de Oliveira L A, da Silva C P, Nuvunga J J, da Silva A Q, Balestre M
Faculdade de Ciências Exatas e Tecnologia, Universidade Federal da Grande Dourados, Dourados, MS, Brasil.
Departmento de Ciências Exatas, Universidade Federal de Lavras, Lavras, MG, Brasil.
Genet Mol Res. 2016 Jun 17;15(2):gmr8612. doi: 10.4238/gmr.15028612.
The additive main effects and multiplicative interaction (AMMI) and the genotype main effects and genotype x environment interaction (GGE) models stand out among the linear-bilinear models used in genotype x environment interaction studies. Despite the advantages of their use to describe genotype x environment (AMMI) or genotype and genotype x environment (GGE) interactions, these methods have known limitations that are inherent to fixed effects models, including difficulty in treating variance heterogeneity and missing data. Traditional biplots include no measure of uncertainty regarding the principal components. The present study aimed to apply the Bayesian approach to GGE biplot models and assess the implications for selecting stable and adapted genotypes. Our results demonstrated that the Bayesian approach applied to GGE models with non-informative priors was consistent with the traditional GGE biplot analysis, although the credible region incorporated into the biplot enabled distinguishing, based on probability, the performance of genotypes, and their relationships with the environments in the biplot. Those regions also enabled the identification of groups of genotypes and environments with similar effects in terms of adaptability and stability. The relative position of genotypes and environments in biplots is highly affected by the experimental accuracy. Thus, incorporation of uncertainty in biplots is a key tool for breeders to make decisions regarding stability selection and adaptability and the definition of mega-environments.
加性主效应和乘积互作（AMMI）模型以及基因型主效应和基因型×环境互作（GGE）模型在基因型×环境互作研究中使用的线性-双线性模型中脱颖而出。尽管使用这些方法来描述基因型×环境（AMMI）或基因型以及基因型×环境（GGE）互作具有优势，但这些方法存在固定效应模型固有的已知局限性，包括处理方差异质性和缺失数据的困难。传统双标图不包括关于主成分的不确定性度量。本研究旨在将贝叶斯方法应用于GGE双标图模型，并评估其对选择稳定且适应性良好的基因型的影响。我们的结果表明，应用于具有非信息先验的GGE模型的贝叶斯方法与传统GGE双标图分析一致，尽管纳入双标图的可信区域能够基于概率区分基因型的表现及其在双标图中与环境的关系。这些区域还能够识别在适应性和稳定性方面具有相似效应的基因型和环境组。双标图中基因型和环境的相对位置受实验精度的影响很大。因此，在双标图中纳入不确定性是育种者在稳定性选择和适应性决策以及大环境定义方面做出决策的关键工具。
