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

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.

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.

References

  1. 1.

    Nesbitt, M. & Samuel, D. From staple crop to extinction? The archaeology and history of the hulled wheats. in Proc. 1st Int. Workshop Hulled Wheats (eds Padulosi, S. et al.) 41–100 (Italy International Plant Genetic Resources Institute, 1996).

  2. 2.

    Tanno, K.-I. & Willcox, G. How fast was wild wheat domesticated? Science 311, 1886 (2006).

    CAS  Article  Google Scholar 

  3. 3.

    Luo, M.-C. et al. The structure of wild and domesticated emmer wheat populations, gene flow between them, and the site of emmer domestication. Theor. Appl. Genet. 114, 947–959 (2007).

    Article  Google Scholar 

  4. 4.

    Ozkan, H., Willcox, G., Graner, A., Salamini, F. & Kilian, B. Geographic distribution and domestication of wild emmer wheat (Triticum dicoccoides). Genet. Resour. Crop Evol. 58, 11–53 (2011).

    Article  Google Scholar 

  5. 5.

    Kihara, H. Discovery of the DD-analyser, one of the ancestors of Triticum vulgare. Agric. Hortic. 19, 889–890 (1944).

    Google Scholar 

  6. 6.

    Dvorak, J., Luo, M. C., Yang, Z. L. & Zhang, H. B. The structure of the Aegilops tauschii genepool and the evolution of hexaploid wheat. Theor. Appl. Genet. 97, 657–670 (1998).

    CAS  Article  Google Scholar 

  7. 7.

    Smith, O. et al. Sedimentary DNA from a submerged site reveals wheat in the British Isles 8000 years ago. Science 347, 998–1001 (2014).

    Article  Google Scholar 

  8. 8.

    Long, T. et al. The early history of wheat in China from 14C dating and Bayesian chronological modelling. Nat. Plants 4, 272–279 (2018).

    Article  Google Scholar 

  9. 9.

    Haudry, a et al. Grinding up wheat: a massive loss of nucleotide diversity since domestication. Mol. Biol. Evol. 24, 1506–1517 (2007).

    CAS  Article  Google Scholar 

  10. 10.

    Akhunov, E. D. et al. Nucleotide diversity maps reveal variation in diversity among wheat genomes and chromosomes. BMC Genomics 11, 702 (2010).

    CAS  Article  Google Scholar 

  11. 11.

    Kerber, E. R. Wheat: reconstitution of the tetraploid component (AABB) of hexaploids. Science 143, 253–255 (1964).

    CAS  Article  Google Scholar 

  12. 12.

    Dvorak, J., Luo, M. & Akhunov, E. D. N. I. Vavilov' s theory of centres of diversity in the light of current understanding of wheat diversity, domestication and evolution. Czech. J. Genet. Plant Breed. 47, 1–8 (2011).

    Article  Google Scholar 

  13. 13.

    Dvorak, J., Akhunov, E. D., Akhunov, A. R., Deal, K. R. & Luo, M.-C. Molecular characterization of a diagnostic DNA marker for domesticated tetraploid wheat provides evidence for gene flow from wild tetraploid wheat to hexaploid wheat. Mol. Biol. Evol. 23, 1386–1396 (2006).

    CAS  Article  Google Scholar 

  14. 14.

    Salojärvi, J. et al. Genome sequencing and population genomic analyses provide insights into the adaptive landscape of silver birch. Nat. Genet. 49, 904–912 (2017).

    Article  Google Scholar 

  15. 15.

    Rendón-anaya, M. et al. Genomic history of the origin and domestication of common bean unveils its closest sister species. Genome Biol. 18, 1–17 (2017).

    Article  Google Scholar 

  16. 16.

    Wang, L. et al. The interplay of demography and selection during maize domestication and expansion. Genome Biol. 18, 1–13 (2017).

    Article  Google Scholar 

  17. 17.

    Hardigan, M. A. et al. Genome diversity of tuber-bearing Solanum uncovers complex evolutionary history and targets of domestication in the cultivated potato. Proc. Natl Acad. Sci. USA 114, E9999–E10008 (2017).

    CAS  Article  Google Scholar 

  18. 18.

    Hübner, S. et al. Islands and streams: clusters and gene flow in wild barley populations from the Levant. Mol. Ecol. 21, 1115–1129 (2012).

    Article  Google Scholar 

  19. 19.

    International Wheat Genome Sequencing Consortium. Shifting the limits in wheat research and breeding using a fully annotated reference genome. Science 361, eaar7191 (2018).

    Article  Google Scholar 

  20. 20.

    Jordan, K. et al. A haplotype map of allohexaploid wheat reveals distinct patterns of selection on homoeologous genomes. Genome Biol. 16, 48 (2015).

    Article  Google Scholar 

  21. 21.

    Liu, Q., Zhou, Y., Morrell, P. L., Gaut, B. S. & Ge, S. Deleterious variants in Asian rice and the potential cost of domestication. Mol. Biol. Evol. 34, 908–924 (2017).

    CAS  Article  Google Scholar 

  22. 22.

    Mezmouk, S. & Ross-Ibarra, J. The pattern and distribution of deleterious mutations in maize. G3 (Bethesda) 4, 163–171 (2014).

    Article  Google Scholar 

  23. 23.

    Avni, R. et al. Wild emmer genome architecture and diversity elucidate wheat evolution and domestication. Science 97, 93–97 (2017).

    Article  Google Scholar 

  24. 24.

    Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).

    CAS  Article  Google Scholar 

  25. 25.

    Cavanagh, C. R. et al. Genome-wide comparative diversity uncovers multiple targets of selection for improvement in hexaploid wheat landraces and cultivars. Proc. Natl Acad. Sci. USA 110, 8057–8062 (2013).

    CAS  Article  Google Scholar 

  26. 26.

    Wang, S. et al. Characterization of polyploid wheat genomic diversity using a high-density 90,000 single nucleotide polymorphism array. Plant Biotechnol. J. 12, 787–796 (2014).

    CAS  Article  Google Scholar 

  27. 27.

    Poets, A. M. et al. The effects of both recent and long-term selection and genetic drift are readily evident in North American barley breeding populations. G3 (Bethesda) 6, 609–622 (2016).

    Article  Google Scholar 

  28. 28.

    Patterson, N. et al. Ancient admixture in human history. Genetics 192, 1065–1093 (2012).

    Article  Google Scholar 

  29. 29.

    Martin, S. H., Davey, J. W. & Jiggins, C. D. Evaluating the use of ABBA-BABA statistics to locate introgressed loci. Mol. Biol. Evol. 32, 244–257 (2015).

    CAS  Article  Google Scholar 

  30. 30.

    Smith, J. & Kronforst, M. R. Do Heliconius butterfly species exchange mimicry alleles? Biol. Lett. 9, 1–4 (2013).

    Article  Google Scholar 

  31. 31.

    Hufford, M. B. et al. The genomic signature of crop-wild introgression in maize. PLoS Genet. 9, e1003477 (2013).

    CAS  Article  Google Scholar 

  32. 32.

    Nave, M., Avni, R., Ben-Zvi, B., Hale, I. & Distelfeld, A. QTLs for uniform grain dimensions and germination selected during wheat domestication are co-located on chromosome 4B. Theor. Appl. Genet. 129, 1303–1315 (2016).

    CAS  Article  Google Scholar 

  33. 33.

    Simons, K. J. et al. Molecular characterization of the major wheat domestication gene Q. Genetics 172, 547–555 (2006).

    CAS  Article  Google Scholar 

  34. 34.

    Günther, T. & Coop, G. Robust identification of local adaptation from allele frequencies. Genetics 195, 205–220 (2013).

    Article  Google Scholar 

  35. 35.

    Chen, H., Patterson, N. & Reich, D. Population differentiation as a test for selective sweeps. Genome Res. 20, 393–402 (2010).

    CAS  Article  Google Scholar 

  36. 36.

    Kant, S., Bi, Y. & Rothstein, S. J. Understanding plant response to nitrogen limitation for the improvement of crop nitrogen use efficiency. J. Exp. Bot. 62, 1499–1509 (2011).

    CAS  Article  Google Scholar 

  37. 37.

    Forde, B. G. Glutamate signalling in roots. J. Exp. Bot. 65, 779–787 (2014).

    CAS  Article  Google Scholar 

  38. 38.

    Lu, G. et al. Application of T-DNA activation tagging to identify glutamate receptor-like genes that enhance drought tolerance in plants. Plant Cell Rep. 33, 617–631 (2014).

    CAS  Article  Google Scholar 

  39. 39.

    Kiba, T., Krapp, A. & Science, R. Plant nitrogen acquisition under low availability: regulation of uptake and root architecture. Plant Cell Physiol. 57, 707–714 (2016).

    CAS  Article  Google Scholar 

  40. 40.

    Kono, T. J. Y. et al. The role of deleterious substitutions in crop genomes. Mol. Biol. Evol. 33, 2307–2317 (2016).

    CAS  Article  Google Scholar 

  41. 41.

    Jordan, K. W. et al. The genetic architecture of genome‐wide recombination rate variation in allopolyploid wheat revealed by nested association mapping. Plant J. 95, 1039–1054 (2018).

    CAS  Article  Google Scholar 

  42. 42.

    Kilian, B. et al. Independent wheat B and G genome origins in outcrossing Aegilops progenitor haplotypes. Mol. Biol. Evol. 24, 217–227 (2007).

    CAS  Article  Google Scholar 

  43. 43.

    Choulet, F. et al. Structural and functional partitioning of bread wheat chromosome 3B. Science 345, 1249721 (2014).

    Article  Google Scholar 

  44. 44.

    Akhunova, A. R., Matniyazov, R. T., Liang, H. & Akhunov, E. D. Homoeolog-specific transcriptional bias in allopolyploid wheat. BMC Genomics 11, 1–16 (2010).

    Article  Google Scholar 

  45. 45.

    Veitia, R. A., Bottani, S. & Birchler, J. A. Cellular reactions to gene dosage imbalance: genomic, transcriptomic and proteomic effects. Trends Genet. 24, 390–397 (2008).

    CAS  Article  Google Scholar 

  46. 46.

    Gusev, A. et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014).

    CAS  Article  Google Scholar 

  47. 47.

    Peleg, Z., Fahima, T., Korol, A. B., Abbo, S. & Saranga, Y. Genetic analysis of wheat domestication and evolution under domestication. J. Exp. Bot. 62, 5051–5061 (2011).

    CAS  Article  Google Scholar 

  48. 48.

    Stitzer, M. C. & Ross-Ibarra, J. Maize domestication and gene interaction. New Phytol. 220, 395–408 (2018).

    Article  Google Scholar 

  49. 49.

    Morrell, P. L., Buckler, E. S. & Ross-Ibarra, J. Crop genomics: advances and applications. Nat. Rev. Genet. 13, 85–96 (2011).

    Article  Google Scholar 

  50. 50.

    Krasileva, K. V. et al. Uncovering hidden variation in polyploid wheat. Proc. Natl Acad. Sci. USA 114, 913–E921 (2017).

    Article  Google Scholar 

  51. 51.

    Ramu, P. et al. Cassava haplotype map highlights fixation of deleterious mutations during clonal propagation. Nat. Genet. 49, 959–963 (2017).

    CAS  Article  Google Scholar 

  52. 52.

    Zhao, Y. et al. Genome-based establishment of a high-yielding heterotic pattern for hybrid wheat breeding. Proc. Natl Acad. Sci. USA 112, 15624–15629 (2015).

    CAS  PubMed  Google Scholar 

  53. 53.

    Hao, Y. et al. Patterns of population variation in two paleopolyploid eudicot lineages suggest that dosage-based selection on homeologs is long-lived. Genome Biol. Evol. 10, 999–1011 (2018).

    CAS  Article  Google Scholar 

  54. 54.

    Yu, X., Woolliams, J. A. & Meuwissen, T. H. E. Prioritizing animals for dense genotyping in order to impute missing genotypes of sparsely genotyped animals. Genet. Sel. Evol. 46, 1–8 (2014).

    Article  Google Scholar 

  55. 55.

    Browning, B. L. & Browning, S. R. Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics 194, 459–471 (2013).

    Article  Google Scholar 

  56. 56.

    Thuillet, A.-C., Bataillon, T., Poirier, S., Santoni, S. & David, J. L. Estimation of long-term effective population sizes through the history of durum wheat using microsatellite data. Genetics 169, 1589–1599 (2005).

    CAS  Article  Google Scholar 

  57. 57.

    Keightley, P. D. & Jackson, B. C. Inferring the probability of the derived vs. the ancestral allelic state at a polymorphic site. Genetics 209, 897–906 (2018).

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Luo, M.-C. et al. Genome sequence of the progenitor of the wheat D genome Aegilops tauschii. Nature 551, 498–502 (2017).

    CAS  Article  Google Scholar 

  59. 59.

    Ling, H.-Q. et al. Draft genome of the wheat A-genome progenitor Triticum urartu. Nature 496, 87–90 (2013).

    CAS  Article  Google Scholar 

  60. 60.

    Mascher, M. et al. A chromosome conformation capture ordered sequence of the barley genome. Nature 544, 427–433 (2017).

    CAS  Article  Google Scholar 

  61. 61.

    De Baets, G. et al. SNPeffect 4.0: on-line prediction of molecular and structural effects of protein-coding variants. Nucleic Acids Res. 40, D935–D939 (2012).

    Article  Google Scholar 

  62. 62.

    Jakobsson, M. & Rosenberg, N. A. CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 1801–1806 (2007).

    CAS  Article  Google Scholar 

  63. 63.

    Green, R. E. et al. A draft sequence of the Neandertal genome. Science 328, 710–722 (2010).

    CAS  Article  Google Scholar 

  64. 64.

    Neph, S. et al. BEDOPS: high-performance genomic feature operations. Bioinformatics 28, 1919–1920 (2012).

    CAS  Article  Google Scholar 

  65. 65.

    Conesa, A. & Götz, S. Blast2GO: a comprehensive suite for functional analysis in plant genomics. Int. J. Plant Genomics 2008, 619832 (2008).

    Article  Google Scholar 

  66. 66.

    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B 57, 289–300 (1995).

    Google Scholar 

  67. 67.

    Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. Revigo summarizes and visualizes long lists of gene ontology terms. PLoS ONE 6, e21800 (2011).

    CAS  Article  Google Scholar 

  68. 68.

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

    Article  Google Scholar 

  69. 69.

    Yang, J. et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nat. Genet. 43, 519–525 (2011).

    CAS  Article  Google Scholar 

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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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Authors

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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