Phenome-wide association studies across large population cohorts support drug target validation. Issue 1 (December 2018)

• Record Type: Journal Article
• Authors: Diogo, Dorothée; Tian, Chao; Franklin, Christopher; Alanne-Kinnunen, Mervi; March, Michael; Spencer, Chris; Vangjeli, Ciara; Weale, Michael; Mattsson, Hannele; Kilpeläinen, Elina; Sleiman, Patrick; Reilly, Dermot; McElwee, Joshua; Maranville, Joseph; Chatterjee, Arnaub; Bhandari, Aman; Nguyen, Khanh-Dung; Estrada, Karol; Reeve, Mary-Pat; Hutz, Janna; Bing, Nan; John, Sally; MacArthur, Daniel; Salomaa, Veikko; Ripatti, Samuli; Hakonarson, Hakon; Daly, Mark; Palotie, Aarno; Hinds, David; Donnelly, Peter; Fox, Caroline; Day-Williams, Aaron; Plenge, Robert; Runz, Heiko;
• Is Part Of: Nature communications. Volume 9:Issue 1(2018)
• Page Start: 1
• Page End: 13
• Publication Date: 2018-12
• Subjects: Biology -- Periodicals; Physical sciences -- Periodicals; 505
• DOI: 10.1038/s41467-018-06540-3
• Languages: English
• ISSNs: 2041-1723

Abstract

Abstract Phenome-wide association studies (PheWAS) have been proposed as a possible aid in drug development through elucidating mechanisms of action, identifying alternative indications, or predicting adverse drug events (ADEs). Here, we select 25 single nucleotide polymorphisms (SNPs) linked through genome-wide association studies (GWAS) to 19 candidate drug targets for common disease indications. We interrogate these SNPs by PheWAS in four large cohorts with extensive health information (23andMe, UK Biobank, FINRISK, CHOP) for association with 1683 binary endpoints in up to 697, 815 individuals and conduct meta-analyses for 145 mapped disease endpoints. Our analyses replicate 75% of known GWAS associations (P < 0.05) and identify nine study-wide significant novel associations (of 71 with FDR < 0.1). We describe associations that may predict ADEs, e.g., acne, high cholesterol, gout, and gallstones with rs738409 (p.I148M) inPNPLA3 and asthma with rs1990760 (p.T946A) inIFIH1 . Our results demonstrate PheWAS as a powerful addition to the toolkit for drug discovery. Testing the association between genetic variants and a range of phenotypes can assist drug development. Here, in a phenome-wide association study in up to 697, 815 individuals, Diogo et al. identify genotype–phenotype associations predicting efficacy, alternative indications or adverse drug effects.