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Empirical Hierarchical Bayes Approach to Gene-Environment Interactions: Development and Application to Genome-Wide Association Studies of Lung Cancer in TRICL

Authors

  • Melanie Sohns,

    1. Department of Genetic Epidemiology, University Medical Center, Georg-August University of Goettingen, Goettingen, Germany
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    • These authors contributed equally to the manuscript.

  • Elena Viktorova,

    1. Department of Genetic Epidemiology, University Medical Center, Georg-August University of Goettingen, Goettingen, Germany
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    • These authors contributed equally to the manuscript.

  • Christopher I. Amos,

    1. Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, Texas
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  • Paul Brennan,

    1. International Agency for Research on Cancer (IARC), Lyon, France
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  • Gord Fehringer,

    1. Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
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  • Valerie Gaborieau,

    1. International Agency for Research on Cancer (IARC), Lyon, France
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  • Younghun Han,

    1. Department of Epidemiology, University of Texas M.D. Anderson Cancer Center, Houston, Texas
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  • Joachim Heinrich,

    1. Institute of Epidemiology I, Helmholtz Zentrum München, Neuherberg, Germany
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  • Jenny Chang-Claude,

    1. Unit of Genetic Epidemiology, Division of Cancer Epidemiology, Deutsches Krebsforschungszentrum, Heidelberg, Germany
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  • Rayjean J. Hung,

    1. Samuel Lunenfeld Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
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  • Martina Müller-Nurasyid,

    1. Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology and Chair of Genetic Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
    2. Institute of Genetic Epidemiology, Helmholtz Zentrum München – German Research Center for Environmental Health, Neuherberg, Germany
    3. Department of Medicine I, University Hospital Grosshadern, Ludwig-Maximilians-Universität, Munich, Germany
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  • Angela Risch,

    1. Division of Epigenomics and Cancer Risk Factors, German Cancer Research Center, Heidelberg, Germany
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  • Juan Pablo Lewinger,

    1. Department of Preventive Medicine, University of Southern California, Los Angeles, California
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  • Duncan C. Thomas,

    1. Department of Preventive Medicine, University of Southern California, Los Angeles, California
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  • Heike Bickeböller

    Corresponding author
    1. Department of Genetic Epidemiology, University Medical Center, Georg-August University of Goettingen, Goettingen, Germany
    • Correspondence to: Heike Bickeböller, Department of Genetic Epidemiology, University Medical Center, Georg-August University of Goettingen, Humboldtallee 32, 37073 Goettingen, Germany. E-mail: hbickeb@gwdg.de

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ABSTRACT

The analysis of gene-environment (G × E) interactions remains one of the greatest challenges in the postgenome-wide association studies (GWASs) era. Recent methods constitute a compromise between the robust but underpowered case-control and powerful case-only methods. Inferences of the latter are biased when the assumption of gene-environment (G-E) independence in controls fails. We propose a novel empirical hierarchical Bayes approach to G × E interaction (EHB-GE), which benefits from greater rank power while accounting for population-based G-E correlation. Building on Lewinger et al.'s ([2007] Genet Epidemiol 31:871–882) hierarchical Bayes prioritization approach, the method first obtains posterior G-E correlation estimates in controls for each marker, borrowing strength from G-E information across the genome. These posterior estimates are then subtracted from the corresponding case-only G × E estimates. We compared EHB-GE with rival methods using simulation. EHB-GE has similar or greater rank power to detect G × E interactions in the presence of large numbers of G-E correlations with weak to strong effects or only a low number of such correlations with large effect. When there are no or only a few weak G-E correlations, Murcray et al.'s method ([2009] Am J Epidemiol 169:219–226) identifies markers with low G × E interaction effects better. We applied EHB-GE and competing methods to four lung cancer case-control GWAS from the Interdisciplinary Research in Cancer of the Lung/International Lung Cancer Consortium with smoking as environmental factor. A number of genes worth investigating were identified by the EHB-GE approach.

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