# Prediction of suicide death using EHR and polygenic risk scores

> **NIH NIH R01** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2020 · $738,338

## Abstract

ABSTRACT
Suicide is a leading cause of death that continues to increase, with over 47,000 preventable suicide deaths per
year in the U.S. Although we have made great strides in using electronic health records (EHR) and other
factors to predict suicidal ideation and behavior, our ability to reliably predict suicide death is close to zero.
From a healthcare standpoint, predicting suicide deaths is tricky. We know that the incidence of suicide
behaviors is far more common (~4%-5% per year) compared to suicide death (~0.01%-0.02% per year).
Essentially, only a small fraction of those who engage in suicidal behaviors will go on to die by suicide.
Knowledge of who these highest risk individuals are is critically important in directing prevention efforts and
development of future targeted interventions. In addition, well over half of suicide deaths occur with no prior
attempts, even accounting for lack of documentation of attempts in diagnostic codes. These “out of the blue”
cases suggest one or more high-risk groups even more elusive to accurate prediction and prevention.
Including genetic data of suicide deaths may offer substantial predictive improvement; genetic factors account
for close to 50% of the risk of suicide death. Using the extensive genetic data, statewide longitudinal EHR
resources, demographic, and familial data available to the Utah Suicide Genetic Risk Study (USGRS), we are
uniquely poised to address this critical knowledge gap. Our primary focus will be to use machine learning
methods develop models that predict suicide deaths. In addition, our large suicide death research resource will
also allow us to model differences of suicide deaths with vs. without prior attempts. Of the ~9,000 Utah suicide
deaths with demographics and environmental data, familial data, and 2 decades of longitudinal EHR data, the
USGRS also currently has DNA from >6,000, which will increase to ~10,000 during the award period. Genome-
wide molecular data is in hand for over 5,000 of these Utah suicides, allowing for tests of association of suicide
subtypes identified using EHR data with “genetic phenotypes” represented by polygenic risk scores. The
USGRS also has demographics, familial data, and longitudinal EHR data from 5 age/sex- matched Utah
population controls for each suicide death, allowing for comparisons of non-lethal attempts to suicide deaths. In
addition, we will collaborate with colleagues at the Mount Sinai School of Medicine, who are currently
developing EHR and polygenic risk models to study substance use disorder, anxiety, and major depressive
disorder in 37,510 participants in the Mount Sinai BioMe Biorepository. They will expand this work to include
suicidality to provide an additional resource of suicide attempt for our model development and testing. We will
additionally study polygenic risk scores associated with suicide death vs. attempt using our resources, Mount
Sinai BioMe, and a collaboration with Vanderbilt University for access to th...

## Key facts

- **NIH application ID:** 10027263
- **Project number:** 1R01MH123489-01
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Hilary Coon
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $738,338
- **Award type:** 1
- **Project period:** 2020-08-15 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10027263

## Citation

> US National Institutes of Health, RePORTER application 10027263, Prediction of suicide death using EHR and polygenic risk scores (1R01MH123489-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10027263. Licensed CC0.

---

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