ArticleMolecular biology and evolution2016
Detecting Genomic Signatures of Natural Selection with Principal Component Analysis: Application to the 1000 Genomes Data.
Article in Molecular biology and evolution, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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68 citing papers in PubMed, 180 citations in OpenAlex.
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- Prognostic value and immune landscapes of immunogenic cell death-associated lncRNAs in lung adenocarcinoma.Scientific reports · 2023Article
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- Recent natural selection conferred protection against schizophrenia by non-antagonistic pleiotropy.Scientific reports · 2023Article
- Population genetic structure of the maize weevil, Sitophilus zeamais, in southern Mexico.PloS one · 2023Article
- Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated.Scientific reports · 2022Article
8 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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 (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.
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