Implications of univariate and multivariate analyses on the dissimilarity of common bean accessions

Abstract

Univariate and multivariate analyses have been routinely used to discriminate genotypes in breeding programs, but their relationship is not always considered. The objective of this study was to relate univariate and multivariate analyses on the dissimilarity among common bean accessions based on characteristics such as days until emergence, plant height, first pod insertion height, harvest days, final stand, number of pods per plant, pod length, 100-grain weight, grain yield and seed length, width and thickness. Data were submitted to univariate analysis of variance, with a comparison of means by the Scott-Knott test, and multivariate analysis to estimate the genetic divergence based on the generalized Mahalanobis distance, grouping the accesses through Tocher’s method. The relative importance of the characters was also estimated by Singh's method, performing the multicollinearity diagnosis. There was a significant difference for all the evaluated characteristics, showing the existence of variability between the accesses. The greater variability found was related to seed characteristics. It was possible to relate univariate and multivariate analyses, since there was a relationship between the groups generated by Scott-Knott’s test and the percentage of the relative contribution of the evaluated characters. However, the clustering obtained in the univariate analysis for the characteristic that contributed the most to the divergence between the accessions was not compatible with the cluster generated by Tocher’s method. The coupled use of these analyses can help breeders to decide, choosing superior genotypes and that present wide genetic divergence, mainly for the characteristics of greater interest.