# 3/4: Leveraging EHR-linked biobanks for deep phenotyping, polygenic risk score modeling, and outcomes analysis in psychiatric disorders

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2021 · $429,337

## Abstract

PROJECT ABSTRACT
Major depressive disorder (MDD), anxiety disorders, and substance use disorders (SUDs) are common, complex
psychiatric traits that frequently co-occur and are associated with significant functional impairment, increased
healthcare utilization and cost, and higher mortality risk. Not only are these three conditions highly prevalent in
the general population and generate a huge societal burden, but recent studies by our team and others have
shown that shared covariance from common genetic variation significantly contributes to these psychiatric
comorbidities. Large data sets are needed to understand how the multifaceted interplay of genetics, including
polygenic risk scores (PRSs), and social determinants of health factors, such as employment and educational
attainment, can increase the risk of these psychiatric disorders and clinical outcomes, such as multiple
psychiatric hospitalizations. PRSs have shown potential for risk prediction, but the clinical utility of PRSs for
psychiatric conditions is just starting to be explored. Use of Electronic Health Records (EHRs) offers the promise
of large data sets to examine these relationships in cohorts of patients seen in clinical practice. However, the
use of EHRs is in its infancy in the study of psychiatric disorders and their treatment. This study will address
critical knowledge gaps in “genotype-psychiatric phenotype” relationships in large, demographically and
geographically diverse population-based samples derived from EHR-linked biobanks across four medical
centers - Columbia, Cornell, Mayo Clinic and Mount Sinai. Our objectives are to (1) develop improved methods
for EHR phenotyping of MDD, anxiety, and SUDs, and related outcomes based on a data-set of >30 million
EHRs, (2) evaluate associations between PRSs and these conditions, as well as (3) assess the association
between PRSs and outcomes including treatment resistance in MDD and healthcare utilization in patients with
MDD, anxiety and SUD. The PRS analyses will utilize data from biobanks with >50,000 persons with both EHR
and GWAS data. Successful completion of this study will generate new data in improving our understanding of
the clinical utility of PRSs for commonly occurring psychiatric disorders.

## Key facts

- **NIH application ID:** 10197807
- **Project number:** 5R01MH121923-03
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** ALEXANDER W CHARNEY
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $429,337
- **Award type:** 5
- **Project period:** 2019-09-05 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10197807

## Citation

> US National Institutes of Health, RePORTER application 10197807, 3/4: Leveraging EHR-linked biobanks for deep phenotyping, polygenic risk score modeling, and outcomes analysis in psychiatric disorders (5R01MH121923-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10197807. Licensed CC0.

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