# Integration of Omic Data in the Analysis of Gene x Environment Interaction

> **NIH NIH P01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2022 · $283,463

## Abstract

Project 3: Integration of Omic Data in the Analysis of Gene x Environment Interaction
Abstract
The availability of high-volume ‘omic’ data, including gene expression, metabolome, methylation,
and microbiome, provides exciting opportunities to identify novel gene-environment (G×E) and
omic × E interactions affecting cancer and other complex traits. For example, the FIGI colorectal
cancer consortium has generated transcriptomic (gene expression) data on both normal tissue
and colon organoids to inform the discovery of G×E and expression × E interactions for colorectal
cancer in a sample of over 130,000 cases and controls, with exposure data on established risk
factors including tobacco, alcohol, obesity, and red meat. The multi-ethnic cohort includes over
215,000 subjects followed for up to 30 years, with biomarkers, metabolomic, and microbiome data
available on selected subsamples and nested case-control samples of breast and colorectal
cancer. In addition to potentially improving power for identifying novel interactions, the use of
omic data holds promise to inform the biological mechanisms by which genes and exposures
affect a particular trait. This project will develop two types of novel methods that leverage omic
data to identify interactions in a genomewide scan. The first (Aim 1) considers one factor at a
time (e.g. one SNP, one gene) and uses novel two-step screening/testing methods to discover
G×E or omic × E interactions. The second (Aim 2) approach is a joint model considering SNPs
and omic data simultaneously, using novel hierarchical modeling techniques to guide the
discovery of G×E and omic × E interactions. For both Aims 1 and 2, we will consider the various
types of exposure data that may be available, ranging from simple yes/no indicators from
questionnaires to integrated exposure measures constructed using statistical models, with or
without relevant omic data. Aim 3 will focus on applying the methods from Aims 1 and 2 to several
cancer-related data resources, including epidemiological investigations such as FIGI and MEC
and a clinical trial examining modifiers of treatment outcomes in colorectal cancer patients.
Overall, this project will develop statistical methods to use both integrative omic and
environmental exposure approaches to improve power for identifying novel G×E and omic × E
interactions as well as to inform the biological mechanism by which these factors affect the risk
or prognosis of cancer. We will leverage our collaborations on several cancer-related studies to
guide our methods development process, to design realistic simulation studies for evaluating the
methods, and to assure that methods we develop are translated into real-data applications.

## Key facts

- **NIH application ID:** 10411241
- **Project number:** 2P01CA196569-07A1
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** William JAMES GAUDERMAN
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $283,463
- **Award type:** 2
- **Project period:** 2016-07-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10411241

## Citation

> US National Institutes of Health, RePORTER application 10411241, Integration of Omic Data in the Analysis of Gene x Environment Interaction (2P01CA196569-07A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10411241. Licensed CC0.

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