Human Genetics

, Volume 131, Issue 10, pp 1591–1613

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

Authors

    • Department of EpidemiologyHarvard School of Public Health
  • Sharon Lutz
    • Departments of Epidemiology & Biostatistics and Informatics, Colorado School of Public HealthUniversity of Colorado Anschutz Medical Campus
  • Bärbel Maus
    • Systems and Modeling Unit, Montefiore InstituteUniversity of Liège
    • Bioinformatics and Modeling, GIGA-RUniversity of Liège
  • Eric J. Duell
    • Unit of Nutrition, Environment and Cancer, Epidemiology Research ProgramCatalan Institute of Oncology (ICO), Bellvitge Biomedical Research Institute (IDIBELL)
  • Tasha E. Fingerlin
    • Departments of Epidemiology & Biostatistics and Informatics, Colorado School of Public HealthUniversity of Colorado Anschutz Medical Campus
  • Nilanjan Chatterjee
    • Division of Cancer Epidemiology and GeneticsNational Cancer Institute
  • Peter Kraft
    • Department of EpidemiologyHarvard School of Public Health
    • Department of BiostatisticsHarvard School of Public Health
  • Kristel Van Steen
    • Systems and Modeling Unit, Montefiore InstituteUniversity of Liège
    • Bioinformatics and Modeling, GIGA-RUniversity of Liège
Review Paper

DOI: 10.1007/s00439-012-1192-0

Cite this article as:
Aschard, H., Lutz, S., Maus, B. et al. Hum Genet (2012) 131: 1591. doi:10.1007/s00439-012-1192-0

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.

Copyright information

© Springer-Verlag 2012