Comparison of projection of distance techniques for genetic diversity studies

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.

Research Article Neural networks and dimensionality reduction to increase predictive efficiency for complex traits

“…In general, the MLPNN method is less affected by the different genetic architectures evaluated (Figure 4). Figure 4a shows that, with the inclusion of dominance effects, RMSE increased, on average, from 74 to 311, approximately, using RR-BLUP, and from 7 to 14, approximately, using MLPNN, in agreement with the results obtained by other authors (Sant'anna et al, 2019;Sant'anna et al, 2021). Sant'Anna et al ( 2019) carried out studies using simulated populations with different levels of dominance and heritability and verified the predictive superiority of the RBF neural networks when compared with the G-BLUP method, given the lower RMSE values obtained.…”

“…Specifically, for T10 -D120H35Ep, for example, RMSE decreased from 577.249 to 24.483. Other authors had already reported the superiority of neural networks in prediction studies (Sant'anna et al, 2015;Silva et al, 2016;Silva et al, 2017;Sant'anna et al, 2019;Sant'anna et al, 2021). Sant'anna et al ( 2019) evaluated the genome-enable prediction by Radial Basis Neural Networks (RBFNN) model compared to RRBLUP and obtained greater prediction accuracy for the RBFNN model.…”

Life in the desert: The impact of geographic and environmental gradients on genetic diversity and population structure of Ivesia webberi

“…We conducted AMOVA in GenAlEx to characterize the partitioning of genetic variation on the landscape. Principal coordinate analysis (PCoA) was conducted using F ST values to investigate population structuring (Jombart et al, 2009; Sant’Anna et al, 2020); using the pcoa function in the ape R package (Paradis & Schliep, 2019; R Development Core Team, 2020). Effective population size ( N e ) was calculated for each population and Bayesian genotype cluster identified using the linkage disequilibrium (LD) method in NeEstimator v.2.0 (Do et al, 2014).…”

Multivariate analyses of indigenous bread wheat (Triticum aestivum L.) landraces of Oman

“…PCA and AHC based on Euclidian distances showed differential nature of clustering and did not endorse each other. However, critical examination of composition of accessions in four clusters of PCAs and three clusters illustrated by dendrogram in the present study clearly depicted the fact that the discriminatory factors involved in three multivariate were helpful in screening the common accessions gradually to reduce their number to a minimum for ultimate selection of parents for use in crop improvement programs (Walker, 2014;Sant'Anna et al, 2020). In this sense, comparison of each of the respective four clusters of PCA without and with varimax rotation indicated that cluster nos.…”