Integration of genetic evidence to identify approved drug targets

BACKGROUND: Drugs targeting genes supported by human genetic evidence are more likely to succeed in clinical trials. While previous approaches have benchmarked individual methods such as genome-wide association studies (GWAS), rare variant burden testing, and quantitative trait locus (QTL)-informed Mendelian randomization, it remains unclear how best to integrate these signals for drug target discovery. METHODS: We compared gene-prioritization strategies across 30 complex traits, evaluating their ability to recover approved drug targets compiled into lenient and moderate gold-standard sets from six curated databases. Gene-level association scores from GWAS, expression QTL, protein QTL, and exome-based analyses were integrated using five unsupervised approaches. Predictive performance was assessed with area under the receiver operating characteristic curve (AUROC) and enrichment-based statistics. RESULTS: Across traits, GWAS alone ranked known drug targets on average ∼652 ranks (3.42%) above random expectation, and the minimum-rank-based integration strategy further improved performance by approximately ∼558 positions (2.93%), achieving the best AUROC in 23 of 30 traits. Genetic correlation and drug target overlap across trait pairs showed a significant positive association ([Formula: see text]). Cross-trait analyses further revealed that prioritization scores derived from related diseases could at times equal or even surpass a trait's own performance. For instance, coronary artery disease data improved the prediction of stroke targets ([Formula: see text]), while inflammatory bowel disease data enhanced the prioritization of chronic kidney disease targets ([Formula: see text]). CONCLUSIONS: These results demonstrate that using the strongest signal from complementary genetic prioritization methods, combined with information from genetically related traits, systematically strengthens drug target identification across complex diseases.