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The role of gene flow and chromosomal instability in shaping the bread wheat genome


Bread wheat (Triticum aestivum) is one of the world’s most important crops; however, a low level of genetic diversity within commercial breeding accessions can significantly limit breeding potential. In contrast, wheat relatives exhibit considerable genetic variation and so potentially provide a valuable source of novel alleles for use in breeding new cultivars. Historically, gene flow between wheat and its relatives may have contributed novel alleles to the bread wheat pangenome. To assess the contribution made by wheat relatives to genetic diversity in bread wheat, we used markers based on single nucleotide polymorphisms to compare bread wheat accessions, created in the past 150 years, with 45 related species. We show that many bread wheat accessions share near-identical haplotype blocks with close relatives of wheat’s diploid and tetraploid progenitors, while some show evidence of introgressions from more distant species and structural variation between accessions. Hence, introgressions and chromosomal rearrangements appear to have made a major contribution to genetic diversity in cultivar collections. As gene flow from relatives to bread wheat is an ongoing process, we assess the impact that introgressions might have on future breeding strategies.

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Fig. 1: PCA plots of hexaploid bread wheat accessions for each chromosome.
Fig. 2: Manhattan plots of −log10-transformed P values assigned to each SNP marker derived from the Mahalanobis distance test statistic calculated via PCAdapt.
Fig. 3: Detection of translocations/introgressions in accessions Russet and Diablo using GISH.
Fig. 4: Summary of the introgression data.
Fig. 5: Average total introgression size for wheat collections based on the predicted donor wheat relative.
Fig. 6: Alignment of the exome capture data with predicted introgressions and diversity measures.

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Data availability

The genotype data that support the findings of this study are available in the European Variation Archive (EVA) with the identifier PRJEB29561. Source data are provided with this paper.

Code availability

The custom PERL scripts described in this study are available via GitHub (


  1. Dubcovsky, J. & Dvorak, J. Genome plasticity a key factor in the success of polyploid wheat under domestication. Science 316, 1862–1866 (2007).

    Article  CAS  Google Scholar 

  2. Shewry, P. R. Wheat. J. Exp. Bot. 60, 1537–1553 (2009).

    Article  CAS  Google Scholar 

  3. Pont, C. et al. Tracing the ancestry of modern bread wheats. Nat. Genet. 51, 905–911 (2019).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  5. Harlan, J. R. & de Wet, J. M. Toward a rational classification of cultivated plants. Taxon 20, 509–517 (1971).

    Article  Google Scholar 

  6. Moore, G. Strategic pre-breeding for wheat improvement. Nat. Plants 1, 15018 (2015).

    Article  CAS  Google Scholar 

  7. He, F. et al. Molecular cytogenetic identification of a wheat–Thinopyrum ponticum translocation line resistant to powdery mildew. J. Genet. 96, 165–169 (2017).

    Article  CAS  Google Scholar 

  8. Cheng, H. et al. Frequent intra- and inter-species introgression shapes the landscape of genetic variation in bread wheat. Genome Biol. 20, 136 (2019).

    Article  Google Scholar 

  9. He, 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).

    Article  CAS  Google Scholar 

  10. Molnár-Láng, M. et al. in Genomics of Plant Genetic Resources (eds Tuberosa, R. et al.) 255–283 (Springer, 2014).

  11. Worland, A. J. & Snape, J. W. in The World Wheat Book (eds Bonjean, A. P. & Angus, W. J.) 59–100 (Lavoisier, 2001).

  12. Villareal, R. L., Rajaram, S., Mujeebkazi, A. & Deltoro, E. The effect of chromosome 1B/1R translocation on the yield potential of certain spring wheats (Triticum aestivum). Plant Breed. 106, 77–81 (1991).

    Article  Google Scholar 

  13. Schlegel, R. & Meinel, A. A quantitative trait locus (QTL) on chromosome arm 1RS of rye and its effect on yield performance of hexaploid wheat. Cereal Res. Commun. 22, 7–13 (1994).

    Google Scholar 

  14. Gardner, K. A., Wittern, L. M. & Mackay, I. J. A highly recombined, high-density, eight-founder wheat MAGIC map reveals extensive segregation distortion and genomic locations of introgression segments. Plant Biotechnol. J. 14, 1406–1417 (2016).

    Article  CAS  Google Scholar 

  15. Winfield, M. O. et al. High-density SNP genotyping array for hexaploid wheat and its secondary and tertiary gene pool. Plant Biotechnol. J. 14, 1195–1206 (2016).

    Article  CAS  Google Scholar 

  16. Gale, M. D. et al. An α-amylase gene from Aegilops ventricosa transferred to bread wheat together with a factor for eyespot resistance. Heredity 52, 431–435 (1984).

    Article  CAS  Google Scholar 

  17. Jafarzadeh, J. et al. Breeding value of primary synthetic wheat genotypes for grain yield. PLoS ONE 11, e0162860 (2016).

    Article  Google Scholar 

  18. Hao, M. et al. A breeding strategy targeting the secondary gene pool of bread wheat: introgression from a synthetic hexaploid wheat. Theor. Appl. Genet. 132, 2285–2294 (2019).

    Article  CAS  Google Scholar 

  19. Lange, T. M. et al. In silico quality assessment of SNPs—a case study on the Axiom® Wheat genotyping arrays. Curr. Plant Biol. 21, 100140 (2020).

    Article  Google Scholar 

  20. Luu, K., Bazin, E. & Blum, M. G. pcadapt: an R package to perform genome scans for selection based on principal component analysis. Mol. Ecol. Resour. 17, 67–77 (2017).

    Article  CAS  Google Scholar 

  21. Allen, A. M. et al. Characterization of a Wheat Breeders’ Array suitable for high throughput SNP genotyping of global accessions of hexaploid bread wheat (Triticum aestivum). Plant Biotechnol. J. 15, 390–401 (2016).

    Article  Google Scholar 

  22. Jones, H. et al. Strategy for exploiting exotic germplasm using genetic, morphological, and environmental diversity: the Aegilops tauschii Coss. example. Theor. Appl. Genet. 126, 1793–1808 (2013).

    Article  CAS  Google Scholar 

  23. Fradgley, N. et al. A large-scale pedigree resource of wheat reveals evidence for adaptation and selection by breeders. PLoS Biol. 17, e3000071 (2019).

    Article  Google Scholar 

  24. Winfield, M. O. et al. Targeted re-sequencing of the allohexaploid wheat exome. Plant Biotechnol. J. 10, 733–742 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  27. Fang, T. et al. Stripe rust resistance in the wheat cultivar Jagger is due to Yr17 and a novel resistance gene. Crop Sci. 51, 2455–2465 (2011).

    Article  CAS  Google Scholar 

  28. Bulos, M. et al. Occurrence of the rust resistance gene Lr37 from Aegilops ventricosa in Argentine cultivars of wheat. Electron. J. Biotechnol. 9, 580–586 (2006).

    Article  Google Scholar 

  29. Xue, S. et al. Mapping of leaf rust resistance genes and molecular characterization of the 2NS/2AS translocation in the wheat cultivar Jagger. G3 (Bethesda) 8, 2059–2065 (2018).

    Article  CAS  Google Scholar 

  30. Allard, R. W. & Shands, R. G. Inheritance of resistance to stem rust and powdery mildew in cytologically stable spring wheats derived from Triticum timopheevi. Phytopathology 44, 266–274 (1954).

    Google Scholar 

  31. Montenegro, J. D. et al. The pangenome of hexaploid bread wheat. Plant J. 90, 1007–1013 (2017).

    Article  CAS  Google Scholar 

  32. Burridge, A. J. et al. in Wheat Biotechnology: Methods and Protocols (Bhalla, P. L. & Singh, M. B.) 293–306 (Humana Press, 2017).

  33. Gardiner, L. J. et al. Integrating genomic resources to present full gene and putative promoter capture probe sets for bread wheat. GigaScience 8, giz018 (2019).

    Article  Google Scholar 

  34. Wilkinson, P. A. et al. CerealsDB—new tools for the analysis of the wheat genome: update 2020. Database 2020, 1–13 (2020).

    Article  Google Scholar 

  35. Milne, I. et al. Flapjack—graphical genotype visualization. Bioinformatics 26, 3133–3134 (2010).

    Article  CAS  Google Scholar 

  36. Zheng, X. et al. A high-performance computing toolset for relatedness and principal component analysis of SNP data. Bioinformatics 28, 3326–3328 (2012).

    Article  CAS  Google Scholar 

  37. Kato, A., Lamb, J. C. & Birchler, J. A. Chromosome painting using repetitive DNA sequences as probes for somatic chromosome identification in maize. Proc. Natl Acad. Sci. USA 101, 13554–13559 (2004).

    Article  CAS  Google Scholar 

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We thank the Bristol Genomics Facility for the Illumina sequencing data and the Germplasm Resource Unit (GRU) for providing many of the accessions used in this paper. We thank the Biotechnology and Biological Sciences Research Council, UK, for funding this work (award nos BB/N021061/1 and BBS/E/J/000PR9781).

Author information

Authors and Affiliations



A.M.P.-A., P.A.W., A.J.B., M.O.W., G.L.A.B. and K.J.E. conceived and planned the experiments. S.G., L.U.W., R.H., A.R.B. and P.S. provided the plant material for the analysis. A.J.B., J.K. and C.Y. carried out the lab experiments. A.M.P.-A., P.A.W., M.O.W., X.D., M.B., G.L.A.B. and K.J.E. planned and carried out the computational analyses. A.M.P.-A. took the lead in writing the manuscript. All authors provided critical feedback and helped interpret the data and shape the research, analysis and manuscript.

Corresponding author

Correspondence to Alexandra M. Przewieslik-Allen.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Plants thanks Rudi Appels, Agnieszka Aleksandra Golicz and Isobel Parkin for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1

Genetic diversity calculated for five collections of T. aestivum, averaged across genomes (a) and chromosomes (b). The mean is displayed as a white cross (a) or red dots (b). For each collection chromosomes are ordered 1A, 1B, 1D, … 7D. A two-sided Kruskal-Wallis (non-parametric ANOVA) showed that that mean diversity values were significantly different among the three genomes (H = 10,270, n = 3,376,940, d.f. = 2, p = 0.000) and among the five populations (H = 41,383, n = 3,376,940, d.f. = 4, p = 0.000). Post-hoc Mann-Whitney two-sided tests with Bonferroni correction also showed that all pairwise comparisons between the three genomes were significant as were all comparisons between the five populations with p = 0.000 in all cases.

Source data

Extended Data Fig. 2

The standard deviation (STDEV) of introgression scores for each chromosome. Chromosomes are divided into approximately 50 Mb bins (x-axis) and separate lines are shown for each bread wheat collection based upon accession release date: blue, Collection 1 (1790-1930); yellow, Collection 2 (1931-1965); green, Collection 3 (1966-1985); orange, Collection 4 (1986-2015); grey, Collection 5 (novel synthetics). The STDEV values are used to summarise data from all varieties, with peaks in the values representing regions where introgression scores vary because a sub-set of varieties have an introgression not shared by all varieties examined. For example, the clear peaks in Chromosome 1B arise because a sub-set of varieties have the 1BL/1RS introgression.

Supplementary information

Supplementary Information

Supplementary Figs. 1–3 and Table 1.

Reporting Summary

Supplementary Tables

Supplementary Tables 2, 3, 5, 7, 8 and 9.

Supplementary Table 4

PCA coordinates and plots of hexaploid bread wheat accessions for each chromosome.

Supplementary Table 6

CNV events detected in hexaploid wheat accessions. CNV analysis was performed using the Affymetrix CNV Tool software (v.1.1). Events were defined as copy number gain and copy number loss using the segmentation algorithm in Nexus Copy Number v.9.0.

Supplementary Table 10

Predicted introgressions from T. dicoccoides in Robigus (Sheet 1). One predicted T. dicoccoides introgression on 4A was highlighted as under selection in Collection 4 by the PCAdapt analysis and was exclusive to Robigus and its progeny (Sheet 2). The exome captured sequence data and predicted SNPs for the 4A region (Sheet 3).

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 5

Source data for the figure.

Source Data Extended Data Fig. 1

Statistical source data.

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Przewieslik-Allen, A.M., Wilkinson, P.A., Burridge, A.J. et al. The role of gene flow and chromosomal instability in shaping the bread wheat genome. Nat. Plants 7, 172–183 (2021).

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