Genetic Diversity using Cluster and Principal Component Analysis in Chickpea (Cicer arietinum L.)

Abstract

A comprehensive assessment of genetic diversity was conducted in 46 chickpea genotypes using Principal Component Analysis (PCA) and hierarchical cluster analysis based on seven quantitative traits. PCA revealed that the first three principal components (PCs) had eigenvalues greater than one and together accounted for 72.87% of the total phenotypic variation. PC1 was primarily associated with days to flowering and maturity, PC2 with seed yield and PC3 with plant height and test weight. These components effectively captured the major dimensions of variability, facilitating trait-based genotype classification. The PCA biplot identified genotypes such as DCR 24-2, RVSSG-137 and NBeG 1149 as promising for seed traits while VCD-23-3 and KCD-120029 exhibited superior yield potential. Cluster analysis further grouped the genotypes into four distinct clusters, with the blue cluster showing maximum genetic divergence. The clustering pattern supported PCA results and highlighted genotypes with unique trait combinations suitable for breeding. This integrated multivariate approach proved effective in identifying genetically diverse and agronomically desirable genotypes, offering valuable insights for parent selection and trait-specific improvement in chickpea breeding programs.