In Defense of Genopolitics

2013 ◽  
Vol 107 (2) ◽  
pp. 362-374 ◽  
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.

scholarly journals Gene-Environment Interplay in Twin Models

2013 ◽  
Vol 21 (3) ◽  
pp. 368-389 ◽  
Author(s):  
Brad Verhulst ◽  
Peter K. Hatemi
Keyword(s):  
Genetic Similarity ◽  
Behavior Genetics ◽  
Empirical Studies ◽  
Environment Interaction ◽  
Twin Design ◽  
Gene Environment Interaction ◽  
Twin Model ◽  
Gene Environment ◽  
Twin Models ◽  
Classical Twin Design

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.

Behavior Genetic Approaches for Situation Research

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.

Gene-Environment Interaction in the Determination of Levels of Haemostatic Variables Involved in Thrombosis and Fibrinolysis

1997 ◽  
Vol 78 (01) ◽  
pp. 457-461 ◽  
Author(s):  
S E Humphries ◽  
A Panahloo ◽  
H E Montgomery ◽  
F Green ◽  
J Yudkin
Keyword(s):  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Gene Environment

Gene-environment Interaction on the Risk of Type 2 Diabetes Among Ethnic Minority Populations Living in Europe and North America: A Systematic Review

2020 ◽  
Vol 16 (5) ◽  
pp. 457-470 ◽  
Author(s):  
Mohammad H. Zafarmand ◽  
Parvin Tajik ◽  
René Spijker ◽  
Charles Agyemang
Keyword(s):  
Type 2 Diabetes ◽  
North America ◽  
Ethnic Minority ◽  
Ethnic Minorities ◽  
Minority Groups ◽  
Environment Interaction ◽  
Ethnic Minority Groups ◽  
Gene Environment Interaction ◽  
Gene Environment

Background: The body of evidence on gene-environment interaction (GEI) related to type 2 diabetes (T2D) has grown in the recent years. However, most studies on GEI have sought to explain variation within individuals of European ancestry and results among ethnic minority groups are inconclusive. Objective: To investigate any interaction between a gene and an environmental factor in relation to T2D among ethnic minority groups living in Europe and North America. Methods: We systematically searched Medline and EMBASE databases for the published literature in English up to 25th March 2019. The screening, data extraction and quality assessment were performed by reviewers independently. Results: 1068 studies identified through our search, of which nine cohorts of six studies evaluating several different GEIs were included. The mean follow-up time in the included studies ranged from 5 to 25.7 years. Most studies were relatively small scale and few provided replication data. All studies included in the review included ethnic minorities from North America (Native-Americans, African- Americans, and Aboriginal Canadian), none of the studies in Europe assessed GEI in relation to T2D incident in ethnic minorities. The only significant GEI among ethnic minorities was HNF1A rs137853240 and smoking on T2D incident among Native-Canadians (Pinteraction = 0.006). Conclusion: There is a need for more studies on GEI among ethnicities, broadening the spectrum of ethnic minority groups being investigated, performing more discovery using genome-wide approaches, larger sample sizes for these studies by collaborating efforts such as the InterConnect approach, and developing a more standardized method of reporting GEI studies are discussed.

scholarly journals Estimating the effective sample size in association studies of quantitative traits

2021 ◽  
Author(s):  
Andrey Ziyatdinov ◽  
Jihye Kim ◽  
Dmitry Prokopenko ◽  
Florian Privé ◽  
Fabien Laporte ◽  
...  
Keyword(s):  
Statistical Power ◽  
Quantitative Traits ◽  
Mixed Model ◽  
Association Studies ◽  
Effective Sample Size ◽  
Environment Interaction ◽  
Uk Biobank ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
The Uk

Abstract The effective sample size (ESS) is a metric used to summarize in a single term the amount of correlation in a sample. It is of particular interest when predicting the statistical power of genome-wide association studies (GWAS) based on linear mixed models. Here, we introduce an analytical form of the ESS for mixed-model GWAS of quantitative traits and relate it to empirical estimators recently proposed. Using our framework, we derived approximations of the ESS for analyses of related and unrelated samples and for both marginal genetic and gene-environment interaction tests. We conducted simulations to validate our approximations and to provide a quantitative perspective on the statistical power of various scenarios, including power loss due to family relatedness and power gains due to conditioning on the polygenic signal. Our analyses also demonstrate that the power of gene-environment interaction GWAS in related individuals strongly depends on the family structure and exposure distribution. Finally, we performed a series of mixed-model GWAS on data from the UK Biobank and confirmed the simulation results. We notably found that the expected power drop due to family relatedness in the UK Biobank is negligible.

Sign in / Sign up

Export Citation Format

Share Document