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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.

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 ...

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.

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.

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.

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.

High expectations for new technologies coexist with wide variability in the actual adoption and impact of information technology (IT) projects in clinical settings, and the frequent failure to incorporate otherwise "successful" projects into routine practice. This paper draws on actor-network theory to present an in-depth, sociotechnical analysis of one such project--a computer kiosk designed to diagnose and expedite treatment of urinary tract infections (UTI) in adult women. Research at a hospital urgent care clinic demonstrated the kiosk program's effectiveness at diagnosing UTI and reducing patient wait times, and the kiosk was subsequently adopted by the clinic for routine patient care. However, a study promoting the adoption of the device at emergency departments (ED) was characterized by persistent staff resistance and lower-than-expected patient eligibility for kiosk-assisted care. The device was ultimately abandoned at all but one of the new sites. Observations and interviews with ED staff and the design/research team were conducted at four California EDs between April and July 2011 and point to conflicting understandings of evidence for the device's usefulness and reasons for its (non)adoption. The kiosk program's designers had attempted to "rationalize" medical work by embedding a formal representation of triage practices in the kiosk's software. However, the kiosk's "network" failed to stabilize as it encountered different patient populations, institutional politics, and the complex, pragmatic aspects of ED work at each site. The results of this evaluation challenge the persistent myth that a priori qualities and meanings inhere in technology regardless of context. The design and deployment of new IT projects in complex medical settings would benefit from empirically informed understandings of, and responses to, the contingent properties of human-technology relations.

High expectations for new technologies coexist with wide variability in the actual adoption and impact of information technology (IT) projects in clinical settings, and the frequent failure to incorporate otherwise "successful" projects into routine practice. This paper draws on actor-network theory to present an in-depth, sociotechnical analysis of one such project--a computer kiosk designed to diagnose and expedite treatment of urinary tract infections (UTI) in adult women. Research at a hospital urgent care clinic demonstrated the kiosk program's effectiveness at diagnosing UTI and reducing patient wait times, and the kiosk was subsequently adopted by the clinic for routine patient care. However, a study promoting the adoption of the device at emergency departments (ED) was characterized by persistent staff resistance and lower-than-expected patient eligibility for kiosk-assisted care. The device was ultimately abandoned at all but one of the new sites. Observations and interviews with ED staff and the design/research team were conducted at four California EDs between April and July 2011 and point to conflicting understandings of evidence for the device's usefulness and reasons for its (non)adoption. The kiosk program's designers had attempted to "rationalize" medical work by embedding a formal representation of triage practices in the kiosk's software. However, the kiosk's "network" failed to stabilize as it encountered different patient populations, institutional politics, and the complex, pragmatic aspects of ED work at each site. The results of this evaluation challenge the persistent myth that a priori qualities and meanings inhere in technology regardless of context. The design and deployment of new IT projects in complex medical settings would benefit from empirically informed understandings of, and responses to, the contingent properties of human-technology relations.

In this article, we explore current thinking and practices around the logics of difference in gene–environment interaction research in the post-genomic era. We find that scientists conducting gene–environment interaction research continue to invoke well-worn notions of racial difference and diversity, but use them strategically to try to examine other kinds of etiologically significant differences among populations. Scientists do this by seeing populations not as inherently homogeneous or heterogeneous, but rather by actively working to produce homogeneity along some dimensions and heterogeneity along others in their study populations. Thus we argue that homogeneity and heterogeneity are situational properties – properties that scientists seek to achieve in their study populations, the available data, and other aspects of the research situation they are confronting, and then leverage to advance post-genomic science. Pointing to the situatedness of homogeneity and heterogeneity in gene–environment interaction research underscores the work that these properties do and the contingencies that shape decisions about research procedures. Through a focus on the situational production of homogeneity and heterogeneity more broadly, we find that gene–environment interaction research attempts to shift the logic of difference from solely racial terms as explanatory ends unto themselves, to racial and other dimensions of difference that may be important clues to the causes of complex diseases.

INTRODUCTION: Implementation of genome-scale sequencing in clinical care has significant challenges: the technology is highly dimensional with many kinds of potential results, results interpretation and delivery require expertise and coordination across multiple medical specialties, clinical utility may be uncertain, and there may be broader familial or societal implications beyond the individual participant. Transdisciplinary consortia and collaborative team science are well poised to address these challenges. However, understanding the complex web of organizational, institutional, physical, environmental, technologic, and other political and societal factors that influence the effectiveness of consortia is understudied. We describe our experience working in the Clinical Sequencing Evidence-Generating Research (CSER) consortium, a multi-institutional translational genomics consortium. METHODS: A key aspect of the CSER consortium was the juxtaposition of site-specific measures with the need to identify consensus measures related to clinical utility and to create a core set of harmonized measures. During this harmonization process, we sought to minimize participant burden, accommodate project-specific choices, and use validated measures that allow data sharing. RESULTS: Identifying platforms to ensure swift communication between teams and management of materials and data were essential to our harmonization efforts. Funding agencies can help consortia by clarifying key study design elements across projects during the proposal preparation phase and by providing a framework for data sharing data across participating projects. CONCLUSIONS: In summary, time and resources must be devoted to developing and implementing collaborative practices as preparatory work at the beginning of project timelines to improve the effectiveness of research consortia.