Vanderbilt University Medical Center has received a five-year, $4.5 million grant from the National Human Genome Research Institute, part of the National Institutes of Health, to assess clinical outcomes and economic value of screening large, diverse health care populations for disease risk using polygenic risk scores.
A polygenic risk score (PRS) uses hundreds to thousands of genetic variants in a person’s genome to measure genetic risk for a given disease.
“VUMC is particularly well suited to lead this type of translational research, thanks to decades of investment in data banking and data tools for studying real-world biomedical phenomena with an eye to advancing precision medicine,” said principal investigator Josh Peterson, MD, MPH, professor of Biomedical Informatics and Medicine, director of the Center for Precision Medicine, and vice-president for Personalized Health.
Peterson will collaborate with two other principal investigators, David Veenstra, PhD, PharmD, of the University of Washington in Seattle, and Jing Hao, PhD, MD, MS, MPH, of Geisinger Health System, headquartered in Danville, Pennsylvania. The project also relies on modeling expertise of John Graves, PhD, associate professor of Health Policy, Jonathan Schildcrout, PhD, professor of Biostatistics, and Shawn Garbett, MS, assistant in Biostatistics.
“Polygenic risk scores are new screening tests to predict common diseases like breast cancer or heart disease,” Peterson said. “But for these methods to be used routinely, we have to understand the cost and clinical impact across patients’ lifetimes. We also need to establish a framework for bringing scores into the clinic on an equitable basis, as many published scores do not perform equally well across ancestries. That’s what we’re setting out to provide with this project.”
While all sorts of correlations between polygenic variation and disease risk may already be well established, experimental evidence that returning risk scores to clinicians provides a cost-effective means of improving patient outcomes remains a separate matter. A researcher starting this work in mid-career might advance to emerita status before experimentally establishing a clinical outcome benefit for PRS.
In light of this latency between testing and clinical outcomes, the team will use so-called decision-analytic modeling techniques, in which current evidence from various sources is synthesized to assess the likely efficiency of a health technology innovation. Focusing on common diseases with well-defined preventive measures, the team will search for any likely incremental benefit of PRS compared to current disease risk-modeling methods.
The team will evaluate current evidence on the clinical value of PRS for comprehensive genomic risk assessment; study the incremental clinical benefit and cost effectiveness of PRS for breast cancer, cardiovascular disease and colorectal cancer; assess how PRS would be apt to impact lifetime outcomes across diverse populations; and set out research priorities for the equitable development and implementation of PRS across underserved and underrepresented populations.
The project is supported by the National Institutes of Health (HG012262).