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The convergence of increasingly efficient high-throughput genetic sequencing technology and ubiquitous Internet use has fueled the proliferation of companies that provide direct-to-consumer (DTC) personal genetic information. The emergence of consumer genetics reflects several shifts in the governance of genetic testing and management of human genetic data. This article discusses DTC genetics as a case study of neoliberalism and contemporary transformations in medicine that construe disease and its management through economic rationalities. At stake are shifts in subjectivities from "patient" to "consumer" and the meaning of being a "good citizen" in the context of precision medicine. Engaging concepts of biopower, biosociality, and biovalue in the public consumption of genetic information, this article analyzes DTC genetics and its effect on social connection, identity, and modes of participation in the production of biomedical knowledge and the management of health and risk.

This article discusses the institutional practices of classifying and creating taxonomies of difference within biobanks (repositories that store a broad range of biological materials, including DNA) and the technical and sociopolitical priorities that ultimately create biobanks. I argue that biobanks operate as political artifacts and that the social circumstances surrounding the development and use of biobanks determine what counts as meaningful difference within human genetic research. The massive collection of human DNA, blood, and tissues is critical to genomic medicine and the development and governance of biobanks structure knowledge that will ultimately bear on how population differences are interpreted and health disparities are framed. Careful consideration of how to avoid the conflation of concepts of race, ethnicity, and nationality with biological differences is necessary to identify effective interventions that will bear positively on health.

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.

Introduction: Federal agencies have instituted guidelines to prioritize the enrollment and retention of diverse participants in precision medicine research (PMR). Prior studies examining participation of minoritized communities have shown that potential benefits represent a key determinant. Human subject research guidance, however, conceptualizes potential benefits narrowly, emphasizing generalized advances in medical knowledge. Further, few studies have provided qualitative data that critically examine how the concept of "benefit" is interpreted or challenged in the context of research practice. This paper examines the experiences of PMR investigators and frontline research staff to understand how standard approaches to benefit are received, contested, and negotiated "on the ground." Methods: Findings are drawn from a qualitative project conducted across five US-based, federally funded PMR studies. Data collection included 125 in-depth interviews with a purposive sample of investigators, research staff, community advisory board members, and NIH program officers associated with these PMR studies. Results: Researchers report that the standard approach to benefit – which relies on the premise of altruism and the promise of incrementally advancing scientific knowledge – is frequently contested. Researchers experience moral distress over the unmet clinical, psychosocial, and material needs within the communities they are engaging. Many believe the broader research enterprise has a responsibility to better address these needs. Conclusion: Researchers frequently take issue with and sometimes negotiate what is owed to participants and to their communities in exchange for the data they provide. These experiences of moral distress and these improvisations warrant systematic redress, not by individual researchers but by the broader research ethics infrastructure.

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.