Plant Breeding Reviews, Volume 46 E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Serie: Plant Breeding Reviews
• Sprache: Englisch

Seitenzahl: 1473

Veröffentlichungsjahr: 2022

Beschreibung

Plant Breeding Reviews presents state-of-the-art reviews on plant genetics and the breeding of all types of crops by both traditional means and molecular methods. Many of the crops widely grown today stem from a very narrow genetic base; understanding and preserving crop genetic resources is vital to the security of food systems worldwide. The emphasis of the series is on methodology, a fundamental understanding of crop genetics, and applications to major crops.

Leseprobe

Table of Contents

Cover

Title Page

Copyright Page

List of Contributors

1 Dani Zamir

I. INTRODUCTION

II. UNDERSTANDING QUANTITATIVE GENETIC VARIATION

III. CLONING OF QUANTITATIVE TRAIT LOCI

IV. CHARACTERIZATION OF GENETIC PHENOMENA

V. SEQUENCING THE TOMATO GENOME

VI. PRACTICAL PLANT BREEDING

VII. SCIENTIFIC IMPACT

VIII. LIST OF SCIENTIFIC JOURNAL PUBLICATIONS OF DANI ZAMIR

ACKNOWLEDGMENTS

LITERATURE CITED

2 Muscadine Grape Breeding

I. INTRODUCTION

II. HISTORY OF IMPROVEMENT

III. BREEDING TECHNIQUES

IV. MOLECULAR BREEDING RESOURCES

V. BREEDING FOR SPECIFIC CHARACTERS

VI. INTERSUBGENERIC HYBRIDIZATION

VII. FUTURE PROSPECTS

LITERATURE CITED

3 Breeding Intermediate Wheatgrass for Grain Production

I. INTRODUCTION

II. PLANT BIOLOGY AND BEHAVIOR

III. HISTORY OF IWG BREEDING

IV. BREEDING METHODOLOGIES BY PROGRAM

V. BREEDING GOALS AND PROGRESS

VI. MODERN BREEDING TOOLS

VII. RATE OF INTERMEDIATE WHEATGRASS DOMESTICATION

VIII. FUTURE DIRECTIONS

LITERATURE CITED

4 Understanding Environmental Modulation of Heterosis

I. INTRODUCTION OF HETEROSIS

II. MODELS AND MECHANISMS TO EXPLAIN HETEROSIS

III. GENOTYPE‐BY‐ENVIRONMENT INTERACTION

IV. INBRED LINES GENERALLY HAVE MORE INSTABILITY ACROSS ENVIRONMENTS THAN HYBRIDS

V. HIGHER HETEROSIS LEVELS ARE OBSERVED UNDER STRESS CONDITIONS

VI. VARIATION IN HETEROSIS IS ALSO OBSERVED UNDER NATURAL CONDITIONS

VII. CONCLUSION AND FUTURE PROSPECTS

ACKNOWLEDGMENTS

LITERATURE CITED

5 Breeding of Hemp (

Cannabis sativa

)

I. INTRODUCTION

II. TAXONOMY AND DOMESTICATION OF HEMP

III. SEX DETERMINATION IN HEMP

IV. CONTROL OF POLLINATION

V. BREEDING AND SELECTION SCHEMES

VI. TARGET TRAITS FOR GENETIC IMPROVEMENT

VII. GERMPLASM RESOURCES

VIII. GENOMIC RESOURCES

IX. FUTURE DIRECTIONS

ACKNOWLEDGMENTS

LITERATURE CITED

6 Genetic Resources and Breeding Priorities in Phaseolus Beans

I. DESCRIPTION OF CROP VULNERABILITY AND ITS RELEVANCE IN

PHASEOLUS

II. BACKGROUND ON THE ORIGIN, DIVERSIFICATION, AND DOMESTICATION OF THE GENUS

PHASEOLUS

III. URGENCY AND EXTENT OF CROP VULNERABILITIES AND THREATS TO FOOD SECURITY

IV. GENETIC EROSION IN THE CENTERS OF ORIGIN

V. STATUS OF PLANT GENETIC RESOURCES IN THE NPGS

VI. GENOMIC AND GENOTYPIC CHARACTERIZATION DATA

VII. PROSPECTS, FUTURE DEVELOPMENT, AND GAPS IN GENETIC DIVERSITY

VIII. EPILOGUE

ACKNOWLEDGMENTS

LITERATURE CITED

7 Club Wheat – A Review of Club Wheat History, Improvement, and Spike Characteristics in Wheat

I. INTRODUCTION

II. SPIKE ARCHITECTURE IN GRASSES

III. CLUB WHEAT HISTORY

IV. CLUB WHEAT BREEDING

V. MAJOR GENES FOR CONTROL OF SPIKE CHARACTERSITICS IN WHEAT

VI. CONCLUSION

ACKNOWLEDGMENTS

LITERATURE CITED

8 Predicting Genotype × Environment × Management (G × E × M) Interactions for the Design of Crop Improvement Strategies:

I. THREE PERSPECTIVES OF G × E × M INTERACTIONS

II. FOUNDATIONS FOR G × E × M PREDICTION

III. THE BREEDER’S EQUATION AND BEYOND

IV. G × E × M CONSIDERATIONS FOR DESIGNING MULTI‐ENVIRONMENT TRIALS

V. BREEDER’S QUESTIONS: G × E × M → G × (E × M)

VI. AGRONOMIST’S QUESTIONS: G × E × M → M × (E × G)

VII. FARMER’S QUESTIONS: G × E × M → (G × M) × E

VIII. INTEGRATING THE DIFFERENT G × E × M PERSPECTIVES

IX. G × E × M PREDICTIONS BEYOND THE TRAINING DATA BOUNDARIES

X. PREDICTION‐BASED CROP IMPROVEMENT: FUTURE PROSPECTS

ACKNOWLEDGMENTS

LITERATURE CITED

9 Root Phenes for Improving Nutrient Capture in Low‐Fertility Environments

I. THE NEED FOR NUTRIENT‐EFFICIENT CROPS

II. ROOT PHENES ARE IMPORTANT FOR RESOURCE AQUSITION AND PLANT GROWTH

III. ROOT IDEOTYPES FOR IMPROVED NUTRIENT ACQUISITION

IV. PHENOTYPING METHODOLOGY AND TECHNOLOGY

V. DEPLOYMENT STRATEGIES FOR ROOT PHENES IN CROP BREEDING PROGRAMS

VI. CONCLUSIONS

LITERATURE CITED

10 Role of the Genomics–Phenomics–Agronomy Paradigm in Plant Breeding

I. INTRODUCTION

II. AGRONOMY AND GENOMICS (A‐G)

III. GENOMICS AND PHENOMICS (G‐P)

IV. PHENOMICS AND AGRONOMY (P‐A)

V. MERGE G‐P‐A THROUGH GWAS

VI. MERGE G‐P‐A THROUGH BLUP

VII. MERGE G‐P‐A THROUGH BAYESIAN METHODS

VIII. MERGE G‐P‐A THROUGH ML

IX. CONCLUSION AND FUTURE PROSPECTS

ACKNOWLEDGMENT

LITERATURE CITED

Cumulative Contributor Index

Cumulative Subject Index

End User License Agreement

List of Tables

Chapter 2

Table 2.1 Morphological traits which differentiate subgenus

Muscadinia

from...

Table 2.2 Muscadine Cultivars.

Table 2.3 Diseases, severity, and type of damage caused by several organism...

Chapter 3

Table 3.1 Cultivars released, germplasm registered, vegetatively propagated...

Table 3.2 Number of quantitative trait loci (QTL) discovered in different I...

Table 3.3 Response to eight cycles of selection for grain production traits...

Chapter 4

Table 4.1 Research reports assessing the degree of heterosis under differen...

Chapter 5

Table 5.1 Mean terminal flowering date and early presence of axillary flowe...

Table 5.2 Summary yield and quality data representing the means of data col...

Chapter 6

Table 6.1 Worldwide production of grain legumes (FAO data) (accessed on 18 ...

Table 6.2 Twenty countries with the largest dry bean production (FAO data) ...

Table 6.3 Twenty largest vegetable bean‐producing countries (aggregate of gr...

Table 6.4 Diversity among domesticated species in the genus

Phaseolus

.

Table 6.5 Accessions of

Phaseolus

species with the largest collections, inc...

Table 6.6 Distribution of sample origins among

Phaseolus

species in the USD...

Table 6.7 Number of wild

Phaseolus vulgaris

accessions collected per decade ...

Table 6.8 Factors affecting genetic vulnerability and resilience of

Phaseol

...

Chapter 7

Table 7.1 Major hexaploid wheat

a

subspecies and loci for major spike compon...

Table 7.2 Major QTL discovered in wheat mapping populations for spike compo...

Chapter 8

Table 8.1 Overview of breeder, agronomist, and farmer perspectives of genot...

Table 8.2 Characterization of six agronomic treatments used to sample the m...

Chapter 10

Table 10.1 Historical development of BLUP methods to predict breeding value...

Table 10.2 Historical development of Bayesian methods for genomic predictio...

Table 10.3 The major properties of the winning models in the Syngenta Crop ...

List of Illustrations

Chapter 1

Fig. 1.1. Dani Zamir, Professor Emeritus, Hebrew University of Jerusalem, Is...

Fig. 1.2. The

S. pennellii

Introgression Line (IL) population. (a) Genome in...

Fig. 1.3. Dani Zamir with multi‐loculed tomato germplasm.

Fig. 1.4. AB1 tomato cultivar.

Fig. 1.5. Field trials of tomato germplasm in Israel.

Chapter 2

Fig. 2.1.

Vitis rotundifolia

var.

rotundifolia

(left),

V. rotundifolia

var.

Fig. 2.2. County‐level distribution of

Vitis rotundifolia

and origin of majo...

Fig. 2.3. A wide variation of fruit size, color, and shape exists in current...

Fig. 2.4. Muscadine inflorescence in an organza isolation bag.

Fig. 2.5. Female (pistillate, left), hermaphroditic (perfect; center), and m...

Fig. 2.6. Lobed leaf and berries (left) of “Southern Home” muscadine. Red co...

Fig. 2.7. Pedigree of the complex bridge hybrid DRX60‐40. Parents with 100%

Fig. 2.8. Pedigree of the complex bridge hybrid JB81‐107‐11. Parents with 10...

Chapter 3

Fig. 3.1. Origin of 142 intermediate wheatgrass accessions deposited at the ...

Fig. 3.2. Principal component analysis of 1,916 intermediate wheatgrass geno...

Fig. 3.3. Intermediate wheatgrass used as a perennial grain crop: (a) seedli...

Fig. 3.4. Movement of intermediate wheatgrass elite germplasm among breeding...

Fig. 3.5. Diagram of population improvement and cultivar development for int...

Fig. 3.6. Quantitative trait loci (QTL) associated with agronomic, domestica...

Fig. 3.7. Changes in six traits across eight cycles of intermediate wheatgra...

Chapter 4

Fig. 4.1. Schematic diagram of terms used to describe the magnitude and gene...

Fig. 4.2. Heterosis is subject to genotype by environment interactions. (a–d...

Chapter 5

Fig. 5.1. Hemp planting and harvesting equipment. (a) Seed drill. (b) Sickle...

Fig. 5.2. Genetic diversity of hemp. Using 190 hemp cultivars, crosses, and ...

Fig. 5.3. Inflorescences on male (a), monoecious (b), and female (c) plants....

Fig. 5.4. Inflorescence of a female plant masculinized using silver thiosulf...

Fig. 5.5. Images of reproductive tissues of

C. sativa

collected with a scann...

Fig. 5.6. Number of seeds per female plant established in sentinel plots at ...

Fig. 5.7. Six row cone seeder (a) and small plot combine (b) for scale‐up an...

Fig. 5.8. Separation ofchemotypes I, II, III, and IV in 5,283 samples of

C.

...

Fig. 5.9. Depiction of the cannabinoid biosynthetic pathway including the en...

Fig. 5.10. Powdery mildew on hemp.

Fig. 5.11. Survey results for powdery mildew on hemp in trials in New York. ...

Fig. 5.12. Dry retted dry straw yields of 28‐fiber and dual‐purpose cultivar...

Chapter 6

Fig. 6.1. The seven domesticated gene pools of

Phaseolus

exhibit perhaps the...

Fig. 6.2. The seven domesticated gene pools of

Phaseolus

, including both wil...

Fig. 6.3. Global production of dry bean and green beans. Global production o...

Fig. 6.4. Climate change will dramatically change the suitability of

Phaseol

...

Fig. 6.5. Typical local dishes based on beans from around the world reflecti...

Fig. 6.6. Crossability relationships among domesticated

Phaseolus

species an...

Fig. 6.7. Distribution of the wild ancestral relatives of the five domestica...

Fig. 6.8. Genealogy of the common bean (

P. vulgaris

) evolutionary lineage....

Fig. 6.9. Different causes of genetic erosion in the centers of origin of

Ph

...

Fig. 6.10. Introductions into the CIAT collection of wild

Phaseolus vulgaris

Fig. 6.11. Inter‐gene pool and interspecific introgressions in common bean l...

Fig. 6.12. Global yield trends in soybean and dry bean. Soybean yields have ...

Chapter 7

Fig. 7.1. (a) Field plot of awned club wheat, Pullman WA, 2020.(b) Spike...

Fig. 7.2. Migration of club wheat from center of origin in Turkey throughout...

Fig. 7.3. Display of spike‐type segregation in wheat by W.J. Spillman, 1901....

Fig. 7.4. (a) Distribution of club wheat in the United States in 1919.(b...

Fig. 7.5. Orville Vogel comparing the height of semi‐dwarf “Gaines” wheat (l...

Fig. 7.6. Cross sections of Japanese sponge cakes evaluated by the Japanese ...

Fig. 7.7. Dosage effect of the

Q

gene (a–e) and the

q

gene (f–j) (named the ...

Chapter 8

Fig. 8.1. Schematic representation of the linear Breeder → Agronomis...

Fig. 8.2. Example of a maize multi‐environment trial (MET) designed to evalu...

Fig. 8.3. Estimated grain yield components of variance (with standard errors...

Fig. 8.4. Representation of agronomist views of results from a maize multi‐e...

Fig. 8.5. Representation of breeder views of results from a maize multi‐envi...

Fig. 8.6. Representation of farmer views of results from a maize multi‐envir...

Fig. 8.7. Line plot visualization of grain yield of six hybrids plotted agai...

Fig. 8.8. Schematic representation of conditional perspectives of the breede...

Fig. 8.9. Schematic representation of a hybrid maize breeding program with a...

Fig. 8.10. Multi‐environment trial (MET) analysis emphasizing breeder’s pers...

Fig. 8.11. Multi‐environment trial (MET) analysis emphasizing breeder’s pers...

Fig. 8.12. Multi‐environment trial (MET) analysis emphasizing agronomist’s p...

Fig. 8.13. Multi‐environment trial (MET) analysis emphasizing agronomist’s p...

Fig. 8.14. Multi‐environment trial (MET) analysis emphasizing agronomist’s p...

Fig. 8.15. Multi‐environment trial (MET) analysis emphasizing farmer’s persp...

Fig. 8.16. Multi‐environment trial (MET) analysis emphasizing farmer’s persp...

Fig. 8.17. Schematic of process for enabling whole‐genome prediction (see te...

Fig. 8.18. Schematic representations of a crop growth model (CGM) at differe...

Fig. 8.19. RPG‐MET‐TPE alignment for prediction applications. Alignment of m...

Fig. 8.20. Application of yield‐evapotranspiration (yield–ET) front and yiel...

Fig. 8.21. Long‐term improvement of maize yield productivity in comparison w...

Fig. 8.22. Schematic representation of an iterative experimental modeling ap...

Fig. 8.23. Two examples of applications of an iterative experimental modelin...

Chapter 9

Fig. 9.1. Comparison of maize (

Zea mays

) and common bean (

Phaseolus vulgaris

Fig. 9.2. “Steep, cheap, and deep” and “topsoil foraging” root ideotypes in ...

Fig. 9.3. The “steep, cheap, and deep” root ideotype for enhanced plant perf...

Fig. 9.4. Examples of anatomical and architectural phenotyping. (a) LAT imag...

Chapter 10

Fig. 10.1. Relationship and dissection among genomics, phenomics, and agrono...

Guide

Cover Page

Title Page

Copyright Page

List of Contributors

Table of Contents

Begin Reading

Cumulative Contributor Index

Cumulative Subject Index

Wiley End User License Agreement

Pages

iii

iv

xi

xii

xiii

xiv

xv

xvi

xvii

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

292

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

412

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

506

507

508

509

510

511

512

513

514

515

516

517

518

519

520

521

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

550

551

552

553

554

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

572

573

574

575

576

577

578

579

580

581

587

588

589

590

591

592

593

594

595

596

597

598

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

620

621

622

623

624

625

627

628

629

630

631

632

633

634

635

636

637

638

639

640

641

642

643

644

645

646

647

648

649

650

651

652

653

654

655

656

657

658

659

660

661

662

663

664

665

666

667

668

669

670

671

672

673

675

676

677

678

679

680

681

682

683

684

685

686

687

688

689

690

691

692

693

694

695

696

697

698

699

700

701

702

703

704

705

706

707

708

709

710

711

712

713

714

715

PLANT BREEDING REVIEWS

Volume 46

Edited by

Irwin Goldman

University of Wisconsin–Madison

Madison, Wisconsin, USA

This edition first published 2023© 2023 John Wiley & Sons, Inc.

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

The right of Irwin Goldman to be identified as the author of this editorial material has been asserted in accordance with law.

Registered OfficeJohn Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA

Editorial OfficeThe Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

Wiley also publishes its books in a variety of electronic formats and by print‐on‐demand. Some content that appears in standard print versions of this book may not be available in other formats.

Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

Library of Congress Cataloging‐in‐Publication Data has been applied forHardback ISBN: 9781119874126

Cover Design: WileyCover Image: © browndogstudios/Getty Images

List of Contributors

Kayla R. AltendorfSeed and Cereal Research Unit, USDA ARS, Prosser, WA, USA

James A. AndersonDepartment of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN, USA

Prabin BajgainDepartment of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN, USA

James BeaverDepartamento de Cultivos y Ciencias Agro‐Ambientales, University of Puerto Rico, Mayagüez, PR, USA

Mark BrickDepartment of Soil and Crop Sciences, Colorado State University, Fort Collins, CO, USA

Judith K. BrownSchool of Plant Sciences, University of Arizona, Tucson, AZ, USA

Douglas J. CattaniDepartment of Plant Science, University of Manitoba, Winnipeg, MB, Canada

Chunpeng James ChenDepartment of Animal and Poultry Sciences, Virginia Tech, Blacksburg, VA, USA

Karen CichySugarbeet and Bean Research Unit, USDA‐ARS, East Lansing, MI, USA

Patrick J. ConnerDepartment of Horticulture, University of Georgia, Tifton, GA, USA

Mark CooperQueensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, Queensland, AustraliaARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Queensland, Australia

Jared L. CrainDepartment of Plant Pathology, Kansas State University, Manhattan, KS, USA

Timothy E. CrewsThe Land Institute, Salina, KS, USA

Jose CrossaBiometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, México

Daniel G. DebouckPrograma de Recursos Genéticos, CIAT, Cali, Colombia

Lee R. DeHaanThe Land Institute, Salina, KS, USA

Alfonso Delgado‐SalinasInstituto de Biología, Universidad Nacional Autónoma de México, Ciudad de México, México

Christine H. DiepenbrockDepartment of Plant Sciences, University of California, Davis, CA, USA

Sarah DohleDepartment of Plant Science, Delaware Valley University, Doylestown, PA, USA

Emmalea ErnestCooperative Extension Vegetable & Fruit Program, University of Delaware, Georgetown, DE, USA

Jorge Acosta GallegosCampo Experimental Bajío, INIFAP, Celaya, México

Kimberly A. Garland‐CampbellUSDA‐ARS Wheat Health, Genetics and Quality Unit, Washington State University, Pullman, WA, USA

Paul GeptsDepartment of Plant Sciences, University of California, Davis, CA, USA

Carla GhoCorteva Agriscience, Johnston, IA, USA

Irwin L. GoldmanDepartment of Horticulture, University of Wisconsin‐Madison, Madison, WI, USA

Francisco GomezDepartment of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, USA

Graeme L. HammerQueensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, Queensland, AustraliaARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Queensland, Australia

Ben J. HayesQueensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, Queensland, Australia

Barbara HellierPlant Germplasm Introduction and Testing Research Unit, USDA‐ARS, Pullman, WA, USA

Candice N. HirschDepartment of Agronomy and Plant Genetics, University of Minnesota, St. Paul, MN, USA

Ying HuCollege of Plant Protection, Jilin Agricultural University, Changchun, Jilin, China

Consuelo Estevez de JensenDepartamento de Cultivos y Ciencias Agro‐Ambientales, University of Puerto Rico, Mayagüez, PR, USA

Xiuliang JinMinistry of Agriculture, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Institute of Crop Sciences, Beijing, China

Alexander V. KarasevDepartment of Entomology, Plant Pathology and Nematology, University of Idaho, Moscow, ID, USA

James D. KellyDepartment of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI, USA

Steven R. LarsonForage and Range Research, USDA ARS, Logan, UT, USA

Zhi LiState Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng, China

Phillip McCleanDepartment of Plant Sciences, North Dakota State University, Fargo, ND, USA

Carlos D. MessinaCorteva Agriscience, Johnston, IA, USAHorticultural Sciences Department, University of Florida, Gainesville, FL, USA

Phillip MiklasGrain Legume Genetics Physiology Research Unit, USDA‐ARS, Prosser, WA, USA

Luis A. MonserrateSchool of Integrative Plant Science, Cornell University, Geneva, NY, USA

Seth C. MurrayDepartment of Soil and Crop Sciences, Texas A&M University, College Station, College Station, TX, USA

James R. MyersDepartment of Horticulture, Oregon State University, Corvallis, OR, USA

Juan M. OsornoDepartment of Plant Sciences, North Dakota State University, Fargo, ND, USA

Travis A. ParkerDepartment of Plant Sciences, University of California, Davis, CA, USA

Julie S. PascheDepartment of Plant Sciences, North Dakota State University, Fargo, ND, USA

Marcial A. Pastor‐CorralesBeltsville Agricultural Center, Soybean Genomics and Improvement Center, USDA‐ARS, Beltsville, MD, USA

Dean W. PodlichCorteva Agriscience, Johnston, IA, USA

Jesse A. PolandKing Abdullah University of Science and Technology, Thuwal, Makkah, Saudi Arabia

Timothy PorchTropical Agriculture Research Station, USDA‐ARS, Mayagüez, PR, USA

Owen M. PowellQueensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Brisbane, Queensland, AustraliaARC Centre of Excellence for Plant Success in Nature and Agriculture, The University of Queensland, Brisbane, Queensland, Australia

Jessica RutkoskiDepartment of Crop Sciences, University of Illinois at Urbana‐Champaign, Urbana, IL, USA

James C. SchnableDepartment of Agronomy and Horticulture, University of Nebraska‐Lincoln, Lincoln, NE, USA

Hannah M. SchneiderCentre for Crop Systems Analysis, Wageningen University & Research, Wageningen, The Netherlands

Christine D. SmartSchool of Integrative Plant Science, Cornell University, Geneva, NY, USA

Lawrence B. SmartSchool of Integrative Plant Science, Cornell University, Geneva, NY, USA

George M. StackSchool of Integrative Plant Science, Cornell University, Geneva, NY, USA

James R. SteadmanDepartment of Plant Pathology, University of Nebraska, Lincoln, NE, USA

Benjamin StichInstitute for Quantitative Genetics and Genomics of Plants, Heinrich Heine University, Düsseldorf, Germany

Christopher F. StrockBreeding Insight, Cornell University, Ithaca, NY, USA

Jiabin SunState Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng, China

Tom TangCorteva Agriscience, Johnston, IA, USA

Frank TechnowCorteva Agriscience, Tavistock, Ontario, Canada

Jacob A. TothSchool of Integrative Plant Science, Cornell University, Geneva, NY, USA

M. Kathryn TurnerThe Land Institute, Salina, KS, USA

Carlos UrreaDepartment of Agronomy and Horticulture, University of Nebraska, Scottsbluff, NE, USA

Lyle WallacePlant Germplasm Introduction and Testing Research Unit, USDA‐ARS, Pullman, WA, USA

Lizhi WangDepartment of Industrial and Manufacturing Systems Engineering, Iowa State University, Ames, IA, USA

Anna WesterberghDepartment of Plant Biology, Uppsala BioCenter, Linnean Centre for Plant Biology in Uppsala, Swedish University of Agricultural Sciences, Uppsala, Sweden

Margaret L. WorthingtonDepartment of Horticulture, University of Arkansas, Fayetteville, AR, USA

Zhiwu ZhangDepartment of Crop and Soil Sciences, Washington State University, Pullman, WA, USA

1Dani Zamir: Pioneer in Tomato Genetics and Quantitative Trait Dissection

Irwin L. Goldman

Department of Horticulture, University of Wisconsin‐Madison, Madison, WI, USA

ABSTRACT

The dedicatory chapters for PBR have traditionally not had abstracts; but if there is a strong sense that there should be one I can write it‐ however this hasn’t always been done.

KEYWORDS: Tomato, quantitative trait loci, introgression lines, tomato genome, overdominance, introgression breeding

OUTLINE

I. INTRODUCTION

II. UNDERSTANDING QUANTITATIVE GENETIC VARIATION

III. CLONING OF QUANTITATIVE TRAIT LOCI

IV. CHARACTERIZATION OF GENETIC PHENOMENA

V. SEQUENCING THE TOMATO GENOME

VI. PRACTICAL PLANT BREEDING

VII.SCIENTIFIC IMPACT

VIII. LIST OF SCIENTIFIC JOURNAL PUBLICATIONS OF DANI ZAMIR

ACKNOWLEDGMENTS

LITERATURE CITED

ABBREVIATIONS

ILs

Introgression Lines

QTL

Quantitative Trait Locus

CWR

Crop Wild Relative

I. INTRODUCTION

For more than four decades, Dani Zamir has been among the most influential scientists in plant breeding throughout the world (Figure 1.1). Professor Zamir has spent his career at the Hebrew University of Jerusalem, in Rehovot, Israel, where he and his students have made great strides in improving our understanding of the genetic basis of quantitative traits in crop plants and in developing tools for advancing the science of plant breeding. Zamir was able to combine those efforts with practical plant breeding, leading to the development of highly productive cultivars and the establishment of practical breeding programs in horticultural species. He also mentored many undergraduate and graduate students in plant genetics and taught a popular course. Now Professor Emeritus, Dani Zamir's work on tomato genomics, genetics, and breeding continues into his fifth decade of work on the crop. This dedication focuses on a few of his key accomplishments in the field of plant breeding and plant genetics.

Fig. 1.1. Dani Zamir, Professor Emeritus, Hebrew University of Jerusalem, Israel.

Source: Photo credit: D. Zamir.

Dani Zamir was born in 1950 in Israel. Following his military service, he received degrees from the Hebrew University of Jerusalem (undergraduate) and the University of California‐Davis (graduate), completing his doctorate in 1981. He started his career as a lecturer in genetics at the Hebrew University's Faculty of Agriculture in Rehovot, Israel, in 1982 and was appointed senior lecturer in 1985. In 1992, he became associate professor and in 1996, professor of Genetics. He retired from his formal teaching and research at the University in 2018 and is now Professor Emeritus. Zamir also held adjunct appointments in genetics at Seoul University in South Korea and Cornell University in Ithaca, New York. Throughout his career, Zamir taught a popular course in general genetics to undergraduates at Hebrew University and was a mentor for numerous students.

Zamir has also founded two companies, each of which has achieved substantial success. The first, AB Seeds, initiated approximately 20 years ago, is a breeding and genetics company specializing in crop seeds including tomato. The company was sold to De Ruiter in 2008 and later purchased by Monsanto. More recently, Zamir and his student Yaniv Semel established the company Phenome Networks, which has developed proprietary software for managing complex breeding programs and the phenotypic and genotypic data that they generate. The company, based in Rehovot, Israel, serves a wide variety of public and private customers and helps users track crossing, trialing, phenotyping, and genotyping activities that are core components of breeding programs.

Zamir has served on the advisory boards of a number of institutions, journals, and projects, including Genoplante (France), the Max Planck Institute for Plant Breeding (Germany), the Department of Plant Molecular Biology at the University of Barcelona (Spain), the Grapevine Genome Project (Italy), the International SOL Genome Project, and the journals G3: Genes, Genomes, Genetics; Scientific Data; The Plant Journal; and Scientific Reports.

Zamir was also recipient of the Kaye Innovation Prize from the Hebrew University of Jerusalem in 2007, the EMET Prize in Agriculture in 2015, which recognizes excellence in academic and professional achievements that have significant influence on society, and the highly prestigious Israel Prize 2020. The Israel Prize is awarded by the State of Israel and is considered the highest honor the state bestows on an individual. It is highly selective and awarded annually in a formal state ceremony attended by the President, Prime Minister, and other dignitaries. The recipients of the prize are Israeli citizens or organizations who have displayed excellence in their field(s) or have contributed strongly to the culture of Israel. Receipt of this award is a singular achievement and a powerful indicator of the impact of Dani Zamir's work in agricultural science.

II. UNDERSTANDING QUANTITATIVE GENETIC VARIATION

Dani Zamir has long been a proponent of understanding and utilizing genetic variability, particularly that from crop wild relatives (CWRs), to improve modern crops. Among his most well‐known projects was the development of tomato (Solanum lycopersicon) introgression lines (ILs) containing small, molecular marker‐defined chromosomal segments from the wild species Solanum pennellii. His approach, which became known as Introgression Breeding, is predicated on the idea that crop domestication may have left behind useful allelic variation. In a publication that has been cited nearly 1,300 times, Eshed and Zamir (1995) argued that some of this variation may be valuable in a modern breeding context and that genetic tools could be developed to identify and introgress that variation into modern cultivars without the disadvantages of using CWRs directly as parents in a breeding program. The approach gained worldwide acclaim and has been attempted in a number of crop species. The resulting progenies from these types of approaches are called Introgression Lines, or ILs.

Zamir (2001) later suggested that a genetic infrastructure could be developed based on “exotic libraries” where individual breeding lines or cultivars in the library would contain a marker‐defined chromosomal segment from a CWR that had been introgressed through sexual recombination. A full set of lines of this sort would constitute a library of the genome of the CWR, albeit nested inside the genome of cultivated crop accessions. A scientist could obtain lines from the library to screen for traits of interest and potentially identify one or more lines carrying segments with valuable traits. These lines could then be easily introgressed into breeding material or cultivars using the markers flanking the introgression. These ideas were later more fully expanded to consider how this approach could be used to source natural variation for plant breeding programs (Zamir 2008).

Zamir's key insights into the value of allelic variation in CWRs were (1) that there were ways to access their value without using their entire genome as a parent in a breeding program and (2) that the genome of the wild relative could be assembled piece by piece into a library that was based on a cultivated genetic background. Plant breeders have long been aware of the pitfalls of using CWR as parents, including substantial linkage drag with undesirable traits, introduction of sterility and incompatibility, and limited recombination between wild and cultivated chromosomes. Granted, there are numerous examples of introgressions of important alleles from CWRs into cultivated crops; though these almost always involved substantial backcrossing to the cultivated parent to remove the genome of the wild parent and retain only the small segment associated with the trait of interest. In a number of these cases, unwanted segments of wild species chromosomes remain and are difficult to remove because of limited recombination at or near the unwanted genes of interest. The IL approach circumvents this problem by pre‐developing a set of marker‐defined ILs and allowing for a more custom‐designed breeding approach (Figure 1.2).

But of even greater value may be the use of CWRs as a source for valuable quantitative trait locus (QTL) variation. Zamir and his students and colleagues were among the first to propose and demonstrate a practical approach to utilizing the potential of CWRs as sources of important quantitative variation (Zamir 2008). Prior to this time, CWRs were primarily considered as sources of valuable qualitative genetic variants, particularly for traits such as disease resistance. Traits like yield and productivity were considered mainly in the context of cultivated genetic backgrounds. But an important insight offered by Zamir and colleagues focused on the observation that bottlenecks caused by domestication and modern breeding may have left behind valuable quantitative traits. By going back to CWRs, some of these valuable quantitative traits could be accessed; however, introgressing them carefully into cultivated backgrounds using very precise marker‐delineated segments was the key to harnessing their potential. The IL concept provided a framework for how this could be accomplished.

Fig. 1.2. The S. pennellii Introgression Line (IL) population. (a) Genome introgressions in the 76 S. pennellii ILs, which are nearly isogenic to each other and to M82 and differ only for the marked introgressed chromosome segments. (b) Green fruits of the wild species, S. pennellii, the lycopene‐rich red fruits of S. lycopersicum, their F1 hybrid and six ILs.

Source: D. Zamir.

More specifically, Eshed et al. (1996) conducted a series of field trials with ILs and their hybrids in two distinct genetic backgrounds. Seven out of 8 hybrids displayed from 7 to 13% higher yield than their near‐isogenic controls (without introgressions). This finding demonstrated a significant interaction between the introgression and genetic background for yield in tomato. When the two introgressions with the largest yield advantage were combined into a single genetic background, a 20% yield increase compared to the control was realized.

The IL system is used by hundreds of researchers and breeders in academia and industry around the world. It has become the most helpful tool for identifying and introducing beneficial genes into cultivated varieties from their wild relatives. Moreover, the success of the tomato ILs has become a model for the development of similar systems in other agricultural crops (rice, barley, and wheat, for example) in China, Japan, Korea, and other countries.

III. CLONING OF QUANTITATIVE TRAIT LOCI

Dani Zamir has been among the world pioneers in applying molecular markers for mapping quantitative traits in plants. One of the most important achievements in this area was the first example of cloning and characterization of a QTL, performed by Zamir and his student Eyal Fridman, currently a researcher in Israel's Volcani Institute. This was the first example of a cloned QTL from any organism. Their efforts identified a QTL for levels of soluble solids, primarily sugars, in tomato fruit that was determined by a variant of the enzyme invertase. The milestone article, published in April 2000 (Fridman et al. 2000), has been one of the most notable achievements of Israeli science in genetics and agriculture. In parallel with Zamir's work, a QTL gene for fruit size in tomato that segregated in one of the ILs was identified by Steven Tanksley from Cornell University, one of Zamir's research partners for many years, and this article was published in July 2000 (Frary et al. 2000).

To clone this QTL, Fridman et al. (2000) first identified a moderate QTL known as Brix‐9‐2‐5, which increases sugar yield of tomatoes without compromising yield. This QTL was mapped to a single‐nucleotide polymorphism (SNP)‐defined region of 484 base pairs within a flower‐ and fruit‐specific cell wall invertase gene (LIN5). LIN5 is considered a “sink gene” that is involved with the unloading of sugars into the fruit. Further QTL analysis with segregating populations from five tomato species localized the functional polymorphism of Brix‐9‐2‐5 to an amino acid near the catalytic site of the invertase crystal, which affects enzyme kinetics and fruit sink strength (Fridman et al. 2004). The work helped demonstrate the relationship between genetic variation at the sequence level and the manifestation of a QTL. This first cloning of an important crop QTL highlighted the value of the IL approach, and the enormous collection of characterized lines, advanced by Zamir and colleagues over many years.

IV. CHARACTERIZATION OF GENETIC PHENOMENA

Dani Zamir's commitment to working with tomato extends through five decades, and he is therefore in an enviable position to study and describe the history of tomato genetics and breeding. Working with a large multinational team, Zamir and colleagues examined the modern history of tomato domestication and breeding through the lens of the cumulative genetic information collected by researchers throughout the years (Lin et al.2014). Their work revealed that modern tomato can be partially described by two independent sets of QTLs that conferred important changes to tomato fruit. These QTL, particularly fruit mass QTLs known as fw1.1, fw5.2, fw7.2, fw12.1, and len12.1, are responsible for the large size of modern tomato fruit, which is more than 100× larger than its wild progenitor. They also proposed a two‐step evolution of tomato fruit mass through domestication sweeps associated with these QTL. In addition to changes in fruit mass, they reported several QTL on chromosome 5 that confer greater fruit firmness (fir5.1) and higher soluble solids (ssc5.1, ssc5.2, and ssc5.3) were likely selected during the development of processing tomatoes. Processing tomatoes are largely used for the production of tomato paste, which is a staple of processed foods such as ketchup. This genetic signature of processing tomato was facilitated by the presence of a very large centromere on chromosome 5, which likely reduced the amount of recombination present in the region where these QTL reside (Lin et al. 2014) (Figure 1.3).

Fig. 1.3. Dani Zamir with multi‐loculed tomato germplasm.

Source: Photo credit: M. Schwartz.

Among Zamir's important contributions to agricultural research are his insights into understanding the genetic basis of overdominance (Semel et al. 2006), epistasis (Eshed and Zamir 1995), and heterosis itself (Lippman and Zamir 2007; Lippman et al. 2007; Krieger et al. 2010). For as long as humans have bred plants and animals, they have recognized the phenomenon of hybrid vigor, or heterosis, in which the F1 progeny of a cross exceeds the value of the parents in terms of productivity. Despite the obvious importance of heterosis to global food production, its genetic basis has remained poorly understood; perhaps in part because many loci contribute to yield and productivity traits, and these loci behave in a variety of different ways.

In a collaboration with Uri Krieger and Zachary Lippman, Zamir worked out genetic effects at the locus known as SINGLE FLOWER TRUSS (SFT), which codes for a protein that produces the flowering hormone florigen (Lifschitz et al. 2006). Heterosis has been associated with several potential explanations, including the dominance hypothesis, the overdominance hypothesis, and epistasis. The overdominance hypothesis suggests that interaction between alleles at a locus is the cause of hybrid vigor. Identification of a number of examples of putative overdominance have revealed the phenomenon of pseudo‐overdominance, where dominant loci are linked and appear as an overdominant locus. Krieger et al. (2010) examined a tomato mutant, sft‐e4537, which displayed overdominant‐type heterosis and possessed a missense mutation in the gene SFT. Plants carrying this mutation in the homozygous recessive condition flower very late and their flowering branches quickly revert to vegetative branches. Heterozyogtes display substantial heterosis, derived from a suppression of growth termination mediated by the SELF PRUNING (SP) gene, an antagonist of SFT. This elegant example of a mechanism for overdominance illustrates how this elusive genetic phenomenon is a plausible explanation for heterosis in tomato. That is not to say that all loci behave in this manner, but the confirmation of a truly overdominant locus goes a long way to confirming the truth of one of the most widely held hypotheses of heterosis. Interestingly, this example also confirms the importance of epistasis, one of the three primary hypotheses for heterosis, given that the SFT and SP loci interact in this example.

V. SEQUENCING THE TOMATO GENOME

Zamir was the leader of the SOL Genome Project, in which the complete DNA sequence of the tomato was deciphered (Tomato Genome Consortium 2012). To this end, Prof. Zamir organized research groups from the United States, the United Kingdom, the Netherlands, Italy, France, India, Korea, and Japan into a group known as the International Tomato Genome Sequencing Project, who worked together to sequence the tomato genome. Zamir was one of the two corresponding authors of the article on the tomato genome published in 2012 in Nature that garnered the issue's cover and a special feature (Tomato Genome Consortium 2012). This paper has now been cited more than 2,000 times and represents a tremendous multinational effort to sequence the genome of one of our most important crops.

The International Tomato Genome Sequencing Project was begun in 2004 by an international consortium of scientists from Korea, China, the United Kingdom, India, the Netherlands, France, Japan, Spain, Italy, and the United States. The group found that tomato genome was highly syntenic with other sequenced solanaceae crops and comprised more low‐copy sequences than other crop genomes. They compared the cultivated genome to the related wild species Solanum pimpinellifolium, and the two genomes were divergent for only 0.6% of their nucleotides. However, the cultivated genome was 8% divergent from potato with a number of chromosomal inversions differing between the two. The researchers found two genome triplications in the history of tomato, one of which is approximately 130 million years ago and the other about 60 million years ago. These large‐scale events were key to the diversification of genes for fruit fleshiness and color, particularly the more recent triplication event.

As critically important as this international effort was, Zamir's involvement in the ultimate success of the tomato genome project goes far deeper. Over many decades, Zamir collaborated with Cornell University scientist Steve Tanskley, who played a key role in building the molecular marker linkage map that was used to piece together much of the early information about the tomato genome and the location of traits of interest. Zamir's career spans the critical period from the early 1980s through the early 2000s which saw the development of molecular markers for plant breeding applications. Beginning with allozyme markers in the 1980s, then restriction fragment length polymorphisms (RFLPs) in the late 1980s and early 1990s, polymerase chain reaction (PCR)‐based markers in the 1990s, and finally sequence‐based markers in the 2000s, the possibility of associating chromosome segments with some type of molecular marker improved dramatically during this period. High‐density molecular marker linkage maps became common by the 1990s and expanded dramatically in the 2000s with sequence‐based markers. These developments were greatly facilitated by improvements in genome sequencing, particularly next‐generation technologies that became available more recently. In addition, tomato, along with maize and rice, was always among the most well‐developed models for marker systems in crops. Zamir was instrumental in the iterative development of marker‐based information in tomato, contributing to virtually all of these developments over a period of decades. Marker‐based regions were critical to the sequencing effort. Thus, the sequencing of the tomato genome represents one of the more recent successes of Zamir's collaborations, built piece by piece on a foundation of tomato breeding and genetics knowledge.

VI. PRACTICAL PLANT BREEDING

Dani Zamir lives up to his principles by being involved in practical breeding. Based on the new methods he had developed, he has bred, together with a seed company he founded, AB Seeds, a processed tomato variety, ‘AB2’, which was a leading variety in California for a number of years. In many ways, this hybrid variety served as practical proof of the principles described by Zamir's scholarly work. The QTL for yield in this variety were first identified and described in Zamir's earlier work (Eshed et al. 1996). Zamir won the Kaye Innovation Award from the Hebrew University for his achievements in applied research based on this development.

Over the years, Zamir has collected tomato varieties from all major collections in the world. In the years 2007–2010, he grew more than 5,000 varieties simultaneously on the experimental farms in Acre, Israel, in the framework of the EU consortium EUSOL. He described them all phenotypically and invited the scientific community to examine and use them. Hundreds of scientists from all over the world came to Acre and searched for phenotypes of interest. Likewise, in the early 2000s, Zamir and colleagues performed a large‐scale experiment on mutagenesis in the cultivated tomato. After scanning hundreds of thousands of second‐generation (F2) plants in the field, hundreds of new mutations were isolated. Each mutation was genetically and phenotypically described, and the results were posted online for the benefit of the entire scientific community. These mutants are available to anyone in the world who requests them. Molecular characterization of these mutations, whether in collaboration with the Zamir lab or by others, is responsible for numerous important discoveries in plant development and metabolism. The results of these efforts have fueled the development of new tomato cultivars and spawned new research projects based on the phenotypic diversity present in the collections (Figures 1.4 and 1.5).

Zamir has not limited his activities to tomato. Throughout his career, he has established infrastructure for programs in genomic‐based breeding of rose and Lisianthus in the Faculty of Agriculture. He assembled an extensive collection of rose varieties and added additional researchers from the Faculty of Agriculture to a long‐term project in which these varieties are being characterized genetically and metabolically. These efforts are particularly important given the loss of public sector breeding programs in recent decades. Establishment of new breeding programs is a way to re‐energize public breeding programs in horticultural crops that have been neglected, perhaps because of the difficulty in finding funding to support them.

Marker‐assisted selection has been used extensively for many plant breeding applications, including introgressing chromosomal segments from wild species. Because small segments can be defined by marker‐linked regions, it may be possible to reduce the linkage drag associated with less desirable traits. A number of examples of wild species introgressions in rice and tomato have led to improved agronomic performance and new cultivars. Marker‐linked Solanum pennellii introgressions backcrossed into cultivated tomato by Zamir and colleagues led to the incorporation of the QTL Brix‐9‐2‐5, which conferred higher levels of soluble solids in processing tomatoes (Lippman et al. 2007). Zamir's company AB Seeds, based in Israel, used this introgression to develop the processing tomato cultivar ‘AB2’, which was widely grown in California's tomato processing industry (Vogel 2014). The success of this approach combines the power of the IL concept pioneered by Eshed and Zamir with pyramiding of genes for traits of interest. Sacco et al. (2013) showed how introgressions from S. pennellii carrying useful QTL could be pyramided in a single genotype. In their study, QTL controlling ascorbic acid, phenol composition, and soluble solids were introgressed into a recurrent parent and stabilized in the homozygous condition.

Fig. 1.4. AB1 tomato cultivar.

Source: Photo credit: D. Zamir.

Zamir has also argued for a more thoughtful and comprehensive database of crop phenotypes, which can be used to improve crops in the future. Zamir's work to develop large collections of tomato genotypes described earlier, including mutants, and make them available to the scientific community, is an example of his interest in preserving phenotypic information. In a landmark paper in PLoS Biology in 2013 (Zamir 2013), he advocates for an online system that is capable of storing, managing, and retrieving data related to crop phenotypes and their genotypes. This would take advantage of prior work in phenotyping, which is largely lost as individual scientists retire or stop their projects for other reasons. A public repository would advance the collective effort, as it would preserve unique phenotypes in a catalog that could be retrieved by any worker interested in that crop. It is perhaps this emphasis that led Zamir and his former student Yaniv Semel to develop Phenome Networks, an Israeli company that has built proprietary software for managing the complex data stream necessary for modern breeding programs. The software helps the breeder manage phenotypic and genotypic data, as well as pedigrees and trial information.

Fig. 1.5. Field trials of tomato germplasm in Israel.

Source: Photo credit: D. Zamir.

VII. SCIENTIFIC IMPACT

Dani Zamir's scientific influence on crop breeding and genetics is reflected in the large number of citations his publications have received – over 34,000 citations to date in scientific journals (Google Scholar, https://scholar.google.com/ 2021), which is an extraordinary number in the field of plant breeding. This is also reflected in his high h‐index, measured at 95. The h‐index is a measure of the degree to which an author is cited by others, and demonstrates the impact of Zamir's work in the research community. The paper with the largest number of citations from Zamir's work, in addition to the tomato genome sequence paper which has more than 2,000 citations, is the IL concept of Eshed and Zamir (1995) which has nearly 1,300 citations. In addition, Zamir is a co‐author on 15 publications that have more than 500 citations each, a truly remarkable accomplishment.

For 35 years, Prof. Zamir taught the introductory course in genetics to students of the Faculty of Agriculture at Hebrew University in Rehovot, Israel. It is likely that he personally instructed more than 9,000 students during this period. Zamir has mentored generations of graduate students, including 17 MS students, 20 PhD students, and 8 postdoctoral fellows, most of whom continue to pursue genetics and plant breeding research. Several of his students continue in academic and applied research at the leading universities in Israel and abroad. Many of his students are involved in breeding in seed companies. Prof. Zamir is an avid and dedicated supporter of teaching and education in applied genetics and has published an important article on this subject (Fridman and Zamir 2012).

Dani Zamir is a decorated scientist, academic, plant breeder, entrepreneur, and mentor. His career stands as an outstanding example of how a sustained focus on a particular species, coupled with persistence and insight, can lead to dramatic advancements in science and technology. When Zamir began his career in plant breeding in the 1970s, we may not have predicted that specific chromosome segments from tomato's wild relatives would play such an important role in the improvement of quantitative traits. Likewise, we may not have predicted that tools would become available to clone these genes or to dissect the genetic basis of some of the most perplexing phenomena in crop genetics. Dani Zamir's work has encompassed all of these achievements. By any measure, Zamir's career has been an unqualified success. He is recognized not only for the knowledge he has helped to uncover, but for the efforts to organize the community to harness their resources toward improvement of the tomato.

VIII. LIST OF SCIENTIFIC JOURNAL PUBLICATIONS OF DANI ZAMIR

Zamir, D., R.A. Jones, and N. Kedar. 1980. Anther culture of male sterile

Lycopersicon esculentum

mutants.

Plant Sci. Lett

. 17:353–361.

Zamir, D., S.D. Tanksley, and R.A. Jones. 1981. Genetic analysis of the origin of plants regenerated from anther tissues of

Lycopersicon esculentum

.

Plant Sci. Lett

. 21:121–127.

Zamir, D., S.D. Tanksley, and R.A. Jones. 1981. Low temperature effect on selective fertilization of pollen mixtures of wild and cultivated tomato species.

Theor. Appl. Genet

. 59:235–238.

Tanksley, S.D., D. Zamir, and C.M. Rick. 1981. Evidence for extensive overlap in sporophytic and gametophytic gene expression in

Lycopersicon esculentum

.

Science

213:453–455.

Zamir, D., S.D. Tanksley, and R.A. Jones. 1982. Haploid selection for low temperature tolerance of tomato pollen.

Genetics

101:129–137.

Palmer, J.D., and D. Zamir. 1982. Chloroplast DNA evolution and phylogenetic relationships in

Lycopersicon

.

Proc. Natl. Acad. Sci. U.S.A

. 79:5006–5010.

Zamir, D. 1983. Pollen irradiation in tomato: minor effects on enzymic gene transfer.

Theor. Appl. Genet

. 66:147–151.

Zamir, D., and G. Ladizinsky. 1984. Genetics of allozyme variants in lentil.

Euphytica

33:329–336.

Zamir, D., N. Navot, and J. Rudich. 1984. Enzyme polymorphism in

Citrullus lanatus

and

C. colocynthis

in Israel and Sinai.

Plant Syst. Evol

. 146:163–170.

Zamir, D., T. Ben‐David, and J. Rudich. 1984. Frequency distributions and linkage relationships of 2‐tridecadone in interspecific segregating generations of tomato.

Euphytica

33:481–488.

Miltau, O., D. Zamir, and J. Rudich. 1984. Breeding for chilling tolerance in tomato: an examination of selection criteria.

Eucarpia

9:45–50.

Zamir, D., and I. Chet. 1985. Electrophoretic patterns of soluble enzymes in

Trichoderma viride

.

Can. J. Microbiol

. 31:578–580.

Goldring, A., D. Zamir, and Ch. Degani. 1985. Duplicated phosphoglucose isomerase genes in acocado.

Theor. Appl. Genet

. 71:491–494.

Perl‐Treves, R., D. Zamir, N. Navot, and E. Galun. 1985. Phylogeny of

Cucumis

based on isozyme variability and its comparison with the plastom phylogeny.

Theor. Appl. Genet

. 71:430–436.

Pinkas, R., D. Zamir, and G. Ladizinsky. 1985. Allozyme divergence and evolution in the genus

Lens

.

Plant. Syst. Evol

. 151:131–140.

Miltau, O., D. Zamir, and J. Rudich. 1985. Growth rates of

Lycopersicon

species at low temperatures.

Z. Pflanzenzuchtg

. 96:193–199.

Navot, N., and D. Zamir. 1986. Linkage relationships of 19 protein coding genes in watermelon.

Theor. Appl. Genet

. 72:274–278.

Zamir, D. and Y. Tadmor. 1986. Unequal segregation of nuclear genes in plants.

Bot. Gaz

. 147:355–358.

Zamir, D., and M. Tal. 1987. Genetic analysis of sodium, potassium and chloride in an interspecific

Lycopersicon

cross.

Euphytica

36:187–191.

Gadish, I., and D. Zamir. 1987. Differential zygotic abortion abortion in an interspecific

Lycopersicon

cross.

Genome

29:156–159.

Navot, N., and D. Zamir. 1987. Isozyme and seed protein phylogeny of the genus

Citrullus

(Cucurbitaceae).

Plant Syst. Evol

. 156:61–67.

Tadmor, Y., D. Zamir, and G. Ladizinsky. 1987. Genetic mapping of an ancient translocation in the genus

Lens

.

Theor. Appl. Genet

. 73:883–892.

Zamir, D., and I. Gadish. 1987. Pollen selection for low temperature adaptation in tomato.

Theor. Appl. Genet

. 74:545–548.

Czosnek, H., R. Ber, Y. Antignus, S. Cohen, and D. Zamir. 1988 Isolation of the tomato yellow leaf curl virus – a gemini virus.

Phytopathology

78:508–512.

Czosnek, H., R. Ber, N. Navot, D. Zamir, Y. Antignus, and S. Cohen. 1988. Detection of tomato yellow leaf curl virus in lysates of plants and insects by hybridization with viral DNA probe.

Plant Dis

. 72:949–591.

Tanksley, S.D., and D. Zamir. 1988. Double tagging of a male sterile gene in tomato using morphological and enzymic marker genes.

Hort Sci

. 23:387–388.

Young, N.D., D. Zamir, M. Ganal, and S.D. Tanksley. 1988. Use of isogenic lines and simultaneous probing to identify DNA markers tightly linked to the Tm‐2a gene in tomato.

Genetics

120:579–585.

Zamir, D., and S.D. Tanksley. 1988. Tomato genome is comprised largely of fast evolving, low copy number sequences.

Mol. Gen. Genet

. 213:254–261.

Czosnek, H., R. Ber, N. Navot, Y. Antignus, S. Cohen, and D. Zamir, 1989. Tomato yellow leaf curl virus DNA forms in the viral capsid, in infected plants and in the insect vector.

J. Phytopathol

. 125:47–54.

Sarfatti, M., J. Katan, R. Fluhr, and D. Zamir. 1989. An RFLP marker in tomato linked to the

Fusarium oxysporum

resistance gene I2.

Theor. Appl. Genet

. 78:755–759.

Navot, N., M. Sarfatti, and D. Zamir. 1990. Linkage relationships of genes affecting bitterness and flesh color in watermelon.

J. Hered

. 81:162–165.

Kagan‐Zur, V., Y. Mizrahi, D. Zamir, and N. Navot. 1990. A tomato triploid hybrid whose double genome parent is the male.

J. Am. Soc. Hortic. Sci

. 116:342–345.

Zakay, Y., N. Navot, M. Zeidan, N. Kedar, H.D. Rabinowitch, H. Czosnek, and D. Zamir. 1990. Screening

Lycopersicon

accessions for resistance to the tomato yellow leaf curl virus: presence of viral DNA and symptom development.

Plant Dis

. 75:279–281.