# Integrative analysis of genetic variation and transcription factor networks to elucidate mechanisms of mental health disorders

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2024 · $776,594

## Abstract

PROJECT SUMMARY
In this project we will bridge the traditionally largely distinct fields of quantitative genetics and mechanistic
biology to obtain a mechanistic understanding of regulatory effects of genetic variants in humans. Leveraging
on large human data sets providing parallel whole genome and transcriptome sequencing data, we will extend
proof-of-principle studies and computational approaches developed and validated in model organisms to achieve
improved functional interpretation of GWAS loci associated to mental health disorders. We focus
specifically on the role of transcription factors as both upstream regulators of genetic risk variants as well as
mediators of downstream network-level effects. As Aim 1, we will develop extend methods to allow accurate
modeling of transcription factor activity from transcriptome data from large cohorts of human tissue samples
in GTEx, PsychENCODE, and TOPMed cohorts. These data will be used in Aim 2 to dissect the mechanisms
underlying proximal genetic regulatory variants in cis. We hypothesize that dynamics of transcription factor
activity and binding modifies the effect size of genetic regulatory variants across individuals, tissues, and cell
types, and that by modeling this relationship we can detect TFs regulating specific regulatory variants and
noncoding disease-associated loci. In parallel Aim 3, we will map network-level trans-acting genetic variants
for inter-individual variation in TF activity. Going beyond treating TF activity as a tissue-specific parameter
of the cellular environment, we will now consider it as a variable quantitative trait itself, and by GWAS/TWAS for
inferred TF activity, we map specific polymorphisms that affect TF activity within each tissue. We anticipate that
the trans-acting loci discovered in this analysis will be of major interest not only to basic biology of regulatory
networks, but also for explaining GWAS associations to complex diseases, and to mental health in particular.

## Key facts

- **NIH application ID:** 10745315
- **Project number:** 5R01MH106842-08
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Harmen J Bussemaker
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $776,594
- **Award type:** 5
- **Project period:** 2015-04-01 → 2025-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10745315

## Citation

> US National Institutes of Health, RePORTER application 10745315, Integrative analysis of genetic variation and transcription factor networks to elucidate mechanisms of mental health disorders (5R01MH106842-08). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10745315. Licensed CC0.

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