Some concrete examples here: 👉 modern cardiovascular drug development has been largely shaped by targets, where variation in their genes has been linked to disease risk (PCSK9, APOC3, ANGPTL3, FXI, LPA, IL6 etc.) 👉 specifically, human genetic evidence has been translated to approved drugs for hypercholesterolemia (PCSK9, approved), boosted the clinical development of new categories of drugs (e.g. LPA, IL6, in phase 3), or informed new translational pathways and indications for others (e.g. FXI, awaiting approval for non-cardioembolic ischemic stroke) 👉 GWAS hits in Alzheimer's disease (e.g. TREM2, CLU, BIN1) have pointed to neuroinflammation and microglial biology, influencing drug development, even if clinical translation is still ongoing (TREM2) 👉 similar story for ALS, where beyond pointing to Mendelian cases, for which targeted therapies are being developed, drugs emerging from GWAS hits are about to start being tested in clinical trials (UNC13A) 👉 many similar examples from autoimmune diseases, e.g. IL23/IL23R approved for psoriasis and inflammatory bowel disease, TYK2 inhibition approved for psoriasis now expanding to other indications, PTPN22/LYP emerging as a promising target in clinical development 👉 first approved drugs for dry age-related macular degeneration (C3 and C5 inhibitors) targeting the complement, were largely influenced by discovery of hits in several complement genes in GWAS 👉 the whole booming field of RNA therapeutics (especially liver-targeting) depends on sequencing the targets and so is directly linked to the human genome project

By fine-mapping GWAS signals across disease endpoints, biomarkers, and molecular traits (eQTLs/pQTLs), and then colocalizing across credible sets, the study offers the most systematic exploration of genetic pleiotropy to date.

This new preprint by Open Targets is one of the most comprehensive efforts to date to connect the dots across all available (>100K) GWAS datasets that have been generated over the last two decades👇