Gene × environment interaction studies have not properly controlled for potential confounders: the problem and the...

Area of Science:

• Behavioral Genetics
• Psychiatric Genetics
• Statistical Genetics

Background:

• Candidate gene × environment (G × E) interaction research investigates how genetic variations modify environmental influences on outcomes.
• Nonexperimental nature of G × E studies raises concerns about confounding variables (e.g., ethnicity, gender, age, socioeconomic status) mimicking true interactions.
• Current practices often fail to adequately control for these confounders in statistical models.

Purpose of the Study:

• To analytically demonstrate the correct method for controlling confounders in G × E interaction analyses.
• To highlight the prevalence of improper covariate control in the existing G × E literature.
• To emphasize the implications of inadequate confounder control for the validity of G × E findings.

Main Methods:

• Analytical demonstration of statistical modeling for G × E interactions.
• Examination of general linear models and the necessity of including covariate-environment and covariate-gene interaction terms.
• Review of common practices in the G × E interaction literature.

Main Results:

• Standard methods of including covariates do not adequately control for their influence on G × E interactions.
• Proper control requires including interaction terms between covariates and both genetic and environmental factors.
• The analysis reveals that improper covariate control is widespread in published G × E research.

Conclusions:

• Many detected G × E interactions may be artifacts of uncontrolled confounding variables.
• Researchers must adopt appropriate statistical models to ensure the validity of G × E findings.
• Correctly accounting for confounders is crucial for advancing our understanding of gene-environment interplay in health and disease.