Individual genes have the ability to influence more than one trait or characteristic. But this feature is often discovered through a type of analysis called pleiotropy, which requires merging a patient’s data from electronic health records at many different places—a challenge given privacy stipulations. A team at the Perelman School of Medicine created a new method that could make pleiotropy easier to perform and more widely used, called Sum-Share. The statistical model pulls summary-level information from many different sites to generate significant insights.
In a test of the method, published in Nature Communications, Sum-Share’s developers detected more than 1,700 DNA-level variations that could be associated with five different cardiovascular conditions. Using patient-specific information from just one site, which is the current norm, would have found just one variation.
This story is by Frank Otto. Read more at Penn Medicine News.
