# Large-scale transcriptome and epigenome association analysis across multiple traits

> **NIH VA I01** · JAMES J PETERS VA  MEDICAL CENTER · 2020 · —

## Abstract

PROJECT SUMMARY
Precision Medicine refers to the customization of medical treatment to the individual characteristics of each
patient. The Million Veteran Program (MVP) provides a unique opportunity to perform large-scale genome-wide
association studies (GWAS) and further our understanding of Precision Medicine across multiple traits and
diseases. While well powered GWAS have identified multiple risk variants, there has been limited conclusive
findings on the genetic factors contributing to complex traits due to small effect sizes. In addition, the majority
of common risk variants are within non-coding regions of the genome and, as such, the functional relevance of
most discovered loci remains unclear. Our group and others have shown that a large portion of phenotypic
variability in disease risk can be explained by regulatory variants, i.e. genetic variants that affect epigenetic
mechanisms and the expression levels of genes. Studying gene expression and epigenome changes directly in
MVP samples is not feasible as such data are not available. To overcome these limitations, we propose to
apply a machine learning approach that leverages existing molecular data (unrelated to MVP) as a reference
panel and directly impute multi-tissue and genome-wide gene expression and epigenome profiles in MVP
samples using the existing MVP genotypes. As reference panel, we will use large-scale datasets with
genotyping and molecular profiling that our group and others have generated, including, but not limited to, the
CommonMind consortium, psychENCODE, AD-AMP, STARNET and GTEx. Imputed MVP gene expression
and epigenome data provides a powerful cohort to “translate” genetic findings to dysregulation of specific
molecular pathways across multiple traits that will enhance drug discovery. We propose to study gene
expression and epigenome perturbations in neuropsychiatric -- including schizophrenia, bipolar disorder, post-
traumatic stress disorder, alcohol abuse, recurrent depression and suicidal ideations -- and cardiometabolic --
including type 2 diabetes, hypertension, hyperlipidemia, coronary heart disease, history of myocardial infarction
and bloodwork-quantified (glucose, Hb1Ac and lipid profile) -- traits. These disease-associated signatures can
be further explored in terms of enrichment with specific molecular networks. We propose to construct tissue
specific weighted gene-gene interaction and causal probabilistic networks and assess the enrichment with
disease-associated signatures to identify subnetworks, molecular processes and key drivers. Overall, the scale
of data generation and its integration into predictive models will provide a wealth of data for other diseases
beyond the immediate goals of this proposal that have the potential to increase our understanding of Precision
Medicine.

## Key facts

- **NIH application ID:** 9815337
- **Project number:** 5I01BX004189-02
- **Recipient organization:** JAMES J PETERS VA  MEDICAL CENTER
- **Principal Investigator:** Panagiotis Roussos
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2020
- **Award amount:** —
- **Award type:** 5
- **Project period:** 2018-10-01 → 2022-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9815337

## Citation

> US National Institutes of Health, RePORTER application 9815337, Large-scale transcriptome and epigenome association analysis across multiple traits (5I01BX004189-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9815337. Licensed CC0.

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