EXGEP : a framework for predicting genotype-by-environment interactions using ensembles of explainable machine-learning models
Material type:
ArticleLanguage: English Publication details: United Kingdom : Oxford University Press, 2025.ISSN: - 1467-5463
- 1477-4054 (Onine)
| Item type | Current library | Collection | Status | |
|---|---|---|---|---|
| Article | CIMMYT Knowledge Center: John Woolston Library | CIMMYT Staff Publications Collection | Available |
Peer review
Open Access
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.
Text in English
Tingxi Yu : Not in IRS staff list but CIMMYT Affiliation
Hao Zhang : Not in IRS staff list but CIMMYT Affiliation
Shoukun Chen : Not in IRS staff list but CIMMYT Affiliation
Shang Gao : Not in IRS staff list but CIMMYT Affiliation
Ze Liu : Not in IRS staff list but CIMMYT Affiliation
National Natural Science Foundation of China Chinese Academy of Agricultural Sciences (CAAS) Bill & Melinda Gates Foundation (BMGF) Breeding for Tomorrow Genebanks
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