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

• DOI: 10.1371/journal.pone.0242705
• Publication Date: 2020-11-20T19:05:29Z
• Authors: Igor Ferreira Coelho; Marco Antônio Pei...; Jeniffer Santana ...; Rodrigo Silva Alves; Suellen Sales; Marcos Deon Vilel...; Jefferson Fernand...; Edésio Fialho dos...; Leonardo Lopes Bh...
• Keywords: Akaike information criterion; Restricted maximum likelihood; Trait; Genetic gain; Mixed model

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