Was done using the adegenet package67 in R to identify and describe clusters based on genetic relationships using a diploid form of genotyping data. The feature find.clusters was used to identify the number of clusters within the population. The K-means clustering decomposes the variable’s total variance into between-group and within-group components. The lowest associated BIC had defined the best number of subpopulations. The correct number of principal components (PCs) to be maintained was verified using a cross-validation feature (Xval.dapc). In this analysis, the data is divided into two sets: a training set (90 percent of the data) and a validation set (10 percent of the data). The members of each group are chosen by stratified random sampling, ensuring that at least one member of each group or population is reflected in the original data in both training and validation sets. DAPC is performed on the training set with a variable number of retained PCs, and the degree to which the analysis can accurately predict group membership of excluded individuals (those in the validation set) is used to determine the optimum number of retained PC. The sampling and DAPC procedures are repeated many times at every PC retention level. The best number of PCs that should be taken is associated with the lowest root mean square error. SNPZIP analysis was used to identify alleles with the largest contributions to form the linear discriminants and allocate the genotypes to the clusters. The coefficient of genetic differentiation among groups (Fst) was calculated using stamppFst in StAMPP package68 in R.

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