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.
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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).
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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
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Juliana, P., Poland, J., Huerta-Espino, J. et al. Improving grain yield, stress resilience and quality of bread wheat using large-scale genomics. Nat Genet 51, 1530–1539 (2019). https://doi.org/10.1038/s41588-019-0496-6
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