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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

Genotype imputation in case-only studies of gene-environment interaction: validity and power

TL;DR: The statistical power was found to be reduced by imputation, particularly for SNPs with low MAF, and a gradual loss of statistical power resulted when the level of LD to the SNPs driving the imputation decreased, which highlights that genotypes should be employed with great care in CO studies of G×E interaction.
Dissertation

Interaction between dietary factors and genetic risk for lipoprotein traits and cardiovascular disease

TL;DR: Results showed that intake of long-chain omega-3 (ω-3) PUFAs can modify the associated effects of FADS1 genetic variations on LDL-C concentrations, and suggested that a high quality diet that reflects the Swedish nutrition recommendations might attenuate the association between genetic risk for high HDL-C and increased risk of ischemic stroke compared to a low quality diet.

Genetics of Two Mendelian Traits and Validation of Induced Pluripotent Stem Cell (iPSC) Technology for Disease Modeling

TL;DR: Novel technologies for genome analysis have provided almost unlimited opportunities to uncover structural gene variants behind human disorders and Whole exome sequencing (WES) is especially useful for this purpose.
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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