Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report.
paper
Cited
Public
- Authors
- Hutter, Carolyn M; Mechanic, Leah E; Chatterjee, Nilanjan; Kraft, Peter; Gillanders, Elizabeth M; NCI Gene-Environment Think Tank
- Year
- 2013
- Journal
- Genetic epidemiology
- PMID
- 24123198
- DOI
- 10.1002/gepi.21756
- PMCID
- PMC4143122
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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| # | Section | Preview |
|---|---|---|
| 20 | Statistical Methods for Testing and Estimating GxE Interactions — Methods for efficient analysis of GxE interactions | There was active discussion at the Think Tank as to whether violations of the assumption of GxE… |
| 21 | Statistical Methods for Testing and Estimating GxE Interactions — Methods for efficient analysis of GxE interactions | Hedge methods have also been proposed to have improved power, and are less susceptible to violations… |
| 22 | Statistical Methods for Testing and Estimating GxE Interactions — Methods for efficient analysis of GxE interactions | The field is rapidly evolving, and additional approaches continue to be developed. For example, the… |
| 23 | Statistical Methods for Testing and Estimating GxE Interactions — Methods for efficient analysis of GxE interactions | As discussed above, characterization studies focus on the joint effects, looking at relative and… |
| 24 | Statistical Methods for Testing and Estimating GxE Interactions — Methods for the joint analysis of G and GxE | In the discovery setting, focus is often on identifying novel genetic loci associated with disease.… |
| 25 | Statistical Methods for Testing and Estimating GxE Interactions — Methods for the joint analysis of G and GxE | simultaneously in a 2-degree of freedom test [Kraft, et al. 2007]. Alternative versions of this test… |
| 26 | Statistical Methods for Testing and Estimating GxE Interactions — Methods for the joint analysis of G and GxE | test that do not show strong evidence for multiplicative interaction when a standard GxE interaction… |
| 27 | Statistical Methods for Testing and Estimating GxE Interactions — Additional Methods | Some methods propose testing GxE interactions at the gene level, rather than the SNP or genetic… |
| 28 | Statistical Methods for Testing and Estimating GxE Interactions — Additional Methods | There are also several machine learning approaches, including multifactor dimensionality reduction… |
| 29 | Key Considerations for Characterization and Discovery — Measurement Error and Improved Exposure Assessment | Participants at the Think Tank noted every GxE study must address challenges in measuring, assessing… |
| 30 | Key Considerations for Characterization and Discovery — Measurement Error and Improved Exposure Assessment | Think Tank participants discussed methods that correct for bias and loss of power due to exposure… |
| 31 | Key Considerations for Characterization and Discovery — Measurement Error and Improved Exposure Assessment | Recent reports discuss the clear need for developing improved measures of environmental exposures,… |
| 32 | Key Considerations for Characterization and Discovery — Measurement Error and Improved Exposure Assessment | There is additional uncertainty in environmental data stemming from our lack of knowledge of the… |
| 33 | Key Considerations for Characterization and Discovery — Power and Sample Size | Think Tank participants discussed the related issues of power and sample size. Because of small… |
| 34 | Key Considerations for Characterization and Discovery — Power and Sample Size | Studies with sufficiently large samples for analyzing interactions with common exposures may still… |
| 35 | Key Considerations for Characterization and Discovery — Assessing Statistical Significance and Evidence for Interaction | Another key issue that was raised throughout the Think Tank was the question of statistical… |
| 36 | Key Considerations for Characterization and Discovery — Assessing Statistical Significance and Evidence for Interaction | For characterization studies the goal is often estimation rather than significance testing. When… |
| 37 | Key Considerations for Characterization and Discovery — Assessing Statistical Significance and Evidence for Interaction | For GEWIS studies of SNP × E for discovery, the Think Tank participants discussed setting a… |
| 38 | Key Considerations for Characterization and Discovery — Assessing Statistical Significance and Evidence for Interaction | Specifically, are we using a threshold to determine when an interaction is real, or using a… |
| 39 | Key Considerations for Characterization and Discovery — Assessing Statistical Significance and Evidence for Interaction | Despite these concerns, some Think Tank participants suggested adjusting GWAS thresholds to account… |
| Name | Type |
|---|---|
| 8q22.3 local | variant |
| alcohol | phenotype |
| ALDH2 | gene |
| benzidine local | drug |
| benzidine-exposed workers local | cohort |
| bladder cancer | phenotype |
| bladder cancer GWAS (10,519 cases, 13,218 controls) local | cohort |
| body mass index | phenotype |
| breast cancer | phenotype |
| cancer | phenotype |
| cancer phenotypes | phenotype |
| cancer risk | phenotype |
| CASP8 | gene |
| colorectal cancer | phenotype |
| common complex trait local | phenotype |
| common variants | cohort |
| complex diseases | phenotype |
| Complex diseases and outcomes local | phenotype |
| diabetes | phenotype |
| disease | phenotype |
| Early childhood exposure local | drug |
| environmental exposures | drug |
| environmental factors | drug |
| epigenetic components local | drug |
| esophageal cancer | phenotype |
| Esophageal squamous cell carcinoma | phenotype |
| ever/never smoking local | drug |
| GAME-ON Initiative local | cohort |
| genetic factors | cohort |
| genetic variants | cohort |
| GEWIS studies local | cohort |
| GxE local | phenotype |
| imputed genotypes local | variant |
| In utero exposure local | drug |
| large consortium local | cohort |
| Longitudinal study | cohort |
| LSP1 local | gene |
| NAT2 | gene |
| nested case-control study local | cohort |
| non-coding RNA local | gene |
| non-smokers | phenotype |
| obesity | phenotype |
| PAHs local | drug |
| parity local | drug |
| PhenX initiative local | cohort |
| polygenic risk-scores local | phenotype |
| rare disease | phenotype |
| rare variant | cohort |
| rs1495741 local | variant |
| smoking | phenotype |
| SNP | cohort |
| SNP×E case-control study local | cohort |
| tamoxifen | drug |
| vegetable consumption local | drug |
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|---|---|---|
| Candidate gene-environment interaction research: reflections and recommendations. | 2015 | 25620996 |
| Genetic determinants of depression: recent findings and future directions. | 2015 | 25563565 |
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