# EHR Phenotyping and Genomics of Opioid Addiction (Project 1)

> **NIH NIH P50** · RESEARCH TRIANGLE INSTITUTE · 2024 · $424,504

## Abstract

PROJECT SUMMARY/ABSTRACT
 Drug overdose is the leading cause of injury-related death in the United States, and more than 2 million
people in the United States are struggling with some form of opioid addiction (OA). Notably, many patients
with OA are first introduced to opioids with a prescription for treatment of acute and chronic pain. Health care
systems are also significantly impacted by the opioid epidemic, with opioid-related hospitalizations increasing
by 150% and emergency department visits for opioid-related treatment doubling over the past 20 to 30
years. Thus, the use of prescription and clinical data from existing health system records offers a powerful
opportunity to improve our understanding of opioid use and abuse. Several health systems with longitudinal
data on millions of patients have also created biobanks to facilitate electronic health record (EHR)-based
genomic research and implementation of genomic medicine. In 2007, the National Human Genome
Research Institute organized the Electronic Medical Records and Genomics (eMERGE) network to develop
EHR algorithms for medical disorders, and this was expanded in 2018 to include psychiatric disorders
(PsycheMERGE). To date, however, EHR-based risk prediction and genomics have not been widely
leveraged for substance abuse research. Evidence suggests that substance use disorders are highly
heritable, although the underlying genetic risk factors remain unknown. In Project 1, we will leverage two
powerful health system biobanks to develop EHR opioid phenotypes using prescription records and clinical
diagnoses on more than 5 million people.
 We aim to (1) validate and harmonize case and control phenotypes across multiple disorders, (2)
complete genome-wide association studies (GWAS) of opioid use phenotypes and the largest GWAS of OA
to date, and (3) examine the interaction between genomics and brain structure in opioid-using patients.
Successful completion of these aims will represent a major advance in demonstrating the utility of EHR
resources for furthering our understanding of OA and will build a multi-site opioid research network for
continued scientific discovery. Integrating Project 1 in the broader context of the Integrative Omics Center for
Accelerating Neurobiological Understanding of Opioid Addiction (ICAN) creates multi-omic synergy that
extends the impact of achieving these aims, linking them directly to differential gene regulation (Project 2)
and experimental follow-up of key findings in rodent models (Project 3), as well as gene networks
identification (Project 4). In this way, other ICAN Projects will enhance interpretation of Project 1 findings,
and Project 1 GWAS and imaging results will provide opportunities to extend the other ICAN Projects,
collectively achieving our goal to identify biologically meaningful drivers of OA.

## Key facts

- **NIH application ID:** 10888290
- **Project number:** 5P50DA054071-03
- **Recipient organization:** RESEARCH TRIANGLE INSTITUTE
- **Principal Investigator:** Vanessa Troiani
- **Activity code:** P50 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $424,504
- **Award type:** 5
- **Project period:** 2022-09-15 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10888290

## Citation

> US National Institutes of Health, RePORTER application 10888290, EHR Phenotyping and Genomics of Opioid Addiction (Project 1) (5P50DA054071-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10888290. Licensed CC0.

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