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Exome sequencing highlights the role of wild-relative introgression in shaping the adaptive landscape of the wheat genome

Nature Genetics volume 51, pages 896–904 (2019)Cite this article

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A Publisher Correction to this article was published on 13 June 2019

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Abstract

Introgression is a potential source of beneficial genetic diversity. The contribution of introgression to adaptive evolution and improvement of wheat as it was disseminated worldwide remains unknown. We used targeted re-sequencing of 890 diverse accessions of hexaploid and tetraploid wheat to identify wild-relative introgression. Introgression, and selection for improvement and environmental adaptation, each reduced deleterious allele burden. Introgression increased diversity genome wide and in regions harboring major agronomic genes, and contributed alleles explaining a substantial proportion of phenotypic variation. These results suggest that historic gene flow from wild relatives made a substantial contribution to the adaptive diversity of modern bread wheat.

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Fig. 1: Population structure and genetic differentiation of wild and domesticated emmer, and bread wheat.
Fig. 2: Identification of wild emmer introgression in the wheat genome.
Fig. 3: Distribution of introgressions, selective sweeps and regions showing environmental adaptation across the wheat genome.
Fig. 4: Distribution of dSNPs across the wheat genome.
Fig. 5: SNPs from the introgressed regions explain a large proportion of phenotypic variance in wheat.

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Data availability

Data have been deposited in the European Variation Archive (EVA) under project PRJEB31218 and NCBI SRA under project PRJNA517692, and are available for viewing and download from http://wheatgenomics.plantpath.ksu.edu/1000EC.

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  • 13 June 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Acknowledgements

This project was supported by the Agriculture and Food Research Initiative Competitive Grants 2017-67007-25939 (Wheat-CAP) and 2016-67013-24473 from the USDA National Institute of Food and Agriculture, and grants from the Bill and Melinda Gates Foundation and Kansas Wheat Commission. Exome sequencing of Canadian wheat cultivars was supported through the Canadian Triticum Applied Genomics grant funded by Genome Canada, Genome Prairie, Saskatchewan Ministry of Agriculture, and the Western Grains Research Foundation. P.L.M. was supported by grant IOS-1339393 from the US National Science Foundation. Corteva Agriscience, Agriculture Division of DowDuPont provided financial support through collaboration with Agriculture Victoria Services enabling the development of the SNP dataset and technologies used in this manuscript. The authors would like to thank International Wheat Genome Sequencing Consortium for providing access to wheat genome sequence under Toronto agreement, D. Andresen for assistance with the computing resources of the KSU Beocat cluster funded by NSF grant ACI-144054 and K. Jordan for valuable suggestions and editing the manuscript.

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Author notes

  1. Ben Hayes

    Present address: Queensland Alliance for Agriculture and Food Innovation, Centre for Animal Science, University of Queensland, St Lucia, Queensland, Australia

Authors and Affiliations

  1. Department of Plant Pathology, Kansas State University, Manhattan, KS, USA

    Fei He, Alina Akhunova & Eduard Akhunov

  2. Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Victoria, Australia

    Raj Pasam, Fan Shi, Surya Kant, Gabriel Keeble-Gagnere, Pippa Kay, Kerrie Forrest, German Spangenberg, Ben Hayes, Hans Daetwyler, Josquin Tibbits & Matthew Hayden

  3. Department of Agronomy, Kansas State University, Manhattan, KS, USA

    Allan Fritz

  4. Crop Development Centre, University of Saskatchewan, Saskatoon, Saskatchewan, Canada

    Pierre Hucl, Krystalee Wiebe & Curtis Pozniak

  5. Swift Current Research and Development Centre, Swift Current, Saskatchewan, Canada

    Ron Knox & Richard Cuthbert

  6. Integrated Genomics Facility, Kansas State University, Manhattan, KS, USA

    Alina Akhunova

  7. Department of Agronomy and Plant Genetics, University of Minnesota, St Paul, MN, USA

    Peter L. Morrell

  8. Corteva Agriscience, Agriculture Division of DowDuPont, Indianapolis, IN, USA

    John P. Davies & Steve R. Webb

  9. School of Applied Systems Biology, La Trobe University, Bundoora, Victoria, Australia

    German Spangenberg, Hans Daetwyler, Josquin Tibbits & Matthew Hayden

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Contributions

F.H. led the bioinformatic and statistical analyses of data and helped to draft the first version of the manuscript. R.P. led phenotypic analyses. F.S. contributed to genomic analyses. S.K. was responsible for field trials and phenotype collection. G.K.-G. contributed to bioinformatic analyses of data. P.K. was responsible for exome sequencing of most wheat lines. K.F. was responsible for exome sequencing and 90K SNP data analyses. A.F. contributed to generating wild emmer exome capture data. P.H., K.W., R.K., R.C. and C.P. generated and contributed exome sequencing data for wild and domesticated emmer, and Canadian wheat cultivars. A.A. contributed to exome capture of wild and domesticated emmer, and wheat. P.L.M. contributed to data interpretation and manuscript writing. C.P., J.P.D., S.R.W. and G.S. contributed to project design. B.H., H.D. and J.T. contributed to project coordination and data analyses. M.H. provided project leadership, coordinated data collection and next generation sequencing (NGS) data analyses, and contributed to manuscript writing. E.A. conceived the idea, coordinated data collection and NGS data analyses and data interpretation, and wrote the manuscript.

Corresponding authors

Correspondence to Matthew Hayden or Eduard Akhunov.

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Supplementary information

Supplementary Information

Supplementary Figures 1–13, Supplementary Tables 2, 3, 14–16, 19 and 20, and Supplementary Note

Reporting Summary

Supplementary Table 1

List of hexaploid wheat accessions used in the study.

Supplementary Table 4

Genetic differentiation between wheat landraces and cultivars

Supplementary Table 5

Distribution of population-based fd statistic and frequency of introgression (FI) across genome.

Supplementary Table 6

Distribution of introgression statistics across the wheat genome

Supplementary Table 7

Ancestral allelic states inferred using multiple outgroup species

Supplementary Table 8

Locations of introgressed genomic regions (IGRs).

Supplementary Table 9

Climatic and bioclimatic data from WorldClim database used in Bayenv analyses

Supplementary Table 10

Genomic regions associated with environmental adaptation

Supplementary Table 11

The genomic regions showing the evidence of improvement selection

Supplementary Table 12

The genomic regions shared by all three scans for introgression, XP-CLR and Bayenv

Supplementary Table 13

GO terms enriched for genes located in the regions detected using the XP-CLR, Bayenv and fd – statistics analyses

Supplementary Table 17

Overlap of GWAS signals with introgression

Supplementary Table 18

Homoeolog-specific bias in gene expression between introgressed (I) and non-introgressed (NI) genomic regions

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He, F., Pasam, R., Shi, F. et al. Exome sequencing highlights the role of wild-relative introgression in shaping the adaptive landscape of the wheat genome. Nat Genet 51, 896–904 (2019). https://doi.org/10.1038/s41588-019-0382-2

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