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

• Published in: BMC bioinformatics 2021-05, Vol.22 (1), p.230-230, Article 230
• Main Authors: Johnsen, Pål V, Riemer-Sørensen, Signe, DeWan, Andrew Thomas, Cahill, Megan E, Langaas, Mette
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Biobanks; Gene expression; Gene-Environment Interaction; Genes; Gene–gene and gene–environment interactions; Genome-wide association studies; Genome-Wide Association Study; Genomes; Genotype & phenotype; GWAS; Machine learning; Methodology; Methods; Model explainability; Obesity; Phenotypes; Polymorphism, Single Nucleotide; Power; Regression analysis; Regression models; SHAP; Single nucleotide polymorphisms; Single-nucleotide polymorphism; Tree ensemble models; Trees; XGBoost
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-021-04041-7

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. 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. 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.