Detecting genomic signatures of natural selection with principal component analysis: application to the 1000 Genomes data
Nicolas Duforet-Frebourg et al.
(Submitted on 8 Apr 2015)
Large-scale genomic data offers the perspective to decipher the genetic architecture of natural selection. 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. We show that the common Fst index of genetic differentiation between populations can be viewed as a proportion of variance explained by the principal components. Looking at 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) after removal of recently admixed individuals resulting in 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 (3X). The correlations between genetic variation and each principal component provide well-known targets for positive selection (EDAR, SLC24A5, SLC45A2, DARC), and also new candidate genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and non-coding RNAs. In addition to identifying genes involved in biological adaptation, we identify two biological pathways involved in polygenic adaptation that are related to the innate immune system (beta defensins) and to lipid metabolism (fatty acid omega oxidation). PCA-based statistics retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially in non-model species for which defining populations can be difficult. Genome scan based on PCA is implemented in the open-source and freely available PCAdapt software.
Fast principal components analysis reveals independent evolution of ADH1B gene in Europe and East Asia
Kevin J Galinsky et al.
Principal components analysis (PCA) is a widely used tool for inferring population structure and correcting confounding in genetic data. We introduce a new algorithm, FastPCA, that leverages recent advances in random matrix theory to accurately approximate top PCs while reducing time and memory cost from quadratic to linear in the number of individuals, a computational improvement of many orders of magnitude. We apply FastPCA to a cohort of 54,734 European Americans, identifying 5 distinct subpopulations spanning the top 4 PCs. Using a new test for natural selection based on population differentiation along these PCs, we replicate previously known selected loci and identify three new signals of selection, including selection in Europeans at the ADH1B gene. The coding variant rs1229984 has previously been associated to alcoholism and shown to be under selection in East Asians; we show that it is a rare example of independent evolution on two continents.