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EXGEP: a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models

文献类型： 外文期刊

作者： Yu, Tingxi 1 ; Zhang, Hao 1 ; Chen, Shoukun 1 ; Gao, Shang 1 ; Liu, Ze 1 ; Wang, Jiankang 1 ; Crossa, Jose 3 ; Montesinos-Lopez, Osval A. 4 ; Hearne, Sarah 3 ; Li, Huihui 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Crop Sci, Int Maize & Wheat Improvement Ctr CIMMYT China Off, State Key Lab Crop Gene Resources & Breeding, 12 Zhongguancun South St, Beijing 100081, Peoples R China

2.Chinese Acad Agr Sci, Nanfan Res Inst, Sanya 572024, Hainan, Peoples R China

3.Int Maize & Wheat Improvement Ctr CIMMYT, Apdo Postal 6-641, Texcoco 06600, Mexico

4.Univ Colima, Fac Telemat, Colima 28040, Estado De Colim, Mexico

关键词： maize; machine learning; phenotypic prediction; genotype-by-environment interactions; explainable artificial intelligence

期刊名称：BRIEFINGS IN BIOINFORMATICS （ 影响因子：7.7； 五年影响因子：8.7 ）

ISSN： 1467-5463

年卷期： 2025 年 26 卷 4 期

页码：

收录情况： SCI

摘要： Phenotypic variation results from the combination of genotype, the environment, and their interaction. The ability to quantify the relative contributions of genetic and environmental factors to complex traits can help in breeding crops with superior adaptability for growth in varied environments. Here, we developed and extensively evaluated the performance of an explainable machine-learning framework named explainable genotype-by-environment interactions prediction (EXGEP) to accurately predict the grain yield in crops. To assess the performance of EXGEP, we applied it to a dataset comprising 70 693 phenotypic records of grain yield traits for 3793 hybrids (also including both genotype and environmental condition data). When used with four different combinations of genotypes and environmental data, EXGEP exceeded the yield prediction performance of the classic model Bayesian ridge regression model by 17.37%-42.35%. Moreover, EXGEP incorporates SHapley Additive exPlanations values that can uncover complex nonlinear relationships between genotype and environment and identify key features, and their interactions, that provide the main contributions to model performance, thus enhancing our understanding of genotype-by-environment interactions. Additionally, data from a series of tests support that EXGEP exhibits superior performance in terms of prediction accuracy and explainability. Our development of EXGEP and comparisons of it against alternative models provides valuable insights into methods for accurately predicting complex traits in multiple environments.

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