Researchers Rework Polygenic Risk Scores Built on 1 Ancestry to Better Predict Disease
Updated
Updated · The New York Times · Jul 20
Researchers Rework Polygenic Risk Scores Built on 1 Ancestry to Better Predict Disease
3 articles · Updated · The New York Times · Jul 20
Summary
Polygenic risk scores—used to estimate chances of diseases such as heart disease and breast cancer—often miss badly for non-European patients, with some forecasts for people of color barely better than a coin flip.
European DNA dominates the data used to train these tools, leaving models less accurate for other populations even as scientists push them toward clinical use from infancy onward.
Researchers are trying to close the gap through new modeling methods and broader recruitment of minority groups, aiming to make the scores work across ancestries.
The effort has gained urgency as the NIH expands its All of Us genome database, because biased tools could deepen health-care disparities instead of advancing personalized medicine.
With the largest diverse DNA database now open, when will genetic tests be accurate for all ancestries?
Why do tools for 'personalized' medicine fail for most of the world's population?
Multi-Ancestry PRS in Clinical Guidelines: Advances, Equity, and the Future of Preventive Medicine
Overview
Preventive healthcare is rapidly changing in 2026, with multi-ancestry polygenic risk scores (PRS) becoming a key tool for personalized risk assessment. These PRS are now part of major clinical guidelines, especially for cardiovascular disease prevention. The 2026 American dyslipidemia guideline endorses the PREVENT equations, which are recommended by the ACC and AHA for estimating 10-year ASCVD risk in adults. The PREVENT-ASCVD equations provide lower risk estimates than older methods, helping to identify more high-risk patients and supporting more tailored preventive care. This marks a new era where genetics play a central role in guiding treatment decisions.