# Understanding gene regulation in schizophrenia-associated loci using high-throughput epigenetic CRISPR screens

> **NIH NIH F31** · DUKE UNIVERSITY · 2024 · $42,519

## Abstract

ABSTRACT: Although schizophrenia (SCZ) is highly heritable, the genetic underpinnings of SCZ are largely
unknown. Over the last decade, genome wide association (GWA) studies have made huge strides in identifying
non-coding loci associated with increased risk of SCZ. However, our ability to interpret this data and identify
causal variants has significantly lagged behind our ability to identify associations with this disorder. Work in our
lab utilizes CRISPR-Cas9 technologies to dissect disease-associated variants and loci at large scale to
determine if they function in gene regulation. Our current goal is to utilize high-throughput CRISPR screens to
characterize SCZ-associated variants that map within regulatory elements and link them to their target genes.
This proposal outlines the use of epigenetic CRISPR screens to dissect SCZ-associated loci that have been
detected by both common variant GWA and rare variant whole exome sequencing (WES) studies, as well as loci
that contain a single gene. Successful completion of this work will provide a mechanistic understanding of SCZ
GWA regions and will provide an optimized strategy for tackling complex disease genetics.

## Key facts

- **NIH application ID:** 10993269
- **Project number:** 1F31MH138142-01
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Marisa C. Hamilton
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $42,519
- **Award type:** 1
- **Project period:** 2024-07-11 → 2027-07-10

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10993269

## Citation

> US National Institutes of Health, RePORTER application 10993269, Understanding gene regulation in schizophrenia-associated loci using high-throughput epigenetic CRISPR screens (1F31MH138142-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10993269. Licensed CC0.

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