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Estimation of Genetic Divergence in Mung Bean (Vigna radiata L.) under Temperate Ecology of Kashmir

Aijaz Ahmad1, Syed Mudasir Razvi1, Manzor Ahmad Rather1, Mushtaq Ahmad1, Gul Zaffar1, Shabeer Ahmad Ganie2, Mohmmad Ramzan Mir3 and Khalid Rehman Hakeem4*

1Division of Genetics and Pant Breeding, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir Shalimar campus, Srinagar, India

2Division of Plant Pathology, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir Shalimar campus, Srinagar -191 121, India

3Department of Botany, Aligarh Muslim University, Aligarh 202002, India

4Faculty of Forestry, University Putra Malaysia, Serdang-43400, Selangor, Malaysia

*Corresponding Author:
Hakeem KR
Faculty of Forestry, University Putra Malaysia, Serdang-43400, Selangor, Malaysia,
Tel:
+60 3-8946 6000
E-mail: kur.hakeem@gmail.com

Received date: 11/01/2016; Accepted date: 03/03/2016; Published date: 05/03/2016

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Abstract

Thirty five genotypes of mung bean were evaluated during kharif 2011 with the objective to work out the genetic divergence in the genotypes for future breeding studies. Significant differences were observed for all the traits studied. The material was subjected to genetic analysis and further, based on Mahalanobis D2 statistics genotypes were grouped into seven clusters, with cluster II having maximum of 13 genotypes, cluster-I 11 genotypes and cluster IV and V, (4 genotypes each) and the rest of the cluster were monogenotypic. Inter-cluster distance was maximum between cluster VI and VII (9308.29) followed by cluster IV and VII and Cluster I and VII (5236.69) the cluster means for seed yield plant-1 revealed that the magnitude of differences among the means ranged from (4.65 g) in cluster IV to (2.83 g) in cluster V. On the basis of divergence between clusters, genotypes from cluster-VI and VII followed by cluster-IV and VII can be involved in the hybridization program for recovery of superior recombinants or transgressive segregants in segregating generations.

Keywords

Mung bean, Divergence, Clusters, Hybridization.

Introduction

Legumes constitute the third largest family of higher plants, with 20,000 species and second after cereals in agricultural importance based on area and total production (Graham and Vance, 2003). These are unique crops in having an inbuilt capacity for fixing atmospheric nitrogen. Thus, these crops meet their own nitrogen requirement to a great extent. They also leave nitrogen in the soil that enrich it and become available to the succeeding crop. They are also important constituents in the diets of a very large number of people, especially in the developing countries and are good sources of protein, which help to supplement cereal diets. Protein content of pulses is twice that of cereals (>20%), whereas their by products provide nutritious fodder for livestock. With the growing demand for nutritional security, pulses are becoming ever more important as a plant based source of protein in the human diet. Unfortunately, unlike those of cereals, the existing production levels of pulses cannot meet the emerging demands. Among pulses, mung bean (Vigna radiata L. Wilczek) is also known as green bean, choroko, mung, mash bean, munggo, green gram, golden gram and green soy. Mung bean is native to Bangladesh, India and Pakistan. The mung bean is one of many species recently moved from the genus Phaseolus to Vigna. The nutritional value of mung bean per 100 g, energy (347 kcal), carbohydrates (62.62 g), sugars (6.60 g), dietary fibre (16.3 g), fat (1.15 g), protein (23.86 g), vitamin C (4.8 mg), calcium (132 mg) and magnesium (189 mg). (USDA Nutrient Database, 1997). Mung bean is also considered as the natural source of many bioactive compounds [1].

Genetic divergence is also of utmost importance for identifying appropriate genotypes as parents with their long-term potentialities. Mahalanobis D2 statistics is a powerful tool in quantifying the degree of divergence at the genotypic level with regard to characters which needed to be improved and provides an opportunity to identify putative parents for executing an effective breeding strategy [1]. Till date limited efforts have been made in characterizing mung bean accessions in India in general and in the Kashmir valley in particular. Precise information about genetic divergence is critical for productive breeding programs as genetically diverse parents are known to produce high heterotic effects consequently increasing yield in desirable segregants. D2 statistics, it has now become possible to quantify the degree of genetic divergence among the biological population [2]. Therefore, the present investigation was aimed at ascertaining the nature and magnitude of genetic diversity among 35 mung bean genotypes for morphological yield and yield attributing traits to identify suitable parents for hybridization program.

Materials and Methods

An experiment was carried out at experiential farm of Division of Plant Breeding and Genetics, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K) Shalimar campus, Srinagar. Overall, 35 genotypes (Table 1) of mung bean including Shalimar mung-1 as check were evaluated in a randomized complete block design (RBD) with 3 replications during kharif 2011. Each experimental plot comprised two rows each of 3 m length, with 30 and 10 cm inter- and intra-row spacing. Recommended agronomic package of practices were followed to raise a healthy crop.

S.No. Name of Genotype Source Pedigree
1 SKUM-101 TNAU Coimbatore COGWG 923 ×VC 6040A
2. SKUM-102 Mad hive MGG 295 × MGG 341
3. SKUM-103 ARS, Durgapura ML 613 × BDYER-2
4 SKUM-105 CSAU, Kanpur Samrat× ML-13
5 SKUM-106 PAU, Ludhiana M1 267 × ML 949
6 SKUM-107 PAU, Ludhiana NO 54 × HY 645
7 SKUM-108 S.K. Nagar GM 9303 × PB-1
8 SKUM-109 CSAU Kanpour PDM 54 ×Pusa 9872
9 SKUM-110 CCSAU, Hissar CO-6 × BDYR-1
10 SKUM-111 IIPR Kanpur PBM 139 × PB2
11 SKUM-112 Shillong PDM 84-143 × ML 337 × SGI
12 SKUM-113 IARI, New Delhi IPM 99-125 × IM P02-1
13 SKUM-114 PAU Ludhiana  ML 267 × ML 955
14 SKUM-115 CSAUA&T Kanpur Samrat× PDM 54
15 SKUM-116 IARI,New Delhi Pusa Bald 2 × SEL.11
16 SKUM-117 CCSAU,Hissar Aisha × MH 98-7
17 SKUM-118 NDUAT, Faizabad NDM1 × NDM Sel 11
18 SKUM-119 IIPR, Kanpur PPM 139 × PB-2
19 SKUM-120 PAU, Ludhiana  5145 / 87 × ML 267
20 SKUM-121  S.K. Nagar.  GM 9402 × E92-3
21 SKUM-122  Berhampur (0)  Mutant of Sujata
22 SKUM-123 Madhira  MGG 295 × MGG 332-2
23 SKUM-124 Shillong  T44 × AAU 34
24 SKUM-125 Shillongani  PDM 91-243 × WGG 62
25 SKUM-126  S.K. NAGAR  GM3 × TM 96-1
26 SKUM-127 CSAUA&T kanpur Samrat× PDM 54
27 SKUM-130 GBPUA&T Pant Mung 2 × AMP-36
28 SKUM-131  BARC Jabalpur Kopergnon× TARM-B
29 SKUM-132 BARC Mumbai.  Mutant of 96-2
30 SKUM-133 Jalgaon. Vaibhav× Tata Mung
31 SKUM-134  Akola  JLM 3 ×Kopergoon
32 SKUM-135 PAU Ludhiana  No 54 × HY 645
33 SKUM-136 NDUAT Faizabad. NDM -70-2 × NDM-1
34 SKUM-137 PAU Ludhiana  MC 1020 × UPM 98
35 Shalimar mung-1 SKUAST-K Sel-2 × local

Table 1. List of germplasm lines, their source and pedigree.

Observations were recorded on eleven morpho-agronomic, yield and quality traits viz., days to 50% flowering, number of clusters plant-1, number of primary branches plant-1, number of pods plant-1, Pod length (cm), days to 80% maturity, plant height (cm), number of seeds pod-1, 100-seed weight (g), Seed yield plant-1 (g) and protein content (%).

Ten competitive representative plants were selected at random from each experimental plot in each replication and tagged for recording the observations. The genetic divergence was computed using the procedure as described by Rao, Singh and Choudhary.

Results and Discussion

Estimation of Genetic Divergence

Analysis of variance for dispersion revealed that the genotypes tested expressed significant variability for the morphological, maturity, quality, yield and yield component traits. The ‘V’ statistics, which is a measure of Wilk’s criterion was significantly higher than the corresponding chi-square value (3245.05) revealing thereby that the genotypes possessed a significant diversity and thus, could be studied for divergence studies.

Based on the performance of the genotypes the 35 genotypes, including a check got grouped into 7 clusters (Table 2) as per the Mahalanobis D2 analysis employing Tocher’s method (Rao). Cluster-II comprised maximum genotypes (13) followed by cluster-I (11), cluster-IV and cluster-V (4) each. The rest of the cluster was mono-genotypic. The check Shalimar mung bean-1 got grouped in cluster-IV. Similar results have been reported by Rahim et al. and Katiyar et al. [3,4].

Cluster No. No. of Genotypes in the cluster Variety/accession No. of the genotypes
I 11 SKUM-112, SKUM-118, SKUM-133, SKUM-123, SKUM-111 SKUM-119, SKUM-137. SKUM-120, SKUM-114, SKUM-134, SKUM-122.
I 13 SKUM-109, SKUM-117, SKUM-127, SKUM-125, SKUM-130. SKUM-126, SKUM-108, SKUM-110, SKUM-102, SKUM-113, SKUM-115, SKUM-105, SKUM-106.
III 1 SKUM-136.
IV 4 SKUM-131, Shalimar mung-1, SKUM-101, SKUM-132.
V 4 SKUM-103, SKUM-135, SKUM-107, SKUM-124.
VI 1 SKUM-121.
VII 1 SKUM-116.

Table 2: Distribution of different Mung bean (Vignaradiata L. Wilczek), Genotypes into clusters based on D2 statistics.

The mean intra and inter-cluster distance (D2) values (Table 3) revealed that cluster-V had the highest estimate of intra cluster distance (D2) 947.96 followed by cluster-II (490.84), cluster-I (421.86) and cluster-IV (378.90). The estimate for intercluster distance (D2) was found highest between cluster-VI and cluster-VII (9308.29) followed by the distance between cluster-IV and cluster-VII (8708.22), cluster-I and cluster-VII (5236.69), cluster-IV and cluster-V (4782.11), cluster-V and cluster-VI (4209.98), cluster-III and cluster-VII (3194.89), cluster-II and cluster-VI (3006.26). The minimum inter-cluster distance was observed between cluster-III and cluster-V (784.32), followed by cluster-I and cluster-III (819.31) and cluster-II and cluster-III (987.29). The classification of genotypes into different clusters have been reported in different studies [5,6]. Recently, Singh et al. have grouped drought tolerant genotypes of Lentil (Lens culinaris Medik.) in different clusters based on their sensitivity to drought stress [7]. Wild accessions were separated from cultivars on the basis of both population structure and cluster analysis [8].

Clusters ↓→ I II III IV V VI VII
I 421.86 1048.39 819.39 1250.57 2084.22 973.88 5236.69
II   490.84 987.29 2199.61 1335.80 3006.26 2817.10
III     0.00 2911.82 784.32 1880.00 3194.89
IV       378.90 4782.11 1477.65 8708.22
V         947.96 4209.98 1702.56
VI           0.00 9308.29
VII             0.00

Table 3. Average inter cluster (above diagonal) and intra cluster (diagonal) D2 values among mung bean (Vignaradiata L. Wilczek).

The cluster means for different traits (Table 4) revealed that the magnitude of differences among the mean of traits for different clusters was significant. The range of variation in cluster means for days to 50 per cent flowering was 43.25 (cluster-IV) to 53.21 (cluster-I). Mean of days taken to 80 per cent maturity ranged from 93.33 (cluster-III) to 110.00 (cluster-VI). The lowest mean of clusters plant-1 were expressed by cluster-VII (3.97) and highest by cluster-III (5.57). The minimum mean number of primary branches plant-1 were expressed by cluster-VII (2.57) and highest by cluster-III (3.47). Maximum mean number of pods plant-1 were recorded in cluster-III (22.30) and minimum in cluster-VII (16.00). Mean pod length (cm) ranged from 6.72 in cluster-III to 7.86 in cluster-IV. Plant height recorded lowest mean of 66.03 in cluster -VI and highest mean of 118.60 in cluster-V. Maximum mean of seeds pod-1 were recorded in cluster-IV (8.17) and minimum in cluster-V (7.05). Cluster-I had maximum 100-seed weight of 3.24 g and cluster-III with minimum of 2.20 g. The mean seed yield plant-1 ranged from 4.65 g in cluster-IV to 2.83 g in cluster-V. Maximum and minimum mean protein content was expressed by cluster-IV (22.41) and cluster-VI (20.59), respectively.

Cluster Days to 50% flowering Days to 80% maturity No. of clusters plant -1 No. of primary branches plant -1 No. of pods plant -1 Pod length (cm) Plant height (cm) No. of seeds pod -1 100- seed weight(g) Seed yield plant-1 (g) Protein content (%)
I 53.21 105.27 4.38 2.70 16.36 7.65 86.73 7.40 3.24 3.86 21.43
II 52.10 96.92 4.58 2.64 16.54 7.29 104.88 7.52 2.78 3.54 22.17
III 46.67 93.34 5.57 3.47 22.30 6.72 103.25 7.36 2.20 3.64 20.81
IV 43.25 93.33 4.27 2.83 17.70 7.86 70.44 8.17 3.21 4.65 22.41
V 48.83 96.00 4.16 2.99 16.70 6.81 118.60 7.05 2.22 2.83 21.14
VI 52.00 110.00 4.60 2.83 16.77 7.24 66.03 7.72 2.94 4.19 20.59
VII 50.00 102.00 3.97 2.57 16.00 7.33 148.01 7.66 2.88 3.57 21.85

Table 4. Cluster means for quality yield and yield component traits in mung bean (Vignaradiata L. Wilczek).

Analysis of genetic diversity is a platform for stratified sampling of breeding population and to identify the genotypes for hybridization [8]. Involving genetically diverse parents is known to provide an opportunity for bringing together gene constellation yielding desirable transgressive segregants in advanced generations. In order to classify large number of potential genotypes into few numbers of homogenous clusters, the D2 statistic of (Mahalanobis) is now well established in plant breeding [1]. The use of Mahalanobis D2 statistic for estimating genetic divergence have been emphasized by many workers because it permits precise comparison among all the possible pair of populations in any group before effecting actual crosses [9]. Murthy and Arunachalam hypothesized that (Mahalanobis,) generalized distance as a measure of metric distance between population centroids could be very useful multivariate statistical tool for effective discrimination among parents, as high yield parents, with greater genetic diversity and expected to develop productive hybrids [10,11]. Multi-variate analysis quantifies the degree of divergence between populations so as to understand the trend of their evolutionary pattern and to assess the relative contribution of different components to the total divergence together with nature of forces operating at intra and inter-cluster levels.

The pattern of group constellations in the present study, suggested that geographical diversity was not an essential factor to group the genotypes from a particular source or origin into one particular cluster. This means that, geographical diversity, though important, was not the only factor in determining the genetic divergence. Earlier workers have discussed genetic drift, selection pressure and environment as major factors that could cause greater diversity than geographical distance [12]. Genetic diversity is the outcome of several factors, including geographical diversification. Therefore, selection of parents should be based on genetic diversity rather than geographical diversity and statistical distance D2, presented the index of genetic diversity among these clusters.

Cluster means of different clusters identify the characters to be chosen for hybridization. Hence in the present set of material, genotypes in cluster IV represent highest cluster means for most of the traits, so genotypes from cluster IV should be used for hybridization program should be selected from this cluster for the improvement of these traits. Sardana et al. observed that cluster means and coefficient of variation are an interesting picture of the nature of diversity [13].

Improvement over existing germplasm is a continuous process in plant breeding. Any successful hybridization program for varietal improvement depends mainly on the selection of parents with high genetic variability so that desirable character combinations could be selected for the target traits to be improved upon. Maurya and Singh suggested that more diverse the parents, within overall limits of fitness, the greater are the chances of obtaining the higher magnitude of heterotic expression in F1’s and subsequently result in the release of broad spectrum of genetic variability in the segregating generations [14].

The results obtained in present investigation indicate that considerable variability and diversity is available in the experimental material for converging the elite allelic resources though a systemic breeding and selection approach to recover high yielding segregates that possess good quality characteristics as well. Therefore, selection of parents for hybridization should be done form different clusters having wider inter-cluster distance. The parents to be selected form such clusters should also have high per se performance for traits. In general, combining high yielding potential with wide genetic diversity is emphasized for further selection and choice of parents for hybridization. Thus, crosses between genotypes of the cluster IV and cluster VII are likely to exhibit high heterosis and produce superior recombinants with desired traits.

Conclusion

Average inter-cluster D2 value was maximum between cluster-VI and cluster-VII, followed by cluster-IV and cluster-VII. Minimum intra-cluster distance was observed in cluster-V followed by cluster-II. The cluster means for different morphological, yield and quality traits revealed substantial genetic variability for all the traits. In the present set of material, genotypes in cluster IV represent highest cluster means for most of the traits, so genotypes from cluster IV should be selected to use in the hybridization programme for the improvement of these traits and further indicated that crosses between genotypes in cluster-VI and cluster-VII are likely to produce superior recombinants for yield and yield contributing traits.

References