Evaluation of Rice Genetic Diversity and Variability in a Population Panel by Principal Component Analysis

A population panel of 192 rice genotypes comprising traditional landraces and exotic genotypes from 12 countries was evaluated for 12 agro - morphological traits by principal component analysis for determining the pattern of genetic diversity and relationship among individuals. Twelve quantitative characters i.e. plant height, leaf length, number of productive tillers, panicle length, number of filled grains, spikelet fertility, days to 50% flowering; days to harvest maturity, grain length, grain width, grain length width ratio, and single plant yield were measured. The largest variation was observed for number of productive tillers with Coefficient of Variation (CV) of 28.03% followed by number of filled grains per panicle, single plant yield, leaf length , grain length width ratio. Days to maturity has shown the least variation with the CV of 9.74%. Principal component analysis was utilized to examine the variation and to estimate the relative contribution of various traits for total variability. In the current study, Component 1 had the contribution from the traits such as days to 50% flowering, leaf length, plant height, panicle length, days to maturity and number of filled grains which accounted 28.46% of the total variability. Grain width and grain length width ratio has contributed 16.8% of total variability in component 2. The remaining variability of 14.4%, 11.7% and 9.3% was consolidated in component 3, component 4 and component 5 by various traits such as spikelet fertility, single plant yield, grain length and number of productive tillers. The cumulative variance of 80.56% of total variation among 12 characters was explained by the first five axes. Thus the results of principal component analysis used in the study have revealed the high level of genetic variation and the traits contributing for the variation was identified. Hence this population panel can be utilized for trait improvement in breeding programs for the traits contributing for major variation.