Gene-Environment Interplay in Twin Models

In this article, we respond to Shultziner's critique that argues that identical twins are more alike not because of genetic similarity, but because they select into more similar environments and respond to stimuli in comparable ways, and that these effects bias twin model estimates to such an extent that they are invalid. The essay further argues that the theory and methods that undergird twin models, as well as the empirical studies which rely upon them, are unaware of these potential biases. We correct this and other misunderstandings in the essay and find that gene-environment (GE) interplay is a well-articulated concept in behavior genetics and political science, operationalized as gene-environment correlation and gene-environment interaction. Both are incorporated into interpretations of the classical twin design (CTD) and estimated in numerous empirical studies through extensions of the CTD. We then conduct simulations to quantify the influence of GE interplay on estimates from the CTD. Due to the criticism's mischaracterization of the CTD and GE interplay, combined with the absence of any empirical evidence to counter what is presented in the extant literature and this article, we conclude that the critique does not enhance our understanding of the processes that drive political traits, genetic or otherwise.

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In Defense of Genopolitics

American Political Science Review ◽

10.1017/s0003055413000063 ◽

2013 ◽

Vol 107 (2) ◽

pp. 362-374 ◽

Cited By ~ 30

Author(s):

JAMES H. FOWLER ◽

CHRISTOPHER T. DAWES

Keyword(s):

Political Science ◽

Voter Turnout ◽

Church Attendance ◽

Behavior Genetics ◽

American Political Science Review ◽

Environment Interaction ◽

Gene Environment Interaction ◽

National Longitudinal Study ◽

Gene Environment ◽

American Political Science

The American Political Science Review recently published a critique of an article we published in the Journal of Politics in 2008. In that article we showed that variants of the genes 5HTT and MAOA were significantly associated with voter turnout in a sample of 2,300 subjects from the National Longitudinal Study of Adolescent Health. Here, we address the critique first by conducting a replication study using an independent sample of 9,300 subjects. This study replicates the gene-environment interaction of the 5HTT gene variant with church attendance, but not the association with MAOA. We then focus on the general argument of the critique, showing that many of its characterizations of the literature in genetics and in political science are misleading or incorrect. We conclude by illustrating the ways in which genopolitics has already made a lasting contribution to the field of political science and by offering guidelines for future studies in genopolitics that are based on state-of-the-art recommendations from the field of behavior genetics.

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Behavior Genetic Approaches for Situation Research

The Oxford Handbook of Psychological Situations ◽

10.1093/oxfordhb/9780190263348.013.35 ◽

2017 ◽

pp. 24-39

Author(s):

Daniel A. Briley

Keyword(s):

Environmental Variables ◽

Behavior Genetics ◽

Behavioral Genetics ◽

Situational Factors ◽

Environment Interaction ◽

Selection Effects ◽

Personality Psychology ◽

Gene Environment Interaction ◽

Gene Environment ◽

History Of

As a field, behavior genetics has a long and often underappreciated focus on environmental and situational factors. This chapter describes the methodological details and empirical findings of this line of work, as well as what situation research can gain from behavior genetics and vice versa. Genetically informative designs offer tools to quantify the extent to which people actively create their situational experiences as opposed to randomly encountering them, and novel advances in situation research have the potential to clarify the scattered history of environmental variables in behavioral genetics. Current progress in personality psychology will be highlighted. Parallels between behavior genetics and personality work can be found both in terms of mechanisms (e.g., gene-environment correlation and gene × environment interaction contrasting with selection effects and person × situation effects) and explanatory pitfalls. Researchers interested in delineating the pathways from situations to behavior would do well to draw from and build upon work in behavior genetics.

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Birmingham and Beyond

Twin Research and Human Genetics ◽

10.1017/thg.2020.27 ◽

2020 ◽

Vol 23 (2) ◽

pp. 68-71

Author(s):

Lindon Eaves

Keyword(s):

Mate Selection ◽

Cultural Transmission ◽

Large Scale ◽

Behavior Genetics ◽

Model Fitting ◽

Environment Interaction ◽

Twin Design ◽

Components Of Variance ◽

Classical Twin Design ◽

The University

AbstractNick Martin was a doctoral student of mine at the University of Birmingham in the mid 1970s. In this review, I discuss two of Nick’s earliest and most seminal contributions to the field of behavior genetics. First, Martin and Eaves’ (1977) extension of the model-fitting approach to multivariate data, which laid the theoretical groundwork for a generation of multivariate behavior genetic studies. Second, the Martin et al.’s (1978) manuscript on the power of the classical twin design, which showed that thousands of twin pairs would be required in order to reliably estimate components of variance, and has served as impetus for the formation of large-scale twin registries across the world. I discuss these contributions against the historical backdrop of a time when we and others were struggling with the challenge of figuring out how to incorporate gene-by-environment interaction, gene–environment correlation, mate selection and cultural transmission into more complex genetic models of human behavior.

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umx: Twin and Path-Based Structural Equation Modeling in R

Twin Research and Human Genetics ◽

10.1017/thg.2019.2 ◽

2019 ◽

Vol 22 (1) ◽

pp. 27-41 ◽

Cited By ~ 12

Author(s):

Timothy C. Bates ◽

Hermine Maes ◽

Michael C. Neale

Keyword(s):

Structural Equation Modeling ◽

Ordinal Data ◽

Structural Equation ◽

Behavior Genetics ◽

Model Specification ◽

Equation Modeling ◽

Environment Interaction ◽

Gene Environment ◽

Twin Models ◽

Pathway Models

AbstractStructural equation modeling (SEM) is an important research tool, both for path-based model specification (common in the social sciences) and also for matrix-based models (in heavy use in behavior genetics). We developed umx to give more immediate access, relatively concise syntax and helpful defaults for users in these two broad disciplines. umx supports development, modification and comparison of models, as well as both graphical and tabular outputs. The second major focus of umx, behavior genetic models, is supported via functions implementing standard multigroup twin models. These functions support raw and covariance data, including joint ordinal data, and give solutions for ACE models, including support for covariates, common- and independent-pathway models, and gene × environment interaction models. A tutorial site and question forum are also available.

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