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Title: Data from: Detecting genomic signatures of natural selection with principal component analysis: application to the 1000 Genomes data      
keywords:
FST
principal component analysis
population structure
population genomics
landscape genetics
selection scan
local adaptation
1000 genomes
type:
Article
description:
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.
ID:
oai:datadryad.org:10255/dryad.82938
accesstypes:
download
landingPage: http://hdl.handle.net/10255/dryad.82938
authentication:
none
authorization:
none
ID:
doi:10.5061/dryad.5s77q
Duforet-Frebourg N, Luu K, Laval G, Bazin E, Blum MGB (2016) Detecting genomic signatures of natural selection with principal component analysis: application to the 1000 Genomes data. Molecular Biology and Evolution 33(4): 1082-1093.
http://hdl.handle.net/10255/dryad.82938
PGENETICS-D-15-00867
setID:
hdl_10255_3
recNum:
5
setName:
Main
dateReleased:
03-03-2016
issueDate:
20151229
accessionDate:
01-05-2016
dateCreated:
01-05-2016
abbreviation:
Dryad
ID:
SCR:005910
name:
Dryad Digital Repository
homepage: http://www.datadryad.org
ID:
SCR:005910
name:
Dryad Data Repository

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