A statistical framework to identify gene-gene interactions underlying multiple dichotomous phenotypes from genotype data

Identifying gene-gene (G x G) interactions across multiple dichotomous phenotypes is challenging due to the extreme sparsity of SNP-derived interaction matrices and reduced statistical power by binary outcomes. Existing GxG association methods are restricted to either single or multiple continuous phenotypes. Here we introduce GiMat (Gene Interaction and Multiple-phenotype Association Test), a statistical framework that extends multivariate kernel regression to model G x G interactions jointly across dichotomous phenotypes, while explicitly capturing homogeneous and heterogeneous interaction effects. Extensive simulations demonstrate that GiMat controls type I error conservatively and adapts power flexibly to different types of relationships between interaction effects and phenotypes. Applied to type 2 diabetes and hypertension comorbidity in the UK Biobank, GiMat identified four previously unreported GxG interaction pairs associated with both phenotypes. This scalable framework enables robust discovery of complex genetic interactions underlying multiple correlated phenotypes.