Assessment of Yield and Quality Traits in Rice (Oryza sativa L.) Restorer Lines through Genetic Diversity and Principal Component Analysis

The experiment was conducted during kharif (July to November, 2020) at Seed Breeding Farm, Rice Improvement Project, Department of Plant Breeding & Genetics, College of Agriculture, JNKVV, Jabalpur, Madhya Pradesh, India to study genetic diversity and principal component analysis (PCA) in 80 fertility restorer lines of rice. Using the Tocher method, the genotypes were divided into 13 clusters, with Cluster I being the largest, comprising 64 genotypes. Cluster V included five genotypes, while the other clusters each contained only one genotype. The D2 statistics revealed that Cluster V, which comprised the genotypes Laxmi-144, CANP-318, ANP-526, JNPT-782, and JNPT-767, exhibited the highest intra-cluster distance. Maximum inter-cluster distance was found between Clusters IX and XIII, followed by Clusters IV and XIII. Cluster mean values suggest that genotypes R-710 and 1E-TP-2 contribute major yield and quality traits, while genotype AD02207 contribute to improve yield-attributing traits. PCA identified eight principal components with Eigenvalue exceeding 1.00, captured 77.4% of total variability. These PCs contributed the traits having high value in terms of yield and quality. The rotated component matrix revealed that the PC1 accounted for the highest variability. Genotypes JNPT-782, AD02207, NP-9165 and ANP-526 reported elevated scores in these PCs hence will lead to superior cross combinations in terms of both yield and quality. Biplot analysis was also performed for trait-based genotype selection.