| Title: | Integration of genetic evidence to identify approved drug targets |
| Journal: | Genome Medicine |
| Published: | 6 Jul 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42410617/ |
| DOI: | https://doi.org/10.1186/s13073-026-01689-9 |
| URL: | https://link.springer.com/content/pdf/10.1186/s13073-026-01689-9.pdf |
Publication 20421
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Abstract
BackgroundDrugs 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.MethodsWe 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.ResultsAcross traits, GWAS alone ranked known drug targets on average $$\sim $$652 ranks (3.42%) above random expectation, and the minimum-rank-based integration strategy further improved performance by approximately $$\sim $$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 ($$r = 0.193;\ p = 5.46e{-}5$$). 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 ($$p = 0.004$$), while inflammatory bowel disease data enhanced the prioritization of chronic kidney disease targets ($$p = 0.014$$).ConclusionsThese 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.</p>
4 Keywords
- Drug Discovery
- Genome-Wide Association Study
- Humans
- Quantitative Trait Loci
3 Authors
- Samuel Moix
- Marie C. Sadler
- Zoltán Kutalik
1 Application
| Application ID | Title |
|---|---|
| 16389 | Exploring Diet/Lifestyle factors as causes and modifiers of genetic determinants of obesity and metabolic traits |
Enabling scientific discoveries that improve human health