Diversity analysis in rice breeding lines for yield and its components using principal component analysis

Abstract

One hundred and nineteen rice breeding lines with two local checks were subjected to the principle component analysis (PCA) and cluster analysis to estimate the existing genetic diversity for yield contributing characters. The first three principal components having Eigen value more than one are cumulatively contributing 68.69% to the total variability. PC1 has the contribution from the traits days to 50% flowering (0.497), days to maturity (0.484) and ear bearing tillers (0.359) which accounted 31.84% of total variability indicating these traits contributed more to the total variance. Cluster analysis revealed that the rice lines were classified into 12 divergent clusters by both PCA and Tocher’s method. Among the 12 clusters, cluster 1 had highest number of breeding lines (18) and cluster 11 had least number of lines (2) in PCA cluster analysis whereas in Tocher’s method highest number of lines observed in cluster 5 (34) followed by cluster 1(33). This analysis reveals the presence of wide genetic variance in rice breeding lines.