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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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Unravelling the complex genetics of common kidney diseases: from variants to mechanisms

TL;DR: The authors propose that identification of causal variants, genetic regulatory mechanisms, target-gene products and disease-associated phenotypes is crucial to this process and the use of advanced computational approaches to combine datasets orthogonal to GWAS data, including molecular quantitative trait studies, single-cell transcriptomics and epigenetic information, is necessary for the prioritization of GWAS variants.
References
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Journal ArticleDOI

‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?

TL;DR: Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.
Journal ArticleDOI

Sick individuals and sick populations

TL;DR: Aetiology confronts two distinct issues: the determinant of individual cases, and the determinants of incidence rate: if exposure to a necessary agent is homogeneous within a population, then case/control and cohort methods will fail to detect it.
Journal ArticleDOI

Replication validity of genetic association studies.

TL;DR: It is concluded that a systematic meta-analytic approach may assist in estimating population-wide effects of genetic risk factors in human disease.
Journal ArticleDOI

Epistasis: what it means, what it doesn't mean, and statistical methods to detect it in humans

TL;DR: It is noted that the degree to which statistical tests of epistasis can elucidate underlying biological interactions may be more limited than previously assumed.
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