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Drug-gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval.

TitleDrug-gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval.
Publication TypeJournal Article
Year of Publication2014
AuthorsAvery CL, Sitlani CM, Arking DE, Arnett DK, Bis JC, Boerwinkle E, Buckley BM, Chen Y-DIda, 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
Secondary AuthorsPsaty BM
JournalPharmacogenomics J
Volume14
Issue1
Pagination6-13
Date Published2014 Feb
ISSN1473-1150
KeywordsComputer Simulation, Cross-Sectional Studies, Drug-Related Side Effects and Adverse Reactions, Electrocardiography, European Continental Ancestry Group, Gene-Environment Interaction, Genome-Wide Association Study, Humans, Linear Models, Long QT Syndrome, Markov Chains, Pharmacogenetics, Polymorphism, Single Nucleotide, Quantitative Trait, Heritable
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.

DOI10.1038/tpj.2013.4
Alternate JournalPharmacogenomics J
PubMed ID23459443
PubMed Central IDPMC3766418
Grant ListR01 HL075366 / HL / NHLBI NIH HHS / United States
N1AG62101A / AG / NIA NIH HHS / United States
R01 HL059367 / HL / NHLBI NIH HHS / United States
N01HC85086 / HL / NHLBI NIH HHS / United States
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HL080295 / HL / NHLBI NIH HHS / United States
HHSN268201100012C / HL / NHLBI NIH HHS / United States
UL1RR025005 / RR / NCRR NIH HHS / United States
UL1 RR033176 / RR / NCRR NIH HHS / United States
R00 HL098458 / HL / NHLBI NIH HHS / United States
R01 HL103612 / HL / NHLBI NIH HHS / United States
HHSN268201100009I / HL / NHLBI NIH HHS / United States
R01HL59367 / HL / NHLBI NIH HHS / United States
HHSN268201100010C / HL / NHLBI NIH HHS / United States
UL1 RR025005 / RR / NCRR NIH HHS / United States
R01 AG015928 / AG / NIA NIH HHS / United States
N01 AG062101 / AG / NIA NIH HHS / United States
HHSN268201100008C / HL / NHLBI NIH HHS / United States
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