Optimized phenotype definitions boost GWAS power

Complex diseases are a major challenge, and genetics underlie a large fraction of the risk for these diseases. Observational data are helpful for this research due to large scale, cost-effectiveness, information on many different conditions, and future scalability, but they reflect factors such as healthcare processes, access to care, and broader societal effects like systemic biases. Here, we introduce MaxGCP, a phenotyping method designed to purify the genetic signal in observational data. MaxGCP optimizes a phenotype definition to maximize its coheritability—the genetic covariance between two traits normalized by their phenotypic standard deviations—with the complex trait of interest. Unlike previous phenotype-combination methods, MaxGCP is phenotype-specific, has linear computational complexity in the number of features, and does not require manual feature selection. In an analysis of stroke, we found that MaxGCP boosts study power by more than 13 percent 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.Author summary: Genome-wide association studies (GWAS) seek to identify genetic variants associated with disease risk. However, phenotype definitions derived from electronic health records contain substantial environmental noise that reduces statistical power. We developed MaxGCP, a method that constructs optimized phenotype definitions by combining multiple observed phenotypes (e.g., diagnosis codes) into a single index that maximizes the shared genetic signal with a target disease. MaxGCP requires only summary-level genetic covariance estimates and a phenotypic covariance matrix, and it has an exact, closed-form solution that scales linearly with the number of input phenotypes. In simulations and real-data analyses of stroke and Alzheimer’s disease using the UK Biobank, MaxGCP consistently improved GWAS sensitivity compared to conventional single-code phenotype definitions, with the strongest gains observed when high-quality genetic covariance estimates were available. MaxGCP is freely available as a Python package and can be applied broadly to improve genetic discovery in observational data.