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Targeted capture and resequencing of 1040 genes reveal environmentally driven functional variation in grey wolves.

Authors
  • Rm, Schweizer
  • J, Robinson
  • R, Harrigan
  • P, Silva
  • M, Galverni
  • M, Musiani
  • Re, Green
  • J, Novembre
  • Rk, Wayne
Type
Published Article
Journal
Molecular Ecology
Publisher
Wiley (Blackwell Publishing)
Volume
25
Issue
1
Pages
357–379
Identifiers
DOI: 10.1111/mec.13467
Source
UCSC Bioinformatics biomedical-ucsc
License
Unknown

Abstract

In an era of ever-increasing amounts of whole-genome sequence data for individuals and populations, the utility of traditional single nucleotide polymorphisms (SNPs) array-based genome scans is uncertain. We previously performed a SNP array-based genome scan to identify candidate genes under selection in six distinct grey wolf (Canis lupus) ecotypes. Using this information, we designed a targeted capture array for 1040 genes, including all exons and flanking regions, as well as 5000 1-kb nongenic neutral regions, and resequenced these regions in 107 wolves. Selection tests revealed striking patterns of variation within candidate genes relative to noncandidate regions and identified potentially functional variants related to local adaptation. We found 27% and 47% of candidate genes from the previous SNP array study had functional changes that were outliers in sweed and bayenv analyses, respectively. This result verifies the use of genomewide SNP surveys to tag genes that contain functional variants between populations. We highlight nonsynonymous variants in APOB, LIPG and USH2A that occur in functional domains of these proteins, and that demonstrate high correlation with precipitation seasonality and vegetation. We find Arctic and High Arctic wolf ecotypes have higher numbers of genes under selection, which highlight their conservation value and heightened threat due to climate change. This study demonstrates that combining genomewide genotyping arrays with large-scale resequencing and environmental data provides a powerful approach to discern candidate functional variants in natural populations.

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