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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.
Journal ArticleDOI

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.
Journal ArticleDOI

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

Trick or treat: The effect of placebo on the power of pharmacogenetic association studies

TL;DR: It is shown that the placebo 'response' strongly affects the statistical power of association studies -- even a highly penetrant drug-response allele requires at least a 500-patient trial in order to reach 80 per cent power, several-fold more than the value estimated by standard tools that are not calibrated to pharmacogenetics.
Book ChapterDOI

24 The role of interacting determinants in the localization of genes

TL;DR: It is found that a test of heterogeneity in IBD sharing probabilities across strata defined by sharing of environmental factors can offer greater power for detecting linkage than the simple mean test, provided the interaction effect is sufficiently strong.
Journal ArticleDOI

Testing linkage and gene x environment interaction: comparison of different affected sib-pair methods.

TL;DR: Results showed that when exposure cancels the effect of the gene, or changes the direction of this effect (i.e., the protective allele becomes the risk allele), the PST, sMLS, and MIT may provide, under some models, greater power to detect linkage than the MLS.
Journal ArticleDOI

Sample Size Calculations for Main Effects and Interactions in Case–control Studies using Stata's nchi2 and npnchi2 Functions:

TL;DR: In this article, the non-central � 2 distribution is used to calculate power for t ests detecting departure from a null hypothesis and required sample size is proportional to the noncentrality parameter for the distribution.
Journal ArticleDOI

Using Multiple Drug Exposure Levels to Optimize Power in Pharmacogenetic Trials

TL;DR: The conclusion is that trial designs that use more than one drug exposure or dose will have an increased likelihood of discovering statistically significant pharmacogenetic associations.
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