Departamento de Biologia Geral, Universidade Federal de Viçosa (UFV), Viçosa, Minas Gerais, Brazil.
Departamento de Estatística, INCT Café / Universidade Federal de Viçosa (UFV), Viçosa, Minas Gerais, Brazil.
PLoS One. 2020 Nov 20;15(11):e0242705. doi: 10.1371/journal.pone.0242705. eCollection 2020.
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.
在玉米育种计划中，需要一种高效且信息量丰富的统计方法来分析基因型与环境互作（GxE）。因此，本研究的目的是比较多性状模型（MTM）、随机回归模型（RRM）和复合对称模型（CSM）在玉米育种中多环境试验（MET）分析中的有效性。为此，使用了一个在四个环境下评估 84 个玉米杂交种的产量（GY）性状数据进行分析。方差分量通过受限极大似然法（REML）进行估计，遗传值通过最佳线性无偏预测法（BLUP）进行预测。通过赤池信息量准则（AIC）确定了最佳的 MTM、RRM 和 CSM，并通过似然比检验（LRT）检验了遗传效应的显著性。考虑了四个选择强度（5、10、15 和 20 个杂交种）来预测遗传增益。所选择的 MTM、RRM 和 CSM 模型拟合了异质残差。此外，对于 RRM，遗传效应是通过二阶勒让德多项式来建模的。评估了不同玉米杂交种之间的 GY 遗传变异。一般来说，使用 MTM 和 RRM 时，广义遗传力、选择性准确性和预测选择增益的估计值略高。因此，考虑到简约性标准和预测未测试环境中杂交种遗传值的可能性，RRM 是分析玉米育种中 MET 的首选方法。
