Gene-Environment Interactions in Cancer Epidemiology: A National Cancer Institute Think Tank Report

Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, United States of America.
Genetic Epidemiology (Impact Factor: 2.6). 11/2013; 37(7). DOI: 10.1002/gepi.21756
Source: PubMed


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.

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    • "fficient for the longitudinal BMI revealed that the interaction was the strongest in the age interval 40 – 49 years ( supplementary Fig . S6 ) , in contrast to age intervals 20 – 29 and 30 – 39 years for SNP rs8050136 in the FTO gene . Our analyses here exemplify the need to look at the entire range of longitudinal exposure in the G × E analysis [ Hutter et al . , 2013 ] and the proposed FLR model coupled with the FPCA thereby provides a powerful tool for such investigations ."
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