BAYESIAN ADDITIVE REGRESSION TREES FOR GENOTYPE BY ENVIRONMENT INTERACTION MODELS

• 成果类型: Article
• 署名作者: Sarti, Danilo A.; Prado, Estevao B.; Inglis, Alan N.; Dos Santos, Antonia A. L.; Hurley, Catherine B.; Moral, Rafael A.; Parnell, Andrew C.
• 署名单位: Maynooth University; Maynooth University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/22-AOAS1698
• 发表日期: 2023
• 页码: 1936-1957
• 关键词: ammi analysis; gge biplot; adaptation; STABILITY; evaluate; wheat

摘要

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.