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A genome scan for selection signatures comparing farmed Atlantic salmon with two wild populations: Testing colocalization among outlier markers, candidate genes, and quantitative trait loci for production traits
Author(s) -
Liu Lei,
Ang Keng Pee,
Elliott J. A. K.,
Kent Matthew Peter,
Lien Sigbjørn,
MacDonald Danielle,
Boulding Elizabeth Grace
Publication year - 2017
Publication title -
evolutionary applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.776
H-Index - 68
ISSN - 1752-4571
DOI - 10.1111/eva.12450
Subject(s) - biology , quantitative trait locus , candidate gene , genetics , population , genome scan , domestication , locus (genetics) , evolutionary biology , genome , gene , microsatellite , allele , demography , sociology
Comparative genome scans can be used to identify chromosome regions, but not traits, that are putatively under selection. Identification of targeted traits may be more likely in recently domesticated populations under strong artificial selection for increased production. We used a North American Atlantic salmon 6K SNP dataset to locate genome regions of an aquaculture strain (Saint John River) that were highly diverged from that of its putative wild founder population (Tobique River). First, admixed individuals with partial European ancestry were detected using STRUCTURE and removed from the dataset. Outlier loci were then identified as those showing extreme differentiation between the aquaculture population and the founder population. All Arlequin methods identified an overlapping subset of 17 outlier loci, three of which were also identified by BayeScan. Many outlier loci were near candidate genes and some were near published quantitative trait loci ( QTL s) for growth, appetite, maturity, or disease resistance. Parallel comparisons using a wild, nonfounder population (Stewiacke River) yielded only one overlapping outlier locus as well as a known maturity QTL . We conclude that genome scans comparing a recently domesticated strain with its wild founder population can facilitate identification of candidate genes for traits known to have been under strong artificial selection.

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