# A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disorders

> **NIH NIH K01** · UNIVERSITY OF CALIFORNIA-IRVINE · 2023 · $113,036

## Abstract

PROJECT ABSTRACT
 The incidence of diagnosed psychiatric disorders has been increasing for decades,
leaving millions of afflicted individuals. Despite the high heritability, their underlying molecular
mechanisms remain elusive. Most risk loci are located in noncoding genomic elements without
direct effects on protein products. Comprehensive functional annotation and variant impact
quantification are essential to provide new molecular insights and discover therapeutic targets.
 Recent advances in novel sequencing technologies and community efforts to share
genomic data provide unprecedented opportunities to understand how genetic variants contribute
to psychiatric diseases. This application describes the development of integrative strategies and
machine learning methods to combine novel assays (such as STARR-seq) with population-scale
genomic profiles to elucidate the genetic regulatory grammar in the human prefrontal cortex (PFC)
and to prioritize genetic variants in psychiatric disorders. Specifically, we will (1) dissect the cis-
regulatory landscape of the PFC using population-scale epigenetics data, (2) construct multi-
model gene regulatory networks by linking distal cis-regulatory elements to genes using chromatin
co-variability analyses, (3) integrate genetic, epigenetic, and transcriptional data to identify key
transcription factors and variants that contribute to psychiatric disorders. Distinct from existing
efforts focusing on one genome, this proposed work presents a truly novel big-data approach for
both modeling gene regulation and investigating disease-risk factors by incorporating
heterogeneous multi-omics profiles from hundreds of individuals. The resultant comprehensive
list of cis-regulatory elements will expand the number of known functional regions in the human
brain by at least an order. We will release our methods and resources in the form of web services,
distributed open-source software, and annotation databases, which will also benefit other
investigators exploring the genetic underpinnings of neuropsychiatric disorders.
 In addition to its scientific content, this application proposes a comprehensive training
program for preparing an independent investigator in computational genomics and neurogenetics.
This training will take place at Yale University (in the Dept. of Molecular Biophysics and
Biochemistry) under the mentorship of Prof. Mark Gerstein (functional genomics), Prof. Nenad
Sestan (neurogenetics), and Prof. Hongyu Zhao (statistical genetics and machine learning). A
committee of experienced psychiatric disease experts and data scientists will also provide advice
on both scientific research and career development.

## Key facts

- **NIH application ID:** 10640918
- **Project number:** 5K01MH123896-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA-IRVINE
- **Principal Investigator:** JING ZHANG
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $113,036
- **Award type:** 5
- **Project period:** 2020-07-17 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10640918

## Citation

> US National Institutes of Health, RePORTER application 10640918, A big data approach to explore epigenetic heterogeneity and interpret noncoding variants for psychiatric disorders (5K01MH123896-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10640918. Licensed CC0.

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