Batista Do Prado, Estevao

de Andrade Moral, Rafael

Hurley, Catherine

Inglis, Alan

Lemos dos Santos, Alessandra

Parnell, Andrew C

Sarti, Danilo

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

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Bayesian additive regression trees for genotype by environment interaction models

https://eprints.lancs.ac.uk/id/eprint/185619/1/2021.05.07.442731.full_2_.pdf

Bayesian additive regression trees for genotype by environment interaction models

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