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Journal ArticleDOI

Gene-environment interactions for complex traits: Definitions, methodological requirements and challenges

TL;DR: 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
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.
Abstract: Much of the public media discussion of genetics of common diseases has centered on opportunities for targeted preventive actions. At the same time, in the specialist literature, there has been extensive discussion of “interaction,” both between genes and between genes and environment. 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. Here these debates are revisited and illustrated with results from an analysis of the genetics of type 1 diabetes (T1D). Details of this analysis are provided in section 1 of Text S1.

254 citations

Journal ArticleDOI
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.

230 citations


Cites background from "Gene-environment interactions for c..."

  • ...Thus, an individual's genetic profile could determine if he/ she has a greater or lesser susceptibility to a disease under the influences of the same environment (Dempfle et al., 2008)....

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Journal ArticleDOI
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.
Abstract: Understanding the pathogenesis of complex immunologic disorders such as multiple sclerosis (MS) is challenging Abnormalities in many different cell types are observed in the immune system and CNS of patients with MS and the identification of the primary and secondary events is difficult Recent studies suggest that the model of MS as a disorder mediated only by T cells is overly simplistic and propose an important role for B cells in the propagation of the disease B-cell activation in the form of oligoclonal bands (OCB) production is the most consistent immunologic finding in patients with MS Notably, markers of B-cell activation within the CSF of patients with MS predict conversion from clinically isolated syndrome to clinically definite MS and correlate with MRI activity, onset of relapses, and disability progression In addition, the main genetic risk factor in MS is associated with OCB production, and environmental agents associated with MS susceptibility (vitamin D and the Epstein-Barr virus) influence B-cell proliferation and function Finally, the only cell-specific treatments that are effective in patients with MS are monoclonal antibodies targeting the B-cell antigen CD20, suggesting a potentially causative role for B cells Based on current evidence there is no longer doubt that B cells are relevant to the etiology and pathogenesis of MS Elucidating the role of B cells in MS will be a fruitful strategy for disease prevention and treatment

203 citations

Journal ArticleDOI
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.
Abstract: The prevalence and incidence of type 2 diabetes, representing >90% of all cases of diabetes, are increasing rapidly throughout the world. The International Diabetes Federation has estimated that the number of people with diabetes is expected to rise from 366 million in 2011 to 552 million by 2030 if no urgent action is taken. Furthermore, as many as 183 million people are unaware that they have diabetes (www.idf.org). Therefore, the identification of individuals at high risk of developing diabetes is of great importance and interest for investigators and health care providers. Type 2 diabetes is a complex disorder resulting from an interaction between genes and environment. Several risk factors for type 2 diabetes have been identified, including age, sex, obesity and central obesity, low physical activity, smoking, diet including low amount of fiber and high amount of saturated fat, ethnicity, family history, history of gestational diabetes mellitus, history of the nondiabetic elevation of fasting or 2-h glucose, elevated blood pressure, dyslipidemia, and different drug treatments (diuretics, unselected β-blockers, etc.) (1–3). There is also ample evidence that type 2 diabetes has a strong genetic basis. The concordance of type 2 diabetes in monozygotic twins is ~70% compared with 20–30% in dizygotic twins (4). The lifetime risk of developing the disease is ~40% in offspring of one parent with type 2 diabetes, greater if the mother is affected (5), and approaching 70% if both parents have diabetes. In prospective studies, we have demonstrated that first-degree family history is associated with twofold increased risk of future type 2 diabetes (1,6). The challenge has been to find genetic markers that explain the excess risk associated with family history of diabetes. Advances in genotyping technology during the last 5 years have facilitated rapid progress in large-scale genetic studies. Since 2007, …

170 citations

Journal ArticleDOI
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.
Abstract: Genome-wide association studies (GWAS) have emerged as powerful means for identifying genetic loci related to complex diseases. However, the role of environment and its potential to interact with key loci has not been adequately addressed in most GWAS. Networks of collaborative studies involving different study populations and multiple phenotypes provide a powerful approach for addressing the challenges in analysis and interpretation shared across studies. 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. GENEVA consists of several academic institutions, including a coordinating center, two genotyping centers and 14 independently designed studies of various phenotypes, as well as several Institutes and Centers of the National Institutes of Health led by the National Human Genome Research Institute. Minimum detectable effect sizes include relative risks ranging from 1.24 to 1.57 and proportions of variance explained ranging from 0.0097 to 0.02. Given the large number of research participants (N>80,000), an important feature of GENEVA is harmonization of common variables, which allow analyses of additional traits. Environmental exposure information available from most studies also enables testing of gene-environment interactions. Facilitated by its sizeable infrastructure for promoting collaboration, GENEVA has established a unified framework for genotyping, data quality control, analysis and interpretation. By maximizing knowledge obtained through collaborative GWAS incorporating environmental exposure information, GENEVA aims to enhance our understanding of disease etiology, potentially identifying opportunities for intervention.

159 citations


Cites background from "Gene-environment interactions for c..."

  • ...SUMMARY Nearly all GWAS to date have concentrated on detecting and characterizing main effects of genes and have underemphasized the potential role the environment plays in modifying genetic risk [Clayton and McKeigue, 2001; Dempfle et al., 2008; Martinez, 2008]....

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  • ...Nearly all GWAS to date have concentrated on detecting and characterizing main effects and have not fully explored the potential role environmental factors play in modifying genetic risk [Clayton and McKeigue, 2001; Dempfle et al., 2008; Martinez, 2008]....

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References
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Journal ArticleDOI
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.
Abstract: Associations between modifiable exposures and disease seen in observational epidemiology are sometimes confounded and thus misleading, despite our best efforts to improve the design and analysis of studies. Mendelian randomization-the random assortment of genes from parents to offspring that occurs during gamete formation and conception-provides one method for assessing the causal nature of some environmental exposures. The association between a disease and a polymorphism that mimics the biological link between a proposed exposure and disease is not generally susceptible to the reverse causation or confounding that may distort interpretations of conventional observational studies. Several examples where the phenotypic effects of polymorphisms are well documented provide encouraging evidence of the explanatory power of Mendelian randomization and are described. The limitations of the approach include confounding by polymorphisms in linkage disequilibrium with the polymorphism under study, that polymorphisms may have several phenotypic effects associated with disease, the lack of suitable polymorphisms for studying modifiable exposures of interest, and canalization-the buffering of the effects of genetic variation during development. Nevertheless, 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.

3,646 citations

Journal ArticleDOI
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.
Abstract: Aetiology confronts two distinct issues: the determinants 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: they will only identify markers of susceptibility. The corresponding strategies in control are the 'high-risk' approach, which seeks to protect susceptible individuals, and the population approach, which seeks to control the causes of incidence. The two approaches are not usually in competition, but the prior concern should always be to discover and control the causes of incidence.

3,377 citations

Journal ArticleDOI
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.
Abstract: The rapid growth of human genetics creates countless opportunities for studies of disease association. Given the number of potentially identifiable genetic markers and the multitude of clinical outcomes to which these may be linked, the testing and validation of statistical hypotheses in genetic epidemiology is a task of unprecedented scale. Meta-analysis provides a quantitative approach for combining the results of various studies on the same topic, and for estimating and explaining their diversity. Here, we have evaluated by meta-analysis 370 studies addressing 36 genetic associations for various outcomes of disease. We show that significant between-study heterogeneity (diversity) is frequent, and that the results of the first study correlate only modestly with subsequent research on the same association. The first study often suggests a stronger genetic effect than is found by subsequent studies. Both bias and genuine population diversity might explain why early association studies tend to overestimate the disease protection or predisposition conferred by a genetic polymorphism. We conclude that a systematic meta-analytic approach may assist in estimating population-wide effects of genetic risk factors in human disease.

1,900 citations

Journal ArticleDOI
TL;DR: The present paper provides a description of theEPIC study, with the aim of simplifying reference to it in future papers reporting substantive or methodological studies carried out in the EPIC cohort.
Abstract: The European Prospective Investigation into Cancer and Nutrition (EPIC) is an ongoing multi-centre prospective cohort study designed to investigate the relationship between nutrition and cancer, with the potential for studying other diseases as well. The study currently includes 519 978 participants (366 521 women and 153 457 men, mostly aged 35-70 years) in 23 centres located in 10 European countries, to be followed for cancer incidence and cause-specific mortality for several decades. At enrollment, which took place between 1992 and 2000 at each of the different centres, information was collected through a non-dietary questionnaire on lifestyle variables and through a dietary questionnaire addressing usual diet. Anthropometric measurements were performed and blood samples taken, from which plasma, serum, red cells and buffy coat fractions were separated and aliquoted for long-term storage, mostly in liquid nitrogen. To calibrate dietary measurements, a standardised, computer-assisted 24-hour dietary recall was implemented at each centre on stratified random samples of the participants, for a total of 36 900 subjects. EPIC represents the largest single resource available today world-wide for prospective investigations on the aetiology of cancers (and other diseases) that can integrate questionnaire data on lifestyle and diet, biomarkers of diet and of endogenous metabolism (e.g. hormones and growth factors) and genetic polymorphisms. First results of case-control studies nested within the cohort are expected early in 2003. The present paper provides a description of the EPIC study, with the aim of simplifying reference to it in future papers reporting substantive or methodological studies carried out in the EPIC cohort.

1,641 citations

Journal ArticleDOI
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.
Abstract: Epistasis, the interaction between genes, is a topic of current interest in molecular and quantitative genetics. A large amount of research has been devoted to the detection and investigation of epistatic interactions. However, there has been much confusion in the literature over definitions and interpretations of epistasis. In this review, we provide a historical background to the study of epistatic interaction effects and point out the differences between a number of commonly used definitions of epistasis. A brief survey of some methods for detecting epistasis in humans is given. We note that the degree to which statistical tests of epistasis can elucidate underlying biological interactions may be more limited than previously assumed.

1,056 citations

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