# 2/4: Leveraging EHR-linked biobanks for deep phenotyping, polygenic risk score modeling, and outcomes analysis in psychiatric disorders

> **NIH NIH R01** · MAYO CLINIC ROCHESTER · 2021 · $405,409

## 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, such as employment and
educational attainment, can impact 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. Research utilizing 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, and (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
substantially advance our understanding of the clinical utility of PRSs for commonly occurring psychiatric
disorders.

## Key facts

- **NIH application ID:** 10176262
- **Project number:** 5R01MH121924-03
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** Joanna M Biernacka
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $405,409
- **Award type:** 5
- **Project period:** 2019-09-05 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10176262

## Citation

> US National Institutes of Health, RePORTER application 10176262, 2/4: Leveraging EHR-linked biobanks for deep phenotyping, polygenic risk score modeling, and outcomes analysis in psychiatric disorders (5R01MH121924-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10176262. Licensed CC0.

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