ArticleBMC bioinformatics2021
A new method for exploring gene-gene and gene-environment interactions in GWAS with tree ensemble methods and SHAP values.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Machine Learning-Identified Potential Interaction Between Clazosentan and Nicardipine in Patients with Subarachnoid Hemorrhage.Journal of clinical medicine · 2026Article
- Bayesian neural networks for genomic prediction: uncertainty quantification and SNP interpretation with SHAP and GWAS.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Article
- Applying gradient tree boosting to QTL mapping with Shapley additive explanations.Breeding science · 2025Article
- Advancing genome-based precision medicine: a review on machine learning applications for rare genetic disorders.Briefings in bioinformatics · 2025Review
- Gene-environment interactions in human health.Nature reviews. Genetics · 2024Review
- Genetic Inheritance Models of Non-Syndromic Cleft Lip with or without Palate: From Monogenic to Polygenic.Genes · 2023Review
- E-GWAS: an ensemble-like GWAS strategy that provides effective control over false positive rates without decreasing true positives.Genetics, selection, evolution : GSE · 2023Article
- Inferring feature importance with uncertainties with application to large genotype data.PLoS computational biology · 2023Article
- Machine learning classifier approaches for predicting response to RTK-type-III inhibitors demonstrate high accuracy using transcriptomic signatures andBioinformatics advances · 2023Article
- Gene-gene interaction detection with deep learning.Communications biology · 2022Article
- Compressive Strength Estimation of Steel-Fiber-Reinforced Concrete and Raw Material Interactions Using Advanced Algorithms.Polymers · 2022Article
- Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions.Materials (Basel, Switzerland) · 2022Article
- Compressive Strength Evaluation of Ultra-High-Strength Concrete by Machine Learning.Materials (Basel, Switzerland) · 2022Article
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5 authors.
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Abstract
backgroundThe identification of gene-gene and gene-environment interactions in genome-wide association studies is challenging due to the unknown nature of the interactions and the overwhelmingly large number of possible combinations. Parametric regression models are suitable to look for prespecified interactions. Nonparametric models such as tree ensemble models, with the ability to detect any unspecified interaction, have previously been difficult to interpret. However, with the development of methods for model explainability, it is now possible to interpret tree ensemble models efficiently and with a strong theoretical basis.
resultsWe propose a tree ensemble- and SHAP-based method for identifying as well as interpreting potential gene-gene and gene-environment interactions on large-scale biobank data. A set of independent cross-validation runs are used to implicitly investigate the whole genome. We apply and evaluate the method using data from the UK Biobank with obesity as the phenotype. The results are in line with previous research on obesity as we identify top SNPs previously associated with obesity. We further demonstrate how to interpret and visualize interaction candidates.
conclusionsThe new method identifies interaction candidates otherwise not detected with parametric regression models. However, further research is needed to evaluate the uncertainties of these candidates. The method can be applied to large-scale biobanks with high-dimensional data.
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