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).