Genetic Diversity Studies for Yield, and Quality Traits using PCA and Cluster Analysis in Timely Sown Chickpea (Cicer arietinum L.) Genotypes

Abstract

Chickpea (Cicer arietinum L.) is an important pulse crop that contributes substantially to nutritional security because of its high protein content and adaptability to diverse environments. The present study assessed the extent of genetic diversity among 31 chickpea genotypes evaluated in a randomized block design (RBD) with three replications using morphological, physiological and biochemical traits. Multivariate techniques, including principal component analysis (PCA) and cluster analysis, were used to identify the key traits contributing to genetic divergence. PCA revealed that the first three principal components collectively explained 66.79% of the total phenotypic variation, indicating the effectiveness of dimensionality reduction. Traits related to yield components, phenology and physiological responses, such as days to flowering, number of pods per plant, proline content, malondialdehyde (MDA) and protein content, contributed substantially to genetic variability. Cluster analysis grouped the genotypes into three distinct clusters, reflecting considerable genetic heterogeneity, with greater divergence observed between clusters C1 and C3. Genotypes exhibiting higher proline accumulation coupled with lower MDA levels indicated better physiological efficiency, while wide variation in protein content revealed significant nutritional diversity among the genotypes. Based on overall performance across yield, physiological and quality traits, RKGM 20-2, ICCV 191611 and GNG 1958 were identified as promising genotypes for use in future chickpea breeding programmes. The study demonstrates that the combined use of PCA and cluster analysis is an effective approach for identifying genetically diverse and superior genotypes, thereby providing useful information for selecting parents aimed at improving yield, nutritional quality and adaptability in chickpea.