Yingnan Wang , Guangxun Qi , Hongkun Zhao , Cuiping Yuan , Xiaodong Liu , Yuqiu Li , Yumin Wang , Yingshan Dong

Jilin Academy of Agricultural Sciences, Changchun, 130033, P.R. China
Author    Correspondence author
Legume Genomics and Genetics, 2023, Vol. 14, No. 1   doi: 10.5376/lgg.2023.14.0001
Received: 07 Feb., 2023    Accepted: 17 Feb., 2023    Published: 22 Feb., 2023

This article was first published in Molecular Plant Breeding in Chinese, and here was authorized to translate and publish the paper in English under the terms of Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Wang Y.N., Qi G.X., Zhao H.K., Yuan C.P., Liu X.D., Li Y.Q., Qang Y.M., and Dong Y.S., 2023, Genetic diversity of soybean landraces with different seed coat color, Legume Genomics and Genetics, 14(1): 1-10 (doi: 10.5376/lgg.2023.14.0001)

In this study, the genetic diversity of 52 soybean landraces were analyzed using 60 SSR (Simple sequence repeat) markers. The results showed that a total of 545 alleles were detected by 60 pairs of SSR primers, with a range of 2-18 alleles and an average of 9.08 alleles per SSR locus. The gene diversity of 60 SSR markers was 0.4529-0.9210, with an average of 0.7579; the polymorphism information content was 0.3562-0.9157, with an average of 0.7288. The results showed that the genetic diversity of black and green soybeans was relatively high. The results of cluster analysis showed that 52 soybean landraces could be divided into 5 groups. AMOVA analysis showed that 11.27% of the genetic variation existed among groups. The Fst between yellow and black seed coat soybean, and the Fst between yellow and light green seed coat soybean were 0.0788 and 0.0867, respectively, indicating moderate genetic differentiation. This study can provide reference information for further utilization of soybean landraces and genetic improvement of black and green soybean varieties.

Soybean; SSR marker; Genetic diversity; Cluster analysis