Bayesian Additive Regression Trees for Genotype by Environment Interaction Models - AMBARTI

Sarti, D. A.; Prado, E. B.; Inglis, A.; dos Santos, A. A. L.; Hurley, C.; Moral, R. d. A.; Parnell, A.

doi:10.1101/2021.05.07.442731

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bioRxiv · 10.1101/2021.05.07.442731

Bayesian Additive Regression Trees for Genotype by Environment Interaction Models - AMBARTI

Sarti, D. A.·Prado, E. B.·Inglis, A.·dos Santos, A. A. L.·Hurley, C.·Moral, R. d. A.·Parnell, A.

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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 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.

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Sarti, D. A., Prado, E. B., Inglis, A., dos Santos, A. A. L., Hurley, C., Moral, R. d. A., Parnell, A.. 2021-05-09. Bayesian Additive Regression Trees for Genotype by Environment Interaction Models - AMBARTI. https://doi.org/10.1101/2021.05.07.442731

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