Plant Genetics Research Unit, U. S. Department of Agriculture-Agricultural Research Service, Columbia, Missouri 65211
Division of Biological Sciences, University of Missouri, Columbia, Missouri 65211.
Genetics. 2018 May;209(1):321-333. doi: 10.1534/genetics.118.300857. Epub 2018 Mar 15.
Important traits in agricultural, natural, and human populations are increasingly being shown to be under the control of many genes that individually contribute only a small proportion of genetic variation. However, the majority of modern tools in quantitative and population genetics, including genome-wide association studies and selection-mapping protocols, are designed to identify individual genes with large effects. We have developed an approach to identify traits that have been under selection and are controlled by large numbers of loci. In contrast to existing methods, our technique uses additive-effects estimates from all available markers, and relates these estimates to allele-frequency change over time. Using this information, we generate a composite statistic, denoted [Formula: see text] which can be used to test for significant evidence of selection on a trait. Our test requires pre- and postselection genotypic data but only a single time point with phenotypic information. Simulations demonstrate that [Formula: see text] is powerful for identifying selection, particularly in situations where the trait being tested is controlled by many genes, which is precisely the scenario where classical approaches for selection mapping are least powerful. We apply this test to breeding populations of maize and chickens, where we demonstrate the successful identification of selection on traits that are documented to have been under selection.
重要的农业、自然和人类群体特征越来越多地被证明是由许多基因控制的，这些基因单独只贡献了遗传变异的一小部分。然而，数量遗传学和群体遗传学的大多数现代工具，包括全基因组关联研究和选择作图协议，都是为了识别具有较大影响的单个基因而设计的。我们开发了一种识别受选择控制并由大量基因座控制的特征的方法。与现有方法不同，我们的技术使用所有可用标记的加性效应估计值，并将这些估计值与随时间变化的等位基因频率相关联。利用这些信息，我们生成一个综合统计量，记为 [Formula: see text]，可用于检验一个特征是否受到选择的显著影响。我们的检验需要选择前后的基因型数据，但只需要一个具有表型信息的时间点。模拟表明，[Formula: see text] 在识别选择方面非常有效，特别是在被测试的特征由许多基因控制的情况下，这正是经典选择作图方法最无效的情况。我们将此检验应用于玉米和鸡的育种群体，成功地识别了已被证明受到选择的特征上的选择。
