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CardioVar Consortium Wins $8.2M From NHLBI to Map Heart Disease Variants, Functional Effects

NEW YORK – A team of researchers from Vanderbilt University Medical Center, Stanford Medicine, the University of Toronto, and Brigham and Women’s Hospital has won a four-year, $8.2 million grant from the National Institutes of Health's National Heart, Lung, and Blood Institute (NHLBI) to study the functional effects of variants in key cardiac disease genes associated with heart disease or other changes in heart function.

The newly formed CardioVar Consortium intends to generate a database of so-called variant effect maps, aiming to distinguish disease-causing variants from harmless changes across a target group of about 25 genes.

"As genetic testing in patients with heart disease becomes increasingly adopted, a common result is a 'variant of uncertain significance,'" Dan Roden, VUMC VP and project principal investigator, said in a statement. "Our high-throughput studies will provide data on function for thousands of variants [to] both help guide treatment for individual patients and provide insights into underlying biology."

Euan Ashley, professor of medicine, genetics, and biomedical data science at Stanford School of Medicine; and Frederick Roth, professor of molecular genetics at the University of Toronto’s Donnelly Centre, are co-PIs for CardioVar.

According to Ashley, "at the current rate of clinical sequencing, it would take over a hundred years to find most genetic variants relevant for heart disease even once in the population."

"The variant maps we are building will allow us to dramatically accelerate that timeline, providing vital information for families we are seeing in clinic today," he said in a statement.

The project's first step will be to validate various high-throughput cellular assays that can directly measure variant function and discriminate pathogenic from benign variants. Investigators will employ editing techniques to mutate or insert altered gene sequencies into pools of cells in which they can then generate and validate variant effect maps.

Eventually, the team hopes to reveal relationships between variant effects, protein structure and function, and human phenotypes informed by both hypothesis-driven analysis and machine learning models. This could be the backbone of a future variant-centric decision support system to help clinicians evaluate functional evidence in patients undergoing genetic testing for heart disease.

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