In This Article
- The popularity and affordability of do-it-yourself DNA tests is leading to an increase in research participants, which is enabling researchers to uncover the long-indecipherable mysteries hidden in our genes
- Researchers at Massachusetts General Hospital, armed with an unprecedented amount of genetic data, are developing polygenic scores to more accurately predicting cardiovascular disease risk
- These new risk scores, coupled with current protocols, could increase physicians’ ability to better predict disease
Clinicians and scientists at Massachusetts General Hospital are using an unprecedented increase in genetic data to make improved genetic-based disease predictions. Due to the popularity and affordability of do-it-yourself DNA tests, there has been an increase in research participants, which is enabling researchers to uncover the long-indecipherable mysteries hidden in our genes.
Amit Khera, MD, cardiovascular geneticist at Mass General, uses these data to find correlations between an individual’s genome and their risk of cardiovascular disease. The massive amount of data is allowing Dr. Khera and Sekar Kathiresan, MD, director of the Center for Genomic Medicine, to see complex genetic variants and smaller signals tied to diseases and traits. They are using these findings to develop polygenic scores that involve thousands of genes, not just one. These scores can be used to predict a person’s chance of developing atrial fibrillation or an irregular heartbeat. The new scores can identify as much risk for disease as the rare genetic flaws that physicians currently evaluate.
“Where I see this going is that at a young age you’ll basically get a report card,” says Dr. Khera. “And it will say for these 10 diseases, here’s your score. You are in the 90th percentile for heart disease, 50th for breast cancer, and the lowest 10 percent for diabetes.”
The question now is how this risk information will make it into the hands of patients. Right now, Dr. Khera uses a combination of age, weight, cholesterol levels and habits to predict risk. Adding a genetic model score to those models would improve accuracy.
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Learn more about Amit Khera's research