A new method for exploring gene–gene and gene–environment interactions in GWAS with tree ensemble methods and SHAP values

BMC Bioinformatics

卷:22

Megan E. Cahill¹  Andrew Thomas DeWan¹  Mette Langaas²  Signe Riemer-Sørensen³  Pål V. Johnsen³ 

[1] Department of Chronic Disease Epidemiology and Center for Perinatal, Pediatric and Environmental Epidemiology, Yale School of Public Health;

[2] Department of Mathematical Sciences, Norwegian University of Science and Technology;

[3] SINTEF DIGITAL;

关键词: GWAS;    Tree ensemble models;    XGBoost;    SHAP;    Model explainability;    Gene–gene and gene–environment interactions;   

DOI  :  10.1186/s12859-021-04041-7

来源: DOAJ

【 摘 要 】

Abstract Background 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.

【 授权许可】

Unknown