Literature DB >> 24123198

Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report.

Carolyn M Hutter1, Leah E Mechanic, Nilanjan Chatterjee, Peter Kraft, Elizabeth M Gillanders.   

Abstract

Cancer risk is determined by a complex interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified hundreds of common (minor allele frequency [MAF] > 0.05) and less common (0.01 < MAF < 0.05) genetic variants associated with cancer. The marginal effects of most of these variants have been small (odds ratios: 1.1-1.4). There remain unanswered questions on how best to incorporate the joint effects of genes and environment, including gene-environment (G × E) interactions, into epidemiologic studies of cancer. To help address these questions, and to better inform research priorities and allocation of resources, the National Cancer Institute sponsored a "Gene-Environment Think Tank" on January 10-11, 2012. The objective of the Think Tank was to facilitate discussions on (1) the state of the science, (2) the goals of G × E interaction studies in cancer epidemiology, and (3) opportunities for developing novel study designs and analysis tools. This report summarizes the Think Tank discussion, with a focus on contemporary approaches to the analysis of G × E interactions. Selecting the appropriate methods requires first identifying the relevant scientific question and rationale, with an important distinction made between analyses aiming to characterize the joint effects of putative or established genetic and environmental factors and analyses aiming to discover novel risk factors or novel interaction effects. Other discussion items include measurement error, statistical power, significance, and replication. Additional designs, exposure assessments, and analytical approaches need to be considered as we move from the current small number of success stories to a fuller understanding of the interplay of genetic and environmental factors.
© 2013 WILEY PERIODICALS, INC.

Entities:  

Keywords:  complex phenotypes; gene-environment interactions; genetic epidemiology

Mesh:

Year:  2013        PMID: 24123198      PMCID: PMC4143122          DOI: 10.1002/gepi.21756

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  130 in total

1.  Using hierarchical modeling in genetic association studies with multiple markers: application to a case-control study of bladder cancer.

Authors:  Rayjean J Hung; Paul Brennan; Christian Malaveille; Stefano Porru; Francesco Donato; Paolo Boffetta; John S Witte
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2004-06       Impact factor: 4.254

2.  Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes.

Authors:  Marilyn C Cornelis; Eric J Tchetgen Tchetgen; Liming Liang; Lu Qi; Nilanjan Chatterjee; Frank B Hu; Peter Kraft
Journal:  Am J Epidemiol       Date:  2011-12-22       Impact factor: 4.897

3.  Invited commentary: GE-Whiz! Ratcheting gene-environment studies up to the whole genome and the whole exposome.

Authors:  Duncan C Thomas; Juan Pablo Lewinger; Cassandra E Murcray; W James Gauderman
Journal:  Am J Epidemiol       Date:  2011-12-22       Impact factor: 4.897

Review 4.  Analysing biological pathways in genome-wide association studies.

Authors:  Kai Wang; Mingyao Li; Hakon Hakonarson
Journal:  Nat Rev Genet       Date:  2010-12       Impact factor: 53.242

Review 5.  Gene-environment interactions in human diseases.

Authors:  David J Hunter
Journal:  Nat Rev Genet       Date:  2005-04       Impact factor: 53.242

6.  Powerful multilocus tests of genetic association in the presence of gene-gene and gene-environment interactions.

Authors:  Nilanjan Chatterjee; Zeynep Kalaylioglu; Roxana Moslehi; Ulrike Peters; Sholom Wacholder
Journal:  Am J Hum Genet       Date:  2006-10-20       Impact factor: 11.025

7.  Analysis of case-only studies accounting for genotyping error.

Authors:  K F Cheng
Journal:  Ann Hum Genet       Date:  2006-09-08       Impact factor: 1.670

8.  When one depends on the other: reporting of interaction in case-control and cohort studies.

Authors:  Mirjam J Knol; Matthias Egger; Pippa Scott; Mirjam I Geerlings; Jan P Vandenbroucke
Journal:  Epidemiology       Date:  2009-03       Impact factor: 4.822

9.  Commentary: reporting and assessing evidence for interaction: why, when and how?

Authors:  David Clayton
Journal:  Int J Epidemiol       Date:  2012-05-16       Impact factor: 7.196

10.  From "big epidemiology" to "colossal epidemiology": when all eggs are in one basket.

Authors:  Miguel A Hernán; David A Savitz
Journal:  Epidemiology       Date:  2013-05       Impact factor: 4.822

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  60 in total

1.  Genetic Control of Environmental Variation of Two Quantitative Traits of Drosophila melanogaster Revealed by Whole-Genome Sequencing.

Authors:  Peter Sørensen; Gustavo de los Campos; Fabio Morgante; Trudy F C Mackay; Daniel Sorensen
Journal:  Genetics       Date:  2015-08-12       Impact factor: 4.562

2.  Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification.

Authors:  Philip S Boonstra; Bhramar Mukherjee; Stephen B Gruber; Jaeil Ahn; Stephanie L Schmit; Nilanjan Chatterjee
Journal:  Am J Epidemiol       Date:  2016-01-10       Impact factor: 4.897

Review 3.  Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies.

Authors:  Chirag J Patel; Jacqueline Kerr; Duncan C Thomas; Bhramar Mukherjee; Beate Ritz; Nilanjan Chatterjee; Marta Jankowska; Juliette Madan; Margaret R Karagas; Kimberly A McAllister; Leah E Mechanic; M Daniele Fallin; Christine Ladd-Acosta; Ian A Blair; Susan L Teitelbaum; Christopher I Amos
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2017-07-14       Impact factor: 4.254

4.  Polygenic approaches to detect gene-environment interactions when external information is unavailable.

Authors:  Wan-Yu Lin; Ching-Chieh Huang; Yu-Li Liu; Shih-Jen Tsai; Po-Hsiu Kuo
Journal:  Brief Bioinform       Date:  2019-11-27       Impact factor: 11.622

5.  Gene-diet interactions and their impact on colorectal cancer risk.

Authors:  Elizabeth D Kantor; Edward L Giovannucci
Journal:  Curr Nutr Rep       Date:  2015-03

Review 6.  Genetic determinants of depression: recent findings and future directions.

Authors:  Erin C Dunn; Ruth C Brown; Yael Dai; Jonathan Rosand; Nicole R Nugent; Ananda B Amstadter; Jordan W Smoller
Journal:  Harv Rev Psychiatry       Date:  2015 Jan-Feb       Impact factor: 3.732

7.  A powerful and data-adaptive test for rare-variant-based gene-environment interaction analysis.

Authors:  Tianzhong Yang; Han Chen; Hongwei Tang; Donghui Li; Peng Wei
Journal:  Stat Med       Date:  2018-11-20       Impact factor: 2.373

8.  Functional logistic regression approach to detecting gene by longitudinal environmental exposure interaction in a case-control study.

Authors:  Peng Wei; Hongwei Tang; Donghui Li
Journal:  Genet Epidemiol       Date:  2014-09-12       Impact factor: 2.135

9.  EasyStrata: evaluation and visualization of stratified genome-wide association meta-analysis data.

Authors:  Thomas W Winkler; Zoltan Kutalik; Mathias Gorski; Claudio Lottaz; Florian Kronenberg; Iris M Heid
Journal:  Bioinformatics       Date:  2014-09-25       Impact factor: 6.937

10.  Multiancestry Study of Gene-Lifestyle Interactions for Cardiovascular Traits in 610 475 Individuals From 124 Cohorts: Design and Rationale.

Authors:  D C Rao; Yun J Sung; Thomas W Winkler; Karen Schwander; Ingrid Borecki; L Adrienne Cupples; W James Gauderman; Kenneth Rice; Patricia B Munroe; Bruce M Psaty
Journal:  Circ Cardiovasc Genet       Date:  2017-06
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