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Improving grain yield, stress resilience and quality of bread wheat using large-scale genomics

Nature Genetics volume 51, pages 1530–1539 (2019)Cite this article

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Abstract

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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Fig. 1: Distribution of GBS markers in the bread wheat genome.
Fig. 2: Genomic prediction accuracies for DTHD, DTMT and GY in different environments, disease resistance and end-use quality traits, within and across panels at different marker densities using the genomic-best linear unbiased prediction approach.
Fig. 3: Marker–trait associations for GY and agronomic traits.
Fig. 4: Marker–trait associations for disease resistance and quality-related traits.
Fig. 5: The reference genotype–phenotype map.
Fig. 6: Genomic fingerprinting analysis for markers that are significantly associated with GY across different environments.
Fig. 7: Trends in the favorable allele frequencies of 47 GY-associated markers in the ESWYTs over 15 years (2013–2017).

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Data availability

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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Acknowledgements

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.

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Authors and Affiliations

  1. International Maize And Wheat Improvement Center (CIMMYT), Texcoco, Mexico

    Philomin Juliana, José Crossa, Leonardo Crespo-Herrera, Fernando Henrique Toledo, Velu Govindan, Suchismita Mondal, Sridhar Bhavani, Pawan K. Singh, Xinyao He, Carlos Guzman, Susanne Dreisigacker & Ravi Prakash Singh

  2. Wheat Genetics Resource Center, Department of Plant Pathology, Kansas State University, Manhattan, KS, USA

    Jesse Poland, Sandesh Shrestha, Daljit Singh & Mohammad Mokhlesur Rahman

  3. Campo Experimental Valle de Mexico, Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias (INIFAP), Chapingo, Mexico

    Julio Huerta-Espino

  4. Borlaug Institute for South Asia (BISA), New Delhi, India

    Uttam Kumar

  5. International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya

    Mandeep S. Randhawa

  6. Departamento de Genética, Escuela Técnica Superior de Ingeniería Agronómica y de Montes, Campus de Rabanales, Universidad de Córdoba, Cordoba, Spain

    Carlos Guzman

  7. United States Department of Agriculture, Agricultural Research Service (USDA-ARS), Cereal Disease Laboratory and Department of Plant Pathology, University of Minnesota, St Paul, MN, USA

    Matthew N. Rouse & Yue Jin

  8. Colegio de Post graduados, Montecillos, Mexico

    Paulino Pérez-Rodríguez

  9. Facultad de Telematica, Universidad de Colima, Colima, Mexico

    Osval A. Montesinos-López

  10. Regional Agricultural Research Station, Bangladesh Agricultural Research Institute (BARI), Jamalpur, Bangladesh

    Mohammad Mokhlesur Rahman

  11. Instituto Nacional de Innovación Agropecuaria y Forestal (INIAF), La Paz, Bolivia

    Felix Marza

Authors
  1. Philomin Juliana
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  2. Jesse Poland
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  3. Julio Huerta-Espino
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  4. Sandesh Shrestha
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  5. José Crossa
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  6. Leonardo Crespo-Herrera
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  7. Fernando Henrique Toledo
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  8. Velu Govindan
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  9. Suchismita Mondal
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  10. Uttam Kumar
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  11. Sridhar Bhavani
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  12. Pawan K. Singh
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  13. Mandeep S. Randhawa
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  14. Xinyao He
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  15. Carlos Guzman
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  16. Susanne Dreisigacker
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  17. Matthew N. Rouse
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  18. Yue Jin
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  19. Paulino Pérez-Rodríguez
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  20. Osval A. Montesinos-López
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  21. Daljit Singh
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  22. Mohammad Mokhlesur Rahman
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  23. Felix Marza
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  24. Ravi Prakash Singh
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Contributions

P.J. drafted the manuscript and performed the analyses. R.P.S., J.P., J.H.-E. and J.C. planned the study and supervised the analysis. F.H.T., P.P.-R. and O.A.M.-L. performed some of the analyses. L.C.-H., V.G., S.M., U.K., S.B., P.K.S., M.S.R., X.H., C.G., M.N.R., Y.J., D.S., M.M.R. and F.M. generated the phenotyping data. S.D. performed the DNA extraction and S.S. called the marker polymorphisms.

Corresponding author

Correspondence to Ravi Prakash Singh.

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Competing interests

The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–6

Reporting Summary

Supplementary Tables

Supplementary Tables 1–7

Supplementary Data 1

Phenotyping data for the lines in the YT, EYTs, SABWYTs, IBWSNs, SRRSNs and ESWYTs

Supplementary Data 2

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

Supplementary Data 3

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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  • DOI: https://doi.org/10.1038/s41588-019-0496-6

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