Detecting genomic signatures of natural selection with principal component analysis: application to the 1000 Genomes data

To characterize natural selection, various analytical methods for detecting candidate genomic regions have been developed. We propose to perform genome-wide scans of natural selection using principal component analysis (PCA). We show that the common FST index of genetic differentiation between populations can be viewed as the proportion of variance explained by the principal components. Considering the correlations between genetic variants and each principal component provides a conceptual framework to detect genetic variants involved in local adaptation without any prior definition of populations. To validate the PCA-based approach, we consider the 1000 Genomes data (phase 1) considering 850 individuals coming from Africa, Asia, and Europe. The number of genetic variants is of the order of 36 millions obtained with a low-coverage sequencing depth (3×). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A...

为表征自然选择，学界已开发出多种用于检测候选基因组区域的分析方法。本文提出利用主成分分析（principal component analysis, PCA）开展全基因组范围的自然选择扫描。研究表明，常见的群体间遗传分化指数FST可视为主成分所解释的方差比例。通过考量遗传变异与各主成分间的相关性，可构建一套无需预先定义群体的概念框架，用于检测参与局部适应的遗传变异。为验证基于PCA的方法，本文采用千人基因组计划（1000 Genomes）第一阶段（phase 1）的数据，该数据集包含来自非洲、亚洲与欧洲的850名个体，共获得约3600万条遗传变异数据，测序覆盖度为3×。遗传变异与各主成分的相关性可得到已知的正选择靶点（如EDAR、SLC24A...）