Adaptation Analysis
Nucleotide diversity (π) was calculated for the four groups using a 100
kb nonoverlapping sliding window by using VCFtools (Danecek et al.,
2011). Additionally, the fixation index (FST) and
nucleotide divergence (DXY) between each of the four
groups were calculated by PIXY (Danecek et al., 2011) in 100 kb sliding
windows. Ninety-five percent confidence intervals for mean
FST and DXY values were obtained by
bpnreg packages in R. PopLDdecay (C. Zhang, Dong, Xu, He, & Yang, 2019)
software was applied to compute linkage disequilibrium (LD) decay among
different groups and chromosomes separately. To make the following
analyses easier to complete, we employed PLINK v.1.9 (Purcell et al.,
2007) to filter SNPs with the following parameters: –indep-pairwise
50 10 0.2, 143,318 SNPs were kept and phased using Beagle v.3.3.2
(Browning & Browning, 2007). We ran Mantel tests to test for
correlations between FST and geographic distance to
assess
isolation
by distance (IBD) in the R package vegan (Oksanen et al., 2013). The
QST were calculate using R package
‘QSTFSTComp’ (Gilbert & Whitlock,
2015).
To explore the effect of adaptation on 11 quantitative traits, we used
the single-phenotype
QST-FSTtest with the R package
‘QSTFSTComp’
(Gilbert & Whitlock, 2015). If QST >
FST, the differentiation of traits is the major effect
of divergent selection and shows local adaptation. We used the half-sib
dam model and 10000 resampling steps for each
QST-FST analysis. Taking into account
the recent divergence between NE and EL lineages, we compared the levels
of FST between CN and NW lineages to identify candidate
loci under natural selection from 143,318 SNPs by BayeScan v.2.1 (Foll
& Gaggiotti, 2008) software with default parameters, and PGDSpider
(Lischer & Excoffier, 2012) was used to produce an input file for
BayeScan. SNPs with a q value lower than 0.05 were considered
potentially selected loci. Fisher’s exact test was used to perform
significance in GO enrichment analysis on positively selected genes by
the clusterProfiler package (Yu, Wang, Han, & He, 2012) in R.