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Meta-Analysis
. 2015 Oct 30;10(10):e0140496.
doi: 10.1371/journal.pone.0140496. eCollection 2015.

Drug-Gene Interactions of Antihypertensive Medications and Risk of Incident Cardiovascular Disease: A Pharmacogenomics Study from the CHARGE Consortium

Affiliations
Meta-Analysis

Drug-Gene Interactions of Antihypertensive Medications and Risk of Incident Cardiovascular Disease: A Pharmacogenomics Study from the CHARGE Consortium

Joshua C Bis et al. PLoS One. .

Abstract

Background: Hypertension is a major risk factor for a spectrum of cardiovascular diseases (CVD), including myocardial infarction, sudden death, and stroke. In the US, over 65 million people have high blood pressure and a large proportion of these individuals are prescribed antihypertensive medications. Although large long-term clinical trials conducted in the last several decades have identified a number of effective antihypertensive treatments that reduce the risk of future clinical complications, responses to therapy and protection from cardiovascular events vary among individuals.

Methods: Using a genome-wide association study among 21,267 participants with pharmaceutically treated hypertension, we explored the hypothesis that genetic variants might influence or modify the effectiveness of common antihypertensive therapies on the risk of major cardiovascular outcomes. The classes of drug treatments included angiotensin-converting enzyme inhibitors, beta-blockers, calcium channel blockers, and diuretics. In the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, each study performed array-based genome-wide genotyping, imputed to HapMap Phase II reference panels, and used additive genetic models in proportional hazards or logistic regression models to evaluate drug-gene interactions for each of four therapeutic drug classes. We used meta-analysis to combine study-specific interaction estimates for approximately 2 million single nucleotide polymorphisms (SNPs) in a discovery analysis among 15,375 European Ancestry participants (3,527 CVD cases) with targeted follow-up in a case-only study of 1,751 European Ancestry GenHAT participants as well as among 4,141 African-Americans (1,267 CVD cases).

Results: Although drug-SNP interactions were biologically plausible, exposures and outcomes were well measured, and power was sufficient to detect modest interactions, we did not identify any statistically significant interactions from the four antihypertensive therapy meta-analyses (Pinteraction > 5.0×10-8). Similarly, findings were null for meta-analyses restricted to 66 SNPs with significant main effects on coronary artery disease or blood pressure from large published genome-wide association studies (Pinteraction ≥ 0.01). Our results suggest that there are no major pharmacogenetic influences of common SNPs on the relationship between blood pressure medications and the risk of incident CVD.

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Conflict of interest statement

Competing Interests: The authors have read the journal's policy and have the following competing interests: Bruce M. Psaty serves on a DSMB of a clinical trial of a device funded by the manufacturer (Zoll LifeCor), on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson, on the NHLB Advisory Council, and on the FDA Science Board. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Plots show the individual interaction p-values based on Stage I (indicated as solid dots) or Stage I + Stage II meta-analysis (indicated as outlined dots with “+” symbol) against their genomic position for the combined cardiovascular disease (CVD) outcome for the four antihypertensive medication exposures: (a) Angiotensin-converting enzyme (ACE) inhibitors, (b) Beta-blockers, (c) Calcium Channel Blockers, and (d) Thiazide Diuretics.
Within each chromosome, shown on the x-axis, the results are plotted left to right from the p-terminal end. The nearest genes are indicated for variants with an interaction p-value less than 1×10−5 in the meta-analysis.

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