Challenges in the use of literature-based meta-analysis to examine gene-environment interactions

Am J Epidemiol. 2010 Jun 1;171(11):1225-32. doi: 10.1093/aje/kwq051. Epub 2010 Apr 20.


Statistical interactions between genes and environmental exposures with respect to disease outcomes may help to identify biologic mechanisms and pathways and inform behavioral interventions. The number of persons required for a single study to have sufficient statistical power to detect such interactions may be considered prohibitively large, making a meta-analysis of published literature an apparently attractive alternative. However, meta-analysis of gene-environment interactions using published literature is challenging, with the conclusions being likely to suffer from bias and lack of generalizability. The authors highlight these challenges and biases using an illustrative example: meta-analysis of interactions between the Pro12Ala variant of the peroxisome proliferator-activated receptor gamma (PPARgamma) gene and various diet and lifestyle factors in the risk of diabetes. The authors conclude that literature-based meta-analysis conducted to examine gene-environment interactions is unlikely to provide a meaningful quantitative conclusion. Alternative strategies are required, including analyses in scientific consortia established to assess main genetic effects, where individual participant data can be shared, allowing both greater power and consistency of analysis methods. However, these consortia are likely to be limited by lack of standardization of the measures of environmental factors. This issue may ultimately only be resolvable by the de novo establishment of large single or multicenter cohorts using comparable methods.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bias
  • Data Collection / methods
  • Data Collection / standards
  • Diabetes Mellitus, Type 2 / etiology
  • Diabetes Mellitus, Type 2 / genetics
  • Disease / etiology*
  • Environmental Exposure / adverse effects*
  • Epidemiologic Research Design
  • Genetic Predisposition to Disease* / epidemiology
  • Genetic Predisposition to Disease* / genetics
  • Genotype
  • Humans
  • Life Style
  • Meta-Analysis as Topic*
  • PPAR gamma / genetics


  • PPAR gamma