2.5 Genetic diversity, population assignment and admixture
The number of alleles and allele frequencies for the selected SNPs were
calculated with vcftools 0.1.16 (Petr et al. , 2011). To measure
genetic diversity, we estimated expected heterozygosity (He ) and
observed heterozygosity (Ho ). We used Arlequin v3.5 (Excoffier &
Lischer, 2010) to estimate genetic differentiation by calculating
pairwise values of differences among populations (Fst ). To
compare molecular diversity between and within populations, we used
analysis of molecular variance (AMOVA) and a hierarchical analysis of
subdivision (Excoffier, Smouse, & Quattro, 1992; Weir, 1996; Weir &
Cockerham, 1984).
We estimated population genetic structure with a Bayesian Markov Chain
Monte Carlo model (MCMC) implemented in FastStructure v1.0 (Raj,
Stephens, & Pritchard, 2014). We used the default setting with 10-fold
cross-validation on the 112 individuals of C. chuniana , testing
for subpopulations (K ) ranging from 1 to 11. The python script
Choose K in FastStructure was used to choose the optimal K , i.e.,
the value that maximizes the marginal likelihood. Results were
graphically represented and edited with Adobe Illustrator. We performed
principal component analysis (PCA) using the PCA function in SNPRelate
(Zheng et al., 2012) and visualized the results using the scripts of
Tanya Lama
(https://github.com/ECOtlama/SNPRelate.git)
in the R package.