Optimized phenotype definitions boost GWAS power

Abstract

AbstractComplex diseases are among the central challenges facing the world, and genetics underlie a large fraction of the risk. Observational data, such as electronic health records (EHR), offer numerous advantages in the study of complex disease genetics. These include their large scale, cost-effectiveness, information on many different conditions, and future scalability with the widespread adoption of EHRs. Observational data, however, are challenging for research as they reflect various factors including the healthcare process and access to care, as well as broader societal effects like systemic biases. Here, we introduce MaxGCP, a novel phenotyping method designed to purify the genetic signal in observational data. Our approach optimizes a phenotype definition to maximize its coheritability with the complex trait of interest. We validated the method in simulations and applied it to real data analyses of stroke and Alzheimer’s disease. We found that MaxGCP improves genomewide association study (GWAS) power compared to conventional, single-code phenotype definitions. MaxGCP is a powerful tool for genetic discovery in observational data, and we anticipate that it will be broadly useful for studying complex diseases using observational data.