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

> **NIH NIH P01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2023 · $282,237

## Abstract

Project 2: 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:** 10707459
- **Project number:** 5P01CA196569-08
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** William JAMES GAUDERMAN
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $282,237
- **Award type:** 5
- **Project period:** 2016-07-01 → 2027-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10707459

## Citation

> US National Institutes of Health, RePORTER application 10707459, Integration of Omic Data in the Analysis of Gene x Environment Interaction (5P01CA196569-08). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10707459. Licensed CC0.

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