scholarly journals A Tale of Odds and Ratios: Political Preference Formation in Postindustrial Democracies

2021 ◽  
Author(s):  
David M. Wineroither ◽  
Rudolf Metz
Keyword(s):  
Social Stratification ◽  
Political Process ◽  
Environment Interaction ◽  
Preference Formation ◽  
Political Preference ◽  
Individual Level ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Wide Range ◽  
The Individual

AbstractThis report surveys four approaches that are pivotal to the study of preference formation: (a) the range, validity, and theoretical foundations of explanations of political preferences at the individual and mass levels, (b) the exploration of key objects of preference formation attached to the democratic political process (i.e., voting in competitive elections), (c) the top-down vs. bottom-up character of preference formation as addressed in leader–follower studies, and (d) gene–environment interaction and the explanatory weight of genetic predisposition against the cumulative weight of social experiences.In recent years, our understanding of sites and processes of (individual) political-preference formation has substantially improved. First, this applies to a greater variety of objects that provide fresh insight into the functioning and stability of contemporary democracy. Second, we observe the reaffirmation of pivotal theories and key concepts in adapted form against widespread challenge. This applies to the role played by social stratification, group awareness, and individual-level economic considerations. Most of these findings converge in recognising economics-based explanations. Third, research into gene–environment interplay rapidly increases the number of testable hypotheses and promises to benefit a wide range of approaches already taken and advanced in the study of political-preference formation.

scholarly journals The Ethics of Translational Science: Imagining Public Benefit in Gene-Environment Interaction Research

2017 ◽  
Vol 3 ◽  
pp. 351 ◽  
Author(s):  
Sara L. Ackerman ◽  
Katherine Weatherford Darling ◽  
Sandra Soo-Jin Lee ◽  
Robert A. Hiatt ◽  
Janet K. Shim
Keyword(s):  
Biomedical Research ◽  
Scientific Knowledge ◽  
Life Sciences ◽  
Environment Interaction ◽  
Translational Science ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Wide Range ◽  
Causes Of Disease ◽  
Interaction Research

Biomedical research is increasingly informed by expectations of “translation,” which call for the production of scientific knowledge that can be used to create services and products that improve health outcomes. In this paper, we ask how translation, in particular the idea of social responsibility, is understood and enacted in the post-genomic life sciences. Drawing on theories examining what constitutes “good science,” and interviews with 35 investigators who study the role of gene-environment interactions in the etiology of cancer, diabetes, and cardiovascular disease, we describe the dynamic and unsettled ethics of translational science through which the expected social value of scientific knowledge about complex disease causation is negotiated. To describe how this ethics is formed, we first discuss the politics of knowledge production in interdisciplinary research collectives. Researchers described a commitment to working across disciplines to examine a wide range of possible causes of disease, but they also pointed to persistent disciplinary and ontological divisions that rest on the dominance of molecular conceptions of disease risk. The privileging of molecular-level causation shapes and constrains the kinds of knowledge that can be created about gene-environment interactions. We then turn to scientists’ ideas about how this knowledge should be used, including personalized prevention strategies, targeted therapeutics, and public policy interventions. Consensus about the relative value of these anticipated translations was elusive, and many scientists agreed that gene-environment interaction research is part of a shift in biomedical research away from considering important social, economic, political and historical causes of disease and disease disparities. We conclude by urging more explicit engagement with questions about the ethics of translational science in the post-genomic life sciences. This would include a consideration of who will benefit from emerging scientific knowledge, how benefits will accrue, and the ways in which normative assumptions about the public good come to be embedded in scientific objects and procedures.

Genetic and Epigenetic Models

2019 ◽  
pp. 546-560
Author(s):  
Rudolf Uher
Keyword(s):  
Environmental Exposure ◽  
Genetic Variants ◽  
Current Knowledge ◽  
Environmental Exposures ◽  
Environment Interaction ◽  
Gene Environment Interaction ◽  
Treatment And Prevention ◽  
Gene Environment ◽  
The Individual ◽  
The Impact

Both genetic variation and environmental exposures play key roles in the development of mental health or psychopathology. Their roles are interdependent: The effects of genetic variants depend on environment, and the impact of environment depends on the genetic variants. This chapter will explain and critically review the most important models of gene–environment interplay, including gene–environment correlation, gene–environment interaction, and epigenetics. Gene–environment correlation describes a mechanism where genetic variants influence the likelihood of environmental exposure. Gene–environment interactions refer to a mechanism where genetic variants influence the impact of an environmental exposure on the individual. Finally, epigenetics provides a molecular mechanism through which environmental exposures affect the function of genes for long periods of time. The chapter concludes with a discussion of the limits of current knowledge, its implications for treatment and prevention, and directions for further research.

scholarly journals A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables

2021 ◽  
Author(s):  
Julian Hecker ◽  
Dmitry Prokopenko ◽  
Matthew Moll ◽  
Sanghun Lee ◽  
Wonji Kim ◽  
...  
Keyword(s):  
Statistical Power ◽  
Quantitative Traits ◽  
Risk Scores ◽  
Environment Interaction ◽  
Test Statistics ◽  
Robust Test ◽  
Gene Environment Interaction ◽  
Gene Environment ◽  
Wide Range ◽  
Interaction Testing

AbstractThe identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since statistical power is often limited, the specification of environmental effects is nontrivial, and such misspecifications can lead to false positive findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy increases power to detect interactions, identifying contributing key genes and pathways is difficult based on these global results.Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate multiple genetic variants and/or multiple environmental factors. Using sample splitting, a screening step enables the selection and combination of potential interactions into scores with improved interpretability, based on the user’s unrestricted choices for statistical/machine learning approaches. In the testing step, the application of robust test statistics minimizes the susceptibility of the results to main effect misspecifications.Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified genome-wide significant interactions with subcomponents of genetic risk scores. While the contributing single variant interactions are moderate, our analysis results indicate interesting interaction patterns that result in strong aggregated signals that provide further insights into gene-environment interaction mechanisms.

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.

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