A team of researchers from The Broad Institute at MIT and Harvard University, in collaboration with 10 other academic medical centers have implemented the use of polygenic risk scores (PRS) for use in clinical research, with an eye toward moving them into clinical care for chronic diseases. The research, published in the journal Nature, details the selection of the chronic conditions and the optimization and validation of the PRS.
Data for the research was sourced via a collaboration with the national Electronic Medical Records and Genomics (eMERGE) network a National Human Genome Research Institute (NHGRI) initiative that is examining how patients’ genetic data is being used within their electronic medical records (EMRs) to influence care decision to improve clinical outcomes. The collaborating medical centers are tasked with enrolling the 25,000 patients needed for the study, while researchers at the Broad Clinical Labs carry out the PRS analysis.
There have been a lot of ongoing conversations and debates about polygenic risk scores and their utility and applicability in the clinical setting,” said Niall Lennon, chief scientific officer of Broad Clinical Labs, an institute scientist at the Broad, and first author of the new paper. “With this work, we’ve taken the first steps toward showing the potential strength and power of these scores across a diverse population. We hope in the future this kind of information can be used in preventive medicine to help people take actions that lower their risk of disease.”
While PRS, designed to identify those people at higher risk of developing a disease over their lifetime has shown potential as a tool for providing early preventative interventions, they have also been hampered by the genetic data used to develop them, which, to date, have largely been derived using genetic data from people of European ancestry.
With this in mind, the MIT/Harvard investigators searched existing literature for those PRS studies that have include participants from at least two different genetic ancestries. In addition, they looked for those chronic conditions where it is known that disease risk can be reduced via early interventions such as medical treatments, lifestyle changes, and disease screening.
Using these criteria, the team narrowed their list down to 10 different PRS for chronic conditions: atrial fibrillation, breast cancer, chronic kidney disease, coronary heart disease, hypercholesterolemia, prostate cancer, asthma, type 1 diabetes, obesity, and type 2 diabetes. For each, the team identified the exact location on the genome they would analyze to develop the PRS and then verified that those locations could be accurately genotyped by comparing the results of their tests with the whole genome sequences of each participant.
To make the PRS work effectively across different genetic ancestries, the team turned to data from the NIH’s All of Us research program to understand how genetic variants differ across populations. The team then used these data to calibrate the PRS based on a person’s genetic ancestry.
“We can’t fix all biases in the risk scores, but we can make sure that if a person is in a high-risk group for a disease, they’ll get identified as high risk regardless of what their genetic ancestry is,” explained Lennon.
From this information, Lennon and colleagues at the Broad Clinical Labs developed 10 optimized tests for the target diseases that are now being used to calculate the PRS of the 25,000 eMERGE study participants.
“Ultimately, the network wants to know what it means for a person to receive information that says they’re at high risk for one of these diseases,” Lennon concluded.
