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Bayesian Additive Regression Trees for Genotype by Environment Interaction Models

View ORCID ProfileDanilo A. Sarti, View ORCID ProfileEstevão B. Prado, View ORCID ProfileAlan N. Inglis, Antônia A. L. dos Santos, View ORCID ProfileCatherine B. Hurley, View ORCID ProfileRafael A. Moral, View ORCID ProfileAndrew C. Parnell
doi: https://doi.org/10.1101/2021.05.07.442731
Danilo A. Sarti
1Hamilton Institute, Department of Mathematics and Statistics, Maynooth University, Ireland
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Estevão B. Prado
1Hamilton Institute, Department of Mathematics and Statistics, Maynooth University, Ireland
2Insight Centre for Data Analytics, Maynooth University, Ireland
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  • For correspondence: estevao.prado@hotmail.com
Alan N. Inglis
1Hamilton Institute, Department of Mathematics and Statistics, Maynooth University, Ireland
2Insight Centre for Data Analytics, Maynooth University, Ireland
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Antônia A. L. dos Santos
1Hamilton Institute, Department of Mathematics and Statistics, Maynooth University, Ireland
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Catherine B. Hurley
1Hamilton Institute, Department of Mathematics and Statistics, Maynooth University, Ireland
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Rafael A. Moral
1Hamilton Institute, Department of Mathematics and Statistics, Maynooth University, Ireland
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Andrew C. Parnell
1Hamilton Institute, Department of Mathematics and Statistics, Maynooth University, Ireland
2Insight Centre for Data Analytics, Maynooth University, Ireland
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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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Version revised to update reviewers' questions and suggestions.

  • http://github.com/ebprado/ambarti

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted November 07, 2022.
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Bayesian Additive Regression Trees for Genotype by Environment Interaction Models
Danilo A. Sarti, Estevão B. Prado, Alan N. Inglis, Antônia A. L. dos Santos, Catherine B. Hurley, Rafael A. Moral, Andrew C. Parnell
bioRxiv 2021.05.07.442731; doi: https://doi.org/10.1101/2021.05.07.442731
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Bayesian Additive Regression Trees for Genotype by Environment Interaction Models
Danilo A. Sarti, Estevão B. Prado, Alan N. Inglis, Antônia A. L. dos Santos, Catherine B. Hurley, Rafael A. Moral, Andrew C. Parnell
bioRxiv 2021.05.07.442731; doi: https://doi.org/10.1101/2021.05.07.442731

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