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.
