Polygenic scores—calculations of a person’s likelihood of developing a disease based on the millions of minute genetic differences in their genome—have been developed by researchers over the past ten years. These scores have gotten better for some diseases and groups of people, but they still don’t work well for people of non-European ancestry because the genetic datasets used to calculate them mostly come from Europeans.
The accuracy of genetic risk prediction for heart disease across all ancestries is significantly improved by a new strategy developed by a team led by researchers from the Cardiovascular Disease Initiative at the Broad Institute of MIT and Harvard and Massachusetts General Hospital (MGH).
Using information from more than one million genetic studies, the researchers developed a polygenic score. To additionally work on the score, they likewise consolidated in their estimations hereditary changes related to 10 related characteristics, for example, circulatory strain and weight record. In predicting risk for coronary artery disease, the leading cause of death worldwide, among participants of African, European, Hispanic, and South Asian ancestry, their new score outperformed all previous scores.
“The ability to identify genetic risk early in life—technically possible even at birth—is powerful, because we don’t have to wait for clinical factors like elevated cholesterol to arise,”
Co-senior author Amit V. Khera, who developed polygenic scores as a Merkin Institute Fellow at the Broad
Clinicians may be able to recommend interventions like cholesterol-lowering medications or lifestyle changes that have been shown to offset and even normalize high genetic risk with the help of this method in the future. The findings, which were published in Nature Medicine, suggest that the framework can be utilized to enhance genetic risk prediction for a variety of traits and diseases as well.
Co-senior author Amit V. Khera, who is now vice president of genomic medicine at Verve Therapeutics and a cardiologist at Brigham and Women’s Hospital, said, “The ability to identify genetic risk early in life—technically possible even at birth—is powerful because we don’t have to wait for clinical factors like elevated cholesterol to arise.” Khera developed polygenic scores while he was a Merkin Institute Fellow at the Broad.
Co-senior author Pradeep Natarajan, a Broad associate member who is director of preventive cardiology and the Paul & Phyllis Fireman Endowed Chair in Vascular Medicine at MGH, as well as an associate professor of medicine at Harvard Medical School, stated, “Using larger, more diverse datasets, our score can better identify individuals at high risk who would otherwise fly under the radar.”
Prediction progress The scientists gathered information from over a million people to create the new score, including nearly 270,000 people with coronary artery disease. This is a significant increase from their previous study from 2018, which only looked at tens of thousands of people with the disease. The team was also able to incorporate data on more people with African, Hispanic, and South Asian ancestry thanks to new, more diverse studies that came out in the past year, like the U.S. Veterans Affairs Million Veteran Program.
“The European-based scores created in 2018 didn’t function admirably in that frame of mind for individuals with different families,” said Aniruddh Patel, co-first creator of the new review and a cardiologist and scientist in the Natarajan lab at MGH. “As a result, the scientific community has concentrated on enhancing prediction across ancestries.”
Likewise, Minxian (Wallace) Wang, a review co-first creator and previous computational scholar at the Expansive, fostered a pipeline to all the more unequivocally catch the impact of hereditary variations with more modest effects on coronary illness risk by focusing on DNA changes known to impact both risk for coronary illness and related characteristics, for example, weight record, smoking status, and pulse. Curiously, about half of the score’s prescient power came from investigations of coronary illness itself, and the other half from investigations of these other risk factors.
When applied to a different dataset from people of different families, the new score, called GPSMult, distinguished more individuals at the most noteworthy and least risk of coronary illness than every past score. For instance, whereas those in the highest percentile had a 16% chance of being diagnosed with heart disease by middle age, those in the lowest percentile had a chance of less than 1%. Strikingly, the group had the option to distinguish 3% of unaffected people who, in light of normal DNA variety alone, have a risk for a future cardiovascular occasion, for example, respiratory failure, as high as individuals who’ve previously been determined to have the illness.
When it comes to calculating polygenic risk scores, the findings demonstrate the advantages of combining traits and multi-ancestry data, and they also suggest that the method could improve risk prediction for other diseases. In order to make the scores more useful to doctors, the researchers are incorporating larger, more diverse datasets, new computational methods that take into account the intricate genome architecture, and clinical risk factors.
According to Khera, “What’s exciting is that we still haven’t reached the theoretical maximum for how good a genetic predictor of heart disease can be,” indicating that “these tests will continue to get even better in the coming years.” Additionally, we have a lot of work to do in order to determine the most effective way to incorporate these tests into clinical practice and eventually make them the standard of care.”
More information: Aniruddh P. Patel et al, A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease, Nature Medicine (2023). DOI: 10.1038/s41591-023-02429-x
