Bayesian additive regression trees for genotype by environment interaction models

Sarti, Danilo and Batista Do Prado, Estevao and Inglis, Alan and Lemos dos Santos, Alessandra and Hurley, Catherine and de Andrade Moral, Rafael and Parnell, Andrew C (2023) Bayesian additive regression trees for genotype by environment interaction models. Annals of Applied Statistics, 17 (3). pp. 1936-1957. ISSN 1932-6157

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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.