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四十年多环境试验中的遗传关联揭示了普通菜豆农艺性状的演变。

Genetic Associations in Four Decades of Multienvironment Trials Reveal Agronomic Trait Evolution in Common Bean.

机构信息

Integrative Biology, The University of Texas at Austin, Texas 78712

U.S. Arid Land Agricultural Research Center, U.S. Department of Agriculture-Agricultural Research Service, Maricopa, Arizona 85239.

出版信息

Genetics. 2020 May;215(1):267-284. doi: 10.1534/genetics.120.303038. Epub 2020 Mar 23.

Abstract

Multienvironment trials (METs) are widely used to assess the performance of promising crop germplasm. Though seldom designed to elucidate genetic mechanisms, MET data sets are often much larger than could be duplicated for genetic research and, given proper interpretation, may offer valuable insights into the genetics of adaptation across time and space. The Cooperative Dry Bean Nursery (CDBN) is a MET for common bean () grown for > 70 years in the United States and Canada, consisting of 20-50 entries each year at 10-20 locations. The CDBN provides a rich source of phenotypic data across entries, years, and locations that is amenable to genetic analysis. To study stable genetic effects segregating in this MET, we conducted genome-wide association studies (GWAS) using best linear unbiased predictions derived across years and locations for 21 CDBN phenotypes and genotypic data (1.2 million SNPs) for 327 CDBN genotypes. The value of this approach was confirmed by the discovery of three candidate genes and genomic regions previously identified in balanced GWAS. Multivariate adaptive shrinkage (mash) analysis, which increased our power to detect significant correlated effects, found significant effects for all phenotypes. Mash found two large genomic regions with effects on multiple phenotypes, supporting a hypothesis of pleiotropic or linked effects that were likely selected on in pursuit of a crop ideotype. Overall, our results demonstrate that statistical genomics approaches can be used on MET phenotypic data to discover significant genetic effects and to define genomic regions associated with crop improvement.

摘要

多环境试验 (METs) 被广泛用于评估有前途的作物种质资源的表现。尽管很少用于阐明遗传机制,但 MET 数据集通常比遗传研究的可重复数据集大得多,并且如果进行适当的解释,可能会为了解跨时间和空间的适应的遗传机制提供有价值的见解。合作干豆苗圃 (CDBN) 是一种用于普通豆 () 的 MET,在美国和加拿大已经种植了 >70 年,每年在 10-20 个地点种植 20-50 个品种。CDBN 提供了丰富的表型数据,涵盖了品种、年份和地点,可以进行遗传分析。为了研究这种 MET 中分离的稳定遗传效应,我们使用跨年份和地点的最佳线性无偏预测值进行了全基因组关联研究 (GWAS),用于 21 个 CDBN 表型和 327 个 CDBN 基因型的基因型数据 (120 万个 SNPs)。通过发现三个以前在平衡 GWAS 中发现的候选基因和基因组区域,证实了这种方法的价值。多变量自适应收缩 (mash) 分析增加了我们检测显著相关效应的能力,发现所有表型都有显著效应。Mash 发现了两个对多个表型有影响的大基因组区域,支持了多效性或连锁效应的假设,这些效应可能是在追求作物理想型的过程中被选择的。总的来说,我们的结果表明,统计基因组学方法可用于 MET 表型数据,以发现显著的遗传效应,并定义与作物改良相关的基因组区域。

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