PsycheMERGE: Leveraging electronic health records and genomics for mental health research

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Neuropsychiatric disorders are the leading causes of disability in the US and are associated with increased mortality (e.g. through suicide and associations with chronic diseases and their risk factors). Evidence suggests that early detection and treatment of psychiatric illness is essential to improving long-term outcomes and may even modify illness trajectories at a biological level. Unfortunately, a substantial proportion of patients undergo a long diagnostic odyssey before receiving an appropriate diagnosis and initiating effective treatment. Efforts to improve surveillance for emerging or occult psychopathology are often complex, costly, and have limited yield. Thus, there is an urgent public health need to improve clinical decision support for the early detection of psychiatric disorders in clinical settings. The growing availability of large-scale biobanks linking EHRs to biospecimens has created a powerful, but still relatively untapped, opportunity for psychiatric research. In 2007, the NHGRI organized the Electronic Medical Records and Genomics (eMERGE) network which has brought together investigators around the U.S. to facilitate EHR-based genomic research and the implementation of genomic medicine. To date, however, EHR-based risk prediction and genomics have not been widely leveraged for psychiatric research. To address this gap, we have created a new, large-scale collaborative consortium—PsycheMERGE—which leverages the resources and existing infrastructure of the eMERGE network, the Psychiatric Genomics Consortium (PGC), and local EHR and biobank resources. In this proposal, we aim to: (1) phenotypically and genomically validate and harmonize case and control phenotypes across multiple disorders (2) build clinically-useful risk surveillance models for mood disorders that also leverage cross-institutional genomewide data, and (3) examine whether EHR- and genomic-based risk profiles are associated with clinically-relevant health outcomes. We will further use these risk profiles to examine disparities in diagnostic delay by age, sex and race/ethnicity. The resulting diagnostic and risk prediction algorithms will be made available to the scientific community through the eMERGE network. Successful completion of these aims would represent a major advance in demonstrating the utility of EHR resources for precision medicine approaches to psychiatry, provide the first step toward clinical decision support tools that can be implemented within health systems, and create an invaluable resource for the scientific community.