Nature CommunicationsGeneticsPeer-reviewed journal article
Validation of lipid-related therapeutic targets for coronary heart disease prevention using human genetics
María Gordillo‐MarañóniD, Magdalena ZwierzynaiD, Pimphen CharoeniD, Fotios DrenosiD, Sandesh ChopadeiD, Tina ShahiD, Jorgen EngmanniD, Nish ChaturvediiD, Olia PapacostaiD, S. Goya WannametheeiD, Andrew WongiD, Reecha SofatiD, Mika Kivimaki BiD, Jackie F. PriceiD, Alun David HughesiD, Tom R. GauntiD, Debbie A. LawloriD, Anna GaultoniD, Aroon Dinesh HingoraniiD, Amand Floriaan SchmidtiD, Chris FinaniD
Abstract
Drug target Mendelian randomization (MR) studies use DNA sequence variants in or near a gene encoding a drug target, that alter its expression or function, as a tool to anticipate the effect of drug action on the same target. Here, we applied MR to prioritize drug targets for their causal relevance for coronary heart disease (CHD). The targets were further prioritized using genetic co-localization, protein expression profiles from the Human Protein Atlas and, for targets with a licensed drug or an agent in clinical development, by sourcing data from the British National Formulary and clinicaltrials.gov. Out of the 341 drug targets identified through their association with circulating blood lipids (HDL-C, LDL-C and triglycerides), we were able to robustly prioritize 30 targets that might elicit beneficial treatment effects in the prevention or treatment of CHD. The prioritized list included NPC1L1 and PCSK9, the targets of licensed drugs whose efficacy has been already proven in clinical trials. To conclude, we discuss how this approach can be generalized to other targets, disease biomarkers and clinical end-points to help prioritize and validate targets during the drug development process. One Sentence SummaryWe provide genetic support for lipid-modifying drug targets for coronary heart disease prevention using drug target Mendelian randomization and further prioritization based on clinical and biological evidence.
- organism: human
- assay: GWAS summary statistics
- research focus: drug response / drug discovery
- approach: statistical model
- Citations
- 31
- FWCI
- 2.82
- Field percentile
- top 8%
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