Open Access

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

Hereditas2017154:9

DOI: 10.1186/s41065-017-0030-8

Received: 8 December 2016

Accepted: 13 April 2017

Published: 27 April 2017

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.

Keywords

Soybean Genetic diversity SSR markers Seed colour

Background

Soybean (Glycine max (L.) Merr) is an important legume crop which contains 37–42% protein, 17–24% oil and 35% carbohydrates [1], that served as an excellent source of oil and protein for human consumption and animal feed. The wild and cultivated soybeans showed significant phenotypic diversity but the small reproductive difference, and they have very similar genomes in both its size and content [2]. Soybean is grown under varied climatic conditions and geographical locations in India. It occupies an area of 10.8 million hectare and accounting to a production of 11.5 million tone with the productivity of 1065 kg/ha [3]. A potential source of protein and oil makes soybeans a large share in human nutrition, and also improves soil fertility therefore; soybean is also an important crop for research [4].

In soybean, evaluation of genetic diversity is enhanced by the use of DNA markers. Researchers have studied the genetic divergence among soybean genotypes for various agronomic traits [58] with molecular markers [911]. Among different DNA markers, restriction fragment length polymorphisms (RFLPs), random amplified polymorphic DNAs (RAPDs), amplified fragment length polymorphisms (AFLPs), single nucleotide polymorphisms (SNPs) and microsatellites or simple sequence repeats (SSRs) have been extensively used in soybean, each with its own advantages and limitations [1217].

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].

Analyses of the genetic variation and population structure of Kala bhat genotypes are important for their effective conservation and utilization of the valuable genetic resource. The present study was done to estimate the genetic variability and population structure present in Kala bhat cultivated in Uttarakhand state using SSR markers, as the information on the level of diversity present in local landraces (Kala bhat) and population structure had not been studied systematically. The genetic diversity of Kala bhat was also compared with other improved soybean varieties cultivated in Uttarakhand.

Methods

Collection of plant materials

Seeds of 75 soybean genotypes were procured from NBPGR regional station located at Bhowali, Uttarakhand, India. The Seeds were sown in pots under controlled conditions inside the Green house of NBPGR, New Delhi. Black seed coat genotypes were the landraces (Kala bhat) whereas, reddish-brown and yellowish-white genotypes were improved varieties, which were introduced earlier and naturalized as the population in that agro-ecological region. The leaf samples were collected at 3–4 leaves stage for DNA isolation. The details of each genotype along with passport data, National ID, i.e. Indigenous Collection (IC) number, cultivar name, seed colour, district, region and state are given in Table 1.
Table 1

List of Soybean genotypes used in the study with their cultivar name, IC numbers, seed coat colour, district, region and state

S. No.

Cultivar name

IC numbers

Seed coat colour

District

Region

State

1

Bhatt

IC281596

Imperfect black

Bageshwar

Kumaon

Uttarakhand

2

Soybean

IC281602

Yellowish white

Bageshwar

Kumaon

Uttarakhand

3

Bhatt

IC281616

Imperfect black

Chamoli

Garhwal

Uttarakhand

4

Soybean

IC281618

Yellowish white

Almora

Kumaon

Uttarakhand

5

Soybean

IC281629

Yellowish white

Almora

Kumaon

Uttarakhand

6

Soybean

IC281644

Yellowish white

Almora

Kumaon

Uttarakhand

7

Soybean

IC281652

Yellowish white

Almora

Kumaon

Uttarakhand

8

Soybean

IC281655

Yellowish white

Almora

Kumaon

Uttarakhand

9

Soybean

IC281671

Yellowish white

Almora

Kumaon

Uttarakhand

10

Soybean

IC281684

Yellowish white

Almora

Kumaon

Uttarakhand

11

Soybean

IC281694

Yellowish white

Tehri

Garhwal

Uttarakhand

12

Kala bhatt

IC281815

Imperfect black

Almora

Kumaon

Uttarakhand

13

Bhatt

IC281838

Imperfect black

Almora

Kumaon

Uttarakhand

14

Soybean

IC281843

Yellowish white

Almora

Kumaon

Uttarakhand

15

Soybean

IC316141

Yellowish white

Bhowali

Kumaon

Uttarakhand

16

Bhatt

IC316142

Imperfect black

Bhowali

Kumaon

Uttarakhand

17

Soybean

IC316154

Yellowish white

Bhowali

Kumaon

Uttarakhand

18

Bhatt

IC316155

Imperfect black

Nainital

Kumaon

Uttarakhand

19

Bhatt

IC316163

Imperfect black

Nainital

Kumaon

Uttarakhand

20

Kala soybean

IC316170

Imperfect black

Almora

Kumaon

Uttarakhand

21

Bhatt

IC316171

Imperfect black

Almora

Kumaon

Uttarakhand

22

Kala soybean

IC316172

Imperfect black

Almora

Kumaon

Uttarakhand

23

Bhatt

IC316178

Imperfect black

Almora

Kumaon

Uttarakhand

24

Soybean

IC316181

Yellowish white

Bhowali

Kumaon

Uttarakhand

25

Soybean

IC316182

Yellowish white

Nainital

Kumaon

Uttarakhand

26

Bhatt

IC316183

Imperfect black

Nainital

Kumaon

Uttarakhand

27

Bhatt

IC316184

Imperfect black

Nainital

Kumaon

Uttarakhand

28

Bhatt

IC316186

Imperfect black

Nainital

Kumaon

Uttarakhand

29

Soybean

IC316188

Yellowish white

Nainital

Kumaon

Uttarakhand

30

Kala bhatt

IC316192

Imperfect black

Nainital

Kumaon

Uttarakhand

31

Kala soybean

IC316193

Imperfect black

Almora

Kumaon

Uttarakhand

32

Bhatt

IC317428

Imperfect black

Chamoli

Garhwal

Uttarakhand

33

Bhatt

IC317431

Yellowish white

Chamoli

Garhwal

Uttarakhand

34

Bhatt

IC317437

Imperfect black

Chamoli

Garhwal

Uttarakhand

35

Bhatt

IC317465

Reddish brown

Chamoli

Garhwal

Uttarakhand

36

Soybean

IC317578

Yellowish white

Dehradun

Garhwal

Uttarakhand

37

Soybean

IC317581

Yellowish white

Dehradun

Garhwal

Uttarakhand

38

Bhatt

IC317660

Imperfect black

Dehradun

Garhwal

Uttarakhand

39

Bhatt

IC317663

Imperfect black

Dehradun

Garhwal

Uttarakhand

40

Soybean

IC337280

Yellowish white

Pauri

Garhwal

Uttarakhand

41

Bhatt

IC338509

Imperfect black

Almora

Kumaon

Uttarakhand

42

Bhatt

IC338622

Imperfect black

Pauri

Garhwal

Uttarakhand

43

Bhatt

IC338626

Imperfect black

Nainital

Kumaon

Uttarakhand

44

Soybean

IC338702

Imperfect black

Champawat

Kumaon

Uttarakhand

45

Soybean

IC338713

Imperfect black

Champawat

Kumaon

Uttarakhand

46

Soybean

IC338717

Imperfect black

Champawat

Kumaon

Uttarakhand

47

Soybean

IC338720

Yellowish white

Champawat

Kumaon

Uttarakhand

48

Soybean

IC338729

Imperfect black

Champawat

Kumaon

Uttarakhand

49

Soybean

IC338732

Reddish brown

Champawat

Kumaon

Uttarakhand

50

Soybean

IC338749

Imperfect black

Champawat

Kumaon

Uttarakhand

51

Soybean

IC419875

Yellowish white

Chamoli

Garhwal

Uttarakhand

52

Bhatt

IC419896

Imperfect black

Chamoli

Garhwal

Uttarakhand

53

Bhatt

IC419909

Imperfect black

Chamoli

Garhwal

Uttarakhand

54

Kala bhatt

IC430009

Imperfect black

Bageshwar

Kumaon

Uttarakhand

55

Soybean

IC430038

Yellowish white

Bageshwar

Kumaon

Uttarakhand

56

Soybean

IC430041

Yellowish white

Bageshwar

Kumaon

Uttarakhand

57

Soybean

IC430063

Yellowish white

Bageshwar

Kumaon

Uttarakhand

58

Kala bhatt

IC430066

Imperfect black

Bageshwar

Kumaon

Uttarakhand

59

Soybean

IC430075

Imperfect black

Bageshwar

Kumaon

Uttarakhand

60

Soybean

IC430076

Yellowish white

Bageshwar

Kumaon

Uttarakhand

61

Black bhatt

IC436967

Imperfect black

Rudraprayag

Garhwal

Uttarakhand

62

Bhatt

IC444239

Imperfect black

Pithoragarh

Kumaon

Uttarakhand

63

Bhatt

IC444241

Imperfect black

Pithoragarh

Kumaon

Uttarakhand

64

Bhatt

IC444249

Reddish brown

Pithoragarh

Kumaon

Uttarakhand

65

Kala bhatt

IC469759

Imperfect black

Champawat

Kumaon

Uttarakhand

66

Kala bhatt

IC469767

Imperfect black

Champawat

Kumaon

Uttarakhand

67

Kala bhatt

IC469833

Imperfect black

Champawat

Kumaon

Uttarakhand

68

Soybean

IC469881

Yellowish white

Pithoragarh

Kumaon

Uttarakhand

69

Kala bhatt

IC469902

Imperfect black

Pithoragarh

Kumaon

Uttarakhand

70

Kala bhatt

IC524256

Imperfect black

Pauri

Garhwal

Uttarakhand

71

Bhatt

IC538013

Imperfect black

Nainital

Kumaon

Uttarakhand

72

Bhatt

IC538042

Imperfect black

Nainital

Kumaon

Uttarakhand

73

Bhatt

IC538070

Yellowish white

Champawat

Kumaon

Uttarakhand

74

Kala bhatt

IC548612

Imperfect black

Almora

Kumaon

Uttarakhand

75

Kala bhatt

IC548623

Imperfect black

Chamoli

Garhwal

Uttarakhand

DNA extraction

Five grams of young fresh leaves were crushed in liquid nitrogen using a motor pestle and DNA was isolated using CTAB method [21]. The DNA quality was first checked on 0.8% agarose gel and then quantified using Nanodrop (Thermo Fisher, USA). A working concentration of 10 ng/μl DNA stock was prepared for all the 75 soybean genotypes and stored at 4 °C.

Genotyping of soybean genotypes using SSR markers

Total 51 SSR markers were selected for initial screening. Gradient PCR was done for each primer with selected soybean samples to standardize the temperature for amplification (Ta). 21 SSR primers (Table 2) out of 51 showed good amplification and were considered for further study. These 21 primers were subjected to PCR analysis with 75 soybean samples.
Table 2

List of SSR primers used for genotyping of 75 soyabean genotypes along with their product size, no. of alleles amplified, gene diversity, heterozygosity and PIC value

Marker

Size (bp)

Forward primer

Reverse primer

Major allele frequency

Allele No

Gene diversity

Heterozygosity

PIC

sat005

141

TATCCTAGAGAAGAACTAAAAAA

GTCGATTAGGCTTGAAATA

0.6000

2.0000

0.4800

0.0571

0.3648

sat385

310

AATCGAGGATTCACTTGAT

CATTGGGCCACACAACAAC

0.6081

2.0000

0.4766

0.0000

0.3630

sat415

297

GCGTCTCCCTTAATCTTCAAGC

GCGTGTGACGGTTCAAAATGATAGTT

0.6197

3.0000

0.4999

0.0000

0.4104

sat577

119

CAAGCTTAAGTCTTGGTCTTCTCT

GGCCTGACCCAAAACTAAGGGAAGTG

0.6884

3.0000

0.4637

0.0145

0.4039

sat180

242

TCGCGTTTGTCAGC

TTGATTGAAACCCAACTA

0.3542

4.0000

0.7210

0.1250

0.6689

sat277

243

GGTGGTGGCGGGTTACTATTACT

CCACGCTTCAGTTGATTCTTACA

0.6923

2.0000

0.4260

0.0000

0.3353

sat422

250

ATTAGGGGAGGGGAGGTAAAAAGT

TGAAGGCCCGATATCCAAATAAA

0.5208

3.0000

0.5651

0.0139

0.4742

sat600

195

GCGCAGGAAAAAAAAACGCTTTTATT

GCGCAATCCACTAGGTGTTAAT

0.5625

4.0000

0.6189

0.1389

0.5753

sat389

232

GCGGCTGGTGTATGGTGAAATCA

GCGCCAAAACCAAAAGTTATATC

0.9400

2.0000

0.1128

0.0400

0.1064

sat411

97

TGGCCATGTCAAACCATAACAACA

GCGTTGAAGCCGCCTACAAATATAAT

0.5462

2.0000

0.4957

0.0154

0.3729

sat554

261

GCGATATGCTTTGTAAGAAAATTA

GCGCAAGCCCAAATATTACAAATT

0.5530

4.0000

0.5874

0.0758

0.5200

sat285

236

GCGACATATTGCATTAAAAACATACTT

GCGGACTAATTCTATTTTACACCAACAAC

0.9400

2.0000

0.1128

0.0133

0.1064

sat183

240

TAGGTCCCAGAATTTCATTG

CACCAACCAGCACAAAA

0.6800

2.0000

0.4352

0.0000

0.3405

sat431

250

GCGTGGCACCCTTGATAAATAA

GCGCACGAAAGTTTTTCTGTAACA

0.4857

3.0000

0.6171

0.0571

0.5409

sat247

221

GCGCCCATGTGGCTATTTCTTTATTT

GCGGATCAATAATAAACAAAGTGACAA

0.8933

2.0000

0.1906

0.0000

0.1724

sat175

163

GACCTCGCTCTCTGTTTCTCAT

GGTGACCACCCCTATTCCTTAT

0.8733

2.0000

0.2212

0.0133

0.1968

sat306

212

GCGCTTAAGGACACGGATGTAAC

GCGTCTCTTTCGATTGTTCTATTAG

0.5074

2.0000

0.4999

0.9853

0.3749

sat255

141

GCGCTTTTAGCGTCGTCTGGC

TACCCCTCTCTTATTCTTCTT

0.8493

3.0000

0.2597

0.0274

0.2324

sat584

189

GCGCCCAAACCTATTAAGGTATGAACA

GCGGGTCAGAAGATGCTACCAAACTCT

0.7719

2.0000

0.3521

0.0000

0.2901

sat420

232

GCGTATTCAGCAAAAAAATATCAA

TTATCGCACGTGTAAGGAGACAAAT

0.7800

2.0000

0.3432

0.0133

0.2843

sat478

190

CAGCCAAGCAAAAGATAAATAATA

TCCCCCACAAGAGAACAAGAAGGT

0.5423

4.0000

0.6341

0.8592

0.5886

   

Mean

0.6671

2.6190

0.4340

0.1166

0.3677

PCR reaction was set in a total volume of 10 μl containing 2 μl genomic DNA (10 ng/μl), 1 μl of 10X buffer, 0.8 μl of 25 mM MgCl2, 0.2 μl of 10 mM dNTPs, 0.2 μl of each primer (10 nmol), 0.2 μl of Taq DNA polymerase (Fermentas, Life Sciences, USA) and 5.6 μl distilled water. Amplification was performed in a thermocycler (G Storm, UK) using following program; Initial denaturation at 94 °C for 4 min followed by 36 cycles of 94 °C for 30 s, Ta for 45 s, 72 °C for 1 min and a final extension at 72 °C for 10 min. The amplified products were analyzed on 4% metaphor agarose gel for 4 h at a constant supply of 120 V. Gel pictures were recorded using gel documentation System (Alpha Imager®, USA).

Statistical analysis

SSR bands generated near expected product size were scored visually for all 75 genotypes of Soybean. The band size of amplified products was determined by comparing with 100 bp DNA ladder (Fermentas, Life Sciences, USA). The SSR bands scored in soybean genotypes was subjected to statistical analysis. Major allele frequency, gene diversity, heterozygosity and polymorphic information content (PIC) for each locus for SSR markers were calculated using Power Marker 3.25 [22]. In addition, genetic distances across the soybean genotypes were calculated using Power Marker 3.25, and a phylogenetic tree was constructed and viewed in Mega version 6 [23] . Principle Coordinate Analysis (PCoA) and Analysis of Molecular Variance (AMOVA) were performed using software GenAlEx V6.5 [24]. The model-based program, STRUCTURE 2.3.3 [25] was used to infer the population structure. For each K, three replications were run. Each run was implemented over a burn-in period of 100,000 steps with 100,000 Monte Carlo Markov Chain replicates. The membership of each genotype was run for a range of genetic clusters from the value of K = 1 to 20 by taking admixture model and correlated allele frequency into account. LnPD derived for each K was then plotted to find the plateau of the ΔK values [26]. The “Structure harvester” program was used (http: //taylor0. biology.ucla.edu) to determine the final population. Venn diagram analysis was performed to identify common accessions between cluster and population using software Venny 2.1 [27].

Results

Total 21 SSR primers were used for genetic diversity study of 75 soybean genotypes. A total of 60 alleles were amplified with an average of 2.85 alleles per locus. The number of alleles amplified per SSR primer varied from 2 to 4 (Table 2) and maximum numbers of alleles were amplified with primer Sat180, Sat600, Sat554 and Sat478 (four alleles). Gene diversity varied from 0.72 (Satt 180) to 0.11 (Satt 389 and Satt 285) with a mean value of 0.43. The heterozygosity ranged from 0.00 (Satt385, Satt415, Satt277, Satt183, Satt247, Satt584) to 0.98 (Satt 306). Major allele frequency was lowest for Satt180 (0.35) and maximum for Satt389 and Satt285 (0.94). The maximum PIC was observed for primer Satt 180 (0.66) and the minimum was observed for Satt285 and Satt389 (0.10) with a mean value of 0.36. (Table 2).

Hierarchical cluster analysis

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.
Fig. 1

NJ tree of 75 soybean genotypes based on SSR markers

Population structure

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.
Fig. 2

Population structure of 75 soybean genotypes based on SSR markers

Fig. 3

Estimation of population using LnP(D) derived Δk for k from 1 to20

Table 3

Allele-frequency divergence among populations computed using estimates of P (Model based approach)

 

POP1

POP2

POP3

POP4

POP5

POP6

POP1

-

0.1944

0.1781

0.2448

0.1634

0.1877

POP2

0.1944

-

0.1609

0.2932

0.3036

0.2782

POP3

0.1781

0.1609

-

0.1892

0.2029

0.2013

POP4

0.2448

0.2932

0.1892

-

0.2197

0.1772

POP5

0.1634

0.3036

0.2029

0.2197

-

0.2044

POP6

0.1877

0.2782

0.2013

0.1772

0.2044

-

Analysis of molecular variance (AMOVA)

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).
Table 4

Summary of AMOVA for three soybean populations

Source

df

SS

MS

Est. Var.

%

Among Pops

10

164.219

16.422

0.628

12%

Among Indiv

66

528.158

8.002

3.443

66%

Within Indiv

74

86.000

1.162

1.162

22%

Total

150

778.377

 

5.188

100%

Principal coordinate analyses (PCoA)

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.
Fig. 4

Principal Coordinate Analysis (PCoA) of 75 soybean genotypes (Populations based on seed coat colour)

Co-linearity between hierarchical cluster and model based population analysis

Since the similar pattern of a grouping of genotypes was observed in the hierarchical cluster as well as in population structure, therefore, the Co-linearity between a grouping of genotypes in hierarchical cluster and model based population structure was confirmed using Venn diagram (Fig. 5a and b). The Venn diagram (Fig. 5a) showed that, out of 32 genotypes tested; 30 genotypes were common between population 4, 5, 6 and cluster 1 (93.8%) similarly, Venn diagram (Fig. 5b) showed that 41 genotypes were common between population 1, 2, 3 and cluster 3 (91.1%). This study supports that grouping of soybean genotypes based on the hierarchical cluster and model based approaches were more than 90% similar.
Fig. 5

a Venn diagram showing co linearity between cluster 1 and pop4, 5, 5 b Venn diagram showing co linearity between cluster 3 and pop1, 2, 3

Discussion

The assessment of genetic diversity is not only important for crop improvement but also important for the efficient management and protection of the available genetic resource. The reliable and authentic results of molecular profiling have made it preferred in genetic diversity study. The molecular study is less influenced by environmental fluctuations, stands another reason for its preference in breeding [28]. Also, it is less biased when compared with estimates obtained by the coefficient of parentage and phenotypic characters [19]. Genetic diversity study has several aspects, first, to identify distinct genetic groups for the retention of germplasm [29], second, to identify genes that correspond to important phenotypic traits and genetic shifts during domestication approach, third, is to find the aspects of history and timing of domestication.

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.

Conclusions

Our study showed that Kala bhat, which has medicinal properties possess large diversity in comparison to yellow and brown seed coat soybean genotypes cultivated in Uttarakhand, India. This study confirms the hypothesis that the landraces are thought to possess rare alleles and therefore, good genetic diversity. This study also provides useful insights about the Kala bhat (black coloured soybean) among different districts of Uttarakhand and simultaneous isolation of yellow coloured soybean. Improving the genetic base requires an introduction of new alleles into the breeding program, and this can only be done by exploiting the genetic variability found in Kala bhat.

Abbreviations

AMOVA: 

Analysis of molecular variance

PCoA: 

Principal Coordinate Analysis

PIC: 

Polymorphic information content

SSR: 

Simple sequence repeats

Declarations

Acknowledgements

We are thankful to the Director, NBPGR, New Delhi, who provided facilities for this work. Financial support granted by Indian Council of Agricultural Research, New Delhi, India, is also gratefully acknowledged.

Funding

Indian Council of Agricultural Research, New Delhi, India.

Availability of data and material

All the details data and materials are given in this article.

Authors’ contributions

Conceived and designed the experiments: RS, Performed the experiments: YH, DRC, Analyzed the data: DRC, RS, Contributed reagents/materials/analysis tools: VG, Wrote the paper: RS, YH and DRC. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

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Authors’ Affiliations

(1)
Division of Plant Genetic Resources, ICAR-Indian Agricultural Research Institute
(2)
Division of Genomic Resources, ICAR-National Bureau of Plant Genetic Resources
(3)
Division of Germplasm Conservation, ICAR-National Bureau of Plant Genetic Resources

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