TY - JOUR T1 - Bayesian Additive Regression Trees for Genotype by Environment Interaction Models JF - bioRxiv DO - 10.1101/2021.05.07.442731 SP - 2021.05.07.442731 AU - Danilo A. Sarti AU - Estevão B. Prado AU - Alan N. Inglis AU - Antônia A. L. dos Santos AU - Catherine B. Hurley AU - Rafael A. Moral AU - Andrew C. Parnell Y1 - 2022/01/01 UR - http://biorxiv.org/content/early/2022/11/07/2021.05.07.442731.abstract N2 - 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 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 available at https://github.com/ebprado/ambarti.Competing Interest StatementThe authors have declared no competing interest. ER -