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The impact of exposure-biased sampling designs on detection of gene–environment interactions in case–control studies with potential exposure misclassification

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Abstract

With limited funding and biological specimen availability, choosing an optimal sampling design to maximize power for detecting gene-by-environment (G–E) interactions is critical. Exposure-enriched sampling is often used to select subjects with rare exposures for genotyping to enhance power for tests of G–E effects. However, exposure misclassification (MC) combined with biased sampling can affect characteristics of tests for G–E interaction and joint tests for marginal association and G–E interaction. Here, we characterize the impact of exposure-biased sampling under conditions of perfect exposure information and exposure MC on properties of several methods for conducting inference. We assess the Type I error, power, bias, and mean squared error properties of case-only, case–control, and empirical Bayes methods for testing/estimating G–E interaction and a joint test for marginal G (or E) effect and G–E interaction across three biased sampling schemes. Properties are evaluated via empirical simulation studies. With perfect exposure information, exposure-enriched sampling schemes enhance power as compared to random selection of subjects irrespective of exposure prevalence but yield bias in estimation of the G–E interaction and marginal E parameters. Exposure MC modifies the relative performance of sampling designs when compared to the case of perfect exposure information. Those conducting G–E interaction studies should be aware of exposure MC properties and the prevalence of exposure when choosing an ideal sampling scheme and method for characterizing G–E interactions and joint effects.

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Abbreviations

CC:

Case–control

CO:

Case-only

EB:

Empirical Bayes

G:

Genetic variant

E:

Environmental exposure

D:

Disease/outcome status

αg :

Marginal log-odds ratio associated with the genetic factor

MAg :

Marginal genetic association

αe :

Marginal log-odds ratio associated with the environmental factor

MAe :

Marginal environmental association

ORge :

Odds ratio for the association between the genetic and environmental variables in controls

βg :

Main effect log-odds ratio associated with the genetic factor

ORg :

Exp(βg)

βe :

Main effect log-odds ratio associated with the environmental factor

ORe :

Exp(βe)

βg×e :

Gene by environment interaction log-odds ratio

ORg×e :

Exp(βg×e)

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Acknowledgments

Support for this study was provided by National Science Foundation DMS 1007494, National Institutes of Health ES 20811, National Institutes of Health CA 156608, and National Institutes of Health CA 148107. Funding for SLS was provided by the National Human Genome Research Institute at the National Institutes of Health (T32 HG00040), the National Institute of Environmental Health Sciences at the National Institutes of Health (T32 ES013678), and a fellowship from the University of Michigan Rackham Graduate School. Funding for PB and BM was partially provided by the University of Michigan Cancer Center Support Grant NIH P30 CA 046592.

Conflict of interest

The authors declare no conflicts of interest.

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Authors and Affiliations

  1. Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA

    Stephanie L. Stenzel

  2. Department of Statistics, College of Literature, Science, and the Arts, University of Michigan, Ann Arbor, MI, USA

    Stephanie L. Stenzel

  3. Department of Biostatistics and Bioinformatics, Georgetown University, Washington, DC, USA

    Jaeil Ahn

  4. Department of Biostatistics, School of Public Health, University of Michigan, M4166 SPH II, 1415 Washington Heights, Ann Arbor, MI, 48109, USA

    Philip S. Boonstra & Bhramar Mukherjee

  5. Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, USA

    Stephanie L. Stenzel & Stephen B. Gruber

Authors
  1. Stephanie L. Stenzel
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  2. Jaeil Ahn
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  4. Stephen B. Gruber
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  5. Bhramar Mukherjee
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Correspondence to Stephanie L. Stenzel.

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Stenzel, S.L., Ahn, J., Boonstra, P.S. et al. The impact of exposure-biased sampling designs on detection of gene–environment interactions in case–control studies with potential exposure misclassification. Eur J Epidemiol 30, 413–423 (2015). https://doi.org/10.1007/s10654-014-9908-1

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  • DOI: https://doi.org/10.1007/s10654-014-9908-1

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