Skip to main content
SpringerLink
Log in

Genomic Selection for Wheat Improvement

  • Chapter
  • First Online:
Physiological, Molecular, and Genetic Perspectives of Wheat Improvement

Abstract

Recent advances in plant breeding and agronomic practices have contributed significantly to the annual genetic gain in crop productivity to the tune of 0.8–1.2%. However, the present rate of gain is insufficient to meet out the fast-growing food demand of the expected global population of 2050. Till 1980s genetic enhancement of crop plants was primarily based on conventional plant breeding approaches. Although conventional breeding is continued to be breeder’s choice, faster genetic gain is hampered particularly for complex traits. Increasing the rate of genetic gain through modern breeding technologies is essential for food and nutritional security. Genomic selection (GS) is one such proven technology in animal breeding and recently incorporated in plant breeding programmes, especially large-scale private sector. GS is a promising approach for the rapid selection of superior genotypes and accelerating the breeding cycle. A comprehensive review of the existing GS literature in crop plants may provide insights for integrating GS in crop breeding programmes. Incorporation and effective use of GS in breeding programme depend upon several factors such as breeding method, genetic architecture and heritability number of targeted traits, statistical models, availability of genotyping and phenotyping facilities and the budget of breeding program. In this chapter, we discuss GS in wheat while highlighting various studies carried for improvement of grain yield, biotic and abiotic stresses, disease resistance and grain quality parameters. Also discussed are the challenges and key considerations to be followed for successful implementation of GS in varietal development programmes. Most of the GS studies are used to predict the additive genetic value and lag behind for non-additive and Genotype X Environment Interaction (GEI). Multi-trait and multi-environment modelling is essential for improving the prediction accuracy for environment-sensitive traits. Another potential of GS is mining of genes in gene bank accessions to access unexplored diversity into breeding programmes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Araus JL, Cairns JE (2014) Field high-throughput phenotyping: the new crop breeding frontier. Trends Plant Sci 19:52–61

    Article  CAS  PubMed  Google Scholar 

  • Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE (2018) Translating high-throughput phenotyping into genetic gain. Trends Plant Sci 23:451–466

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Arruda MP, Brown PJ, Lipka AE, Krill AM, Thurber C, Kolb FL (2015) Genomic selection for predicting head blight resistance in a wheat breeding program. Plant Genome 8:1–12. https://doi.org/10.3835/plantgenome2015.01.0003

    Article  CAS  Google Scholar 

  • Arruda MP, Lipka AE, Brown PJ, Krill AM, Thurber C, Brown-Guedira G, Dong Y, Foresman BJ, Kolb FL (2016) Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum L.). Mol Breed 36:1–11

    Article  CAS  Google Scholar 

  • Asad MA, Bai B, Lan C, Yan J, Xia X, Zhang Y, He Z (2014) Identification of QTL for adult-plant resistance to powdery mildew in Chinese wheat landrace Pingyuan 50. Crop J 2:308–314

    Article  Google Scholar 

  • Baenziger PS (2016) Wheat breeding and genetics. Ref Modul Food Sci 2016

    Google Scholar 

  • Barmeier G, Schmidhalter U (2016) High-throughput phenotyping of wheat and barley plants grown in single or few rows in small plots using active and passive spectral proximal sensing. Sensors 16(11):1860–1873

    Article  PubMed Central  Google Scholar 

  • Batool N, Ilyas N, Shahzad A, Hauser BA, Arshad M (2018) Quantitative trait loci (QTLs) mapping for salt stress tolerance in wheat at germination stage. Pak J Agric Sci 55(1):47–55

    Google Scholar 

  • Battenfield SD, Guzmán C, Gaynor RC, Singh RP, Peña RJ, Dreisigacker S, Fritz AK, Poland JA (2016) Genomic selection for processing and endues quality traits in the CIMMYT spring bread wheat breeding program. Plant Genome 9(2):1–12. https://doi.org/10.3835/plantgenome2016.01.0005

    Article  CAS  Google Scholar 

  • Bekele D, Tesfaye K, Fikre A (2019) Recent developments in genomic selection for minor gene quantitative disease resistance plant breeding. J Plant Pathol Microbiol 10:1–8

    Article  Google Scholar 

  • Bernardo R (2008) Molecular markers and selection for complex traits in plants: learning from the last 20 years. Crop Sci 48:1649–1664

    Article  Google Scholar 

  • Bhandari A, Bartholomé J, Cao-Hamadoun TV, Kumari N, Frouin J, Kumar A, Ahmadi N (2019) Selection of trait-specific markers and multi-environment models improve genomic predictive ability in rice. PLoS One 14(5):1–21. https://doi.org/10.1371/journal.pone.0208871

    Article  Google Scholar 

  • Bokore FE, Cuthbert RD, Knox RE, Singh A, Campbell HL, Pozniak CJ, N’Diaye A, Sharpe AG, Ruan Y (2019) Mapping quantitative trait loci associated with common bunt resistance in a spring wheat (Triticum aestivum L.) variety Lillian. Theor Appl Genet 132:3023–3033

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Bonnett D, Rebetzke GJ, Spielmeyer W (2005) Strategies for efficient implementation of molecular markers in wheat breeding. Mol Breed 15:75–85

    Article  CAS  Google Scholar 

  • Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  • Burgueno J, de los Campos G, Weigel K, Crossa J (2012) Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci 52:707–719

    Article  Google Scholar 

  • CGIAR Research Program on Wheat (CRP) (2018) Wheat in the world. https://wheat.org/wheat-in-the-world/

  • Chakradhar T, Hindu V, Reddy PS (2017) Genomic-based-breeding tools for tropical maize improvement. Genetica 145(6):525–539

    Article  CAS  PubMed  Google Scholar 

  • Chantret N, Mingeot D, Sourdille P, Bernard M, Jacquemin JM, Doussinault G (2001) A major QTL for powdery mildew resistance is stable over time and at two development stages in winter wheat. Theor Appl Genet 103:962–971

    Article  CAS  Google Scholar 

  • Charmet G, Tran LG, Auzanneau J, Rincent R, Bouchet S (2020) BWGS: AR package for genomic selection and its application to a wheat breeding programme. PLoS One 15(4):e0222733

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Cooper M, Messina CD, Podlich D, Totir LR, Baumgarten A, Hausmann NJ, Wright D, Graham G (2014) Predicting the future of plant breeding: complementing empirical evaluation with genetic prediction. Crop Pasture Sci 65:311–336

    Article  CAS  Google Scholar 

  • Crain JL, Wei Y, Barker J, Thompson SM, Alderman PD, Reynolds M, Zhang N, Poland J (2016) Development and deployment of a portable field phenotyping platform. Crop Sci 56:965–975

    Article  Google Scholar 

  • Crain J, Mondal S, Rutkoski J, Singh RP, Poland J (2018) Combining high-throughput phenotyping and genomic information to increase prediction and selection accuracy in wheat breeding. Plant Genome 11(1):1–14. https://doi.org/10.3835/plantgenome2017.05.0043

    Article  Google Scholar 

  • Crossa J, de los Campos G, Perez P, Gianola D, Burgueño J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan JB, Arief V, Banziger M, Braun HJ (2010) Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186:713–724

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Crossa J, Perez P, Campos GDL, Mahuku G, Dreisigacker S, Magorokosho C (2011) Genomic selection and prediction in plant breeding. J Crop Improv 25:239–261

    Article  Google Scholar 

  • Crossa J, Pérez P, Hickey J, Burgueño J, Ornella L, Cerón-Rojas J, Zhang X, Dreisigacker S, Babu R, Li Y, Bonnett D (2014) Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity 112:48–60

    Article  CAS  PubMed  Google Scholar 

  • Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de los Campos G, Burgueño J, González-Camacho JM, Pérez-Elizalde S, Beyene Y, Dreisigacker S (2017) Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci 22:961–975

    Article  CAS  PubMed  Google Scholar 

  • Daetwyler HD, Bansal UK, Bariana HS, Hayden MJ, Hayes BJ (2014) Genomic prediction for rust resistance in diverse wheat landraces. Theor Appl Genet 127:1795–1803

    Article  CAS  PubMed  Google Scholar 

  • De Souza CHW, Lamparelli RAC, Rocha JV, Magalhães PSG (2017) Height estimation of sugarcane using an unmanned aerial system (UAS) based on structure from motion (SfM) point clouds. Int J Remote Sens 38:2218–2230

    Article  Google Scholar 

  • Deery D, Jimenez-Berni J, Rebetzke GJ, James R, Bovill WD, Furbank R, Condon AG (2016) Methodology for high-throughput field phenotyping of canopy temperature using airborne and ground based thermography. Front Plant Sci 7:1808–1817

    Article  PubMed  PubMed Central  Google Scholar 

  • Deery DM, Rebetzke G, Jimenez-Berni JA, Bovill B, James RA, Condon AG, Furbank R, Chapman S, Fischer R (2019) Evaluation of the phenotypic repeatability of canopy temperature in wheat using continuous-terrestrial and airborne measurements. Front Plant Sci 10:875–893

    Article  PubMed  PubMed Central  Google Scholar 

  • Deng SM, Wu XR, Wu YY, Zhou RH, Wang HG, Jia JZ, Liu SB (2011) Characterization and precise mapping of a QTL increasing spike number with pleiotropic effects in wheat. Theor Appl Genet 122:281–289

    Article  PubMed  Google Scholar 

  • Desta ZA, Ortiz R (2014) Genomic selection: genome-wide prediction in plant improvement. Trends Plant Sci 19:592–601

    Article  CAS  PubMed  Google Scholar 

  • Devi R, Ram S, Rana V, Malik VK, Pande VSingh GP (2019) QTL mapping for salt tolerance associated traits in wheat (Triticum aestivum L.). Euphytica 215(12):210–232. https://doi.org/10.1007/s10681-019-2533-z

    Article  CAS  Google Scholar 

  • Dexter J, Clear R, Preston K (1996) Fusarium head blight: effect on the milling and baking of some Canadian wheats. Cereal Chem 73:695–701

    CAS  Google Scholar 

  • Dunckel S, Crossa J, Wu S, Bonnett D, Poland J (2017) Genomic selection for increased yield in synthetic-derived wheat. Crop Sci 57(2):713–725

    Article  Google Scholar 

  • FAO (2018) The state of the world’s land and water resources for food and agriculture: managing systems at risk. Food and Agriculture Organization of the United Nations, Rome, Italy, 2011 FAOSTAT. http://www.fao.org/faostat/

  • Fiedler JD, Salsman E, Liu Y, Michalak de Jiménez M, Hegstad JB, Chen B, Manthey FA, Chao S, Xu S, Elias EM, Li X (2017) Genome-wide association and prediction of grain and semolina quality traits in durum wheat breeding populations. Plant Genome 10(3):1–12. https://doi.org/10.3835/plantgenome2017.05.0038

    Article  CAS  Google Scholar 

  • Fischer RA, Maurer R (1978) Drought resistance in spring wheat cultivars. I. Grain yield responses. Aust J Agric Res 29:897–912

    Article  Google Scholar 

  • Frisch M, Melchinger AE (2005) Selection theory for marker-assisted backcrossing. Genetics 170:909–917

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Furbank RT, Tester M (2011) Phenomics–technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–644

    Article  CAS  PubMed  Google Scholar 

  • Gahlaut V, Jaiswal V, Tyagi BS, Singh G, Sareen S, Balyan HS, Gupta PK (2017) QTL mapping for nine drought-responsive agronomic traits in bread wheat under irrigated and rain-fed environments. PLoS One 12(8):e0182857. https://doi.org/10.1371/journal.pone.0182857

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Gebrewahid TW, Zhang P, Zhou Y, Yan X, Xia X, He Z, Liu D, Li Z (2020) QTL mapping of adult plant resistance to stripe rust and leaf rust in a Fuyu 3/Zhengzhou 5389 wheat population. Crop J. https://doi.org/10.1016/j.cj.2019.09.013

  • Gianola D, Van Kaam JB (2008) Reproducing Kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics 178:2289–2303

    Article  PubMed  PubMed Central  Google Scholar 

  • Habier D, Fernando RL, Kizilkaya K, Garrick DJ (2011) Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12(1):186–197

    Article  PubMed  PubMed Central  Google Scholar 

  • Haghighattalab A, Pérez LG, Mondal S, Singh D, Schinstock D, Rutkoski J, Ortiz-Monasterio I, Singh RP, Goodin D, Poland J (2016) Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseries. Plant Methods 12(1):35–49

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Haghighattalab A, Crain J, Mondal S, Rutkoski J, Singh RP, Poland J (2017) Application of geographically weighted regression to improve grain yield prediction from unmanned aerial system imagery. Crop Sci 57(5):2478–2489

    Article  Google Scholar 

  • Haile JK, Nachit MM, Hammer K, Badebo A, Röder MS (2012) QTL mapping of resistance to race Ug99 of Puccinia graminis f. sp. tritici in durum wheat (Triticum durum Desf.). Mol Breed 30:1479–1493

    Article  Google Scholar 

  • Haile JK, N’Diaye A, Clarke F, Clarke J, Knox R, Rutkoski J, Bassi FM, Pozniak CJ (2018) Genomic selection for grain yield and quality traits in durum wheat. Mol Breed 38(6):75–92. https://doi.org/10.1007/s11032-018-0818-x

    Article  CAS  Google Scholar 

  • HarvestPlus (2017) Crops. http://www.harvestplus.org/what-we-do/crops

    Google Scholar 

  • Hassan MA, Yang M, Rasheed A, Yang G, Reynolds M, Xia X, Xiao Y, He Z (2019) A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Sci 282:95–103

    Article  CAS  PubMed  Google Scholar 

  • Hayes B, Goddard ME (2001) The distribution of the effects of genes affecting quantitative traits in livestock. Genet Sel Evol 33:209–229

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Hayes BJ, Panozzo J, Walker CK, Choy AL, Kant S, Wong D, Tibbits J, Daetwyler HD, Rochfort S, Hayden MJ, Spangenberg GC (2017) Accelerating wheat breeding for end-use quality with multi-trait genomic predictions incorporating near infrared and nuclear magnetic resonance-derived phenotypes. Theor Appl Genet 130:2505–2519

    Article  CAS  PubMed  Google Scholar 

  • He S, Schulthess AW, Mirdita V, Zhao Y, Korzun V, Bothe R, Ebmeyer E, Reif JC, Jiang Y (2016) Genomic selection in a commercial winter wheat population. Theor Appl Genet 129:641–651

    Article  CAS  PubMed  Google Scholar 

  • Heffner EL, Sorrells ME, Jannink JL (2009) Genomic selection for crop improvement. Crop Sci 49:1–12

    Article  CAS  Google Scholar 

  • Heffner EL, Lorenz AJ, Jannink JL, Sorrells ME (2010) Plant breeding with genomic selection: gain per unit time and cost. Crop Sci 50:1681–1690

    Article  Google Scholar 

  • Heffner EL, Jannink JL, Iwata H, Souza E, Sorrells ME (2011a) Genomic selection accuracy for grain quality traits in biparental wheat populations. Crop Sci 51(6):2597–2606

    Article  Google Scholar 

  • Heffner EL, Jannink JL, Sorrells ME (2011b) Genomic selection accuracy using multifamily prediction models in a wheat breeding program. Plant Genome 4:65–75

    Article  Google Scholar 

  • Heidari B, Sayed-Tabatabaei BE, Saeidi G, Kearsey M, Suenaga K (2011) Mapping QTL for grain yield, yield components, and spike features in a doubled haploid population of bread wheat. Genome 54(6):517–527

    Article  PubMed  Google Scholar 

  • Henderson CR (1963) Selection index and expected genetic advance. Stat Genet Plant Breed 982:141–163

    Google Scholar 

  • Herrera-Foessel SA, Lagudah ES, Huerta-Espino J, Hayden MJ, Bariana HS, Singh D, Singh RP (2011) New slow rusting leaf rust and stripe rust resistance genes Lr67 and Yr46 in wheat are pleiotropic or closely linked. Theor Appl Genet 122:239–249

    Article  PubMed  Google Scholar 

  • Herter CP, Ebmeyer E, Kollers S, Korzun V, Miedaner T (2019) An experimental approach for estimating the genomic selection advantage for Fusarium head blight and Septoria tritici blotch in winter wheat. Theor Appl Genet 132:2425–2437

    Article  PubMed  Google Scholar 

  • Hickey LT, Hafeez AN, Robinson H, Jackson SA, Leal-Bertioli SCM, Tester M, Gao C, Godwin ID, Hayes BJ, Wulff BBH (2019) Breeding crops to feed 10 billion. Nat Biotechnol 37:744–754

    Article  CAS  PubMed  Google Scholar 

  • Hoffstetter A, Cabrera A, Huang M, Sneller C (2016) Optimizing training population data and validation of genomic selection for economic traits in soft winter wheat. G3: Genes Genomes Genetics 6(9):2919–2928

    Article  PubMed  PubMed Central  Google Scholar 

  • Holman FH, Riche AB, Michalski A, Castle M, Wooster MJ, Hawkesford MJ (2016) High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sens 8(12):1031–1053

    Article  Google Scholar 

  • Howes NK, Woods SM (1998) Simulations and practical problems of applying multiple marker assisted selection and doubled haploids to wheat breeding programs. Eur J Plant Pathol 100:225–230

    Google Scholar 

  • Hu X, Carver BF, Powers C, Yan L, Zhu L, Chen C (2019) Effectiveness of genomic selection by response to selection for winter wheat variety improvement. Plant Genome 12(3):1–15. https://doi.org/10.3835/plantgenome2018.11.0090

    Article  PubMed  Google Scholar 

  • Hu Y, Knapp S, Schmidhalter U (2020) Advancing high-throughput phenotyping of wheat in early selection cycles. Remote Sens 12(3):574–583

    Article  Google Scholar 

  • IWGSC (International Wheat Genome Sequencing Consortium) (2018) Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science 361:eaar7191. https://doi.org/10.1126/science.aar7191

    Article  CAS  Google Scholar 

  • Jannink J, Lorenz AJ, Iwata H (2010) Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9:166–177

    Article  CAS  PubMed  Google Scholar 

  • Jiang Y, Schulthess AW, Rodemann B, Ling J, Plieske J, Kollers S, Ebmeyer E, Korzun V, Argillier O, Stiewe G, Ganal MW (2017) Validating the prediction accuracies of marker-assisted and genomic selection of Fusarium head blight resistance in wheat using an independent sample. Theor Appl Genet 130:471–482

    Article  CAS  PubMed  Google Scholar 

  • Jiang Y, Li C, Robertson JS, Sun S, Xu R, Paterson AH (2018) Gphenovision: a ground mobile system with multi-modal imaging for field-based high throughput phenotyping of cotton. Sci Rep 8:1213–1228

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Jimenez-Berni JA, Deery DM, Rozas-Larraondo P, Condon AT, Rebetzke GJ, James RA, Bovill WD, Furbank RT, Sirault XR (2018) High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LiDAR. Front Plant Sci 9:237–255. https://doi.org/10.3389/fpls.2018.00237

    Article  PubMed  PubMed Central  Google Scholar 

  • Jin F, Zhang D, Bockus W, Baenziger PS, Carver B, Bai G (2013) Fusarium head blight resistance in US winter wheat cultivars and elite breeding lines. Crop Sci 53:2006–2013

    Article  Google Scholar 

  • Juliana P, Singh RP, Singh PK, Crossa J, Huerta-Espino J, Lan C, Bhavani S, Rutkoski JE, Poland JA, Bergstrom GC, Sorrells ME (2017a) Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat. Theor Appl Genet 130:1415–1430

    Article  PubMed  PubMed Central  Google Scholar 

  • Juliana P, Singh RP, Singh PK, Crossa J, Rutkoski JE, Poland JA, Bergstrom GC, Sorrells ME (2017b) Comparison of models and whole-genome profiling approaches for genomic-enabled prediction of Septoria tritici blotch, Stagonospora nodorum blotch, and tan spot resistance in wheat. Plant Genome 10(2):1–16. https://doi.org/10.3835/plantgenome2016.08.0082

    Article  CAS  Google Scholar 

  • Juliana P, Singh RP, Poland J, Mondal S, Crossa J, Montesinos-López OA, Dreisigacker S, Pérez-Rodríguez P, Huerta-Espino J, Crespo-Herrera L, Govindan V (2018) Prospects and challenges of applied genomic selection – a new paradigm in breeding for grain yield in bread wheat. Plant Genome 11(3):180017

    Article  Google Scholar 

  • Juliana P, Montesinos-López OA, Crossa J, Mondal S, Pérez LG, Poland J, Huerta-Espino J, Crespo-Herrera L, Govindan V, Dreisigacker S, Shrestha S, Shrestha S (2019a) Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat. Theor Appl Genet 132(1):177–194

    Article  CAS  PubMed  Google Scholar 

  • Juliana P, Poland J, Huerta-Espino J, Shrestha S, Crossa J, Crespo-Herrera L, Toledo FH, Govindan V, Mondal S, Kumar U, Bhavani S (2019b) Improving grain yield, stress resilience and quality of bread wheat using large-scale genomics. Nat Genet 51(10):1530–1539

    Article  CAS  PubMed  Google Scholar 

  • Kelly AM, Smith AB, Eccleston JA, Cullis BR (2007) The accuracy of varietal selection using factor analytic models for multi-environment plant breeding trials. Crop Sci 47:1063–1070

    Article  Google Scholar 

  • Kipp S, Mistele B, Baresel P, Schmidhalter U (2014) High-throughput phenotyping early plant vigour of winter wheat. Eur J Agron 52:271–278

    Article  Google Scholar 

  • Krattinger SG, Lagudah ES, Spielmeyer W, Singh RP, Huerta-Espino J, McFadden H, Bossolini E, Selter LL, Keller B (2009) A putative ABC transporter confers durable resistance to multiple fungal pathogens in wheat. Science 323:1360–1363

    Article  CAS  PubMed  Google Scholar 

  • Krause MR, González-Pérez L, Crossa J, Pérez-Rodríguez P, Montesinos-López O, Singh RP, Dreisigacker S, Poland J, Rutkoskiy J, Sorrells M, Gore MA (2018) Use of hyperspectral reflectance-derived relationship matrices for genomic prediction of grain yield in wheat. BioRxiv:e389825. https://doi.org/10.1101/389825

  • Kthiri D, Loladze A, N'Diaye A, Nilsen KT, Walkowiak S, Dreisigacker S, Ammar K, Pozniak CJ (2019) Mapping of genetic loci conferring resistance to leaf rust from three globally resistant durum wheat sources. Front Plant Sci 10:1247–1262. https://doi.org/10.3389/fpls.2019.01247

    Article  PubMed  PubMed Central  Google Scholar 

  • Lee U, Chang S, Putra GA, Kim H, Kim DH (2018) An automated, high-throughput plant phenotyping system using machine learning-based plant segmentation and image analysis. PLoS One 13(4). https://doi.org/10.1371/journal.pone.0196615

  • Lehermeier C, Krämer N, Bauer E, Bauland C, Camisan C, Campo L, Flament P, Melchinger AE, Menz M, Meyer N, Moreau L (2014) Usefulness of multiparental populations of maize (Zea mays L.) for genome-based prediction. Genetics 198(1):3–16

    Article  PubMed  PubMed Central  Google Scholar 

  • Lelong C, Burger P, Jubelin G, Roux B, Labbé S, Baret F (2008) Assessment of unmanned aerial vehicles imagery for quantitative monitoring of wheat crop in small plots. Sensors 8:3557–3585

    Article  PubMed  PubMed Central  Google Scholar 

  • Liu G, Jia L, Lu L, Qin D, Zhang J, Guan P (2014) Mapping QTLs of yield-related traits using RIL population derived from common wheat and Tibetan semi-wild wheat. Theor Appl Genet 127:2415–2432

    Article  CAS  PubMed  Google Scholar 

  • Liu B, Asseng S, Müller C, Ewert F, Elliott J, Lobell DB, Martre P, Ruane AC, Wallach D, Jones JW, Rosenzweig C (2016) Similar estimates of temperature impacts on global wheat yield by three independent methods. Nat Clim Chang 6:1130–1136

    Article  Google Scholar 

  • Lopez-Cruz M, Crossa J, Bonnett D, Dreisigacker S, Poland J, Jannink JL, Singh RP, Autrique E, de los Campos G (2015) Increased prediction accuracy in wheat breeding trials using a marker× environment interaction genomic selection model. G3: Genes Genomes Genetics 5(4):569–582

    Article  PubMed  PubMed Central  Google Scholar 

  • Lorenz AJ, Smith KP, Jannink JL (2012) Potential and optimization of genomic selection for Fusarium head blight resistance in six-row barley. Crop Sci 52:1609–1621

    Article  Google Scholar 

  • Lozada DN, Mason RE, Sarinelli JM, Brown-Guedira G (2019) Accuracy of genomic selection for grain yield and agronomic traits in soft red winter wheat. BMC Genet 20(1):82–94

    Article  PubMed  PubMed Central  Google Scholar 

  • Lu J, Hu J, Zhao G, Mei F, Zhang C (2017) An in-field automatic wheat disease diagnosis system. Comput Electron Agric 142:369–379

    Article  Google Scholar 

  • Malik S, Malik TA (2015) Genetic mapping of potential QTLs associated with drought tolerance in wheat. J Anim Plant Sci 25(4):1032–1040

    CAS  Google Scholar 

  • Manickavelu A, Hattori T, Yamaoka S, Yoshimura K, Kondou Y, Onogi A, Matsui M, Iwata H, Ban T (2017) Genetic nature of elemental contents in wheat grains and its genomic prediction: toward the effective use of wheat landraces from Afghanistan. PLoS One 12(1):e0169416

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Martin LBB, Fei Z, Giovannoni JJ, Rose JKC (2013) Catalyzing plant science research with RNA-seq. Front Plant Sci 4:66–75

    Article  PubMed  PubMed Central  Google Scholar 

  • Merida-Garcia R, Liu G, He S, Gonzalez-Dugo V, Dorado G, Galvez S, Solis I, Zarco-Tejada PJ, Reif JC, Hernandez P (2019) Genetic dissection of agronomic and quality traits based on association mapping and genomic selection approaches in durum wheat grown in Southern Spain. PLoS One 14(2):e0211718

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157:1819–1829

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Michel S, Ametz C, Gungor H, Epure D, Grausgruber H, Löschenberger F, Buerstmayr H (2016) Genomic selection across multiple breeding cycles in applied bread wheat breeding. Theor Appl Genet 129(6):1179–1189

    Article  PubMed  PubMed Central  Google Scholar 

  • Michel S, Kummer C, Gallee M, Hellinger J, Ametz C, Akgöl B, Epure D, Löschenberger F, Buerstmayr H (2018) Improving the baking quality of bread wheat by genomic selection in early generations. Theor Appl Genet 131(2):477–493

    Article  CAS  PubMed  Google Scholar 

  • Michel S, Löschenberger F, Ametz C, Pachler B, Sparry E, Bürstmayr H (2019) Simultaneous selection for grain yield and protein content in genomics-assisted wheat breeding. Theor Appl Genet 132(6):1745–1760

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Miedaner T, Korzun V (2012) Marker-assisted selection for disease resistance in wheat and barley breeding. Phytopathology 102:560–566

    Article  PubMed  Google Scholar 

  • Mirdita V, He S, Zhao Y, Korzun V, Bothe R, Ebmeyer E, Reif JC, Jiang Y (2015) Potential and limits of whole genome prediction of resistance to Fusarium head blight and Septoria tritici blotch in a vast central European elite winter wheat population. Theor Appl Genet 128:2471–2481

    Article  CAS  PubMed  Google Scholar 

  • Moore JK, Manmathan HK, Anderson VA, Poland JA, Morris CF, Haley SD (2017) Improving genomic prediction for pre-harvest sprouting tolerance in wheat by weighting large-effect quantitative trait loci. Crop Sci 57(3):1315–1324

    Article  Google Scholar 

  • Moose SP, Mumm RH (2008) Molecular plant breeding as the foundation for 21st century crop improvement. Plant Physiol 147:969–977

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Muleta KT, Bulli P, Zhang Z, Chen X, Pumphrey M (2017) Unlocking diversity in germplasm collections via genomic selection: a case study based on quantitative adult plant resistance to stripe rust in spring wheat. Plant Genome 10(3):1–15. https://doi.org/10.3835/plantgenome2016.12.0124

    Article  Google Scholar 

  • Ornella L, Singh S, Perez P, Burgueño J, Singh R, Tapia E, Bhavani S, Dreisigacker S, Braun HJ, Mathews K, Crossa J (2012) Genomic prediction of genetic values for resistance to wheat rusts. Plant Genome 5:136–148

    Article  CAS  Google Scholar 

  • Ortiz R, Sayre KD, Govaerts B, Gupta R, Subbarao GV, Ban T, Hodson D, Dixon JM, Ortiz-Monasterio JI, Reynolds M (2008) Climate change: can wheat beat the heat? Agric Ecosyst Environ 126:46–58

    Article  Google Scholar 

  • Ortiz-Monasterio JI, Palacios-Rojas N, Meng E, Pixley K, Trethowan R, Pena RJ (2007) Enhancing the mineral and vitamin content of wheat and maize through plant breeding. J Cereal Sci 46(3):293–307

    Article  CAS  Google Scholar 

  • Park T, Casella G (2008) The Bayesian lasso. J Am Stat Assoc 103:681–686

    Article  CAS  Google Scholar 

  • Parlevliet JE (2002) Durability of resistance against fungal, bacterial and viral pathogens; present situation. Euphytica 124:147–156

    Article  CAS  Google Scholar 

  • Perez-Rodriguez P, Gianola D, González-Camacho JM, Crossa J, Manes Y, Dreisigacker S (2012) Comparison between linear and non-parametric regression models for genome-enabled prediction in wheat. G3: Genes Genomes Genetics 2:1595–1605

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Poland JA, Rife TW (2012) Genotyping-by-sequencing for plant breeding and genetics. Plant Genome J 5(3):92–102

    CAS  Google Scholar 

  • Poland J, Rutkoski J (2016) Advances and challenges in genomic selection for disease resistance. Annu Rev Phytopathol 54:79–98

    Article  CAS  PubMed  Google Scholar 

  • Poland J, Endelman J, Dawson J, Rutkoski J, Wu SY, Manes Y, Dreisigacker S, Crossa J, Sanchez-Villeda H, Sorrells M, Jannink JL (2012) Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome 5:103–113

    CAS  Google Scholar 

  • Prasad PVV, Pisipati SR, Momčilović I, Ristic Z (2011) Independent and combined effects of high temperature and drought stress during grain filling on plant yield and chloroplast EF-Tu expression in spring wheat. J Agron Crop Sci 197:430–441

    Article  CAS  Google Scholar 

  • Qin F, Shinozaki K, Yamaguchi-Shinozaki K (2011) Achievements and challenges in understanding plant abiotic stress responses and tolerance. Plant Cell Physiol 52(9):1569–1582

    Article  CAS  PubMed  Google Scholar 

  • Rapp M, Lein V, Lacoudre F, Lafferty J, Müller E, Vida G, Bozhanova V, Ibraliu A, Thorwarth P, Piepho HP, Leiser WL (2018) Simultaneous improvement of grain yield and protein content in durum wheat by different phenotypic indices and genomic selection. Theor Appl Genet 131(6):1315–1329

    Article  CAS  PubMed  Google Scholar 

  • Rasmusson DC, Phillips RL (1997) Plant breeding progress and genetic diversity from de novo variation and elevated epistasis. Crop Sci 37:303–310

    Article  Google Scholar 

  • Ray DK, Ramankutty N, Mueller ND, West PC, Foley JA (2012) Recent patterns of crop yield growth and stagnation. Nat Commun 3:1293–1299

    Article  PubMed  CAS  Google Scholar 

  • Ray DK, Mueller ND, West PC, Foley JA (2013) Yield trends are insufficient to double global crop production by 2050. PLoS One 8(6):e66428

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Reynolds D, Baret F, Welcker C, Bostrom A, Ball J, Cellini F, Lorence A, Chawade A, Khafif M, Noshita K, Mueller-Linow M (2019) What is cost-efficient phenotyping? Optimizing costs for different scenarios. Plant Sci 282:14–22

    Article  CAS  PubMed  Google Scholar 

  • Rincent R, Charpentier JP, Faivre-Rampant P, Paux E, Le Gouis J, Bastien C, Segura V (2018) Phenomic selection is a low-cost and high-throughput method based on indirect predictions: proof of concept on wheat and poplar. G3: Genes Genomes Genetics 8(12):3961–3972

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Rutkoski J, Benson J, Jia Y, Brown-Guedira G, Jannink JL, Sorrells M (2012) Evaluation of genomic prediction methods for Fusarium head blight resistance in wheat. Plant Genome 5:51–61

    Article  CAS  Google Scholar 

  • Rutkoski JE, Poland JA, Singh RP, Huerta-Espino J, Bhavani S, Barbier H, Rouse MN, Jannink JL, Sorrells ME (2014) Genomic selection for quantitative adult plant stem rust resistance in wheat. Plant Genome 7(3):1–10. https://doi.org/10.3835/plantgenome2014.02.0006

    Article  Google Scholar 

  • Rutkoski J, Singh RP, Huerta-Espino J, Bhavani S, Poland J, Jannink JL, Sorrells ME (2015) Genetic gain from phenotypic and genomic selection for quantitative resistance to stem rust of wheat. Plant Genome 8(2):1–10. https://doi.org/10.3835/plantgenome2014.10.0074

    Article  CAS  Google Scholar 

  • Rutkoski J, Poland J, Mondal S, Autrique E, Pérez LG, Crossa J, Reynolds M, Singh R (2016) Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat. G3: Genes Genomes Genetics 6:2799–2808

    Article  PubMed  PubMed Central  Google Scholar 

  • Saccomanno A, Matny O, Marone D, Laidò G, Petruzzino G, Mazzucotelli E, Desiderio F, Blanco A, Gadaleta A, Pecchioni N, De Vita P (2018) Genetic mapping of loci for resistance to stem rust in a tetraploid wheat collection. Int J Mol Sci 19:3907–3922. https://doi.org/10.3390/ijms19123907

    Article  CAS  PubMed Central  Google Scholar 

  • Saintenac C, Zhang W, Salcedo A, Rouse MN, Trick HN, Akhunov E, Dubcovsky J (2013) Identification of wheat gene Sr35 that confers resistance to Ug99 stem rust race group. Science 341:783–786

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Sallam AH, Endelman JB, Jannink JL, Smith KP (2015) Assessing genomic selection prediction accuracy in a dynamic barley breeding population. Plant Genome 8:1):1–1)15. https://doi.org/10.3835/plantgenome2014.05.0020

    Article  CAS  Google Scholar 

  • Sallam A, Alqudah AM, Dawood MF, Baenziger PS, Börner A (2019) Drought stress tolerance in wheat and barley: advances in physiology, breeding and genetics research. Int J Mol Sci 20(13):3137–3172. https://doi.org/10.3390/ijms20133137

    Article  CAS  PubMed Central  Google Scholar 

  • Sarinelli JM, Murphy JP, Tyagi P, Holland JB, Johnson JW, Mergoum M, Mason RE, Babar A, Harrison S, Sutton R, Griffey CA (2019) Training population selection and use of fixed effects to optimize genomic predictions in a historical USA winter wheat panel. Theor Appl Genet 132:1247–1261

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Shikha M, Kanika A, Rao AR, Mallikarjuna MG, Gupta HS, Nepolean T (2017) Genomic selection for drought tolerance using genome-wide SNPs in maize. Front Plant Sci 8:550–561. https://doi.org/10.3389/fpls.2017.00550

    Article  PubMed  PubMed Central  Google Scholar 

  • Singh RP, Singh PK, Rutkoski J, Hodson DP, He X, Jørgensen LN, Hovmøller MS, Huerta-Espino J (2016) Disease impact on wheat yield potential and prospects of genetic control. Annu Rev Phytopathol 54:303–322

    Article  CAS  PubMed  Google Scholar 

  • Singh PK, He X, Sansaloni CP, Juliana P, Dreisigacker S, Duveiller E, Kumar U, Joshi AK, Singh RP (2018) Resistance to spot blotch in two mapping populations of common wheat is controlled by multiple QTL of minor effects. Int J Mol Sci 19(4054):4069. https://doi.org/10.3390/ijms19124054

    Article  Google Scholar 

  • Singh D, Wang X, Kumar U, Gao L, Noor M, Imtiaz M, Singh RP, Poland J (2019) High-throughput phenotyping enabled genetic dissection of crop lodging in wheat. Front Plant Sci 10:394–405

    Article  PubMed  PubMed Central  Google Scholar 

  • Sukumaran S, Jarquin D, Crossa J, Reynolds M (2018) Genomic-enabled prediction accuracies increased by modeling genotype × environment interaction in durum wheat. Plant Genome 11(2):1–11. https://doi.org/10.3835/plantgenome2017.12.0112

    Article  CAS  Google Scholar 

  • Sun J, Rutkoski JE, Poland JA, Crossa J, Jannink JL, Sorrells ME (2017) Multitrait, random regression, or simple repeatability model in high-throughput phenotyping data improve genomic prediction for wheat grain yield. Plant Genome 10(2):1–12. https://doi.org/10.3835/plantgenome2016.11.0111

    Article  Google Scholar 

  • Tattaris M, Reynolds MP, Chapman SC (2016) A direct comparison of remote sensing approaches for high-throughput phenotyping in plant breeding. Front Plant Sci 7:1131–1140

    Article  PubMed  PubMed Central  Google Scholar 

  • Thompson AL, Thorp KR, Conley M, Andrade-Sanchez P, Heun JT, Dyer JM, White JW (2018) Deploying a proximal sensing cart to identify drought-adaptive traits in upland cotton for high-throughput phenotyping. Front Plant Sci 9:507–520. https://doi.org/10.3389/fpls.2018.00507

    Article  PubMed  PubMed Central  Google Scholar 

  • Trnka M, Feng S, Semenov MA, Olesen JE, Kersebaum KC, Rötter RP, Semerádová D, Klem K, Huang W, Ruiz-Ramos M, Hlavinka P (2019) Mitigation efforts will not fully alleviate the increase in water scarcity occurrence probability in wheat-producing areas. Sci Adv 5(9):eaau2406. https://doi.org/10.1126/sciadv.aau2406

    Article  PubMed  PubMed Central  Google Scholar 

  • van Eeuwijk FA, Bustos-Korts D, Millet EJ, Boer MP, Kruijer W, Thompson A, Malosetti M, Iwata H, Quiroz R, Kuppe C, Muller O, Blazakis KN, Yu K, Tardieu F, Chapman SC (2019) Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding. Plant Sci 282:23–39. https://doi.org/10.1016/j.plantsci.2018.06.018

    Article  CAS  PubMed  Google Scholar 

  • Vanderplank JE (2012) Disease resistance in plants. Elsevier, Philadelphia

    Google Scholar 

  • Vega FA, Ramirez FC, Saiz MP, Rosua FO (2015) Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop. Biosyst Eng 132:19–27

    Article  Google Scholar 

  • Velu G, Ortiz-Monasterio I, Cakmak I, Hao Y, Singh RP (2014) Biofortification strategies to increase grain zinc and iron concentrations in wheat. J Cereal Sci 59(3):365–372

    Article  CAS  Google Scholar 

  • Velu G, Crossa J, Singh RP, Hao Y, Dreisigacker S, Perez-Rodriguez P, Joshi AK, Chatrath R, Gupta V, Balasubramaniam A, Tiwari C (2016) Genomic prediction for grain zinc and iron concentrations in spring wheat. Theor Appl Genet 129(8):1595–1605

    Article  CAS  PubMed  Google Scholar 

  • Wang N, Liu B, Liang X, Zhou Y, Song J, Yang J, Yong H, Weng J, Zhang D, Li M, Nair S (2019) Genome-wide association study and genomic prediction analyses of drought stress tolerance in China in a collection of off-PVP maize inbred lines. Mol Breed 39(8):113–132

    Article  CAS  Google Scholar 

  • Watanabe K, Guo W, Arai K, Takanashi H, Kajiya-Kanegae H, Kobayashi M, Yano K, Tokunaga T, Fujiwara T, Tsutsumi N, Iwata H (2017) High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling. Front Plant Sci 8:421–431. https://doi.org/10.3389/fpls.2017.00421

    Article  PubMed  PubMed Central  Google Scholar 

  • Watt M, Fiorani F, Usadel B, Rascher U, Muller O, Schurr U (2020) Phenotyping: new windows into the plant for breeders. Annu Rev Plant Biol 71:689–712. https://doi.org/10.1146/annurev-arplant-042916-041124

    Article  CAS  PubMed  Google Scholar 

  • Weckwerth W, Ghatak A, Bellaire A, Chaturvedi P, Varshney RK (2020) PANOMICS meets germplasm. Plant Biotechnol J 18(7):1507–1525. https://doi.org/10.1111/pbi.13372

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Weigand C (2011) Wheat import projections towards 2050. US Wheat Associates, Arlington

    Google Scholar 

  • White JW, Conley MM (2013) A flexible, low-cost cart for proximal sensing. Crop Sci 53:1646–1649

    Article  Google Scholar 

  • White JW, Andrade-Sanchez P, Gore MA, Bronson KF, Coffelt TA, Conley MM, Feldmann KA, French AN, Heun JT, Hunsaker DJ, Jenks MA (2012) Field-based phenomics for plant genetics research. Field Crops Res 133:101–112. https://doi.org/10.1016/j.fcr.2012.04.003

    Article  Google Scholar 

  • William M, Singh R, Huerta-Espino J, Islas SO, Hoisington D (2003) Molecular marker mapping of leaf rust resistance gene Lr46 and its association with stripe rust resistance gene Yr29 in wheat. Phytopathology 93:153–159

    Article  CAS  PubMed  Google Scholar 

  • Würschum T, Leiser WL, Kazman E, Longin CFH (2016) Genetic control of protein content and sedimentation volume in European winter wheat cultivars. Theor Appl Genet 129:1685–1696. https://doi.org/10.1007/s00122-016-2732-0

    Article  CAS  PubMed  Google Scholar 

  • Xu C, Zhang H, Sun J, Guo Z, Zou C, Li WX, Xie C, Huang C, Xu R, Liao H, Wang J (2018) Genome-wide association study dissects yield components associated with low-phosphorus stress tolerance in maize. Theor Appl Genet 131(8):1699–1714

    Article  CAS  PubMed  Google Scholar 

  • Xu C, Kohler TA, Lentonf TM, Svenning J, Schefferc M (2020) Future of the human climate niche. Proc Natl Acad Sci 117(21):11350–11355. https://doi.org/10.1073/pnas.1910114117

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yang J, Sears R, Gill B, Paulsen G (2002) Growth and senescence characteristics associated with tolerance of wheat-alien amphiploids to high temperature under controlled conditions. Euphytica 126(2):185–193

    Article  CAS  Google Scholar 

  • York LM (2019) Functional phenomics: an emerging field integrating high-throughput phenotyping, physiology, and bioinformatics. J Exp Bot 70(2):379–386

    Article  CAS  PubMed  Google Scholar 

  • Yu X, Ren S, Zhao L, Guo J, Bao Y, Ma Y, Wang H, Ohm HW, Yu D, Li H, Kong L (2018) Molecular mapping of a novel wheat powdery mildew resistance gene Ml92145E8-9 and its application in wheat breeding by marker-assisted selection. Crop J 6:621–627

    Article  Google Scholar 

  • Yuan L, Zhang H, Zhang Y, Xing C, Bao Z (2017) Feasibility assessment of multi-spectral satellite sensors in monitoring and discriminating wheat diseases and insects. Optik 131:598–608

    Article  Google Scholar 

  • Yuan FP, Zeng QD, Wu JH, Wang QL, Yang ZJ, Liang BP et al. (2018) QTL mapping and validation of adult plant resistance to stripe rust in Chinese wheat landrace Humai 15. Front Plant Sci 9:968

    Google Scholar 

  • Zampieri M, Ceglar A, Dentener F, Toreti A (2017) Wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales. Environ Res Lett 12(6):064008. https://doi.org/10.1088/1748-9326/aa723b

    Article  Google Scholar 

  • Zhang P, Li X, Gebrewahid TW, Liu H, Xia X, He Z, Li Z, Liu D (2019) QTL mapping of adult-plant resistance to leaf and stripe rust in wheat cross SW 8588/Thatcher using the wheat 55K SNP array. Plant Dis 103:3041–3049

    Article  CAS  PubMed  Google Scholar 

  • Zhao Y, Gowda M, Longin FH, Würschum T, Ranc N, Reif JC (2012) Impact of selective genotyping in the training population on accuracy and bias of genomic selection. Theor Appl Genet 125:707–713. https://doi.org/10.1007/s00122-012-1862-2

    Article  CAS  PubMed  Google Scholar 

  • Zhao YS, Zeng J, Fernando R, Reif JC (2013) Genomic prediction of hybrid wheat performance. Crop Sci 53:802–810

    Article  Google Scholar 

  • Zhao Y, Mette MF, Reif JC (2015) Genomic selection in hybrid breeding. Plant Breed 134(1):1–10

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ICAR-Indian Grassland and Fodder Research Institute, Jhansi, India

    Neeraj Kumar, Maneet Rana, Brijesh Kumar & Subhash Chand

  2. ICAR-Indian Institute of Sugarcane Research, Lucknow, India

    Aalok Shiv

  3. Mountain Research Centre for Field Crops, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Anantnag, Jammu and Kashmir, India

    Shabir H. Wani

  4. ICAR-Indian Institute of Wheat & Barley Research, Karnal, India

    Satish Kumar

Authors
  1. Neeraj Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maneet Rana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Brijesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Subhash Chand
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Aalok Shiv
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Shabir H. Wani
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Satish Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Mountain Research Centre For Field Crops, Sher e Kashmir University of Agricultural Sciences and Technology, Khudwani, Jammu and Kashmir, India

    Shabir H Wani

  2. Department of Biology, University of Pennsylvania, Philadelphia, PA, USA

    Amita Mohan

  3. ICAR-Indian Institute of Wheat & Barley Research, Karnal, Haryana, India

    Gyanendra Pratap Singh

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kumar, N. et al. (2021). Genomic Selection for Wheat Improvement. In: Wani, S.H., Mohan, A., Singh, G.P. (eds) Physiological, Molecular, and Genetic Perspectives of Wheat Improvement. Springer, Cham. https://doi.org/10.1007/978-3-030-59577-7_9

Download citation

Publish with us

Policies and ethics

Search

Navigation