Data from: 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, SLC24A5, SLC45A2, DARC), and also new candidate
genes (APPBPP2, TP1A1, RTTN, KCNMA, MYO5C) and noncoding 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 fast
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.