Legume Research

  • Chief EditorJ. S. Sandhu

  • Print ISSN 0250-5371

  • Online ISSN 0976-0571

  • NAAS Rating 6.80

  • SJR 0.391

  • Impact Factor 0.8 (2023)

Frequency :
Monthly (January, February, March, April, May, June, July, August, September, October, November and December)
Indexing Services :
BIOSIS Preview, ISI Citation Index, Biological Abstracts, Elsevier (Scopus and Embase), AGRICOLA, Google Scholar, CrossRef, CAB Abstracting Journals, Chemical Abstracts, Indian Science Abstracts, EBSCO Indexing Services, Index Copernicus

Genetic Diversity Assessment in Dolichos Bean (Lablab purpureus L.) Based on Principal Component Analysis and Single Linkage Cluster Analysis

S.R. Singh1,*, S. Rajan1, Dinesh Kumar1, V.K. Soni1
1ICAR-Central Institute for Subtropical Horticulture, Rehmankhera, Kakori, Lucknow-226-119, Uttar Pradesh, India.
  • Submitted05-12-2020|

  • Accepted24-02-2021|

  • First Online 02-04-2021|

  • doi 10.18805/LR-4561

Background: Dolichos bean occupies a unique position among the legume vegetables of Indian origin for its high nutritive value and wider climatic adaptability. Despite its wide genetic diversity, no much effort has been undertaken towards genetic improvement of this vegetable crop. Knowledge on genetic variability is an essential pre-requisite as hybrid between two diverse parental lines generates broad spectrum of variability in segregating population. The current study aims to assess the genetic diversity in dolichos genotypes to make an effective selection for yield improvement.

Methods: Twenty genotypes collected from different regions were evaluated during year 2016-17 and 2017-18. Data on twelve quantitative traits was analysed using principal component analysis and single linkage cluster analysis for estimation of genetic diversity.

Result: Principal component analysis revealed that first five principal components possessed Eigen value > 1, cumulatively contributed > 82.53% of total variability. The characters positively contributing towards PC-I to PC-V may be considered for dolichos improvement programme as they are major traits involved in genetic variation of pod yield. All genotypes were grouped into three clusters showing non parallelism between geographic and genetic diversity. Cluster-I was best for earliness and number of cluster/plant. Cluster-II for vine length, per cent fruit set, pod length, pod width, pod weight and number of seed /pod, cluster III for number of pods/cluster and pod yield /plant. Selection of parent genotypes from divergent cluster and component having more than one positive trait of interest for hybridization is likely to give better progenies for development of high yielding varieties in Dolichos bean.
Dolichos bean (Lablab purpureus L.) is an important leguminous crop grown by nutri-gardeners and commercial vegetables grower for green pods, tender leaves, immature green seeds as vegetable and dry seeds as pulse throughout country. It has wide adaptability in different diverse climate conditions such as arid, semi-arid, subtropical and humid region. Crop is rich source of protein, minerals and vitamins besides a significant contributor to agriculture sustainability because of its ability to fix the atmospheric nitrogen as a leguminous crop (Basu et al., 1999) and high drought tolerance capacity. Despite its importance, this crop remained underutilized due to low productivity of the existing varieties, long gestation period, photosensitivity, indeterminate growing nature (Magalingam et al., 2013). India is one of the primary centres of origin and a large variability exists for plant and pod characters. Green, fleshy, string less pods coupled with high yield are preference of consumers and growers. There is a need of genetic restructuring of dolichos bean considering the consumer preference for attractive fleshy green pods with high yield to bridge the gap between potential yield and actual yield realised at framers field. Genetic variability is pre requisite for effective improvement programme of any crop (Azmat et al., 2011). Diverse genetic back ground of parental lines provides a desirable allelic variation to attempt novel combinations (Taran et al., 2005). A good understanding of genetic variability in different characters is a useful tool for identification of suitable genes to improve the genetic behaviour of crop (Nwangburuka et al., 2011). Classification of genetic variability, among the genotype is valuable for maintenance and further utilization as parental line for development of improved varieties (Amurrio et al., 2007). It is necessary to utilize existing natural genetic resources with minimum genetic erosion (Nissar et al., 2011). Moreover principal component analysis (PCA) is a useful tool for measuring variability in plant genetic resources for agronomic and physiological traits (Julier et al., 1995, Veasey et al., 2001, Bhargava et al., 2007). PCA is a descriptive method, which shows the pattern of co-variation of characters among the individual (Rhodes and Martin, 1972). However, PCA is not adequate for representation of characters in terms of relative importance, when numerous characters are considered simultaneously (Nwangburuka et al., 2011). To compliment the result of such multivariate analysis, single linkage cluster analysis is an essential analytical technique to classify the variation existing in germlasm. Single linkage cluster analysis (SLCA) is an agglomerative technique which shows the patterns of relationship between the individuals of population (Ario and Odulaza, 1991). Thus the study was conducted with an objective to identify the major characters responsible for variation among the accessions and to classify potential parental stock into groups employing the combined multivariate analysis.
Present investigation was carried out during year 2016-17 and 2017-18 at experimental farm of ICAR-Central Institute for subtropical Horticulture Rehmankhera Kakori, Lucknow in block I. The experimental site situated at 26o 45' to 27o 10' N latitude, 80o 30' to 80o 5' E longitude and 123 m above the sea level. Twenty diverse genotypes of dolichos bean collected from different regions of India were used in the study (Table 1).

Table 1: Details on the Dolichos bean genotypes used for genetic diversity analysis.

Seeds were sown in first week of July in both the year at 120 x 60 cm inter and intra row spacing at depth 5cm. The experiment was conducted in a randomised complete block design with three replications. The soil of experimental field was loamy in nature with 6.8 pH and 0.36 dsm-1 EC. Plants were staked and supported. Uniform agronomic cultural practices were adopted to obtain the good phenotypic expression of characters. Five plants from each replication were randomly selected to record the 12 quantitative traits representing vegetative characteristics of plants related to yield and yield attributes. Data collected on the quantitative characters were analysed using the SAS Microsoft Windows 9.2 (SAS Institute, 2011). PCA and SLCA were used to assess the genetic variation and percentage similarity within the genotype. The PCA produced Eigen – vectors and principal component score were used to assess the relative discriminatory power of its axis and their associated characters. The cluster procedure was used to identify distinct groups of 20 genotypes on the basis of genetic relationship using the character variation. SLCA analyse the position of accessions into a dendogram at an  interval of 5% level of dissimilarity starting from 100% level of dissimilarity.
Variability in phenotypic characters expressed by range, standard deviation and coefficient of variation reflected significant variation among the delineated genotypes (Table 2). The vine length ranged from 1.70 to 5.90 meter. The highest vine length of 5.90 meter was recorded by genotype CISH-DC-16 whereas minimum vine length of 1.70 meter was recorded with CISH-DC-18. Number of primary braches ranged 5.67 (CISH-DC-5) to 11.00 (CISH-DC-13) plant. Days taken for first flowering determine the harvest time which ranged from 65.00 (CISH-DC-1) to 225.00 days (CISH-DC-20) Number of flower/cluster ranged from 11.33 (CISH-DC-5) 28.00(CISH-DC-3). Fruit set percentage ranged from 39.24 to 94.74 %. Genotype CISH-DC-15 recorded maximum fruit set 94.74% whereas minimum fruit set (39.24%) was recorded by CISH-DC-20. Number of cluster/plant which directly associated with yield ranged from 20 (CISH -DC-2) to 70.3 (CISH-DC -11). Number of pods/cluster ranged from 6.67 (CISH-DC-3) to 19.67 (CISH-DC-13). Pod length ranged from 5.55 cm (CISH-DC-3) to 16.30 cm (CISH-DC-6). Pod weight, which is one of the most important trait for determining yield of crop ranged from 3.60 g (CISH-DC-20) to 11.22 g (CISH-DC-7). Number of seeds/pod varied from 2.97 (CISH-DC-18) to 5.97 (CISH-DC-16). Pod yield /plant ranged from 1.89 kg (CISH-DC-20) to 10.17 kg (CISH-DC-7). The genotypes having highest values for particular desirable traits may be selected as parents for future breeding aiming for genetic improvement programme of yield in dolichos. Similar observations have been reported by Parmar et al., 2013 and Peer et al., 2018 in the germplasm evaluation of Dolichos bean.

Table 2: Variability for different traits of dolichos bean genotypes.

Coefficient of variation (%) reflected the high magnitude of variability among the evaluated phenotypic traits. Highest coefficient of variation was observed for pod yield /plant (g) (50.51%) followed by days taken to first flowering (44.39%), pod weight (33.01%) indicated an appreciable variability in delineated genotypes which is a prerequisite for crop improvement programme. The results are in accordance with findings of the Ali et al., 2005.     
Principal component analysis transforms a number of possible correlated variables into smaller number of uncorrelated variables by reducing the outliers. (Table 3) Eigen values with more than one has high explanatory power of original variables. Based on degree of divergence, 20 genotypes were grouped into five principal components having Eigen value > 1 accounted for 82.53% of total variability. The first principal component (PC-I) explained 29.44% of total variation and the traits which positively contributed towards the first component were pod yield /plant (0.497), pod weight (0.407) pod length (0.345) pod with (0.332) whereas primary branch /plant contributed negatively to this component. Numbers of flowers/cluster (0.518) and vine length (0.383) were found to be the main traits contributing towards PC-II which were responsible for 17.67 % of total variability. The traits positively contributing towards PC- III were number of seeds /plant (0.600) number of clusters/plant (0.514) which was responsible for 15.07 % of total variability. PC-IV accounted for 10.74 % of total variability positively contributed by percent fruit set (0.631), number of pods /cluster (0.354) whereas 9.61% of the total variability was positively contributed by PC V from primary branches/plant (0.666). The traits with largest absolute closer to unit within the first component influence the clustering more than those with lower absolute value closer to zero (Chahal and Gosal, 2002). Thus the differentiation within the delineated genotypes in different principal component was because of high contribution of few characters rather than small contribution of each character. The bi-plot of PC-I and PC-II showed a considerable variability presenting a dispersion pattern of delineated genotypes (Fig 1). The genotypes having negative values for PC-I and PC-II had smaller pods with less number of seeds /pods, low pod weight and yield. The genotypes with maximum positive loadings for PC-I and negative for PC-II were characterised with high number of flowers/cluster, number of flower clusters/plant and number of pods/cluster. The characters which are positively contributing towards PC-I to PC-V are important because they explain more than 82.53% of total variability. The dataset revealed higher contribution from the traits viz., pod yield /plant, pod weight, pod length, pod width, number of flower/cluster, vine length and primary branches/plant. The genotypes grouped in the separate clusters (Fig 2) as shown by dendrogram are to be considered important during the selection of parents for hybridization or direct selection. Similar findings were earlier reported by Hadwani et al., 2018 in dolichos bean. The use of PCA to identify the most divergent genotypes in germplasm collection has also been reported (Singh et al., 2017, Singh et al., 2020). The scatter plot generated through PCA illustrated the diverse genotypes located farther from the point of origin. Genotypes namely CISH-DC-12, CISH-DC-20, CISH-DC-3 and CISH-DC-11 were found most divergent for different traits (Fig 1). These diverse genotypes may be employed as parents in hybridisation in future breeding program in hybridization. Generally, it is customary to select one of the important variables from these identified groups for targeted improvement programme. Hence, PC I for pod yield/plant PC II for number of flower /clusters, PC III for number of seeds/pod, PC IV for maximum fruit set percentage and PC-V for number of primary branches /plant. The results demonstrated the usefulness of PCA in the identification of traits that contributed most variation within a group of genotypes.  This technique has more practical utility in the selection of parental lines for breeding purposes.

Table 3: Principal component analysis of 12 traits in 20 genotypes of dolichos bean.


Fig 1: Scatter diagram of Bi-plot presenting dolichos bean genotypes.


Fig 2: Dendogram depicting the genetic relationship among the dolichos bean genotypes based horticultural traits produced by ASH analysis (scale- Euclidean distance at .05).

Single linkage cluster analysis grouped all genotypes into three clusters by quantifying cluster means of all traits and their individual contribution (Table 4). The maximum number of genotypes were accommodated in cluster III (8) followed by cluster I (6) and cluster II (6) comprising 40, 30 and 30 % share of the total population, respectively. On the cluster mean basis, cluster I was important for days to first flowering (47.83) earliness and number of clusters /plant (54.44). Cluster II is important for the traits viz., vine length (4.58), per cent fruit set (79.30), pod length (11.97), pod width (1.53) pod weight (7.58) and number of seeds /pod (5.14) whereas cluster III three was important for primary branches /plant (8.88), number of flowers/cluster (19.79), number of pods/cluster (14.96) and pod yield /plant (5.13).  The genotypes of highest cluster mean of specific traits can be utilized as parents for realising targeted genetic improvement for specific traits restructuring of dolichos bean for a particular character. Arya et al., (2017) also suggested that cluster having high mean values of specific traits may be used for hybridization programme to get superior recombinants.

Table 4: Cluster mean values for twelve important horticultural traits along with number and percent contribution of genotypes in each cluster.

Proximity matrix obtained suggests that the resolution for 20 genotypes of dolichos bean distributed in three clusters with a wide range of diversity for traits (Table 5). The highest inter cluster distance between clusters I and cluster II (136.237) followed by cluster-II and cluster-III (102.313) elucidated a high degree of divergence between the genotypes of these clusters. The hybrids developed from genotypes having maximum cluster distance resulted in high heterosis in yield and yield attributing traits (Kujur et al., 2017). Thus, crosses between the genotypes of cluster-I and Cluster-II and Cluster-II and cluster-III may be used in dolichos breeding for achieving maximum heterosis and isolating useful recombinants in segregating generation as well as introgressing useful traits in commercial dolichos cultivar. These findings are in conformity with the observations of Hadawani et al., 2018, Singh et al., 2017 and Birari and Ghanekar, 1992 that genotypes having placed farther in clusters resulted in a wide spectrum of variation when used in hybridization distance used in hybridization resulted in a wide spectrum of variation in segregants. The genotypes grouped in same cluster presumably diverge very little from each other and does not produce desired segregates when used as parents for hybridization (Roy et al., 2013). Hence hybridization programme should be initiated with putative parents belonging to different characters. Therefore, parents with higher cluster distance are likely to be beneficial for further improvement in dolichos.

Table 5: Average Inter cluster distance.

Dendogram obtained from single linkage cluster analysis by using the Euclidean distance depicted the clear relationship and exact position of genotype in clusters (Fig 2). ‘All the genotypes were distinct at 100 percent of dissimilarity and and formed three clusters at 65% of dissimilarity. Dissimilarity range 65 to 100% among the evaluated genotypes is enough to suggest the variability available in the dolichos bean genotypes. Distance among the genotype on the Euclidean scale reflected the position of genotype. CISH-DC-20, CISH-DC-3, CISH-DC-15, CISH-DC-5 and CISH-DC-2 were most divergent and potential for future breeding programmes in Dolichos bean and to isolate desirable recombinants for high pod yield. This study on genetic diversity assessment identified diverse genotypes in respect of pod yield that could be used as parental clones in hybridization and breeding programme in dolichos bean.
The principal component analysis and single linkage cluster analysis proved to be a better tool to assess the genetic diversity by providing the clear relationship among dolichos bean genotypes. The information on genotypes and distinct traits contributing for more variation facilitates breeders to develop newer high yielding cultivars. Selection of genotypes from divergent clusters for use in hybridisation may lead to genetic improvement in yield and quality of Dolichos bean.
Authors are highly thankful to Director ICAR-CISH Lucknow for extending the facilities to conduct the experiment.

  1. Ali, F., Sikdar, B., Roy, A.K. and Joar, O.I. (2005). Correlation and genetic variation of different genotypes of lablab beans (Lablab purpureus L). Sweet. Bengal. J. Bot. 34: 125-128.

  2. Amurrio, C.O., Adebao, M.A., Aria, O.J. and Adewale, B.B. (2007). Classification of genetic diversity and choice of parent for hybridization in cowpea (Vigna unguiculata L). Walp for humid savana ecology. Afro. J. of Biotech. 6: 2333-2339.

  3. Ario, O.J. and Odulaza. (1991). Numerical analysis of variation among accessions of okra [A. esculentus (L.) Moench] malvaceae. Ann. Bot. 67: 527-531.

  4. Arya, J.S., Singh, N., Arya, P. and Kant, A. (2017). Morphological variation and relationship among the onion germplasm for quantitative and qualitative traits at Trans Himalayas Laddakh India. Australian J. Crop Sci. 11: 229-237.

  5. Azmat, M.A., Nausherwan, N.N., Asif, A.K., Ashraf, M., Shahid N. and Khalid, M. (2011). Characterization of pea germplasm. Int. J. of Veg. Sci. 17: 246-258. 

  6. Basu, A.K., Pal, D., Sasmala, S.C. and Samanta, S.K. (1999). Genetic analysis for embryo weight, cotyledon weight and seed protein in lablab bean. Vegetable Sci. 26: 37-40.

  7. Bhargava, A., Shukla, S., Rajan, S. and Ohri, D. (2007). Genetic diversity for morphological and quality traits in quinoa (Chenopodium quinoa Wild.) germplasm. Genet. Resour. Crop Evol. 54: 167-173. 

  8. Birari, S.P. and Ghanekar, S.L. (1992). Genetic diversity in lablab beans. J. Maharastra University. 17: 62-65.

  9. Chahal, G.S. and Gosal, S.S. (2002). Principles and Procedures of Plant Breeding: Biotechnology and Conventional Approaches. Alpha Science International, United Kingdom, ISBN: 9781842650363, Pages: 604.

  10. Hadavani, J.K., Mehta, D.R., Ansodariya, S.N. and Gadhiya, K.K. (2018). Principal component analysis and cluster analysis in Indian beans (Lablab purpureus L.). International Journal of Chemical Studies. 1448-1453.

  11. Julier, B., Porcheron, A., Ecalle, C. and Guy, P. (1995). Genetic variability for morphology growth and forage yield among the perennial deployed and tetraploid lucerne population (Medicago sativa L.). Agron. 15: 295-04

  12. Kujur, P.K., Bahadur, V. and Pankaj, P. (2017). Study on Genetic variability, heritability and genetic advance in Dolichos bean (Lablab purpureus L.) genotypes. Trends in Biosci. 10(13): 2418-2421.

  13. Magalingam,V., Yassin, M. and Kumar, S.R. (2013). Genetic variability and character association in dolichos bean. SAARC J. Agri. 11: 161-171 

  14. Nissar, M., Gafoor, A. and Khan, M. (2011). Phenotypic variation in agronomic and morphological traits of Pisum savium L. germ plasm obtained from different part of world. Rus. J. of Genet. 47: 19-25.

  15. Nwangburuka, C.C., Kehinde, O.B., OJO, D.K., Dentan, O.A. and Popoola, A.R. (2011). Morphological classification of genetic diversity in cultivated Okra [Abelmoschus esculentus (L.) Moench] using principal component analysis (PCA) and single linkage cluster analysis (SLCA). Afric. J. of Biot. 10: 11165-11172.

  16. Parmar, A.M., Singh, A.P., Dhillon N.P.S. and Jamwal, M. (2013). Genetic variability of morphological and yield traits in Dolichos bean (Lablab purpureus L.). African Journal of Agricultural Research. 8: 1022-1027.

  17. Peer, Sadak, S., Syam Sundar Reddy, P., Sadarunnisa, S., Reddy, D.S. and Pandravada, S.R. (2018). Genetic variability and heritability for yield and yield attributes in field bean (Lablab purpureus L.) genotypes. Int. J. Curr. Microbiol. App. Sci. 7: 2131-2137. 

  18. Rhodes, A.M. and Martin, F.W. (1972). Multivariate studies of variation of varieties in yams (D. alata L.). J. Am. Soc. Hort. Sci. 97: 685-688. 

  19. Roy, S., Islam, M.A., Sarkar, A., MaliK, M.A., Rafi, M.Y. and Ismail, M.R. (2013). Determination of genetic diversity in lentil germplasm based on quality traits. Aust. J. Crop Sci. 7: 14-21.

  20. SAS Institute. (2011). SAS Institute Inc., SAS 9.1.3 Help and Documentation, Cary, NC: SAS Institute Inc., 2002-2004.

  21. Singh, S.R., Ahmad, N., Kumar, D., Trivedi, A.K., Singh, A. and Mir, M. (2020). Assessment of genetic diversity in chilli in Western Himalayan region. Journal of Env. Bio. 41: 266-273. 

  22. Singh, S.R., Ahmed, N., Srivastva, K.K., Singh, D.B., Singh, A. and Yousuf, S. (2017). Genetic divergence assessment in European carrot type. Indian J. Hort. 74: 551-554.

  23. Taran, B., Zhang, C., Warkentin, T. and Tullu, A.V. (2005). Genetic diversity among varieties and wild species accession of pea (Pisum sativum L.) based on molecular marker, morphological and physiological Characters. Genome. 2: 257-272.

  24. Veasey, A.E., Aparecida, S.E. and Vencovsky, R. (2001). Germplasm characterization of Sesbania accessions based on multivariate analyses. Genetic Resources and Crop Evolution. 48: 79-91. https ://doi.org/10.1023/A:1011238320630.

Editorial Board

View all (0)