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Gene–Environment Interactions (G×E) in Behavioral Genetics

  • Laramie Duncan
Chapter
Part of the Advances in Behavior Genetics book series (AIBG, volume 2)

Abstract

This chapter focuses on gene–environment interactions, often abbreviated “G × E” and pronounced “G-by-E.” G × Es are interaction effects, distinct from genetic main effects. The presence of a G × E implies that the effect of an environmental variable (on phenotype) depends on genotype and vice versa and that the effect of genotype (on phenotype) depends on the environment. G × E effects have been studied in psychiatry and psychology for decades using a variety of methodological techniques, reviewed here. The first of four sections in this chapter is the “Introduction,” which provides a description of what G × Es are (and are not) and how they have historically been studied. It also covers conceptual issues related to G × E including gene–environment correlation (rGE), the form of G × Es, biological plausibility of various types of G × Es, and epigenetics as it pertains to G × E. Section “Current Issues” is focused primarily on the most common G × E method currently employed: the candidate G × E (cG × E) approach, also known as the measured G × E approach. A brief review of findings from the first decade of cG × E research in psychiatry and psychology is provided. Evidence that raises questions about the validity of cG × E findings is presented via discussion of statistical power, publication bias, and the estimated field-wise false discovery rate (FDR). Section “Conclusion” discusses the parallel course that G × E research has taken with genetic (main effects) research. The final section “Future Directions” briefly explains genome-wide interaction studies (GWIS) and suggests the most promising avenues for future G × E research.

Keywords

Publication Bias False Discovery Rate Stressful Life Event Heritability Estimate Candidate Gene Study 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Harvard School of Public Health, Massachusetts General Hospital, Harvard Medical SchoolBroad Institute of MIT and HarvardCambridgeUSA

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