76. INCORPORATING PHENOTYPE HETEROGENEITY IN DISEASE GWAS IMPROVES POWER WHILE MAINTAINING SPECIFICITY

Background Common complex diseases are clinically heterogeneous, yet most genome-wide association studies (GWAS) assume genetic homogeneity among cases. Growing evidence suggests that phenotypic heterogeneity reflects underlying variation in genetic architecture. To address this, we developed StratGWAS, a scalable framework that leverages clinically relevant measures of heterogeneity to construct a new phenotype that better reflects genetic liability within diseases. Methods StratGWAS stratifies cases using secondary phenotypic information such as age of onset, medication burden, or recruitment definition. StratGWAS then estimates genetic covariance between strata, and derives a transformed phenotype that upweights cases with higher inferred genetic liability. We evaluated the performance of StratGWAS through simulations (N = 100k) and application to 21 common traits and major depressive disorder in the UK Biobank (N = 368k). Results In simulations, StratGWAS consistently outperformed existing methods, with greatest power gains observed when shared genetic liability between auxiliary and target variable was high. Applied to 21 UK Biobank traits, StratGWAS upweighted individuals with earlier disease onset and higher medication burden, yielding respectively 17% and 4% more independent genome-wide significant loci than standard case–control GWAS. Applied to depression, StratGWAS upweighted individuals with multiple diagnoses, greater psychiatric comorbidity, or higher self-reported depressive symptoms, identifying eight additional independent loci compared to case-control GWAS. Discussion StratGWAS provides a powerful approach to incorporate clinical heterogeneity into genetic studies, improving locus discovery and enabling more precise dissection of disease architecture beyond case-control phenotypes.