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Article

Assessment of Simple Sequence Repeat (SSR) Markers Derived from Whole-Genome Sequence (WGS) of Mungbean (Vigna radiata L. Wilczek): Cross-Species Transferability and Population Genetic Studies in Vigna Species

by
Pawan Saini
1,2,
Asmita Sirari
1,
Belaghihalli N. Gnanesh
3,*,
Kamalpreet Singh Mandahal
1,
Navkiran Kaur Ludhar
4,
Sharon Nagpal
5,
S. A. H. Patel
6,
Javed Akhatar
1,
Pooja Saini
7,
Aditya Pratap
8,
Tejinderjit Singh Bains
1 and
Inderjit Singh Yadav
9
1
Department of Plant Breeding and Genetics, Punjab Agricultural University (PAU), Ludhiana 141 004, Punjab, India
2
CSB-Central Sericultural Research & Training Institute (CSR & TI), Pampore 192 121, Jammu-Kashmir, India
3
Department of Genetics and Plant Breeding, Sampoorna International Institute of Agri Science & Horticultural Technology, Maddur 571 433, Karnataka, India
4
Department of Plant Pathology, Punjab Agricultural University (PAU), Ludhiana 141 004, Punjab, India
5
Department of Microbiology, School of Bioengineering and Biosciences, Lovely Professional University, Phagwara 144 411, Punjab, India
6
Department of Vegetable Science, Punjab Agricultural University (PAU), Ludhiana 141 004, Punjab, India
7
Department of Plant Breeding, Genetics and Biotechnology, Dr. Khem Singh Gill Akal College of Agriculture, Eternal University (EU), Baru Sahib 173 101, Himachal Pradesh, India
8
ICAR-Indian Institute of Pulses Research (IIPR), Kanpur 208 024, Uttar Pradesh, India
9
School of Agricultural Biotechnology, Punjab Agricultural University (PAU), Ludhiana 141 004, Punjab, India
*
Author to whom correspondence should be addressed.
Horticulturae 2024, 10(1), 34; https://doi.org/10.3390/horticulturae10010034
Submission received: 15 November 2023 / Revised: 22 December 2023 / Accepted: 25 December 2023 / Published: 28 December 2023
(This article belongs to the Section Genetics, Genomics, Breeding, and Biotechnology (G2B2))
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Abstract

:

Simple Summary

Whole-genome sequencing (WGS)-based mungbean SSR markers have a high rate of cross-species transferability and are very useful in mungbean improvement programs.

Abstract

The genus Vigna is pan-tropical, having more than 200 species with many desirable economically important traits. This study aimed to validate the in silico polymorphism of whole-genome-sequence-developed mungbean-specific SSR markers and their transferability among different Vigna species. The present study utilized a set of 200 SSR markers developed from the whole-genome sequence of mungbean and validated them using a diversity panel of 25 accessions that belong to 13 Vigna species. Out of 200 SSR markers, 130 markers (65%) were polymorphic across the various Vigna species, and the number of alleles amplified varied from 7 to 24. The SSR markers showed more than 90 percent transferability across the different Vigna species accessions. Utilizing allelic data, the 25 Vigna accessions were clustered into three groups using the unweighted pair group method with arithmetic mean (UPGMA). The two integral coalitions explained 50.79 and 15.42% of the total variance. The principal coordinate analysis (PCA) biplot graph and UPGMA-based neighbor-joining clustering diagram showed a similar pattern of Vigna accession distribution. A population structure assessment grouped the cultivated and wild species accessions into two sub-populations based upon a maximum delta K value of 144.79, which drew a sharp peak at K = 2. The estimated marker parameters, such as the polymorphic information content (0.09–0.84), marker index (0.091–3.342), and effective multiplex ratio (1.0–4.0), suggested their adequacy for several genetic studies, such as parental selection, hybrid testing, genetic mapping, and marker-aided breeding programs, for the genetic enhancement of species belonging to the Vigna genus.

1. Introduction

Among the legumes, Vigna is an agriculturally important taxon. The genus Vigna belongs to the subgenus Ceratotropis, tribe Phasleoleae, and family Fabaceae and includes more than 150 Vigna species. The majority of the members belonging to Vigna are wild species from the Asian and African continents [1,2]. The domesticated Vigna members are mungbean, urdbean, ricebean, adzuki bean, cowpea, etc. Among Vigna, mungbean (Vigna radiata L. Wilczek) is a major pulse popularly referred to as green gram, golden gram, Oregon pea, chickasawpea, and mung [3]. Mungbean is used as a vegetable in the form of green pods and sprouts in salads. It is an autogamous crop with 2n = 2x = 22 chromosomes that span about 574 Mega base pairs of genetic material [4]. The small genome size makes it a suitable model crop for studying evolutionary and genetic diversity [5].
In the current context, cultivating mungbean varieties that exhibit quick maturation, are insensitive to the photoperiod, display stability, possess high resistance to diseases and insect pests, and demonstrate a high yield potential offers an opportunity to incorporate mungbean as a catch crop in cereal cropping systems, such as rice–wheat–mungbean. The diverse edaphoclimatic conditions of India are well suited for sustainable food production and food security [6]. The further expansion of mungbean cultivation is linked to the pace of genetic improvement, which depends upon genetic and genomic resources. Mungbean is lagging in genomic research and the application of genomics-assisted breeding techniques as compared to other legumes. To date, 18 genetic linkage maps are available for mungbean based on Restriction Fragment Length Polymorphism (RFLP), Randomly Amplified Polymorphic DNA (RAPD), Sequence-Tagged Sites (STSs), and simple sequence repeats (SSRs) [7]. Several researchers [8,9,10] developed mungbean-specific DNA markers, i.e., genic SSRs, and the markers used to amplify the mungbean genome are mostly specific to other legume crops. SSR markers from Vigna species (cowpea, common bean, adzuki bean) and other genera, such as soybean, have been applied in mungbean. Adzuki bean and common bean SSRs showed high rates of amplification of 72.7% and 60.6% [11,12]. Unigene-based SSR markers showed a high transferability rate of 88% in different Vigna species [13].
In mungbean, the marker transferability rates varied from 1.5% [14] to 92.85% [15]. Somta et al. [8] reported 5.7% polymorphism using 200 SSRs on 17 mungbean accessions. About 70% of adzuki bean primers successfully amplified the mungbean genome [16]. The SSR transferability study conducted by Gupta and Gopalakrishnan [13] tested 65 SSR markers on 16 accessions of eight Vigna species, which showed an 84.6% transferability rate. In another study by Dikshit et al. [2], 78 SSR markers of adzuki bean across 18 accessions of six Vigna species showed high marker transferability % of 60.97%, 87.80%, 62.2%, 91.8%, 78%, and 80% in V. radiata, V. mungo, V. unguiculata, V. umbellata, V. mungo var. slyvestris, and V. trilobata, respectively. Recently, Choudhary et al. (2022) [17] reported 100% SSR transferability in 70 elite mungbean cultivars belonging to the CEDG, VM, and BM series. Haddoudi et al. [18] used 200 SSR markers of a Medicago truncatula Gaertna legume pasture crop for genetic diversity assessment in five populations of M. polymorpha L., and 50 markers showed amplification, with a transferability rate of 25%. The high rate of marker transferability indicates the conservation of microsatellite sequences in the genus Vigna during evolution. Marker transferability provides the opportunity to understand crop evolution and speciation through comparative mapping, synteny, and collinearity studies.
SSR markers are particularly popular in laboratories with limited resources [19]. The general methodology of SSR development consists of three steps, i.e., the preparation of an SSR library, PCR, and sequencing. This process is very laborious and expensive. To date, several workers [8,20,21,22,23,24,25,26,27,28] have developed SSR markers, but still, limited SSR markers are available in mungbean. This has further limited the molecular mapping of many desirable characteristics of stress resistance in the crop. Trait-based mapping is urgently required for mungbean to strengthen the molecular-marker-based improvement program. With the help of next-generation sequencing (NGS) technologies, it has become possible to develop and identify large numbers of SSRs and other markers at low prices. NGS technologies, coupled with bioinformatics approaches, can massively increase the number of SSRs available for carrying out genetic investigations in understudied and economically important crops such as mungbean. The whole-genome sequences of mungbean and urdbean varieties (ML 267 and Mash 114) were assembled at Punjab Agricultural University (PAU), Ludhiana, and an aggregate of 443,867 SSR markers were discovered in V. radiata (cv. ML267) and V. mungo (cv. Mash 114), of which 410,282 were found to be polymorphic in silico.
In the present study, we developed 250 WGS-based SSR markers specific to the mungbean genome. These developed markers were evaluated for their cross-species transferability with 25 accessions of 13 Vigna species for their utility in elucidating the underlying genetic diversity in the genus Vigna, hybrid testing, genetic mapping, and marker-assisted breeding programs. The objective of our study is to test the in silico polymorphism of WGS-developed SSR markers and their cross-species transferability for future mungbean improvement programs.

2. Materials and Methods

2.1. Plant Material

Phenotypically diverse accessions from different geographic regions were included to enhance the likelihood of detecting polymorphic marker loci. The diversity panel, comprising 25 Vigna accessions across 13 species, was procured from ICAR-Indian Institute of Pulse Research (IIPR), Kanpur, Uttar Pradesh (Table 1). The present work was carried out in the experimental area of the Department of Plant Breeding and Genetics, Punjab Agricultural University (PAU), Ludhiana, located at 244 m above mean sea level (AMSL) (latitude: 30°90′ N; longitude: 75°85′ E) in a semi-arid climate zone. Each accession was sown in a single line in a bed of 3 m in length with a spacing of 40 cm between rows during the kharif season in 2019.

2.2. DNA Extraction and Quantification

Total genomic DNA was isolated from fresh young and tender leaves of each accession by employing the standard CTAB method [29]. RNA contamination was removed with RNase at 37 °C for 45 min. The quantity and quality of DNA were examined with agarose gel (0.8%) with lambda DNA as a reference. The integrity and quantity of DNA based on agarose gel were optimized to 20 ng/µL and used for the amplification process.

2.3. SSR Marker Design

The whole-genome contig assembly and scaffolding of WGS-based SSR markers were carried out by using CLC assembler (source: https://clcbio.com accessed on 14 March 2018) at default parameter settings and SOAP de novo [30], respectively, at the School of Agricultural Biotechnology, Punjab Agricultural University, Ludhiana, and further used for SSR mining and the identification of in silico polymorphism through MIcroSAtelllite (MISA) with default parameters, such as 10 repeating units for mononucleotides, 6 repeating units for dinucleotides, and 5 repeating units for tri-, tetra-, penta-, and hexanucleotides [31]. A total of 218,508 and 225,359 SSRs were detected from 471,725 and 444,059 sequences in V. mungo cv. Mash114 and V. radiata cv. ML267, respectively (Table 2) [32]. Using these SSR sequences, 250 in silico polymorphic mungbean SSR primers were mapped on urdbean contigs based upon a 20–50 bp distance between mungbean and urdbean using e-PCR [33]. e-PCR has two steps: hashing and primer alignment. For hashing, the number of bases used was 12, and the number of mismatches allowed in primer alignment was 1. The amplicon size of the V. radiata and V. mungo were compared for the identification of in silico primers, which were further filtered using the following parameters: mismatch = 0, gap = 0, >10 amplicon length difference, and removal of duplicates. In silico polymorphism was discovered by performing e-PCR on SSR markers obtained from V. radiata cv. ML267 and V. mungo cv. Mash114. Descriptive information, including the strand, marker type, repeat numbers, contig, amplicon size, GC content, start and end positions, and chromosome numbers, were obtained for each SSR marker. The primers were designed using Primer3 software with default parameters: melting temperature of 55–65 °C, guanine–cytosine (GC) content of 40–70%, primer size of 18–27 bp length, and product size of 150–280 bp. A series of 250 dinucleotide whole-genome sequence (WGS)-based SSR markers were synthesized by Promega Biotech, and of these, 200 SSR markers were used for the validation of WGS-derived polymorphism, as well as their transferability to other Vigna species (Table S1, Figure 1).

2.4. SSR Validation

The PCR reaction (SSR amplification) was performed in a total reaction volume of 20 µL with 40 ng/µL DNA template, 10 µM primers (forward and reverse), 10 mM dNTPs, 4.0 µL of 5× PCR buffer, 25 mM MgCl2, and 5 µL of one unit of Taq polymerase (Promega). The PCR profile for the amplification of DNA was set as follows: initial denaturation at 94 °C for 3 min followed by denaturation at 94 °C for 1 min, annealing at 55 °C for 1 min, which comprised 35 cycles, extension at 72 °C for 1 min, and final extension at 72 °C for 10 min and a hold at 4 °C. The amplified PCR product was run on 2.5% agarose gel, stained with ethidium bromide in a horizontal gel electrophoresis unit, and visualized with a gel documentation system (American Instrument Exchange {AIE}, Western Ave, Haverhill, MA, USA).

2.5. Genetic Diversity, AMOVA, and PCoA in Vigna Species

Amongst the 25 accessions, the total number of alleles, amplicon size, and number were recorded for each Vigna species. The amplified fragment was scored in base pair size and converted to 1 (amplification) and 0 (no amplification) format. Then, each marker was assessed for the number of alleles (Na), the number of effective alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), unbiased heterozygosity (uHe), Shannon Information Index (I), and fixation index (F), which were calculated using GENALEx V.6.5 software [34]. GENALEx V.6.5 was also used to detect population differentiation utilizing SSR markers by performing an analysis of molecular variance (AMOVA) and principal coordinate analysis (PCoA).

2.6. Population Structure Analysis

Population structure analysis was carried out with STRUCTURE V.2.3.1 [35]. For the identification of the number of populations (K), the project run time was set to 100,000 Markov Chain Monte Carlo (MCMC) iterations and a 100,000 burning period length with a probability of admixture and independent allele frequency. The K value was set between 1 and 10 K in each of 10 independent runs. The optimal delta K value was determined with STRUCTURE HARVESTER [36]. Further, the accessions were assembled into clusters based upon the dissimilarity matrix using an unweighted pair group method with arithmetic mean (UPGMA) neighbor-joining method using DARwin6 software [37].

2.7. Data Analysis

2.7.1. Polymorphic Information Content (PIC)

The polymorphic information content (PIC) value provides an estimate of the discriminatory power of a locus or loci by taking into consideration the number of alleles; the relative frequency of alleles was estimated using the Botstein et al. [38] equation.
P I C = 1 − ∑ i = 1 n ( P i j ) 2 − { ∑ i = 1 n ( P i j ) 2 } 2 + ∑ i = 1 n ( P i j ) 2 } 2
where Pij is the frequency of the jth allele in the ith primer, and summation extends over ‘n’ patterns.

2.7.2. Effective Multiplex Ratio (EMR)

The average number of DNA fragments amplified or detected per genotype using a marker system is considered the multiplex ratio (n). The number of polymorphic loci in the germplasm set of interest, analyzed per experiment, is known as the effective multiplex ratio.
E f f e c t i v e   M u l t i p l e x   R a t i o   E M R = n × β
where n = average number of fragments amplified by a genotype; β = fraction of polymorphic bands to the total polymorphic and monomorphic bands:
β = P B ( P B + M B )
where PB = number of polymorphic bands; MB = number of monomorphic bands.

2.7.3. Marker Index (MI)

The marker index is measured as a product of the polymorphic information content (PIC) and effective multiplex ratio (EMR). It is estimated using the formula given by Powell et al. [39]:
M a r k e r   I n d e x   ( M I ) = P I C   × E M R
where PIC = polymorphic information content; EMR = effective multiplex ratio.

2.7.4. Resolving Power (RP)

The resolving power is a measure of the ability of each primer to detect the level of variation between individuals. It is calculated according to Prevost and Wilkinson [40].
R e s o l v i n g   P o w e r   ( R P ) = ∑ I b
where Ib = informative fragments.
I b = 1 − [ 2 0.5 − P i ]
where Pi = proportion of genotypes containing the ith band.

3. Results

3.1. WGS-Based SSR Marker Development

The whole-genome sequencing (WGS) of ML267 and Mash114 was performed by [32] at the School of Agricultural Biotechnology, PAU, Ludhiana. From WGS, a total of 443,867 SSRs were identified in V. radiata cv. ML267 and V. mungo cv. Mash114, of which 410,282 poly SSR primers were designed in silico by e-PCR. Out of these primers, a total of 250 in silico polymorphic mungbean SSR primers were mapped to urdbean contigs based upon the 20–50 bp distance between mungbean and urdbean using e-PCR (Figure 1). These 250 SSR markers flanked dinucleotide SSR motifs and covered all 11 linkage groups of mungbean and urdbean. The maximum number of SSRs (45) were from chromosome 7, and the minimum number of SSRs (8) were from chromosome 9. The remaining SSRs (197) were distributed unequally, with 41, 36, 24, 21, 18, 17, 14, 13, and 13 on chromosomes 5, 8, 6, 1, 11, 4, 3, 2, and 10. Among these SSR repeats, 10 different dinucleotide repeats, (AT)n, (AG)n, (AC)n, (TA)n, (TG)n, (TC)n, (GA)n, (GT)n, (CA)n and (CT)n, were observed (Table 3). The (AT)n and (TA)n repeats were the most abundant dinucleotide repeat motifs at 69 (27.6%) and 62 (24.8%), respectively. These two dinucleotide repeats (AT/TA) account for 52.4% of the total repeat motifs.

3.2. Validation of SSR Markers in Vigna Species Accessions for Transferability Studies

For validation, a set of 25 different Vigna species accessions belonging to 13 species were genotyped with 200 WGS-developed SSR markers. All 200 SSR markers produced varying levels of amplification in all the accessions, except four (one from V. radiata var. radiata (GP15) and three from V. mungo var. mungo (GP16, GP17 and GP18)) (Table S2). Out of the 200 SSRs used for validation, 130 markers (65%) showed polymorphism, while 70 markers (35%) exhibited monomorphism in different Vigna species accessions.
Size-based polymorphism was observed for 402 alleles of the total amplified 2121 alleles, with an average of 8.1 alleles per locus. The PCR amplification profiles of WGS-based SSR markers in different Vigna accessions are given in Figure 2. The number of alleles amplified by WGS-SSRs ranged from 7 (SSR 274) to 24 (SSR 271). The average number of alleles amplified per marker was estimated at 15.7. Seven SSR markers, viz., SSR 271 (24 alleles), SSR 123 (23 alleles), SSR 208, SSR 262, SSR 273, SSR 287, and SSR 289 (21 alleles), amplified more than 20 alleles. For the Vigna species accessions, a minimum of 8 accessions and a maximum of 21 accessions showed PCR amplification with these WGS-derived SSR markers, and the amplicon size varied from 50 to 1000 base pairs (Table S2).

3.3. SSR Marker Analysis

The marker analysis is based upon the average PIC estimates for all of the markers arrayed, ranging between 0.09 (SSR 262) and 0.84 (SSR 269), with 0.31 as the average PIC value (Table S3). Of the 130 polymorphic markers, 85 markers (65.38%) were highly informative (PIC ≥ 0.45), 26 (20.50%) were reasonably informative (PIC = 0.25–0.45), and 19 (14.62%) were slightly informative (PIC < 0.25). The MI value ranged between 0.091 (SSR 262) and 3.342 (SSR 269). Similarly, the EMR varied from 1.0 to 4.0 (SSR 269). The average MI and EMR for the 130 polymorphic markers were recorded as 0.54 and 1.01, respectively. The RP for all 200 SSR markers varied from 0.56 (SSR 274) to 2.00 (SSR 177), with an average value of 1.27 (Table S3). Other marker utility parameters, such as observed and effective allele number, Shannon diversity index, and estimates of heterozygosity, were also computed (Table S3). The effective number of alleles (Ne) ranged from 1 to 2 (average estimate 1.374), and the Shannon diversity index (I) varied from 0.693 to 0 (average estimate 0.321). The Shannon information index was the highest for SSR106 and SSR 234 (0.693), followed by SSR253 (0.686), SSR 198 and SSR 251 (0.685), SSR 156 (0.683), SSR241 (0.679), and SSR 135 (0.675). The values obtained for observed heterozygosity ranged from 0.188 to 0, with a 0.016 average, while the estimates of expected heterozygosity ranged from 0.50 to 0, with a 0.216 average value. The unbiased expected heterozygosity (uHe) recorded was between 0 and 0.526, with an average of 0.235.

3.4. Genetic Diversity and Relationship among Different Vigna Species

The Vigna accessions were clustered into three main clusters based upon genetic dissimilarity, which was estimated using an unweighted pair group method with an arithmetic mean (UPGMA) neighbor-joining approach (Table 4 and Figure 3). Cluster 1 consists of 10 Vigna accessions, which were further divided into two major sub-clusters (sub-cluster 1a and sub-cluster 1b). Sub-cluster 1a included seven accessions (GP5 (V. sublobata), GP4 (V. sublobata), GP9 (V. trilobata), GP15 (V. radiata var. radiata), GP22 (V. vexillata), GP21 (V. radiata var. setulosa), and GP20 (V. glabrescence)), while sub-cluster 1b comprised three accessions (GP24 (V. dalzelliana), GP23 (V. hainiana), and GP25 (V. unguiculata)). The second cluster comprised nine accessions, with eight (GP17 (V. radiata var. mungo), GP16 (V. radiata var. mungo), GP18 (V. radiata var. mungo), GP19 (V. slyestris), GP11 (V. aconitifolia), GP10 (V. aconitifolia), GP2 (V. umbellata cultivated), and GP1 (V. umbellata cultivated)) and one accession (GP12) in sub-clusters 2a and 2b, respectively. The third cluster consisted of six accessions grouped into two sub-clusters, namely, 3a with five accessions (GP8 (V. trilobata), GP6 (V. trilobata), GP7 (V. trilobata), GP14 (V. stipulacea), and GP13 (V. stipulacea)) and 3b with one accession (GP3 V. umbellata). Nei’s unbiased genetic distance (GD) and genetic identity (GI) were also estimated, and based on the genetic distance, the Vigna accessions were categorized into four populations (pops) (Table 5). The genetic distance between pops ranged from 0.189 (between pops 4 and 3) to 0.458 (between pops 4 and 2). The Vigna accessions of pops 4 and 3 are closely related, while accessions from pops 4 and 2 are distantly related. Pop 1 and pop 3 comprised four (one of V. sublobata and three of V. mungo) and three accessions (one of V. sublobata, one of V. radiata var. radiata, and one of V. radiata var. setulosa). Pop 2 comprised the highest number of Vigna accessions at eleven (three accessions each of V. umbellata and V. trilobata; two accessions each of V. aconitifolia and V. stipulacea; and one accession of V. glabrescence), followed by pop 3 with seven accessions (one accession each of V. unguiculata, V. trilobata, V. aconitifolia var. TMV, V. sylvestris, V. vaxillata, V. hainiana, and V. dalzelliana).

3.5. Population Structure Analysis

The population structure analysis of 25 Vigna accessions was performed with 130 polymorphic SSR markers. Based upon the admixture model with independent alleles, the maximum delta K value (144.79) drew a sharp peak at K = 2 (Figure 4), which divided the genotypes into two sub-populations (SP1 and SP2) (Table 6 and Figure 4). SP1 comprised 11 accessions, whereas SP2 had 14 accessions. Sub-population 1 (SP1) included the accessions GP1 (V. umbellata cultivated), GP2 (V. umbellata cultivated), GP10 (V. aconitifolia), GP11 (V. aconitifolia), GP12 (V. aconitifolia TMV-1), GP16 (V. radiata var. mungo), GP17 (V. radiata var. mungo), GP18 (V. radiata var. mungo), GP20 (V. glabrescence), GP22 (V. vexillata), and GP24 (V. dalzelliana), while SP2 included GP3 (V. umbellata), GP4 (V. sublobata), GP5 (V. sublobata), GP6 (V. trilobata), GP7 (V. trilobata), GP8 (V. trilobata), GP9 (V. trilobata), GP13 (V. stipulacea), GP14 (V. stipulacea), GP15 (V. radiata var. radiata), GP19 (V. slyestris), GP21 (V. radiata var. setulosa), GP23 (V. hainiana), and GP25 (V. unguiculata).

3.6. Analysis of Molecular Variance (AMOVA) and Principal Coordinate Analysis (PCoA)

An analysis of molecular variance was performed within and among individuals’ diversity modules. Significantly higher genetic variance was observed among the individuals (89%) as compared to within individuals (1%) (Table 7 and Figure 5). Principal coordinate analysis (PCoA) revealed that the first and second integral coalitions explained 50.79 and 15.42 percent of the total variance. PCoA categorized the accessions into four groups involving different species, similar to UPGMA neighbor-joining clustering (Figure 6). The PCA showed a correlation with the UPGMA-based dendrogram for the grouping of Vigna species accessions.

4. Discussion

Crop improvement is important for making every crop species available to humankind. For every crop improvement program, the availability of accessible genetic variation in the crop genetic resources is indispensable. The determination of genetic diversity provides an opportunity for the exploitation of the useful variation present in the available germplasm in breeding programs as promising parents [41]. Pre-breeding is an approach that harnesses the useful variability in unadapted genetic material that cannot be utilized as such in breeding populations and necessarily serves as a major stride in the utilization of genetic variation in improvement programs [42,43,44,45,46,47,48,49]. The genetic variability existing in gene banks help in the conservation, characterization, and implementation of genetic variation in crop improvement programs [50]. The Vigna gene pool serves as a source of an ample amount of untapped genetic polymorphism available in wild Vigna species [51,52,53]. To unlock the available genetic variation, DNA-based molecular markers are required, but limited genomic resources are available in mungbean.
The present study involved the validation of 200 SSR markers amongst 250 developed from mungbean cv. ML 267 and urdbean cv. Mash 114 using a whole-genome sequence strategy at the School of Agriculture Biotechnology (SAB), Punjab Agricultural University (PAU), Ludhiana [32]. These SSR markers flank dinucleotide SSR motifs and cover all 11 linkage groups of mungbean and urdbean. Chromosome 7 has the maximum number of SSR markers (45), whereas chromosome 9 has the minimum number of SSR markers (8). The rest of the SSR markers are distributed unevenly on chromosomes 5, 8, 6, 1, 11, 4, 3, 2, and 10. The SSR markers comprised ten different types of dinucleotide repeat motifs, and two repeat motifs, i.e., (AT)n and (TA)n, were predominant. These two dinucleotide repeats (AT/TA) accounted for 52.40 percent of the total repeat motifs. In general, it has been observed that dinucleotide repeats are mainly present in many legumes [54], but trinucleotide repeats have been commonly found in mungbean [26,55,56] and in other legumes (pea [57], cowpea [13], chickpea [58], common bean [59], and horse gram) [60]. Mononucleotide repeat motifs have been observed in relative abundance in mungbean [61]. Higher numbers of mono- and tetranucleotide repeats were also reported from the transcriptome sequencing of adzuki bean [62]. Transcriptome-based SSRs can be developed from mononucleotide repeats because this type of marker exhibits high polymorphism. Similarly, a whole-genome-based SSR developed from mononucleotide repeats will also be more polymorphic than other repeats. However, the chances of errors like DNA slippage during PCR amplification by the polymerase enzyme machinery cannot be ruled out. Therefore, dinucleotide repeats were selected for this study. Conversely, high-fidelity PCR is an alternative strategy that utilizes a DNA polymerase with a low error rate and results in a high degree of accuracy in DNA amplification.
Simple sequence repeats (SSRs) are tandem repeated sequences (1–6 nucleotides) with a high rate of polymorphism, high reproducibility, and a co-dominant nature and are abundantly distributed throughout the genome. SSRs exhibit an excellent degree of transferability between and amongst closely related species or genera, which makes the SSR a useful molecular marker for the estimation of variation at the gene level, the mapping of economically important loci, and breeding programs based on molecular markers. The SSR marker transferability relies on the divergence between individual accessions. When the genetic distance between the accessions is closer, the transferability of SSR markers is higher compared to accessions with a greater genetic distance between them [63]. Within the same species of the same genus or across related genera within families, SSR transferability is higher than between different genera and families [64]. Marker transferability is a parameter to describe the closeness and crossability between species. Mungbean and other species-specific SSR markers have been used in different studies for assessing polymorphism among and between Vigna accessions or introgression lines. Following previous reports by Somta et al. [65], Tangphatsornruang et al. [24], Gupta et al. [66], Dikshit et al. [2], Singh et al. [67], Gupta et al. [54], Satinder Kaur et al. [15], Simranjit Kaur et al. [68], the present investigation showed more than 90 percent marker transferability across different Vigna accessions. Daware et al. [69] developed 4170 SSR markers with in silico polymorphism from the genomic sequences of 11 indica, japonica, aus, and wild rice accessions. Out of these, 4048 SSRs showed amplification (97.1%), and 3819 were polymorphic (94.3%) markers. In another study, 120 Nicotiana multiple (X) genome (NIX) SSR markers developed from seven genomes were tested on five Nicotiana accessions, i.e., N. syl, N. tom, N. tab K326, N. ben, and unknown genomic sequence, and 100% amplification was recorded in four accessions [70]. Similarly, using Chinese cherry, which is a fruiting cherry species, Liu et al. [71] applied 39 genomic SSR markers derived from the whole genome and seven preliminary assembled genome sequences from 94 Chinese cherry accessions and 13 Cerasus taxa. Of these 39 SSRs, 19 SSRs have 100% transferability in all 94 cherry accessions. Eight (08) SSR markers applied to all 13 taxa of Cerasus. The successful applicability of whole-genome-sequence-based SSRs between different Vigna species accessions and other crop species showed that the flanking regions of these SSRs are adequately conserved for the amplification of genomic regions. The very high cross-species transferability percentage depends on the number of species analyzed and the genetic distance among them.
The newly developed SSR markers in our study amplified 7 to 24 alleles (average estimate of 15.7). The amplification of a higher number of alleles is indicative of the high genetic diversity prevalent among Vigna species. Between 4 and 16 alleles per locus have been obtained in Asiatic mungbean accessions using 53 SSR markers [72]. In another study, Geeta Kumari [73] reported 9 to 31 alleles per locus in 119 mungbean accessions of 19 Vigna species. Studies by Dachapak et al. [74], Sarr et al. [75], and Singh et al. [76] also amplified alleles in the range of 15–25 in zombie pea, cowpea, and mungbean, respectively. Similarly, 94 Chinese cherry accessions were characterized with 39 genomic SSRs, which amplified a total of 143 allelic loci, with an average of 3.6 allelic loci [73]. In another study, 65 novel genomic SSR markers were used for the characterization of 26 accessions of Tinospora cordifolia and one accession each of T. rumphii and T. sinensis. Of these genomic SSRs, 49 were polymorphic and showed 125 alleles, with an average of 2.55 alleles per locus, and the cross-species transferability of T. cordifolia g-SSR in T. rumphii and T. sinensis was 95.3% and 93.8%, respectively [77]. Heterozygosity and the PIC value are two important estimates of genetic diversity at the genotypic level. The high PIC value in the present study is in accordance with other studies [51,72,73,74,75,76], indicating that the microsatellite-flanking regions are conserved and highly useful in inferring the phylogenetic relationship between several species. Higher estimates of the MI and EMR of SSRs suggest the high polymorphism of SSR markers. The high resolving power (RP) of SSRs (0.5 to 2.0) is another diversity parameter that reveals the markers’ power for distinguishing between genotypes. Thus, it becomes clear that SSR markers have potential in different genetic studies, such as crop germplasm characterization, genetic diversity assessment, marker–trait association, and marker-assisted breeding, which helps in the development of improved versions of crop varieties.
In general, the results of PCoA and UPGMA clustering were not completely consistent with the structure analysis. The progenitor species of mungbean and urdbean, i.e., Vigna sublobata and Vigna silvestris, clustered into two separate clusters, as they have been categorized under primary and secondary gene pools. In contrast, Geeta Kumari et al. [73] reported the grouping of progenitor species into one sub-cluster. The mixed grouping of members of all three gene pools were also observed after clustering. The primary gene pool (V. radiata var. setulosa) grouped with secondary (V. trilobata) and tertiary gene pool (V. glabrescence, V. vexillata) members under sub-cluster 1a. Similarly, secondary and tertiary gene pool species clustered together in one cluster with two sub-clusters. Similar observations were recorded by Geeta Kumari et al. [73], where secondary (V. trilobata) and tertiary gene pool (V. dalzelliana, V. umbellata, and V. vexillata) species accessions were grouped into a sub-cluster.
The population structure analysis depicted two types of populations: SP1 and SP2. The highest number of genotypes (14) were grouped into SP2. The accessions in SP1 were mainly of the cultivated type, whereas most of the wild relatives were grouped into SP2. Based upon the suitable K value, which captures the best structure of the population, Chen et al. [56] and Noble et al. [78] also divided the mungbean genotypes into cultivated and wild mungbean genotypes, having higher genetic similarity. In other Vigna species like cowpea, the appropriate K value proved helpful in differentiating the genotypes based upon geographical as well as genetic similarity [78,79,80,81]. The accessions of progenitors of mungbean and urdbean (V. sublobata and V. silvestris) and their relative species V. radiata var. setulosa and V. radiata var. mungo were categorized separately in SP1 and SP2, while Geeta Kumari et al. [73], Singh et al. [76], Pratap et al. [72], Saxena et al. [82], Pandiyan et al. [83], and Kumar et al. [84] categorized the progenitors of mungbean and urdbean accessions in one group. The secondary gene pool species accession of V. aconitifolia grouped with the tertiary gene pool accessions of V. umbellata, V. vexillata, and V. dalzelliana in SP1 due to their close relationship with each other [52]. Similar to Geeta Kumari et al.’s results [73], V. umbellata and V. trilobata are categorized into two groups (SP1 and SP2). The V. hainiana, V. stipulacea, V. glabrescence, and V. unguiculata accessions are categorized in SP2 as an admixture.
The AMOVA provides clues regarding the genetic variation present within and among individuals. The greater variance of 89% among individuals revealed the presence of high genetic diversity. The low genetic diversity among populations indicates the exchange of germplasm between different regions and the distribution of similar Vigna species [56,73,78]. Among populations, a low level of genetic diversity of 10% was observed in our study, while Geeta Kumari et al. [73] obtained a high level of genetic diversity of 88.33% among populations. In the case of oil-tea Camellia species, 77% genetic variation was observed within the population, whereas there was a small fraction of genetic variation among species (23%) [85]. Fst is an estimate of population differentiation on account of genetic composition. Frankham et al. [86] stated that an Fst estimate < 0.15 is an important criterion for population discrimination. The obtained Fst value of 0.105 is near the significant value, indicating low differentiation between individuals. The results of principal coordinate analysis (PCoA) and UPGMA-based clustering were in agreement, showing gene diversity and the clear differentiation of cultivated and wild Vigna species. Similarly, 140 oil-tea Camellia species accessions were divided into three groups based on the PCoA and UPGMA analysis [85].

5. Conclusions

The Vigna species gene pool harbors huge genetic diversity with variable alleles that can be harnessed for developing cultivars having high yield potential. Examining the inherent genetic variation found in both wild and cultivated species is crucial for expanding the genetic foundation of breeding lines. This enables the marker-assisted introgression of desirable traits into modern cultivars, contributing to successful genetic improvement programs. In the present study, we developed 200 WGS-based SSR markers, out of which 130 markers were highly polymorphic and showed a high rate of cross-species transferability among Vigna accessions. Of these polymorphic markers, seven SSRs amplified more than 20 alleles among the 25 Vigna accessions. The 130 polymorphic SSRs covered all of the linkage groups of mungbean. The high polymorphism, transferability rate, and distribution of WGS-SSRs indicate their usefulness in pre-breeding and the genetic dissection of novel genes/QTLs linked to agronomic performance, nutritional quality, resistance to diseases and insect pests, and tolerance to abiotic stresses.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/horticulturae10010034/s1, Table S1: Whole-genome sequence (WGS)-based mungbean SSR markers; Table S2: Amplification of WGS-based SSR markers in 25 Vigna accessions; Table S3: Performance of WGS-based SSR markers on a panel of Vigna species.

Author Contributions

P.S. (Pawan Saini), T.S.B. and A.S.: conceived and designed the study; P.S. (Pawan Saini): performed field and laboratory experiments; I.S.Y.: designed the SSR primers; N.K.L. and S.A.H.P.: assisted in designing SSR primers; A.P.: provided plant material; K.S.M.: assisted in field and laboratory experiments; J.A.: performed the analysis of data and proofreading; B.N.G.: performed formal analysis, validation, and visualization; P.S. (Pawan Saini): completed the original draft; P.S. (Pooja Saini) and S.N.: corrected the manuscript; P.S. (Pawan Saini), B.N.G., T.S.B. and A.S.: reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not Applicable.

Informed Consent Statement

Not Applicable.

Data Availability Statement

The data used for the analysis in this study are available within the article and Supplementary Materials.

Acknowledgments

The Department of Plant Breeding and Genetics, Punjab Agricultural University (PAU), Ludhiana, is highly appreciated for facilitating the implementation of this research.

Conflicts of Interest

The authors of the manuscript state that there are no competing interest regarding financial or individual conflicts.

References

  1. Schrire, B.D. Tribe Phaseoleae. In Legumes of the World; Lewis, G., Schrire, B., Mackinder, B., Lock, M., Eds.; Royal Botanic Gardens: Kew, UK, 2005; pp. 393–431. [Google Scholar]
  2. Dikshit, H.K.; Singh, D.; Singh, A.; Jain, N.; Kumari, J.; Sharma, T.R. Utility of adzuki bean [Vigna angularis (Willd.) Ohwi & Ohashi] simple sequence repeat (SSR) markers in genetic analysis of mungbean and related Vigna spp. Afr. J. Biotechnol. 2012, 11, 13261–13268. [Google Scholar] [CrossRef]
  3. Akbar, W.; Akhtar, M.A.; Murtaza, G.; Hussain, A.; Javed, H.M.; Arshad, M.; Maqbool, M.A. Mungbean Yellow Mosaic Disease and its Management. J. Agric. Basic Sci. 2019, 4, 34–44. [Google Scholar]
  4. Kang, Y.J.; Kim, S.K.; Kim, M.Y.; Lestari, P.; Kim, K.H.; Ha, B.K.; Jun, T.H.; Hwang, W.J.; Lee, T.; Lee, J.; et al. Genome sequence of mungbean and insights into evolution within Vigna species. Nat. Commun. 2014, 5, 5443. [Google Scholar] [CrossRef] [PubMed]
  5. Rohilla, V.; Yadav, R.K.; Poonia, A.; Sheoran, R.; Kumari, G.; Shanmugavadivel, P.S.; Pratap, A. Association Mapping for Yield Attributing Traits and Yellow Mosaic Disease Resistance in Mung Bean [Vigna radiata (L.) Wilczek]. Front. Plant Sci. 2022, 12, 749439. [Google Scholar] [CrossRef] [PubMed]
  6. Hinz, R.; Sulser, T.B.; Hüfner, R.; Mason-D’Croz, D.; Dunston, S.; Nautiyal, S.; Ringler, C.; Schuengel, J.; Tikhile, P.; Wimmer, F.; et al. Agricultural development and land use change in India: A scenario analysis of trade-offs between UN sustainable development goals (SDGs). Earth’s Future 2020, 8, e2019EF001287. [Google Scholar] [CrossRef]
  7. Saini, P. Inheritance Studies and Mapping of Yellow Mosaic Disease Resistance in an Interspecific Cross of Mungbean (Vigna radiata (L.) Wilczek) and Urdbean (Vigna mungo (L.) Hepper). Ph.D. Dissertation, Punjab Agricultural University, Ludhiana, India, 2020; pp. 1–150. [Google Scholar]
  8. Somta, P.; Musch, W.; Kongsamai, B.; Chanprame, S.; Nakasathien, S.; Toojinda, T.; Sorajjapinun, W.; Seehalak, W.; Tragoonrung, S.; Srinives, P. New microsatellite markers isolated from mungbean (Vigna radiata (L.) Wilczek). Mol. Ecol. Resour. 2008, 8, 1155–1157. [Google Scholar] [CrossRef] [PubMed]
  9. Lestari, P.; Kim, S.K.; Reflinur; Kang, Y.J.; Dewi, N.; Lee, S.-H. Genetic diversity of mungbean (Vigna radiata L.) germplasm in Indonesia. Plant Genet. Resour. 2014, 12 (Suppl. S1), S91–S94. [Google Scholar] [CrossRef]
  10. Savithramma, D.L.; Ramakrishnan, C.K.D. Development and characterization of newly developed genomic SSR markers in Mung bean (Vigna radiata (L.) Wilczek). In Proceedings of the 2nd International Conference on Genetic & Protein Engineering, Atlanta, GA, USA, 14–16 November 2016; 43p. [Google Scholar]
  11. Somta, P.; Srinives, P. Genome research in mungbean [Vigna radiata (L.) Wilczek] and blackgram [V. mungo (L.) Hepper]. Sci. Asia 2007, 1, 69–74. [Google Scholar] [CrossRef]
  12. Chaitieng, B.; Kaga, A.; Tomooka, N.; Isemura, T.; Kuroda, Y.; Vaughan, D.A. Development of a black gram [Vigna mungo (L.) Hepper] linkage map and its comparison with an azuki bean [Vigna angularis (Willd.) Ohwi and Ohashi] linkage map. Theor. Appl. Genet. 2006, 113, 1261–1269. [Google Scholar] [CrossRef]
  13. Gupta, S.K.; Gopalakrishna, T. Development of unigene-derived SSR markers in cowpea (Vigna unguiculata) and their transferability to other Vigna species. Genome 2010, 53, 508–523. [Google Scholar] [CrossRef]
  14. Zhong, M.; Cheng, X.Z.; Wang, L.X.; Wang, S.H. Transferability of mungbean genomic-SSR markers in other Vigna species. Acta Agron Sin 2012, 38, 223–230. [Google Scholar] [CrossRef]
  15. Kaur, S.; Gill, R.K.; Bains, T.S.; Kaur, M.; Thakur, S. Comparative assessment of SSR markers derived from different sources in genetic diversity analysis of Vigna genotypes. Agric. Res. J. 2017, 54, 462–468. [Google Scholar] [CrossRef]
  16. Wang, L.X.; Cheng, X.Z.; Wang, S.H.; Liu, C.Y. Transferability of SSR markers for adzuki bean into mungbean. Acta Agron Sin 2009, 35, 816–820. [Google Scholar] [CrossRef]
  17. Choudhary, K.B.; Pratap, A.; Tomar, R. Cross genera marker transferability and genetic diversity analysis in elite cultivars of mungbean [Vigna radiata (L.) wilczek]. Legume Res.-Int. J. 2022, 45, 1065–1073. [Google Scholar] [CrossRef]
  18. Haddoudi, L.; Hdira, S.; Cheikh, N.B.; Mahjoub, A.; Abdelly, C.; Ludidi, N.; Badri, M. Assessment of genetic diversity in Tunisian populations of Medicago polymorpha based on SSR markers. Chil. J. Agric. Res. 2021, 81, 53–61. [Google Scholar] [CrossRef]
  19. Singh, B.; Das, A.; Parihar, A.K.; Bhagawati, B.; Singh, D.; Pathak, K.N.; Dwivedi, K.; Das, N.; Keshari, N.; Midha, R.L.; et al. Delineation of Genotype-by-Environment interactions for identification and validation of resistant genotypes in root-knot nematode (Meloidogyne incognita) using GGE biplot. Sci. Rep. 2020, 10, 4108. [Google Scholar] [CrossRef] [PubMed]
  20. Kumar, S.V.; Tan, S.G.; Quah, S.C.; Yusoff, K. Isolation of microsatellite markers in mungbean, Vigna radiata. Mol. Ecol. Notes 2002, 2, 96–98. [Google Scholar] [CrossRef]
  21. Kumar, S.V.; Tan, S.G.; Quah, S.C.; Yusoff, K. Isolation and characterization of seven tetranucleotide microsatellite loci in mungbean, Vigna radiata. Mol. Ecol. Notes 2002, 2, 293–295. [Google Scholar] [CrossRef]
  22. Miyagi, M.; Humphry, M.; Ma, Z.Y.; Lambrides, C.J.; Bateson, M.; Liu, C.J. Construction of bacterial artificial chromosome libraries and their application in developing PCR-based markers closely linked to a major locus conditioning bruchid resistance in Mungbean [Vigna radiata (L.) Wilczek]. Theor. Appl. Genet. 2004, 110, 151–156. [Google Scholar] [CrossRef]
  23. Gwag, J.G.; Chung, W.K.; Chung, H.K.; Lee, J.H.; Ma, K.H.; Dixit, A.; Park, Y.J.; Cho, E.G.; Kim, T.S.; Lee, S.H. Characterization of new microsatellite markers in mungbean. Mol. Ecol. Notes 2006, 6, 1132–1134. [Google Scholar] [CrossRef]
  24. Tangphatsornruang, S.; Somta, P.; Uthaipaisanwong, P.; Chanprasert, P.; Sangsrakru, D.; Seehalak, W.; Sommanas, W.; Tragoonrung, S.; Srinives, P. Characterization of microsatellites and gene contents from genome shotgun sequences of mungbean (Vigna radiata (L.) Wilczek). BMC Plant Biol. 2009, 9, 137. [Google Scholar] [CrossRef] [PubMed]
  25. Singh, N. Development of Cost Efficient and Rapid Method for Developing Homozygous SSR Markers in Mungbean Vigna radiata (L.) Wilczek and Its Validation in Vigna Species. Master’s Thesis, Tamil Nadu Agricultural University, Coimbatore, India, 2011. [Google Scholar]
  26. Singh, N.; Singh, H.; Nagarajan, P. Development of SSR markers in mung bean, Vigna radiata (L.) Wilczek using in silico methods. J. Crop Weed 2013, 9, 69–74. [Google Scholar]
  27. Shrivastava, D.; Verma, P.; Bhatia, S. Expanding the repertoire of microsatellite markers for polymorphism studies in Indian accessions of mungbean (Vigna radiata L. Wilczek). Mol. Biol. Rep. 2014, 41, 5669–5680. [Google Scholar] [CrossRef] [PubMed]
  28. Bangar, P.; Chaudhary, A.; Umdale, S.; Kumari, R.; Tiwari, B.; Kumar, S.; Gaikwad, A.B.; Bhat, K.V. Detection and characterization of polymorphic simple sequence repeats markers for the analysis of genetic diversity in Indian mungbean [Vigna radiata (L.) Wilczek]. Indian J. Genet. 2018, 78, 111–117. [Google Scholar] [CrossRef]
  29. Doyle, J.J.; Doyle, J.L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull. 1987, 19, 11–15. [Google Scholar]
  30. Li, R.; Li, Y.; Kristiansen, K.; Wang, J. SOAP: Short oligonucleotide alignment program. Bioinformatics 2008, 24, 713–717. [Google Scholar] [CrossRef] [PubMed]
  31. Thiel, T.; Michalek, W.; Varshney, R.K.; Graner, A. Exploiting EST databases for the development of cDNA derived microsatellite markers in barley (Hordeum vulgare L.). Theor. Appl. Genet. 2003, 106, 411–422. [Google Scholar] [CrossRef] [PubMed]
  32. Thakur, S. Whole Genome De Novo Assembly of Vigna mungo and Vigna radiata and In Silico Comparative Analysis for Marker Development. Ph.D. Dissertation, Punjab Agricultural University, Ludhiana, India, 2018; pp. 1–96. [Google Scholar]
  33. Shyu, C.; Foster, J.A.; Forney, L.J. Electronic polymerase chain reaction (ePCR) search algorithm. In Proceedings of the IEEE Computer Society Bioinformatics Conference, Stanford, CA, USA, 16 August 2002; p. 338. [Google Scholar]
  34. Peakall, R.; Smouse, P.E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research: An update. Bioinformatics 2012, 28, 2537–2539. [Google Scholar] [CrossRef]
  35. Pritchard, J.K.; Stephens, M.; Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 2000, 155, 945–959. [Google Scholar] [CrossRef]
  36. Earl, D.A. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 2012, 4, 359–361. [Google Scholar] [CrossRef]
  37. Perrier, X.; Jacquemoud-Collet, J.P. DARwin Software, 5th ed.; Cirad: Montpellier, France, 2006. Available online: http://darwin.cirad.fr/darwin (accessed on 13 July 2023).
  38. Botstein, D.; White, R.L.; Skolnick, M.; Davis, R.W. Construction of a genetic linkage map in man using restriction fragment length polymorphism. Am. J. Hum. Genet. 1980, 32, 314–331. [Google Scholar] [PubMed]
  39. Powell, W.; Morgante, M.; Andre, C.; Hanafey, M.; Vogel, J.; Tingey, S.; Rafalski, A. The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Mol. Breed. 1996, 2, 225–238. [Google Scholar] [CrossRef]
  40. Prevost, A.; Wilkinson, M.J. A new system of comparing PCR primers applied to ISSR fingerprinting of potato cultivars. Ther. Appl. Genet. 1999, 98, 107–112. [Google Scholar] [CrossRef]
  41. Nayak, S.N.; Song, J.; Villa, A.; Pathak, B.; Ayala-Silva, T.; Yang, X.; Todd, J.; Glynn, N.C.; Kuhn, D.N.; Glaz, B.; et al. Promoting utilization of Saccharum spp. genetic resources through genetic diversity analysis and core collection construction. PLoS ONE 2014, 9, 110856. [Google Scholar] [CrossRef] [PubMed]
  42. Nass, L.L.; Paterniani, E. Pre-breeding: A link between genetic resources and maize breeding. Sci. Agric. 2000, 57, 581–587. [Google Scholar] [CrossRef]
  43. Haussmann, B.I.G.; Parzies, H.K.; Presterl, T.; Susic, Z.; Mieddaner, T. Plant genetic resources in crop improvement. Plant Genet. Resour. 2004, 2, 3–21. [Google Scholar] [CrossRef]
  44. Acosta-Gallegos, J.; Kelly, J.D.; Gepts, P. Prebreeding in common bean and use of genetic diversity from wild germplasm. Crop Sci. 2007, 47, 44–59. [Google Scholar] [CrossRef]
  45. Shimelis, H.; Laing, M. Timelines in conventional crop improvement: Pre-breeding and breeding procedures. Aus. J. Crop Sci. 2012, 11, 1542–1549. [Google Scholar]
  46. Sharma, S.; Upadhyaya, H.D.; Varshney, R.K.; Gowda, C.L.L. Pre-breeding for diversification of primary gene pool and genetic enhancement of grain legume. Front. Plant Sci. 2013, 4, 309. [Google Scholar] [CrossRef]
  47. Kumar, V.; Shukla, Y.M. Pre-breeding: Its applications in crop improvement. Res. News U (RNFU) 2014, 16, 199–202. [Google Scholar]
  48. Meena, A.K.; Gurjar, D.; Kumhar, B.L. Pre-breeding is a bridge between wild species and improved genotypes—A review. Chem. Sci. Rev. Lett. 2017, 6, 1141–1151. [Google Scholar]
  49. Jain, S.K. Omprakash Pre-breeding: A Bridge between Genetic Resources and Crop Improvement. Int. J. Curr. Microbiol. App. Sci. 2019, 8, 1998–2007. [Google Scholar] [CrossRef]
  50. Singh, R.B.; Singh, B.; Singh, R.K. Evaluation of genetic diversity in Saccharum species clones and commercial varieties employing molecular (SSRs) and physiological markers. Indian J. Plant Genet. Resour. 2018, 31, 17–26. [Google Scholar] [CrossRef]
  51. Wang, M.L.; Barkley, N.A.; Gillaspie, G.A.; Pederson, G.A. Phylogenetic relationships and genetic diversity of the USDA Vigna germplasm collection revealed by gene-derived markers and sequencing. Genet. Res. 2008, 90, 467–480. [Google Scholar] [CrossRef] [PubMed]
  52. Takahashi, Y.; Somta, P.; Muto, C.; Iseki, K.; Naito, K.; Pandiyan, M.; Natesan, S.; Tomooka, N. Novel Genetic Resources in the Genus Vigna Unveiled from Gene Bank Accessions. PLoS ONE 2016, 11, e0147568. [Google Scholar] [CrossRef] [PubMed]
  53. Bisht, I.S.; Bhat, K.V.; Lakhanpaul, S.; Latha, M.; Jayan, P.K.; Biswas, B.K.; Singh, A.K. Diversity and genetic resources of wild Vigna species in India. Genet. Resour. Crop Evol. 2005, 52, 53–68. [Google Scholar] [CrossRef]
  54. Blair, M.W.; Hurtado, N.; Chavarro, C.M.; Munoz-Torres, M.C.; Pedraza, M.F.; Tomins, J.; Wing, R. Gene-based SSR markers for common bean (Phaseolus vulgaris L.) derived from root and leaf tissue ESTs: An integration of the BMC series. BMC Plant Biol. 2011, 11, 50. [Google Scholar] [CrossRef] [PubMed]
  55. Gupta, S.K.; Bansal, R.; Gopalakrishna, T. Development and characterization of genic SSR markers for mungbean (Vigna radiata (L.) Wilczek). Euphytica 2014, 195, 245–258. [Google Scholar] [CrossRef]
  56. Chen, H.; Qiao, L.; Wang, L.; Wang, S.; Blair, M.W.; Cheng, X. Assessment of genetic diversity and population structure of mungbean (Vigna radiata) germplasm using EST-based and genomic SSR markers. Gene 2015, 566, 175–183. [Google Scholar] [CrossRef]
  57. Gong, Y.M.; Xu, S.C.; Mao, W.H.; Hu, Q.Z.; Zhang, G.W.; Ding, J.; Li, Y.D. Developing new SSR markers from ESTs of pea (Pisum sativum L.). J. Zhejiang Univ. Sci. B 2010, 11, 702–707. [Google Scholar] [CrossRef]
  58. Choudhary, S.; Sethy, N.K.; Shokeen, B.; Bhatia, S. Development of chickpea EST-SSR markers and analysis of allelic variation across related species. Theor. Appl. Genet. 2009, 118, 591–608. [Google Scholar] [CrossRef] [PubMed]
  59. Blair, M.W.; Hurtado, N. EST-SSR markers from five sequenced cDNA libraries of common bean (Phaseolus vulgaris L.) comparing three bioinformatic algorithms. Mol. Ecol. Resour. 2013, 13, 688–695. [Google Scholar] [CrossRef]
  60. Bhardwaj, J.; Chauhan, R.; Swarnkar, M.K.; Chahota, R.K.; Singh, A.K.; Shankar, R.; Yadav, S.K. Comprehensive transcriptomic study on horse gram (Macrotyloma uniflorum): De novo assembly, functional characterization and comparative analysis in relation to drought stress. BMC Genom. 2013, 14, 647. [Google Scholar] [CrossRef] [PubMed]
  61. Jasrotia, R.S.; Yadav, P.K.; Iquebal, M.A.; Bhatt, S.B.; Arora, V.; Angadi, U.B.; Tomar, R.S.; Jaiswal, S.; Rai, A.; Kumar, D. VigSat DB: Genome-wide microsatellite DNA marker database of three species of Vigna for germplasm characterization and improvement. Database 2019, 2019, baz055. [Google Scholar] [CrossRef] [PubMed]
  62. Chen, H.; Liu, L.; Wang, L.; Wang, S.; Somta, P.; Cheng, X. Development and validation of EST SSR markers from the transcriptome of adzuki bean (Vigna angularis). PLoS ONE 2015, 10, e0131939. [Google Scholar] [CrossRef]
  63. Luro, F.L.; Constantino, G.; Terol, J.; Argout, X.; Allario, T.; Wincker, P.; Talon, M.; Ollitrault, P.; Morillon, R. Transferability of the ESR-SSRs developed on Nules clementine (Citrus clementine Hort ex Tan) to other Citrus species and their effectiveness for genetic mapping. BMC Genom. 2008, 9, 287. [Google Scholar] [CrossRef] [PubMed]
  64. Mnejja, M.; Garcia-Mas, J.; Audergon, J.M.; Arús, P. Prunus microsatellite marker transferability across rosaceous crops. Tree Genet. Genomes 2010, 6, 689–700. [Google Scholar] [CrossRef]
  65. Somta, P.; Seehalak, W.; Srinives, P. Development, characterization and cross-species amplification of mungbean (Vigna radiata) genic microsatellite markers. Conserv. Genet. 2009, 10, 1939–1943. [Google Scholar] [CrossRef]
  66. Gupta, S.K.; Bansal, R.; Vaidya, U.J.; Gopalakrishna, T. Development of EST-derived microsatellite markers in mungbean [Vigna radiata (L.) Wilczek] and their transferability to other Vigna species. Indian J. Genet. 2012, 72, 468–471. [Google Scholar]
  67. Singh, A.; Dikshit, H.K.; Jain, N.; Singh, D.; Yadav, R.N. Efficiency of SSR, ISSR and RAPD markers in molecular characterization of mungbean and other Vigna species. Indian J. Biotechnol. 2014, 13, 81–88. [Google Scholar]
  68. Kaur, S.; Bains, T.S.; Sirari, A.; Kaur, S. Evaluation and molecular characterization of advanced interspecific lines for genetic improvement in mungbean [Vigna radiate (L.) Wilczek]. Legume Res. 2019, 42, 729–735. [Google Scholar] [CrossRef]
  69. Daware, A.; Das, S.; Srivastava, R.; Badoni, S.; Singh, A.K.; Agarwal, P.; Parida, S.K.; Tyagi, A.K. An Efficient Strategy Combining SSR Markers- and Advanced QTL-seq-driven QTL Mapping Unravels Candidate Genes Regulating Grain Weight in Rice. Front. Plant Sci. 2016, 7, 1535. [Google Scholar] [CrossRef] [PubMed]
  70. Wang, X.; Yang, S.; Chen, Y.; Zhang, S.; Zhao, Q.; Li, M.; Gao, Y.; Yang, L.; Bennetzen, J.L. Comparative genome-wide characterization leading to simple sequence repeat marker development for Nicotiana. BMC Genom. 2018, 19, 500. [Google Scholar] [CrossRef] [PubMed]
  71. Liu, Z.; Zhang, J.; Wang, Y.; Wang, H.; Wang, L.; Zhang, L.; Xiong, M.; He, W.; Yang, S.; Chen, Q.; et al. Development and Cross-Species Transferability of Novel Genomic-SSR Markers and Their Utility in Hybrid Identification and Trait Association Analysis in Chinese Cherry. Horticulturae 2022, 8, 222. [Google Scholar] [CrossRef]
  72. Pratap, A.; Gupta, S.; Malviya, N.; Tomar, R.; Maurya, R.; John, K.J.; Madhavan, L.; Singh, N.P. Genome scanning of Asiatic Vigna species for discerning population genetic structure based on microsatellite variation. Mol. Breed. 2015, 35, 178. [Google Scholar] [CrossRef]
  73. Kumari, G.; Roopa Lavanya, G.; Shanmugavadivel, P.S.; Singh, Y.; Singh, P.; Patidar, B.; Madhavan, L.; Gupta, S.; Singh, N.P.; Pratap, A. Genetic diversity and population genetic structure analysis of an extensive collection of wild and cultivated Vigna accessions. Mol. Genet. Genom. 2021, 296, 1337–1353. [Google Scholar] [CrossRef] [PubMed]
  74. Dachapak, S.; Somta, P.; Poonchaivilaisak, S.; Yimram, T.; Srinives, P. Genetic diversity and structure of the zombi pea (Vigna vexillata (L.) A. Rich) gene pool based on SSR marker analysis. Genetica 2017, 145, 189–200. [Google Scholar] [CrossRef]
  75. Sarr, A.; Bodian, A.; Gbedevi, K.M.; Ndir, K.N.; Ajewole, O.O.; Gueye, B.; Foncéka, D.; Diop, E.A.; Diop, B.M.; Cissé, N.; et al. Genetic Diversity and population structure analyses of wild relatives and cultivated cowpea (Vigna unguiculata (L.) Walp.) from senegal using simple sequence repeat markers. Plant Mol. Biol. Rep. 2020, 39, 112–124. [Google Scholar] [CrossRef]
  76. Singh, R.B.; Mahenderakar, M.D.; Jugran, A.K.; Singh, R.K.; Srivastava, R.K. Assessing genetic diversity and population structure of sugarcane cultivars, progenitor species and genera using microsatellite (SSR) markers. Gene 2020, 753, 144800. [Google Scholar] [CrossRef]
  77. Paliwal, R.; Singh, R.; Choudhury, D.R.; Tiwari, G.; Kumar, A.; Bhat, K.C.; Singh, R. Molecular Characterization of Tinospora cordifolia (Willd.) Miers Using Novel g-SSR Markers and Their Comparison with EST-SSR and SCoT Markers for Genetic Diversity Study. Genes 2022, 13, 2042. [Google Scholar] [CrossRef]
  78. Noble, T.J.; Tao, Y.; Mace, E.S.; Williams, B.; Jordan, D.R.; Douglas, C.A.; Mundree, S.G. Characterization of linkage disequilibrium and population structure in a mungbean diversity panel. Front. Plant Sci. 2018, 8, 2102. [Google Scholar] [CrossRef] [PubMed]
  79. Xiong, H.; Shi, A.; Mou, B.; Qin, J.; Motes, D.; Lu, W.; Ma, J.; Weng, Y.; Yang, W.; Wu, D. Genetic diversity and population structure of cowpea (Vigna unguiculata L. Walp). PLoS ONE 2016, 11, e0160941. [Google Scholar] [CrossRef] [PubMed]
  80. Chen, H.; Chen, H.; Hu, L.; Wang, L.; Wang, S.; Wang, M.L.; Cheng, X. Genetic diversity and a population structure analysis of accessions in the Chinese cowpea [Vigna unguiculata (L.) Walp.] germplasm collection. Crop J. 2017, 5, 363–372. [Google Scholar] [CrossRef]
  81. Luo, Z.; Brock, J.; Dyer, J.M.; Kutchan, T.; Schachtman, D.; Augustin, M.; Ge, Y.; Fahlgren, N.; Abdel-Haleem, H. Genetic Diversity and Population Structure of a Camelina sativa Spring Panel. Front. Plant Sci. 2019, 10, 184. [Google Scholar] [CrossRef]
  82. Saxena, S.; Kole, P.R.; Bhat, K.V.; Pandey, S.P. Species diversity and relationship among Vigna radiata (L.) wilczek and its close wild relatives. Bangladesh J. Botany 2016, 45, 567–573. [Google Scholar]
  83. Pandiyan, M.; Senthil, N.; Ramamoorthi, N.; Muthiah, A.R.; Tomooka, N.; Duncan, V.; Tomooka, N.; Jayaraj, T. Interspecifc hybridization of Vigna radiata × 13 wild Vigna species for developing MYMV donor. Electron. J. Plant Breed. 2010, 1, 600–610. [Google Scholar]
  84. Kumar, S.; Gupta, S.; Chandra, S.; Singh, B.B. How wide is the genetic base of pulse crops. In Pulses in New Perspective; Ali, M., Singh, B.B., Kumar, S., Dhar, V., Eds.; Indian Society of Pulses Research and Development: Kanpur, India, 2004; pp. 211–221. [Google Scholar]
  85. Yan, H.; Qi, H.; Li, Y.; Wu, Y.; Wang, Y.; Chen, J.; Yu, J. Assessment of the Genetic Relationship and Population Structure in Oil-Tea Camellia Species Using Simple Sequence Repeat (SSR) Markers. Genes 2022, 13, 2162. [Google Scholar] [CrossRef]
  86. Frankham, R.; Ballou, J.D.; Briscoe, D.A. Introduction to Conservation Genetics; Cambridge University Press: Cambridge, UK, 2002. [Google Scholar] [CrossRef]
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Figure 1. Schematic flowchart of development of primer mining and design and the PCR validation of WGS-based SSR markers.
Figure 1. Schematic flowchart of development of primer mining and design and the PCR validation of WGS-based SSR markers.
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Figure 2. PCR profiles of WGS-based SSR markers in different Vigna accessions. M (Marker—100 bp); (1) V. umbellata (cultivated); (2) V. umbellata (cultivated); (3) V. umbellata; (4) V. sublobata; (5) V. sublobata; (6) V. trilobata; (7) V. trilobata; (8) V. trilobata; (9) V. trilobata; (10) V. aconitifolia; (11) V. aconitifolia; (12) V. aconitifolia (TMV-1); (13) V. stipulacea; (14) V. stipulacea; (15) V. radiata var. radiata; (16) V. radiata var. mungo; (17) V. radiate var. mungo; (18) V. radiate var. mungo; (19) V. slyestris; (20) V. glabrescence; (21) V. radiata var. satulosa; (22) V. vexillata; (23) V. hainiana; (24) V. dalzelliana; (25) V. unguiculata; (26–33) Cajanus and Glycine spp.
Figure 2. PCR profiles of WGS-based SSR markers in different Vigna accessions. M (Marker—100 bp); (1) V. umbellata (cultivated); (2) V. umbellata (cultivated); (3) V. umbellata; (4) V. sublobata; (5) V. sublobata; (6) V. trilobata; (7) V. trilobata; (8) V. trilobata; (9) V. trilobata; (10) V. aconitifolia; (11) V. aconitifolia; (12) V. aconitifolia (TMV-1); (13) V. stipulacea; (14) V. stipulacea; (15) V. radiata var. radiata; (16) V. radiata var. mungo; (17) V. radiate var. mungo; (18) V. radiate var. mungo; (19) V. slyestris; (20) V. glabrescence; (21) V. radiata var. satulosa; (22) V. vexillata; (23) V. hainiana; (24) V. dalzelliana; (25) V. unguiculata; (26–33) Cajanus and Glycine spp.
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Figure 3. Dendrogram of 25 Vigna accessions derived from a UPGMA cluster analysis based on the neighbor-joining method.
Figure 3. Dendrogram of 25 Vigna accessions derived from a UPGMA cluster analysis based on the neighbor-joining method.
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Figure 4. Population structure analysis of 25 Vigna accessions based on 200 SSR markers (K = 2) and graph of estimated membership fraction for K = 2. (Red: Sub-population 1 and Green: Sub-population 2).
Figure 4. Population structure analysis of 25 Vigna accessions based on 200 SSR markers (K = 2) and graph of estimated membership fraction for K = 2. (Red: Sub-population 1 and Green: Sub-population 2).
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Figure 5. Analysis of molecular variance (AMOVA) showing the percentage of molecular variance among and within populations and among the various Vigna species genotypes.
Figure 5. Analysis of molecular variance (AMOVA) showing the percentage of molecular variance among and within populations and among the various Vigna species genotypes.
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Figure 6. Principal coordinate analysis (PCA) grouping of 25 Vigna accessions in plot 1 and 2 coordinates.
Figure 6. Principal coordinate analysis (PCA) grouping of 25 Vigna accessions in plot 1 and 2 coordinates.
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Table 1. List of Vigna accessions genotyped in the study.
Table 1. List of Vigna accessions genotyped in the study.
DesignationAccessionsDesignationAccessions
GP1V. umbellata (Cultivated)GP14V. stipulacea
GP2V. umbellata (Cultivated)GP15V. radiata var. radiata
GP3V. umbellataGP16V. radiata var. mungo
GP4V. sublobataGP17V. radiata var. mungo
GP5V. sublobataGP18V. radiata var. mungo
GP6V. trilobataGP19V. slyestris
GP7V. trilobataGP20V. glabrescence
GP8V. trilobataGP21V. radiata var. satulosa
GP9V. trilobataGP22V. vexillata
GP10V. aconitifoliaGP23V. hainiana
GP11V. aconitifoliaGP24V. dalzelliana
GP12V. aconitifolia (TMV-1)GP25V. unguiculata
GP13V. stipulacea
Table 2. Summary of SSR mining and frequency of different repeat types identified through whole-genome sequencing of V. radiata cv. ML267 and V. mungo cv. Mash114 [32].
Table 2. Summary of SSR mining and frequency of different repeat types identified through whole-genome sequencing of V. radiata cv. ML267 and V. mungo cv. Mash114 [32].
ParametersNumber of SSRs
V. radiata cv. ML267V. mungo cv. Mash114
SSR Mining
 SSR sequences examined444,059471,725
 SSRs identified225,359218,508
 SSR-containing sequences130,125126,749
 Sequences containing more than 1 SSR50,76046,626
 SSRs present in compound formation16,20115,565
Repeat Types
 Mononucleotide173,536 (77%)170,071 (77.83%)
 Dinucleotide29,559 (13.12%)27,625 (12.64%)
 Trinucleotide19,732 (8.76%)18,490 (8.46%)
 Tetranucleotide1939 (0.86%)1794 (0.82%)
 Pentanucleotide410 (0.18%)369 (0.17%)
 Hexanucleotide183 (0.08%)159 (0.08%)
Data in parentheses are the percentage values of the repeat type.
Table 3. The abundance of dinucleotide repeats in in silico-developed SSR markers between V. radiata (cv. ML267) and V. mungo (cv. Mash 114).
Table 3. The abundance of dinucleotide repeats in in silico-developed SSR markers between V. radiata (cv. ML267) and V. mungo (cv. Mash 114).
Dinucleotide RepeatNNumberPercentage
(AT)n6, 7, 8, 9, 10, 11, 12, 13, 14, 176927.6
(AG)n6, 7, 8, 9, 10, 13, 14, 16, 17, 223012.0
(AC)n6, 7, 8, 9166.40
(TA)n6, 7, 8, 9, 10, 11, 12, 13, 206224.8
(TC)n6, 7. 8, 9, 10, 11, 13, 14, 16, 173112.4
(TG)n6, 7, 9114.40
(CT)n6, 7, 8, 9, 12, 14135.20
(CA)n6, 7020.80
(GA)n6, 7, 8, 10, 12, 19, 20104.00
(GT)n6, 7, 14062.40
Table 4. Grouping of 25 accessions of 13 Vigna species based upon the UPGMA cluster analysis.
Table 4. Grouping of 25 accessions of 13 Vigna species based upon the UPGMA cluster analysis.
Cluster 1Cluster 2Cluster 3
Sub-Cluster 1aSub-Cluster 1bSub-Cluster 2aSub-Cluster 2bSub-Cluster 3aSub-Cluster 3b
GP5 (V. sublobata)GP24 (V. dalzelliana)GP17 (V. radiata var. mungo)GP12 (V. aconitifolia TMV-1)GP8 (V. trilobata) GP3 (V. umbellata)
GP4 (V. sublobata)GP23 (V. hainiana)GP16 (V. radiata var. mungo) GP6 (V. trilobata)
GP9 (V. trilobata)GP25 (V. unguiculata)GP18 (V. radiata var. mungo) GP7 (V. trilobata)
GP15 (V. radiata var. radiata) GP19 (V. slyestris) GP14 (V. stipulacea)
GP22 (V. vexillata) GP11 (V. aconitifolia) GP13 (V. stipulacea)
GP21 (V. radiata var. setulosa) GP10 (V. aconitifolia)
GP20 (V. glabrescence) GP2 (V. umbellata
cultivated)
GP1 (V. umbellata
cultivated)
Table 5. Nei’s unbiased measures of genetic identity and genetic distance based on 200 SSR markers.
Table 5. Nei’s unbiased measures of genetic identity and genetic distance based on 200 SSR markers.
Pop IDPop 1Pop 2Pop 3
Pop 20.374--
Pop 30.2080.442-
Pop 40.2500.4580.189
Table 6. Grouping of 25 accessions of 13 Vigna species based on population structure analysis.
Table 6. Grouping of 25 accessions of 13 Vigna species based on population structure analysis.
Composition of Sub-Population 1Composition of Sub-Population 2
GP1 (V. umbellata cultivated)GP3 (V. umbellata)
GP2 (V. umbellata cultivated)GP4 (V. sublobata)
GP10 (V. aconitifolia)GP5 (V. sublobata)
GP11 (V. aconitifolia)GP6 (V. trilobata)
GP12 (V. aconitifolia TMV-1)GP7 (V. trilobata)
GP16 (V. radiata var. mungo)GP8 (V. trilobata)
GP17 (V. radiata var. mungo)GP9 (V. trilobata)
GP18 (V. radiata var. mungo)GP13 (V. stipulacea)
GP20 (V. glabrescence)GP14 (V. stipulacea)
GP22 (V. vexillata)GP15 (V. radiata var. radiata)
GP24 (V. dalzelliana)GP19 (V. slyestris)
GP21 (V. radiata var. setulosa)
GP23 (V. hainiana)
GP25 (V. unguiculata)
Table 7. Summary of analysis of molecular variance (AMOVA).
Table 7. Summary of analysis of molecular variance (AMOVA).
Source of
Variation		DfSum of SquaresMean Sum of SquaresEstimated Variance% VarianceF Statistics
Among populations2470.62235.317.24100.105
Among individuals222706.18123.0061.15890.989
Within individuals2517.500.7000.7010.990
Total493194.30 69.09100
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Saini, P.; Sirari, A.; Gnanesh, B.N.; Mandahal, K.S.; Ludhar, N.K.; Nagpal, S.; Patel, S.A.H.; Akhatar, J.; Saini, P.; Pratap, A.; et al. Assessment of Simple Sequence Repeat (SSR) Markers Derived from Whole-Genome Sequence (WGS) of Mungbean (Vigna radiata L. Wilczek): Cross-Species Transferability and Population Genetic Studies in Vigna Species. Horticulturae 2024, 10, 34. https://doi.org/10.3390/horticulturae10010034

AMA Style

Saini P, Sirari A, Gnanesh BN, Mandahal KS, Ludhar NK, Nagpal S, Patel SAH, Akhatar J, Saini P, Pratap A, et al. Assessment of Simple Sequence Repeat (SSR) Markers Derived from Whole-Genome Sequence (WGS) of Mungbean (Vigna radiata L. Wilczek): Cross-Species Transferability and Population Genetic Studies in Vigna Species. Horticulturae. 2024; 10(1):34. https://doi.org/10.3390/horticulturae10010034

Chicago/Turabian Style

Saini, Pawan, Asmita Sirari, Belaghihalli N. Gnanesh, Kamalpreet Singh Mandahal, Navkiran Kaur Ludhar, Sharon Nagpal, S. A. H. Patel, Javed Akhatar, Pooja Saini, Aditya Pratap, and et al. 2024. "Assessment of Simple Sequence Repeat (SSR) Markers Derived from Whole-Genome Sequence (WGS) of Mungbean (Vigna radiata L. Wilczek): Cross-Species Transferability and Population Genetic Studies in Vigna Species" Horticulturae 10, no. 1: 34. https://doi.org/10.3390/horticulturae10010034

APA Style

Saini, P., Sirari, A., Gnanesh, B. N., Mandahal, K. S., Ludhar, N. K., Nagpal, S., Patel, S. A. H., Akhatar, J., Saini, P., Pratap, A., Bains, T. S., & Yadav, I. S. (2024). Assessment of Simple Sequence Repeat (SSR) Markers Derived from Whole-Genome Sequence (WGS) of Mungbean (Vigna radiata L. Wilczek): Cross-Species Transferability and Population Genetic Studies in Vigna Species. Horticulturae, 10(1), 34. https://doi.org/10.3390/horticulturae10010034

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