Genetic diversity and population structure analysis of Kala bhat (Glycine max (L.) Merrill) genotypes using SSR markers

Abstract

BackgroundKala bhat (Black soybean) is an important legume crop in Uttarakhand state, India, due to its nutritional and medicinal properties. In the current study, the genetic variabilities present in Kala bhat were estimated using SSR markers and its variability was compared with other improved soybean varieties cultivated in Uttarakhand state, India.ResultsSeventy-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.ConclusionsThe 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.

Genetic diversity analysis of soybean (Glycine max (L.) Merr.) genotypes making use of SSR markers

“…Genotype GMSE0634 (0.5) had the lowest heterozygosity value, whereas Satt001 (0.87) had the highest value. The average heterozygosity value obtained was 0.7485 and were much higher than results reported values on soybean (Wang et al, 2006;Li et al, 2008;Wang et al, 2010;Zhang et al, 2013;Hipparagi et al, 2017) . Figure 1 shows a dendrogram for the 30 soybean genotypes constructed using the unweighted pair group method with arithmetic mean (UPGMA) clustering algorithm based on 20 SSR markers.…”

“…Recently, Bisen et al (2015) reported two major clusters, which were further divided into two sub-groups in the analysis of 38 soybean genotypes using 16 SSR markers. In addition, Hipparagi et al (2017) found three distinct clusters in 75 genotypes using 21 SSR markers, and Hirota et al Wang et al (2006) and Ghosh et al (2014) observed two major clusters. A previous study on revolutionary relationship between Glycine soja and Glycine max revealed two clusters (Wen et al, 2009).…”

“…SSR markers used in this study generated 216 alleles among the 30 soybean genotypes with an average of 10.8 alleles per locus. Hipparagi et al (2017) analyzed 75 genotypes using 21 SSR markers and reported 60 alleles with an average of 2.85 alleles per locus. In another study, 38 soybean genotypes were analyzed using 16 SSR markers resulting in 51 alleles with an average of 2.22 alleles per locus (Bisen et al, 2015).…”

“…High PIC values suggested a potential to detect differences among soybean genotypes. Hipparagi et al (2017) showed an average PIC value of 0.36 with the markers, Sat554, Sat180, Sat600, and Sat478 having 4 alleles per locus each. The PIC values ranged from 0.55 to 0.66.…”

Analysis of Genetic Variations in Soybean using Simple Sequence Repeat (SSR) Markers

“…The study detected a total of 71 alleles with an average of 2.8 alleles per locus. Seventyfive soybean genotypes of soybean were analyzed for genetic variability using 21 SSR markers (Hipparagi et al, 2017). A total of 60 alleles were amplified with an average of 2.85 alleles per locus.…”