Methodology of laboratory measurements in prospective studies on gene-environment interactions: the experience of GenAir

Several large prospective investigations are under way or are planned in different parts of the world, aiming at the investigation of gene-environment interactions for chronic diseases. Technical, practical and ethical issues are raised by such large investigations. Here we describe how such issues were approached within a case-control study nested in EPIC, a large European cohort, and the kind of validation studies that have been set up. The GenAir investigation aimed at measuring the effects of air pollution and environmental tobacco smoke on human health in EPIC with a nested design and with biological measures. Validation studies included (a) comparisons between cotinine measurements, hemoglobin adducts and questionnaire data; (b) an analysis of the determinants of DNA adduct concentration; (c) comparison among different genotyping methods; (d) an analysis of the determinants of plasma DNA amounts. We also describe how the ethical issues were dealt with in our investigation.     

Exploiting gene-environment interaction to detect genetic associations

Complex disease by definition results from the interplay of genetic and environmental factors. However, it is currently unclear how gene-environment interaction can best be used to locate complex disease susceptibility loci, particularly in the context of studies where between 1,000 and 1,000,000 markers are scanned for association with disease. We present a joint test of marginal association and gene-environment interaction for case-control data. We compare the power and sample size requirements of this joint test to other analyses: the marginal test of genetic association, the standard test for gene-environment interaction based on logistic regression, and the case-only test for interaction that exploits gene-environment independence. Although for many penetrance models the joint test of genetic marginal effect and interaction is not the most powerful, it is nearly optimal across all penetrance models we considered. In particular, it generally has better power than the marginal test when the genetic effect is restricted to exposed subjects and much better power than the tests of gene-environment interaction when the genetic effect is not restricted to a particular exposure level. This makes the joint test an attractive tool for large-scale association scans where the true gene-environment interaction model is unknown.     

Genetic association analysis of human longevity in cohort studies of elderly subjects: an example of the PON1 gene in the Danish 1905 birth cohort

Although the case-control or the cross-sectional design has been popular in genetic association studies of human longevity, such a design is prone to false positive results due to sampling bias and a potential secular trend in gene-environment interactions. To avoid these problems, the cohort or follow-up study design has been recommended. With the observed individual survival information, the Cox regression model has been used for single-locus data analysis. In this article, we present a novel survival analysis model that combines population survival with individual genotype and phenotype information in assessing the genetic association with human longevity in cohort studies. By monitoring the changes in the observed genotype frequencies over the follow-up period in a birth cohort, we are able to assess the effects of the genotypes and/or haplotypes on individual survival. With the estimated parameters, genotype- and/or haplotype-specific survival and hazard functions can be calculated without any parametric assumption on the survival distribution. In addition, our model estimates haplotype frequencies in a birth cohort over the follow-up time, which is not observable in the multilocus genotype data. A computer simulation study was conducted to specifically assess the performance and power of our haplotype-based approach for given risk and frequency parameters under different sample sizes. Application of our method to paraoxonase 1 genotype data detected a haplotype that significantly reduces carriers' hazard of death and thus reveals and stresses the important role of genetic variation in maintaining human survival at advanced ages.     

Review of moderate alcohol consumption and reduced risk of coronary heart disease: is the effect due to beer,wine, or spirits.

OBJECTIVES: To review the effect of specific types of alcoholic drink on coronary risk. DESIGN: Systematic review of ecological, case-control, and cohort studies in which specific associations were available for consumption of beer, wine, and spirits and risk of coronary heart disease. SUBJECTS: 12 ecological, three case-control, and 10 separate prospective cohort studies. MAIN OUTCOME MEASURES: Alcohol consumption and relative risk of morbidity and mortality from coronary heart disease. RESULTS: Most ecological studies suggested that wine was more effective in reducing risk of mortality from heart disease than beer or spirits. Taken together, the three case-control studies did not suggest that one type of drink was more cardioprotective than the others. Of the 10 prospective cohort studies, four found a significant inverse association between risk of heart disease and moderate wine drinking, four found an association for beer, and four for spirits. CONCLUSIONS: Results from observational studies, where alcohol consumption can be linked directly to an individual''s risk of coronary heart disease, provide strong evidence that all alcoholic drinks are linked with lower risk. Thus, a substantial portion of the benefit is from alcohol rather than other components of each type of drink.     

Gene-environment interactions in human disease: nuisance or opportunity?

Many environmental risk factors for common, complex human diseases have been revealed by epidemiologic studies, but how genotypes at specific loci modulate individual responses to environmental risk factors is largely unknown. Gene-environment interactions will be missed in genome-wide association studies and could account for some of the 'missing heritability' for these diseases. In this review, we focus on asthma as a model disease for studying gene-environment interactions because of relatively large numbers of candidate gene-environment interactions with asthma risk in the literature. Identifying these interactions using genome-wide approaches poses formidable methodological problems, and elucidating molecular mechanisms for these interactions has been challenging. We suggest that studying gene-environment interactions in animal models, although more tractable, might not be sufficient to shed light on the genetic architecture of human diseases. Lastly, we propose avenues for future studies to find gene-environment interactions.     

Smoking and Risk of Erectile Dysfunction: Systematic Review of Observational Studies with Meta-Analysis

Background

There are many recent observational studies on smoking and risk of erectile dysfunction (ED) and whether smoking increases the risk of ED is still inconclusive. The objective of this meta-analysis was to synthesize evidence from studies that evaluated the association between smoking and the risk of ED.

Methods

We searched PubMed, Embase, Web of Science, and Scopus in January 2013 to identify cohort and case-control studies that evaluated the association between smoking and ED. Study quality of included studies was assessed by the Newcastle-Ottawa scale. Random-effects meta-analyses were used to combine the results of included studies.

Results

Four prospective cohort studies and four case-control studies involving 28, 586 participants were included. Because of significant heterogeneity after including case-control studies in meta-analysis, the consistent results of prospective cohort studies were considered more accurate, Because of significant heterogeneity after including case-control studies in meta-analysis, the consistent results of prospective cohort studies were considered more accurate, Compared with non-smokers, the overall odd ratio of ED in prospective cohort studies was 1.51(95% CI: 1.34 to 1.71) for current smokers, and it was 1.29 (95% CI: 1.07 to 1.47) for former smokers. Evidence of publication bias was not found.

Conclusion

Evidence from epidemiological studies suggests that smoking, especially current smoking, may significantly increase the risk of ED     

Nature versus nurture: death of a dogma, and the road ahead

Interaction between the genome and the environment has been widely discussed in the literature, but has the importance ascribed to understanding these interactions been overstated? In this opinion piece, we critically discuss gene-environment interactions and attempt to answer three key questions. First, is it likely that gene-environment interactions actually exist? Second, what is the realistic value of trying to unravel these interactions, both in terms of understanding disease pathogenesis and as a means of ameliorating disease? Finally, and most importantly, do the technologies and methodologies exist to facilitate an unbiased search for gene-environment interactions? Addressing these questions highlights key areas of feasibility that must be considered in this area of research.     

Gene-environment interactions in human diseases

Studies of gene-environment interactions aim to describe how genetic and environmental factors jointly influence the risk of developing a human disease. Gene-environment interactions can be described by using several models, which take into account the various ways in which genetic effects can be modified by environmental exposures, the number of levels of these exposures and the model on which the genetic effects are based. Choice of study design, sample size and genotyping technology influence the analysis and interpretation of observed gene-environment interactions. Current systems for reporting epidemiological studies make it difficult to assess whether the observed interactions are reproducible, so suggestions are made for improvements in this area.     

Identification of SNP interactions using logic regression

Interactions of single nucleotide polymorphisms (SNPs) are assumed to be responsible for complex diseases such as sporadic breast cancer. Important goals of studies concerned with such genetic data are thus to identify combinations of SNPs that lead to a higher risk of developing a disease and to measure the importance of these interactions. There are many approaches based on classification methods such as CART and random forests that allow measuring the importance of single variables. But none of these methods enable the importance of combinations of variables to be quantified directly. In this paper, we show how logic regression can be employed to identify SNP interactions explanatory for the disease status in a case-control study and propose 2 measures for quantifying the importance of these interactions for classification. These approaches are then applied on the one hand to simulated data sets and on the other hand to the SNP data of the GENICA study, a study dedicated to the identification of genetic and gene-environment interactions associated with sporadic breast cancer.     

Why study gene-environment interactions?

PURPOSE OF REVIEW: We examine the reasons for investigating gene-environment interactions and address recent reports evaluating interactions between genes and environmental modulators in relation to cardiovascular disease and its common risk factors. RECENT FINDINGS: Studies focusing on smoking, physical activity, and alcohol and coffee consumption are observational and include relatively large sample sizes. They tend to examine single genes, however, and fail to address interactions with other genes and other correlated environmental factors. Studies examining gene-diet interactions include both observational and interventional designs. These studies are smaller, especially those including dietary interventions. Among the reported gene-diet interactions, it is important to highlight the strengthened position of APOA5 as a major gene that is involved in triglyceride metabolism and modulated by dietary factors, and the identification of APOA2 as a modulator of food intake and obesity risk. SUMMARY: The study of gene-environment interactions is an active and much needed area of research. Although technical barriers of genetic studies are rapidly being overcome, inclusion of comprehensive and reliable environmental information represents a significant shortcoming of genetics studies. Progress in this area requires inclusion of larger populations but also more comprehensive, standardized, and precise approaches to capturing environmental information.     

Simple and efficient analysis of disease association with missing genotype data

Missing genotype data arise in association studies when the single-nucleotide polymorphisms (SNPs) on the genotyping platform are not assayed successfully, when the SNPs of interest are not on the platform, or when total sequence variation is determined only on a small fraction of individuals. We present a simple and flexible likelihood framework to study SNP-disease associations with such missing genotype data. Our likelihood makes full use of all available data in case-control studies and reference panels (e.g., the HapMap), and it properly accounts for the biased nature of the case-control sampling as well as the uncertainty in inferring unknown variants. The corresponding maximum-likelihood estimators for genetic effects and gene-environment interactions are unbiased and statistically efficient. We developed fast and stable numerical algorithms to calculate the maximum-likelihood estimators and their variances, and we implemented these algorithms in a freely available computer program. Simulation studies demonstrated that the new approach is more powerful than existing methods while providing accurate control of the type I error. An application to a case-control study on rheumatoid arthritis revealed several loci that deserve further investigations.     

Mutual Information for Testing Gene-Environment Interaction

Despite current enthusiasm for investigation of gene-gene interactions and gene-environment interactions, the essential issue of how to define and detect gene-environment interactions remains unresolved. In this report, we define gene-environment interactions as a stochastic dependence in the context of the effects of the genetic and environmental risk factors on the cause of phenotypic variation among individuals. We use mutual information that is widely used in communication and complex system analysis to measure gene-environment interactions. We investigate how gene-environment interactions generate the large difference in the information measure of gene-environment interactions between the general population and a diseased population, which motives us to develop mutual information-based statistics for testing gene-environment interactions. We validated the null distribution and calculated the type 1 error rates for the mutual information-based statistics to test gene-environment interactions using extensive simulation studies. We found that the new test statistics were more powerful than the traditional logistic regression under several disease models. Finally, in order to further evaluate the performance of our new method, we applied the mutual information-based statistics to three real examples. Our results showed that P-values for the mutual information-based statistics were much smaller than that obtained by other approaches including logistic regression models.     

A latent variable approach to study gene-environment interactions in the presence of multiple correlated exposures

Many existing cohort studies initially designed to investigate disease risk as a function of environmental exposures have collected genomic data in recent years with the objective of testing for gene-environment interaction (G × E) effects. In environmental epidemiology, interest in G × E arises primarily after a significant effect of the environmental exposure has been documented. Cohort studies often collect rich exposure data; as a result, assessing G × E effects in the presence of multiple exposure markers further increases the burden of multiple testing, an issue already present in both genetic and environment health studies. Latent variable (LV) models have been used in environmental epidemiology to reduce dimensionality of the exposure data, gain power by reducing multiplicity issues via condensing exposure data, and avoid collinearity problems due to presence of multiple correlated exposures. We extend the LV framework to characterize gene-environment interaction in presence of multiple correlated exposures and genotype categories. Further, similar to what has been done in case-control G × E studies, we use the assumption of gene-environment (G-E) independence to boost the power of tests for interaction. The consequences of making this assumption, or the issue of how to explicitly model G-E association has not been previously investigated in LV models. We postulate a hierarchy of assumptions about the LV model regarding the different forms of G-E dependence and show that making such assumptions may influence inferential results on the G, E, and G × E parameters. We implement a class of shrinkage estimators to data adaptively trade-off between the most restrictive to most flexible form of G-E dependence assumption and note that such class of compromise estimators can serve as a benchmark of model adequacy in LV models. We demonstrate the methods with an example from the Early Life Exposures in Mexico City to Neuro-Toxicants Study of lead exposure, iron metabolism genes, and birth weight.     

The Federal Government's Agricultural Health Study: A Critical Review with Suggested Improvements

The Agricultural Health Study (AHS) has approximately 90,000 pesticide applicators and their spouses enrolled in a number of studies to determine whether exposures to specific pesticides are associated with various cancers and other adverse health outcomes. Although the AHS was intended to be an integrated program of studies, some significant difficulties have emerged. In this report, we examine the design of the AHS, identify important program strengths and flaws, suggest various improvements in the program, and recommend ancillary studies that could be undertaken to strengthen the AHS. Overall, the AHS is collecting a large amount of information on potential determinants of health status among farmers and farm families. A promising feature of the AHS is the prospective cohort study of cancers among farmers in which the research design determines exposures prior to the diagnosis of disease. More effort needs to be devoted to reducing selection bias and information bias. Success of the cohort study will depend in part on follow-up surveys of the cohort to determine how exposures and disease states change as the cohort ages. The cross-sectional and case-control studies planned in the AHS are less promising because they will be subject to some of the same criticisms, such as potentially biased and imprecise exposure assessment, that have characterized the existing literature in this field. Important limitations of the AHS include low and variable rates of subject response to administered surveys, concerns about the validity of some self-reported non-cancer health outcomes, limited understanding of the reliability and validity of self-reporting of chemical use, an insufficient program of biological monitoring to validate the exposure surrogates employed in the AHS questionnaires, possible confounding by unmeasured, nonchemical risk factors for disease, and the absence of detailed plans for data analysis and interpretation that include explicit, a priori hypotheses. Although the AHS is already well underway, most of these limitations can be addressed by the investigators if adequate resources are made available. If these limitations are not addressed, the large amounts of data generated in the AHS will be difficult to interpret. If the exposure and health data can be validated, the scientific value of the AHS should be substantial and enduring. A variety of research recommendations are made to strengthen the AHS. They include reliability and validity studies of farmer reporting of chemical use, biological monitoring studies of farmers and members of farm families, and validity studies of positive and negative self-reports of disease status. Both industry and government should consider expanded research programs to strengthen the AHS.     

Association of angiotensin-converting enzyme I/D polymorphism with heart failure: a meta-analysis

Heart failure (HF) is a complex clinical syndrome and is thought to have a genetic basis. Numerous case-control studies have investigated the association between heart failure and polymorphisms in candidate genes. Most studies focused on the angiotensin-converting enzyme insertion/deletion (ACE I/D) polymorphism, however, the results were inconsistent because of small studies and heterogeneous samples. The objective was to assess the association between the ACE I/D polymorphism and HF. We performed a meta-analysis of all case-control studies that evaluated the association between ACE I/D polymorphism and HF in humans. Studies were identified in the PUBMED and EMBASE databases, reviews, and reference lists of relevant articles. Two reviewers independently assessed the studies. Seventeen case-control studies with a total of 5576 participants were included in the meta-analysis, including 2453 cases with HF and 3123 controls. The heterogeneity between studies was significant. No association was found under all the four genetic models (D vs. I, DD vs. ID and II, DD and ID vs. II, DD vs. ID). Subgroup analyses for ischemic HF (IHF) and HF because of dilated cardiomyopathy (DHF) also showed no significant association between ACE I/D polymorphism and HF. No significant association between the ACE I/D polymorphism and risk of HF was found in this meta-analysis. The future studies should focus on large-scale prospective and case-control studies which designed to investigate gene-gene and gene-environment interactions to shed light on the genetics of HF.     

Homocysteine,MTHFR gene polymorphisms, and cardio-cerebrovascular risk

Vascular diseases are commonly associated with traditional risk factors, but in the last decade scientific evidence has suggested that elevated plasma levels of homocysteine are associated with an increased risk of atherosclerosis and cardiovascular ischaemic events. Cardio- and cerebrovascular diseases are multifactorial, as their aetiopathogenesis is determined by genetic and environmental factors and by gene-gene and gene-environment interactions. Experimental studies have shown that many possible mechanisms are implicated in the pro-atherogenic effect of homocysteine. Hyperhomocysteinaemia may confer a mild risk alone, but it increases the risk of disease in association with other factors promoting vascular lesions. Variants in genes encoding enzymes involved in homocysteine metabolism, or depletion of important cofactors or substrates for those enzymes, including folate, vitamin B12 and vitamin B6, may result in elevated plasma homocysteine levels. Several studies have been performed to elucidate the genetic determinant of hyperhomocysteinaemia in patients with vascular disease, and the MTHFR 677C>T polymorphism is the one most extensively investigated. However, the lack of homogeneity in the data and the high number of factors influencing plasma homocysteine concentrations remain conflicting. Moreover, studies on the evaluation of therapeutic interventions in improving the atherogenic profile, lowering plasma homocysteine levels, and preventing vascular events, have shown inconsistent results, which are reviewed in this paper. More prospective, double-blind, randomized studies, including folate and vitamin B interventions, and genotyping for polymorphisms in genes involved in homocysteine metabolism, might better define the relationship between mild hyperhomocysteinaemia and vascular damage.     

Genes and environment in arthritis: can RA be prevented?

Understanding of how interactions between genes and environment contribute to the development of arthritis is a central issue in understanding the etiology of rheumatoid arthritis (RA), as well as for eventual subsequent efforts to prevent the disease. In this paper, we review current published data on genes and environment in RA as well as in certain induced animal models of disease, mainly those in which adjuvants only or adjuvants plus organ-specific autoantigens are used to induce arthritis. We refer to some new data on environmental and genetic factors of importance for RA generated from a large case-control study in Sweden (1200 patients, 1200 matched controls). We found an increased risk of seropositive but not of seronegative RA in smokers, and there are indications that this effect may be due to a gene-environment interaction involving MHC class II genes. We also found an increased risk of RA in individuals heavily exposed to mineral oils. This was of particular interest because mineral oils are strong inducers of arthritis in certain rodent strains and because polymorphisms in human genetic regions syntenic with genes predisposing for oil-induced arthritis in rats have now been shown to associate with RA in humans. Taken together, our data support the notion that concepts and data on gene-environment interactions in arthritis can now be taken from induced animal models of arthritis to generate new etiological hypotheses for RA.     

Using genomics for birth defects epidemiology: can epigenetics cut the GxE Gordian knot?

Most birth defects are etiologically complex disorders caused by combinations of genetic and environmental factors, but most studies of birth defect etiology have examined only genetic factors or only environmental factors and have not considered interactions among them. Genome-wide epigenetic studies, which use the same genomic technologies that have revolutionized our ability to identify genetic causes of disease, provide an attractive way to study gene-environment interactions. However, finding an association between epigenetic variation and an etiologically complex birth defect without knowledge of the genetic variation and environmental exposures affecting the individuals who were studied usually provides little or no information regarding the cause of the disorder. In order for genome-wide studies of epigenetic variation to contribute to our understanding of the causes of birth defects, these studies must be combined with studies of environmental exposures and studies of genetic variation in the same subjects. Under such circumstances, epigenetic studies may help to establish the molecular basis for gene-environment interactions.     

Challenges and opportunities in genome-wide environmental interaction (GWEI) studies

The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies-when the number of environmental or genetic risk factors is relatively small-has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene-gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to "joining" two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.     

Gene-environment interactions in sporadic Parkinson's disease

Much has been learned in recent years about the genetics of familial Parkinson's disease. However, far less is known about those malfunctioning genes which contribute to the emergence and/or progression of the vast majority of cases, the 'sporadic Parkinson's disease', which is the focus of our current review. Drastic differences in the reported prevalence of Parkinson's disease in different continents and countries suggest ethnic and/or environmental-associated multigenic contributions to this disease. Numerous association studies showing variable involvement of multiple tested genes in these distinct locations support this notion. Also, variable increases in the risk of Parkinson's disease due to exposure to agricultural insecticides indicate complex gene-environment interactions, especially when genes involved in protection from oxidative stress are explored. Further consideration of the brain regions damaged in Parkinson's disease points at the age-vulnerable cholinergic-dopaminergic balance as being involved in the emergence of sporadic Parkinson's disease in general and in the exposure-induced risks in particular. More specifically, the chromosome 7 ACHE/PON1 locus emerges as a key region controlling this sensitive balance, and animal model experiments are compatible with this concept. Future progress in the understanding of the genetics of sporadic Parkinson's disease depends on globally coordinated, multileveled studies of gene-environment interactions.