# Using 'Big Data' and Precision Medicine to Assess and Manage Suicide Risk in U.S. Veterans

> **NIH VA I01** · DURHAM VA MEDICAL CENTER · 2021 · —

## Abstract

Reducing suicide and suicidal behavior (i.e., self-directed violence) is a top priority for the Department of
Veterans Affairs. Recent statistics indicate that, on average, 20 Veterans die by suicide in the U.S. each day.
Family, adoption, and twin studies indicate that genetic factors account for 30-50% of the heritability in suicidal
behavior. Numerous candidate gene and genome wide association studies (GWAS) have been conducted to
identify variants associated with suicidal behavior; however, a major limitation of all prior genetic studies in this
area of research is low statistical power due to small sample sizes and the infrequency with which suicidal
behavior occurs. Another significant limitation concerns the failure of most prior genetic studies of suicidal
behavior to include Veterans, despite the fact that Veterans are at significantly increased risk for suicide and
suicidal behavior.
The proposed research will address these limitations by leveraging the genetic and phenotypic data available
through the Million Veteran Program (MVP) and other key administrative databases to perform the largest and
most well-powered GWAS of suicidal behavior to date. The potential impact of identifying novel genetic
markers that reliably predict suicidal behavior would be enormous. It could fundamentally shift current
understanding of the biology of suicide, lead to new and improved approaches to suicide prevention for
Veterans and civilians alike, and significantly improve VA's ongoing efforts to identify and intervene with high
risk Veterans before they engage in suicidal behavior.
Our long-term goal is to develop effective screening and intervention strategies to reduce the occurrence of
suicide and suicidal behavior. The overall objective of this application is to discover novel genetic variants that
increase Veterans' risk for suicidal behavior. The rationale for the proposed research is that identification of
genetic variants that are reliably associated with suicidal behavior could lead to the discovery of novel,
clinically-meaningful biological pathways that could, in turn, lead to new and improved suicide prevention
approaches for Veterans. We will accomplish our overall objective by pursuing the following specific aims:
In Aim 1, we will refine the phenotypes that we will use to define cases of suicidal behavior within MVP. In Aim
2, we will use GWAS to identify novel genetic variants associated with suicide attempts and suicidal ideation
among Veterans in MVP. In Aim 3, we will replicate significant findings obtained from the MVP cohort in the
Mid-Atlantic MIRECC and Army STARRS Cohorts. In Aim 4, we will explore whether the genetic findings
obtained from MVP can be used to improve VA's ability to identify Veterans at risk for suicidal behavior.

## Key facts

- **NIH application ID:** 9842275
- **Project number:** 5I01CX001729-02
- **Recipient organization:** DURHAM VA MEDICAL CENTER
- **Principal Investigator:** JEAN C. BECKHAM
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2021
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-01-01 → 2020-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9842275

## Citation

> US National Institutes of Health, RePORTER application 9842275, Using 'Big Data' and Precision Medicine to Assess and Manage Suicide Risk in U.S. Veterans (5I01CX001729-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9842275. Licensed CC0.

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