Exploiting Gene-Environment Interaction to Detect Genetic Associations

Kraft, Peter; Yen, Yu-Chun; Stram, Daniel O.; Morrison, John L.; Gauderman, W. James

doi:10.1159/000099183

Cited by 411 publications

(462 citation statements)

References 53 publications

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“…Genetic health care has been offered for several decades by specialist doctors, nurses and counsellors working in the context of genetics clinics, but the relatively new science of genomics (interaction between genes and the environment that influence health and disease) is also having an effect on health-care delivery. 4 As understanding of the effect of gene variation on the complex or common diseases increases, knowledge of genetics is needed by health professionals to practise in the current genomic era. 5 Moreover, the science of pharmacogenomics is steadily affecting the quality of health care.…”

Section: The Effect Of Genomics On Health Carementioning

confidence: 99%

Genetic education and the challenge of genomic medicine: development of core competences to support preparation of health professionals in Europe

et al. 2010

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International audienceThe use of genetics and genomics within a wide range of health care settings requires health professionals to develop expertise in order to practise appropriately. There is a need for a common minimum standard of competence in genetics for health professionals in Europe but due to differences in professional education and regulation between European countries, setting curricula may not be practical. Core competences are used as a basis for health professional education in many fields and settings. An Expert Group working under the auspices of the EuroGentest project and European Society of Human Genetics Education Committee agreed that a pragmatic solution to the need to establish common standards for education and practice in genetic healthcare was to agree a set of core competences that could apply across Europe. These were agreed through an exhaustive process of consultation with relevant health professionals and patient groups. Sets of competences for practitioners working in primary, secondary and tertiary care have been agreed and were approved by the European Society of Human Genetics. The competences provide an appropriate framework for genetics education of health professionals across national boundaries and the suggested learning outcomes are available to guide development of curricula that are appropriate to the national context, educational system and healthcare setting of the professional involved. Collaboration between individuals from many European countries and professions has resulted in an adaptable framework for both pre-registration and continuing professional education. This competence framework has the potential to improve the quality of genetic healthcare for patients globally

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Section: The Effect Of Genomics On Health Carementioning

confidence: 99%

Genetic education and the challenge of genomic medicine: development of core competences to support preparation of health professionals in Europe

et al. 2010

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“…Especially for high-throughput genotyping of polymorphisms in hundreds of candidate genes or genome-wide association studies with several hundred thousands of polymorphisms, the inclusion and testing of interactions greatly increase the number of statistical tests and thus the need to correct for multiple testing. Joint tests of marginal and interaction effects 25 may provide power over a wide range of unknown true situations. However, in the absence of very strong interaction, tests for marginal gene effects are still the most powerful to identify a disease-related gene.…”

Section: When Should G â E Interactions Be Investigated?mentioning

confidence: 99%

Gene–environment interactions for complex traits: definitions, methodological requirements and challenges

et al. 2008

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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 Â 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 Â 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 Â 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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“…Finally, a Kraft et al study, 22 similar in content to the Cornelis et al study in that it also focused on logadditive gene models, formulated a likelihood ratio test of association between disease and locus with the possibility that the genetic effect may be modified by an environmental factor. The specific environment model they investigated was similar to one of the experiments examined in our study-namely, a chemical spill-an all-or-nothing type of exposure.…”

Section: Gene-gene Interaction Studiesmentioning

confidence: 99%

Assessing Gene-Environment Interactions in Genome-Wide Association Studies: Statistical Approaches

Cooley¹,

Clark²,

Folsom

2014

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This PDF document was made available from www.rti.org as a public service of RTI International. More information about RTI Press can be found at http://www.rti.org/rtipress. RTI International is an independent, nonprofit research organization dedicated to improving the human condition by turning knowledge into practice. The RTI Press mission is to disseminate information about RTI research, analytic tools, and technical expertise to a national and international audience. RTI Press publications are peer-reviewed by at least two independent substantive experts and one or more Press editors. AbstractIn this report, we address a scenario that uses synthetic genotype case-control data that is influenced by environmental factors in a genome-wide association study (GWAS) context. The precise way the environmental influence contributes to a given phenotype is typically unknown. Therefore, our study evaluates how to approach a GWAS that may have an environmental component. Specifically, we assess different statistical models in the context of a GWAS to make association predictions when the form of the environmental influence is questionable. We used a simulation approach to generate synthetic data corresponding to a variety of possible environmental-genetic models, including a "main effects only" model as well as a "main effects with interactions" model. Our method takes into account the strength of the association between phenotype and both genotype and environmental factors, but we focus on low-risk genetic and environmental risks that necessitate using large sample sizes (N = 10,000 and 200,000) to predict associations with high levels of confidence. We also simulated different Mendelian gene models, and we analyzed how the collection of factors influences statistical power in the context of a GWAS. Using simulated data provides a "truth set" of known outcomes such that the association-affecting factors can be unambiguously determined. We also test different statistical methods to determine their performance properties. Our results suggest that the chances of predicting an association in a GWAS is reduced if an environmental effect is present and the statistical model does not adjust for that effect. This is especially true if the environmental effect and genetic marker do not have an interaction effect. The functional form of the statistical model also matters. The more accurately the form of the environmental influence is portrayed by the statistical model, the more accurate the prediction will be. Finally, even with very large samples sizes, association predictions involving recessive markers with low risk can be poor.

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Exploiting Gene-Environment Interaction to Detect Genetic Associations

Cited by 411 publications

References 53 publications

Genetic education and the challenge of genomic medicine: development of core competences to support preparation of health professionals in Europe

Genetic education and the challenge of genomic medicine: development of core competences to support preparation of health professionals in Europe

Gene–environment interactions for complex traits: definitions, methodological requirements and challenges

Assessing Gene-Environment Interactions in Genome-Wide Association Studies: Statistical Approaches

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