Mendelian randomisation: a new spin or real progress?

Cannabis use and psychosis: the origins and implications of an association

Evidence for the effectiveness of treatment or secondary prevention of psychotic illness such as schizophrenia is often disappointing. This situation reflects our limited understanding of the aetiology of psychosis. There is good evidence that both genetic and environmental factors are implicated but the precise identity of these is unclear. Cannabis use is one candidate as a possible, modifiable environmental influence on both incidence and prognosis of psychosis. Evidence supporting this candidature is exclusively observational, and its strength has perhaps been overestimated and problems related to its interpretation underestimated by some. Nevertheless the possibility that cannabis does cause psychosis remains. Because of this, and because there are other good public health reasons to prevent cannabis use, interventions targeting use need to be evaluated. This evaluation, along with other imaginative approaches to future research, is needed to further our understanding of the determinants of mental illness and how we can most effectively improve the population's mental health.

Bone mineral density is associated with vitamin D related rs6013897 and estrogen receptor polymorphism rs4870044: The Tromsø study

Background

Bone mineral density (BMD) is determined by bone remodeling processes regulated by endocrine, autocrine and genetic mechanisms. Thus, some studies have reported that BMD is associated with single nucleotide polymorphisms (SNPs) associated with vitamin D receptor (VDR), serum 25(OH)D levels and estrogen receptor 1 (ESR1), but without consensus. Therefore, we aimed to map and compare the risk genotypes for forearm and total hip low BMD.

Methods and findings

Data were derived from a population-based study in northern Norway; the Tromsø Study. Distal forearm BMD was measured with a single x-ray absorptiometric device, while total hip BMD was measured with a dual-energy x-ray absorptiometric device. There were 7,317 and 4,082 successful analyses of distal forearm and total hip BMD, respectively, and at least one SNP of interest. We evaluated plausible BMD modulating factors and associations of BMD and SNPs related to vitamin D metabolism (FokI, Cdx2, BsmI, rs2298850, rs10741657, rs3794060, rs6013897), ApaI-BsmI-TaqI haplotypes and ESR1 SNP rs4870044.

Results

Age, BMI, physical activity and smoking were significantly associated with BMD. In a linear regression model with adjustment for age and gender and with the major homozygote as reference, rs6013897 had a standardized beta coefficient (β) of –0.031 (P = 0.024) for total hip BMD. β for ESR1 SNP rs4870044 was –0.016 (P = 0.036) for forearm BMD and –0.034 (P = 0.015) for total hip BMD. The other SNPs nor serum 25(OH)D were significantly associated with BMD.

Conclusions

Both forearm and total hip BMD were associated with ESR1 SNP rs4870044. Of the vitamin D–related genes, only CYP24A1 gene rs6013897 was associated with total hip BMD, but the association was weak and needs confirmation in other studies. Serum 25(OH)D was not associated with BMD in our population, probably due to the generally sufficient vitamin D levels in the population.

Selecting instruments for Mendelian randomization in the wake of genome-wide association studies

Mendelian randomization (MR) studies typically assess the pathogenic relevance of environmental exposures or disease biomarkers, using genetic variants that instrument these exposures. The approach is gaining popularity-our systematic review reveals a greater than 10-fold increase in MR studies published between 2004 and 2015. When the MR paradigm was first proposed, few biomarker- or exposure-related genetic variants were known, most having been identified by candidate gene studies. However, genome-wide association studies (GWAS) are now providing a rich source of potential instruments for MR analysis. Many early reviews covering the concept, applications and analytical aspects of the MR technique preceded the surge in GWAS, and thus the question of how best to select instruments for MR studies from the now extensive pool of available variants has received insufficient attention. Here we focus on the most common category of MR studies-those concerning disease biomarkers. We consider how the selection of instruments for MR analysis from GWAS requires consideration of: the assumptions underlying the MR approach; the biology of the biomarker; the genome-wide distribution, frequency and effect size of biomarker-associated variants (the genetic architecture); and the specificity of the genetic associations. Based on this, we develop guidance that may help investigators to plan and readers interpret MR studies.

Genetically lowered concentrations of circulating sRAGE might cause an increased risk of cancer: Meta-analysis using Mendelian randomization

Objectives

To undertake a systematic meta-analysis of all variants in the gene encoding receptor for advanced glycation end products (RAGE) to summarize their associations with cancer risk and changes in the levels of circulating soluble RAGE (sRAGE), with the aim of determining possible causality between circulating sRAGE and cancer risk.

Methods

Articles written in English were retrieved from MEDLINE® and EMBASE® databases. Two researchers independently identified eligible articles and extracted the data (analysed using STATA® software version 12.0).

Results

Fifteen articles qualified for inclusion in the meta-analysis of the RAGE–cancer association and three examined the RAGE–sRAGE relationship. The 82Ser/82Ser genotype was significantly associated with overall cancer risk compared with the 82Gly/Gly genotype (odds ratio 1.75, 95% confidence interval [CI] 1.46, 2.10). Carriers of the 82Ser/82Ser genotype had significantly reduced circulating sRAGE concentrations compared with the 82Gly/82Gly genotype. Mendelian randomization analysis demonstrated that a reduction of 100, 200 and 300 pg/ml in circulating sRAGE concentrations was associated with a 1.11-fold (95% CI 1.06, 1.25), 1.24-fold (95% CI 1.11, 1.57) and 1.38-fold (95% CI 1.18, 1.96) increased risk of developing cancer, respectively.

Conclusions

Genetically lowered concentrations of circulating sRAGE might cause an increased risk of cancer.

Methodological challenges in mendelian randomization

We give critical attention to the assumptions underlying Mendelian randomization analysis and their biological plausibility. Several scenarios violating the Mendelian randomization assumptions are described, including settings with inadequate phenotype definition, the setting of time-varying exposures, the presence of gene-environment interaction, the existence of measurement error, the possibility of reverse causation, and the presence of linkage disequilibrium. Data analysis examples are given, illustrating that the inappropriate use of instrumental variable techniques when the Mendelian randomization assumptions are violated can lead to biases of enormous magnitude. To help address some of the strong assumptions being made, three possible approaches are suggested. First, the original proposal of Katan (Lancet. 1986;1:507-508) for Mendelian randomization was not to use instrumental variable techniques to obtain estimates but merely to examine genotype-outcome associations to test for the presence of an effect of the exposure on the outcome. We show that this more modest goal and approach can circumvent many, though not all, the potential biases described. Second, we discuss the use of sensitivity analysis in evaluating the consequences of violations in the assumptions and in attempting to correct for those violations. Third, we suggest that a focus on negative, rather than positive, Mendelian randomization results may turn out to be more reliable.

Association of interleukin-6 circulating levels with coronary artery disease: a meta-analysis implementing mendelian randomization approach

BACKGROUND

We aim to investigate whether the association between circulating interleukin 6 (IL-6) levels and the risk for coronary artery disease (CAD) is robust and perhaps even causal by a meta-analysis implementing mendelian randomization approach with IL-6 gene G-174C polymorphism as an instrument.

METHODS

Data were available from 19 articles encompassing 9417 CAD patients and 15982 controls. A random effects model was applied irrespectively of between-study heterogeneity, and publication bias was examined using a funnel plot and the corresponding statistics.

RESULTS

Overall, comparison of IL-6 gene alleles -174C with -174G had 4% increased risk for CAD (95% confidence interval [95% CI]: 0.97-1.10; P=0.285), accompanying marginal heterogeneity (I(2)=38.3%; P=0.033). This association was potentiated in dominant model as odds ratio (OR) reached 1.08 (95% CI: 0.96-1.22; P=0.204) and heterogeneity was significant (I(2)=58.4%; P<0.0005). Subgroup analysis by ethnicity indicated that carriers of -174C allele were associated with a 12% increased risk for CAD in prospective studies involving White populations (OR=1.12; 95% CI: 0.95-1.33; P=0.184), whereas the association in East Asians was remarkably reversed with 37-46% reduced risk. Relative to -174GG homozygotes, carriers of -174C allele had an overall 0.24 pg/ml high circulating IL-6 levels (P=0.047). The predicted OR for 1 pg/ml elevation in IL-6 levels was 1.60 (95% CI: 1.44-1.72; P<0.01) in prospective studies involving White populations. Publication biases were absent for all comparisons (P>0.1).

CONCLUSION

Our findings provided strong evidence on the causal association of circulating IL-6 levels with the development of CAD in White populations.

Bias in the case-only design applied to studies of gene-environment and gene-gene interaction: a systematic review and meta-analysis

BACKGROUND

The case-only study, proposed as a design specifically for assessing departure from multiplicative gene-environment and gene-gene interactions, is of considerable potential value but there are concerns about its validity. The objective of this study was to evaluate the extent and sources of bias in the case-only design by means of a systematic review and meta-regression analysis.

METHODS

The MEDLINE, CINAHL, EMBASE and PUBMED databases were searched through to 7 October 2009. Studies that assessed bias in the case-only design applied to the study of gene-environment and gene-gene interaction were identified. Qualitative comments on the sources and extent of bias were extracted. A meta-regression analysis of the ratio (IOR(CC)/IOR(CO)) of the case-control (IOR(CC)) and case-only (IOR(CO)) interaction odds ratios was conducted based on studies in which both methods were applied to the same data set.

RESULTS

The search yielded 365 unique articles of which 38 met the inclusion criteria. Potential sources of bias in the case-only design included non-independence of genotype and exposure in the source population. Meta-regression analysis, based on 24 evaluations, produced a mean IOR(CC)/IOR(CO) of 1.06 [95% confidence interval (95% CI) 0.93-1.22], suggesting that bias in case-only designs is not common in practice. The I(2) statistic indicated that 23.9% (95% uncertainty interval 0-53.9%) of the observed variation was due to heterogeneity between studies, which was not explained by any methodological characteristics of the included studies.

CONCLUSION

As understanding of the relationships between genes and environmental exposures in the population improves, the case-only design may prove to be of considerable value.

Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants

BACKGROUND

Mendelian Randomization (MR) studies assess the causality of an exposure-disease association using genetic determinants [i.e. instrumental variables (IVs)] of the exposure. Power and IV strength requirements for MR studies using multiple genetic variants have not been explored.

METHODS

We simulated cohort data sets consisting of a normally distributed disease trait, a normally distributed exposure, which affects this trait and a biallelic genetic variant that affects the exposure. We estimated power to detect an effect of exposure on disease for varying allele frequencies, effect sizes and samples sizes (using two-stage least squares regression on 10,000 data sets-Stage 1 is a regression of exposure on the variant. Stage 2 is a regression of disease on the fitted exposure). Similar analyses were conducted using multiple genetic variants (5, 10, 20) as independent or combined IVs. We assessed IV strength using the first-stage F statistic.

RESULTS

Simulations of realistic scenarios indicate that MR studies will require large (n > 1000), often very large (n > 10,000), sample sizes. In many cases, so-called 'weak IV' problems arise when using multiple variants as independent IVs (even with as few as five), resulting in biased effect estimates. Combining genetic factors into fewer IVs results in modest power decreases, but alleviates weak IV problems. Ideal methods for combining genetic factors depend upon knowledge of the genetic architecture underlying the exposure.

CONCLUSIONS

The feasibility of well-powered, unbiased MR studies will depend upon the amount of variance in the exposure that can be explained by known genetic factors and the 'strength' of the IV set derived from these genetic factors.

A systems genetics approach implicates USF1, FADS3, and other causal candidate genes for familial combined hyperlipidemia

We hypothesized that a common SNP in the 3' untranslated region of the upstream transcription factor 1 (USF1), rs3737787, may affect lipid traits by influencing gene expression levels, and we investigated this possibility utilizing the Mexican population, which has a high predisposition to dyslipidemia. We first associated rs3737787 genotypes in Mexican Familial Combined Hyperlipidemia (FCHL) case/control fat biopsies, with global expression patterns. To identify sets of co-expressed genes co-regulated by similar factors such as transcription factors, genetic variants, or environmental effects, we utilized weighted gene co-expression network analysis (WGCNA). Through WGCNA in the Mexican FCHL fat biopsies we identified two significant Triglyceride (TG)-associated co-expression modules. One of these modules was also associated with FCHL, the other FCHL component traits, and rs3737787 genotypes. This USF1-regulated FCHL-associated (URFA) module was enriched for genes involved in lipid metabolic processes. Using systems genetics procedures we identified 18 causal candidate genes in the URFA module. The FCHL causal candidate gene fatty acid desaturase 3 (FADS3) was associated with TGs in a recent Caucasian genome-wide significant association study and we replicated this association in Mexican FCHL families. Based on a USF1-regulated FCHL-associated co-expression module and SNP rs3737787, we identify a set of causal candidate genes for FCHL-related traits. We then provide evidence from two independent datasets supporting FADS3 as a causal gene for FCHL and elevated TGs in Mexicans.

By integrating a genetic polymorphism with genome-wide gene expression levels, we were able to attribute function to a genetic polymorphism in the USF1 gene. The USF1 gene has previously been associated with a common dyslipidemia, FCHL. FCHL is characterized by elevated levels of total cholesterol, triglycerides, or both. We demonstrate that this genetic polymorphism in USF1 contributes to FCHL disease risk by modulating the expression of a group of genes functionally related to lipid metabolism, and that this modulation is mediated by USF1. One of the genes whose expression is modulated by USF1 is FADS3, which was also implicated in a recent genome-wide association study for lipid traits. We demonstrated that a genetic polymorphism from the FADS3 region, which was associated with triglycerides in a GWAS study of Caucasians, was also associated with triglycerides in Mexican FCHL families. Our analysis provides novel insight into the gene expression profile contributing to FCHL disease risk, and identifies FADS3 as a new gene for FCHL in Mexicans.

Integrated weighted gene co-expression network analysis with an application to chronic fatigue syndrome

Background

Systems biologic approaches such as Weighted Gene Co-expression Network Analysis (WGCNA) can effectively integrate gene expression and trait data to identify pathways and candidate biomarkers. Here we show that the additional inclusion of genetic marker data allows one to characterize network relationships as causal or reactive in a chronic fatigue syndrome (CFS) data set.

Results

We combine WGCNA with genetic marker data to identify a disease-related pathway and its causal drivers, an analysis which we refer to as "Integrated WGCNA" or IWGCNA. Specifically, we present the following IWGCNA approach: 1) construct a co-expression network, 2) identify trait-related modules within the network, 3) use a trait-related genetic marker to prioritize genes within the module, 4) apply an integrated gene screening strategy to identify candidate genes and 5) carry out causality testing to verify and/or prioritize results. By applying this strategy to a CFS data set consisting of microarray, SNP and clinical trait data, we identify a module of 299 highly correlated genes that is associated with CFS severity. Our integrated gene screening strategy results in 20 candidate genes. We show that our approach yields biologically interesting genes that function in the same pathway and are causal drivers for their parent module. We use a separate data set to replicate findings and use Ingenuity Pathways Analysis software to functionally annotate the candidate gene pathways.

Conclusion

We show how WGCNA can be combined with genetic marker data to identify disease-related pathways and the causal drivers within them. The systems genetics approach described here can easily be used to generate testable genetic hypotheses in other complex disease studies.

Methodological challenges in causal research on racial and ethnic patterns of cognitive trajectories: measurement, selection, and bias

Research focused on understanding how and why cognitive trajectories differ across racial and ethnic groups can be compromised by several possible methodological challenges. These difficulties are especially relevant in research on racial and ethnic disparities and neuropsychological outcomes because of the particular influence of selection and measurement in these contexts. In this article, we review the counterfactual framework for thinking about causal effects versus statistical associations. We emphasize that causal inferences are key to predicting the likely consequences of possible interventions, for example in clinical settings. We summarize a number of common biases that can obscure causal relationships, including confounding, measurement ceilings/floors, baseline adjustment bias, practice or retest effects, differential measurement error, conditioning on common effects in direct and indirect effects decompositions, and differential survival. For each, we describe how to recognize when such biases may be relevant and some possible analytic or design approaches to remediating these biases.

Methods for meta-analysis in genetic association studies: a review of their potential and pitfalls

Meta-analysis offers the opportunity to combine evidence from retrospectively accumulated or prospectively generated data. Meta-analyses may provide summary estimates and can help in detecting and addressing potential inconsistency between the combined datasets. Application of meta-analysis in genetic associations presents considerable potential and several pitfalls. In this review, we present basic principles of meta-analytic methods, adapted for human genome epidemiology. We describe issues that arise in the retrospective or the prospective collection of relevant data through various sources, common traps to consider in the appraisal of evidence and potential biases that may interfere. We describe the relative merits and caveats for common methods used to trace inconsistency across studies along with possible reasons for non-replication of proposed associations. Different statistical models may be employed to combine data and some common misconceptions may arise in the process. Several meta-analysis diagnostics are often applied or misapplied in the literature, and we comment on their use and limitations. An alternative to overcome limitations arising from retrospective combination of data from published studies is to create networks of research teams working in the same field and perform collaborative meta-analyses of individual participant data, ideally on a prospective basis. We discuss the advantages and the challenges inherent in such collaborative approaches. Meta-analysis can be a useful tool in dissecting the genetics of complex diseases and traits, provided its methods are properly applied and interpreted.

Gene-environment studies: any advantage over environmental studies?

Gene-environment studies have been motivated by the likely existence of prevalent low-risk genes that interact with common environmental exposures. The present study assessed the statistical advantage of the simultaneous consideration of genes and environment to investigate the effect of environmental risk factors on disease. In particular, we contemplated the possibility that several genes modulate the environmental effect. Environmental exposures, genotypes and phenotypes were simulated according to a wide range of parameter settings. Different models of gene-gene-environment interaction were considered. For each parameter combination, we estimated the probability of detecting the main environmental effect, the power to identify the gene-environment interaction and the frequency of environmentally affected individuals at which environmental and gene-environment studies show the same statistical power. The proportion of cases in the population attributable to the modeled risk factors was also calculated. Our data indicate that environmental exposures with weak effects may account for a significant proportion of the population prevalence of the disease. A general result was that, if the environmental effect was restricted to rare genotypes, the power to detect the gene-environment interaction was higher than the power to identify the main environmental effect. In other words, when few individuals contribute to the overall environmental effect, individual contributions are large and result in easily identifiable gene-environment interactions. Moreover, when multiple genes interacted with the environment, the statistical benefit of gene-environment studies was limited to those studies that included major contributors to the gene-environment interaction. The advantage of gene-environment over plain environmental studies also depends on the inheritance mode of the involved genes, on the study design and, to some extend, on the disease prevalence.

Apolipoprotein E genotype affects the response to lipid-lowering therapy in Chinese patients with type 2 diabetes mellitus

OBJECTIVE

To evaluate the effect of apolipoprotein E (apoE) genotype on baseline lipid levels and the response to hydroxy-methyl glutaryl coenzyme A reductase inhibitors (statins) therapy in Chinese patients with type 2 diabetes mellitus (DM).

RESEARCH DESIGN AND METHODS

We consecutively recruited Chinese patients with type 2 DM requiring lipid-lowering therapy according to current guidelines. Patients were started on either simvastatin 10 mg daily or given an equivalent dose of lovastatin 20 mg. After 12 weeks of statin therapy, patients had fasting lipid profiles repeated. ApoE genotyping was performed by restriction fragment length polymorphism (RFLP).

RESULTS

Ninety-six patients were studied. The epsilon3/epsilon3 genotype was in 62.5%, epsilon2/epsilon3 and epsilon3/epsilon4, 16.7 and 20.8%, respectively. After adjusting for confounding variables, baseline total cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels were significantly higher in those with epsilon3/epsilon4 compared with epsilon2/epsilon3 genotype (6.7 vs. 5.5 mm for TC, 4.5 vs. 3.6 mm for LDL-C; p = 0.015 and p = 0.025, respectively). With statin therapy, epsilon3/epsilon4 patients had significantly greater LDL-C lowering compared with epsilon2/epsilon3 patients (48 vs. 27.7%; p = 0.04). There was no gender difference in baseline lipid parameters or response to statin therapy.

CONCLUSIONS

ApoE genotype accounts for interindividual variability of baseline cholesterol levels, and response to statin therapy in Chinese patients with type 2 DM.

Limits to causal inference based on Mendelian randomization: a comparison with randomized controlled trials

"Mendelian randomization" refers to the random assortment of genes transferred from parent to offspring at the time of gamete formation. This process has been compared to a randomized controlled trial of genetic variants. This could greatly aid observational epidemiology by potentially allowing an unbiased estimate of the effects of gene products on disease outcomes. However, studies utilizing Mendelian randomization to estimate effects of gene products on outcomes should be interpreted with caution. In this paper, the authors discuss some of the challenges facing epidemiologists in the analysis and interpretation of Mendelian randomization studies, particularly those that become apparent when the analogy with randomized controlled trials is closely examined. The authors conclude that Mendelian randomization is a powerful addition to etiologic research tools. However, care must be taken, because drawing valid causal inferences from its application depends upon more extensive assumptions than are required in randomized controlled trials.

Genetic epidemiology and public health: hope, hype, and future prospects

Genetic epidemiology is a rapidly expanding research field, but the implications of findings from such studies for individual or population health are unclear. The use of molecular genetic screening currently has some legitimacy in certain monogenic conditions, but no established value with respect to common complex diseases. Personalised medical care based on molecular genetic testing is also as yet undeveloped for common diseases. Genetic epidemiology can contribute to establishing the causal nature of environmentally modifiable risk factors, through the application of mendelian randomisation approaches and thus contribute to appropriate preventive strategies. Technological and other advances will allow the potential of genetic epidemiology to be revealed over the next few years, and the establishment of large population-based resources for such studies (biobanks) should contribute to this endeavour.

Obstacles and opportunities in meta-analysis of genetic association studies

Genetic association studies have the potential to advance our understanding of genotype-phenotype relationships, especially for common, complex diseases where other approaches, such as linkage, are less powerful. Unfortunately, many reported studies are not replicated or corroborated. This lack of reproducibility has many potential causes, relating to study design, sample size, and power issues, and from sources of true variability among populations. Genetic association studies can be considered as more similar to randomized trials than other types of observational epidemiological studies because of "Mendelian randomization" (Mendel's second law). The rationale and methodology for synthesizing randomized trials is highly relevant to the meta-analysis of genetic association studies. Nevertheless, there are a number of obstacles to overcome when performing such meta-analyses. In this review, the impacts of Type I error, lack of power, and publication and reporting biases are explored, and the role of multiple testing is discussed. A number of special features of association studies are especially pertinent, because they may lead to true variability among study results. These include population dynamics and structure, linkage disequilibrium, conformity to Hardy-Weinberg Equilibrium, bias, population stratification, statistical heterogeneity, epistatic and environmental interactions, and the choice of statistical models used in the analysis. Approaches to dealing with these issues are outlined. The supreme importance of complete and consistent study reporting and of making data readily available is also highlighted as a prerequisite for sound meta-analysis. We believe that systematic review and meta-analysis has an important role to play in understanding genetic association studies and should help us to separate the wheat from the chaff.

How many genes underlie the occurrence of common complex diseases in the population?

BACKGROUND

Most common human diseases are due to complex interactions among multiple genetic variants and environmental risk factors. There is debate over whether variants of a relatively small number of genes, each with weak or modest individual effects, account for a large proportion of common diseases in the population, or whether a large number of rare variants with large effects underlie genetic susceptibility to these diseases. It is not clear how many genes are necessary to account for an appreciable population-attributable fraction of these diseases.

METHODS

In this analysis, we estimated the number of disease susceptibility genes needed to account for varying population attributable fractions of a common complex disease, taking into account the genotype prevalence, risk ratios for individual genes, and the model of gene-gene interactions (additive or multiplicative).

RESULTS

Very large numbers of rare genotypes (e.g. those with frequencies of 1 per 5000 or less) are needed to explain 50% of a common disease in the population, even if the individual risk ratios are large (RR = 10-20). On the other hand, only approximately 20 genes are usually needed to explain 50% of the burden of a disease in the population if the predisposing genotypes are common (> or = 25%), even if the individual risk ratios are relatively small (RR = 1.2-1.5).

CONCLUSIONS

Our results suggest that a limited number of disease susceptibility genes with common variants can explain a major proportion of common complex diseases in the population. Our findings should help focus the search for common genetic variants that provide the most important predispositions to complex human diseases.

TNF-alpha-, TNF-beta-, IL-6-, and IL-10-promoter polymorphisms in patients with chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a major health problem. The disease is driven by abnormal inflammatory reactions in response to inhaled particles and fumes. Therefore, inflammatory mediators are postulated to be of distinct importance. In the present case-control study, we investigated interleukin (IL)-promoter polymorphisms known to correlate with altered transcription levels of their gene products in patients with COPD. We analyzed tumor necrosis factor-alpha (TNF-alpha)-308, TNF-beta-intron1-252, IL-6-174, IL-10-819, and IL-10-1082 polymorphisms in 469 individuals using restriction fragment length polymorphism-based converted polymerase chain reaction. The study population consisted of 113 patients with COPD based on chronic bronchitis, divided into subgroups by severity (I degrees -III degrees ), 113 matched hospitalized individuals suffering from severe coronary heart disease without pulmonary disease (age-, sex-, and smoking-matched control group), and 243 healthy individuals (population control group). The matched analysis showed no significant differences in genotype distribution of all tested polymorphisms between the matched controls and the COPD patients. However, comparison with the population controls revealed significant differences in IL-10-1082 A/G genotype frequencies (P = 0.0247 for the whole COPD group, P = 0.009 for smokers only), with the genotypes carrying the G allele more common in the COPD cases [odds ratio (OR) = 1.66, 95% confidence interval (CI) 1.01-2.75; P = 0.046]. Interestingly, this shift toward more G alleles was even more pronounced in the matched control group (OR = 2.55, 95% CI 1.47-4.41; P = 0.0007), suggesting both presented groups share corresponding underlying mechanisms. The IL-10-1082_G allele is known to correlate with altered IL-10 levels. Therefore, it might be associated with altered or abnormal inflammatory response, a mechanism that could be postulated to be important in both chronic bronchitis and coronary heart disease.

What can mendelian randomisation tell us about modifiable behavioural and environmental exposures?

Using genetic variants as a proxy for modifiable environmental factors that are associated with disease can circumvent some of the problems of observational studies

Do we need genomic research for the prevention of common diseases with environmental causes?

Concerns have been raised about the value of genomic research for prevention and public health, especially for complex diseases with risk factors that are amenable to environmental modification. Given that gene-environment interactions underlie almost all human diseases, the public health significance of genomic research on common diseases with modifiable environmental risks is based not necessarily on finding new genetic "causes" but on improving existing approaches to identifying and modifying environmental risk factors to better prevent and treat disease. Such applied genomic research for environmentally caused diseases is important, because 1) it could help stratify disease risks and differentiate interventions for achieving population health benefits; 2) it could help identify new environmental risk factors for disease or help confirm suspected environmental risk factors; and 3) it could aid our understanding of disease occurrence in terms of transmission, natural history, severity, etiologic heterogeneity, and targets for intervention at the population level. While genomics is still in its infancy, opportunities exist for developing, testing, and applying the tools of genomics to clinical and public health research, especially for conditions with known or suspected environmental causes. This research is likely to lead to population-wide health promotion and disease prevention efforts, not only to interventions targeted according to genetic susceptibility.

Genetics and public health--evolution, or revolution?

During the 19th and early 20th century, public health and genetics shared common ground through similar approaches to health promotion in the population. By the mid-20th century there was a division between public health and genetics, with eugenicists estranged and clinical genetics focused on single gene disorders, usually only relevant to small numbers of people. Now through a common interest in the aetiology of complex diseases such as heart disease and cancer, there is a need for people working in public health and genetics to collaborate. This is not a comfortable convergence for many, particularly those in public health. Nine main concerns are reviewed: fear of eugenics; genetic reductionism; predictive power of genes; non-modifiable risk factors; rights of individuals compared with populations; resource allocation; commercial imperative; discrimination; and understanding and education. This paper aims to contribute to the thinking and discussion about an evolutionary, multidisciplinary approach to understanding, preventing, and treating complex diseases.

The Epidemiologic Approach to Pharmacogenomics

The epidemiologic approach enables the systematic evaluation of potential improvements in the safety and efficacy of drug treatment which might result from targeting treatment on the basis of genomic information. The main epidemiologic designs are the randomized control trial, the cohort study, and the case-control study, and derivatives of these proposed for investigating gene-environment interactions. However, no one design is ideal for every situation, and methodological issues, notably selection bias, information bias, confounding and chance, all play a part in determining which study design is best for a given situation. There is also a need to employ a range of different designs to establish a portfolio of evidence about specific gene-drug interactions. In view of the complexity of gene-drug interactions, pooling of data across studies is likely to be needed in order to have adequate statistical power to test hypotheses. We suggest that there may be opportunities (i) to exploit samples from trials already completed to investigate possible gene-drug interactions; (ii) to consider the use of the case-only design nested within randomized controlled trials as a possible means of reducing genotyping costs when dichotomous outcomes are being investigated; and (iii) to make use of population-based disease registries that can be linked with tissue samples, treatment information and death records, to investigate gene-treatment interactions in survival.