Bayesian Additive Regression Trees for Genotype by Environment Interaction Models - AMBARTI

Abstract

¹ 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 semi-parametric Bayesian Additive Regression Trees to accurately capture marginal genotypic and environment effects along with their interaction in a fully Bayesian model. We demonstrate that our approach is competitive or superior to the traditional AMMI models widely used in the literature via both simulation and a real world data set. 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 available at https://github.com/ebprado/ambarti.