Bayesian factor analytic model: An approach in multiple environment trials.

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ID: 21194
2019
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Abstract
One of the main challenges in plant breeding programs is the efficient quantification of the genotype-by-environment interaction (GEI). The presence of significant GEI may create difficulties for breeders in the selection and recommendation of superior genotypes for a wide environmental network. Among the diverse statistical procedures developed for this purpose, we highlight those based on mixed models and factor analysis that are called factor analytic (FA) models. However, some inferential issues are related to the factor analytic model, such as Heywood cases that make the model non-identifiable. Moreover, the representation of the loads and factors in the conventional biplot does not involve any measurement of uncertainty. In this work, we propose dealing with the FA model using the Bayesian framework with direct sampling of factor loadings via spectral decomposition; this guarantees identifiability in the estimation process and eliminates the need for the rotationality of factor loadings or imposition of any ad hoc constraints. We used simulated and real data to illustrate the method's application in multi-environment trials (MET) and to compare it with traditional FA mixed models on controlled unbalancing. In general, the Bayesian FA model was robust under different simulated unbalanced levels, presenting the superior predictive ability of missing data when compared to competing models, such as those based on FA mixed models. In addition, for some scenarios, the classical FA mixed model failed in estimating the full FA model, illustrating the parametric problems of convergence in these models. Our results suggest that Bayesian factorial models might be successfully used in plant breeding for MET analysis.
Reference Key
nuvunga2019bayesianplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nuvunga, Joel Jorge;da Silva, Carlos Pereira;de Oliveira, Luciano Antonio;de Lima, Renato Ribeiro;Balestre, Marcio;
Journal PloS one
Year 2019
DOI
10.1371/journal.pone.0220290
URL
Keywords Keywords not found

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