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Multiple-trait, random regression, and compound symmetry models for analyzing multi-environment trials in maize breeding.

Authors :
Igor Ferreira Coelho
Marco Antônio Peixoto
Jeniffer Santana Pinto Coelho Evangelista
Rodrigo Silva Alves
Suellen Sales
Marcos Deon Vilela de Resende
Jefferson Fernando Naves Pinto
Edésio Fialho Dos Reis
Leonardo Lopes Bhering
Source :
PLoS ONE, Vol 15, Iss 11, p e0242705 (2020)
Publication Year :
2020
Publisher :
Public Library of Science (PLoS), 2020.

Abstract

An efficient and informative statistical method to analyze genotype-by-environment interaction (GxE) is needed in maize breeding programs. Thus, the objective of this study was to compare the effectiveness of multiple-trait models (MTM), random regression models (RRM), and compound symmetry models (CSM) in the analysis of multi-environment trials (MET) in maize breeding. For this, a data set with 84 maize hybrids evaluated across four environments for the trait grain yield (GY) was used. Variance components were estimated by restricted maximum likelihood (REML), and genetic values were predicted by best linear unbiased prediction (BLUP). The best fit MTM, RRM, and CSM were identified by the Akaike information criterion (AIC), and the significance of the genetic effects were tested using the likelihood ratio test (LRT). Genetic gains were predicted considering four selection intensities (5, 10, 15, and 20 hybrids). The selected MTM, RRM, and CSM models fit heterogeneous residuals. Moreover, for RRM the genetic effects were modeled by Legendre polynomials of order two. Genetic variability between maize hybrids were assessed for GY. In general, estimates of broad-sense heritability, selective accuracy, and predicted selection gains were slightly higher when obtained using MTM and RRM. Thus, considering the criterion of parsimony and the possibility of predicting genetic values of hybrids for untested environments, RRM is a preferential approach for analyzing MET in maize breeding.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
19326203
Volume :
15
Issue :
11
Database :
Directory of Open Access Journals
Journal :
PLoS ONE
Publication Type :
Academic Journal
Accession number :
edsdoj.6b4ff9bdf0a0491ab884ca84ca70a9db
Document Type :
article
Full Text :
https://doi.org/10.1371/journal.pone.0242705