Contrasting whole-genome and reduced representation sequencing for population demographic and adaptive inference: an alpine mammal case study

Genomes capture the adaptive and demographic history of a species, but the choice of sequencing strategy and sample size can impact such inferences. We compared whole genome and reduced representation sequencing approaches to study the population demographic and adaptive signals of the North American mountain goat (Oreamnos americanus). We applied the restriction site-associated DNA sequencing (RADseq) approach to 254 individuals and whole genome resequencing (WGS) approach to 35 individuals across the species range at mid-level coverage (9X) and to 5 individuals at high coverage (30X). We used ANGSD to estimate the genotype likelihoods and estimated the effective population size (Ne), population structure, and explicitly modelled the demographic history with δaδi and MSMC2. The data sets were overall concordant in supporting a glacial induced vicariance and extremely low Ne in mountain goats. We evaluated a set of climatic variables and geographic location as predictors of genetic diversity using redundancy analysis. A moderate proportion of total variance (36% for WGS and 21% for RADseq data sets) was explained by geography and climate variables; both data sets support a large impact of drift and some degree of local adaptation. The empirical similarities of WGS and RADseq presented herein reassuringly suggest that both approaches will recover large demographic and adaptive signals in a population; however, WGS offers several advantages over RADseq, such as inferring adaptive processes and calculating runs-of-homozygosity estimates. Considering the predicted climate-induced changes in alpine environments and the genetically depauperate mountain goat, the long-term adaptive capabilities of this enigmatic species are questionable.