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

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $791,339

## Abstract

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.

## Key facts

- **NIH application ID:** 9873997
- **Project number:** 5R01MH118233-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Lea K Davis
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $791,339
- **Award type:** 5
- **Project period:** 2019-02-15 → 2022-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9873997

## Citation

> US National Institutes of Health, RePORTER application 9873997, PsycheMERGE: Leveraging electronic health records and genomics for mental health research (5R01MH118233-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9873997. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*