A scientific team funded by the National Institutes of Health (NIH) has found new ways to improve the accuracy of a genetic testing method called a polygenic risk score. The team's research aims to address health disparities that can arise from this emerging genomic technology.
Polygenic risk scores are tools used to assess a person's risk for disease by analyzing genomic variants - the small differences between individuals' genomes. However, these genetic tests are ineffective for all populations. The team recalibrated the tests using ancestrally diverse genomic data to overcome this issue.
According to a report in Nature Medicine, the optimized tests provide a more accurate disease risk assessment across diverse populations. This is an important development as there is a concern that the genomic datasets used to calculate the scores often heavily over-represent people of European ancestry.
Niall Lennon, Ph.D., a scientist at the Broad Institute in Cambridge, Massachusetts and first author of the publication, said, "Recently, more and more studies incorporate multi-ancestry genomic data into the development of polygenic risk scores. However, there are still gaps in genetic ancestral representation in many scores developed."
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 are the result of systemic discrimination rather than genetic factors, these puzzling findings suggest that health disparities may be even greater. The Polygenic risk score data is from the Public Health Research Project, an NIH-funded project to collect health data on more than 1 million people from diverse backgrounds.
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