# Deciphering the Relationship between Substance Use and Psychiatric Disorders from Whole Genome Sequencing Data

> **NIH NIH DP1** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2020 · $468,000

## Abstract

Abstract
Substance use disorders have a large degree of co-morbidity with psychiatric disorders including bipolar
disorder, schizopherina, and depression. A key challenge to understanding the genetic basis of substance use
disorders is to understand at a genetic level its relationship with psychiatric disorders. Whole genome
sequencing data of individuals with substance use and psychiatric disorders has the potential to provide
extensive information on the relationship between them. However effectively interpreting variants from such
data particularly in the vast non-coding regions of the human genome will require novel computational
approaches to better annotate the human genome. We will develop several approaches to produce a more
relevant annotation of the human genome for interpreting such whole genome sequencing data. One limitation
of existing epigenome based annotations of the genome for relevant samples from brain regions is they are
derived from a complex mixture of cell types. We will define epigenome annotations such as chromatin states
computationally at a single cell type level by deconvoluting population based ChIP-seq data in a framework
work that integrates single cell RNA-seq data and Hi-C or other information associating distal regions with
genes. We will also develop approaches to better map highly relevant epigenomic data on substance use
disorders from model organisms to human through a novel approach that learns a mapping based on common
activity from a compendium of existing epigenomic data. We will also develop approaches that will learn from
high-throughput functional testing genomewide predictions of the functionally important positions and variants
even in cell types not tested using epigenomic features and specially constructed sequence features that will
generalize across cell types. Through collaborations the genome annotations produced here will be applied to
analyze multiple whole genome sequencing data sets of individuals with substance use disorders, psychiatric
disorders, or both. We will identify annotation classes as being associated specifically with variants of
substance use disorders, psychiatric disorders, or jointly between them to gain biological insights into the
biological relationship between the disorders. All computational methods developed and genome annotations
produced will be broadly disseminated.

## Key facts

- **NIH application ID:** 9978014
- **Project number:** 5DP1DA044371-04
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Jason Ernst
- **Activity code:** DP1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $468,000
- **Award type:** 5
- **Project period:** 2017-09-01 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9978014

## Citation

> US National Institutes of Health, RePORTER application 9978014, Deciphering the Relationship between Substance Use and Psychiatric Disorders from Whole Genome Sequencing Data (5DP1DA044371-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9978014. Licensed CC0.

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