Multivariate Assessment of Genetic Variation and Trait Associations in Moth bean (Vigna aconitifolia Jacq.)

Khushwant B.  Choudhary; Hans Raj  Mahla; Ramavtar Sharma

doi:10.56093/aaz.v65i1.172537

Authors

Khushwant B. Choudhary ICAR-Central Arid Zone Research Institute, Jodhpur 342 003, India
Hans Raj Mahla ICAR-Central Arid Zone Research Institute, Jodhpur 342 003, India
Ramavtar Sharma ICAR-Central Arid Zone Research Institute, Jodhpur 342 003, India

https://doi.org/10.56093/aaz.v65i1.172537

Keywords:

Genetic variability, moth bean, multi - trait selection indices, principal component analysis, trait associations

Abstract

Genetic variability, trait associations, and genetic divergence was studied among 49 moth bean genotypes. Analysis of variance showed significant genetic variation for most of the growth and yield-related traits. Correlation analysis revealed strong positive associations of total pods, clusters, and reproductive traits on branches with seed yield, while test weight was largely independent, allowing simultaneous improvement of yield and seed size. Principal component analysis explained 67.4% of total variability with the first two components; PC1 highlighted reproductive traits driving yield variation, and PC2 emphasized seed size and pod distribution. Leveraging these results, three PCA-based multi-trait selection indices—Yield Index, Seed Size Index, and Architecture Index—were constructed using respective trait loadings to streamline simultaneous selection of complex traits aligned with breeding objectives. Cluster analysis using Mahalanobis D² distances and Ward’s minimum variance method grouped genotypes into five distinct clusters revealing substantial genetic divergence. Unique genotypes in single-genotype clusters offer valuable genetic resources for broadening crop diversity. This integrative approach combining phenotypic variance, multivariate analysis, and genetic clustering provides a robust framework for effective parent selection and accelerated genetic gain in moth bean.

Downloads

Download data is not yet available.

References

Ahlinder, J., Hall, D., Suontama, M., and Sillanpää, M.J. 2024. Principal component analysis revisited: fast multitrait genetic evaluations with smooth convergence. G3: Genes|Genomes|Genetics 14(12): jkae228. https://doi.org/10.1093/g3journal/ jkae228

Ben-Israel, A. and Greville, T.N.E. 2003. Generalized Inverses: Theory and Applications. Springer, New York. https://doi.org/10.1007/b97366.

Bhadmus, A., Abberton, M., Idehen, E., Ekanem, U., Paliwal, R. and Oyatomi, O. 2023. Genetic diversity assessment of winged bean [Psophocarpus tetragonolobus (L.) DC.] accessions using agronomic and seed morphometric traits. Crops 3: 170-183. https://doi.org/10.3390/ crops3020017

Bhavsar, V.V. and Birari, S.P. 1989. Variability, correlation and path analysis in moth bean. Journal of Maharashtra Agricultural Universities 14: 148–150.

Chaudhari, S.R., Dahat, D.V., Rajput, H.J. and Chavan, B.H. 2021. Correlation, direct and indirect effect on yield and yield contributing characters in moth bean [Vigna aconitifolia (Jacq.)]. Journal of Pharmacognosy and Phytochemistry 10(1): 1365-1368. https://www.phytojournal.com/ archives/2021/vol10issue1/PartS/10-1-199-490. pdf

Chaudhary, N.B., Patel, A.M., Chaudhari, R.H., Viradiya, Y.A. and Patel, H.S. 2021. Genetic analysis for yield and quality related traits in moth bean (Vigna aconitifolia). International Journal of Chemical Studies 9(1): 2658-2661. doi: 10.22271/ chemi.2021.v9.i1ak.11629

Choudhary, K.B., Sharma, R., Solanki, R.K., Mahla, H.R., Jadon, K.S., Choudhary, M., Wani, S.H., Al-Ashkar, I., Abdelhamid, M.T. and Sabagh, A.E. 2024. Genetic relatedness in elite cultivars of moth bean using morpho-agronomic and molecular markers. Legume Research 47(3): 378- 384. Doi 10.18805/LRF-774

Debnath, S., Sarkar, A., Perveen, K., Bukhari, N.A., Kesari, K.K., Verma, A., Chakraborty, N.R. and Tesema, M. 2022. Principal component and path analysis for trait selection based on the assessment of diverse lentil populations developed by gamma-irradiated physical mutation. Biomed Research International 2022: 9679181. doi: 10.1155/2022/9679181

Halimi, R.A., Raymond, C.A., Barkla, B.J., Mayes, S. and King, G.J. 2021. Development of selection indices for improvement of seed yield and lipid composition in Bambara groundnut (Vigna subterranea (L.) Verdc.). Foods 11(1): 86. doi: 10.3390/foods11010086

Jain, S.K., Sharma, L.D., Gupta, K.C., Kumar, V. and Sharma, R.S. 2023. Principal component and genetic diversity analysis for seed yield and its related components in the genotypes of chickpea (Cicer arietinum L.). Legume Research 46(9): 1174- 1178. doi: 10.18805/LR-4489

Jolliffe, I.T. and Cadima, J. 2016. Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374(2065): 20150202. doi: 10.1098/rsta.2015.0202

Kanavi, M.S.P., Reddy, B.V.S. and Rao, P.V. 2019. Genetic diversity analysis through Mahalanobis D² statistic for green gram [Vigna radiata (L.) Wilczek]. International Journal of Current Microbiology and Applied Sciences 8(6): 847-855. 10.20546/ijcmas.2019.806.102.

Kohakade, S.N., Bhavsar, V.V. and Pawar, V.Y. 2017. Evaluation of genetic variability for quantitative characters in moth bean. International Journal of Current Microbiology and Applied Sciences 6(9): 3163–3168. doi: https://doi.org/10.20546/ ijcmas.2017.609.390

Mahalanobis, P.C. 1936. On the generalized distance in statistics. Proceedings of the National Institute of Science of India 2: 49-55.

Moore, E.H. 1920. On the reciprocal of the general algebraic matrix. Bulletin of the American Mathematical Society 26(9): 394-395.

Nainu, A.J., Murugan, S., Senthil Kumar, N. and John Christopher, D. 2020. Principal component analysis for yield contributing traits in mungbean [Vigna radiata (L.) Wilczek] genotypes. Plant Archives 20(2): 3585-3590. https://www. plantarchives.org/20-2/3585-3590%20(6201).pdf

Punia, M., Rolaniya, L.K., Jat, R.L., Parihar, A.K. and Kumar, S. 2023. Phenotypic variability analysis of moth bean (Vigna aconitifolia) accessions under drought and heat stress conditions. Biological Forum – An International Journal 15(11): 483-488.

Raghavan, K., Singh, R. and Yadav, R. 2022. Genetic diversity and clustering in mung bean using Mahalanobis D² and principal component analysis. Legume Research 45(3): 455-462.

Rajora, M.P., Bhatt, R.K. and Ram, R. 2012. Analysis for seed yield and its components in moth-bean (Vigna aconitifolia) under hot arid conditions. Madras Agricultural Journal 99(1-3): 21-25. https:// doi.org/10.29321/MAJ.10.100007

Ram, S., Kumar, P. and Singh, D. 2020. Genetic divergence studies in pigeon pea [Cajanus cajan (L.) Millspaugh] genotypes using Mahalanobis D² statistic. Legume Research 43(4): 735-743.

Sachan, V. and Parveen, S. 2025. Assessment of variability, heritability, genetic advance and correlation in elite germplasm of moth bean [Vigna aconitifolia (Jacq.)]. Agricultural Science Digest 1-7. doi: 10.18805/ag.D-6363.

Sahoo, S., Sharma, A.K., Sanadya, S.K. and Kumar, A. 2018. Character association and path coefficient analysis in moth bean germplasm. International Journal of Current Microbiology and Applied Sciences 7(8): 833-839. doi: 10.20546/ijcmas.2018.708.093

Santos, I.G., Cruz, C.D., Nascimento, M., Rosado, R.D.S. and Ferreira, R.P.D. 2018. Direct, indirect and simultaneous selection as strategies for alfalfa breeding on forage yield and nutritive value. Pesquisa Agropecuária Tropical 48: 178-189. doi: 10.1590/1983-40632018v4851950

Sharma, R., Kumar, S., Mahla, H.R., Choudhary, K.B. and Khandelwal, V. 2024. Moth bean (Vigna aconitifolia (Jacq.) Maréchal). In: S.H. Wani (Ed.), CABI Books. pp. 264-286. doi: 10.1079/9781800624658.0014

Shojaei, S., Bihamta, M., Mousavi, S.M.N., Qasemi, S., Keshavarzi, M.H.B. and Omrani, A. 2024. Application of graphical analysis and principal components to identify the effect of genotype × trait in maize hybrids. Agrosystems, Geosciences & Environment 7: e20548. doi: 10.1002/agg2.20548

Soni, R.K. and Modi, G. 2023. Trade-offs and synergies: Understanding the relationship between traits in moth bean (Vigna aconitifolia Jacq.). Journal of Chemical Health Risks 13(4): 1028- 1033. https://www.jchr.org/index.php/JCHR/ article/view/1019

Vaggar, S., Kumar, U., Prasad, K., Yadav, L.M., Pramila and Sinha, B.M. 2022. Study of genetic divergence for yield and quality traits in cowpea [Vigna unguiculata (L.) Walp.]. Legume Research 45(4): 410-414. doi: 10.18805/LR-4297.

Ward, J.H. 1963. Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58(301): 236–244. https:// doi.org/10.1080/01621459.1963.10500845

Yadav, A., Singh, C., Kalia, R., Mittal, S., Wankhede, D., Kakani, R., Ujjainwal, S., Aakash, Saroha, A., Nathawat, N.S., Rani, R., Panchariya, P., Choudhary, M., Solanki, K., Chaturvedi, K., Archak, S., Singh, K., Singh, G. and Singh, A.

2023. Genetic diversity, population structure, and genome-wide association study for the flowering trait in a diverse panel of 428 moth bean (Vigna aconitifolia) accessions using genotyping by sequencing. BMC Plant Biology 23: 4215. doi: 10.1186/s12870-023-04215-w.

Yadav, A., Nath, S., Chourasiya, V., Boro, J., Kumar, D., Srikanth, B. and Bharti, D. 2025. Multivariate genetic diversity assessment in chickpea (Cicer arietinum L.) genotypes under different environmental conditions. Legume Research – An International Journal. doi: 10.18805/LR-5407.