To determine the number of populations and the assignment of samples to clusters, we used the Bayesian clustering analysis implemented in STRUCTURE 3.2 (Pritchard et al., 2000) with both the admixed and correlated allele frequency models. Twenty independent runs were conducted for each number of clusters (K) ranging from 1 to 9 using 50,000 iterations following a burn‐in length of 5,000 iterations. The most likely number of clusters was determined based on the log‐likelihood score of each K and the ΔK method (Evanno et al., 2005) using its implementation in the CLUMPAK software (Kopelman et al., 2015). For visualization, bar plots were generated with the Structure plot V2.0 interactive web application (Ramasamy et al., 2014).

Next, a neighbor‐joining tree was constructed using the genetic information obtained from all accessions in addition genetic data generated for 8 rootstock varieties (Vitis rupestris) which were included as an outgroup. The dendrogram was generated from a Bruvo's genetic distance (Bruvo et al., 2004) calculated with 1,000 bootstrap replicates using the R package poppr (Kamvar et al., 2014).

Analysis of genetic variation within and between the identified clusters using the analysis of molecular variance (AMOVA) and PCoA (principal coordinate analysis) was calculated as implemented in GenAlex v6.501 (Peakall & Smouse, 2012). The level of significance was computed based on 999 permutations. In addition, population genetics statistics were calculated for each cluster and included the fixation index (F), the observed heterozygosity (Ho), unbiased expected heterozygosity (He), the number of effective alleles (Ne), Hardy–Weinberg equilibrium test (HWE), and the number of private alleles, extent of gene flow (Nm), and genetic differentiation (FST) (Meirmans & Hedrick, 2011) using the GenAlex v6.501 software (Peakall & Smouse, 2012). Estimation of null allele frequencies was conducted with package “PopGenReport” (Adamack & Gruber, 2014) in R. A heatmap plot for the pairwise FST matrix was generated using “ggplot2” package (Wickham, 2016) in R.

R scripts used in this study are provided as Supplementary R script file.

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