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Results: Seventy-five genotypes cultivated in different districts of Uttarakhand were collected, and molecular analysis was done using 21 SSR markers. A total of 60 alleles were amplified with an average of 2.85 alleles per locus. The mean value of gene diversity and PIC was estimated to be 0.43 and 0.36, respectively. The unrooted phylogenetic tree grouped soybean genotypes into three major clusters, where, yellow seed coat (improved varieties) genotypes were grouped in one cluster, while reddish brown (improved varieties) and Kala bhat showed intermixing. Population structure divided the soybean genotypes into six different populations. AMOVA analysis showed 12% variance among the population, 66% variance among individual and 22% variance was observed within individuals. Principal Coordinate Analysis (PCoA) also showed that yellow seed coat genotypes were grouped in one cluster, whereas, the Kala bhat showed scattered distribution and few genotypes of Kala bhat showed grouping with red and yellow genotypes.
Conclusions: The different genetic diversity parameters used in the present study indicate that Kala bhat genotypes were more diverse than the yellow seed coat and brown seed coat colour genotypes. Therefore, Kala bhat genotypes can be a good source for the soybean breeding programme due to its better genetic diversity as well as its medicinal properties.
Seventy-five genotypes cultivated in different districts of Uttarakhand were collected, and molecular analysis was done using 21 SSR markers. A total of 60 alleles were amplified with an average of 2.85 alleles per locus. The mean value of gene diversity and PIC was estimated to be 0.43 and 0.36, respectively. The unrooted phylogenetic tree grouped soybean genotypes into three major clusters, where, yellow seed coat (improved varieties) genotypes were grouped in one cluster, while reddish brown (improved varieties) and Kala bhat showed intermixing. Population structure divided the soybean genotypes into six different populations. AMOVA analysis showed 12% variance among the population, 66% variance among individual and 22% variance was observed within individuals. Principal Coordinate Analysis (PCoA) also showed that yellow seed coat genotypes were grouped in one cluster, whereas, the Kala bhat showed scattered distribution and few genotypes of Kala bhat showed grouping with red and yellow genotypes.
The different genetic diversity parameters used in the present study indicate that Kala bhat genotypes were more diverse than the yellow seed coat and brown seed coat colour genotypes. Therefore, Kala bhat genotypes can be a good source for the soybean breeding programme due to its better genetic diversity as well as its medicinal properties.
Black seed coat soybean, locally known by different names such as Bhat, Bhatmash and Kala bhat is grown in Kumaon and Garhwal region and in frontiers of Uttarakhand state [18]. In Uttarakhand, these soybean varieties are commonly known as Kala Bhat. It is believed that soybean was introduced by traders via Myanmar from Indonesia. As a result, it has been traditionally grown on a small scale in states like Himachal Pradesh, Kumaon and Garhwal hills of Uttarakhand, East Bengal, Khasi hills and small parts of central India. Kala bhat is also considered as the treasure trove of different medicinal properties. Kala bhat and its products are the richest sources of iso-flavones. Kala bhat, in Uttarakhand is grown in 5734 ha area, with a production and productivity is 5636 tonne and 9.82 q/ha, respectively (Anonymous, 2011). A traditional cultivar of Kala bhat is much low yielder than normal soybean varieties hence this can be improved further by crossing with diverse exotic as well as indigenous germplasm. Morphological characterization of 21 soybean cultivars was done by Oda et al. [19] and 24 Kala bhat genotypes was done by Bhartiya et al. [20].
Soybean genotypes were grouped into three major clusters (Fig. 1). Kala bhat got distributed in all the three clusters whereas, brown seed coat colour soybean got grouped only into cluster3 that was mainly dominated by Kala bhat, which shows that there is mixing up of the genetic background between them. However yellow seeded soybeans were grouped into only cluster1 but five genotypes (IC316142, IC430009, IC316172, IC316192 and IC317660) of Kala bhat also grouped with yellow seed coat colour genotypes in cluster1. This hierarchical cluster analysis showed that Kala bhat is sharing genetic similarity with both, yellow and brown seed coat colour soybean, but, there is no sharing of genetic similarity between brown and yellow seed coat colour soybeans.
The 75 soybean genotypes got distributed into six populations (Figs. 2 and 3). Seven pure and five admix individuals were present in population1; twelve pure and eight admix individuals were in population 2; five pure and seven admix individuals in population 3; eight pure and four admix individuals in population 4, 10 pure and three admix individuals in population 5, and three pure and three admix individuals in population 6. Mean Fst value for pop1, pop2, pop3, pop4, pop5 and pop6 were 0.464, 0.498, 0.332, 0.608, 0.345, and 0.688 respectively with a mean alpha value of 0.058. The allele frequency divergence among populations is given in Table 3. Average distances (expected heterozygosity) between individuals in the same cluster were between the range of 0.148 for cluster 6 and 0.378 for cluster 5. Population 1, 2 and 3 were dominated by Kala bhat and brown seed coat colour genotypes (highlighted with brown box) got distributed in all the three populations (Fig. 2) while, population 4, 5 and 6 were dominated by yellow seed coat colour genotypes (Fig. 2). Population structure based grouping supports the hierarchical cluster analysis and genotypes grouped in cluster1 corresponds to pop4,5 and 6 while genotypes grouped in cluster3 corresponds to pop1, 2 and 3.
Analysis of molecular variance (AMOVA) of soybean genotypes based on seed coat color was performed to analyze the distribution of genetic diversity between and within the populations. AMOVA analysis showed 12% diversity among populations, 22% diversity within individuals and a maximum of 66% diversity among individuals (Table 4).
Principal coordinate analyses (PCoA) showed two distinct groups represented by Kala bhat and yellow seed coat colour soybean respectively. The brown seed coat colour soybean got distributed in both the groups. The yellow seed coat colour soybean was confined to one group, a similar pattern was also observed during the cluster analysis. The first three axes of PCoA have explained a cumulative percent variation of 33.15% (Fig. 4). This shows large diversity exists in the genotypes studied.
The SSR primers used in the present study amplified an average number of 2.61 alleles per locus with a gene diversity value of 0.43. Li et al. [30] reported 19.7 alleles per locus with gene diversity value of 0.72 during characterization of 1863 Chinese soybean landraces with 59 SSR markers. Similarly, Guan et al. [31] reported 16.2 alleles per locus with a gene diversity of 0.84 while comparing the genetic diversity of 205 Chinese landraces and also Liu et al. [32] reported 7.14 alleles per locus in his study on 91 Shaanxi soybean landraces. These reports show a higher number of alleles per locus in comparison to present study. Doldi et al. [33] reported two to six alleles per locus during characterization of 18 soybean cultivars using 12 microsatellite primers and Tantasawat et al. [34] reported 4.82 alleles per locus. Therefore, allelic richness (average number of alleles per locus) is an effective index for diversity evaluation but it is largely dependent on the sample size [35]. Hence to improve the allelic richness more landraces needs to be introduced into the system thus, enhancing genetic diversity. The mean PIC value obtained in the present study was 0.36, where sat180, sat600, sat554 and sat478 are having 4 alleles per locus and PIC value between 0.55-0.66. These markers with high PIC values become informative for distinguishing among the soybean genotypes. Similar values have been reported by Zhang et al. [36] (0.38), Hisano et al. [37] (0.40), Wang et al. [35] (0.50) and Kim et al. [38] (0.87) with good genetic diversity in their set of samples. As a self -fertilizing crop soybean is expected to have low heterozygosity than hybrid crops [36], here we got low heterozygosity (0.11) much lower than the value reported by Zhang et al. [36] (0.46). Li et al. [30] reported heterozygosity of 0.014 in grain soybean whereas, 0.069 and 0.446 were reported in wild soybean by Liu et al.[39] and Wang et al. [40] respectively. Gene diversity observed in the present study was 0.43; this low level of gene diversity may be ascribed to the emphasis on direct introductions from introduced germplasm and single cross hybrids in the soybean breeding programs. Therefore, diverse germplasm needs to be introduced for more genetic variability [41] Narvel et. al. [14] analyzed 79 elite soybean cultivars with 74 SSR markers showing a low value of gene diversity. Gene diversity reported by Li et al. [42] Wang et al. [43] and Hudcovicova and Kraic [44] showed a substantially higher -value i.e. 0.77, 0.80 and 0.71 respectively on different sets of soybean genotypes. Hierarchical clustering divided the soybean landraces into three distinct clusters, and yellow seed coat colour soybean got grouped into one cluster. In this study, seed coat colour based grouping was more logical than grouping based on geographical location. The analysis based on geographical location showed mixing of genotypes from one location to another location and indicated frequent seed exchange across the geographical location. But when cluster analysis was done based on seed coat colour, the yellow seed coat colour genotypes were grouped together except one genotype(IC-469881). This shows that yellow seed coat colour genotypes are a recent introduction into this area, and breeders have not utilized yellow seed colour genotypes in the breeding programs. Tantasawat et al. [34] reported four major clusters in 25 soybean genotypes analysed by 11 SSR markers. Wang et al. [40] obtained two groups with five wild soybean population assessed by ten SSR markers and Wen et al. [45] also reported two clusters while studying the evolutionary relationship among ecotypes of Glycine max and G. soja in China. Ghosh et al. [46] reported two clusters and six sub clusters while studying 32 soybean cultivars with 10 SSR markers. Hirota et al. [47] studied black soybean landraces of Tanba region and got two distinct clusters, where as three clusters were obtained by Kondetti et al. [48] while studying 55 Indian Soybean varieties. Population structure divided the soybean genotypes into six different populations. Qiu et al. [49] reported three populations as wild, semi wild and cultivated soybean from Yangstee region whereas; two populations were obtained by Chung et al. [50] in Korean wild and cultivated accessions of soybean and Gyu-Taek Cho et al. [51] reported three populations in Korean land races. PCoA analysis also showed consistent results when seen in terms of a grouping of landraces in cluster analysis. AMOVA showed 12% variance between populations, 22% variance within individuals and 66% variance among individuals. Since soybean is a self pollinated crop, therefore, less variation within individual and more variation among varieties/land races are expected. The analysis done by Venn diagrams showed that, more than 90% co-linearity between cluster 3 and pop1, pop2, pop3 and between cluster 1 and pop4, pop5, pop6. This study proves that SSR based genotyping is a better way to study the genetic diversity in soybean because grouping done by the Hierarchical method and population structure method were more than 90% similar.
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