Evaluation of genetic diversity in rice (oryzasativaL.) germ plasm accessions employing multivariate analysis and association studies for yield and nutritional traits

Genetic variability and correlation of characters in rice (Oryzasativa L.) are crucial to the development of high-yielding and nutritionally improved cultivars. Principal Component Analysis (PCA) was used in the current study to determine phenotypic variation between 183 rice germplasm accessions tested over four consecutive seasonskharif 2021 and 2022 and rabi 2022 and 2023 with regard to yield and nutritional traits. Observations were recorded for eleven yield attributes like plant height, panicle length, tillers number, productive tillers number, days to reach 50% flowering, panicle weight, number of filled grains, number of unfilled grains, fertile spike lets, per plant yield and test weight, and two nutritional attributes viz., grain iron and zinc contents. PCA retained six principal components with eigenvalues greater than one, explained a combined 78.5% of the total phenotypic variance. Yield-contributing characteristics and micronutrient levels were the primary drivers of variability and discriminated among genotypes as key discriminators. Five different clusters were classified using cluster analysis, with Cluster I having the highest number of accessions and Cluster V having the least. Among all the traits, zinc content showed maximum genetic divergence. Biplot analysis also revealed a significant positive correlation of single plant yield with all the traits, except plant height, which showed a negative correlation. Similarly, these relationships were confirmed by correlation analysis, suggesting that the traits positively correlating with yield may be effective selection traits.Yield traits shown high environmental variability, whereas micronutrients have low phenotypic variation but high analytical sensitivity which was a challenge of the study and it was overcome by eliminating biasedness through proper standardization of principle component analysis. In all, the findings provide useful information about the multivariate pattern of phenotypic variation and facilitate identification of donor lines for combined improvement of yield and micronutrient use efficiency in rice.