An integrative multivariate and clustering analysis dissecting genetic variability in bread wheat (Triticum aestivum L.)

A detailed investigation was conducted to assess analysis of variance, genetic divergence, correlation and principal component analysis (PCA) among 381 wheat genotypes from the ICAR-NBPGR Core Collection with three local cultivated varieties. We analyzed 12 agro-morphological traits, yielding clear and significant results. The analysis of variance clearly shows that the treatments have a significant effect on almost all traits. High Genotypic Coefficient of variation observed for yield-related traits and high heritability observed among spike length, days to 50 % flowering and plant height. For grain yield and the number of grains per spike, a positive correlation was observed. The PCA results indicated that the first and second principal components (PC-1 and PC-2) have eigenvalues greater than one, accounting for an impressive 50.73 % of the total variability. Notably, for PC-2 exhibited strong positive loadings for both 1000-grain weight and grain yield. The cluster analysis distinctly categorized the genotypes into eight separate clusters using Euclidean distance, identifying the greatest genetic distance between Cluster V and Cluster III.