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

Repurposing existing medicines to target disease-associated genes represents a promising strategy for developing effective treatments for complex diseases. However, progress has been hindered by a lack of viable candidate drug targets identified through genome-wide association studies. Gene-based association tests provide a more powerful alternative to traditional 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 develop GenT and its various extensions, comprising a framework of gene-based tests utilizing summary-level data from genome-wide association studies. Using GenT, we identify 16, 15, 35, and 83 candidate genes linked to Alzheimer's disease, amyotrophic lateral sclerosis, major depression, and schizophrenia, respectively, not detected by Genome-Wide Association Studies (GWAS). Additionally, we use our multi-ancestry gene-based test (MuGenT) to identify 28 candidate genes associated with type 2 diabetes. By integrating brain expression and protein quantitative trait loci into our analysis, we identify 43 candidate genes associated with Alzheimer's disease that have supporting xQTL evidence. We also perform experimental assays to demonstrate that the NTRK1 inhibitor GW441756 significantly reduces tau hyper-phosphorylation (including p-tau181 and p-tau217) in Alzheimer's disease patient-derived iPSC neurons, providing mechanistic support for our predictions.