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Accounting for linkage disequilibrium in genome scans for selection without individual genotypes: The local score approach
Author(s) -
Fariello María Inés,
Boitard Simon,
Mercier Sabine,
Robelin David,
Faraut Thomas,
Arnould Cécile,
Recoquillay Julien,
Bouchez Olivier,
Salin Gérald,
Dehais Patrice,
Gourichon David,
Leroux Sophie,
Pitel Frédérique,
Leterrier Christine,
SanCristobal Magali
Publication year - 2017
Publication title -
molecular ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.619
H-Index - 225
eISSN - 1365-294X
pISSN - 0962-1083
DOI - 10.1111/mec.14141
Subject(s) - linkage disequilibrium , biology , selection (genetic algorithm) , disequilibrium , haplotype , genome , background selection , genome wide association study , genome scan , population , computational biology , genetics , genetic association , evolutionary biology , genotype , single nucleotide polymorphism , computer science , artificial intelligence , gene , allele , microsatellite , medicine , demography , sociology , ophthalmology
Detecting genomic footprints of selection is an important step in the understanding of evolution. Accounting for linkage disequilibrium in genome scans increases detection power, but haplotype‐based methods require individual genotypes and are not applicable on pool‐sequenced samples. We propose to take advantage of the local score approach to account for linkage disequilibrium in genome scans for selection, cumulating (possibly small) signals from single markers over a genomic segment, to clearly pinpoint a selection signal. Using computer simulations, we demonstrate that this approach detects selection with higher power than several state‐of‐the‐art single‐marker, windowing or haplotype‐based approaches. We illustrate this on two benchmark data sets including individual genotypes, for which we obtain similar results with the local score and one haplotype‐based approach. Finally, we apply the local score approach to Pool‐Seq data obtained from a divergent selection experiment on behaviour in quail and obtain precise and biologically coherent selection signals: while competing methods fail to highlight any clear selection signature, our method detects several regions involving genes known to act on social responsiveness or autistic traits. Although we focus here on the detection of positive selection from multiple population data, the local score approach is general and can be applied to other genome scans for selection or other genomewide analyses such as GWAS .