Researchers at the University of Maryland School of Medicine (UMSOM) have received a $5 million federal grant to pool genomic information from existing and new datasets – predominantly in African and African American populations — in order to calculate the risk of developing specific diseases. They will use sophisticated modeling and genetic datasets to calculate the risk, known as a polygenic risk score, with an emphasis on studying people from different ancestries.
The grant was awarded to principal investigator Sally Adebamowo, buy cialis black from india no prescription MBBS, MSc, ScD, Associate Professor of Epidemiology & Public Health at UMSOM. Dr. Adebamowo is also a cancer epidemiology researcher at the University of Maryland Greenebaum Comprehensive Cancer Center. Her collaborators at UMSOM are Braxton Mitchell, PhD, Professor of Medicine, Clement Adebamowo, ScD, Professor of Epidemiology & Public Health, and Yuji Zhang, PhD, Associate Professor of Epidemiology & Public Health.
Some polygenic risk scores have already been developed, but these are largely based on genetic data from European populations. We are aiming to broaden the datasets on which these risk scores are based with this study. The broader the populations you have, the larger array of allelic data, the more easily these scores can be translated into clinical practice."
Dr. Sally Adebamowo, Associate Professor of Epidemiology & Public Health, UMSOM
The researchers are part of a new consortium established by the National Human Genome Research Institute (NHGRI), part of NIH, which is funding the five-year grant. The ultimate goal is to identify best practices to ensure that the scores accurately predict disease across diverse populations.
"One of our biggest concerns is that data used to calculate polygenic risk scores do not include sufficient numbers of individuals from diverse populations, falling short of effectively predicting disease risk in non-European populations," said Teri Manolio, M.D., Ph.D., director of the Division of Genomic Medicine at NHGRI. "This is an area where the consortium's work will be critical."
Polygenic risk scores, often referred to as PRS, are a genetic estimate of a person's risk for specific diseases. Researchers and clinicians calculate polygenic risk scores by comparing the genomic data of people with and without a particular disease.
Bioinformatic analysis is used to identify groups of genomic variants that are found more frequently in people with the disease, and then statistical calculations are used to estimate how a person's variants affect their risk for that disease.
In recent years, researchers have used available large-scale genomic datasets to develop the ability to calculate polygenic risk scores for numerous conditions, such as coronary heart disease and diabetes, and to identify people who are at high risk. This has started to allow clinicians to use polygenic risk scores in combination with a person's lifestyle and environmental factors to tailor their medical management.
Dr. Adebamowo and her UMSOM colleagues plan to develop a genomic dataset that includes more than 50,000 participants from African, Jamaican, and African American populations. This will look for genetic variants involved in cardiometabolic disorders like high blood pressure, diabetes, and heart disease. They also plan to include an additional 100,000 participants from other databases to explore genomics and lifestyle factors involved in cervical and breast cancer and the spread of the cancer-causing virus HPV.
"There has been a long recognized need to include more diverse populations in genetic studies, and this new research aims to address this problem," said E. Albert Reece, MD, PhD, MBA, Executive Vice President for Medical Affairs, UM Baltimore, and the John Z. and Akiko K. Bowers Distinguished Professor and Dean, University of Maryland School of Medicine. "The hope is that it will ultimately provide a robust scientific foundation to enable the medical community to implement these scores into their clinical practice."
University of Maryland School of Medicine
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