Post-GWAS machine learning prioritizes key genes regulating blood pressure

Abstract Over one thousand blood pressure (BP) loci have been identified by genetic association studies. However, determination of causal genes remains a bottleneck for further translational discovery. Here we triage genes identified by a BP genome-wide association study (GWAS) using optimized machine learning (ML) methodologies. We investigated regression models with nested cross-validation, benchmarking fourteen models (tree-based, ensemble and generalized linear models) using multi-omic features and 293 training genes. The top-performing model was extreme gradient boosting (0.897 predicted r2) that prioritized 794 genes. These genes showed significantly more intolerance to variation and were more often termed as essential. 27/794 genes showed evidence of direct interaction with blood pressure medications potentially highlighting opportunities for genetic stratification of response. Notably some BP drug mechanisms were not well represented in GWAS, while 51 genes showed no interaction with known BP drugs, highlighting possible target and repositioning opportunities. This study exploits ML to prioritize signals within BP-GWAS associations based on similarities with established BP-drug interacting genes, streamlining identification of genes underpinning BP that could inform disease management and drug discovery.