*4.3. Analysis of Genotypic Data and Genetic Diversity*

Pairwise genetic distances between accessions were calculated using the Powermarker software package ver.3.25 by Nei et al. [52] D<sup>A</sup> distance. The dendrogram was constructed on the basis of the distance matrix. We estimated the similarity between genotypes for each accession by awarding a score to each microsatellite (i.e., 0 when an allele was absent, 1 when the allele was present). The cluster analysis was carried out using the unweighted pair group method using arithmetic average (UPGMA) and the dendrogram resulting from these calculations was plotted using MEGA 6.0 to visualize and edit the dendrogram. The basic summary statistics for biallelic data were calculated using the POWERMARKER software package version 3.25 [76]. The polymorphism information content (PIC) of an SSR marker was determined according to the method described by Anderson et al. [77] based on the allele frequency of all genotypes.

$$\text{PIC} \quad = \begin{array}{c} 1 \ - \end{array} \sum\_{i=1}^{n} \text{pi2} \tag{1}$$

where *Pij* is the frequency of the allele for locus *i* and the summation covered *n* patterns.

A PIC value of 1 indicates that the marker can differentiate each line, and 0 indicates a monomorphic marker. The informative potential of a marker is high if its PIC value is more than 0.5, moderate if its PIC is between 0.5 and 0.25, and only slightly informative if its PIC value is below 0.25. Other statistics calculated were the number of alleles and availability and gene diversity for each marker. Further analysis of genetic structure was done by means of Principal co-ordinate analysis (PCOA) using XLSTAT, 2014 [78] and a three-dimensional diagram was constructed. Dominant data (0, 1 binary data) were used for the PCoA analysis.
