GenomeMapping andMolecular Breeding in Plants: Oilseeda

Genome Mapping and Molecular Breeding in Plants Volume 2 Series Editor: Chittaranjan Kole

Volumes of the Series Genome Mapping and Molecular Breeding in Plants

Volume 1 Cereals and Millets Volume 2 Oilseeds Volume 3 Pulses, Sugar and Tuber Crops Volume 4 Fruits and Nuts Volume 5 Vegetables Volume 6 Technical Crops Volume 7 Forest Trees

Chittaranjan Kole (Ed.)

Oilseeds With 42 Illustrations, 7 in Color

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Chittaranjan Kole Department of Horticulture 316 Tyson Building The Pennsylvania State University University Park, PA 16802 USA e-mail: [email protected]

Library of Congress Control Number: 2006926550

ISBN-10 3-540-34387-3 Springer Berlin Heidelberg New York ISBN-13 978-3-540-34387-5 Springer Berlin Heidelberg New York

This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permissions for use must always be obtained from Springer. Violations are liable for prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springer.com © Springer-Verlag Berlin Heidelberg 2007 The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Editor: Dr. Sabine Schreck, Heidelberg, Germany Desk Editor: Dr. Jutta Lindenborn, Heidelberg, Germany Cover design: WMXDesign GmbH, Heidelberg, Germany Typesetting and production: LE-TEX Jelonek, Schmidt & Vöckler GbR, Leipzig, Germany 39/3100/YL 5 4 3 2 1 0 – Printed on acid-free paper

Preface to the Series

Genome science has emerged unequivocally as the leading discipline of this new millennium. Progress in molecular biology during the last century has provided critical inputs for building a solid foundation for this discipline. However, it has gained fast momentum particularly in the last two decades with the advent of genetic linkage mapping with RFLP markers in humans in 1980. Since then it has been flourishing at a stupendous pace with the development of newly emerging tools and techniques. All these events are due to the concerted global efforts directed at the delineation of genomes and their improvement. Genetic linkage maps based on molecular markers are now available for almost all plants of significant academic and economic interest, and the list of plants is growing regularly. A large number of economic genes have been mapped, tagged, cloned, sequenced, or characterized for expression and are being used for genetic tailoring of plants through molecular breeding. An array of markers in the arsenal from RFLP to SNP; tools such as BAC, YAC, ESTs, and microarrays; local physical maps of target genomic regions; and the employment of bioinformatics contributing to all the “-omics” disciplines are making the journey more and more enriching. Most naturally, the plants we commonly grow on our farms, forests, orchards, plantations, and labs have attracted emphatic attention, and deservedly so. The two-way shuttling from phenotype to genotype (or gene) and genotypte (gene) to phenotype has made the canvas much vaster. One could have easily compiled the vital information on genome mapping in economic plants within some 50 pages in the 1980s or within 500 pages in the 1990s. In the middle of the first decade of this century, even 5,000 pages would not suffice! Clearly genome mapping is no longer a mere “promising” branch of the life science; it has emerged as a full-fledged subject in its own right with promising branches of its own. Sequencing of the Arabidopsis genome was complete in 2000. The early 21st century witnessed the complete genome sequence of rice. Many more plant genomes are waiting in the wings of the national and international genome initiatives on individual plants or families. The huge volume of information generated on genome analysis and improvement is dispersed mainly throughout the pages of periodicals in the form of review papers or scientific articles. There is a need for a ready reference for students and scientists alike that could provide more than just a glimpse of the present status of genome analysis and its use for genetic improvement. I personally felt the gap sorely when I failed to suggest any reference works to students and colleagues interested in the subject. This is the primary reason I conceived of a series on genome mapping and molecular breeding in plants. There is not a single organism on earth that has no economic worth or concern for humanity. Information on genomes of lower organisms is abundant and highly useful from academic and applied points of view. Information on higher animals including humans is vast and useful. However, we first thought to concentrate only on the plants relevant to our daily lives, the agronomic, horticultural and technical crops, and forest trees, in the present series. We will come up soon with commentaries on food and fiber animals, wildlife and companion animals, laboratory animals, fishes and aquatic animals, beneficial and harmful insects,

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plant- and animal-associated microbes, and primates including humans in our next “genome series” dedicated to animals and microbes. In this series, 82 chapters devoted to plants or their groups have been included. We tried to include most of the plants in which significant progress has been made. We have also included preliminary works on some so-called minor and orphan crops in this series. We would be happy to include reviews on more such crops that deserve immediate national and international attention and support. The extent of coverage in terms of the number of pages, however, has nothing to do with the relative importance of a plant or plant group. Nor does the sequence of the chapters have any correlation to the importance of the plants discussed in the volumes. A simple rule of convenience has been followed. I feel myself fortunate to have received highly positive responses from nearly 300 scientists of some 30-plus countries who contributed the chapters for this series. Scientists actively involved in analyzing and improving particular genomes contributed each and every chapter. I thank them all profoundly. I made a conscientious effort to assemble the best possible team of authors for certain chapters devoted to the important plants. In general, the lead authors of most chapters organized their teams. I extend my gratitude to them all. The number of plants of economic relevance is enormous. They are classified from various angles. I have presented them using the most conventional approach. The volumes thus include cereals and millets (Volume I), oilseeds (Volume II), pulse, sugar and tuber crops (Volume III), fruits and nuts (Volume IV), vegetables (Volume V), technical crops including fiber and forage crops, ornamentals, plantation crops, and medicinal and aromatic plants (Volume VI), and forest trees (Volume VII). A significant amount of information might be duplicated across the closely related species or genera, particularly where results of comparative mapping have been discussed. However, some readers would have liked to have had a chapter on a particular plant or plant group complete in itself. I ask all the readers to bear with me for such redundancy. Obviously the contents and coverage of different chapters will vary depending on the effort expended and progress achieved. Some plants have received more attention for advanced works. We have included only introductory reviews on fundamental aspects on them since reviews in these areas are available elsewhere. On other plants, including the “orphan” crop plants, a substantial amount of information has been included on the basic aspects. This approach will be reflected in the illustrations as well. It is mainly my research students and professional colleagues who sparked my interest in conceptualizing and pursuing this series. If this series serves its purpose, then the major credit goes to them. I would never have ventured to take up this huge task of editing without their constant support. Working and interacting with many people, particularly at the Laboratory of Molecular Biology and Biotechnology of the Orissa University of Agriculture and Technology, Bhubaneswar, India as its founder principal investigator; the Indo-Russian Center for Biotechnology, Allahabad, India as its first project coordinator; the then-USSR Academy of Sciences in Moscow; the University of Wisconsin at Madison; and The Pennsylvania State University, among institutions, and at EMBO, EUCARPIA, and Plant and Animal Genome meetings among the scientific gatherings have also inspired me and instilled confidence in my ability to accomplish this job. I feel very fortunate for the inspiration and encouragement I have received from many dignified scientists from around the world, particularly Prof. Arthur

Preface to the Series

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Kornberg, Prof. Franklin W. Stahl, Dr. Norman E. Borlaug, Dr. David V. Goeddel, Prof. Phillip A. Sharp, Prof. Gunter Blobel, and Prof. Lee Hartwell, who kindly opined on the utility of the series for students, academicians, and industry scientists of this and later generations. I express my deep regards and gratitude to them all for providing inspiration and extending generous comments. I have been especially blessed by God with an affectionate student community and very cordial research students throughout my teaching career. I am thankful to all of them for their regards and feelings for me. I am grateful to all my teachers and colleagues for the blessings, assistance, and affection they showered on me throughout my career at various levels and places. I am equally indebted to the few critics who helped me to become professionally sounder and morally stronger. My wife Phullara and our two children Sourav and Devleena have been of great help to me, as always, while I was engaged in editing this series. Phullara has taken pains (“pleasure” she would say) all along to assume most of my domestic responsibilities and to allow me to devote maximum possible time to my professional activities, including editing this series. Sourav and Devleena have always shown maturity and patience in allowing me to remain glued to my PC or “printed papers” (“P3” as they would say). For this series, they assisted me with Internet searches, maintenance of all hard and soft copies, and various timely inputs. Some figures included by the authors in their chapters were published elsewhere previously. The authors have obtained permission from the concerned publishers or authors to use them again for their chapters and expressed due acknowledgement. However, as an editor I record my acknowledgements to all such publishers and authors for their generosity and good will. I look forward to your valuable criticisms and feedback for further improvement of the series. Publishing a book series like this requires diligence, patience, and understanding on the part of the publisher, and I am grateful to the people at Springer for having all these qualities in abundance and for their dedication to seeing this series through to completion. Their professionalism and attention to detail throughout the entire process of bringing this series to the reader made them a genuine pleasure to work with. Any enjoyment the reader may derive from this books is due in no small measure to their efforts. Pennsylvania, 10 January 2006

Chittaranjan Kole

Preface to the Volume

I believe some sort of explanation is due to readers regarding the contents of this volume. The conventional grouping of economic plants species, particularly crop plants, is conveniently based on the mode or purpose of their use. Some crop plants are unique in their use and find place in a particular group, say, rice under cereals, peanut under oilseeds, or apple under fruits. Certain crops have two or more important agricultural purposes and may belong to different groups. Such crops posed a problem for chapter allocation under the volumes in this series. My initial intention was to have a volume for edible oil-yielding crops. This would have required grouping together oilseeds, oil palm, coconut, cottonseed, and olive, and then why not corn or rice! In that case, agricultural scientists would doubt on my minimum knowledge about plantation and cereal crops, and I had to retreat. I went for just the oilseed crops. At least four oilseed crops have multifarious agronomic purposes. Soybean could with equal justification be categorized as an oilseed or as a pulse crop. But considering its dominating presence among oil-producing field crops, it should be included in the oilseed volume. The second problematic entrant was Brassica rapa. It could boast of being an oilseed as well as a vegetable. Moreover, it has several distinct subspecies under these two categories. Traditional breeding must have dealt with them separately with distinct objectives for genetic improvement. However, genetics, basic or modern, needed them together. Nucleotides do not distinguish between oil and vegetable! We placed it in this volume with detailed review on molecular aspects and also in the volume on vegetables with details on the basic aspects. Black mustard, which is a traditional condiment. But it has been of immense use in studies of comparative genomics in Brassicaceae and provided many clues to the evolution of genes and genomes. We placed it in this volume as well. A horticulturist would surely prefer to treat sunflower as an ornamental. However, it is among the four leading seed-oil- producing species, and so it is here in volume II on oilseed crops. Some oilseed crops are grown in Asian countries, like sesame and safflower, which have recently attracted the attention of molecular biologists and been the object of considerable efforts at genome analysis. We had to omit them from the current volume with the hope of including chapters on them in future editions. To our delight we were able to sign on highly eminent scientists to author the chapters in this volume. In addition, some of the chapters represent the results of the multilab and multinational efforts of their authors, who took pains for coordinated and concerted endeavors. I am thankful to all the authors for the high academic quality of their final output. I worked on oilseed Brassicas, specifically B. rapa and B. napus, in the lab of Prof. Thomas C. Osborn at UW-Madison and later continued works in my own labs in India and have many close colleagues in that fraternity. It was a real pleasure and enriching to work on this volume with some of these friends, whom I used to meet annually in January at the Town & Country Hotel in San Diego during the Plant and Animal Genome conferences. I also wish to record my thanks to my wife, Phullara, who used to work with me at UW-Madison and India, and my colleagues and research students who worked with me on oilseed Brassicas for their continued

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interactions and inputs that made my life easier while editing this volume. The first volume produced by the publishers of this series has been well received by readers. The publishers have done an equally elegant job for this volume as well. I am thankful for their dedicated service to science. Suggestions from any corner on how to improve this volume for future editions are welcome. Pennsylvania, 26 February 2006

Chittaranjan Kole

Contents

Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XVI Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . XXI 1 Soybean G.-J. Lee, X. Wu, J. G. Shannon, D. A. Sleper, H. T. Nguyen . . . . . . . . . . . . . . . . 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Construction of Soybean Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Soybean Genetic Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 First Generation of Soybean Maps Constructed Based on a Single Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.3 Integrated Soybean Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Mapping of Genes in Soybean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.1 Resistant Genes to Soybean Diseases . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 Genes for Herbicide Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.3 Nodulation Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.4 Genes for Growth Habit, Flowering, and Morphology . . . . . . . . 1.3.5 Soybean Sterility Genes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.6 Genes for Mineral Toxicity or Deficiency . . . . . . . . . . . . . . . . . . . 1.3.7 Genes for Soybean Pigmentation . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.8 Genes for Fatty Acid Composition . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 QTL Mapping in Soybean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Pest Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 Tolerance to Abiotic Stresses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.3 Growth and Development Responses . . . . . . . . . . . . . . . . . . . . . . 1.4.4 Seed Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.5 Yield-Related Traits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Marker-Assisted Breeding in Soybean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Advantages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.2 Marker Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.3 Marker-Assisted Introgression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.4 Gene Pyramiding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Progress in Map-Based Cloning, Transformation, and Other Candidate Gene Approaches in Soybean . . . . . . . . . . . . . . . . . 1.6.1 Map-Based Cloning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.2 Soybean Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.3 Functional Genomics for Candidate Gene Discovery . . . . . . . . . 1.7 Future Scope . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Oilseed Rape R. Snowdon, W. Lühs, W. Friedt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Origin and History of Oilseed Rape . . . . . . . . . . . . . . . . . . . . . . . . 2.1.2 Botanical Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Economic Importance of Oilseed Rape . . . . . . . . . . . . . . . . . . . . . 2.1.4 Nutritional and Chemical Composition of Rapeseed Oil . . . . . .

1 1 7 7 7 9 10 10 20 20 20 22 22 23 23 24 24 36 37 38 38 40 40 40 41 42 42 42 43 43 44 45 55 55 55 56 56 57

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Breeding of Oilseed Rape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Breeding Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Breeding for Improved Productivity . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Improvement of Seed Components . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Hybrid Breeding and Cytoplasmic Male Sterility Systems . . . . . 2.2.5 Use of Male Sterility Systems in Oilseed Rape Breeding . . . . . . 2.2.6 Genetic Diversity for Heterosis and Hybrid Breeding . . . . . . . . 2.2.7 Expanding the Genetic Variability in Oilseed Rape by Interspecific Hybridization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Cytogenetic Studies of Brassica Crops and Interspecific Hybrids . . . . . . 2.3.1 History of Cytogenetic Studies in Brassica . . . . . . . . . . . . . . . . . . 2.4 Genetic Diversity Studies in Brassica napus . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Genetic Modification: Status and Potential of Transgenic Brassica napus . . . . . . . . . . . . . . . . . . 2.5.1 Herbicide Tolerance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.2 Genetic Engineering of Fatty Acid Biosynthesis . . . . . . . . . . . . . 2.6 Molecular Markers and Genetic Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.1 Use of Isoenzymes in Oilseed Rape Breeding . . . . . . . . . . . . . . . 2.6.2 Brassica napus Genetic Maps: From RFLP to PCR Markers . . . 2.6.3 Mapping of Genes and QTLs for Morphological and Quality Traits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.4 Mapping of Genes and QTL for Disease Resistance . . . . . . . . . . 2.6.5 Mapping QTLs for Abiotic Stress . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6.6 Towards an Integrated B. napus Genetic Map . . . . . . . . . . . . . . . 2.7 Comparative Genomic Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8 Physical Mapping and Genomics Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8.1 Physical Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8.2 Public Genome Resources: The Multinational Brassica Genome Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8.3 Genome Sequencing in B. oleracea and B. rapa . . . . . . . . . . . . . . 2.9 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Peanut S. L. Dwivedi, D.J. Bertioli, J. H. Crouch, J. F. Valls, H. D. Upadhyaya, A. Fávero, M. Moretzsohn, A. H. Paterson . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Origin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Botanical Types and Distribution . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.3 Crop Production and Uses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.4 Improved Quality Requirements: Reduced Allergenicity and Toxicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Genetic Resources in Peanut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Wild Arachis Species and Interspecific Gene Introgression into Cultivated Peanut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Cultivated Germplasm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Core Collections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Appropriate Germplasm and Evaluation Systems for Mapping Economically Important Traits in Peanut . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Phenotypic Screens, Resistance/Tolerance Mechanism, and Genetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

59 59 59 60 62 64 64 65 67 67 71 72 72 73 77 77 77 88 91 92 93 94 98 98 100 100 103 103

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Contents

Germplasm with Beneficial Traits for Mapping and Genetic Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Genomic Resources in Peanut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 DNA Markers (RFLPs, RAPDs, AFLPs, SSRs) . . . . . . . . . . . . . . . 3.4.2 Molecular Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.3 Mapping Population . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.4 Genetic Linkage Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.5 Comparative Mapping with Model Genomes . . . . . . . . . . . . . . . . 3.4.6 BAC Libraries and New Generation Markers . . . . . . . . . . . . . . . . 3.5 Successes and Limitations of Conventional Breeding in Peanut . . . . . . . 3.6 Biotechnological Applications to Genetic Enhancement in Peanut . . . . 3.6.1 Marker/Trait Associations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.2 Unlocking the Genetic Variation from Wild Genetic Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.3 Transgenics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 Conclusions and Future Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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126 128 128 131 131 132 133 133 136 137 137 137 140 142 143

4 Sunflower N. Paniego, R. Heinz, P. Fernandez, P. Talia, V. Nishinakamasu, H. Esteban Hopp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Brief History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Botanical Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.3 Economic Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.4 Conventional Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Molecular Markers and Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Genomics and Transcriptomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Structural Analysis by in situ Hybridization . . . . . . . . . . . . . . . . . . . . . . . 4.5 Resistance Genes in Cultivated and Wild Sunflowers . . . . . . . . . . . . . . . . 4.6 QTL Analysis for Developmental and Agronomic Traits . . . . . . . . . . . . . 4.7 In vitro Tissue-Culture-Aided Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8 Genetic Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8.1 Transgenic Traits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8.2 Transgenic Sunflowers: Biosafety Concerns . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

153 153 153 153 154 155 156 157 163 164 168 169 170 170 170 172

5 Indian Mustard D. Edwards, P. A. Salisbury, W. A. Burton, C. J. Hopkins, J. Batley . . . . . . . . . . 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Brief History of Brassica juncea . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.2 Botanical Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.3 Economic Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.4 Breeding Objectives and Progress . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.5 Overcoming Limitations of Classical Endeavours . . . . . . . . . . . . 5.1.6 Classical Mapping Efforts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.7 Utility of Molecular Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Construction of Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Genetic Mapping in B. juncea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 First-Generation Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Second-Generation Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

179 179 179 179 180 181 183 185 185 186 186 186 188

XIV

Contents

5.2.4 Comparative Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gene Mapping and Marker-Assisted Selection . . . . . . . . . . . . . . . . . . . . . 5.3.1 White Rust Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Seed Coat Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.3 Fatty Acid/Oil Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.4 Glucosinolate Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Marker-Assisted Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Transgene-Assisted Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6 Advanced Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.6.1 Gene Discovery and Expression . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Future Scope of Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

193 195 195 197 198 201 202 203 203 203 204 205

6 Brassica Rapa P. Quijada J. Cao, X. Wang, M. Hirai, C. Kole . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 History of the Crop . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.2 Botanical Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.3 Economic Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.4 Breeding Objectives and Achievements . . . . . . . . . . . . . . . . . . . . 6.2 Construction of Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Brief History of Mapping Efforts . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.2 Mapping Populations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.3 Mapping Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.4 Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.5 Comparative Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3 Gene Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Seed Coat Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Disease Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.3 Vernalization Requirements and Flowering Time . . . . . . . . . . . . 6.3.4 Fatty Acid Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.5 Self-Incompatibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.6 Dwarfism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.7 Markers Linked to Microspore Embryogenic Ability . . . . . . . . . 6.4 QTL Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 Morphological Traits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.2 Heat Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.3 Resistance to Clubroot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.4 Resistance to White Rust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.5 Linolenic Acid Content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.6 Flowering Time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.7 Abiotic Stresses: Winter Survival and Freezing Tolerance . . . . . 6.4.8 Mendelization of QTLs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5 Marker-Assisted Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.1 Marker Conversions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.2 Germplasm Screening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.3 Marker-Assisted Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.4 Marker-Assisted Introgression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.5 Gene Pyramiding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 Map-Based Cloning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6.1 Principles of Map-Based Cloning . . . . . . . . . . . . . . . . . . . . . . . . . .

211 211 211 211 213 213 215 215 216 217 218 221 224 224 227 229 231 231 232 233 233 233 236 236 236 237 239 240 242 243 243 243 246 248 250 251 251

5.3

Contents

XV

6.6.2 Genetic Resources and Mapping Populations . . . . . . . . . . . . . . . 6.6.3 BAC Libraries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6.4 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.7 Future Scope of Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

252 252 253 253 254

7 Black Mustard S. Das, U. Lagercrantz, M. Lascoux . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.1 Taxonomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.2 Genetic Relationship and Evolution of Brassica species . . . . . . . 7.1.3 Breeding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 DNA Marker Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3 Genetic Linkage Mapping in Brassica nigra . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.2 Comparative Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Mapping of Flowering-Time Trait in Brassica nigra . . . . . . . . . . . . . . . . . 7.5 Future Scope of Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

265 265 265 265 266 267 267 267 269 270 271 272

8 Flax C.A. Cullis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.1 Origin and History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.2 Biological Descriptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.3 Karyotype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.4 Genome Size . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.5 Economic Importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1.6 Breeding Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 Construction of Genetic Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.1 Classical Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Molecular Maps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3 Germplasm Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.1 Germplasm Characterization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.3.2 Molecular Markers for Germplasm Identification . . . . . . . . . . . . 8.3.3 Inducing New Variability or Traits . . . . . . . . . . . . . . . . . . . . . . . . . 8.4 Gene Discovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 Quantitative Trait Loci . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.6 Future Scope of Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

275 275 275 275 277 278 278 280 282 282 285 287 287 289 290 291 291 291 292

Subject Index

297

Contributors

Jacqueline Batley Plant Biotechnology Centre Primary Industries Research Victoria Department of Primary Industries Victorian AgriBiosciences Centre 1 Park Drive, Bundoora Victoria 3083, Australia [email protected] D.J. Bertioli Universidade Catolica de Brasilia Pos Graduacao Campus II SGAN 916, DF CEP 70.790-160 Brasilia, Brazil [email protected] Wayne A. Burton Grains Innovation Park Private Bag 260 Horsham, Victoria 3401, Australia [email protected] Jiashu Cao Laboratory of Cell & Molecular Biology Institute of Vegetable Science Zhejiang University No. 268 Kaixuan Road, Hangzhou 310029, China [email protected] J.H. Crouch International Crops Research Institute for the Semi Arid Tropics (ICRISAT) ICRISAT Patancheru PO, 502324, AP India [email protected] Christopher A. Cullis Department of Biology Case Western Reserve University Cleveland, OH 44106-7080, USA [email protected]

Sandip Das Max Planck Institute for Developmental Biology Department of Molecular Biology Spemannstrasse 37–39, Tuebingen 72076, Germany and Present Address: Center for Biotechnology Hamdard University, Delhi 110 062 India [email protected] S.L. Dwivedi International Crops Research Institute for the Semi Arid Tropics (ICRISAT) ICRISAT Patancheru PO, 502324, AP India [email protected] David Edwards Plant Biotechnology Centre Primary Industries Research Victoria Department of Primary Industries Victorian AgriBiosciences Centre 1 Park Drive, Bundoora Victoria 3083, Australia [email protected] Alessandra Pereira Fávero EMBRAPA Recursos Genéticos e Biotecnologia (CENARGEN) Parque Estação Biológica-pqEB Final Av. W5 Norte, Brasília-DF CEP: 70770-900, Brazil [email protected] Paula Fernandez Unidad Integrada de Investigación y Docencia CNIA-INTA y FCEyN-UBA, Instituto de Biotecnología CICVyA-INTA CC 25, 1712 Castelar, Argentina

XVIII

Contributors

Wolfgang Friedt Department of Plant Breeding Research Centre for Biosystems, Land Use and Nutrition Justus Liebig University of Giessen Heinrich-Buff-Ring 26–32 35392 Giessen, Germany [email protected] Ruth Heinz Unidad Integrada de Investigación y Docencia CNIA-INTA y FCEyN-UBA, Instituto de Biotecnología CICVyA-INTA CC 25, 1712 Castelar, Argentina [email protected] Masashi Hirai National Research Institute of Vegetables, Ornamental Plants & Tea, Ano, Mie 5142392, Japan [email protected] Clare J. Hopkins Plant Biotechnology Centre Primary Industries Research Victoria Department of Primary Industries Victorian AgriBiosciences Centre 1 Park Drive, Bundoora Victoria 3083, Australia [email protected] H. Esteban Hopp Unidad Integrada de Investigación y Docencia CNIA-INTA y FCEyN-UBA, Instituto de Biotecnología CICVyA-INTA, CC 25, 1712 Castelar, Argentina [email protected] Chittaranjan Kole Department of Horticulture The Pennsylvania State University 316 Tyson Building University Park, PA 16802, USA [email protected] Ulf Lagercrantz Department of Evolutionary Functional Genomics Evolutionary Biology Centre

Uppsala University, Norbyv. 18D SE-752 36 Uppsala, Sweden [email protected] Martin Lascoux Department of Evolutionary Functional Genomics Evolutionary Biology Centre Uppsala University, Norbyv. 18D SE-752 36 Uppsala, Sweden [email protected] Geung-Joo Lee Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri 1-31 Agriculture Building, Columbia MO 65211, USA [email protected] Wilfried Lühs Department of Plant Breeding Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University of Giessen Heinrich-Buff-Ring 26–32 35392 Giessen, Germany [email protected] Marcio de Carvalho Moretzsohn EMBRAPA Recursos Genéticos e Biotecnologia (CENARGEN) Parque Estação Biológica-pqEB Final Av. W5 Norte, Brasília-DF CEP: 70770-900, Brazil [email protected] Veronica Nishinakamasu Unidad Integrada de Investigación y Docencia CNIA-INTA y FCEyN-UBA, Instituto de Biotecnología CICVyA-INTA, CC 25, 1712 Castelar, Argentina Henry T. Nguyen Division of Plant Sciences and National Center for Soybean Biotechnology University of Missouri 1-31 Agriculture Building, Columbia MO 65211, USA [email protected]

Contributors

Norma Paniego Unidad Integrada de Investigación y Docencia CNIA-INTA y FCEyN-UBA, Instituto de Biotecnología CICVyA-INTA, CC 25, 1712 Castelar, Argentina [email protected] A.H. Paterson Distinguished Research Professor and Director Plant Genome Mapping Laboratory University of Georgia, Rm. 228 111 Riverbend Road, Athens GA 30602, USA [email protected] Pablo A. Quijada Laboratory of Genetics University of Wisconsin-Madison 425-G Henry Mall, Madison, WI 53706 USA [email protected]

XIX

Rod Snowdon Department of Plant Breeding Research Centre for Biosystems, Land Use and Nutrition Justus Liebig University of Giessen Heinrich-Buff-Ring 26–32 35392 Giessen, Germany [email protected] Paola Talia Unidad Integrada de Investigación y Docencia CNIA-INTA y FCEyN-UBA, Instituto de Biotecnología CICVyA-INTA, CC 25, 1712 Castelar, Argentina H.D. Upadhyaya International Crops Research Institute for the Semi Arid Tropics (ICRISAT) ICRISAT Patancheru PO, 502324, AP India [email protected]

Philip A. Salisbury Faculty of Land and Food Resources The University of Melbourne Victoria 3010, Australia [email protected]

J.F. Valls EMBRAPA Recursos Genéticos e Biotecnologia (CENARGEN) Parque Estação Biológica-pqEB Final Av. W5 Norte, Brasília-DF CEP: 70770-900, Brazil [email protected]

J. Grover Shannon Division of Plant Sciences and National Center for Soybean Biotechnology University of Missouri, P.O. Box 160 147 State Highway T, Portageville MO 63873, USA [email protected]

Xiaowu Wang Institute of Vegetables and Flowers Chinese Academy of Agricultural Science No. 12, Zhongguancun Nandajie Beijing, 100081, China [email protected]

David A. Sleper Division of Plant Sciences and National Center for Soybean Biotechnology University of Missouri 271-F Life Sciences Center Columbia, MO 65211, USA [email protected]

Xiaolei Wu Division of Plant Sciences and National Center for Soybean Biotechnology University of Missouri 1-31 Agriculture Building, Columbia MO 65211, USA [email protected]

Abbreviations

Δ9DES AAFC AC ACP ACS AFLP AHAS ANOVA ARS AS-PCR ASPE AT ATP BAC BAGI BC BSA BSR CAPS cDNA CENARGEN CID CIM CLG cM CMS CO CR CSHL DAF DAG DAGAT DAP DAS DD-RT-PCR DH DH DRE DTF DUS ECSs ELISA ELS EMBRAPA EMS

Δ9-Steroyl-ACP-Desaturase Agriculture and Agri-Food Canada Albugo candida Acetyl Carrier Protein Acyl-CoA Synthase Amplified Fragment Length Polymorphism Acetohydroxyacid Syntheses Analysis of Variance Agricultural Research Service Allele-Specific Polymerase Chain Reaction Allele-Specific Primer Extension Acyltransferase Adenosine Triphosphate Bacterial Artificial Chromosome Brassica/Arabidopsis Genomic Institute Backcross Bulked Segregant Analysis Brown Stem Rot Cleaved Amplified Polymorphic Sequences Complementary DNA National Center of Genetic Resources Carbon Isotope Discrimination Composite Interval Mapping Classical Linkage Group centi-Morgan Cytoplasmic Male Sterility Constans (gene) Clubroot Resistance Cold Spring Harbor Laboratory DNA Amplification Fingerprint DiAcylGlycerol DiAcylGlyceral AcylTransferase Days After Pollination Days After Sowing Differential Display Reverse Transcription PCR Direct Hybridization Doubled Haploid Drought Responsive Elements Days To Flowering Distinctness, Uniformity and Stability Evolutionary Conserved Sequences Enzyme-Linked ImmunoSorbent Assay Early Leaf Spot Empresa Brasileira de Pesquisa Agropecuaria Ethyl Methane Sulphonate

XXII

Abbreviations

EPSP EST FA FAD FAEI FAO FAS FISH FLC G3P G3PAT GFP GISH GMO GO GPI GRAS GRAV GRV GS GST GUS HDL HEAR HI HPLC IC ICRISAT IDC IFDB IMI InDel IP IPCV ISSR JSC KAS LDL LEAR LG LLS LOD LPA LPAAT MAS MBGP MCFA MD MIPS MMT MTA

5-EnolPyruvyl 3-Shikimate Phosphate Expressed Sequenced Tag Fatty Acid Fatty Acid Desaturase (gene) Fatty Acid Elongase (gene) Food and Agricultural Organization Fatty Acid Synthase Fluorescence In Situ Hybridization Flowering Locus C Glycerol-3-Phosphate Glycerol-3-Phosphate AcylTransferase Green Florescence Protein Genomic In Situ Hybridization Genetically Modified Organism Gene Ontology Glucose-6-Phosphate Isomerase Generally Recognized As Safe Groundnut Rosette Assistor Virus Groundnut Rosette Virus Glutamine Synthetase Gene Sequence Tag Glucuronidase High Density Lipoprotein High Erucic Acid Rapeseed Harvest Index High Performance Liquid Chromatography Intercross International Crops Research Institute for the Semi-Arid Tropics Iron Deficiency Chlorosis International Flax Data Base Imidazalinones Insertion Deletion Interaction Phenotype Indian Peanut Clump Virus Inter-Simple Sequence Repeat Jaccard’s Similarity Coefficient KetoAcyl-ACP Synthase Low Density Lipoprotein Low Erucic Acid Rapeseed Linkage Group Late Leaf Spot Logarithm Of Odds LysoPhosphatidic Acid-phosphate LysoPhosphatidic Acid Transferase Marker-Assisted Selection Multinational Brassica Genome project Medium-Chain Fatty Acid Microspore-Derived Munich Information Center for Protein Million Metric tons Material Transfer Agreement

Abbreviations

MUFA NBS NIAB NILs NIR NMR NOR OL ORF PAP PBC PBNV PBP PC PCR PCV PD PEP PFM PGRC PH PMC PPR PPT PSND PT PTRD PUFA PW QTL RAPD RFLP RGA RGC RIL RKN RNAi RT-PCR RWC Sat RNA SBE SCAR SCH SCN SCR SDS SFA SI SLA SLG SLN

Monounsaturated Fatty Acid Nucleotide-Binding Sites National Institute of Agricultural Biotechnology Near Isogenic Lines Near Infrared Nuclear Magnetic Resonance Nuclear Organizing Region Oligonucleotide Ligation Open Reading Frame Phosphatidic Acid Phosphates Plant Biotechnology Center Peanut Bud Necrosis Virus Plant by Plant PhosphatidylCholine Polymerase Chain Reaction Peanut Clump Virus Pod Dehiscence PhosphoEnol Pyruvate Physical Functional Markers Plant Gene Resource of Canada Plant Height Peanut Mottle Potyvirus PentatricoPeptide Repeat PhosPhonoThricin Peanut Stem Necrosis Disease Petiole Thickness Peanut Transcript Responsive to Drought PolyUnsaturated Fatty Acid Petiole Width Quantitative Trait Loci Random Amplified Polymorphic DNA Restriction Fragment Length Polymorphism Resistant Gene Analogue Recipient Genome content Recombinant Inbred Line Root-Knot Nematode RNA Interference Reverse Transcription PCR Relative Water Content Satellite RiboNucleic Acid Single-Base Extension Sequence Characterized Amplified Region Seed Coat Hardiness Soybean Cyst Nematode S-locus Cysteine Rich (Protein) Sudden Death Syndrome Single Factor Analysis Self-Incompatibility Specific Leaf Area S-Locus Glycoprotein Specific Leaf Nitrogen

XXIII

XXIV

Abbreviations

SLW SMT SMV SNP SP SPD SRAP SRK SSD SSR STS SUS SWP TAG TBA TE TE TIGR TLCSF TSF TSM TSV TSWV TuMV TuYv UCB UNESP UPGMA USDA VFR WLL WUE WUE YAC

Specific Leaf Weight Selenocysteine Methyl Transferase Soybean Mosaic Virus Single Nucleotide Polymorphism S-locus Protein Single-Pod-Descendent Sequence Related Amplified Polymorphism S-locus Receptor Kinase Single-Seed-Descent Simple Sequence Repeat Sequence-Tagged Site Sulphoneluries Saskatchewan Wheat Pool TriAcylGlycerols Thio Barbiutric Acid ThioEsterases Transpiration Efficiency The Institute for Genomic Research Total Long Chain Saturated Fatty Acid Total Saturated Fat Thousand Seed Mess Tobacco Streak Virus Tomato Spotted Wilt Virus Turnip Mosaic Virus Turnip Yellow Virus Catolica de Brasilia Universidade Estadual de Sao Paulo Unweighted Pair Group Method with Arithmetic Mean United States Department of Agriculture Vernalization-Responsive Flowering-Time in (Brassica) Rapa Water Loss from (Excised) Leaves Water-Use Efficiency Water Use Efficiency Yeast Artificial Chromosome

CHAPTER 1

1 Soybean Geung-Joo Lee1 , Xiaolei Wu1 , J. Grover Shannon2 , David A. Sleper3 , and Henry T. Nguyen1 1

2

3

Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri, 1-31 Agriculture Building, Columbia, MO 65211, USA e-mail: [email protected] Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri, P.O. Box 160, 147 State Highway T, Portageville MO 63873, USA Division of Plant Sciences and National Center for Soybean Biotechnology, University of Missouri, 271F Life Sciences Center, Columbia, MO 65211, USA

1.1 Introduction Soybean [Glycine max (L.) Merr.], grown for its edible seed protein and oil, is often called the miracle crop because of its many uses. Seed composition averages 40% protein, 20% oil, 35% carbohydrate, and 5% ash on a dry-weight basis (Liu 1997). Each component is affected by the growing environment and varies among soybean genotypes (Fig. 1). A 50-kg bag of soybeans yields about 40 kg of protein-rich meal and 9 kg of oil. The expansion in world soybean production and the increasing importance of soybeans as a world crop are great (http://www.soystats.com). Soybean is now an essential and dominant source of protein and oil with numerous uses in feed, food, and industrial applications (Table 1). It is the world’s primary source of vegetable oil (Fig. 2) and protein feed supplement for livestock (Fig. 3). Recent nutritional studies claim that

Fig. 1. Composition of soybean seed (Liu 1997) and major factors affecting seed composition

consumption of soybean reduces cancer, blood serum cholesterol, osteoporosis, and heart disease (Birt et al. 2004). It has sparked increased demand for the many edible soybean products (Table 1). The priority for more meat in diets among the world’s population has also increased the demand for soybean protein for livestock and poultry feed. In addition to feed and food, soybean has numerous industrial applications (Table 1) such as building materials, plastics, printing inks, paints, hydraulic fluids, cosmetics, pharmaceuticals, and soy diesel fuel that burns cleaner and pollutes less than petroleum-derived fuels. World production of soybeans has tripled in the last 20 years (www.soystats.com), rising from about 70 million metric tons to over 200 million metric tons (Fig. 4). The soybean plant is bushy and green and is a legume related to clover, peas, and alfalfa. Soybean has a different seed composition than other legumes in that it is high in both protein and fat with little carbohydrate content. Soybean protein is well balanced compared to other protein sources. The oil portion of the seed is composed primarily of five fatty acids. Palmitic and stearic acids are saturated fatty acids and comprise 15% of the oil. Soybean is rich in the unsaturated fatty acids, oleic, linoleic, and linolenic which make up 85% of the oil. Soybeans are a good source of minerals, B vitamins, folic acid, and isoflavones, which are credited with slowing cancer development, heart disease, and osteoporosis (Wilson 2004). Soybean products have long been consumed by humans in various forms for its protein in China and parts of eastern and southern Asia. The center of origin of soybean is China, and domestication probably took place about 3000 to 1500 BC. From northern and southern China,

Genome Mapping and Molecular Breeding in Plants, Volume 2 Oilseeds C. Kole (Ed.) © Springer-Verlag Berlin Heidelberg 2007

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G.-J. Lee et al.

Table 1. Uses of soybean (Source: The American Soybean Association; www.soygrowers.com) WHOLE SOYBEAN PRODUCTS

OIL PRODUCTS

Glycerol, sterols, Refined soyoil fatty acids

Oleochemistry EDIBLE USES Soy diesel Seed Solvents Stock feed Soy sprouts Baked soybeans Full fat soy flour Bread Candy Doughnut mix Frozen desserts Instant milk drinks Pancake flour Pan grease extender Pie crust Sweet goods Roasted Soybeans Candies/confections Cookie ingredient/ Topping Crackers Dietary items Soynut butter Soy coffee Soybean derivates Miso Soymilk Tempeh Tofu

Soybean lecithin

SOYBEAN PROTEIN PRODUCTS Soy flour concentrates and isolates

EDIBLE USES Alimentary pastes Baby food Bakery ingredients Candy products Cereals Diet food products Food drinks Nutritional uses Hypoallergenic milk Dietary Meat products Medical Noodles TECHNICAL USES Prepared mixes Sausage casings Antifoam agents Yeast Alcohol TECHNICAL USES Beer and ale Anticorrosion agents Yeast Antistatic agents TECHNICAL USES Caulking compounds Antispattering Adhesives Composite building agents Antibiotics Margarine material Asphalt emulsions Concrete release Dispersing agents Composite building agents Material Paint Core oils Fermentation aids/ Ink Crayons nutrients Insecticides Dust control agent Fibers Electrical insulation Magnetic tape Films for packaging Paper Epoxies Fire fighting foams Rubber Fungicides Inks Hydraulic fluids Leather substitutes Stabilizing agent Inks – printing Paints – water based Shortening Linoleum backing Paper coatings Wetting agents Lubricants Calf milk replacers Particle boards Metal casting/ Plastics Cosmetics working Polyesters Paint pigments Oiled fabrics Pharmaceuticals Paints Pesticides/fungicides Pesticides Textiles Plasticizers Protective coatings Putty Soap/shampoos/ Detergents Vinyl plastics Waterproof cement EDIBLE USES Coffee creamers Cooking oils Filled milks Margarine Mayonnaise Medicinals Pharmaceuticals Salad dressings Salad oils Sandwich spreads Shortenings

EDIBLE USES Emulsifying agent Bakery products Candy/chocolate Coatings Pharmaceuticals

Soybean meal

FEED USES Aquaculture Bee foods Calf milk replacer Fish food Fox and mink feed Livestock feeds Poultry feeds Protein concentrates Pet foods HULLS Dairy Feed

Chapter 1 Soybean

soybeans moved to Korea, Japan, and other parts of Southeast Asia by the first century AD. In the 17th century soybean was introduced into Europe. It was brought into the United States in 1765, then into South America during the mid-1900s (Hymowitz 2004). It is the world’s number one oil seed, crop well ahead of rapeseed, cottonseed, peanut, and sunflower seed (Fig. 5). About 80% of the soybeans are produced in North and South America (www.soystats.com). The United States, Brazil, and Argentina are the major soybean-producing countries (Fig. 6). Soybean is a member of the genus Glycine willd., which is a member of the legume family Leguminoseae, subfamily Papilionoideae, and tribe Phaseoleae. The tribe Phaseoleae is the most important tribe of the Leguminoseae, with members that have great importance for food and feed, such as common bean, lima bean, mungbean, and cowpea. Soybeans are divided into two subgenera, Glycine (perennials) and Soja (Moench) F. J. Herm. (annuals). The subgenus Soja includes Glycine max, the cultivated soybean, and G. soja, the wild annual soybean (Hymowitz 2004). Wild soybean grows in China, Japan, Korea, Russia, and Taiwan in fields and hedgerows and along roadsides and riverbanks. G. soja plants are annual, procumbent with slender twining growth, and generally have purple flowers and tawny pubescence. Soybean plants are diploid with 20 pairs of chromosomes on which currently 20 linkage groups (LGs) have been constructed (Hymowitz 2004; Song et al. 2004). The soybean genome consists of ∼1.1 Mbp, which is relatively larger than those of Arabidopsis (7.5 times; Mbp/C) or rice (2.5 times; Mbp/C) but smaller than corn (2.4 times; Mbp/C) or wheat (14 times; Mbp/C) (Arumuganathan and Earle 1991). Cultivated soybean [Glycine max (L.) Merr.] was domesticated by humans from wild soybean G. soja. G. max also has 20 chromosomes (2n = 40) and can be intercrossed or hybridized with G. soja. Cultivated soybean is morphologically variable because of the development of specific soybean landraces with specific traits by individual farm families throughout East Asia (Hymowitz 2004). Morphological traits are often qualitatively inherited and controlled by a few genes. Plants generally grow erect and are sparsely or densely branched depending on genotype and growing conditions such as day length, soil fertility, plant spacing, and water availability. Leaves are primarily tri-

3

foliolate alternatively arranged in two opposite rows with leaflets varying from oblong to ovate to lanceolate in shape. Pubescence on leaves, pods, and stems can be dense to almost absent. Pubescence color is gray, tawny, or light tawny. The root system consists of a taproot and a large number of fibrous, secondary roots. Root nodules are 3- to 6-mm spherical swellings of the root cortex inhabited by Bradyrhizobium japonicum bacteria, which establish a symbiotic relationship with the soybean to fix N2 from the air and supply N to the plant (Lersten and Carlson 2004). Soybean flowers have bilateral symmetry and are white or purple. Seeds are spherical produced in one to four seeded pods with pod walls at maturity colored tan, brown, or black. Seeds of soybean have black, brown, green, yellow, or mottled seed coats and yellow or green cotyledons. However, commercially grown soybeans usually have yellow seed coats and yellow cotyledons. Seed size of commercially grown soybeans are generally 10 to 20 g/100 seeds but can be significantly larger than 30 g/100 seeds or significantly smaller than 6 g/100seeds in size (Lersten and Carlson 2004). Farmers generally plant soybeans in the spring to early summer. During the summer, soybean plants flower and produce 60 to 80 pods, each holding from one to four pea-sized

Fig. 2. World vegetable oil consumption 2003 (SoyStats: www.soystats.com/2004)

Fig. 3. World protein meal consumption 2003 (SoyStats: www.soystats.com/2004)

4

G.-J. Lee et al.

Fig. 4. World soybean production in million metric tons from 1965 to 2006 (projected) (Chicago Board of Trade – www.cbot.com)

beans. In the early fall, farmers harvest their crop for the beans, which are high in protein and oil. In tropical environments, soybeans can be grown year round. Efforts to seriously improve soybeans through plant breeding did not begin until the 1940s. An effort to improve soybeans through breeding has expanded into all major soybean-growing regions of the world. Most of the improvement has been through conventional breeding practices, but molecular breeding techniques have played a more important role in recent soybean improvements. The most important traits in traditional breeding programs include soybean yield, pest resistance, and seed composition. Improved yield is the most important trait in soybean breeding because it has the most impact on growers’ profits (Orf et al. 2004). Estimates indicate that soybean yields are improving at a rate of 23 kg ha−1 year−1 due to improved varieties, production practices, and higher atmospheric CO2 (Specht et al. 1999). To insure yield stability over various growing conditions, considerable effort has been made to select for resistance to soybean pathogens such as diseases and nematodes. Tolerance to abiotic stresses such as drought, flooding, and nutrient deficiency or toxicity is also of interest to increase and stabilize yields. Recently traits that improve the value and functionality of soybeans such as modification of protein and oil to give greater utility in food, health, and industrial uses have been emphasized by soybean breeders. Soybean breeding research has been directed toward modifying the fatty acid profile in the oil to expand uses in food and industrial applications.

Modifying soybean oil to lower saturates to less than 7%, increase the oleic acid content to 50 to 60%, and lower the linolenic acid content to <3% would greatly improve soybean quality for greater functionality in food and industrial applications. Other areas of emphasis include improving protein by modifying the amino acid profile, improving digestible phosphorus, and reducing antinutritional factors such as trypsin inhibitors and allergenic factors (Wilson 2004). Breeding of the self-pollinating soybean includes parental selection followed by hybridization of the selected parents. Parent selection is one of the most important decisions, affecting gene frequencies and genetic variability of populations, which eventually change population means. Germplasm resources for soybean breeding include commercial cultivars, advanced breeding lines, and plant introductions. Depending on breeding goals, parental combinations can be between superior parents for higher mean performance or between an elite line and plant introduction, which will hopefully result in transgressive segregation The genetic base, especially of US cultivars, is narrow and traces back to ca. 15 ancestors. Efforts are being made to utilize more plant introductions in breeding programs to broaden the germplasm base and to introduce new genes for yield, pest resistance, oil quality, and protein quality (Carter et al. 2004). Procedures following hybridization of the selected parents are to identify desirable individuals or families from segregating generations, which include pedigree- selection, bulk-population, and single-seed-descent methods. The pedigree-selection method is based on practical selection among F2 plants. The procedure includes making crosses

Chapter 1 Soybean

Fig. 5. World www.soystats.com)

Oilseed

Production 2003

5

(SoyStats:

Fig. 6. World Soybean Production 2003 (Soy Stats: www.soystats.com)

among the chosen parents followed by individual plant selections until the desired level of homozygosity is achieved. Usually in the F4 or F5 generation, the plant row will be bulked or composited. This seed is then used to conduct preliminary yield tests. A high percentage of the loci are homozygous in the F3 and F4 generations, and families are largely homogeneous at this time, which hopefully provides for effective selection among the composited F4 or F5 families (Poehlman and Sleper 1995). This method is desirable for selecting qualitative traits that can be identified in the early segregating generations and allows for the elimination of undesirable individuals before testing. Selection in each cycle represents expression of diverse variability from different environments, but this method is labor intensive, requires extensive record keeping of parent-progeny relationships, and is less efficient for quantitative traits. The bulk-population method is a breeding procedure for advancing segregating populations in bulk until a desired level of homozygosity is achieved. Advancement of the early generations is carried out as bulk populations without regard to individual plant selections. The breeder might allow for natural selection in the early bulk generations. This could be particularly useful for pest resistance if the environment contains natural infestations of the pest. Another example where bulk-population breeding could be successful is in the selection for seed size. Segregating populations could be harvested and desirable seed size selected for advancement in the next bulk generation. The procedure in this method is to keep planting and harvesting seeds in bulk until F3 or later generations, then plant individual spaced-

plants, single-plant thresh, finally, grow the plants in progeny rows. Progeny rows would likely be F5 but could be earlier or later generations depending upon the objectives and resources available (Poehlman and Sleper 1995). Compared to pedigree selection, the bulk-population method is simpler and less labor intensive. Natural selection can be very effective in the bulk-population method, particularly in breeding for certain biotic or abiotic stresses, by eliminating undesirable genotypes in early segregating generations. The most favored selection method in soybean cultivar development is the single-seed-descent (SSD) method. In soybean, single pod descent (SPD) modified from the SSD has been used in which one or a few pods from a single plant are harvested and bulked to grow the next generation. The procedures for the SPD method include bulking seeds in the F1 and harvesting one or a few pods per plant in early segregating generations until the desired level of homozygosity is achieved. After the desired level of homozygosity is achieved, perhaps in the F4 generation, plants are single-plant threshed and seed from each plant seeded to a progeny or plant row (Poehlman and Sleper 1995). Selected progeny rows are bulked and yield-tested in the following generation. Advancement toward homozygosity can be hastened through the use of winter nurseries. One of the major disadvantages of the single-seed-descent method is the narrow exploitation of the F2 generation where only one seed or pod represents the genetic base of the F2 population. However, single-seed descent is more often the method of choice for variety development because it is often less expensive and less labor intensive than other methods.

6

G.-J. Lee et al.

The backcrossing method is used to substitute one trait for which a recurrent parent (elite cultivar) is deficient by introgressing a desirable allele from a donor parent. The aim of this method is to rapidly recover the genomic constituents of the recurrent parent along with substituting the desirable allele after successive backcrosses. The procedure involves backcrossing desirable individuals to the recurrent parent until the desired level of the recurrent parent is captured. This method is desirable for improving an otherwise excellent genotype with the addition of one or a few desirable alleles from the donor parent. It works most easily for quantitative traits where only a few genes are involved. With the practical use of molecular markers, backcrossing is being increasingly used for quantitative traits and yield testing may be limited compared to other breeding methods. Many complex traits important in the development of cultivars can be difficult to measure or are environmentally sensitive, and thus it is often difficult to make progress in breeding programs (Lee 1995). This is especially true for traits with low heritability such as soybean yield. For these traits, extensive testing is required over multiple locations and years prior to release of the improved variety. As outlined above, with some classical breeding methods release of new soybean cultivars can take 8 to 10 years. Also, it is difficult to combine numerous important traits simultaneously into cultivars in the processes of classical breeding. Technological advancements in molecular biology have allowed the opportunity for genetic dissection of complex traits affected by several genes. The association of phenotypic data with genetic markers among lines segregating for genes conditioning a complex trait provides a way to identify a set of specific quantitative trait loci (QTLs) affecting that trait. New germplasm in the classical breeding system is explored by phenotypic evaluation of the individuals collected, but identification of superior genotypes and their genetic relationships can be achieved using DNA markers associated with desirable traits. Also, information on DNA markers closely linked to a gene of interest is useful for selecting individuals without phenotypic investigation in breeding programs, especially for agronomic traits that are difficult to phenotype. Better estimation of breeding values at identified loci for traits with low heritability might be achieved using molecular markers for selection (Sills and Nienhuis 1998). There are many traits whose genetic potential is masked by epistatic interactions between genes or by

genes in the repulsion linkage phase. In this case, dissection and linkage breakdown of the unfavorable relationship at the genetic level will be obtained by using molecular markers (Lee 1995). DNA marker analysis can measure the effect of an individual locus at the allelic level. For example, the contribution of alleles from each parent at a particular locus can be determined separately from other loci affecting the trait. This provides an effective method to pyramid desired genes for complex traits or traits with low heritability into adapted varieties (Orf et al. 2004). Therefore, high-throughput molecular breeding techniques, coupled with traditional breeding approaches, will be more effective and more efficient for introducing important quantitative traits into soybean. Large sets of DNA markers have been developed that cover the soybean genome. Several highdensity genetic maps of soybean are available (Cregan et al. 1999; SoyBase, http://soybase.ncgr.org/cgibin/ace/generic/search/soybase). Orf et al. (2004) give a history of molecular-marker development in soybean. Soybean restriction fragment length polymorphisms (RFLPs) were first used in the late 1980s. Random amplified polymorphic DNA (RAPD) and DNA amplification fingerprint (DAF) markers were developed in the early 1990s. In the mid-1990s simple sequence repeat (SSR) markers were introduced. SSR markers are highly polymorphic in elite soybean populations. They are adaptable to automation, affordable, and amenable to high-throughput mapping technology. Single nucleotide polymorphism (SNP) markers are on the horizon and will have a greater impact on molecular breeding than SSR and other markers because they are even more polymorphic, cheaper, easier to use, and also very adaptable to high-throughput mapping methods (P. Cregan, pers. comm.). Molecular markers have been used to map the genomic location of both major genes and QTLs for many agronomic, physiological, pest-resistance, and seed-composition traits in soybeans. Orf et al. (2004) reported at least 319 QTLs significantly associated with various quantitative traits, with 162 unique QTLs conditioning 10% or more of the phenotypic variation. Most DNA marker analysis has been applied to discovery of QTLs for traits of high economic importance to soybean growers. The highest priority generally has been given to mapping QTLs for resistance to pests such as soybean cyst nematode and for seed protein and oil.

Chapter 1 Soybean

Marker-assisted selection (MAS) with genetic markers among soybean breeders is of great interest but not widely used at present. Markers will be widely applied only after they have proven effective and economical. Soybean-breeding programs conduct simultaneous selection for ten or more traits over several environments and will use MAS as a tool to enhance conventional breeding methods. Potential uses of genetic markers in soybean breeding include: (1) selection of parental combinations with the highest potential for producing a superior variety, (2) use in backcrossing for introducing a trait and for recovering the recurrent parent phenotype more quickly, (3) trait selection in segregating populations to select only phenotypes for further testing with desired loci or QTLs of interest, and (4) identification of positive alleles from exotic, unadapted germplasm (Orf et al. 2004).

1.2 Construction of Soybean Genetic Maps 1.2.1 Soybean Genetic Map The main goal of crop breeders is to develop new cultivars that have desired traits superior to the existing cultivars. A conventional breeding method is to make a cross of two parents, followed by selecting recombinants with the trait of interest among the segregation progenies, which is laborious and time consuming, requiring several crosses, generations, and trait evaluations. One technical handicap of the conventional breeding procedure is linkage dragging of tight linkage of the desired locus with undesired loci. Several types of genetic markers and molecular breeding strategies are now available to crop breeders that help them overcome those problems that arise in conventional breeding. Also, DNA markers are applicable to locations of the genes of interest and subject to dissection of the gene effect. One of the powerful applications of genetic markers is linkage analysis and map construction. Two types of genetic markers are morphological and molecular markers. Additional details on the DNA markers available in soybean are described in Sect. 1.5 in the current soybean chapter. In this section, we introduce the current state of

7

progress in soybean genetic maps that have been constructed at different institutes (Tables 2 and 3).

1.2.2 First Generation of Soybean Maps Constructed Based on a Single Population Prior to the use of DNA markers, the first soybean linkage map was constructed using the 57 classical markers spanning ca. 420 cM (Palmer and Kilen 1987). Another classical soybean genetic map contains 49 markers and covers 530 cM (Palmer and Kiang 1990). The first genetic map of soybean published in 1990 was based on RFLP markers (Apuya et al. 1988; Keim et al. 1990a). The complete soybean genetic map was constructed using 150 RFLPs and three classical markers in an F2 segregating population from the interspecific cross of G. max (A81-356022) and G. soja (PI 468916) by USDA/Iowa State University. Twentysix genetic LGs containing ca. 1200 recombination units were reported in the map. G. soja (Seib. and Zucc.) is a wild relative of the current domesticated soybean and is considered the progenitor of G. max. The interspecific population used in the study exhibited higher polymorphism (40%), which was a twofold increase over two G. max parents used earlier (Apuya et al. 1988). Except for 20 markers that remained unlinked, 130 linked markers were used to construct 26 LGs. This segregating population was also used to associate eight agronomic traits (R1, R8, seed-fill length, stem diameter, canopy height, stem length, internode length, leaf width, and leaf length) with RFLP markers, and those traits were linked to cosegregating markers (Keim et al.1990a). Some DNA probes were found to be multiple RFLP loci, indicating that soybean had many duplicated regions. In fact, ∼90% of all RFLP probes were reported to have duplicated loci in soybean (Shoemaker et al. 1996). Based on the initial construction of the map, Diers et al. (1992a,b) expanded the map to 246 markers in 31 LGs covering 2,147 cM. The genetic map was continuously saturated with the additional 100 RFLP markers having 25 LGs encompassing 355 RFLPs, 10 RAPDs, 3 classical markers, and 3 isozymes (Shoemaker and Olson 1993). This population was used for integrating different soybean maps along with other genetic maps (Cregan et al. 1999). The first map constructed with families from the intraspecific cross of two G. max parents, Minsoy

31 35 26 29 20 35 21

68 F2 lines

69 F3 lines 284 RILs 60 F2 lines

190 F2 lines

149 F2 lines 184 RILs

E.I. DuPont Bonus × G. soja PI 81762 Minsoy × Noir 1

1400 3596

1550 1981 1056 1486 2909

1200 2147 3371 2678 3 1 7 7

5 3

26 23 22 28 20

60 F2 lines 59 F2 lines

240 RILs 57 F2 lines 59 F2 lines

240 RILs 57 F2 lines 233 RILs 240 RILs

25

59 F2 lines

A81-356022 (G. max) × PI 468916 (G. soja) Clark × Harosoy A 81-356022 (G. max) × PI 468916 (G. soja) Minsoy × Noir 1 Clark × Harosoy A 81-356022 (G. max) × PI 468916 (G. soja) Minsoy × Noir 1 Clark × Harosoy Minsoy × Archer Archer × Noir 1 2413 2787 2534

1004 3003

2473

2 7 10

4

4

Linkage Map group length (cM) Isozyme

Population size and type

Mapping population

10 14 24

20 3

3

Classical

4 4

5 6 13 13 5

3 3 3

Classical

Table 3. Integrated soybean genetic maps (modified from Shoemaker et al. 2004)

Misuzudaizu × Moshidou Gong503 Noir 1 × BARC-2 Kefeng No. 1 × Nannong 1138-2

Clark × Harosoy

26 31 23 21

59 F2 lines

G. max A81-356022 × G. soja PI468916

Linkage Map group length (cM) Isozyme

Population size and type

Mapping population

209 95 709

110 501

358

RFLP

39 229

148 224 110 110 401

50 238 355 600+

RFLP

57 73

8 10

10

Marker type RAPD

17

8 8 1

10

Marker type RAPD

11 6

AFLP

105

AFLP

412 339 1015

486

SSR

25 219

40 96

45

SSR

Table 2. Initial and saturated soybean genetic maps constructed based on single population (modified from Shoemaker et al. 2004)

633 523 1849

138 1004

375

Total

207 452

156 276 138 178 503

153 253 371 600+

Total

Song et al. 2004

Cregan et al. 1999

Shoemaker and Specht 1995

Reference

Matthews et al. 2001 Zhang et al. 2004

Lark et al. 1993 Mansur et al. 1996 Shoemaker and Specht 1995 Akkaya et al. 1995 Yamanaka et al. 2001

Keim et al. 1990a Diers et al. 1992a,b Shoemaker and Olson 1993 Rafalski and Tingey 1993

Reference

8 G.-J. Lee et al.

Chapter 1 Soybean

(PI 27890) and Noir 1 (PI 290136) (MN), was released by the University of Utah (Lark et al. 1993). Of 156 marker loci used for segregation analysis among progeny, 132 markers were genetically linked and placed into 31 LGs with a LOD (logarithm of odds) score of 3 or greater. These markers include RFLPs, five morphological and biochemical markers [w1 (flower color), textitI (seed-coat color), Pb (pubescence tip), Ep (seed-coat peroxidase), and R (hilum color)], and three isozymes [Aco4 (aconitase), ME (malic enzyme), Idh2 (isocitrate dehydrogenase)]. These progenies showed a high degree of trangressive variation, resulting in many extreme offspring that surpassed either parent in reproductive and morphological seed traits and yield. The LGs comprised 1,550 cM of the soybean genome. Based on RFLP features that were not usual in previous soybean maps, transposable elements in soybean were proposed to exist, which possibly led to the distribution of the repeated elements. Recombinant inbred lines (RILs) were developed from this cross, and the restriction fragments were found to be stable sufficiently through the F1 0 generation (Mansur et al. 1993). The first G. max population was further investigated for a genetic map and an association analysis using newly developed RILs (Mansur et al. 1996). RILs are good resources for genetic studies because they were inbred to near homozygosity, which provides more opportunity for segregation and recombination and less environmental attribution to particular traits necessary for establishing small genetic differences. This population developed from a cross of Minsoy and Noir 1 consisted of 284 F7 -derived RILs by SSD and was used for locating genetic loci controlling several agronomic traits including days to flower (R1), days to maturity (R8), reproductive period (R8R1), plant height, lodging score, height divided by lodging, seed protein and oil content, seed size, yield, seed number, yield divided by height, and leaf traits (width, length, area). The total linkage of the genetic map was 1,981 cM, encompassing 224 RFLPs, 45 SSRs, 1 isozyme, and 6 classical markers in 35 LGs. Sixty F2 isogenic lines from the cross of the nearisogenic parents of the cultivars Clark and Harosoy at the University of Nebraska were developed to initiate map integration (Shoemaker and Specht 1995). This population was used for the construction of a genetic map using classical genetic markers as well as molecular markers, consisting of 13 classical markers, 7 isozymes, 110 RFLP markers, and 8 RAPD markers. An initial effort was made to integrate the

9

various marker types into a common linkage map using this population, where anchoring RFLP markers were used to identify the same LGs between the maps from the above two populations (Shoemaker and Specht 1995). Akkaya et al. (1995) saturated the previous genetic map with the developed soybean SSR markers, which overcame the problems of using the RFLP markers with lower polymorphisms and higher genome duplication. This new integrated map using SSR markers resulted in higher resolution and solved ambiguous assignments of many RFLP loci across mapping populations. Linkage analysis using the SSR markers exhibited a total map length of 1,486 cM in 29 LGs, which yielded a relatively limited amount of clustering of soybean map and highly informative loci associated with five out of seven pigmentation traits, four of the six morphological traits, and three of the seven isozymes in the segregating population.

1.2.3 Integrated Soybean Genetic Maps The advent of comparative mapping and new software brought integration of the different soybean genetic maps. The classical LGs having many informative loci controlling various morphological and biochemical traits were integrated into the molecular linkage map (Shoemaker and Specht 1995). An interspecific population between G. max and G. soja and an intraspecific population derived from the cross of the near-isogenic lines (NILs) in the Clark and Harosoy were used to merge more of the LGs of the classical linkage map with the soybean RFLP map. The integration effort resulted in about half of the 19 soybean classical LGs that were found to be associated with those morphological and biochemical traits. Along with classical markers, the first widely accepted map in soybean was developed from three previous maps from the populations of A81-356022 × G. soja PI 468916, Minsoy × Noir 1, and Clark × Harosoy, which were widely used as a frame of linkage mappings for various traits associated with molecular markers (Cregan et al. 1999). Marker loci were grouped at LOD 5.0 within each of the three mapping populations using Mapmaker 3.0b (Lincoln and Lander 1993) and then aligned with the order of the genetic loci by repetitive use of the Ripple command of Mapmaker at a window size of 6 among the populations and the classical markers. A total of 1423 unique loci were mapped in one or more of three popula-

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tions distributed among the 20 plus LGs, presumably corresponding to the 20 pairs of soybean chromosomes. These loci encompass 606 SSRs, 689 RFLPs, 79 RAPDs, 11 amplified fragment length polymorphisms (AFLPs), 10 isozymes, and 26 classical loci. This integrated map has 544 new loci that had not been reported previously (Akkaya et al. 1995; Mansur et al. 1996). This consensus map exhibited the consistency of marker order and genetic distance among three different populations. A genetic map based on the Minsoy × Noir 1 population that was used for the construction of the consensus map was further positioned with additional 412 SSR loci and improved to 67 distinct intervals with less than 0.01 cM between adjacent SSR markers, but 36 intervals of at least 20 cM or 79 intervals of at least 10 cM distance, indicating the necessity of saturating more genetic markers into the genomic gaps. The most recent complete integration of the maps based on the five mapping populations consists of a total of 1845 markers, including 1,010 SSRs, 798 RFLPs, 73 RAPDs, 6 AFLPs, 24 classical traits, 10 isozymes, and 12 others (Fig. 7; Song et al. 2004). These five populations consist of a USDA/Iowa State University population of F2 - derived A81-356022 and PI 468916 (G. soja) families, three RIL populations from the University of Utah [284 families from the cross of Minsoy (PI 27890) and Noir 1 (PI 290136) (MN), 233 families from the cross of Minsoy and Archer (MA), and 240 families from the cross of Noir 1 and Archer (NA)], and a population derived from the cross of NILs of Clark and Harosoy from the University of Nebraska. Four hundred fourteen new SSR markers were further added to the map constructed by Cregan et al. (1999), which improved map resolution from 12 to 29 markers saturated to each LG and with 90 SSRs positioned to 30 gaps of 20 cM or more in the previous map. Information on primer sequence, polymerase chain reaction (PCR) reagents for amplification, and thermocycling profile for amplification of all publicly accessible SSR markers is available at the SoyBase Website (http://soybase.agron.iastate.edu).

which includes disease- related genes, agronomic trait-related genes, metabolite genes, herbicide resistant genes, growth and morphology genes, and seed-quality genes. The current section of this soybean chapter deals with gene reports and location in the soybean map, which will be helpful for future directions of gene mapping of those genes that have not been located. A summary of the most useful and well-recognized genes is presented in Table 4.

1.3.1 Resistant Genes to Soybean Diseases

Bacterial blight caused by Pseudomonas syringae pv. glycinea causes small and circular lesions and yellow-to-brown spots on leaves, especially leaves on the top of plants (http://www.soydiseases.uiuc.edu/ index.cfm). Genes resistant to bacterial blight have been reported Rpg1 (resistant to P. syringae pv. glycinea race 1) (Mukherjee et al. 1966; Keen and Buzzel 1991; Ashfield et al. 1998), Rpg2 (resistant to race 4) (Palmer and Kilen 1987), Rpg3 (resistant to race 4) (Palmer and Kilen 1987), and Rpg4 (resistant to race 4) (Palmer and Kilen 1987). Of these genes, Rpg1 and Rpg4 were mapped in the LG-F and N, respectively, having flanking markers K644 and B212 for Rpg1, and B162_1 and Satt521 for Rpg4 (Palmer and Kilen 1987; Ashfield et al. 1998). Bacterial pustule caused by Xanthomonas axonopodis pv. glycines leads to premature defoliation, which in turn leads to yield decrease by reducing seed size and number. Initially small and pale green spots form pustules in the center of lesions, but mostly later under the leaf surface. The reported recessive resistant gene, rxp, was located between SSR markers Satt372 and Satt014 in LG-D2 (Palmer and Kilen 1987; Narvel et al. 2001a). A molecular pedigree analysis confirmed the locus and resistant source of the cultivar CNS, indicating that the flanking markers would be useful for MAS for lines resistant to this disease. Brown stem rot is a fungal disease caused by Phialophora gregata, which causes greater yield loss when wet cool weather during the plant pod-fill stage is followed by hot, dry weather. Disease symptoms 1.3 are visualized around leaf vein tissues at the R3 or Mapping of Genes in Soybean R4 stage, and a dark brown discoloration of the vascular elements and pith is evident in longitudinally Currently a total of 469 soybean genes have been split stems. Three resistant genes (Rbs1, Rbs2, Rbs3) reported in a USDA-ARS soybean genetics and were reported from resistant sources of L78- 4094, genome database (http://soybase.agron.iastate.edu), PI 437833, and PI 437970, respectively (Hanson et al.

Chapter 1 Soybean Fig. 7. Consensus genetic map of soybean representing 20 linkage groups integrated from five mapping populations (Song et al. 2004). The mapped markers consisted of 1015 SSR, 798 RFLP, 73 RAPD, 6 AFLP, 24 classical traits, 10 isozyme, and 12 others

11

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G.-J. Lee et al.

Fig. 7. (continued)

Chapter 1 Soybean Fig. 7. (continued)

13

14

G.-J. Lee et al.

Fig. 7. (continued)

Chapter 1 Soybean

15

Table 4. Genes mapped in soybean genetic map Reaction

Trait

Genea

Linkage groupb

Flanking markers (gene) and distance

Reference

Disease

Bacterial blight

Rpg1 Rpg2 Rpg3 Rpg4 rxp Rbs1 Rbs2 Rbs3 Rcs1 Rcs2 Rcs3 Rpm1 Rpm2 Rmd Rps1 Rps2 Rps3 Rps4 Rps5 Rps6 Rps7

F – – N D2 J J J – – J – – J N J F G G G N

K644-B212 (5.6 cM)

Ashfield et al. 1998

B162_1-Satt521 (5 cM) Satt372-Satt014 Satt215-Satt431 (32.9 cM) Satt244-Satt431 (33.8 cM) K375-Satt431 (14 cM)

Narvel et al. 2001a Bachman et al. 2001 Bachman et al. 2001 Lewers et al. 1999

Satt244-Satt547 (1.5 cM)

Mian et al. 1999

A233-A724 (3.8 cM) Satt159-Satt152 (0.5 cM) Satt287-Satt547 (40.6 cM) HSP176-Satt114 (2.2 cM) Satt109-Satt472 (35.5 cM) T005 Satt472-Satt191 (2.1 cM) R022-1-K395-2 (29.1 cM)

Rps8 Rdc1 Rdc2 Rdc3 Rdc4 Rfs Rpp1 Rpp2

A2 – – – – G – –

Polzin et al. 1994 Demirbas et al. 2001 Demirbas et al. 2001 Demirbas et al. 2001 Demirbas et al. 2001 Diers et al. 1992a Demirbas et al. 2001 Lohnes and Schmitthenner 1997 Burnham et al. 2003

Rpp3

–

Rpp4 Rsv1 Rsv3 Rsv4 Rpv1 rpv2 rhg1 rhg2 rhg3 Rhg4 Rhg5

– F – D1b – – G – – A2 –

Bacterial pustule Brown stem rot

Frog leaf spot

Downy mildew Powdery mildew Phytophthora root and stem rot

Stem canker

Sudden death syndrome Soybean rust

Soybean mosaic virus

Peanut mottle virus Soybean cyst nematode

a

Sat_040-Satt228 (43 cM)

Satt309-Bng122 (4.5 cM)

Satt309-O103 (2.5 cM)

Meksem et al. 1999 Mclean and Byth 1980 Hartwig and Bromfield 1983 Hartwig and Bromfield 1983 Hartwig 1986 Yu et al. 1994; Hayes et al. 2000 Hayes et al. 2000 Boerma and Kuhn 1976 Shipe et al. 1979 Meksem et al. 1999

A085-i locus (16.6)

Cregan et al. 1999

K644-B212 (4.1 cM) Satt542-Satt588 (12.5 cM)

Genes controlling disease resistance, nodule formation, abscission induction, normal root, late and insensitive long daylength or long juvenile trait, long or normal stem/petiole, normal height, three foliolate leaflets and normal leaf shape, normal density and shape of pubescence, fertility, efficient nutrient responses, green leaf and yellow seed, light hilum and yellow/nonsaddle seed coat, tawny pubescence, normal palmitate/stearate/oleate/linolenate content b Linkage group based on integrated USDA consensus genetic map of soybean

16

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Table 4. (continued) Reaction

Herbicide

Trait

Genea

Linkage groupb

Reniform nematode

rrn rn1 rn2 Rmi1 hs1 hs2 hs3 Als1 Hb Hm CP4 Rj1

– – – – – – – – – N – D1b

rj2 rj3 rj4 Rj5 rj6 Rj7 rj7 rfg1 Ab Rn1 Rn2 E1 E2 E3 E4 E5 e6 E7 j Dt1 dt2 F Lps1 Lps2 S(or e1) Df2 Df3 Df4 Df5 Df6 Df7 Mn Pm lf1 Lf2 Ln

J – G1∗ – – – – – – – – C2 O L – – – C2 – L CLG18∗ D1b – – – CLG6∗ – – CLG1c∗ – – – – A2 CLG16∗ I

Root-knot nematode Sulfonylurea

Bentazon Metribuzin Roundup Nodulation

Growth, flower and morphology

Leaf abscission Root necrosis Flowering and maturity

Stem, petiole, plant growth

Leaf form

∗

Genes mapped on classical linkage group (CLG)

Flanking markers (gene) and distance

Reference

Williams et al. 1981 Kosslak et al. 1997 Kosslak et al. 1997 Luzzi et al. 1994

Rps1

Palmer et al. 2004

Idh1 rmd-A233 (3.9 cM)

Devine and Kuykendall 1996 Polzin et al. 1994

ACTAAA312 (25.3 cM)

Matthews et al. 2001

E7 B157_1-Mng211(20 cM) Pgd1-Dt1

Cober and Voldeng 2001

T(3.9 cM)

Cober and Voldeng 2001

A461_1-Satt006 Satt141-Satt703 (15 cM)

Kiang 1990 Karakaya et al. 2002

Satt333-Satt133 (7.5 cM)

Cregan et al. 1999 Devine 2003 Cregan et al. 1999

Satt270-Sat_268 (5.0 cM)

Chapter 1 Soybean

17

Table 4. (continued) Reaction

Trait

Pubescence type

Seed-coat structure

Fertilitysterility

Seed shape Synaptic sterility

Structural sterility Partial sterility Complete sterility

Mineral toxicity or deficiency

Nutrient response

Pigmentation

Chlorophyll deficiency

Genea

Linkage groupb

Lo Lnr lw1lw2 lb1lb2 Pa1 Pa2 p1 P2 Pb Pc pd1 pd2 ps B1 B2 B3 N Shr St2 St3 St4 St5 St6 St7 St8 Fs1Fs2 Ft Msp Ms1 Ms2 Ms3 Ms4 Ms5 Ms6 Ms7 Ms8 Ms9 Fe Np Ncl Nr V1 V2 Y3 Y4 Y5 Y6 Y7(Y8) Y9

– – – – B1 F K I E – D1a CLG16∗ H F – – – F – – – F-CLG8c∗ – –

Flanking markers (gene) and distance

Reference

B031-Satt197 (6.8 cM) Sct_188-Satt072 (1.8 cM) Sat_126-Satt588 (8.8 cM) Ln Satt411-Sat_124 (3.0 cM)

Lee et al. 1999 Lee et al. 1999 Cregan et al. 1999 Xu et al. 2000 Cregan et al. 1999

C063_1 (6 cM) Sat_158-Satt302 (7.5 cM) Sat_375-Sat_313 (3.8 cM)

Cregan et al. 1999 Devine 2003 Cregan et al. 1999 Chen and Shoemaker 1998

Satt490-Satt144 (4.1 cM)

Chen and Shoemaker 1998

ms6

Ilarslan et al. 1997

W1 Pgm1

Sneller et al. 1992; Ilarslan et al. 1997

W1

Sneller 1992, Ilarslan et al. 1997

Ln

Cregan et al. 1999

Satt411-A661 (44.6 cM)

Cregan et al. 1999

– – F-CLG8c∗ O-CLG15 – – – F-CLG8c∗ – – – – – – – I-CLG4∗ – – – – – – E

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Table 4. (continued) Reaction

Trait

Flower color

Seed coat and hilum color

Pubescence color Fatty acid

Palmitic acid

Stearic acid

Oleic acid Linolenic acid

Genea

Linkage groupb

Flanking markers (gene) and distance

Reference

Y10 Y11 Y12 Y13 Y14 Y15 Y16 Y17 Y18 Y19 Y20 Y21 Y22 Y23 D1(D2) G1 G2 G3 W1 W3 W4 Wm Wp I K1 K2 K3 O R

N F C2-CLG1∗ A2 – – – E – – – – – F D1a D1a – – F – – F-CLG8∗ D1b A2 B1 – – A2 K

Satt022 Df2 Sp1-E1 Sat_115-A638_1 (3.3 cM)

Zou et al. 2003 Mahama et al. 2002 Cregan et al. 1999 Cregan et al. 1999

Pb

Devine 1998 Burzlaff and Palmer 1999

T Td Fap1 Fap2 Fap3 Fap4 Fap5 Fap6 Fap7 Fas St1 St2 Ol Fan1 Fan2 Fan3

C2 – – A_611-A_537 D – – – – – B2-CLG17∗ Satt070 (12.3 cM) – – – Idh2, Fas B2 – –

Burzlaff and Palmer 1999

Ms6-St5 Satt071-Sat_169 (3.9 cM) Satt071-D1 (3 cM)

Mahama et al. 2002 Lohnes et al. 1997 Luquez and Guiamet 2001

Sat_039-Sat_298 (4.4 cM) Dihydroflavonol 4-reductase W1 Satt660-K011_4 (11 cM) Satt315-Sat_400 (4.1 cM) Satt197-Satt298 (25.3 cM)

Karakaya et al. 2002 Fasoula et al. 1995

Sat_212-Satt424 (4.3 cM) Sat_352-Sat_293 (5.5 cM) E7 (3.9 cM)

Hegstad et al. 2000b Senda et al. 2002 Cregan et al. 1999 Chen and Palmer 1998 Weiss 1970 Karakaya et al. 2002 Cober and Voldeng 2001 Primomo et al. 2002 Nickell et al. 1994 Bravo et al. 1999 Stoltzfus et al. 2000 Stoltzfus et al. 2000 Stoltzfus et al. 2000 Spencer et al. 2003 Rahman et al. 1997 Rahman et al. 1997 Rahman et al. 1996 Brummer et al. 1995

Chapter 1 Soybean

1988; Willmot and Nickell 1989). Those three genes resistant to brown stem rot reside at the close region, linking to SSR marker Satt431 on LG-J (Bachman et al. 2001). Frogeye leafspot is a fungal disease caused by Cercospora sojina. Symptoms include small, gray spots with reddish brown borders on the upper leaves in hot summer. The fungus infects seed, and consequently the seed coat of infected seeds turns gray. Resistance was found to be in the varieties Lincoln and Wabash caused by a dominant gene Rcs1 resistant to race 1 (Athow and Probst 1952), in Kent by Rcs2 resistant to race 2 (Athow et al. 1962), and in Davis by Rcs3 resistant to race 5 and all known Brazilian isolates (Phillips and Boerma 1982; Mian et al. 1999). The Rcs3 gene was mapped on LG-J, near SSR markers Satt244 and Satt547 (Mian et al. 1999). Downy Mildew is caused by an obligate fungal disease, Peronospora manshurica. Symptoms of downy mildew appear on leaf, pod, and seed, as light-green to yellow spotted leaves, small pods, and a pale coating of spores on seeds. Genes Rpm1 and Rpm2, which are resistant to downy mildew race 1 and race 2, respectively, were reported in soybean (Bernard and Cremeens 1971; Lim 1989). However, none of these was located in the soybean genetic map. Powdery mildew is a fungal leaf disease, Microsphaera manshurica, with greater incidence in seasons with cooler than normal temperatures. The first symptom is usually a circular area of white to light gray, superficial powdery growth on the upper leaf surface. A resistant gene, Rmd, was closely mapped with other soybean genes (Rps2 and Rj2 for phytophthora root and stem rot resistance and ineffective bradyrhizobia nodulation, respectively) with flanking markers of RFLP markers A233 and A724 within a 3.8-cM region on LG-J (Polzin et al. 1994). Phytophthora root and stem rot caused by Phytophthora sojae is a fungal disease that occurs in wet and poor drainage soil. To date, eight dominant loci resistant to the Phytophthora rot have been reported in soybean, including Rps1 (Bernard et al. 1957), Rps2 (Kilen et al. 1974), Rps3 (Mueller et al. 1978), Rps4 (Athow et al. 1980), Rps5 (Buzzell and Anderson 1981), Rps6 (Athow and Laviolette 1982), Rps7 (Anderson and Buzzell 1992), and Rps8 (Burnham et al. 2003). Linkage analysis indicated genomic locations of Rps1 and Rps7 on LG-N, Rps2 on LGJ, Rps3 on LG-F, Rps4, Rps5, and Rps6 on LG-G, and Rps8 on LG-A2 (Demirbas et al. 2001; Weng et al. 2001; Burnham et al. 2003).

19

Stem canker caused by the fungus Diaporthe phaseolorum leads to symptoms of reddish brown, slightly sunken lesions at the base of branches or leaf petioles and eventually brown discoloration inside the stem. Four genes resistant to the stem canker disease reported include Rdc1, Rdc2, Rdc3, and Rdc4 (Kilen and Hartwig 1987), but none of these has yet been mapped in the soybean molecular map. Sudden death syndrome caused by the fungus Fusarium solanii has symptoms of initially scattered chlorotic spots between the veins on leaves and, later, cupped and curled leaves. Resistant gene Rfs was first reported in a cultivar Ripley and mapped on LG-G (Stephens et al. 1993; Meksem et al. 1999). Soybean rust was first reported in Japan in 1902 and spread to other continents including other Asian countries, Africa, and, recently, Central and North America. The spread of soybean rust has rapidly picked up steam in recent years. For example, in Brazil in 2002, soybean rust was observed in about 4% of soybean-growing acres, but it was observed in 99% of those same acres in 2003. Soybean rust is caused by the fungi Phakopsora pachyrhizi and P. meibomiae. The host range is quite broad, with at least 41 and 34 natural hosts including kudzu and soybean for P. meibomiae and P. pachyrhizi, respectively. Four dominant genes have been reported to be resistant to soybean rust, which include Rpp1, Rpp2, Rpp3, and Rpp4, and none has been mapped yet (McLean and Byth 1980; Hartwig and Bromfield 1983; Hartwig 1986). Soybean mosaic virus (SMV) causes three responses including resistance, susceptibility with symptoms of blistering and cupping of the leaves from the initial mosaic, and necrotic lesions on leaflets, petioles, and stems. Many resistant genes respond differently to various SMV strains, including Rsv1, which has seven alleles (Rsv1-t, Rsv1-y, Rsv1-m, Rsv1-k, Rsv1-s, Rsv1-r, Rsv1-sk), Rsv3, and Rsv4 (Gunduz et al. 2002). Those SMV resistant genes were assigned to different soybean genome in the genetic map (Table 4). Peanut mottle potyvirus (PMV) causes systemic dark green mottling, crinkling, blistering, and malformation of soybean leaves. There are two genes resistant to PMV (Rpv1 and rpv2) identified in the cultivars of Dorman and Peking, but neither has been located yet in the soybean map (Boerma and Kuhn 1976; Shipe et al. 1979). Soybean cyst nematode (SCN, Heterodera glycines) is the most destructive pathogen worldwide affecting soybean yield (Wrather et al. 2001). Of 130

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SCN resistant cultivars released in the USA, most (>95%) have resistant alleles derived from a plant introduction PI 88788 due to favorable agronomic performance (Skorupska et al. 1994). Inheritance of SCN resistance was first reported in a cultivar called Peking, and three recessive genes (rhg1, rhg2, rhg3) were assigned (Caldwell et al. 1960). In Peking, a dominant resistance gene (Rhg4) was later identified that is found to be linked to the i locus underlying seed coat color (Matson and Williams 1965). Another resistant gene, Rhg5, was reported to be in PI 88788 (Rao-Arelli 1994). It has been suggested that genes necessary for full resistance to SCN include Rhg1–5 and may extend to ten independently inherited genes. Currently, two genes, rhg1 and Rhg4 on LG-G and A2 respectively, have been independently isolated and characterized as strong candidates for resistance to SCN (Hauge et al. 2001; Lightfoot and Meksem 2002). Concibido et al. (1994) first reported the genomic region of the Rhg4 gene in a segregation population, which was closely linked to the i locus on LG-A2 and was confirmed by independent studies (Mahalingam and Shorupska 1995; Webb et al. 1995; Chang et al. 1997). Since the RFLP marker flanking rhg1 (K069) on LG-G was initially found to be associated with SCN resistance in PI 209332, followup research has confirmed the presence of the gene in different resistant soybean sources such as Peking, PI 88788, PI 90763, and PI 437654 (Concibido et al. 1994, 1995, 1997; Webb et al. 1995; Mudge et al. 1997). Reniform nematode, Rotylenchulus reniformis, was first found on cowpea roots in Hawaii and first reported as a parasite of cotton in Georgia and of tomato in Florida in the USA. Today, it is found worldwide including in tropical, subtropical, and warm temperate zones in South America, North America, the Caribbean Basin, Africa, southern Europe, the Middle East, Asia, Australia, and the Pacific (Ayala and Ramirez 1964). Inheritance of the reniform nematode response indicated that the recessive rrn gene is related to resistance in soybean (Williams et al. 1981). However, the gene has not yet been located in the soybean map. Root-knot nematode by Meloidogyne spp. causes roots to produce characteristic swellings or galls that are easily distinguishable from nitrogen-fixing nodules. Based on gall counts on soybean roots in F1 , F2 , and F3 generations, a single additive gene for resistance to galling, designated Rmi1, was found (Luzzi et al. 1994). Two QTLs were reported that are associated with low gall numbers on roots on LG-O and G,

but the gene location has not yet been located (Li et al. 2001).

1.3.2 Genes for Herbicide Resistance There are several reported genes that are resistant to herbicides, including genes tolerant or with enhanced tolerance to bentazon (dominant Hb), metribuzin (dominant Hm), sulfonylurea (recessive hs1, hs2, hs3), roundup (CP4), and sulfonylurea (dominant Als1) (Palmer et al. 2004). The Hm tolerant to metribuzin was located 7 cM from Rps1, which is resistant to Phytophthora root and stem rot on LG-N.

1.3.3 Nodulation Genes During interaction between Rhizobium and legume plants, formation of the peribacteroid membrane, where the bacteria are protected from direct contact with the host cell cytoplasm, is essential for rhizobia function and maintenance inside the host cells (Verma 1992). The recessive gene rj1, which is closely mapped to the f (fasciated stem) and Idh1 (isocitrate dehydrogenase) loci in LG-D1b-W (classical LG 11), controls the nonnodulation in genotypes of T181 and T201 (Devine and Kuykendall 1996). The dominant Rj2 detected in cultivars Hardee and CNS is associated with ineffective nodulation response with Bradyrhizobium japonicum and was mapped in LG-J, which is closely linked to the phytophthora resistance gene Rps2 (Devine et al. 1991; Polzin et al. 1994). Also, the dominant allele Rj4 showed ineffective nodulation in response to B. elkanii and was mapped in the soybean genetic map (Matthews et al. 2001). The other nodulation-inducing genes (rj3, Rj5, Rj6, Rj7, rfg1) and hypernodulating gene (rj7) have not yet been mapped (Vest 1970; Pracht et al. 1993; Devine and Kuykendall 1994).

1.3.4 Genes for Growth Habit, Flowering, and Morphology A gene associated with abscission, Ab, was reported, but it has not yet been mapped (Probst 1950). The shedding of plant parts, both reproductive and veg-

Chapter 1 Soybean

etative, is important for harvest, reproduction, plant defense, resistance to drought and flooding, and continuation of perennial growth (Sexton and Roberts 1982). A necrotic root mutant having brown inner cortical cells adjacent to the stele was susceptible to hyphal infection by the fungus Phytophthora sojae (Kosslak et al. 1996). Necrotic root genes, rn1, rn2, and rn3, were reported to be associated with the accumulation of isoflavonoid phytoalexins and group 2 peroxidases under axenic conditions (Kosslak et al. 1996, 1997). The necrosis genes soon after germination also seemed to be associated with resistance to reniform nematode (Kosslak et al. 1997), but those genes were not located. Flowering time and maturity are directly related to soybean yield due to the length of vegetative period and leaf development; thus many soybean breeders are interested in finding genotypes with these characteristics. A delayed flowering under short day length termed “long juvenile” is a trait controlled by the recessive jj allele in an E6 locus (Bonato and Vello 1999; Cairo et al. 2002). Six more genes (E1 to E5, E7) have been reported as being associated with flowering time and maturity or photoperiod sensitivity (Cober and Voldeng 2001). Although genes for flowering time, maturity, and photoperiod insensitivity were proposed to be at the same genomic region, linkage analysis using probes designed from greater gene homologs for flowering time, maturity, and photoperiod insensitivity in Arabidopsis exhibited that none of the cDNA probes matched the previously positioned loci for those traits (Tasma et al. 2001; Tasma and Shoemaker 2003). Many genes control the growth of the stem (determinate vs. indeterminate vs. semideterminate; normal vs. fasciated), petiole (normal vs. short), whole plant (normal vs. dwarf; normal vs. miniature), and leaf (normal vs. dwarf and crinkled). The genes Dt1 (vs. homozygous dt1 for the determinate stem) and Dt2 (vs. dt2 for indeterminate stem) condition continuous stem elongation and node production after flowering and semideterminate stem growth, respectively (Thompson et al. 1997; Lewers et al. 1998). Soybean cultivars with indeterminate stems are usually grown for their early maturity in the northern USA and Canada, whereas cultivars with determinate stems are planted in the southern USA for their later maturity group. Thus the early determinate cultivars may result in harvest difficulties or low yield in seasons of poor growth. Many soybean geneticists and breeders

21

have concerns regarding interactions between genes for stem elongation and other flowering, maturity, or internode length. The Dt1 locus is epistatic to Dt2, which is expressed only with Dt1 and is blocked in the homozygous recessive dt1dt1 genotype (Bernard 1972). Also, one report indicated that plant height could be modified by internode length, which is controlled by the S allele affecting short internode stem type. Two alleles, Dt2 and S, resulted in early maturity, short plant height and decreased lodging, and enhanced harvest index but decreased seed weight and protein content (Lewers et al. 1998). Fasciated soybean is characterized by a broad and flattened stem, which is controlled by a single recessive gene, f (Karakaya et al. 2002). A study using a fasciated mutant revealed that the mutation had a pleiotropic effect on plant development and pattern formation so that meristem enlargement and strong apical dominance in mutants generated increased leaf numbers and a branchless phenotype (Tang and Knap 1998). Two recessive genes, lps1 and lps2, were reported to control a short petiole phenotype in soybean (You et al. 1998). Short petiole and leaf movement with a pulvinus region at the base of the petiole are associated with soybean yield in terms of controlling the amount of light energy and plant density and are heritable traits. The lps2 was also found to be associated with abnormal pulvinus, and the two genes expressed at the different stages were assumed to control the development of the petiole and pulvinus (You et al. 1998). Six dwarf-related genes have been reported in soybean (df2, df3, df4, df5, df6, and df7df8) (Werner et al. 1987; Soybean Genetics Committee 1995). These genes were identified in soybean lines of T210, T243, T244, T256, T263, T286, and T261 and cause short internode, rugose leaves and relatively low seed yield (Werner et al. 1987; Soybean Genetics Committee 1995). A mutant treated with ethyl methanesulfonate (EMS) revealed that the difference in leaf size was due primarily to a difference in cell size and not the number of epidermal cells (Werner et al. 1987). Brachytic character with short internodes in a normal number of stem-noded soybeans was studied genetically and was found to be controlled by a duplicate recessive factor, sb1sb2 (Boerma and Jones 1978). Of those soybean height- or length- related traits, only df2 and df5 genes were mapped in the classical linkage map (Table 4). Some genes related to leaf forms (lf1, Lf2, Ln, Lo, Lnr, lw1 lw2, lb1lb2) are heritable, and usually necrotic

22

G.-J. Lee et al.

or abnormal leaf forms are controlled by a single gene, but duplicated genes form a wavy or bullate leaf. Genes controlling leaflet number (lf1 and lf2) were mapped on LG- A2 and classical LG-16, respectively (Cregan et al. 1999; Devine 2003). The gene for leaf shape (ovate vs. narrow controlled by a gene ln) resided in the middle of LG-I (Cregan et al. 1999). Trichome density and shape were recognized to be associated with negative spread of pathogens such as soybean mosaic virus and insects and positive improvement of soybean yield (Gunashinghe et al. 1988; Zhang et al. 1992). Many genes related to pubescence types (density, tip shape, growth habit, etc.) have been identified in different genotypes. Most of the genes are mapped in different LGs including LG-B1 (a recessive pa1 for semiappressed), F ( a recessive pa2 for erect or semiappressed), K (a dominant P1 for glabrous), I (a recessive p2 for puberulent), E (a recessive pb for blunt hair tip), D1a (a dominant Pd1 for dense trichomes), CLG16 linked to Lf2 locus (a dominant Pd2 for dense trichomes), and H (a dominant Ps for sparse pubescence) as shown in Table 4. Genes for seed coat bloom (dominant B1, B2, B3) and lack of abscission layer on soybean hilum (recessive n) were reported to be associated with seed coat structure (Palmer et al. 2004). Only the B1 gene was mapped on LG-F, where other seed trait genes (Gy5 for glycine subunit, Shr for shriveled seed, and Cgy1 for β-conglycinin subunit) linked together (Chen and Shoemaker 1998).

tural abnormalities is controlled genetically by duplicate recessive genes, fs1 and fs2 (Johns and Palmer 1982). Using a gamma ray-induced mutant, Singh and Jha (1978) proposed a recessive gene, ft, assigned as a transformed flower, that controls multiple floral parts (phyllody, sepalody, petalody, staminody, carpellody). In these sterile plants, fertile pollen is produced, but plants are male-sterile because of poor anther dehiscence. However, none of these genes has been mapped yet (Table 4). Partially or complete sterile soybeans were also identified. Partial male sterility in soybean is controlled by a single recessive gene, msp, and nine genes associated with complete sterility of soybean have been reported (Stelly and Palmer 1980; Palmer 2000). Of those genes, three complete male and female sterile genes have been mapped in the genetic map (Cregan et al. 1999). Male sterility, while leaving female reproductive capacity unaffected, is useful in soybean breeding in terms of production of F1 hybrid seeds and insect-mediated pollination. Use of linkage information between other phenotypic or DNA markers and male sterility has been used for selecting sterile plants normally controlled by homozygous recessive genes without a segregation test in the next generation. For example, morphological marker purple hypocotyls and flower color (W1) is associated with one male sterile gene, ms6 (Lewers et al. 1996), and SSR markers Satt157 and Satt412 on LG-D1b are linked to the nuclear male-sterile gene, ms (Jin et al. 1998). This genetic linkage information could be used for secetion of male sterile plants.

1.3.5 Soybean Sterility Genes Reports show diverse sterility systems in soybean including synaptic, structural, partial, and complete sterility. Most synaptic phenomena affecting chromosome pairing and disjunction leading to male and female sterilility were inherited by a single recessive gene (st1 to st8). Some microspores at the tetrad stage or male cells after microspore mitosis were found to be collapsed in the male sterile mutant (Palmer and Horner 2000). Only the st5 gene in synaptic sterility of soybean was mapped in the classical LG F-CLG8c and linked to other male sterile genes (ms1, ms6). Structural malformation of floral parts caused natural sterilility or mutant-induced sterilility and resulted in meiosis and anthesis or prevented another dehiscence (Singh and Jha 1978; Johns and Palmer 1982). A mutant exhibiting male sterility due to struc-

1.3.6 Genes for Mineral Toxicity or Deficiency Genes associated with responses to soil nutrient concentration include Fe (efficient iron utilization), Np (tolerant to high phosphorous level), Ncl (chlorideexcluding type at the root surface), and Nr (presence of constitutive nitrate reductase). Fe-deficient chlorosis in soybean is a typical symptom in alkaline soil where the nutrient is rarely dissolved in soil solution (Lin et al. 1997). There are some reports on genetic control of Fe-deficiency chlorosis either by a single recessive gene or multiple genes, but no such gene was located in the soybean map (Weiss 1943; Lin et al. 1997). Tolerance to Zn in acidic soil or deficiency of Mn in high-pH soil has been investigated, but none of the genes has been mapped yet (Hartwig et al. 1991;

Chapter 1 Soybean

Graham et al. 1995). In response to high phosphorus levels in growing solution, soybeans showed genetically diverse responses, and a single recessive pair of the gene npnp was assigned to plants with severe brown splotching, chlorosis, and stunting appearance (Bernard and Howell 1964). Chloride exclusion was found to be associated with the symptom of no necrosis in salt-tolerant soybean Lee cultivar and its progenies and inherited by a dominant gene, Ncl (Abel 1969). Nitrogen reductase (NR) controlled by a gene, Nr, is responsible for converting the accumulated NO−3 absorbed by plant root into a reduced form of nitrogen and minimizing NO−3 toxicity so that the NR mutant without inherent NO−3 reductase grows poorly or dies when NO−3 is the sole N source (Ryan et al. 1983). Those genes associated with efficiency of use of those nutrients, however, were not located in the soybean genetic map (Table 4).

1.3.7 Genes for Soybean Pigmentation Many genes conditioning expression of chlorophyll pigment in leaves and seeds have been reported (Palmer et al. 2004), including two genes for normal or variegated leaves (V1 and V2), 21 genes for normal colored green-yellow/pale or green/whitish green seedlings and plants, with the seedling becoming yellow (Y3–Y23), two genes for green seed embryo due to chlorophyll retention (D1 and D2), and three genes for green seed coat due to chlorophyll retention (G1–G3) (Table 3). Of these genes, only ten (V1, Y9–Y13, Y23, D1, G1) were located in the classical and integrated genetic map of soybean (Cregan et al. 1999). Variation in flower pigment, seed coat, and pubescence serves as useful markers for hybridization in soybean breeding. Soybean has purple (controlled by W1, Wm, or Wp), white (controlled by w1), and pink (controlled by wp) flowers, but some transposable elements in a soybean control chimeric flowers (w4-m for purple or white chimeric) at different nodes in a plant (Hegstad et al. 2000a,b). A mutation to the pink color locus also causes changes in flower color (wp-m for pink and purple) across generations (Johnson et al. 1998). Soybean has yellow, saddle, brown, and black seed coats, which are conditioned by I, K type, O, and R genes, respectively (Table 4). Soybean has two distinct pubescence colors, tawny and gray, inherited by dominant T or Td and recessive t, respectively. Temperatures below

23

15 ◦ C during seed development cause discoloration of hila and seed coat (Takahashi and Abe 1994; Takahashi and Asanuma 1996). Linkage analysis of those pigment- conditioning genes with other traits have been conducted for protein and oil content, flowering and maturity, or resistance to SCN, as a type of visual marker of the soybean pigment (Todd and Vodkin 1998; Hegstad et al. 2000a; Cober and Voldeng 2001).

1.3.8 Genes for Fatty Acid Composition Soybean has two groups of fatty acid (FA): saturated FA without double bonds in the carbon chains including palmitic (16:00) and stearic acid (18:0) and unsaturated FA with double bonds including oleic (18:1), linoleic (18:2), and linolenic acid (18:3). The average FA content in soybean is 11% palmitic, 4% stearic, 24% oleic, 54% linoleic, and 7% linolenic acid (Fehr 1991). In terms of edible oil, soybean breeding targets cultivars with low saturated FA (3% for palmitic acid), high oleic acid (48%), and low linolenic acid (3.5%). Linolenic acid is the most easily oxidizable FA and is considered to be a major cause of flavor problems in soybean oil. Decreased linolenic acid content will reduce or eliminate the need for hydrogenation and associated trans fatty acid production (List et al. 2000). Seven genes for controlling palmitic acid content in soybean have been reported, but only the fap2 contributed by the C1727 genotype and a major gene from N87-2122-4 were mapped on LG- D and A1, respectively (Nickell et al. 1994; Li et al. 2002). The genes controlling another saturated FA, stearic acid, are Fas, St1, and St2. Usually, most of the genotypes carrying the fas alleles exhibit dramatic seed yield, but a newly developed line FAM94-41 was found to have an agronomically robust high stearic acid line (Pantalone et al. 2002; Spencer et al. 2002). The fas gene was mapped on LG-B2, but the others have not been located yet (Spencer et al. 2003). A soybean cultivar with mid to high oleic acid content (24 to 48%) is a desirable target of soybean breeding, which has been funded recently by the United Soybean Board. Previous studies on the oleic acid content in soybean reported that the trait was quantitatively governed by multiple genes, but only the Ol gene was studied genetically (Rahman et al. 1996). The gene has not been mapped yet. A major factor controlling linolenic acid (18:3) level in plant tissues is omega-3 fatty acid de-

24

G.-J. Lee et al.

saturase, which converts linoleic (18:2) to linolenic acid (18:3) (Bilyeu et al. 2003). Of three genes (fan1, fan2, fan3) associated with linolenic acid content in soybean, only fan1 has been mapped on LG-B2, which is closely linked to other genes such as Idh2 and fas (Brummer et al. 1995).

1.4 QTL Mapping in Soybean One application of molecular markers is to locate genes for quantitative traits that are generally controlled by multiple loci with relatively low genetic contribution and more likely affected by environmental variation. Any traits with higher heritability can obviously be tagged by molecular markers in segregating populations, and the flanking markers can be beneficially applied to selecting progenies and/or germplasm that have favorable alleles for particular traits among diverse genetic materials as well. Likewise any DNA markers flanking the traits inherited with low heritability can be used to select lines and used to introgress the positive alleles into elite germplasm without any phenotypic evaluations in descendant generations if the QTL is confirmed across different genetic backgrounds. In the last decade, development of DNA markers in soybean has allowed identification of many QTLs, dissection of genetic attributes of the QTLs, and exploration of the positive alleles for the traits. The construction of soybean genetic maps has enabled many soybean geneticists and breeders to dissect the genetic loci of interest into their genetic contribution to the trait variation, additive or dominant effects, and their interactions. Along with genetic heritability, information of the genetic contribution of each individual locus to the trait variation can be used to decide numbers of loci conditioning the particular traits, major or minor loci, and genomewide locations of the loci. Several morphological and molecular markers used in soybean were introduced in preceding sections. In this section, many DNA markers for the QTLs that are associated with pest resistance, physiological response, seed composition, and agronomic traits are reviewed and summarized (Table 5). Because information on DNA markers links qualitative traits and known genes were introduced in the previous section on soybean gene mapping, all reported tagged mark-

ers to known (cloned) genes are omitted in Table 5. A summary of the reported QTLs listed in Table 5 is modified and provides more details of the reviewed chapter by Orf et al. (2004). In this section, we provide mapping populations that were used for mapping the QTLs, LGs, flanking markers and their interval (cM), phenotypic contribution (%), and software or analyzing tools used for QTL analysis. As explained by Orf et al. (2004), only putative QTLs explaining more than 10% of the phenotypic variation from a group of linked markers reported in SoyBase (a total of 963) are indicated in Table 5. Readers can see the highly significant QTLs that have higher R2 (%) values and are detected in the same genomic locations across mapping populations, which will be highly likely to be used in MAS. Also, many quantitative traits need to be tagged with the available DNA markers, and saturation of the reported markers is necessary if those markers are to be used for MAS in soybean-breeding programs. Recent progress in the development of SNP markers and diverse genotyping platforms will allow acceleration of QTL detection and precise location of QTLs (Zhu et al. 2003).

1.4.1 Pest Resistance Mapping of QTLs for soybean resistance to insects has been demonstrated mainly against defoliating insect species including the corn earworm, which is a common soybean pest in the southern USA. Two insect resistance mechanisms were reported as antibiosis (mortality of insect growth or development) and nonpreferential antixenosis (repellence of insects from soybean plant) (Clark et al. 1972; Lambert and Kilen 1984). A limited resistance source can be traced back to soybean plant introductions PI 171451, PI 227687, and PI 229358 that were used for QTL mapping associated with corn earworm resistance in soybean (Table 5). A total of 27 QTLs have been reported, and a major QTL explaining the highest variation of antibiosis and antixenosis resistance was located on LG-M, which is being fine-mapped using recombinant substitution lines identified from a population of 3,000 BC7 F2 plants (Zhu et al. 2004). QTLs that condition resistance to three root knot nematodes (RKNs), including peanut RKN (Meloidogyne arenaria), southern RKN (M. incognita), and Javanese RKN (M. javanica), have been mapped on LG-

PI 96354 × Bossier 110 F2:3

Southern RKN

CNS × PI 230977 68 F2:3

PI 96354 ×Bossier 110 F2:3, Prichard × G93-9009 BC2F2

PI 200538 × CNS 105 F2:3

Fukuyutaka × Himeshirazu 143 F2:3

Root knot nematode Peanut RKN

Common cutworm

(Antibiosis)

Minsoy × Noir 1 240 RILs Cobb × PI 229358 100 F2:3

Cobb × PI 171451 (110 F2:3), Cobb × PI 227687 (95 F2:3), Cobb × PI 229358 (103 F2:3)

Population

b

Total number of reported QTLs are shown in parentheses Only independent QTL contributing >10% to particular trait variation c SFA (single factor analysis)

a

Insect Corn earworm

Pest resistance

(Antixenosis)

Traita

Reaction

B212_1-R045_1(3.5) B212_2-A111_2(31) A882_1-G248_1(13) K493_1-Cs008_1(5) Satt492-Satt358 (9) Satt012-Satt505 (8)

G

Satt567–Satt463

M F E O G O

Satt141-Satt290 (3.7) Sat_122-Satt541(0.5) Satt463-A584_4 (5.0) Satt472-Satt191 (4.4) Satt220-Satt536 (2.5) Satt220–Satt175

A132-A670 Bng047 B212-A757 L183-L002 (13) A131-R249 A064-K401 (41) A584-A226 Satt575-Sat_112 (6)

C2 D1b F G H J M E D1b H M G M M

A343-K411 (31)

Flanking loci (cM)b

B2

LG

Table 5. QTL conditioning various soybean traits mapped in genetic map (modified from Orf et al. 2004)

18

32 16 31 14 56

16

10 15 37 14 21 28

11 12 20 19 19 19 37 17

12

3.6

8.6 2.4 6.9 2.4 16.3

6.8

2.3 3.6 9.8 3 4.6 12.7

– – 4.8 – 2–4.0 – 10 9

–

R2 (%) LOD

Mapmaker QTL

Mapmaker QTL

Mapmaker QTL

QTL Cartographer

PLABQTL Mapmaker QTL/QTL Cartographer

Mapmaker QTL

Software or analysisc

Tamulonis et al. 1997c

Tamulonis et al. 1997a Tamulonis et al. 1997b Li et al. 2001

Komatsu et al. 2005

Terry et al. 2000 Narvel et al. 2001b

Rector et al. 1999, 2000

Reference

Chapter 1 Soybean 25

Reaction

Race 5

Race 3

Race 2

SCN Race 1

Javanese RKN

Traita

Table 5. (continued)

Evans × PI 209332 Evans × Peking 110 F2:3 Peking × Essex 200 F2:3

Hamilton × PI 90763 226 F2:3

PI 468916 × A81-356022 57 F2:4

Hamilton × PI 438489B 184 F2:3

Williams 82 × Hartwig 200 F2:3 Evans × PI 90763 115 F2:3 Peking × Essex 200 F2:3 Hamilton × PI 89772 250 F2:3

Hamilton × PI 90763 226 F2:3 Magellan × PI 404198A 224 F2:3

J87-233 × Hutcheson 125 F2:3 Hamilton × PI 438489B 184 F2:3

Hamilton × PI 438489B 184 F2:3

Magellan × PI 404198A 224 F2:3 Hamilton × PI 89772 250 F2:3

Evans × PI 209332 Peking × Essex 200 F2:3

Population D1a F L B2 H G B1 D2 B1 B2 G A2 C1 G G B1 G B1 J H E G A2 D1a G E G A2 G D2 N I

LG A725_2 B212_1 A023 A593 B072 Satt309-Satt688 A006-Satt583 (25) B132-Satt372 Satt583-Sat_123 (19) Satt168-A329 (11) A096- Satt130 (11) BLT65V A463-Satt396 (40) A096- Satt130 (11) Satt163-Satt688 Satt453 Satt163-Satt309 A006 B032 B072 A135-Satt231 (19) B053-Satt309 (12) K400- T155 (13) A398-K478 (22) Satt130- Satt012 (26) Satt573-Satt598 (4) Satt288-Satt472 (24) Sat_400-Satt424 Satt163-Satt688 A064_2 A280_1 K011

Flanking loci (cM)b 13 46 24 21 13 20 17 10 13 12 16 11 10 13 15 11 13 91 19 13 16 23 19 11 14 23 27 18 28 11 14 11 – – – 10 6.8 4.6 4.2 2.8 9.1 – 2.6 7.5 7.9 5.5 7.1 – – – 3.6 13.7 7 5.5 4.5 3.1 3.8 14.5 22.1 – – –

R2 (%) LOD

ANOVA ANOVA Mapmaker QTL

QTL Cartographer

QTL Cartographer

Mapmaker QTL

Stepwise regression ANOVA Mapmaker QTL Mapmaker QTL

QTL Cartographer QTL Cartographer

Mapmaker QTL Mapmaker QTL

Mapmaker QTL

QTL Cartographer Mapmaker QTL

Mapmaker QTL

Mapmaker QTL

Software or analysisc

Concibido et al. 1996 Concibido et al. 1997 Qui et al. 1999

Guo et al. 2005

Wang et al. 2001

Yue et al. 2001b

Vierling et al. 1996 Concibido et al. 1997 Qui et al. 1999 Yue et al. 2001a

Guo et al. 2005 Guo et al. 2006

Heer et al. 1998 Yue et al. 2001b

Yue et al. 2001b

Guo et al. 2006 Yue et al. 2001a

Concibido et al. 1997 Qui et al. 1999

Reference

26 G.-J. Lee et al.

Abiotic stress

Reaction

LG

Young × PI 416937 120 F4d Minsoy × Noir 1 236 RILs

Carbon isotope discrimination

BSR101 × PI 437654 320 RILs Bell × Colfax 93 RILs & NILs Young × PI 416937 116 F4:7

Essex × Forrest 80 NILs Pyramid × Douglas 90 RILs

Essex × Forrest 100 RILs

N G N G G C2 N J J F D2 J L L

G E B1 Magellan × PI 404198A 224 F2:3 B1 N Hamilton × PI 89772 250 F2:3 B1 G Hamilton × PI 438489B 184 F2:3 B1 Hamilton × PI 438489B 184 F2:3 C1 E Evans × PI 209332 D2 Evans × Peking 110 F2:3 G N Hartwig × D2 BR92-31983 126BC3F2:3 Hamilton × PI 438489B 184 F2:3 C1 E Essex × Forrest 100 RILs C2

Hamilton × PI 90763 226 F2:3

Population

Water use efficiency

Bud blight

Brown stem rot

Sudden death syndrome

Race 14

Race 6

Traita

Table 5. (continued)

19 20 16 63 16 14 16 45 45 82 12 13 14 27

11 19 22

A059- A463 (23) A656- Satt452 (26) K455_1 OF04-1600 OI03-450 OC01_650 Satt309 Satt309-Satt163 (5) Satt307 Satt080-Satt387 (7) K375I-1 (5cM) 21E22.sp2 Satt114-Satt510 (14) K258_2 Cr497_1 A489_1 Dt1

13 13 11 13 10 10 10 11 11 19 11 18 14 41

– 5.1 1.8 6.2 5.2 2.5 2.7 – 6 – – – – 20

3.6 5 –

7.1 7.2 6 6.7 3 3.7 5.2 2.7 3.6 5 – – – –

R2 (%) LOD

Satt163-Satt688 Satt573-Satt204 Satt453-Satt359 Satt453 Sat_280-Satt549 A118-A006 (13) B053-Satt309 (12) Satt583- Sat_123 (19) A059-A463 (23) A656-Satt452 (26) A064-2 C006 A280 Satt082-Satt574 (7)

Flanking loci (cM)b

Yue et al. 2001a Yue et al. 2001b Yue et al. 2001b Concibido et al. 1996 Concibido et al. 1997

Mapmaker QTL Mapmaker QTL Mapmaker QTL ANOVA ANOVA

QTL Cartographer

ANOVA

QTL Cartographer ANOVA &MapQTL Mapmaker QTL

ANOVA Mapmaker QTL

ANOVA

Mapmaker QTL

Mapmaker QTL

Specht et al. 2001

Mian et al. 1996

Lewers et al. 1999 Patzoldt et al. 2005 Fasoula et al. 2003

Meksem et al. 1999 Njiti et al. 2002

Chang et al. 1997

Hnetkovsky et al. 1996

Yue et al. 2001b

Schuster et al. 2001

Guo et al. 2006

QTL Cartographer

Mapmaker QTL

Guo et al. 2005

Reference

QTL Cartographer

Software or analysisc

Chapter 1 Soybean 27

Growth and development response

Nodulation

Reaction

E L O O B1 C2 H – C2 M

PI 97100 × Coker 237 111 F2d SJ2 × Suwon 157 136 RILs SJ2 × Suwon 157 136 RILs Young × PI 416937 120 F4d

Minsoy × Noir 1 69 F2:5

Number of nodules

Nodule weight

Specific leaf weight

Leaf area

Herbicide sensitivity Chlorimuron ethyl

Salt tolerance Chilling tolerance

Iron deficiency

Al tolerance

G

Archer × Minsoy 122 RILs, Archer × Noir 1 86 RILs A5403 × Archer 103 RILs P9641 × Archer 67 RILs

Water logging

A1 F Young × PI 416937 120 F4d A2 B1 A81-356022 × PI 468916 60 F2:3 A2 D1a G Pride B216 × A15 120 F2:4 B2 I H Anoka × A7 92 F2:4 N I S100 × Tokyo 106 F2:5 N Toyomusume × Hayahikari C2 104 RILs L H

LG

Population

Traita

Table 5. (continued)

Blt043_1 A122_1 A381_1 Gc409_2 A397-Blt029 (7) A584-R079 (29)

Sat_274

Cr168_1 A106-B164 (11) Sat_038

Satt229 Satt635

Satt385 Satt269 M0103_1 Cr207_1 I locus C063_1 K069_1 Satt070-A593_1 A515-K644 A404-B69 Blt15-Sat_033 A515-K644 Satt237-Sat_091 T

Sat_064

Flanking loci (cM)b

18 12 12 13 20 25

19

82 14 22

10 16 10 10 17 31 11 11 19 22 73 80 45

–

– – – – 2.9 4.2

–

– – –

4.4 4.1

2.5 2 – – – – – 2.4 2.6 2.4 13.1 3.5 9–13 13.5

3

R2 (%) LOD

Mapmaker QTL

ANOVA and Multiple regression analysis ANOVA and Multiple regression analysis ANOVA

ANOVA

Mansur et al. 1993

Mian et al. 1998

Tanya et al. 2005

Tanya et al. 2005

Mian et al. 1997

Lee et al. 2004c Funatsuki et al. 2005

Lin et al. 1997

Mapmaker QTL

Map Manager QTX QTL Cartographer

Diers et al. 1992c

Bianchi-Hall et al. 2000

Cornelious et al. 2005

VanToai et al. 2001

Reference

SFA SFA ANOVA

QTL Cartographer

ANOVA

Software or analysisc

28 G.-J. Lee et al.

Reaction

Leaf ash

Stem diameter Canopy width Plant height

Early plant vigor

Leaf width

Leaf length

Young × PI 416937 120 F4d Minsoy × Archer 233 RILs Minsoy × Noir 1 240 RILs Young × PI 416937 120 F4d

LG

A2 M M G G A81-356022 × PI 468916 60 F2:3 D1a Minsoy × Noir 1 284 RILs L Minsoy × Archer 233 RILs M A81-356022 × PI 468916 60 F2:3 A2 F D1b Minsoy × Noir 1 284 RILs M Minsoy × Archer 233 RILs M S100 × Tokyo 116 F2:5 C2 F E A81-356022 × PI 468916 60 F2:3 L S100 × Tokyo 116 F2:5 C2 Minsoy × Noir 1 284 RILs L C2 PI 97100 × Coker 237 111 F2d L Young × PI 416937 120 F4d C1 M Minsoy × Noir 1 (240 RILs),¸ Archer × Minsoy (233 RILs), Noir 1 × Archer (240 RILs) A81-356022 × soja PI 468916 BC3 Kefeng No. 1 × Nannong 1138-2 184 RILs I B1 C2 C2 C2

Population

Traita

Table 5. (continued)

11 13 18 14 13 19 11 12 24 17 16 14 13 21 25 19 24 12 32 18 68 10 16

29 13 21 24 24

Satt127 GmKF082c-GmKF168b A397I-B131V Satt431-GmKF059a GmKF143-Satt319

2.8 7.3 11 16.4 12.6

– 7.2 10 – – – – – – – – – – 5.2 6.4 – – 2.8 – – 25 – 8.9

R2 (%) LOD

A085 Satt150 R079_1 A112_1 A458_1 K478_1 Satt006 Satt150 A111_1 K390_1 K411_1 R079_1 Satt150 K418-A397 (2.4) HSP176-B212 (8.4) Cr406 G173_1 A397-K365 (2.4) Satt006 Satt079 Dt1-K385_1 (24) A063_1 Satt150

Flanking loci (cM)b

Sebolt et al. 2000 Zhang et al. 2004

Lee et al. 1996b Lee et al. 1996c Orf et al. 1999

Mapmaker QTL ANOVA PLABQTL

Mapmaker QTL WINQTLCART

Keim et al. 1990a Mian et al. 1998 Mansur et al. 1996

Mansur et al. 1996 Orf et al. 1999 Mian et al. 1998

ANOVA PLABQTL Mapmaker QTL

ANOVA Mapmaker QTL ANOVA

Keim et al. 1990a Mansur et al. 1996 Orf et al. 1999 Keim et al. 1990a

Mian et al. 1998 Orf et al. 1999 Orf et al. 1999 Mian et al. 1996

Reference

ANOVA ANOVA PLABQTL ANOVA

ANOVA PLABQTL PLABQTL ANOVA

Software or analysisc

Chapter 1 Soybean 29

Traita

Protein content

Reaction

Seed composition

Table 5. (continued) LG

A81-356022 × PI 468916 60 F2:3 I E B2 L G F2d eight populations A2 C1 D1a E G I Young × PI 416937 120 F4d C1 N B2 PI 97100 × Coker 237 111 F2d H K Peking × Essex 200 F2:3 H F Noir 1 × Archer 240 RILs C1 L Minsoy × Archer 233 RILs A1 C1 A81-356022 × I soja PI 468916 BC3 Minsoy × Noir 1236 RILs M Essex × Williams 131 RILs C2 F K M N87-984-16 × TN93-99 G 101 RILs Kefeng No. 1 × B2 Nannong 1138-2 184 RILs

Population 42 24 19 16 12 11 17 28 11 16 28 13 11 10 14 11 32 17 12 11 15 12 65 27 28 18 24 13 20 12

Satt567 Satt277-Satt202 Satt335-Satt144 Satt539-Satt102 Satt540-Satt463 Satt570 A953_1H-Satt560

3.5

12.8 9.8 4.4 4.3 3 3.5

– – – – – – – – – – – – – – – – – – – – – – –

R2 (%) LOD

K011 SAC7_1 A242_1 A023_1 A245_2 A505_1 A063_1 A398_1 B174_1 A890_1 A144_1 Gc97_1 A071_2 B142_1 A566_2 A065_3 B072 B148 Satt578 Satt166 T155_1 SoyGPATR Satt127

Flanking loci (cM)b

Qui et al. 1999 Orf et al. 1999 Orf et al. 1999 Sebolt et al. 2000

Mapmaker QTL PLABQTL PLABQTL Mapmaker QTL

WINQTLCART

Zhang et al. 2004

Panthee et al. 2005

Lee et al. 1996a

ANOVA

QTL Cartographer

Lee et al. 1996a

ANOVA

Specht et al. 2001 Hyten et al. 2004

Brummer et al. 1997

ANOVA

QTL Cartographer QTL Cartographer

Diers et al. 1992b

Reference

ANOVA

Software or analysisc

30 G.-J. Lee et al.

Reaction

Linolenic acid

Oil content

Methionine

N87-984-16 × TN93-99 101 RILs F G N87-984-16 × TN93-99 101 RILs F G M A81-356022 × PI 468916 60 F2:3 I E B2 L PI 27890 × PI 290136 69 F2:5 A2 K F2d eight populations A1 B1 G H K Young × PI 416937 120 F4d D2 PI 97100 × Coker 237 111 F2d C1 G Minsoy × Noir 1 240 RILs A1 Archer × Minsoy 233 RILs A1 C1 Noir 1 × Archer 240 RILs C2 L Peking × Essex 200 F2:3 H Essex × Williams 131 RILs C2 L M N87-984-16 × TN93-99 101 RILs D1b O O A81-356022 × PI 468916 60 F2:3 E L K

Cysteine

LG

Population

Traita

Table 5. (continued)

Satt252 Satt235 Satt252 Satt564 Satt590 A407_1 SAC7_1 A242_1 A023_1 T153_1-A111_1 (18) BC1-A315_1 (26) A104_1 A109_1 A584_1 A069_1 K387_1 Cr142_1 A063_1 L154_1 T155_1 Satt174 SOYGPATR Satt432 A489_1 B072 Satt277-Satt460 Satt166-Dt1 Satt540-Satt463 Satt274 Satt420 Satt479 SAC7_1 A023 A065_3

Flanking loci (cM)b 11 13 15 19 23 28 43 39 32 36 24 19 31 19 18 16 13 13 17 13 10 11 11 19 21 32 10 12 12 15 12 31 26 20

2.2 2.8 2.8 2.6 2.4 – – – – 5.5 2.9 – – – – – – – – 3.4 4 3.3 3.3 6.1 – 12 3.3 3.6 3 3.5 3.1 – – –

R2 (%) LOD

Orf et al. 1999

Qui et al. 1999 Hyten et al. 2004

Panthee et al. 2005

PLABQTL

Mapmaker QTL QTL Cartographer

QTL Cartographer

Diers and Shoemaker 1992

Lee et al. 1996a Lee et al. 1996a

ANOVA ANOVA

ANOVA

Brummer et al. 1997

Mansur et al. 1993

Diers et al. 1992b

Panthee et al. 2006

Panthee et al. 2006

Reference

ANOVA

ANOVA

QTL Cartographer

QTL Cartographer

Software or analysisc

Chapter 1 Soybean 31

Yield-related trait

Reaction

Maturity

Glycitein

Genistein

Daidzein

Oligosaccharide Sucrose content Total isoflavone

Oleic acid

Palmitic acid

A81-356022 × PI 468916 60 F2:3 A1 E B1 A81-356022 × PI 468916 60 F2:3 B2 J Cook × N87-2122-4 A1 A81-356022 × PI 468916 60 F2:3 A1 E B2 Keunolkong × Iksan 10 115RILs L V71-370 × PI 407162 149 F2:3 I AC756 × RCAT Angora 207 RILs A1 M AC756 × RCAT Angora 207 RILs A1 M Essex × Forrest 100 RILs A1 N AC756 × RCAT Angora 207 RILs M Essex × Forrest 100 RILs B2 AC756 × RCAT Angora 207 RILs F Essex × Forrest 100 RILs B1 N A81-356022 × PI 468916 60 F2:3 D1a C2 PI 27890 × PI 290136 284 RILs M C2 L PI 97100 × Coker 237 111 F2d K Young × PI 416937 120 F4d B1 A81-356022 × Soja PI 468916 BC3 I Kefeng No. 1 × B1 Nannong 1138-2 184 RILs B1 B1

Linoleic acid

LG

Population

Traita

Table 5. (continued)

38 21 20 24 18 33 23 21 19 14 12 12 26 15 18 10 10 31 38 39 50 11 18 21 19 19 12 31 22 44 28 24 11

A520T-Sat_128 GmKF082c-GmKF168b

8.6 6

– – – – – 5–7 – – – – – 2 5.5 2.1 3.4 2.7 3.2 7.6 2.9 2.9 11 2.3 – – – – – 6.7 – 6 13.1

R2 (%) LOD

A082_1 A242_2 A118_1 A343_1 K375_1 Satt684 A170_1 Pb A619_2 Satt278 A144 Satt200 Satt201 Satt200 Satt201 Satt276 Satt080 Satt201 Satt063 Satt516 Satt251 Satt237 R013_2 K474_2 R079_1 Satt079 Satt006 R051-N100 (17.7) Blt043_1 Satt127 Satt597-A118T

Flanking loci (cM)b

Keim et al. 1990a Mansur et al. 1996

Lee et al. 1996b Lee et al. 1996c Sebolt et al. 2000 Zhang et al. 2004

ANOVA ANOVA

Mapmaker QTL ANOVA Mapmaker QTL WINQTLCART

Primomo et al. 2005 Kassem et al. 2004 Primomo et al. 2005 Kassem et al. 2004

Kassem et al. 2004

Mapmaker QTL QTL Cartographer Mapmaker QTL QTL Cartographer Mapmaker QTL

Primomo et al. 2005

QTL Cartographer

Kim et al. 2005 Maughan et al. 2000 Primomo et al. 2005

Li et al. 2002 Diers and Shoemaker 1992

Mapmaker QTL ANOVA

ANOVA ANOVA QTL Cartographer

Diers and Shoemaker 1992

Diers and Shoemaker 1992

Reference

ANOVA

ANOVA

Software or analysisc

32 G.-J. Lee et al.

Reaction

Flowering date

Pod dehiscence Seed coat hardness

L C2 L L C2 L C2 F D1b C2

PI 27890 × PI 290136 284 RILs

Lodging

C2 C2 Young × PI 416937 120 F4d J A81-356022 × PI 468916 60 F2:3 A2 L D1b N A81-356022 × PI 468916 60 F2:3 C2 PI 27890 × PI 290136 284 RILs C2 M Minsoy × Noir 1 240 RILs M L Archer × Minsoy 233 RILs C2 M Noir 1 × Archer 240 RILs C2 L Kefeng No. 1 × B1 Nannong 1138-2 184 RILs B1 B1 B1 C2 C2

Noir 1 × Archer 240 RILs Kefeng No. 1 × Nannong 1138-2 184 RILs

Archer × Minsoy 233 RILs

PI 97100 × Coker 237 111 F2d Young × PI 416937 120 F4d Minsoy × Noir 1 240 RILs

LG

Population

Traita

Table 5. (continued)

19 15 44 34 15 13 12 23 31 22 39 10 31 26 31 25 11 16 13 12 23 22

GmKF177-GmKF082c GmKF082c-GmKF168b GmKF168b-Gmpti_D A397I-B131V Satt431-GmKF059a

28 18 56 13 15 27 21 17 10 18

8.4 7.3 6.9 10.1 13.4

11.4 7.2 – – – – – – – – 25 5.3 19 15 19 15 5.7

– – 18 – 8.1 16 12 9.2 5 8.3

R2 (%) LOD

GmKF059a GmKF143-Satt319 B122_1 I locus G173_1 K411_1 K418_1 K474_1 A109_2 R079_1 Satt567 G173_1 Satt365 Satt150 Satt489 A489_1 GmKF104b-GmKF177

Satt006 A109_2 Dt1-K385 (24) A169 Satt489 Dt1 Satt277 Satt335 Sat_096 A397I-B131V

Flanking loci (cM)b

Keim et al. 1990a Mansur et al. 1996 Orf et al. 1999

Zhang et al. 2004

PLABQTL

WINQTLCART

Bailey et al. 1997 Keim et al. 1990b

Zhang et al. 2004

Lee et al. 1996b Lee et al. 1996c Orf et al. 1999

Mansur et al. 1996

Reference

ANOVA ANOVA

ANOVA ANOVA

WINQTLCART

Mapmaker QTL ANOVA PLABQTL

ANOVA

Software or analysisc

Chapter 1 Soybean 33

Reaction

Seed size

Seed weight

Reproductive period

Traita

Table 5. (continued) LG

C2 E PI 27890 × PI 290136 284 RILs L Minsoy × Noir 1 240 RILs M Archer × Minsoy 233 RILs C1 M Noir 1 × Archer 240 RILs L C1 PI 27890 × PI 290136 284 RILs A2 Young × PI 416937 120 F4d C1 E G PI 97100 × Coker 237 111 F2d G L M PI 27890 × PI 290136 284 RILs A2 V71-370 × PI 407162 152 F2:3 B1 G J L A81-356022 × soja PI 468916 BC3 I Ma Belle × Proto 82 F2d O I Minsoy × Noir 1 236 RILs M Pureun × Jinpum2 100 F2d B1 D2 E F Kefeng No. 1 × B1 Nannong 1138-2 184 RILs D2 Essex × Williams 131 RILs C2 D1a F

Population 20 15 13 14 12 14 19 21 13 10 14 22 10 10 11 11 14 10 11 11 29 12 12 10 11 10 10 13 10 11 14 14 10

B146H-Satt458 Satt277-Satt460 Satt179-Satt071 Satt114-Satt335

4.8 6.7 4 4.7

9.6 4.3 – 7.6 6.2 7.8 11 12 – – – – – – – – – – – – 3 4.3 – 7 – – – – 3.8

R2 (%) LOD

GmKF143-Satt319 Satt496-A374H G173_1 Satt567 G214_24 Satt150 A489_1 Sat_077 K443_2 A059_1 Blt49_2 B031_1 A235_1 Dt1 locus Cr529_1 K443_2 A118_1 A816_1 K384_1 K385_1 A144_1 Satt219 Satt562 Satt590 A089 A095 A069 Cr321 Satt509

Flanking loci (cM)b

Mansur et al. 1996 Maughan et al. 1996

Sebolt et al. 2000 Csanadi et al. 2001 Specht et al. 2001 Lee et al. 2001a

Zhang et al. 2004

ANOVA ANOVA

Mapmaker QTL Mapmaker QTL QTL Cartographer ANOVA

WINQTLCART

Hyten et al. 2004

Mian et al. 1996

ANOVA

QTL Cartographer

Mansur et al. 1996 Mian et al. 1996

Mansur et al. 1996 Orf et al. 1999

Reference

ANOVA ANOVA

ANOVA PLABQTL

Software or analysisc

34 G.-J. Lee et al.

Reaction

Sprout yield Hypocotyl length Abnormal seedlings

Seed yield

Traita

Table 5. (continued) LG

L N87-984-16 × TN93-99 101 RILs D1a D1a D2 PI 27890 × PI 290136 284 RILs M Essex × Forrest 100 RILs N Minsoy × Noir 1 240 RILs M Noir 1 × Archer 240 RILs F A81-356022 × soja PI 468916 BC3 I Minsoy × Noir 1 236 RILs M Kefeng No. 1 × B1 Nannong 1138-2 184 RILs C2 C2 C2 M Pureun × Jinpum2 100 F2d G Pureun × Jinpum2 100 F2d B1 Pureun × Jinpum2 100 F2d M

Population 28 17 11 10 13 31 19 13 26 37 10 12 12 13 10 11 11 12

Satt319-K11_3T K11_3T-Satt277 Satt557 A60V-Satt150 L154 K011n Bng222

6 5.9 6.9 3.8 – – –

14.5 4.6 3.5 2.9 – – 11 7 2.5 37 5.4

R2 (%) LOD

Satt156-Dt1 Satt147 Satt184 Satt002 R079_1 OC01_650 Satt150 Satt144 Satt127 Satt150-Satt567 (19) GmKF168b-Gmpti_D

Flanking loci (cM)b

Lee et al. 2001a Lee et al. 2001 Lee et al. 2001

Sebolt et al. 2000 Specht et al. 2001 Zhang et al. 2004

Mapmaker QTL QTL Cartographer WINQTLCART

Multiple regression Multiple regression ANOVA

Mansur et al. 1996 Hnetkovsky et al. 1996 Orf et al. 1999

Panthee et al. 2005

Reference

ANOVA Mapmaker QTL PLABQTL

QTL Cartographer

Software or analysisc

Chapter 1 Soybean 35

36

G.-J. Lee et al.

E and F, LG-O and G, and LG-D1a and F, respectively (Table 5). Of these, only QTLs associated with southern RKN resistance were confirmed by using markerassisted lines having different allele combinations (Li et al. 2001). The soybean cyst nematode (SCN) is the most destructive pest in soybean, and thus a number of studies on QTL mapping for SCN resistance have been conducted. A total of 61 QTLs associated with SCN were reported, which were located on 18 soybean LGs, but most of them await confirmation (Concibido et al. 2004). DNA markers flanking Rhg1 on LG-G and rhg4 genes on LG-A2 conditioning resistance to SCN were not included in this section because they had been cloned and characterized earlier and listed in the previous section (Table 4). So far, about 130 SCN-resistant cultivars have been developed (57 from the public sector and 69 from the private sector), but the resistant alleles in most of the resistant cultivars were traced back to only a few sources (PI 88788, Peking, PI 90763). To avoid virulence of the different SCN races (or HG types) on those resistant cultivars, it is essential to broaden and explore genetic sources for SCN resistance. Currently, breeding efforts on SCN resistance are focusing on other sources that have broadband resistance to multiple races or wild ancestors such as G. soja that have a relatively low genetic relationship with elite lines (Wang et al. 2001; Concibido et al. 2004). Phenotypic evaluation of resistance to sudden death syndrome (SDS) in soybean is difficult because expression of the disease symptom is variable depending on environmental conditions. So the use of molecular markers linked to QTLs associated with SDS resistance is a preferential tool for soybean cultivar development. Three QTL regions on LG-C2, G, and N were detected to have resistance genes to SDS (Table 5), and genomic regions on LG-G and N seem to be common in different genetic backgrounds of soybean (Chang et al. 1997; Njiti et al. 2002). Brown stem rot (BSR) caused by soilborne fungus is a disease prevalent in soybean growing in cooler regions including northern USA and Canada. Three resistance genes were identified as shown in the previous section, but other resistance sources and their genetic control have been investigated (Lewers et al. 1999). Of the five QTLs conditioning BSR resistance, only the QTL on LG-J explains 45% of the phenotypic variation (Table 5). Bud blight, considered more destructive in soybean yield in tropical and subtropical regions and in China, is one of the three most dominant diseases

of soybean (Orellana 1981). Two QTLs contributing more than 10% to the disease expression were located on LG-F and D2, and the resistant allele was inherited from the cultivar Young. In a different population crossed between the susceptible PI 416937 and Benning, Lee et al. (2003) identified those two QTLs in naturally infected field plots with the pathogen, tobacco ringspot virus.

1.4.2 Tolerance to Abiotic Stresses In soybean, drought stress should be studied in connection with yield performance rather than only crop survival, meaning that genes for increasing transpiration of water under drought stress need to be emphasized. There are many physiological determinations for yield performance under drought stress, and some QTLs related to those specific traits have been reported (Table 5). Direct evaluation of soybean yield in irrigated and rainfed environments was conducted in a population of 160 F4 -derived lines from the cross of Hutcheson (high yielding) × PI471938 (drought tolerant) (Lee et al. 2002). Seed yield was determined in a total of 14 environments (nine irrigated and five rainfed) in the southern USA for 2 years, and information on QTL for yield under drought stress will be reported soon. In water-limited environments, Passioura (1977) formulated seed yield in the function of seasonal transpiration, water-use efficiency, and harvest index. Water-use efficiency (WUE) is measured as total dry weight of plant per liter of water consumed, while carbon isotope discrimination (CID) for measuring transpiration efficiency is the ratio of C13 /C12 measured in a juvenile trifoliolate leaf. Of nine reported QTLs for WUE, only two QTLs explaining the variation of the trait greater than 10% were provided in Table 5. Of five QTLs for CID, one QTL on LG-L closely linked to Dt1 (a gene for determinate stem in soybean) explains 27% of the variation (Table 5). In contrast to drought, excess water in soil due to periodic flooding and inadequate drainage of soil water can cause waterlogging (submergence of root or part of shoot) or complete submergence, which results in soybean growth and production adversely (Reyna et al. 2003). A QTL study indicated that lines with Archer alleles at the locus on LG-G exhibited improved plant growth (11 to 18%) and grain yield (47 to 180%) in waterlogging conditions (Table 5).

Chapter 1 Soybean

Tolerance to Al toxicity was measured as root extension under high Al treatment and relative percentage of root extension compared to control without Al (Bianchi-Hall et al. 2000). Of the six QTLs reported to be associated with root growth under high Al stress, two QTLs on LG-A2 and B1 contributed more than 10% to the trait variation (Table 5). Based on information of the inheritance of the resistant allele from PI 416937, lines with different allele combinations at two QTLs on LG-A2 in a different genetic background (Benning × PI 416937) demonstrated that lines homozygous for the PI alleles at both loci exhibited higher root extension (Lee et al. 2001b). Iron deficiency chlorosis (IDC) occurs in high-pH soils such as calcareous soil where Fe solubility was decreased in the soil solution. The chlorotic and/or necrosis symptom was evaluated on soybean plants at V4 or V5 stage (four or five nodes with fully developed leaves) based on visual scorings of no yellowing to severe yellowing and necrosis. Three mapping populations were used for mapping IDC QTL, and seven QTLs were found to explain more than 10% of IDC variation of the total 36 QTLs reported. However, only one locus on LG-I was commonly detected to be associated with IDC (Table 5). Salt stress in soybean production becomes important because the field may be salinized heavily through inefficient fertilizer practices, seawater flooding, and irrigation practices with poor water quality (Lee et al. 2004c). A major QTL was found to reside on LG-N explaining 30, 29, and 42% of the phenotypic variation, which account for 41, 60, and 79% of the total genetic variation for salt tolerance in the field, greenhouse, and combined environments (Lee et al. 2004c; our Table 5). By evaluating the inherited alleles at two flanking loci of the QTL in descendants of two parents, Lee et al. (2004c) found that there is a strong relationship between alleles at the SSR loci and salt tolerance. This suggests that these markers could be used for MAS in commercial breeding for salt-tolerant soybean. Chlorimuron ethyl is a herbicide for weed control in soybean, and genotypic differences in chlorimuron ethyl sensitivity exist (Lloyd and Wax 1984). Of 14 total QTLs identified in a population of PI 97100 and Coker 237, two QTLs on LG-E and L contributed more than 10% to the variation of sensitivity to chlorimuron ethyl (Table 5).

37

1.4.3 Growth and Development Responses Specific leaf weight (SLW) expressed as dry weight (mg) divided by leaf area (cm−2 ) was reported to be positively related with photosynthetic proteins per unit ground area and a canopy photosynthesis rate but negatively related with individual leaf size (Wiebold and Kenworthy 1985; Wells et al. 1986). The use of molecular markers linked to the SLW trait can improve the photosynthetic rate for higher yield in soybean without declining photosynthetic leaf area. A total of six QTLs for SLW were reported and four loci in one population were found to have more than 10% higher phenotypic contribution (Table 5). A total of 16 QTLs for leaf area were detected in four genetic populations, and three QTLs on LG-C2, M, and A2 were found to be associated with leaf area. Because none of the QTLs for SLW and leaf area are linked together, the pleiotropic effect of the QTLs for the two traits does not seem to be a concern. Leaf ash was measured as milligrams per gram of dry weight (mg g−1 ) of leaves harvested 36 d after planting. A total of 11 QTLs conditioning the amount of leaf ash were reported, but only two independent QTLs on LG-G accounted for more than 10% trait variation (Table 5). Traits of leaf length and width were studied in the three mapping populations (Table 5). A total of 15 QTLs for leaf length and leaf width were identified, but one QTL on LG-M closely tagged with Satt150 marker conditions both traits, but two other QTLs on LG-D1a and L for leaf length are independent from three QTLs on LG-A2, F, and D1b for leaf width. Stem diameter is the average length of stems between the unifoliolate and the first trifoliolate nodes for three mature plants. Among three QTLs reported in a population, one locus linked to RFLP marker G173_1 appeared to account for more than 10% variation in stem diameter (Table 5). Vigorous growth of soybean at early growth stages affects suppression of weed invasion and growth, which leads to reduced use of herbicide and results in economic and environmental benefits. Of three QTLs for plant height at V7 and V10 stages that explain more than 10% phenotypic variation, two QTLs on LG-C2 and F were found to be associated with canopy width (Mian et al. 1998). Of 32 total QTLs associated with plant height, five QTLs contributing to variation (>10%) in plant height were reported in six mapping populations and found to locate on LG-L, C2, C1, M,

38

G.-J. Lee et al.

and I. Only one QTL closely linked to a Dt1 gene of these QTLs were detected in a single population, but (determinate stem growth) on LG-L was commonly one QTL on LG-A1 was confirmed in different genetic identified in southern and northern USA populations backgrounds. There were reports on seven, six, five, and six QTLs (Table 5). for linolenic, linoleic, palmitic, and oleic acid, respectively, in soybean fatty acid (FA) composition in SoyBase (http://soybase.agron.iastate.edu). All QTLs with 1.4.4 more than 10% of phenotypic contribution were sumSeed Composition marized in Table 5, including three QTLs for linolenic Soybean is the predominant source for seed protein, acid on LG-E, K, and L; three QTLs for linoleic acid on which ranges from 347 to 552 g kg−1 on a dry seed ba- LG-A1, B1, and E; three QTLs for palmitic acid on LGsis among USDA soybean germplasms (Chung et al. A1, B2, and J; and three QTLs for oleic acid on LG-A1, 2003). QTLs conditioning protein content in soybean B2, and E. Only one QTL on LG- E conditions the conwere widely investigated on 17 soybean populations tent of three FAs such as linolenic, linoleic, and oleic and found to be located on the soybean genome ex- acids. Recently six QTLs associated with increased cept for LG-B1, D1b, D2, J, and O (Table 5). A total oleic acid were mapped on LG-A1, D2, G, and L with of 61 QTLs for protein content were reported and 16 SSR markers in a population of G99-G725 (low) × putative QTLs (25%) accounting for more than 10% N00-3350 (high), and four of them were confirmed phenotypic variation were identified. Among them, in a different population of G99-G3438 x N00-3350 ten QTLs were detected in more than two soybean (Monteros et al. 2004). Soybean has desirable carbohydrates (i.e., glucose, populations. The traits for protein content is highly heritable, as shown in the locus on LG-I (Satt127) fructose, and sucrose) and undesirable carbohydrates with a maximum phenotypic contribution of 65% (i.e., raffinose, stachyose). Out of a total of 17 mark(Table 5). Fine-mapping studies have been used to ers significantly associated with seed sucrose content clone the gene close to the Satt127 marker on LG-I on seven LGs, only QTL on LG-I was found to have and resulted in QTL localization within 1.1 cM and a phenotypic contribution greater than 10% (Table 5). using BAC libraries (Joseph et al. 2004; Nichols et al. Based on a comparison of the reported QTLs for seed 2004). MAS of lines with or without positive alleles at composition traits, it is likely to be a gene cluster or the QTL on LG-I was applied to compare the allelic ef- a major QTL with pleiotropic effect (Maughan et al. fect and confirm the QTL, which resulted in a 56 g kg−1 2000). Among the seven genomic regions underlying increase in protein content in lines carrying homozy- QTLs for sucrose content, four regions (LG-E, F, I, L) gous alleles from the high protein parent (Yates et al. also affect protein content, and three regions (LG-A2, I, L) are associated with oil content as well. 2004). Soybean is also an important oil source for humans, as illustrated in Figs. 2 and 5. Among the accessions in the USDA soybean germplasm, seed oil con- 1.4.5 tent ranges from 65 to 287 g kg−1 (Chung et al. 2003). Yield-Related Traits QTL for oil content are distributed widely on the soybean genome except for LG-D1a, D1b, F, J, N, and O Soybean maturity is determined by the number of (Table 5) so that three LGs (D1b, J, O) do not carry days after 31 August when 50% of the individuals genes for both protein and oil content. Eleven out of in a plot had mature seed pod color (95% of the 16 (69%) and 19 QTLs (58%) that were associated with pods/plant) or the number of days after 31 July when protein and oil content, respectively, commonly con- 95% of the normal pods on the main stem had reached trol both traits. Because strong negative correlations their mature pod color (Keim et al. 1990a; Mansur exist between protein and oil content, it needs to be et al. 1993). Of a total of 48 marker loci for maturity redetermined genetically whether the negative relation- ported in SoyBase (http://soybase.agron.iastate.edu), ship is due to pleiotropism or repulsion linkages be- 7 genomic regions appeared to account for more than tween protein and oil alleles. Of 53 oil QTLs reported 10% phenotypic variation on LG-B1, C2, D1a, I, K, L, in SoyBase (http://soybase.agron.iastate.edu), 19 pu- and M in five soybean populations (Table 5). Only one tative QTLs explain more than 10% of the phenotypic QTL region on LG-C2 was identified in two different variation (Table 5). In contrast to protein QTLs, most populations.

Chapter 1 Soybean

The lodging score was determined either by the average angle the plants in the plot made with the soil surface or the visual score with ranges from all erect plants to all prostrate plants (Mansur et al. 1993; Lee et al. 1996b). A total of 36 marker loci were reported to be associated with the soybean lodging trait in six different populations, and five genomic regions on LG-C2, D1b, F, and L (two putative loci) were found to have more than 10% phenotypic contribution (Table 5). Of these five QTL regions, two loci on LG-C2 and L were detected in three independent studies. A genomic region on LG-L flanking a gene for stem determinate (Dt1) was also found to be associated with stem diameter (Table 5). Pod dehiscence (PD; shattering) directly affects soybean yield potential, which is more likely a problem in cultivars derived from the original cross with the wild G. soja. Of 12 marker loci associated with the trait on five genomic regions on LG-E, J, L, and two unlinked loci, one locus on LG-J explains 44% of the PD variation, which accounts for 48% of the total genetic variation, meaning that one or two minor QTLs remain to be identified. Seed coat hardiness (SCH) is determined by the percent seed germination in the absence of scarification compared with scarificated seeds as control (Keim et al. 1990b). A total of seven loci were reported to condition the SCH trait, and four putative loci on LG-A2, D1b, L, and N were found to have greater contribution to the trait variation (Table 5). Interestingly one locus on LG-A2 closely linked to the I locus underlying seed coat color is also associated with the SCH, and cultivars with black seed color are generally considered to have a hard seed coat. Flowering date is determined either by the date on which 50% of the plants in a plot have begun to flower or by the first date after 31 May when an open flower is found at any node on the main stem (Keim et al. 1990a; Mansur et al. 1993). Reports indicated that there were 32 total loci controlling the trait of flowering date in the five soybean populations in SoyBase (http://soybase.agron.iastate.edu). Three genomic regions on LG-C2, L, and M were found to explain the greater part of phenotypic variation of the flowering date and were identified in four, two, and three independent studies, respectively (Table 5). These three QTL regions were reported to be associated with the maturity trait, as expected. The reproductive period is based on the difference in dates between the first blooming (R1) and matu-

39

rity (R8) where the particular stages were determined by the number of days after planting when 50% of the plants in a plot have reached the R1 or R8 stage (Mansur et al. 1993). A total of 14 loci were identified as conditioning the trait, and three genomic regions on LG-C1, L, and M seemed to have major genes (Table 5). Of these three regions, two loci on LG-L and M were reported to have genes for maturity and flowering date. Seed weight is determined either by the weight of 100 seeds in grams or by milligrams per seed from the weight of 200 seed samples per plot. A total of 66 QTLs for seed weight were reported in SoyBase, and loci accounting for a relatively larger (>10%) proportion of the effect on the trait variation were located on 12 LGs in the nine soybean populations (Table 5). Only one locus flanking the Dt1 gene on LG-L was found to be detected in three different populations, and two loci on LG-B1 and G were detected in two populations. The locus on LG-L was also associated with yieldrelated traits of maturity, lodging, seed coat hardness, flowering date, and reproductive period. Seed yield determined by kilograms per hectare on a 13% moisture basis has been the trait of the highest interest in soybean breeding, but the identified QTLs were characterized as having a relatively small effect. Only 29 marker loci were identified as being significantly associated with seed yield, but four of those loci have a larger phenotypic effect, including genomic regions on LG-F, I, M, and N in six mapping populations investigated (Table 5). Only one locus on LG-M was identified in multiple populations. Two of the clustered intervals for reproductive or morphological traits were found to be associated with seed yield (Mansur et al. 1993). Interestingly one locus on LG-I that conditions seed protein content was found to be associated with seed yield, implying that further study is required to uncover the allelic relations (i.e., pleiotropic effect or repulsion phase of the opposite alleles for two traits). Soybean as a vegetable source was evaluated with traits of seed weight (<120 mg seed−1 , hypocotyls length, germination rate, water absorption rate, and sprout yield (Lee et al. 2001a). Mapping of QTLs conditioning hypocotyl length, abnormal seedlings, and sprout yield was conducted in the F2 -derived lines. Multiple regression analysis resulted in finding four QTL regions for sprout yield on LG- B1, G, K, and L; three regions for hypocotyl length on LG- B1, F, and L; and three regions for abnormal seedlings on LG- B1, C2, and N (Lee et al. 2001a). Of these, one

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QTL for each trait was found to have more than 10% 1.5.2 phenotypic contribution, and these include a QTL on Marker Systems LG-G, B1, and M for sprout yield, hypocotyl length, and abnormal seedlings, respectively (Table 5). Seed composition and resistance against pathogens and pests in soybean breeding can be considerably improved by molecular markers (RFLPs, RAPDs, AFLPs, 1.5 Marker-Assisted Breeding in Soybean SSRs, STSs, SNPs) as they facilitate (1) efficient genotyping and estimation of genetic diversity, (2) reliable selection on a single plant level independent of comprehensive performance in the field, (3) acceleration 1.5.1 of backcrossing procedures, (4) pyramiding of mulAdvantages tiple genes, (5) detection of QTL- and marker-based DNA markers that are closely linked to target genes combination of positive alleles, and (6) isolation of have been used in soybean-breeding programs since targeted genes via map-based cloning. The first large-scale effort to produce the soythey are stable and easily detect favorite alleles from a large number of breeding populations without the bean genetic map was performed mainly using RFLP need to perform phenotype tests that are influenced markers (Keim et al. 1990a). However, with the deby environmental factors. Compared to traditional velopment of PCR and the discovery of SSRs (Cregan breeding methods, marker-assisted breeding offers et al. 1994), the RFLP marker system was gradually great advantages by shortening the breeding time as replaced by microsatellites that have multiple alleles a result of (1) increased reliability, (2) increased effi- and more advantages over RFLP. In the past decade ciency, and (3) reducing cost. Efficiency is increased the integrated linkage map of soybean has been unby the ability to determine the genotype at any de- der development and now contains over 1,800 linked velopmental stage. By selecting at the seedling stage, markers on 20 LGs with an average of one marker considerable amounts of time and space can be saved. per 1.4 cM (Song et al. 2004). This map consists priFor example, at the seedling stage, we can select lines marily of RFLP and SSR markers and has been used that have high oleic acid content in seeds based on to identify over 320 unique QTLs (Song et al. 2004). genotypes. This is especially advantageous when se- As for the other marker types, such as RAPD and lecting for traits that are expressed only at later stages AFLP, their applications to genetic analysis seem to of development, such as seed characteristics. With be limited due to the fact that they are dominant, but the application of high- throughput PCR assay and RAPDs and AFLPs have the great advantage of ease automated diagnostic technologies, the material and of use in the laboratory. Coupled with the bulked segconsumable cost for a PCR-based genotyping assay regant analysis (BSA) approach, they are extensively is much lower than the cost of phenotype evalua- used for quickly generating high-density maps in partion. Only plants that fit breeding goals can be se- ticular regions when prior sequence information is lected before planting and will save a considerable lacking (Meksem et al. 2001). However, the drawback amount of money, field space, and labor. The use of is that the markers are generally dominant and generDNA markers for indirect selection offers the greatest ated at random. One major problem with the RAPDs benefits for quantitative traits with low heritability is their low reproducibility, depending highly on the as it is extremely difficult and very costly to assess PCR conditions. Contrariwise, AFLP markers can still these traits in field experiments. The success of MAS be a good choice for QTL mapping or diversity studies is dependent upon many factors, including availabil- in species devoid of dense marker maps. For highity of markers closely linked to the traits of interest, throughput genotype scoring in MAS, AFLP bands relative cost, and time required for MAS compared to can be converted into polymorphic (STS) markers by the conventional phenotypic selection. The efficiency sequencing AFLP bands and primer extension to rethat high-throughput MAS brings to the breeding pro- construct the polymorphism that generates the AFLP. cess is measured by the success in capturing desired Use of bacterial artificial chromosome (BAC) libraries traits in a large population throughout the breeding and physical maps can improve the efficiency of AFLP band conversion to STS. This approach was applied scheme. in the discovery of new markers linking to one of two

Chapter 1 Soybean

loci, rhg1 on LG-G and Rhg4 on LG-A2 (Meksem et al. 2001). SNPs are the most abundant form of sequence variability in the soybean genome (Zhu et al. 2003), and polymorphisms in gene coding are likely to contribute to the phenotypic differences between lines. As abundant DNA markers, SNPs are valuable for genetic association studied for complex traits. The expanding availability of soybean genome sequence data and the microarray-based technologies for high-throughput discovery of polymorphic markers around or within gene- coding regions is allowing the efficient development of targeted SNP markers for important QTL or candidate genes. The use of SNPs in MAS will further reduce the cost of MAS and increase the accuracy of capturing targeted genes. Allele-specific polymerase chain reaction (ASPCR) assay was developed for the detection and genotyping of SNPs in the genomic DNA fragments tightly linked to two soybean mosaic virus resistance genes, Rsv1 and Rsv3. Using the modified procedure, many SNP loci in eight soybean parental lines and F2 individuals of three mapping populations could be genotyped, which could greatly facilitate small- to medium-scale MAS programs for agronomically important genes (Jeong and Maroof 2004). SNP genotyping assays that are high-throughput, accurate, and inexpensive are needed for MAS in plant- improvement programs. High-throughput SNP genotyping requires an accurate SNP identification method and a compatible automated or semiautomated platform technology that allows rapid handling and scoring of the data. Luminex 100 flow cytometer is used as a versatile and compatible platform for soybean SNP genotyping in several laboratories. Lee et al. (2004b) compared the effectiveness and robustness of different genotyping assays: single-base extension (SBE), allele-specific primer extension (ASPE), oligonucleotide ligation (OL), and direct hybridization (DH), performed on a Luminex 100 flow cytometric platform, to compare the genotyping accuracy of these assays with that of the SNaPshot assay. The results showed that ASPE is more cost effective and simpler than SBE and would therefore be a good method for genetic mapping and diversity studies. DH is the most economical assay suitable for MAS, though optimization for DH would be required for some SNP markers. The conversion of AFLP bands into polymorphic STS markers is necessary for high-throughput genotyping. There are several technical hurdles that arise

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from genome complexity (particularly sequence duplication), from the low- molecular-weight nature of the AFLP bands, and from the location of polymorphism within the AFLP band. The markers that are linked to one of two loci, rhg1 on LG-G and Rhg4 on LG-A2, were successfully converted, which confers resistance to the soybean cyst nematode (Meksem et al. 2001). When the polymorphic AFLP band sequence contained a duplicated sequence or could not be converted to a locus-specific STS marker, direct sequencing of BAC clones anchored to a physical map generated locus-specific flanking sequences at the polymorphic locus. When the polymorphism was adjacent to the restriction site used in the AFLP analysis, single primer extension was performed to reconstruct the polymorphism. The converted AFLP markers could be insertions or deletions, microsatellites or SNPs. The polymorphic sequences are used to design a high-throughput assay for marker-assisted breeding for resistance to cyst nematode. Meksem et al. (2001) indicated that the converted AFLP markers contained polymorphism at a 10- to 20-fold higher frequency than expected for adapted soybean cultivars and that the efficiency of AFLP band conversion to STS could be improved using BAC libraries and physical maps. The method provides an efficient tool for SNP and STS discovery suitable for marker-assisted breeding and genomics.

1.5.3 Marker-Assisted Introgression MAS is used to accelerate recovery of the elite parent genome in backcross programs. Marker-assisted backcrossing has made it possible to introduce two or more genes from different sources into the genetic background of one recipient genotype by recurrent backcrossing. Besides the transfer of the target genes, the main goal in such a breeding program is to recover the recurrent parent genome as rapidly and completely as possible. A core set of SSR markers with a good coverage of the entire genome can be used for background selection of individuals with a high proportion of recurrent parent genome. At the Soybean Genome Mapping Lab at the University of Missouri, microsatellite- based primer panels across the whole genome at approximately 20-cM intervals are used to select recurrent background genomes, and some polymorphic SSR flanking markers associated with high oleic acid content are used to capture donor alleles that

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contribute to the high oleic acid content of soybean seeds. In practice, we can perform marker-assisted backcrossing for six loci. Some general principles can be used for this project. First, when beginning a backcrossing project, the background of all parental lines (recurrent and donor) should be assessed. Second, two to four hybrids having the highest percentage of recurrent contribution, as determined by microsatellite analysis, will be used as males to make crosses with recurrent lines. The aim is to produce at least ten heterozygotes for subsequent background assessment. Third, this process of selected breeding will be repeated at each generation out until individuals contain greater than 98% of the recipient background. Near-isogenic lines (NILs) are widely used in QTL fine mapping, map-based cloning, and novel variety development. They provide insight into gene structure and the contribution of the gene to their phenotypes by using a series of NILs that are identical at all genetic loci except for one region of interest. Utilizing traditional, random backcrossing methods it may take many years to develop a line containing 99% of the recurrent (elite line) genome combining the favorable genes from the donor. Selectively breeding individuals containing more of the recurrent genome from each generation will accelerate the procedure of producing an ideal genotype. Genetic diversity is limited in southern elite soybean. Soybean breeders in the United States are attempting to do introgression of high-yield alleles at three QTLs from the northern cultivar Archer into southern cultivars. The results indicate that it may be difficult to capture the value assigned to QTL alleles derived from diverse parents with variable relative genetic value when the alleles are introgressed into populations with different genetic backgrounds or when tested in different environments (Reyna and Sneller 2001). Exotic germplasms such as wild soybean are valuable in broadening the genetic base of soybean cultivars. However, unwanted linkages often thwart the successful incorporation of beneficial genes from exotic germplasms into elite lines. Thus, MAS can facilitate the utilization of exotic germplasms in soybeanbreeding programs, which makes the process of introgression easier and more accurate. The availability of closely linked molecular markers makes it possible only to isolate specific genomic regions and transfer them to elite lines with minimal linkage drag. A yieldenhancing QTL located on LG-B2 was found from G.

soja (PI 407305). This study demonstrates the potential of exotic germplasms for yield enhancement in soybean, though the yield effect was consistently observed in only two of six genetic backgrounds (Concibido et al. 2003).

1.5.4 Gene Pyramiding The purpose of gene pyramiding is to develop an ideotype that is homozygous for favorable alleles at different loci of interest that correspond to all target genes. Usually, these target genes exist in different donor genotypes so that the breeding scheme for pyramiding multiple genes is more complicated than that for a single gene. Servin et al. (2004) proposed that the best way to combine a series of target genes into a single genotype was through marker- assisted gene pyramiding. The success of gene pyramiding is dependent on the breeding scheme, selection strategy, and population size (Frisch and Melchinger 2001). MAS and gene introgression programs for the development of mid-oleic acid, low linolenic acid, and SCN-resistant soybean are being used by different groups. As an example, DNA markers were identified for three microsomal omega-3 FA desaturase genes and are being used in breeding for low linolenic acid content in soybean (Bilyeu et al. 2003).

1.6 Progress in Map-Based Cloning, Transformation, and Other Candidate Gene Approaches in Soybean 1.6.1 Map-Based Cloning The map-based-cloning approach has been extensively used for gene isolation in model plant systems. In soybean, map-based cloning is still tedious because of the lack of high-density genetic and physical maps. However, with the saturating marker technologies and gene golfing strategy, two SCN-resistant genes, rhg1 on LG-G and Rhg4 on LG-A2, have been cloned (Meksem et al. 2001). Advances in the methods used to detect DNA polymorphisms, the discovery

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and mapping of a large number of SNP markers, and 1.6.3 the availability of an integrated genetic and physical Functional Genomics map will make the map-based cloning process much for Candidate Gene Discovery easier. Microarray analysis of gene expression is a promising technology to monitor the transcriptional machinery 1.6.2 of the organism. This technology has attracted the Soybean Transformation attention of many researchers interested in exploring the functional similarities or differences among Modern genetic and gene function analysis and thousands of genes simultaneously. As more tissues the manipulation of transgenic soybean (G. max) and developmental stages are profiled, a gene exwith desirable traits depend heavily on an ef- pression atlas can be created that describes the exficient and dependable transformation process. pression pattern of every gene in the genome. Such Two transformation systems have been used most an atlas would be a tremendously useful tool to frequently in soybean over the past decade. One the biologist interested in the expression profile of is biolistic-mediated transformation of soybean a specific gene (Borevitz and Chory 2004). An arembryogenic cultures (Finer and McMullen 1991; ray consists of immobilized biomolecules spatially Finer et al. 1992; Parrott et al. 1994; Hadi et al. 1996; arranged on a slide, which is used for identifying Stewart et al. 1996; Hazel et al. 1998; Santarem and known and unknown DNA samples based on base Finer 1999). The other is Agrobacterium-mediated pairing. These microarrays on the slides can be cretransformation of soybean cotyledonary nodes ated manually or by robots that deposit the samthrough direct organogenesis (Hinchee et al. 1988; ple spots, which are typically less than 200 μm in Di et al. 1996; Zhang et al. 1999; Clemente et al. diameter and are detected by specialized scanning 2000; Xing et al. 2000). The Plant Transformation and imaging equipment. The biomolecules include Core at the University of Missouri is working on oligonucleotides, PCR products, proteins, lipids, pepdeveloping a high- throughput transformation tides, and carbohydrates, but this section is limited to system for the Williams 82 genotype that has been DNA arrays. used as the subject for EST, genome mapping, There are two categories of microarrays, cDNA and functional genomics analyses. Zeng et al. microarray and oilgonucleotide array, depending (2004) optimized both Agrobacterium infection and on the property of arrayed sequence, cDNA or glufosinate selection in the presence of l-cysteine oligonucleotide (Zhu 2003; Venkatasubbarao 2004). for Williams 82 and recovered transgenic lines of GeneChip is a specialized oligonucleotide probe this genotype with an enhanced transformation array used to detect the sequence similarity and efficiency using this herbicide selection system. abundance of target DNA or RNA molecules through This work will greatly aid soybean functional complementary-sequence binding. High-density genomics and soybean transgenic technology in DNA probe arrays are powerful tools for a broad set general. of applications including gene expression monitorGenetic engineering of soybean is currently be- ing, sequence analysis, and genotyping (Zhu 2003; ing carried out using three distinct target explants, Lipshultz et al. 1999). cotyledonary node, embryonic axis, and somatic emThe soybean expressed sequence tag (EST) project bryos. Roundup Ready soybean is an input trait in- led by representatives of soybean commodity groups, troduced into soybean germplasm via genetic en- including public and private scientists and producgineering that provides growers with a convenient ers and growers, was initiated to develop 300,000 and effective weed-control tool. Ongoing research ef- cDNA libraries (gene messages) and gene sequences forts have expanded the targets of other novel in- of economic importance from many developmenput and output traits for soybean. Input traits be- tal stages or organs of soybean. Through collaboing investigated include alternative herbicide resis- rative efforts, currently over 80 libraries and over tance and tolerance to both biotic and abiotic stresses, 300,000 EST sequences have been generated from while output traits are primarily focused on modified roots, shoots, leaves, stems, pods, cotyledons, germioil and protein composition in the seed (Clemente nating shoot tips, flower meristems, tissue-culture2004). derived embryos, and pathogen-challenged tissues

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(Shoemaker et al. 2002). Cluster analysis revealed that the entire public EST collection yields 61,127 unigenes of which 36,357 are contigs and 24,770 are singletons (Vodkin et al. 2004). Similarly, The Institute for Genomic Research (TIGR) has a total of 63,676 unigenes consisting of 31,918 TC sequences and 31,748 singletons (http://www.tigr.org/tdb/tgi). To enable cDNA microarray experiments for expression profiles, the selected cDNA clones were processed into three sets of 9,216 cDNAs per array representing low-redundancy copy and at least once on an array (Vodkin et al. 2004). Recent GeneBank-submitted EST sequences from the subtracted library of droughtstressed soybean root tips contribute stress-specific unigenes for the further functional genomics approach. A long oligoarray featuring more strict and precise selection of transcripts is being developed and will be available commercially soon from Lila Vodkin’s group at the University of Illinois. Recently, soybean GeneChips have been made commercially available for studying the gene expression of over 37,500 G. max transcripts, over 15,800 Phytophthora root and stem rot transcripts, and over 7,500 SCN transcripts (http://www.affymetrix.com). Size of the soybean array on the GeneChip is an 11-probe, 11-μm pair. Proteomic analysis of soybean in responding to Bradyrhizobium was presented with 96 protein spots from root hairs inoculated, as well as 37 protein spots from the inoculated roots over different time points (Wan et al. 2004). Several proteins previously reported (peroxidase and phenylalanine-ammonia lyase) and novel proteins (phospholipase D, phosphoglucomutase) were identified, and further advances will be made. Protein analysis for seed proteins in soybean was conducted using cotyledon tissues, and nine basic proteins were isolated and are being analyzed (Woo et al. 2004). Several drought-stress-induced proteins were identified in soybean roots and leaves using novel proteomics technology, and the comparison of the tissue- specific transcriptome with proteome will help identify and reveal novel genes regulating the stresstolerance mechanism in soybean. Functional analysis of several genes of interest using RNAi silencing is in progress in soybean. Recently, RNAi suppression of some genes responsible for seed phospholipid degradation and defense genes to pathogen invasion (Phytophthora root and stem rot and SCN) was conducted; confirmation of the findings is in progress (Graham et al. 2004; Lee et al. 2004a; Trick et al. 2004).

An integrated functional genomics approach will facilitate the identification of novel genes and transcription factors for crop improvement.

1.7 Future Scope DNA markers are vital to leveraging knowledge from noncrop model systems to crop species of economic importance. They form the foundation for gene tagging and QTL mapping. They enable genome comparisons among different species and provide tools for assessing molecular variation within and between species. Efficient sharing of genetic information between soybean and other legumes requires the availability of common DNA markers across all legume genomes. Comparative analysis among the legumes will enable advances in one crop to be applied to other species. Development of a high-resolution physical map of the soybean cultivar Williams 82 has been identified as a research priority in the USA (Stacey et al. 2004). A large EST resource was derived from this genotype and is targeted for whole genome sequencing in the future. Completion of a physical map with BAC end sequences will advance numerous soybean genomic goals, such as SNP marker development, comparative mapping, and genome sequencing, and will help to reveal ancient duplications within the soybean genome that are problematic for many genetic studies. Mapping cDNAs from the EST or unigenes onto the physical map will yield information useful to basic studies of genome structure as well as the discovery of candidate genes for QTLs. In addition, this approach will identify gene-rich regions that could be targeted for genome sequencing. Even though the virtue of genomic analysis privileges Arabidopsis as a model plant, soybean is considered a model species of the phaseoloid clade of the legume family. The tools of functional genomics such as transcriptomics, proteomics, and metabolomics and related bioinformatics approaches will lead to the identification of a high degree of crosstalk and overlap between different response pathways and gene discovery in soybean. These comprehensive advances elevate the importance of a systems biology approach in soybean and integrate the comparative genomics analysis in the process. Comparative genomics analysis is the potential angle of in silico analysis to under-

Chapter 1 Soybean

stand biological systems and processes across plant species with the aim of identifying genes of agronomic interest. Discovery of novel genes and transcription factors need to be translated for better crop production using efficient gene transformation technologies. Also, the scope of promoter elements is high for tissue-specific or stress-specific gene expression. Much attention should be given to identifying regulatory regions and investigating the structure that controls the transcription of genes of interest. Studies on crosstalk between pathways and the regulatory network and comparative genomics will help to correlate the effects of genetic background and environmental factors on soybean plants. The development of new markers and the identification of candidate genes through a genomics approach will provide potential tools for soybean breeders for crop-improvement applications.

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Chapter 1 Soybean Rahman SM, Takagi Y, Kinoshita T (1996) Genetic control of high oleic acid content in seed oil of two soybean mutants. Crop Sci 36:1125–1128 Rahman SM, Takagi Y, Kinoshita T (1997) Genetic control of high stearic acid content in seed oil of two soybean mutants. Theor Appl Genet 95:772–776 Rao-Arelli AP (1994) Inheritance of resistance to Heterodera glycines race 3 in soybean accessions. Plant Dis 78:898–900 Rector BG, All JN, Parrot WA, Boerma HR (1999) Quantitative trait loci for antixenosis resistance to corn earworm in soybean. Crop Sci 39:531–538 Rector BG, All JN, Parrot WA, Boerma HR (2000) Quantitative trait loci for antibiosis resistance to corn earworm in soybean. Crop Sci 40:233–238 Reyna N, Sneller CH (2001) Evaluation of marker-assisted introgression of yield QTL alleles into adapted soybean. Crop Sci 41:1317–1321 Reyna N, Cornelious B, Shannon JG, Sneller CH (2003) Evaluation of a QTL for waterlogging tolerance in southern soybean germplasm. Crop Sci 43:2077–2082 Ryan SA, Nelson RS, Harper JE (1983) Soybean mutants lacking constitutive nitrate reductase activity. II. Nitrogen assimilation, chlorate resistance, and inheritance. Plant Physiol 72:510–514 Santarem ER, Finer JJ (1999) Transformation of soybean [Glycine max (L.) Merrill] using proliferative embryogenic tissue maintained on semisolid medium. In Vitro Cell Dev Biol Plant 35:451–455 Schuster I, Abdelnoor RV, Marin SRR, Carvalho VP, Kiihl RAS, Silva JFV, Sediyama CS, Barros EG, Moreira MA (2001) Identification of a new major QTL associated with resistance to soybean cyst nematode (Heterodera glycines). Theor Appl Genet 102:91–96 Sebolt AM, Shoemaker RC, Diers BW (2000) Analysis of a quantitative trait locus allele from wild soybean that increases seed protein concentration in soybean. Crop Sci 40:1438–1444 Senda M, Jumonji A, Yumoto S, Ishikawa R, Harada T, Niizeki M, Akada S (2002) Analysis of the duplicated CHS1 gene related to the suppression of the seed coat pigmentation in yellow soybeans. Theor Appl Genet 104:1086–1091 Servin B, Martin OC, Mezard M, Hospital F (2004) Toward a theory of marker-assisted gene pyramiding. Genetics 168:513–523 Sexton R, Roberts JA (1982) Cell biology of abscission. Ann Rev Plant Physiol 33:133–162 Shipe ER, Buss GR, Tolin SA (1979) A second gene for resistance to peanut mottle virus in soybeans. Crop Sci 19:656–658 Shoemaker RC, Olson TC (1993) Molecular linkage map of soybean (Glycine max L. Merr.). In: O’Brien SJ (ed) Genetic Maps: Locus Maps of Complex Genomes. Cold Spring Harbor Press, Cold Spring Harbor, NY, pp 6131–6138 Shoemaker RC, Specht JE (1995) Integration of the soybean molecular and classical genetic linkage groups. Crop Sci 35:436–446

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Shoemaker RC, Polzin K, Labate J, Specht J, Brummer EC, Olson T, Young N, Concibido V, Wilcox J, Tamulonis JP, Kochert G, Boerma HR (1996) Genome duplication in soybean (Glycine subgenus soja). Genetics 144:329–338 Shoemaker R, Keim P, Vodkin L, Retzel E, Clifton SW, Waterston R, Smoller D, Coryell V, Khanna A, Erpelding J, Gai X, Brendel V, Raph-Schmidt C, Shoop EG, Vielweber CJ, Schmatz M, Pape D, Bowers Y, Theising B, Martin J, Dante M, Wylie T, Granger C (2002) A complication of soybean ESTs: generation and analysis. Genome 45:329–338 Shoemaker RC, Cregan PB, Vodkin LO (2004) Soybean genomics. In: Boerma HR, Specht JE (eds) Soybeans: Improvement, Production, and Uses. Agron Monogr, 3rd edn. No. 16, ASA-CSSA-SSSA, Madison, WI, pp 235–263 Sills G, Nienhuis J (1998) Changes in DNA-marker frequencies associated with response to contrasting selection methods in Arabidopsis. Theor Appl Genet 97:275–282 Singh BB, Jha AN (1978) Abnormal differentiation of floral parts in a mutant strain of soybean. J Hered 69:143–144 Skorupska HT, Choi IS, Rao-Arelli AP, Bridges WC (1994) Resistance to soybean cyst nematode and molecular polymorphism in various sources of Peking soybean. Euphytica 75:63–70 Sneller CH, Isleib TG, Carter TE Jr (1992) Isozyme screening of near-isogenic male-strile soybean lines to uncover potential linkages: linkage of the Pgm1 and ms2 loci. J Hered 83:457–459 Song QJ, Marek LF, Shoemaker RC, Lark KG, Concibido VC, Delannay X, Specht JE, Cregan PB (2004) A new integrated genetic linkage map of the soybean. Theor Appl Genet 109:122–128 Soybean Genetics Committee (1995) Soybean genetics committee report. Soybean Genet Newslett 22:11–14 Specht JE, Hume DJ, Kumundini SV (1999) Soybean yield potential – a genetic and physiological perspective. Crop Sci 39:1560–1570 Specht JE, Germann M, Markwell JP, Lark KG, Orf JH, Macrander M, Chase K, Chung J, Graef GL (2001) Soybean response to water: a QTL analysis of drought tolerance. Crop Sci 41:493–509 Spencer MM, Pantalone VR, Meyer EJ, Landau-Ellis D, Hyten DL (2003) Mapping the Fas locus controlling stearic acid content in soybean. Theor Appl Genet 106:615–619 Stacey G, Vodkin L, Parrott WA, Shoemaker RC (2004) National Science Foundation-sponsored workshop report. Draft plan for soybean genomics. Plant Physiol 135:59–70 Stelly DM, Palmer RG (1980) A partially male-sterile mutant line of soybeans Glycine max (L.) Merr.: characterization of msp phenotype variability. Euphytica 29:539–546 Stephens PA, Nickell CD, Kolb FL (1993) Genetic analysis of resistance to Fusarium solanii in soybean. Crop Sci 33:929–930 Stewart CN Jr, Adang MJ, All JN, Boerma HR, Cardineau G, Tucker D, Parrott WA (1996) Genetic transformation, recovery, and characterization of fertile soybean trans-

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Vest G (1970) Rj3-a gene conditioning ineffective nodulation in soybean. Crop Sci 10:34–35 Vierling RA, Faghihi J, Ferris VR, Ferris JM (1996) Association of RFLP markers conferring broad-based resistance to the soybean cyst nematode (Heterodera glycines). Theor Appl Genet 92:83–86 Vodkin LO, Khanna A, Shealy R, Clough SJ, Gonzalez DO, Philip R, Zabala G, Thibaud-Nissaen F, Sidarous M, Stromvik MV, Shoop E, Schmidt C, Retzel E, Erpelding J, Shoemaker RC, Rodriguez-Huete AM, Polacco JC, Coryell V, Keim P, Gong G, Liu L, Pardinas J, Schweitzer P (2004) Microarrays for global expression constructed with a low redundancy set of 27,500 sequenced cDNAs representing an array of developmental stages and physiological conditions of the soybean plant. BMC Genomics 5 (Published online 29 September 2004) Wan J, Torres M, DaGue B, Mooney B, Xu D, Stacey G (2004) Proteomic analysis of soybean root hairs after infection by Bradyrhizobium japonicum. In: Soy2004 10th Biennial Conference of the Cellular and Molecular Biology of the Soybean, Columbia, MO, p 117 Wang D, Arelli PR, Shoemaker RC, Diers BW (2001) Loci underlying resistance to race 3 of soybean cyst nematode in Glycine soja plant introduction 468916. Theor Appl Genet 103:561–566 Webb DM, Baltazar BM, Rao-Arelli PA, Schupp J, Clayton K, Keim P, Beavis WD (1995) Genetic mapping of soybean cyst nematode race-3 resistance loci in soybean PI437.654. Theor Appl Genet 91:574–581 Weiss MG (1943) Inheritance and physiology of efficiency in iron utilization in soybeans. Genetics 28:253–268 Weiss MG (1970) Genetic linkage in soybeans. Linkage group II and III. Crop Sci 10:300–303 Wells R, Ashley DA, Boerma HR, Reger BJ (1986) Physiological comparisons of two soybean cultivars differing in canopy photosynthesis. II. Variation in specific leaf weight, nitrogen, and protein components. Photosyn Res 9:295–305 Weng C, Yu K, Anderson TR, Poysa V (2001) Mapping genes conferring resistance to Phytophthora root rot of soybean, Rps1a and Rps7. J Hered 92:442–446 Werner BK, Wilcox JR, Housley TL (1987) Inheritance of an ethyl methanesulfonate-induced dwarf in soybean and analysis of leaf cell size. Crop Sci 27:665–668 Wiebold WR, Kenworthy WJ (1985) Leaflet expansion rates for 15 soybean cultivars. Field Crops Res 12:271–279 Williams C, Gilman DF, Fontenot DS, Birchfield WB (1981) Inheritance of reaction to the reniform nematode Rotylenchulus reniformis in soybean. Crop Sci 21:93–94 Willmot DB, Nickell CD (1989) Genetic analysis of brown stem rot resistance in soybean. Crop Sci 29:672–674 Wilson RF (2004) Seed composition. In: Boerma HR, Specht JE (eds) Soybeans: Improvement, Production, and Uses. Agron Monogr, 3rd edn. No. 16, ASA-CSSA-SSSA, Madison, WI, pp 621–677

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CHAPTER 2

2 Oilseed Rape Rod Snowdon, Wilfried Lühs, and Wolfgang Friedt Department of Plant Breeding, Research Centre for Biosystems, Land Use and Nutrition, Justus Liebig University of Giessen, Heinrich-Buff-Ring 26–32, 35392Giessen, Germany e-mail: [email protected]

2.1 Introduction 2.1.1 Origin and History of Oilseed Rape Rapeseed (Brassica napus L.; genome AACC, 2n = 38) is today the most widely cultivated crop species in the crucifer family (Brassicaceae). The species is now divided into two subspecies, comprising on the one hand the swedes (B. napus ssp. napobrassica) and on the other hand B. napus ssp. napus, which includes winter and spring oilseed, fodder and vegetable rape forms. The latter include the distinct leaf rape forms (B. napus ssp. napus var. pabularia), which used to be common as a winter-annual vegetable in many parts of the world (Siberian kale, Hanover salad; German: Schnittkohl; French: chou à faucher; Chinese: xi yang you cai). The species originated through spontaneous interspecific hybridization between turnip rape (Brassica rapa L., syn. campestris; genome AA, 2n = 20) and cabbage (Brassica oleracea L.; genome CC, 2n = 18), resulting in an amphidiploid genome comprising the full chromosome complements of its two progenitors. Because no wild B. napus forms are known, it is assumed that the species arose relatively recently, in the Mediterranean region, where both of its two parental species concurred. The occurrence of spontaneous chromosome doubling in crosses among closely related Brassica diploid species is well documented; the related amphidiploids Indian or brown mustard (B. juncea; genome AABB, 2n = 36) and Abyssinian or Ethiopian mustard (B. carinata; genome BBCC, 2n = 34) arose in the same manner after crosses of black mustard (B. nigra, genome BB, 2n = 16) with B. rapa and B. oleracea, respectively. Brassica vegetables and oilseeds were among the earliest plants to be systematically cropped by mankind. There are indications that a vegetable crucifer was widely cultivated as early as 10,000 years

ago. In India records have been identified which suggest that oilseed brassicas (probably B. rapa) were being used as early as 4000 BC, and 2000 years ago their use had spread into China and Japan. Swedes (B. napus ssp. napobrassica) were known in Europe at the time of the Romans, and utilisation (probably of B. rapa) for oil purposes in northern Europe is thought to have begun around the 13th century. By the 16th century, rapeseed was the major source of lamp oil in Europe, although it was not until the 18th century that significant cultivation areas of the crop were recorded (Kroll 1994; Kimber and McGregor 1995). Today, oilseed rape is the most heavily produced oilseed crop in Europe and only soybean has a greater importance worldwide. Oilseed rape production is dominated by North America (particularly Canada), Western Europe and China; however the crop is also significant in Eastern Europe, the Indian subcontinent and Australia. Oilseed rape has become a major international crop only in the past three decades, however (Lühs and Friedt 1994a). The use of rapeseed oil for lamp fuel was largely superseded by petroleum from the end of the 19th century onwards, and only the high quality of rapeseed fats as lubricants for industrial machinery guaranteed continued production of the crop throughout the 20th century. Oil from early rapeseed varieties contained a high quantity of erucic acid (cis 13-docosenoic acid, 22:1n-9), which in high doses can lead to cardiac damage and related health problems. Erucic acid also has a bitter taste, meaning that the oil was generally used only by the poor as a food oil. In times of poverty and crisis, of course, such negative aspects tended to be outweighed by necessity; hence rapeseed production peaked significantly during the wars in Europe in the 20th century, particularly in World War II when rapeseed oil was used especially for the production of margarine. The poor reputation of rapeseed oil as a foodstuff was overcome only by the development of 0 and

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00 rapeseed varieties in the 1970s (Stefansson 1983; Downey and Röbbelen 1989; Downey 1990). The first major breakthrough came with the initial 0-quality cultivars with erucic acid levels of less than 1% (Stefansson and Hougen 1964). Earlier rapeseed cultivars contained up to 50% erucic acid in the seed oil. The identification of the fatty acid mutants from which the first 0-rapeseed derived was made possible by major improvements in high-throughput seed analysis techniques, in particular gas chromatography. The first erucic acid-free variety, derived from a spontaneous mutant of the German spring rapeseed cultivar Liho, was released in Canada in the early 1970s. The value of the crop was still suppressed by the presence in the seed of high quantities of glucosinolates, however, which made rapeseed meal unsuitable as a livestock feed. In monogastric animals the digeston of glucosinolates results in the release of toxic byproducts that can cause liver and kidney damage along with lymph dysfunction. In 1969 the Polish spring rape variety Bronowski was identified as a low glucosinolate form, and this cultivar provided the basis for an international backcrossing program to introduce this polygenic trait (Bronowski was found to possess at least three recessive genes for low glucosinolate content) into high-yielding erucic acid-free material. The result was the release in 1974 of the first 00-quality spring rapeseed variety, Tower, with zero erucic acid and low glucosinolate content, and thus began the advance of oilseed rape (canola) in the following decades to one of the most important oil crops in temperate regions. The particular value of rapeseed oil lies in its diverse range of uses. Besides its use as a highly nutritional food oil, rapeseed oil also provides a raw material for an astounding array of products ranging from rapeseed methyl ester (biodiesel) to industrial lubricants, tensides for detergent and soap production and biodegradable plastics. 2.1.2 Botanical Description Oilseed rape is cultivated in Europe and Asia predominantly as winter rapeseed, whereas in Canada, northern Europe and Australia only spring forms are suitable. The differentiation into winter and spring forms is governed by a genetic mechanism controlling the requirement for vernalisation to promote the onset of flowering. Spring oilseed rape does not require vernalisation and is not winter-hardy, hence the crop is sown in spring and stem development begins

immediately after germination. Winter oilseed rape on the other hand is sown in autumn and survives the winter in a leaf rosette form on the soil surface. In the following spring a long vertical stem develops, and shortly before the floral development lateral branches are formed. Flowering generally occurs in late spring, with pod development and ripening taking place over a period of around 6 to 8 weeks until mid-summer. As a member of the family Brassicaceae (Cruciferae), B. napus possesses a Brassica-typical radial flower comprised of four petals in the typical crucifer cross-form, alternating with four sepals. The inflorescence is racemose, with indeterminate flowering beginning at the lowest bud on the main raceme and continuing upward during the following days. The stigma is receptive from about 3 d prior until 3 d after the opening of the flower. The normally yellow flowers have one pair of lateral stamens with short filaments and four median stamens with longer filaments. Oilseed rape anther sutures are introrse in the bud stage; however the anthers of the four long stamens become extrorse after the flowers open. In contrast to the majority of B. rapa and B. oleracea, its diploid progenitors, B. napus is a facultative outcrossing species with a high degree of self-pollination. When insect pollinators are abundant, a greater proportion of cross-pollination can occur, although through targeted fertilization-direction (e.g. using male-sterility systems for hybrid varieties, see below) it is possible to obtain up to 100% outcrossing. Brassica flowers possess two functional nectaries at the base of the short stamens, along with two nonfunctional nectaries at the base of the pairs of long stamens. The synocarpous ovary, consisting mainly of two but sometimes up to four carpels, develops after fertilization into a bivalved silique with a longitudinal septum. 2.1.3 Economic Importance of Oilseed Rape Rapeseed/canola (mainly B. napus) has become a significant agricultural product during the past 30 years and is now the world’s third leading source of both vegetable oil (after soybean and oil palm) and oil meal (after soybean and cotton). In 2005 world production of the seven major oilseeds was 371 million metric tonnes (MMT), with soybeans dominating (210 MMT) followed by rapeseed/canola with 46 MMT (Table 1; FAOSTAT data 2006). This annual production comes mainly from the European

Chapter 2 Oilseed Rape Table 1. World production of major oilseed crops in millions of metric tons (FAOSTAT data, 2006: http://faostat.fao.org/) Crop

2003

2004

2005

Soybeans Rapeseed Cottonseed Groundnuts in shell Sunflower seed Sesame seed Linseed Total

190.60 36.61 34.36 36.43

206.41 46.17 43.33 36.42

209.98 46.41 41.65 35.91

27.80 3.20 2.13 331.13

26.47 3.28 1.96 364.04

31.07 3.32 2.71 371.04

Union (EU-25: 14.98 MMT), China (13.05 MMT), Canada (8.44 MMT), India (6.40 MMT), and Australia (1.13 MMT). The world average yield of 2.71 metric tonnes per hectare in 2005 covered a wide range, with up to 4.14 t/ha in Western Europe, 1.81 in China, 1.63 in Canada and 1.04 in Australia to 0.90 t/ha in India (FAOSTAT data 2006). These differences are due to varieties (e.g. winter vs. spring type), climate and soil as well as the use of agricultural inputs (seed quality, fertilizers and agrochemicals) and different agronomic techniques.

2.1.4 Nutritional and Chemical Composition of Rapeseed Oil Oilseeds generally are crushed to yield oil (40 to 45% in the case of rapeseed) and residual meal that is rich in protein and used mainly for animal feed and, to a lesser extent, for human food. In contrast to soybean meal, rapeseed meal is not widely used for human consumption (Lühs and Friedt 1994a; Friedt et al. 2004). Seed oils, which are composed mainly of triacylglycerols (TAGs), are an important source of fatty acids for human nutrition and hydrocarbon chains for industrial products. Commodity rapeseed oil is now produced mainly from low erucic low glucosinolate varieties (canola), but there are also high-erucic cultivars and newer varieties with modified fatty acid composition furnishing specialty uses and niche markets, such as oleochemistry or frying purposes (Friedt and Lühs 1998). Over the whole range of oils and fats about 80% is used as human food, a further 6% goes into animal feed, and the balance (14%) provides the

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basis of the oleochemical industry (Lühs and Friedt 1994b).

Rapeseed oil for human nutrition Today, the oil of modern rapeseed varieties, which is almost entirely lacking in nutritionally undesirable long-chain fatty acids, is highly appreciated due to its fatty acid profile, which meets new health recommendations to reduce total dietary saturated fat intake, mainly derived from animal sources. The current interest in the nutritional and health effects of fatty acids, such as laurate (12:0), myristate (14:0), palmitate (16:0) and to a certain extent also stearate (18:0), relates to evidence associating high intake of these fatty acids in the diet with increased levels of blood cholesterol, arteriosclerosis and high coronary heart disease risk (Grundy and Denke 1990; Gurr 1992; Hu et al. 1997). With regard to commonly consumed vegetable oils and fats, low-erucic-acid-content rapeseed or canola oil contains the lowest level (ca. 6 to 7%) of saturated fatty acids. Furthermore, with 58 to 60% of its total fatty acids as oleic acid (18:1n-9), canola oil is an important source of this monounsaturated fatty acid (MUFA) whose nutritional significance has increased during the last 20 years (Mattson and Grundy 1985; Dupont et al. 1989; Marsic et al. 1992; Valsta et al. 1992; McDonald 1995; Trautwein 1997). Canola oil received GRAS (Generally Recognized as Safe) status on the United States market in 1985, and its high nutritional quality is being further enhanced today by the admission to the market of low-saturated-fatty-acid-content oilseeds, often accompanied by low linolenic acid and/or high oleic acid content. Contrary to the nutritional necessity of certain n-6 and n-3 polyunsaturated fatty acids (PUFA), canola or soybean oils containing 8 to 10% α-linolenic acid (18:3n-3) are more liable to rapid oxidative damage than oils with little or no 18:3n-3. For example oxidation of linoleate (18:2n-6) and linolenate is ca. 10 and 25 times higher, respectively, than that of oleic acid (Frankel 1991; Kinsella 1991; Carlson 1995; Horrobin 1995; Lands 1997). Vegetable oils with a high 18:1n-9 and/or low 18:3n-3 content are marketed for bottled salad oil and salad dressings as well as for food applications requiring high cooking and frying temperature stability, including extended shelf-life products (such as snack foods). Increasing the 18:1n-9 content while reducing PUFA levels decreases the development of unpleasant flavors and off-odors indicating oil ran-

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cidity (Warner and Mounts 1993). Providing oxidative stability without extensive hydrogenation, high-oleiccontent and low-linolenic-content oils are being developed primarily to reduce trans fatty acids formed during the hydrogenation of vegetable oils. Nutritional research suggests that these stereo-isomers of cis-fatty acids may have negative nutritional effects. Trans fatty acids appear to increase serum low-density lipoprotein (LDL) cholesterol levels and may reduce serum high-density lipoprotein (HDL) cholesterol levels to a greater extent than saturated fatty acids (Mensink and Katan 1993). These concerns led to a recommendation to reduce their amount in the diet. Since trans fatty acids are unavoidable during oil processing, demand is increasing for MUFA oils, which do not require extensive hydrogenation (Hu et al. 1997). The nutritional advantages of canola as a cooking oil or margarine are rivalled only by olive oil, where rapeseed oil exhibits a higher content of essential fatty acids, especially α-linolenic acid, and is the only plant oil with an optimal 2:1 proportion of linoleic to α-linolenic acid. There is growing evidence that n-3 fatty acids play a role in the prevention and therapy of a number of chronic diseases, such as some forms of cancer, inflammatory diseases like rheumatoid arthritis and possibly abnormal cognitive functions such as depression. Most evidence, however, relates to the reduction of the risk for coronary heart disease (Trautwein 2001). The scientific rationale for ‘dietary fat strategies’ like the Lyon Diet Heart Study (cf. De Lorgeril et al. 2001) is to adopt a beneficial so-called ‘Mediterranean’ diet. In this study patients had to drastically reduce the consumption of foods rich in saturated (essentially animal) fat. Among vegetable oils only olive oil, despite its lack of 18:3n-3, and rapeseed oil (canola), despite a relatively high amount of linoleic acid (18:2n-6), have the necessary fatty acid composition that reflects the beneficial effects of the Mediterranean diet. The best combination between dietary saturated, polyunsaturated and monounsaturated fatty acids (essentially 18:1n-9) is obtained with olive and canola oil. Thus, rapeseed (canola) oil and canola-oil-based margarine are recommended as a major source of 18:3n-3 in the secondary prevention of coronary heart disease (cf. De Lorgeril et al. 2001).

and Foglia 2001). Historically, rapeseed oil was used mainly for industry and for domestic lighting. However, the industrial applications were limited until steam power came into use. Until the replacement of steam power by diesel engines, the use of rapeseed oil as a lubricant or additive to petroleum-based lubricants was developed because rapeseed oil clings to water-treated metal surfaces better than other lubricants. Besides being used industrially in many applications in which almost any vegetable oil can be used, rapeseed oil with high content of erucic acid (22:1n-9) in particular has considerable advantages in specific applications due to the properties of this longchain fatty acid. High-erucic-acid rapeseed (HEAR) oil contains ca. 50% erucic acid (00-quality rapeseed contains less than 2% erucic acid in the seed oil). The special properties of HEAR oil include high smoke and flash points, stability at high temperatures, durability and the ability to remain fluid at low temperatures. The principal end use of HEAR is to produce erucamide, which is used as a slip additive in polyethylene and polypropylene manufacture to reduce surface friction and prevent adhesion between film surfaces. Erucamide is a relatively large complex molecule, making it difficult and expensive to produce synthetically from petrochemicals. HEAR oil is also used in printing inks and lubricants and has a range of other applications (Lühs and Friedt 1994b; Piazza and Foglia 2001). Rapeseed oil is currently also incorporated into lubricants for two-stroke petrol engines, and methyl esters derived from rapeseed oil are widely used as a diesel substitute (biodiesel). Commercial biodiesel production occurs in several countries worldwide. In Europe low-erucic-acid-content rapeseed oil is the primary feedstock. The driving forces behind the use of biodiesel fuels are mainly environmental and energy concerns. In general, vegetable oil biodiesel fuels, being simple alkyl esters, have the following advantages over diesel fuel: as a neat fuel or in blends with diesel fuel, they produce less smoke and particulates, have higher octane values, produce lower carbon monoxide and hydrocarbon emissions, and are biodegradable and non-toxic. Biodiesel is less volatile than petroleum diesel, which is reflected by its higher flash point, resulting in its safer handling and storage (Körbitz 1995). Conversely, biodiesel fuels present technical challenges of their own, such as higher pour, Non-food uses of rapeseed oil and protein cloud and cold filter plugging points and slow lowRapeseed oil has many potential uses other than as temperature flow, possibly reduced oxidative stability an oil for nutrition (Lühs and Friedt 1994b; Piazza in storage, and incomplete combustion. The advan-

Chapter 2 Oilseed Rape

tages and disadvantages of fat- and oil-derived alkyl ester diesel fuels with respect to fuel properties, engine performance and emissions have been reviewed in detail by Graboski and McCormick (1998). The use of oilseed rape oils as industrial lubricants has considerable environmental benefits because they are inherently biodegradable, of low ecotoxicity and toxicity towards humans, derived from renewable resources and have no net carbon dioxide contribution to the atmosphere. The cost of rapeseed oil falls in the range between mineral and synthetic oils. Furthermore, rapeseed oil has a high viscosity index and the oil structure endures mechanical stresses well. Its low friction coefficient reduces heat development during use and the freezing temperature is also very low. Due to its polarity, the oil adheres well to metal surfaces and provides good protection against corrosion. Because of these characteristics it is not surprising that considerable efforts are being directed towards increased use of rapeseed oil and oil derivatives in designing environmental fuels and lubricants (Piazza and Foglia 2001). Protein-based plant products for non-food markets are another option for utilisation of rapeseed meal. Currently, soybean and wheat proteins represent the most important resource for protein production, with some emerging availability of pea proteins and cottonseed proteins and, very recently, rapeseed proteins. Vegetable proteins are already used in a number of products including plastics and adhesives, and the potential for expansion of such applications is significant. The thermoplastic properties and good biodegradation properties predispose rapeseed oil for use in bioplastic production.

2.2 Breeding of Oilseed Rape 2.2.1 Breeding Methods The majority of oilseed rape cultivars are pure lines derived from breeding schemes designed for selffertilizing crops, i.e. pedigree selection or modifications thereof. Backcrossing has been successfully used to transfer simply inherited traits such as low erucic acid and glucosinolate content into adapted breeding material. B. napus is also one of the most amenable crop species to improvement through biotechnology.

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For instance, it is possible to reproducibly obtain haploid and subsequently doubled-haploid (DH) plants through anther and/or microspore culture (e.g. Weber et al. 2005). The principal advantage of haploid techniques is the rapid fixation of segregating genotypes, occurring in lower frequency, in which recessive genes coding for specific traits are combined in the homozygous condition. Thus, utilisation of microspore culture can allow a substantial acceleration of the breeding cycle. Due to the generally high response of B. napus genotypes, the use of DH production has become common practice in commercial breeding programs and has already resulted in numerous licensed cultivars. Besides haploid techniques, wide hybridizations using embryo rescue techniques or protoplast fusion can also be used to create novel genetic variation. However, once a useful property has been identified in a basic breeding stock, e.g. a mutant line or germplasm from a wild relative, it may take many years to accomplish the development of cultivars possessing this novel desirable trait. Marker-assisted selection (MAS) has had a significant impact on the efficiency of plantbreeding routines such as backcrossing programs. In cases where conventional approaches have not been sufficient, further improvements can be achieved by genetic engineering. Genetic engineering for herbicide tolerance and modified oil quality will be described in more detail later in this chapter.

2.2.2 Breeding for Improved Productivity Unlike soybeans, peanut and most other oilseeds, breeding progress in Brassica oil crops has always been connected with drastic improvements in seed quality, followed by relatively quick acceptance by growers and the processing industry. However, research on seed quality does not ignore the importance of high yields. According to the morphological and agronomical characteristics, the yield of rapeseed consists of the number of siliques per unit area, the number of seeds per silique and the 1000-seed weight (Diepenbrock 2000). Furthermore, improvement in productivity includes several agronomic parameters, such as early maturing, resistance to lodging and shattering and resistance to weeds, insects and, particularly, to the major diseases. A recent review of oilseed rape diseases was presented by Rimmer and Buchwaldt (1995). Their

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conclusions are summarised here: Sclerotinia stem rot (Sclerotinia sclerotiorum) and stem canker (Leptosphaeria maculans, anamorph Phoma lingam), also known as blackleg disease, are the major diseases of oilseed rape in Canada. These are also commonly found in Europe, where there are a number of additional diseases of local importance. Verticillium wilt caused by Verticillium longisporum is a particular problem in Sweden and Germany, light leaf spot (Pyrenopeziza brassicae) in northern parts of Europe and clubroot (Plasmodiophora brassicae) in Scandinavian countries and the northern United Kingdom. In China, sclerotinia stem rot is a major disease, and viral disease also causes substantial yield losses. Alternaria (Alternaria brassicae) causes problems for B. rapa in northern parts of India and simultaneous infection of white rust (Albugo candida) and downy mildew (Perenospora parasitica) is common on both B. rapa and B. juncea in this region. Control of disease has involved a range of strategies. It has been possible to utilise natural variation to breed varieties, particularly in B. napus, with improved resistance to a number of diseases, including light leaf spot and blackleg disease. For other diseases, like alternaria, sclerotinia stem rot, white rust and verticillium wilt, availability of resistant lines is more limited. Cultural control methods, particularly rotation, are important means of controlling diseases such as clubroot and sclerotinia, and good agronomic practice will limit the number of susceptible crops in the rotation (Walker and Booth 2001). In some cases, transfer of resistant germplasm to B. napus from other Brassica species and related crucifers has been successfully applied, as described later in this chapter.

2.2.3 Improvement of Seed Components The gross composition of Brassica oil crop species varies widely depending on both genetic (i.e. species, variety, cultivar) and environmental (e.g. temperature, water and nutrient supply) factors. The oil content ranges from 36 to 50% (on a dry-matter basis), while the oil-free meal contains 33 to 48% protein (Canvin 1965; Appelqvist and Ohlson 1972; Arnholdt and Schuster 1981; Marquard and Schuster 1981; Salunkhe et al. 1992). The improvement of oil content is an important goal due to the primary economic value of the oil component and its relatively high heritability (Grami et al. 1977), and it has to date been

quite successful due to the ease and speed with which oil content can be measured by non-destructive NMR (nuclear magnetic resonance) techniques. Although oil and protein content are negatively correlated, improvements can be achieved through selecting for the sum of the two seed components (Grami et al. 1977; Arnholdt and Schuster 1981; Stefansson 1983). Low-glucosinolate rapeseed meal still presents several problems, however. Besides the presence of undesirable compounds like sinapic acid esters, phytic acid and phytates, phenolic acids and tannins, the comparatively high crude fiber content (approx. 15% of dry oil-free meal) is disadvantageous (Shahidi 1990; Thies 1991; Salunkhe et al. 1992). Due to the small size of the seeds the hull, accounting for about 10 to 20% of the seed weight, imparts most of the fiber content to the meal (Appelqvist and Ohlson 1972; Anjou et al. 1977). Currently, the most promising route to reducing fiber and hull content genetically is to breed cultivars with a yellow (light) seed coat, like pure yellow-seeded cultivars occurring in the sarson subspecies of B. rapa or in B. juncea. Since yellow seed coats are significantly thinner than brown or black ones, the development of pure yellow-seeded B. napus cultivars with agronomically acceptable performance remains an important goal in quality breeding towards increased oil and protein content. The value and suitability of rapeseed oil for nutritional or industrial purposes are again determined by its fatty acid composition. The identification of naturally occurring zero-erucic mutants in both B. napus and B. rapa was indeed the first discovery opening the era of mutant-derived quality improvement in oil crops (Downey 1964; Stefansson and Hougen 1964; Röbbelen 1990). Canola-quality rapeseed low in saturated fatty acids and almost lacking nutritionally undesirable very long-chain fatty acids meets all the requirements of a prime edible oil (Ackman 1990; Downey and Bell 1990; Trautwein 1997). Despite the beneficial nutritional properties of α-linolenic acid (18:3n-3), the oxidative stability of the oil can be improved by decreasing the linolenate content from average 10% to less than 3%, which results in enhanced shelf life (Rakow et al. 1987; Pleines and Friedt 1988, 1989; Downey and Bell 1990). In the last three decades improvements in the C18 fatty acid composition in rapeseed (B. napus) have been achieved by selecting altered linoleate/linolenate genotypes after chemical mutagenesis. The fatty acid profiles of these lines indicated that nearly all of the linolenic acid was being directed to linoleic acid and that the level of oleate

Chapter 2 Oilseed Rape

increased only insignificantly (Rakow 1973; Röbbelen and Nitsch 1975; Röbbelen and Thies 1980; Rakow et al. 1987; Röbbelen 1990). In 1988, the spring rapeseed cultivar Stellar, which produces oil containing less than 3% linolenate, was released for commercial production in Canada, although its agronomic performance was less than satisfactory (Scarth et al. 1988). Due to strong environmental and marked maternal influences, only low correlations have been found between the contents of polyenoic fatty acids determined in half-seeds and their progenies. The most important factor influencing the biogenesis of the unsaturated fatty acids is the prevailing temperature during seed development (Pleines and Friedt 1988, 1989). Recent reports describe mutants with reduced levels of polyunsaturated fatty acids (PUFAs) obtained by blocking oleic acid desaturation. The development of canola cultivars with reduced levels of PUFAs accompanied by higher oleate content would produce a dietary oil with additional markets (Marsic et al. 1992). For industrial applications a very high content of oleic acid (80 to 90%) is preferred because this is most suitable for certain consecutive chemical reactions (Lühs and Friedt 1994b). The low-linolenic-acid-content trait was created in rapeseed (B. napus) using chemical mutagenesis and subsequent selection for altered ratios of linoleic/linolenic acid facilitated by rapid screening methods like the thiobarbituric acid (TBA) test. Several low-linolenic-content Canadian spring rapeseed varieties have been released (e.g. Stellar, Apollo and Allons), and later this trait was also transferred into suitably adapted winter rapeseed lines (Kräling and Röbbelen 1991; Rücker and Röbbelen 1996; Scarth et al. 1997). To increase the oleic acid content to above 80% and, concomitantly, to lower the level of PUFAs, different breeding procedures have been utilised. These include mutagenesis applied to seeds (Auld et al. 1992; Rücker and Röbbelen 1995) or microsporederived embryos (Wong et al. 1991). Furthermore, relevant genes of Δ12 or Δ15 desaturases, responsible for the biosynthesis of 18:2n-6 and 18:3n-3, have been isolated, and subsequently rapeseed was genetically engineered, leading either to high-oleic-acid or highlinoleic-acid profiles (Hitz et al. 1995; Scheffler et al. 1997). Traditional varieties of Brassica oilcrop species typically contain appreciable levels of long-chain fatty acids like eicosenoic (20:1n-9), erucic (22:1n-9) and nervonic acid (24:1n-9). Studies on the inheritance of erucic acid in Brassica oil crops revealed that erucic

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acid is under the control of the embryonic genotype. In monogenomic species like turnip rape (B. rapa), the erucic acid synthesis is governed by one gene locus. Consequently, in the amphidiploid B. napus two major gene loci participate. Multiple alleles occur at each locus, acting in a largely additive manner. Homozygous genotypes with various alleles produce levels of erucic acid ranging from less than 0.1 to about 60%. In the winter forms of B. napus, alleles are present which in a single dose give about 15 to 18% erucate (cf. Jönsson 1977). The accumulation of fatty acids of the preceding chain elongation steps, namely oleate and eicosenoate, is probably influenced by the same major gene loci (Zhou and Liu 1987; Chen and Beversdorf 1990). With regard to oleic acid operating simultaneously as the substrate for desaturation, it is assumed that in HEAR at least two minor gene loci are also involved that control the desaturation of oleic acid to form linoleate and linolenate, respectively (Jönsson 1977; Chen and Beversdorf 1990). In resynthesised rapeseed produced with the aim of genetic modification of oil quality (Chen and Heneen 1989; Friedt and Lühs 1998), the effect of alleles responsible for a specific erucic acid content is probably influenced additionally by the level of ploidy and the genetic background. Due to the procedure of resynthesis commonly used, i.e. hybridization of the monogenomic parents followed by artificial diploidisation, synthetic B. napus forms are homozygous lines, so that a change in their fatty acid composition, as compared to that of their parents, could also be explained by interactions between the non-homologous genes of the A and C genomes. These interactions have to be attributed to epistasis (non-allelic gene actions) rather than dominance (Chen and Heneen 1989; Chopra and Prakash 1991). Lühs and Friedt (1995) suggested that it might be possible to achieve recombinants with new allele combinations via introgression of resynthesised germplasm into conventional high erucic acid breeding material. However, the possibility of increasing erucic acid synthesis by accumulation and combination of desirable alleles is obviously limited due to the restriction of the subsequent TAG synthesis. Cruciferous seed oils seem to contain erucic acid almost exclusively in the sn-1 and sn-3 positions of the glycerol backbone, so that mutants in rapeseed with a desirable erucic content beyond the theoretical limit (>66%) are unlikely, and a maximum of ca. 60 to 63% erucic acid is currently achievable (Lühs and Friedt 1995). With regard to well-known industrial and non-food uses, trierucoylglycerol (trierucin)

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would make the processing of comparatively pure erucic acid much easier and more commercially attractive. However, in the near future it might be possible to introduce the property for producing trierucinenriched seed oil into B. napus by genetic engineering (Friedt and Lühs 1998; Han et al. 2001).

2.2.4 Hybrid Breeding and Cytoplasmic Male Sterility Systems Although for many years the emphasis in oilseed rape breeding was strongly focussed on open-pollinating varieties, up to 30% heterosis for seed yield has been reported for B. napus (e.g. Schuster 1969; Grant and Beversdorf 1985; Lefort-Buson et al. 1987; Brandle and McVetty 1989), and for both winter rapeseed and spring canola hybrid varieties have rapidly gained in importance over the past decade as effective systems for controlled pollination were developed. The first restored winter rapeseed hybrids were released in 1995. In current European winter rapeseed material yield improvements of up to 15% have been reported for F1 hybrids compared to non-hybrid openpollinating varieties. This has led to a major increase in production of hybrid rapeseed in the leading producing countries. For example, although only 14 of the 53 approved German 00 winter rapeseed cultivars listed by the German Plant Variety Office in 2004 were hybrids (Bundessortenamt 2004), more than 50% of the 1.3 million hectares of German winter rape in 2003/2004 were planted with hybrid varieties. Furthermore, in 2003/2004 the hybrid cultivar Talent replaced the open-pollinating Express as the most widely cultivated winter oilseed rape variety in Germany, the first time a hybrid cultivar has achieved the top position. One of the most important reasons for the upsurge in interest in hybrid varieties is that they tend to have higher yield stability and better adaptation to lowinput cropping systems than conventional cultivars (Budewig and Léon 2003; Friedt et al. 2003). Numerous cytoplasmic male sterility (CMS) systems have been discovered and are used in crop brassicas. Because CMS arises from specific interactions between the mitochondrial and nuclear genomes, the combination of cytoplasm and nucleus from different species often results in complete or partial male sterility and in many cases functional mutations of floral structure. Two spontaneous male sterile cytoplasms, nap and pol, are found in B. napus. The nap system was

the first to be identified, originating from intraspecific crosses using Bronowski (Thompson 1972) or Hokuriku 23 (Shiga and Baba 1973) as the male parent. As with many other forms of CMS, the mtDNA regions implicated in specifying the nap and pol forms of male sterility contain novel open reading frames (ORFs). Unlike other CMS-associated ORFs, however, a high level of sequence similarity extends over the entire length of the nap and pol CMS-associated ORFs (Brown 1999). In other plant species where more than one form of CMS is found, the nuclear genes that restore fertility to various male sterile cytoplasms represent distinct genes that map to different nuclear loci. The restorers for the nap and pol cytoplasms (Rfn and Rfp, respectively), however, were found to represent different alleles or haplotypes of a single nuclear locus. Both alleles specify factors that influence mtRNA processing events, but the specific processing events conditioned by the two alleles are different, suggesting that the factors encoded by these genes recognize distinct RNA structural features. Unlike other nuclear genes that affect mitochondrial gene expression, Rfn is capable of modifying the expression of multiple mtDNA regions, some of which are not associated with CMS (Brown 1999). Most other B. napus CMS systems result from interspecific or intergeneric crosses, often using known sterility-inducing systems from other species. The best example for this is the widely used INRAOgura CMS originating from Raphanus sativus (Ogura 1968), which was transferred to oilseed rape by French scientists some 30 years ago (Bannerot et al. 1974). Although this system was described by Tokumasu (1951) as a genic male sterility, in B. napus it is expressed as CMS. The Ogura CMS in radish (Raphanus sativus) is caused by an aberrant mitochondrial gene, orf138, that prevents the production of functional pollen without affecting female fertility. Rfo, a nuclear gene from radish that restores male fertility, alters the expression of orf138 at the posttranscriptional level. In recent years a large effort has been made in the genetic characterization of the Ogura CMS system for generation of effective selection markers for fertility restoration and cloning of the genes involved. Using bulked segregant analysis and comparative mapping, Delourme et al. (1998) identified molecular markers linked to the Rfo restorer gene. These markers were then used to localise the radish introgression on the B. napus genetic map constructed from the cross Darmor-bzh × Yudal. From the comparison

Chapter 2 Oilseed Rape

of the linkage group (LG) containing the introgression with the corresponding LG constructed from an F2 progeny segregating for the radish introgression, it was concluded that the introgression was derived from a homoeologous recombination, that it was not distal and that it had replaced a B. napus region of around 50 cM. A QTL involved in aliphatic seed glucosinolate content was located on the same LG at a position corresponding to one end of the introgression. Bulked segregant analysis was also used by Hansen et al. (1997) to identify random amplified polymorphic DNA (RAPD) markers linked to the Ogura CMS fertility restorer gene in oilseed rape. After screening for polymorphisms using 960 primers, 14 randomly amplified polymorphic DNA (RAPD) markers were mapped in a population of 242 F2 individuals to a 25cM region including the restorer locus. The map was used to select 11 markers that were investigated for polymorphisms between the restorer donor line and 46 recipient lines. A set of four RAPD markers were used in MAS of plants homozygous for the restorer allele. One marker in attraction phase with the restorer allele and three in attraction to the non-restorer allele were informative in all 46 combinations and allowed identification of 906 homozygous restored plants among 4,605 BC1 F2 plants analysed. Bellaoui et al. (1999) analysed the effect of the restorer gene on the expression of the ORF138 protein associated with Ogura CMS. It was shown that the presence of the Rfo gene in the genome of the restored plants decreased the amount of ORF138 protein in floral buds and that this effect was most dramatic in anthers at the stage of development when the sterile phenotype is normally expressed. However, the amount of orf138 transcripts was not affected by the Rfo gene in the same organs at the same stages. Total polysome analyses of buds and anthers showed that the orf138 transcripts are translated with the same efficiency in sterile and restored plants. It was concluded that the Rfo gene product acts on the posttranslational stability of the ORF138 protein, leading to a decrease in the accumulation of the protein and a restoration of fertility. Giancola et al. (2003) used the region of the Arabidopsis genome syntenic to the Rfo gene to characterize the radish introgression in restored rapeseed. Amplified consensus genetic markers (ACGM) in restored rapeseed plants were employed, alongside the construction of a precise genetic map around the Rfo gene in a segregating radish population. The use of ACGMs made it possible to detect radish orthologues of Ara-

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bidopsis genes in the restored rapeseed genome. The positions of markers linked to the Rfo gene were used to produce a schematic diagram of the radish introgression in rapeseed. Markers which could be mapped both on radish and restored rapeseed indicated that at least 50 cM of the radish genome was integrated in restored rapeseed. Using markers closely linked to the Rfo gene in rapeseed and radish, a contig was identified spanning six bacterial artificial chromosome (BAC) clones carrying the orthologous Rfo gene on A. thaliana chromosome 1. Desloire et al. (2003) also applied a combination of positional cloning and microsynteny analysis between A. thaliana and radish to delimit the Rfo locus, in this case to a 15-kb DNA segment. Analysis of this segment confirmed that the Rfo gene is a member of the pentatricopeptide repeat (PPR) family. Ultimately, Brown et al. (2003) used a map-based cloning approach relying on this synteny between the corresponding genome regions of radish and Arabidopsis to clone Rfo. A radish gene encoding a 687-amino-acid protein with a predicted mitochondrial targeting presequence was found to confer male fertility upon transformation into Ogura CMS B. napus. This gene codes for a PPR-containing protein with multiple PPR domains. Two similar genes that did not appear to function as Rfo were found to flank this gene. Furthermore, comparison of the Rfo region with the syntenic Arabidopsis region indicated that no PPR gene was present at the equivalent Rfo locus in Arabidopsis, although a smaller and related PPR gene was found about 40 kb from this site. In Arabidopsis, the PPR gene family contains more than 450 members of unknown function, most of which are predicted to be targeted to mitochondria and chloroplasts and are thought to have roles in organellar gene expression. Bett and Lydiate (2004) used three R. sativus populations (BC1 , F2 and R8 ) segregating for the restoration of Ogura CMS to map restorer loci in the donor species. Three restorer loci, Rf1, Rf2 and Rf3, were localised, whereby each exhibited dominant restoring alleles and together the loci were mutually epistatic. The complex genetic control of the restoration of Ogura CMS in Raphanus was compared with the more simple genetic control of this trait previously described in B. napus. Markers linked to each of the three restorer loci will now allow the routine generation and verification of defined restorer and maintainer lines for various combinations of defined restorer loci.

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The Japanese radish cv. Kosena has also been used to transfer CMS to B. napus (Sakai and Imamura 1990). Iwabuchi et al. (1999) showed that the CMS-associated gene orf125 from Kosena radish has a sequence homologous to that of the Ogura CMSassociated gene orf138. Only two amino acid substitutions and a 39-bp deletion in the orf138 coding region distinguish the two gene sequences. In Kosena radish, orf125 was found to be linked to orfB, whereas the orf125 locus differed in a B. napus CMS cybrid derived from protoplast fusion between Kosena radish and B. napus. A novel mtDNA sequence was identified in the 3’ flanking region of orf125 in the B. napus Kosena CMS cybrid. The orf125 was expressed both in radish and the B. napus Kosena CMS cybrid, and its accumulation was found to be strongly associated with the CMS phenotype in B. napus, whereas fertility restoration was accompanied by a decrease in the amount of ORF125 protein. Imai et al. (2003) used a positional cloning strategy to isolate the fertility restoration gene Rfk1 from radish. RAPD-sequence tagged site (STS) markers tightly linked to the gene in radish were isolated, and a RAPD map surrounding the Rfk1 locus was constructed. Recombinants for bulk segregant analysis were identified among 948 F2 plants with adjacent RAPD-STS markers. This enabled isolation of tightly linked amplified fragment length polymorphism (AFLP) markers surrounding the gene of interest. Ten linked AFLP markers were obtained and used to construct a high-resolution map of the genome region involved in fertility restoration. The closest AFLP-STS markers flanking Rfk1 were 0.1 cM and 0.2 cM away. Screening of lambda and cosmid libraries with the four adjacent AFLP markers enabled the identification of genomic clones that were aligned by examination of end sequences and restriction fragment length polymorphism (RFLP) patterns for each clone and by hybridization to the DNA isolated from recombinants. This led to construction of a 198-kb contig, comprised of 20 lambda and two cosmid clones, that spanned the Rfk1 gene. By analysis of the breakpoints in recombinants with the rfk1/rfk1 or Rfk1/- genotype, the Rfk1 locus could be assigned to a 43-kb region comprising four lambda clones and one cosmid clone. This exact localisation in the radish genome made it possible to identify the gene by sequence analysis, giving rise to the possibility of genetically transforming cytoplasmic male-sterile B. napus plants for fertility restoration.

2.2.5 Use of Male Sterility Systems in Oilseed Rape Breeding Although numerous CMS systems are available from different sources, their use in oilseed rape breeding is often inhibited by instability, the absence of suitable restorer or maintainer lines, or negative effects of the cytoplasm used to induce the male sterility. Environmental instability of the expression of nap male sterility means this system is unsuitable for hybrid production, and the Polima (pol) system was only made workable by screening of huge numbers of lines in different environments (Bartkowiak-Broda et al. 1991) in order to identify stable maintainer genotypes. The monogenically inherited restorer genes for B. napus Polima CMS can be readily introduced into elite lines, and pol is therefore now effectively used to produce registered F1 hybrid spring canola varieties in numerous countries. Male-sterile-inducing cytoplasm can also have negative effects on flower morphology, nectar production or yield, and sometimes chlorophyll deficiencies also need to be overcome. In some cases, suitable B. napus restorer lines have been produced for B. tournefortii CMS (Banga et al. 1995; Stiewe et al. 1995a,b). Restored F1 hybrids based on the Ogura CMS system are under increasing production in France and other European countries, and hybrid cultivars based on the commercial Male-Sterility Lembke (MSL) system are currently among the best-selling winter oilseed rape varieties in Germany.

2.2.6 Genetic Diversity for Heterosis and Hybrid Breeding The relative lack of genetic variability within oilseed rape breeding material can to a large extent be attributed to the limited geographical area, the Mediterranean region, where the natural habitats of the progenitor species overlap. For winter oilseed rape only three distinct local landraces are known. These evolved in different European climate zones and hence display variation in vegetative growth and winter hardiness. The first released cultivar, Lembkes, selected in Germany from a Mecklenburg landrace in the early 20th century, was extensively exploited in French, Swedish, German and Polish breeding programs. The genetic base of oilseed rape is today even narrower because the introduced double-low (00) quality again

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originates from single sources, the spring cultivars Liho and Bronowski. Consequently, there is a need to introduce new genetic variation to breeding material since most cultivars share a more or less common parentage (Thompson 1983; Downey and Rakow 1987). Compared to the narrow gene pool of present-day 00-quality oilseed-rape-breeding material, which severely limits the formation of heterotic pools, erucic-acid- and glucosinolate-containing plant material represents a comparatively genetically divergent source for the development of heterotic rapeseed forms (Röbbelen 1975; Thompson 1983; Schuster 1987). Because of the emphasis on oil quality, such material has found only limited use in practical rapeseed breeding in the past few decades. However, strong heterotic effects are observed in experimental crosses between material of distant geographical and genetic origin (Lefort-Buson et al. 1987; Brandle and McVetty 1990), and efforts are increasing to develop new cytoplasmic-genetic male-sterile and restorer lines as the most promising system for the production of new hybrid cultivars. Following appropriate quality conversion, inbred lines and DH lines with a high genetic distance to existing 00-quality varieties have the potential to become an important resource for the development of high-performance pools with improved combining ability compared to existing 00-rapeseed material.

2.2.7 Expanding the Genetic Variability in Oilseed Rape by Interspecific Hybridization One strategy to broaden the genetic basis of oilseedrape-breeding material is the production of resynthesised rapeseed by crossing the original ancestors, B. oleracea and B. rapa. This has the potential not only to increase genetic variability with a view to hybrid breeding but also to broaden the genetic base with respect to pest and disease resistances, which in some cases is severely eroded in B. napus. For such interspecific hybridizations a variety of biotechnological tools, for example embryo rescue techniques or protoplast fusion, are used to circumvent existing incompatibility barriers. In some cases resynthesised rape forms have resulted in successful release of cultivars carrying novel resistance genes from the diploid species. For example, Diederichsen and Sacristan (1996) successfully used protoplast fusion to transfer resistance to

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clubroot (Plasmodiophora brassicae) from B. oleracea to B. napus. Through advanced backcrossing a racespecific resistance was subsequently transferred from resynthesised rapeseed progeny to elite winter oilseed rape material, and the winter oilseed rape varieties Mendel and Tosca derived from this material were released in the early 2000s to specifically combat this disease in affected areas of Britain and Germany. In another example, Mithen and Magrath (1992) generated synthetic lines of B. napus carrying resistance to blackleg disease (Leptosphaeria maculans, anamorph: Phoma lingam) derived from B. rapa via embryo culture. The resistance was then integrated successfully into spring canola, resulting in the release of the cv. Surpass in the late 1990s and subsequent efforts to introgress this resistance into winter oilseed rape material. Although this Phoma resistance from B. rapa has in the meantime been overcome by virulent L. maculans isolates in Australia, and the clubroot resistance from B. oleracea is also race-specific and hence not durable without careful agronomic management, these examples nevertheless demonstrate the potential utility of B. oleracea and B. rapa for the identification and combination of novel resistance genes to important oilseed rape pathogens. This strategy has the potential to prove particularly valuable for development of resistance to Verticillium wilt. This disease, caused by the host-adapted pathogen V. longisporum, causes grave yield losses in affected areas of Sweden, Denmark, Great Britain and the north of Germany. The fungus forms microsclerotia, which can persist in the soil for more than a decade, and because accredited fungicides are not available, the only current alternative for effective control of the disease in short crop rotations is the breeding of resistant cultivars. Very little resistance is available in either winter or spring rapeseed, however, necessitating a search for resistance sources in related species. Transfer of resistance from B. oleracea to B. napus was reported by Happstadius et al. (2003), and in our own work we have identified further resistance donors in ongoing screening of diverse turnip rape and cabbage accessions. In order to develop durable polygenic resistance to Verticillium wilt, we aim to combine resistances from B. oleracea and B. rapa in novel resynthesised B. napus genotypes by interspecific hybridization, assisted by embryo rescue (ovule culture). After characterising the resistance by genetic mapping it should be possible using marker-assisted backcrossing to simultaneously transfer A- and Cgenome resistance genes into elite rapeseed lines as

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a starting point for the development of new varieties with combined resistance from the diploid progenitors. Resynthesised rapeseed also represents an interesting source of genetic variation for quality improvement in oilseed rape. For example, we have made crosses between B. rapa ssp. trilocularis (Yellow Sarson) and several selected cauliflowers (B. oleracea convar. botrytis var. botrytis) to create new oilseed rape germplasm with a high erucic acid content. The offspring displayed desirable variation in the content of major fatty acids, raising the possibility of producing breeding lines with an erucic acid content of 60% or even more. Furthermore, the high genetic distance of these lines from conventional cultivars (see Seyis et al. 2003a) also makes them potential candidates for improving heterosis. In field trials at two locations experimental hybrids based on these resynthesised lines gave a higher yield potential compared to check cultivars (Seyis et al. 2003b). Interspecific crosses are also an important source of seed-color variants for breeding of light-seeded rape. Brown or yellow seeds are of particular interest for breeding of oilseed rape because of their association with a thinner seed coat that results in reduced dietary fiber content. This considerably improves the feed quality of rapeseed meal after oil extraction (Shirzagedan and Röbbelen 1985; Slominski et al. 1994, 1999). Light seed color and low fiber content are considered to coincide because the biochemical pathways leading to lignin (fiber) and pigment synthesis have common precursors, such as p-cumarate (Theander et al. 1977; Whetten et al. 1998). Furthermore, the reduction in testa thickness in yellowseeded oilseed rape has also been found to be associated with increased seed oil and/or protein content per dry weight (Xiao and Liu 1982, Piotrowska et al. 2003). A variety of different yellow-seeded rapeseed material has been generated by interspecific crosses between yellow-seeded B. rapa and brown-seeded B. oleracea (Schwetka 1981) or B. alboglabra (Chen et al. 1988; Rahman 2001, 2003). The yellow-seed trait has also been introduced to B. napus from B. chinensis (Liu 1983), B. juncea (Rashid et al. 1994) and B. carinata (Rashid et al. 1994; Meng et al. 1998; Rahman et al. 2001, 2003). We have studied the genetics of yellow seed color and raw fiber content in crosses involving yellow-seeded lines from two genetically divergent B. rapa sources, and found that in each case the trait was controlled by a major dominant gene along with either one or two epistatic loci (Badani et al., in press). This

finding, corroborated by QTL localisation and segregation analyses, supports results published by Somers et al. (2001) and Liu et al. (2005) for two further independent yellow-seed sources. Viewed together, these studies suggest that the same genetic loci appear to control seed color in genetically diverse B. napus material, even though more than 20 different genes are known in A. thaliana that give rise to an analogous transparent testa phenotype in the corresponding mutants. Other Brassica species and even less closely related genera are also important as potential sources of disease resistance for oilseed rape breeding. A prime example for this is the use of interspecific and intergeneric hybrids as a source for new resistance against blackleg disease. The genetic basis of blackleg resistance in B. napus in European cultivars originates for the most part from the French cultivar Jet Neuf, which possesses a partial, polygenically controlled adult plant resistance not expressed at the seedling stage (Cargeeg and Thurling 1980). In contrast, all Brassica species containing the B genome exhibit an absolute and stable resistance to most of the aggressive pathogen isolates studied to date. B-genome resistance is mono- or oligogenically controlled (Rimmer and van den Berg 1992; Dixelius 1999) and efficient from the seedling stage onwards. Thus, B-genome donors like B. nigra (L.) Koch (BB, 2n = 16) and B. juncea (L.) Czern (BBCC, 2n = 36) have been extensively used as a genetic pool in an attempt to develop resistant oilseed rape (e.g. Roy 1978; Sacristán and Gerdemann 1986; Sjödin and Glimelius 1989; Chèvre et al. 1996; Struss et al. 1996; Plieske et al. 1998; Dixelius 1999). On the other hand, some aggressive isolates of the pathogen have been shown to overcome the resistance of B. juncea (Purwantara et al. 1998; Winter et al. 1999). Leptosphaeria maculans exhibits a broad variation in virulence, giving it the potential to adapt quickly to a given resistance (Kuswinanti et al. 1999). Generation of durable resistance therefore necessitates the application of a broad spectrum of resistance sources in oilseed rape breeding. For this reason, interspecific and intergeneric transfer of blackleg resistance from wild crucifers is an interesting alternative, and in recent years progress has been made in introgressing resistance into oilseed rape from different sources, including Sinapis arvensis (Snowdon et al. 2000; Winter et al. 2003) and Coincya monensis (Winter et al. 2003). Other examples of intergeneric hybridization for resistance gene transfer into B. napus include resistance to beet cyst ne-

Chapter 2 Oilseed Rape

matodes on Raphanus sativus addition chromosomes (Thierfelder and Friedt 1995; Voss et al. 2000; Peterka et al. 2004), whereas Klewer et al. (2003) used sexual and somatic hybridization in an attempt to transfer resistance to Alternaria blackspot into B. napus from B. elongata, Sinapis alba, Diplotaxis tenuifolia and D. erucoides. In such broad intergeneric hybrids ovary culture techniques are absolutely necessary to overcome incompatibility barriers; however, a successful transfer of the desired trait is often achieved. The prerequisite for this is that intergenomic chromosome recombination takes place in early backcross generations before the loss of non-homologous donor chromosomes.

2.3 Cytogenetic Studies of Brassica Crops and Interspecific Hybrids 2.3.1 History of Cytogenetic Studies in Brassica After Morinaga and U discovered through cytogenetic studies in the early 1930s that amphidiploid Brassica species originate from diploid progenitors and contain the complete chromosome sets of their parental species, chromosome studies came to play a leading role in genome analysis among the Brassicaceae. The age of classical cytogenetics has, however, been largely superseded by the implementation of DNA techniques during the past few decades, and the difficulties associated with Brassica chromosomes as a cytological object – in particular their small size and lack of distinctive cytological landmarks – have made Brassica cytogenetics a rare art amongst the proliferating molecular marker technologies. For many years little more could be achieved than simple chromosome counts or meiotic studies of the offspring from interspecific or intergeneric crosses, giving insight into genome homologies amongst the various Brassica relatives. In recent years, however, advances in the molecular cytogenetic technique of fluorescence in situ hybridization (FISH), which enables the direct chromosomal localisation of labelled DNA probes, have enabled a resurgence of cytogenetic analyses in plant genome research and molecular breeding. The field of Brassica cytogenetics dates back to the early decades of the 20th century, when a number of

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predominantly Asian scientists began with detailed investigations of chromosome numbers and chromosome pairing in some of the important crucifer species. The first major achievement was the publication of the chromosome number for B. rapa by Takamine (1916), followed 8 years later by the synthesis and analysis of Raphanobrassica by Karpechenko (1927) and the experiments of Morinaga, who began working intensively on interspecific hybrids during the 1920s and investigating chromosome pairing and homology in detail. It was during this period that others began to publish surveys of chromosome counts for large numbers of crucifer species – in particular Manton (1932), who was one of the pioneers in this area – and to investigate the somatic chromosomes in more detail. However it was the work of Morinaga (1934) and U (1935) that gave rise to another generation of researchers who began to look more deeply into genome homology in the Brassicacaeae. The development of ovary culture and embryo rescue techniques in the 1950s enabled enormous progress in the study of genome homologies based on chromosome pairing analyses. Additionally, technological advances in optical equipment and microscopy brought great improvement in cytological techniques in general. Based on these techniques Röbbelen (1960) was the first to publish detailed cytological descriptions of Brassica somatic chromosome structure. From a cytogenetics perspective the period between the 1960s and the end of the 1980s was dominated by an intensive effort to collect and classify botanical representatives of the crucifer tribe and to study the evolutionary and genomic relationships among this array of species. One of the major personalities in this movement was Harberd (1972), whose study of chromosome pairing among a huge number of species eventually led to the classification of cytodemes describing homologous genomes. Based largely upon this work we know now that there is extensive genome homology or homoeology throughout the entire Brassica coenospecies, and from a plantbreeding perspective in particular it has become well known that we consequently have the possibility to broaden gene pools for the introgression of novel genes or alleles, well beyond the species boundary. Related Brassica species and their relatives among the Brassicaceae represent a huge pool of potential gene donors for agronomically relevant traits in oilseed rape. Table 2presents an overview of sexual and somatic interspecific and intergeneric crosses between B. napus and other crucifers, with examples of rele-

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Table 2. Examples of sexual (x) and somatic (+) Brassica napus interspecific and intergeneric hybrids with genome donors from family Brassicaceae Cross

Genome donor

Character transferred or studied

Reference

B. napus x

Brassica carinata Brassica chinensis Brassica cossoniana Brassica elongata

Yellow seed color Yellow seed color Genome homology Blackleg resistance

Brassica juncea Brassica juncea Brassica juncea Brassica juncea Brassica gravinae Brassica nigra Brassica rapa Coincya monensis Diplotaxis erucoides Eruca sativa Hirschfeldia incarna Orychophragmus violaceus Raphanus raphinistrum Raphanus sativus Raphanus sativus

Yellow seed color Blackleg resistance Earliness Shattering resistance Alloplasmy Blackleg resistance Blackleg resistance Blackleg resistance Genome homology Altered oil quality Potential transgene transfer Pigmentation Potential transgene transfer Ogura CMS Nematode resistance

Sinapis alba Sinapis alba Sinapis arvensis Sinapis arvensis Sinapis arvensis

Nematode resistance Alternaria resistance Potential transgene transfer Genome homology Blackleg resistance

Sinapis pubescens Arabidopsis thaliana Arabidopsis thaliana Barbarea vulgaris Crambe abyssinica Lesquerella fendleri Raphanus sativus Sinapis alba

Resistance Acetolactate synthase Blackleg resistance Cold tolerance High erucic acid Lesquerolic acid Kosena CMS Alternaria resistance

Thlaspi perfoliatum

Nervonic acid

Rashid et al. (1994) Liu (1983) Harberd and McArthur (1980) Plümper and Sacristán (1995), Klewer et al. (2003) Rashid et al. (1994) Roy (1984), Chèvre et al. (1997) Rao et al. (1993) Prakash and Chopra (1988) Nanda-Kumar et al. (1989) Chèvre et al. (1991b, 1996) Chèvre et al.(2003) Winter et al. (2003) Harberd and McArthur (1980) Bijral and Sharma (1996) Kerlan et al. (1993) Li and Luo (1993) Kerlan et al. (1993) Takeshita et al. (1980) Lelivelt and Krens (1992), Peterka et al. (2004) Lelivelt et al. (1993) Klewer et al. (2003) Kerlan et al. (1993) Mizushima (1950) Snowdon et al. (2000), Winter et al. (2003) Inomata (1994) Bauer-Weston et al. (1993) Forsberg et al. (1994) Fahleson et al. (1994b) Wang et al. (2004) Skarzhinskaya et al. (1996) Sakai and Imamura (1990) Plümper and Sacristán (1995), Klewer et al. (2003) Fahleson et al.(1994a)

B. napus +

vant traits that in numerous cases could be successfully introgressed from the donor into the oilseed rape genome. FISH techniques offer the potential not only for more reliable chromosome identification in Brassica, but also in terms of the information they might be able to offer regarding the integration of genetic and physical maps, for ordering molecular markers and measuring physical genome distances, and

for structural and functional chromosome analysis. FISH methods for the accurate localisation of repetitive DNA sequences at chromosomal subarm level, particularly ribosomal DNA sequences, have enabled the elucidation of karyotypes for B. napus and its progenitor species and the identification of A- and C-genome chromosomes in the amphidiploid species (Armstrong et al. 1998; Fukui et al. 1998; Snowdon et al. 2002; our Fig. 1). FISH hybridization of BAC

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Fig. 1. Karyotypes based on fluorescence in situ hybridization patterns with 5S (green) and 25S (red) rDNA probes and DAPI staining (blue), for Brassica rapa L., B. oleracea L. and their amphidiploid B. napus L. Closed arrowheads: co-localisation of 5S and 25S loci; open arrowheads: weak 5S locus and weak 25S locus on B. napus chromosomes C5 and C7, respectively. Red asterisks: position of large 25S rDNA locus located on satellite of B. napus chromosome A2, which in this spread was lost during chromosome preparation. The B. napus karyotype is divided into two sets of chromosomes with differing chromatin condensation patterns resembling, respectively, those of B. rapa (a) and B. oleracea (c). Each B. napus chromosome is aligned and numbered in accordance with its putative homologue in the B. rapa or B. oleracea genome. [Reproduced with permission from Snowdon et al. (2002)]

clones to B. oleracea (Howell et al. 2002) and B. rapa (Jackson et al. 2000) chromosomes represents a first step towards integration of physical and genetic maps with the karyograms of the diploid species and their amphidiploid hybrid B. napus. In the latter study, FISH techniques were adapted for comparative physical mapping between A. thaliana and B. rapa. Six BAC clones representing a 431-kb contiguous region of chromosome 2 of A. thaliana were mapped on both chromosomes and DNA fibers of B. rapa. Although the DNA fragment investigated is single-copy in A. thaliana, it hybridized on up to six B. rapa chromosomes, indicating multiple duplications in the B. rapa genome. The fiber-FISH signals in B. rapa were similar to those in A. thaliana for each BAC, suggesting that the genomic region investigated is duplicated but not expanded in the B. rapa genome. Such comparative fiber-FISH mapping results support other evidence that chromosomal duplications, rather than regional expansion due to accumulation of repetitive

sequences in the intergenic regions, played the major role in the evolution of the diploid Brassica genomes. The use of total genomic DNA as a FISH probe (genomic in situ hybridization, or GISH; HeslopHarrison and Schwarzacher 1996) is especially useful for diagnostic studies of the amount and integration of foreign chromatin in interspecific and intergeneric plant hybrids. Hybrids between high-yielding rapeseed cultivars and related species are relatively easily produced and have often been used to develop new lines containing introgressed traits like novel pest or disease resistances. Great advances in interspecific hybridization have resulted from the application of in vitro techniques for the generation of viable offspring from interspecific and intergeneric hybrids (Lühs et al. 2002). Identification of alien DNA in wide crosses has been achieved by quantification of chromosome content by flow cytometry (Sabharwal and Dolezel 1993) and by tracing chromosome and DNA transfer using molecular markers. Visu-

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alisation of alien chromatin in interspecific hybrids using in situ hybridization techniques, on the other hand, potentially enables pinpointing of introgressions to specific chromosomes (Heslop-Harrison and Schwarzacher 1996; Snowdon et al. 1997). As an example, Skarzhinskaya et al. (1998) studied the chromosome complements of somatic hybrids produced between B. napus and Lesquerella fendleri with novel fatty acid compositions by analysing their karyotypes and performing GISH. Symmetric fusions of protoplasts fused with no pretreatment resulted in hybrids containing L. fendleri chromosomes in numbers varying from two chromosomes to two full chromosome complements. Asymmetric hybrids were also generated by irradiating L. fendleri protoplasts before fusion. In this case plants with 38 to more than 76 chromosomes were obtained. In the hybrids with 38 chromosomes, the presence of L. fendleri chromosomes was not detected by GISH analysis, even though L. fendleri DNA was revealed by Southern blotting. Intra- and intergenomic recombinations were observed in hybrids from both symmetric and asymmetric fusions, but particularly in plants resulting from asymmetric fusions. Intergeneric sexual bybrids between B. napus and Sinapis arvensis containing novel genes for resistance against blackleg disease on chromosome additions and introgressions were analysed via GISH by Snowdon et al. (2000). Selfed BC3 progenies included fertile plants exhibiting high seedling and adult plant resistance associated with the presence of an acrocentric addition chromosome from S. arvensis. Furthermore, some individuals with adult plant resistance but cotyledon susceptibility were observed to have a normal B. napus karyotype with no visible GISH signals, indicating introgression lines carrying at least a subset of the S. arvensis resistance genes. Schelfhout et al. (2004, 2006) used a B-genome-specific centromeric repeat sequence as a PCR and FISH marker to characterize B-genome introgressions in sexual progeny from B. napus × B. juncea crosses exhibiting various traits of agronomic interest, including resistance against blackleg disease and pod shattering. Genotypes with normal B. napus karyotype were identified in which the minisatellite sequence could be detected by PCR, although no FISH signals were observed, indicating small chromosomal introgressions that carried the gene of interest. Voss et al. (2000) generated intergeneric crosses between spring oilseed rape and nematode-resistant oil radish (R. sativus) genotypes, using embryo res-

cue to overcome incompatibility barriers. In three backcross generations, highly resistant progeny with a minimal number of R. sativus chromosomes were selected by resistance testing accompanied by GISH analysis. This strategy led to the identification of a resistant BC3 plant with a monosomic, acrocentric addition chromosome. This individual was backcrossed once again to produce a stable disomic addition line; however, efforts to introgress the resistance on a stable introgression failed. Similarly, Peterka et al. (2004) also generated oilseed rape interspecific hybrid lines containing nematode resistance on a monosomic R. sativus addition chromosome. Here, R. sativus chromatin was identified by PCR and FISH with a Raphanus-specific centromeric repeat sequence. In this case also, however, no intergenomic transfer of the resistance was reported. Fahleson et al. (1997) analysed somatic hybrids between Eruca sativa and B. napus using in situ hybridization with two E. sativa-specific repetitive DNA sequences accompanied by GISH. One of the repetitive sequences showed 100% similarity with a part of the E. sativa rDNA intergenic spacer and localised to the three pairs of E. sativa rDNA loci, whereas the other clone was a tandemly repeated element located close to the telomeres on at least 10 E. sativa chromosomes. Analysis of progenies derived from the somatic hybrids revealed the presence of E. sativa DNA; however, no intergenomic translocations could be detected by GISH, although the somatic hybrid progeny contained one or two complete E. sativa chromosomes. Together these results emphasise the fact that chromosome translocations among non-homologous genomes are more likely in the presence of homoeologous chromosome pairing allowing intergenomic recombination. Genome homoeology at the chromosomal level is expected to be more extensive between oilseed rape and its closer relatives, and this is confirmed by the relative ease with which agronomic traits have been transferred to B. napus from B. nigra, B. juncea, B. carinata and Sinapis species in comparison with the difficulties observed in more distant crosses. On the other hand, successful transfer of genes of interest in intertribal asymmetric hybrids has also been demonstrated on a number of occasions and may indicate unknown or partial genome homologies. Interesting results in this respect were obtained by Wang et al. (2004), who produced sexual progenies of asymmetric somatic hybrids between B. napus and Crambe abyssinica in an effort to improve the fatty acid composition of oilseed rape seed. Through

Chapter 2 Oilseed Rape

meiotic GISH these authors were able to identify intergenomic chromatin bridges and detect asynchrony between the B. napus and C. abyssinca meiotic cycles. Lagging, bridging and late disjunction of univalents derived from C. abyssinica were observed, whereas analysis of cleaved amplified polymorphic sequence (CAPS) markers derived from the FAE1 gene showed novel patterns different from the B. napus recipient in some hybrid offspring. This indicated the existance of novel allelic variation in the interspecific hybrids that presumably arose from introgression of Crambe chromatin to one or more B. napus chromosomes. Some of the recombinant offspring contained significantly higher amounts of seed erucic acid than the B. napus parent, demonstrating that it is possible to introgress agronomic traits from distantly related crucifers into elite oilseed rape material. In another example, Winter et al. (2003) used mitotic GISH to characterize recombination lines containing genes for blackleg resistance from Moricandia arvensis. Resistant lines were identified which exhibited a normal B. napus karyotype but carried the Moricandia resistance genes on putative chromosome introgressions. Although such crosses can exhibit significant linkage drag and hence must be viewed as extremely basic material from a breeding perspective, such prebreeding is of enormous interest in terms of broadening the genetic variability for particular traits where little variation is available within B. napus itself.

2.4 Genetic Diversity Studies in Brassica napus Besides spring and winter oilseed rape types, B. napus is often also grown as a fodder crop or as green manure. Swede cultivars (B. napus ssp. napobrassica) are also relatively common, particularly in Great Britain and Scandinavia, and a small number of rape kale vegetable forms (B. napus ssp. napus var pabularia) are also known, predominantly in Asia. Owing to their generally unsuitable seed characters, however, in particular high content of seed erucic acid, glucosinolates, and other anti-nutritive substances, fodder and vegetable rape forms have been generally overlooked for breeding of oilseed cultivars in recent decades. This emphasis on specific oil quality traits has led to a considerable narrowing of the gene pool of elite oilseed rape breeding material in recent

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decades. On the other hand, genetically diverse material among vegetable and fodder rape represents a potentially valuable source for improved pathogen and pest resistance (Lühs et al. 2003a,b), and introduction of untapped germplasm into breeding lines also has the potential to improve heterotic potential. However, the construction of genetic pools, as used for example in maize hybrid breeding, has not been achieved for oilseed rape to date. Because of linkage drag for seed yield and quality traits associated with non-oilseed rape morphotypes, identification of exotic germplasm amongst the respective gene pools of winter and spring oilseed forms is of particular interest in this respect. Due to the genetic bottleneck introduced in the 1970s and 1980s by the focus on breeding for material with double-low seed quality, genetic variability in oilseed rape is restricted with regard to many characters of value for breeding purposes. On the other hand, considerable genetic variation has been discovered within the species as a whole using molecular marker analyses. Becker et al. (1995) compared cultivars and resynthesised lines using allozyme and restriction fragment length polymorphism (RFLP) markers and concluded that resynthesised forms are a suitable resource for broadening the genetic base of rapeseed. Song et al. (1995) described the rapid genome changes that occur in synthetic Brassica polyploids and discussed the evolutionary implications arising from the ability of polyploid species to generate extensive genetic diversity in a short period of time. Thormann et al. (1994) used RFLP and RAPD markers to determine genetic distances in and between cruciferous species. Halldén et al. (1994) compared B. napus breeding lines with RFLPs and RAPDs, while Diers and Osborn (1994) compared RFLP patterns in winter and spring rapeseed genotypes and concluded that the two forms constitute two genetically different groups. The relationship between genetic distance and heterosis in oilseed rape was investigated by Diers et al. (1996) using RFLP markers and by Riaz et al. (2001) with sequence-related amplified polymorphisms (SRAPs). Plieske and Struss (2001) were able to clearly differentiate winter and spring rapeseed in a cluster analysis using simple sequence repeat (SSR, microsatellite) markers. The use of RAPDs for discrimination among rapeseed cultivars was also described by Mailer et al. (1994), whereas Lombard et al. (2000) utilised amplified fragment length polymorphisms (AFLPs; Vos et al. 1995) to genotype winter rapeseed cultivars and estimate

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genetic similarities. Using SSR markers Hasan et al. (2006) found remarkable genetic variation in exotic vegetable and fodder rape genotypes compared to the gene pools of conventional spring and winter oilseed material. Similar extreme genetic variation compared to conventional rapeseed cultivars was also found in resynthesised rapeseed lines analysed by Becker et al. (1995) using allozyme and RFLP markers, and in other resynthesised rapeseed material investigated by Seyis et al. 2003a) using AFLP markers. In the latter study the genetic differences were correlated to heterotic yield potential in experimental hybrids (Seyis et al. 2003b). Such exotic material must obviously be viewed from a long-term perspective with regard to use in oilseed rape breeding; however, genetic diversity analyses using molecular markers have the potential to identify novel genetic variation that might assist in future improvement of heterotic potential in B. napus. Furthermore, the analysis of large sets of genetically diverse material using mapped markers in linkage disequilibrium can potentially provide a valuable data basis for the creation of so-called ‘graphical genotypes’ for allele-trait association studies.

2.5 Genetic Modification: Status and Potential of Transgenic Brassica napus Genetic engineering is considered a powerful tool for practical plant breeding since the transfer of specific traits to a target genotype is possible without changing the phenotype and agronomic performance of the recipient plant. Oilseed rape is particularly amenable to Agrobacterium tumefaciensmediated transformation, and during the last two decades considerable progress has been made in the development of transgenic varieties. Consequently, the global area of transgenic crops has grown continuously. The area planted in GM crops increased dramatically from 1.7 million ha in 1996 to almost 90 million ha in 2005 (James 2005). In 2003, four countries accounted for 98% of the global genetically modified crop area. The United States grew 42.8 million ha (63% of global total), followed by Argentina with 13.9 million ha (21%), Canada 4.4 million ha (6%), Brazil 3.0 million ha (4%) and China 2.8 million ha (4%). Amongst the different crops, soybean, cotton, corn and canola are the four principal crops in which

transgenic technology is utilized. Herbicide tolerant soybean is the dominant transgenic crop commercially grown, representing 61% of the global transgenic crop area in 2003. The second most dominant crop is insect-tolerant Bt-corn, which occupied 9.1 million ha (13% of global total) in 2003. After Btcotton (7.2 million ha) the fourth most dominant crop was herbicide tolerant canola, which was grown on 3.6 million ha, equivalent to 5% of the global transgenic area in 2003. The huge success of genetically modified crop plants in America and China has not been continued in the European Union member states. This is mainly due to limited public acceptance and unclear administrative legislation (cf. Friedt and Lühs 1998). One of the major markets for transgenic canola is Canada, where today the vast majority of the crop comprises herbicide-tolerant varieties, and a significant proportion of the continually expanding production of oilseed rape in China is also comprised of genetically modified varieties. The first generation of transgenic varieties showed a strong emphasis on herbicide tolerance and hybrid breeding systems; however, efforts are increasing in the areas of genetically modified fatty acid biosynthesis and to a certain extent in the introgression of transgenic pest and disease resistance (e.g. National Research Council Committee on Genetically Modified Pest-Protected Plants, Board on Agriculture and Natural Resources 2000).

2.5.1 Herbicide Tolerance Making crop plants tolerant to herbicides allows farmers to use these herbicides to control weeds without harming the crop. This has been achieved in some crops for certain narrow-spectrum herbicides, using tissue culture techniques. The two most promising developments produced by genetic modification to date are oilseed rape varieties tolerant to the postemergent broad-spectrum herbicides glyphosate [N-(phosphonomethyl)glycine] and glufosinate-ammonium [2-amino-4-(hydroxymethylphosphinyl)butanoic acid] (Oelck et al. 1991). Since its introduction in 1974, glyphosate, the active ingredient in the herbicide RoundUp, has increased dramatically in use, particularly with the advent in the 1990s of crops genetically engineered to be tolerant to this herbicide, e.g. Roundup Ready (RR) canola, soybeans, cotton and corn (Shaner 2000). The

Chapter 2 Oilseed Rape

cellular target of glyphosate in plants is the enzyme 5enolpyruvyl-3-shikimate phosphate (EPSP) synthase, which catalyses the formation of EPSP from phosphoenolpyruvate (PEP) and shikimate-3-phosphate. Inhibition of this step of the shikimate pathway causes starvation of aromatic amino acids, accumulation of shikimate and, eventually, cellular death. Herbicide tolerance in plants was mediated by overexpression of an Agrobacterium-derived EPSP synthase with decreased affinity for glyphosate, but unaffected kinetic efficiency and tight binding of PEP (cf. Padgette et al. 1995). Glufosinate-ammonium is the chemically synthesised form of the bacterial product phosphinothricin (PPT) and is used worldwide as a non-selective herbicide. It is a foliage-applied contact herbicide that controls or suppresses most annual and perennial weeds. Glufosinate-ammonium inhibits glutamine synthetase (GS), an enzyme critical to the metabolism of nitrogen by plants, causing a greatly reduced availability of amino acids required for photorespiratory glyoxylate transamination. Thus accumulation of glyoxylate in GS-inhibited plants produces phytotoxic effects that result in rapid cessation of photosynthesis and death (Dekker and Duke 1995). Two genes that confer tolerance to PPT have been isolated from bacteria and cloned: the bar gene from Streptomyces hygroscopicus, which confers resistance to bialaphos (Murakami et al. 1986), and the pat gene from S. viridochromogens, which confers resistance to phosphinothricin (Strauch et al. 1988). Because of their stable expression, both of these genes, which behave in a dominant fashion, have been used extensively as selectable markers in transformation experiments. The enzyme phosphinothricin acetyltransferase, encoded by the bar and pat genes, deactivates the active ingredient phosphinothricin by acetylation of its free NH2 group, thereby neutralising its toxic effect on plant tissue (Botterman and Leemans 1988; Yoder and Goldsbrough 1994). Several studies have demonstrated that the pat gene conferring glufosinate-ammonium tolerance, when introgressed into B. napus via Agrobacterium-mediated transformation, behaves as a dominant gene and shows Mendelian inheritance (Budar et al. 1986; De Block et al. 1987, 1989; Kumar et al. 1998). The first glufosinate-ammonium-tolerant B. napus spring cv. Innovator was registered for production in Canada in 1995 (Oelck et al. 1995). Although weed control in canola is possible with available herbicides, multiple treatments with chemicals of dif-

73

ferent herbicide families are often required for control of all weeds. Certain cruciferous weeds such as wild mustard (Sinapis arvensis) and stinkweed (Thlaspi arvense) are difficult to control, and the use of specialty herbicides for cruciferous weed control is sometimes required. In addition, the use of multiple herbicides increases production costs and the chemical load on soils. The availability of several types of herbicide-tolerant plants allows for rotation of herbicides, minimising the risk of weeds becoming resistant to any particular one. Several varieties of transgenic herbicide-tolerant oilseed rape are grown and processed in the USA, Canada and China. In 2004 herbicide-tolerant varieties comprised more than 85% of the canola crop in Canada, with the majority of the herbicide tolerance being of transgenic origin (http://www.canola-council.org/).

2.5.2 Genetic Engineering of Fatty Acid Biosynthesis Rapeseed is an important source of energy both for human consumption and for feeding of livestock, and also provides raw material for a wide range of industrial products for many non-food purposes. Modification of the fatty acid composition to make rapeseed oil more competitive in various segments of the food and industrial oil markets has become an important objective of oilseed rape molecular genetics and breeding. One of the central objectives in this context is the genetic modification of the seed storage oil by maximising the proportion of specific or functional fatty acids in order to obtain tailor-made raw materials suited for various industrial purposes (Friedt and Lühs 1998; Biermann et al. 2000). However, the quality of vegetable food products has increased in relevance for human nutrition in recent decades, with the advent of so-called ‘functional foods’. With regard to specific properties of such nutritive substances, genetic engineering offers the possibility of adapting plant storage lipids to meet specific nutritional and even therapeutic requirements (Leckband et al. 2002; Friedt et al. 2004). Rapeseed oil is unique in having a large spectrum of usability and positive properties for food and non-food applications. Genetic engineering of lipid biosynthesis in rapeseed has already led to commercialisation, with transgenic varieties expressing genetically modified fatty acid patterns being available since 1995 (cf. Friedt and Lühs 1998).

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The isolation of the majority of genes encoding enzymes of storage oil synthesis made this basic metabolism one of the first targets for gene transfer to plants. In the following section we will highlight both the achievements made and the impediments observed with regard to the modification of oilseed quality by genetic transformation. Technical aspects and efforts, ranging from regeneration of fertile plants from tissue cultures to the final selection and field evaluations of transformants with the desired phenotype, are discussed in more detail by Weber et al. (2000). The biochemical pathways of plant lipid synthesis are well understood and have been comprehensively reviewed by a number of authors (Ohlrogge et al. 1991; Töpfer et al. 1995; Murphy 1999; Drexler et al. 2003). In plants consecutive steps of de novo fatty acid biosynthesis take place in different cell compartments. Fatty acid synthesis starts in the stroma of the plastids where malonyl-CoA is synthesised from acetyl-CoA and carbonate by an acetyl-CoA carboxylase (ACCase) enzyme. Condensing enzymes (ketoacyl-ACP synthases, KAS), as part of the FAS (fatty acid synthase) multienzyme complex, elongate the initial malonyl-CoA consecutively by adding C2 units derived from malonyl-CoA under release of CO2 . During this process the growing fatty acid chain is bound to an acetyl carrier protein (ACP). After seven condensation cycles, a C16 acyl thioester (palmitoyl-ACP) is elongated by another KAS specific to stearoyl-ACP, which is then desaturated to oleoyl-ACP by the action of a Δ9-stearoyl-ACP desaturase. The three latter acyl residues are released from ACP via hydrolisation by acyl-ACP thioesterases (TE), resulting in palmitic (16:0), stearic (18:0) and oleic (18:1n-9) as the primary fatty acids. Acyl-ACPTEs are distinguished by their evolutionary origin into FatA and FatB TEs. The FatA types have unsaturated acyl groups as substrates, while FatB types prefer saturated acyl ACPs (Jones et al. 1995). In certain plant species, e.g. Cuphea sp., specific FatB TEs terminate the chain length of storage lipid fatty acids by hydrolysing acyl-ACPs before they reach a length of 18 carbons, resulting in short- to medium-chain fatty acids: capric (8:0), caprylic (10:0), lauric (12:0) and myristic (14:0) acid, respectively. However, fatty acids hydrolysed from ACP are exported into the cytoplasm, where they are activated with CoA by a membranebound acyl-CoA synthase (ACS). In Brassicas and some other plant species, oleoyl-CoA is subject to further elongation, which results in long-chain fatty

acid residues: eicosenoyl (20:1n-9), erucoyl (22:1n-9) and nervonoyl (24:1n-9)-CoA, which complete the acyl-CoA. At the endoplasmic reticulum the assembly of TAGs is catalysed by acyltransferases (AT) which convert CoA-activated acyls and glycerol-3-phosphate (G3P) under the release of inorganic phosphate to TAGs. In a first step, lysophosphatidic acid (LPA) is formed by glycerol-3-phosphate acyltransferase (G3PAT), which is then converted into diacylglycerol (DAG)-phosphate by the action of a substrate-specific lysophosphatidic acid acyltransferase (LPAAT). The substrate specificity of LPAAT results in a distinct occupation of the central sn2 position of the resulting TAG with a specific fatty acid, which has an impact on the fatty acid pattern of the seed oil. For complementation of TAG synthesis, the phosphate group bound to the diacylglycerol is released by phosphatidic acid phosphatase (PAP) prior to conversion of the diacylglycerol by diacylglycerol acyltransferase (DAGAT), the only enzyme unique to storage lipid synthesis (Murphy 1999), to the final TAG. Alternatively, DAG can be converted reversibly to phosphatidylcholine (PC), which may enter membrane lipid synthesis or is used as substrate for membrane-bound desaturases that in turn desaturate the acyl residues at positions Δ9, Δ12 and Δ15 or Δ6. Additionally, in some plant species, functional groups, e.g. hydroxy residues in castor seeds (Ricinus communis L.), are added to the PC-bound fatty acid residue prior to desaturation. The PCs with such altered fatty acid residues can then, by acyl exchange, be reconverted to various diacylglycerols (DAG) which complement the substrate pool for DAG acyltransferase for triacylglycerid synthesis of storage lipids. A wide range of genes encoding various enzymes involved in plant storage lipid synthesis have been isolated and cloned (cf. Martínez de Ilárduya et al. 1999; Mekhedov et al. 1999; Drexler et al. 2003) and thus are available for genetic engineering of fatty acid composition of seed oils. The quality of oils and fats is determined primarily by the composition of fatty acids, their chain length, their degree of desaturation and their functional groups. The latter can be divided into two general groups. On the one hand, there is the nutritional sector in which saturated fatty acids are not desired because of their unfavorable effects (e.g. cardiovascular diseases, arteriosclerosis, etc.). On the other hand is the industrial sector, in which saturated fatty acids are valuable raw materials for the produc-

Chapter 2 Oilseed Rape

tion of cosmetics, softeners, lubricants, pesticides and related products. An increase in the content of an existing or a new fatty acid in order to facilitate industrial processing of the raw material is one of the most important objectives of genetic engineering in oil crops (Friedt and Lühs 1998; Biermann et al. 2000). Each plant has a typical fatty acid pattern that is predetermined by its enzyme configuration. A modification of typical fatty acid patterns may be derived by either increasing or reducing the expression of enzyme activities. Previous approaches to alter the fatty acid composition in oilseed rape by modifying C18 desaturation were accomplished by Knutzon et al. (1992) and Zarhloul et al. (1999). For this purpose, a construct of Δ9stearoyl-ACP desaturase (Δ9 DES) was expressed in antisense orientation, which resulted in an increased level of stearic acid of up to 40% total fatty acids. It was concluded that the antisense Δ9-stearoyl-ACPdesaturase transcript hybridized with the RNA of the native Δ9-desaturase, which results in a suppression of the endogenous desaturase expression followed by an accumulation of stearoyl-ACP from which stearic acid can be released by the activity of the stearoyl-ACP-TE (Töpfer et al. 1995). The alteration of fatty acid chain length has been a further aim of modifying lipid composition of rapeseed. In a first step, a major breakthrough was achieved when seeds of transgenic Arabidopsis thaliana plants engineered with a TE gene from California bay (Umbellularia californica), a plant containing 70% lauric acid in its oil (Pollard et al. 1991), showed an accumulation of up to 25% of this fatty acid (Voelker et al. 1992). The expression of the same gene in rapeseed led to an accumulation of 40% lauric acid (Voelker et al. 1996). Some Cuphea species include more than 85% (Crane et al. 2003) of one single saturated medium-chain fatty acid (MCFA), for example the Mexican shrub C. hookeriana with seed oil containing up to 75 mol % caprylic acid (8:0) and capric acid (10:0). Therefore, the presence of medium-chain specific enzymes could be assumed. Jones et al. (1995) isolated a cDNA of C. hookeriana (ChFatB1), expressed in the whole plant, which was active on the 14:0- and 18:1(n-9)-acyl-ACP with strong preference for 16:0acyl-ACP. In contrast, ChFatB2, which is expressed only in seeds possessing a substrate specificity for 8:0- and 10:0-acyl-ACP, has maximum values of 11% and 27% C8:0 and 10:0 in B. napus, respectively (Dehesh et al. 1996). For another species belonging to

75

the genus Cuphea, namely C. lanceolata, specific acylACP TE genes have been cloned (Töpfer and Martini 1994). Among these, ClFatB3 and ClFatB4 have been used in transformation experiments with rapeseed, resulting in an increase in MCFA to 1% and 3% of caprylic and capric acid, respectively, whereas ClFatB4 led to the formation of 7% myristic and 15% palmitic acid in the storage oil (Töpfer et al. 1995). A further achievement was reported by Voelker et al. (1997), who described an enrichment of C14-18 and C10-18 saturated fatty acids in rapeseed by expression of FatB TEs from nutmeg (Myristica fragrans) and elm (Ulmus americana), respectively. The expression of a TE from the tropical tree mangosteen (Garcinia manostana) in transgenic rapeseed resulted in an accumulation of ca. 20% stearic acid (Hawkins and Kridl 1998). Another approach is the heterologous expression of different β-ketoacyl-acyl carrier protein synthase (KAS) isoforms, which catalyse the condensation of acetyl-CoA with a specific acyl-carrier protein (ACP). These condensing enzymes are available and possess different specificities: KAS III, the starting enzyme of the fatty acid synthesis, catalyses the condensation of acetyl-CoA and malonyl-ACP. KAS I is specific for elongation of the substrates from 4:0-ACP to 16:0ACP, KAS II is responsible for the condensation of the substrates from 16:0-ACP to 18:0-ACP, and KAS IV directs its condensing activity on medium-chain acyl substrates (Dehesh et al. 1998, 2001). This suggests that the enzymes in the fatty acid synthesis may be coordinately regulated to counteract changes brought by genetic overexpression and thus maintain the flux of fatty acid production (Dehesh et al. 2001). Different studies reveal that an overexpression of a mediumchain specific KAS III from Cuphea lanceolata leads to an increased level of palmitic acid in the seed oil of rape (Dehesh et al. 2001; Stoll et al. 2003). It is obvious that KAS enzymes in combination with a specific TE, overexpressed in rapeseed, strongly enhance the levels of the respective fatty acids in contrast to a TE expressed alone (Dehesh et al. 1998). KAS IV from Cuphea hookeriana (ChKASIV) in combination with the respective medium-chain specific TE (ChFatB2) resulted in a 30 to 40% increase in the levels of MCFA compared with lines expressing ChFatB2 alone (Dehesh et al. 1998). The same group created Brassica plants expressing ChKASIV, and a TE from C. palustris (CpFatB1) contained on average 40% more C8:0 and 10:0 fatty acids than plants expressing the TE alone.

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We also were able to modify the fatty acid pattern of rapeseed by coexpression of a modified KASIII originating from C. lanceolata (Abbadi et al. 2000) with TEs from C. lanceolata (ClFatB3, Töpfer et al. 1995) and C. hookeriana (ChFatB2, Dehesh et al. 1996). This resulted in an increase of MCFA to 2.9% capric and 9.8% palmitic acid for the first combination and 1% caprylic and 6% capric acid for the second combination, respectively (unpublished data). In order to explore the regulation site of KASIII, which is sensitive for regulatory acyl-ACPs (predominantly decanoylACP) inhibiting enzyme activity, a mutant KASIIIN291D was designed by site-specific mutagenesis (Abbadi et al. 2000). This mutant showed a slightly reduced enzymatic activity but is not inhibited by acyl-ACP. A further approach to increase single fatty acids or to modify the fatty acid pattern takes place in the cytosol. At the endoplasmic reticulum the assembly of TAGs is catalysed by acyltransferases (ATs), which attach CoA-activated acyls on the glycerol backbone under the release of inorganic phosphate. In a first step, lysophosphatidic acid (LPA) is formed by glycerol3-phosphate acyltransferase (G3PAT), which is then converted into diacylglycerol (DAG) phosphate by the action of a substrate specific LPA acyltransferase (LPAAT). The substrate specificity of LPAAT results in a distinct occupation of the central sn-2 position of the resulting TAG with a specific fatty acid, which has an impact on the fatty acid pattern of the seed oil. For complementation of TAG synthesis, the phosphate group bound to the DAG is released by phosphatidic acid phosphatase (PAP) prior to conversion of the DAG by DAG acyltransferase (DAGAT), the only enzyme unique to storage lipid synthesis (Murphy 1999), to the final TAG. Rapeseed bearing the California bay TE contains up to 40% lauric acid, while only the sn-1 and the sn-3 positions of the TAG are occupied (Voelker et al. 1996). In order to further improve the amount of lauric acid, a LPAAT from coconut (Cocos nucifera) (Knutzon et al. 1999), which enables a laurate deposition at the sn-2 position and leading to trilaurin, was co-expressed in the bay TE rapeseed (Knutzon et al. 1992). These authors were able to increase the content of laurate above 50% in the seed oil. Despite the success of some research programs in utilising parallel mutagenesis and lipid profilingtechniques

to demonstrate novel fatty acid synthesis, commercially viable increases in oil yields or specific designer fatty acids have for the most part not been realised. In the context of the modification of the fatty acid structure some effort has been spent on determining the limiting factors. Wiberg et al. (1997) studied the partitioning of laurate between membrane and storage lipids in developing seeds of laurateproducing transgenic rape. They revealed that a substantial decrease of laurate in mature seeds follows the accumulation of laurate in the TAG during seed development, in accordance with a rising amount of lauroyl-phosphatidyl-choline-metabolising enzymes. It was suggested that there is only one substrate pool for both phospholipids and TAGs, and that rapeseed ATs specifically remove or exclude lauroyl moieties and, therefore, differ from corresponding enzymes of plants naturally accumulating laurate. The same effect could be true for caprate-producing rapeseed lines. If the high amount of MCFA in the membrane lipids is a limit for accumulating caprate, traditional breeding methods should be used to select germplasm with a higher capacity for the exclusion of MCFA from the membranes (Wiberg et al. 1997). One future alternative could be the identification of genes encoding enzymes responsible for the exclusion mechanism, and their co-expression with medium-chain specific TEs and condensing enzymes to produce oilseed rape with very high levels of MCFA (Wiberg et al. 1997). On the other hand, the importance of the genetic background should not be underestimated. According to Tang and Scarth (2004), selection for appropriate genetic backgrounds is vital to maximising the expression of the target fatty acids. In the case of MCFA-producing rapeseed, acylCoA profiling has been conducted to determine limiting factors (Larson et al. 2002; Graham et al. 2002), and the lack of the necessary enzymatic repertoire to incorporate MCFA efficiently into seed oils was highlighted as a general problem. In conclusion, it has become obvious that the genetic modification of fatty acid synthesis is still poorly understood, and changes in the content of specific fatty acids are often unpredictable. Nevertheless a great potential exists for genetic engineering of novel seed oil compositions in oilseed rape beyond intraspecific boundaries.

Chapter 2 Oilseed Rape

2.6 Molecular Markers and Genetic Mapping

2.6.1 Use of Isoenzymes in Oilseed Rape Breeding Before the advent of DNA marker systems, isoenzyme analysis was the quickest and most effective method for characterization and identification of morphologically difficult-to-distinguish rapeseed varieties, populations and individuals. In the late 1980s isoenzymes were still being widely used in Brassica vegetable and oilseed breeding for verification of hybrids and characterization of interspecific sexual and somatic cross products. Gupta and Röbbelen (1986), Mündges-Christmann and Köhler (1990) and Chèvre et al. (1991a) applied isoenzymes for varietal identification in oilseed rape cultivars, whereas Sundberg et al. (1987), Jourdan et al. (1989) and Mündges et al. (1989) used isoenzymes separated by starch gel electrophoresis to confirm the genomic composition of resynthesised rapeseed generated by somatic hybridization. A number of groups working on somatic interspecific hybridization for transfer of characters agronomically important to oilseed rape used isoenzyme analysis for characterization of the resulting hybrid and backcross offspring (Sacristan et al. 1989; Sjödin and Glimelius 1989; Sundberg and Glimelius 1991). Isoenzyme banding patterns were also a useful tool for estimation of outcrossing rates (Jain 1979; Hackenberg et al. 1990; Becker et al. 1992). The utility of isoenzyme markers for markerassisted selection (MAS) in crop Brassicas was limited by the lack of diagnostic markers for morphological traits. One notable exception was the discovery by Hinata and Nishio (1978) of allele specificity of stigma glycoproteins in B. oleracea and B. rapa. In oilseed rape breeding, moreover, one of the best examples of successful MAS for an agronomically important trait involves isoenzyme markers. Selection of glucose-6phosphate isomerase (GPI) alleles from radish present on the oilseed rape chromosome introgression carrying the Ogura CMS fertility-restorer gene can assist in early identification of restorer plants (Delourme and Eber 1992; Horacek and Acanova 2003), and this GPI marker is still used today in hybrid breeding programs utilising the Ogura CMS system.

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As the first Brassica RFLP markers (Song et al. 1988) and the resulting genetic maps (see below) became available in the 1990s, interest in isoenzymes rapidly dwindled. Due to the low cost, simple equipment and ease of use, however, use of isoenzymes for homogeneity tests or confirmation of hybrids can still play a role in commercial breeding programs and variety registration today, e.g. in smaller breeding companies without more sophisticated DNA laboratories.

2.6.2 Brassica napus Genetic Maps: From RFLP to PCR Markers As mentioned above, protein and isoenzyme markers still play a role for specific questions in commercial oilseed rape breeding, but genome research and marker-assisted applications in Brassica first began to flourish in the late 1980s with the development of the first restriction fragment length polymorphism (RFLP) linkage maps for B. oleracea (Slocum et al. 1990), B. rapa (Song et al. 1991) and B. napus (Landry et al. 1991). In the meantime, a large number of B. napus genetic maps have been generated from a large number of different rapeseed crosses, and considerable efforts have been invested in the localisation of genes and QTL controlling agronomically relevant traits. Figure 2 shows a detailed consensus genetic map, aligned using common RFLP markers mapped in four mapping populations by the group of Thomas C. Osborn at the University of Wisconsin at Madison. Table 3 presents an overview outlining details of a number of published B. napus genetic maps, including cross parents, number and types of markers, map size and the traits that have been studied in the respective crosses. The following section highlights some of the major genetic mapping efforts of the past one and a half decades. While this overview gives a good impression of the multitude of data that have been collated by different oilseed rape researchers during this time, it also underlines the difficulties in collating and comparing this information amongst different genetic maps. In particular, for comparison of QTLs, but also for integration of genetic, physical and karyotpe maps, it is essential that all available mapping data be integrated as much as possible in future using standardised chromosome nomenclature and common markers. Fortunately such initiatives have slowly begun to gain momentum in recent years, and the genetic tools

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Fig. 2. Consensus genetic linkage map of molecular markers compiled from individual maps constructed for four segregating populations of Brassica napus doubled-haploid lines (see Udall et al. 2005; map populations are described in Table 3). Linkage groups N1–N10 correspond to B. rapa A genome LGs A1–A10, whereas LGs N11–N19 correspond to B. oleracea C genome LGs C1–C9, respectively. Marker locus names and map positions (in cM) are in the first two columns of each LG. Individual maps contributed complementary sets of polymorphic loci to the consensus map, as shown by bars in four columns (SYN, HUA, MF, RV) aligned with loci in each LG. Circles: loci that had different orders (greater than 2 cM) in individual DH maps compared to consensus map. Linkage group N11 of MF map was not included in consensus map due to a very different locus order. HNRT indicates loci that were part of a homoeologous non-reciprocal transposition on N11 for which genetic distances could not be estimated. Loci on N7 and N16 having P1804 alleles the same size as fragments found in B. rapa are in italics. Loci on N7 and N16 that had segregating monomorphic loci in the SYN population are underlined. Map image kindly provided by Tom Osborn, University of Wisconsin, Madison, WI, USA, and is used with permission from the Genetics Society of America. For further details see Udall et al. (2005)

Chapter 2 Oilseed Rape

Fig. 2. (continued)

79

80

R. Snowdon, W. Lühs, W. Friedt

Fig. 2. (continued)

Chapter 2 Oilseed Rape

Fig. 2. (continued)

81

277

RFLP

RFLP

50 DH lines

92 DH lines 82 DH lines (reciprocal crosses)

50 DH lines 92 DH lines 2 × 90 lines (complementary backcross populations) 156 DH lines

SYN1 × N-o-9 (resynthesised rapeseed × winter DH cultivar) N-o-1 × N-o-9 (DH from Westar × DH from British biennial breeding line) SYN1 × N-o-9 N-o-1 × N-o-9 N-o-93 × N-o-1 (Spring canola line × DH from Westar)

Mansholts Hamburger Raps × Samourai (European landrace × French 00-quality winter rapeseed)

392

RFLP

105 DH lines

RFLP RAPD SSR

RFLP

RFLP (later expanded with RFLP, AFLP, isozyme and gene loci)

204 2 4

201 common markers 189 and 176, respectively

132 (expanded to a total of 480 loci)

120

Major × Stellar (Blackleg-resistant French winter rapeseed × blackleg-susceptible Canadian spring canola variety)

RFLP

90 F2:F3-families

Number of markers

Westar × Topas (both Canadian spring canola varieties)

Marker type(s)

Population type and size

Map parents

Comparison of A and C genome organisation, observation of locus duplications; microspore culture responsiveness Blackleg resistance, white rust resistance, growth habit, flowering time, winter tolerance, glucosinolate content, erucic and linolenic acid contents Comparative genome analysis, identification of A and C genome LGs Comparative genome analysis, reciprocal crosses

Traits investigated

1441

Seed erucic acid, seed glucosinolates, oil content, tocopherol composition

1512 (aligned) Integrated map from two crosses 1544 and 1577, Comparison of meiotic respectively recombination in reciprocal crosses

1741 and 1606, respectively

1656

1016

1413

Map size (cM)

Table 3. Selected Brassica napus genetic mapping studies: Map parents, segregating populations and markers used, map size and investigated traits

Ecke et al. (1995), Uzunova et al. (1995), Uzunova and Ecke (1999), Marwede et al. (2005)

Kelly et al. (1997)

Parkin and Lydiate (1997)

Sharpe et al. (1995)

Sharpe et al. (1995), Parkin et al. (1995)

Ferreira et al. (1994, 1995a–c), Toroser et al. (1995), Thormann et al. (1996), Osborn et al. (1997), Kole et al. (2002b)

Landry et al. (1991), Cloutier et al. (1995)

Reference

82 R. Snowdon, W. Lühs, W. Friedt

134 DH lines

94 DH lines

152 DH lines 134 DH lines 94 DH lines

95 DH lines

235 DH lines

Stellar × Drakkar (Canadian low-linolenic canola × French spring type)

Darmor-bzh × Yudal Darmor × Samourai Stellar × Drakkar

90-DHW-1855-4 × 87-DHS-002 (winter × spring breeding lines)

Express × R54 (German 00-quality winter variety × resynthesised rapeseed)

RAPD AFLP RFLP Isozyme SSR SCAR Bzh gene RAPD AFLP RFLP SCAR Isozyme RAPD AFLP Isozyme SCAR SSR Isozyme RAPD RFLP AFLP RFLP RAPD STS AFLP

152 DH lines

Darmor × Samourai (French winter oilseed rape varieties)

RFLP

200 backcross lines

Tapidor × Victor French 00-quality winter rapeseed × ++-quality Swedish winter rapeseed Darmor-bzh × Yudal (French dwarf winter type × Korean spring type)

Marker type(s)

Population type and size

Map parents

Table 3. (continued)

274 66 2 143

1141

2125

2429

1912

Resistance to TuMV

Genome organisation

Consensus map from three populations

Linolenic acid

Blackleg resistance

Dwarf gene, resistance to blackleg and light leaf spot, erucic acid content, linolenic acid content

2023

271 219 79 10 7 3 1 150 122 68 1 3 167 163 6 3 1 540 1574

Seed glucosinolates, oil content, fatty acid composition

Traits investigated

1238

Map size (cM)

158

Number of markers

Dreyer et al. (2001)

Cheung et al. (1997)

Lombard and Delourme (2001)

Jourdren et al. (1996b), Lombard and Delourme (2001)

Pilet et al. (2001), Lombard and Delourme (2001)

Foisset et al. (1995, 1996), Jourdren et al. (1996a), Pilet et al. (1998a,b), Lombard and Delourme (2001)

Howell et al. (1996, 2003), Burns et al. (2003)

Reference

Chapter 2 Oilseed Rape 83

MF216 × P1804 (DH line from (Major × Stellar) × male-fertility restorer line)

170 DH lines

RFLP

RFLP SSR

162 DH lines

202 DH lines

RFLP

SSR RFLP AFLP SNP

166 DH lines

162 DH lines

AFLP SSR

105 DH lines

25629-3 × K26-96 (yellow-seeded 00-quality inbred line × black-seeded ++-quality DH line) Express × 1012/98 (German winter oilseed rape variety × yellow-seeded breeding line, both 00-quality) Tapidor × Ningyou 7 (French 00-quality winter rapeseed × ++ quality old Chinese semi-winter variety)

RV289 × P1804 (derivate from Chinese winter rape cultivar × male-fertility restorer line) TO1147 × P1804 (Resynthesised B. napus × male-fertility restorer line)

RFLP AFLP SSR RAPD AFLP SSR

123 F2:F3-families

H5200 × Ning RS1 (MS restorer line × partial Sclerotinia resistant)

Marker type(s)

Population type and size

Map parents

Table 3. (continued)

218

309 2

1398

1668

1460

Comparative mapping for analysis of homoeologous chromosome recombination Comparative mapping for analysis of homoeologous chromosome recombination Comparative mapping for analysis of homoeologous chromosome recombination

Yield and yield components, seed oil, erucic acid, glucosinolate and protein contents

1532

124 18 15 350

239

Seed color, dietary fiber

Seed color, dietary fiber

Resistance to Sclerotinia sclerotiorum

Traits investigated

1721

1397

1625

Map size (cM)

262 85

72 30 3 2 211 35

Number of markers

Udall et al. (2005)

Udall et al. (2005)

Udall et al. (2005)

Pers. comm., Dan Qiu and Jinling Meng, Huazhong Agricultural Unversity, Wuhan, China; see http://brassica.bbsrc.ac.uk/ IMSORB

Badani et al., (in press)

Badani et al., (in press)

Zhao and Meng (2003)

Reference

84 R. Snowdon, W. Lühs, W. Friedt

RFLP

SSR SCAR

164 DH lines

574 F2 plants from unbalanced diallel cross between six parental lines

RV128 × P1804 (Westar × Samourai introgression line × male-fertility restorer line) Comet, Jaguar, Vivol (French winter rapeseed cultivars) and three French commercial breeding lines

Marker type(s)

Population type and size

Map parents

Table 3. (continued)

304 59

205

Number of markers

2619

1453

Map size (cM) Comparative mapping for analysis of homoeologous chromosome recombination SSR consensus map from six segregating populations

Traits investigated

Piquemal et al. (2005)

Udall et al. (2005)

Reference

Chapter 2 Oilseed Rape 85

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R. Snowdon, W. Lühs, W. Friedt

necessary to realise them are beginning to be made available in the public domain. It remains to hope that a common, integrated, public oilseed rape map with extensive marker coverage, QTL data and alignment to physical maps will be available to international Brassica researchers in coming years. The first B. napus map, published by Landry et al. (1991), was based on F2 segregation analyses from a cross between the canola cultivars Westar and Topas, using RFLPs from a seedling-specific cDNA library and independent digestions with BamHI, EcoRI, EcoRV and HindIII. A total of 120 RFLP loci were mapped on 19 LGs covering a total of 1,413 cM. Due to low polymorphism among the RFLP markers in this cross the number of loci that could be mapped was relatively low; however, it was nevertheless possible to detect considerable locus duplication corresponding to the amphidiploid genome organisation, and the first evidence was observed for extensive rearrangements of the linear order of the duplicated loci. Comparisons of this map with the corresponding B. oleracea and B. rapa maps enabled the first detailed investigations of genome organisation among the respective Brassica genomes. Furthermore, the fact that functional DNA sequences were used as markers meant that the results were immediately relevant for applications in canola breeding. A further map was reported by Hoenecke and Chyi (1991) shortly afterwards, this time based on a cross between two breeding lines. In this case 125 RFLP markers covering 1,350 cM were mapped to 19 LGs. Subsequently a number of groups produced RFLP maps using segregating F2 or doubled-haploid (DH) populations derived from homozygous DH lines as parents. The map made by Ferreira et al. (1994), developed from a cross between a winter oilseed rape cv. Major and the spring canola variety Stellar, was used initially to localise QTLs associated with the annual/biennial growth habit and subsequently for extensive studies of flowering time genes (Ferreira et al. 1995b; Osborn et al. 1997). In the initial study a total of 132 RFLP loci were grouped on 22 LGs covering 1,016 cM. The map positions for a subset of the mapped markers were compared with the locus ordering in F2 progeny from the same cross, and no significant differences could be established between the two maps. Comparisons of this map with the maps for B. rapa and B. oleracea containing the same markers suggested that less recombination was observed in the B. napus cross than would be expected from the combined map distances of the two diploid progenitors.

A high percentage (32%) of segregating marker loci were duplicated in the DH map, and conserved linkage arrangements of some duplicated loci indicated intergenome homoeology in the amphidiploid, or intragenome duplications from the diploid progenitors. Toroser et al. (1995) also used the Major × Stellar mapping population to localise QTLs which regulate the total seed aliphatic-glucosinolate content in B. napus. A population of 99 F1 -derived DH recombinant lines were used for single-marker analysis and interval mapping of QTL associated with total seed glucosinolates. In further studies Ferreira et al. (1995a,c) localised loci contributing to blackleg and white rust resistance using the same cross, and genes controlling erucic and linolenic acid biosynthesis were localised in this map by Thormann et al. (1996). The first integrated map constructed using two different segregating populations was developed by Sharpe et al. (1995). This study revealed considerable chromosome instability in one of the crosses, which involved a resynthesised rapeseed crossed with a normal rapeseed variety. Parkin et al. (1995) mapped this cross with 399 RFLP markers and discovered that the majority of loci exhibited disomic inheritance of parental alleles. This provided evidence demonstrating that B. rapa chromosomes were each pairing exclusively with recognisable A genome homologues in B. napus and that B. oleracea chromosomes were pairing similarly with C-genome homologues. That enabled identification of the ten A genome and nine C genome LGs of B. napus and showed that the nuclear genomes of B. napus, B. rapa and B. oleracea have remained essentially unaltered since the formation of B. napus. This result was confirmed in a comparative mapping study including natural and resynthesised rapeseed described by Udall et al. (2005). According to Sharpe et al. (1995), the chromosome abnormalities they observed were probably caused by associations between homoeologous chromosomes in the resynthesised parent and the F1 plant at meiosis, leading to non-disjunction and homoeologous recombination. Kelly et al. (1997) used an F1 individual derived from a cross between two distinct lines of spring oilseed rape to produce a pair of complementary backcross populations comprising 90 individuals each. Genetic maps were generated from both populations and aligned using 117 common loci to produce an integrated genome map of B. napus with 243 RFLP loci. No differences could be observed between the frequencies and distributions of crossovers in the two male and female populations of F1 gametes, and the respec-

Chapter 2 Oilseed Rape

tive genetic maps each consisted of 19 LGs spanning 1,544 and 1,577 cM. The maps were found to be comparable with other B. napus maps in terms of their genetic size and general organisation. With the discovery of the polymerase chain reaction (PCR; Mullis and Faloona 1987) the potential arose to greatly increase the marker density in existing genetic maps through amplification of highly polymorphic anonymous PCR fragments, first with RAPD markers (Williams et al. 1990) and more recently with AFLPs (Vos et al. 1995) and intersimple sequence repeats (ISSRs; Zietkiewicz et al. 1994). Foisset et al. (1995, 1996) integrated RAPD and isozyme markers into an RFLP map based on DH offspring from the F1 of a cross between the French dwarf winter oilseed rape variety Darmor-bzh and the Korean spring rapeseed Yudal. Using 153 DH lines a total of 254 markers were mapped to 19 LGs covering 1,765 cM, and a number of agronomically relevant genes could be localised. In particular, the dwarf gene Bzh was localised and linked markers were identified by a combination of genetic mapping, on the one hand, and targeting of the dwarf gene using near-isogenic lines (NILs) and bulked segregant analysis (BSA) on the other hand. The BSA approach was found to be more efficient in finding DNA markers linked to Bzh, whereas the efficiency of the NIL approach was limited by the similarity of the genetic background between the dwarf donor parent and the recurrent lines. In the late 1990s RAPD and subsequently AFLP markers began to be more broadly incorporated in new and existing B. napus genetic maps and provided relatively cheap and less labour-intensive alternatives for saturation of genome regions containing genes of interest. These high-throughput, highly polymorphic marker systems also improved the application of BSAs for quick identification of markers linked to qualitative and also quantitative traits of interest. For example, Delourme et al. (1994) used BSA to identify RAPD markers linked to the restorer gene Rfo used in the Ogura Raphanus sativus cytoplasmic male sterility of rapeseed. DNA polymorphisms generated by four RAPD primers were found to be completely linked to the restorer gene, with the polymorphic DNA fragments being associated either with the fertility restorer allele or with the sterility maintainer allele. Southern hybridization of labelled RAPD fragments on digested genomic DNA from the same three pairs of bulks revealed fragments specific to either the male sterile bulks or the restored bulks, and a few fragments common to all bulks. The four RAPD frag-

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ments which were completely linked to the restorer locus were cloned and sequenced to develop sequence characterized amplified region (SCAR) markers. SSR markers (microsatellites) represent a class of variable-number tandem repeats comprised of a short (1 to 6 bp) nuceotide motif repeated up to 100 times or more. SSRs exist in eukaryotic nuclear genomes and in the chloroplast genome of some plants (Powell et al. 1995) and are of great interest to population geneticists because of their high mutation rate, which in plants has been estimated to be as high as 10−6 (Udupa and Baum 2001). SSR markers are generated by PCR amplification of specific microsatellite loci using primers developed from the sequences flanking the simple repeat. Because of their robust nature, generally codominant inheritance and relatively high level of polymorphism, SSR markers are an extremely valuable tool for genetic mapping; along with RFLPs they have become the markers of choice for alignment of oilseed rape genetic maps from different crosses. Uzunova and Ecke (1999) screened some 45,000 clones of a small insert library of B. napus genomic DNA and estimated that GA/TC and CA/TG SSRs occurred at a rate of approx. one repeat every 100 kb and 400 kb, respectively. This high abundance suggests that SSR sequences are also common within coding regions, which can make SSR markers particularly effective tools for MAS and map-based gene cloning. The crosstaxa amplification of SSRs among the Brassicaceae was demonstated by Westman and Kresovich (1998), who showed that primer pairs designed to amplify singlelocus SSRs in A. thaliana could in some cases be used to amplify multiple marker loci in different Brassica crop species. Sets of substitution lines have advantages over segregating populations for the analysis of loci influencing quantitative traits because the effects of individual QTLs can be compared in a uniform genetic background. Howell et al. (1996) developed a strategy for the rapid production of B. napus substitution lines, involving the systematic application of MAS over two to four backcrossing generations. A genetic map containing 158 loci was generated from a population of 200 first-backcross (B1) individuals. Six complementary B1 individuals enriched for recurrent genotype and collectively carrying almost all of the donor genome were selected. A total of 288 B2 plants derived from the selected B1 individuals were analysed, and complementary individuals carrying five or fewer donor segments were identified. Similar selection, carried out on 250 B3 plants from two distinct B1

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R. Snowdon, W. Lühs, W. Friedt

lineages, identified 74 B3 individuals carrying one or two donor segments. Together, 12 of these isolated segments represented 33% of the mapped genome. Lines homozygous for single substituted segments were derived from selfed progeny of selected B3 plants. Full sets of substitution lines can be used to elucidate the genetic control of quantitative production traits in oilseed rape over several environments. Besides representing ‘immortal’ populations that can be continually tested in different years and environments, substitution lines also enable an exact dissection of quantitative traits by enabling observation and comparison of effects from individual QTLs in a uniform genetic background. Butruille et al. (1999) developed four populations of inbred backcross lines of oilseed rape and used them to map genomic regions from the donor parent (a winter-type cultivar) which influenced agronomic traits in spring-type inbreds and hybrids. RFLP markers identified among the introgression lines were used to enrich a composite genetic map of B. napus with 72 new RFLP loci. The selfed and hybrid progenies of the inbred backcross lines were evaluated during two growing seasons for several agronomic traits. Both pedigree structure and map information were incorporated into the QTL analysis by using a regression approach and shown to be valuable for both genetic mapping and QTL analysis. Comparison of gene loci and particularly QTL among different mapping populations can only be achieved when consensus markers are available for different crosses. In one example, Lombard and Delourme (2001) developed a framework B. napus consensus map by integrating three DH mapping populations derived from crosses between or within spring and winter rapeseed parents. A total of 992 isozyme, RFLP, RAPD and AFLP markers were mapped to at least one population, and 540 markers could be included in the consensus map. A total of 253 markers were common to at least two populations. Markers were distributed over 19 LGs and covered 2,429 cM. The markers were more or less evenly spaced on the entire genome, although on several LGs RAPD and AFLP markers were not uniformly distributed. The growing availability of Arabidopsis EST collections and their integration into comparative Brassica genome maps (e.g. Fourmann et al. 2002; Babula et al. 2003) enables the fine mapping of genome rearrangements and the delineation of gene-coding regions in crop genomes, enabling the correlation of traits in Brassica crops with Arabidopsis candidate

genes and development of genetic markers considerably more closely linked to the relevant genes. In some cases, however, it is not even necessary to know the gene sequence. For example, Li and Quiros (2001) developed sequence-related amplified polymorphic (SRAP) markers that preferentially amplify and detect polymorphisms in open reading frames (ORFs). Primers containing CCGG motifs were found to preferentially anneal in ORFs due to the predominance of GC bases in coding sequences. Because exonic sequences generally exhibit few or no length polymorphisms, the ORF primers were combined with primers containing an AATT core near their 3 end. These sequences occur more frequently in promoter regions and introns which are prone to sequence variation, meaning that the resulting anonymous exon-intron markers showed relatively high polymorphism. After sequencing the SRAP amplification products it was found that a large proportion of the markers matched known genes, and their utility for gene tagging was demonstrated by localisation of a glucosinolate desaturation gene in B. oleracea. In future, methods that combine candidate gene information with highthroughput technologies for detection and screening of closely linked markers are likely to have the best success in the study and isolation of gene loci involved in important quantitative traits in rapeseed.

2.6.3 Mapping of Genes and QTLs for Morphological and Quality Traits A large amount of work over the past decade has been done to investigate the genetic basis of morphological and quality traits in oilseed rape. Teutonico and Osborn (1994) constructed an RFLP linkage map for oilseed B. rapa, using anonymous genomic DNA and cDNA clones from Brassica along with cloned genes from A. thaliana, and used this map to localise genes controlling simply inherited traits including yellow seeds, seed erucic acid and pubescence. The map included 139 RFLP loci organised into ten LGs and one small group, covering a total of 1,785 cM. Comparisons of the linkage arrangements between B. rapa and B. oleracea revealed extensive colinearity among the Brassica A and C genomes, and nine of the ten B. rapa LGs had conserved linkage arrangements with B. napus LGs. The majority of loci in common were in the same order among the three species, although the distances between loci were largest on the B. rapa map.

Chapter 2 Oilseed Rape

Genome organisation was also compared between B. rapa and A. thaliana using RFLP loci detected with 12 cloned genes in the two species. This provided some of the first evidence that linkage arrangements are to a certain extent conserved among even distantly related crucifer genomes. Considerable knowledge has been gained on the genetic control of flowering time through QTL analysis and comparative mapping of genes associated with this trait in ortholgous Brassica and Arabidopsis genome regions. Osborn et al. (1997) found that a major QTL for flowering time, VFR2, occurs in a region homologous to a region in B. napus which controls the same trait, and that this region is also homologous to the top of chromosome 5 in Arabidopsis, where several flowering-time genes are located. After backcrossing, VFR2 was found to segregate as a discrete character, and by comparative fine mapping it was shown to co-segregate with the Arabidopsis gene flc, which regulates flowering time (Kole et al. 2001). Thus, VFR2 appears to be homologous to FLC and may control flowering time though a similar mechanism as in A. thaliana. Uzunova et al. (1995) constructed a linkage map of the rapeseed genome comprising 204 RFLP markers, two RAPD markers and one phenotypic marker using an F1 -derived DH population from a cross between the old German winter rapeseed landrace Mansholt’s Hamburger Raps and the French winter rapeseed cv. Samourai. The mapped markers were distributed on 19 LGs covering 1,441 cM. About 43% of the markers proved to be of dominant nature, 36% of the mapped marker loci were duplicated, and conserved linkage arrangements reflected extensive duplicated regions in the rapeseed genome. Using cDNA probes for the genes of acyl-carrier-protein (ACP) and beta-ketoacyl-ACP-synthase I (KASI) the respective homoeologous loci of these genes were localised, and the linkage map was also used to localise QTL for seed glucosinolate content by interval mapping. Four QTLs were mapped on different LGs. This map was used by Ecke et al. (1995) to localise the genes controlling the synthesis of erucic acid and loci involved in variation for oil content. The observed three-class segregation for erucic acid confirmed the inheritance of this trait by two erucic acid genes, which were mapped to two different LGs on the RFLP map. Although the parents of the segregating DH population showed no significant difference in seed oil content, in the DH population a transgressive segregation in oil content was observed. This segregation followed a normal dis-

89

tribution, characteristic of a quantitative trait. Three QTLs for seed oil content were localised on three different LGs. Their additive effects together explained about 51% of the phenotypic variation for oil content and two of the QTLs co-localised with the two erucic acid genes, indicating a direct effect of these genes on oil content. Based on a candidate gene approach, two DNA sequences homologous to the FAE1 gene involved in erucic acid synthesis were isolated from a B. napus immature embryo cDNA library by Barret et al. (1998). The sequences of the two cDNA clones were highly homologous, yet distinct, sharing 97% nucleotide identity and 98% identity at the amino acid level. Southern hybridization showed the rapeseed beta-ketoacyl-CoA synthase to be encoded by a small multigene family, whereas Northern hybridization showed the expression of the rapeseed FAE1 gene(s) to be restricted to the immature embryo. One of the genes was found to be tightly linked to one of two loci controlling erucic acid content in rapeseed. Fourmann et al. (1998) developed polymorphic markers within the respective B. napus FAE1 gene loci from the A and C genomes and showed that the two genes co-segregated with the erucic acid loci identified by Ecke et al. (1995). Finally, Das et al. (2002) cloned the fatty acid elongase genes (FAE1) from B. campestris and B. oleracea and demonstrated their effects on erucic acid levels. Jourdren et al. (1996a) also mapped the gene loci responsible for expression of erucic acid in DH progeny derived from a low × high erucic acid F1 hybrid. In this case the aim was to identify genetic markers closely linked to the genes that could be used to assist selection for high erucic acid levels, which is complicated by an inability to clearly distinguish between homozygous and heterozygous genotypes with high levels of this fatty acid. RAPD markers were used to map the two genes to two independent LGs through a QTL approach. A close association was found between individual plant genotypes and the erucic acid content of the doubled-haploid (DH) progeny, and it was shown that the two genes do not contribute uniformly to the erucic acid level. A gene determining alpha-linolenic acid (18:3) content was localised by Hu et al. (1995), whereas Jourdren et al. (1996b) identified specific polymorphisms within one of the B. napus FAD3 delta-desaturase gene copies which were also associated with variations in linolenic acid concentration. Schierholt et al. (2000) mapped a high oleic acid mutation in B. napus and showed that linked AFLP markers localised near

90

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a copy of the FAD2 gene. Previously, Tanhuanpaa et al. (1998) had identified an association between the FAD2 gene, which codes 18:1 desaturase, and a QTL for oleic acid in B. rapa. Allele-specific PCR markers for this locus were developed based on a single nucleotide polypmorphism (SNP) in the B. rapa FAD2 gene which resulted in a functionally relevant amino acid substitution. Somers et al. (1998) identified RAPD markers associated with linoleic acid desaturation in B. napus and found that the gene FAD3 localised near one of the identified QTLs for this trait. Markers associated with low linolenic acid loci were identified in a DH population derived from a cross between the B. napus lines Apollo (low linolenic) and YN90-1016 (high linolenic) using RAPDs and BSA. A total of 16 markers were distributed over 3 LGs, which individually accounted for 32, 14 and 5% of the phenotypic variation in 18:3-content. The rapeseed FAD3 gene was mapped near the locus controlling 14% of the variation. The mode of inheritance appeared to be additive, and a QTL analysis showed that collectively the three loci explained 51% of the phenotypic variation within this population. PCR fragments for low linolenic acid Apollo alleles were identified at all three loci, and simultaneous selection for these alleles at all three loci resulted in a group of DH lines with 4.0% linolenic acid. Hu et al. (1999) also identified two RAPD markers linked to 18:3-content in rapeseed oil and furthermore developed sequence-tagged markers associated with low linolenic acid content that appeared to be linked to an omega-3 desaturase gene. A SCAR marker was developed that amplified an allele associated with low linolenic acid content. A different allele was associated with high linolenic acid content in the cross investigated. This marker, which explained some 25% of the genetic variation for the trait, was mapped and found to co-localise with an omega-3 desaturase gene in B. napus. Howell et al. (2003) localised loci controlling seed glucosinolate content in aligned oilseed rape maps generated from their two intervarietal backcross populations. Four QTLs were localised in a population derived from the cross Victor × Tapidor, together accounting for 76% of the phenotypic variation. Three of these loci also appeared to control the accumulation of seed glucosinolates in the cross Bienvenu × Tapidor, where they accounted for 86% of the phenotypic variation. The three QTLs common to both mapping populations mapped to homoeologous genomic regions, suggesting that seed glucosinolate ac-

cumulation is controlled by duplicate genes. A comparative analysis of QTLs controlling seed glucosinolate accumulation was performed by aligning these two maps with published genetic maps generated by other research groups. This demonstrated that highglucosinolate varieties often carry low-glucosinolate alleles at one or more of the loci controlling seed glucosinolate accumulation. Such information could be particularly interesting for the development of heterotic pools for oilseed rape hybrid breeding because it underlines the possibility of transgressive segregation for reduced glucosinolate content in crosses among genetically diverse genotypes with potentially improved yield heterosis. The development of genetic markers is of particular interest in efforts to select for complex quantitative traits that are strongly influenced by environment, for example seed color. Various groups have in recent years investigated the inheritance of yellow seed color in different segregating crosses involving yellow- and black-seeded genotypes of different origin. Somers et al. (2001) investigated seed-color inheritance in a DH population derived from the F1 generation of the cross Apollo (black-seeded) × YN90-1016 (yellow-seeded). The offspring were analysed via BSA to identify molecular markers associated with the yellow-seed trait, and a single major gene flanked by eight RAPD markers was found to cosegregate with the yellow seed coat color trait in the population. This gene explained over 72% of the phenotypic variation in seed coat color. Further analysis of the yellow-seeded portion of this DH population revealed two additional genes favoring Apollo alleles, which respectively explained 11 and 8.5% of the variation in seed coat color. The data suggested a dominant, epistatic interaction between the major locus and the two additional genes, a hypothesis that was supported by segregation data presented by Liu et al. (2005) in crosses between a yellow-seeded DH line, derived from a resynthesised B. napus, with different black-seeded genotypes. However, the latter study also found that in one of the two populations only one of the two epistatic loci was segregating. We have also analysed QTL and segregation patterns for seed color in two independent populations segregating for the yellow-seed trait (Badani et al. in press). The first was a DH population derived from a cross between a yellow-seeded B. napus winter-type 25629-3 with high erucic acid and high glucosinolate (++) quality and the black-seeded ++-quality DH line K26-96. For comparative QTL and segregation analyses, an F2 :F3

Chapter 2 Oilseed Rape

population, along with a large DH population, was also derived from a second cross involving a different source of yellow seed color. This cross involved an inbred line of the black-seeded 00-quality German winter oilseed rape cv. Express and the yellow-seeded ++-line 1012/98. Mapping was performed including reference AFLP and SSR markers mapped in the Mansholt’s × Samourai population used by Uzunova et al. (1995) and Ecke et al. (1995), meaning that the maps could be closely aligned for comparison of the respective QTLs. In concurrence with Somers et al. (2001) and Liu et al. (2005), it was found that the yellow-seed trait in both crosses was controlled by a major QTL controlling around 50% of the phenotypic variance for seed color in each cross. Furthermore, this locus was found to co-localise with a major QTL contributing to reduced raw fiber content in the seed meal, establishing a causative relationship between seed coat thickness and seed color. This observation suggests that the same major gene contributes to expression of the yellow-seed trait in yellow-seeded lines of different origin. Further contributing QTL were localised on different chromosomes in the two crosses, indicating that seed color in different B. napus material is influenced by different epistatic genes. As described by Somers et al. (2001) one cross had two epistatic loci. The other cross showed only two epistatic loci, corresponding to the suggestion of Liu et al. (2005) that epistatic genes for seed color may not segregate in all crosses between black and yellow genotypes. The hypothesised bigenic and trigenic inheritance of seed color in the respective crosses were confirmed by segregation analyses, and markers which are closely linked to the seed-color loci are being used in ongoing work to attempt a map-based cloning of the gene corresponding to the main-effect QTL.

2.6.4 Mapping of Genes and QTL for Disease Resistance A significant focus of mapping efforts in oilseed rape has been the genetic characterization of disease resistances. Pilet et al. (1998a,b, 2001) identified QTL for field resistance to blackleg (L. maculans) in crosses of different genetic background, whereas other authors (e.g. Ferreira et al. 1995a; Mayerhofer et al. 1997) mapped major loci associated with race-specific L. maculans resistance genes. In the former work, genomic regions controlling blackleg resistance at the

91

adult plant stage were detected using 152 DH lines derived from the F1 of the cross Darmor-bzh × Yudal. Blackleg resistance of each DH line was evaluated in field tests in 1995 and 1996 by measuring the mean disease index (I) and the percentage of lost plants (P). In the first year of field trials ten QTLs were detected. Seven QTLs for I and six QTLs for P explained 57 and 41% of the genotypic variation, respectively. Three of these QTLs were common to I and P. In a second year of trials seven QTLs were identified, including five for I and two different QTLs for P that accounted for 50 and 23% of the genotypic variation, respectively. In the second year, one QTL located close to a dwarf gene Bzh was detected with a very strong effect, masking more QTL detection. Four major genomic regions were revealed in both years with the same parental contribution, one of which carried a resistance allele from the susceptible parent. Genes involved in blackleg resistance were also mapped by Dion et al. (1995) with RFLP markers. One hundred seventy-five polymorphic RFLP loci were mapped in a segregating population composed of 98 DH lines from the cross Cresor (blackleg resistant) × Westar (susceptible). A single chromosomal region, accounting for a high proportion of the variation in blackleg reaction, was found to be responsible for resistance in each of four environments tested. A second QTL, with a smaller effect on blackleg reaction, was present in one of four year/site assays. A Mendelian approach, using blackleg disease ratings for classifying DH lines as resistant or susceptible, allowed a single resistance locus to be mapped in the region of the major QTL. This strongly suggested the presence of a single major gene controlling adult plant resistance to blackleg in the resistant spring canola cv. Cresor. Mayerhofer et al. (1997) used BSA of DH lines derived from a cross between the Australian spring canola cultivars Shiralee and Maluka to identify 13 RAPD and two RFLP markers linked to blackleg resistance in this cross. QTLs for blackleg resistance were localised in the map from the segregating DH population and suggested the presence of a single major locus controlling resistance in the cultivar Shiralee. This confirmed previous results obtained from Mendelian genetic analyses. Furthermore, mapping data for the cultivar Maluka also supported a single locus model for resistance and indicated that the resistance genes from Shiralee and Maluka were either linked or possibly identical. In further work on blackleg resistance breeding and analysis, Plieske and Struss (2001) converted RFLP and RAPD markers

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linked with B-genome blackleg resistance into STS markers for use in marker-assisted backcrossing of the introgressed trait in B. napus, and Wretblad et al. (2003) isolated the cDNA sequence Lm1 from B. nigra, which gave enhanced resistance to Leptosphaeria maculans when overexpressed in oilseed rape. Genes involved in resistance to turnip yellows virus (TuYV) were mapped by Dreyer et al. (2001) in a DH population derived from a cross between a resynthesised rapeseed line (as donor for TuYV resistance) and the elite winter oilseed rape cv. Express. After screening 17 AFLP primer combinations (PstI/MseI and EcoRI/MseI), 143 AFLP markers were mapped to 20 LGs. QTLs for TuYV resistance were localised by composite interval mapping. A major QTL was found that explained some 50% of the phenotypic variation for resistance in this cross. Because no other factors displaying a significant effect on the expression of resistance could be identified, a simple mode of inheritance for TuYV resistance was suggested, and linked markers should hence be useful for MAS of resistant lines. Walsh et al. (1999) also mapped genes responsible for turnip mosaic virus (TuMV) resistance. In this case a dominant gene, designated TuRBO1, was localised using a set of DH lines from the population used by Sharpe et al. (1995) to develop a B. napus reference RFLP map. TuRBO1 was found to confer extreme resistance to some isolates of TuMV. The positioning of the gene on LG N6 of the B. napus A-genome indicated that the gene probably originated from B. rapa. The specificity of TnRBO1 was determined using a wide range of TuMV isolates. A second locus, TuRBO2, that appeared to control the degree of TuMV susceptibility in a quantitative manner was identified on the C-genome LG N14. By combining these resistance genes using marker-assisted breeding it should be possible to develop durable resistance to TuMV. A further genetic map was produced by Zhao and Meng (2003) specifically for QTL analysis of sclerotinia stem rot in partially resistant material from China. Quantitative loci involved in resistance to Sclerotinia sclerotiorum were detected in a population of 128 F2:3 families derived from a cross between a male sterility restorer line (H5200) and a partially resistant line (Ning RS-1). A total of 107 molecular markers including 72 RFLPs, 30 AFLPs, 3 SSRs and 2 RAPDs were used to construct a genetic linkage map with 23 LGs covering 1,625 cM. Resistance data from detached leaf inoculation at the seedling stage and in vivo stem inoculation at the mature plant stage were used to identify QTL involved in resistance. Of six de-

tected QTLs, three were associated with leaf resistance at the seedling stage and collectively accounted for 41% of the total phenotypic variation. Three further QTLs were found to correspond to the disease resistance at the mature plant stage, with additive epistatic interactions among the loci. The Major × Stellar cross described previously was used by Ferreira et al. (1995c) to localise a single dominant gene controlling resistance to white rust (Albugo candida) in B. napus, and the position of this locus relative to genes controlling A. candida resistance in oilseed B. rapa was later compared by Kole et al. (2002b). A minor resistance QTL was identified in B. rapa on the same homologous A-genome LG containing the dominant resistance gene in B. napus, however, at a different map position according to an alignment of the LGs using common loci. Furthermore, no resistance locus was identified in B. napus on the LG homologous to that carrying the gene controlling resistance in B. rapa. This indicates potential positions of additional resistance loci in both species that have not been mapped in the individual studies. Alignment of the respective maps to the physical map of the Arabidopsis genome was used to identify regions to target for comparative fine mapping.

2.6.5 Mapping QTLs for Abiotic Stress One of the most important forms of biotic stress for oilseed rape is cold stress in winter forms sown in autumn, which in particularly cold climates or harsh winters must possess a certain degree of freezing tolerance to survive the winter. Besides acclimatised and non-acclimatised freezing tolerance, winter survival may also be affected by genetic variation for other cold-regulated traits like vernalisationresponsive flowering time. Because biennial B. napus forms generally have a higher winter survival than annual forms, populations that segregate for resistance to cold stress can be generated by crosses between the two. Using the DH population produced by Ferreira et al. (1994) from the cross between the winter-hardy, freezing-tolerant winter rapeseed variety Major and the cold-sensitive spring canola cultivar Stellar, Teutonico et al. (1995) detected different QTLs for acclimatised and non-acclimatised freezing tolerance. Using the same immortal population, Kole et al. (2002a) localised QTLs for winter survival, freezing tolerance and flowering time and compared the map positions

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with corresponding loci in B. rapa. The B. napus population was evaluated in multiple winters, and 6 out of a total of 16 significant QTLs for winter survival were detected in more than one winter. Some QTLs for the different traits were found to co-localise within both B. napus and B. rapa, suggesting that some alleles causing greater acclimatised freezing tolerance and later flowering time also contributed to increased winter survival. Correspondence in the map positions of QTLs between species provided evidence for allelic variation at homologous loci in B. rapa and B. napus. Interestingly, many of the DH lines were found to exhibit better winter survival than the winter-hardy parent Major, suggesting that favorable alleles may also have been contributed by the cold-sensitive parent. This was supported by results from QTL mapping, in which alleles increasing winter survival were detected from both parents.

2.6.6 Towards an Integrated B. napus Genetic Map The most useful current technology for map integration is provided by SSR markers, which due to their highly polymorphic and robust nature and simple, relatively inexpensive analysis are a particularly valuable resource for map alignment among different crosses. The number of publicly available Brassica microsatellite primers is increasing as a result of publicly funded international initiatives (see www.brassica.info/ssr/SSRinfo.htm); however, in comparison to other important crop species relatively few markers are freely available to date, which has hindered the effective integration of genetic maps produced by different groups worldwide. In 2005 the primer sequences from a set of mapped B. napus SSR markers developed in the Celera AgGen Brassica Consortium involving 16 breeding companies from different countries were released to the public domain (Piquemal et al. 2005), and other groups are also planning the release of mapped SSR markers. The availablility in the public domain of robust, polymorphic, mapped SSR markers spanning the entire B. napus genome will without doubt assist the entire Brassica genetics community in consensus mapping and genome integration. Integration of consensus markers into existing and new genetic maps will considerably accelerate the progress of map and QTL alignment among diverse oilseed rape crosses and hence will ultimately play a pivotal role in

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the correlation of candidate gene loci with important QTLs. During recent discussions at the Joint Meeting of the 14th Crucifer Genetics Workshop and the 4th ISHS Symposium on Brassicas in Daejeon, South Korea, it was agreed that a common international nomenclature must be adopted for the naming of B. napus LGs and chromosomes, respectively. The suggested standard is the convention already used by many authors that numbers the ten A-genome chromosomes N1– N10 and the nine C-genome chromosomes N11–N19, respectively (e.g. Parkin et al. 1995; Sharpe et al. 1995; our Fig. 2). By integrating mapped SSR markers from reference maps being developed from publicly available populations of B. rapa, B. oleracea and B. napus into existing maps, it should ultimately be possible to integrate and align all B. napus maps and establish corresponding associations to newly developed physical maps and physical karyotypes. This iniative, when achieved, will vastly increase the ability to exchange and compare information among different oilseed rape mapping populations. For example, the comparison of QTLs for agronomically important traits will be feasible among different crosses, giving a considerably broader overview of the genetic control of quantitative traits than was available to date in individual populations. To this end, an international collaboration has been initiated to publish for the first time an integrated map that aligns the LGs of numerous genetic maps, developed from different crosses in different countries, and compares the locations and effects of previously localised QTLs in different genetic backgrounds (R. Delourme, INRA, France, pers. comm.). Of enormous benefit to this task will be the increasing availablility of public SSR markers derived for example from bioinformatics approaches. Based on BAC-end sequence data from the ongoing B. rapa sequencing program, or using the already available B. oleracea shotgun sequences from TIGR (see below), the computer software Sputnik (originally by Chris Abajian at Washington University, now available at http://sputnik.btk.fi/) enables the extraction of putative SSR sequences and design of primers for SSR analyses. Mapping of the resulting SSR loci will potentially provide a direct link between genetic and physical maps and a correspondence to annotated sequences in A. thaliana, and as the collection of Brassica genomic sequences continues to grow the availability of such markers will increase correspondingly.

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Among the growing resources being developed and distributed by the Multinational Brassica Genome Project (MBGP, see below) are genetic and physical maps for a B. rapa cross between inbred lines of the Chinese cabbage (B. rapa L. ssp. pekinensis) varieties Chiifu and Kenshin. A group of international researchers has begun the construction of a highdensity genetic map of this cross using AFLP, PCRRFLP, EST and SSR markers. In October 2004 the map comprised around 900 markers with an average distance of around 2 cM, and the aim was to saturate this map with up to 5,000 markers during 2005. Because the Chiifu inbred line is the genotype being used for the complete sequencing of the B. rapa genome, this map provides the opportunity for a direct alignment with the B. rapa physical map and annotation to the genome sequence. Further progress in anchoring of ESTs to the B. rapa genetic map, along with the anchoring of a growing collection of B. rapa genomic sequence tags to the Arabidopsis physical map, will in the foreseeable future provide a powerful new set of integrated data linking genetic and physical map information between the model and crop genomes. This information will be of enormous relevance to B. napus genome analysis, particularly when the markers on the B. rapa reference map are used to create similar high-densiity annotated maps in B. napus. One example of such an initiative is the multinational collaboration IMSORB (Integrated Marker System for Oilseed Rape Breeding; http://brassica.bbsrc.ac.uk/IMSORB/). In this collaboration between European and Chinese scientists, a reference B. napus genetic map is being established by mapping EST sequences in a DH population derived from a cross between the European oilseed rape cv. Tapidor and the Chinese cv. Ningyou 7. Genespecific hybridization probes from A. thaliana are being used to identify clones from a B. napus Tapidor BAC library representing around 1,100 regions of the genome of B. napus. To allow alignment of the B. napus BAC contigs with A- and C-genome physical maps currently under construction, corresponding gene sequences or PCR products are being obtained from the appropriate clones from A- and C-genome BAC libraries. By sequencing alleles from Ningyou 7 corresponding to the respective gene copy in Tapidor, SNP markers are to be identified that allow the genetic mapping of up to 500 genes in the segregating DH population. The resulting map will be aligned with existing B. napus maps using public-domain RFLP and SSR markers. Ultimately the intention is

to construct a publicly available database containing details of gene sequences, markers and polymorphisms, along with detailled genetic, physical and comparative mapping data.

2.7 Comparative Genomic Studies Due to the close phylogenetic relationship of crop Brassicas with Arabidopsis, for which the entire genome sequence has been available since 2000, it was anticipated that knowledge transfer for Brassica crop improvement would be straightforward. However, although the physiology and developmental biology of Arabidopsis and Brassica are very similar, the genomes of Brassica species are much more complex than that of A. thaliana, as a result of multiple rounds of polyploidy during their ancestry. For example, B. napus may contain over 100,000 genes, compared to only around 28,000 in A. thaliana. This makes the identification of orthology relationships of genes extremely difficult, and the presence in Brassica of multiple homologues of each gene in A. thaliana provides ample opportunity for divergence of gene function. Genome colinearity, or conservation of marker order, has been widely investigated in comparative genomic studies between the model crucifer A thaliana, which possesses the most extensively studied higher plant genome, and the closely related Brassica crops. Comparative genetic mapping experiments have established colinearity of genomes for species of the Brassicaceae. A plethora of physical mapping and sequencing experiments have revealed considerable conservation of gene sequence and order between Arabidopsis and Brassica, although genome rearrangements are often considerably more complex than they appear at first glance. Physical genome maps and sequence data from A. thaliana together with comparative analysis of its syntenic relationships to Brassica genomes provide potentially powerful tools for genome analysis and gene discovery in oilseed rape, the closest major crop relative to the model plant. Generally 80 to 90% homology is found between the exons of putative orthologous genes in Arabidopsis and Brassica (Schmidt 2002), meaning that knowledge from Arabidopsis is highly relevant for gene isolation and characterization in Brassica crops. In comparative studies of genome

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regions flanking known genes, extensive colinearity between Arabidopsis and Brassica genome segments has been observed on a microsyntenic level. However, minor deletions, insertions and translocations are relatively common in regions surrounding Brassica orthologues of Arabidopsis genes. For example, Quiros et al. (2001) described small-scale colinearity between the region of A. thaliana chromosome 4 containing the ABI1-Rps2-Ck1 gene complex and a homoeologous segment of B. oleracea chromosome 4. Although almost complete microsynteny was observed, the B. oleracea regions contained an extra gene with homology to genes located on Arabidopsis chromosomes 2 and 5. In other words, even regions with well-preserved colinearity on a microsyntenic scale can be interrupted by translocations. In fact, one or several homologues of Arabidopsis genes may be missing from any particular triplicated region in Brassica (Schmidt 2002). The different orthologous regions in Brassica are therefore often comprised of a different gene repertoire. Nevertheless, due to the large-scale synteny over long chromosome stretches, it is still often feasible to utilise sequence information from markers flanking genes or QTLs of interest in Brassica crops, to identify possible candidate genes from the corresponding chromosome regions in Arabidopsis. For example, different homoeologous regions in B. rapa and B. napus, which contain various QTLs influencing flowering time, each show significant colinearity to Arabidopsis chromosome sections containing a number of genes relevant to flowering time (Lagercrantz et al. 1996; Osborn et al. 1997; Kole et al. 2001). The use of Arabidopsis as a tool in marker development, map-based gene cloning and candidate gene identification in Brassica crop species is complicated by the complex arrangement of the (ancestral) polyploid Brassica genomes. As the genome relationships between Arabidopsis and Brassica have been unravelled, however (e.g. Paterson et al. 2000; Schmidt et al. 2001, Parkin et al. 2005), the model plant has developed into the most important resource for gene isolation and characterization in Brassica crops. In recent years it has become increasingly feasible to integrate genetic mapping with a candidate gene approach (Pflieger et al. 2001) using Arabidopsis resources and genome tools to identify gene loci involved in both simple and complex traits. Fourmann et al. (2002) and Chalhoub et al. (2003) described an effective method to use functional PCR markers for physical mapping of A. thaliana gene loci in B. napus. This approach

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is based on so-called physical functional markers (PFMs) for candidate genes and centers on the observation that most A. thaliana gene loci are present as multiple copies in polyploid Brassica genomes. The availability of detailed Arabidopsis sequence information enables the use of syntenic regions surrounding candidate genes for better characterization of orthologous locus copies. For example, BAC clones identified by gene-specific filter hybridization or PCR can be separated into locus-specific contigs through the presence or absence of PCR markers that are amplified using specific primers within or flanking the gene sequence. These markers not only enable the variable intron-exon structure of the orthologous gene copies to be better characterized, but can also be used as a basis for development of locus-specific SNPs. The latter are a promising basis for allele-trait association studies of relevant candidate genes and simultaneously provide a basis for integration of the loci in physical functional genetic maps. Cavell et al. (1998) assessed genomic colinearity to B. napus chromosome regions over a 7.5-Mbp region of the long arm of A. thaliana chromosome 4, equivalent to 30 cM. Estimates of copy number indicated that sequences present in a single copy in the haploid genome of A. thaliana were present in two to eight copies in the haploid genome of B. napus, while sequences present in multiple copies in A. thaliana were present in over ten copies in B. napus. Genetic mapping in B. napus of DNA markers derived from a segment of A. thaliana chromosome 4 revealed duplicated homologous segments in the B. napus genome. Physical mapping in A. thaliana of Brassica homologues derived from these regions confirmed the identity of six duplicated segments with substantial homology to the 7.5-Mbp region of chromosome 4 in A. thaliana. These six duplicated Brassica regions (on average 22 cM in length) were found to be colinear, except that two of the six copies contained the same large internal inversion. Ryder et al. (2001) investigated colinearity of marker order between two different genome regions of B. oleracea and homologous regions of A. thaliana. Although widespread replication of marker loci was observed in both A. thaliana and B. oleracea, a combination of RFLP, CAPS and SSR markers mapped in B. oleracea enabled analysis and identification of mediumscale chromosomal organisation and rearrangements. Probes were hybridized onto BAC contigs representing the whole A. thaliana genome. A total of 20 marker loci were sampled from throughout the shortest B. ol-

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eracea LG and 21 from a 30-cM section of the longest LG which contained locus duplications. Locus order was conserved between a putative duplicated region of 10.5 cM and a discrete region comprising 25 cM of A. thaliana chromosome 1. This was supported by evidence from seven paralogous loci, three of which were duplicated in a 30.6-cM region of the largest B. oleracea LG. The pattern of locus order for the remainder of this LG and the sampled section of the shortest LG was more complex when compared with the A. thaliana genome. Although there was some conservation of locus order in this case, this was superimposed upon a complex pattern of additional loci which were replicated in both A. thaliana and B. oleracea. Parkin et al. (2002) investigated chromosomal colinearity between chromosome 5 of A. thaliana and the genome of B. napus using in silico sequence homology to identify conserved loci between the two species. An 8-Mb region of chromosome 5 was found in six highly conserved copies in the B. napus genome. A single inversion appeared to be the predominant rearrangement that had separated the two lineages leading to the formation of Arabidopsis chromosome 5 and the homologous chromosome regions in B. napus. The observed results were explained by the putative fusion of three ancestral genomes with strong similarities to modern-day Arabidopsis, which subsequently led to the constituent diploid B. napus genomes. This result supports the hypothesis that the diploid Brassica genomes evolved from a common hexaploid ancestor. Alignment of the genetic linkage map of B. napus with the Arabidopsis genomic sequence indicated that for specific regions a genetic distance of 1 cM in B. napus was equivalent to only 285 kb of Arabidopsis DNA sequence. This underlines the high suitability of Arabidopsis for marker development, map-based gene cloning, and candidate gene identification in oilseed rape and other Brassica crops. Sillito et al. (2000) mapped Arabidopsis ESTs with homology to cloned plant disease resistance genes in A. thaliana and B. napus in order to identify candidate resistance gene loci and investigate intergenomic colinearity. A total of 103 resistance gene EST loci were localised on a B. napus genetic map, 48 of which could also be locted on the Columbia × Landsberg A. thaliana map. The mapped loci identified colinear regions between Arabidopsis and Brassica that had been observed in previous comparative mapping studies, and the detection of syntenic genomic regions indicated

that there was no apparent rapid divergence of the identified genomic regions containing the resistance EST loci. Genetic maps of B. napus generally show almost complete colinearity (e.g. Fig. 2; also Lydiate et al. 1993; Lombard and Delourme 2001), and it has been found that the A- and C-genome LGs in B. napus have remained essentially unchanged in comparison to the diploid genomes (Parkin et al. 1995). Because of the high level of similarity between the A and C genomes this means that the diploid species provide an extremely useful tool for analyses of colinearity and duplication between Arabidopsis and the complex amphidiploid genome of B. napus. Comparison of B. rapa with B. oleracea and B. napus supports the close evolutionary relationship between the two diploids but indicates that deletions and insertions may have occurred after divergence of the two species (Hoenecke and Chyi 1991). The genome of synthetic B. napus is essentially unrearranged with respect to its B. oleracea and B. rapa progenitors (Lydiate et al. 1993; Parkin et al. 1995; Sharpe et al. 1995), although the evolution of wild B. napus may have been been accompanied by more complex rearrangements (Cheung and Landry 1996). Extensive segmental duplications are found in both diploid and amphidiploid maps, supporting the hypothesis that diploid Brassica species are derived from an ancestor with a lower original basic chromosome number. However, no duplications of whole LGs have been found. Homoeologues of B. oleracea are differentiated from one another by a minimum of 22 chromosomal rearrangements (Lan et al. 2000; Paterson et al. 2000). Figure 3 (from Paterson et al. 2000) gives a detailed and impressive view of homology and duplications between the genomes of Arabidopsis and B. oleracea revealed by comparative RFLP linkage mapping of B. oleracea and alignment of loci to the map of A. thaliana. Recently Parkin et al. (2005) published a comprehensive comparative map of the A. thaliana and B. napus genomes based on Brassica RFLP markers, whereby the five Arabidopsis chromosomes could be allocated via macrosyntenic comparisons to a minimum of 22 conserved blocks that are duplicated and rearranged throughout the B. napus genome. Although such representations emphasise the underlying complexity of the genome rearrangements between Brassica and Arabidopsis; on the other hand they also reveal the extensive colinearity and hence the great potential the model genome offers for comparative genetic analyses of oilseed rape.

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Fig. 3. Composite RFLP linkage map of Brassica oleracea and its alignment to the map of Arabidopsis thaliana. Filled circles next to loci: homoeologous Brassica loci (chromosomes 1 to 9, near right) or homologous Arabidopsis loci (chromosomes 1 to 5, far right) detected by the same probe. When all the circles are open, no polymorphism was detected for homoeologous (Brassica) or homologous (Arabidopsis) loci. An R next to the probe name indicates that the probe hybridizes to a repetitive DNA sequence in Arabidopsis. Specific colors were assigned to each homoeologous (Brassica) and homologous (Arabidopsis) chromosome. Markers that appear to represent duplication of Brassica chromatin or orthology between Brassica and Arabidopsis (based on criteria described in Lan et al. 2000) were connected by colored columns. Open columns indicate possible triplicated (Brassica) or duplicated (Arabidopsis) regions. Vertical axis: centimorgans. This image, modified from Lan et al. (2000), was kindly provided by Andrew Paterson, University of Georgia, Athens, GA, USA and is used with permission from Cold Spring Harbor Laboratory Press

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Fig. 3. (continued)

2.8 Physical Mapping and Genomics Tools 2.8.1 Physical Mapping The genomes of Brassica species, although four to ten times larger than that of A. thaliana, are still of a manageable size for application of genomic technologies, and in recent years increasing emphasis has been placed on phyiscal mapping of B. napus and particularly its diploid progenitor genomes. Physical maps are currently under construction for the Brassica A genome in Korea and for both the A and C genomes in the United Kingdom, and partial phys-

ical mapping of the genome of B. napus is being conducted by groups in Canada and in the EU. Although such physical maps will be of great value for the identification of specific regions in the genomes of these important crops, they will not permit the detailed analysis of the entire Brassica genome, the preparation of microarrays to analyse the transcriptome, or the efficient design of markers associated with the sequences of specific genes for use in breeding programs. To achieve these objectives, the complete sequence of at least one of the B. napus diploid progenitor genomes is required. Due to the extensive chromosomal and sequence colinearity of the A and C genomes, the sequence of one of these genomes will represent a powerful genomic resource for physical genome analysis in B. oleracea, B. rapa and B. napus alike.

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Numerous countries have established national plant genome programs during the past decade with specific oilseed rape genome research projects. In Germany, for example, the program Genome Analysis in the Biological System Plant (GABI; http://www.gabi.de) has to date funded two consortia investigating different aspects of commercially relevant winter oilseed rape genomics. These efforts have included, for example, the development of genetic maps for analysis of different seed quality traits and a large-insert BAC library from the German winter rapeseed cv. Express, and are continuing with the development and testing of SNP markers for association studies. The French Génoplánte program (http://www.genoplante.fr) has been particularly active in the development of functional genomics tools for oilseed rape, placing particular emphasis on the use of Arabidopsis genome sequence data to maximally exploit a BAC library from the French dwarf rapeseed variety Darmor-Bzh. The approach taken is to develop physical functional markers (Fourmann et al. 2002) from a large set of A. thaliana coding sequences to physically map oilseed rape ESTs on the oilseed rape BAC library. All A. thaliana coding sequences or oilseed rape ESTs presumed to correspond to a biologically or agronomically important function in oilseed rape were selected as entry points for this approach. Based on the data gathered, functional physical and genetic databases are being developed which are linked at high resolution to the genome sequence of A. thaliana. These resources will facilitate rapid gene and QTL cloning in oilseed rape using both forward and reverse genetics approaches. In Canada, the project Enhancing Canola Through Genomics funded by Genome Prairie (http://www.genomeprairie.ca/canola) was founded in 2003 with the aim of developing integrated functional genomics tools for B. napus. At the time this chapter was prepared 27 embryo and seed-specific cDNA libraries had been constructed and used for the identification of more than 10,000 Brassica unigenes from over 60,000 ESTs related to seed development. Particular emphasis is being placed on analyses of gene expression during seed development. As part of this work, Dong et al. (2004) identified a set of highly expressed genes from cDNAs isolated from canola seeds 15 d after pollination (DAP) and analysed their differential expression during seed development. From 104 differentially expressed sequence tags (ESTs) 54

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unique genes could be identified, of which 33 had putative homologues in Arabidopsis or B. napus. These genes encoded diverse proteins ranging from proteins of unknown function to metabolic enzymes and proteins associated with cell structure and development. Twenty-five genes were seed-specific, and 11 of these started to express as early as 5 or 10 DAP. A large set of public EST sequences is also being generated in a joint initiative between the Agriculture and Agri-Food Canada (AAFC) Research Centre of Saskatoon and HRI Warwick, UK. The aim is to provide 30,000 EST sequences from the B. oleracea line A12DHd, for which high-quality genetic, physical and karyotype maps are already available. The ESTs resulting from this project will provide an excellent resource for annotation of the TIGR B. oleracea shotgun sequence (see below) and for improved navigation between B. napus and A. thaliana physical maps. Tools for annotation between the Arabidopsis and Brassica genomes have also been developed as part of the Brassica/Arabidopsis Genomics Initiative (BAGI) at AAFC Saskatoon. One interesting accessory provided by BAGI is the Brassica/Arabidopsis Comparative Genome Browser, with which B. napus ESTs developed by AAFC (3 and 5 sequences for 60,000 cDNAs) can be visualised relative to the Arabidopsis genome sequence as annotated by TIGR. The B. napus ESTs are not yet available in the public version of the browser; however, it is hoped that at least a subset will be made public in coming years. The browser allows visualisation of Brassica DNA sequences relative to homologous sequences in the Arabidopsis genome, allowing provisional physical mapping of ESTs through colinearity, access to the corresponding Arabidopsis gene annotation and clustering of related members of Brassica multigene families. The construction of the database allows users to view all ESTs with similarity to any part of the Arabidopsis genome and see how the different ESTs align with Arabidopsis genes. Furthermore it is possible to search for a specific gene or EST, to access gene annotations and to identify duplicate genes in Arabidopsis. At the time this chapter went to press negotiations were well advanced for the generation of a new Affymetrix gene chip comprising more than 100,000 Brassica ESTs from public sources and a large number of ESTs from private sources. Release of the Brassica chip was expected in September 2006.

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2.8.2 Public Genome Resources: The Multinational Brassica Genome Project Recently the MBGP was established by international Brassica researchers to coordinate Brassica genomics activities and pool resources to achieve common goals. The primary aim of this initiative is the provision of freely available genetics resources for Brassica genome analysis, including mapping populations, markers, genomic libraries, ESTs and genomic sequences. The first publicly available physical maps of the B. rapa and B. oleracea genomes were estabished by researchers affiliated with the BBSRC in the United Kingdom. BAC libraries were constructed from B. oleracea (JBo and BoB) and B. rapa (JBr) at the John Innes Centre in Norwich and Horticulture Research International in Wellesbourne, UK. Fingerprinting and contig assembly were achieved using computer software developed at the Sanger Institute in Cambridge, UK, and physical maps of the A and C genomes were generated by fingerprinting around 35,000 genomic clones each from the two genomes and integrating these with the genome sequence of A. thaliana by hybridization with selected genomic sequence tags (GSTs). This enabled coverage of some 462 Mb of the B. rapa genome with 2041 BAC contigs, along with the construction of 2433 B. oleracea contigs. The physical map databases can be freely accessed via the Internet (http://brassica.bbsrc.ac.uk/IGF), including a browsing facility and an online order form for BAC clones. Query forms are provided to enable users to easily interrogate the database, obtain the hybridization results for each probe and link to any contigs that it might anchor. As a participant in the MBGP, the Plant Biotechnology Centre (PBC) at Latrobe University in Bundoora, Australia is involved in developing a set of bioinformatic tools for Brassica functional genomics (http://hornbill.cspp.latrobe.edu.au/brassica.html). Among other things applications have been developed for the rapid discovery of SSR and SNP markers in Brassica species using data analysis of accumulated sequence data for primer design (Barker et al. 2003), and the data have been incorporated within an integrated gene annotation database, Brassica ASTRA, that includes modules for the gene ontology (GO) annotation of Brassica sequences and comparative mapping with A. thaliana. Further ongoing developments include the integration of gene expression data from microarray analyses along with

molecular genetic and allele diversity data, which will be interrogated through an Ensembl database. Of particular interest in terms of the whole-genome sequencing of B. rapa is a comparative BAC viewer for the identification of Brassica BACs which are syntenic to regions of the Arabidopsis genome. The application of this tool has the potential to enable rapid identification of candidate Brassica genes based on microsynteny to corresponding regions between the Brassica and Arabidopsis genomes. A leading role in the MGBP is being played by scientists from the Korean National Institute of Agricultural Biotechnology (NIAB) and the Chungnam National University in Daejeon, South Korea. In particular this has involved the provision of BAC libraries from Chinese cabbage (B. rapa ssp. pekinensis), which together with high-density filters are freely available for use in the public domain, and an active participation in the ongoing sequencing and physical mapping activities. At the time of writing, a library of more than 10,000 cDNAs had already been developed. More than 1000 molecular markers have been localised on a newly established B. rapa reference map, with alignment to the A-genome B. napus chromosomes. Integration of this map with the physical and karyotype maps will provide a similarly powerful resource for the B. napus A genome to that being developed in the abovementioned activities for the C genome of B. oleracea. Together this rapid accumulation of functional and physical genome orientation data and tools will revolutionise the study of the B. napus genome and enable massive strides in the utilisation of genome and map information for oilseed rape breeding.

2.8.3 Genome Sequencing in B. oleracea and B. rapa In recent years considerable progress has also been made in the endeavour to sequence the complete genome of the diploid B. napus progrenitor species. A total of 454,274 B. oleracea genomic sequence reads, with an average length of around 650 bp, have been generated from a joint whole-genome shotgunsequencing initiative between The Institute for Genomic Research, Rockville, MD, USA (TIGR) and Cold Spring Harbor Laboratory, NY, USA, funded by the United States National Science Foundation. The reads (available at http://www.tigr.org) cover some 295 Mbp (about 0.45x) of the B. oleracea genome, and around a quarter have a match to known proteins. Interest-

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ingly, however, only around 40% of the sequences appear to have a high-quality match to the genome sequence of Arabidopis, although some 90% of the Arabidopsis proteome was represented in the B. oleracea sequence reads. As expected, matches to Arabidopsis sequences are very good in exons and reduced in introns, whereby the conserved regions generally extend into the introns somewhat (i.e. intron-exon splice sites are also converved). According to Town et al. (2006), the B. oleracea sequence data not only provide an exciting new resouce for analysis of Brassica genomes, but they will also be extremely useful for improved gene annotation in Arabidopsis. First estimates using the B. oleracea sequence data to reprogram A. thaliana gene prediction models appeared to identify some 2,000 to 5,000 novel genes in the model genome. Very rough first estimates of the gene number in B. oleracea predicted a minimum number of around 41,000 genes based on the sequences available. One important outcome of the MBGP initiative has been the establishment of a project aimed at sequencing the complete genome of B. rapa as a basic DNA sequence resource for Brassica A- and C-genome crops. The Brassica genome sequencing project aims initially to generate fully oriented and ordered Phase 2 sequence (meaning that it will contain some small sequence gaps and low-quality sequences) using BAC clones covering the 500-Mb genome of B. rapa ssp. pekinensis (Chinese cabbage). The genome sequence will be anchored to a reference genetic map containing some 1,000 molecular markers. Scientists requiring finished sequence from a specific region will be able to complete it themselves by accessing trace files that will be archived in an agreed format at TIGR (http://www.tigr.org), MIPS (Munich Information Center for Protein Sequences, Germany; http://mips.gsf.de) and NIAB (National Institute of Agricultural Biotechnology, Suweon, South Korea; http://www.niab.go.kr/homepage/english). The systematic annotation of the genome sequence will be performed with the help of the annotated A. thaliana genome sequence. The B. rapa sequencing is being conducted by an international consortium using common resources. Integration, annotation and public serving of the data will be coordinated by TIGR, MIPS and NIAB. The sequencing program has been divided into three stages. Initially, an online Brassica information resource was established (http://www.brassica.info) along with an online portal for the MBGP (http://brassica.bbsrc.ac.uk/

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and http://www.niab.go.kr). Communal databases have been constructed for the deposition of genetic (http://ukcrop.net/perl/ace/search/BrassicaDB) and physical mapping data (http://brassica.bbsrc.ac.uk/ IGF/index.htm). Reference genome libraries have been produced for the B. rapa variety Chiifu (Chinese cabbage, B. rapa ssp. pekinensis). The KBrH and KBrB libraries, each consisting of 144 × 384 well plates, were generated using HindIII (KBrH) and BamHI (KBrB) digested genomic DNA, respectively. A total of 110,592 clones are available, providing 20-fold redundant genome coverage. International distribution centers for the two libraries have been set up at NIAB in South Korea and at the John Innes Centre, UK. Reference DH and RIL populations for low- and high-resolution genetic mapping of the Chinese cabbage variety Chiifu are under construction, and high-quality genetic maps for each population are being constructed using publicly available markers. International distribution centers for the reference populations are being established at Chungnam National University, Daejeon, South Korea, and at Horticulture Research International, Wellesbourne, UK. The full set of 110,592 BAC clones in the reference libraries are currently being end-sequenced, with data being deposited in searchable public databases at TIGR, MIPS and NIAB. The aim of the project is to deliver the flanking sequences of all available BACs during 2005. Using the Brassica ASTRA computational tool developed by the Plant Biotechnology Centre of Latrobe University in Australia, the end sequences of the B. rapa BACs are being comparatively mapped onto the Arabidopsis genome (Love et al. 2005; our Fig. 4). Brassica BACs are electronically mapped onto A. thaliana by comparing the BAC end sequences with the Arabidopsis genome sequence, followed by filtering for matches in an inverse orientation within a distance of less than 500,000 bp. The publicly available C-genome shotgun sequences have also been compared with the Arabidopsis genome sequence. These sequences were aligned to the Arabidopsis genome using only sequences with the highest BLAST hit in order to avoid duplicated genome regions (Love et al. 2005). As more A-genome sequence data become available the results of this analysis will give powerful new data regarding the comparative structure of the B. rapa, B. oleracea and A. thaliana genomes and will also give important data on the distribution of A-genome BACs throughout the genome, which is vital for the

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Fig. 4. Physical comparison of B. rapa BAC-end sequences (red) and B. oleracea shotgun sequences (blue) by alignment to the five chromosomes of Arabidopsis (green). B. oleracea sequences, available at http://www.tigr.org, represent around 295 Mbp of B. oleracea genome and cover a considerable proportion of the A. thaliana genome. Brassica rapa BAC end sequences are being generated as part of the first phase of a multinational whole-genome B. rapa sequencing initiative. This image was kindly provided by Christopher Love and Dave Edwards from the Plant Biotechnology Centre at Latrobe University, Australia. For further details on the Brassica ASTRA database tools used to create this alignment see Love et al. (2005)

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selecton of seeding BACs for the B. rapa genome sequencing. The continuing accumulation of such data will lead to completion of the planned second phase for the B. rapa sequencing project, which foresees an unambiguous genetic anchoring of around 1,000 seed BACs to the Arabidopsis genome sequence using both end sequences, accompanied by the anchoring to one of the the B. rapa reference genetic maps using single-locus SSR, SNP or InDel markers. In the third and final stage, the actual genome sequencing will be performed, with division on a chromosome-bychromosome basis as in other multinational genomesequencing programs. Participants will begin by sequencing all seed BACs on their chromosome or defined subregion of a chromosome. All sequence data will be submitted to TIGR, MIPS and NIAB as soon as Phase 2 quality is achieved.

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dardised nomenclature. As these deficits are rectified it can be expected that the coming decade will see enormous advances in genome mapping and molecular breeding in oilseed rape. Coupled with technological developments for high-throughput genotyping and whole-genome sequencing, the focus of genetic research will ultimately advance from analysis of gene functions underlying traits of interest to a broader investigation of complete biosynthesis pathways underlying complex metabolic expression patterns. The increasing availability and accessibility of Brassica genomics tools and data, along with the close relationship between B. napus and the model plant Arabidopsis, make oilseed rape one of the major crop plants set to benefit most from the developing technological platforms for functional genomics, proteomics and metabolomics.

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2.9 Outlook In recent years enormous progress has been achieved in the international Brassica research community towards the accumulation of public genetic markers, mapping populations, genetic and physical maps, EST collections and genomic sequence data for B. rapa, B. oleracea and B. napus. Together these resources, combined with the available information from the model crucifer Arabidopsis and the increasing data from comparative mapping studies, are paving the way for increasingly detailled annotation and navigation between the Arabidopsis and Brassica genomes. This facility, based on knowledge from the model plant and from the Brassica diploid genomes, will in the coming decade undoubtedly continue to accelerate our ever-increasing understanding of the genetic functionality underlying complex genetic traits in oilseed rape. Furthermore, large-scale expression analyses, for example based on chip technologies, will also contribute to a better understanding of the genes involved in agronomically relevant traits and lead to new marker technologies for exploitation of allelic variation in oilseed rape breeding. One area where international B. napus genome research has been somewhat slow in comparison to other major crop species, for example barley, is in the availability of public marker and EST collections and the integration of genetic and physical maps via stan-

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Weber S, Ünker F, Friedt W (2005) Improved doubled haploid production protocol for Brassica napus using microspore colchicine treatment in vitro and ploidy determination by flow cytometry. Plant Breed 124:511–513 Westman AL, Kresovich S (1998) The potential for cross-taxa simple-sequence repeat (SSR) amplification between Arabidopsis thaliana L. and crop brassicas. Theor Appl Genet 96:272–281 Whetten RW, Mackay JJ, Sederoff R (1998) Recent advances in understanding lignin biosynthesis. Annu. Rev. Plant Physiol. Plant Mol Biol 49:585–609 Wiberg E, Banas A, Stymne S (1997) Fatty acid distribution and lipid metabolism in developing seeds in laurate-producing rape (Brassica napus L.). Planta 203:341–348 Williams JGK, Kubelik AR, Livak KJ, Rafalski JA, Tingey SV (1990) DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Res 18:6531–6535 Winter H, Snowdon RJ, Diestel A, Gärtig S, Sacristán MD (1999) Untersuchungen zum Transfer von Resistenzen gegen Leptosphaeria maculans aus Wildcruciferen in den Raps. Vortr Pflanzenzüchtg 46:340–342 Winter H, Diestel A, Gärtig S, Krone N, Sterenberg K, Sacristan MD (2003) Transfer of new blackleg resistances into oilseed rape. In: Proc 11th Int Rapeseed Congress, Copenhagen, 1:19–21 Wong R, Patel JD, Grant I, Parker J, Charne D, Elhalwagy M, Sys E (1991) Development of high oleic acid canola lines through seed mutagenesis. In: Proc 8th Int Rapeseed Congress, Saskatoon, Sasketchewan, Canada, 1:207–212 Wretblad S, Bohman S, Dixelius C (2003) Overexpression of a Brassica nigra cDNA gives enhanced resistance to Leptosphaeria maculans in B. napus. Mol Plant Microbe Interact 16:477–484 Xiao D, Liu HL (1982) Correlation analysis of seed color and seed oil in Brassica napus L. Acta Agron Sin 8:24–27 Yoder JI, Goldsbrough AP (1994) Transformation systems for generating marker-free transgenic plants. Biotechnology 12:263–267 Zarhloul MK, Lühs W, Ehemann AS, Hausmann L, Friedt W, Töpfer R (1999) Molecular Approaches to the biosynthesis of medium-chain triacylglycerols in Brassica napus. In: Proc 10th Int Rapeseed Congress, Canberra, Australia. http://www.regional.org.au/au/gcirc/4/445.htm Zhao JW, Meng JL (2003) Genetic analysis of loci associated with partial resistance to Sclerotinia sclerotiorum in rapeseed (Brassica napus L.). Theor Appl Genet 106:759–764 Zhou YM, Liu HL (1987) Studies on the inheritance of major fatty acid composition in the oil of rapeseed (Brassica napus L.). Acta Agron Sin 13:1–10 Zietkiewicz E, Rafalski A, Labuda D (1994) Genome fingerprinting by simple sequence repeat (SSR)-anchored polymerase chain reaction amplification. Genomics 20:176–183

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3 Peanut S.L. Dwivedi1 , D.J. Bertioli2 , J.H. Crouch1 , J.F. Valls3 , H.D. Upadhyaya1 , A. Fávero3 , M. Moretzsohn3 , and A.H. Paterson4 1

2 3

4

International Crops Research Institute for the Semi Arid Tropics (ICRISAT), ICRISAT Patancheru PO, 502324, AP, India e-mail: [email protected] Universidade Catolica de Brasilia, Pos Graduacao Campus II, SGAN 916, DF CEP 70.790-160, Brasilia, Brazil EMBRAPA Recursos Genéticos e Biotecnologia (CENARGEN), Parque Estação Biológica-pqEB, Final Av. W5 Norte, CEP: 70770-900, Brasília-DF, Brazil Distinguished Research Professor and Director, Plant Genome Mapping Laboratory, University of Georgia, Rm. 228, 111 Riverbend Road, GA 30602Athens, USA

3.1 Introduction 3.1.1 Origin The legume genus Arachis is of South American origin and contains about 80 known species with natural distributions restricted to Brazil, Bolivia, Paraguay, Argentina, and Uruguay (Valls and Simpson 1994). These wild Arachis species are divided into nine taxonomical sections, based upon morphology and sexual compatibilities. A. hypogaea, the cultivated tetraploid peanut (also known as groundnut), is found in the section Arachis, along with some 25 wild diploid species. This section also contains another tetraploid species, A. monticola, which readily hybridizes with A. hypogaea, is almost indistinguishable using DNA markers, and may best be considered as conspecific. It seems that the origin of A. hypogaea was through the hybridization of two diploid species with distinct genomes giving rise to a sterile hybrid. A spontaneous duplication of chromosomes restored fertility, but left the plant reproductively isolated from its wild relatives (Kochert et al. 1991; Jung et al. 2003; Seijo et al. 2004). It is most likely that these events occurred once or only a few times. There is doubt about exactly which diploid species were involved and as to where these events occurred. However, it seems logical that the diploid species involved were probably brought together by human action through the cultivation of species that have distinct and separate natural distributions. In support of this, archaeological finds of shells of fruits closely resembling A. duranensis Krapov. and W.C. Gregory; A. magna Krapov., W.C. Gregory and C.E.

Simpson; A. ipaensis Krapov. and W.C. Gregory; and A. monticolaKrapov. and Rigoni were excavated near Casma in coastal Peru. These species today are considered entirely wild but were apparently cultivated in the remote past. It is not necessarily the case that the origin of A. hypogaea was in this region; indeed, it seems that this would be more likely to have happened in the Eastern slopes of the Andes where A. monticola exists today in a wild state. The climate in this region is moister, not as good for the preservation of archaeological remains, but more favorable for plant growth, and a better environment for the wild bees that would have done the necessary initial hybridization (Simpson et al. 2001). 3.1.2 Botanical Types and Distribution A. hypogaea is divided into two subspecies, hypogaea and fastigiata, and six botanical varieties (Krapovickas and Gregory 1994). The subsp. hypogaea var. hypogaea has a long cycle, no flowers on the central stem, and regularly alternating vegetative and reproductive side branches. It is exemplified by the Virginia types that are widely present along the tributaries of the right margin of the Amazon Basin in Brazil and Bolivia. Also classified within subsp. hypogaea, but with more hirsute leaflets and even longer cycle, is the variety hirsuta Kohlër. Nowadays this variety is concentrated in the coastal regions of Peru, from where it extends to Central America and Mexico, Asia, and Madagascar. The variability of this variety found in the Old World even suggests the possibility of pre-Colombian contacts (Simpson et al. 2001).

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The subspecies fastigiata Waldron has a shorter cycle, flowers on the central stem, and reproductive and vegetative shoots distributed in a disorganized way. This subspecies includes four varieties. The variety vulgaris C. Harz has its distribution/spread along the basin of the river Uruguay, usually the fruits are two seeded, and correspond to the agricultural type Spanish. The variety fastigiata has fruits with more than two seeds and a smooth pericarp. This variety corresponds to the agricultural type Valencia; centers of diversity are in Paraguay and in central and northeastern Brazil extending to Peru. The other two varieties, aequatoriana Krapov. and W. C. Gregory (Equador and North of Peru) and peruviana Krapov. and W.C. Gregory (Peru, northeast Bolivia, and the Brazilian state of Acre), have fruits with more than two seeds, heavy reticulation of the pericarp, and very restricted distributions (Krapovickas and Gregory 1994). In addition, Brazilian germplasm includes material that is difficult to fit into the above classification. Particularly notable in this respect is material from the Xingu river basin (Freitas and Valls 2001). Modern cultivars generally have a narrow genetic base (Isleib et al. 2001). Nevertheless they are generally classified into the agricultural types Spanish, Valencia, or Virginia. These cultivars have been widely used as representatives of the botanical varieties in analyses of genetic variability. These cultivars often have their origin in more than one variety or subspecies and are not correct taxonomic representatives; their use as such may lead to incorrect conclusions. 3.1.3 Crop Production and Uses Peanut is the major oilseed crop in the world, grown on 26 million ha producing nearly 36 million tons annually. Although the global average productivity is low (1.35 t ha−1 ), many countries achieve much higher levels of productivity, including the USA (3.54 t ha−1 ) and China (2.62 t ha−1 ) (FAO 2003). Developing countries contribute about 94% of the world peanut production, grown mostly under rainfed conditions (predominantly in Asia and Africa) (Table 1). Analysis of peanut productivity from the 1960s to the 1990s reveals interesting profiles across regions and in specific countries. While the average global

peanut productivity has steadily increased during that period, from 8% in the 1970s (average yield 0.85 t ha−1 in the 1960s) to 18% each in the 1980s (average yield 0.92 t ha−1 in the 1970s) and 1990s (average yield 1.09 t ha−1 in the 1980s), changes in productivity have been highly variable across different regions. For example, peanut yield in Asia increased by 14% in the 1970s (average yield 0.82 t ha−1 in the 1960s), 20%in the 1980s (average yield 0.93 t ha−1 in the 1970s), and 32% in the 1990s (average yield 1.12 t ha−1 in the 1980s), while in Africa productivity has largely stagnated or even declined during certain periods. In north Central America, tremendous yield increases (45%) were achieved during the 1970s (average yield 1.62 t ha−1 in the 1960s) but thereafter only marginal increases were seen as the importance of the crop in this region began to decline. A similar trend was seen in the USA with 54% increase in peanut productivity during the 1970s (average yield 1.73 t ha−1 in the 1960s) but little increase thereafter due to emphasis on stabilizing the yield by incorporating resistance/tolerance to pests and diseases and improving seed quality. In contrast, peanut yield remained stagnated during the 1960s and 1970s in South America but then increased by 28% in the 1980s (average yield 1.24 t ha−1 in the 1970s) and 16% in 1990s (average yield 1.58 t ha−1 in the 1980s). The peanut yield in Argentina and Brazil remained stagnated in the 1960s and 1970s but registered a 89% increase in Argentina (average yield 1.16 t ha−1 in the 1970s) and a 31%increase in Brazil (average yield 1.32 t ha−1 in the 1970s) in the 1980s and remained stagnated at this level in the 1990s. The greatest sustained improvement has been seen in China with a 19%increase during the 1970s (average yield 1.03 t ha−1 in the 1960s), 47% in the 1980s (average yield 1.23 t ha−1 in the 1970s), and 41% in the 1990s (average yield 1.81 t ha−1 in the 1980s). These statistics seem to indicate a direct correlation of perceived national economic importance (and presumably in turn investment in research and breeding) with increases in productivity. The prevalence of biotic and abiotic stresses and the level of technological innovation at the farm level are probably a major source of yield variation observed across regions and between countries within regions. However, it is equally inevitable that a major portion can be attributed to progress in genetics and breeding research that have been captured in new varieties. This is surely a strong justification for increased investment in plant research for genetics

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Table 1. Area, production and productivity of the peanut across globe, regions, and major peanut producing countries within the region (FAO 2003) Region

Country within region

Asia India China Indonesia Myanmar Vietnam Africa Nigeria Sudan Senegal Chad Congo (DR) Burkina Fasso Mozambique Cameroon Zimbabwe Niger North Central America

USA Mexico

South America Argentina Brazil Oceanea Australia World +

Area (000 ha)

Production (000 t)

Average yield (t ha−1 )

14988.60 (56.64)+ 8000.00 (53.37)++ 5125.40 (34.19) 682.94 (4.56) 575.00 (3.84) 240.30 (1.60) 10472.57 (39.57) 2800.00 (26.74) 1900.00 (18.14) 900.00 (8.59) 480.00 (4.58) 456.59 (4.36) 331.00 (3.16) 292.54 (2.79) 283.00 (2.70) 260.00 (2.48) 230.00 (2.19) 657.10 (2.48) 530.95 (80.80) 62.01 (9.44) 307.08 (1.16) 156.40 (50.93) 85.02 (27.68) 25.76 (0.097) 22.00 (85.40) 26462.86

24000.55 (67.31)+ 7500.00 (31.25)++ 13447.45 (56.03) 1377.00 (5.74) 730.00 (3.04) 400.00 (1.67) 8969.19 (25.15) 2700.00 (30.10) 1200.00 (13.38) 900.00 (10.03) 450.00 (5.02) 355.18 (3.96) 301.00 (3.55) 109.92 (1.22) 294.89 (3.29) 125.00 (1.39) 100.00 (1.11) 2077.77 (5.83) 1879.75 (90.47) 74.64 (3.57) 559.92 (1.57) 315.60 (56.36) 177.06 (31.62) 40.88 (0.12) 37.00 (90.51) 35658.43

1.60 0.94 2.62 2.02 1.27 1.66 0.86 0.96 0.63 1.00 0.94 0.78 0.91 0.38 1.04 0.48 0.43 3.16 3.54 1.20 1.82 2.02 2.08 1.59 1.68 1.35

% of global area and production % of region area and production

++

and genomics-enhanced peanut-breeding programs. China, the USA, Argentina, and Vietnam together contribute 75.5% of the world shelled peanut trade (1.13 million tons), with a total export value of US$ 593.6 million (FAO 2002). The US shelled peanut seeds command a premium price of US$ 786 per metric ton in the international market. Abiotic and biotic stresses are the major constraints on world peanut production and are extensively reviewed in Dwivedi et al. (2003a). In addition, devastating new diseases are also emerging. For example, a new disease diagnosed as peanut stem necrosis disease (PSND) caused by tobacco streak virus (TSV) affected nearly 225,000 hapeanut crops that resulted in yield losses of over US$65 million in India during 2000 (Rao et al. 2003a).

Peanut is a rich source of oil, protein, minerals (Ca, Mg, P, and K), and vitamins (E, K, and B1 ) (Savage and Keenan 1994). Freeman et al. (1999) predicted a continued increase in peanut production in Asia, a slow increase in sub-Saharan Africa, and decline in Latin America. There will be a gradual shift away from peanut oil and meals to peanut confectionary products in Asia, Latin America, and the Caribbean. The cake remaining after oil extraction is used in human food or incorporated into animal feeds (Savage and Keenan 1994). Peanut haulm is excellent forage for cattle as it is rich in protein and more palatable than many other fodders (Cook and Crosthwaite 1994). Wild Arachis species are used in pasture improvement in the Americas and Australia (Kerridge and Hardy 1994).

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3.1.4 Improved Quality Requirements: Reduced Allergenicity and Toxicity

terson et al. 2004). Thus in this section we will only provide a brief overview of the current status of genetic resources being maintained in national and international gene banks and the range of wild and culPeanut is widely used in the food and confectionery tivated accessions with beneficial traits. industry due to its high nutritive value. However, it is well known for its allergenic properties affecting both children and adults. A study in the USA revealed 3.2.1 that about 3 million American children and adults Wild Arachis Species and Interspecific Gene suffer from allergy to peanut or tree nuts (Sicherer Introgression into Cultivated Peanut et al. 1999). Most importantly, just a trace of peanut can provoke an abnormal IgE-mediated immunolog- ICRISAT gene bank maintains 453 accessions, repical reaction ranging from nausea or drowsiness and resenting 9 sections and 44 wild Arachis species. vomiting to anaphylactic shock and death. Peanut has Of these, 352 accessions belonging to 41 species seven distinct allergenic proteins, Ara h1 to Ara h7 are seed producing, 100 accessions of 2 species are (Burks et al. 1991, 1992; De Jong et al. 1998; Kleber- vegetatively propagated, and 1 accession (A. batiJanke et al. 1999; Herman 2004), that together include zogaea) is a natural hybrid (seed producing). One the vast majority of proteins in seeds and include both hundred ninety-five accessions of 16 species are storage protein families (Koppelman et al. 2001). Ara annual and 232 accessions of 17 species are perennial. h1 and Ara h2 are major allergens (Burks et al. 1995; Information on 26 accessions of 11 species is not Stanley et al. 1997; Kleber-Janke et al. 1999) and the known. The gene bank acquired 418 accessions remaining five are minor allergens (Kleber-Janke et al. as donations from 8 cooperators in 4 countries 1999). North American populations are more prone (Brazil 22, United Kingdom 3, India 8, USA 285) to Ara h1 than the European populations (Koppel- and 31 accessions through 4 collecting missions man et al. 2001). A vaccine that can protect people in Brazil (Rao et al. 2003b; http://www.icrisat.org/ from peanut allergies has been developed, and tests GroundNut/Arachis/Start.htm). The whereabouts revealed that the vaccine effectively protected mice of the remaining four accessions maintained from peanut allergies, which provides some hope that in the ICRISAT gene bank are not known. we should be able to protect humans from peanut The Southern Regional Plant Introduction Station, USDA-ARS at Griffin, GA, USA, maintains allergy (http://www.nature.com/reviews/immunol). Quality characters are also of great importance over 700 accessions of 60 wild Arachis species in peanut production. For example, Aspergillus flavus (USDA-ARS 2002; http://www.ars-grin.gov/cgi-bin/ Link ex Fries infection does not significantly affect npgs/html/site_holding.pl?S9). Large collections peanut yields but its production of aflatoxins makes of wild Arachis species are also maintained at contaminated grain dangerous for animal and human Texas A&M and North Carolina State University, consumption. The presence of aflatoxins also dramat- Raleigh, NC, USA. The National Center of Genetic ically influences the marketing of peanut kernels and Resources (CENARGEN) in Brazil maintains over cake because of stringent international standards for 1200 accessions of 81 species belonging to 9 sections. Unlike cultivated peanut germplasm, wild permissible levels of aflatoxin contamination set by importing countries in Europe, North America, and Arachis species are reported to possess high levels of resistance to rust, leaf spots, nematodes, peanut Australia. bud necrosis virus (PBNV), tomato spotted wilt virus (TSWV), groundnut rosette virus (GRV) and groundnut rosette assistor virus (GRAV), leaf miner, 3.2 Spodoptera, aphids, thrips, and jassids (Stalker Genetic Resources in Peanut and Simpson 1995; Dwivedi et al. 2003a; Rao et al. 2003b). Wild Arachis species are also reported to There are a number of review articles covering the ge- show wide variation for most of the morphological netic resources of cultivated and wild Arachis species traits (Singh et al. 1996; Chandran and Pandya 2000). (Singh and Simpson 1994; Stalker and Simpson 1995; ICRISAT scientists characterized/evaluated 267 wild Dwivedi et al. 2003a; Holbrook and Stalker 2003; Pa- Arachis accessions from 37 species under greenhouse

Chapter 3 Peanut

conditions for 33 qualitative and 15 quantitative traits. The frequency distribution of 17 qualitative descriptors was uniformly distributed whereas for the other 16 traits it was skewed (70% or more) toward one class. These species, except for the height of the main stem, stem thickness, and basal leaflet on the main stem, showed large variation for lateral branches, plant width, stipule length, adnation of stipule on the main stem, petiole length on the main stem, apical leaflet length and width on the main stem, apical length and width on the primary lateral, hypanthium length, standard petal length, and peg length as revealed by the Shanon-Weaver diversity index that ranged from 0.022 for hairiness on the margin of the stipule of the main stem to 0.836 for basal leaflet shape on the primary lateral (H.D. Upadhyaya, ICRISAT unpubl. data). The primary gene pool in peanut consists of accessions that belong to cultivated peanut (Arachis hypogaea) and the wild tetraploid species A. monticola. The secondary gene pool is represented by diploid species of the section Arachis that are cross compatible with cultivated peanut, while the tertiary gene pool includes species of the other sections that cannot be hybridized with A. hypogaea by conventional means. Both pre- and postzygotic hybridization barriers have been shown to restrict crossing between Arachisspecies. Despite the crossing barriers, several interspecific tetraploid derivatives have been developed that possess high levels of resistance to rust, early leaf spot (ELS), late leaf spot (LLS), nematodes, southern corn rootworm, corn earworm, Spodoptera, and jassids (see Dwivedi et al. 2003a and references therein) that are semi-improved genetic resources that researchers may use for mapping and genetic enhancement in peanut. From the interspecific crossing, two root-knot nematode resistant peanut lines, Coan and Nema TAM, have been released for cultivation in areas heavily infested with nematodes in USA (Simpson and Starr 2001; Simpson et al. 2003).

3.2.2 Cultivated Germplasm The world’s largest peanut collection of 14,966 accessions from 93 countries is housed at the RS Paroda Gene Bank in ICRISAT, Patancheru, India. This collection represents six botanical varieties: 45.8% var. hypogaea (6,838 accessions), 36.6% var. vulgaris (5,493 accessions), 15.7% var.

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fastigiata (2,351 accessions), 0.1% var. aequitorania (14 accessions), 0.13% var. hirsuta (19 accessions), and 1.7% var. peruviana(251 accessions). Approximately 43% of the collection consists of landrace germplasm, 7% cultivars, 31% breeding lines, and 19% other genetic stocks (mutants and experimental germplasm) (Upadhyaya et al. 2001a). Passport and characterization data are accessible through the internet (http://www.icrisat.org/ GroundNut/Project1/pfirst.asp?gname=entire) and the germplasm is freely available for distribution providing the requisitioned signs a material transfer agreement with ICRISAT. Other gene banks holding sizable numbers of cultivated peanut accessions are 9,027 accessions at USDA Southern Plant Introduction Center, Griffin, GA, USA; about half of these collections are unimproved landraces collected in the crop’s centers of diversity in South America (Holbrook 2001). A series of descriptors has been developed for standardizing the characterization of peanut genetic resources using various morphophysiological, reproductive, and biochemical traits (IBPGR and ICRISAT 1992). The majority of the ICRISAT peanut germplasm showed a large variation for qualitative and quantitative traits, seed quality traits, and resistance to biotic and abiotic stresses (Upadhyaya et al. 2001a). Field evaluation of these germplasms identified a large number of accessions possessing tolerance to drought and resistance to biotic stresses (Table 2). Some of these genetic resources have been used in breeding programs to develop improved breeding lines/cultivars resulting in significant economic gains to peanut farmers. For example, the largest impacts have been from the development of cultivars with resistance to sclerotinia blight (Sclerotinia minor Jagger), the peanut root-knot nematode [Meliodogyne arenaria (Neal) Chitwood race 1], and TSWV, which had an estimated economic impact of more than US$ 200 million annually for US peanut farmers (Holbrook 2001).

3.2.3 Core Collections The development of a core collection could facilitate easier access to peanut genetic resources, improve the efficiency of germplasm evaluations by reducing the number of accessions to be evaluated while increasing the probability of locating genes of interest thus

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Table 2. Sources of resistance to rust, leaf spots, sclerotinia blight, groundnut rosette virus, aflatoxin, nematode, defoliator, aphid, and drought reported in cultivated and wild Arachis species Trait

Peanut accessions with beneficial traits reported Cultivated Reference Wild species Arachis species

Rust Late leaf spot Early leaf spot Groundnut rosette virus Nematode Seed infection and/or aflatoxin production by Aspergillus flavus Sclerotinia blight Defoliator (Leaf miner and Spodoptera)

169 69 37 116

Aphid

EC 36892 and ICG 12991 40

Drought

Reference

Singh et al. 1997 Singh et al. 1997 Singh et al. 1997 Subrahmanyam et al. 1998

29 27 11 12

Subrahmanyam et al. 1995 Upadhyaya et al. 2001a Upadhyaya et al. 2001a Subrahmanyam et al. 2001

21 21

Holbrook et al. 2000 Singh et al. 1997

– 4

– Thakur et al. 2000

50 9

Damicone et al. 2003 Dwivedi et al. 1993; Wightman and Rao 1994; Rao and Wightman 1999; Stalker and Lynch 2002 Padagham et al. 1990; Minja et al. 1999

– – 38 Wightman and Rao 1994; (leaf miner) Lynch and Mack 1995 and 67 (Spodoptera) Wild species not evaluated

Nigam et al. 2003b; Seetharama et al. 2003

Wild species not evaluated

enhancing their use in crop improvement programs, and simplify the gene bank management. A very small proportion of the germplasm accessions are being used in peanut-breeding programs (Upadhyaya et al. 2002a). At ICRISAT, where about 14,966 accessions of cultivated peanut and 453 accessions of wild Arachis are available for use, only 132 cultivated germplasm and 10 wild accessions have been used in developing 8,279 breeding lines in 17 years from 1986 to 2002 (H.D. Upadhyaya, ICRISAT, unpubl. data). Few accessions have been extensively used in breeding programs: Chico (ICG 476) 1180 times and Robut 33-1 also known as Kadiri 3 (ICG 799) 3,096 times. Holbrook et al. (1993) were the first to develop a core collection of 831 accessions from a set of 7432 USA peanut germplasms based on 6 morphological variables. Subsequently, a global core consisting of 1,704 accessions (14 morphological descriptors on 14,310 accessions) (Upadhyaya et al. 2003) and an Asia region core of 504 accessions (15 morphological descriptors on 4,738 accessions) (Upadhyaya et al. 2001b) were developed. However, when the size of col-

lection is too large and a core collection (10% of entire collection) becomes unmanageable, Upadhyaya and Ortiz (2001) suggested a strategy to select a minicore collection (10% of core or 1% of entire collection). Using this strategy, Upadhyaya et al. (2002a) developed a minicore consisting of 184 accessions (based on 16 to 18 agronomic and quality traits scored on 1704 core collection accessions in 2 constrasting seasons) that captured variability present in the core collection (1,704 accessions) and also in the entire collection (14,310 accessions). Holbrook and Dong (2003) evaluated the USDA peanut core collection (831 accessions) for 16 morphological traits to develop a core of the core consisting of 111 accessionsdemonstrating that the genetic variation expressed in the core had been preserved in this core of the core collection. The accessions included in the core potentially possess new sources of variation for economically important traits. When the peanut global core, Asia region core and/or minicore collections, were evaluated for various traits in multienvironment trials, ICRISAT scientists identified new sources

Chapter 3 Peanut

of early maturity (21 accessions) (Upadhyaya et al. 2006) and tolerance to low temperature (12°C) at germination (158 accessions) from the global core (Upadhaya et al 2001a), for drought-tolerance traits (18 accessions) from the minicore (Upadhaya 2005), and 60 accessions (15 fastigiata, 20 vulgaris, and 25 hypogaea) from the Asia region core that showed high yield potential, greater meat content (also known as shelling percentage), and 100-seed weight (Upadhyaya et al. 2005). These new accessions have trait-specific characteristics similar to the best control, for example Chico, for early maturity but were agronomically similar or superior but diverse. The use of these diverse sources would help in bringing in much needed diversity and broaden the genetic base of cultivars (Upadhyaya 2005). Similarly, when the USDA peanut core collection was evaluated, 21 accessions were resistant to peanut root-knot nematode (Meloidogyne arenaria(Neal) Chitwood race 1 (Holbrook et al. 2000); 55 accessions were resistant to TSWV (Anderson et al. 1996); 11 and 12 accessions were resistant to cylindrocladium black rot [Cylindrocladeum crotalariae (Loos) Bell and Sobers] and ELS, respectively (Isleib et al. 1995); 6 accessionsexhibited a 90% reduction in preharvest aflatoxin contamination (Holbrook et al. 1998); 6 accessions were resistant to rhizoctomia limb rot (Rhizoctonia solanii Kuhn) (Franke et al. 1999); and 20 and 30 accessions were classified as highly resistant (no disease) and resistant (<10% disease incidence) to sclerotinia blight [Sclerotinia minor Jagger and Sclerotinia sclerotiorum(Lib) de Bary], whereas 10 accessions were resistant to pepper spot [Leptosphaerulina crassiasca (Sechet) Jackson and Bell] (Damicone et al. 2003). These new sources performed better than or similar to the best cultivars for the target traits but were known to be diverse from all previous sources of those traits. Thus, these accessions are a good resource for analyzing genetic relationships and detecting allelic variation associated with beneficial traits through association analysis. Moreover, where allelic tests indicate an independent genetic basis for a given trait, there is an opportunity for pyramiding genes to enhance the level of expression of that trait, provided the genetic basis is highly additive and epistatically positive. It is noteworthy that the core and minicore collections discussed in this section do not include accessions from wild Arachisspecies. These genotypes tend to have a much longer cropping duration than the cultivated germplasm and need special care when

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grown under field conditions outside their natural habitat. Thus, it is very difficult to conduct routine large-scale evaluation trials that include both wild and cultivated material. However, there are considerable ongoing efforts focused on the characterization of wild accessions for various morphophysiological, reproductive, and quality traits (Sect. 2.1) that should lead to increased exploitation of wild germplasm in peanut-breeding programs.

3.3 Appropriate Germplasm and Evaluation Systems for Mapping Economically Important Traits in Peanut The efficiency of marker-assisted selection (MAS) is highly dependent on the quality of the prior mapping process, which requires parental genotypes with substantially contrasting phenotypes (for the target trait), highly accurate and precise evaluation techniques, and large mapping populations. In addition, the cost-effective application of MAS requires one marker very close to (or preferably within) the gene of interest and two markers closely flanking either side and that these markers be based on simple robust PCR-based marker assays. In this section, we provide an overview of genetically diverse sources of economically important traits that may be useful in genome mapping and marker-assisted genetic enhancement in peanut. Then we briefly discuss the mechanisms and genetics of resistance/tolerance to important biotic and abiotic stresses and the key steps involved in generating accurate phenotyping data for those target traits.

3.3.1 Phenotypic Screens, Resistance/Tolerance Mechanism, and Genetics

Rust and Leaf Spots It is essential that a pure disease inoculum be used for inoculating the test materials. This should be generated and maintained on incubated, inoculated, detached leaves of a highly susceptible cultivar such as TMV 2 in a plant growth chamber using a temperature of 23 ◦ Cand 12-h photoperiod. The early leaf spot

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(ELS) and late leaf spot (LLS) conidia and rust urediniospores should be harvested with a cyclone spore collector and used for inoculation of the test materials separately. A two-step evaluation is suggested for phenotyping: the field and glasshouse evaluation. The former is useful as a first step wherein a large number of genotypes/mapping populations can be evaluated for overall disease score and related to yield loss, whereas the latter is recommended for dissecting the components of resistance under more controlled conditions. For field evaluation, the infector row technique is adapted wherein the resistant and susceptible controls are sown alternately every five rows of test materials. Sunflower is sown in a 4-m wide strip around the experimental plot to avoid aerial contamination by other foliar fungal pathogens. At 35 d after sowing (DAS) the infector row and the test materials are inoculated with disease inoculum containing either 20,000 ml−1 rust urediniospores or 20,000 ELS or LLS conidia, late in the evening. Prior to fungal inoculation, the whole experimental field is given light overhead sprinkler irrigation for 30 min to create leaf wetness on the foliage throughout the night. Thereafter, overhead irrigation is given every day for 30 min in the late evening for 10 d to maintain alternate wet (night) and dry (day) periods to obtain maximum disease development (Butler et al. 1994). The experimental plot should be protected against leaf spots when screening for resistance to rust [using weekly application of Chlorothalonil (Kavach) at 2 g l−1 water (500 lha−1 )] while rust should be controlled when screening for resistance to leaf spots [using weekly application of calixin at 0.5 ml l−1 water (500 l ha−1 )]. There is no fungicide that gives differential protection (i.e., protection against ELS in LLS screening trial and protection against LLS in ELS screening trial), so the phenotyping should be conducted in hot spots prone only either to ELS or LLS. A nine-point modified scale is used, where 1 = no disease and 9 = 81 to 100% disease (Subrahmanyam et al. 1995), to record % leaf area damaged, % leaf defoliation (in case of leaf spots), and disease scored at 15-d intervals from 35 d after inoculation until 1 week before harvest. For greenhouse evaluation, the test materials along with susceptible and resistant controls are grown in 15-cm-diameter plastic pots containing autoclaved soil and farmyard manure (v/v 4:1 ratio). The inoculum procedure is the same as described in the case of field screening. Immediately after

inoculation, the pots are shifted into dew chambers (Clifford 1973) at 23 ◦ C to ensure wetness of the leaf surface during the night. The pots are removed from the dew chambers on the morning of the following day and returned to the greenhouse to maintain a dry period during the day. This alternation of wet (16 h) and dry (8 h) periods is repeated for 10 d. The pots are then kept permanently in the glasshouse until completion of the experiment. Two undamaged fully expanded quadrifoliate leaves of the main axis of a plant per pot, five plants per replication for each genotype, are tagged to study the components of resistance (Dwivedi et al. 2002a). The experiment should be terminated at 45 to 50 d after inoculation or when the disease is fully developed and most of the leaves are either completely weathered due to rust or defoliated due to leaf spots. Reports on the genetic basis of resistance to rust and leaf spots vary from a few genes to quantitative inheritance (reviewed by Dwivedi et al. 2003a). Resistance to rust is due to longer incubation and latent periods, fewer pustules per leaf, smaller pustule diameter, lower sporulation index, less leaf area damage, and lower disease score. Since these components interact in an additive manner, selection is based on evaluation of individual components of resistance together with the determination of green leaf area remaining on the plant. Resistance to LLS is due to longer incubation and latent periods, fewer lesions per leaf, smaller lesion diameter, lower sporulation index, less leaf area damage, and lower disease score. Selection based on components of resistance to LLS may not lead to plants with higher retained green leaf area. The remaining green leaf area on the plant and disease score should be the basis of selecting for resistance to LLS (Dwivedi et al. 2002a). The ELS-resistant germplasm accessions have longer incubation periods, reduced sporulation rates, lesion diameter, and infection frequencies, and less defoliation (Nevil 1981; Waliyar et al. 1993).

Aflatoxin Aflatoxins are produced by Aspergillus flavusLink ex Fries, which produces only aflatoxin B1 . Resistance to A. flavus in peanut operates independently in at least three tissues: pod, seed coat, and cotyledons (Mixon 1986). Resistance to pod infection is attributed to pod-shell structure, while resistance to seed invasion and colonization has been correlated with thickness, density of palisade cell layers, absence of fissures and cavities, and presence of wax layers

Chapter 3 Peanut

on the seed coat. A number of genes are involved in seed response to colonization by A. flavus, aflatoxin production, and preharvest infection (Utomo et al. 1990). Estimates of broad sense heritability vary from low to high for natural infection and seed colonization by A. flavus and from low to moderate for aflatoxin production (http://www.aflatoxin.info/ groundnut_breeding.asp). Three-phase evaluation is recommended for phenotyping of the parental and mapping populations: preharvest infection (Mehan 1989), in vitro seed colonization (Mixon and Rogers 1973; Mehan and McDonald 1980), and aflatoxin production (Mehan and McDonald 1980). Drought during pod formation substantially increases the level of aflatoxin contamination. Preharvest infection requires a drought period of 30 to 35 d and a mean soil temperature of 29 to 31 ◦ C in the podding zone (Cole et al. 1989, 1995). The susceptibility of peanut to aflatoxin contamination is related to lower water activity (0.80 to 0.95) in the kernel and favorable temperature (25 to 32 ◦ C) for growth of A. flavus (Sicherer et al. 1999). As the kernel moisture content decreases under end-of-season drought, protection from the natural defense mechanisms is lost and the kernels become vulnerable to colonization by A. flavus and aflatoxin contamination. A line-source sprinkler irrigation system (Hanks et al. 1976) that imposes a water deficit gradient is suggested to evaluate populations for their reaction to A. flavus and aflatoxin production. The most common immunoassay used for mycotoxin detection are enzyme-linked immunosorbent assay (ELISA); analyzing a single analyte by ELISA costs ca. 5.9%and 2.5% of that by gas chromatography or gas chromatography-mass spectroscopy, respectively (Vogt 1984). Chu et al. (1987) developed an improved ELISA technique that is more sensitive than previously reported methods (El-Nakib et al. 1981; Ram et al. 1986a,b) and analytical time shortened to less than 1 h for quantitative analysis and less than 30 min for screening tests, with a detection power as little as 5 pg B1 . A few commercial kits available in the market are Aflatest, Agriscreen, AflaCup 10, AflaCup 20, and EZ-Screen, with 1 to 20 ppb detection power; approximate cost per sample ranges from US$ 8.5 to 65.0 for quantitative estimates and US$ 6.0 to 7.5 for visual tests (www.oznet.ksu.edu/grsiest).

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food products (Holzhauser and Vieths 1999), whereas peanut allergens are measured in foods by immunoassays with human IgE antibodies (Keating et al. 1990). However, these assays do not provide quantitative measurements of exposure to food allergens. Hird et al. (2003) developed a sensitive and robust assay for the identification of peanut in commercial products using real-time PCR technology, while Pomes et al. (2003) developed a new sensitive and specific monoclonal antibody-based ELISA to monitor Ara h1 content in food products and in developing thresholds for sensitization or allergic reaction in persons with peanut allergy. Dodo et al. (2002) provided a detailed procedure to detect peanut allergens that consists of defatting peanut seeds, extracting peanut proteins, and conducting an ELISA test.

Nematodes The peanut root-knot nematode Meloidogyne arenaria (Neal) Chitwood race 1 is widely distributed across peanut-growing areas. Holbrook et al. (1983) described the screening technique wherein greenhouse-grown plants are inoculated with 3000 eggs of M. arenaria race 1 cultured on tomato (Lycopersicon esculentum Mill. cv. Rutgers) or eggplant (Solanum melongena L. cv. Black Beauty). The nematode inoculum should be prepared using the NaOCl method (Hussey and Barker 1973) and applied 14 d after planting. Approximately 70 dafter inoculation, plants are uprooted and washed clean of soil. The roots are then placed in 1000-ml beakers containing 300 ml of 0.05% ploxine B solution for 3 to 5 min (Daykin and Hussey 1985) and scored for root galls and egg masses [using dissecting microscope (×20) to determine the number of eggs per gram of fresh root weight] based on a 0 to 5 rating where 0 = no galls or no egg masses, 1 = 1 to 2, 2 = 3 to 10, 3 = 11 to 30, 4 = 31 to 100, and 5 = more than 100 galls or egg masses per root system (Taylor and Sasser 1978). Some variation in screening procedure may be adapted as reported in other publications (Nelson et al. 1989; Starr et al. 1995; Choi et al. 1999). Resistance to M. arenaria is moderate in cultivated peanut and is due to reduction in the percentage of second-stage juveniles (J2) that establish a functional Allergens feeding site (Timper et al. 2000). In contrast to culELISA is the most commonly used method to de- tivated peanut, very high levels of resistance to M. tect peanut in the food manufacturing process and in arenariaexist in wild Arachis species conditioned by

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two dominant genes: Mag inhibits root galling and and length, leaf hairiness, standard petal length and Mae inhibits egg production by M. arenaria (Garcia petal markings, basal leaflet width, and main stem thickness and hairiness, and stipule adnation length et al. 1996). and width showed significant correlation and/or regression coefficients with damage by defoliators Defoliating Insect Pests including S. litura(Sharma et al. 2003). The major defoliators in peanut are leaf miner [Aproaerema modicella (Deventer)] and tobacco armyworm (Spodoptera litura F.). It is difficult to Groundnut Rosette Virus screen for resistance to defoliators under natural field The rosette (chlorotic and green) is the most deinfestation because of variation in infestation in space structive disease of peanut in sub-Saharan Africa. and time. Sharma et al. (2002) developed a no-choice Three agents interact to produce rosette disease syncage technique to screen for resistance to S. litura. drome: groundnut rosette virus (GRV), groundnut Essentially the technique consists of raising the rosette assistor virus (GRAV), and satellite RNA (sat nuclear insect cultures and maintaining these on an RNA) (Bock et al. 1990). GRV is transmitted by aphid artificial diet (Taneja and Leuschner 1985), releasing (Aphis craccivora) but only from the plants that also a known number of first- or third-instar larvae on contain GRAV. GRAV is not mechanically transmis15-d-old greenhouse plants (temperature 28 ± 5 ◦ C sible and causes no apparent symptoms in peanut. and RH > 65%) grown in pots (30 cm diameter, 30 cm The sat RNA, which is dependent on GRV for muldeep) under a plastic jar cage (11 cm diameter and tiplication and on GRAV for aphid transmission, is 26 cm height) with two wire-mesh-screened windows largely responsible for rosette symptoms (Murant (4 cm diameter) for varying periods of time and et al. 1988). Variation in sat RNA has been corthen recording observations on insect survival and related with the different forms of rosette disease leaf area damage. Two plants are grown in each pot, (Murant and Kumar 1990). All three agents must where one plant is infested with the larvae inside the be present together in the host plant for successcage and the other plant remains outside the cage ful transmission of the disease by the aphid vecand is left as an uninfested control. Observations are tor. Bock and Nigam (1988) developed the infectorthen recorded on the number of surviving larvae, the larval weight (4 hfollowing termination of the row technique where a highly susceptible peanut culexperiment), and percentage leaf area damage on a 1 tivar is grown in every two rows of the test materito 9 scale, where 1 ≤ 10% leaf area damage, 2 = 11 to als under field conditions. Potted spreader plants of 20%, 3 = 21 to 30%, 4 = 31 to 40%, 5 = 41 to 50%, 6 = the highly susceptible cultivar showing severe rosette 51 to 60%, 7 = 61 to 70%, 8 = 71 to 80%, and 9 > 80% symptoms and heavily infested with aphids are raised in a glasshouse and transplanted to the infector rows leaf area damaged. At present screening for resistance to leaf miner (1 plant per 3 m rows) 10 d after sowing the test mais dependent on natural infestation under field con- terials. The disease development in the infector rows ditions as it is a difficult pest to devise a cage-based is monitored ca. 2 weeks later, and viruliferous aphids greenhouse screening procedure for. However, pro- are released onto the plants that are free from the longed drought and planting soybean as infester crop disease. The disease incidence is then assessed at the favor high intensity of leaf miner infestation under pod-filling stage. The percentage of disease incidence field conditions. A 1 to 9 scale, similar to that used is determined based on the total number of plants per for screening resistance to Spodoptera, is suggested to plot and the number of plants showing rosette symptoms with severe stunting; using a 1 to 3 scale for clasrecord percentage leaf area damage by leaf miner. A high level of tolerance to leaf miner and sifying the disease reaction where 1 = plants with no Spodoptera has been observed in breeding line ICGV symptoms on foliage and no stunting, 2 = plants with 86031 and is manifested as the enhanced ability of obvious rosette leaf symptoms and stunted to about the vegetative tissue to regrow subsequent defoliation 50% of the size of the normal plants, 3 = plants with se(Wightman and Rao 1994). Several wild Arachis vere rosette leaf symptoms and stunting greater than species have also shown resistance to leaf miner and 50% (Olorunju et al. 1991) with some modification Spodoptera with morphological traits such as main (Subrahmanyam et al. 1998). The disease index is then stem thickness, hypanthium length, leaflet shape calculated based on this rating as (A + 2B +3C)/total

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number of plants assessed per plant, where A, B, and C equal the number of plants with ratings of 1, 2, and 3, respectively. The selected lines from field trials should be evaluated (50 to 60 d after aphid inoculations) for the presence of GRAV using TAS-ELISA (Rajeshwari et al. 1987). GRV and sat RNA are detected by RT-PCR as described by Naidu et al. (1998), and a field and greenhouse evaluation is recommended to screen for resistance to aphid (Padagham et al. 1990). Resistance to GRV virus has been reported to vary from monogenic dominance to two independent recessive genes (Nigam and Bock 1990; Olorunju et al. 1992), while aphid resistance may be controlled by a single recessive gene (Herselman et al. 2004). Peanut Clump Virus Viruses of the genus Pecluvirus cause peanut clump disease (Torrance and Mayo 1997), referred to as peanut clump virus (PCV) in West Africa and Indian peanut clump virus (IPCV) in South Asia. The fungus Polymyxa ssps transmits IPCV (Ratna et al. 1991) and PCV is suspected to have the same vector. Both IPCV and PCV have an extremely wide host range including many monocots (Ratna et al. 1991; Delfosse et al. 1996), are soilborne, and produce similar symptoms on peanut. However, IPCV is not serologically related to two West African isolates (Reddy et al. 1983). Severely stunted plants with dark green leaves and mosaic mottling with chlorotic rings on new quadrifoliates characterize the peanut clump disease in peanut (Reddy et al. 1983). The disease occurs in patches in the field, which reappears in the same positions in the following season, indicating high resilience but slow movement of the vector. A field with a known history of high disease incidence should be selected for screening for resistance to peanut clump disease. An ELISA and nucleic acid hybridization test is recommended to confirm the presence of the virus in the infected plants from the field (Delfosse et al. 1999). The intensity of disease incidence is measured by visual symptoms and ELISA test. Drought Genotypes are usually evaluated for drought tolerance under field conditions during the dry season with controlled supplementary irrigation. However, it is difficult to fully represent the natural drought environment where moisture stress occurs during the

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rainy season in which the crop is largely grown by the farmers. Thus, it is recommended that rainout shelters be created for drought stress screening during the rainy season, but this does require considerable investment. Crop phenology is the single most important trait for enhancing performance under drought stress. Fortunately, peanut is temperature sensitive but not daylight sensitive like many crops, so its phenology is not especially driven by the environment. Specht et al. (1986) defined drought tolerance in three general categories: (a) escape for drought by tailoring plants with appropriate phonologies to fit the most appropriate growing period, (b) dehydration avoidance through identification and incorporation of traits that lessen evaporatory water loss or increase water uptake through deeper and more extensive root system, and (c) dehydration tolerance by selecting traits that maintain cell turgor (a driving force for plant growth) or enhancing cellular constituents that protect cytoplasmic proteins and membranes from desiccation. In contrast, empirical or yield-based definitions of drought tolerance fall into two categories: (i) absolute, where breeders select for the highest yielding genotypes in environments where seasonal drought is predictably recurrent, and (ii) relative, where breeders select for genotypes with the smallest yield decline per unit of reduced seasonal rainfall (Specht et al. 1986). Three profiles of drought stress can be differentiated for all crops including peanut – (i) early-season drought, (ii) midseason drought, and (iii) end-ofseason (terminal) drought – and the effect of these on yield depends on the severity and duration of water deficit stress and the drought management practices applied. Moderate early drought stress is actually beneficial for the establishment of the peanut crop, while midseason and terminal drought can cause substantial reduction in peanut yield (Nageswara Rao et al. 1989). To impose midseason drought stress in peanut, the irrigation is withheld from 40 d after sowing (DAS) to 80 DAS, and then the stress is released (by irrigating the crop on a regular basis until maturity) to measure the recovery response of the stressed genotypes. These 40 d of stress concide with flowering and early pod development in peanut. For end-of-season drought, the irrigation is withheld from 80 DAS until the crop is harvested. The line-source sprinkler technique (Hanks et al. 1976) is used to create a gradient of irrigation across contiguous plots. The plot nearest to the sprinkler head receives ca. 50 mm of water, thus providing an

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irrigated control. The amount of water then decreases in a linear fashion as the distance of the plot from the sprinkler head increases. Several sources of tolerance to midseason and/or terminal drought have been reported in peanut (Nageswara Rao et al. 1989; Nigam et al. 2003a,b) that showed variation for physiological traits such as specific leaf area (SLA), water use efficiency (WUE), amount of water transpired (T), transpiration efficiency (TE), and harvest index (HI) under drought stress conditions (Nageswara Rao et al. 1993; Nageswara Rao and Wright 1994; Wright et al. 1994, 1996; Craufurd et al. 1999; Nageswara Rao and Nigam 2001). The component trait-based approach based on assessing variation in SLA, WUE, T, TE, and HI under drought stress conditions is recommended for accurate and precise dissection of drought tolerance in peanut. Variation in WUE arises mainly from genotypic differences in water use. Carbon isotope discrimination (Δ) can be used to select genotypes with improved WUE under drought conditions in the field. A strong relationship between WUE and SLA and between Δ and SLA revealed that genotypes with thick leaves had greater WUE (Wright et al. 1994). SLA could therefore be used as a rapid and inexpensive indirect selection criterion for WUE to facilitate selection for terminal drought tolerant genotypes (Nageswara Rao and Wright 1994). However, there appears to be a negative relationship between WUE and HI under terminal drought stress conditions, suggesting that selection for high WUE might enhance dry matter production under stress but not necessarily improve pod yield (Nageswara Rao and Wright 1994; Wright et al. 1994). SLA is also highly influenced by G × E interaction. Nageswara Rao et al. (2001) demonstrated the use of a portable SPAD chlorophyll meter for rapid assessment of SLA and specific leaf nitrogen (SLN) as surrogate traits to measure TE in peanut. Both additive and additive × additive epistasis for SLA and HI and additive genetic effect for Δ are reported (Jayalakshmi et al. 1999; Nigam et al. 2001).

(NMR) spectrometer (Jambunathan et al. 1985) is preferred over the conventional Soxhlet (Kuck and St Angelo 1980) method for determining the oil content as these tests are rapid (2 to 3 min), do not require any extraction reagents or supporting equipment, and the seeds can be used after the test. However, NIR requires a larger sample (150 g) over NMR (3 to 5 g) but can also provide additional information on protein content and other quality parameters. A Technicon autoanalyzer may also be used to determine nitrogen concentration (Singh and Jambunathan 1980) and then multiply the value by a factor of 5.46to convert nitrogen into crude protein content (United Nations University 1980). Fatty acid composition is determined following Hovis et al. (1979), and from it the following quality parameters can be determined (Mozingo et al. 1988). 1. Iodine value (IV): (% oleic acid) (0.8601) + (% linoleic acid) (1.7321) + (% eicosenoic acid) (0.7854) 2. Oleic (O)/linoleic (L) acid ratio = % oleic acid/% linoleic acid 3. Total saturated fatty acids (%) (TSF) = % palmitic acid + %stearic acid + % arachidic acid + % behenic acid + % lignoceric acid 4. Polyunsaturated (P)/saturated (S) ratio = % linoleic acid/ % TSF 5. Total long-chain saturated fatty acids (%) (TLCSF) = % arachidic acid + % behenic acid + % lignoceric acid

3.3.2 Germplasm with Beneficial Traits for Mapping and Genetic Enhancement

A large number of accessions possessing resistance/tolerance to abiotic and biotic stresses have been identified both in cultivated and wild Arachis species (Table 2). Of these, a number of promising accessions/breeding lines, mostly based on genetic diversity, differing in resisSeed Quality Total oil and protein content and fatty acid profile are tance/tolerance mechanism to biotic and abiotic the important seed quality traits that substantially stresses, or varying in seed quality, including influence the edible uses of peanut. Near-infrared peanut allergens have been recommended for use (NIR) transmittance spectroscopy (Panford 1990; in mapping and genetic enhancement in peanut Misra et al. 2000) and nuclear magnetic resonance (Table 3).

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Table 3. List of potential germplasm/breeding lines resistant to rust, leaf spots, aflatoxin, rosette, nematode, defoliators, aphid, drought, low temperature, and those with improved agronomic traits, oil chemistry and low allergens for mapping and genetic enhancement in cultivated peanut Trait

Identity

Reference

Rust

ICGV# 99003 and ICGV 99005

Dwivedi et al. 2002a; ICRISAT 2004

Leaf spots

ICG # 405, 1705, 6284, 9987, 9991, 9989, 10000, and 10914; ICGV# 99001 and 99104; PI 565287 (TxAG-6) and PI 565288 (TxAG-7)

Dwivedi et al. 2002a; Dwivedi and Gurtu 2002; Simpson et al. 1993; ICRISAT 2004

Groundnut rosette virus Nematode

ICG#3436, 6323, 7827, 9558, 11044, and 11968

Dwivedi et al. 2003a

PI 565287 (TxAG-6) and PI 565288(TxAG-7)

Simpson et al. 1993

Seed infection and/or aflatoxin production by Aspergillus flavus

ICG# 1326, 1448, 1471, 4681, 4749, and 7101 ICGV 88145 and ICGV 89104 ICGV 91278, ICGV 91279, and ICGV 91283

Dwivedi et al. 2001; Mehan et al. 1985 Rao et al. 1995 Upadhyaya et al. 2001c

Defoliators (Leaf miner and Spodoptera)

ICGV# 86031, 87154 and 87160; ICG# 2271 and 1697 + from GP-NCWS7, GP-NCWS8, GP-NCWS9, and GP-NCWS10

Dwivedi et al. 1993; Wightman and Rao 1994 Stalker and Lynch 2002

Aphid

ICG 12991

Herselman et al. 2004

Drought

ICG 1471; ICGV# 94106, 94113, 96294, 97068, 97093, 98381, 98382, 99231; 99233, 99235, 99236; 99237, 99238, 99241, 99243, 99247; 99249, and 99255; and CSMG 84-1 ICG# 766 and 14523; ICGV 86031 and TAG 24 ICG# 118, 532, 862, 2106, 2511, 2773, 4527, 5236, 5827, 6654, 6766, 7243, 8285, 11219, 11855, 14475, 14523, 14985 UF 435-2-1, and UF 435-2-2

Dwivedi and Varma 2002

Norden et al. 1987

PI 261924 and PI 338386

Dodo et al. 2002

ICG # 3200, 3540, 3631, 4558, 4729, 4890, 5512, 5560, 5881, 9427, 9930, 9968, 11605, 11914, 13585, 13606, 13647, 14390, 14788, 14814, 14815 158 accessions ICGV 92267

Upadhyaya et al. 2006

60 accessions

Upadhyaya et al. 2005

Oleic/linoleic fatty acid ratio Allergen Early maturity

Low temperature Yield and agronomic traits

ICRISAT 2004 Upadhyaya 2005

ICRISAT unpubl. data Upadhyaya et al. 2002b

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3.4 Genomic Resources in Peanut 3.4.1 DNA Markers (RFLPs, RAPDs, AFLPs, SSRs) Early studies on peanut genomics focused on screening cultivated peanut germplasm and/or tetraploid interspecific breeding lines with 67 polymorphic RAPD markers (Burow et al. 1996; Bhagwat et al. 1997; Choi et al. 1999; Subramanian et al. 2000; Raina et al. 2001; Dwivedi et al. 2001, 2002b) and 404 polymorphic RFLP markers (Garcia et al. 1995; Burow et al. 2001). However, these marker assays are not ideal for use in MAS. RFLP, although providing high-quality codominant information, is labor intensive and time consuming, requires large amount of DNA, and is dependent on radioisotope-based protocols, while RAPD analysis provides only dominant information and frequently suffers from reproducibility problems. However, it is possible to convert tightly linked RFLP markers into PCR-based sequence-tagged site (STS) markers (Olson et al. 1989) and similarly to convert RAPD bands into sequence-characterized amplified region (SCAR) markers (Paran and Michelmore 1993). STS and SCAR assays provide substantially more reliable markers with a relative high-throughput potential. Most of the genetically mapped RFLPs have also been sequenced. Like RAPDs, AFLPs are also a dominant marker class but can be converted into codominant marker such as SCAR (Paran and Michelmore 1993; Negi et al. 2000; Huaracha et al. 2004) and CAPS (Konieczny and Ausubel 1993). Forty-five of the 64 EcoRI/MseI primer pairs were polymorphic in cultivated peanut germplasm/cultivars and interspecific derivatives (Table 4). Herselman (2003) used two different rare cutters enzymes, EcoRI and MluI, in combination with the frequent cutter MseI, and found that both EcoRI/MseI and MIul/MseI AFLP enzyme combinations efficiently detected polymorphisms within closely related cultivated peanut accessions, although the EcoRI/MseI enzyme combination detected more fragments per primer combination (on average 67.8) as opposed to 29.1 by the MIul/MseI enzyme combination on the similar peanut accessions. Simple sequence repeat (SSR, also known as microsatellites) markers have become the assay of choice

for molecular breeding of most crops. SSR markers are valuable for a multitude of applications due to their abundance and uniformity of distribution throughout most genomes, their multiallelic, codominance inheritance, and their highly polymorphic and reproducible nature where analysis is simple and readily transferable (Weber 1990). Hopkins et al. (1999) were the first to report six polymorphic SSR markers in cultivated peanut. Further search for SSRs in peanut led to the development of 553 SSRs of which 192 SSRs were polymorphic in a diverse range of cultivated peanut accessions (Table 5). Moretzsohn et al. (2004) reported high marker transferability for markers from species related to peanut: up to 76% from species of the section Arachis and up to 45% from species of the other eight Arachis sections. Similarly, efforts at ICRISAT have supported this finding (Mace et al. unpubl. data). However, there appears to be a high level of redundancy in this approach, such that, although SSR markers from related species and genera do amplify in Arachis germplasm, few are found to be polymorphic in groundnut breeding populations. Empresa Brasileira de Pesquisa Agropecuaria (EMBRAPA) and Universidade Catolica de Brasilia (UCB), Brazil, have recently put considerable effort into large-scale development of additional SSR markers. Genomic libraries enriched for TC and AC repeats (Rafalski et al. 1996) have yielded 126 new SSR markers. To facilitate the selection of SSR-containing sequences, and the assembly of forward and reverse sequencing runs, a dedicated module (available from David Bertioli on request) has been developed for the Staden sequence assembly software (Staden et al. 2003a,b). In addition to the enriched genomic libraries, the module has also been used to “data-mine” 31 EST-SSR markers from data available in Genbank and 117 EST-SSR markers from A. stenosperma EST data. Similar efforts have also been made by ICRISAT through the library enrichment process. Based on these recent marker development projects, groundnut is now one of the best-served tropical legumes in terms of SSR markers for linkage mapping and molecular breeding (Table 5). However, it is notable that these recent advances have been driven largely by labs based in developing countries rather than by advanced labs in Western countries that have traditionally fulfilled this role. The availability of such a large number of AFLPs and SSRs markers holds great promise for diversity studies,

Chapter 3 Peanut Table 4. Polymorphic AFLP primer pairs reported in cultivated peanut accessions AFLP primer

E-AAC/M-CAT E-AAC/M-CTA E-AAC/M-CTC E-AAC/M-CTG E-AAC/M-CTT E-AAC/M-CAA E-AAC/M-CAC E-AAC/M-CAG E-AAG/M-CAC E-AAG/M-CAG E-AAG/M-CAT E-AAG/M-CTT E-ACC/M-CAA E-ACC/M-CTG E-ACC/M-CTC E -ACC/M-CAT E-ACC/M-CAC E-ACC/M-CAG E-ACC/M-CTA E-ACG/M-CAC E-ACG/M-CTA E-ACG/M-CAT E-ACG/M-CTG E-ACG/M-CTT E-ACG/M-CAG E-ACG/M-CAA E-ACG/M-CTC E-ACT/M-CAA E-ACT/M-CAT E-ACT/M-CTA E-ACT/M-CTC E-ACT/M-CTG E-ACT/M-CTT E-ACT/M-CAG E-AGC/M-CAA E-AGC/M-CTG E-AGC/M-CTT E-AGG/M-CAA E-AGG/M-CAC E-AGG/M-CAT E-AGG/M-CTA E-AGG/M-CTT E-AGG/M-CAG E-ACA/M-CAG E-ACA/M-CTC E-ACA/M-CTG E-ACA/M-CAA

Herselman 2003

Gimenes et al. 2002

∗ ∗ ∗ ∗

Dwivedi et al. 2002b

He and Prakash 2001

He and Prakash 1997

∗

∗

∗ ∗ ∗

∗ ∗ ∗

∗ ∗ ∗ ∗

∗ ∗ ∗

∗ ∗ ∗ ∗ ∗ ∗

∗ ∗ ∗

∗ ∗ ∗ ∗

∗ ∗ ∗ ∗

∗

∗ ∗ ∗ ∗ ∗ ∗ ∗

∗

∗

∗ ∗

∗ ∗ ∗ ∗ ∗

∗

∗ ∗

∗ ∗ ∗

∗ ∗ ∗ ∗ ∗ ∗

∗ ∗ ∗ ∗ ∗ ∗ ∗

∗ ∗ ∗ ∗ ∗ ∗ ∗

∗ ∗

129

Total number

26

56 226 67 122 112 38 125

Source and method of isolation

cDNA libraries from peanut cultivar Florunner and New Mexico Valencia

SSR enrichment procedure (Edwards et al. 1996)

cDNA libraries from peanut cultivar Florunner

Rafalski et al. (1996)

SSR enrichment procedure (Rafalski et al. 1996)

EMBRAPA ESTs and Genbank deposited sequences

Data-mining other sequences

Edwards enrichment process (Edwards et al. 1996)

GT CA

CT AG

AAG AAT CT

TC AC

TTG AAC

ATT GA

GA CT

GA

Most frequently repeat

32 diverse genotypes include 24 from Ferguson et al. 2004b

16 cultivated peanut accessions

16 cultivated peanut accessions

16 cultivated peanut accessions

60 cultivated peanut accessions

24

24

19

55

5

9

53

3

110

19

5

2.1 (1–4)

4.75 (4–6)

4.50 (2–8)

5.96 (2–12)

9.8 (2–27)

3.4 (2–5.7)

4.3 (2–8)

6.3 (2–14)

Results from screening cultivated germplasm Germplasm Number detecting Number of alleles represented polymorphism detected

Table 5. List of total number of SSRs and those polymorphic in cultivated peanut accessions

Tho et al. unpub data (ICRISAT)

EMBRAPA (unpub)

EMBRAPA (unpub)

EMBRAPA (unpub)

Moretzsohn et al. 2004

Ferguson et al. 2004a

He et al. 2003

Hopkins et al. 1999

Reference

130 S.L. Dwivedi et al.

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genetic mapping and MAS, and gene discovery in bacterial wilt (Mace et al. unpubl. data). In these studies, SSR markers were able to detect a surprisingly peanut. high level of polymorphism. In some cases, more than half of the markers detected polymorphism with PIC values of over 0.5. This has also opened the possi3.4.2 bility of following association mapping in cultivated Molecular Diversity peanut germplasm for the identification of markers Diversity assessment and construction of genetic link- for disease resistance. Although recent studies on genetic diversity reage maps are the two important steps in the development of molecular breeding programs. The Inter- vealed polymorphisms in cultivated peanut, the levels national Crops Research Institute for the Semi-Arid of polymorphism detected are still low for easy conTropics (ICRISAT), Patancheru, India, hosts the world struction of saturated maps. In contrast, much higher collection of 14,966 accessions of the cultivated and polymorphism is reported in wild Arachis species. 453 of wild Arachis species. These accessions differ in For example, only three of the 67 SSRs were polymany morphophysiological, reproductive, and qual- morphic on 60 cultivated accessions belonging to 6 ity traits, and in response to biotic and abiotic stresses botanical varieties, whereas 28 were polymorphic on (Rajgopal et al. 1997; Singh and Nigam 1997; Upad- two wild Arachisaccessions (A. duranensis K7988 and hyaya et al. 2001a, 2003). For enhancing the use of A. stenospermaV10309) (Moretzsohn et al. 2004) used peanut germplasm in breeding, two-core and mini- to make the diploid AA genome mapping populacore collections (ICRISAT core of 1,704 and minicore tion (see Sect. 4.3. for further details). This higher of 184 accessions and USDA core of 831 accessions and level of polymorphism greatly facilitates genetic mapcore of core 111 accessions) are reported in peanut. ping. Conventionally, the use of interspecific mapThe accessions included in the core and minicores ping populations has not been encouraged by plant have the potential to identify new sources of variation breeders because of the divergent recombination patfor use in peanut genomics and breeding (see Sect. 2.3. terns evident in such populations as compared with for further details). The minicore is good starting ma- the intraspecific breeding populations where resulterial for association mapping and for the detection of tant markers would be applied. This divergence often rare allelic variation associated with beneficial traits. leads to a loss of selective power of the marker. HowIn contrast with the historical generalization that ever, peanut is an amphiploid, viz. an allotetraploid cultivated peanut lacks genetic variation (Griesham- with two different genomes, that behaves genetically mer and Wynne 1990; Kochert et al. 1991; Bhag- as if two separate diploids are in the same cell. Thus, wat et al. 1997; He and Prakash 1997; Subramanian the application of diploid maps may be much more et al. 2000), genetic diversity studies in the last few directly applicable and effective than conventional inyears have revealed sufficient polymorphic variations terspecific mapping populations. among cultivated peanut germplasms that could be tapped to identify markers associated with beneficial traits and possibly effect marker-assisted ge- 3.4.3 netic enhancement in peanut (Hopkins et al. 1999; Mapping Population Dwivedi et al. 2001; He and Prakash 2001; Raina et al. 2001; Dwivedi et al. 2002b; Dwivedi and Gurtu Near-isogenic lines (NILs) (Muehlbauer et al. 1988), 2002; Dwivedi and Varma 2002; Gimenes et al. 2002; recombinant inbred lines (RILs) (Burr et al. 1988), Dwivedi et al. 2003a,b; Herselman 2003; Krishna et al. doubled-haploid (DH) populations (Heun 1992), and 2003; Ferguson et al. 2004b; Moretzsohn et al. 2004). advanced backcross-derived RILs are the preferred Both AFLP and SSR are useful for estimating diver- types of mapping populations in plant genomic studsity among the Arachis species and six botanical types ies as these are immortal genetic stocks (unlike F2 ) of cultivated peanut (He and Prakash 2001; Gimenes that can be recurrently tested in replicated trials across et al. 2002; Herselman 2003; Ferguson et al. 2004b; locations and seasons. Tanksley and Nelson (1996) Moretzsohn et al 2004). have proposed advanced backcross lines for the siMost recently, ICRISAT has been using SSR mark- multaneous discovery and transfer of valuable QTLs ers to analyze the genetic diversity among cultivated from unadapted and wild germplasm into elite breedgermplasm resistant to late leaf spot (LLS), rust, and ing lines. Similarly, Podlich et al. (2004) have proposed

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a refinement and expansion of this type of approach. This approach acknowledges that the size of mapping populations and the presence of a consistently adapted agronomic background across all members of that population are critically important factors for the precise and accurate mapping of economically important characters, particularly complex traits. A population of 250 to 300 F2 derived RILs should be sufficient to detect the chromosomal region (more precisely the QTL location) associated with most beneficial traits. The small population sizes all too often used for QTL detection lead to overestimation of QTL effect and underestimation of QTL number and interaction due to what is now commonly referred to as the “Beavis Effect” (Beavis 1998; Melchinger et al. 1998). Efforts are being made by ICRISAT and EMBRAPA to develop mapping populations involving A. hypogaea × A. hypogaea, A. hypogaea × wild Arachis, and wild Arachis × wild Arachis species crosses. RIL mapping populations are now available for rust, LLS, and drought for evaluation, and seeds from these populations are available upon request under the respective institutional material transfer agreement. Recently, EMBRAPA-CENARGEN (EMBRAPA Recursos Genéticos e Biotecnologia), UCB, and UNESP (Universidade Estadual de São Paulo)-Botucatu (Brazil) have developed diploid mapping populations from crosses of wild Arachis. F2 populations have been made that represent the AA and BB genomes of cultivated peanut, A. duranensis and A. ipaensis crossed with closely related species A. stenosperma and A. magna, respectively. The original hybrids are being maintained and the F2 plants multiplied by cuttings. Currently the mapping populations are of 93 plants that are being advanced to develop RILs. Through this strategy, concensus maps will be created by comparative analysis of diploid and tetraploid maps. These diploid maps will facilitate the marker-assisted introgression of a wide range of important agronomic traits into cultivated peanut. In addition, a tetraploid mapping population has been developed from the cross of a synthetic amphidiploid (A. duranensis × A. ipaensis)c with cultivated A. hypogaea.

terson et al. 1988; Lander and Botstein 1989), for cloning genes by chromosome walking (Wicking and Williamson 1991), and for developing MAS systems for desirable traits in breeding programs (Burr et al. 1983; Tanksley et al. 1989). Cultivated peanut is a disomic polyploid (2n = 4x = 40); thus in genetic maps we expect 20 linkage groups (LGs), each representing one haploid complement chromosome. Halward et al. (1993) developed the first low-density RFLP-based genetic linkage map in peanut, derived from an interspecific F2 population involving A genome diploid species A. stenosperma and A. cardenasii. This map comprised 117 markers on 11 LGs with a total map distance of ca. 1,063 cM and average marker density of 9.08 cM. Subsequently, Burow et al. (2001) reported an RFLP-based tetraploid genetic linkage map derived from a BC1 population (n = 78) from the cross: synthetic amphidiploid {[A. batizocoi K9484 × (A. cardenasiiGK10017 × A. digoi GKP10602)]4x } crossed with cv. Florunner. A. cardenasii and A. digoi are A genome while A. batizocoi belongs to B genome species. This map consists of 370 RFLP loci distributed into 23 LGs with a total map distance of 2,210 cM and average marker density of 5.97 cM. These RFLP loci will detect alleles in populations involving crosses between wild species or between A. hypogaea× wild Arachis species. They are unlikely to detect alleles in A. hypogaea × A. hypogaea crosses. In the last few years, there has been substantial progress on identifying polymorphic AFLPs and SSR markers (see sections 4.1 and 4.2). A partial AFLPbased genetic linkage map, based on an intraspecific A. hypogaea cross, has been developed for mapping aphid resistance that mapped 12 markers to five LGs covering a map distance of 139.4 cM (Herselman et al. 2004). Significant progress has also been made at ICRISAT in the mapping of disease resistances using AFLP, SSR, and RGA markers (Mace et al. unpubl. data). Preliminary maps comprise around 75 markers across 16 LGscovering a map length of 423 cM (rust resistance) and around 70 markers across 9 LGs covering a map length of 175 cM (LLS resistance). A skeleton map has also been generated for resistance to ELS, and mapping of bacterial wilt resistance is ongoing. As new SSR markers are becoming available, they are 3.4.4 being integrated into these maps in order to increase Genetic Linkage Map the total map length and marker density. The new SSR markers developed at EMHigh-density genetic linkage maps are a useful basis for identifying markers tightly linked to QTLs BRAPA/UCB and ICRISAT are being used for the that contribute to economically important traits (Pa- development of diploid maps of the A and B genomes.

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the diploid AA genome Arachis mapping population (David Bertioli, unpubl. data). Comparing the map positions of ECS markers in different legumes should allow the development of a preliminary comparative map across legume crops and model systems. The inclusion of Arachis within this analysis is likely to be especially informative because Arachis together with lupin occupies a basal phylogenetic position within the Papilionoideae. This work will then enable model organisms with wellcharacterized genomes to serve as genic frameworks for the poorly characterized Arachis genome. The genomes of two model legumes are currently being sequenced, L. japonicus by the Kazusa DNA Research Institute (http://www.kazusa.or.jp/lotus/) and M. truncatula by the Medicago truncatulaConsortium 3.4.5 (http://www.medicago.org/genome/). These model Comparative Mapping with Model Genomes legumes have genomes of about 420 to 470 Mbp (Young et al. 2003), almost ten times smaller An ongoing project between Aarhus University, Den- than that of the A. hypogaea genome (3,479 Mbp, mark, EMBRAPA, and UCB Brazil aims to integrate http://www.rbgkew.org.uk/cval/database1.html). Arachis into a single unified legume genetic frame- Thus, these conserved gene-based markers will work using “legume family anchor markers” for greatly assist researchers to quickly identify orthololegumes. These are gene-based markers with a single gous genic markers in peanut. These will significantly homolog representation in the Arabidopsis proteome speed up the identification of candidate genes for that are being mapped in Lotus japonicus, lupin, bean, MAS and the positional cloning of genes for the development of transgenic varieties. and the diploid AA genome of Arachis. To efficiently identify potential anchor marker sequences, a computer “pipeline” that uses multispecies EST- and genome-sequence data has been devel- 3.4.6 oped. Comparison of ESTs from Medicago truncatula, BAC Libraries and New Generation Markers Glycine max, and L. japonicus identifies evolutionary conserved sequences (ECSs) that have a high probability of being conserved in less well-characterized BAC Libraries legumes. Alignment of ECSs and a corresponding ge- Bacterial artificial chromosome (BAC) libraries, pronomic sequence defines sets of PCR primer sites flank- viding whole-genome coverage in segments of about ing introns. Introns are targeted because purifying se- 100 kb, have become central to a wide range of goals in lection is less stringent for coding regions, and they biology and genomics. Recently, the first large-insert are more likely to be polymorphic. The length of in- DNA library for A. hypogaea was constructed (B. Yuktrons is important because short introns are less likely sel and A.H. Paterson, manuscript in preparation). to be polymorphic than longer ones and because the The library contains 182,784 clones; only 5,484 (3%) final PCR reaction is limited to a maximum ampli- of them had no inserts; and average insert size is con size of ∼2.5 kbusing standard polymerases. Fi- 104 kb. About 1,208 (0.66% of) clones appear to cornally, only marker sequences with single homologs in respond to the 45S ribosomal DNA, and only 9 clones the Arabidopsis proteome are selected for further de- hybridize to chloroplast probes. The depth of covervelopment. Using this approach, 867 ECSs have been age is estimated to be 6.5 times, allowing the isolation identified, and these are being used for marker devel- of virtually any single-copy locus. The identification opment. Polymorphisms are identified by size or se- of multiple loci by most probes in polyploids comquence differences in PCR products, amplified from plicates anchoring of physical and genetic maps. The mapping parents, and CAPs or dCAPs markers de- research group at the University of Georgia, Athens, veloped. So far, 40 markers have been developed for GA, USA, explored the practicality of a hybridizationTo date, an SSR-based linkage map of the AA genome of Arachis contains 153 SSR markers mapped on 11 LGs with a total map distance of 1,138.39 cM and 7.43 cM average marker density. In addition, a BBgenome linkage map is being made in collaboration with UNESP-Botucatu. It is anticipated that these diploid linkage maps will enable mapping of some markers and traits that would be difficult to deal with in a tetraploid background. Diploid maps will then be verified using a tetraploid mapping population based on an F2 population derived from a cross between a synthetic amphidiploid (A. duranensis × A. ipaensis)c and A. hypogaea.

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based approach for determination of map locations of BAC clones in peanut by analyzing 94 clones detected by seven different overgos. The banding patterns on Southern blots were good predictors of contig compositions. This BAC library has great potential in terms of advancing the future research about the peanut genome.

Expressed Sequenced Tags (ESTs) Hundreds of thousands of ESTs are available for soybean (Shoemaker et al. 2002; Tian et al. 2004), M. truncatula (Fedorova et al. 2002; Journet et al. 2002; htpp://www.medicago.org), and Phaseolus (Hernandez et al. 2004). In constrast, there are only 1,825 ESTs available for peanut derived from two cDNA libraries constructed using mRNA from immature pods of a drought-tolerant line (A13) and from leaves of tomato spotted wilt virus (TSWV) and leaf-spotresistant line (C33-24): NCBI GeneBank accession numbers CD037499 to CD038843. These have been successfully used to develop 44 EST-derived SSR markers of which over 20% were polymorphic among 24 cultivated peanut accessions (Luo et al. 2003). This group has also arrayed about 400 unigenes of adversity resistance on glass slides. This macroarray is being used with mRNA probes from different lines that have been exposed to various profiles of drought stress or fungal infection to identify genes related to biotic or abiotic stresses. Another group at the University of Florida, Gainesville, USA, has constructed leaf, seed, and peg/pod cDNA libraries from developmentally pooled tissues of “SunOleic 97R” (peanut cultivar with high O/L ratio). These libraries are currently being sequenced to develop gene expression profiles that will lead to greater understanding of peanut’s responses to various abiotic and biotic stresses. It is hoped that this will provide the necessary knowledge and tools to alter peanut to achieve maximum performance under given growth conditions (Chengalrayan and Gallo-Meagher 2003). Aflatoxin is a serious quality problem in peanut. Drought and hightemperature stresses are conducive to Aspergillus flavus infection and aflatoxin contamination. Differential display reverse transcription PCR (DD-RTPCR) (Liang and Pardee 1992) and EST/microarray are now used to locate multiple genes that enable plants to withstand biotic and abiotic stresses. Using DD-RT-PCR, Guo et al. (2003) revealed that some cDNA fragments are up- or down-regulated by induced drought stress and identified a novel PLD gene

that encodes a putative phospholipase D, a primary enzyme responsible for the drought-induced degradation of membrane phospholipids in plants. They studied the PLD gene expression under drought stress in the greenhouse using two peanut lines, Tifton 8 (drought tolerant) and Georgia Green (drought sensitive). Northern analyses showed that the PLD gene expression is induced sooner by drought stress in Georgia Green than in Tifton 8. After the PLD gene in peanut is characterized, the researchers plan to attempt gene silencing using genetic transformation to suppress PLD gene expression and induce drought tolerance. An A. flavus ESTs program at USDA/ARS Southern Regional Research Center in New Orleans, LA (Yu et al. 2002) and USDA-ARS Labs at Tifton, GA has resulted in about 8000 expressed unique genes that will help to identify genes that could be used to inhibit fungal growth or aflatoxin formation by the fungi. Finally, a group at the University of Agricultural Sciences, Bangalore, India, has developed subtractive libraries for water use efficiency. ESTs from this project are being sequenced, and the most promising candidate gene markers will be mapped using an RIL population specifically designed for this purpose (Udaya Kumar et al. unpubl. data).

Transcriptional Profiling Jain et al. (2001) used an RT-PCR-based procedure (differential display) to identify cDNA corresponding to transcripts affected by water stress in peanut and identified several mRNA transcripts that are upregulated or down-regulated following water stress. With 21 primer combinations, they observed 1235 and 950 differential-display products in irrigated and drought-stressed samples, respectively. Forty-three peanut transcripts responsive to drought (PTRDs) were significantly altered due to water stress. Slot blot analysis of 16 PTRDs revealed that 12 were completely suppressed by prolonged drought while 2 were down-regulated, and 2 were up-regulated under drought-stress conditions. RNA dot-blot analysis of the 12 completely suppressed transcripts revealed that PTRD-1, PTRD-10, and PTRD-16 were expressed for a longer period in the tolerant line compared to the susceptible line. All these sequences may be useful candidate gene markers for mapping components of drought tolerance in peanut.

Chapter 3 Peanut

Variation in fatty acid profile is the major determinant of oil quality in peanut. Oils high in monounsaturated (oleic) and low in polyunsaturated fatty acids (linoleic and linolenic) are commercially and nutritionally desirable. Polyunsaturated fatty acyl residues are susceptible to oxidation, the products of which cause unpleasant odors and tastes commonly associated with rancidity. These oxidized products have potential atherogenic effects, while oils high in monosaturates have been reported to be effective in lowering cholesterol levels (St Angelo and Ory 1973; Broun et al. 1999). Mapping or isolation of genes associated with increased oleic acid accumulation would provide opportunities to alter fatty acid composition in peanut by MAS as has been achieved in soybean (Kinney and Knowlton 1998) and rapeseed (Friedt and Luhs 1999; Tanhuanpaa and Vilkki 1999). Jung et al. (2000a) isolated two cDNA sequences coding for microsomal oleoyl-PC desaturases (ahFAD2A and ahFAD2B) from the developing peanut seed with a normal oleate phenotype; these desaturases are nonallelic but homeologous genes originating from two different diploid species. The gene ahFAD2Ais expressed in both normal and high oleate peanut seeds, but the ahFAD2B transcript is severely reduced in the high oleate peanut, suggesting that the reduction in ahFAD2B transcript level in the developing seeds is correlated with a high oleate trait. Further studies revealed that a mutation in ahFAD2A and a significant reduction in levels of the ahFAD2B transcript together cause the high oleate phenotype in peanut, and that of expressed gene encoding a functional enzyme appears to be sufficient for the normal oleate phenotype (Jung et al. 2000b).

Single Sequence Polymorphism (SNPs) SNPs are the most elemental difference between genotypes, a difference in DNA sequence; therefore they are the most direct means of DNA fingerprinting that can ever exist. SNPs have replaced SSRs as the preferred marker in mammalian genomics. A wide range of emerging, high-efficiency techniques for finding SNPs, even when polymorphism is rare (such as in peanut), sets the stage for use of genomic tools on a scale not previously possible. SNPs provide enabling biotechnologies in the form of low-cost molecular markers and genetic fingerprinting tools suitable not only for plant variety protection but broadly applicable to the implementation of environmentally friendly

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genetic solutions to challenges that increase the economic and environmental costs of peanut production. SNP discovery in polyploids such as peanut poses a problem not faced in diploids, i.e., that most PCR amplification products are likely to be mixtures of sequences from two or more divergent loci. This precludes many otherwise attractive SNP discovery strategies based on direct resequencing of PCR products. It remains to be determined exactly the degree to which this will be a problem in peanut as there is very little comparative sequence data for both cultivated peanut and its diploid progenitors. Further, the antiquity of polyploid formation in peanut will also bear on this problem – if polyploid formation were ancient, there might be an appreciable degree of “diploidization” or loss of some duplicated gene copies, suggesting that reasonable populations of truly single-copy loci might be found. However, polyploid formation is thought to be relatively recent in view of the generally low polymorphism rate. Further investigation of the structure and evolutionary history of the peanut genome will be needed to evaluate various SNP discovery strategies and implement optimal strategies across the genome and the gene pool. A few SNPs for particular high-priority genes have already been discovered. Lopez et al. (2000) used peanut lines with a low (T-90) or high (F435) oleic (O) to linoleic (L) fatty acid ratio to isolate and characterize the Δ12 -fatty acid desaturase (FAD) gene. The Δ12 -FAD contains a putative intron, the coding region at the 3 end, and an open reading frame (ORF) of 1,140 bp encoding 379 amino acids. A comparison of coding sequences from the high and low oleic acid genotypes revealed several SNPs: one SNP in the flanking region at 229 bpupstream of the start codon and a cluster of four SNPs in the coding region. Two polymorphisms appear to be associated with the high O/L trait. The first is an “A” insertion 442 bp after the start codon that shifts the amino acid reading frame, probably resulting in a truncated, inactive protein and the loss of one of three histidine boxes believed to be involved in metal ion complexation required for the reduction of oxygen and another polymorphism at 448 bp from the start codon that results in an amino acid change. Several independently derived backcross lines with high O/L ratio had either the “A” insertion or the amino acid substitution. This association of the molecular polymorphisms with the low and high oleate trait in peanut should allow peanut breeders to develop an effec-

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tive and low-cost molecular assay for the high O/L a good resource for the peanut research and breeding community. trait.

Resistant Gene Analog (RGA) Plants have distinct mechanisms for defending themselves against diseases. One of these involves the specific recognition of, and response to, pathogens. Many of the genes that control this type of resistance encode proteins with an NBS (nucleotide-binding site) domain (Meyers et al. 1999). The only function so far associated with the NBS in plants is disease resistance. Amino acid motifs within the NBS can be used to design degenerate PCR primers that amplify diverse NBS encoding regions from plant genomic or total cDNA. These NBS encoding regions, isolated by cloning and converted into genetic markers, have, in some studies, been shown to be genetically linked to known R-genes, or indeed to be fragments of the known R-genes themselves (Kanazin et al. 1996; Aarts et al. 1998; Collins et al. 1998, 1999, 2001; Shen et al. 1998; Hayes and Saghai-Maroof 2000; Donald et al. 2002). NBS encoding regions are therefore ideal candidate gene markers for disease resistances in peanut. There is a very large number of NBS encoding regions in plant genomes (about 150 in Arabidopsis and many more in larger genomes) potentially creating a high level of redundancy for this process. However, since many resistance genes occur in clusters it may not be necessary to specifically detect the correct candidate gene but instead effective MAS systems may be derived from any resistance gene analog in the correct genomic region. Thus, Bertioli et al. (2003) have used degenerate primers to isolate 78 complete NBS encoding regions from genomic DNA of a number of Arachis species. EMBRAPA and UCB have been working to convert these NBS sequences to molecular markers for the diploid AA genome mapping population (see Sect. 4.3 for further details). Southern blots using NBS-based probes show high polymorphism, in many cases cosegregation of homologs, and often differences in the numbers of homologs between the mapping parents. In our opinion it certainly is worthwhile to place the major resistance gene clusters on the Arachis genetic map. Incorporated within a framework of transferable PCR-based markers (SSRs), these markers should serve as

3.5 Successes and Limitations of Conventional Breeding in Peanut Progress in conventional peanut breeding has recently been reviewed elsewhere (Dwivedi et al. 2003a). Peanut-breeding programs, in developed and developing countries, have made significant progress toward developing cultivars with crop durations ranging from 90 to 150 d and pod yield potentials from 3.0 t ha−1 to 9.0 t ha−1 . However, farmers in most countries do not come close to realizing these types of yields (i.e., world average yield of 1.35 t ha−1 ). The highest average national yields are 2.6 t ha−1 in China and and 3.5 t ha−1 in the USA) (FAO 2003), although even higher yields have been reported in isolated farmers’ fields in China and Zimbabwe (Smartt 1978; Yanhao and Caibin 1990). Resistance to rust, bacterial wilt, and groundnut rosette virus (GRV) has been successfully incorporated into improved genetic background. Bacterialwilt-resistant cultivars in Southeast Asia and the Far East and GRV-resistant cultivars in sub-Saharan Africa are now grown on large areas. A few cultivars with moderate resistance to rust and leaf spots have also been developed; however, these have not become popular among farming communities in the semiarid tropics because of their relatively long duration, low shelling out-turn, and inferior pod/seed characteristics compared to preferred cultivars. More recently, several peanut-breeding programs have been successful in diluting this undesirable linkage, facilitating the development of breeding lines with a shorter duration and moderate resistance to rust and/or LLS plus excellent pod/seed characteristics (Upadhyaya et al. 2002b). In contrast, peanut breeders have not been successful in developing cultivars that show complete resistance to Aspergillus flavus in order to eliminate aflatoxin contamination. Several germplasm accessions and breeding lines are available that offer various components of genetic resistance (such as resistance to pod infection, to seed invasion, and to aflatoxin production). But again, when these traits are introgressed into breeding programs, the genetic resistance has not

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improved over what is already available in germplasm lines. Interspecific crossing and selection has resulted in the release of two nematode resistant varieties, Coan and NemaTAM, in the USA (Simpson and Starr 2001; Simpson et al. 2003). Conventional breeding has had some success in selecting for drought tolerance in peanut. However, trait-based (specific leaf area, wateruse efficiency, amount of water transpired, transpiration efficiency, and harvest index) selection is likely to be a more rewarding strategy to substantially enhance drought tolerance. ICRISAT has developed breeding lines originating from trait-based selection that are being compared with breeding lines originating from conventional selection for their response to drought and yield potential (Nigam et al. 2003a). Peanut oil quality is determined by the ratio of oleic (O) fatty acid/linoleic (L) fatty acid: a higher ratio results in a better storage quality of the oil and longer shelf life of peanut products. With the availability of peanut germplasm with an exceptionally high O/L fatty acid ratio (Norden et al. 1987), US peanut breeders have been successful in transferring this trait into improved genetic backgrounds, and several newly developed cultivars with improved oil chemistry are now commercially grown in the USA.

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Mag gall formation. RAPD and RFLP markers closely linked to the resistance loci have been identified (Table 6). The RFLP loci R2430E and R2545E are easy to score and sufficiently close to the resistance allele for an acceptable selective power. Similarly, a RAPD marker, Z3/265, linked at 10 cM and 14 cM from Mag and Mae, respectively, has been converted into a SCAR marker and RFLP probe that confirmed linkage with nematode resistance. Association of an RFLP probe R2430E linked to a locus for resistance to root-knot nematode race 1 in four breeding populations has further validated these markers. US peanut breeders now routinely use these markers to select for nematode resistance. RAPD markers associated with resistance to southern corn rootworm, ELS, and cylindrocladium black rot have also been reported (Table 6). Aphid is a vector of GRV that causes substantial yield losses in peanut production in sub-Saharan Africa, and identification of markers linked with aphid resistance should help peanut breeders select for combined resistance to vector and the GRV. A single recessive gene that confers resistance to aphid has been mapped on LG 1, 3.9 cM from a marker originating from the susceptible parent explaining 76% of the phenotypic variation for aphid resistance (Herselman et al. 2004). Resistance to a number of diseases (ELS and LLS, rust, and bacterial wilt) is being mapped using SSR and AFLP markers at ICRISAT. Preliminary results have identified a number of markers closely linked 3.6 to QTL for each resistance trait (Mace et al. unpubl. Biotechnological Applications data). to Genetic Enhancement in Peanut Urgent needs include the development of large numbers of user-friendly genetic mapping tools; sequencing of substantial populations of expressed se3.6.1 quences from diverse tissues, genotypes, and stress Marker/Trait Associations profiles; assembly of a genetically anchored physical map and its alignment to the emerging model legume Unlike with other oilseed crops such as soybean, genomes such as Lotus and Medicago; and sampling oilseed rape, and sunflower, the genomics and molec- the gene-rich regions to quantify the additional inforular breeding of peanut is still in its infancy. Although mation that may be gained by further sequencing of peanut is a complex polyploid (like oilseed rape), the peanut genome (Paterson et al. 2004). the primary reason for slow progress is the lack of detectable molecular variation in cultivated peanut. This problem has been somewhat resolved by the 3.6.2 large-scale development of SSR markers. A few eco- Unlocking the Genetic Variation nomically important traits have now been mapped in from Wild Genetic Resources peanut. Resistance to root-knot nematode has been mapped using RFLP and/or RAPD assays in an inter- Although there is high morphological diversity specific cross A. hypogaea Florunner × wild Arachis among varieties and landraces of A. hypogaea, species. Two dominant genes confer resistance to molecular genetic diversity and variability for some root-knot nematode: Maerestricts egg number and important traits of agronomic interest are low.

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Table 6. Summary of marker-trait relationships reported in mapping populations derived from interspecific crosses in peanut Trait

Root-knot nematode (Meloidogyne arenaria (Neal) Chitwood)

Early leaf spot and southern corn rootworm

Summary of DNA markers linked with beneficial traits

Arachis hypogaea × Wild Arachis species crosses BSA identified RAPD markers, RKN410, RKN440, and RKN229, linked with nematode resistance in BC4 F2 population of the cross Florunner × TxAG7 and further validated by screening 21 segregating BC4 F2 and 63 BC5 F2 single plants. Recombination fraction between RKN410 and resistance and between RKN440 and resistance was 5.4 cM and 5.8 cM, respectively. These two markers identified a resistance gene derived from either A. cardenasii or A. digoi and were closely linked to each other. Marker RKN229, that inherited from A. cardenasii or A. digoi, was 9 cM away from resistance locus. Two dominant genes conditioning resistance to the root-knot nematode reported in segregating F2 populations involving a root-knot nematode introgression line GA 6 (A. hypogaea × A. cardenasii) and a highly susceptible recurrent parent PI 261942. The gene Mae restricts egg number and Mag gall formation. A RAPD marker Z3/265 was linked at 10 cM and 14 cM from Mag and Mae, respectively. They cloned this marker to make SCAR and RFLP probes, and these markers confirmed the linkages with nematode resistance. An RFLP probe R2430E linked to a locus for resistance to Meloidogyne arenaria race 1 in four breeding populations and three peanut lines, demonstrating that RFLP probe R2430E linked to nematode resistance provide a useful selection method for identifying resistance to the peanut root-knot nematode. Three RFLP loci (R2430E, R2545E, and S1137E) linked with resistance to nematode at distances of 4.2 to 11.0 cM in BC2 F2:4 population of the cross Florunner × TxAG7. R2430E and R2545E are easy to score and sufficiently close to the resistance allele that can be used with a high level of confidence to select resistant progenies based on marker information. Evaluated six polymorphic RAPD markers (AD 1, AI 11, AI 19, AJ 19, AK 20, and AN 15) for components of resistance to early leaf spot (ELS) and southern corn rootworm resistance in F2 population involving A. hypogaeacultivar (NC 7) and ELS resistant tetraploid interspecific derivative NC GP WS 1, and established association between RAPD markers and sporulation, lesion diameter, and defoliation and for southern corn rootworm resistance.

Reference

Burow et al. 1996

Garcia et al. 1996

Seib et al. 2003

Choi et al. 1999; Church et al. 2000

Stalker and Mozingo 2001

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Table 6. (continued) Trait

Summary of DNA markers linked with beneficial traits

Reference

Arachis hypogaea × Arachis hypogaea crosses Early leaf spot and cylindrocladium black rot Aphid

Cylindrocladium black rot resistance was associated with RAPD marker AM 1101 and ELS sporulation was associated with AM 1102 in F2 population of the cross NC 7 × PI 109839. Aphid resistance in ICG 12991 was mapped on linkage group 1, 3.9 cMfrom a AFLP marker originating from the susceptible parent, that explained 76.1% of the phenotypic variation for aphid resistance.

This is because of an extreme genetic bottleneck at the origin of this species. Peanut evolved through the hybridization of two wild diploid species followed by spontaneous duplication of chromosomes. The resultant allotetraploid (or amphidiploid) plant would have captured good hybrid vigour but been reproductively isolated from its wild relatives. There are also good reasons to believe that the lack of allelic diversity in A. hypogaea has also led to genetic restrictions to increasing productivity. The best sources of disease resistance genes are found in wild species (Dwivedi et al. 2003a). A. monticola is the only wild relative that is sexually compatible with cultivated A. hypogaea. A few cultivars including Spancross and Tamnut 74 have been developed that include A. monticola within their ancestry (Isleib et al. 2001). However, the bulk of agronomically useful genetic diversity lies in the diploid species. There are three main pathways that have been proposed for the incorporation of this diversity into breeding programs (Simpson 2001): (1) A diploid wild species is crossed with A. hypogaea to generate a sterile triploid hybrid. This hybrid is treated with colchicine to double the chromosomes and produce a hexaploid plant with 60 chromosomes, which is crossed and backcrossed with A. hypogaea until the progeny regains the normal chromosome number of 40. (2) Two wild species, one with genome type AA and the other BB, are treated with colchicine to create tetraploids. These are then crossed to give a plant with a genome type AABB that is then crossed and backcrossed with A. hypogaea to regain the

Stalker and Mozingo 2001

Herselman et al. 2004

cultivated agronomic background while selecting for the exotic trait of interest. (3) Two wild diploid plants are crossed; the primary hybrid is treated with colchicine to double the chromosomes and produce a synthetic amphidiploid (allotetraploid). This amphidiploid is then crossed and backcrossed with A. hypogaea. This pathway is likely to be most successful when species with AA and BB genomes are used to make this primary cross, as synthetics with other genomes may not be readily cross fertile with cultivated peanut. The first pathway has been successfully used for the development of new varieties (reviewed by Dwivedi et al. 2003a and Holbrook and Stalker 2003). The second pathway is reported to have had limited success because of sterility problems. The third (resynthesis) pathway essentially attempts to artificially recreate events similar to those that gave rise to the evolutionary speciation of A. hypogaea. A variant of this pathway in a cross has led to the development of cultivars that incorporate wild resistance genes (Simpson and Starr 2001; Simpson et al. 2003). The crossing used a hybrid between two AA-genome species (A. cardenasii and A. diogoi) as the A donor crossed with A. batizocoi as the B donor. However, it is now known that A. batizocoi has a very different genome to the B genome of A. hypogaea. For instance, the chromosomes of A. batizocoi have centromeric heterochromatic bands absent in the B genome of A. hypogaea. Nevertheless, these contrasting differentiations did not lead to the sterility problems that might have been expected.

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Nowadays our knowledge of the affinities of the genomes of species within the taxonomical section Arachis are much better defined. In particular, the wild species with genomes most similar to the ancestral genomes of A. hypogaea have been identified: A. duranensis as the contributor of the A genome and A. ipaensis as the contributor of the B genome (Seijo et al. 2004). With this in mind EMBRAPA scientists, in collaboration with Charles Simpson of Texas A&M University, USA, has recently undertaken work to “resynthesize” A. hypogaea using the ancestral and related species (A. Fávero et al. unpubl. data). This approach of resynthesis is attractive because it may minimize both sterility barriers and suppression of recombination, both major barriers in the utilization of wild species in breeding. The resynthesis of allopolyploid crops has been successfully used for introgressing exotic traits in both oilseed rape (Akbar 1989; Chen and Heneen 1989; Lu et al. 2001) and wheat (Fernandes et al. 2000). So far, five synthetic Arachis amphidiploids have been generated: [A. hypogaea × (A. ipaensis × A. duranensis)c , A. hypogaea × (A. hoehnei × A.cardenasii)c , A. hypogaea × (A.aff.magna × A. villosa)c , (A. aff.magna × A.aff.diogoi)c , and (A. hoehnei × A. helodes)c ]. Fertile hybrids from crosses between three of these with A. hypogaea × (A. hoehnei KG30006 × A. cardenasii GKP10017)c , A. hypogaea× (A. aff.magna V6389 × A. villosaV12812)c , and A. hypogaea × (A. ipaensisKG30076 × A. duranensis V14167)c have been obtained. The c in the description of the amphidiploid crosses indicates that the plants have been treated with colchicine and have chromosome number 2n = 40. The accessions used for these crosses were chosen based on the results of bioassays with late leaf spot (LLS) [Phaeoisariopsis personata(Berk. and Curtis) Deighton] and rust (Puccinia arachidis Speg) isolates from Brazil. It was noteworthy that all of the 97 wild accessions tested had higher levels of resistance than the 10 control cultivars of A. hypogaea, and that there was great heterogeneity within species as regards disease resistance. Therefore, it is invalid to regard a particular species as being uniformly resistant against any of the fungi tested. These synthetic amphidiploids incorporate new disease resistance genes from both the A and B genomes. The usefulness of resistances of these wild sources has been further confirmed in greenhouse screens using a severe combined challenge with LLS and rust. All A. hypogaeacultivars were severely affected, many losing

almost all of their photosynthetic leaf area, while all synthetic amphidiploids and their F1 hybrids with A. hypogaea showed high levels of resistance (A. Fávero et al. unpubl. data). In order to efficiently use the synthetic amphidiploids in prebreeding, it will be necessary to apply foreground and background marker-assisted introgression and backcross breeding. Genetic maps, constructed with markers that have good transferability across species and that are reasonably easy to use (SSRs would seem to be the best choice), will facilitate the effective introgression of target traits while simultaneously efficiently eliminating the remainder of the wild species genome. This approach will be heavily dependent on the microsatellite-based genetic maps that have already been developed for the A genome and that are ongoing for the B genome. These diploid maps will be validated in a tetraploid population derived from a cross of an amphidiploid with A. hypogaea. The combination of SSR-based genetic maps of diploid species and synthetic amphidiploids incorporating various exotic genomes are first steps toward a new molecular-enhanced paradigm in peanut breeding that will unlock the value of wild Arachis germplasm that has been hitherto largely beyond the reach of most breeders. New peanut varieties incorporating wild Arachis genes will have improved resistance to biotic stresses and tolerance to abiotic stress together with allelic combinations for enhanced yield potential and increased quality profiles that would never have been possible through conventional approaches.

3.6.3 Transgenics An efficient tissue culture and transformation system to introduce foreign DNA into peanut has been reported (Sharma and Anjaiah 2000), and transgenic peanuts carrying genes for resistance to TSWV, lesser cornstalk borer, and sclerotinia blight in the USA; a gene carrying resistance to peanut stripe virus in China; and genes carrying IPCV cp or IPCV replicase for resistance to peanut clump virus, GRAV cp , for resistance to groundnut rosette assistor virus (GRAV), rice chitinase for resistance to fungal diseases, and drought responsive elements of Arabidopsis(rd29A:DREB1A) for improving drought tolerance have been produced at ICRISAT (Table 7). These transgenics are in var-

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141

Table 7. List of transgenics carrying genes for resistance to peanut clump virus, groundnut rosette assistor virus, tomato spotted wilt virus, peanut stripe virus, fungal and soil born diseases, drought and allergens in peanut Trait

Gene and host cultivar Current status of transgenics

Reference

Peanut clump virus (PCV)

IPCV cp or IPCV replicase gene

ICRISAT 2004

Groundnut rosette assistor virus (GRAV) Tomato Spotted Wilt Virus (TSWV) Peanut Stripe virus (PStV) Fungal diseases (rust and leaf spots) Sclerotinia blight (Sclerotinia minor)

GRAV cp gene

Drought

Allergens

2-year field trials of transgenic peanuts having coat protein-mediated resistance (IPCV cp12, IPCV cp12, and IPCV cp51) or replicase-mediated resistance (IPCV rep3) have consistently shown resistance to PCV. Over 50 T2 -T3 generation transgenic lines containing GRAV cp gene characterized for gene integration and expression using RT-PCR and Southern hybridization.

ICRISAT 2004

Nucleocap-sid protein gene

The transgenic peanut showed lower TSWV incidence in comparison to nontransgenic control, both in the field and in controlled environment conditions.

Yang et al. 2004

Coat protein gene

Transgenic plant carrying copies of viral coat protein gene exhibited high levels of resistance to PStV.

Dietzgen et al. 2004

Rice chitinase gene

Thirty-six transgenic plants were evaluated for resistance to rust using a detached leaf technique and at 25 d after inoculation, 7 and 20 transgenic plants showed over 85% and 50% reduction in rust pustules, respectively. Antifungal gene A 3-year study of evaluation of several transgenic lines containing antifungal gene under high disease pressure revealed an average reduction of 32%S. minor infection compared to susceptible control Okrun and were comparable for shelling percentage and 100-seed weight to that of nontransgenic control Okrun. Two lines consistently averaged S. minorincidence similar to resistant control Southwest Runner. Oxalate oxidase gene Transgenic peanuts containing oxalate oxidase gene, evaluated in greenhouse, expressed higher levels of oxalate oxidase activity than untransformed controls. 79% of Perry, 78% of Wilson, and 35% of NC7 transgenics showed significantly elevated expression, and few selected lines showed 3 to 4 times as much activity as untransformed controls. rd29A:DREB1A T2 transgenic lines revealed positive gene integration and expression. DDRT-PCR test performed on these lines, subjected to various abiotic stresses (salinity, high and low temperature) under in vitro conditions, revealed the up and down regulation of several mRNAs besides identifying some new mRNA clones. Stable transgenic peanut with knock down expression p DK2 construct and a plasmid p CB13 of Ara h2 gene produced and Northern hybridization revealed that Ara h2gene is expressed only in peanut seeds containing hygromycine marker and not in vegetative tissues.

ICRISAT 2004

Chenault and Melouk 2003

Livingstone et al. 2003

ICRISAT 2004

Konan et al. 2002

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ious stages of characterization under containment glasshouse and/or controlled field evaluations. In addition, Peggy Ozias-Akins’s group in Tifton/Georgia, USA, has adopted a three-tier approach to develop transgenic peanut by incorporating resistance to insect damage using a Bt gene, resistance to fungal growth using the tomato anionic peroxidase gene (tap1), or antifungal peptide D4E1, and inhibition of aflatoxin biosynthetic pathway using the lipoxygenase gene lox1(Ozias-Akins et al. 2002). Transgenics clearly offers tremendous potential for introgressing genes not available in the Arachisgenus or that have pleiotropic effects on yield potential or quality profiles. Once favorable genes have been introduced into a cultivated peanut genotype, they can quickly be introgressed in a wide range of locally adapted and preferred backgrounds through marker-assisted breeding.

3.7 Conclusions and Future Outlook The genus Arachis probably arose during the tertiary period, and the genus now contains genetically diverse accessions and species. Most of this variation is not directly available to plant breeders because genes of interest are within genomes that are too diverged from the genome of A. hypogaea for sexual compatibility and too large and uncharacterized to allow positional cloning. The genomes of accessions within the taxonomical section Arachis are, however, available through the routes detailed in Sect. 6.2. To date, however, there are very few examples of released cultivars that contain genes from wild species. The events that gave rise to A. hypogaea imposed a severe genetic bottleneck at the origin of the crop, and the genetic diversity in cultivated germplasm today results from only some 4,000 years of mutation and selection. In addition, a second genetic bottleneck has been imposed by modern breeding programs, which so far have only used a tiny fraction of the variation within A. hypogaea. Therefore, commercial cultivars grown today have a very narrow genetic base, and the allelic combinations available from working with elite germplasm are limited. Therefore, there is an urgent need to broaden the genetic base of cultivated peanut germplasm.

Genetic resources of peanut currently available in germplasm banks consist of ca. 15,000 cultivated and 400 wild Arachis species accessions maintained at ICRISAT, USDA, and CENARGEN. These genetic resources harbor genes for resistance/tolerance to biotic and abiotic stresses in addition to showing variability for a range of morphophysiological, reproductive, and seed quality traits. There are also two well-defined core and minicore collections representing the majority of variation present in the cultivated peanut germplasm. These are good resources to analyze genetic relationships and detect allelic variation linked with beneficial traits through association mapping, and they provide an effective entry point to the entire collection. Peanut genomics has progressed rapidly during the past decade such that the peanut genomic resources now include availability of a large number of RAPD, AFLP, RFLP, and SSR markers with EST and SNP markers just beginning to emerge. These markers are being used in genetic diversity and marker-trait associations and for the development of genetic linkage maps. An RFLP-based map of tetraploid Arachis, derived from an interspecific backcross population, is already available to peanut researchers. However, it has limited value to peanut breeders as the RFLP loci placed on this map are unlikely to detect polymorphic alleles in intraspecific cultivated A. hypogaea crosses. There is an urgent need to develop more PCRbased genetic linkage maps as only a few sparsely spaced AFLP- and SSR-based genetic maps have been reported for intraspecific A. hypogaea crosses. Similarly, there is need to saturate the preexisting maps with more PCR-based markers. Considerable effort has been directed toward generating new SSR markers. EST and SNP markers are already available for oil quality and drought tolerance, but large-scale development of EST, RGA, and SNP markers will now be of substantial importance. Efforts are also being directed toward developing an SSR-based linkage map of the A and B diploid genomes that should enable mapping of exotic and/or complex traits (especially polygenic ones) that would have been difficult or impossible to deal with in a conventional tetraploid background. Peanut is also being included in the development of a consensus legume genetic linkage map using legume family anchor markers. Eight hundred sixtyseven evolutionary conserved sequences (ECSs) that are likely to be well conserved within the legumes have been identified, and these are being used for marker development. Comparison of the map positions of these markers in different legumes should

Chapter 3 Peanut

allow the development of a single genetic framework map for legumes that in turn should assist peanut researchers to use genomic information from the model plants and facilitate the generation of genic markers, the identification of candidate genes, and positional cloning in Arachis. Transgenic peanut carrying genes for resistance to several fungal and virus diseases and for some insect pests, which are in various stages of evaluation, will be available to peanut researchers for introgression into their target peanut cultivars. It is proposed to adopt an approach that combines transgenic techniques, MAS, and conventional breeding to provide intrinsic, low-cost, and environmentally benign solutions to the many challenges that increase the cost and risk of peanut production and cause peanut to fall short of consumer needs and desires.

Acknowledgement. The senior author wishes to thank the staff of ICRISAT library for their tireless efforts to conduct literature searches and arrange reprints; to KDV Prasad for text editing, references, and tables; and to ICRISAT management for providing the opportunity to contribute to the writing of this invited book chapter.

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Bock KR, Nigam SN (1988) Methodology of groundnut rosette screening and vector-ecology studies in Malawi. In: Coordinated Research on Groundnut Rosette Virus Disease, ICRISAT. Patancheru, AP, India, pp 6–10 Broun P, Gettner S, Somerville C (1999) Genetic engineering of plant lipids. Annu Rev Nutr 19:197–216 Burks AW, Williams LW, Helm RM, Connaughton C, Cockrell G, O’Brien TJ (1991) Identification of a major peanut allergen, Ara h I, in patients with atopicdermatitis19 and positive peanut challenges. J Allergy Clin Immunol 88:172–179 Burks AW, Williams LW, Connaughton C, O’Brien TJ, Helm RM (1992) Identification and characterization of a second major peanut allergen Ara h II, with the use of the sera of patients with atopic dermatitis and positive peanut challenge. J Allergy Clin Immunol 90:962–969 Burks AW, Cockrell G, Stanley JS, Helm RM, Bannon GA (1995) Recombinant peanut allergen Ara h1 expression and IgE binding in patients with peanut hypersensitivity. J Clin Invest 96:1715–1721 Burow MD, Simpson CE, Paterson AH, Starr JL (1996) Identification of peanut (Arachis hypogaea L.) RAPD markers diagnostic of root-knot nematode (Meloidogyne arenaria (Neal) Chitwood) resistance. Mol Breed 2:369–379 Burow MD, Simpson CE, Starr JL, Paterson AH (2001) Transmission genetics of chromatin from a synthetic amphiploid in cultivated peanut (Arachis hypogaea L.): Broadening the gene pool of a monophyletic polyploidy species. Genetics 159:823–837 Burr B, Evola SV, Burr FA, Beckmann JS (1983) Application of restriction fragment length polymorphism to plant breeding. In: Setlow JK, Hollaender A (eds) Genetic Engineering, Vol 5. Plenum, New York, pp 45–49 Burr B, Burr FA, Thompson KH, Albertson MC, Stubber CW (1988) Gene mapping with recombinant inbred in maize. Genetics 118:519–526 Butler DR, Wadia KD, Jadhav DR (1994) Effect of leaf wetness and temperature on late leaf spot infection of groundnut. Plant Pathol 43:112–120 Chandran K, Pandya SM (2000) Morphological characterization of Arachis species of section Arachis. Plant Genet Resource Newslett 121:38–41 Chen B-Y, Heneen WK (1989) Resynthesized Brassica napus L: a review of its potential in breeding and genetic analysis. Hereditas 111:255–263 Chenault KD, Melouk HA (2003) Resistance to Sclerotinia minor infection in transgenic peanut – a three year study. Proc Am Peanut Res Edu Soc 35. http://www.apres.okstate.edu/ old%20proceedings/APRES%202003%20Proceedings%20 vol%2035.pdf#search=‘APRES%202003’ Chengalrayan K, Gallo-Meagher M (2003) Developing peanut expressed sequence tag (EST) libraries. ASGSB 2003 Annual Meeting (Abstr) Choi K, Burow MD, Church G, Burow G, Paterson AH, Simpson CE, Starr JL (1999) Genetics and mechanism of resistance

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Chapter 3 Peanut Rao GVR, Wightman JA (1999) Status of the integrated management of groundnut pests in India. In: Upadhyaya RK, Mukerji KG, Rajak RL (eds) IPM System in Agriculture 5. Aditya, New Delhi, pp 435–459 Rao RDVJP, Reddy DVR, Nigam SN, Reddy AS, Waliyar F, Reddy TY, Subramanyam K, Sudheer MJ, Naik KSS, Bandhyopdahyay A, Desai S, Ghewande MP, Basu MS, Somasekhar (2003a) Peanut stem necrosis: a new disease of groundnut in India. Information Bull. No. 67. ICRISAT, Patancheru, AP, India, p 12 Rao NK, Reddy LJ, Bramel PJ (2003b) Potential of wild species for genetic enhancement of some semi-arid food crops. Genet Resource Crop Evol 50:707–721 Ratna AS, Rao AS, Nolt BL, Reddy DVR, Vijayalakshmi, McDonald D (1991) Studies on the transmission of Indian peanut clump virus disease by Polymyxa graminis. Ann Appl Biol 118:71–78 Reddy DVR, Rajeshwari R, Izuka N, Lesemann DE, Norlt BL, Goto T (1983) The occurrence of Indian peanut clump, a soil-borne virus disease of groundnut (Arachis hypogaea L.) in India. Ann Appl Genet 102:305–310 Savage GP, Keenen JL (1994) The composition and nutritive value of groundnut kernels. In: Smart J (ed) The Groundnut Crop: A Scientific Basis of Improvement. Chapman and Hall, London, pp 173–213 Seib JC, Wunder L, Gallo-Meagher M, Carpentieri-Pipolo V, Gorbet DW, Dickson DW (2003) Marker-assisted selection in screening peanut for resistance to root-knot nematode. APRES 35:90 (abstr) Seijo JG, Lavia GI, Fernandez A, Krapovickas A, Ducasse D, Moscone EA (2004) Physical mapping of the 5s and 18s-25s rRNA genes by FISH as evidence that Arachis duranensis and A. ipaensis are the wild diploid progenitors of A. hypogaea (Leguminosae). Am J Bot 91:1294–1303 Seetharama N, Dwivedi SL, Saxena NP (2003) Enhancing productivity of rainfed oilseed crops in India by mitigating effects of drought. In: Rai M, Singh H, Hegde DM (eds) National Seminar on Stress Management in Oilseeds for Attaining Self-Reliance in Vegetable Oils: Thematic papers. Indian Soc Oilseeds Res, Directorate of Oilseed Research, Rajendranagar, Hyderabad, AP, India, pp 305–318 Sharma KK, Anjaiah V (2000) An efficient method for the production of transgenic plants of peanut (Arachis hypogaea L.) through Agrobacterium temefaciens-mediated genetic transformation. Plant Sci 159:7–19 Sharma HC, Pampapathy G, Kumar R (2002) Technique to screen peanut for resistance to the tobacco armyworm, Spodoptera litura (Lepidoptera: Noctuidae) under nochoice cage conditions. Peanut Sci 29:35–40 Sharma HC, Pampapathy G, Dwivedi SL, Reddy LJ (2003) Mechanisms and diversity of resistance to insect pests in wild relatives of groundnut. J Econ Entomol 96:1886–1897 Shen KA, Meyers BC, Islam-Faridi MN, Chin DB, Stelly DM, Michelmore RW (1998) Resistance gene candidates identified by PCR with degenerate oligonucleotide primers map

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Tian A-G, Wang J, Cui P, Han Y-J, Xu H, Cong L-J, Huang XG, Wang X-L, Ziao Y-Z, Wang B-J, Wang Y-J, Zhang JS, Chen S-Y (2004) Characterization of soybean genomic features by analysis of its expressed sequence tags. Theor Appl Genet 108:903–913 Timper P, Holbrook CC, Xue HQ (2000) Expression of nematode resistance in plant introductions of Arachis hypogaea. Peanut Sci 27:78–82 Torrance L, Mayo MA (1997) Proposed reclassification of furoviruses. Archives Virol 142:435–439 United Nations University (1980) Analytical methods for the determination of nitrogen and amino acids in foods. In: Pallett PL, Young VR (eds) Nutritional Evaluation of Protein Foods. A report of a working group sponsored by the International Union of Nutritional Sciences and the United Nations University World Hunger Programme. United Nations University, Japan Upadhyaya HD (2005) Variability for drought resistance related traits in the mini core collection of peanut. Crop Sci 45:1432–1440 Upadhyaya HD, Ortiz R (2001) A minicore subset for capturing diversity and promoting utilization of chickpea genetic resources in crop improvement. Theor Appl Genet 102:1292–1298 Upadhyaya HD, Ferguson ME, Bramel PJ (2001a) Status of Arachis germplasm collection at ICRISAT. Peanut Sci 28:89–96 Upadhyaya HD, Ortiz R, Bramel PJ, Singh S (2001b) Development of Asia region groundnut core collection. Paper presented in the Diamond Jubilee Symposium on Hundred Years of Post-Mendelian Genetics: Retrospect and Prospects. 6–9 Nov 2001. Indian Agricultural Research Institute, New Delhi Upadhyaya HD, Nigam SN, Mehan VK, Reddy AGS, Yellaiah N (2001c) Registration of Aspergillus flavus seed infection resistant peanut germplasm ICGV 91278, ICGV 91283, and ICGV 91284. Crop Sci 41:599–600 Upadhyaya HD, Bramel PJ, Ortiz R, Singh S (2002a) Developing a mini core of peanut for utilization of genetic resources. Crop Sci 42:2150–2156 Upadhyaya HD, Nigam SN, Reddy AGS, Yellaiah N (2002b) Registration of early maturing, rust, late leaf spot, and low temperature tolerant peanut germplasm line ICGV 92267. Crop Sci 42:2220–2221 Upadhyaya HD, Ortiz R, Bramel PJ, Singh S (2003) Development of a groundnut core collection using taxonomical, geographical and morphological descriptors. Genet Resource Crop Evol 50:139–148 Upadhyaya HD, Mallikarjuna Swamy BP, Goudar PVK, Kullaiswamy BY, Singh S (2005) Identification of diverse accessions of groundnut through multienvironment evaluation of core collection for Asia. Field Crops Res 93:293–299

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CHAPTER 4

4 Sunflower Norma Paniego, Ruth Heinz, Paula Fernandez, Paola Talia, Veronica Nishinakamasu, and H. Esteban Hopp Unidad Integrada de Investigación y Docencia CNIA-INTA y FCEyN-UBA, Instituto de Biotecnología CICVyA-INTA, CC 25, 1712 Castelar, Argentina e-mail: [email protected]

4.1 Introduction 4.1.1 Brief History The center of diversity of sunflower is localized in northern Mexico and southwestern USA (Heiser 1954). Domestication occurred at least 3000 BC by pre-Hispanic American cultures. It became a common crop from which they harvested the calcium-rich seeds to be grounded into flour or cracked for direct consumption (Putt 1997). As most crops grown in earlier times, sunflower had many alternative uses. It was used in medical applications as it has known diuretic and expectorant effect in pulmonary and laryngeal affections. The yellow dye was extracted from the petals for coloration, oil was extracted for ceremonial body painting, and the rest of the plant was used as a building material (after drying the stalk). The flower was revered as documented in representations showing priestesses crowned with sunflowers. Reports from early Spanish explorers indicate the presence of sunflower decorations in the temples of the sun that were made of gold. Spanish explorer Monardes brought the plant to Europe in 1569, where it was widely adopted as an ornamental plant. Peter the Great is credited for introducing sunflower to Russia (Selmeczi-Kovacs 1975), where it became the main source of edible vegetable oil until the present times. The first indication of a food use comes from a patent for squeezing oil from sunflower seeds, which was granted in 1716 in England. However, it was not until 1830 that sunflower oil was commercially manufactured in Russia, where it became a widely cultivated crop. Ukrainian immigrants of Jewish origin reintroduced the crop into America in the late 18th century. By 1892, 315 ha were commercially cultivated in the province of Buenos Aires, Argentina (Jewish

Colonization Association 1914). In North America, the first commercial use of sunflower was for poultry feeding, and it was not until 1926 that its processing for oil started. World wars produced a general lack of traditional edible oils and triggered a substitution process of traditional oils by sunflower oils. However, at that time the common cultivated sunflower plant had a long cycle and relatively low oil content. This led to important breeding efforts at Krasnodar (Pustovoit 1964), with a drastic rise in oil content as seed yield remained constant. In 1940, the average oil content of the main cultivar in the Soviet Union was 330 g/kg, and by 1965 the USSR program was testing strains with 550 g/kg oil (Putt 1997). However, the first official sunflower-breeding programs started in North and South America. In Canada, during the 1930s germplasm material from Mennonite (Russian immigrants) gardens was used, and shortly afterwards in Argentina a short-cycle and high-oil-content (39%) variety called Klein was bred in 1938. After that sunflower cultivation area steadily increased in both North and South America, reaching 1.8 million ha in 1948 in Argentina and 2.2 million ha in the late 1970s, leading finally to its first rank in the world in sunflower production. In the USA, however, the preponderance of early use was not for oil but for silage (Putt 1997). In the 1960s the use of sunflowers with high oil content was started after the reintroduction of cultivars from the USSR. The discovery of genetic male sterility (Leclercq 1969) and cytoplasmic male sterility sources allowed efficient production of hybrid seed in the late 1970s with a twofold increase in relative yields. 4.1.2 Botanical Description The commercial crop is a predominantly outcrossing annual erect plant with a long stem of up to 3 m, with a large flower head (inflorescence called capitulum)

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reaching a diameter of up to 30 cm, well known for its peculiarity of turning its face toward the sun. This property baptizes its denomination in languages like French (tournesol) and Spanish (girasol). The capitulum disc is composed of numerous flowers called disc florets that are crowded together. The outer flowers are called ray florets. They are sterile and can be yellow, maroon, orange, or other colors. The arrangement of florets forms a pattern of spirals where the number of left spirals and the number of right spirals are successive Fibonacci numbers. Anthesis starts in the periphery and proceeds to the center of the disc. The disc florets give rise to the fruits (botanically named achenes), which constitute what we commonly called the “seeds,” the true seeds being encased in a husk. Some recently developed varieties have drooping heads. These varieties are less attractive to gardeners growing the flowers as ornamentals but appeal to farmers because they reduce bird damage and losses from some plant diseases. The scientific name of sunflower is Helianthus annuus L. Helianthus derives from two Greek words: helios, meaning sun, and anthos, meaning flower. It is a diploid species (2n = 2x = 34) that belongs to the Helianthinae subtribe, Asteroideae subfamily, and Compositae family (Seiler and Rieseberg 1997). The genus Helianthus includes 12 annual and 36 perennial species. The Jerusalem artichoke (Helianthus tuberosa) is related to the sunflower. Helianthus is also related to another genus (Lactuca) to which lettuce belongs. The Mexican sunflower is Tithonia rotundifolia. False sunflower refers to plants of the genus Heliopsis. H. annuus comprises three subspecies: H. annuus ssp. macrocarpus, which is the cultivated sunflower, and H. annuus ssp. lenticularis and H. annuus ssp. annuus, which are wild relatives of the cultivated crop. 4.1.3 Economic Importance Total world production of sunflower seed is about 25 million metric tons in recent years (Table 1). World total production oscillated during the last years between 22 and 29 million tons. The whole seed contains about 40% oil and about 25% protein (which can reach up to 42% after removal of the husk), which is well suited for animal feeding and used as such in many countries. The meal remaining after the seeds have been processed for oil is used as a livestock feed for ruminants, pigs, and poultry.

It is rich in fibers, with lower caloric content and lysine than soybean but with larger methione content than soybean. Some varieties of sunflowers have large striped seeds, which are roasted for snack food or blended with other grains to make birdseed. Special oilseed varieties produce small black seeds that contain up to 50% oil. Thus, from their breeding objectives and applications, there are at least three varieties of sunflowers: 1. Oilseed sunflowers (divided into three varieties according to their relative oleic acid composition). 2. Confectionery sunflowers: used for raw, roasted or salted snack food, as well as for food for birds and small animals. The seeds are typically larger than the oilseed variety. 3. Ornamental sunflowers. By far, the major portion of sunflower production is devoted to oil extraction (Dorrell and Vick 1997). Thus, the most important objectives of breeding are dedicated to this trait. Sunflower oil is the world’s third most important vegetable oil accounting for about 13% of the total world’s edible oil production. Only soybean oil and palm oil are produced in greater abundance. Sunflower oil is considered premium oil due to its high unsaturated FA composition and low content of linolenic acid (see comparisons with other oil seeds in Table 2). There are three qualities of sunflower oil composition depending on the genotype, some of which are specially suited for cooking since they do not need hydrogenation to be used in frying (mid-oleic and high oleic variants developed in the 1980s). Their stability makes them suitable for the elaboration of baby foods, for example. The comparatively high price of sunflower oil prevents some other potential applications of industrial use, which are not exploited. However, it was investigated and corroborated that it could be used advantageously for the fabrication of certain paints and plastics. Its relative transparency is much better than other oils with high linolenic content. Due to its relative abundance it is used in the elaboration of soaps and detergents in eastern Europe. It is also used as “carrier” in the production of agrochemicals, particularly hydrophobic pesticides, surfactants, adhesives, plastics, softeners, and lubricants. Sunflower oil is also used in massage oil blends and aromatherapy. It has great potential to produce biodiesel (as a replacement for diesel fuel), for which it is less expensive than the olive product.

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Table 1. Sunflower seed production (in 1,000 metric tons)

Item

Sunflower seed World supply & disappearance (in 1,000 metric tons) 2000/01 2001/02 2002/03 2003/04

Area harvested (1,000 HA) yield (MT/HEC)

19,540 1.18

18,485 1.18

19,892 1.2

Seed Production Argentina Eastern Europe European Union China (Peoples Republic of) Russia/Ukraine United States India Turkey Other TOTAL

2,950 1,657 3,333 1,954 7,368 1,608 730 630 2,880 23,110

3,720 1,861 3,030 1,750 4,936 1,551 870 530 3,551 21,799

Seed Import Mexico European Union Other TOTAL

23 1,999 704 2,726

Oilseed crushed Seed export Argentina United States Russia/Ukraine Other TOTAL

2004/05 Revised

2005/06 Forecast

22,918 1.17

21,262 1.23

22,791 1.29

3,340 1,648 3,718 1,946 7,194 1,112 1,060 830 3,108 23,956

2,990 2,295 4,078 1,820 9,348 1,209 1,160 560 3,467 26,927

3,650 2,270 4,133 1,750 8,001 930 1,300 640 3,505 26,179

3,800 1,950 3,765 1,850 10,450 1,824 1,250 790 3,665 29,344

10 1,155 467 1,632

104 1,007 812 1,923

38 1,473 1,249 2,760

11 763 813 1,587

23 1,000 801 1,824

21,116

18,514

21,149

23,442

23,115

25,510

94 153 1,768 711 2,726

342 176 100 1,084 1,702

232 122 524 1,112 1,990

44 136 1,271 1,277 2,728

97 116 73 1,257 1,543

121 225 560 957 1,863

(Source http://www.sunflowernsa.com/stats/table.asp?contentID=109&htmlID=74&submit170=View&submit.x=57&submit.y=12)

4.1.4 Conventional Breeding Commercial sunflower breeding started in most of the producing countries (eastern and western Europe, North and South America) between 1920 and 1950. Selected traits included increased oil content, capitulum diameter, precocity, and resistance to Botrytis and mildew. Significant yield increases came after the introduction of heterosis, which was first described in 1966 by Leclercq (1966) and extensively utilized after incorporation of cytoplasmic male sterility (Leclercq 1969) following interspecific crossing with H. petio-

laris Nutt. Up to now, the cytoplasmic male sterility (CMS) source, PET1, has been effectively used worldwide for hybrid breeding in sunflower. The CMS was associated with the expression of a 16-kDa protein encoded by orfH522 in the PET1 cytoplasm, which is also present in other male-sterile cytoplasms of sunflower (Horn et al. 1996). It is associated with the atpA mitochondrial gene, which codes for a subunit of F1 ATPase. This feature has drastically changed breeding strategies since then. The most frequent breeding method consists of genealogic selection separately applied in the process of generation of male (called R, for restorer, maintainer, or androfertile line) and female

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Table 2. Oil composition of sunflower and comparison to other oilseeds Source

Conventional sunflower oil Mid oleic sunflower oil High oleic sunflower oil Olive oil Canola oil Cottonseed oil

Contents (%) Oleic Linoleic

Saturated

20 65 82 77 62 18

11 9 9 14 16 27

69 26 9 8 21 54

Source: US Sunflower Crop Quality Report 2003 http://www.sunflowernsa.com/uploads/cqr/cqr2003.pdf

(called A, or androsterile) lines, during six or more successive inbreeding cycles accompanied by selection of the best plants. The most important traits for breeding are yield, relative oil and protein content, FA composition (linoleic and oleic acids), disease resistance (Sclerotinia, Phomopsis, mildew), insect resistance (Liriomyza spancerella, Rachiplusia nu, and Spilosoma virginica), and abiotic stress tolerance. The genetic basis for breeding programs comes from the different H. annuus subspecies and interspecific crossing with other Helianthus species.

4.2 Molecular Markers and Genetic Maps Following the steps of model plant species, the first molecular markers used for constructing a denser linkage map in sunflower were restriction fragment length polymorphism (RFLP) (Berry et al. 1995, 1996, 1997; Gentzbittel et al. 1995, 1999; Jan et al. 1998), random amplified polymorphic DNA (RAPD) (Rieseberg et al. 1993; Rieseberg 1998), and amplified fragment length polymorphisms (AFLPs) (Peerbolte and Peleman 1996; Flores Berrios et al. 2000; Gedil et al. 2001; Al-Chaarani et al. 2002). However, as is known, RFLPs are technically laborious for routine use as molecular markers, and, while RAPD and AFLP markers have many advantages, they are mostly dominant, abundant, but often nonspecific and not very useful for comparison of a genomewide synteny of molecular markers for cross referencing genetic linkage maps. Until relatively recently, microsatellite markers (also called single sequence repeats, SSRs) and expressed sequence tag (EST)-derived single nucleotide

polymorphism (SNP) markers have been lacking in sunflower. Microsatellites are, when available, the markers of choice for linkage analysis due to the fact that they are highly polymorphic, usually inherited in a codominant manner, and, in most cases, chromosome specific. In recent years, development of microsatellites by South American researchers at INTA in Argentina (HAx), together with European (CARTISOL; CRS) and North American (ORSx) researchers, summed up 2,040 markers (Dehemer and Friedt 1998; Gedil 1999; Paniego et al. 2002; Tang et al. 2002; Yu et al. 2002).These markers were used for the development of reference maps using different F2 and recombinant inbred line (RIL) populations, which are almost pure lines derived from crosses between highly contrasting sunflower inbred lines. The recombinant inbred line parental crosses used were PAC2 × RHA266 and RHA280 × RHA801 and PHA × PHAB (Flores Berrios et al. 2000; Tang et al. 2002, 2003; Yu et al. 2003; Al-Chaarani et al. 2004). Until now, the map described by Al-Chaarani et al. (2004) on average is the longest map reported in the literature. It includes 371 AFLPs and 38 SSR markers selected from the CRS and ORS collections and covers a length of 2,915.9 cM with a marker density of 7.9 cM. However, the number of linkage groups (LGs) was higher than the number of total chromosomes (17) described for sunflower. In contrast, the maps constructed using only SSR_ORS and SSR_ORS plus INDELs respectively (Tang et al. 2002; Yu et al. 2003) were shorter but highly saturated as expected for these kinds of markers. Tang et al. (2002) demonstrated that a screening of 459 SSR marker loci is enough for a genomewide analysis of sunflower showing an average spacing of 3.1 cM and coverage of 1,368.3 cM. These authors provided the first sunflower cross-referenced maps by mapping 701 SSR and 89 RFLP or INDEL marker loci into three populations derived from crosses between contrasting germplasms, such as confectionary, oilseed, fertility restorer, and sterility maintainer lines (Yu et al. 2003). In the same work, the researchers also presented the first integrated map by adding 120 SSR loci from the public SSR map to the HA370 × HA372 RFLP map of Berry et al. (1997). Finally, they published a composite linkage map of sunflower that integrated 657 loci in a 1,423-cM long map with a mean density of 2.2 cM per locus. This is the most saturated map described to date, but it still has gaps longer than 30 cM on LGs 2 (31.3 cM), 4 (36.4 cM), 6 (32.6 cM), and 13 (30.7 cM) (Tang et al. 2003). This map allowed the selection of

Chapter 4 Sunflower

95 single-locus SSRs at an average spacing of 12.9 cM representing a near-genomewide collection for a firstpass scan of the sunflower genome. Seventy-eight single-locus SSRs from this standard genotyping set were selected regarding map position, heterozygosity level, allele-length ranges, and genotyping qualities to create a 13 six − locus PCR multiplex set spanning 1,067 cM and including three to five SSR markers per LG, thus increasing the genotyping throughput of the set (Tang et al. 2003). A second set of 78 SSRs for sunflower variety identification and diversity assessment was described recently by Zhang et al. (2005). A unified consensus molecular genetic map integrating the independently developed linkage maps has been reported recently throughout the collaboration between our group and the group of Professor Dr. A Sarrafi (ENSAT, France) by mapping a selection of SSRs from the composite map (ORSx) plus a group of single-locus highly polymorphic SSRs from the collections of INTA (HAx) and GIE CARTISOL (SSLx and SSUx) markers on the RIL population derived from the cross RHA266 × PAC2. The new map integrates 161 SSR markers from a previous map described by Al-Chaarani et al. (2004) reaching a map length of 2,180.7 cM and density of 4.1 cM (S. Poormohammad Kiani et al. unpubl. data). These markers distribute themselves throughout all map LGs, allowing the complete cross reference to the public SSR map (Fig. 1). The inclusion of such diverse markers as SSRs and AFLPs in this framework has contributed to approximate to 96% the full genome coverage of sunflower genome. In addition, the incorporation of new SSRs to the PAC2 × RHA266 map allows the filling of some gaps present in previous maps; meanwhile no gaps longer than 19 cM (LG 17) were found in the entire map. The first functional map for sunflower based on the mapping of expressed genes was described by Lai et al. (2005a). This map comprises 196 SSR framework markers from the RHA280 × RHA801 map (Tang et al. 2002; Yu et al. 2003) and 243 EST markers. These markers derive from a collection of 22,920 and 21,133 sequences from both RHA280 × RHA801 source materials; potential polymorphisms were first detected by means of computational approaches and then confirmed and genotyped via denaturing high-performance liquid chromatography (DHPLC) of SNPs and length polymorphisms. The resulting map covered a map distance of 1,349.3 cM and included ESTs with candidate functions in traits

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related to development, cell transport, metabolism, plant defense, and tolerance to abiotic stress. Most of the maps were functional for mapping phenotypic and quantitative trait loci described later in this chapter (León et al. 1995, 1996, 2000, 2001, 2003; Lawson et al. 1998; Mestries et al. 1998; Lu et al. 1999; Flores Berrios et al. 1999a,b; Bert et al. 2001; Hervé et al. 2001; Al-Chaarani et al. 2002; Mokrani et al. 2002; Pérez-Vich et al. 2002; Tang et al. 2003; S. Poormohammad Kiani et al. unpubl. data).

4.3 Genomics and Transcriptomics In addition to the resources described above, other valuable tools have been developed recently for sunflower that facilitates the genomic study of this plant. In recent years two bacterial artificial chromosome (BAC) libraries have been described (Gentzbittel et al. 2002; Horn et al. 2002), and different EST sequencing programs have been carried out increasing the molecular information available for sunflower in public databases to 66,098 accessions for H. annuus plus another 23,127 from wild species. A small fraction of this collection represents transcription sequences that were characterized from organ-specific cDNA libraries constructed by suppressed subtractive hybridization (Diatchenko et al. 1996) as an alternative way to identify low-copy mRNA and differentially expressed sunflower transcripts (Fernández et al. 2003; Tamborindeguy et al. 2004). The first one describes the characterization of 318 nonredundant organ-specific ESTs generated from leaf, stem, root, and flower bud at two developmental stages (R1 and R4) with the aim of identifying novel genes differentially expressed in sunflower as a source of organ-specific genetic markers that can be functionally associated to important traits. This work helped to successfully isolate a significant number of newly reported sequences related to responses to abiotic and biotic stresses as well as low abundant transcripts with high similarity to homeobox genes, transcription factors, and signaling components (Fernández et al. 2003). This collection is the base of a SNP/InDel discovery project that allowed the identification of 25 polymorphisms by means of sequencing a panel of 16 representative sunflower accessions (work in progress, unpubl. data).

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Fig. 1. Actual status of a consensus public composite genetic linkage map (map B, Poormohammad Kiani et al. unpubl. data) and the reference PAC2 × RHA266 SSR map (map A, Tang et al. 2003). The present map incorporates new markers including 58 HA-SSR (Paniego et al. 2002)

Chapter 4 Sunflower

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Fig. 1. (continued)

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Fig. 1. (continued)

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Fig. 1. (continued)

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The second report involves the production of 1502 unique sequences out of 2479 high-quality ESTs from the characterization of four cDNA libraries created using sunflower protoplasts growing under embryogenic and nonembryogenic conditions (Tamborindeguy et al. 2004). This analysis allowed the identification of 821 previously uncharacterized sunflower sequences and a group of candidate genes putatively implicated in embryo polarity that are being confirmed by functional genomic approaches using these libraries in the construction and evaluation of cDNA microarrays essays (unpubl. data). Comprehensive EST data are found at GenBank dbEST division, at the Compositae Genome Project (CGPDB; http://cgpdb.ucdavis.edu), and at the Compositae DataBase (http://compositdb.ucdavis.edu), while gene indexing is offered at GenBank UniGene division, at TIGR (http://www.tigr.org/tdb/tgi/ plant.shtml) and at SPUTNIK EST database (http://sputnik.btk.fi/ests), comparative records against Lactuca sativa and Arabidopsis thaliana are available at CGPDB and at Interspecific Comparative Clustering and Annotation for EST (ICCARE, http://bioinfo.genopole-toulouse.prd.fr/iccare, Müller et al. 2004). SNPs were characterized for 81 genes previously mapped as RFLP markers and dispersed throughout the genome. DNA fragments representing the cDNA probes were amplified from 12 genotypes and 68 loci using long-distance PCR, and the amplicons were cloned and single-pass sequenced from each end to produce ∼1,000 bp of DNA sequence per locus per genotype. SNPs were found in every gene at a mean frequency of 1/68 bp in this collection, the mean insertion-deletion (INDEL) frequency was 1/200 bp, and the mean haplotype frequency was 12/kb (Kolkman et al. 2002). This set of informative markers has been enlarged with the identification of 273 SNP/InDel out of 535 putative polymorphism inferred in silico within the public EST collections (Lai et al. 2005a). These functional markers were mapped in a RIL population (described above) and could progressively replace RFLP markers to launch last-generation genetic linkage maps. This will definitively help to compare and validate, for example, quantitative trait loci (QTL) and other traitmapping results, which cannot yet be reproduced between different locations and among research groups. The development of such a resource will considerably affect genomic research by adding expressed

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sequence landmarks for in silico syntenic comparisons with genetic maps developed in other plant species.

4.4 Structural Analysis by in situ Hybridization The genus Helianthus is complex, involving both perennial and annual species as well as natural and artificial interspecific hybrids with different levels of wild species genomic introgression, polyploids in perennial species, and a wide geographic distribution that leads to a large phenotypic variation. Wild H. annuus subspecies have been described as being able to extend their range by crossing with wild species followed by introgression of the new gene variants (Seiler and Rieseberg 1997). Classical cytogenetic studies have been important for the analysis of interspecific hybrids in the genus. Chromosomal rearrangements have been important in the speciation process; thus different species differ in one or more translocations and/or inversions. It has been postulated that chromosomal rearrangements could play a key role in reproductive barriers (Chandler et al. 1986). The genomic size varies with species and also among cultivars (from 4.9 to 9.9 pg; Sims and Price 1985). Development of molecular cytogenetic techniques such as genomic in situ hybridization (GISH) and fluorescence in situ hybridization (FISH) are helping genomic studies for application in genome organization, physical mapping of interesting genomic regions, diversity and evolutionary studies between species of the genus Helianthus (Rocco 2002, Rocco et al. 2003). It also offers an alternative to classical cytogenetics for chromosomal identity due to the lack of specific chromosomal characteristics at the morphological level and classical banding patterns for some of the chromosomes. The GISH technique that uses genomic DNA as probes on cytological preparations has been successfully used in different plant species to evaluate introgression of wild species in cultivated crops (Benabdelmouna 2003; Shigyo et al. 2003; Wei 2003) as well as in evolutionary studies using wild polyploids (Bennett 1995; Poggio et al. 1999). Preliminary molecular cytogenetic studies on sunflower showed that the wild species H. petiolaris is closely related to the cultivated sunflower, although a differential hybridization density among chromosomes could

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indicate a divergence during the breeding process of cultivated sunflower (Rocco 2002). Comparative genetic linkage maps and colinearity studies indicate the presence of 11 rearrangements (8 translocations and 3 inversions) between the species H. petiolaris and H. annus (Burke at al. 2004). The detection and localization of specific genes using FISH technology has been used in different species to detect different genes (typically ribosomal genes, see Fig. 2), but also transgenes (Fransz et al. 1996; Ten Hoopen et al. 1996; Pedersen et al. 1997; Abranches et al. 2000; Leggett et al. 2000; Svitashev et al. 2000; Salvo-Garrido et al. 2001). In sunflower, FISH studies using repetitive sequences derived from retroelements from H. annuus allowed the detection of sequences of two families of retroelements dispersed along the length of all chromosomes in all species studied. However, the Ty3/gypsylike sequences were localized preferentially at the centromeric regions in most of the studied species, whereas Ty1/copialike sequences were less represented or absent around the centromeres and plentiful at the chromosome ends only in H. annus (Santini et al. 2002; Natali et al. 2006). These findings suggest that these two sequence families played a role in Helianthus genome evolution and species divergence, evolved independently in the same genomic backgrounds and in annual or perennial species, and acquired different possible functions in the host genomes. FISH studies using different repetitive sequences as the 45S ribosomal DNA sequences allowed the differentiation among diploids, tetraploid, and hexaploid species (Vanzela et al. 2002). The use of a rDNA from wheat including the 18S, 5.8S, and 26S ribosomal DNA in studies that compared different genotypes allowed the detection of six positive regions corresponding to primary constrictions (Fig. 2), while in the confectionery sunflower two of these signals are weak (Rocco 2002). The use of different repetitive sequences showed a differential hybridization pattern among different chromosomes allowing the identification of those chromosomes that cannot be differentiated by other techniques (Fig. 3, Rocco 2002). The use of BACs containing large genomic DNA inserts in physical mapping by FISH technology enabled studies of genome diversity, evolutionary pathways, and chromosomal location of specific genes or gene families in different species (Nagaki et al. 2003; Wei et al. 2003; Ji et al. 2004; Zhang et al. 2004). In sunflower these techniques have not been widely used mainly due to difficulties in cytological preparations,

but new advances both in classical and molecular cytological techniques should contribute to the application of these tools for physical mapping of interesting genes or gene regions. Sunflower BAC libraries have been developed (Gentzbittel et al. 2002; Horn et al. 2002), and thus the application of these techniques should be explored to improve our knowledge regarding localization of agronomically interesting characters.

4.5 Resistance Genes in Cultivated and Wild Sunflowers Cultivated sunflower is susceptible to several economically important fungal and bacterial diseases, and the severity of the infections greatly depends on environmental conditions. Diseases that cause economical losses worldwide in most of the sunflowergrowing areas include wilt, middle stalk rot, and head rot (mainly Sclerotinia sclerotiorum), downy mildew (Plasmopara halstedii), stem canker (Phomopsis helianthi = Diaporthe helianthi), rust (Puccinia helianthi), and Verticillium wilt (Verticillium dahliae). Other diseases such as head rots (Rhizopus arrhizus, R. stolonifera, Botrytis cinerea), phomopsis black stem (Phoma macdonaldii), Alternaria leaf and stem spot (Alternaria helianthi or A. zinniae), Septoria leaf spot (Septoria helianthi), charcoal rot (Macrophomina phasiolina), bacterial infections (Pseodomonas syringae pv. Tagetis), and powdery mildew (Erysiphe cichoracearum) have local impacts in some productive areas (Seiler 1992; Pereyra and Escande 1994; Schneiter 1997). The development of resistant cultivars is the most efficient and sustainable strategy for controlling the disease and pest impact, and genes that confer disease resistance have been introduced in cultivated sunflower through both conventional and transgenic breeding programs. Conventional breeding has produced commercial sunflower hybrids that are resistant to several races of rust and downy mildew. During the last two decades the use of DNA molecular markers has been successful in the development of genetic maps in sunflower allowing the detection of loci controlling quantitative trait variations, including the location of QTLs associated with resistance to downy mildew, white rot, and phomopsis black stem.

Chapter 4 Sunflower

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Fig. 2. In situ hybridizationof ribosomal DNA sequences. Photograph of hybridization pattern of FISH using a fluorescent ribosomal probe from wheat (pTa 71) over sunflower chromosomal preps stained with DAPI (from Rocco 2002). Six conspicuous hybridization signals (A–D). Confectionary sunflower variety showed weaker signals for two of the six loci (E–F)

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Fig. 3. Chromosome identification using FISH and retroposon-related sequences. In situ hybridization of clone c785 (from Rocco 2002) indicates that this repetitive sequence has a dispersed distribution in sunflower genome (A–B) and that is useful to distinguish between chromosomes of similar size and shape (C–E)

Chapter 4 Sunflower

The genetic basis of resistance to Plasmopara halstedii (Farl.) Berlese et de Toni has been extensively studied in cultivated and wild sunflower. The first genetic studies of resistance to downy mildew have shown that dominant major genes, denoted Pl, control resistance to different races of P. halstedii. So far, 11 Pl genes have been described (Rahim et al. 2002) from both the cultivated sunflower (Vranceanu and Stoenescu 1970) and wild Helianthus species (Miller and Gulya 1991). Pl6 , Pl7 , and Pl8 , found in wild Helianthus species, confer resistance to almost all races of P. halstedii (Bert et al. 2001; Bouzidi et al. 2002). Pl6 was obtained from wild H. annuus, whereas Pl7 came from H. praecox and Pl8 from H. argophyllus. Pl6 has been described as a complex locus with at least two genetically distinct regions and 11 functional Pl genes conferring resistance to different Plasmopara races (Vear et al. 1997; Bouzidi et al. 2002). Numerous RGAs clustered and linked to the Pl1 , Pl6 (Genztbittel et al. 1998; Gedil et al. 2001; Bouzidi et al. 2002) and Pl5 /Pl8 loci have been described (Radwan et al. 2003, 2004), and recently a new source of resistance against P. halstedii has been detected and mapped on Helianthus argophyllus. The newly described locus, PlArg , was mapped to a LG different from all other Pl genes previously mapped with SSRs (Dussle et al. 2004). On the other hand, functional studies of the resistance to this pathogen reported induced expression of an RGA isolated from an inbred line resistant to Pl8 associated with the induction of a delay hypersensitive response marker in hypocotyls (Radwan et al. 2005a,b). Resistance to other diseases, such as white rot and phomopsis, is more complex, involving several loci with different effects and is highly dependent on environmental conditions (Castaño et al. 1993). Sunflower production is seriously affected by Sclerotinia sclerotiorum infection when plants are grown in temperate and humid environments. Although the fungus can infect different plant parts, including roots, stem bases, leaves, and terminal buds, the infection on capitula produces the greatest loss. To date, no complete resistance to S. sclerotiorum is available in cultivated sunflower, even if differences in susceptibility exist (Tourvieille et al. 1996). The number of QTLs associated with resistance to this pathogen varies according to the host-pathogen interaction considered, ranging from two (Landry et al. 1992) to seven (Schön et al. 1993) loci and also depends on the plant organ affected. Regarding this point, Mestries et al. (1998)

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detected four QTLs for leaf and two for capitulum resistance. More recent studies involved six different genetic crosses between cultivated inbred lines bearing reported QTLs distributed in at least 14 LGs (Bert el al. 2004). The results showed that seven QTLs explained less than 10% of phenotypic variance and that four QTLs explained up to 10% of capitulum resistance. To add even more complexity to this situation, relative QTL effect levels vary between different years and locations considered. Thus, QTL effects studied during a specific year and location differ within a range of 23% (Bert et al. 2004) to 44% (Mestries et al. 1998) and also differ from those studied over two or more years in different locations for which the variation ranged from 13 to 16% (Gentzbittel et al. 1998; Bert et al. 2002). Regarding Sclerotinia midstalk rot resistance, three to four QTLs were detected for each resistance trait that explained between 40.8 and 72.7% of the genotypic variance (Micic et al. 2005). For stem lesion two genomic regions explained 26.5% of the genotypic variance (Micic et al. 2004, 2005). A candidate gene approach revealed that a protein kinaselike gene was a marker cosegregating with a locus that explained up to 50% of the phenotypic variation for capitulum resistance (Gentzbittel et al. 1998). Up to now, the main QTLs detected from different crosses appear to be different, thus suggesting that pyramiding QTLs for resistance to Sclerotinia is possible, although the nature of the interaction between allelic loci is still unknown. Colocalization studies with other disease resistance loci detected coincidences with a QTL affecting the resistance to D. helianthi (Bert at al. 2002) and with the Pl5 locus conferring resistance to P. halstedii (Bert at al. 2001). An alternative approach to molecular-marker-assisted breeding programs for resistance to Sclerotinia was the transgenic expression of an oxalate oxidase transgene from wheat (see Transgenic traits section, Hu et al. 2003), based on the detoxification of the pathogenocity factor oxalacetic acid produced by the pathogenic fungus. Expression of this gene in sunflower looks promising as it induces activation of endogenous defense pathways leading to inhibition of S. sclerotiorum by antifungal proteins (Hu et al. 2003). A related strategy based on the increase of oxalate oxidase activity to enhance resistance to Sclerotinia focuses on the identification of natural allelic sources in cultivated and wild germplasm lines. Preliminary studies suggested that oxalate oxidase tran-

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script levels vary among sunflower lines (Peluffo et al. 2004; Fernández et al. 2005). Accumulation of soluble phenolic compounds in sunflower capitula was also correlated with resistance to S. sclerotiorum (Prats et al. 2003). Association studies of transcript expression of candidate genes with disease resistance represents an alternative strategy to find new sources of resistance to Sclerotinia. The development of a large EST public database for sunflower (http://www.ncbi.nlm.nih.gov/ dbEST/dbEST_summary.html), including entries from sequenced differential cDNA libraries with highly represented defense genes (Fernández et al. 2003), enables the utilization of the candidate gene approach for searching new QTL determinants. Resistance to phomopsis stem canker also shows continuous variation, and different genotypes appear to harbor resistance loci to different phases of the disease cycle (Viguié at al. 2000). Three major LGs were involved, with common or very close location (Bert et al. 2002). In the case of black stem caused by Phoma macdonaldii, QTL analysis of resistance showed in one of the studies seven QTLs in different LGs that together explained 92% of the phenotypic variation (Al-Chaarani et al. 2002), while in another study four QTLs were detected. In both cases individual effects were moderate, ranging from 5 to 20% of total phenotypic variation (Bert et al. 2002). The most damaging insect pests of cultivated sunflower are those that infest developing seed heads (weevil, moth, and midge larvae) and those that transmit disease (e.g., stem weevils that transmit phoma black stem). In wild relatives of H. annuus, insect resistance is either absent or polygenic, and efforts to introgress strong resistance into the crop have been unsuccessful (Seiler 1992). A high priority for transgenic commercial hybrids is resistance conferred by Bt toxins (see Transgenic traits section), which are specific to different groups of insects such as Lepidoptera (moths, butterflies), Coleoptera (weevils, beetles), or Diptera (flies, midges). Another serious problem of sunflower crop, mainly in the Mediterranean region, is the root parasite Orobanche cumana Wall, which depletes the plant of nutrients and water. Genetic studies suggest that resistance to broomrape in sunflower is controlled by a combination of quantitative race specific resistance affecting the presence or absence of broomrape and quantitative nonspecific resistance affecting their number (Pérez-Vich et al. 2004).

4.6 QTL Analysis for Developmental and Agronomic Traits Yield is a complex trait regulated by a number of factors that can be studied as component traits. The development of the different genetic maps for sunflower allowed the mapping of QTL for a wide range of characteristics such as somatic embryogenesis and in vitro organogenesis (Flores Berrios et al. 2000), photosynthesis, and water status (Hervé et al. 2001); seed-oil content, seed weight, height, and maturity days (Mestries et al. 1998; Mokrani et al. 2002; Bert et al. 2003). Seed oil concentration is a complex trait determined by the genotype and the environmental conditions. Search for seed-oil-concentration QTLs using a genetic map of 205 loci defined by RFLP (León et al. 1995) and composite interval mapping resulted in the detection of eight QTLs on seven LGs that accounted for 88% of the phenotypic variation for seedoil concentration across environments (León et al. 2003). Gene action was additive for four QTLs and dominant or overdominant for the others. Four of the eight QTLs were detected in two or more environments, and the parental effects were the same across generations and environments. Another important determinant for crop adaptation is flowering date. León et al. (2001) dissected the flowering date into growing-degree days and photoperiod components in multiple environments and reported two QTLs for photoperiod colocated with two of the six QTLs associated with growing-degree days. Based on their chromosomal positions it has been suggested that some QTLs for photoperiod sensitivity and basic vegetative loci, which are the main determinants of this trait, are the same loci of major genes (Yano et al. 1997; Lin et al. 2000; Yamamoto et al. 2000; Zhou et al. 2001). It is important to stress that some QTLs seem to be genotype specific and that for some traits it will be important to compare maps to determine on a large number of crosses which QTLs are common to different genotypes. QTLs for photosynthesis traits (net photosynthesis, stomatal conductance, intercellular CO2 concentration, and transpiration) studied under well-watered (Hervé et al. 2001) and water stress conditions and their association to closely linked markers were reported recently (Poormohammad Kiani et al. unpubl. data). Studies of colocalization of developmental and agronomic traits with resistance to pathogens like

Chapter 4 Sunflower

Sclerotinia and D. helianthi have shown association in some cases. A QTL for percentage of plants attacked by S. sclerotiorum was detected in a LG close to one for flowering date with one of the parental alleles that reduced the days to flowering being linked with increases in resistance to this pathogen (Bert et al. 2003). Other colocalization was reported for QTLs for resistance to S. sclerotiorum and QTLs for seed weight and oil content (Mestries et al. 1998; Bert et al. 2003). Some morphology-related traits such as branching regulated by the gene b1 are being studied for their association with S. sclerotiorum resistance and agronomic characters (Bert et al. 2003). Another important agronomic trait recently incorporated in sunflower is herbicide resistance to imidazolinones. Resistance to two classes of acetohydroxyacid synthase (AHAS)-inhibiting herbicides, imidazolinones (IMIs) or sulfonylureas (SUs), have been discovered in wild sunflower populations treated with imazethapyr or chlorsulfuron, respectively. Three AHAS genes were isolated from herbicide-resistant (mutant) and susceptible (wild type) genotypes About 48 SNPs were identified in AHAS1 while a single six-base pair insertion-deletion and a single SNP were detected in AHAS2 and AHAS3, respectively. One of the AHAS1 genes from imazethapyr-resistant inbreds harbored a C-to-T mutation conferring resistance to IMI herbicides, whereas AHAS1 from chlorsulfuron-resistant inbreds harbored a C-to-T mutation conferring resistance to SU herbicides. SNP and single-strand conformational polymorphism markers for the three isolated genes were developed and genetically mapped. The C/T SNP cosegregated with a partially dominant gene for resistance to imidazolinone herbicides in two mutant × wild-type populations, thus providing new molecular markers to sunflower breeding programs (Kolkman et al. 2004). In an attempt to provide evidence of colocalization of ESTs and QTLs in sunflower, Lai el al. (2005a) reported different candidate genes that map in the population RHA280 × RHA801 coincident with QTLrelated traits such as salt tolerance, morphology and development, cell division, photomorphogenesis regulation, and plant growth. Although these associations need to be confirmed experimentally, the approach described by these authors represents an important step toward identifying the genes underlying complex genetic traits. On the other hand, a high level of linkage disequilibrium has been reported in cultivated sunflower. Thus it has been suggested that

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association-based approaches will provide a high degree of resolution for the mapping of functional variations in sunflower (Liu and Burke 2005). Interspecific QTL mapping has been reported for the wild annual species H. annus and H. petiolaris (Lexer et al. 2005) and for three hybrid sunflower species derived from them: H. anomalus, H. deserticola, and H. paradoxus (Lai et al. 2005b). QTL analysis in these studies indicated that karyotypic differences among species contributed to reproductive isolation and evaluated inter- and intraspecific QTL magnitudes.

4.7 In vitro Tissue-Culture-Aided Breeding In vitro tissue culture techniques were rapidly incorporated in sunflower breeding because immature embryo rescue allows the development of four to six generations per year notably accelerating inbreeding speed. Embryo rescue and protoplast fusion also helped to sort sterility or incompatibility barriers in wide interspecies crossings. Examples of protoplast fusion techniques applied to rescue of wide crossings are those between H. annuus and H. petiolaris or with H. debilis (Alibert et al. 1994). Plant regeneration was obtained by direct or indirect organogenesis (depending on the induction and passage through an undifferentiated callus stage), somatic embryogenesis, and, more recently, vegetative multiplication. First efforts to regenerate sunflower plants from shoot-tip meristems date to 1954 (Henrickson 1954). However, very soon it was realized that sunflower is far from being a model system for efficient and reproducible plant regeneration and transformation (see, for example, Alibert et al. 1994). Paterson (1984) showed genotype and cytokinin concentration dependence in a medium after trying more than 100 different genotypes for shoot regeneration. Since then, much work was devoted to overcoming the difficulties. Indirect organogenesis and embryogenesis through callus stages were soon discarded because of seldom regenerated viable shoots or embryos (Greco et al. 1984; Paterson and Everett 1985; Wilcox McCann et al. 1988). The most successful approach seems to be direct organogenesis, which was reported to occur from different explant sources: shoot-tip meristems or embryo axes (Lupi et al. 1987; Knittel et al. 1994;

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Moliner et al. 2002), immature embryos (Power 1987; Bronner et al. 1994; Jeannin et al. 1995), mature-seedderived cotyledons (Chraibi et al. 1991, 1992; Knittel et al. 1991; Ceriani et al. 1992; Deglene et al. 1997; Baker et al. 1999; Flores Berrios et al. 1999a,b; Dhaka and Kothari 2002; Mayor et al. 2003; Parody 2003), leaves (Konov et al. 1998), and protoplasts (Burrus et al. 1991; Krasnyanski and Menczel 1993), while somatic embryogenesis was mainly obtained from immature zygotic embryos (Finner 1987; Freyssinet and Freyssinet 1988; Jeannin and Hahne 1991; Bronner et al. 1994; Jeannin et al. 1995; Sujatha and Prabakaran 2001).

4.8 Genetic Transformation Genetic transformation was achieved by Agrobacterium tumefaciens-mediated techniques, by biolistic systems, and by a combination of both. However, as a reflection of regeneration difficulties, all the published protocols of transformation show a low efficiency (Bidney et al. 1992; Knittel et al. 1994; MaloneSchoneberg et al. 1994; Burrus et al. 1996; Alibert et al. 1999; Lucas et al. 2000; Müller et al. 2001; Hewezi et al. 2002; Weber et al. 2003; Parody 2003; Lewi 2004). Since regeneration usually has a multicellular origin, transgenic plants are often chimeric, which may or may not result in transgenic inheritance to descendents to foster transgenic regeneration (Schrammeijer et al. 1990) cotransformed with a cytokinin biosynthesis transgene ipt, which promotes cell division (Moliner et al. 2002). Different approaches were explored to increase transformation efficiency: to stimulate A. tumefaciens vir genes by adding phenolic compounds like acetosyringone, to hurt tissues by microparticle bombardment (Bidney et al. 1992), glass powder (Grayburn and Vick 1995), enzyme treatment (Alibert et al. 1999), sonication, or combinations of these treatments (Weber et al. 2003).

for breeding (see, among many others cited in the references, Everett et al. 1987; Schrammeijer et al. 1990; Escandón and Hahne 1991; Bidney et al. 1992; Knittel et al. 1994; Malone-Schoneberg et al. 1994; Grayburn and Vick 1995; Rao and Rohini 1999; Müller et al. 2001; Hewezi et al. 2002; Weber et al 2003; Lewi 2004). Three agronomically important transgenic traits were incorporated in sunflower by seed companies: glyphosate tolerance by expressing Agrobacterium EPSPS gene cp4, Bacillus thuringiensis enthomotoxin gene cry1A for the control of Lepidoptera (usually known as Bt), and the oxalate-oxidaseexpressing gene for the control of Sclerotinia (known as oxox), which are in the precommercial field trial stage in the USA and in Argentina (see years 1999–2003 in http://www.sagpya.gov.ar/new/ 0-0/programas/conabia/liberaciones_ogm.php and http://www.isb.vt.edu). Transgenic Bt sunflower was also obtained by public research institutions (Lewi 2004). Bt-induced resistance to Coleoptera was first field-tested in the USA in 1996, and resistance to Lepidoptera was approved for field testing in 1999 (http://www.sagpya.gov.ar/new/ 0-0/programas/conabia/liberaciones_ogm_1999.php and http://www.isb.vt.edu). Broad-spectrum resistance involving multiple Bt genes and other genes for insect resistance (e.g., Stewart et al. 2000) could also be developed. S. sclerotiorum synthesizes and secretes large amounts of oxalacetic into infected host tissues. This acid is a mobile toxin that causes a wilting syndrome in infected sunflower (Noyes and Hancock 1981) suppressing the oxidative burst associated with a hypersensitive response (Cessna et al. 2000). Transgenic oxalate oxidase expression is able to convert S. sclerotiorum oxalic acid toxin and O2 to CO2 and hydrogen peroxide (H2 O2 ) activating defense and conferring disease resistance (Hu et al. 2003).

4.8.2 Transgenic Sunflowers: Biosafety Concerns 4.8.1 Transgenic Traits

Cultivated sunflower in North America is partially sympatric with some of its ancestors and related Initially transgenic plants were developed with selec- species. Some wild subspecies and relatives are tive markers and reporter genes like kanamycin re- also present in other parts of the world partially sistance gene nptII, phosphinotricin resistance gene overlapping with cultivated sunflower areas. Pollen bar, glucouronidase (GUS) gene uidA, green fluores- from cultivated sunflower can spread to adjacent cent protein (GFP) gene egfp, which were not meant wild populations carried by foraging insects, espe-

Chapter 4 Sunflower

cially bees. Biosafety regulation in Argentina has established that distances required for transgenic sunflower field trials need 3 of isolation between the assay and the closest production field and/or wild sunflower to prevent contamination by “foreign” pollen (http://www.sagpya.gov.ar/new/0-0/programas/ conabia/condiciones_de_aislamiento.php). Pollen movement capacity is greatest at the crop edge, where up to 42% of hybrid seeds can generate, diminishing to nearly zero at distances of 800 to 1,000 m (Arias and Rieseberg 1994; Whitton et al. 1997). Thus, distances between populations determine the relative frequency and extent of gene exchange. Hybrids of the crop with wild H. annuus subspecies are fertile, but they typically produce fewer flower heads per plant than purely wild genotypes (Snow et al. 1998). Once crop genes enter wild populations, they can spread farther by both pollen and seed dispersal. Whitton et al. (1997) and Linder et al. (1998) have documented long-term persistence of crop genes in populations of wild sunflower. Wild H. annuus is an outcrossing annual widespread throughout much of the USA, reaching its greatest abundance in midwestern states (Heiser 1954). This species occurs as a common but relatively manageable weed. Populations are typically patchy and ephemeral, relying on the soil seed bank and long-distance dispersal for opportunities to become established in available clearings. The imminent intended release of transgenic sunflowers requires a previous analysis of the population biology of wild relatives to assess the potential added fitness or detrimental effects that agronomic traits might have on the ecosystem of the wild relatives. In the USA and in Argentina (Poverene et al. 2002), baseline information of reproduction and population dynamics of the wild relatives is under study. It is important to predict how a given transgene might affect the abundance and dispersion of wild populations, particularly their weed behavior. This information will help in making decisions about risk management of the different transgenic events, including their potential release to the environment. The recent release of imidazolinone-resistant sunflowers by conventional breeding (see above) gives an excellent opportunity to study the potential effect of similar traits derived from genetic engineering (like glyphosate and gluphosinate tolerance or resistance) because it will have a similar environmental impact showing the implicit contradiction of regulat-

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ing GMO release while equivalent traits are released without regulation if obtained by conventional breeding. As expected, gene flow to cultivated and wild sunflower relatives was rapidly demonstrated. Gene flow from imidazolinone-resistant hybrids to common and prairie sunflower showed that gene flow occurred and could be detected up to 30 m from the pollen source for both species and decreased as distance from the pollen source increased (Massinga et al. 2003). Since gene flow is expected to occur, it is a matter of time to evaluate if the weedy properties of wild sunflower will result in a drastic increase in its abundance becoming a problem in farms using imidazolinone-based weed control. However, as shown for other crop species, it is hardly expected that herbicide resistance traits will significantly affect ecosystems other than those subjected to agricultural practices involving the given herbicide. Resistance to diseases and insects poses a more challenging problem for risking evaluation since they may confer clear Darwinian selective advantages to wild populations and thus potentially disturb natural ecosystems. Resistance to Sclerotinia was addressed by Seiler (1992), who surveyed hundreds of wild sunflower populations without detecting Sclerotinia symptoms in mature plants, thus suggesting that Sclerotinia very unlikely regulates or limits the abundance of wild sunflowers in nature. Accordingly, no fitness benefit of oxalate oxidase transgene for white mold resistance was detected (Burke and Rieseberg 2003). In contrast, transgenic resistance to insect seed predators might be beneficial to wild plants, which sometimes lose as many as 20 to 30% of their seeds to these insects (Pilson 2000). Fitness benefits of Bt transgene (cry1A) in wild sunflowers showed 14 to 55% more seeds per plant depending on the geographic location (Snow et al. 2003). Assuming that Bt genes lead to greater survival or fertility of wild sunflower, the most relevant biosafety question is whether their transfer will thus cause wild populations to become more troublesome weeds, change natural ecosystems, or colonize new cultivated or wild habitats.

Acknowledgement. Dr. N. Paniego and Dr. R. Heinz are career members of the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET, Argentina), and Dr. H. E. Hopp is a career member of the Comisión de Investigaciones Científicas

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de la Provincia de Buenos Aires (CIC) and Professor at the Facultad de Ciencias Exactas y Naturales, University of Buenos Aires (UBA). This research was supported by the ANPCyT/FONCYT, BID 1201 AC/AR PID 024 and BID 267 and ANPCyT/FONCYT, PICTO ASAGIR 13165.

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CHAPTER 5

5 Indian Mustard D. Edwards1 , P.A. Salisbury2 , W.A. Burton3 , C.J. Hopkins1 , and J. Batley1 1

2 3

Plant Biotechnology Centre, Primary Industries Research Victoria, Department of Primary Industries, Victorian AgriBiosciences Centre, 1 Park Drive, Bundoora, Victoria 3083, Australia e-mail: [email protected] Grains Innovation Park, Private Bag 260, Horsham, Victoria 3401, Australia Faculty of Land and Food Resources, The University of Melbourne, Victoria 3010, Australia

5.1 Introduction

species B. rapa and B. nigra have sympatric distribution (Gomez-Campo and Prakash 1999). This would account for the various centers of diversity observed in the species.

5.1.1 Brief History of Brassica juncea Brassica juncea L. Czern. and Coss., alternatively known as Indian, Oriental or Brown mustard, is widely believed to be one of the earliest domesticated plants, with mustard known as a condiment (spice) since early times. It is described in Sanskrit and Sumerian texts from as early as 3000 BC (Hemingway 1995). B. juncea initially spread to Europe in the Middle Ages as a medicinal crop and was later grown as a vegetable for human consumption. Today, B. juncea is used worldwide as an oilseed, a condiment and a vegetable. U (1935) demonstrated that Brassica crop species comprise three diploid species, B. rapa (genome AA, 2n = 20), B. nigra (BB, 2n = 16) and B. oleracea (CC, 2n = 18), plus three amphidiploid species, B. napus (AACC, 2n = 38), B. juncea (AABB, 2n = 36) and B. carinata (BBCC, 2n = 34). The amphidiploid species originated through interspecific hybridization between two of the three diploid species. B. juncea is an amphidiploid species that combines the genomes of both B. rapa and B. nigra. The center of origin of B. juncea is uncertain, with China and the Middle East the most favored options (Gomez-Campo and Prakash 1999). Due to the sympatric range of distribution of its two diploid progenitors, B. juncea could have originated anywhere between eastern Europe and China. Prakash and Hinata (1980) proposed that it first evolved in the Middle East, where B. rapa and B. nigra had geographic sympatry. Various forms of all three crops still grow on the plains there today. It is possible that there have been multiple origins for B. juncea where the parental

5.1.2 Botanical Description B. juncea is an annual crop which grows as cultivated, weedy escapes or wild forms in coastal lowlands, sandy beaches, plateaus and mountainous regions. It has a wide geographical range, spanning the continents of Europe, Africa, Asia, America and Australia. B. juncea is closely related botanically to canola (B. napus) and turnip rape (B. rapa) and has a similar growth habit (Hemingway 1976). The cytogenetic relationship between the Brassica species established by U (1935) was later confirmed by chromosome pairing and artificial synthesis (Axelsson et al. 2000), nuclear DNA content, DNA analysis and the use of genome-specific chromosome markers (Hasterok et al. 2001). Röbbelen (1960) proposed that the three diploid species originated from one common ancestor. However, recent molecular investigations summarised by Gomez-Campo and Prakash (1999) point to a common origin for B. rapa and B. oleracea, with B. nigra evolved from a separate progenitor. B. juncea is an amphidiploid containing conserved diploid genomes from the species B. rapa and B. nigra (Axelsson et al. 2000). This polyphyletic origin of B. juncea is supported by a study utilising random amplified polymorphic DNA (RAPD) markers (Demeke et al. 1992). The genome size of B. juncea has been determined using two different methods. Bennett and Smith (1976) used the Feulgen reaction to estimate a genome size of 1,495 Mbp. This was later revised by Arumuganathan and Earle (1991), who utilised flow

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cytometry to estimate the genome size of B. juncea to be 1,105 Mbp. Brassica species have small chromosomes which are morphologically similar, making it extremely difficult to differentiate the chromosomes using traditional staining methods, and studies of this type are often limited to diploid Brassica species. Hasterok and Maluszynska (2000) performed morphometric analysis based on the length of the chromosome and the position of the centromere. This allowed rough classification into median (Nr 1–6) and submedian (Nr 7– 15) groups; however two nuclear organising regions (NORs) bearing chromosomes can also be identified with unusually prominent secondary constrictions in the short arm (Nr 17–18). The relatively large variation in chromosome size and morphology observed in B. juncea species is due to the chromosomes from the A genome, which is the most asymmetric among the three genomes (Kulak et al. 2002). Kulak et al. (2002) applied a combined morphometric and multicolor FISH, with 5S and 25S rRNA probes, to amphidiploid species. This study was based on eight different rDNAbearing chromosomal types as described in Hasterok et al. (2001). It was found that the mitotic complement comprises 36 chromosomes, equivalent to the diploid progenitors, with chromosome lengths ranging from 1.38 to 3.25 μm. Ten sites of 5S hybridization and 16 sites of 25S hybridization were identified, which, combined with the morphometric characterization, allowed clear identification of chromosomes (Nr 4, 8, 10, 12, 14 and 16). Chromosomes 4, 10 and 16 have co-localised 5S and 25 S rRNA genes, which, among the three progenitor Brassica genomes, is a feature of the A genome. 5.1.3 Economic Importance B. juncea is cultivated worldwide as a condiment, an oilseed and a vegetable. B. juncea is one of the two main species used for condiment mustard production worldwide, along with Sinapis alba (white mustard or yellow mustard). Seed color is important in mustard, with both brown-seeded (brown mustard) and yellow-seeded (oriental mustard) B. juncea cultivars used. Brown mustard seed is ground into flour which is used to produce a hot mustard in European products (Skrypetz 2003), including dijon mustard (Lionneton et al. 2002). The flour is also used in mayonnaise, salad dressing and sauces (Skrypetz 2003). Oriental mustard cultivars have been bred with a range of oil

Table 1. B. juncea types used for vegetable production (adapted from Labana and Gupta 1993) Subspecies or variety

Common name

Use

Capitata Crispifolia Faciliflora Lapitata Multiceps Rapifera Rugosa Spicea Tsa-tsai

Head mustard Cut leaf mustard Broccoli mustard Large petiole mustard Multi-shoot mustard Root mustard Leaf mustard Mustard Big stem mustard

Vegetable Vegetable/fodder Vegetable Vegetable Vegetable/fodder Vegetable Vegetable Pickle/vegetable

content and volatility to meet alternative market requirements. Low-volatility, low-oil-content cultivars are suitable for dry milling purposes (Skrypetz 2003). Canada is the dominant exporter and the second largest producer of mustard seed in the world. It is the largest producer of mustard for condiment purposes. Oriental and brown mustard are both produced in Canada, along with yellow mustard. Annual Canadian condiment mustard seed production has ranged from 105,000 to 319,000 tonnes in the last decade (Skrypetz 2003). The relative production of each type varies from year to year. Brown and oriental mustard combined are typically 50 to 70% of the Canadian production, with yellow mustard 30 to 50% (Skrypetz 2003). India produces the bulk of the world mustard seed, where it is predominantly used for oilseed production. The forecast world oilseed production of rapeseed and mustard for 2004 was 42 million tonnes (http://www.fas.usda.gov/wap/circular/2004/04-08/ Oils.xls). While production figures from India do not differentiate between B. juncea and B. rapa, B. juncea is the major oilseed crop in India, accounting for around 80% of the 4 to 6 million ha of oilseed Brassica production annually (Kumar et al. 2000; Negi et al. 2004). Production is primarily in the north-western part of the country during the winter season. The oil extracted from mustard seed is largely used for edible purposes in India and other South Asian countries (Sharma et al. 2002b). B. juncea is also widely used for vegetable production, particularly in Asia. Due to eco-geographic variation and human selection, a number of morphologically distinct vegetable forms are available (Table 1).

Chapter 5 Indian Mustard

5.1.4 Breeding Objectives and Progress For breeding purposes, two distinct B. juncea germplasm groups have been identified (Oram et al. 1999). The China-Eastern Europe-Canada geographic B. juncea is characterized by brown or yellow seeds containing predominantly allyl (propenyl) glucosinolate. These plants require long days for flowering and are resistant to leaf blight caused by Pseudomonas syringae. In contrast to this, the India-Pakistan geographic group has brown seeds containing a combination of butenyl and allyl glucosinolates, and plants which are relatively day neutral and susceptible to leaf blight. The traits required for adaptation can differ significantly between environments. From a breeding perspective, desired traits are often found in nonadapted backgrounds. For example, B. juncea lin