Trends of genetic changes uncovered by Env- and Eigen-GWAS in wheat and barley.
Trends of genetic changes uncovered by Env- and Eigen-GWAS in wheat and barley.
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DOI：
10.1007/s00122-021-03991-z
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发表时间：
2022-03
期刊：
影响因子：
--
通讯作者：
Mackay IJ
中科院分区：
文献类型：
--
作者：
Sharma R;Cockram J;Gardner KA;Russell J;Ramsay L;Thomas WTB;O'Sullivan DM;Powell W;Mackay IJ
品种年龄和群体结构检测小麦和大麦产量和适应性的新QTL，而不需要表型。上个世纪的作物育种过程已经产生了具有增加的遗传增益的新品种，从而导致更高的作物性能和产量。然而，在许多情况下，支持这种成功的等位基因和基因组区域仍然未知。这部分是由于很难产生足够的表型数据，大量的历史品种，使这种分析。在这里，我们证明了绕过这些瓶颈的能力，通过确定基因组区域选择超过100年的作物育种使用品种的年龄作为产量的替代品。我们没有收集表型数据，而是根据世界上两种最重要的作物小麦和大麦的品种年龄部署了“环境全基因组关联扫描”（EnvGWAS），并检测到两个基因组的强烈选择信号。EnvGWAS在大麦中确定了16个基因组区域，在小麦中确定了10个基因组区域，这两种作物的春季和冬季类型之间存在对比模式。为了进一步研究基因组结构的变化，我们使用基因型数据的基因组关系矩阵来推导特征向量，以便在EigenGWAS中进行分析。这检测到七个主要的染色体渗入，有助于适应小麦。基于品种年龄的EigenGWAS和EnvGWAS避免了昂贵的表型分析，并促进了在育种过程中选择的基因组片段的鉴定。我们的研究结果表明，使用历史品种集合加上基因组数据，以确定染色体区域的选择，并可能有助于指导未来的植物育种策略，以最大限度地提高遗传增益和适应率的潜力。在线版本包含补充材料，可通过10.1007/s 00122 -021-03991-z获得。
Variety age and population structure detect novel QTL for yield and adaptation in wheat and barley without the need to phenotype. The 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. The online version contains supplementary material available at 10.1007/s00122-021-03991-z.
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