The virtues of a deliberately mis-specified disease model in demonstrating a gene-environment interaction

Interactions between exposure to polycyclic aromatic hydrocarbons and xenobiotic metabolism genes, and risk of breast cancer

PURPOSE

Polycyclic aromatic hydrocarbons (PAHs) are a group of environmental pollutants associated with multiple cancers, including female breast cancer. Several xenobiotic metabolism genes (XMGs), including the CYP450 family, play an important role in activating and detoxifying PAHs, and variations in the activity of the enzymes they encode can impact this process. This study aims to examine the association between XMGs and breast cancer, and to assess whether these variants modify the effects of PAH exposure on breast cancer risk.

METHODS

In a case-control study in Vancouver, British Columbia, and Kingston, Ontario, 1037 breast cancer cases and 1046 controls had DNA extracted from blood or saliva and genotyped for 138 single nucleotide polymorphisms (SNPs) and tagSNPs in 27 candidate XMGs. Occupational PAH exposure was assessed using a measurement-based job-exposure matrix.

RESULTS

An association between genetic variants and breast cancer was observed among six XMGs, including increased risk among the minor allele carriers of AKR1C3 variant rs12387 (OR 2.71, 95% CI 1.42-5.19) and AKR1C4 variant rs381267 (OR 2.50, 95% CI 1.23-5.07). Heterogeneous effects of occupational PAH exposure were observed among carriers of AKR1C3/4 variants, as well as the PTGS2 variant rs5275.

CONCLUSION

Our findings support an association between SNPs of XMGs and female breast cancer, including novel genetic variants that modify the toxicity of PAH exposure. These results highlight the interplay between genetic and environmental factors, which can be helpful in understanding the modifiable risks of breast cancer and its complex etiology.

Trade-offs of Personal Versus More Proxy Exposure Measures in Environmental Epidemiology

The technological ability to make personal measurements of toxicant exposures is growing rapidly. While this can decrease measurement error and therefore help reduce attenuation of effect estimates, we argue that as measures of exposure or dose become more personal, threats to validity of study findings can increase in ways that more proxy measures may avoid. We use directed acyclic graphs (DAGs) to describe conditions where confounding is introduced by use of more personal measures of exposure and avoided via more proxy measures of personal exposure or target tissue dose. As exposure or dose estimates are more removed from the individual, they become less susceptible to biases from confounding by personal factors that can often be hard to control, such as personal behaviors. Similarly, more proxy exposure estimates are less susceptible to reverse causation. We provide examples from the literature where adjustment for personal factors in analyses that use more proxy exposure estimates have little effect on study results. In conclusion, increased personalized exposure assessment has important advantages for measurement accuracy, but it can increase the possibility of biases from personal factors and reverse causation compared with more proxy exposure estimates. Understanding the relation between more and less proxy exposures, and variables that could introduce confounding are critical components to study design.

Rethinking cumulative exposure in epidemiology, again

The use of cumulative exposure, the product of intensity and duration, has enjoyed great popularity in epidemiology of chronic diseases despite numerous known caveats in its interpretation. We briefly review the history of use of cumulative exposure in epidemiology and propose an alternative method for relating time-integrated exposures to health risks. We argue, as others before us have, that cumulative exposure metrics obscures the interplay of exposure intensity and duration. We propose to use a computationally simple alternative in which duration and intensity of exposure are modelled as a main effect and their interaction, cumulative exposure, only be added if there is evidence of deviation from this additive model. We also consider the Lubin-Caporaso model of interplay of exposure intensity and duration. The impact of measurement error in intensity on model selection was also examined. The value of this conceptualization is demonstrated using a simulation study and further illustrated in the context of respiratory health and occupational exposure to latex dust. We demonstrate why cumulative exposure has been so popular because the cumulative exposure metric per se gives a robust answer to the existence of an association, regardless of the underlying true mechanism of disease. Treating cumulative exposure as the interaction of main effects of exposure duration and intensity enables epidemiologists to derive more information about mechanism of disease then fitting cumulative exposure metric by itself, and without the need to collect additional data. We propose that the practice of fitting duration, intensity and cumulative exposure separately to epidemiologic data should lead to conceptualization of cumulative exposure as interaction of main effects of duration and intensity of exposure.

The role of genotypes that modify the toxicity of chemical mutagens in the risk for myeloproliferative neoplasms

BACKGROUND

The etiology of myeloproliferative neoplasms (MPN) (polycythemia vera; essential thrombocythemia; primary myelofibrosis) is unknown, however they are associated with a somatic mutation--JAK2 V617F--suggesting a potential role for environmental mutagens.

METHODS

We conducted a population-based case-control study in three rural Pennsylvania counties of persons born 1921-1968 and residing in the area between 2000-2008. Twenty seven MPN cases and 292 controls were recruited through random digit dialing. Subjects were genotyped and odds ratios estimated for a select set of polymorphisms in environmentally sensitive genes that might implicate specific environmental mutagens if found to be associated with a disease.

RESULTS

The presence of NAT2 slow acetylator genotype, and CYP1A2, GSTA1, and GSTM3 variants were associated with an average 3-5 fold increased risk.

CONCLUSIONS

Exposures, such as to aromatic compounds, whose toxicity is modified by genotypes associated with outcome in our analysis may play a role in the environmental etiology of MPNs.

Aggregation of exposure level and probability into a single metric in job-exposure matrices creates bias

Job-exposure matrices (JEMs) are often used in occupational epidemiological studies to provide an exposure estimate for a typical person in a 'job' during a particular time period. A JEM can produce exposure estimates on a variety of scales, such as (but not limited to) binary assessments of presence or absence of exposure, ordinal ranking of exposure level and frequency, and quantitative exposure estimates of exposure intensity and frequency. Specifically, one popular approach to construct a JEM, engendered in a Finnish job exposure matrix (FINJEM), provides a probability that a worker within an occupational group is exposed and an estimate of intensity of exposure among the exposed workers within this occupation. Often the product of the probability and intensity (aka level) is used to obtain the estimate of exposure for the epidemiological analyses. This procedure aggregates exposure across exposed and non-exposed individuals and the effect of this particular procedure on epidemiological analyses has never been studied. We developed a theoretical framework for understanding how these aggregate exposure estimates relate to true exposure (either unexposed or log-normally distributed for 'exposed'), assuming that there is no uncertainty about estimates of level and probability of exposure. Theoretical derivations show that multiplying occupation-specific exposure level and probability of non-zero exposure results in both systematic and differential measurement errors. Simulations demonstrated that under certain conditions bias in odds ratios in a cohort study away from the null are possible and that this bias is smaller when (a) arithmetic rather than geometric mean is used to assess exposure level and (b) exposure level and prevalence are positively correlated. We illustrate the potential impact of using the specified JEM in a simulation based on a case-control study of non-Hodgkin lymphoma and exposure to ionizing and non-ionizing radiation. Inflation of standard errors in the log-odds was observed as well as bias away from null for two out of three specific exposures/data structures. Overall, it is clear that influence of the phenomenon we studied on epidemiological results is complex and difficult to predict, being influenced a great deal by the structure of data. We recommend exploring the influence of JEMs that use the product of exposure level and probability in epidemiological analyses through simulations during planning of such studies to assess both the expected extent of the potential bias in risk estimates and impact on power. The SAS and R code required to implement such simulations are provided. All our calculations are either theoretical or based on simulated data.

A Bayesian mixture modeling approach for assessing the effects of correlated exposures in case-control studies

Predisposition to a disease is usually caused by cumulative effects of a multitude of exposures and lifestyle factors in combination with individual susceptibility. Failure to include all relevant variables may result in biased risk estimates and decreased power, whereas inclusion of all variables may lead to computational difficulties, especially when variables are correlated. We describe a Bayesian Mixture Model (BMM) incorporating a variable-selection prior and compared its performance with logistic multiple regression model (LM) in simulated case-control data with up to twenty exposures with varying prevalences and correlations. In addition, as a practical example we re analyzed data on male infertility and occupational exposures (Chaps-UK). BMM mean-squared errors (MSE) were smaller than of the LM, and were independent of the number of model parameters. BMM type I errors were minimal (≤1), whereas for the LM this increased with the number of parameters and correlation between exposures. The numbers of type II errors were comparable. Re analysis of Chaps-UK data demonstrated more convincingly than by using a LM that occupational exposure to glycol ethers and VOCs are likely risk factors for male infertility. This BMM proves an appealing alternative to standard logistic regression when dealing with the analysis of (correlated) exposures in case-control studies.

Impact of pesticide exposure misclassification on estimates of relative risks in the Agricultural Health Study

BACKGROUND

The Agricultural Health Study (AHS) is a prospective study of licensed pesticide applicators and their spouses in Iowa and North Carolina. We evaluate the impact of occupational pesticide exposure misclassification on relative risks using data from the cohort and the AHS Pesticide Exposure Study (AHS/PES).

METHODS

We assessed the impact of exposure misclassification on relative risks using the range of correlation coefficients observed between measured post-application urinary levels of 2,4-dichlorophenoxyacetic acid (2,4-D) and a chlorpyrifos metabolite and exposure estimates based on an algorithm from 83 AHS pesticide applications.

RESULTS

Correlations between urinary levels of 2,4-D and a chlorpyrifos metabolite and algorithm estimated intensity scores were about 0.4 for 2,4-D (n=64), 0.8 for liquid chlorpyrifos (n=4) and 0.6 for granular chlorpyrifos (n=12). Correlations of urinary levels with kilograms of active ingredient used, duration of application, or number of acres treated were lower and ranged from -0.36 to 0.19. These findings indicate that a priori expert-derived algorithm scores were more closely related to measured urinary levels than individual exposure determinants evaluated here. Estimates of potential bias in relative risks based on the correlations from the AHS/PES indicate that non-differential misclassification of exposure using the algorithm would bias estimates towards the null, but less than that from individual exposure determinants.

CONCLUSIONS

Although correlations between algorithm scores and urinary levels were quite good (ie, correlations between 0.4 and 0.8), exposure misclassification would still bias relative risk estimates in the AHS towards the null and diminish study power.

Bayesian inference of gene-environment interaction from incomplete data: what happens when information on environment is disjoint from data on gene and disease?

Inference in gene-environment studies can sometimes exploit the assumption of mendelian randomization that genotype and environmental exposure are independent in the population under study. Moreover, in some such problems it is reasonable to assume that the disease risk for subjects without environmental exposure will not vary with genotype. When both assumptions can be invoked, we consider the prospects for inferring the dependence of disease risk on genotype and environmental exposure (and particularly the extent of any gene-environment interaction), without detailed data on environmental exposure. The data structure envisioned involves data on disease and genotype jointly, but only external information about the distribution of the environmental exposure in the population. This is relevant as for many environmental exposures individual-level measurements are costly and/or highly error-prone. Working in the setting where all relevant variables are binary, we examine the extent to which such data are informative about the interaction, via determination of the large-sample limit of the posterior distribution. The ideas are illustrated using data from a case-control study for bladder cancer involving smoking behaviour and the NAT2 genotype.

Comparison of occupational exposure assessment methods in a case-control study of lead, genetic susceptibility and risk of adult brain tumours

OBJECTIVES

There is great interest in evaluating gene-environment interactions with chemical exposures, but exposure assessment poses a unique challenge in case-control studies. Expert assessment of detailed work history data is usually considered the best approach, but it is a laborious and time-consuming process. We set out to determine if a less intensive method of exposure assessment (a job exposure matrix (JEM)) would produce similar results to a previous analysis that found evidence of effect modification of the association between expert-assessed lead exposure and risk of brain tumours by a single nucleotide polymorphism in the ALAD gene (rs1800435).

METHODS

We used data from a study of 355 patients with glioma, 151 patients with meningioma and 505 controls. Logistic regression models were used to examine associations between brain tumour risk and lead exposure and effect modification by genotype. We evaluated Cohen's κ, sensitivity and specificity for the JEM compared to the expert-assessed exposure metrics.

RESULTS

Although effect estimates were imprecise and driven by a small number of cases, we found evidence of effect modification between lead exposure and ALAD genotype when using expert- but not JEM-derived lead exposure estimates. κ Values indicated only modest agreement (<0.5) for the exposure metrics, with the JEM indicating high specificity (∼0.9) but poor sensitivity (∼0.5). Disagreement between the two methods was generally due to having additional information in the detailed work history.

CONCLUSION

These results provide preliminary evidence suggesting that high quality exposure data are likely to improve the ability to detect genetic effect modification.