From lipid locus to drug target through human genomics. Issue 9 (23rd May 2018)

• Record Type: Journal Article
• Authors: van der Laan, Sander W; Harshfield, Eric L; Hemerich, Daiane; Stacey, David; Wood, Angela M; Asselbergs, Folkert W
• Is Part Of: Cardiovascular research. Volume 114:Issue 9(2018)
• Page Start: 1258
• Page End: 1270
• Publication Date: 2018-05-23
• Subjects: Genomics -- Lipidomics -- Metabolomics -- Cardiovascular disease -- Causality; Cardiovascular system -- Diseases -- Periodicals; Cardiovascular system -- Periodicals; 616.1
• DOI: 10.1093/cvr/cvy120
• Languages: English
• ISSNs: 0008-6363

Abstract

Abstract: In the last decade, over 175 genetic loci have robustly been associated to levels of major circulating blood lipids. Most loci are specific to one or two lipids, whereas some ( SUGP1, ZPR1, TRIB1, HERPUD1, and FADS1 ) are associated to all. While exposing the polygenic architecture of circulating lipids and the underpinnings of dyslipidaemia, these genome-wide association studies (GWAS) have provided further evidence of the critical role that lipids play in coronary heart disease (CHD) risk, as indicated by the 2.7-fold enrichment for macrophage gene expression in atherosclerotic plaques and the association of 25 loci (such as PCSK9, APOB, ABCG5-G8, KCNK5, LPL, HMGCR, NPC1L1, CETP, TRIB1, ABO, PMAIP1-MC4R, and LDLR ) with CHD. These GWAS also confirmed known and commonly used therapeutic targets, including HMGCR (statins), PCSK9 (antibodies), and NPC1L1 (ezetimibe). As we head into the post-GWAS era, we offer suggestions for how to move forward beyond genetic risk loci, towards refining the biology behind the associations and identifying causal genes and therapeutic targets. Deep phenotyping through lipidomics and metabolomics will refine and increase the resolution to find causal and druggable targets, and studies aimed at demonstrating gene transcriptional and regulatory effects of lipid associated loci will further aid in identifying these targets. Thus, we argue the need for deeply phenotyped, large genetic association studies to reduce costs and failures and increase the efficiency of the drug discovery pipeline. We conjecture that in the next decade a paradigm shift will tip the balance towards a data-driven approach to therapeutic target development and the application of precision medicine where human genomics takes centre stage.