Genetic diversity analysis of peppers: a comparison of discarding variable methods

Abstract

There are a lot of variables in genetic diversity studies, and it is necessary to know whether or not they areall important and which ones can be discarded. There are often little changes in clustering patterns if a subset ofthese variables is used, because the discarded variables are redundant or of little contribution to the variability.This study aimed at comparing two discards of variables methods – the Singh method and the principal componentsmethod – as well as evaluating the effect of the discards on the cluster analysis. In this analysis data of six ripefruits traits were used. Other characters with previously known variability or collinearity were added to theanalysis. The method considered being the most efficient was the one, which indicated variables that did notalter the initial clustering pattern when discarded. The Singh method did not detect variation differences whenstandardized data were used. When the distance was obtained by the non-standardized data, the pericarp thickness(0.018%), total soluble solids (0.1668%) and minimum width (2.99%) had the lowest contribution to thedivergence. The principal components pointed out that the characteristics fruit length, total soluble solids andseeds yield/fruit were considered as dispensable variables. There were changes in the initial clustering patternwhen the variable pericarp thickness was discarded, and the Singh method was not efficient in detecting theimportance of this variable. There were no changes in the initial clustering pattern when fruit length was discarded.The data showed that the two compared methods differed, since Singh’s and principal component methodsshowed different variables to be discarded. The Singh method was not efficient in detecting multicollinearityamong variables. The principal component method was more efficient in pointing out the variables that can bediscarded. It is advisable that the genetic divergence is calculated based on the scores of the principal components.In future studies, when there is no replicated data, the genetic divergence and the pinpoint of characters shouldbe calculated based on the principal component scores to avoid discarding some important variables whendetermining divergence. However, if the variable values differ independently, the Singh method based on Euclideandistance is appropriate.