RT Journal Article
SR Electronic
T1 Bayesian Additive Regression Trees for Genotype by Environment Interaction Models
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.05.07.442731
DO 10.1101/2021.05.07.442731
A1 Danilo A. Sarti
A1 Estevão B. Prado
A1 Alan N. Inglis
A1 Antônia A. L. dos Santos
A1 Catherine B. Hurley
A1 Rafael A. Moral
A1 Andrew C. Parnell
YR 2022
UL http://biorxiv.org/content/early/2022/11/07/2021.05.07.442731.abstract
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.