Gene-Environment Interaction: Overcoming Methodological Challenges

While interacting biological effects of genes and environmental exposures (G × E) form a natural part of the causal framework underlying disorders of human health, the detection of G × E relies on inference from statistical interactions observed at population level. The validity of such inference has been questioned because the presence or absence of statistical interaction depends on measurement scale and statistical model. Furthermore, the feasibility of G × E research is threatened by the fact that tests of statistical interaction require large samples and their power is substantially reduced by unreliability in the assessments of genes, environmental exposures and pathology. It is demonstrated that concerns about statistical models and scaling can be addressed by integration of observational and experimental data. Judicious selection of genes and environmental factors should limit multiple testing. To overcome the challenge of low statistical power, it is suggested to maximize the reliability of measurement, integrate prior knowledge under Bayesian framework and facilitate pooling of data across studies by use of standardized stratified reporting. Consistencies and discrepancies among studies can be exploited for methodological analysis and model specification.