PT  - JOURNAL ARTICLE
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
TI  - Bayesian Additive Regression Trees for Genotype by Environment Interaction Models
AID  - 10.1101/2021.05.07.442731
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2021.05.07.442731
4099  - http://biorxiv.org/content/early/2022/11/07/2021.05.07.442731.short
4100  - http://biorxiv.org/content/early/2022/11/07/2021.05.07.442731.full
AB  - 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.