학술논문
Drug–gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval
Document Type
article
Author
Avery, CL; Sitlani, CM; Arking, DE; Arnett, DK; Bis, JC; Boerwinkle, E; Buckley, BM; Ida Chen, Y-D; de Craen, AJM; Eijgelsheim, M; Enquobahrie, D; Evans, DS; Ford, I; Garcia, ME; Gudnason, V; Harris, TB; Heckbert, SR; Hochner, H; Hofman, A; Hsueh, W-C; Isaacs, A; Jukema, JW; Knekt, P; Kors, JA; Krijthe, BP; Kristiansson, K; Laaksonen, M; Liu, Y; Li, X; MacFarlane, PW; Newton-Cheh, C; Nieminen, MS; Oostra, BA; Peloso, GM; Porthan, K; Rice, K; Rivadeneira, FF; Rotter, JI; Salomaa, V; Sattar, N; Siscovick, DS; Slagboom, PE; Smith, AV; Sotoodehnia, N; Stott, DJ; Stricker, BH; Stürmer, T; Trompet, S; Uitterlinden, AG; van Duijn, C; Westendorp, RGJ; Witteman, JC; Whitsel, EA; Psaty, BM
Source
The Pharmacogenomics Journal. 14(1)
Subject
Language
Abstract
Variability in response to drug use is common and heritable, suggesting that genome-wide pharmacogenomics studies may help explain the 'missing heritability' of complex traits. Here, we describe four independent analyses in 33 781 participants of European ancestry from 10 cohorts that were designed to identify genetic variants modifying the effects of drugs on QT interval duration (QT). Each analysis cross-sectionally examined four therapeutic classes: thiazide diuretics (prevalence of use=13.0%), tri/tetracyclic antidepressants (2.6%), sulfonylurea hypoglycemic agents (2.9%) and QT-prolonging drugs as classified by the University of Arizona Center for Education and Research on Therapeutics (4.4%). Drug-gene interactions were estimated using covariable-adjusted linear regression and results were combined with fixed-effects meta-analysis. Although drug-single-nucleotide polymorphism (SNP) interactions were biologically plausible and variables were well-measured, findings from the four cross-sectional meta-analyses were null (Pinteraction>5.0 × 10(-8)). Simulations suggested that additional efforts, including longitudinal modeling to increase statistical power, are likely needed to identify potentially important pharmacogenomic effects.