# Identifying Bio-signatures of Suicidal Subtypes in Veterans

> **NIH VA I01** · JAMES J PETERS VA  MEDICAL CENTER · 2022 · —

## Abstract

Suicidal behavior is a complex phenomenon, ranging from low-lethality, low-intent impulsive acts to high-
lethality high-intent suicidal acts, and thus likely to be associated with multiple underlying subtypes. Genetic
associates of suicidal behavior have been identified in several studies, yet the effect sizes for are usually
modest, possibly because of the heterogeneity in the suicidal population and the behavior. Using the Million
Veteran Program Gamma computational platform, we propose a unique combination of statistical and machine
learning methods to develop our subtypes based on Electronic Patient Records and self-reports; followed by a
careful genomic analysis of the resulting subtypes compared to two control groups chosen from the same
cohort, with and without mental health disorders. This project aims to develop sophisticated diagnosis tools for
preventing future suicidal behavior in US Veterans at high risk. Moreover, the biomarkers identified from this
study will be directly applied for validation in the PI and co-investigators’ longitudinal study of high-risk VA
patients, a natural validation sample from the same population; promising a combination of the power of both
studies.

## Key facts

- **NIH application ID:** 10683055
- **Project number:** 5I01CX001728-04
- **Recipient organization:** JAMES J PETERS VA  MEDICAL CENTER
- **Principal Investigator:** FATEMEH G HAGHIGHI
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2022
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2019-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10683055

## Citation

> US National Institutes of Health, RePORTER application 10683055, Identifying Bio-signatures of Suicidal Subtypes in Veterans (5I01CX001728-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10683055. Licensed CC0.

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