# Identifying specific genetic pathway interactions for drug use and abuse through integrative omics

> **NIH NIH DP1** · SCRIPPS RESEARCH INSTITUTE, THE · 2022 · $532,500

## Abstract

PROJECT SUMMARY
 Cannabis use disorders (CUD) are prevalent in the U.S., and highly comorbid with other substance use
disorders (SUD) such as alcohol use disorder (AUD), as well as with other mental health problems. While the
etiology of cannabis use/misuse have both environmental and genetic components, cannabis use and
problematic use are found to be highly heritable. Thus, studies that identify the genetic risk factors for CUD in
the general U.S. populations, and in the high-risk populations, are of high public health importance. However,
the genetic factors identified in the human genome thus far by conventional methods are sparse and appear to
have only captured a very small fraction of the overall heritability for the disorder. One key challenge in
addiction genetics is how to identify genetic interactions and epistatic regulations that may play a more
important role in determining risk for addictive behaviors than what gene variants do individually, and that may
help explain a critical part of the missing link. Genetic interactions have rarely been systematically considered
in studies of substance use, primarily due to lack of statistical power and shortage of computational
methodology. To address the challenge, we propose a framework to systematically detect disease-relevant
context specific genetic pathway interactions that underlie the risk for SUD. The framework will be applied to
CUD and comorbid AUD to identify crucial genetic interactions and pleiotropic interactions, filling a critical gap
in uncovering the genetic architectures of CUD. We will leverage genetic network and pathway topology and
integrate multiple layers of omics including genomics, transcriptomic and epigenomic signals in drug abuse
relevant tissues. By sharpening the focus on the functionally connected gene and regulation subsets through a
priori analyses, we will be able to dramatically boost the statistical power to detect genetic interactions, arrive
at highly biologically relevant and readily interpretable findings, and potentially provide clinically actionable
insights. The proposed study will utilize outcomes from large GWAS studies for CUD and AUD, together with
three high-risk population cohorts with elevated levels of severe cannabis and alcohol use disorders that have
whole genome sequence data. We will complement the context specific pathway-level interaction analysis with
high-dimensional variable screening machine-learning algorithms to identify both low and high order genetic
interactions and regulatory epistatic effects associated with CUD. The findings that are carefully validated
using independent study cohorts will be incorporated into a larger disease model of CUD for prediction and
potential intervention, and will open up new avenues of research by allowing interrogation of the addiction
genetics from a system’s level. The framework will be build in such a way that is readily transferable to other
SUD and mental health studies and sets the stage...

## Key facts

- **NIH application ID:** 10461185
- **Project number:** 5DP1DA054373-02
- **Recipient organization:** SCRIPPS RESEARCH INSTITUTE, THE
- **Principal Investigator:** Qian Peng
- **Activity code:** DP1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $532,500
- **Award type:** 5
- **Project period:** 2021-09-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10461185

## Citation

> US National Institutes of Health, RePORTER application 10461185, Identifying specific genetic pathway interactions for drug use and abuse through integrative omics (5DP1DA054373-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10461185. Licensed CC0.

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