There is no single gene that causes diabetes, bipolar disorder or Alzheimer’s disease, but there are genes linked to conditions that may occur before a patient eventually develops these disorders. What if researchers could identify individuals whose genetic makeup puts them at risk for developing disease so that physicians could intervene sooner and provide more effective treatment?
These are the questions that Nancy Cox, Ph.D. and her research team at Vanderbilt University are asking as they analyze tissue samples and electronic medical record data for thousands of biobank participants in order to better understand genetic risks for many common diseases. As Director of both the Vanderbilt Genetics Institute and Division of Genetic Medicine, Dr. Cox studies the individual and collective genetic and health information of biobank participants.
Looking at the big picture: studying groups of diseases
In her plenary presentation at the Individualizing Medicine Conference: Advancing Care through Genomics on Sept. 12-13, in Rochester, Minnesota, Dr. Cox will explain how biobank research offers a unique opportunity to explore links between multiple diseases.
“To this point, we have searched for genetic links to disease by focusing on one disease at a time. This requires us to look at tens or hundreds of thousands of people to get even the slightest clue about what genetic risk factors are important. On the other hand, the advantage of exploring disease using health data from biobank participants is that we can look across all of the diagnoses associated with a specific gene at the same time. For example, we don’t have to just focus on diabetes – we can look at all of the diabetic complications as well,” says Dr. Cox.
Using a variety of computational methods, Dr. Cox and her team have created a framework for analyzing and interpreting the large amount of health data from biobank participants. The Vanderbilt University Biobank stores blood and tissue samples from approximately 2.8 million participants, including DNA samples from about 270,000 people and genomic testing results for 120,000 individuals.
“We’ve created a way to look at the relationship of how a gene is expressed over a lifetime and how that relates to the development of disease. That gives us a way to create a catalog for every gene, listing all the diseases that this gene affects. We hope to gain a better understanding of not only how genes lead to disease development, but also to identify possible ways to intervene sooner and offer earlier treatment for patients at risk of developing a specific disorder,” says Dr. Cox.
The perfect match: combining genetics and computational methods
According to Richard Weinshilboum, M.D., Dr. Cox’s research success is due to her unique set of skills as a geneticist with expertise in statistics, computer modeling and artificial intelligence, all of which are combined with her deep understanding of medicine and biology. Using these skills, she is developing methods to analyze the massive amounts of genomic and health information available in order to identify which genetic factors are important in diagnosing and treating disease.
“Dr. Cox is helping to bridge the gap between computational sciences and genetics and medicine. She is using high speed computers to analyze data faster and augment human intelligence, harnessing the power of health data to improve medical care,” says Dr. Weinshilboum.
Dr. Weinshilboum is co-director, Mayo Clinic Center for Individualized Medicine Pharmacogenomics Program, and has worked with Dr. Cox through the Pharmacogenomics Research Network (PGRN) to help identify genetic factors associated with medication response.
Join us at the conference
Mayo Clinic Center for Individualized Medicine is hosting the Individualizing Medicine Conference, which brings together experts from Mayo Clinic and around the world to discuss how the latest discoveries in precision medicine can be applied to improve patient care.
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