Gene-environment interactions for complex traits: Definitions, methodological requirements and challenges
Astrid Dempfle,André Scherag,André Scherag,Rebecca Hein,Lars Beckmann,Jenny Chang-Claude,Helmut Schäfer +6 more
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 discussedread more
Citations
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Prediction and Interaction in Complex Disease Genetics: Experience in Type 1 Diabetes
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The gene, environment association studies consortium (GENEVA): maximizing the knowledge obtained from GWAS by collaboration across studies of multiple conditions
Marilyn C. Cornelis,Arpana Agrawal,John W. Cole,Nadia N. Hansel,Kathleen C. Barnes,Terri H. Beaty,Siiri N. Bennett,Laura J. Bierut,Eric Boerwinkle,Kimberly F. Doheny,Bjarke Feenstra,Eleanor Feingold,Myriam Fornage,Christopher A. Haiman,Emily L. Harris,M. Geoffrey Hayes,John A. Heit,Frank B. Hu,Jae H. Kang,Cathy C. Laurie,Hua Ling,Teri A. Manolio,Mary L. Marazita,Rasika A. Mathias,Daniel B. Mirel,Justin Paschall,Louis R. Pasquale,Elizabeth W. Pugh,John P. Rice,Jenna Udren,Rob M. van Dam,Xiaojing Wang,Janey L. Wiggs,Kayleen Williams,Kai Yu +34 more
TL;DR: The Gene, Environment Association Studies (GENEVA) consortium was initiated to identify genetic variants related to complex diseases; identify variations in gene‐trait associations related to environmental exposures; and ensure rapid sharing of data through the database of Genotypes and Phenotypes.
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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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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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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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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