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

TL;DR

This paper introduces a PCA-based method for detecting natural selection in genomes, validated on the 1000 Genomes data, identifying known and novel adaptive genetic variants without predefined populations.

Contribution

The study presents a novel PCA-based framework for genome-wide scans of natural selection that does not require prior population definitions, validated on large-scale human genomic data.

Findings

Identified known adaptive genes like EDAR and SLC24A5.

Discovered new candidate genes and non-coding RNAs involved in adaptation.

Detected pathways related to immune response and lipid metabolism.

Abstract

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. 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) 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). An additional analysis of European data shows that a genome scan based on PCA retrieves classical examples of local adaptation even when there are no well-defined populations. PCA-based statistics, implemented in the PCAdapt R package and the PCAdapt open-source software, retrieve well-known signals of human adaptation, which is encouraging for future whole-genome sequencing project, especially when defining populations is difficult.