Analyzing How Genetics May Affect Response to High Blood Pressure Medications

2014-08-27 03:34:53 | BioPortfolio


High blood pressure is one of the most common health problems in the United States. There are many medications to treat high blood pressure, but there is a large variance in how people respond to these medications. It is believed that genetic variations may contribute to the inconsistent treatment response. This study will use genetic analysis to determine whether particular genes interact with high blood pressure medications to modify the risk of certain cardiovascular diseases.


High blood pressure affects nearly one in three individuals in the Unites States. There are many factors that can cause high blood pressure, including family history and genetic traits, kidney disease, stress, diabetes, and diet. If left untreated, high blood pressure can increase one's risk for coronary heart disease (CHD), stroke, heart attack, and heart failure. While high blood pressure can be managed with medication, people receiving medication treatment for high blood pressure are still variably at risk for CHD and other cardiovascular conditions. This risk variation may stem from varying drug reactions that are likely due to genetics. This study will use genetic analysis to determine whether particular genes interact with high blood pressure medications to modify the risk of certain cardiovascular diseases.

This is a continuation study to the antihypertensive and lipid-lowering treatment to prevent heart attack trial (ALLHAT), which included a randomized trial of the four high blood pressure drugs chlorthalidone, amlodipine, lisinopril, and doxazosin. Using samples from ALLHAT participants, this study will analyze the interactions of candidate gene pathways of relevance with medications from the ALLHAT study. Researchers will examine both single DNA building blocks and multiple genes in the candidate gene pathways and determine whether their interaction with the ALLHAT drugs modifies the risk of cardiovascular outcomes. Researchers will perform genetic analysis on 96 genetic markers using structured association testing (SAT) and false discovery rate (FDR) methods. These methods will control for population stratification and multiple testing. Finally, the study will establish a mechanism for other researchers to continue further analysis of the genetic variants examined in this study.

Study Design

Observational Model: Cohort, Time Perspective: Retrospective




University of Minnesota
United States




University of Alabama at Birmingham

Results (where available)

View Results


Published on BioPortfolio: 2014-08-27T03:34:53-0400

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