Diversity Analysis for Yield and Its Contributing Traits in Rice Germplasm (Oryza sativa L.) Using Principal Component Analysis Approach

Abstract

The present study was aimed to assess genetic variation among rice germplasm lines employing a multivariate biometrical approach, viz., Principal Component Analysis. Analysis of variance as per Augmented Block Design indicated the presence of sufficient genetic variation among rice germplasm lines, while estimates of components of variation revealed a maximum contribution of genotypic variance to the phenotypic variance, suggesting its exploitation through selection and hybridization. Simultaneously, correlation estimates revealed a significant positive association of grain yield with panicle length, 1000 grain weight and grain length indicating suitability of these traits for indirect selection. D2 statistics grouped germplasm lines into sixteen clusters and among these cluster I consists of 67 germplasm lines forming the largest cluster followed by cluster III (26 lines), cluster II (18 lines), cluster IV (15 lines), cluster V (13 lines), cluster VI (5 lines), cluster VIII (3 lines), cluster VII and cluster IX (2 lines each), while clusters X, XI, XII, XIII, XIV, XV, XVI (1 line each). Inter cluster distances were found to be higher than intra cluster distances and the maximum inter cluster distance was observed between cluster IX and VI (73.47) followed by cluster IX and X (73.14). 
 Principal component analysis transformed eleven interrelated variables into four major principal components having an eigen value of more than 1, thereby, indicating that these components are responsible for a higher magnitude of variance in the population (76.60 per cent). Among these principal components, the first component accounted for 32.80 per cent of the total variation while, the second, third and fourth components explained 18.50 per cent, 13.90 per cent and 11.40 per cent of total variation, respectively. Factor loadings of the principal components revealed that principal component 1 had high positive loadings for grain length, panicle length, length/breadth ratio, 1000 grain weight and plant height whereas, principal component 2 had high positive loadings for total number of tillers per plant and number of effective tillers per plant indicating that the first two principal factors can be collectively designated as yield attributing factors. Principal component 3 had high positive loadings for grain breadth, 1000 grain weight and plant height, whereas, principal component 4 had high positive loadings for 1000 grain weight, days to maturity and days to 50% flowering. PCA biplots revealed that germplasm lines viz., GPL-1, GPL-4, GPL-131, GPL-128, GPL-20, GPL-127, GPL-135, GPL-100, GPL-130 were found to be superior performers for desirable traits and can be used as parents in hybridization programme.

Appraising Components of Genetic Variation and Association among Rice (Oryza sativa L.) Breeding Lines for Yield and Its Component Traits

“…Analysis of variance (Table 1) as per augmented block design revealed significant differences among the lines for all traits recorded indicating enormous amount of differences among the lines thereby, hinting sufficient scope of selection among the lines followed by hybridization. These results are in close agreement with the results of Devi et al [8] and Choudhary et al [9] in their respective research studies. Genetic components of variation estimated (Table 2) revealed that phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV), thereby, indicating that environment has played mega role in the infestation of these traits.…”

“…Days to 50 per cent flowering, days to maturity and panicle length were found to have moderate values of PCV and GCV suggesting that these can be improved through positive selection based on phenotypic variation. Results of present study are in agreement with the results of Kumar et al [10] and Choudhary et al [9]. Components of variation calculated through PCV and GCV delineates limit of genetic differences in a plant population but provide no information related to genetic and environmental factors affecting selection pressure therefore, heritability estimates depicts proportion of total variation which can be inherited from generation to generation and guides breeders to develop efficient and goal oriented breeding programme.…”

“…The findings of present study confirmed that characters 1000 grain weight, plant height, days to 50 per cent flowering and grain yield per plant were found to have high genetic advance (GAM) coupled with high heritability implying that these traits efficiently inherited from generation to generation. Sandeep et al [11], Mushtaq et al [12] and Choudhary et al [9] also reported similar results in their respective studies. Yield being polygenic control (Table 3) can not be selected directly as it is influenced by cumulative action of genes with unknown effects.…”