Researchers improved existing polygenic risk scores using health records and ancestrally diverse genomic data. Credit: Darryl Leja, National Human Genome Research InstituteNational Institutes of Health. Since polygenic risk scores have not been effective for all populations, the researchers recalibrated these genetic tests using ancestrally diverse genomic data.
These “gaps” or missing data can cause false results, where a person could be at high risk for a disease but not receive a high-risk score because their genomic variants are not represented. Although health disparities often stem from systemic discrimination, not genetics, these false results are a way that inequitable genetic tools can exacerbate existing health disparities.
With the optimized scores, the researchers analyzed disease risk for an ancestrally diverse group of 2,500 individuals. About 1 in 5 participants were found to be at high risk for at least one of the 10 diseases. “Our model strongly increases the likelihood that a person in the high-risk end of the distribution should receive a high-risk result regardless of their genetic ancestry,” said Dr. Lennon. “The diversity of theHowever, these optimized scores cannot address health disparities alone.