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  • 2021.05.07.442731.full (2)

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Bayesian additive regression trees for genotype by environment interaction models

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Bayesian additive regression trees for genotype by environment interaction models. / Sarti, Danilo; Batista Do Prado, Estevao; Inglis, Alan et al.
In: Annals of Applied Statistics, Vol. 17, No. 3, 01.09.2023, p. 1936-1957.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Sarti, D, Batista Do Prado, E, Inglis, A, Lemos dos Santos, A, Hurley, C, de Andrade Moral, R & Parnell, AC 2023, 'Bayesian additive regression trees for genotype by environment interaction models', Annals of Applied Statistics, vol. 17, no. 3, pp. 1936-1957. https://doi.org/10.1214/22-AOAS1698

APA

Sarti, D., Batista Do Prado, E., Inglis, A., Lemos dos Santos, A., Hurley, C., de Andrade Moral, R., & Parnell, A. C. (2023). Bayesian additive regression trees for genotype by environment interaction models. Annals of Applied Statistics, 17(3), 1936-1957. https://doi.org/10.1214/22-AOAS1698

Vancouver

Sarti D, Batista Do Prado E, Inglis A, Lemos dos Santos A, Hurley C, de Andrade Moral R et al. Bayesian additive regression trees for genotype by environment interaction models. Annals of Applied Statistics. 2023 Sept 1;17(3):1936-1957. doi: 10.1214/22-AOAS1698

Author

Sarti, Danilo ; Batista Do Prado, Estevao ; Inglis, Alan et al. / Bayesian additive regression trees for genotype by environment interaction models. In: Annals of Applied Statistics. 2023 ; Vol. 17, No. 3. pp. 1936-1957.

Bibtex

@article{c40db51c8b884f6daed8f25c977f06f9,
title = "Bayesian additive regression trees for genotype by environment interaction models",
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.",
keywords = "Bayesian non-parametric regression, Bayesian additive regression trees, additive main effects multiplicative interactions model, genotype-by-environment interactions",
author = "Danilo Sarti and {Batista Do Prado}, Estevao and Alan Inglis and {Lemos dos Santos}, Alessandra and Catherine Hurley and {de Andrade Moral}, Rafael and Parnell, {Andrew C}",
year = "2023",
month = sep,
day = "1",
doi = "10.1214/22-AOAS1698",
language = "English",
volume = "17",
pages = "1936--1957",
journal = "Annals of Applied Statistics",
issn = "1932-6157",
publisher = "Institute of Mathematical Statistics",
number = "3",

}

RIS

TY - JOUR

T1 - Bayesian additive regression trees for genotype by environment interaction models

AU - Sarti, Danilo

AU - Batista Do Prado, Estevao

AU - Inglis, Alan

AU - Lemos dos Santos, Alessandra

AU - Hurley, Catherine

AU - de Andrade Moral, Rafael

AU - Parnell, Andrew C

PY - 2023/9/1

Y1 - 2023/9/1

N2 - 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.

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 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.

KW - Bayesian non-parametric regression

KW - Bayesian additive regression trees

KW - additive main effects multiplicative interactions model

KW - genotype-by-environment interactions

U2 - 10.1214/22-AOAS1698

DO - 10.1214/22-AOAS1698

M3 - Journal article

VL - 17

SP - 1936

EP - 1957

JO - Annals of Applied Statistics

JF - Annals of Applied Statistics

SN - 1932-6157

IS - 3

ER -