Diversity Analysis through Principle Component Analysis in Wheat (Triticum aestivum)

Abstract

Wheat is agronomically and nutritionally most important cereal essential for the food security, poverty alleviation and improved livelihoods. Genetic diversity is necessary to be present in the base population from which potential parents are to be selected as diverse parent for hybridization. For this purpose, various multivariate analytical techniques, which simultaneously analyze multiple measurements on each individual under investigation, are widely used in analysis of genetic diversity. Among these, principal component analysis (PCA) is useful method used for diversity analysis. For the principal component analysis each genotype was identified on the basis of correlation matrix as a single point in a standardized multidimensional space. The axes of this space were principal components obtained from the original data as orthogonal transformation of the original variety. In this way each principal component becomes a linear combination of the varietal scores corresponding to the original variables.