Leveraging Electronic Medical Records (EMR) for Psychiatric Genetic Research Workshop

Research aimed at identifying common and rare susceptibility variants for mental illnesses has demonstrated that sample size is the rate limiting step for discovery. To address this challenge, this workshop focused on merging electronic medical record (EMR), genomic data in large-scale biobanks, and population based registries to create ready resources for examining phenomewide effects of individual and aggregate genetic risk factors. The use of structured codified data and text mining by natural language processing enables the accrual and analysis of detailed, longitudinal clinical data for research purposes. In 2016, the U.S. Precision Medicine Initiative (PMI) began enrolling a cohort of 1 million individuals with EMR and mobile health data linked to biospecimens. The goal of the PMI is to improve health outcomes by using genetic and other individual data to develop more effective, tailored treatment approaches.

The application of EMR-based genomics to mental illnesses offers substantial opportunities but also raises a number of challenges. The goal of this workshop was to anticipate and address these challenges by bringing together thought leaders and experts in genomics, informatics, big data analytics, and psychiatric phenotyping. The workshop included presentations and discussion of key issues in leveraging EMR data for psychiatric genetic research; lessons learned from ongoing efforts in EMR genomics nationally and internationally; and, descriptions of available and emerging resources comprising psychiatric phenotypes and genomic data including the PMI. The workshop also addressed prospects for future collaborative research projects and opportunities.