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

Nature Plants volume 7, pages 172–183 (2021)Cite this article

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Abstract

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 (https://github.com/pr0kary0te/).

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Acknowledgements

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

  1. Life Sciences, University of Bristol, Bristol, UK

    Alexandra M. Przewieslik-Allen, Paul A. Wilkinson, Amanda J. Burridge, Mark O. Winfield, Xiaoyang Dai, Mark Beaumont, Gary L. A. Barker & Keith J. Edwards

  2. Institute of Systems, Molecular & Integrative Biology, University of Liverpool, Liverpool, UK

    Paul A. Wilkinson

  3. Plant Sciences Building, School of Biosciences, The University of Nottingham, Sutton Bonington, UK

    Julie King & Cai-yun Yang

  4. The John Innes Centre, Norwich, UK

    Simon Griffiths & Luzie U. Wingen

  5. NIAB, Cambridge, UK

    Richard Horsnell

  6. International Maize and Wheat Improvement Center (CIMMYT), El Batán, Mexico

    Alison R. Bentley

  7. Rothamsted Research, Harpenden, UK

    Peter Shewry

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  1. Alexandra M. Przewieslik-Allen
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Contributions

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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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). https://doi.org/10.1038/s41477-020-00845-2

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