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| Illustration Credit: Ricardo Job-Reese, Broad Communications. |
Researchers have developed statistical tools called polygenic risk scores (PRSs) that can estimate individuals’ risk for certain diseases with strong genetic components, such as heart disease or diabetes. However, the data on which PRSs are built is often limited in diversity and scope. As a result, PRSs are less accurate when applied to populations that differ demographically from the PRS training data.
A new scoring approach featured in Cell Genomics and developed by researchers at the Broad Institute of MIT and Harvard and Massachusetts General Hospital (MGH) uses a comprehensive approach to generate more accurate and informative PRSs. Aptly named PRSmix due to its ability to “mix” all previously developed PRSs for a given trait, the approach generates scores that estimate a patient’s genetic disease risk more accurately than PRSs generated from individual studies.
“A major challenge with PRSs is that they’re derived in one population and then unleashed broadly with the assumption that the scores can be generalized,” explained Pradeep Natarajan, the study’s corresponding author. Natarajan is an associate member in Broad's Cardiovascular Disease Initiative and director of preventive cardiology at MGH. “The overall motivation for this work is to better identify individuals who are prematurely at high risk for heritable conditions.”

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