Selection, optimization and validation of genetic tests for 10 common diseases

By analyzing millions of small genetic differences in a person’s genome, researchers can calculate a polygenic risk score to estimate someone’s chances of developing a particular disease in their lifetime. Over the past decade, scientists have developed these risk scores for dozens of diseases, including heart disease, kidney disease, diabetes and cancer, with the hope that patients could someday use this information to reduce their increased risk of disease. However, determining whether such tests work effectively in all populations and how they can guide clinical decision making has been a challenge.

Now, a team of researchers at the Broad Institute of MIT and Harvard, in collaboration with 10 academic medical centers in the US, has implemented 10 such tests for use in clinical research. In a study published in Nature Medicinethe team described how they selected, optimized, and validated the tests for 10 common diseases, including heart disease, breast cancer, and type 2 diabetes. They also calibrated the tests for use in people of non-European ancestry.

The scientists worked in collaboration with the National Electronic Medical Records and Genomics (eMERGE) network, which is funded by the National Human Genome Research Institute to study how patients’ genetic data can be integrated into their electronic medical records to improve clinical care and health outcomes. . The 10 participating medical centers are part of the project and are enrolling 25,000 participants in it, while researchers at Broad Clinical Labs, an affiliate of the Broad Institute, are performing the polygenic risk score screening for these participants.

There has been much ongoing debate and discussion regarding polygenic risk scores and their utility and applicability in the clinical setting. With this work, we have taken the first steps to demonstrate the potential strength and power of these scores in a diverse population. We hope that in the future this kind of information can be used in preventive medicine to help people take steps that reduce the risk of disease.”

Niall Lennon, chief scientific officer of Broad Clinical Labs, an institute scientist at the Broad and first author of the new paper

What’s the score?

Most polygenic risk scores have been developed based on genetic data largely from people of European descent, raising questions about whether the scores are applicable to people of other origins.

To optimize polygenic risk scores for a variety of people, Lennon and colleagues first combed the literature for polygenic risk scores that had been tested in people from at least two different genetic backgrounds. They also looked for scores that indicated a disease risk that patients could reduce with medical treatments, screening and/or lifestyle changes.

“It was important that we didn’t give people results they couldn’t do anything about,” Lennon said.

The team chose 10 conditions to focus on for the polygenic risk scores: atrial fibrillation, breast cancer, chronic kidney disease, coronary artery disease, hypercholesterolemia, prostate cancer, asthma, type 1 diabetes, obesity and type 2 diabetes.

For each condition, the researchers identified the exact spots in the genome that they would analyze to calculate the risk score. They confirmed that all of these spots could be accurately genotyped by comparing their test results to whole-genome sequences from each patient’s blood sample.

Finally, the researchers wanted to make the polygenic risk scores work across different genetic backgrounds. They studied how genetic variants differ between populations by analyzing data from the National Institutes of Health’s All of Us research program, which collects health information from a million people from diverse backgrounds in the US. The team used this information to create a model to calibrate a person’s polygenic risk score according to the person’s genetic ancestry.

“We can’t correct all biases in risk scores, but we can make sure that if a person is in a high-risk group for a disease, they will be identified as high-risk regardless of their genetic background.” Lennon explained.

After completing this optimization, Lennon’s team at Broad Clinical Labs came up with 10 tests that they now use to calculate risk scores for the 25,000 people enrolled in the eMERGE study. With their partners at eMERGE, they are also designing detailed follow-up studies to analyze how polygenic risk scores may affect patient health care.

“Ultimately, the network wants to know what it means for a person to receive information that says they are at high risk for one of these diseases,” Lennon said.