Clustered environments and randomized genes: a fundamental distinction between conventional and genetic epidemiology

Background

In conventional epidemiology confounding of the exposure of interest with lifestyle or socioeconomic factors, and reverse causation whereby disease status influences exposure rather than vice versa, may invalidate causal interpretations of observed associations. Conversely, genetic variants should not be related to the confounding factors that distort associations in conventional observational epidemiological studies. Furthermore, disease onset will not influence genotype. Therefore, it has been suggested that genetic variants that are known to be associated with a modifiable (nongenetic) risk factor can be used to help determine the causal effect of this modifiable risk factor on disease outcomes. This approach, mendelian randomization, is increasingly being applied within epidemiological studies. However, there is debate about the underlying premise that associations between genotypes and disease outcomes are not confounded by other risk factors. We examined the extent to which genetic variants, on the one hand, and nongenetic environmental exposures or phenotypic characteristics on the other, tend to be associated with each other, to assess the degree of confounding that would exist in conventional epidemiological studies compared with mendelian randomization studies.

Methods and Findings

We estimated pairwise correlations between nongenetic baseline variables and genetic variables in a cross-sectional study comparing the number of correlations that were statistically significant at the 5%, 1%, and 0.01% level (α = 0.05, 0.01, and 0.0001, respectively) with the number expected by chance if all variables were in fact uncorrelated, using a two-sided binomial exact test. We demonstrate that behavioural, socioeconomic, and physiological factors are strongly interrelated, with 45% of all possible pairwise associations between 96 nongenetic characteristics (n = 4,560 correlations) being significant at the p < 0.01 level (the ratio of observed to expected significant associations was 45; p-value for difference between observed and expected < 0.000001). Similar findings were observed for other levels of significance. In contrast, genetic variants showed no greater association with each other, or with the 96 behavioural, socioeconomic, and physiological factors, than would be expected by chance.

Conclusions

These data illustrate why observational studies have produced misleading claims regarding potentially causal factors for disease. The findings demonstrate the potential power of a methodology that utilizes genetic variants as indicators of exposure level when studying environmentally modifiable risk factors.     

Environmental exposure and mitochondrial epigenetics: study design and analytical challenges

The environment can influence human health and disease in many harmful ways. Many epidemiological studies have been conducted with the aim of elucidating the association between environmental exposure and human disease at the molecular and pathological levels, and such associations can often be through induced epigenetic changes. One such mechanism for this is through environmental factors increasing oxidative stress in the cell, and this stress can subsequently lead to alterations in DNA molecules. The two cellular organelles that contain DNA are the nucleus and mitochondria, and the latter are particularly sensitive to oxidative stress, with mitochondrial functions often disrupted by increased stress. There has been a substantial increase over the past decade in the number of epigenetic studies investigating the impact of environmental exposures upon genomic DNA, but to date there has been insufficient attention paid to the impact upon mitochondrial epigenetics in studying human disease with exposure to environment. Here, in this review, we will discuss mitochondrial epigenetics with regard to epidemiological studies, with particular consideration given to study design and analytical challenges. Furthermore, we suggest future directions and perspectives in the field of mitochondrial epigenetic epidemiological studies.     

Identifying genetic risk factors for osteoporosis

Over the past decades epidemiological research of so-called "complex" diseases, i.e., common age-related disorders such as cancer, cardiovascular disease, diabetes, and osteoporosis, has identified anthropometric, behavioural, and serum parameters as risk factors. Recently, genetic polymorphisms have gained considerable interest, propelled by the Human Genome Project and its sequela that have identified most genes and uncovered a plethora of polymorphic variants, some of which embody the genetic risk factors. In all fields of complex disease genetics (including osteoporosis) progress in identifying these genetic factors has been hampered by often controversial results. Because of the small effect size for each individual risk polymorphism, this is mostly due to low statistical power and limitations of analytical methods. Genome-wide scanning approaches can be used to find the responsible genes. It is by now clear that linkage analysis is not suitable for this, but genome-wide association analysis has much better possibilities, as is illustrated by successful identification of risk alleles for several complex diseases. Candidate gene association analysis followed by replication and prospective multi-centred meta-analysis, is currently the best way forward to identify genetic markers for complex traits, such as osteoporosis. To accomplish this, we need large (global) collaborative studies using standardized methodology and definitions, to quantify by meta-analysis the subtle effects of the responsible gene variants.     

Bayesian Detection of Causal Rare Variants under Posterior Consistency

Identification of causal rare variants that are associated with complex traits poses a central challenge on genome-wide association studies. However, most current research focuses only on testing the global association whether the rare variants in a given genomic region are collectively associated with the trait. Although some recent work, e.g., the Bayesian risk index method, have tried to address this problem, it is unclear whether the causal rare variants can be consistently identified by them in the small--large- situation. We develop a new Bayesian method, the so-called Bayesian Rare Variant Detector (BRVD), to tackle this problem. The new method simultaneously addresses two issues: (i) (Global association test) Are there any of the variants associated with the disease, and (ii) (Causal variant detection) Which variants, if any, are driving the association. The BRVD ensures the causal rare variants to be consistently identified in the small--large- situation by imposing some appropriate prior distributions on the model and model specific parameters. The numerical results indicate that the BRVD is more powerful for testing the global association than the existing methods, such as the combined multivariate and collapsing test, weighted sum statistic test, RARECOVER, sequence kernel association test, and Bayesian risk index, and also more powerful for identification of causal rare variants than the Bayesian risk index method. The BRVD has also been successfully applied to the Early-Onset Myocardial Infarction (EOMI) Exome Sequence Data. It identified a few causal rare variants that have been verified in the literature.     

Combining environmental exposure and genetic effect measurements in health outcome assessment.

The presence of overwhelming difficulties in assessing the extent or even the presence of a causal association between modern environmental exposures and disease has promoted the use of more complex models in the design of human biomonitoring studies. The concatenation of environmental exposure, genetic effect and individual susceptibility is a key issue in the assessment of risks for populations exposed to environmental pollutants. The use of a biological event laying in the causal pathway from exposure to outcome as surrogate end-point of disease, can potentially anticipate clinical diagnosis, offering a number of possibilities for application of preventive measures. Numerous biomarkers are currently employed to study human populations exposed to environmental carcinogens, among these, the frequency of chromosomal aberration (CA) in peripheral blood lymphocytes has the most abundant literature linking a genetic effect with the occurrence of cancer. Findings from recent epidemiological studies which have followed-up a large group of healthy subjects screened for CAs have lent further support to the use of chromosomal breakage as a relevant biomarker of cancer risk. The applicability of surrogate end-points of cancer on an individual basis thus far seems to be limited to few examples. On the other hand, from a public health outlook, increases in the frequency of surrogate end-points are suggestive of an increased risk of cancer, and for validated biomarkers such as CAs intervention policies and actions in exposed populations showing increased frequency of these end-points should be always recommended.     

Gout. Hyperuricemia and cardiovascular disease: how strong is the evidence for a causal link?

An association between high levels of serum urate and cardiovascular disease has been proposed for many decades. However, it was only recently that compelling basic science data, small clinical trials, and epidemiological studies have provided support to the idea of a true causal effect. In this review we present recently published data that study the association between hyperuricemia and selected cardiovascular diseases, with a final conclusion about the possibility of this association being causal.     

Risk assessment issues in breast cancer

Breast cancer is the most common malignancy affecting women, and its incidence has been increasing in many countries. The aetiology of breast cancer is poorly understood, so there is concern as to which factors in our environment or lifestyle are responsible for the increase. There is a need for reliable risk assessment, which involves the steps of hazard identification, hazard evaluation, exposure evaluation and risk estimation. Short-term laboratory tests and long-term tests in animals are useful for priority-setting, but quantitative human risk assessment should preferably involve observations of humans. Epidemiological studies vary in the degree of reliance that can be placed on their results. The main types of epidemiological investigation are illustrated by recent examples from the literature on breast cancer. Careful judgement is required in assessing whether any association between a factor and a disease is likely to be causal. The injectable contraceptive, depot medroxyprogesterone acetate (DMPA, ‘Depo-Provera’), has been controversial because it caused malignant mammary tumours in beagle dogs. Two recent case-control studies found no overall association between DMPA and the risk of breast cancer in women. There was some evidence of increased risk in certain sub-groups of women, which could be interpreted with more confidence if there were a better understanding of the biology of human breast cancer. Nevertheless, the results do not support the prediction from beagle experiments that DMPA might increase the overall risk of breast cancer.     

Prediction of individual genetic risk of complex disease

Most common diseases are caused by multiple genetic and environmental factors. In the last 2 years, genome-wide association studies (GWAS) have identified polymorphisms that are associated with risk to common disease, but the effect of any one risk allele is typically small. By combining information from many risk variants, will it be possible to predict accurately each individual person's genetic risk for a disease? In this review we consider the lessons from GWAS and the implications for genetic risk prediction to common disease. We conclude that with larger GWAS sample sizes or by combining studies, accurate prediction of genetic risk will be possible, even if the causal mutations or the mechanisms by which they affect susceptibility are unknown.     

The short die young: the interrelationship between stature and longevity-evidence from skeletal remains

It has long been observed that tall people display longer life spans. The current data were employed to verify this association within the bioarchaeological context. To this end, stature and its association with age-at-death were analyzed in a pooled sample of 2,923 skeletons. Height was estimated from proxy indicators based on the maximum length of the humerus, radius, femur, and tibia. Stature estimation followed the procedure outlined by Pearson ([1899] Philos. Trans. R. Soc. Lond. [A] 192:169-244), incorporating minor modifications by R?sing ([ 1988] Handbuch der vergleichenden Biologie des Menschen; Stuttgart: Gustave Fischer, p 586-600). Individual age estimates were classified into three mutually exclusive age groups: 20-39 years (591 males, 667 females), 40-59 years (876 males, 499 females), and 60+ years (171 males, 119 females). The results document that both sexes display a statistically significant inverse relationship between adult height and age-at-death (males, P < 0.01; females, P < 0.05). Taking an epidemiological approach, the risk model implies that the estimated odds of survival beyond age 40 improve by approximately 16% for 1 SD in bone length. However, not all bones may be equally adept at displaying the association. The radius failed to support the positive association between stature and longevity, which may be indicative of a relatively greater contribution of environmental factor to radius length. Overall, the relationship between body height and longevity is not causal but coincidental: mitigated by diverse environmental factors such as nutrition, socioeconomic stressors, and disease load.     

Special feature for the Olympics: effects of exercise on the immune system: overview of the epidemiology of exercise immunology

The application of epidemiological methods to exercise immunology is reviewed briefly, with particular reference to the possible influences of physical activity, exercise and training on susceptibility to upper respiratory infections. Available reports are arbitrarily rated in terms of limiting factors: the quality of the assessment of physical activity, the precision of diagnosis of upper respiratory infection and overall methodology. The pattern of physical activity has often been clearly established but, in part because of the problems associated with the competitive environment, assessments of infection and overall methodology have often been less than optimal. Although there is some evidence that susceptibility to infection is increased by either a single bout of very heavy activity or a period of heavy training, reports are far from unanimous, and in certain respects fail to meet the classical epidemiological criteria of a causal relationship. The issue is important to both the health and the success of the international competitor, and merits definitive investigation, using optimal methods to assess both activity patterns and infection.     

Detailed analysis of the relative power of direct and indirect association studies and the implications for their interpretation

OBJECTIVES: Genetic association studies are usually based upon restricted sets of 'tag' markers selected to represent the total sequence variation. Tag selection is often determined by some threshold for the r(2) coefficients of linkage disequilibrium (LD) between tag and untyped markers, it being widely assumed that power to detect an effect at the untyped sites is retained by typing the tag marker in a sample scaled by the inverse of the selected threshold (1/r(2)). However, unless only a single causal variant occurs at a locus, it has been shown [Eur J Hum Genet 2006;14:426-437] that significant power loss can occur if this principle is applied. We sought to investigate whether unexpected loss of power might be an exceptional case or more general concern. In the absence of detailed knowledge about the genetic architecture at complex disease loci, we developed a mathematical approach to test all possible situations. METHODS: We derived mathematical formulae allowing the calculation of all possible odds ratios (OR) at a tag marker locus given the effect size that would be observed by typing a second locus and the r(2) between the two loci. For a range of allele frequencies, r(2) between loci, and strengths of association at the causal locus (OR from 0.5 to 2) that we consider realistic for complex disease loci, we next determined the sample sizes that would be necessary to give equivalent power to detect association by genotyping tag and causal loci and compared these with the sample sizes predicted by applying 1/r(2). RESULTS: Under most of the hypothetical scenarios we examined, the calculated sample sizes required to maintain power by typing markers that tag the causal locus at even moderately high r(2) (0.8) were greater than that calculated by applying 1/r(2). Even in populations with apparently similar measurements of allele frequency, LD structure, and effect size at the susceptibility allele, the required sample size to detect association with a tag marker can vary substantially. We also show that in apparently similar populations, associations to either allele at the tag site are possible. CONCLUSIONS: Indirect tests of association are less powered than sizes predicted by applying 1/r(2) in the majority of hypothetical scenarios we examined. Our findings pertain even for what we consider likely to be larger than average effect sizes in complex diseases (OR = 1.5-2) and even for moderately high r(2) values between the markers. Until a substantial number of disease genes have been identified through methods that are not based on tagging, and therefore biased towards those situations most favourable to tagging, it is impossible to know how the true scenarios are distributed across the range of possible scenarios. Nevertheless, while association designs based upon tag marker selection by necessity are the tool of choice for de novo gene discovery, our data suggest power to initially detect association may often be less than assumed. Moreover, our data suggest that to avoid genuine findings being subsequently discarded by unpredictable losses of power, follow up studies in other samples should be based upon more detailed analyses of the gene rather than simply on the tag SNPs showing association in the discovery study.     

Obesogens and obesity—An alternative view?

It is accepted that diet is a major contributor to the obesity epidemic, but environmental ‘obesogenic’ chemicals have also been suggested recently as playing a role, based on in vitro, animal and epidemiological studies. Using two such ‘obesogen’ examples (bisphenol A, certain phthalate esters), we argue that their association with obesity and obesity‐related disorders in humans could be circumstantial, and thus non‐causal, because a Western style diet increases exposure to these compounds. This possibility needs to be addressed before further (confounded) epidemiological studies on ‘obesogens’ are undertaken.     

Parent–child pair design for detecting gene–environment interactions in complex diseases

It is becoming clear that the etiology of complex diseases involves not only genetic and environmental factors but also gene–environment (GE) interactions. Therefore, it is important to take account of all these factors to improve the power of an epidemiological study design. We propose here a novel parent–child pair (PCP) design for this purpose. In comparison with conventional designs, this approach has the following advantages: (a) PCP is a 4 × 16 design consisting of pairs of parent–child (PC) genotype statuses, PC exposure statuses and PC disease statuses. Therefore, it utilizes more information than the traditional approaches in association studies; (b) It can determine whether findings in studies of association between disease and genetic or environmental factors and their interaction are spurious, arising from Hardy–Weinberg disequilibrium or the other factors; (c) Since the information from both parents and children of the PC pairs are used in this design, it has high power for detecting association of candidate gene, exposure with a complex disease and GE interaction. We also present a set of estimates of relative risks of candidate genes, exposures and GE interactions under the multiplicative model and a method for computing the sample size requirements to test for these relative risks in the context of the PCP design.     

Mendelian randomization in health research: Using appropriate genetic variants and avoiding biased estimates

Mendelian randomization methods, which use genetic variants as instrumental variables for exposures of interest to overcome problems of confounding and reverse causality, are becoming widespread for assessing causal relationships in epidemiological studies. The main purpose of this paper is to demonstrate how results can be biased if researchers select genetic variants on the basis of their association with the exposure in their own dataset, as often happens in candidate gene analyses. This can lead to estimates that indicate apparent “causal” relationships, despite there being no true effect of the exposure. In addition, we discuss the potential bias in estimates of magnitudes of effect from Mendelian randomization analyses when the measured exposure is a poor proxy for the true underlying exposure. We illustrate these points with specific reference to tobacco research.     

Gene-Gene and Gene-Environment Interactions in Meta-Analysis of Genetic Association Studies

Extensive genetic studies have identified a large number of causal genetic variations in many human phenotypes; however, these could not completely explain heritability in complex diseases. Some researchers have proposed that the “missing heritability” may be attributable to gene–gene and gene–environment interactions. Because there are billions of potential interaction combinations, the statistical power of a single study is often ineffective in detecting these interactions. Meta-analysis is a common method of increasing detection power; however, accessing individual data could be difficult. This study presents a simple method that employs aggregated summary values from a “case” group to detect these specific interactions that based on rare disease and independence assumptions. However, these assumptions, particularly the rare disease assumption, may be violated in real situations; therefore, this study further investigated the robustness of our proposed method when it violates the assumptions. In conclusion, we observed that the rare disease assumption is relatively nonessential, whereas the independence assumption is an essential component. Because single nucleotide polymorphisms (SNPs) are often unrelated to environmental factors and SNPs on other chromosomes, researchers should use this method to investigate gene–gene and gene–environment interactions when they are unable to obtain detailed individual patient data.     

Legionella: from environmental habitats to disease pathology, detection and control

Studies on Legionella show a continuum from environment to human disease. Legionellosis is caused by Legionella species acquired from environmental sources, principally water sources such as cooling towers, where Legionella grows intracellularly in protozoa within biofilms. Aquatic biofilms, which are widespread not only in nature, but also in medical and dental devices, are ecological niches in which Legionella survives and proliferates and the ultimate sources to which outbreaks of legionellosis can be traced. Invasion and intracellular replication of L. pneumophila within protozoa in the environment play a major role in the transmission of Legionnaires' disease. Protozoa provide the habitats for the environmental survival and reproduction of Legionella species. L. pneumophila proliferates intracellularly in various species of protozoa within vacuoles studded with ribosomes, as it also does within macrophages. Growth within protozoa enhances the environmental survival capability and the pathogenicity (virulence) of Legionella . The growth requirements of Legionella , the ability of Legionella to enter a viable non-culturable state, the association of Legionella with protozoa and the occurrence of Legionella within biofilms complicates the detection of Legionella and epidemiological investigations of legionellosis. Polymerase chain reaction (PCR) methods have been developed for the molecular detection of Legionella and used in environmental and epidemiological studies. Various physical and chemical disinfection methods have been developed to eliminate Legionella from environmental sources, but gaining control of Legionella in environmental waters, where they are protected from disinfection by growing within protozoa and biofilms, remains a challenge, and one that must be overcome in order to eliminate sporadic outbreaks of legionellosis.     

Quantifying the underestimation of relative risks from genome-wide association studies

Genome-wide association studies (GWAS) have identified hundreds of associated loci across many common diseases. Most risk variants identified by GWAS will merely be tags for as-yet-unknown causal variants. It is therefore possible that identification of the causal variant, by fine mapping, will identify alleles with larger effects on genetic risk than those currently estimated from GWAS replication studies. We show that under plausible assumptions, whilst the majority of the per-allele relative risks (RR) estimated from GWAS data will be close to the true risk at the causal variant, some could be considerable underestimates. For example, for an estimated RR in the range 1.2-1.3, there is approximately a 38% chance that it exceeds 1.4 and a 10% chance that it is over 2. We show how these probabilities can vary depending on the true effects associated with low-frequency variants and on the minor allele frequency (MAF) of the most associated SNP. We investigate the consequences of the underestimation of effect sizes for predictions of an individual's disease risk and interpret our results for the design of fine mapping experiments. Although these effects mean that the amount of heritability explained by known GWAS loci is expected to be larger than current projections, this increase is likely to explain a relatively small amount of the so-called "missing" heritability.     

Detecting disease associations due to linkage disequilibrium using haplotype tags: a class of tests and the determinants of statistical power

In the 'indirect' method of detecting genetic associations between a trait and a DNA variant, we type several markers in a gene or chromosome region of linkage disequilibrium. If there is association between markers and the trait, we presume the existence of one or more causal polymorphisms in the region. In order to obtain a sufficiently dense set of markers it will almost always be necessary to use single nucleotide polymorphisms (SNPs). Although there is an emerging literature on methods for choosing an optimal set of 'haplotype tag SNPs' (htSNPs) to detect association between a genetic region and a trait, less attention has been given to the problem of how such studies should be analysed when completed, and how the initial data which was used to select the htSNPs should be incorporated into the analysis. This paper discusses this problem for both population- and family-based association studies. The role of the R2 measure of association between a causal locus and various methods of scoring of marker haplotypes is highlighted. In most cases, the simplest method of scoring (locus coding), which does not require phase resolution, is shown generally to be more powerful than scoring methods that include haplotype information. A new 'multi-locus TDT' is also proposed.     

Fish disease as a monitor for marine pollution: the case of the North Sea

Summary The use of fish diseases to monitor marine pollution is reviewed and evaluated, with particular reference to the North Sea and associated waters. Criteria for epidemiological surveys are outlined, an international overview of research is given, and recent studies in the North Sea area are described and evaluated.The basic approach is to identify spatial and temporal patterns of disease prevalence, which can be related to pollution. A major obstacle is to distinguish effects of pollution from those of other variables, especially as most diseases appear to have a multifactorial aetiology. Field studies can be evaluated against a number of criteria: these include the accuracy and precision of prevalence estimates, the extent to which possible causal factors other than pollution are taken into account, and whether or not exposure of the study population to pollution is measured directly.A distinction can be made between intensive, thorough studies, which frequently use a histopathological approach, and the more extensive surveys of large numbers of fish for grossly observable lesions. Broadly speaking, North American research has emphasized the former approach, and research in the North Sea the latter. Nevertheless, although the most comprehensive evidence for a causal relationship between disease and pollution has been gathered in North America, there are also good examples from the North Sea area, particularly in local areas with distinct sources of pollution. The data from wider-ranging surveys are more ambiguous: while some provide circumstantial evidence for a role of pollution, the apparent complexity of disease aetiology and the limitations of the epidemiological approach may prevent any clear demonstration of pollution as a cause over wide geographical areas. Extensive surveys are nevertheless useful for detecting long-term trends in disease prevalence and hot-spots of anomalously high prevalence, and for examining the relationship between disease and a complex of environmental variables.For the future, greater emphasis should be placed on the recording of liver lesions, on the measurement of exposure to pollution, and on experimental work.     

Genetics of thrombophilia: impact on atherogenesis

PURPOSE OF REVIEW: The goal of this review is to present an update on basic and epidemiological findings associating variants in prothrombotic genes with atherogenesis and atherothrombotic disease. RECENT FINDINGS: The relation between atherosclerosis and thrombosis has long been recognized but only recently has it been understood that certain hemostatic factors affect not only thrombus formation, but also have a direct atherogenic role. Atherosclerosis is a complex disorder that results from the interaction of multiple genetic and environmental factors. Numerous polymorphisms and mutations in genes related to the hemostatic system and to vascular redox determinants that modulate nitric oxide bioavailability have been identified in the past decade; their role in atherogenesis and the risk of cardiovascular disease, however, remain uncertain. We will discuss the functional implications and association with disease risk of polymorphisms in coagulation factors (fibrinogen, prothrombin, and factor V); fibrinolytic factors (plasminogen activator inhibitor 1 and lipoprotein(a)); platelet surface receptors; and vascular redox determinants (methylenetetrahydrofolate reductase, endothelial nitric oxide synthase, and the antioxidant enzymes cellular glutathione peroxidase and paraoxonase). SUMMARY: Overall, these genetic variants have a modest effect on risk when considered individually but gain potency when acting synergistically with other genetic or environmental risk factors. We conclude that a better characterization of these interactions, in addition to the identification of potential novel genetic determinants, constitute key issues in the future understanding of the pathogenesis of atherothrombosis.