SSR Marker Data Analysis

Several parameters of genetic diversity including the most important PIC value and the number of alleles/locus were used to assess the extent of genetic diversity available in the common bean core set. The GenAlEx software program (Peakall and Smouse, 2006) was used to calculate genetic diversity parameters, such as genetic distance, number of alleles, number of effective alleles, number of private alleles, number of common alleles, observed heterozygosity, and expected heterozygosity. The diversity parameters were calculated separately for random genomic SSR markers and genic SSR markers, as well as together on the whole population. The analysis was repeated separately by classifying the core set of 96 lines into exotic vs. local landraces and Mesoamerican vs. Andean gene pool landraces. The PIC value for each SSR was calculated manually using Microsoft Excel following Botstein et al. (1980). DARwin version 5.0 was used to calculate pair-wise genetic distances and to construct the dissimilarity matrix (Perrier et al., 2003). The dissimilarity matrix thus obtained was subjected to cluster analysis using the unweighted neighbor-joining (UNJ) method (Gascuel, 1997), followed by bootstrap analysis with 1000 permutations to obtain a dendrogram (Perrier et al., 2003; Mir et al., 2012a).

To test the genetic variation within and between cultivars of exotic and local landraces, analysis of molecular variance (AMOVA) was carried out using the software program GenAlEx (Peakall and Smouse, 2006).

Population structural analysis, which is a model-based clustering, was done to find out the number of subpopulations in our common bean population of 96 lines, using the software program STRUCTURE version 2.3.4 (Pritchard et al., 2000). We tested the number of subpopulations (K) from 1 to 10, and each was repeated three times. For each run, burn-in was set at 100,000, iteration was set at 200,000, and a model without admixture and correlated allele frequencies was used. The run with maximum likelihood was used to assign our 96 common bean lines into subpopulations. This assignment obtained through maximum-likelihood approach was further confirmed by a modified Delta-K (ΔK) method, which provides the real number of clusters/subpopulations (Evanno et al., 2005). Within a subpopulation, the genotypes with affiliation probabilities (inferred ancestry) ≥ 80% were assigned to a distinct subpopulation, and those with < 80% were treated as admixture, i.e., these genotypes seem to have a mixed ancestry from parents belonging to different gene pools or geographical origin (Mir et al., 2012b).