Data from: Genetic subdivision and candidate genes under selection in North American gray wolves
Schweizer, Rena M. et al. (2015), Data from: Genetic subdivision and candidate genes under selection in North American gray wolves, Dryad, Dataset, https://doi.org/10.5061/dryad.c9b25
Previous genetic studies of the highly mobile gray wolf (Canis lupus) found population structure that coincides with habitat and phenotype differences. We hypothesized that these ecologically distinct populations (ecotypes) should exhibit signatures of selection in genes related to morphology, coat color, and metabolism. To test these predictions, we quantified population structure related to habitat using a genotyping array to assess variation in 42,036 SNPs in 111 North American gray wolves. Using these SNP data and individual-level measurements of 12 environmental variables, we identified six ecotypes: West Forest, Boreal Forest, Arctic, High Arctic, British Columbia, and Atlantic Forest. Next, we explored signals of selection across these wolf ecotypes through the use of three complementary methods to detect selection: FST/haplotype homozygosity bivariate percentile, BayeScan, and environmentally correlated directional selection with Bayenv. Across all methods, we found consistent signals of selection on genes related to morphology, coat coloration, metabolism, as predicted, as well as vision and hearing. In several high-ranking candidate genes, including LEPR, TYR, and SLC14A2, we found variation in allele frequencies that follow environmental changes in temperature and precipitation, a result that is consistent with local adaptation rather than genetic drift. Our findings show that local adaptation can occur despite gene flow in a highly mobile species and can be detected through a moderately dense genomic scan. These patterns of local adaptation revealed by SNP genotyping likely reflect high fidelity to natal habitats of dispersing wolves, strong ecological divergence among habitats, and moderate levels of linkage in the wolf genome.