Trends of genetic changes uncovered by Env- and Eigen-GWAS in wheat and barleySharma, R., Cockram, J., Gardner, K. A., Russell, J., Ramsay, L., Thomas, W. T. B., O'Sullivan, D. M. ORCID: https://orcid.org/0000-0003-4889-056X, Powell, W. and Mackay, I. J. ORCID: https://orcid.org/0000-0002-1109-4730 (2022) Trends of genetic changes uncovered by Env- and Eigen-GWAS in wheat and barley. Theoretical and Applied Genetics, 135. pp. 667-678. ISSN 0040-5752
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1007/s00122-021-03991-z Abstract/SummaryThe process of crop breeding over the last century has delivered new varieties with increased genetic gains, resulting in higher crop performance and yield. However, in many cases, the alleles and genomic regions underpinning this success remain unknown. This is partly due to the difficulty of generating sufficient phenotypic data on large numbers of historical varieties to enable such analyses. Here we demonstrate the ability to circumvent such bottlenecks by identifying genomic regions selected over 100 years of crop breeding using age of a variety as a surrogate for yield. Rather than collecting phenotype data, we deployed ‘environmental genome-wide association scans’ (EnvGWAS) based on variety age in two of the world’s most important crops, wheat and barley, and detected strong signals of selection across both genomes. EnvGWAS identified 16 genomic regions in barley and 10 in wheat with contrasting patterns between spring and winter types of the two crops. To further examine changes in genome structure, we used the genomic relationship matrix of the genotypic data to derive eigenvectors for analysis in EigenGWAS. This detected seven major chromosomal introgressions that contributed to adaptation in wheat. EigenGWAS and EnvGWAS based on variety age avoid costly phenotyping and facilitate the identification of genomic tracts that have been under selection during breeding. Our results demonstrate the potential of using historical cultivar collections coupled with genomic data to identify chromosomal regions under selection and may help guide future plant breeding strategies to maximise the rate of genetic gain and adaptation.
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