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New statistical method exponentially increases ability to discover genetic insights


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Pleiotropy analysis, which provides insight on how individual genes result in multiple characteristics, has become increasingly valuable as medicine continues to lean into mining genetics to inform disease treatments. Privacy stipulations, though, make it difficult to perform comprehensive pleiotropy analysis because individual patient data often can t be easily and regularly shared between sites. However, a statistical method called Sum-Share, developed at Penn Medicine, can pull summary information from many different sites to generate significant insights. In a test of the method, published in
Nature Communications, Sum-Share s developers were able to detect more than 1,700 DNA-level variations that could be associated with five different cardiovascular conditions. If patient-specific information from just one site had been used, as is the norm now, only one variation would have been determined. ....

Rui Duan , Hakon Hakonarson , Marylynd Ritchie , Yong Chen , Robert Carroll , Sarah Pendergrass , Christopher Bauer , Joshc Denny , Ruowang Li , Jonathand Mosley , Patrick Sleiman , Wei Qi , Jason Moore , Thomas Lumley , Davids Carrell , Dignar Velez Edwards , Xinyuan Zhang , Institute For Biomedical Informatics , Nature Communications , National Institutes Of Health , Consortium For Clinical Characterization , Penn Medicine , Biomedical Informatics , Clinical Characterization , National Institutes , Georgia Wiesner ,