Gene ontology and species-specific adaptations
To gain insight into the ecological and biological functions of putative
adaptive loci, we identified candidate loci found within genes and the
Gene Ontology (GO) terms associated with such genes (Primmer et
al. 2013). We used the Ensembl variant effect predictor (VEP) to
perform annotation of the candidate loci (McLaren et al. 2016)
and the software SNP2GO (Szkiba et al. 2014) to identify cellular
component, biological process, and molecular function GO terms
associated with the candidate loci using an FDR of 0.05, and following
the annotations of the thirteen-lined ground squirrel genome. We tested
for enrichment considering all the candidates identified by the four
analyses combined, as well as for each analysis individually. To
evaluate the impact of the different method assumptions on GO term
enrichment, we further tested for enrichment considering the identified
candidate loci divided in several categories: population structure
outlier approach (pcadapt ), representing those candidates
uniquely identified for the pcadapt analysis; genotype
environment associations (‘GEA’), considering the candidates uniquely
identified for the LFMM, RDA and pRDA analyses; candidates identified
including population structure (‘POP’), considering those loci uniquely
identified for the pcadapt and RDA analyses; and candidates
identified when excluding population structure (‘noPOP’), considering
those loci uniquely identified for the LFMM and pRDA analyses. For all
three datasets (NIDGS, SIDGS, and the combined IDGS), we further
considered the candidate SNPs resulting in non-synonymous substitutions
and used the online databases Ensembl and UniProt to identify the genes
and proteins involved (Bateman 2019; Yates et al. 2020).