Bread wheat improvement using genomic tools is essential for accelerating trait genetic gains. Here we report the genomic predictabilities of 35 key traits and demonstrate the potential of genomic selection for wheat end-use quality. We also performed a large genome-wide association study that identified several significant marker–trait associations for 50 traits evaluated in South Asia, Africa and the Americas. Furthermore, we built a reference wheat genotype–phenotype map, explored allele frequency dynamics over time and fingerprinted 44,624 wheat lines for trait-associated markers, generating over 7.6 million data points, which together will provide a valuable resource to the wheat community for enhancing productivity and stress resilience.
Subscribe to Journal
Get full journal access for 1 year
only $18.75 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
The phenotyping data for the lines used in this study are available in Supplementary Data 1. The marker P values, additive effects and percentage variation explained by each marker are available in Supplementary Table 2. The genomic fingerprints of 44,624 wheat lines for 195 trait-associated markers are available in Supplementary Table 4a–d. The raw genotyping data for the lines are available in FigShare (https://doi.org/10.6084/m9.figshare.8940257.v1).
CGIAR Research Program on Wheat. Wheat in the World https://wheat.org/wheat-in-the-world/ (CRP, 2018).
Shewry, P. R. & Hey, S. J. The contribution of wheat to human diet and health. Food Energy Secur. 4, 178–202 (2015).
Shiferaw, B. et al. Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Secur. 5, 291–317 (2013).
Curtis, T. & Halford, N. G. Food security: the challenge of increasing wheat yield and the importance of not compromising food safety. Ann. Appl. Biol. 164, 354–372 (2014).
FAOSTAT http://www.fao.org/faostat/ (FAO, 2018).
Ray, D. K., Mueller, N. D., West, P. C. & Foley, J. A. Yield trends are insufficient to double global crop production by 2050. PLoS ONE 8, e66428 (2013).
Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3, 1293 (2012).
Singh, R. P. et al. Disease impact on wheat yield potential and prospects of genetic control. Annu. Rev. Phytopathol. 54, 303–322 (2016).
Wheeler, T. & von Braun, J. Climate change impacts on global food security. Science 341, 508–513 (2013).
Zampieri, M., Ceglar, A., Dentener, F. & Toreti, A. Wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales. Environ. Res. Lett. 12, 064008 (2017).
Meuwissen, T. H. E., Hayes, B. J. & Goddard, M. E. Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829 (2001).
Meuwissen, T., Hayes, B. & Goddard, M. Genomic selection: a paradigm shift in animal breeding. Anim. Front. 6, 6–14 (2016).
Heffner, E. L., Sorrells, M. E. & Jannink, J.-L. Genomic selection for crop improvement. Crop Sci. 49, 1–12 (2009).
Crossa, J. et al. Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci. 22, 961–975 (2017).
Yu, J. & Buckler, E. S. Genetic association mapping and genome organization of maize. Curr. Opin. Biotechnol. 17, 155–160 (2006).
Thornsberry, J. M. et al. Dwarf8 polymorphisms associate with variation in flowering time. Nat. Genet. 28, 286–289 (2001).
Quarrie, S. A. A. et al. A high-density genetic map of hexaploid wheat (Triticum aestivum L.) from the cross Chinese Spring × SQ1 and its use to compare QTLs for grain yield across a range of environments. Theor. Appl. Genet. 110, 865–880 (2005).
Snape, J. W. et al. Dissecting gene × environmental effects on wheat yields via QTL and physiological analysis. Euphytica 154, 401–408 (2007).
International Wheat Genome Sequencing Consortium (IWGSC) Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science 361, eaar7191 (2018).
Lantican, M. A. et al. Impacts of International Wheat Improvement Research, 1994–2014 (CIMMYT, 2016).
Poland, J. et al. Genomic selection in wheat breeding using genotyping-by-sequencing. Plant Genome 5, 103–113 (2012).
Poland, J. A. & Rife, T. W. Genotyping-by-sequencing for plant breeding and genetics. Plant Genome 5, 92–102 (2012).
Elshire, R. J. et al. A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6, e19379 (2011).
Helguera, M., Khan, I. A., Kolmer, J., Lijavetzky, D. & Dubcovsky, J. PCR assays for the Lr37-Yr17-Sr38 cluster of rust resistance genes and their use. Crop Sci. 43, 1839–1847 (2003).
Ma, L. et al. TaGS5-3A, a grain size gene selected during wheat improvement for larger kernel and yield. Plant Biotechnol. J. 14, 1269–1280 (2016).
Rustgi, S. et al. Genetic dissection of yield and its component traits using high-density composite map of wheat chromosome 3A: bridging gaps between QTLs and underlying genes. PLoS ONE 8, e70526 (2013).
Mason, R. E., Mondal, S., Beecher, F. W. & Hays, D. B. Genetic loci linking improved heat tolerance in wheat (Triticum aestivum L.) to lower leaf and spike temperatures under controlled conditions. Euphytica 180, 181–194 (2011).
Zang, X. et al. Overexpression of wheat ferritin gene TaFER-5B enhances tolerance to heat stress and other abiotic stresses associated with the ROS scavenging. BMC Plant Biol. 17, 14 (2017).
Hou, J. et al. Global selection on sucrose synthase haplotypes during a century of wheat breeding. Plant Physiol. 164, 1918–1929 (2014).
Díaz, A., Zikhali, M., Turner, A. S., Isaac, P. & Laurie, D. A. Copy number variation affecting the Photoperiod-B1 and Vernalization-A1 genes is associated with altered flowering time in wheat (Triticum aestivum). PLoS ONE 7, e33234 (2012).
Griffiths, S. et al. Meta-QTL analysis of the genetic control of ear emergence in elite European winter wheat germplasm. Theor. Appl. Genet. 119, 383–395 (2009).
Yan, L. et al. Positional cloning of the wheat vernalization gene VRN1. Proc. Natl Acad. Sci. USA 100, 6263–6268 (2003).
Yan, L. et al. The wheat and barley vernalization gene VRN3 is an orthologue of FT. Proc. Natl Acad. Sci. USA 103, 19581–19586 (2006).
Himi, E. & Noda, K. Red grain colour gene (R) of wheat is a Myb-type transcription factor. Euphytica 143, 239–242 (2005).
Sun, X., Bai, G., Carver, B. F. & Bowden, R. Molecular mapping of wheat leaf rust resistance gene Lr42. Crop Sci. 50, 59 (2010).
Kassa, M. T. et al. Highly predictive SNP markers for efficient selection of the wheat leaf rust resistance gene Lr16. BMC Plant Biol. 17, 45 (2017).
Edae, E. A., Pumphrey, M. O. & Rouse, M. N. A genome-wide association study of field and seedling response to individual stem rust pathogen races reveals combinations of race-specific genes in North American spring wheat. Front. Plant Sci. 9, 52 (2018).
Nirmala, J. et al. Markers linked to wheat stem rust resistance gene Sr11 effective to Puccinia graminis f. sp. tritici race TKTTF. Phytopathology 106, 1352–1358 (2016).
Turner, M. K., Jin, Y., Rouse, M. N. & Anderson, J. A. Stem rust resistance in ‘Jagger’ winter wheat. Crop Sci. 56, 1719–1725 (2016).
Gao, L. et al. Genetic characterization of stem rust resistance in a global spring wheat germplasm collection. Crop Sci. 57, 2575–2589 (2017).
Hiebert, C. W., Rouse, M. N., Nirmala, J. & Fetch, T. Genetic mapping of stem rust resistance to Puccinia graminis f. sp. tritici race TRTTF in the Canadian wheat cultivar harvest. Phytopathology 107, 192–197 (2017).
Mago, R. et al. An accurate DNA marker assay for stem rust resistance gene Sr2 in wheat. Theor. Appl. Genet. 122, 735–744 (2011).
Krattinger, S. G. et al. A putative ABC transporter confers durable resistance to multiple fungal pathogens in wheat. Science 323, 1360–1363 (2009).
Rouse, M. N., Talbert, L. E., Singh, D. & Sherman, J. D. Complementary epistasis involving Sr12 explains adult plant resistance to stem rust in Thatcher wheat (Triticum aestivum L.). Theor. Appl. Genet. 127, 1549–1559 (2014).
Hiebert, C. W. et al. Major gene for field stem rust resistance co-locates with resistance gene Sr12 in ‘Thatcher’ wheat. PLoS ONE 11, e0157029 (2016).
Yang, E. N. et al. QTL analysis of the spring wheat ‘Chapio’ identifies stable stripe rust resistance despite inter-continental genotype × environment interactions. Theor. Appl. Genet. 126, 1721–1732 (2013).
McDonald, D. B., McIntosh, R. A., Wellings, C. R., Singh, R. P. & Nelson, J. C. Cytogenetical studies in wheat XIX. Location and linkage studies on gene Yr27 for resistance to stripe (yellow) rust. Euphytica 136, 239–248 (2004).
Singh, R. P., William, H. M., Huerta-Espino, J. & Crosby, M. Identification and mapping of gene Yr31 for resistance to stripe rust in Triticum aestivum cultivar Pastor. In Proc. 10th International Wheat Genetics Symposium. (eds Pogna N. E. et al.) 411–413 (Instituto Sperimentale per la Cerealicoltura, 2003).
Lu, P. et al. Fine genetic mapping of spot blotch resistance gene Sb3 in wheat (Triticum aestivum). Theor. Appl. Genet. 129, 577–589 (2016).
Morris, C. F. Puroindolines: the molecular genetic basis of wheat grain hardness. Plant Mol. Biol. 48, 633–647 (2002).
Færgestad, E. M. et al. Relationships between storage protein composition, protein content, growing season and flour quality of bread wheat. J. Sci. Food Agric. 84, 877–886 (2004).
Zhen, S. et al. Deletion of the low-molecular-weight glutenin subunit allele Glu-A3a of wheat (Triticum aestivum L.) significantly reduces dough strength and breadmaking quality. BMC Plant Biol. 14, 367– (2014).
Bonafede, M. D., Tranquilli, G., Pflüger, L. A., Peña, R. J. & Dubcovsky, J. Effect of allelic variation at the Glu-3/Gli-1 loci on breadmaking quality parameters in hexaploid wheat (Triticum aestivum L.). J. Cereal Sci. 62, 143–150 (2015).
Wang, Y. et al. Low molecular weight glutenin subunit gene Glu-B3h confers superior dough strength and breadmaking quality in wheat (Triticum aestivum L.). Sci. Rep. 6, 27182 (2016).
Cooper, J. K., Stromberger, J. A., Morris, C. F., Bai, G. & Haley, S. D. End-use quality and agronomic characteristics associated with the Glu-B1al high-molecular-weight glutenin allele in U.S. hard winter wheat. Crop Sci. 56, 2348–2353 (2016).
Maucher, T., Figueroa, J. D. C., Reule, W. & Peņa, R. J. Influence of low molecular weight glutenins on viscoelastic properties of intact wheat kernels and their relation to functional properties of wheat dough. Cereal Chem. 86, 372–375 (2009).
Guzmán, C. et al. Sources of the highly expressed wheat bread making (wbm) gene in CIMMYT spring wheat germplasm and its effect on processing and bread-making quality. Euphytica 209, 689–692 (2016).
Uauy, C., Distelfeld, A., Fahima, T., Blechl, A. & Dubcovsky, J. A. A NAC gene regulating senescence improves grain protein, zinc, and iron content in wheat. Science 314, 1298–1301 (2006).
Avni, R. et al. Functional characterization of GPC-1 genes in hexaploid wheat. Planta 239, 313–324 (2014).
Assanga, S. O. et al. Mapping of quantitative trait loci for grain yield and its components in a US popular winter wheat TAM 111 using 90K SNPs. PLoS ONE 12, e0189669 (2017).
Ma, D., Yan, J., He, Z., Wu, L. & Xia, X. Characterization of a cell wall invertase gene TaCwi-A1 on common wheat chromosome 2A and development of functional markers. Mol. Breed. 29, 43–52 (2012).
Hanif, M. et al. TaTGW6-A1, an ortholog of rice TGW6, is associated with grain weight and yield in bread wheat. Mol. Breed. 36, 1 (2016).
Qin, L. et al. TaGW2, a good reflection of wheat polyploidization and evolution. Front. Plant Sci. 8, 318 (2017).
Juliana, P. et al. Prospects and challenges of applied genomic selection—a new paradigm in breeding for grain yield in bread wheat. Plant Genome 11, 180017 (2018).
Crossa, J. et al. Genomic prediction in CIMMYT maize and wheat breeding programs. Heredity 112, 48–60 (2014).
Reif, J. C., Zhao, Y., Würschum, T., Gowda, M. & Hahn, V. Genomic prediction of sunflower hybrid performance. Plant Breed. 132, 107–114 (2013).
Voss-Fels, K. P., Cooper, M. & Hayes, B. J. Accelerating crop genetic gains with genomic selection. Theor. Appl. Genet. 132, 669–686 (2019).
Pryce, J. E. & Daetwyler, H. D. Designing dairy cattle breeding schemes under genomic selection: a review of international research. Anim. Prod. Sci. 52, 107–114 (2012).
Hayes, B. J., Bowman, P. J., Chamberlain, A. J. & Goddard, M. E. Invited review: Genomic selection in dairy cattle: progress and challenges. J. Dairy Sci. 92, 433–443 (2009).
García-Ruiz, A. et al. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proc. Natl Acad. Sci. USA 113, E3995–E4004 (2016).
Luan, T. et al. The accuracy of genomic selection in Norwegian red cattle assessed by cross-validation. Genetics 183, 1119–1126 (2009).
Lenz, P. R. N. et al. Factors affecting the accuracy of genomic selection for growth and wood quality traits in an advanced-breeding population of black spruce (Picea mariana). BMC Genom. 18, 335 (2017).
Moser, G., Khatkar, M. S., Hayes, B. J. & Raadsma, H. W. Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers. Genet. Sel. Evol. 42, 37 (2010).
Bariana, H. S. & Mcintosh, R. A. Cytogenetic studies in wheat. XV. Location of rust resistance genes in VPM1 and their genetic linkage with other disease resistance genes in chromosome 2A. Genome 36, 476–482 (1993).
Cruz, C. D. et al. The 2NS translocation from Aegilops ventricosa confers resistance to the Triticum pathotype of Magnaporthe oryzae. Crop Sci. 56, 990–1000 (2016).
Zhang, X., Rouse, M. N., Nava, I. C., Jin, Y. & Anderson, J. A. Development and verification of wheat germplasm containing both Sr2 and Fhb1. Mol. Breed. 36, 85 (2016).
Zhao, Y. et al. Characterization of wheat MYB genes responsive to high temperatures. BMC Plant Biol. 17, 208 (2017).
Zhang, Y. et al. OsMPH1 regulates plant height and improves grain yield in rice. PLoS ONE 12, e0180825 (2017).
Brevis, J. C. & Dubcovsky, J. Effects of the chromosome region including the Gpc-B1 locus on wheat grain and protein yield. Crop Sci. 50, 93–104 (2010).
Su, Z., Hao, C., Wang, L., Dong, Y. & Zhang, X. Identification and development of a functional marker of TaGW2 associated with grain weight in bread wheat (Triticum aestivum L.). Theor. Appl. Genet. 122, 211–223 (2011).
Singh, R. P. et al. Emergence and Spread of new races of wheat stem rust fungus: continued threat to food security and prospects of genetic control. Phytopathology 105, 872–884 (2015).
Cruz, C. D. & Valent, B. Wheat blast disease: danger on the move. Trop. Plant Pathol. 42, 210–222 (2017).
Torriani, S. F. F. et al. Zymoseptoria tritici: a major threat to wheat production, integrated approaches to control. Fungal Genet. Biol. 79, 8–12 (2015).
Tack, J., Barkley, A. & Nalley, L. L. Effect of warming temperatures on US wheat yields. Proc. Natl Acad. Sci. USA 112, 6931–6936 (2015).
Trnka, M. et al. Adverse weather conditions for European wheat production will become more frequent with climate change. Nat. Clim. Change 4, 637–643 (2014).
Herrera-Foessel, S. A. et al. Lr68: A new gene conferring slow rusting resistance to leaf rust in wheat. Theor. Appl. Genet. 124, 1475–1486 (2012).
Juliana, P. et al. Genomic and pedigree-based prediction for leaf, stem, and stripe rust resistance in wheat. Theor. Appl. Genet. 130, 1415–1430 (2017).
Roelfs, A. P., Singh, R. P. & Saari, E. E. Rust Diseases of Wheat: Concepts and Methods of Disease Management (CIMMYT, 1992).
Chen, S. et al. Fine mapping and characterization of Sr21, a temperature-sensitive diploid wheat resistance gene effective against the Puccinia graminis f. sp. tritici Ug99 race group. Theor. Appl. Genet. 128, 645–656 (2015).
Jin, Y. et al. Characterization of seedling infection types and adult plant infection responses of monogenic Sr gene lines to race TTKS of Puccinia graminis f. sp. tritici. Plant Dis. 91, 1096–1099 (2007).
Rouse, M. N. & Jin, Y. Stem rust resistance in A-genome diploid relatives of wheat. Plant Dis. 95, 941–944 (2011).
Stakman, E. C., Stewart, D. M. & Loegering, W. Q. Identification of Physiologic Races of Puccinia graminis var. tritici USDA-ARS E-617 (USDA, 1962).
Juliana, P. et al. Genome-wide association mapping for resistance to leaf rust, stripe rust and tan spot in wheat reveals potential candidate genes. Theor. Appl. Genet. 131, 1405–1422 (2018).
Randhawa, M. S. et al. Identification and validation of a common stem rust resistance locus in two bi-parental populations. Front. Plant Sci. 9, 1788 (2018).
Peterson, R. F., Campbell, A. B. & Hannah, A. E. A diagrammatic scale for estimating rust intensity on leaves and stems of cereals. Can. J. Res. 26c, 496–500 (1948).
Juliana, P. et al. 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 https://doi.org/10.3835/plantgenome2016.08.0082 (2017).
Saari, E. E. & Prescott, J. A scale for appraising the foliar intensity of wheat diseases. Plant Dis. Rep. 59, 376–381 (1975).
Eyal, Z., Scharen, A. L., Prescott, J. M. & van Ginkel, M. The Septoria Diseases of Wheat: Concepts and Methods of Disease Management (CIMMYT, 1987).
Simko, I. & Piepho, H.-P. The Area under the disease progress stairs: calculation, advantage, and application. Phytopathology 102, 381–389 (2012).
Singh, P. et al. 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 (2018).
AACC. Approved Methods of Analysis 11th edn (American Association of Cereal Chemists, 2000); https://doi.org/10.1094/AACCIntMethod-10-05.01
Pena, R. J., Amaya, A., Rajaram, S. & Mujeeb-Kazi, A. Variation in quality characteristics associated with some spring 1B/1R translocation wheats. J. Cereal Sci. 12, 105–112 (1990).
Guzmán, C., Posadas-Romano, G., Hernández-Espinosa, N., Morales-Dorantes, A. & Peña, R. J. A new standard water absorption criteria based on solvent retention capacity (SRC) to determine dough mixing properties, viscoelasticity, and bread-making quality. J. Cereal Sci. 66, 59–65 (2015).
Huber P. J. & Ronchetti, E. M. Robust Statistics 2nd edn (John Wiley & Sons, 2009).
Gilmour, A. R. ASREML for testing fixed effects and estimating multiple trait variance components. Proc. Assoc. Adv. Anim. Breed. Genet. 12, 386–390 (1997).
Glaubitz, J. C. et al. TASSEL-GBS: a high capacity genotyping by sequencing analysis pipeline. PLoS ONE 9, e90346 (2014).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Money, D. et al. LinkImpute: fast and accurate genotype imputation for nonmodel organisms. G3 (Bethesda) 5, 2383–2390 (2015).
Bradbury, P. J. et al. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633–2635 (2007).
Heslot, N., Yang, H., Sorrells, M. E. & Jannink, J.-L. Genomic selection in plant breeding: a comparison of models. Crop Sci. 52, 146–160 (2012).
Rutkoski, J. et al. Evaluation of genomic prediction methods for fusarium head blight resistance in wheat. Plant Genome 5, 51–61 (2012).
VanRaden, P. M. Efficient methods to compute genomic predictions. J. Dairy Sci. 91, 4414–4423 (2008).
Habier, D., Fernando, R. L., Kizilkaya, K. & Garrick, D. J. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics 12, 186 (2011).
Pérez, P. & de los Campos, G. Genome-wide regression and prediction with the BGLR statistical package. Genetics 198, 483–495 (2014).
Yu, J. et al. A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38, 203–208 (2006).
Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).
Frichot, E., Mathieu, F., Trouillon, T., Bouchard, G. & François, O. Fast and efficient estimation of individual ancestry coefficients. Genetics 196, 973–983 (2014).
Zhang, Z. et al. Mixed linear model approach adapted for genome-wide association studies. Nat. Genet. 42, 355–360 (2010).
Wu, Y., Bhat, P. R., Close, T. J. & Lonardi, S. Efficient and accurate construction of genetic linkage maps from the minimum spanning tree of a graph. PLoS Genet. 4, e1000212 (2008).
Taylor, J. & Butler, D. ASMap: Linkage Map Construction using the MSTmap Algorithm. R version 0.4-4 (2015).
Barton, N. H., Briggs, D. E. G., Eisen, J. A., Goldstein, D. B. & Patel, N. H. Evolution (Cold Spring Harbor Laboratory Press, 2007).
This research was supported by the Feed the Future project through the US Agency for International Development (USAID), under the terms of contract no. AID-OAA-A-13-00051 (J.P. and R.P.S.). The opinions expressed herein are those of the authors and do not necessarily reflect the views of the USAID. We thank the innovation laboratory at Kansas State University, the CGIAR Research Program on Wheat, the Indian Council of Agricultural Research (ICAR), the Australian Centre for International Agricultural Research (ACIAR), several national partners (Afghanistan, Bangladesh, Canada, Egypt, India, Morocco, Pakistan and Sudan) and field technicians for their support in generating the genotyping and phenotyping data.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Figs. 1–6
Supplementary Tables 1–7
Phenotyping data for the lines in the YT, EYTs, SABWYTs, IBWSNs, SRRSNs and ESWYTs
Manhattan plots of the absolute marker effects for all the traits estimated using Bayes B approach in the combined EYT panel of 3,485 lines and the genomic prediction accuracies within and across panels using the Bayes B approach
Chromosome-wise linkage disequilibrium between the significant markers, shown by the marker R2 vales (the correlation between the alleles at two loci) and the P values for the test of linkage disequilibrium using a two-sided Fisher’s exact test