Identification of genetically diverse maize (Zea mays L.) inbred lines through cluster and principal component analysis

Abstract

The study analyzed the genetic diversity of 133 maize (Zea mays L.) inbred lines using clustering and principal component analysis (PCA) to identify promising parental lines for hybrid development. Analysis of variance (ANOVA) revealed highly significant differences among genotypes for all ten studied traits, indicating substantial genetic variability. The highest coefficient of variation (18.15%) was observed for grain yield per hectare, while cob girth (4.26%) showed the least variation. Tocher’s clustering method grouped the genotypes into eight distinct clusters, with Cluster I containing 89 genotypes, suggesting high genetic similarity, whereas Cluster VIII had only one genotype (G51), indicating its uniqueness. The highest inter-cluster distance was found between Clusters III and VII (D² = 759.56), suggesting that crosses between these clusters could maximize heterosis. PCA identified two principal components (PC1 and PC2) explaining 58.19% of the total variance. PC1 (35.10%) was predominantly influenced by plant height, ear height, and cob length, while PC2 (23.09%) was associated with flowering traits and kernel traits. The PCA biplot showed that high-yielding genotypes were clustered separately from early-maturing genotypes, indicating a trade-off between early maturity and yield potential. These findings suggest that selecting genetically distant parents, particularly from Clusters III and VII, could lead to hybrids with superior yield potential. Additionally, Clusters I, II, and VI contained high-yielding genotypes that could be directly utilized in hybrid breeding programs. This study provides crucial insights into the genetic structure of maize inbred lines, aiding in the strategic selection of parents for enhanced maize productivity.