Genetic Diversity and Population Structure Analysis of Kala Bhat ( (L.) Merrill) Genotypes Using SSR Markers

Overview

Journal Hereditas

Specialty Genetics

Date 2017 May 3

PMID 28461811

Citations 7

Authors

Yegappa Hipparagi

Rakesh Singh

Debjani Roy Choudhury

Veena Gupta

Affiliations

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Abstract

Background: Kala 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.

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.

Citing Articles

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Genetic Diversity Analysis of Soybean Collection Using Simple Sequence Repeat Markers.

Zatybekov A, Yermagambetova M, Genievskaya Y, Didorenko S, Abugalieva S Plants (Basel). 2023; 12(19).

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PMID: 34894334 PMC: 8863763. DOI: 10.1007/s11033-021-07030-4.

References

Pritchard J, Stephens M, Donnelly P . Inference of population structure using multilocus genotype data. Genetics. 2000; 155(2):945-59. PMC: 1461096. DOI: 10.1093/genetics/155.2.945. View

Priester J, Ge Y, Mielke R, Horst A, Moritz S, Espinosa K . Soybean susceptibility to manufactured nanomaterials with evidence for food quality and soil fertility interruption. Proc Natl Acad Sci U S A. 2012; 109(37):E2451-6. PMC: 3443164. DOI: 10.1073/pnas.1205431109. View

Kim K, Chirumamilla A, Hill C, Hartman G, Diers B . Identification and molecular mapping of two soybean aphid resistance genes in soybean PI 587732. Theor Appl Genet. 2014; 127(5):1251-9. DOI: 10.1007/s00122-014-2296-9. View

Li Y, Guan R, Liu Z, Ma Y, Wang L, Li L . Genetic structure and diversity of cultivated soybean (Glycine max (L.) Merr.) landraces in China. Theor Appl Genet. 2008; 117(6):857-71. DOI: 10.1007/s00122-008-0825-0. View

Maughan P, Saghai Maroof M, Buss G, Huestis G . Amplified fragment length polymorphism (AFLP) in soybean: species diversity, inheritance, and near-isogenic line analysis. Theor Appl Genet. 2013; 93(3):392-401. DOI: 10.1007/BF00223181. View

Wang Y, Zhang X, Fan S . Genetic diversity of wild soybean populations in Dongying, China, by simple sequence repeat analysis. Genet Mol Res. 2015; 14(3):11613-23. DOI: 10.4238/2015.September.28.13. View

Chung W, Jeong N, Kim J, Lee W, Lee Y, Lee S . Population structure and domestication revealed by high-depth resequencing of Korean cultivated and wild soybean genomes. DNA Res. 2013; 21(2):153-67. PMC: 3989487. DOI: 10.1093/dnares/dst047. View

Tamura K, Stecher G, Peterson D, Filipski A, Kumar S . MEGA6: Molecular Evolutionary Genetics Analysis version 6.0. Mol Biol Evol. 2013; 30(12):2725-9. PMC: 3840312. DOI: 10.1093/molbev/mst197. View

Peakall R, Smouse P . GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research--an update. Bioinformatics. 2012; 28(19):2537-9. PMC: 3463245. DOI: 10.1093/bioinformatics/bts460. View

10.

Vinu V, Singh N, Vasudev S, Yadava D, Kumar S, Naresh S . Assessment of genetic diversity in Brassica juncea (Brassicaceae) genotypes using phenotypic differences and SSR markers. Rev Biol Trop. 2014; 61(4):1919-34. View

11.

Qiu J, Wang Y, Wu S, Wang Y, Ye C, Bai X . Genome re-sequencing of semi-wild soybean reveals a complex Soja population structure and deep introgression. PLoS One. 2014; 9(9):e108479. PMC: 4181298. DOI: 10.1371/journal.pone.0108479. View

12.

Evanno G, Regnaut S, Goudet J . Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005; 14(8):2611-20. DOI: 10.1111/j.1365-294X.2005.02553.x. View

13.

Hisano H, Sato S, Isobe S, Sasamoto S, Wada T, Matsuno A . Characterization of the soybean genome using EST-derived microsatellite markers. DNA Res. 2008; 14(6):271-81. PMC: 2779906. DOI: 10.1093/dnares/dsm025. View

14.

Bisen A, Khare D, Nair P, Tripathi N . SSR analysis of 38 genotypes of soybean (Glycine Max (L.) Merr.) genetic diversity in India. Physiol Mol Biol Plants. 2015; 21(1):109-15. PMC: 4312325. DOI: 10.1007/s12298-014-0269-8. View

15.

Liu K, Muse S . PowerMarker: an integrated analysis environment for genetic marker analysis. Bioinformatics. 2005; 21(9):2128-9. DOI: 10.1093/bioinformatics/bti282. View

16.

Keim P, Beavis W, Schupp J, Freestone R . Evaluation of soybean RFLP marker diversity in adapted germ plasm. Theor Appl Genet. 2013; 85(2-3):205-12. DOI: 10.1007/BF00222861. View

17.

Soliman K, Jorgensen R, Allard R . Ribosomal DNA spacer-length polymorphisms in barley: mendelian inheritance, chromosomal location, and population dynamics. Proc Natl Acad Sci U S A. 1984; 81(24):8014-8. PMC: 392284. DOI: 10.1073/pnas.81.24.8014. View

18.

Hirota T, Sayama T, Yamasaki M, Sasama H, Sugimoto T, Ishimoto M . Diversity and population structure of black soybean landraces originating from Tanba and neighboring regions. Breed Sci. 2012; 61(5):593-601. PMC: 3406802. DOI: 10.1270/jsbbs.61.593. View

19.

Zhang G, Xu S, Mao W, Hu Q, Gong Y . Determination of the genetic diversity of vegetable soybean [Glycine max (L.) Merr.] using EST-SSR markers. J Zhejiang Univ Sci B. 2013; 14(4):279-88. PMC: 3625524. DOI: 10.1631/jzus.B1200243. View