carry the b allele in the homozygous recessive condition.

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During the initial phases of inbred development, open-pollinated cultivars and populations were chosen for desirable characteristics. Individual plants from these populations were mated in the greenhouse in small masses, usually containing two to four plants. Direct selfpollination was not possible until the SF allele was introgressed from sugar beet, therefore, initial small plant mass populations resulted in the production of hybrid seed. The small plant mass technique was originally developed by H. A. Jones for onion breeding (Jones 1923; Jones and Emsweller 1934). In this technique, vernalized roots were planted in clay pots in late fall and flowered during the winter months. Flowering plants were covered with paper bags, similar to those used in maize breeding, which were stapled shut at the bottom to prevent pollen release. The table beet flower is protandrous and pollination is by wind. Thus, a small amount of shaking of these pollination bags produces reliable seed set. Larger populations are grown in greenhouse isolations without the use of pollination bags. Working with onion, Jones and Davis (1944) found that inbreeding onion for one to two generations generated uniformity in these partially inbred lines. Inbred lines were originally extracted from open-pollinated cultivars, and then later developed through pedigree methods using established inbred lines. Later work indicated further inbreeding of the inbred lines, up through five generations, resulted in superior hybrids. Although it was anticipated that inbred lines could be produced in table beet, the possibility of inbreeding depression was a cause for concern in this type of breeding method. With table beet germplasm carrying wild-type alleles for selfincompatibility, direct self-pollination was not possible. But with the introduction of the SF allele, self-pollination became feasible. Gabelman and students found that inbreeding depression was not as great in table beet as expected, and despite the very high degree of self-pollination (greater than 99%) with SF, many generations of direct selfing were possible. Inbred lines developed with SF have since been self-pollinated more than 15 generations without declines in vigor that would preclude their continuation in a breeding program. One of the unique features of inbred development in table beet has been the use of the annual gene, B, in assessing both sterility and degree of monogerm seed characteristics during first season of growth. Gabelman obtained this gene from V. F. Savitsky. Because standard table beet germplasm is biennial and of the genotype bb, germplasm carrying the B allele will flower (bolt) during the first season of growth. Although bolting is highly undesirable from the standpoint of crop production, it allows for the identification of floral characteristics prior to harvest. With a biennial life cycle, table beet roots must be harvested, stored, and ver-

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nalized prior to reproductive growth, a process that can take many months and a great degree of labor. The table beet breeding program at the University of Wisconsin relied on a greenhouse environment from November to April in order to make crosses and produce seed for the next generation. Because this greenhouse space was limited, care was taken to identify useful genotypes in the field during the first season of growth. When plants carrying the B allele were flowering in the field during August, it became possible to identify these genotypes prior to harvest. For example, a cross of the genotypes SxxzzSfSfBb × NxxzzSfSfbb yields progeny from the seed or “female” parent which are 50 percent annual (Bb). These plants will flower during the first season of growth in the breeding nursery, where they can be classified for fertility or sterility. If, for example, one wishes to determine whether a fertile plant is carrying recessive alleles at the nuclear restorer locus, one can cross it with a sterile plant that is heterozygous for the B allele and then score the flowering progeny in the field the following season. If these progeny are sterile, the flowering plants can be harvested, and have their flower stalks separated from the roots. These roots of known sterile genotype can then be stored and planted again in the greenhouse, where they will again be crossed with maintainer genotypes. Roots of plants carrying the B allele obviously do not need to be vernalized in the same way as bb plants. Using this technique, continuous inbreeding of the sterile line and continual crossing with its maintainer are managed in a relatively simple fashion. Use of the B allele has also facilitated the development of monogerm inbred lines, because flowering progeny in the nursery can also be checked for the number of flowers in the axil of each bract. Additionally, sterile plants carrying the B allele can be used to detect outcrossing during increases of fertile inbred lines under commercial seed production conditions. When biennial plants carrying sterile cytoplasm are desired, such as during the latter stages of an inbred development program, the remaining 50 percent of the segregating progeny from this cross that were not flowering can be chosen for appropriate testcrosses or commercial use. These are of the desired genotype bb. In practice, use of the B allele in table beet breeding allows for greater flexibility and precision in inbred development, because one can choose annual or biennial (or both) plants in the field and more accurately choose and plan the crosses to be made during winter months. D. Commercial Cultivar Development Table beet cultivar development was historically done by a number of the major vegetable seed production companies in North America that produced table beet seed. Most of this breeding work was accomplished by

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selecting new open-pollinated cultivars from established cultivar populations. While this often made it possible for the seed companies to offer improvements on the traits important to growers, it was also done out of necessity as open-pollinated cultivars can drift genetically and need reselection on a regular basis to maintain quality and cultivar purity. With the release of cytoplasmic-genic male sterile (CMS) and maintainer inbred pairs in table beet by W. H. Gabelman at the University of Wisconsin, it became possible for production seed companies already well-versed in the techniques of cultivar maintenance and evaluation to reliably make their own hybrid cultivars. By the 1980s, several U.S. production seed companies had released their own hybrid table beet cultivars through the use of these CMS inbreds. Invariably, these commercial table beet hybrids are made using one of the sterile inbred lines from the Wisconsin program as the female parent, then crossing it with either a standard open-pollinated cultivar or with a “male line” derived from an open-pollinated cultivar as the male parent. This first type of male parent is simply an open-pollinated table beet cultivar and the resultant hybrid is called a “topcross” or “topcross hybrid.” The second type of male parent used in commercial table beet hybrids is a near inbred line, usually developed using phenotypic recurrent selection with small population sib-matings of less than ten roots. Hybrids derived from both types of pollinators have had great success in table beet production and are increasingly used by producers in the United States and around the world.

VI. FUTURE DIRECTIONS Despite the widespread cultivation and utilization of table beet as a vegetable crop in many parts of the world, this crop has received relatively little attention from plant breeders. While the traditional uses of fresh and processed beet roots are still of primary importance, table beet production and consumption has expanded to include baby salad mix, greens for bunching, and betalain pigments for food colorants. In addition, demand for new cultivar types, such as cylindrical root and novel color phenotypes such as yellow and striped-root, continue to present new opportunities. While overall area and production of table beet is relatively small in the United States, increases in consumption for these new markets is envisioned for the near future, and breeding efforts must be directed toward these new uses. Public table beet breeding efforts in the United States during the twentieth century have been largely limited to a single program, while pri-

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vate sector programs have also been few in number. Beginning with a handful of open-pollinated founding populations, table beet breeding efforts in the United States have largely focused on improving these open-pollinated populations and developing inbreds to be used for F1 hybrid development. During the past 40 years, F1 hybrid table beet cultivars have been produced in the United States and have commanded an increasing share of the seed market. Development of table beet inbreds for use in this breeding strategy has been hastened by the introduction of several key genes from sugar beet, including alleles for annual growth habit (B), monogerm seed (m), sterile cytoplasm, and self-fertility (SF). As table beet has thus far benefited greatly from sugar beet genes, it is likely that advanced genomic resources developed for sugar beet will be useful to table beet improvement. Improvements in inbred lines should continue, particularly in the areas of novel uses, disease resistance, and product quality. However, as public sector support for these efforts decreases, an increasing share of the responsibility for table beet improvement will rest with the private sector. Table beet is often grown in low-input production systems and is a successful crop in organic markets and farmers markets. In recent years there has been an increase in the production of vegetable crops without the use of synthetic fertilizers or pesticides due to public policy concerns and consumer demand. Therefore, there will be an increased need for vegetable cultivars that are productive when grown under such reducedinput agricultural systems. This transition to lowered synthetic chemical inputs will require increased disease resistance (e.g., Rhizoctonia root rot, Cercospora leaf spot, downy mildew, and the damping off complex), as well as resistance to leaf miner damage for the production of baby leaf beets. Single gene resistance to any of these maladies has not been identified. Therefore, any improvements in table beet resistance to these pathogens may have to concentrate on polygenic forms of resistance accomplished as a long-term goal through cycles of selection. Open-pollinated table beet cultivars continue to play an important role in the overall production needs of beet growers in both the fresh market and the processing industry. The maintenance and improvement of open-pollinated cultivars has remained a vital part of the beet industry, perhaps because the genetic elasticity of many of these populations has given them greater adaptive capacity across environments than many currently available hybrids. Certainly, if this is the case, it points to the need for breeding populations with a greater concentration of favorable alleles in which to derive the next generation of inbred lines for table beets. The use of genotypic recurrent selection has been used for this purpose with great success in other crops, most notably maize (Allard

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1960; Hallauer 1985). It may behoove table beet breeders to continue to emphasize population improvement, in concert with the inbred-hybrid method, in their efforts to improve this crop for the future.

LITERATURE CITED Abegg, F. A. 1936. A genetic factor for the annual habit in beets and linkage relationship. J. Agr. Res. 53:493–511. Allard, R. W. 1960. Principles of plant breeding. John Wiley & Sons, New York. p. 282–302. Anon. 2000. Wisconsin Agricultural Statistics. Wisconsin Agr. Stat. Serv., Madison, WI. Austin, D., and I. L. Goldman. 2001. Transmission ratio distortion due to the bl gene in table beet. J. Am. Soc. Hort Sci. 126:340–343. Benjamin, L. R., A. McGarry, and D. Gray. 1997. The root vegetables: beet, carrot, parsnip and turnip. p. 553–581. In: H. C. Wein (ed.), The physiology of vegetable crops. CAB International. Bliss, F., and W. H. Gabelman. 1965. Inheritance of male sterility in beet. Crop Sci. 5:403–406. Bosemark, N. O. 1993. Genetics and breeding. p. 67–119. In: D. A. Cooke and R. K. Scott, (eds.), The sugar beet crop: science into practice, Chapman and Hall, London. Cai, D., M. Kleine, S. Kifle, H-J. Haloff, N. N. Sandal, K. Marcker, R. M. Klein-Lankhorst, E. M. J. Salentijn, W. Lange, W. J. Stiekema, U. Wyss, F. M. W. Grundler, and C. Jung. 1997. Positional cloning of a gene for nematode resistance in sugar beet. Science 275:832–834. Campbell, G. K. G. 1976. Sugar beet. p. 25. In: N. W. Simmonds (ed.), Evolution of crop plants. Longman, London. Clement, J. S., T. J. Mabry, H. Wyler, and A. S. Dreiding. 1992. Chemical review and evolutionary significance of the betalains. p. 247–261. In: H. D. Behnke and T. J. Mabry (eds.), Evolution and systematics of the Caryophyllales. Springer Verlag, Berlin. Fischer, H. E. 1989. Origin of the ‘Weisse Schlesische Rübe’ (white Silesian beet) and resynthesis of sugar beet. Euphytica 41:75–80. Fischer, N., and A. S. Dreiding. 1972. Biosynthesis of betalaines. On the cleavage of the aromatic ring during the enzymatic transformation of dopa into betalamic acid. Helv. Chim. Acta 55:649–658. Ford-Lloyd, B. V. 1995. Sugarbeet and other cultivated beets. p. 35–40. In: J. Smartt and N. W. Simmonds (eds.), Evolution of crop plants. 2nd ed. Longman Scientific and Technical, Essex. Ford-Lloyd, B. V., and J. T. Williams. 1975. A revision of Beta section Vulgares (Chenopodiaceae), with new light on the origin of cultivated beets. Bot. J. Linn. Soc. 71:89–102. Gaskill, J. O. 1954. Viable hybrids from matings of chard with Beta procumbens and B. webbiana. Proc. Am. Soc. Sugar Beet Technol. 8:5. Goldman, I. L. 1996a. F1 hybrid, inbred line, and open-pollinated population releases from the University of Wisconsin onion breeding program. HortScience 31:878–879. Goldman, I. L. 1996b. Inbred line and open-pollinated population releases from the University of Wisconsin beet breeding program. HortScience 31:880–881. Goldman, I. L. 1996c. F1 hybrid, inbred line, and open-pollinated population releases from the University of Wisconsin carrot breeding program. HortScience 31:882–883.

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Goldman, I. L. 1998. Inheritance of ffs, a gene conditioning fasciated flower stem in red beet. J. Am. Soc. Hort Sci. 123:632–634. Goldman, I. L. 2000. Prediction in plant breeding. Plant Breeding Rev. 19:15–40. Goldman, I. L., and D. Austin. 2000. Linkage among the R, Y, and bl genes in table beet. Theor. Appl. Genet. 100:337–343. Goldman, I. L., G. Schroeck, and M. J. Havey. 2000. History of public onion breeding programs and pedigree of public onion germplasm releases in the United States. Plant Breed. Rev. 20:67–103. Goldman, I. L., K. A. Eagen, D. N. Breitbach, and W. H. Gabelman. 1996. Simultaneous selection is effective in increasing betalain pigment concentration but not total dissolved solids in red beet (Beta vulgaris L.). J. Am. Soc. Hort Sci. 121:23–26. Goodwin, T. W., and E. I. Mercer. 1983. Introduction to plant biochemistry. 2nd ed. Pergamon Press, New York. Grzebelus, D. 1997. Genotype × environment interaction for nitrate accumulation in red beet. p. 139–142. In: P. Krajewski and Z. Kaczmarck (eds.), Adv. Biomet. Genet. Proc. 10th EUCARPIA Section Biometrics in Plant Breeding. Hallauer, A. R. 1985. Compendium of recurrent selection methods and their application. CRC Crit. Rev. Plant Sci. 3:1–33. Hecker, R. J., and E. G. Ruppel. 1986. Registration of Rhizoctonia root rot resistant sugarbeet germplasm FC 712. Crop Sci. 26:213–214. Hole, C. C., T. H. Thomas, and J. M. T. McKee. 1984. Sink development and dry matter distribution in storage root crops. Plant Growth Reg. 2:347–358. Impelizzeri, G., and M. Piatelli. 1972. Biosynthesis of betalains: formation of indicaxanthin in Opuntia ficus-indica fruits. Phytochemistry 11:2499–2502. Jaggard, J. W., R. Wickens, D. J. Webb, and R. K. Scott. 1983. Effects of sowing date on plant establishment and bolting and the influence of these factors on yields of sugar beet. J. Agr. Sci. (Cambridge) 101:147–161. Jones, H. A. 1923. Pollination and self-fertility in the onion. Proc. Am. Soc. Hort. Sci. 20:191–197. Jones, H. A., and G. Davis. 1944. Inbreeding and heterosis and their relation to the development of new varieties of onions. USDA Tech. Bul. 874. Jones, H. A., and S. L. Emsweller. 1934. The use of flies as onion pollinators. Proc. Am. Soc. Hort. Sci. 31:160–164. Jung, C., and G. Wricke. 1987. Selection of diploid nematode-resistant sugar beet from monosomic addition lines. Plant Breed. 98:205. Keller, W. 1936. Inheritance of some major color types in beets. J. Agr. Res. 52:27–38. Linde-Laursen, I. 1972. A new locus for colour formation in beet, Beta vulgaris L. Hereditas 70:105–112. Lundqvist, A., U. Østerbye, K. Larsen, and I. Linde-Laursen. 1973. Complex selfincompatibility systems in Ranunculus acris L. and Beta vulgaris L. Hereditas 74:161–168. Magruder, R., V. R. Boswell, H. A. Jones, J. C. Miller, J. F. Wood, L. R. Hawthorn, M. M. Parker, and H. H. Zimmerley. 1940. Descriptions of types of principal earne varieties of red garden beets. USDA, Washington, DC. Munerati, O. 1931. L’heredita della tendenza alla annualita nella earne barbabietola coltivata. Z. Induktive Abstam. Verebungslehre 54:740–743. Munerati, O., and T. Costa. 1930. Osservazioni sulla trasmissione del carattere “pelle ear” nella barbabietola. Z. Induktive Abstam. Verebungslehre 54:458–465. Owen, F. V. 1945. Cytoplasmically inherited male-sterility in sugar beets. J. Agr. Res. 71: 423–440.

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Owen, F. V., and G. K. Ryser. 1942. Some Mendelian characters in Beta vulgaris and linkages observed in the Y-R-B linkage group. J. Agr. Res. 65:155–171. Pink, D. A. C. 1993. Beetroot. p. 473–477. In: G. Kalloo (ed.), Breeding vegetable crops. AVI Press, Westport, CT. Poole, C. F. 1937. Improving the root vegetables. p. 300–325. In: USDA Yearbook, U.S. Government Printing Office, Washington, DC. Savitsky, V. F. 1950. Monogerm sugar beets in the United States. Proc. Am. Soc. Sugar Beet Tech. 6:156–159. Savitsky, H. 1954. Obtaining tetraploid monogerm self fertile, self sterile, and male sterile beets. Proc. Am. Soc. Beet Technol. 8:50–58. Sciuto, S., G. P. Oriente, and M. Piatelli. 1972. Betalain glucosylation in Opuntia dillenii. Phytochemistry 11:2259–2262. Sherf, A. F., and A. A. MacNab. 1986. Beet, p. 93–118. 2nd ed. Vegetable diseases and their control. Wiley, New York. Simon, P. 2000. Domestication, historical development, and modern breeding of carrot. Plant Breed. Rev. 19:157–190. Stein, H., and W. H. Gabelman. 1959. Pollen sterility in Beta vulgaris associated with red pigmentation of the anthers. J. Am. Soc. Sugar Beet Technol. X:612–618. von Elbe, J. H., J. H. Pasch, and J. P. Adams. 1974. Betalains as food colorants. Proc. IV Int. Congress Food Sci. Tech. 1:485–492. Walker, J. C. 1952. Diseases of beet and chard. p. 57–86. Diseases of vegetable crops. McGraw-Hill, New York. Walker, J. C., J. P. Jolivette, and W. W. Hare. 1945. Varietal susceptibility in garden beets to boron deficiency. Soil Sci. 59:461–464. Wallace, H. A., and W. L. Brown. 1988. Corn and its early fathers. Rev. ed. Iowa State Univ. Press., Ames, IA. Wang, M., and I. L. Goldman. 1996. Phenotypic variation in free folic acid content among F1 hybrids and open-pollinated cultivars of red beet. J. Am. Soc. Hort Sci. 121: 1040–1042. Wang, M., and I. L. Goldman. 1999. Genetic distance and diversity in table beet and sugarbeet (Beta vulgaris) accessions measured by random amplified polymorphic DNA (RAPD). J. Am. Soc. Hort Sci. 124:630–635. Watson, J. F., and I. L. Goldman. 1997. Inheritance of a recessive gene conditioning blotchy root color patterning in Beta vulgaris. J. Hered. 88:540–543. Watson, J. F., and W. H. Gabelman. 1984. Genetic analysis of betacyanin, betaxanthin, and sucrose concentrations in roots of table beet. J. Am. Soc. Hort. Sci. 109:386–391. Whitney, E. D., and J. E. Duffus. 1986. Compendium of beet diseases and insects. Am. Phytopath. Soc., St. Paul, MN. Williams, J. T., and B. V. Ford-Lloyd. 1974. The systematics of the chenopodiaceae. Taxon 23:353–354. Winner, C. 1993. History of the crop. p. 1–35. In: D. A. Cooke and R. K. Scott (eds.), The sugar beet crop: science into practice. Chapman and Hall, London. Wolyn, D. J., and W. H. Gabelman. 1986. Effects of planting and harvest date on betalain pigment concentrations in three table beet genotypes. HortScience 21:1339–1340. Wolyn, D. J., and W. H. Gabelman. 1990a. Inheritance of root and petiole pigmentation in red table beet. J. Hered. 80:33–38. Wolyn, D. J., and W. H. Gabelman. 1990b. Selection for betalain pigment concentrations and total dissolved solids in red table beets. J. Am. Soc. Hort. Sci. 115:165–169.

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8 Yeast as a Molecular Genetic System for Improvement of Plant Salt Tolerance* Tracie K. Matsumoto, Ray A. Bressan, and P. M. Hasegawa Department of Horticulture and Landscape Architecture Center for Plant Environmental Stress Physiology Purdue University West Lafayette, Indiana 47907 José M. Pardo Instituto de Recursos Naturales y Agrobiologia Consejo Superior de Investigaciones Cientificas P.O. Box 1052 Sevilla 41080, Spain

I. INTRODUCTION A. Salinity: An Impediment to Crop Production B. Established Salt Tolerance Genes in Plants C. Yeast: A Molecular Genetic System for Application to Plant Improvement D. Universal Salt Tolerance Determinants II. YEAST COMPLEMENTATION A. Construct Mutant Yeast Strain B. Selection or Screening System C. cDNA Library and Shuttle Vector D. Transformation, Selection, and Complementation

*Journal article No. 16708 from the Purdue Agricultural Experiment Station. This research was supported in part by USDA/NRICGP grant 97-00558, NSF Plant Genome #DBI9813360, and by Grant BI02000-0938 from Comision Interministerial de Ciencia y Tecnologia (to JMP).

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E. Reconfirm Complementation III. ORTHOLOGOUS PLANT AND YEAST GENES A. Plant Genes That Complement/Suppress Salt/Osmotic Sensitive Yeast Mutants B. Yeast Genes That Confer Salt Tolerance to Plants IV. SIMILARITY OF CELLULAR SALT TOLERANCE IN PLANTS AND YEAST LITERATURE CITED

I. INTRODUCTION A. Salinity: An Impediment to Crop Production The detrimental impact of salinity on plants (mainly Na+ salts) remains a major limitation to crop production in arid, semi-arid, and irrigated agriculture. Salt is estimated to negatively impact approximately 950 million ha, about 6 percent of the world’s land surface (Flowers and Yeo 1995). Currently, 45 million ha (19.5%) of the 230 million ha of irrigated land and 32 million ha (2.1%) of the 1500 million ha of dry land used for agriculture are affected by salt (FAO AGL 2000). Primary salinization is caused by the accumulation of salt in areas where the amount of evapotranspiration exceeds the amount of precipitation, particularly in arid or semi-arid regions. Secondary salinization/alkalization is a consequence of agricultural practices that facilitate net accumulation of salts in the plant root zone; typically, the use of salt-containing irrigation water, or irrigation of soils without adequate drainage (Epstein et al. 1980; Brady 1984). It is estimated that 3 ha of arable land are lost every minute due to soil salinity (FAO AGL 2000). Appropriate agricultural management practices can maintain or improve crop productivity on salt affected soils. These practices include reducing salt in the root zone by under drainage or leaching of soil with high quality irrigation water, converting alkali carbonates to sulfates by the application of gypsum, and retarding evaporation of water from the soil to reduce movement of salts to the soil surface (Brady 1984). However, implementation of these practices is time-consuming and expensive, and often not possible. Furthermore, these practices cannot alleviate conditions of more extreme salinity. Recently, great emphasis has been directed toward the development of salt tolerant cultivars. Tolerant plants would complement irrigation practices that reduce soil salinity as these plants would not require the excessive amount of water that is often necessary to reduce salt levels to acceptable limits in the soil for crop growth (Flowers and Yeo 1995). Plant breeding has facilitated modest gains in yield stability under stress environments through genetic introgression from wild salt toler-

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ant relatives (Epstein et al. 1980; Flowers and Yeo 1995). However, the taxonomic distance between the crop and its wild relative is an impediment to recombination making it difficult to break linkages between salt tolerance genes and those that result in undesirable agronomic traits (Serrano and Gaxiola 1994). Breeding for salt tolerance is further constrained, because tolerance is due to the interaction of many genes that have not been clearly defined. Consequently, effective recurrent selection is difficult and has met with only moderate success (Serrano and Gaxiola 1994; Flowers and Yeo 1995). B. Established Salt Tolerance Genes in Plants Recent advances in molecular biology have resulted in the identification of many osmotic stress-regulated plant genes (Zhu et al. 1997; Seki et al. 2001), and are providing more precise determination of gene function in physiological processes that contribute to stress tolerance (Serrano and Gaxiola 1994). Intrinsic characteristics of high salt include the disruption of both ion and water homeostasis and in reduced nutrient (i.e., potassium, calcium, and phosphates) uptake by the plant (Hasegawa et al. 2000a; Grattan and Grieve 1999). Both plant and microbial genes, that were predicted to facilitate osmotic or compatible solute biosynthesis, have been expressed in plants (Holmberg and Bülow 1998) resulting in increased production of mannitol, ononitol, proline, or glycine betaine (Tarczynski et al. 1993; Kishor et al. 1995; Lilius et al. 1996; Hayashi et al. 1997; Shen et al. 1997; Sheveleva et al. 1997). Plants expressing a late embryogenesis abundant (LEA) protein exhibited increased, albeit limited, salt tolerance (Xu et al. 1996). Modification of ionic homeostasis through the overexpression of AtNHX1, a vacuolar Na+/H+ antiporter in Arabidopsis, resulted in increased Na+ sequestration in the plant vacuole and increased salt tolerance (Apse et al. 1999). Substantial salt stress tolerance is also achieved through regulating the expression of multiple genes that function in adaptation to salt in a spatial and temporal context. Expression of an activated form of calcineurin, a key signal regulatory component in yeast facilitates salt tolerance in tobacco (Pardo et al. 1996). Conversely, a mutation in sos3 (salt overly sensitive) a key signal regulatory component in plants, results in salt sensitive Arabidopsis (Zhu 2000). Exploration of Arabidopsis as a molecular genetic system to study salt tolerance is already underway largely motivated by the discovery of sos Arabidopsis mutants and utilization of a close halophylic Arabidopsis relative Thellungiella halophila, in the laboratory of Jian-Kang Zhu (Hasegawa et al. 2000a; Sanders 2000; Zhu 2001). The completion of the genomic sequence of Arabidopsis thaliana indicates 115.4 Mb encode

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25,498 genes (The Arabidopsis Genome Initiative 2000). From these predicted genes, 9 percent have been characterized experimentally and 69 percent classified according to sequence similarity to proteins of known function in other organisms. Roughly 30 percent of these genes cannot be assigned to functional categories (The Arabidopsis Genome Initiative 2000). Whereas Arabidopsis is the logical choice for studying salt tolerance in plants, yeast still offers some basic advantages as a molecular genetic system. The greatest advantage of yeast is the ability to create mutations through homologous recombination in which precise gene mutations can be incorporated into specific loci in the yeast genome for rapid phenotype evaluation of gene function. With 30 percent of the genes of yeast and Arabidopsis classified as having unknown functions, yeast can serve as an expeditious, intermediate step to investigate the role of proteins in plants. It may also serve as an intermediate system to screen for more effected alleles of genes after facilitated gene specific mutagenesis (Rubio et al. 1995, 1999). C. Yeast: A Molecular Genetic System for Application to Plant Improvement Bakers yeast, Saccharomyces cerevisiae, has been utilized by humans for many centuries, for baking, brewing, and wine making (Attfield 1997). Alcohol production research initiated the utilization of yeast as a tool in biochemical research, which in turn, led to the discovery and characterization of enzymes and coenzymes of the glycolytic pathway (Roman 1981). Use of S. cerevisiae as a molecular genetic tool first began in the mid-1930s when Winge established that inheritance in yeast genetics was Mendelian. This led to further research in gene conversion, homothallism and heterothallism, polyploidy, control of cell division and colinearity between DNA and its protein products (Roman 1981). These early discoveries promoted the use of yeast as a model eukaryotic organism for molecular genetic research (Botstein and Fink 1988; Botstein et al. 1997). Many characteristics of yeast make it amenable to molecular genetic research. Years of genetic research have generated numerous yeast genotypes with various autotrophic or auxotrophic markers for selection of linked or of cosegregating genes or phenotypes. Large numbers of mutants can be easily screened. The haploid phase of yeast is often utilized to facilitate the isolation of recessive mutations. Homologous recombination enables gene replacement to obtain insertional mutants, and to substitute or replace mutant alleles. Techniques for evaluation of promoter activity utilizing chimeric gene fusions, and identification

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and characterization of interacting proteins are also available (Burns et al. 1994; Evangelista et al. 1996; White 1996; DeRisi et al. 1997; Wodicka et al. 1997; Frederickson 1998). In the current “genomic revolution,” yeast is still considered an ideal model for large-scale functional analysis. The complete sequence of the 12,000 kb genome of yeast was released in 1996 making it the first eukaryotic genome to be sequenced (Mewes et al. 1997). Since its completion, 44 genomes have been sequenced and nearly 800 genomes are currently being sequenced (Kumar and Snyder 2001). The compact genome of yeast consists of 16 chromosomes and is predicted to encode approximately 6,200 genes. An additional 137 genes have been experimentally discovered using an integrated approach consisting of gene-trapping, microarray analysis, and homology searches (Kumar et al. 2002). Of these predicted genes, 3780 have been genetically or biochemically characterized and the function of another 560 have been predicted from homologous proteins in other organisms, leaving approximately 1900 genes of unknown function (Kumar and Snyder 2001). Yeast genes with unknown function are currently being systematically characterized by a number of high throughput schemes that utilize random or directed mutations for phenotype evaluation or protein localization and purification. Utilization of homologous recombination allows the systematic deletion of all predicted open reading frames (ORF) in the yeast genome. Currently, a total of 6925 strains of yeast have been constructed with a precise deletion of 2026 ORFs. Of the total yeast strains obtained, 500 deletion strains exhibited a 40 percent growth defect in rich or minimal medium (Winzeler et al. 1999). Interactions between protein products are being tested through largescale, two-hybrid screens and genome wide transcript or protein profiling are used to characterize these unknown proteins (reviewed in Kumar and Snyder 2001). It is evident that many transcriptional salt stress responses are shared between many diverse species including cyanobacteria (Synechocystis PCC6803), yeast, fungi (Aspergillus nidulans), alga (Dunaliella salina), halophyte ice plant, (Mesembryanthemum crystallinum), glycophyte rice (Oryza sativa), and Arabidopsis thailiana (Bohnert et al. 2001; Yale and Bohnert 2001). This suggests that an ubiquitous salt stress response may exist. D. Universal Salt Tolerance Determinants Our working model assumes that both intolerant and salt tolerant plants mediate stress adaptation through ubiquitous functional mechanisms, although some halophytes have evolved specialized adaptations such as salt glands and bladders. The principal difference is that salt tolerant

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plants either: (1) are preadapted to cope with salinity stress, that is, adaptation mechanisms are already activated; (2) are extremely responsive and more effectively activate the same adaptation mechanisms in response to stress imposition; (3) have specialized alleles encoding proteins with subtle but important differences in biochemical properties. Furthermore, it now seems evident that there are similar intrinsic cellular mechanisms of salt tolerance among diverse organisms. The presumption that salt tolerance determinants are inherently similar in both glycophytes and halophytes, and that many cellular determinants of tolerance are evolutionary conserved, has stimulated a new approach for their identification based on gain- or loss-of-function molecular genetic techniques using model organisms (Haro et al. 1993; Serrano and Gaxiola 1994; Pardo et al. 1996; Prieto et al. 1996; Zhu et al. 1997). Determinants, as used in this chapter, are gene products whose function affects salt sensitivity. These may either be effectors that directly mediate mechanisms of tolerance (e.g., ion transporters) or regulatory molecules (e.g., kinases/phosphatases, transcription factors, etc.) that coordinate the function of multiple gene products required to effect complex processes (e.g., intracellular ion accumulation and vacuolar ion compartmentation) required for tolerance (Niu et al. 1995; Shinozaki and Yamaguchi-Shinozaki 1997). The latter determinants include components of stress signaling cascades that transduce stress signal perception into the activation of effectors that mediate adaptation (Bressan et al. 1998). This chapter will recount the evidence that has established the basis for using yeast as a cellular molecular genetic model for salt tolerance in plants. Examples will be presented to demonstrate that some plant and yeast genes are in some ways interchangeable. Finally, we will demonstrate that yeast and plant genes and gene products can functionally interact to increase salt tolerance in plants.

II. YEAST COMPLEMENTATION Plant gene expression in yeast to compensate for a mutant yeast gene (yeast complementation), is a powerful tool for the identification and isolation of plant genes that modulate important physiological processes. Many plant genes, including those that encode plant transporters, have been identified or their function verified by complementation of yeast mutations (reviewed by Frommer and Ninnemann 1995; Dreyer et al. 1999). Many detailed protocols for yeast complementation are available including Current Protocols in Molecular Biology (Ausubel et al. 1988) and Guide to Yeast Genetics and Molecular Biology (Fink 1991). An

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overview of yeast complementation of a recessive mutation is reviewed in Frommer and Ninnemann (1995) and can be summarized in the following steps: (1) obtain or construct a yeast strain containing the mutation of interest and selectable marker; (2) establish a selection or screening system; (3) clone a plant cDNA library into a shuttle vector carrying a gene that complements the selectable marker; (4) transform the yeast mutant with the cDNA library; (5) either screen for the selectable marker first, then replica plate on selective media or directly perform a double selection; and (6) replate positive colonies, isolate the plasmid DNA, and retransform the mutant strain to exclude artifacts (Frommer and Ninnemann 1995). A. Construct Mutant Yeast Strain A yeast strain used for complementation with plant genes usually contains a mutation, or preferably a deletion, of the yeast gene with the greatest similarity in sequence or function to the plant gene (Minet et al. 1992). Plant genes have been isolated through the utilization of yeast mutations in genes affecting similar albeit distinct processes. For instance, plant sucrose transporter genes have been isolated from yeast, even though yeast metabolizes sucrose extracellularly. By using a yeast strain with a mutation that causes a deficiency in secreted invertase and is thus able to metabolize sucrose only internally, researchers successfully isolated sucrose transporters from spinach and potato (Riesmeier et al. 1993). Reversion of point mutations can often lead to large numbers of false positives in which the expression of a plant gene is not responsible for growth under selection. Therefore, gene disruption should be used to construct stable mutants in the gene of interest (Frommer and Ninnemann 1995). A simple one-step gene disruption technique involves the replacement of the gene of interest with a selectable marker by homologous recombination. In this method, a sequence encoding a selectable marker is either inserted within a single restriction site within the gene of interest (insertion) or inserted between two restriction sites which results in a deletion of all or portions of the gene of interest (deletion). The selectable marker is cloned into the gene of interest, which must retain sufficient flanking sequences to permit homologous pairing with both sides of the chromosomal target sequence, usually greater than 500 bp. The disrupted gene fragment containing the selectable marker and homologous ends is linearized and transformed into yeast by spheroplast formation, permeabilization of whole cells with lithium ions or electroporation. Transformants are checked by genomic blot or PCR and the expected phenotype should be tested and complemented with the “native” yeast gene.

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A new method for gene disruption has been recently employed using “hybrid” PCR primers containing 40 to 50 bases homologous to the replacement gene and 15 to 20 bases homologous to a plasmid containing the selectable marker. The primers are used to amplify the selectable marker and the PCR fragment is subsequently transformed into yeast to create a gene disruption by homologous recombination (Wach et al. 1997). For PCR-based recombination, in which short segments of homologous sequences are used for gene replacement, it is necessary to use selectable markers that have no homologous sequences in the transformed yeast. For instance, the bacterial aminoglycoside phosphotransferase, kanMX gene or the Schizosaccharomyces pombe HIS3 homolog, HIS3MX, which do not have sequence homology in S. cerevisiae are used to confer geneticin resistance and histidine autotrophy, respectively (Wach et al. 1997). B. Selection or Screening System The most common markers for selection of yeast transformed with vector plamids include one of the four cloned yeast genes LEU2, URA3, HIS3, or TRP1 which encode proteins for the biosynthesis of leucine, uracil, histidine, and tryptophan, respectively. Specific selectable markers are chosen according to the mutation within the recipient strain to be auxotrophic for the corresponding amino acid or nucleotide (Rose and Broach 1991). Selection for the gene of interest is often determined by the phenotype of the mutation being complemented. In most cases, the selecting agent confers the phenotype being investigated, such as an increased tolerance to lithium (Lippuner et al. 1996; Quintero et al. 1996), or utilization of a specific metabolite as a sole energy source such as sucrose (Riesmeier et al. 1993), or growth in the absence of a specific substance such as amino acids (Minet et al. 1992). In all these cases, growth of the recipient strain on medium that selects for the plasmid and gene of interest is used as the indication of complementation. Another unique strategy for selection has been utilized to identify genes encoding cycloartenol synthase from Arabidopsis thaliana. A transformed yeast strain lacking an epoxysqualene mutase can be assayed for cycloartenol synthase activity by thin layer chromotography (Corey et al. 1993). C. cDNA Library and Shuttle Vector Preparation of the functional cDNA pool requires the cDNA to be oriented properly with respect to the flanking transcriptional promoter and terminator sequences for proper translation of the encoded protein

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(McKnight and McConaughy 1983). The cDNA libraries used for complementation may be enriched for a gene of interest by selection of the proper material for RNA extraction, that is, tissues or conditions when the gene of interest is highly or preferentially expressed. Isolation of sulfate transporters from a tropical forage legume Stylosanthes hamata, utilized a library derived from plants starved for sulfur (Smith et al. 1995) and phosphate transporters were selected from phosphate starved A. thaliana plants (Muchhal et al. 1996). The isolation of SAL1, a gene involved in salt tolerance, utilized RNA isolated from salt treated roots of A. thaliana (Quintero et al. 1996), whereas the isolation of STO and STZ, two transcription factors involved in salt tolerance did not utilize a salt treated A. thaliana library (Lippuner et al. 1996). Since the construction of cDNA libraries utilizes E. coli, toxicity of the gene product can present an appreciable problem. Toxicity in E. coli ranges from completely lethal to mildly deleterious (Rose and Broach 1991). Expression of integral membrane proteins can often be toxic and may lead to the elimination of genes during amplification in E. coli. Therefore, the use of low copy plasmids or the shuttle vector, pFL61, which prevents the background expression from cloned cDNAs in E. coli should be utilized (Frommer and Ninnemann 1995). D. Transformation, Selection, and Complementation Once the library has been transformed into the recipient strain, selection for the plasmid and the gene of interest may be performed sequentially or at the same time. Separate selection of the plasmid, then screening for the gene of interest is preferable since the plasmid marker is selected via a nonreverting marker, whereas the gene of interest may be revertible or the phenotype that it controls could be too subtle to select efficiently (Rose and Broach 1991). E. Reconfirm Complementation Growth of strains containing a complementing plasmid vector is measured on plates or in liquid culture compared to the wild type strain and a strain transformed with the same plasmid vector not containing a cDNA insert. In cases where the GAL promoter is used, growth on the screening medium is induced in the presence of galactose and complementation should be dependent on the presence of galactose. Although galactose dependence should preclude revertants or spontaneous mutations that occur in the selection process, it is still important to recover the plasmid and retransform the recipient strain to reproduce complementation of the desired phenotype linked to the plasmid.

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Another complication can be genetic suppression in which a gene functionally different from the mutation in the gene of interest can still cause complementation (Frommer and Ninnemann 1995). Mutations in glucose transporter genes can suppress a potassium uptake deficient mutation in yeast by allowing ectopic potassium influx. Also, complementation of the branched-chain amino acid transporter mutant, bap1, which is based on growth in the presence of the toxic compound sulfometurone with plant cDNA libraries did not identify the amino acid transporter but instead allowed the isolation of detoxifying enzymes such as catalase and hydroxymethyl CoA synthetase (Frommer and Ninnemann 1995).

III. ORTHOLOGOUS PLANT AND YEAST GENES A. Plant Genes That Complement/Suppress Salt/Osmotic Sensitive Yeast Mutants 1. Plant Homologues of HOG Pathway Components. In yeast, osmotic tolerance is regulated by a mitogen-activated protein (MAP) kinase (MAPK) cascade that consists of two osmosensing transmembrane proteins, Sln1p and Sho1p, that both initiate the high osmolarity glycerol (HOG) cascade that controls glycerol biosynthesis (Wurgler-Murphy and Saito 1997). This is summarized in Fig. 8.1. Sln1p is the yeast twocomponent regulatory sensor system that perceives changes under high osmolarity and transmits a signal through intermediary protein Ypd1p to Ssk1p (Posas et al. 1996). The N-terminus of Sln1p is comprised of two transmembrane domains that flank an extracellar domain followed by histidine kinase and receiver domains at the C-terminus (Posas et al. 1996; Ostrander and Gorman 1999). The first of the two transmembrane domains functions as the osmosensor that controls kinase activity and the extracellular domain mediates dimerization of the Sln1p protein (Ostrander and Gorman 1999). Under normal osmotic conditions, the histidine kinase domain is activated by autophosphorylation at His576, followed by the transfer of this phosphate to the receiver domain at Asp1144. This phosphate is then sequentially transferred from the receiver domain of Sln1p to the His64 in Ypd1p and Asp554 in Ssk1p. Phosphorylated Ssk1p is unable to activate signaling through the HOG pathway preventing glycerol accumulation (Posas et al. 1996; WurglerMurphy and Saito 1997). Hyperosmolarity inhibits this phosphorelay, allowing the accumulation of unphosphorylated Ssk1p which activates the HOG pathway.

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Fig. 8.1. The high osmolarity glycerol (HOG) pathway in yeast (left) is modulated by a mitogen-activated protein kinase (MAPK) cascade that consists of two osmosensing transmembrane proteins, Sln1p and Sho1p. Both initiate the high osmolarity cascade and mediate glycerol biosynthesis. Sln1p is the sensor of a two component regulatory system that perceives changes in osmolarity and transmits a signal through intermediary protein Ypd1p to Ssk1p. High osmolarity inhibits phosphorylation of Ssk1p by Sln1p and unphosphorylated Ssk1p initiates the MAPK cascade by activating, in sequence, a pair of serinethreonine kinases Ssk2p and Ssk22p. The signal is transduced through Pbs2 (MAPKK) to Hog1 (MAPK) that regulates GPD1, a gene encoding the key enzyme for glycerol production, glycerol-3-phosphate dehydrogenase (Varela and Mager 1996). The second transmembrane protein, Sho1p, perceives high osmolarity to induce glycerol production through a MAP kinase pathway that utilizes Ste11p as the MAPKKK and transduces the signal to Pbs1p and Hog1p (Posas and Saito 1997). Arabidopsis genes that complement the corresponding yeast genes are located on the right.

In Arabidopsis, Arabidopsis thailiana histidine kinase 1 (ATHK1) was isolated by PCR using degenerative primers and a cDNA library prepared from dehydrated Arabidopsis plants as template (Urao et al. 1999). The ATHK1 protein is predicted to contain two hydrophobic regions in the N-terminus and both histidine kinase receiver domains suggesting that ATHK1 is a transmembrane protein structurally similar to osmosensor Sln1p in yeast (Urao et al. 1999). Transcript of ATHK1 accumulates under high salinity and low temperature and the promoter is activated in both leaf bases and roots. Heterologous expression of ATHK1 in yeast cells lacking both native osmosensors (sln1Dsho1D) induced tyrosine phosphorylation of Hog1p and recovered the ability of these cells to

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grow in high osmolarity. Mutations that deleted the extracellular domain or replaced the predicted phosphorylation sites (His508 to Val or Asp1074 to Glu) of ATHK1 failed to complement the sln1-ts yeast mutant. Furthermore, expression of ETR1, a histidine kinase twocomponent receptor for ethylene in Arabidopsis also failed to complement the sln1-ts yeast mutant (Urao et al. 1999). Together, these results suggest that ATHK1 is a functional homolog of Sln1p that is likely to be an osmosensor in Arabidopsis (Urao et al. 1999, 2000). Phosphate transfer from the histidine kinase receptor, Sln1p to Ssk1p is facilitated through the intermediary protein Ypd1 (Posas et al. 1996). Ypd1 physically interacts with the activation domain of Sln1p and receiver domain of Ssk1p as assayed by yeast two-hybrid protein interaction tests. Phosphate is transferred from Sln1p (Asp 1144) to Ypd1p (His64), through a histidine residue located in the histidine containing phosphotransfer (HPt) domain (Posas et al. 1996). Using the conserved protein sequence of the HPt domain to search the Arabidopsis expressed sequence tag (EST) database, two groups identified three cDNAs (ATHP1-3 or AHP1-3) that were able to complement the lethal phenotype of a yeast ypd1D strain (Miyata et al. 1998; Suzuki et al. 1998). Accumulation of ATHP1-3 or AHP1-3 transcript was predominantly found in root tissue under salt, cold, drought, and nonstress conditions (Miyata et al. 1998; Suzuki et al. 1998). Using the two-hybrid assay to detect protein–protein interactions, ATHP1 (AHP2) can interact with the receiver domain of ATHK1 and this interaction is abolished by an Asp1074 to Glu residue change in the receiver domain, the same mutation that results in a loss of ATHK1 complementation of a sln1-ts mutant (Urao et al. 2000). In addition, the phosphoryl group on the His residue of AHP2 (ATHP1) can be rapidly transferred to the Asp residue of the receiver domain of ARR3 and ARR4, (Arabidopsis response regulators) in vitro (Suzuki et al. 1998). However, ATRR1 (ARR4) does not interact with ATHP1 (AHP2) in yeast two-hybrid assays (Urao et al. 2000). Thus, it appears that the Arabidopsis homolog to Ssk1p, the response regulator in yeast, remains elusive but may be present among the 13 ARRs predicted to be encoded by the Arabidopsis genome (Suzuki et al. 1998). MAP kinase pathways are ubiquitous in nature. They are commonly composed of a MAP kinase kinase kinase (MAPKKK), MAP kinase kinase (MAPKK) and the MAP kinase (MAPK). In yeast, the HOG pathway MAPKKK function is carried out by two redundant protein kinases, Ssk2p and Ssk22p, that are 69 percent identical in the protein kinase domain. Both Ssk2p and Ssk22p function in the phosphorylation of MAPKK Pbs2p, which subsequently phosphorylates the MAPK Hog1p (Hohmann 1997). Interestingly, although mutations in both pbs2 and

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hog1 result in sensitivity to high osmolarity, a ssk2ssk22 double mutant is not osmosensitive (Hohmann 1997) suggesting the presence of another less related unidentified functional counterpart. A second osmosensor protein, Sho1p, perceives high osmolarity and activates the MAP kinase pathway through Ste11p as the MAPKKK, which then transduces the signal to Pbs1p and Hog1p (Posas and Saito 1997). Ste11p also participates as a MAPKKK in the pheromone response pathway in yeast which results in G1 arrest upon exposure to phermone in preparation for mating (Gustin et al. 1998). The Arabidopsis thailiana cDNA ATMEKK1 encodes a N-terminal truncated protein that has homology and functionally complements the yeast MAPKKK Ste11p (Covic and Lew 1996; Covic et al. 1999). Complementation tests of other mutants in the HOG response pathway demonstrated that ATMEKK1 suppressed a ssk2 ssk22 sho1 triple mutant but not pbs1 or hog1 single mutants, placing ATMEKK1 at the functional level of MAPKKK Ste11p or Ssk2p/Ssk22p to facilitate glycerol accumulation and osmotic tolerance (Covic et al. 1999). ATMEKK1 mRNA is induced in Arabidopsis as early as 5 min up to 60 min following the imposition of a salt treatment (Covic et al. 1999). Using the yeast two-hybrid assay, protein interaction was detected between ATMEKK1 and the MAPKKs MEK1 and ATMKK2, and with ATMPK4, a MAPK (Ichimura et al. 1998; Mizoguchi et al. 1998). Protein interaction was also detected between ATMKK2 and MEK1 with ATMPK4, suggesting that ATMEKK1, ATMKK2/MEK1, and ATMAPK4 comprise a functional MAP kinase pathway in Arabidopsis (Ichimura et al. 1998; Mizoguchi et al. 2000). Coexpression of ATMEKK1 together with MEK1 or ATMKK2, increased the osmotic tolerance of pbs2D mutants while expression of the MAPKK (MEK1 or ATMKK2) alone could not increase osmotolerance (Ichimura et al. 1998; Mizoguchi et al. 2000). This suggests that higher specificity of activation of the HOG pathway in yeast is achieved through interaction of ATMEKK1 with MEK1/ATMKK2. Phosphorylation and activation of ATMPK4 by MEK1 has been demonstrated in vitro (Huang et al. 2000) and ATMPK4 is activated by hyperosmolarity treatments in vivo suggesting ATMPK4 is a component of a MAP kinase pathway involved in osmotolerance in plants (Ichimura et al. 2000). A MAP kinase homologue from Pisum sativum (PsMAPK) has 47 percent amino acid identity to yeast Hog1p. Overexpression of PsMAPK in a hog1 mutant recovered both cell growth and restored cell morphology under high salt stress conditions (Pöpping et al. 1996). Accumulation of glycerol is one of the effects of activation of the HOG signal pathway largely through the induction of GPD1 (reviewed in Hohmann 1997). Replacement of glycerol accumulation by mannitol by

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expression of the bacterial gene MTLD or sorbitol accumulation encoded by expression S6PDH resulted in only a partial recovery of salt tolerance in a glycerol deficient strain gpd1D gpd2D (Shen et al. 1997), suggesting that the osmotic adjustment is not the only mechanism of osmotolerance conferred by induction of glycerol biosynthesis through the HOG pathway in yeast (Shen et al. 1997; Bohnert and Shen 1999). Mannitol accumulation under high salt conditions in celery is the result of reduced mannitol catabolism and increased mannitol synthesis in place of sucrose. Cloning of 3 sucrose transporter cDNAs from celery by hybridization with a spinach transporter demonstrated that the mRNA is in fact down regulated in the presence of high salt. AgSUT1 expressed in yeast is able to transport sucrose and this transport is unaffected by mannitol confirming the existence of separate transporter systems for mannitol and sucrose (Noiraud et al. 2000). In addition to polyols (glycerol and sorbitol), trehalose is also accumulated as a result of osmotic stress and levels of trehalose correlate with stress tolerance (Shen et al. 1997). The salt sensitivity and thermosensitivity of a tps1 yeast mutant which is deficient in trehalose-6-phosphate is suppressed by the expression of the SlTPS1 from the resurrection plant, Selaginella lepidophylla (Zentella et al. 1999). Whereas osmotic stress is involved in the activation of the HOG MAPK pathway through the sequential phosphorylation of a series of kinases, inactivation involves dephosphorylation of Hog1p by tyrosine phosphatases Ptp2p and Ptp3p that modulates Hog1p nuclear and cytoplasmic localization, respectively (Jacoby et al. 1997; Mattison and Ota 2000). Ptp2 and Ptp3 are both able to suppress the lethal phenotype of a constitutively activated Sln1p under normal osmotic conditions (Jacoby et al. 1997). Using a similar approach to search for negative regulators of MAPK pathways in plants, suppression of the pheromone MAPK pathway by plant cDNAs in yeast resulted in the isolation of Medicago phosphatase 2C (MP2C) (Meskiene et al. 1998). In yeast MP2C functions as a suppressor at the level of Ste11p, a common MAPKKK of both the pheromone response and HOG pathways to negatively regulate G1 phase cell cycle arrest and the lethal phenotype of a constitutively activated HOG pathway. In plants, MP2C inactivates immunocomplex kinase activity of the stress induced SAMK pathway that is activated in response to cold, drought, touch, and wounding (Meskiene et al. 1998). 2. Plant Homologues of Calcineurin Pathway Components. Ion homeostasis of yeast in a salt stress environment is principally regulated by a signal transduction pathway that involves Ca2+ and the Ca2+/calmodulindependent PP2B phosphatase, calcineurin (Nakamura et al. 1993; Men-

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doza et al. 1994) (summarized in Fig. 8.2). Functional calcineurin is a heterodimer composed of catalytic (Cna1p/Cna2p) and regulatory (Cnb1p) subunits (Cyert et al. 1991). This pathway functions to limit net intracellular Na+/Li+ uptake through the transcriptional regulation of the P-type ATPase ENA1, that is responsible for Na+/Li+ efflux across the plasma membrane (Haro et al. 1991). Calcineurin, along with Hal4p and

Fig. 8.2. Ion homeostasis in response to high salt is mediated through calcineurin pathway in yeast cells (left). The [Ca2+]cyt increases through Cchp1-Mid1p to activate Cmd1p (yeast CaM). CaM activates calcineurin that dephosphorylates Tcn1p to facilitate its translocation to the nucleus where it activates ENA1 expression facilitating ion homeostasis and salt tolerance. Calcineurin also mediates the transition of Trk1p-Trk2p system to high affinity for K+. In plant cells (right panel) salt induced [Ca2+]cyt increase activates the SOS Ca2+dependent signal cascade composed of SOS1, a Na+/H+ antiporter, SOS2, a ser/thr protein kinase and SOS3, a protein with greatest homology to the yeast Cnb1p (reviewed in Hasegawa et al. 2000; Sanders 2000; Zhu 2000; Zhu 2001). Reconstitution of the entire SOS pathway (SOS1, SOS2, SOS3) in a yeast mutant strain deficient for Na+ efflux or sequestration (ena1-4 nha1 nhx1) results in a substantial increase in salt tolerance. Coexpression of Arabidopsis CBL1 with rat CNA1 is able to functionally suppress the Li+ tolerance of cnb1 yeast mutants (Kudla et al. 1999). STO1, STZ1 and SLT1 were also isolated as suppressors of the Li+/Na+ sensitivity of a cnb1 yeast mutant (Lippuner et al. 1996; Matsumoto et al. 2001). Plant proteins, HKT1, KUP1, AKT1 and KAT1 functionally complemented the growth of yeast cells deficient for the Trk1-Trk2 K+ transport under limiting K+ (see text and Dreyer et al. 1999).

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Hal5p, also posttranscriptionally mediate transition of the K+ transport system encoded by Trk1-Trk2 (Haro et al. 1993) to the high affinity state to restrict intracellular uptake of Na+/Li+ (Mendoza et al. 1994; Mulet et al. 1999). Expression of a constitutively activated form of calcineurin resulted in salt tolerance sufficiency of wild type yeast strains (Mendoza et al. 1996). In addition to Na+ and Li+ homeostasis, the calcineurin pathway also has a pivotal role in homeostasis of Ca2+, Mn2+, and OH– (Cunningham and Fink 1994; Farcasanu et al. 1995; Garrett-Engele et al. 1995; Tanida et al. 1995; Pozos et al. 1996). Ca2+ homeostasis is achieved through the dephosphorylation of the transcription factor Crz1p/Tcn1p/Hal8p by calcineurin and localization into the nucleus to bind with calcineurin dependent response elements (CDRE) sites in the promoters of PMC1 and PMR1, genes encoding tonoplast and ER Ca2+ pumps respectively, and of other calcineurin dependent genes including ENA1 (Matheos et al. 1998; Mendizabal et al. 1998; Stathopoulous and Cyert 1998; StathopoulousGerontides et al. 1999; Mendizabal et al. 2001). Calcineurin is also responsible for the posttranslational inhibition of Vcx1p, a Ca+/H+ antiporter in the vacuolar membrane in yeast (Cunningham and Fink 1996). Complementation of salt sensitive calcineurin deficient mutants (cna1, cna2, or cnb1) with plant cDNA libraries resulted in the identification of two transcription factors from Arabidopsis, STZ and STO (Lippuner et al. 1996) and a novel protein from tobacco and Arabidopsis NtSLT1 and AtSLT1 (Matsumoto et al. 2001). NtSLT1 and AtSLT1 complementation of the salt sensitive phenotype is dependent on the deletion of the N-terminal region, hypothesized to contain an inhibitory domain. NtSLT1 conveys salt tolerance to yeast through the activation of two calcineurin-dependent outputs, the induction of ENA1 expression and the posttranscriptional regulation of the Trk1-Trk2 K+ uptake system (Matsumoto et al. 2001). Both STZ and STO have sequence homology to zinc finger type transcription factors. STZ is 37 to 68 percent identical in amino acid sequence to a family of DNA-binding Cys2/His2-type zinc finger proteins associated with flowers, and is 47 percent identical to WZF1, a wheat zinc finger DNA-binding protein that is expressed primarily in the root apex. STO has greatest similarity to the Arabidopsis CONSTANS (CO) protein within the two regions that are predicted to be zinc fingers. Suppression of salt sensitivity by STZ was dependent on ENA1, however, STO function was independent of ENA1. STZ and STO mRNA are abundant in leaf and roots but STO mRNA is specifically found in flowers. Both STZ and STO mRNA are induced by salt treatments (Lippuner et al. 1996). Interestingly, STZ mRNA accumulation is lower in wild type

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(Col gl1-1) than in Col gl-1 sos3 salt sensitive mutants treated for 4 h with 160 mM NaCl suggesting that the components of the salt overly sensitive (SOS) pathway, responsible for Na+ ion homeostasis, negatively regulates the expression of STZ (Gong et al. 2001). Mutational screening of Arabidopsis plants sensitive to salt resulted in the isolation of the recessive Arabidopsis thaliana mutants sos1, sos2, and sos3 that are hypersensitive to Na+/Li+. SOS1 encodes a putative plasmamembrane Na+/H+ antiporter whose mutation results in the most Na+ sensitive phenotype of the sos mutants. The SOS2 gene encodes a ser/thr protein kinase that physically interacts with SOS3 to regulate SOS1 for Na+ and K+ ion homeostasis in a Ca2+ dependent manner (reviewed in Zhu 2000; Hasegawa et al. 2000b; Sanders 2000; Zhu 2001). The SOS3 gene was isolated by positional cloning and encodes a protein with 27 to 31 percent identity and 49 to 51 percent similarity to the regulatory subunit of calcineurin from protozoa, yeast and animals, with greatest homology to the yeast Cnb1p. SOS3 is also 30 to 31 percent identical and 49 to 50 percent similar to animal neuronal calcium sensors (Liu and Zhu 1998). Reconstitution of the entire SOS pathway (SOS1, SOS2, SOS3) in a yeast mutant strain deficient for Na+ efflux or sequestration (ena1-4, nha1, nhx1) results in a substantial increase in salt tolerance relative to the slight suppression observed when expressing SOS1 alone, indicating that the regulation of SOS1 by SOS2 and SOS3 is required for salt tolerance (Quintero et al. 2002). In addition to SOS3, sequence homology searches reveal a small gene family of SOS3-like Ca2+ binding proteins (SCaBP) or Arabidopsis thailiana calcineurin B-like (AtCBL) proteins (Guo et al. 2001; Kudla et al. 1999). Members of this family contain three EF-hand structures for Ca2+ binding and some contain N-myristoylation consensus sequences that are similar to those found in yeast Cnb1p and are required for SOS3 function in plants (Guo et al. 2001). AtCBL1 (SCaBP5) contains both the EFhand and N-myristoylation consensus sequences, interacts with rat CNA subunit in the yeast two-hybrid system, and functions together with rat CNA to suppress the Li+ tolerance of yeast cnb1 mutants. In plants, AtCBL1 mRNA is predominantly found in stems and roots with some expression in leaves and can be induced by drought, cold, and wounding stresses (Kudla et al. 1999). These experiments have identified molecular entities whose characteristics support previously conducted pharmacological and biochemical experiments that have provided evidence for calcineurin-like activity that regulates guard cell ion channel function (Luan et al. 1993; Allen and Sanders 1995). Ca2+-induced inactivation of inward-rectifying K+ channels in the plasma membrane of Vicia faba guard cells, that regulates stomatal aperture, was blocked by

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the highly specific inhibitors of calcineurin, cyclophilin:cyclosporin A and FK506-binding protein (FKBP):FK506 complexes (Luan et al. 1993). Release of Ca2+ through a slow activating vacuolar (SV) channel in vacuoles obtained from V. faba guard cells was modulated by bovine calcineurin (Allen and Sanders 1995). Moreover, phenocopies of sos3 mutants were obtained in wild-type plants by the application of calcineurin inhibitors cyclosporin A and fenvalerate. The salt sensitivity induced by these inhibitors was not additive to the sos3 mutation phenotypes, thus supporting the view that SOS3 is a calcineurin B-like protein (Elphick et al. 2001). Even considering all of these studies that implicate a calcineurinlike pathway controlling ion homeostasis in plants, no homologue with clear sequence identity to the catalytic subunit of calcineurin has been identified in the Arabidopsis genome. Calcium is a known modulator of salt tolerance in both yeast (Danielsson et al. 1996) and plants (Läuchli 1990; Bressan et al. 1998) for the activation of calcineurin (Maeda et al. 1993; Mendoza et al. 1994) and the SOS pathway (Zhu 2000; Hasegawa et al. 2000b; Sanders 2000; Zhu 2001). Recently, it has been shown that salt causes a transient rise in cytosolic Ca2+ that is derived in part from external pools in both Arabidopsis (Knight et al. 1997) and yeast (Matsumoto et al. 2002). Although the molecular entities that encode the Ca2+ influx systems in plants are not known, the plasma membrane channels Cch1p and Mid1p of yeast are implicated in Na+ and Li+ tolerance and the salt induced cytosolic Ca2+ transient (Paidhungat and Garrett 1997; Matsumoto et al. 2002). However, expression of low cation affinity (LCT1) from wheat is able to complement loss of Ca2+ uptake of cch1D mid1D cells and restore viability of mid1D cells that are unable to recover from mating pheromone-induced cell cycle arrest (Clemens et al. 1998; Amtmann et al. 2001). Originally isolated as a suppressor of the trk1D trk2D K+ uptake system in yeast, LCT1 has been shown to be involved in the transport of Ca2+, Cd2+, K+, Li+, Mg2+, Na+, Rb+, and Zn2+ (Schachtman et al. 1997; Clemens et al. 1998; Amtmann et al. 2001). Expression of LCT1 in yeast cells that are deficient for ena1-4, increased Na+ sensitivity due to increased Na+ uptake and lower intracellular K+. This Na+ uptake was inhibited by K+, Cs+, high concentrations of Ca2+, and deletions in the N-terminal hydrophilic region of the protein suggesting this region may contain regulatory sequences responsible for LCT1 function (Amtmann et al. 2001). Ca2+ ATPase mRNA have been shown to increase in response to high NaCl treatments and in salt adapted cells, presumably due to high Ca2+ levels in the cytosol (Perez-Prat et al. 1992; Wimmers et al. 1992; Niu et al. 1995). Expression of Ca2+ transporters in yeast has facilitated the

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identification and characterization of many Ca2+ pumps (reviewed in Sze et al. 2000). Expression of ACA4 in a yeast strain deficient for endogenous Ca2+ ATPases (pmc1 pmr1) and calcineurin (cnb1) restored tolerance to low Ca2+, high Na+, K+ and mannitol in a manner similar to expression of yeast PMC1. This suppression is increased if the Nterminus that contains an autoinhibitory domain of ACA4 is removed. In plants, ACA4 mRNA is expressed throughout the plant but is predominately present in roots and expression increases as the concentration salt increases until a threshold of 200 mM NaCl. Like yeast Pmc1p, ACA4 protein is localized to the vacuole (Geisler et al. 2000). Together these results suggest a similar response to Ca2+ homeostasis following salt stress may exist in yeast and plants. The cytosolic Na+:K+ ratio is an important factor for plant salt tolerance (reviewed in Maathuis and Amtmann 1999; Schachtman and Liu 1999). In yeast cells, both calcineurin and Hal4p and Hal5p mediate the transition of the Trk1-Trk2 K+ uptake system from a low to high affinity for K+ over Na+ (Mendoza et al. 1994; Mulet et al. 1999; reviewed in Serrano et al. 1999b). Yeast mutants lacking trk1 trk2 have highly hyperpolarized membranes and cannot grow at low external K+ concentrations (Madrid et al. 1998). Functional complementation of trk1 trk2 yeast mutants have resulted in the isolation K+ transport proteins from plants that fall into three categories: (1) KAT/AKT-like inward rectifying K+ channels, (2) HKT1-like high affinity K+/Na+ transporters and (3) KUP or HAK-like K+ transporters (Maathuis and Amtmann 1999; Schachtman and Liu 1999; Serrano et al. 1999b). AKT1 and KAT1 were the first plant K+ transporters to be cloned by complementation of yeast mutants lacking a functional Trk1-Trk2 K+ uptake system (Anderson et al. 1992; Sentenac et al. 1992). Patch clamp analysis of yeast cells expressing KAT1 demonstrated that KAT1 is able to function like a strong inward rectifying channel that facilitates K+ transport into the cell (Bertl et al. 1995). Knockout mutations of akt1 and kat1 in Arabidopsis were isolated in T-DNA and transposon mutagenized populations of Arabidopsis screened by reverse genetics using PCR techniques (Hirsch et al. 1998; Szyroki et al. 2001). Analysis of the kat1::En-1 Arabidopsis plants revealed that stomatal movements of kat::En-1 mutants were indistinguishable from wild type plants despite the fact that inward rectifying K+ currents were reduced in kat::En-1 guard cell protoplasts. Although KAT1 mRNA is predominantly expressed in the stomata, mRNA for other K+ channels, including AKT1, were also present suggesting that multiple K+ channels may regulate stomatal conductance and function (Szyroki et al. 2001). Arabidopsis mutants lacking akt1 did not display inward rectifying K+ currents and

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Rb+ uptake was reduced, although outward currents were normal in this mutant line. Growth was reduced in akt1 mutant lines in less than 100 µM K+ in the presence of NH4+ and seedling could not fully emerge from seeds grown at 10 µM K+, suggesting that AKT1 along with another NH4+-sensitive K+ uptake system function together in K+ nutrition (Hirsch et al. 1998). Under K+ limiting conditions of 10 µM K+, growth of akt1 mutant lines was stimulated by low Na+ and K+ transport was dependent on an electrochemical potential gradient of Na+ or H+ suggesting that the alternative AKT1 K+ uptake system may be a Na+/K+ or H+/K+ symporter (Spalding et al. 1999). Since KUP/HAK transporters are inhibited by NH4+, these transport systems could mediate the NH4+-sensitive component of K+ uptake (Santa-Maria et al. 1997). The KUP/HAK proteins are highly conserved family of K+ transporters that also suppress the growth of trk1 trk2 mutants in low potassium (Quintero and Blatt 1997; Santa-Maria et al. 1997; Fu and Luan 1998). The HvHAK1 gene from barley is able to facilitate high affinity K+ uptake in yeast cells that is inhibited by ammonium and exhibited competitive inhibition by millimolar concentration of Na+. Although reminiscent of a K+/Na+ discrimination locus (Kna1) originally described in Triticum aestivum to confer salt tolerance, mapping of HvHAK1 and HvHAK2 homologs in Triticum revealed that both genes are different loci. HvHAK1 transcripts were exclusively expressed in roots and were higher in plants that were gown under K+ starvation conditions (Santa-Maria et al. 1997). In Arabidopsis, AtKUP is represented by a family of 13 genes that are differentially expressed in different tissues and under low K+ growth conditions (Kim et al. 1998). Facilitation of growth in low potassium by AtKUP1 is dependent on a C-terminal truncation of the protein suggesting plant specific posttranscriptional regulation of this protein may be necessary for proper function as Arabidopsis cell suspensions overexpressing AtKUP1 are able to mediate high affinity potassium uptake (Kim et al. 1998). K+ uptake of yeast cells transformed with AtKT2 (AtKUP2) and Cterminal truncated AtKUP1 transformed yeast cells demonstrated low or dual affinity K+ uptake unlike effects observed with HvHAK1 genes which are high affinity transporters, suggesting evolutionary divergence of this gene family (Quintero and Blatt 1997; Santa-Maria et al. 1997; Fu and Luan 1998). Arabidopsis mutants lacking trh1, display tiny root hairs that undergo growth arrest soon after their initiation at the epidermis. The TRH1 gene is approximately 66 percent similar to AtKUP1 and ATKUP2 and shares significant homology with other KUP/HAK genes. Preferentially expressed in early stages of seedling growth, TRH1 transcripts accumulate to higher levels in root tissues relative to shoots.

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Uptake of 86Rb+ in the trh1 mutant was 40 percent of that in wild-type Arabidopsis plants and expression of TRH1 cDNA in trk1 yeast cells facilitated growth at concentrations as low as 0.2 mM K+ suggesting TRH1 encodes a high affinity K+ transporter (Rigas et al. 2001). In the same yeast complementation screen that was used to isolate LCT1, HKT1 was also isolated and characterized as a wheat high affinity K+ transporter that could restore growth of trk1 trk2 yeast cells under low K+ (Maathuis and Sanders 1994; Schachtman and Schroeder 1994). Using two-electrode voltage clamp analysis on Xenopus oocytes, HKT1 was shown to mediate co-transport of K+ and Na+. Since HKT1mediated K+ uptake was inhibited at high Na+, a yeast screen was used to isolate mutated versions of the wheat HKT1 protein that allowed growth in the presence of 300 mM NaCl. Mutations conferring Na+ tolerance were found on the sixth hydrophobic domain. Expression of these mutant alleles in oocytes confirmed that mutated HKT1 proteins showed greater K+ uptake and less Na+ uptake (Rubio et al. 1995). Implementing this system to further analyze HKT1 structure/function, PCR mutagenized versions of HKT1 cDNA were expressed in trk1 trk2 yeast cell deficient for high affinity K+ uptake as well as Na+ efflux through the Na+ ATPase ena1-4, to isolate mutations that increased K+ uptake and/or reduced Na+ influx. Mutations in Asn-365 located in the hydrophilic loop domain reduced low affinity Na+ uptake, enhanced K+ permeability over Na+ and reduced the inhibition of high affinity K+ uptake at normally limiting Na+ (Rubio et al. 1999). Together these results demonstrate the usefulness of the yeast system in structure function experiments that may later contribute to improvement of crop plants. The Arabidopsis homolog, AtHKT1 is 41 percent identical and 63 percent similar to wheat HKT1. Unlike HKT1, AtHKT1 when expressed in Xenopus oocytes mediates an inward rectifying current dependent on the presence of external Na+ but not K+. Consistent with this data, expression of AtHKT1 in yeast cells lacking trk1 trk2 ena1-4 increased Na+ sensitivity and could not restore growth under low K+. However, expression of AtHKT1 in an E. coli mutant deficient for all three K+ uptake systems enhanced growth under low K+ (Uozumi et al. 2000). Complementation of the E. coli strain lacking K+ transporters was also reported for the Eucalyptus camaldulensis, EcHKT1 protein that mediates both K+ and Na+ inward rectifying currents in Xenopus oocytes (Fairbairn et al. 2000). Interestingly, two isoforms of rice OsHKT1 proteins with distinct ion transport specificity have been recently isolated (Horie et al. 2001). OsHKT1 is a Na+ transporter whereas OsHKT2 is a Na+- and K+-coupled transporter. This finding strongly suggests that (monocot) plants may

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have a complement of HKT1 proteins with different ion transport capacities working in concert to maintain the Na+ and K+ status of the plant. Using a genetic screen to isolate suppressors of the salt sensitivity of the sos3 mutation in Arabidopsis, multiple mutant alleles of the single copy gene AtHKT1 were found to mediate increased salt tolerance (Rus et al. 2001a). The sos3 plants accumulate more Na+ and retain less K+ under salt stress conditions (50 mM NaCl) and are unable to grow under K+ limiting (20 µM K+) conditions at low concentrations of Ca2+ (0.3 mM). Both Na+ sensitivity and K+ deficiency can be alleviated by increasing the Ca2+ concentration to 3 mM, indicating that Ca2+ enhanced the K+/Na+ selectivity (Liu and Zhu 1998). As predicted by two-electrode voltage clamp analysis of AtHKT1 in Xenopus oocytes and yeast complementation experiments, AtHKT1 facilitates Na+ influx into the cell (Uozumi et al. 2000). Accordingly, there is reduced Na+ accumulation in the sos3 athkt1 mutants compared to wild-type Arabidopsis plants. Interestingly, the K+ deficiency of the sos3 mutant is also suppressed by the hkt1 mutation presumably through the activation of AKT1 or another unidentified K+ transport system (Rus et al. 2001a). HKT proteins in both wheat and Arabidopsis are the ion transporters that share the highest sequence homology to the Trk1p proteins from yeast. In yeast trk1 trk2 mutant cells, ectopic K+ uptake is mediated by several low affinity transporters and is likely the result of a more negative membrane potential. Heterologous expression of other HAK-type K+ transporters can compensate for K+ uptake but do not change the membrane potential. However, expression of HKT1 produces a strong depolarization of the membrane and this function may constitute a novel role for HKT1 in plants (Madrid et al. 1998). Presumably, the hkt1 mutation results in membrane hyperpolarization that could facilitate K+ uptake, thereby suppressing the inability of sos3 plants to take up K+ at low external concentrations. 3. Plant Homologues of Halotolerance. Overexpression of genes that increase salt tolerance of yeast cells has led to the isolation of several halotolerance (HAL) genes in the Serrano lab. HAL2/MET22 functions in sulfate assimilation in yeast through dephosphorylation of 3’phosphoadenosine 5’-phosphate (PAP) and 3’-phosphoadenosine 5’phosphosulfate (PAPS) (Murguía et al. 1996). Since the reduction of PAPS to sulfite is required for methionine biosynthesis, hal2 yeast mutants are auxotrophic for methonine. Hal2p/Met22p protein is very sensitive to inhibition by Na+ and Li+ ions, which leads to the accumulation of PAP and PAPS to toxic levels that affect RNA processing (Murguía et al. 1996). Thus, Hal2p/Met22p is a primary metabolic target for

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Na+ and Li+ toxicity in yeast. The methionine auxotrophy of hal2/met22 yeast is complemented by a family of Arabidopsis HAL2-like phosphatases, AtSAL1, AtSAL2 and AtAHL (Cheong et al. 1996; Quintero et al. 1996; Murguía et al. 1996; Gil-Mascarell et al. 1999). In addition to the HAL2-like gene family in Arabidopsis, RHL1, the homologue from rice, complemented the yeast hal2/met22 mutation, implying that RHL1 has the same function in sulfur assimilation in yeast and plants (Peng and Verma 1995). AtSAL1 was originally isolated from a cDNA library derived from salt treated Arabidopsis thaliana roots by complementation of a yeast ena1-4 mutant (Quintero et al. 1996). AtSAL1 not only counteracted the inhibition of Hal2p by salt but also increased the expression of ENA1, presumably through enhanced inositol-phosphate signaling. Indeed, AtSAL1 and AtSAL2 exhibit inositol polyphosphate I phosphatase activity (Quintero et al. 1996; Gil-Mascarell et al. 1999). In contrast, no inositol phosphatase activity was detected by AtAHL, in which PAP is the preferred substrate (Gil-Mascarell et al. 1999). Moreover, AtAHL was the most sensitive of the three proteins to inhibition by Na+, suggesting that AtAHL is therefore a target for salt toxicity in plants (Gil-Mascarell et al. 1999). Whereas plants do not use PAPS for the sulfate reduction pathway, PAPS is used as a donor of activated sulfate in sulfation to produce PAP, which is toxic to RNA processing enzymes, suggesting the HAL2-like Arabidopsis genes may function in the biosynthesis of sulfate conjugates and RNA processing in plants (Gil-Mascarell et al. 1999). HAL3 confers salt and lithium tolerance to yeast by increasing K+ and decreasing Na+ and Li+ in the cytoplasm, through the inhibition of Ppz1p, a protein phosphatase that is a negative regulator of ENA1 expression (Ferrando et al. 1995; De Nadal et al. 1998). Using the Hal3p sequence in a sequence similarity search of Arabidopsis EST database resulted in a cDNA that was used as a probe to isolate two clones, AtHAL3a and AtHAL3b from an Arabidopsis genomic library (EspinosaRuiz et al. 1999). Purified AtHAL3a protein is yellow in color and has an absorption spectrum characteristic of flavin (riboflavin). AtHAL3a and AtHAL3b mRNA are abundant in flowers, siliques and roots but only AtHAL3a mRNA is expressed in seeds. AtHAL3a mRNA is localized to cotyledons and hypocotyls of mature seeds and is mainly associated with phloem in the vascular tissue. AtHAL3a is able to suppress the Li+ sensitivity of a yeast hal3 mutant but not the Na+ sensitive phenotype. However, in plants both AtHAL3a and AtHAL3b mRNA are induced by salt and overexpression of AtHAL3 in Arabidopsis results in an increased salt and osmotic tolerance compared to wild type and plants expressing antisense AtHAL3a (Espinosa-Ruiz et al. 1999).

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4. Plant Homologues of Other Yeast Determinants. Vacuolar compartmentation of Na+ in plants grown under high salinity averts the deleterious effects of Na+ in the cytosol and allows Na+ to be used as a readily available osmoticum for many salt tolerance plant species. Sequestration of Na+ into the vacuole requires a proton gradient generated by the vacuolar H+ ATPases that facilitates the exchange of Na+ for H+ resulting in alkalization of the vacuole by Na+ (Blumwald et al. 2000; Hasegawa et al. 2000b). In yeast, acidification of the cytosol is achieved by a single amino acid substitution mutation of the plasma membrane H+ ATPase, pma1-a4 and results in increased activity of Nhx1p, a Na+/H+ antiporter, that also functions to increase Na+ sequestration into the vacuole (Nass et al. 1997; Nass and Rao 1998). The role of Nhx1p is also thought to be involved in the regulation of vacuolar volume in acute response to hyperosmotic stress by contributing to the vacuolar osmoticum until acquired osmotolerance is achieved through the increased production of glycerol (Nass and Rao 1999). In addition, Nhx1p is required for protein trafficking out of the prevacuolar compartment (Bowers et al. 2000). Three acidic residues that are conserved in Nhx1p homologs from many diverse species, including AtNHX1 from Arabidopsis, are required for this function (Bowers et al. 2000). Sequence homology searches with Nhx1p from yeast revealed an Arabidopsis homolog, AtNHX1, that localizes to the vacuole of the yeast cells and is capable of complementing the salt and hygromycin B sensitivity of a yeast mutant lacking nhx1 (Gaxiola et al. 1999; Quintero et al. 2000). Salt tolerance is conferred by AtNHX1 through Na+ ion sequestration by an electroneutral Na+/H+ exchange that is functionally coupled to the H+ gradient in the vacuole (Darley et al. 2000; Quintero et al. 2000). Overexpression of AtNHX1 in Arabidopsis plants resulted in higher Na+/H+ exchange rates in vacuoles and enhanced salt tolerance (Apse et al. 1999; reviewed in Frommer et al. 1999). AtNHX1 mRNA is more abundant in Arabidopsis shoots and can be induced by ABA, NaCl, KCl, or sorbitol treatments suggesting AtNHX1 may be regulated by hyperosmotic stress or dessication (Gaxiola et al. 1999; Quintero et al. 2000; Yokoi, S., F. J. Quintero, R. A. Bressan, J. M. Pardo and P. M. Hasegawa, unpublished results). Isolation of five additional genes encoding proteins with significant sequence identity to AtNHX1 revealed that each of the AtNHX transcripts varies in abundance and is differentially expressed in shoots and roots. Treatment of Arabidopsis plants with NaCl, LiCl, sorbitol, or ABA results in distinctive mRNA expression patterns for each family member. Gene upregulation is ABA-dependent but independent of the SOS pathway. Moreover, expression of AtNHX isoforms gave different degrees of suppression of the Li+ and Na+ sensitivity of a yeast mutant, suggesting each family member may have a

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unique role mediating ion homeostasis in response to different environmental stresses (Yokoi, S., F. J. Quintero, R. A. Bressan, J. M. Pardo and P. M. Hasegawa, unpublished results). While Na+/H+ antiporters are thought to function in the sequestration of Na+ in the vacuoles of leaves in Arabidopsis plants, the Na+/H+ antiporter in the halophyte Mesembryanthemum crystallinum is thought to work in concert with the Na+/myo-inositol symporter to remove Na+ ions from the vacuoles of the roots and import them into the leaves where the Na+ is then sequestered into the vacuole via the Na+/H+ antiporter. The Na+/myo-inositol symporter MITR1 is able to complement the growth of yeast mutant ino1 itr1 defective in inositol synthesis and transport under conditions of low myo-inositol (Chauhan et al. 2000). AtGSK1 was isolated by suppression of the salt sensitivity of a yeast mutant lacking functional calcineurin (cna1 cna2) owing to its capacity for restoring expression of ENA1. AtGSK1 is member of a multigene family in Arabidopsis that has significant sequence homology to GSK3/ shaggy-like kinases in mammals, Drosophila melanogaster and yeast. Knockout of MCK1, one of the two GSK3/shaggy-like protein kinases genes in yeast, resulted in a salt sensitive phenotype. Presumably, Mck1p functions in a signal pathway separate from calcineurin in the activation of ENA1. The salt sensitivity the mck1 mutant strain is alleviated by the expression of AtGSK1, suggesting that AtGSK1 is an Arabidopsis functional counterpart of Mck1p. In plants, AtGSK1 mRNA is specifically induced both by NaCl and ABA treatments suggesting it may play a role in salt tolerance (Piao et al. 1999). The Arabidopsis RCI2A and RCI2B genes were isolated by means of differential display screening for mRNAs responsive to ABA. RCI2A mRNA is 50 to 100 times more abundant than RCI2B, is accumulated in response to low temperature or salt stress, and is transiently induced in plants treated with ABA or dessicated. RCI2 proteins are highly hydrophobic and contain two putative membrane regions in their amino acid sequence. Homologous proteins to RCI2 are found in diverse organisms including bacteria, fungi, C. elegans and higher plants. The yeast homolog Sna1p/Pmp3p is a putative regulator of the plasma membrane potential (Navarre and Goffeau 2000; Nylander et al. 2001). A yeast strain harboring a pmp3 mutation is sensitive to NaCl and hygromycin B, but not to osmotic stress or Li+ ions. These sensitivities are partially suppressed by the expression of RCI2A (Navarre and Goffeau 2000; Nylander et al. 2001). In addition, a pmp3 mutation is able to restore the growth of trk1D trk2D in low K+ and low pH, which together with its sensitivity to hygromycin B and enhanced Na+ uptake, suggest that a deletion of pmp3 causes hyperpolarization of the membrane enhancing cation flux into the cell. Interestingly, both Na+ and K+ uptake can be suppressed by the

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addition of Ca2+ or, to some extent, by Mg2+. Thus, Pmp3p is an important determinant in the regulation of membrane potential which may modulate salt tolerance in yeast and plants (Navarre and Goffeau 2000). B. Yeast Genes That Confer Salt Tolerance to Plants Experimental results showing that transgenic plants ectopically expressing genes of heterologous organisms, including yeast, exhibit increased stress tolerance is indicative that certain pivotal cellular processes that impact salt tolerance are ubiquitous among organisms (Bressan et al. 1998; Holmberg and Bülow 1998; Pardo et al. 1998; Serrano et al. 1999b; Hasegawa et al. 2000a). Two halotolerance determinants of yeast have been shown to function in plants, namely calcineurin and Hal1p. 1. Yeast Calcineurin Confers Salt Tolerance to Plants. Constitutive activation of calcineurin by a C-terminal deletion at T459 that removes the CAM-binding and autoinhibitory domains of the catalytic subunit Cna2p dramatically increased the salt tolerance of wild type yeast cells (Mendoza et al. 1996). Activated calcineurin was reconstituted in tobacco plant cells by coexpressing a truncated catalytic Cna2p subunit and the regulatory Cnb1p subunit. Evaluation of several different transgenic lines demonstrated that increased salt tolerance genetically segregated with the inheritance of the calcineurin transgenes (Pardo et al. 1998). Enhanced capacity of transgenic plants to survive salt shock was demonstrated when the evaluation was conducted on seedlings in tissue culture raft vessels or on actively transpiring plants in hydroponic culture. In addition, reciprocal grafting of control and transgenic shoots and roots suggested that the effect of calcineurin is mainly in salt uptake, as plants transformed with calcineurin were more tolerant when used as the rootstock compared to their use as scions (Pardo et al. 1998). These results implicate the presence of a calcineurin activated signaling cascade in plants that modulates salt adaptation. 2. HAL1 Confers Salt Tolerance to Melon and Tomato. The yeast HAL1 gene encodes a soluble cytoplasmic protein that does not have significant amino acid identity to other proteins but is a proven regulator of ion homeostasis in yeast. Hal1p modulates salt tolerance by increasing the K+/Na+ ratio in cells by reducing K+ loss through an unknown K+ efflux system. This is accomplished through transcriptional induction of ENA1, the gene encoding the major for Na+-ATPase responsible for Na+ exclusion (Gaxiola et al 1992; Rios et al. 1997). Melon plants transformed with the HAL1 gene were able to form roots in the presence of 10g/l of NaCl, conditions that are inhibitory to rooting in control plants

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(Bordás et al. 1997). Evaluation of HAL1 transgenic tomato plants revealed that salt tolerance in HAL1 expressing plants was the result of higher K+ and lower Na+ accumulation under salinity stress just as was found in yeast (Gisbert et al. 2000). Fruit yield, weight, and number were reduced less in HAL1 expressing tomato plants than control plants grown under continuous saline irrigation (Rus et al. 2001b).

IV. SIMILARITY OF CELLULAR SALT TOLERANCE IN PLANTS AND YEAST It is well known that differentiated tissue and organs of plants respond differently to salt stress and that tolerance of whole plants is achieved by a co-ordination of these responses (Greenway and Munns 1980; Bohnert et al. 1995; Volkmar et al. 1998). Movement of ions, from the soil solution, radially through the root and into the xylem is highly regulated by the functions of epidermal, cortical and endodermal cells. In the shoot, ions are often accumulated to higher levels in older leaves rather than in rapidly expanding leaves or meristematic regions. Particular halophytes possess unique leaf glands or bladders into which ions are accumulated, effectively partitioning them away from the cytosolic milieu. Higher plants also utilize intrinsic cellular-based mechanisms for salt tolerance, principally those required for osmotic adjustment and ion homeostasis (Niu et al. 1995). Now, it seems likely that intrinsically cellular-based tolerance mechanisms are rudimentary components of salt adaptation in all plants, and many probably represent evolutionary paradigms descending from archetypical organisms (Hasegawa et al. 1990; Serrano and Gaxiola 1994; Niu et al. 1995; Bressan et al. 1998, Serrano et al. 1999a). The principal difference between intolerant and salt tolerant plants may be their state of potentiation for adaptation in response to stress imposition, and/or more effective forms of tolerance determinants. Furthermore, the conservation in plants and yeast of many salt tolerance determinants that modulate ion homeostasis, including those involved in salt stress signal transduction, is becoming more apparent (Haro et al. 1993; Serrano and Gaxiola 1994; Pardo et al. 1996; Bressan et al. 1998; Hasegawa et al. 2000b). As in yeast, signal transduction cascades involving Ca2+ and MAP-kinase (MAPK) pathways have been implicated in the plant response to osmotic stress (Sheen 1996; Urao et al. 2000). Plant Ca2+-dependent protein kinases (CDPK) activated a stress and abscisic acid (ABA) inducible HVA promoter, independent of an external signal (Sheen 1996). ABA is considered to be a pivotal intermediate in some osmotic stress signal transduction pathways (Shinozaki and Yamaguchi-Shinozaki 1997). The SOS3 protein is a Ca2+-binding

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protein with sequence similarity to the regulatory subunit of calcineurin and has been shown to act as an intermediate in plant salt stress signaling that mediates ion homeostasis and salt tolerance (Bressan et al. 1998; Hasegawa et al. 2000b; Sanders; 2000; Zhu 2000, 2001). Discovery of key determinants that can mediate salt tolerance in commercial crop plants is a requisite for the application of biotechnology, and the aforementioned integrates the current scientific knowledge of the topic area and the new technologies that have become available to agriculturists in the last decade. Crop improvement for salt tolerance has been substantially limited because it has been too difficult to link the phenotype to individual functional determinants that are easily introgressed by plant breeders. The efforts have been complicated also by the fact that salt tolerance is viewed by plant breeders and agronomists in terms of yield stability, thereby requiring the integration of both yield as well as tolerance traits. The new molecular genetic approaches will facilitate the identification of salt tolerance determinants by gain- or lossof-function genetics, and these will be resources for dissection of physiological processes that are critically involved in salt tolerance. Subsequently, both molecular and physiological markers can be used for selection of salt tolerant plants.

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Subject Index Volume 22 A Alexander, Denton, E. (biography), 1–7 Arachis, see Peanut breeding

Molecular biology, salt resistance, 389–425 O

B Beet (table) breeding, 357–388 Beta, see Beet breeding Biography, Alexander, D.E., 1–7 Breeding: beet (table), 357–388 heritability, 9–111 oil palm, 165–219 peanut, 297–356

Oilseed breeding, oil palm, 165–219 P Peanut breeding, 297–356 Q Quantitative genetics: heritability, 9–111 variance, 113–163

G Genetics: beet, 357–376 salt resistance,389–425 Germplasm, peanut, 297–356 Grain breeding: maize, 3–4 wheat, 221–297 H Heritability estimation, 9–111 I Insect and mite resistance, wheat, 221–297

S Salt resistance, yeast systems, 389–425 Statistic, advanced methods, 113–163 V Variance estimation, 113–163 Vegetable breeding: beet (table), 357–388 peanut, 297–356 W Wheat, insect resistance, 221–297 Y

M

Yeast, salt resistance, 389–425

Maize, high oil, 3–4

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Cumulative Subject Index (Volumes 1–22)

A Adaptation: blueberry, rabbiteye, 5:351–352 durum wheat, 5:29–31 genetics, 3:21–167 testing, 12:271–297 Alfalfa: honeycomb breeding, 18:230–232 inbreeding, 13:209–233 in vitro culture, 2:229–234 somaclonal variation, 4:123–152 unreduced gametes, 3:277 Alexander, Denton, E. (biography), 22:1–7 Allard, Robert W. (biography), 12:1–17 Allium cepa, see Onion Almond: breeding self-compatible, 8:313–338 transformation, 16:103 Alstroemaria, mutation breeding, 6:75 Amaranth: genetic resources, 19:227–285 breeding, 19:227–285 Aneuploidy: alfalfa, 10:175–176 alfalfa tissue culture, 4:128–130 petunia, 1:19–21 wheat, 10:5–9 Anther culture: cereals, 15:141–186 maize, 11:199–224 Anthocyanin pigmentation, maize aleurone, 8:91–137 Antifungal proteins, 14:39–88

Antimetabolite resistance, cell selection, 4:139–141, 159–160 Apple: genetics, 9:333–366 rootstocks, 1:294–394 Apple transformation, 16:101–102 Apomixis: breeding, 18:13–86 genetics, 18:13–86 reproductive barriers, 11:92–96 rice, 17:114–116 Apricot transformation, 16:102 Arachis, see Peanut breeding in vitro culture, 2:218–224 Artichoke breeding, 12:253–269 Avena sativa, see Oat Azalea, mutation breeding, 6:75–76 B Bacillus thuringensis, 12:19–45 Bacterial diseases: apple rootstocks, 1:362–365 cell selection, 4:163–164 cowpea, 15:238–239 potato, 19:113–122 raspberry, 6:281–282 soybean, 1:209–212 sweet potato, 4:333–336 transformation fruit crops, 16:110 Banana: breeding, 2:135–155 transformation, 16:105–106 Barley: anther culture, 15:141–186

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CUMULATIVE SUBJECT INDEX breeding methods, 5:95–138 diversity, 21:234–235 doubled haploid breeding, 15:141–186 gametoclonal variation, 5:368–370 haploids in breeding, 3:219–252 molelcular markers, 21:181–220 photoperiodic response, 3:74, 89–92, 99 vernalization, 3:109 Bean (Phaseolus): breeding, 1:59–102 breeding mixtures, 4:245–272 breeding (tropics), 10:199–269 heat tolerance, 10:149 in vitro culture, 2:234–237 photoperiodic response, 3:71–73, 86–92, 16:102–109 protein, 1:59–102 Beet (table) breeding, 22:357–388 Beta, see Beet breeding Biochemical markers, 9:37–61 Biography: Alexander, Denton E., 22:1–7 Allard, Robert W., 12:1–17 Bringhurst, Royce S., 9:1–8 Burton, Glenn W., 3:1–19 Downey, Richard K., 18:1–12 Draper, Arlen D., 13:1–10 Duvick, Donald N., 14:1–11 Gabelman, Warren H., 6:1–9 Hallauer, Arnel R., 15:1–17 Harlan, Jack R., 8:1–17 Jones, Henry A., 1:1–10 Laughnan, John R. 19:1–14 Munger, Henry M., 4:1–8 Ryder, Edward J., 16:1–14 Sears, Ernest Robert, 10:1–2 Simmonds, Norman W., 20:1–13 Sprague, George F., 2:1–11 Vuylsteke, Dirk R., 21:1–25 Vogel, Orville A., 5:1–10 Weinberger, John H., 11:1–10 Yuan, Longping, 17:1–13 Birdsfoot trefoil, tissue culture, 2:228–229 Blackberry, 8:249–312 mutation breeding, 6:79 Black walnut, 1:236–266 Blueberry: breeding, 13:1–10 rabbiteye, 5:307–357 Brachiaria, apomixis, 18:36–39, 49–51

429 Bramble transformation, 16:105 Brassica, see Cole crops Brassicaceae: incompatibility, 15:23–27 molecular mapping, 14:19–23 Brassica: napus, see Canola, Rutabaga rapa, see Canola Breeding: alfalfa via tissue culture, 4:123–152 almond, 8:313–338 amaranth, 19:227–285 apple, 9:333–366 apple rootstocks, 1:294–394 apomixis, 18:13–86 banana, 2:135–155 barley, 3:219–252; 5:95–138 bean, 1:59–102; 4:245–272 beet (table), 22:357–388 biochemical markers, 9:37–61 blackberry, 8:249–312 black walnut, 1:236–266 blueberry, rabbiteye, 5:307–357 cactus, 20:135–166 carbon isotope discrimination, 12:81–113 carrot, 19:157–190 cassava, 2:73–134 cell selection, 4:153–173 chestnut, 4:347–397 chimeras, 15:43–84 chrysanthemum, 14:321–361 citrus, 8:339–374 coffee, 2:157–193 coleus, 3:343–360 competitive ability, 14:89–138 cowpea, 15:215–274 cucumber, 6:323–359 diallel analysis, 9:9–36 doubled haploids, 15:141–186 durum wheat, 5:11–40 epistasis, 21:27–92 exotic maize, 14:165–187 fescue, 3:313–342 forest tree, 8:139–188 gene action 15:315–374 genotype x environment interaction, 16:135–178 grapefruit, 13:345–363 grasses, 11:251–274 guayule, 6:93–165 heat tolerance, 10:124–168

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430 Breeding (cont.) herbicide-resistant crops, 11:155–198 heritability, 22:9–111 heterosis, 12:227–251 homeotic floral mutants, 9:63–99 honeycomb, 13:87–139; 18:177–249 hybrid, 17:225–257 hybrid wheat, 2:303–319; 3:169–191 induced mutations, 2:13–72 insect and mite resistance in cucurbits, 10:199–269 isozymes, 6:11–54 lettuce, 16:1–14; 20:105–133 maize, 1:103–138, 139–161; 4:81–122; 9:181–216; 11:199–224; 14:139–163, 165–187, 189–236 molecular markers, 9:37–61 mosaics, 15:43–84 mushroom, 8:189–215 negatively associated traits, 13:141–177 oat, 6:167–207 oil palm, 4:175–201; 22:165–219 onion, 20:67–103 pasture legumes, 5:237–305 pea, snap, 212:93–138 peanut, 22:297–356 pearl millet, 1:162–182 perennial rye, 13:265–292 persimmon, 19:191–225 plantain, 2:150–151; 14:267–320; 21:211–25 potato, 3:274–277; 9:217–332; 16:15–86, 19:59–155 proteins in maize, 9:181–216 quality protein maize (QPM), 9:181–216 raspberry, 6:245–321 recurrent restricted phenotypic selection, 9:101–113 recurrent selection in maize, 9:115–179; 14:139–163 rice, 17:15–156 rose, 17:159–189 rutabaga, 8:217–248 sesame, 16:179–228 snap pea, 21:93–138 somatic hybridization, 20:167–225 soybean, 1:183–235; 3:289–311; 4:203–243; 21:212–307 soybean hybrids, 21:212–307 soybean nodulation, 11:275–318

CUMULATIVE SUBJECT INDEX soybean recurrent selection, 15:275–313 spelt, 15:187–213 statistics, 17:296–300 strawberry, 2:195–214 sugar cane, 16:272–273 supersweet sweet corn, 14:189–236 sweet cherry, 9:367–388 sweet corn, 1:139–161; 14:189–236 sweet potato, 4:313–345 tomato, 4:273–311 triticale, 5:41–93; 8:43–90 Vigna, 8:19–42 virus resistance, 12:47–79 wheat, 2:303–319; 3:169–191; 5:11–40; 11:225–234; 13:293–343 wheat for rust resistance, 13:293–343 white clover, 17:191–223 wild rice, 14:237–265 Bringhurst, Royce S. (biography), 9:1–8 Broadbean, in vitro culture, 2:244–245 Burton, Glenn W. (biography), 3:1–19 C Cactus: breeding, 135–166 domestication, 135–166 Cajanus, in vitro culture, 2:224 Canola, R.K. Downey, designer, 18:1–12 Carbohydrates, 1:144–148 Carbon isotope discrimination, 12:81–113 Carnation, mutation breeding, 6:73–74 Carrot breeding, 19: 157–190 Cassava, 2:73–134 Castanea, see Chestnut Cell selection, 4:139–145, 153–173 Cereal breeding, see Grain breeding Cereal diversity, 21:221–261 Cherry, see Sweet cherry transformation, 16:102 Chestnut breeding, 4:347–397 Chickpea, in vitro culture, 2:224–225 Chimeras and mosaics, 15:43–84 Chinese cabbage, heat tolerance, 10:152 Chromosome, petunia, 1:13–21, 31–33 Chrysanthemum: breeding, 14:321–361 mutation breeding, 6:74 Cicer, see Chickpea Citrus, protoplast fusion, 8:339–374

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CUMULATIVE SUBJECT INDEX Clonal repositories, see National Clonal Germplasm Repository Clover: in vitro culture, 2:240–244 molecular genetics, 17:191–223 Coffea arabica, see Coffee Coffee, 2:157–193 Cold hardiness: breeding nectarines and peaches, 10:271–308 wheat adaptation, 12:124–135 Cole crops: Chinese cabbage, heat tolerance, 10:152 gametoclonal variation, 5:371–372 rutabaga, 8:217–248 Coleus, 3:343–360 Competition, 13:158–165 Competitive ability breeding, 14:89–138 Controlling elements, see Transposable elements Corn, see Maize; Sweet corn Cotton, heat tolerance 10:151 Cowpea: breeding, 15:215–274 heat tolerance, 10:147–149 in vitro culture, 2:245–246 photoperiodic response, 3:99 Cybrids. 3:205–210; 20: 206–209 Cryopreservation, 7:125–126,148–151,167 buds, 7:168–169 genetic stability, 7:125–126 meristems, 7:168–169 pollen, 7:171–172 seed, 7:148–151,168 Cucumber, breeding, 6:323–359 Cucumis sativa, see cucumber Cucurbitaceae, insect and mite resistance, 10:309–360 Cytogenetics: alfalfa, 10:171–184 blueberry, 5:325–326 cassava, 2:94 citrus, 8:366–370 coleus, 3:347–348 durum wheat, 5:12–14 fescue, 3:316–319 Glycine, 16:288–317 guayule, 6:99–103 maize mobile elements, 4:81–122 maize-tripsacum hybrids, 20:15–66

431 oat, 6:173–174 pearl millet, 1:167 perennial rye, 13:265–292 petunia, 1:13–21, 31–32 rose, 17:169–171 rye, 13:265–292 Saccharum complex, 16:273–275 sesame, 16:185–189 triticale, 5:41–93; 8:54 wheat, 5:12–14; 10:5–15; 11:225–234 Cytoplasm: cybrids, 3:205–210; 20:206–209 molecular biology of male sterility, 10:23–51 organelles, 2:283–302; 6:361–393 pearl millet, 1:166 petunia, 1:43–45 wheat, 2:308–319 D Dahlia, mutation breeding, 6:75 Daucus, see Carrot Diallel cross, 9:9–36 Diospyros, see Persimmon Disease and pest resistance: antifungal proteins, 14:39–88 apple rootstocks, 1:358–373 banana, 2:143–147 blackberry, 8:291–295 black walnut, 1:251 blueberry, rabbiteye, 5:348–350 cassava, 2:105–114 cell selection, 4:143–145, 163–165 citrus, 8:347–349 coffee, 2:176–181 coleus, 3:353 cowpea, 15:237–247 durum wheat, 5:23–28 fescue, 3:334–336 herbicide-resistance, 11:155–198 host-parasite genetics, 5:393–433 induced mutants, 2:25–30 lettuce, 1:286–287 potato, 9:264–285, 19:69–155 raspberry, 6:245–321 rutabaga, 8:236–240 soybean, 1:183–235 spelt, 15:195–198 strawberry, 2:195–214 virus resistance, 12:47–79 wheat rust, 13:293–343

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432 Diversity in land races, 21:221–261 DNA methylation, 18:87–176 Doubled haploid breeding, 15:141–186 Downey, Richard K. (biography), 18:1–12 Draper, Arlen D. (biography), 13:1–10 Drought resistance: durum wheat, 5:30–31 soybean breeding, 4:203–243 wheat adaptation, 12:135–146 Durum wheat, 5:11–40 Duvick, Donald N. (biography), 14:1–11 E Elaeis, see Oil palm Embryo culture: in crop improvement, 5:181–236 oil palm, 4:186–187 pasture legume hybrids, 5:249–275 Endosperm: maize, 1:139–161 sweet corn, 1:139–161 Endothia parasitica, 4:355–357 Epistasis, 21:27–92. Evolution: coffee, 2:157–193 grapefruit, 13:345–363 maize, 20:15–66 sesame, 16:189 Exploration, 7:9–11, 26–28, 67–94 F Fabaceae, molecular mapping, 14:24–25 Fescue, 3:313–342 Festuca, see Fescue Floral biology: almond, 8:314–320 blackberry, 8:267–269 black walnut, 1:238–244 cassava, 2:78–82 chestnut, 4:352–353 coffee, 2:163–164 coleus, 3:348–349 fescue, 3:315–316 guayule, 6:103–105 homeotic mutants, 9:63–99 induced mutants, 2:46–50 pearl millet, 1:165–166 pistil in reproduction, 4:9–79 pollen in reproduction, 4:9–79 reproductive barriers, 11:11–154

CUMULATIVE SUBJECT INDEX rutabaga, 8:222–226 sesame, 16:184–185 sweet potato, 4:323–325 Forage breeding: alfalfa inbreeding, 13:209–233 diversity, 21:221–261 fescue, 3:313–342 perennials, 11:251–274 white clover, 17:191–223 Forest crop breeding: black walnut, 1:236–266 chestnut, 4:347–397 ideotype concept, 12:177–187 molecular markers, 19:31–68 quantitative genetics, 8:139–188 Fragaria, see Strawberry Fruit, nut, and beverage crop breeding: almond, 8:313–338 apple, 9:333–366 apple rootstocks, 1:294–394 banana, 2:135–155 blackberry, 8:249–312 blueberry, 13:1–10 blueberry, rabbiteye, 5:307–357 cactus, 20:135–166 cherry, 9:367–388 citrus, 8:339–374 coffee, 2:157–193 ideotype concept, 12:175–177 genetic transformation, 16:87–134 grapefruit, 13:345–363 mutation breeding, 6:78–79 nectarine (cold hardy), 10:271–308 peach (cold hardy), 10:271–308 persimmon, 19:191–225 plantain, 2:135–155 raspberry, 6:245–321 strawberry, 2:195–214 sweet cherry, 9:367–388 Fungal diseases: apple rootstocks, 1:365–368 banana and plantain, 2:143–145, 147 cassava, 2:110–114 cell selection, 4:163–165 chestnut, 4:355–397 coffee, 2:176–179 cowpea, 15:237–238 durum wheat, 5:23–27 host-parasite genetics, 5:393–433 lettuce, 1:286–287 potato, 19:69–155 raspberry, 6:245–281

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CUMULATIVE SUBJECT INDEX soybean, 1:188–209 spelt, 15:196–198 strawberry, 2:195–214 sweet potato, 4:333–336 transformation, fruit crops, 16:111–112 wheat rust, 13:293–343 G Gabelman, Warren H. (biography), 6:1–9 Gametes: almond, self compatibility, 7:322–330 blackberry, 7:249–312 competition, 11:42–46 forest trees, 7:139–188 maize aleurone, 7:91–137 maize anthocynanin, 7:91–137 mushroom, 7:189–216 polyploid, 3:253–288 rutabaga, 7:217–248 transposable elements, 7:91–137 unreduced, 3:253–288 Gametoclonal variation, 5:359–391 barley, 5:368–370 brassica, 5:371–372 potato, 5:376–377 rice, 5:362–364 rye, 5:370–371 tobacco, 5:372–376 wheat, 5:364–368 Garlic, mutation breeding, 6:81 Genes: action, 15:315–374 apple, 9:337–356 Bacillus thuringensis, 12:19–45 incompatibility, 15:19–42 incompatibility in sweet cherry, 9:367–388 induced mutants, 2:13–71 lettuce, 1:267–293 maize endosperm, 1:142–144 maize protein, 1:110–120, 148–149 petunia, 1:21–30 quality protein in maize, 9:183–184 rye perenniality, 13:261–288 soybean, 1:183–235 soybean nodulation, 11:275–318 sweet corn, 1:142–144 wheat rust resistance, 13:293–343 Genetic engineering: bean, 1:89–91 DNA methylation, 18:87–176

433 fruit crops, 16:87–134 host-parasite genetics, 5:415–428 maize mobile elements, 4:81–122 salt resistance, 22:389–425 transformation by particle bombardment, 13:231–260 virus resistance, 12:47–79 Genetic load and lethal equivalents, 10:93–127 Genetics: adaptation, 3:21–167 almond, self compatibility, 8:322–330 amaranth, 19:243–248 Amaranthus, see Amaranth apple, 9:333–366 apomixis, 18:13–86 Bacillus thuringensis, 12:19–45 bean seed protein, 1:59–102 beet, 22:357–376 blackberry, 8:249–312 black walnut, 1:247–251 blueberry, 13:1–10 blueberry, rabbiteye, 5:323–325 carrot, 19:164–171 chestnut blight, 4:357–389 chimeras, 15:43–84 chrysanthemums, 14:321 clover, white, 17:191–223 coffee, 2:165–170 coleus, 3:3–53 cowpea, 15:215–274 DNA methylation, 18:87–176 durum wheat, 5:11–40 forest trees, 8:139–188 fruit crop transformation, 16:87–134 gene action, 15:315–374 herbicide resistance, 11:155–198 host-parasite, 5:393–433 incompatibility, 15:19–42 incompatibility in sweet cherry, 9:367–388 induced mutants, 2:51–54 insect and mite resistance in Cucurbitaceae, 10:309–360 isozymes, 6:11–54 lettuce, 1:267–293 maize aleurone, 8:91–137 maize anther culture, 11:199–224 maize anthocynanin, 8:91–137 maize endosperm, 1:142–144 maize male sterility, 10:23–51 maize mobile elements, 4:81–122

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434 Genetics (cont.) maize mutation, 5:139–180 maize seed protein, 1:110–120, 148–149 male sterility, maize, 10:23–51 mapping, 14:13–37 maturity, 3:21–167 markers to manage germplasm, 13:11–86 metabolism and heterosis, 10:53–59 molecular mapping, 14:13–37 mosaics, 15:43–84 mushroom, 8:189–216 oat, 6:168–174 organelle transfer, 6:361–393 overdominance, 17:225–257 pea, 21:110–120 pearl millet, 1:166, 172–180 perennial rye, 13:261–288 petunia, 1:1–58 photoperiod, 3:21–167 plantain, 14:264–320 potato disease resistance, 19:69–165 potato ploidy manipulation, 3:274–277; 16:15–86 quality protein in maize, 9:183–184 quantitative trait loci, 15:85–139 reproductive barriers, 11:11–154 rice, hybrid, 17:15–156 rose, 17:171–172 rutabaga, 8:217–248 salt resistance, 22:389–425 snap pea, 21:110–120 sesame, 16:189–195 soybean, 1:183–235 soybean nodulation, 11:275–318 spelt, 15:187–213 supersweet sweet corn, 14:189–236 sweet corn, 1:139–161; 14:189–236 sweet potato, 4:327–330 temperature, 3:21–167 tomato fruit quality, 4:273–311 transposable elements, 8:91–137 triticale, 5:41–93 virus resistance, 12:47–79 wheat gene manipulation, 11:225–234 wheat male sterility, 2:307–308 wheat molecular biology, 11:235–250 wheat rust, 13:293–343 white clover, 17:191–223 yield, 3:21–167

CUMULATIVE SUBJECT INDEX Genome: Glycine, 16:289–317 Poaceae, 16:276–281 Genotype x environment, interaction, 16:135–178 Germplasm, see also National Clonal Germplasm Repositories; National Plant Germplasm System acquisition and collection, 7:160–161 apple rootstocks, 1:296–299 banana, 2:140–141 blackberry, 8:265–267 black walnut, 1:244–247 cactus, 20:141–145 cassava, 2:83–94, 117–119 chestnut, 4:351–352 coffee, 2:165–172 distribution, 7:161–164 enhancement, 7:98–202 evaluation, 7:183–198 exploration and introduction, 7:9–18,64–94 genetic markers, 13:11–86 guayule, 6:112–125 isozyme, 6:18–21 maintenance and storage, 7:95–110,111–128,129–158,159–18 2; 13:11–86 maize, 14:165–187 management, 13:11–86 oat, 6:174–176 peanut, 22:297–356 pearl millet, 1:167–170 plantain, 14:267–320 potato, 9:219–223 preservation by tissue culture, 2:265–282 rutabaga, 8:226–227 sesame, 16:201–204 spelt, 15:204–205 sweet potato, 4:320–323 triticale, 8:55–61 wheat, 2:307–313 Gesneriaceae, mutation breeding, 6:73 Gladiolus, mutation breeding, 6:77 Glycine, genomes, 16:289–317 Glycine max, see Soybean Grain breeding: amaranth, 19:227–285 barley, 3:219–252, 5:95–138 diversity, 21:221–261

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CUMULATIVE SUBJECT INDEX doubled haploid breeding, 15:141–186 ideotype concept, 12:173–175 maize, 1:103–138, 139–161; 5:139–180; 9:115–179, 181–216; 11:199–224; 14:165–187; 22:3–4 oat, 6:167–207 pearl millet, 1:162–182 rice, 17:15–156 spelt, 15:187–213 transformation, 13:231–260 triticale, 5:41–93; 8:43–90 wheat, 2:303–319; 5:11–40; 11:225–234, 235–250; 13:293–343; 22:221–297 wild rice, 14:237–265 Grape, transformation, 16:103–104 Grapefruit: breeding, 13:345–363 evolution, 13:345–363 Grass breeding: breeding, 11:251–274 mutation breeding, 6:82 recurrent selection, 9:101–113 transformation, 13:231–260 Growth habit, induced mutants, 2:14–25 Guayule, 6:93–165 H Hallauer, Arnel R. (biography), 15:1–17 Haploidy, see also unreduced and polyploid gametes apple, 1:376 barley, 3:219–252 cereals, 15:141–186 maize, 11:199–224 petunia, 1:16–18, 44–45 potato, 3:274–277; 16:15–86 Harlan, Jack R. (biography), 8:1–17 Heat tolerance breeding, 10:129–168 Herbicide resistance: breeding needs, 11:155–198 cell selection, 4:160–161 decision trees, 18:251–303 risk assessment, 18:251–303 transforming fruit crops, 16:114 Heritability estimation, 22:9–111 Heterosis: gene action, 15:315–374 overdominance, 17:225–257 plant breeding, 12:227–251

435 plant metabolism, 10:53–90 rice, 17:24–33 soybean, 21:263–320 Hordeum, see Barley Honeycomb: breeding, 18:177–249 selection, 13:87–139, 18:177–249 Host-parasite genetics, 5:393–433 Hyacinth, mutation breeding, 6:76–77 Hybrid and hybridization, see also Heterosis barley, 5:127–129 blueberry, 5:329–341 chemical, 3:169–191 interspecific, 5:237–305 overdominance, 17:225–257 rice, 17:15–156 soybean, 21:263;-320 wheat, 2:303–319 I Ideotype concept, 12:163–193 In vitro culture: alfalfa, 2:229–234; 4:123–152 barley, 3:225–226 bean, 2:234–237 birdsfoot trefoil, 2:228–229 blackberry, 8:274–275 broadbean, 2:244–245 cassava, 2:121–122 cell selection, 4:153–173 chickpea, 2:224–225 citrus, 8:339–374 clover, 2:240–244 coffee, 2:185–187 cowpea, 2:245–246 embryo culture, 5:181–236, 249–275 germplasm preservation, 7:125, 162–167 introduction, quarantines, 3:411–414 legumes, 2:215–264 mungbean, 2:245–246 oil palm, 4:175–201 pea, 2:236–237 peanut, 2:218–224 petunia, 1:44–48 pigeon pea, 2:224 pollen, 4:59–61 potato, 9:286–288 sesame, 16:218

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436 In vitro culture (cont.) soybean, 2:225–228 Stylosanthes, 2:238–240 wheat, 12:115–162 wingbean, 2:237–238 zein, 1:110–111 Inbreeding depression, 11:84–92 alfalfa, 13:209–233 cross pollinated crops, 13:209–233 Incompatibility: almond, 8:313–338 molecular biology, 15:19–42 pollen, 4:39–48 reproductive barrier, 11:47–70 sweet cherry, 9:367–388 Incongruity, 11:71–83 Industrial crop breeding, guayule, 6:93–165 Insect and mite resistance: apple rootstock, 1:370–372 black walnut, 1:251 cassava, 2:107–110 clover, white, 17:209–210 coffee, 2:179–180 cowpea, 15:240–244 Cucurbitaceae, 10:309–360 durum wheat, 5:28 maize, 6:209–243 raspberry, 6:282–300 rutabaga, 8:240–241 sweet potato, 4:336–337 transformation fruit crops, 16:113 wheat, 22:221–297 white clover, 17:209–210 Interspecific hybridization: blackberry, 8:284–289 blueberry, 5:333–341 citrus, 8:266–270 pasture legume, 5:237–305 rose, 17:176–177 rutabaga, 8:228–229 Vigna, 8:24–30 Intersubspecific hybridization, rice, 17:88–98 Introduction, 3:361–434; 7:9–11, 21–25 Ipomoea, see Sweet potato Isozymes, in plant breeding, 6:11–54 J Jones, Henry A. (biography), 1:1–10 Juglans nigra, see Black walnut

CUMULATIVE SUBJECT INDEX K Karyogram, petunia, 1:13 Kiwifruit transformation, 16:104 L Lactuca sativa, see Lettuce Landraces, diversity, 21:221–263 Laughnan, Jack R. (bibliography), 19:1–14 Legume breeding, see also Oilseed, Soybean: cowpea, 15:215–274 pasture legumes, 5:237–305 Vigna, 8:19–42 Legume tissue culture, 2:215–264 Lethal equivalents and genetic load, 10:93–127 Lettuce: genes, 1:267–293 breeding, 16:1–14; 20:105–133 Linkage: bean, 1:76–77 isozymes, 6:37–38 lettuce, 1:288–290 maps, molecular markers, 9:37–61 petunia, 1:31–34 Lotus: hybrids, 5:284–285 in vitro culture, 2:228–229 Lycopersicon, see Tomato M Maize: anther culture, 11:199–224; 15:141–186 anthocyanin, 8:91–137 apomixis, 18:56–64 breeding, 1:103–138, 139–161 carbohydrates, 1:144–148 doubled haploid breeding, 15:141–186 exotic germplasm utilization, 14:165–187 high oil, 22:3–4 honeycomb breeding, 18:226–227 hybrid breeding, 17:249–251 insect resistance, 6:209–243 male sterility, 10:23–51 mobile elements, 4:81–122 mutations, 5:139–180 origins, 20:15–66

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CUMULATIVE SUBJECT INDEX overdominance, 17:225–257 protein, 1:103–138 quality protein, 9:181–216 recurrent selection, 9:115–179; 14:139–163 supersweet sweet corn, 14:189–236 transformation, 13:235–264 transposable elements, 8:91–137 unreduced gametes, 3:277 Male sterility: chemical induction, 3:169–191 coleus, 3:352–353 lettuce, 1:284–285 molecular biology, 10:23–51 pearl millet, 1:166 petunia, 1:43–44 rice, 17:33–72 sesame, 16:191–192 soybean, 21:277–291 wheat, 2:303–319 Malus spp, see Apple Malus ×domestica, see Apple Malvaceae, molecular mapping, 14:25–27 Mango transformation, 16:107 Manihot esculenta, see Cassava Medicago, see also Alfalfa in vitro culture, 2:229–234 Meiosis, petunia, 1:14–16 Metabolism and heterosis, 10:53–90 Microprojectile bombardment, transformation, 13:231–260 Mitochondria genetics, 6:377–380 Mixed plantings, bean breeding, 4:245–272 Mobile elements, see also transposable elements: maize, 4:81–122; 5:146–147 Molecular biology: apomixis, 18:65–73 comparative mapping, 14:13–37 cytoplasmic male sterility, 10:23–51 DNA methylation, 18:87–176 herbicide-resistant crops, 11:155–198 incompatibility, 15:19–42 molecular mapping, 14:13–37; 19:31–68 molecular markers, 9:37–61, 10:184–190; 12:195–226; 13:11–86; 14:13–37 quantitative trait loci, 15:85–139 salt resistance, 22:389–425 somaclonal variation, 16:229–268

437 soybean nodulation, 11:275–318 strawberry, 21:139–180 transposable (mobile) elements, 4:81–122; 8:91–137 virus resistance, 12:47–79 wheat improvement, 11:235–250 Molecular markers, 9:37–61 alfalfa, 10:184–190 apomixis, 18:40–42 barley, 21:181–220 clover, white, 17:212–215 forest crops, 19:31–68 fruit crops, 12:195–226 mapping, 14:13–37 plant genetic resource mangement, 13:11–86 rice, 17:113–114 rose, 17:179 somaclonal variation, 16:238–243 wheat, 21:181–220 white clover, 17:212–215 Monosomy, petunia, 1:19 Mosaics and chimeras, 15:43–84 Mungbean, 8:32–35 in vitro culture, 2:245–246 photoperiodic response, 3:74, 89–92 Munger, Henry M. (biography), 4:1–8 Musa, see Banana, Plantain Mushroom, breeding and genetics, 8:189–215 Mutants and mutation: alfalfa tissue culture, 4:130–139 apple rootstocks, 1:374–375 banana, 2:148–149 barley, 5:124–126 blackberry, 8:283–284 cassava, 2:120–121 cell selection, 4:154–157 chimeras, 15:43–84 coleus, 3:355 cytoplasmic, 2:293–295 gametoclonal variation, 5:359–391 homeotic floral, 9:63–99 induced, 2:13–72 maize, 1:139–161, 4:81–122; 5:139–180 mobile elements, see Transposable elements mosaics, 15:43–84 petunia, 1:34–40 sesame, 16:213–217 somaclonal variation, 4:123–152; 5:147–149

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438 sweet corn, 1:139–161 sweet potato, 4:371 transposable elements, 4:181–122; 8:91–137 tree fruits, 6:78–79 vegetatively-propagated crops, 6:55–91 zein synthesis, 1:111–118 Mycoplasma diseases, raspberry, 6:253–254 N National Clonal Germplasm Repository (NCGR), 7:40–43 cryopreservation, 7:125–126 genetic considerations, 7:126–127 germplasm maintenance and storage, 7:111–128 identification and label verification, 7:122–123 in vitro culture and storage, 7:125 operations guidelines, 7:113–125 preservation techniques, 7:120–121 virus indexing and plant health, 7:123–125 National Plant Germplasm System (NPGS), see also Germplasm history, 7:5–18 information systems, 7:57–65 operations, 7:19–56 National Seed Storage Laboratory (NSSL), 7:13–14, 37–38, 152–153 Nectarines, cold hardiness breeding, 10:271–308 Nematode resistance: apple rootstocks, 1:368 banana and plantain, 2:145–146 coffee, 2:180–181 cowpea, 15:245–247 soybean, 1:217–221 sweet potato, 4:336 transformation fruit crops, 16:112–113 Nicotiana, see Tobacco Nodulation, soybean, 11:275–318 O Oat, breeding, 6:167–207 Oil palm: breeding, 4:175–201 in vitro culture, 4:175–201

CUMULATIVE SUBJECT INDEX Oilseed breeding: canola, 18:1–20 oil palm, 4:175–201; 22:165–219 sesame, 16:179–228 soybean, 1:183–235; 3:289–311; 4:203– 245; 11:275–318; 15:275–313 Onion, breeding history, 20:57–103 Opuntia, see Cactus Organelle transfer, 2:283–302; 3:205–210; 6:361–393 Ornamentals breeding: chrysanthemum, 14:321–361 coleus, 3:343–360 petunia, 1:1–58 rose, 17:159–189 Ornithopus, hybrids, 5:285–287 Orzya, see Rice Overdominance, 17:225–257 Ovule culture, 5:181–236 P Panicum maximum, apomixis, 18:34–36, 47–49 Papaya transformation, 16:105–106 Parthenium argentatum, see Guayule Paspalum, apomixis, 18:51–52 Paspalum notatum, see Pensacola bahiagrass Passionfruit transformation, 16:105 Pasture legumes, interspecific hybridization, 5:237–305 Pea: breeding, 21:93–138 flowering, 3:81–86, 89–92 in vitro culture, 2:236–237 Peach: cold hardiness breeding, 10:271–308 transformation, 16:102 Peanut: breeding, 22:297–356 in vitro culture, 2:218–224 Pear transformation, 16:102 Pearl millet: apomixis, 18:55–56 breeding, 1:162–182 Pecan transformation, 16:103 Pennisetum americanum, see Pearl millet Pensacola bahiagrass, 9:101–113 apomixis, 18:51–52 selection, 9:101–113

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CUMULATIVE SUBJECT INDEX Pepino transformation, 16:107 Peppermint, mutation breeding, 6:81–82 Perennial grasses, breeding, 11:251–274 Perennial rye breeding, 13:261–288 Persimmon breeding, 19:191–225 Petunia spp., genetics, 1:1–58 Phaseolin, 1:59–102 Phaseolus vulgaris, see Bean Phytophthora fragariae, 2:195–214 Pigeon pea, in vitro culture, 2:224 Pistil, reproductive function, 4:9–79 Pisum, see Pea Plant introduction, 3:361–434; 7:9–11, 21–25 Plant exploration, 7:9–11, 26–28, 67–94 Plantain breeding, 2:135–155; 14:267–320; 21:1–25 Plastid genetics, 6:364–376, see also Organelle Plum transformation, 16:103–140 Poaceae: molecular mapping, 14:23–24 Saccharum complex, 16:269–288 Pollen: reproductive function, 4:9–79 storage, 13:179–207 Polyploidy, see also Haploidy alfalfa, 10:171–184 alfalfa tissue culture, 4:125–128 apple rootstocks, 1:375–376 banana, 2:147–148 barley, 5:126–127 blueberry, 13:1–10 gametes, 3:253–288 isozymes, 6:33–34 petunia, 1:18–19 potato, 16:15–86 reproductive barriers, 11:98–105 sweet potato, 4:371 triticale, 5:11–40 Population genetics, see Quantitative Genetics Potato: breeding, 9:217–332, 19:69–165 disease resistance breeding, 19:69–165 gametoclonal variation, 5:376–377 heat tolerance, 10:152 honeycomb breeding, 18:227–230 mutation breeding, 6:79–80 photoperiodic response, 3:75–76, 89–92

439 ploidy manipulation, 16:15–86 unreduced gametes, 3:274–277 Protein: antifungal, 14:39–88 bean, 1:59–102 induced mutants, 2:38–46 maize, 1:103–138, 148–149; 9:181–216 Protoplast fusion, 3:193–218; 20: 167–225 citrus, 8:339–374 mushroom, 8:206–208 Prunus: amygdalus, see Almond avium, see Sweet cherry Pseudograin breeding, amaranth, 19:227–285 Psophocarpus, in vitro culture, 2:237–238 Q Quantitative genetics: epistasis, 21:27–92 forest trees, 8:139–188 genotype x environment interaction, 16:135–178 heritability, 22:9–111 overdominance, 17:225–257 statistics, 17:296–300 trait loci (QTL), 15:85–139; 19:31–68 variance, 22:113–163 Quantitative trait loci (QTL), 15:85–138; 19:31–68 Quarantines, 3:361–434; 7:12,35 R Rabbiteye blueberry, 5:307–357 Raspberry, breeding, 6:245–321 Recurrent restricted phenotypic selection, 9:101–113 Recurrent selection, 9:101–113, 115–179; 14:139–163 soybean, 15:275–313 Red stele disease, 2:195–214 Regional trial testing, 12:271–297 Reproduction: barriers and circumvention, 11:11–154 pollen and pistil, 4:9–79 Rhododendron, mutation breeding, 6:75–76

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440 Rice, see also Wild rice: anther culture, 15:141–186 apomixis, 18:65 doubled haploid breeding, 15:141–186 gametoclonal variation, 5:362–364 heat tolerance, 10:151–152 honeycomb breeding, 18:224–226 hybrid breeding, 17:1–15, 15–156 photoperiodic response, 3:74, 89–92 Rosa, see Rose Rose breeding, 17:159–189 Rubus, see Blackberry, Raspberry Rust, wheat, 13:293–343 Rutabaga, 8:217–248 Ryder, Edward J. (biography), 16:1–14 Rye: gametoclonal variation, 5:370–371 perennial breeding, 13:261–288 triticale, 5:41–93 S Saccharum complex, 16:269–288 Salt resistance: cell selection, 4:141–143 durum wheat, 5:31 yeast systems, 22:389–425 Sears, Ernest R. (biography), 10:1–22 Secale, see Rye Seed: apple rootstocks, 1:373–374 banks, 7:13–14, 37–40, 152–153 bean, 1:59–102 lettuce, 1:285–286 maintenance and storage, 7:95–110, 129–158, 159–182 maize, 1:103–138, 139–161, 4:81–86 pearl millet, 1:162–182 protein, 1:59–138, 148–149 rice production, 17:98–111, 118–119 soybean, 1:183–235, 3:289–311 synthetic, 7:173–174 variegation, 4:81–86 wheat (hybrid), 2:313–317 Selection, see also Breeding cell, 4:139–145, 153–173 honeycomb design, 13:87–139; 18:177–249 marker assisted, forest tree, 19:31–68 recurrent restricted phenotypic, 9:101–113

CUMULATIVE SUBJECT INDEX recurrent selection in maize, 9:115–179; 14:139–163 Sesame breeding, 16:179–228 Sesamum indicum, see Sesame Simmonds, N.W. (biography), 21:1–13 Snap pea breeding, 21:93–138 Solanaceae: incompatibility, 15:27–34 molecular mapping, 14:27–28 Solanum tuberosum, see Potato Somaclonal variation, see also Gametoclonal variation alfalfa, 4:123–152 isozymes, 6:30–31 maize, 5:147–149 molecular analysis, 16:229–268 mutation breeding, 6:68–70 rose, 17:178–179 transformation interaction, 16:229–268 utilization, 16:229–268 Somatic embryogenesis, 5:205–212; 7:173–174 oil palm, 4:189–190 Somatic genetics, see also Gametoclonal variation; Somaclonal variation: alfalfa, 4:123–152 legumes, 2:246–248 maize, 5:147–149 organelle transfer, 2:283–302 pearl millet, 1:166 petunia, 1:43–46 protoplast fusion, 3:193–218 wheat, 2:303–319 Somatic hybridization, see also Protoplast fusion 20:167–225 Sorghum: photoperiodic response, 3:69–71, 97–99 transformation, 13:235–264 Southern pea, see Cowpea Soybean: cytogenetics, 16:289–317 disease resistance, 1:183–235 drought resistance, 4:203–243 hybrid breeding, 21:263–307 in vitro culture, 2:225–228 nodulation, 11:275–318 photoperiodic response, 3:73–74 recurrent selection, 15:275–313 semidwarf breeding, 3:289–311

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CUMULATIVE SUBJECT INDEX Spelt, agronomy, genetics, breeding, 15:187–213 Sprague, George F. (biography), 2:1–11 Sterility, see also Male sterility, 11:30–41 Starch, maize, 1:114–118 Statistics: advanced methods, 22:113–163 history, 17:259–316 Strawberry: biotechnology, 21: 139–180 red stele resistance breeding, 2:195–214 transformation, 16:104 Stress resistance: cell selection, 4:141–143,161–163 transformation fruit crops, 16:115 Stylosanthes, in vitro culture, 2:238–240 Sugarcane: and Saccharum complex, 16:269–288 mutation breeding, 6:82–84 Sweet cherry, pollen-incompatibility and selffertility, 9:367–388 transformation, 16:102 Sweet corn, see also Maize: endosperm, 1:139–161 supersweet (shrunken2), 14:189–236 Sweet potato breeding, 4:313–345; 6:80–81 T Tamarillo transformation, 16:107 Taxonomy: amaranth, 19:233–237 apple, 1:296–299 banana, 2:136–138 blackberry, 8:249–253 cassava, 2:83–89 chestnut, 4:351–352 chrysanthemum, 14:321–361 clover, white, 17:193–211 coffee, 2:161–163 coleus, 3:345–347 fescue, 3:314 Glycine, 16:289–317 guayule, 6:112–115 oat, 6:171–173 pearl millet, 1:163–164 petunia, 1:13 plantain, 2:136; 14:271–272

441 rose, 17:162–169 rutabaga, 8:221–222 Saccharum complex, 16:270–272 sweet potato, 4:320–323 triticale, 8:49–54 Vigna, 8:19–42 White clover, 17:193–211 wild rice, 14:240–241 Testing: adaptation, 12:271–297 honeycomb design, 13:87–139 Tissue culture, see In vitro culture Tobacco, gametoclonal variation, 5:372–376 Tomato: breeding for quality, 4:273–311 heat tolerance, 10:150–151 Toxin resistance, cell selection, 4:163–165 Transformation: alfalfa, 10:190–192 cereals, 13:231–260 fruit crops, 16:87–134 mushroom, 8:206 rice, 17:179–180 somaclonal variation, 16:229–268 white clover, 17:193–211 Transpiration efficiency, 12:81–113 Transposable elements, 4:81–122; 5:146–147; 8:91–137 Tree crops, ideotype concept, 12:163–193 Tree fruits, see Fruit, nut and beverage crop breeding Trifolium, see Clover, White Clover Trifolium hybrids, 5:275–284 in vitro culture, 2:240–244 Tripsacum: apomixis, 18:51 maize ancestry, 20:15–66 Trisomy, petunia, 1:19–20 Triticale, 5:41–93; 8:43–90 Triticum: Aestivum, see Wheat Turgidum, see Durum wheat Triticosecale, see Triticale Tulip, mutation breeding, 6:76 U United States National Plant Germplasm System, see National Plant Germplasm System

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442 Unreduced and polyploid gametes, 3:253–288; 16:15–86 Urd bean, 8:32–35

CUMULATIVE SUBJECT INDEX transformation fruit crops, 16:108–110 white clover, 17:201–209 Vogel, Orville A. (biography), 5:1–10 Vuylsteke, Dirk R. (biography), 21:1–25

V Vaccinium, see Blueberry Variance estimation, 22:113–163 Vegetable breeding: artichoke, 12:253–269 bean, 1:59–102; 4:245–272 bean (tropics), 10:199–269 beet (table), 22:357–388 carrot 19: 157–190 cassava, 2:73–134 cucumber, 6:323–359 cucurbit insect and mite resistance, 10:309–360 lettuce, 1:267–293; 16:1–14; 20:105:133 mushroom, 8:189–215 onion, 20:67–103 pea, 21:93–138 peanut, 22:297–356 potato, 9:217–232; 16:15–86l; 19:69–165 rutabaga, 8:217–248 snap pea, 21: 93–138 tomato, 4:273–311 sweet corn, 1:139–161; 14:189–236 sweet potato, 4:313–345 Vicia, in vitro culture, 2:244–245 Vigna, see Cowpea, Mungbean in vitro culture, 2:245–246; 8:19–42 Virus diseases: apple rootstocks, 1:358–359 clover, white, 17:201–209 coleus, 3:353 cowpea, 15:239–240 indexing, 3:386–408, 410–411, 423–425 in vitro elimination, 2:265–282 lettuce, 1:286 potato, 19:122–134 raspberry, 6:247–254 resistance, 12:47–79 soybean, 1:212–217 sweet potato, 4:336

W Walnut (black), 1:236–266 Walnut transformation, 16:103 Weinberger, John A. (biography), 11:1–10 Wheat: anther culture, 15:141–186 apomixis, 18:64–65 chemical hybridization, 3:169–191 cold hardiness adaptation, 12:124–135 cytogenetics, 10:5–15 diversity, 21:236–237 doubled haploid breeding, 15:141–186 drought tolerance, 12:135–146 durum, 5:11–40 gametoclonal variation, 5:364–368 gene manipulation, 11:225–234 heat tolerance, 10:152 hybrid, 2:303–319; 3:185–186 in vitro adaptation, 12:115–162 insect resistance, 22:221–297 molecular biology, 11:235–250 molecular markers, 21:191–220 photoperiodic response, 3:74 rust interaction, 13:293–343 triticale, 5:41–93 vernalization, 3:109 White clover, molecular genetics, 17:191–223 Wild rice, breeding, 14:237–265 Winged bean, in vitro culture, 2:237–238 Y Yeast, salt resistance, 22:389–425 Yuan, Longping (biography), 17:1–13. Z Zea mays, see Maize, Sweet corn Zein, 1:103–138 Zizania palustris, see Wild rice

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Cumulative Contributor Index (Volumes 1–22)

Abdalla, O.S., 8:43 Acquaah, G., 9:63 Aldwinckle, H.S., 1:294 Anderson, N.O., 10:93; 11:11 Aronson, A.I., 12:19 Ascher, P.D., 10:93 Ashri, A., 16:179 Baggett, J.R. 21:93 Baltensperger, D.D., 19:227 Basnizki, J., 12:253 Beck, D.L., 17:191 Beineke, W.F., 1:236 Berzonsky, W.A., 22:221 Bingham, E.T., 4:123; 13:209 Binns, M.R., 12:271 Bird, R. McK., 5:139 Bjarnason, M., 9:181 Bliss, F.A., 1:59; 6:1 Boase, M.R., 14:321 Borlaug, N.E., 5:1 Boyer, C.D., 1:139 Bravo, J.E., 3:193 Brenner, D.M., 19:227 Bressan , R.A., 13:235; 14:39; 22:389 Bretting, P.K., 13:11 Broertjes, C., 6:55 Brown, A.H.D., 221 Brown, J.W.S., 1:59 Brown, S.K., 9:333,367 Burnham, C.R., 4:347

Burton, G.W., 1:162; 9:101 Burton, J.W., 21:263 Byrne, D., 2:73 Campbell, K.G., 15:187 Cantrell, R.G., 5:11 Carvalho, A., 2:157 Casas, A.M., 13:235 Cervantes-Martinez, C.T., 22:9 Chew, P.S., 22:165 Choo, T.M., 3:219 Christenson, G.M., 7:67 Christie, B.R., 9:9 Clark, R.L., 7:95 Clarke, A.E., 15:19 Clegg, M.T., 12:1 Condon, A.G., 12:81 Cooper, R.L., 3:289 Cornu, A., 1:11 Costa, W.M., 2:157 Cregan, P., 12:195 Crouch, J.H., 14:267 Crow, J.F., 17:225 Cummins, J.N., 1:294 Dana, S., 8:19 De Jong, H., 9:217 Deroles, S.C., 14:321 Dhillon, B.S., 14:139 Dickmann, D.I., 12:163 Ding, H., 22:221 Dodds, P.N., 15:19 Donini, P., 21:181

Draper, A.D., 2:195 Dumas, C., 4:9 Duncan, D.R., 4:153 Echt, C.S., 10:169 Ehlers, J.D., 15:215 England, F., 20:1 Eubanks, M.W., 20:15 Evans, D.A., 3:193; 5:359 Everett, L.A., 14:237 Ewart, L.C., 9:63 Farquhar, G.D., 12:81 Fasoula, D.A., 14:89; 15:315; 18:177 Fasoula, V.A., 13:87; 14:89; 15:315; 18:177 Fasoulas, A.C., 13:87 Fazuoli, L.C., 2:157 Fear, C.D., 11:1 Ferris, R.S.B., 14:267 Flore, J.A., 12:163 Forsberg, R.A., 6:167 Forster, R.L.S., 17:191 French, D.W., 4:347 Gai, J. 21:263 Galiba, G., 12:115 Galletta, G.J., 2:195 Gmitter, F.G., Jr., 8:339; 13:345 Gold, M.A., 12:163 Goldman, I.L. 19:15; 20:67; 22:357 Gradziel, T.M., 15:43

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444 Gressel, J., 11:155; 18:251 Gresshof, P.M., 11:275 Grombacher, A.W., 14:237 Grosser, J.W., 8:339 Grumet, R., 12:47 Gudin, S., 17:159 Guimarães, C.T., 16:269 Gustafson, J.P., 5:41; 11:225 Guthrie, W.D., 6:209 Haley, S.D., 22:221 Hall, A.E., 10:129; 12:81; 15:215 Hall, H.K., 8:249 Hallauer, A.R., 9:115; 14:1,165 Hamblin, J., 4:245 Hancock, J.F., 13:1 Hancock, J.R., 9:1 Hanna, W.W., 13:179 Harlan, J.R., 3:1 Harris, M.O., 22: 221 Hasegawa, P.M. 13:235; 14:39: 22:389 Havey, M.J., 20:67 Hillel, J., 12:195 Hodgkin, T., 21:221 Hokanson, S.C., 21:139 Holbrook, C.C., 22: 297 Holland, J.B: 21: 27; 22: 9 Hor, T.Y., 22:165 Hunt, L.A., 16:135 Hutchinson, J.R., 5:181 Hymowitz, T., 8:1; 16:289 Janick, J., 1:xi Jansky, S., 19:77 Jayaram, Ch., 8:91 Johnson, A.A.T., 16:229; 20:167 Jones, A., 4:313 Jones, J.S., 13:209 Ju, G.C., 10:53 Kang, H., 8:139 Kann, R.P., 4:175 Karmakar, P.G., 8:19 Kartha, K.K., 2:215,265 Kasha, K.J., 3:219 Keep, E., 6:245

Page 444

CUMULATIVE CONTRIBUTOR INDEX Kleinhofs, A., 2:13 Knox, R.B., 4:9 Koebner, R.M.D., 21:181 Kollipara, K.P., 16:289 Kononowicz, A.K., 13:235 Konzak, C.F., 2:13 Krikorian, A.D., 4:175 Krishnamani, M.R.S., 4:203 Kronstad, W.E., 5:1 Kulakow, P.A., 19:227 Lamb, R.J., 22:221 Lambert, R.J., 22: 1 Lamborn, C., 21:93 Lamkey, K.R., 15:1 Lavi, U., 12:195 Layne, R.E.C., 10:271 Lebowitz, R.J., 3:343 Lehmann, J.W., 19:227 Levings, III, C.S., 10:23 Lewers, K.R., 15:275 Li, J., 17:1,15 Liedl, B.E., 11:11 Lin, C.S., 12:271 Lovell, G.R., 7:5 Lukaszewski, A.J., 5:41 Lyrene, P.M., 5:307 McCoy, T.J., 4:123; 10:169 McCreight, J.D., 1:267; 16:1 McDaniel, R.G., 2:283 McKeand, S.E., 19:41 McKenzie, R.I.H., 22: 221 McRae, D.H., 3:169 Maas, J. L., 21: 139 Maheswaran, G., 5:181 Maizonnier, D., 1:11 Marcotrigiano, M., 15:43 Martin, F.W., 4:313 Matsumoto, T.K. 22:389 Medina-Filho, H.P., 2:157 Miller, R., 14:321 Mondragon Jacobo, C. 20:135 Morrison, R.A., 5:359 Mowder, J.D., 7:57 Mroginski, L.A., 2:215 Murphy, A.M., 9:217

Mutschler, M.A., 4:1 Myers, J.R., 21:93 Myers, O., Jr., 4:203 Myers, R.L., 19:227. Namkoong, G., 8:139 Navazio, J., 22:357 Neuffer, M.G., 5:139 Newbigin, E., 15:19 Nyquist, W.E., 22:9 Ohm, H.W., 22:221 O’Malley, D.M., 19:41 Ortiz, R., 14:267; 16:15; 21:1 Palmer, R.G., 15:275, 21:263 Pandy, S., 14:139 Pardo, J. M., 22:389 Parliman, B.J., 3:361 Paterson, A.H., 14:13 Patterson, F.L., 22:221 Peairs, F.B., 22:221 Pedersen, J.F., 11:251 Perdue, R.E., Jr., 7:67 Peterson, P.A., 4:81; 8:91 Polidorus, A.N., 18:87 Porter, D.A., 22:221 Porter, R.A., 14:237 Powell, W., 21: 181 Proudfoot, K.G., 8:217 Rackow, G., 18:1 Raina, S.K., 15:141 Ramage, R.T., 5:95 Ramming, D.W., 11:1 Ratcliffe, R.H., 22:221 Ray, D.T., 6:93 Redei, G.P., 10:1 Reimann-Phillipp, R., 13:265 Reinbergs, E., 3:219 Rhodes, D., 10:53 Richards, R.A., 12:81 Roath, W.W., 7:183 Robinson, R.W., 1:267; 10:309 Ron Parra, J., 14:165 Roos, E.E., 7:129 Rotteveel, T., 18:251 Rowe, P., 2:135

3935 P-09 (Index)

8/21/02

12:14 PM

Page 445

CUMULATIVE CONTRIBUTOR INDEX Russell, W.A., 2:1 Rutter, P.A., 4:347 Ryder, E.J., 1:267; 20:105 Samaras, Y., 10:53 Sansavini, S., 16:87 Saunders, J.W., 9:63 Savidan, Y., 18:13 Sawhney, R.N., 13:293 Schaap, T., 12:195 Schroeck, G., 20:67 Scott, D.H., 2:195 Seabrook, J.E.A., 9:217 Sears, E.R., 11:225 Shands, Hazel L. 6:167 Shands, Henry L. 7:1,5 Shannon, J.C., 1:139 Shanower, T.G., 22:221 Shattuck, V.I., 8:217; 9:9 Shaun, R., 14:267 Sidhu, G.S., 5:393 Simmonds, N.W., 17:259 Simon, P.W., 19:157 Singh, B.B., 15:215 Singh, R.J., 16:289 Singh, S.P., 10:199 Singh, Z., 16:87 Slabbert, M.M., 19:227 Sleper, D.A., 3:313 Sleugh, B.B., 19:227 Smith, S.E., 6:361 Socias i Company, R., 8:313 Sobral, B.W.S., 16:269 Soh, A.C., 22:165 Sondahl, M.R., 2:157

Spoor, W., 20: 1 Stalker, H.T., 22:297 Steffensen, D. M., 19:1 Stevens, M.A., 4:273 Stoner, A.K., 7:57 Stuber, C.W., 9:37; 12:227 Sugiura, A., 19:191 Sun, H. 21:263 Tai, G.C.C., 9:217 Talbert, L.E., 11:235 Tan, C.C., 22:165 Tarn, T.R., 9:217 Tehrani, G., 9:367 Teshome, A., 21:221 Thompson, A.E., 6:93 Towill, L.E., 7:159, 13:179 Tracy, W.F., 14:189 Tsaftaris, A.S., 18:87 Tsai, C.Y., 1:103

445 Weeden, N.F., 6:11 Wehner, T.C., 6:323 Westwood, M.N., 7:111 Whitaker, T.W., 1:1 White, D.W.R., 17:191 White, G.A., 3:361; 7:5 Widholm, J.M., 4:153, 11:199 Widmer, R.E., 10:93 Widrlechner, M.P., 13:11 Wilcox, J.R., 1:183 Williams, E.G., 4:9; 5:181, 237 Williams, M.E., 10:23 Wilson, J.A., 2:303 Woodfield, D.R., 17:191 Wright, G.C., 12:81 Wong, G., 22: 165 Wu, L., 8:189 Wu, R., 19:41

Ullrich, S.E., 2:13

Xin, Y., 17:1,15 Xu, S., 22:113 Xu, Y., 15:85

Van Harten, A.M., 6:55 Varughese, G., 8:43 Vasal, S.K., 9:181; 14:139 Veilleux, R., 3:253; 16:229; 20:167 Villareal, R.L., 8:43 Vogel, K.P., 11:251 Vuylsteke, D., 14:267

Yamada, M., 19:191 Yan, W., 13:141 Yang, W.-J., 10:53 Yonemori, K., 19:191 Yopp, J.H., 4:203 Yun, D.-J., 14:39

Wallace, D.H., 3:21; 13:141 Wan, Y., 11:199

Zeng, Z.-B., 19:41 Zimmerman, M.J.O., 4:245 Zohary, D., 12:253