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Faced with rapid advances in human genetics, policy makers are struggling to come to grips with a host of complex ethical, legal and social questions. The HUMGEN website gives policy makers and the public access to legislation, policy, guidelines and recommendations of government and nongovernmental organizations worldwide.

<b><i>Background:</i></b> Data sharing from clinical trials can be key to the development and approval of medicines for rare diseases. Many events during the first half of 2013 have contributed to the movement for increased transparency. These include the development of the European Medicines Agency's new data publication policy, the creation of the AllTrials petition and GlaxoSmithKline's choice to sign it, the launch of GlaxoSmithKline's system for access to patient-level clinical trial data and Roche's commitment to create a similar system, the release of results from the Yale University Open Data Access project's first medicine analysis for Medtronic, and the creation of the Reg4All website. <b><i>Aims/Objectives:</i></b> This paper summarises major developments in clinical trial transparency between January and June 2013 and analyses the composition of datasets released by GlaxoSmithKline. <b><i>Methods:</i></b> GlaxoSmithKline's database of available trials was tabulated and graphs of relevant trial characteristics were produced. <b><i>Results/Conclusions:</i></b> Due to current transparency initiatives, it is likely that much more data will be made available over the next few years through systems similar to GlaxoSmithKline's. Although some aspects of GlaxoSmithKline's model could limit its usefulness, the data currently listed is diverse and could be promising for researchers interested in rare disease treatment.

<b><i>Background:</i></b> The rapidity of technological change in genetics is not always matched by the uptake of this new knowledge into practice. Increasing genetic knowledge has already led to legal liability for those who have not used it properly, such as not informing patients or their families of potential genetic risk. A similar outcome is also of concern in the case of risk prediction models used for hereditary breast cancer. <b><i>Results:</i></b> No legal case has directly addressed the use of risk prediction models. However, as genetic medicine and risk prediction models become more widely used, the prospect of a lawsuit will also increase. Current case law is instructive on the circumstances under which medical liability actions could be pursued and circumstances under which liability is unlikely, such as the provision of faulty family history information by a patient. <b><i>Conclusions:</i></b> There is existing case law on family history and genetics that parallels in many respects the use of risk prediction models. However, the idea of a bad 'prediction' is a difficult legal concept. Outside of a plain misuse or failure to use a risk prediction model when circumstances clearly required it, there is little legal guidance presently available to determine the risk for medical liability.

Genetic information can be used to target interventions that improve health and prevent disease. Indeed, the results of population genomics research could be useful for public health and national pandemic plans. Yet, firm scientific evidence originating from such research and the indicators of the role of health determinants, gene-gene and gene-environment interaction remain to be assessed and validated before being integrated into pandemic plans or public health programmes. It is not clear what is the role of the State in research on the elucidation of the determinants of gene-gene and gene-environment interactions and how, when, and if such data can be accessed and used for such planning. Over a period of 3 years, we sought to address these questions by gathering data and literature relevant to research in public health genomics, preparing issues papers and, finally, consulting with stakeholders on a provisional 'points to consider' document at various times. Examining in turn the issues of privacy, State powers, stakeholder perceptions, and public participation, we propose in this article, for each of these themes, a series of recommendations aiming to provide guidance on the role of the State in the use of genomic information for public health research, prevention and planning.

Bioscience has recently undergone a series of knowledge-based and technological revolutions. A critical consequence has been increasing recognition of the need to invest in infrastructure. Good access to data (and samples) from multiple studies is axiomatic to the value of this infrastructure. Access must be streamlined, secure, and based upon transparent and 'fair' decision making. It must be clear who has created and who has used which data. Ethico-legal policies and guidelines, which reflect dominant local cultural and societal norms, must take account of the increasingly global nature of bioscience research. A robust data infrastructure must also be attentive to the translational aims and social impact of its knowledge generation. In order to maintain the trust of its constituency – the general public as well as professional, political, commercial stakeholders – it must develop mechanisms to take account of all of these perspectives. These considerations form the basis of an emerging data economy. Building on extant achievements and pursuing the ideas outlined here could revolutionise the way we use and manage large-scale data. They have critical implications for biomedical and public health research communities and will be of central relevance for healthcare managers and policy makers, governments and industry. However, if the major challenges are to be met we must continue to invest,both nationally and internationally, in developing the cooperative infrastructures that provide a complementary foil to competitive resourcing mechanisms that drive hypothesis-driven science.