Abstract
This paper investigates how groups of ‘citizen scientists’ in non-traditional settings and primarily online networks claim to be challenging conventional genomic research processes and norms. Although these groups are highly diverse, they all distinguish their efforts from traditional university- or industry-based genomic research as being ‘participant-driven’ in one way or another. Participant-driven genomic research (PDGR) groups often work from ‘labs’ that consist of servers and computing devices as much as wet lab apparatus, relying on information-processing software for data-driven, discovery-based analysis rather than hypothesis-driven experimentation. We interviewed individuals from a variety of efforts across the expanding ecosystem of PDGR, including academic groups, start-ups, activists, hobbyists, and hackers, in order to compare and contrast how they relate their stated objectives, practices, and political and moral stances to institutions of expert scientific knowledge production. Results reveal that these groups, despite their diversity, share commitments to promoting alternative modes of housing, conducting, and funding genomic research and, ultimately, sharing knowledge. In doing so, PDGR discourses challenge existing approaches to research governance as well, especially the regulation, ethics, and oversight of human genomic information management. Interestingly, the reaction of the traditional genomics research community to this revolutionary challenge has not been negative: in fact, the community seems to be embracing the ethos espoused by PDGR, at the highest levels of science policy. As conventional genomic research assimilates the ethos of PDGR, the movement’s ‘democratizing’ views on research governance are likely to become normalized as well, creating new tensions for science policy and research ethics.
Highlights
The field of human genome research provoked controversy at its inception as an example of ‘Big Science’ in biology
These groups often have distinct philosophies, practices, and goals from each other, they share rhetorical commitments to individual empowerment and the democratization of genomic research. We describe their objectives in promoting this vision for genomic research, their scientific practices, and the political and moral principles that inform their work
Angrist (2009) refers to early adopters of this technology as utilizing ‘‘citizen science,’’ and Prainsack (2014, p. 155) has documented how the leading commercial provider of personal genome testing ‘‘fits into the bigger picture of citizen science.’’ We have argued elsewhere that DTC personal genomic testing rearticulated the relationship between biomedical and information technologies through its co-constitution by developers and users (McGowan et al, 2010), and this led us to question whether and how participant-driven genomic research (PDGR) rearticulates the relationship between biomedical research and information technologies
Summary
The field of human genome research provoked controversy at its inception as an example of ‘Big Science’ in biology. The intermingling of academic and commercial genomic research platforms, political patient advocacy, advances in Web 2.0 technologies, ‘citizen science,’ electronic personal health monitoring, open-access and open-source cultures – all within an age of social networking and the corporate university (Delfanti, 2011; Kelty, 2010; Levina, 2010) – has given rise to new forms of translational genomic research While researchers in these domains often cite the same translational efficiencies, communitarian values, and civic virtues espoused by contemporary genome science, they do so to support efforts to liberate and de-institutionalize genomic research from the conventional scientific community altogether, and place it into the hands of consumers, patients, and citizens to promote the design and conduct of genomic research through the use of interactive, dynamic, and web-based tools to enroll participants, gather, manage, and analyze data, and distribute findings. Self-tracking leads to the collection of information that is ‘actionable’ in that it enables feedback loops for behavior changes that are intended to foster better or different outcomes (Swan, 2013)
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