One of the take-home messages from this review is that interpreting results from gene-environment interaction studies is not straightforward. There are valid reasons why “real” gene-environment interaction effects might not be detected, why results might vary across studies, or why our statistics might yield “significant evidence” for gene-environment interaction when none really exists. An uncritical tally of whether specific gene-environment interaction effects replicate or not, without paying attention to issues such as the measurement of the outcome, measurement of the environment, statistics employed, sampling strategy, and sample size, across studies will undoubtedly lead to mixed results for any given gene-environment interaction effect in the literature. Statistics such as meta-analyses have become very popular in genetics, largely due to recognition of the incredibly large sample sizes it will take to identify genes of small effect. Meta-analytic techniques are particularly appropriate in this area, where genotypic data can be standardized across studies, and outcomes are often measured using standardized assessments (e.g., DSM diagnoses as assessed in structured clinical interviews). However, in the area of gene-environment interactions, where studies often have very different
