Gene-Environment Interaction in Genome-Wide Association Studies

Murcray, Cassandra E.; Lewinger, Juan Pablo; Gauderman, W. James

doi:10.1093/aje/kwn353

Cited by 280 publications

(334 citation statements)

References 21 publications

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“…Considering the polygenic, multifactorial, mode of inheritance of asthma, it is useful to identify susceptible genetic variants in environment-specific and population-specific contexts. The corresponding statistical method should be developed to investigate the G/E interactions in GWA studies, 45 particularly for asthma. …”

Section: Future Directionsmentioning

confidence: 99%

The era of genome-wide association studies: opportunities and challenges for asthma genetics

2009

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In the era of genome-wide association (GWA) studies, delineating pathogenic asthma genetic pathways has provided both challenges and opportunities. Initial GWA studies on asthma and asthma-like phenotypes provided some successes in terms of ascertaining new potential asthma candidate genes. However, due to asthma having a heterogeneous etiology, replications of these genotype-phenotype association studies are generally lacking. Furthermore, genes by environment interactions are generally not considered when GWA studies are conducted. Therefore, there is a need for extensive collaborations in multi-disciplinary research fields, including different environments and populations, to investigate the functional importance of variations in the human genome in relation to asthma pathogenesis.

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Section: Future Directionsmentioning

confidence: 99%

The era of genome-wide association studies: opportunities and challenges for asthma genetics

2009

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“…Murcray et al 20 performed a general methodological study that focused on identifying SNPs that demonstrate heterogeneity between subgroups defined by some environmental exposure. They describe a two-step approach for detecting loci involved in gene-environment interactions that is performed independently of any initial scans for main effects.…”

Section: Gene-gene Interaction Studiesmentioning

confidence: 99%

Assessing Gene-Environment Interactions in Genome-Wide Association Studies: Statistical Approaches

Cooley¹,

Clark²,

Folsom

2014

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This PDF document was made available from www.rti.org as a public service of RTI International. More information about RTI Press can be found at http://www.rti.org/rtipress. RTI International is an independent, nonprofit research organization dedicated to improving the human condition by turning knowledge into practice. The RTI Press mission is to disseminate information about RTI research, analytic tools, and technical expertise to a national and international audience. RTI Press publications are peer-reviewed by at least two independent substantive experts and one or more Press editors. AbstractIn this report, we address a scenario that uses synthetic genotype case-control data that is influenced by environmental factors in a genome-wide association study (GWAS) context. The precise way the environmental influence contributes to a given phenotype is typically unknown. Therefore, our study evaluates how to approach a GWAS that may have an environmental component. Specifically, we assess different statistical models in the context of a GWAS to make association predictions when the form of the environmental influence is questionable. We used a simulation approach to generate synthetic data corresponding to a variety of possible environmental-genetic models, including a "main effects only" model as well as a "main effects with interactions" model. Our method takes into account the strength of the association between phenotype and both genotype and environmental factors, but we focus on low-risk genetic and environmental risks that necessitate using large sample sizes (N = 10,000 and 200,000) to predict associations with high levels of confidence. We also simulated different Mendelian gene models, and we analyzed how the collection of factors influences statistical power in the context of a GWAS. Using simulated data provides a "truth set" of known outcomes such that the association-affecting factors can be unambiguously determined. We also test different statistical methods to determine their performance properties. Our results suggest that the chances of predicting an association in a GWAS is reduced if an environmental effect is present and the statistical model does not adjust for that effect. This is especially true if the environmental effect and genetic marker do not have an interaction effect. The functional form of the statistical model also matters. The more accurately the form of the environmental influence is portrayed by the statistical model, the more accurate the prediction will be. Finally, even with very large samples sizes, association predictions involving recessive markers with low risk can be poor.

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“…However, in allergic diseases, environmental factors influence gene regulation/expression for that, geneenvironment interactions could be critical in these diseases (Cárdaba et al, 2007). In fact, in the last four years there has been an increase in the research of this field (review in Vercelli, 2010b) and although the gene-environment interactions known to date have been identified through hypothesis-driven research and candidate gene approaches, several efforts are doing in order to develop novel analytical methods at allowing efficient testing for geneenvironment interactions in GWASs (Murcray et al, 2009, Chatterjee et al, 2009, Vineis et al, 2008. These studies could be a new era of gene-environment-wide interaction studies (GEWIS) (Khoury &Wacholder, 2009) that may change our understanding of geneenvironment interactions and their impact on complex disease susceptibility .…”

Section: Genetic and Genomic Studies In Allergymentioning

confidence: 99%

New Challenges for Old Diseases: The Impact of -Omics Technologies in the Understanding of Allergic Diseases

Cárdaba¹,

Aguerri²,

Calzada³

et al. 2012

Allergic Diseases - Highlights in the Clinic, Mechanisms and Treatment

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Gene-Environment Interaction in Genome-Wide Association Studies

Cited by 280 publications

References 21 publications

The era of genome-wide association studies: opportunities and challenges for asthma genetics

The era of genome-wide association studies: opportunities and challenges for asthma genetics

Assessing Gene-Environment Interactions in Genome-Wide Association Studies: Statistical Approaches

New Challenges for Old Diseases: The Impact of -Omics Technologies in the Understanding of Allergic Diseases

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