Genetics-Guided Therapeutic Target Discovery

Summary

Genetics-guided target discovery uses inherited variation that changes gene expression or protein abundance as a proxy for perturbing a therapeutic mechanism. Mendelian randomization, statistical colocalization and phenome-wide scans can prioritize targets, anticipate safety signals and distinguish plausible indications from correlations.

Evidence Framework

Large biobanks provide thousands of gene-trait associations, but target nomination is strongest when molecular instruments are biologically relevant, the molecular and disease signals colocalize, and results remain coherent across tissues and cohorts. The CIPHER resource harmonized molecular QTLs with 2,003 phenotypes and used approved targets plus biological annotations to rank significant associations.[1]

Examples

• Immune-cell transcriptomic instruments prioritized 21 genes for type 1 diabetes; genetically predicted higher VSIR expression was associated with lower risk, while P2RY12 suggested a repurposing route.[2]
• Protein and arterial or fibroblast transcript instruments implicated molecular mechanisms in spontaneous coronary artery dissection, illustrating tissue-aware target discovery.[3]
• Analyses of HMGCR supported differing pathways from statin-like inhibition to coronary artery disease and type 2 diabetes, demonstrating that benefit and adverse effects need not share a single mediator.[4]
• Genetically proxied IL-6 signaling inhibition was evaluated jointly with lipoprotein(a) and atherosclerotic outcomes, while APOA1-region analyses found no evidence that higher apolipoprotein A-I lowers cardiovascular risk across LDL-cholesterol strata.[5][6]

Related Evidence

• Plasma Proteogenomics — pQTL mapping and disease association evidence used to prioritize protein targets and indications.

Citations

[1] Ferolito et al. (2025), "Leveraging large-scale biobanks for therapeutic target discovery" [2] Sklar et al. (2025), "Immune cell-based transcriptomic Mendelian randomization and colocalization study on type 1 diabetes" [3] Ardissino et al. (2026), "Genetic association of circulating proteins and gene transcripts with spontaneous coronary artery dissection" [4] Hwang et al. (2025), "Human genetics suggests differing causal pathways from HMGCR inhibition to coronary artery disease and type 2 diabetes" [5] Daghlas et al. (2026), "Genetically proxied IL-6 signaling inhibition, lipoprotein(a) levels, and atherosclerotic disease risk" [6] Luo et al. (2026), "No genetic evidence for an association of apolipoprotein A-I with cardiovascular outcomes at different LDL cholesterol levels"