Abstract
The objective of this study was to compare different graphical dispersion analysis techniques in two- or three-dimensional planes. In this study, the data from different published works were used in order to determine the best methodology for analyzing the genetic diversity of different species. In this study, efficiency is measured by the amount of original distance absorbed by the projection of distances technique, which in the case of major components is equal to the amount of total variation originally available and retained by the principal components used for dispersion purposes. The projection of dissimilarity measurement technique, principal component analysis (PCA), and principal coordinate analysis (PCoA) were used. Considering the analysis by means of three orthogonal axes, the graphical dispersion efficiency was 82.22 for PCA, 87.22 for PCoA, and 85.25 for the projection of distances technique. For the 2D analysis, considering the two main axes, the mean dispersion efficiency was 69.90 for the PCA, 75.06 for the projection technique, and 78.16 for PCoA. Considering the studies carried out with experimental data of six different species, it is concluded that the principal coordinate analysis is superior.
Highlights
Studies regarding the discrimination of plant and animal populations are of great importance for the development of breeding programs and biodiversity conservation
Three biometric techniques were used for graphical representation of genetic diversity, and the choice of technique depends on the type of the available data or the author’s preference without adopting a scientific criterion
The projection of distances technique was successfully used to represent genetic diversity by Sant'Anna and Cruz (2015), who found that in simulated populations, this technique placed the parents and the five backcross generations in the Cartesian plane, respecting the genetic similarities, which ranged from 75% to 98%, exactly as expected by the Mendelian principles
Summary
Studies regarding the discrimination of plant and animal populations are of great importance for the development of breeding programs and biodiversity conservation. Through phenotypic characteristics, has guided the selection of suitable parents at the early stages of breeding programs, leading to the optimization of selective gains. Analysis of genetic diversity has allowed the quantification of variability and has facilitated the management of germplasm banks, saving time and resources (Cruz, Ferreira, & Pessoni, 2011). There are several methodologies available for the quantification and evaluation of diversity in population studies, either from phenotypic information or genotypic data. Due to the wide variety of information and given the particularities of each biological material, the choice and the application of the most appropriate methodology are important to obtain reliable results (Mohammadi & Prasana, 2003). The choice falls on individual clusterings based on dispersions in relation to Cartesian axes obtained by the principal components, principal coordinate and projection of distance techniques
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