A variable is a factor, quantity, character or symbol with values, which vary in contrast to a constant value that is fixed. Genetic variability refers to the differences inherent in the genomes of organisms. Multivariate analysis is a set of techniques dedicated to the analysis of data sets with more than one response variable. It involves a mathematical procedure, which transforms a number of correlated variables into smaller number of uncorrelated variables known as principal components. This transformation is responsible for the data sensitivity and outcome. Hence, principal component analysis (PCA) as a statistical tool has found application in various fields, including analyzing genetic variations between and within species. It is a way of identifying patterns in data and expressing the data in such a way as to highlight their similarities and differences. A key advantage of PCA is that once these patterns are found, the data are compressed without much loss of information. It is widely used in pattern recognition applications, image compression and gene clustering. PCA is an increasingly powerful tool enabling analysis of data in studies of genetic variability. Thus, it is paramount that plant scientists, especially those involved in characterization and evaluation of plant genetic resources should adapt its application to advance their findings.

Keywords: Data set, principal component analysis (PCA), genetic variability, multivariate method, dimension reduction.