Loci under selection
Loci potentially influenced by selection were screened from the ‘nuclear mapped’ catalog considering all SNPs using two approaches. The reversible jump Markov chain Monte Carlo approach implemented in BAYESCAN 2.1 (Foll and Gaggiotti 2008) was applied by grouping samples per location, setting default parameters of 50000 burn-in steps, 5000 iterations, 10 thinning interval size and 20 pilot runs of size 5000. Candidate loci under selection with a posterior probability higher than 0.76 (considered as strong according to the Jeffery’s interpretation in the software manual) and a false discovery rate (FDR) lower than 0.05 were selected. We then used the multivariate analysis method implemented in the pcadapt R package, which does not require a prior grouping of the samples, following Luu et al. (2017) recommendations and selected outlier SNPs following the Benjamini-Hochberg procedure. Pairwise linkage disequilibrium between all filtered SNPs obtained from those scaffolds which contained candidate SNPs under selection was measured using the R package LDheatmap. PCAs were performed using the adegenet R package (Jombart and Ahmed 2011) based on outlier SNPs, and variants obtained from one genomic region found to be under high linkage disequilibrium (scaffolds BKCK01000075 (partially) and BKCK01000111) from the ‘nuclear mapped’ and the ‘nuclear mapped + others’ catalogs. Individual heterozygosity values based on SNPs within this region from the ‘nuclear mapped’ were calculated using PLINK (Purcell et al. 2007).