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Challenges and opportunities in genome-wide environmental interaction (GWEI) studies

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Abstract

The interest in performing gene–environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene–environment interaction studies and the technical challenges related to these studies—when the number of environmental or genetic risk factors is relatively small—has been described before. In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze genome-wide environmental interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for genome-wide association gene–gene interaction studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to “joining” two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.

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Acknowledgments

H. Aschard and P. Kraft were supported by grant NIH/NIDDK-R21 DK084529. B. Maus and K. Van Steen acknowledge research opportunities offered by the Belgian Network BioMAGNet (Bioinformatics and Modeling: from Genomes to Networks), funded by the Interuniversity Attraction Poles Program (Phase VI/4), initiated by the Belgian State, Science Policy Office. Their work was also supported in part by the IST Program of the European Community, under the PASCAL2 Network of Excellence (Pattern Analysis, Statistical Modeling and Computational Learning), IST-2007-216886. E.J. Duell was supported by the Spanish Ministry of Health (ISCIII RETICC RD06/0020). The scientific responsibility for this work rests with its authors.

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The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA

    Hugues Aschard & Peter Kraft

  2. Departments of Epidemiology & Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

    Sharon Lutz & Tasha E. Fingerlin

  3. Systems and Modeling Unit, Montefiore Institute, University of Liège, Liège, Belgium

    Bärbel Maus & Kristel Van Steen

  4. Bioinformatics and Modeling, GIGA-R, University of Liège, Liège, Belgium

    Bärbel Maus & Kristel Van Steen

  5. Unit of Nutrition, Environment and Cancer, Epidemiology Research Program, Catalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, Spain

    Eric J. Duell

  6. Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA

    Nilanjan Chatterjee

  7. Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

    Peter Kraft

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  1. Hugues Aschard
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  2. Sharon Lutz
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Correspondence to Hugues Aschard.

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S. Lutz and B. Maus contributed equally to this work.

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Aschard, H., Lutz, S., Maus, B. et al. Challenges and opportunities in genome-wide environmental interaction (GWEI) studies. Hum Genet 131, 1591–1613 (2012). https://doi.org/10.1007/s00439-012-1192-0

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