Combining xQTL and genome-wide association studies from ethnically diverse populations improves druggable gene discovery.

Repurposing existing medicines to target disease-associated genes represents a promising strategy for developing new treatments for complex diseases. However, progress has been hindered by a lack of viable candidate drug targets identified through genome-wide association studies (GWAS). Gene-based association tests provide a more powerful alternative to traditional single nucleotide polymorphism (SNP)-based methods, yet current approaches often fail to leverage shared heritability across populations and to effectively integrate functional genomic data. To address these challenges, we developed GenT and its various extensions, comprising a framework of gene-based tests utilizing summary-level GWAS data. Using GenT, we identified 16, 15, 35, and 83 druggable genes linked to Alzheimer's disease (AD), amyotrophic lateral sclerosis, major depression, and schizophrenia, respectively. Additionally, our multi-ancestry gene-based test (MuGenT) uncovered 28 druggable genes associated with type 2 diabetes that previous trans-ancestry or ancestry-specific GWAS had missed. By integrating brain expression and protein quantitative trait loci (e/pQTLs) into our analysis, we identified 43 druggable genes (e.g., RIPK2, NTRK1, RIOK1) associated with AD that had supporting xQTL evidence. Notably, experimental assays demonstrated that the NTRK1 protein inhibitor GW441756 significantly reduced tau hyper-phosphorylation (including p-tau181 and p-tau217) in AD patient-derived iPSC neurons, thus providing mechanistic support for our predictions. Overall, our findings underscore the power of gene-based association testing as a strategic tool for informed drug target discovery and validation based on human genetic and genomic data for complex diseases.