Research on how interactions between candidate genes and environmental factors influence psychiatric illnesses has generated enthusiasm but not many replicable findings. The authors discovered that the more closely a replication study matched the original research, the less likely it was to have similar results. Publication bias toward positive findings was apparent both in reports of novel findings and in replication studies. Another contributor to false discoveries in many studies of candidate genes is low statistical power due to small study groups or other design factors.

A Critical Review of the First 10 Years of Candidate Gene-by-Environment Interaction Research in Psychiatry

In this tutorial, we provide a broad introduction to the topic of interaction between the effects of exposures. We discuss interaction on both additive and multiplicative scales using risks, and we discuss their relation to statistical models (e.g. linear, log-linear, and logistic models). We discuss and evaluate arguments that have been made for using additive or multiplicative scales to assess interac- tion. We further discuss approaches to presenting interaction analyses, different mechanistic forms of interaction, when interaction is robust to unmeasured confounding, interaction for continuous out- comes, qualitative or "crossover" interactions, methods for attributing effects to interactions, case-only estimators of interaction, and power and sample size calculations for additive and multiplicative In this tutorial, we discuss the concept of interaction, some of the motivation for studying interaction, forms of statistical interaction and the issue of scale dependence, methods for estimating additive and multiplicative interaction, issues of confounding control and the causal interpretation of interaction measures, and how best to present interaction analyses. We also cover a number of more specialized topics including so-called "qualitative" or "crossover" interactions, interaction in the sufficient cause framework and in other mechanistic senses, the limits of statistical inference about biologic or physical interac- tions, methods for attributing effects to interactions, case-only designs for interaction, interaction for continuous outcomes, methods to identify subgroups to target using multiple covariates, the role of unmeasured confounding in interaction analyses, and power and sample size calculations for interac-

https://www.degruyter.com/downloadpdf/j/em.2014.3.issue-1/em-2013-0005/em-2013-0005.xml

A Tutorial on Interaction

Despite the yield of recent genome-wide association (GWA) studies, the identified variants explain only a small proportion of the heritability of most complex diseases. This unexplained heritability could be partly due to gene--environment (G×E) interactions or more complex pathways involving multiple genes and exposures. This Review provides a tutorial on the available epidemiological designs and statistical analysis approaches for studying specific G×E interactions and choosing the most appropriate methods. I discuss the approaches that are being developed for studying entire pathways and available techniques for mining interactions in GWA data. I also explore methods for marrying hypothesis-driven pathway-based approaches with 'agnostic' GWA studies.

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Gene–environment-wide association studies: emerging approaches

Subgroup analyses in randomized trials: risks of subgroup-specific analyses; power and sample size for the interaction test.

Consideration of gene-environment (GxE) interaction is becoming increasingly important in the design of new epidemiologic studies. We present a method for computing required sample size or power to detect GxE interaction in the context of three specific designs: the standard matched case-control; the case-sibling, and the case-parent designs. The method is based on computation of the expected value of the likelihood ratio test statistic, assuming that the data will be analysed using conditional logistic regression. Comparisons of required sample sizes indicate that the family-based designs (case-sibling and case-parent) generally require fewer matched sets than the case-control design to achieve the same power for detecting a GxE interaction. The case-sibling design is most efficient when studying a dominant gene, while the case-parent design is preferred for a recessive gene. Methods are also presented for computing sample size when matched sets are obtained from a stratified population, for example, when the population consists of multiple ethnic groups. A software program that implements the method is freely available, and may be downloaded from the website http://hydra.usc.edu/gxe.

Sample size requirements for matched case‐control studies of gene–environment interaction

Power and sample size considerations are critical for the design of epidemiologic studies of gene-environment interactions. Hwang et al. (Am J Epidemiol 1994;140:1029-37) and Foppa and Spiegelman (Am J Epidemiol 1997;146:596-604) have presented power and sample size calculations for case-control studies of gene-environment interactions. Comparisons of calculations using these approaches and an approach for general multivariate regression models for the odds ratio previously published by Lubin and Gail (Am J Epidemiol 1990; 131:552-66) have revealed substantial differences under some scenarios. These differences are the result of a highly restrictive characterization of the null hypothesis in Hwang et al. and Foppa and Spiegelman, which results in an underestimation of sample size and overestimation of power for the test of a gene-environment interaction. A computer program to perform sample size and power calculations to detect additive or multiplicative models of gene-environment interactions using the Lubin and Gail approach will be available free of charge in the near future from the National Cancer Institute.

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M García-Closas

Papers

Power and sample size calculations in case-control studies of gene-environment interactions: comments on different approaches.