New statistical method exponentially increases ability to discover genetic insights
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.
Nanoparticle blueprints reveal path to smarter medicines
New research involving Penn Engineering shows detailed variation in lipid nanoparticle size, shape, and internal structure, and finds that such factors correlate with how well they deliver therapeutic cargo to a particular destination.
A generous gift from alumni Glenn and Amanda Fuhrman brings the work of internationally acclaimed artist Jaume Plensa to the University of Pennsylvania. The latest addition to the Penn Art Collection expands Philadelphia's public art.
A massive chunk of ice, a new laser, and new information on sea-level rise
For nearly a decade, Leigh Stearns and collaborators aimed a laser scanner system at Greenland’s Helheim Glacier. Their long-running survey reveals that Helheim’s massive calving events don’t behave the way scientists once thought, reframing how ice loss contributes to sea-level rise.
