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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]

Cross-Species Evidence

Integrating mouse liver co-expression networks (leveraging genome-wide transcript/protein data from murine models) with human lipid GWAS loci has been proposed as a route to prioritize causal lipid-metabolism genes for follow-up biochemical validation, complementing the human-only evidence sources above.[7] Only the abstract of this source was inspected (publisher paywall); the specific genes prioritized and the validation methodology were not verified in this pass, so this claim is recorded at confidence: medium and should be treated as a pointer to a promising cross-species approach rather than a validated finding.

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" [7] Votava, Parks (2021), "Cross-species data integration to prioritize causal genes in lipid metabolism", Current Opinion in Lipidology, 32(2), 84-90. Supports: cross-species evidence paragraph above. Location: Abstract only (full text not accessed — publisher paywall).

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