Reverse GWAS: Using Genetics to Identify and Model Phenotypic Subtypes
Recent and classical work has revealed biologically and medically significant subtypes in complex diseases and traits. However, relevant subtypes are often unknown, unmeasured, or actively debated, making automatic statistical approaches to subtype definition particularly valuable. We propose reverse GWAS (RGWAS) to identify and validate subtypes using genetics and multiple traits: while GWAS seeks the genetic basis of a given trait, RGWAS seeks to define trait subtypes with distinct genetic bases. Unlike existing approaches relying on off-the-shelf clustering methods, RGWAS uses a bespoke decomposition, MFMR, to model covariates, binary traits, and population structure. We use extensive simulations to show these features can be crucial for power and calibration. We validate RGWAS in practice by recovering known stress subtypes in major depressive disorder. We then show the utility of RGWAS by identifying three novel subtypes of metabolic traits. We biologically validate these metabolic subtypes with SNP-level tests and a novel polygenic test: the former recover known metabolic GxE SNPs; the latter suggests genetic heterogeneity may explain substantial missing heritability. Crucially, statins, which are widely prescribed and theorized to increase diabetes risk, have opposing effects on blood glucose across metabolic subtypes, suggesting potential have potential translational value.\n\nAuthor summaryComplex diseases depend on interactions between many known and unknown genetic and environmental factors. However, most studies aggregate these strata and test for associations on average across samples, though biological factors and medical interventions can have dramatically different effects on different people. Further, more-sophisticated models are often infeasible because relevant sources of heterogeneity are not generally known a priori. We introduce Reverse GWAS to simultaneously split samples into homogeneoues subtypes and to learn differences in genetic or treatment effects between subtypes. Unlike existing approaches to computational subtype identification using high-dimensional trait data, RGWAS accounts for covariates, binary disease traits and, especially, population structure; these features are each invaluable in extensive simulations. We validate RGWAS by recovering known genetic subtypes of major depression. We demonstrate RGWAS is practically useful in a metabolic study, finding three novel subtypes with both SNP- and polygenic-level heterogeneity. Importantly, RGWAS can uncover differential treatment response: for example, we show that statin, a common drug and potential type 2 diabetes risk factor, may have opposing subtype-specific effects on blood glucose.