Gene-Environment Research and Cancer Epidemiology
- Funding Opportunities
- Funded Projects
- Research Resources
- Selected EGRP GxE Publications
- Discussion Forum
Risk of most cancer types are determined by a complex interplay of genetic and environmental factors. Recent studies provide theoretical and empirical evidence that additional genetic and environmental factors can be identified in studies that examine gene-environment (GxE) interactions. More importantly, GxE interaction research has the potential to facilitate insights into biological mechanisms and strategies for cancer prevention and control. Despite progress, several challenges remain for performing these studies. These challenges stem, in part, from the complex, evolving, and expanding nature of the genetic and environmental data collected.
The Epidemiology and Genomics Research Program (EGRP), at the National Cancer Institute's (NCI) Division of Cancer Control and Population Sciences (DCCPS), supports extramural research that investigates both genetic and environmental factors that may contribute to the etiology of cancer and/or impact cancer outcomes.
EGRP is participating in the following National Institutes of Health (NIH) Funding Opportunity Announcement (FOA):
- Analysis of Genome-Wide Gene-Environment (GxE) Interactions (R21) – expires February 15, 2014, unless reissued
There are not currently any other specific NCI Requests for Applications (RFAs) or Program Announcements (PAs) for gene-environment research; however, EGRP encourages investigator-initiated grant applications on this topic.
EGRP joins with other NCI Divisions, Offices, and Centers and other Institutes and Centers at the National Institutes of Health (NIH) to fund grant applications submitted in response to FOAs.
The National Institute of Environmental Health Sciences (NIEHS) and the National Human Genome Research Institute (NHGRI) also sponsor FOAs related to gene-environment research.
A list of active gene-environment research grants supported by the Epidemiology and Genomics Research Program (EGRP) in NCI's Division of Cancer Control and Population Sciences, can be found by searching the Cancer Genomics and Epidemiology Navigator (CGEN). Tip: Enter "gene-environment" in the search box and click the "Grants" tab.
Additionally, view a list of EGRP-supported grants funded in response to NIH-PAR-11-032, Methods and Approaches for Detection of Gene-Environment Interactions in Human Disease (R21).
Resources and tools specific to GxE research, or which provide specific modules for GxE studies, and analysis are listed below.
- Bayesian model for Detecting Gene-Environment Interactions (BaDGE)
This webpage, from NCI's intramural Division of Cancer Epidemiology and Genetics (DCEG), contains an R package implementing the Bayeisan model for detecting gene environment interaction.
- Case-control.Genetics (CGEN)
Case-control Genetics (CGEN), from NCI's DCEG, is an R package for analyzing genetic data on case-control samples, with particular emphasis on novel methods for detecting Gene-Gene and Gene-Environment interactions.
This program, out of the University of Southern California, detects GxE interactions in a genome-wide association study by implementing a suite of testing methods for GEWIS data, including efficient two-step methods.
This program, from the University of Michigan, performs meta-analysis of SNPxEnvionrment interaction and is a common package for combining existing GWAS results.
This MATLAB software package, from NCI's DCEG, tests association of a disease with a group of SNPs after accounting for their interaction with another group of SNPs or environmental exposures.
This program is a free, open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyses in a computationally efficient manner. This program was developed by the Center for Human Genetic Research (CHGR), Massachusetts General Hospital (MGH), and the Broad Institute of Harvard & MIT. Tests for GxE may be performed in PLINK.
This program is designed to perform association analysis by mean of regression of the outcome of interest onto estimated genotypic probabilities in a fast, memory-efficient and, consequently, genome-wide feasible manner and may be used to test for GxE. This tool is from GenABEL.org.
This SAS macro, a product of the Harvard School of Public Health, can be used for power calculations for gene-environment tests incorporating misclassified exposures.
This windows-based program, from NCI's DCEG, computes sample size and power for binary outcome studies (case-control and cohort studies) based on a logistic-live regression model with one covariate or two covariates (e.g., gene X exposure interactions).
This software's specific uses include: power calculations, GxE and joint test, case-control, case-only, family-based designs, continuous outcome. This software is a product of the University of Southern California.
- Stata Programs for Estimation of Study Power
These programs, out of Cancer Research UK, can be used for power calculations as well as GxE testing from logistic regression.
Additional resources that support GXE research can be found on EGRP's Genetic Susceptibility to Cancer and Environmental Epidemiology pages as well as the Biospecimen Resources for Population Scientists page.
Selected EGRP GxE Publications
- Hutter CM, Mechanic LE, Chatterjee N, Kraft P, Gillanders EM, on behalf of NCI Gene-Environment Think Tank. Gene-Environment Interactions in Cancer Epidemiology: A National Cancer Institute Think Tank Report. Genet Epidemiol. 2013 Nov;37(7):643-657. [Epub 2013 Oct 5]
- Ghazarian A, Simonds N, Bennett K, Pimentel C, Ellison G, Gillanders E, Schully S, and Mechanic L. A review of NCI's extramural grant portfolio: identifying opportunities for future research in genes and environment and cancer. Cancer Epidemiol Biomarkers Prev. April 2013: 22; 501.
- Khoury MJ, Gwinn M, Clyne M, Yu W. Genetic epidemiology with a capital E, ten years after. Genet Epidemiol. 2011 Dec;35(8):845-52.
- Mechanic LE, Chen HS, Amos CI, Chatterjee N, Cox NJ, Divi RL, Fan R, Harris EL, Jacobs K, Kraft P, Leal SM, McAllister K, Moore JH, Paltoo DN, Province MA, Ramos EM, Ritchie MD, Roeder K, Schaid DJ, Stephens M, Thomas DC, Weinberg CR, Witte JS, Zhang S, Zöllner S, Feuer EJ, Gillanders EM. Next generation analytic tools for large scale genetic epidemiology studies of complex diseases. Genet Epidemiol. 2012 Jan;36(1):22-35.
EGRP will be launching a Yammer site in the near future to facilitate on-line discussion of GxE research and cancer epidemiology. Yammer is a social networking tool used by NIH to promote collaborations by forming groups focused on specific interest areas. The goal is to develop a dynamic, collaborative workspace where investigators interested in GXE research can post and answer questions, participate in on-line discussions, and share information. If you are interested in participating in this discussion forum, please e-mail Leah Mechanic, Ph.D, M.P.H.
EGRP co-sponsors workshops and meetings to convene experts in the fields of cancer epidemiology and environmental research to review the state-of-the-science, identify research gaps, and establish scientific agendas/priorities for the future, such as:
- Gene-Environment Think Tank - January 2012
- Birth Defects and Cancer: The Intersection of Genes and Prenatal Exposures – September 2012
- Next-Generation Analytical Tools for Large-Scale Genetic Epidemiology Studies of Complex Diseases – September 2010
For general questions about EGRP's interests in gene-environment research, contact Leah Mechanic, Ph.D., M.P.H.
For questions about the following aspects of GxE research, contacts include:
- Gary L. Ellison, Ph.D., M.P.H. - environmental exposures, specifically chemical and physical agents
- Kelly K. Filipski, Ph.D., M.P.H. – pharmacogenomics related to outcomes and toxicity
- Elizabeth M. Gillanders, Ph.D. - genetic epidemiology, analytical issues and study design considerations
- Tram Kim Lam, Ph.D., M.P.H. – knowledge integration and lifestyle environmental factors
- Sheri D. Schully, Ph.D. – knowledge integration and translation