Drug-gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval
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Author
Avery, Christy L.
Sitlani, Colleen M.
Arking, Dan E.
Arnett, Donna K.
Bis, Joshua C.
Boerwinkle, Eric
Buckley, Brendan M.
Chen, Y.-D. Ida
de Craen, Anton JM
Eijgelsheim, Mark
Enquobahrie, Daniel
Evans, Daniel S.
Ford, Ian
Garcia, Melissa E.
Gudnason, Vilmundur
Harris, Tamara B.
Heckbert, Susan R.
Hochner, Hagit
Hsueh, Wen-Chi
Isaacs, Aaron
Jukema, J. Wouter
Knekt, Paul
Kors, Jan A.
Krijthe, Bouwe P.
Kristiansson, Kati
Laaksonen, Maarit
Liu, Yongmei
Li, Xiaohui
MacFarlane, Peter W.
Nieminen, Markku S.
Oostra, Ben A.
Porthan, Kimmo
Rice, Kenneth
Rivadeneira, Fernando F.
Rotter, Jerome I.
Salomaa, Veikko
Sattar, Naveed
Siscovick, David S.
Slagboom, P. Eline
Smith, Albert V.
Sotoodehnia, Nona
Stott, David J.
Stricker, Bruno H.
Stürmer, Til
Trompet, Stella
Uitterlinden, Andre G.
van Duijn, Cornelia M.
Westendorp, Rudi GJ
Witteman, Jacqueline C.
Whitsel, Eric A.
Psaty, Bruce M.
Note: Order does not necessarily reflect citation order of authors.
Published Version
https://doi.org/10.1038/tpj.2013.4Metadata
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Avery, C. L., C. M. Sitlani, D. E. Arking, D. K. Arnett, J. C. Bis, E. Boerwinkle, B. M. Buckley, et al. 2013. “Drug-gene interactions and the search for missing heritability: a cross-sectional pharmacogenomics study of the QT interval.” The pharmacogenomics journal 14 (1): 6-13. doi:10.1038/tpj.2013.4. http://dx.doi.org/10.1038/tpj.2013.4.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 ten 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-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.Other Sources
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3766418/pdf/Terms of Use
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