Invited commentary: GE-Whiz! Ratcheting gene-environment studies up to the whole genome and the whole exposome.
- Authors
- Thomas, Duncan C; Lewinger, Juan Pablo; Murcray, Cassandra E; Gauderman, W James
- Year
- 2012
- Journal
- American journal of epidemiology
- PMID
- 22199029
- DOI
- 10.1093/aje/kwr365
- PMCID
- PMC3261438
One goal in the post-genome-wide association study era is characterizing gene-environment interactions, including scanning for interactions with all available polymorphisms, not just those showing significant main effects. In recent years, several approaches to such "gene-environment-wide interaction studies" have been proposed. Two contributions in this issue of the American Journal of Epidemiology provide systematic comparisons of the performance of these various approaches, one based on simulation and one based on application to 2 real genome-wide association study scans for type 2 diabetes. The authors discuss some of the broader issues raised by these contributions, including the plausibility of the gene-environment independence assumption that some of these approaches rely upon, the need for replication, and various generalizations of these approaches.
No figures extracted from this document.
No chunks β full text not yet ingested.
No entities extracted from this document yet.
No uploaded files.
No citations found.
In this knowledge base
External
| Title | Authors | Journal | Year | Link |
|---|---|---|---|---|
| Progress in Characterizing the Human Exposome: a Key Step for Precision Medicine. | Martin-Sanchez F et al. | β | 2020 | β |
| Gene-Environment Interactions in Psychiatry: Recent Evidence and Clinical Implications. | Musci RJ et al. | β | 2019 | β |
| Smoking and Parkinson disease: Evidence for gene-by-smoking interactions. | Lee PC et al. | β | 2018 | β |
| Associations between serotonin transporter and behavioral traits and diagnoses related to anxiety. | Talati A et al. | β | 2017 | β |
| Commentary: Fundamental problems with candidate gene-by-environment interaction studies - reflections on Moore and Thoemmes (2016). | Border R et al. | β | 2017 | β |
| Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases. | McAllister K et al. | β | 2017 | β |
| Genome-wide Association Study of Susceptibility to Particulate Matter-Associated QT Prolongation. | Gondalia R et al. | β | 2017 | β |
| Opportunities and Challenges for Environmental Exposure Assessment in Population-Based Studies. | Patel CJ et al. | β | 2017 | β |
| What Does "Precision Medicine" Have to Say About Prevention? | Thomas DC | β | 2017 | β |
| A Framework for Interpreting Type I Error Rates from a Product-Term Model of Interaction Applied to Quantitative Traits. | Rao TJ et al. | β | 2016 | β |
| Analytical Complexity in Detection of Gene Variant-by-Environment Exposure Interactions in High-Throughput Genomic and Exposomic Research. | Patel CJ | β | 2016 | β |
| Opportunities and challenges of big data for the social sciences: The case of genomic data. | Liu H et al. | β | 2016 | β |
| Resolving the etiology of atopic disorders by using genetic analysis of racial ancestry. | Gupta J et al. | β | 2016 | β |
| Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification. | Boonstra PS et al. | β | 2016 | β |
| Candidate gene-environment interaction research: reflections and recommendations. | Dick DM et al. | β | 2015 | β |
| Effect of occupational exposures on lung cancer susceptibility: a study of gene-environment interaction analysis. | Malhotra J et al. | β | 2015 | β |
| Mapping asthma-associated variants in admixed populations. | Mersha TB | β | 2015 | β |
| Identification of new genetic susceptibility loci for breast cancer through consideration of gene-environment interactions. | Schoeps A et al. | β | 2014 | β |
| Is the gene-environment interaction paradigm relevant to genome-wide studies? The case of education and body mass index. | Boardman JD et al. | β | 2014 | β |
| Lack of replication of the GRIN2A-by-coffee interaction in Parkinson disease. | Ahmed I et al. | β | 2014 | β |
| Studying the elusive environment in large scale. | Patel CJ et al. | β | 2014 | β |
| Body mass index, asthma, and genetic variation. | Tobin MD | β | 2013 | β |
| Comparisons of power of statistical methods for gene-environment interaction analyses. | Ege MJ et al. | β | 2013 | β |
| Empirical hierarchical bayes approach to gene-environment interactions: development and application to genome-wide association studies of lung cancer in TRICL. | Sohns M et al. | β | 2013 | β |
| Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank report. | Hutter CM et al. | β | 2013 | β |
| Genome-wide investigation of gene-environment interactions in colorectal cancer. | Siegert S et al. | β | 2013 | β |
| Molecular pathological epidemiology of epigenetics: emerging integrative science to analyze environment, host, and disease. | Ogino S et al. | β | 2013 | β |
| The case-only test for gene-environment interaction is not uniformly powerful: an empirical example. | Wu C et al. | β | 2013 | β |
| Associations between variation in CHRNA5-CHRNA3-CHRNB4, body mass index and blood pressure in the Northern Finland Birth Cohort 1966. | Kaakinen M et al. | β | 2012 | β |
| Confluence of genes, environment, development, and behavior in a post Genome-Wide Association Study world. | Vrieze SI et al. | β | 2012 | β |
| Conventional case-cohort design and analysis for studies of interaction. | Cologne J et al. | β | 2012 | β |
| Data-driven integration of epidemiological and toxicological data to select candidate interacting genes and environmental factors in association with disease. | Patel CJ et al. | β | 2012 | β |
| Gene-environment interactions in the development of type 2 diabetes: recent progress and continuing challenges. | Cornelis MC et al. | β | 2012 | β |
| Genetic prediction of common diseases. Still no help for the clinical diabetologist! | Prudente S et al. | β | 2012 | β |
| Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases. | Aschard H et al. | β | 2012 | β |
| Next generation analytic tools for large scale genetic epidemiology studies of complex diseases. | Mechanic LE et al. | β | 2012 | β |
| On lung function and interactions using genome-wide data. | MelΓ©n E et al. | β | 2012 | β |