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Open AccessJournal ArticleDOI

Gene-environment interactions for complex traits: Definitions, methodological requirements and challenges

TLDR
This review discusses methodological issues involved in investigating gene–environment (G × E) interactions in genetic–epidemiological studies of complex diseases and their potential relevance for clinical application and attempts to clarify conceptual differences of the term ‘interaction’ in the statistical and biological sciences.
Abstract
Genetic and environmental risk factors and their interactions contribute to the development of complex diseases In this review, we discuss methodological issues involved in investigating gene-environment (G x E) interactions in genetic-epidemiological studies of complex diseases and their potential relevance for clinical application Although there are some important examples of interactions and applications, the widespread use of the knowledge about G x E interaction for targeted intervention or personalized treatment (pharmacogenetics) is still beyond current means This is due to the fact that convincing evidence and high predictive or discriminative power are necessary conditions for usefulness in clinical practice We attempt to clarify conceptual differences of the term 'interaction' in the statistical and biological sciences, since precise definitions are important for the interpretation of results We argue that the investigation of G x E interactions is more rewarding for the detailed characterization of identified disease genes (ie at advanced stages of genetic research) and the stratified analysis of environmental effects by genotype or vice versa Advantages and disadvantages of different epidemiological study designs are given and sample size requirements are exemplified These issues as well as a critical appraisal of common methodological concerns are finally discussed

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Journal ArticleDOI

Prediction and Interaction in Complex Disease Genetics: Experience in Type 1 Diabetes

TL;DR: These two topics concern the use and interpretation of statistical models for risk of diseases with several, perhaps many, etiological risk factors, and were both the subject of lively debate some 30 to 40 years ago when such models first came into widespread use in epidemiology.
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Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical consequences.

TL;DR: Inflammation is one of the major components of PVR, and new genetic biomarkers that have the potential to predict its development are described, including one directed towards neuroprotection, which can also be useful for preventing visual loss after any RD.
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The evidence for a role of B cells in multiple sclerosis

TL;DR: There is no longer doubt that B cells are relevant to the etiology and pathogenesis of MS and elucidating the role of B cells in MS will be a fruitful strategy for disease prevention and treatment.
Journal ArticleDOI

Genetic screening for the risk of type 2 diabetes: worthless or valuable?

TL;DR: It is demonstrated that first-degree family history is associated with twofold increased risk of future type 2 diabetes, and advances in genotyping technology during the last 5 years have facilitated rapid progress in large-scale genetic studies.
References
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Journal ArticleDOI

Sample size determination for studies of gene-environment interaction

TL;DR: The formulae allowing the computation of the sample size required to study the interaction between a continuous environmental exposure and a genetic factor on a continuous outcome variable should have a practical utility in assisting the design of studies of appropriate power.
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Family-based association studies

TL;DR: It is concluded that family-based case-control studies are an attractive alternative to population-basedcase-control designs using unrelated control subjects.
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Case-parents design for gene-environment interaction.

TL;DR: Results indicate that the case‐parents design can be more powerful to detect gene‐environment interactions, particularly when the disease susceptible allele is rare, and one of the derived likelihood methods, based on additive effects of alleles, tended to be the most robust in terms of power for a broad range of genetic mechanisms, and so may be useful for broad applications to assess gene‐ Environment interactions.
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Genetics and genomics in infectious disease susceptibility.

TL;DR: Comparative sequence analysis of pathogen strains and functional genomics studies are now underway, hopefully providing new insight into infectious disease susceptibility.
Journal ArticleDOI

Differential Misclassification and the Assessment of Gene-Environment Interactions in Case-Control Studies

TL;DR: The authors identify conditions under which differential misclassification does not introduce bias in the interaction parameter when no multiplicative interaction is present, and it biases the interaction parameters toward the null value when a multiplier is present.
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