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Understanding the Genetic Variation Behind Drug Response
CHORI Researchers Identify Gene that Modulates with Statin Response

November 15, 2012 – In a new study just released on-line in the Published Library of Science (PLoS) Genetics, CHORI Assistant Scientist Marisa W. Medina, PhD, CHORI Senior Scientist Ronald Krauss, MD, and their colleagues have shown for the first time that the gene, RHOA, contributes to the ability of statin drugs to lower cholesterol.

“Not only were we able to identify human genetic variation within RHOA associated with statin response, but we were also able to show a corresponding molecular mechanism and function of RHOA,” says Dr. Medina.

“We identified human genetic variation within RHOA associated with statin response, as well as showing a corresponding molecular mechanism and function of RHOA.”



The most common family of drugs used to lower cardiovascular disease (CVD) risk, statins have been used for decades to lower cholesterol and reduce CVD risk. However, there is a wide degree of variability in effectiveness of statin treatment across individuals. While thirty percent of the patients taking statins do have significantly reduced heart attacks, the other two-thirds of individuals taking statins still end up with CVD, the number one cause of death in the United States.

"Why do some people respond well while others don't?" asks Dr. Medina. "This is a critical question, because statins aren't used for only a short time. It's a medication that people take over decades to prevent heart disease long term, so it could be years before individuals know whether the drug is effective for them," says Dr. Medina.
“What we want is to understand the mechanism underlying why one person responds well and another doesn't. We can then use that information for two purposes one, to help inform whether a given individual will benefit from statins, and two, to potentially identify different targets for new drugs to be developed.”
In the current study, Dr. Medina and her colleagues developed an entirely new approach to identifying genes of interest. While genome-wide association studies (GWAS) can identify genetic contributions to disease, they are much more difficult to use in identifying genes associated with a particular drug response.

"It's really challenging to identify genes and gene variations associated with drug response because it is so difficult to get a large enough population for GWAS to be truly effective," explains Dr. Medina. "This means we have to be more creative about how we tackle the problem of identifying the genes that might be modulating how well an individual responds to a particular drug."

Instead of using GWAS to find gene variants of potential interest, Dr. Medina used information gathered from multiple genome-wide gene expression datasets to identify a list of candidate genes whose variation in expression appeared to be correlated with variation in statin response. Specifically, she used these datasets to establish a set of biological filters, or rules, to define the expression characteristics of a gene that might be involved in statin response.
Using this novel methodology identified RHOA as a possible candidate. To confirm that this findings wasn't merely coincidental, Dr. Medina then performed functional studies to demonstrate that RHOA can modulate cellular cholesterol metabolism. Lastly, she was then able to identify genetic variation within RHOA that was associated with variation in LDL-cholesterol lowering with statin treatment from individuals from two statin clinical trials.

“Using this kind of transcriptome-wide profiling, combined with biological filtering and functional studies provides a new tool that we can use to begin to identify genes and gene variations associated with the efficacy of different drugs.”

As a result, the study is notable not just for identifying RHOA as a key player in the ability of statins to lower cholesterol, but also for establishing a new methodology for identifying candidate genes in the field of pharmacogenetics.

"It's very gratifying when you are able to start at the discovery level, filter it down to identify candidate genes, and that validate a particular candidate by showing how that gene is functionally relevant to drug response. It's a complete story."

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Friday, December 28, 2012 2:25 PM

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