Bayesian additive regression trees for genotype by environment interaction models
Danilo A. Sarti, Estevão B. Prado, Alan N. Inglis, Antônia A. L. dos Santos, Catherine B. Hurley, Rafael A. Moral, Andrew C. Parnell
The Annals of Applied Statistics · 2023 · Volume 17 · Issue 3
Abstract
We propose a new class of models for the estimation of genotype by environment (GxE) interactions in plant-based genetics. Our approach, named AMBARTI, uses semiparametric Bayesian additive regression trees to accurately capture marginal genotypic and environment effects along with their interaction in a cut Bayesian framework. We demonstrate that our approach is competitive or superior to similar models widely used in the literature via both simulation and a real world dataset. Furthermore, we introduce new types of visualisation to properly assess both the marginal and interactive predictions from the model. An R package that implements our approach is also available at https://github.com/ebprado/ambarti.
Keywords
additive main effects multiplicative interactions model, Bayesian additive regression trees, Bayesian nonparametric regression, genotype-by-environment interactions
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