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dc.contributor.authorJohnsen, Pål Vegard
dc.contributor.authorRiemer-Sørensen, Signe
dc.contributor.authorDeWan, Andrew Thomas
dc.contributor.authorCahill, Megan E.
dc.contributor.authorLangaas, Mette
dc.identifier.citationBMC Bioinformatics. 2021, 22 (1), 230.en_US
dc.description.abstractBackground The 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. Results We 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. Conclusions The 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.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.subjectTree ensemble modelsen_US
dc.subjectModel explainabilityen_US
dc.subjectGene-gene and gene-environment interactionsen_US
dc.titleA new method for exploring gene–gene and gene–environment interactions in GWAS with tree ensemble methods and SHAP valuesen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© The Author(s), 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the mate‑ rial. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// The Creative Commons Public Domain Dedication waiver ( cdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.en_US
dc.source.journalBMC Bioinformaticsen_US
dc.relation.projectNorges forskningsråd: 272402en_US

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