Soybean (Glycine max (L.) Merr.) can be divided into yellow, black and green beans according to seed coat color. China consumes about 10 million tons of soybeans every year, with the largest consumption of soybeans. With the improvement of people's living standards and the strengthening of health awareness, black and green beans are also gaining popularity in the market. Black bean is a soybean with black seed coat. According to the color of its cotyledons, it can be divided into green kernel black bean and yellow kernel black bean. Black beans are rich in nutrition, containing protein, fat, vitamins, trace elements and other nutrients, with anti-aging, anti-oxidation and other functions, which are good as medicine and food. Its medicinal mechanism is related to a variety of bioactive substances, especially isoflavones (Zhang et al., 2011). Green beans are soybeans with green seed coat. According to the color of its cotyledon, it can be divided into green kernel green bean and yellow kernel green bean, among which green kernel green bean consumption is larger.

Genetic diversity of germplasm resources is the basis of crop germplasm innovation and genetic improvement.  There are many reports on genetic diversity of soybean germplasm resources. Xie et al. (2003) selected 200 SSR loci from 20 linkage groups of Chinese autumn soybean and screened out 60 SSR core loci. Li et al. (2008) used 59 SSR markers to identify the local soybean varieties of 1863 in China, and each locus contained 19.7 alleles on average. Cluster analysis and population structure analysis could divide the local soybean varieties of 1863 into 7 groups, which were basically consistent with their geographical origin. Guan et al. (2010) used 46 SSR loci to study 205 soybean resources from China and 39 soybean resources from Japan, and the results showed that the genetic diversity of Chinese varieties was higher than that of Japanese varieties. Song et al. (2010) used 100 SSR loci to evaluate the population structure and genetic diversity of Chinese cultivated soybean microcore germplasm, and the results showed that Chinese cultivated soybean microcore germplasm had rich genetic diversity, which could be divided into three groups. Liu et al. (2011) analyzed 91 local soybean varieties in Shanxi Province and found that each locus had 7.14 alleles on average. Xu et al. (2014) used 44 pairs of SSR primers to analyze the genetic diversity of 26 vegetable soybean varieties (lines) in the south of Huaihe River. The results showed that 26 vegetable soybean varieties (lines) were divided into 4 groups, and the 6 pairs of SSR primers with high polymorphism were used to construct the fingerprint map.

Breeding high yield and high quality black bean and green bean varieties is of great significance to promote the development of soybean industry in China. In this study, SSR markers were used to analyze soybean resources with different seed coat colors, so as to clarify the genetic diversity level of soybean with different seed coat colors, and provide reference for genetic improvement of black and green soybean.

1 Results and Analysis

1.1 Genetic diversity of SSR markers

A total of 545 alleles with an average of 9.08 alleles were detected by 60 SSR primers from 52 soybean germplasm with different seed coat colors in Jilin Province. The detected alleles ranged from 2 to 18, Sct_188 in F linkage group was the lowest, and Satt462 in L linkage group was the highest. The genetic diversity of 60 SSR markers ranged from 0.452 9 to 0.921 0, with an average of 0.757 9. The polymorphism information content (PIC) ranged from 0.356 2 to 0.915 7, with an average value of 0.728 8 (Table 1).

Table 1 Major allele frequency, number of alleles, gene diversity, heterozygosity and polymorphism information content of 60 SSR markers in 52 soybean landraces from Jilin Province

1.2 Cluster analysis of 52 local soybean varieties resources in Jilin Province

According to the method of Nei (1972), the genetic distance between 52 soybean resources was calculated, and the cluster analysis of 52 soybean resources was carried out by UPGMA. As can be seen from the clustering diagram (Figure 1), 52 soybean resources can be divided into 5 groups. Group I included 17 materials, mainly green seed coat soybean resources, 12 materials in total, and 4 black seed coat soybean resources and brown seed coat soybean resources. Group II consisted of 8 materials, including 4 green seed coat soybean resources, 3 brown seed coat soybean resources and 1 black seed coat soybean resources. Group III included 17 materials, 10 of which were mainly yellow seed coat soybean resources, 4 brown seed coat soybean resources, 6 green seed coat soybean resources and 1 black seed coat soybean resources. Group IV consisted of 9 materials, mainly brown (tawny) seed coat soybean resources, with 5 materials, including 3 black seed coat soybean resources and 1 green seed coat soybean resources. The nine materials were all Moshidou resources with small 100-grain weight. There is only one material in group Ⅴ, which is Pingdingduludou from Jilin Zhelimu League (Now Tongliao City, Inner Mongolia), and this material is distantly related to other materials (Table 2). 

Figure 1 Dendogram of 52 Soybean landraces from Jilin Province based on SSR markers

Table 2 Comparison of genetic diversity of soybean grouped by seed coat colors

1.3 Comparison of genetic diversity among soybean resources with different seed coat color

By comparing the genetic diversity of soybean resources with different seed coat colors (Table 3), we found that black seed coat soybean resources had more abundant alleles (5.03), while yellow seed coat soybean resources had the least alleles (4.46). In terms of gene diversity, dark green seed coat (green seed coat and green cotyledon) soybean resources had the highest gene diversity (0.6 906), followed by black seed coat soybean resources (0.6 811), yellow seed coat soybean resources had the lowest gene diversity (0.6 288). The polymorphism information content of 60 pairs of SSR markers was the highest (0.6 473) in green cotyledon soybean resources, and the lowest (0.5 816) in yellow seed coat soybean resources. Therefore, among the materials used in this experiment, the genetic diversity of black seed coat and light green seed coat soybean resources was higher, while that of yellow seed coat soybean resources was lower.

Table 3  AMOVA analysis of soybean landraces from Jilin grouped by seed coat colour

1.4 Genetic differentiation and relationship of soybean resources with different seed coat color

According to the seed coat color of black, brown (tawny), dark green, light green and yellow, the 52 materials were divided into 5 groups for molecular variance analysis. The results showed that 87.32% of the variation came from the population, 1.41% from the individual, 11.27% from the interpopulation. The results indicated that there was some genetic differentiation among soybean resources with different seed coat colors.

The genetic differentiation coefficients between the different seed coat colour groups were calculated (Table 4) and the results showed that the genetic differentiation coefficients of yellow seed coat soybean, black seed coat soybean and light green seed coat soybean were 0.0 788 and 0.0 867, respectively, indicating moderate genetic differentiation. Cluster analysis was carried out based on the identification genetic differentiation coefficients of different seed coat colors (Figure 2). The results showed that the local varieties with different seed coat colors could be divided into three groups, among which black seed coat was a separate group, brown seed coat and yellow seed coat clustered into one group, and dark green seed coat and light green seed coat clustered into one group.

Table 4 Genetic differentiation of soybean landraces from Jilin grouped by seed coat color

Figure 2 Cluster analysis of soybean landraces grouped by seed coat color

2 Discussion

Xie (2002, Chinese Academy of Agricultural Sciences, pp.3-7) used 67 SSR markers to study the genetic diversity of cultivated soybean resources in five different ecological regions of China, and found that the genetic diversity of spring soybean in Northeastern China was lower than that in other ecological regions. Cui et al. (2004, Scientia Agricultura Sinica, 37(1): 15-22) used 49 pairs of SSR primers to analyze 96 Huanghuai summer soybean resources, and the average number of alleles per SSR locus was 10.6. The SSR markers used in this study were almost the same as those used in these two studies, and genetic diversity could be compared through the number of alleles. The study of Guan et al. (2007) showed that the number of alleles decreased gradually with the decrease of sample number. Therefore, in this study, 60 pairs of SSR primers were used to study the genetic diversity of 52 soybean resources in Jilin Province, and the average allelic variation of each SSR locus was 9.08, indicating that the genetic diversity of local soybean varieties in Jilin Province was relatively rich. Hirota et al. (2012) analyzed 76 local varieties of black soybeans from Tanba and nearby areas, and 3.3 alleles were detected in average SSR loci. In this study, only 10 black soybeans were analyzed, with an average of 5.03 alleles per SSR locus, indicating that the genetic diversity of black soybeans in Jilin Province was higher.

In the evolution process of wild soybean to cultivated soybean, due to artificial selection, the seeds evolved from small black, muddy film and not easy to absorb water to large yellow, muddy film free and easy to absorb water (Li, 1986). In this study, the genetic diversity of black seed coat soybean was higher, which was distributed in 4 groups. However, due to the small sample size used in this study, the genetic diversity of black bean resources needs to be further clarified by enlarging the sample size. In the National Germplasm Bank, most of the collected and preserved soybean resources are yellow soybean (47%), black bean (13%) and green bean (10%), which together account for more than 70% (Dong et al., 2004). Studies have shown that most of the resources resistant to soybean syst nematode are black bean resources, such as Beijingxiaoheidou, Harbinxiaoheidou, and Huipizhiheidou (Li et al., 2014). Therefore, it is of great significance to excavate the black bean resources to broaden the genetic basis of soybean and to breed soybean syst nematode.

In China, besides yellow soybean, black and green soybean are cultivated in a larger area and used more. The nutritional value of black and green beans is higher than that of ordinary soybeans, which has certain advantages. At present, black and green seed coat soybeans are planted in many provinces in China, but most of them are small farmers, which do not form a scale, have low output and do not have outstanding benefits. At the same time, due to the small planting area, the breeding of black and green soybeans did not attract the attention of breeders, few varieties were bred, and the yield level of black and green soybeans was much lower than that of ordinary soybeans. Therefore, it is suggested to take the main soybean variety as the parent to cross with the superior black soybean resources, select the seed coat color and cotyledon color at the same time, eliminate the bad characters such as split pod, and cultivate high yield and high quality black and green soybean varieties as soon as possible to meet the market demand.

3 Materials and Methods

3.1 Plant materials

The 52 tested soybean materials were all from the National Crop Germplasm Bank (Table 5), and were divided into five types according to seed coat color: black, brown (tawny), dark green, light green and yellow.

Table 5 Soybean materials with different seed color used in this experiment

3.2 DNA extraction and primer synthesis

DNA extraction follows the method of CTAB (Cetyl thylammonium bromide) reported by Rogers and Bendish (1985). 60 pairs of SSR primers were identified from 60 soybean SSR core loci screened by Xie et al. (2003), and primer sequences were downloaded from SoyBase (https://www.soybase.org/) for synthesis (Table 6).

Table 6 SSR markers and their primer sequences used in this experiment

3.3 SSR amplification and electrophoresis detection

SSR reaction system was 25 μL, including 10×PCR Buffer (100 mmol/L Tris-HCl (pH 8.3), 500 mmol/L KCl, 200 mmol/L MgCl₂, 0.001% geltin, 0.1% (Np-40) 2.5 μL, dNTP (2.5 mmol/L) 0.4 μL, SSR upstream and downstream primer (10 pM) 2.0 μL, template DNA (20 ng/μL) 2.0 μL, Taq DNA polymerase (2 U/μL)1.0 μL and sterilized ultrapure water 15.1 μL. Reaction procedure was as follows: pre-denaturation at 95℃ for 5 min; Denaturation at 95℃ for 45 s, annealing at 52℃~57℃ for 45 s, extension at 68℃ for 45 s, 35 cycles; Extension at 72℃ for 10 min.

PCR amplification products were detected by 6% denatured polyacrylamide gel, with 100 bp Marker as molecular weight standard. Constant power 80 W, voltage 3000 V, electrophoresis time 2 h, silver staining, observation, photography and statistical alleles of each material. A material for each allele was selected for re-amplification with 5' fluorescence labeled primers, and the amplification products were analyzed using Megabase 1 000 platform (Amersham Bioscience, Piscataway, NJ, USA). The fragment size of alleles was estimated by Wang et al. (2007). Then the fingerprint data are converted to fingerprint data with molecular weight for analysis.

3.4 Data analysis

Powermark 3.25 (Liu and Muse, 2005) was used to calculate the number of alleles (Na), major allele frequency (M), heterozygosity observed (Ho) and polymorphism information content (PIC) of each SSR locus. According to the method of Nei (1972), the genetic distance between the tested materials was calculated, and UPGMA was used for cluster analysis. Analysis of molecular variance (AMOVA) was performed to calculate the coefficient of genetic differentiation (Weir and Hill, 2002).

Authors’ Contributions

WYN completed the above experiments and the writing of the paper; QGX participated in the planting of experimental materials and the modification of the paper; ZHK and YCP participated in the data analysis; LXD and LYQ participated in the revision of the paper; WYM and DYS are the architects and principals of the project, who are fully involved in guiding the experimental design, data analysis, paper writing and modification. All authors read and approved the final manuscript.

Acknowledgements

This study was supported by Major Projects of Agricultural Science and Technology Innovation of Jilin Province (CXGC2017ZD014) and Jilin Provincial Agricultural Science and Technology Innovation Project (C82230416).

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