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Political scientists tend to focus on environmental triggers as the primary precipitating cause for political violence. However, little has been done to explain why certain individuals faced with certain pressures resort to violence, while others confronting the same situation seek out diplomatic and peaceful resolutions to conflict. Here, using two independent samples, we explore the interaction between genetic disposition and violent early life events and their influence on engaging in physical violence. We find that individuals with the low-activity form of monoamine oxidase-A, who are exposed to violence in youth have a greater likelihood of engaging in physical aggression later in adulthood. Our findings hold important implications for the value of environmental intervention in communities besieged by political violence in order to reduce the likelihood of the intergenerational transfer of its propensity.

We investigated genetic and environmental correlations and gene by environment interactions (GxE) between depressive symptoms measured by the Beck Depression Inventory (BDI) and quantity smoked measured by number of cigarettes smoked per day (CPD) using quantitative genetic modeling. The population-based sample consisted of 12,063 twin individuals from the Finnish Twin Cohort Study. Bivariate Cholesky decomposition revealed that the phenotypic correlation (r = 0.09) between BDI and CPD was explained by shared genetic (rg = 0.18) and environmental (re = 0.08) factors. GxE models incorporating moderator effects were built by using CPD as trait and BDI as moderator and vice versa. The importance of the genetic variance component increased with increasing moderator value in both models. Thus, the influence of genetic effects on variance of smoking quantity was enhanced in individuals with elevated depression score and vice versa; the genetic effects on depression variance were potentiated among heavy smokers. In conclusion, shared genetic and environmental factors as well as GxE underlie the association of smoking with depression.

Political scientists tend to focus on environmental triggers as the primary precipitating cause for political violence. However, little has been done to explain why certain individuals faced with certain pressures resort to violence, while others confronting the same situation seek out diplomatic and peaceful resolutions to conflict. Here, using two independent samples, we explore the interaction between genetic disposition and violent early life events and their influence on engaging in physical violence. We find that individuals with the low-activity form of monoamine oxidase-A, who are exposed to violence in youth have a greater likelihood of engaging in physical aggression later in adulthood. Our findings hold important implications for the value of environmental intervention in communities besieged by political violence in order to reduce the likelihood of the intergenerational transfer of its propensity. [Reprinted by permission of Sage Publications Inc., copyright holder.]

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.

Abstract Behavioural genetics was initially concerned with partitioning population variance into that due to genetics and that due to environmental influences. The implication was that the two were separate and it was assumed that gene–environment interactions were usually of so little importance that they could safely be ignored. Theoretical considerations suggested that that was unlikely to be true and empirical findings are now accumulating on the demonstrated and replicated biological interactions between identified common single genetic variants and the operation of environmentally mediated risks. The paper outlines the evidence and considers why it is changing concepts in ways that matter.

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 ...

AbstractThe possibility that specific environmental factors such as smoking and estrogen use modify the genetic influences (gene–environment interaction) on bone mineral density (BMD) has not been explored in genetic epidemiological studies such as twin studies. The aim of this study was to look for evidence of gene–environment interaction in BMD determination by analyzing data collected on a large number of healthy female twins. BMD of the hip, distal forearm and lumbar spine were measured by dual-energy X-ray absorptiometry on 287 identical and 265 nonidentical volunteer female twin pairs. The environmental factors examined were hormone replacement therapy (HRT) and smoking. In genetic modeling analysis using path analysis, there was evidence of 'HRT-specific' genetic component of BMD variance at the forearm (50% of total variance) but not at the hip. At the lumbar spine the magnitude of the genetic component of variance in HRT users (> 60-month HRT use) was less than the genetic component of variance for little or no exposure to HRT (48% vs. 84%). There was no evidence of gene–environment interaction for smoking. The main evidence for gene–environment interaction was the finding that forearm BMD variance was influenced by a significant HRT-specific genetic component. There was also evidence that in HRT users, the genetic component of total variance for lumbar BMD was lower.

Accurately identifying interactions between genetic vulnerabilities and environmental factors is of critical importance for genetic research on health and behavior. In the previous work of Van Hulle et al. (Behavior Genetics, Vol. 43, 2013, pp. 71–84), we explored the operating characteristics for a set of biometric (e.g., twin) models of Rathouz et al. (Behavior Genetics, Vol. 38, 2008, pp. 301–315), for testing gene-by-measured environment interaction (GxM) in the presence of gene-by-measured environment correlation (rGM) where data followed the assumed distributional structure. Here we explore the effects that violating distributional assumptions have on the operating characteristics of these same models even when structural model assumptions are correct. We simulated N = 2,000 replicates of n = 1,000 twin pairs under a number of conditions. Non-normality was imposed on either the putative moderator or on the ultimate outcome by ordinalizing or censoring the data. We examined the empirical Type I error rates and compared Bayesian information criterion (BIC) values. In general, non-normality in the putative moderator had little impact on the Type I error rates or BIC comparisons. In contrast, non-normality in the outcome was often mistaken for or masked GxM, especially when the outcome data were censored.

The effects of the serotonin receptor 2A (HTR2A) and 1A (HTR1A) polymorphisms and maternal caregiving attitudes on child's later hostile attitudes were examined. The sample consisted of 819 Finnish children and adolescents. Nurturance was reported by the mothers during childhood, while hostile attitudes were reported by the participants in adulthood. Caregiving attitudes predicted hostile attitudes (β = −.069, p = .047), but HTR2A and HTR1A were not related to them. HTR2A rs6313 moderated the effect of caregiving on hostility. Among the carriers of T/T and T/C genotype, caregiving predicted hostility (β = −.26, p = .024 and β = −.18, p = .001, respectively), while no association among C/C carriers was observed. T allele carriers were sensitive to the effects of caregiving on hostility although this association dropped when controlling for depressive symptoms.

© 2015, The Author(s) 2015. Scientists now agree that common diseases arise through interactions of genetic and environmental factors, but there is less agreement about how scientific research should account for these interactions. This paper examines the politics of quantification in gene–environment interaction (GEI) research. Drawing on interviews and observations with GEI researchers who study common, complex diseases, we describe quantification as an unfolding moral economy of science, in which researchers collectively enact competing "virtues." Dominant virtues include molecular precision, in which behavioral and social risk factors are moved into the body, and "harmonization," in which scientists create large data sets and common interests in multisited consortia. We describe the negotiations and trade-offs scientists enact in order to produce credible knowledge and the forms of (self-)discipline that shape researchers, their practices, and objects of study. We describe how prevailing techniques of quantification are premised on the shrinking of the environment in the interest of producing harmonized data and harmonious scientists, leading some scientists to argue that social, economic, and political influences on disease patterns are sidelined in postgenomic research. We consider how a variety of GEI researchers navigate quantification's productive and limiting effects on the science of etiological complexity.