Determination of SSR-markers based genetic diversity with genetic parameters
Vernonia galamensis is a potential novel industrial crops, contains naturally occurring epoxidized oil. However, its potential values are neglected, underestimated and underexploited. In addition, it also exposed to genetic erosion. Therefore, assessment of genetic diversity with SSR markers generally in plants and particularly in V. galamensis is important for in-situ and ex-situ conservation and efficient management, for selection and improvement of the available genetic resource [16]. The SSR study showed considerable genetic diversity, the average number of alleles (3.9) detected in this study was higher than that reported by Keneni et al. [36] , an average of 3.36 alleles per locus using 155 chickpea accessions with 33 SSR markers. But lower than that reported by Olango et al. [37] and Gadissa et al. [38], who reported an average number of alleles 5.94 and 6.40 using 70 enset and 174 Plectranthus edulis accessions with 34 and 20 SSR markers, respectively. Number of effective alleles (Ne) is an important parameter to measure genetic diversity in a finite population, averaged 3.06 and Varied from 1.99 to 3.05.
Polymorphic information content (PIC) is generally used for characterization of marker polymorphism. In this study, the PIC values ranged between 0.50 (Vg-05) and 0.96 (Vg-02) with an average of 0.76, higher than that reported by Adugna et al [39] and Olango et al. [37], reported average PIC of 0.62 and 0.54, using 160 cultivated sorghum bicolor and 70 enset (Ensete ventricosum (Welw.) accessions with 12 and 34 SSR markers, respectively. The diversity parameters showed that a high level of polymorphism among the 20 SSR markers, favoring the genetic variation within V. galamensis collection. For most of the loci, expected heterozygosity (He) values were higher than that of observed heterozygosity (Ho), revealing a high homozygosity at the given loci among the accessions [38, 39].
Genetic Differentiation and Gene Flow
The (AMOVA) demonstrated that V. galamensis had low variation among population (11%). On the other hand, 67% of the total variation was attributed to genetic variability among individuals from different populations and 22% was due to variation among individuals within the same population. The result is similar to the previously reported in chickpea [36], cultivated Sorghum bicolor [38] and Ethiopia potato [39]. In addition, Fst has important in discriminating genetic differentiation among the studied populations, according to IPGRI and Cornell University [40], Fst values ranging from 0.0 to 0.05 was small in genetic differentiation, from 0.05 to 0.15 correspond to moderate, and from 0.15 to 0.25 imply large, and those greater than 0.25 was very large genetic differentiation among populations in terms of allele frequencies. In line with these, the extent of genetic differentiation among the ten populations in terms of allele frequencies measured was moderate (Fst= 0.101), which implied individuals within similar populations was significant. The same trends were reported by Adugna et al. [39], Olango et al. [37] and Gadissa et al. [38].
Genetic distance is the measure of the allelic substitutions per locus that have occurred during the separate evolution of two populations, and in this study the largest genetic distance was observed between Borena and East Harerghe (0.57) populations, while the minimum genetic distance was observed between Borena and Konso (0.24). The overall magnitude of pairwise population matrix of Nei genetic distance was relatively lower than that of Nei’s genetic identity. The genetic identity of two populations could be due to interspecific hybridization that has occurred throughout their evolution, which favors allele sharing [36].
Clustering and principal co-ordinates among Vernonia galamensis accessions
In the present study, a phylogenetic tree was constructed based on the 150 accessions of V. galamensis collected from different geographic and agro-ecological regions. V.galamensis accessions were clustered into four (4) major clusters based on the allelic frequency. Cluster 1 was characterized as the second major group, comprised of 41 accessions, the second cluster contained 25 accessions, the third cluster composed of 59 accessions, and the fourth groups contained 25 accessions that collected from different regions of origin. Generally, the cluster analysis revealed a poor clustering pattern, accessions from different collection sites was clustered together; clusters did not follow a clear pattern of geographic origins, which may imply the presence of gene flow between and within populations/regions/collection sites. Similarily, Adugna (2014) reported that 160 cultivated sorghum bicolor grouped into 3 major clusters, and pattern of the population structure was weak intra-regional similarity. Gadissa et al. (2018) also reported similarly, 12 populations of Ethiopian potatos clustered into four major clusters, and mixed clustering was observed among accessions from different geographic regions (low levels of intra-regional similarities). In contrary, Keneni et al. (2012) reported 155 chickpea grouped into 5 clusters, and the clustering pattern showed the existence of definite pattern of relationships between geographical origins and genetic diversity (high levels of intra-regional similarities).
Principal components (PC) analysis explores complex data sets and transforms a number of associated variables into a smaller number of PCs. In the present investigation, the principal component analysis revealed that the majorities of samples were placed at the center of a two-dimensional coordinate plane and roughly forms three groups with the total variation of 30.04%. This, in turn, agrees with the results of the NJ dendrogram in that there was no unique clustering among accessions from the same population/collection areas. The presence of gene flow between and within populations/collection areas, accompanied by the prevalence of inter-gene pool introgressions/hybrids between the gene pools of origin may be the most probable explanations behind the mixed clustering of accessions from different populations/collection areas together. The result in the PCoA further supported by the previous results of Adugna (2014), mixed grouping of populations was observed among accessions from different collection areas.
Populations genetic structure in Vernonia galamensis
The structure analyses of 150 V. galamensis accessions using a model based Bayesian approach based on highest ΔK Value, according to Gilbert et al. (2012) and Evanno et al. (2005) method. Three sub-populations were detected when K = 3 according to STRUCTURE results (STRUCTURE Harvester). The patterns of population structure was certainly supported by the UPGMA and PCoA analyses, however, accessions collected from the same region of origin did not often grouped entirely together within a given major groups. There was a wide admixture in structuring of V. galamensis populations, which again agreed with neighbor joining trees (Mondini et al., 2009; El-Esawi et al., 2018; Gadissa et al., 2018). Similarly, Adugna (2014) used STRUCTURE analysis of SSR markers in a study of 160 sorghum bicolor accessions and identified two (k =2) sub-groups. Most of the magnitudes identified for the SSR markers were important information for V. galamensis cultivation, breeding and genetic resource conservation.