Researchers optimize genetic tests for diverse populations to tackle health disparities

Key Takeaways

• A scientific team has devised new ways to improve a method of genetic testing for common diseases by recalibrating the tests using ancestrally diverse genomic data.
• The optimized tests provide a more accurate assessment of disease risk across diverse populations.

Genetic tests look at the small differences between individuals’ genomes, known as genomic variants, and polygenic risk scores are tools for assessing many genomic variants across the genome to determine a person’s risk for disease. As the use of polygenic risk scores grows, one major concern is that the genomic datasets used to calculate the scores often heavily overrepresent people of European ancestry. Gaps in genetic ancestral representation in the scores 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.

In a new study published in Nature Medicine, researchers improved existing polygenic risk scores using health records and ancestrally diverse genomic data from the All of Us Research Program, an NIH-funded initiative to collect health data from over a million people from diverse backgrounds. The researchers selected polygenic risk scores for 10 common health conditions, including breast cancer, prostate cancer, chronic kidney disease, coronary heart disease, asthma and diabetes. Polygenic risk scores are particularly useful for assessing risk for conditions that result from a combination of several genetic factors, as is the case for the 10 conditions selected. Many of these health conditions are also associated with health disparities.

The researchers assembled ancestrally diverse cohorts from the All of Us data, including individuals with and without each disease, so they could determine genomic variants associated with each disease. The genomic variants represented in these cohorts allowed the researchers to recalibrate the polygenic risk scores for individuals of non-European ancestry.

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. Most importantly, these participants ranged widely in their ancestral backgrounds, showing that the recalibrated polygenic risk scores are not skewed towards people of European ancestry and are effective for all populations.

“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. Niall Lennon, Ph.D., a scientist at the Broad Institute in Cambridge, Massachusetts and first author of the publication. This work is an important step towards routine use of polygenic risk scores in the clinic to benefit all people. 

Edited by Miriam Kaplan, PhD

Source: NIH News Release, February 19, 2024; see source article