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http://journals.tubitak.gov.tr/agriculture/

Turkish Journal of Agriculture and Forestry Turk J Agric For(2016) 40: 229-240© TÜBİTAKdoi:10.3906/tar-1508-59

Diversity analysis of green gram (Vigna radiata (L.) Wilczek)through morphological and molecular markers

Gunjeet KAUR1, Anurabh JOSHI1, Devendra JAIN1,*, Ravish CHOUDHARY2, Divya VYAS1

1Department of Molecular Biology and Biotechnology, Rajasthan College of Agriculture, Maharana Pratap University of Agriculture and Technology, Udaipur, India

2Indian Agricultural Research Institute Regional Station, Pusa, Samastipur, Bihar, India

* Correspondence: devendrajain@mpuat.ac.in

1. IntroductionPulses are the major source of dietary protein in vegetarian diets in most countries. Among the pulse crops, green gram (Vigna radiata (L.) Wilczek) is an important annual legume. It is cultivated in tropical, subtropical, and temperate zones of Asia including Bangladesh, India, Pakistan, Myanmar, Indonesia, Philippines, Sri Lanka, Nepal, China, Korea, and Japan (Shanmugasundaram, 2001). Being a legume, it has the ability to fix atmospheric nitrogen (30–50 kg/ha) (Chadha, 2010). India is the leading green gram cultivator, with up to 55% of the total world acreage and 45% of total production (Rishi, 2009; Singh N et al., 2013). Green gram is a diploid (2n) with the chromosome number 22, grown primarily in intercropping with wheat, maize, potato, etc. during the monsoon season and as a monoculture at other times (Singh et al., 2014). On account of its short duration, photoinsensitivity, and dense crop canopy, it assumes special significance in crop intensification, diversification, and conservation of natural resources and sustainability of production systems. For yield improvement, it is essential to have knowledge on the variability of different characters

such as days to 50% maturity, days to maturity, plant height (cm), number of branches/plant, number of pods/plant, pod length (cm), 1000-seed weight (g), seed yield per plant (g), biological yield (g), and harvest index (%). The grouping of genotypes based on these traits can be easily detected by naked eye and used in green gram breeding programs for improving the seeds’ physical quality. Morphological traits can be used to assess phenotypic variation in grow-ing environments and are also used as tools for the indirect analysis of genetic variability and diversity. Tabasum et al. (2010) reported the genetic variability in ten green gram genotypes, and the degree to which the abovementioned plant traits associate with yield could be useful for establishing selection criteria for high seed yield in green gram breeding. Genetic diversity is an important factor and also a prerequisite in any breeding program. Multivariate analysis by means of the Mahalanobis generalized distance (D2) statistic is a powerful tool in quantifying the degree of divergence at the genotypic level and might be an efficient tool in the quantitative estimation of genetic diversity in green gram genotypes (Mahalanobis, 1936).

Abstract: Twenty-three genotypes of green gram (Vigna radiata (L.) Wilczek) were collected to determine the extent of genetic diversity through morphological characters as well as random amplified polymorphic DNA (RAPD), inter-simple sequence repeat (ISSR), and simple sequence repeat (SSR) fingerprinting results. Analysis of variance revealed significant mean square values that indicated a substantial degree of genetic variability among the genotypes. Significant genetic variance was observed for days to 50% maturity, days to maturity, plant height (cm), number of branches/plant, number of pods/plant, pod length (cm), 1000-seed weight (g), seed yield/plant (g), biological yield (g), and harvest index (%) during cultivation in three different environments. Hierarchical Euclidean and cluster analysis based on 10 morphological characteristics grouped all 23 genotypes into three divergent clusters. RAPD (15), ISSR (13), and SSR (10) markers produced a total of 216 bands, of which 190 exhibited polymorphism. The similarity coefficients were significant for all markers; however, they were higher in ISSR than in RAPD or SSR. In the UPGMA dendrogram based on the combined morphological, RAPD, ISSR, and SSR results, the 23 genotypes were divided into three main clusters. The present study revealed that morphological and molecular markers may be successfully utilized for determining genetic diversity and relationships in green gram genotypes and could be used in green gram breeding programs.

Key words: Green gram genotypes, genetic diversity, morphological markers, RAPD, ISSR, SSR

Received: 17.08.2015 Accepted/Published Online: 21.11.2015 Final Version: 05.02.2016

Research Article

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Molecular techniques using DNA polymorphism are increasingly used to characterize and identify novel genotypes for uses in the crop breeding process (O’ Neill et al., 2003). DNA-based markers are less affected by age, the physiological condition of samples, and environmental factors. They are not tissue-specific and thus can be detected at any phase of organism development. The power of discrimination of DNA-based markers is so high that very closely related varieties can be differentiated. Genetic markers are the traits that can differentiate between the parents and must be accurately reproduced in the progeny. Assessment of the genetic variation in green gram has been carried out using different types of molecular markers including random amplified polymorphic DNA (RAPD) (Santalla et al., 1998; Lakhanpaul et al., 2000), amplified fragment length polymorphism (AFLP) (Bhat et al., 2005), inter-simple sequence repeat (ISSR) (Reddy et al., 2008), and simple sequence repeat (SSR) (Gwag et al., 2010). Molecular markers are indispensable for genomic study. Among various marker systems such as restriction fragment length polymorphism (RFLP), RAPD, sequence tagged sites (STSs), and AFLP, SSRs have pivotal importance because of their reproducibility, multiallelic nature, codominant inheritance, relative abundance, and good genetic coverage. SSRs are clusters of short tandem repeated nucleotide bases distributed throughout the genome. Major features that make SSRs very popular are their abundant distribution in the genomes examined to date and their hypervariable nature (Singh R et al., 2013). Hence, genetic variability and divergence present in the materials is an important tool for any breeding program. The assessment of variation would provide us a correct picture of the extent of variation, further helping us to improve the genotypes’ responses to biotic and abiotic stresses. The main objective of this study was to characterize green gram genotypes using morphological and molecular markers in order to evaluate the genetic diversity and relationships among genotypes lines.

2. Materials and methodsThe present field investigations were carried out in three environments at the Agricultural Research Station, Durgapura, Jobner Agriculture University, Jaipur (26°59′N, 75°52′E), during summer (‘zaid’) of 2012 and 2013, and in 2013 (monsoon season, ‘kharif ’) at the Instructional Farm, Rajasthan College of Agriculture, MPUAT, Udaipur (24°35′N, 70°42′E), Rajasthan (India).2.1. Plant materialsPlants of 23 genotypes lines, representing native as well as foreign plants collected from different parts of India, were maintained and considered for the present study (Table 1). Newly emerged leaf samples of the cultivars were used for DNA extraction.

2.2. Morphological analysisTen morphometric characters were evaluated for plant specimens from 23 genotypes lines. Data on morphological characters were standardized using the YBAR option of the Stand program from the NTSYS-pc 2.1 software (Rohlf, 2004). Duplicate measurements for each specimen were averaged and were used to design a data matrix of pairwise similarities between genotypes lines. The simple matching coefficient was used to measure similarity, as it was the coefficient with the best results following a cophenetic test. Principal component analysis (PCA) was also used for nonhierarchical relationships among the genotypes. Eigenvalues and eigenvectors were calculated by the Eigen program using a correlation matrix as input (calculated using standardized morphological data), and 2D and 3D plots were used to generate the two-dimensional PCA plot from NTSYS-pc 2.1 (Rohlf, 2004).2.3. Genomic DNA extraction and quantificationTotal genomic DNA was isolated from 23 genotype lines using a cetyltrimethylammonium bromide (CTAB) extraction protocol (Doyle and Doyle, 1987) and was then quantified spectrophotometrically on a nanospectrophotometer (Implen, Germany).2.3.1. RAPD-PCR amplificationTwenty decamer primers (Operon Technologies Inc., USA) were screened in the green gram genotypes, of which 15 primers generated polymorphic and reproducible banding patterns and were selected for final analysis. PCR amplification was carried out in a reaction volume of 20 µL containing 200 µM dNTP mix, 1.5 mM MgCl2, 1 U of Taq polymerase, 1X reaction buffer, 0.5 µM primer, double-distilled water, and 20 ng of genomic DNA. The amplification was performed in an Eppendorf Mastercycler (Germany) with reaction conditions programmed as initial predenaturation at 94 °C for 4 min, followed by 44 cycles of denaturation at 94 °C for 1 min, annealing at 37 °C for 1 min, and extension at 72 °C for 2 min. A final extension was done for 10 min at 72 °C with a hold temperature of 4 °C. Amplification products were separated by electrophoresis on 1.2% agarose gels stained with ethidium bromide at constant voltage (3 V/cm of gel) until bromophenol blue/loading dye migrated to the other end of the gel. The gel was visualized on a UV-transilluminator and photographed using a gel documentation system (Alpha DigiDoc, Germany).2.3.2. ISSR-PCR amplificationA total of 20 primers identified by the University of British Columbia (UBC) were procured from Bangalore Genei Pvt. Ltd., Bangalore, India, and were used for ISSR-PCR optimization trials. Thirteen primers that gave the best amplification results with the sample DNA were selected for final ISSR-PCR analysis. PCR amplification was carried out in a 20-µL reaction volume containing 200 µM dNTP

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mix, 1 U of Taq polymerase, 1.5 mM MgCl2, 1X reaction buffer, 0.5 µM primer, and double-distilled water, and 20 ng of genomic DNA. The amplification was performed in an Eppendorf Mastercycler with reaction conditions programmed as initial predenaturation at 94 °C for 4 min, followed by 35 cycles of denaturation at 94 °C for 1 min, annealing at 42.9–60 °C for 1 min, and extension at 72 °C for 2 min with a hold temperature of 4 °C. A final extension was done for 10 min at 72 °C. Amplification products were separated by electrophoresis on 1.2% agarose gels and photographed using a gel documentation system.2.3.3. SSR-PCR amplificationForty SSRs or microsatellite repeat primers (1–6 bp; monomers to hexamers) were screened in the green gram genotypes, 10 of which generated polymorphic and reproducible banding patterns and were selected for final analysis. PCR amplification was carried out in a 20-µL reaction volume containing 200 µM dNTP mix, 1.5 mM MgCl2, 1 U of Taq polymerase, 1X reaction buffer, 0.5 µM

primer, double-distilled water, and 20 ng of genomic DNA. The amplification was performed with reaction conditions programmed as initial predenaturation at 94 °C for 4 min, followed by 30 cycles of denaturation at 94 °C for 1 min, annealing at 57.3–70.5 °C for 1 min, and extension at 72 °C for 45 s. A final extension was done for 10 min at 72 °C with a hold temperature of 4 °C. Amplification products were separated by electrophoresis on 4% metaphor agarose (Sigma-Aldrich, India) and photographed.2.4. Data analysisAmplified bands generated from RAPD, ISSR, and SSR-PCR amplification were scored based on the presence (1) or absence (0) of bands for each primer and were used to calculate a similarity matrix (SM) using NTSYS-pc version 2.1 (Rohlf, 2004). Cluster analysis was performed on both morphological and molecular data. SMs were compiled for all pairs of accessions using SM similarity coefficients, using SIMQUAL, and then cluster analysis was done using unweighted pair-group method with arithmetic mean

Table 1. Mean value of the morphological traits of different green gram genotypes from 3 different environments.

Genotypes Sourceof genotypes

Daysto 50%flowering

Days to maturity

Plantheight(cm)

No. of branches/plant

No. of pods/plant

Pod length

1000-seed weight

Seed yield/plant

Biological yield/plant

Harvest index

PUSA-672 IARI, New Delhi 29 75 36.99 1.80 22.22 6.00 39.22 6.10 16.88 36.14

AKM- 962 PKV, Akola 28 72 32.00 2.00 15.22 5.50 32.22 3.43 15.22 22.55

UPM-02-18 GBPAU, Pantnagar 28 72 43.16 2.50 28.66 6.40 27.44 5.51 15.44 36.48

ML-729 PAU, Ludhiana 32 74 46.00 1.20 14.00 6.13 33.89 3.32 12.30 27.00

EC-398885 AVRDC, Taiwan 34 76 43.50 2.20 27.22 6.00 32.33 6.16 16.22 37.98

IPM-02-01 IIPR, Kanpur 34 71 37.99 3.30 26.89 5.73 26.45 4.98 14.45 34.45

IPM-02-3 IIPR, Kanpur 32 74 41.40 1.80 24.00 7.00 25.66 4.31 12.11 35.60

IPM-02-14 IIPR, Kanpur 34 74 31.33 2.40 16.77 6.90 28.33 3.33 14.33 23.21

IPOI-1539 IIPR, Kanpur 28 75 35.65 2.00 14.55 5.33 39.00 3.97 17.00 23.37

RMG-62 RAU, Durgapura 28 71 31.80 2.00 16.00 4.33 40.00 4.48 16.00 28.00

PDM-288 IIPR, Kanpur 32 71 48.33 1.33 14.22 6.51 37.44 3.73 13.33 27.96

RMG-353 RAU, Durgapura 32 72 48.66 1.66 12.48 8.66 41.77 3.65 15.11 24.15

PRATEEKSHA-NEPAL AVRDC, Taiwan 33 75 44.83 1.22 16.00 6.16 42.00 4.70 12.65 37.19

MEHA IIPR, Kanpur 31 75 41.99 1.00 12.44 6.00 40.30 3.51 10.66 32.92

PANT GBPAU, Pantnagar 31 72 35.99 2.00 15.48 6.56 40.22 4.36 12.44 35.03

ASHA HAU, Hisar 31 72 52.66 2.00 14.72 8.00 39.44 4.06 11.00 36.94

MG-331 Gurdaspur, Punjab 29 76 45.00 2.33 18.00 8.66 36.44 4.59 14.23 32.27

GM-9925 S.K. Nagar, Gujarat 34 76 58.65 1.33 19.33 7.17 32.33 4.37 15.44 28.33

IC-393407 NBPGR, New Delhi 30 76 48.22 2.00 22.40 5.27 38.10 5.97 17.89 33.39

DRA-24 IIPR, Kanpur 30 75 54.05 1.33 16 6.4 38.44 4.31 17 25.33

SAMRAT IIPR, Kanpur 32 75 33.50 2.00 14.22 6.2 39.2 3.9 16.22 24.06

HUM-1 BHU, Varanasi 30 78 40.17 1.66 18 6.4 35.55 4.48 17.22 26.01

HUM-12 BHU, Varanasi 30 78 35.17 3.00 20 7.17 42 5.88 18 32.67

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(UPGMA) analysis and dendrograms were constructed using the SAHN program. The cophenetic correlation was calculated to find the degree of association between the original SM and the tree matrix in both morphological and molecular analyses. Using the Mantel test (Mantel, 1967), a comparison between both methods was performed for morphological data of the green gram genotypes for which both data sets were available by calculating the correlation between the two data sets in NTSYS-pc. Using the same software, PCA was also carried out to identify any genetic association among the genotypes.2.5. Polymorphism information content, effective multiple ratio, resolving power, and marker indexTo measure the polymorphism information of RAPD, ISSR, and SSR marker systems, the polymorphism information content (PIC) was calculated according to the following formula (Smith et al., 1997):

PIC = 1–pii

n2

1=/ ,

where N = total number of alleles detected for a locus of a marker and Pi = frequency of the first allele.

The effective multiple ratio (EMR = npβ) is the product of the number of polymorphic loci (np) in the genotype analyzed and the fraction of markers that were polymorphic (β).

The resolving power (Rp) of each primer was calculated as Rp = ΣIb, where Ib (band informativeness) takes the value of 1 [ 2 × (0.5 − p) ], p being the proportion of genotypes of different Vigna species containing that band (Prevost and Wilkinson, 1999).

Marker index (MI), defined as the product of the polymorphism percentage and PIC, is used to estimate the overall utility of each marker system and was calculated using the following equation:

MI = PIC × EMR (Milbourne et al., 1997).

3. Results3.1. Morphological analysisIn the present investigation, 10 important yield-related morphological and qualitative characters have been studied to evaluate the pattern and extent of genetic variability and relatedness among 23 genotypes of green gram. The results obtained from the mean value of morphological characters from 3 environments (Table 1) demonstrated that days to 50% flowering or early flowering (28 days) were showed by AKM-962, UPM-02-18, IPOI-1539, and RMG-62, whereas late flowering (34 days) was observed in EC-398885, IPM-02-01, IPM-02-14, and GM-9925. Genotypes IPM-02-01, RMG-62, and PDM-288 were found to mature early (71 days), whereas genotypes HUM-1 and HUM-12

were late (78 days). Genotype GM-9925 was found as the tallest (58.65 cm) among all the genotypes, whereas IPM-02-14 was the shortest (31.33 cm). Maximum number of branches (3.30) was found in IPM-0201 while it was minimum (1.00) in MEHA. Maximum numbers of pods (28.66) were obtained in UPM-02-18, whereas minimum pods (12.44) were found in MEHA. Pod length was found maximum (8.66 cm) in RMG-353 and MG-331, whereas it was minimum (5.27 cm) in IC-393407. The maximum 1000-seed weight (42.00 g) was found in PRATEEKSHA-NEPAL and HUM-12, while it was minimum (25.66 g) in IPM-02-03. Maximum seed yield per plant (6.16 g) was found in EC-398885, while it was minimum (3.32 g) in ML-729. The highest biological yield per plant (18.00 g) was found in HUM-1, while the lowest (10.66 g) was found in MEHA. Harvest index was found maximum (37.98%) in EC-398885 while it was minimum (22.55%) in AKM-962.

Comparative analysis of the 10 morphological characters revealed moderate variation. The pairwise similarity coefficient based on the SM among the genotypes of green gram ranged from 0.01 to 0.30 with an average of 0.15 based on morphological data. A dendrogram generated from morphological data grouped all 23 genotypes into 3 major clusters (Figure 1a). The first cluster was the biggest, comprising 10 genotypes lines, and was subdivided into I-A and I-B. Subcluster I-A comprised 5 genotypes lines: PUSA-672, MEHA, IPOI-1539, DRA-24, and PRATEEKSHA-NEPAL. In this subcluster, PRATEEKSHA-NEPAL was distinct from other genotypes with a similarity value of 0.15. Within this subcluster, the genotypes PUSA-672 and MEHA and the genotypes IPOI-1539 and DRA-24 were quite similar, with a similarity value of 0.21. Subcluster I-B comprised 5 genotypes: AKM-962, UPM-02-18, RMG-62, PANT, and ASHA. Within this subcluster, genotypes PANT and ASHA were quite similar, showing a similarity value of 0.30, while RMG-62 was distinct from other remaining genotypes with a similarity value of 0.15.

The second cluster comprised 6 genotypes and was subdivided into two subclusters, II-A and II-B. Subcluster II-A comprised the ML-729, IPM-02-3, and IPM-02-14 genotypes, in which IPM-02-14 was distinct from the rest of the genotypes and separated with a similarity value of 0.098. Subcluster II-B also comprised 3 genotypes, PDM-288, SAMRAT, and RMG-353. In this subcluster, RMG-353 was distinct from the other genotypes with a similarity value of 0.098.

The third cluster comprised 7 genotypes and subdivided into two subclusters, III-A and III-B. Subcluster III-A comprised 4 genotypes, EC-398885, GM-9925, IPM-02-01, and MG-331. Within this cluster, genotypes EC-398885 and GM-9925 were quite similar,

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Coefficient0.02 0.09 0.16 0.23 0.30

PUSA–672 MEHA IPOI–1539 DRA–24 PRATEEKSHA–NEPAL AKM–962 UPM–02–18 RMG–62 PANT ASHA ML–729 IPM–02–3 IPM–02–14 PDM–288 SAMRAT RMG–353 EC–398885 GM–9925 IPM–02–01 MG–331 IC–393407 HUM–1 HUM–12

I

I–A

I–B

II II–A

II–B

III

III–A

III–B

Dim–1–0.77 –0.34 0.09 0.52 0.95

Dim–2

–0.69

–0.30

0.09

0.48

0.87

PUSA–672

AKM–962

UPM–02–18ML–729

EC–398885

IPM–02–01

IPM–02–3

IPM–02–14

IPOI–1539RMG–62

PDM–288

RMG–353

PRATEEKSHA–NEPAL

MEHAPANT

ASHA

MG–331

GM–9925

IC–393407DRA–24

SAMRATHUM–1 HUM–12

II

I

III

IV

a

b

Figure 1. Dendrogram generated from morphological traits of 23 genotypes of green gram using UPGMA (a) and 2D plot (b) methods.

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showing a similarity value of 0.30. In this subcluster, MG-331 was the most distinct from all other genotypes with a similarity value of 0.06. Subcluster III-B comprised 3 genotypes, IC-393407, HUM-1, and HUM-12. Genotype IC-393407 was distinct from the rest of the genotypes and separated with a similarity value of 0.098. Based on Mantel Z-statistics (Mantel, 1967), the correlation coefficient (r) was estimated as 0.68. This value was considered a good fit of the UPGMA cluster pattern to the data.

The two-dimensional plot generated from PCA showed 4 groups that were found to be similar to the clustering pattern of the UPGMA dendrogram. In the 2D plot, genotype UPM-02-18 was found with EC-398885 and IPM-02-01, constituting one separate group, whereas in UPGMA clustering, UPM-02-18 was present in subcluster I-A, while EC-38885 and IPM-02-01 were grouped together in one cluster. The SAMRAT genotype was grouped together in the 2D plot with IPOI-1539, RMG-62, and AKM-962, while in UPGMA it was found in cluster II with different genotypes. In the 2D plot, PUSA-672 was found in a separate group with HUM-1, HUM-12, and IC-393407, while in UPGMA, it was found in cluster I (Figure 1b).

The analysis gave 19 principal components (PCs), out of which the first 10 principal components contributed 98.79% of the total variability. The first 5 principal components accounted for 87.17% of the total variability, and the first 3 accounted for 72.10% of the variance, in which the highest variation was contributed by the first component (31.74%), followed by the second (23.80%) and third (16.56%) components. The first PC was influenced by the characteristics of plant height, pod length, and 1000-seed weight (Table 2). The second PC was influenced by days to maturity, number of branches per plant, number

of pods per plant, 1000-seed weight, seed yield per plant, biological yield per plant, and harvest index. The third PC was mostly influenced by days to 50% maturity, plant height, number of branches per plant, pod length, seed yield per plant, and biological yield per plant, as shown in Table 2.3.2. Divergence based on RAPD-ISSR and SSR finger printingThe genetic divergence was analyzed among the 23 green gram genotypes using RAPD (15), ISSR (13), and SSR (10) primers. Sequences of all three molecular markers are showed in Table 3. A total of 38 markers amplified 216 bands, of which 190 showed polymorphism while the remaining 26 bands showed monomorphism. The maximum polymorphism was found with RAPD markers (93.48%), followed by SSR (86.66%) and ISSR (82%) markers. The polymorphism percentage, PIC, MI, and Rp for each RAPD, ISSR, and SSR marker were calculated to depict their discriminatory power, as represented in Table 3.

Fifteen primers were selected for RAPD analysis based on reproducibility and banding patterns. A total of 126 bands were generated from 15 RAPD primers, of which 117 bands were polymorphic (84.32%), with an average of 7.8 polymorphic bands per primer. The fragment size ranged from 200 to 2900 bp (Table 3). Each primer was amplified at a range of 2–17 amplicons with an average of 8.4 amplicons per primer. The OPO-01 primer amplified the highest number of amplicons (17) with 100% polymorphism, whereas the OPP-09 primer generated the lowest number of amplicons (2). The polymorphism percentage ranged from 67% to 100%. The average rate of polymorphism across all 23 genotypes was 93.48%. Primers OPA-01 and OPP-06 detected two unique bands ranging between 250 and 2500 bp in two genotypes (PUSA-672

Table 2. Details of principal components.

S. no. Character PC1 PC2 PC3

1. Days to 50% flowering –3.41 –7.84 1.12

2. Days to maturity –7.69 1.13 –3.60

3. Plant height 6.90 –1.14 4.56

4. Number of branches/plant –1.08 8.78 2.10

5. Number of pods/plant –1.14 4.47 6.98

6. Pod length 8.65 –1.09 2.29

7. 1000-seed weight 8.56 2.42 –1.09

8. Seed yield/plant –1.01 9.80 3.88

9. Biological yield/plant –1.14 6.71 4.77

10. Harvest index –3.02 1.11 –8.13

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Table 3. Details of amplified bands generated in 23 genotypes of green gram based on RAPD, ISSR and SSR primers.

Primer Sequence Tm (°C) TB PB Β PIC EMR MI RpRAPDOPA-01 CAGGCCCTTC 37 17 17 1.00 0.31 17.00 5.27 7.65OPA-02 TGCCGAGCTG 37 8 8 1.00 0.32 8.00 2.56 3.74OPC-08 TGGACCGGTG 37 11 11 1.00 0.38 11.00 4.18 6.78OPE-03 CCAGATGCAC 37 9 9 1.00 0.24 9.00 2.16 3.04OPF-17 AACCCGGGAA 37 6 4 0.67 0.17 2.67 0.45 1.3OPF-19 CCTCTAGACC 37 11 11 1.00 0.37 11.00 4.07 6.17OPK-06 CACCTTTCCC 37 7 7 1.00 0.34 7.00 2.38 3.13OPK-07 AGCGAGCAAG 37 10 10 1.00 0.38 10.00 3.8 6.00OPP-04 GTGTCTCAGG 37 10 8 0.8 0.35 6.40 2.24 4.00OPP-05 CCCCGGTAAC 37 9 8 0.89 0.30 7.11 2.13 4.52OPP-06 GTGGGCTGAC 37 12 8 0.67 0.21 5.33 1.12 2.78OPP-07 GTCCATGCCA 37 3 3 1.00 0.20 3.00 0.6 3.91OPP-08 ACATCGCCCA 37 3 3 1.00 0.42 3.00 1.26 1.91OPP-09 GTGGTCCGCA 37 2 2 1.00 0.46 2.00 0.92 2.35OPP-10 TCCCGCCTAC 37 8 8 1.00 0.44 8.00 3.52 1.39Average 8.4 7.8 0.93 0.32 7.36 2.44 3.91ISSRUBC-810 (GA)8T 42.9 11 10 0.91 0.35 10 3.50 5.57UBC-813 (CT)8T 43.3 2 2 1.00 0.22 2 0.44 0.52UBC-815 (CT)8G 44.9 3 3 1.00 0.19 3 0.57 0.70UBC-817 (CA)8A 52.0 4 2 0.50 0.14 2 0.28 0.96UBC-818 (CA)8G 52.0 4 4 1.00 0.13 4 0.52 0.61UBC-820 (GT)8T 50.0 6 6 1.00 0.24 6 1.44 1.91UBC-822 (TC)8A 45.0 8 7 0.88 0.23 7 1.61 2.43UBC-826 (AC)8C 52.0 7 7 1.00 0.34 7 2.38 3.39UBC-836 (AG)8YA 43.3 8 3 0.38 0.07 3 0.21 0.78UBC-840 (GA)8YT 45.0 5 3 0.6 0.12 3 0.36 0.70UBC-848 (CA)8RG 55.5 7 4 0.57 0.17 4 0.68 1.57UBC-873 (GACA)4 45.0 7 6 0.86 0.25 6 1.50 2.26UBC-878 (GGAT)4 60.0 3 3 1.00 0.26 3 0.78 0.96Average 5.76 4.61 0.82 0.208 4.61 1.09 1.72SSR

CEDG006 AATTGCTCTCGAACCAGCTCGGTGTACAAGTGTGTGCAAG

58.053.4 1 1 1.00 0.15 1 0.15 0.17

CEDG010 TGGGCTACCAACTTTTCCTCTGAGCGACATCTTCAACACG2

57.659.4 1 0 0.00 0.00 0 0.00 0.00

CEDG050 GGCAGAATCGTACAAGTGGTCAGATTCTCGCTTGCATG

50.657.9 1 1 1.00 0.45 1 0.45 0.70

CEDG056 GAACTTAACTTGGGTTGTCTGCGCTATGATGGAAGAGGGCAT2GG

56.463.6 2 2 1.00 0.26 2 0.52 0.78

CEDG088 TCTTGTCATTTAGCACTTAGCACGTTGTTGTTTACTAAGAGCCCGTGT

59.459.9 1 0 0.00 0.00 0 0.00 0.00

CEDG092 TCTTTTGGTTGTAGCAGGATGAACTACAAGTGATATGCAACGGTTAGG

60.358.9 1 1 1.00 0.34 1 0.34 0.43

CEDG214 CACTCACTGCAAAGAGCAACCTACCTATCTGAGGGACAC

57.547.5 3 3 1.00 0.17 3 0.51 0.61

CEDG232 GATGACCAAGGTAACGTGGGACAGATCCAAAACGTG

50.253.0 1 1 1.00 0.15 1 0.15 0.17

CEDG253 CACTTCCATGATGACTCACCCACCCTTCTTTATCCTCTTCG

54.656.5 2 2 1.00 0.23 2 0.46 1.52

CEDG305 GCAGCTTCACATGCATAGTACGAACTTAACTTGGGTTGTCTGC

54.556.4 2 2 1.00 0.30 2 0.60 0.78

Average 1.5 1.3 0.8 0.205 1.3 0.318 0.516

Tm - Annealing temperature; TB - total no. of amplified bands; PB - polymorphic bands; β - proportion of polymorphic bands; PIC - polymorphism information content; EMR - effective multiplex ratio; MI - marker index; Rp - resolving power.

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and HUM-12). Jaccard’s similarity coefficient values for RAPD primers ranged from 0.28 to 0.90 with an average of 0.59. Based on the dendrogram generated through the UPGMA method and PCA, most of the genotypes could be organized into four main clusters. Cluster I included three genotypes, while cluster II included two and cluster III included thirteen genotypes. Cluster IV included four genotypes. Genotype EC-398885 lay apart from all four

clusters. A minimum similarity coefficient of 0.28 was observed between genotypes RMG-353 and EC-398885 that indicated maximum genetic divergence. Based on Mantel Z-statistics (Mantel, 1967), the correlation coefficient (r) was estimated as 0.91. This value was considered a good fit of the UPGMA cluster pattern to RAPD data. Amplification profiling of 23 genotypes with the OPF-017 RAPD primer is shown in Figure 2A.

B

A

Figure 2. Representative gel profiles of green gram based on RAPD, ISSR, and SSR markers (A) OPF-017, (B) UBC-848, and (C) CEDG305, respectively. G1: PUSA-672, G2: AKM-962, G3: UPM-02-18, G4: ML-729, G5: EC-398885, G6: IPM-02-1, G7: IPM-02-03, G8: IPM-02-14, G9: IPOI-1539, G10: RMG-62, G11: PDM-288, G12: RMG-353, G13: PRATEEKSHA-NEPAL, G14: MEHA, G15: PANT, G16: ASHA, G17: MG331, G18: GM9925, G19: IC-393407, G20: DRA-24, G21: SAMRAT, G22: HUM-1, and G23: HUM-12.

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Thirteen primers were selected for ISSR analysis based on reproducibility and banding patterns. A total of 75 bands were generated, of which 60 bands were polymorphic (82%). Each primer amplified 2–11 polymorphic amplicons with an average of 6.5 amplicons per primer. Primer UBC-810 amplified the highest number of amplicons at 11, whereas UBC-813 amplified the lowest number of polymorphic amplicons at 2. The polymorphism percentage ranged from 38% (primer UBC-836) to 100%. The average polymorphism rate across all 23 genotypes lines was 82%. The overall size of the PCR-amplified fragments ranged from 200 to 2500 bp (Table 3). Four unique bands were detected in three genotypes, IC-393407, PUSA-672, and GM-9925, with 3 ISSR primers (UBC-818, UBC-820, and UBC-826). Genotype IC-393407 gave the maximum number of distinct bands, i.e. 2. The size of these unique bands ranged from 300 to 950 bp. Jaccard’s similarity coefficient values for ISSR primers ranged from 0.38 to 0.94 with an average of 0.66. Based on the dendrogram generated through the UPGMA method and PCA, most of the genotypes could be divided into two main clusters. Cluster I included twenty genotypes, while cluster II included three genotypes. Genotype GM-9925 lay apart from the two clusters. A minimum similarity coefficient of 0.38 was observed between genotypes GM-9925 and EC-398885 that indicated maximum genetic divergence. Based on Mantel Z-statistics (Mantel, 1967), the correlation coefficient (r) was estimated as 0.93. This value was considered a good fit of the UPGMA cluster pattern to ISSR data. Amplification profiling of 23 genotypes with the UBC-848 ISSR primer is shown in Figure 2B.

Fifteen alleles were analyzed using 10 SSR primers, of which 13 were polymorphic. The number of alleles per locus ranged from one (CEDG006, CEDG010, CEDG050, CEDG088, CEDG092, and CEDG232) to three (CEDG 214), with an average of 1.5 alleles per locus. The overall size of amplified products ranged from 100 to 190 bp (Table 3). Jaccard’s similarity coefficient values for SSR primers ranged from 0.28 to 1.00 with an average of 0.64. Based on the dendrogram generated through the UPGMA method and PCA, most of the genotypes were divided into three main clusters. Cluster I consisted of two genotypes, while cluster II included eleven and cluster III included seven. Genotypes EC-398885, IPM-02-3, and PRATEEKSHA-NEPAL lay apart from all the clusters. A minimum similarity coefficient of 0.40 was observed between genotypes PDM-288 and EC-398885 and between MEHA and EC-398885 that showed maximum genetic divergence. Based on Mantel Z-statistics (Mantel, 1967), the correlation coefficient (r) was estimated as 0.89. This value was considered a good fit of the UPGMA cluster pattern to SSR data. The 2D plot generated from PCA of

SSR data was also consistent with the clustering pattern of the UPGMA dendrogram. Amplification profiling of 23 genotypes with SSR primer set CEDG305 is shown in Figure 2C.3.3. PIC analysesThe highest (0.46) PIC value through RAPD fingerprinting data was recorded for primer OPP-17 and the lowest (0.17) for OPP-09. In ISSR, the highest (0.35) and the lowest (0.07) PIC values were recorded for primers UBC-810 and UBC-836, respectively. PIC values, a measure of allelic diversity, ranged from minimum (0.00) in CEDG010 and CEDG088 to maximum (0.45) in CEDG050. The average PIC values of RAPD, ISSR, and SSR were found to be 0.32, 0.208, and 0.225, respectively (Table 3). All these marker systems efficiently discriminated the different genotypes of green gram. The average MI for RAPD was 2.44, followed by ISSR with 1.09 and SSR with 0.318. Similar trends were obtained in a previous study (Singh N et al., 2013). The resolving power ranged from 0.52 to 3.91 for the studied markers. The highest value of resolving power (3.91) was obtained in RAPD, followed by ISSR (1.72) and SSR (0.52). Similar observations were found in earlier reports (Singh R et al., 2013).3.4. Cumulative data analysis of morphology, RAPD, ISSR, and SSRPairwise similarity among the genotypes ranged from 0.51 to 0.87 with an average of 0.69 based on combined morphometric, RAPD, ISSR, and SSR data. The highest similarity (87%) was observed between MG-331 and ASHA and between DRA-24 and MG-331, whereas the lowest was observed between GM-9925 and EC-398885 with a similarity value of 0.51. A dendrogram based on combined morphometric, RAPD, ISSR, and SSR data clustered all 23 genotypes into 3 major clusters (Figure 3a). The first cluster comprised 3 genotypes, PUSA-672, EC-398885, and IPM-02-3. Within this cluster, EC-398885 and IPM-02-3 were the most similar morphologically and genetically, showing a similarity value of 0.70. In this group, PUSA-672 was distinct from the other genotypes, with a similarity value of 0.67. The second cluster was the biggest one, comprising 17 genotypes: AKM-962, UPM-02-18, ML-729, IPM-02-01, IPOI-1539, RMG-353, PRATEEKSHA-NEPAL, DRA-24, ASHA, MG-331, SAMRAT, RMG-62, PDM-288, MEHA, PANT, IC-393407, and HUM-12. This cluster was again subdivided into two subclusters, II-A and II-B. Subcluster II-A comprised four genotypes: AKM-962, UPM-02-18, ML-729, and IPM-02-01. Within this cluster, AKM-962 and UPM-02-18 were most similar to each other with a similarity value of 0.82. Subcluster II-B comprised 13 genotypes: IPOI-1539, RMG-353, PRATEEKSHA-NEPAL, DRA-24, ASHA, MG-331, SAMRAT, RMG-62, PDM-288, MEHA, PANT, IC-393407, and HUM-12. Within this subcluster, ASHA and MG-331 were similar to

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Coe�icient0.61 0.68 0.74 0.81 0.88

PUSA–672 EC–398885 IPM–02–3 AKM–962 UPM–02–18 ML–729 IPM–02–01 IPOI–1539 RMG–353 PRATEEKSHA–NEPAL DRA–24 ASHA MG–331 SAMRAT RMG–62 PDM–288 MEHA PANT IC–393407 HUM–12 IPM–02–14 GM–9925 HUM–1

I

II

III

II–A

II–B

a

b

Dim–10.68 0.74 0.80 0.86 0.92

Dim–2

–0.21

–0.02

0.16

0.34

0.53

PUSA–672'

UPM–02–18'

EC–398885'

IPM–02–01'

IPM–02–3'

IPM–02–14'

RMG–62'MG–331'

GM–9925'D

RA

–24

HUM–1'

I

II III

PRATEEKSHA

-NEPAL

Figure 3. UPGMA dendrogram (a) and 2D plot (b) of 23 genotype lines of green gram generated based on combined morphometric, RAPD, ISSR, and SSR data.

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each other with a maximum similarity coefficient of 0.88, followed by PRATEEKSHA-NEPAL and DRA-24 with a similarity value of 0.87. The third cluster comprised 3 genotypes: IPM-02-14, GM-9925, and HUM-12. Within this cluster GM-9925 and HUM-12 were the most similar morphologically and genetically, showing a similarity value of 0.74. Based on Mantel Z-statistics (Mantel, 1967), the correlation coefficient (r) was estimated as 0.93. This value was considered a good fit of the UPGMA cluster pattern to the cumulative morphological and molecular data.

The 2D plot generated from the PCA of the combined morphological, RAPD, ISSR, and SSR data (Figure 3b) also supported the clustering pattern of the UPGMA dendrogram. In the 2D plot, genotype PUSA-672 was grouped in cluster II, whereas in UPGMA clustering, it was grouped in cluster I. The analysis gave 22 PCs, out of which the first 10 PCs contributed 81.13% of the total variability of the analyzed genotypes. The first 5 PCs accounted for 61.75% of the total variability; the first 3 accounted for 46.17% of the variance, in which maximum variability was contributed by the first component (22.31%), followed by the second (14.59%) and third (9.27%) components.

The Mantel Z-test also revealed a low level of correlation between the morphological and molecular data matrix, whereas a high level of correlation was observed among RAPD, ISSR, and SSR data (data not shown).

4. DiscussionIn the present investigation, estimations of genetic variability to establish genetic relationships through morphological, RAPD, ISSR, and SSR marker analyses among the genotypes were successfully revealed. Morphological characterization based on 10 characters revealed significant diversity (62%) in leaf, fruit, and stem traits. The RAPD, ISSR, and SSR data generated from the 23 genotypes with 15, 13, and 10 primers, respectively, were sufficient to provide inferences on genetic divergence and relationships. The RAPD, ISSR, and SSR markers showed a high level of polymorphism (87.96%). Similarly, a high level of polymorphism (89.02%) was reported by Singh R et al. (2013), and Chattopadhyay et al. (2008) found 70% polymorphism in green gram with combined RAPD, ISSR, and SSR markers. PIC values were also recorded high with RAPD (0.32), while they were moderate with ISSR (0.208) and SSR (0.205) markers, which helps to detect polymorphism within green gram genotypes. Jaccard’s genetic similarity values of RAPD, ISSR, and SSR were found in the ranges of 0.28 to 0.90 (average: 0.59), 0.38 to 0.94 (average: 0.66), and 0.40 to 1.00 (average: 0.7), respectively, revealing a moderate level of genetic diversity through RAPD and ISSR markers, whereas a low level of diversity was observed through SSR within green gram clusters. This moderate level of genetic diversity within the

self-pollinated members of green gram genotypes from the genus Vigna suggests its moderate genetic base, which is possibly due to accumulation of novel gene combinations in response to dynamic pressures of natural selection.

In all the dendrograms (morphological, RAPD, ISSR, SSR, and combined), AKM-962 and UPM-02-18 were grouped together, showing a close genetic relationship, which might be due to their close genetic bases. For plant breeders close genetic relationships could provide an avenue for introgression of high-yielding and resistant genes into commercial and farmers’ varieties. DNA fingerprinting is a routine method employed to study the extent of genetic diversity across a set of genotypes or cultivars and group them into specific categories. Comparative studies in  Vigna  species involving RAPD, AFLP, ISSR, and SSR marker systems were successfully used and reported by researchers (Souframanien and Gopalakrishna, 2004; Gillaspie et al., 2005; Dikshit et al., 2007; Muthusamy et al., 2008); however, the reports on green gram are very limited. The discriminative power of DNA markers used as tools to characterize green gram is very important because they can be used to assess the genetic diversity among the genotypes of green gram. In the present study, we used three different molecular marker systems along with morphological markers to define genetic relationships between green gram genotypes with high levels of polymorphism, consistent with earlier reports with different marker systems (Muthusamy et al., 2008; Singh N et al., 2013). The Mantel correlation values for the dendrogram based on morphological data were moderate (r = 0.68). Low regression values were reported earlier in Vigna unguiculata genotypes (Gajera et al., 2014). 

In conclusion, the results indicate the presence of moderate genetic variability among the elite green gram genotypes. SSR markers are useful in the assessment of green gram diversity and the selection of a core collection to enhance the efficiency of genotype management for use in green gram breeding and conservation. Characterization and assessment of diversity among the green gram genotypes have great significance in designing breeding strategies, both for qualitative and quantitative traits. In this study, we have successfully assessed the levels of inter and intraspecific diversity relationships among different cultivated and wild genotypes. Results derived from this study would be highly useful in green gram breeding programs and may be used for further crop improvement using advance marker systems.

AcknowledgmentsGunjeet thanks UGC, New Delhi, for awarding SRF under the Maulana Azad National Fellowship for Minority Students. The authors gratefully acknowledge the financial assistance from the RKVY project “Validation of important crop varieties through DNA fingerprinting”.

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References

Bhat KV, Lakhanpaul S, Chadha S (2005). Amplified fragment length polymorphism (AFLP) analysis of genetic diversity in Indian green gram [Vigna radiata (L.) Wilczek] cultivars. Ind J Biotech 4: 55–64.

Chadha ML (2010). Short Duration Green Gram: A New Success in South Asia. Bangkok, Thailand: Asia-Pacific Association of Agricultural Research Institutions.

Chattopadhyay K, Bhattacharya S, Mandal N, Sarkar HK (2008). PCR-based characterization of mung bean (Vigna radiata) genotypes from Indian subcontinent at intra- and inter-specific level. J Pl Biochem Biotech 17: 141–148.

Dikshit HK, Jhang T, Singh NK, Koundal KR, Bansal KC, Chandra N, Tickoo JL, Sharma TR (2007). Genetic differentiation of  Vigna  species by RAPD, URP and SSR markers. Biol Plantarum 51: 451–457.

Doyle JJ, Doyle JL (1987). A rapid DNA isolation procedure for small quantities of fresh leaf material. Phytochem Bull 19: 11–15.

Gajera HP, Domadiya RK, Patel SV, Golakiya BA (2014). Appraisal of RAPD and ISSR markers for genetic diversity analysis among cowpea (Vigna unguiculata L.) genotypes. J Crop Sci Biotech 17: 79–88.

Gillaspie AG, Hopkins MS, Dean RE (2005). Determining genetic diversity between lines of Vigna unguiculata  subspecies by AFLP and SSR markers. Genet Resour Crop Evol 52: 245–247.

Gwag JG, Dixit A, Park YJ, Ma KH, Kwon SJ, Cho GT, Lee GA, Lee SY, Kang HK, Lee SH (2010). Assessment of genetic diversity and population structure in green gram. Genes Genomics 32: 299–308.

Lakhanpaul S, Chadha S, Bhat KV (2000). Random amplified polymorphic DNA (RAPD) analysis in Indian green gram (Vigna radiata (L.) Wilczek) cultivars. Genetica 109: 227–234.

Mahalanobis PC (1936). On the generalised distance in statistics. Proc Nat Inst Sci India 2: 49–55.

Mantel NA (1967). The detection of disease clustering and a generalized regression approach. Cancer Res 27: 209–220.

Milbourne D, Meyer R, Bradshaw J, Baird E, Bonar N, Provan J, Powell W, Waught R (1997). Comparisons of PCR-based marker systems for the analysis of genetic relationships in cultivated potato. Mol Breeding 3: 127–136.

Muthusamy S, Kanagarajan S, Ponnusamy S (2008). Efficiency of RAPD and ISSR markers system in accessing genetic variation of rice bean (Vigna umbellata) landraces. Electron J Biotechn 11: 1–10.

O’Neill R, Snowdon RJ, Kohler W (2003). Population genetics: aspects of biodiversity. In: Esser K, Lüttge U, Beyschlag W, Hellwig F, editors. Progress in Botany, Vol. 64. Berlin, Germany: Springer, pp. 115–137.

Prevost A, Wilkinson MJ (1999). A new system of comparing PCR primers applied to ISSR finger printing of potato cultivars. Theor Appl Genet 102: 440–449.

Reddy KS, Souframanien J, Reddy KS, Dhanasekar P (2008). Genetic diversity in green gram as revealed by SSR and ISSR markers. J Food Legumes 21: 15–21.

Rishi N (2009). Significant plant virus diseases in India and a glimpse of modern disease management technology. J Gen Pl Path 75: 1–18.

Rohlf FJ (2004). NTSYS-pc. Numerical Taxonomy and Multivariate Analysis System. Setauket, NY, USA: Exeter Software.

Santalla M, Power JB, Davey MR (1998). Genetic diversity in green gram genotypes revealed by RAPD markers. Pl Breed 117: 473–478.

Shanmugasundaram S (2001). New breakthrough with green gram. Centerpoint 19: 1–2.

Singh A, Dikshit HK, Jain N, Singh D, Yadav RN (2014). Efficiency of SSR, ISSR and RAPD markers in molecular in molecular characterisation of green gram and other Vigna species. Ind J Biotech 13: 81–84.

Singh N, Singh H, Nagarajan P (2013). Development of SSR markers in mung bean, Vigna radiata (L.) Wilczek using in silico methods. J Crop Weed 9: 69–74.

Singh R, van Heusden AW, Yadav RC (2013). A comparative genetic diversity analysis in green gram (Vigna radiata L.) using inter-simple sequence repeat (ISSR) and amplified fragment length polymorphism (AFLP). Afr J Biotech 12: 6574–6582.

Smith JSC, Chin ECL, Shu H, Smith OS, Wall SJ, Senior ML, Mitchell SE, Kresovich S, Zeigle J (1997). An evaluation of the utility of SSR loci as molecular markers in maize (Zea mays L.): comparisons with data from RFLPs and pedigree. Theor Appl Genet 95: 163–173.

Souframanien J, Gopalakrishna T (2004). A comparative analysis of genetic diversity in blackgram genotypes using RAPD and ISSR markers. Theor Appl Genet 109: 1687–1693.

Tabasum A, Saleem M, Aziz I (2010) Genetic variability, trait association and path analysis of yield and yield components in mungbean (Vigna radiata (L.) Wilczek). Pak J Bot 42: 3915–3924.