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The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits., B.P.C. acknowledges funding from Abigail Wexner Research Institute at Nationwide Children’s Hospital; T.H. Nyrönen acknowledges funding from Academy of Finland grant #31996; A.M.-J., K.N., T.F.B., O.M.H., and Z.S. acknowledge funding from Australian Medical Research Future Fund; M.S. acknowledges funding from Biobank Japan; D. Bujold and S.J.M.J. acknowledge funding from Canada Foundation for Innovation; L.J.D. acknowledges funding from Canada Foundation for Innovation Cyber Infrastructure grant #34860; D. Bujold and G.B. acknowledge funding from CANARIE; L.J.D. acknowledges funding from CANARIE Research Data Management contract #RDM-090 (CHORD) and #RDM2-053 (ClinDIG); K.K.-L. acknowledges funding from CanSHARE; T.L.T. acknowledges funding from Chan Zuckerberg Initiative; T. Burdett acknowledges funding from Chan Zuckerberg Initiative grant #2017-171671; D. Bujold, G.B., and L.D.S. acknowledge funding from CIHR; L.J.D. acknowledges funding from CIHR grant #404896; M.J.S.B. acknowledges funding from CIHR grant #SBD-163124; M. Courtot and M. Linden acknowledge funding from CINECA project EU Horizon 2020 grant #825775; D. Bujold and G.B. acknowledge funding from Compute Canada; F.M.-G. acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – NFDI 1/1 “GHGA – German Human Genome-Phenome Archive; R.M.H.-S. acknowledges funding from Duke-Margolis Center for Health Policy; S.B. and A.J.B. acknowledge funding from EJP-RD EU Horizon 2020 grant #825575; A. Niewielska, A.K., D.S., G.I.S., J.A.T., J.R., M.A.K., M. Baudis, M. Linden, S.B., S.S., T.H. Nyrönen, and T.M.K. acknowledge funding from ELIXIR; A. Niewielska acknowledges funding from EOSC-Life EU Horizon 2020 grant #824087; J.-P.H. acknowledges funding from ETH Domain Strategic Focal Area “Personalized Health and Related Technologies (PHRT)” grant #2017-201; F.M.-G. acknowledges funding from EUCANCan EU Horizon 2020 grant #825835; B.M.K., D. Bujold, G.B., L.D.S., M.J.S.B., N.S., S.E.W., and Y.J. acknowledge funding from Genome Canada; B.M.K., M.J.S.B., S.E.W., and Y.J. acknowledge funding from Genome Quebec; F.M.-G. acknowledges funding from German Human Genome-Phenome Archive; C. Voisin acknowledges funding from Google; A.J.B. acknowledges funding from Health Data Research UK Substantive Site Award; D.H. acknowledges funding from Howard Hughes Medical Institute; S.B. acknowledges funding from Instituto de Salud Carlos III; S.-S.K. and K.T. acknowledge funding from Japan Agency for Medical Research and Development (AMED); S. Ogishima acknowledges funding from Japan Agency for Medical Research and Development (AMED) grant #20kk0205014h0005; C.Y. and K. Kosaki acknowledge funding from Japan Agency for Medical Research and Development (AMED) grant #JP18kk0205012; GEM Japan acknowledges funding from Japan Agency for Medical Research and Development (AMED) grants #19kk0205014h0004, #20kk0205014h0005, #20kk0205013h0005, #20kk0205012h0005, #20km0405401h0003, and #19km0405001h0104; J.R. acknowledges funding from La Caixa Foundation under project #LCF/PR/GN13/50260009; R.R.F. acknowledges funding from Mayo Clinic Center for Individualized Medicine; Y.J. and S.E.W. acknowledge funding from Ministère de l’Économie et de l’Innovation du Québec for the Can-SHARE Connect Project; S.E.W. and S.O.M.D. acknowledge funding from Ministère de l’Économie et de l’Innovation du Québec for the Can-SHARE grant #141210; M.A.H., M.C.M.-T., J.O.J., H.E.P., and P.N.R. acknowledge funding from Monarch Initiative grant #R24OD011883 and Phenomics First NHGRI grant #1RM1HG010860; A.L.M. and E.B. acknowledge funding from MRC grant #MC_PC_19024; P.T. acknowledges funding from National University of Singapore and Agency for Science, Technology and Research; J.M.C. acknowledges funding from NHGRI; A.H.W. acknowledges funding from NHGRI awards K99HG010157, R00HG010157, and R35HG011949; A.M.-J., K.N., D.P.H., O.M.H., T.F.B., and Z.S. acknowledge funding from NHMRC grants #GNT1113531 and #GNT2000001; D.L.C. acknowledges funding from NHMRC Ideas grant #1188098; A.B.S. acknowledges funding from NHMRC Investigator Fellowship grant #APP177524; J.M.C. and L.D.S. acknowledge funding from NIH; A.A.P. acknowledges funding from NIH Anvil; A.V.S. acknowledges funding from NIH contract #HHSN268201800002I (TOPMed Informatics Research Center); S.U. acknowledges funding from NIH ENCODE grant #UM1HG009443; M.C.M.-T. and M.A.H. acknowledge funding from NIH grant #1U13CA221044; R.J.C. acknowledges funding from NIH grants #1U24HG010262 and #1U2COD023196; M.G. acknowledges funding from NIH grant #R00HG007940; J.B.A., S.L., P.G., E.B., H.L.R., and L.S. acknowledge funding from NIH grant #U24HG011025; K.P.E. acknowledges funding from NIH grant #U2C-RM-160010; J.A.E. acknowledges funding from NIH NCATS grant #U24TR002306; M.M. acknowledges funding from NIH NCI contract #HHSN261201400008c and ID/IQ Agreement #17X146 under contract #HHSN2612015000031 and #75N91019D00024; R.M.C.-D. acknowledges funding from NIH NCI grant #R01CA237118; M. Cline acknowledges funding from NIH NCI grant #U01CA242954; K.P.E. acknowledges funding from NIH NCI ITCR grant #1U24CA231877-01; O.L.G. acknowledges funding from NIH NCI ITCR grant #U24CA237719; R.L.G. acknowledges funding from NIH NCI task order #17X147F10 under contract #HHSN261200800001E; A.F.R. acknowledges funding from NIH NHGRI grant #RM1HG010461; N.M. and L.J.Z. acknowledge funding from NIH NHGRI grant #U24HG006941; R.R.F., T.H. Nelson, L.J.B., and H.L.R. acknowledge funding from NIH NHGRI grant #U41HG006834; B.J.W. acknowledges funding from NIH NHGRI grant #UM1HG009443A; M. Cline acknowledges funding from NIH NHLBI BioData Catalyst Fellowship grant #5118777; M.M. acknowledges funding from NIH NHLBI BioData Catalyst Program grant #1OT3HL142478-01; N.C.S. acknowledges funding from NIH NIGMS grant #R35-GM128636; M.C.M.-T., M.A.H., P.N.R., and R.R.F. acknowledge funding from NIH NLM contract #75N97019P00280; E.B. and A.L.M. acknowledge funding from NIHR; R.G. acknowledges funding from Project Ris3CAT VEIS; S.B. acknowledges funding from RD-Connect, Seventh Framework Program grant #305444; J.K. acknowledges funding from Robertson Foundation; S.B. and A.J.B. acknowledge funding from Solve-RD, EU Horizon 2020 grant #779257; T.S. and S. Oesterle acknowledge funding from Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN), supported by the Swiss State Secretariat for Education, Research and Innovation SERI; S.J.M.J. acknowledges funding from Terry Fox Research Institute; A.E.H., M.P.B., M. Cupak, M.F., and J.F. acknowledge funding from the Digital Technology Supercluster; D.F.V. acknowledges funding from the Australian Medical Research Future Fund, as part of the Genomics Health Futures Mission grant #76749; M. Baudis acknowledges funding from the BioMedIT Network project of Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN); B.M.K. acknowledges funding from the Canada Research Chair in Law and Medicine and CIHR grant #SBD-163124; D.S., G.I.S., M.A.K., S.B., S.S., and T.H. Nyrönen acknowledge funding from the EU Horizon 2020 Beyond 1 Million Genomes (B1MG) Project grant #951724; P.F., A.D.Y., F.C., H.S., I.U.L., D. Gupta, M. Courtot, S.E.H., T. Burdett, T.M.K., and S.F. acknowledge funding from the European Molecular Biology Laboratory; Y.J. and S.E.W. acknowledge funding from the Government of Canada; P.G. acknowledges funding from the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-206); J.Z. acknowledges funding from the Government of Ontario; C.K.Y. acknowledges funding from the Government of Ontario, Canada Foundation for Innovation; C. Viner and M.M.H. acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (grant #RGPIN-2015-03948 to M.M.H. and Alexander Graham Bell Canada Graduate Scholarship to C.V.); K.K.-L. acknowledges funding from the Program for Integrated Database of Clinical and Genomic Information; J.K. acknowledges funding from the Robertson Foundation; D.F.V. acknowledges funding from the Victorian State Government through the Operational Infrastructure Support (OIS) Program; A.M.L., R.N., and H.V.F. acknowledge funding from Wellcome (collaborative award); F.C., H.S., P.F., and S.E.H. acknowledge funding from Wellcome Trust grant #108749/Z/15/Z; A.D.Y., H.S., I.U.L., M. Courtot, H.E.P., P.F., and T.M.K. acknowledge funding from Wellcome Trust grant #201535/Z/16/Z; A.M., J.K.B., R.J.M., R.M.D., and T.M.K. acknowledge funding from Wellcome Trust grant #206194; E.B., P.F., P.G., and S.F. acknowledge funding from Wellcome Trust grant #220544/Z/20/Z; A. Hamosh acknowledges funding from NIH NHGRI grant U41HG006627 and U54HG006542; J.S.H. acknowledges funding from National Taiwan University #91F701-45C and #109T098-02; the work of K.W.R. was supported by the Intramural Research Program of the National Library of Medicine, NIH. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. H.V.F. acknowledges funding from Wellcome Grant 200990/A/16/Z ‘Designing, developing and delivering integrated foundations for genomic medicine'.

Enabling genomic and biomedical data to be shared for secondary research purposes is not always straightforward for existing “legacy” data sets. Researchers may not know whether their data meet ethical and regulatory requirements for sharing. As a result, these data, collected using public funds and the good will and efforts of the donors and investigators, may not be used beyond their original purpose. Single-use plastics are now being banned in many countries; single-use research should be avoided if possible. This paper describes a filter developed through the driver projects of the Global Alliance for Genomics and Health that can be used by researchers to help them determine the extent of sharing possible for their legacy data and actions to be taken to enable further sharing.

Background: New data collection in established longitudinal population studies provides an opportunity for studying the risk factors and sequelae of the novel coronavirus disease 2019 (COVID-19), plus the indirect impacts of the COVID-19 pandemic on wellbeing. The Extended Cohort for E-health, Environment and DNA (EXCEED) cohort is a population-based cohort (N>11,000), recruited from 2013 in Leicester, Leicestershire and Rutland. EXCEED includes consent for electronic healthcare record (EHR) linkage, spirometry, genomic data, and questionnaire data. Methods: Between May 2020 and July 2021, a new questionnaire was deployed in EXCEED, which captured COVID-19 symptoms, general physical and mental health, plus socioeconomic and environmental factors during the pandemic. An online system was developed to invite new participants to join EXCEED, with informed consent being provided online. New and existing participants then completed the COVID-19 questionnaire online. A subset of the new questionnaire respondents were invited to participate in COVID-19 serology substudies, using home antibody testing kits. Results: In total, 3,693 participants provided COVID-19 infection status (median age 62.9 (IQR 54.7-69.2), 58.9% female). Trends of monthly incidence proportions of COVID-19 in EXCEED (self-report or symptom-predicted) approximated local and national figures. Regression analysis of 2,768 participants with linked EHR data showed no obvious monotonic relationship between number of chronic diseases (of 16 pre-specified diseases) and COVID-19 infection. There were 2,144 participants with valid results from a kit allowing differentiation between antibodies due to vaccination or infection. Of these, 8.5% had results consistent with previous COVID-19 infection, and 85.9% had evidence of COVID-19 vaccination, but without evidence of infection. Conclusions: Enriching EXCEED with a new COVID-19 questionnaire and serology data may improve understanding of the risk factors, clinical sequelae and broader impacts of the COVID-19 pandemic in the general population. Controlled access to these data for bona fide researchers is via application to the EXCEED study.

The identification of microbiological infection is usually a diagnostic investigation, a complex process that is firstly initiated by clinical suspicion. With the emergence of high-throughput sequencing (HTS) technologies, metagenomic analysis has unveiled the power to identify microbial DNA/RNA from a diverse range of clinical samples (1). Metagenomic analysis of whole human genomes at the clinical/research interface bypasses the steps of clinical scrutiny and targeted testing and has the potential to generate unexpected findings relating to infectious and sometimes transmissible disease. There is no doubt that microbial findings that may have a significant impact on a patient’s treatment and their close contacts should be reported to those with clinical responsibility for the sample-donating patient. There are no clear recommendations on how such findings that are incidental, or outside the original investigation, should be handled. Here we aim to provide an informed protocol for the management of incidental microbial findings as part of the 100,000 Genomes Project which may have broader application in this emerging field. As with any other clinical information, we aim to prioritise the reporting of data that are most likely to be of benefit to the patient and their close contacts. We also set out to minimize risks, costs and potential anxiety associated with the reporting of results that are unlikely to be of clinical significance. Our recommendations aim to support the practice of microbial metagenomics by providing a simplified pathway that can be applied to reporting the identification of potential pathogens from metagenomic datasets. Given that the ambition for UK sequenced human genomes over the next 5 years has been set to reach 5 million and the field of metagenomics is rapidly evolving, the guidance will be regularly reviewed and will likely adapt over time as experience develops.

Anonymization is a recognized process by which identifiers can be removed from identifiable data to protect an individual's confidentiality and is used as a standard practice when sharing data in biomedical research. However, a plethora of terms, such as coding, pseudonymization, unlinked, and deidentified, have been and continue to be used, leading to confusion and uncertainty. This article shows that this is a historic problem and argues that such continuing uncertainty regarding the levels of protection given to data risks damaging initiatives designed to assist researchers conducting cross-national studies and sharing data internationally. DataSHIELD and the creation of a legal template are used as examples of initiatives that rely on anonymization, but where the inconsistency in terminology could hinder progress. More broadly, this article argues that there is a real possibility that there could be possible damage to the public's trust in research and the institutions that carry it out by relying on vague notions of the anonymization process. Research participants whose lack of clear understanding of the research process is compensated for by trusting those carrying out the research may have that trust damaged if the level of protection given to their data does not match their expectations. One step toward ensuring understanding between parties would be consistent use of clearly defined terminology used internationally, so that all those involved are clear on the level of identifiability of any particular set of data and, therefore, how that data can be accessed and shared.

EXCEED is a longitudinal population-based cohort which facilitates investigation of genetic, environmental and lifestyle-related determinants of a broad range of diseases and of multiple morbidity through data collected at baseline and via electronic healthcare record linkage. Recruitment has taken place in Leicester, Leicestershire and Rutland since 2013 and is ongoing, with 9,840 participants aged 30-69 to date. The population of Leicester is diverse and additional recruitment from the local South Asian community is ongoing. Participants have consented to follow-up for up to 25 years through electronic health records and additional bespoke data collection is planned. Data available includes baseline demographics, anthropometry, spirometry, lifestyle factors (smoking and alcohol use) and longitudinal health information from primary care records, with additional linkage to other EHR datasets planned. Patients have consented to be contacted for recall-by-genotype and recall-by-phenotype sub-studies, providing an important resource for precision medicine research. We welcome requests for collaboration and data access by contacting the study management team via exceed@le.ac.uk.

In the United Kingdom (UK), transfer of genomic data to third countries is regulated by data protection legislation. This is a composite of domestic and European Union (EU) law, with EU law to be adopted as domestic law when Brexit takes place. In this paper we consider the content of data protection legislation and the likely impact of Brexit on transfers of genomic data from the UK to other countries. We examine the advice by regulators not to rely upon consent as a lawful basis for processing under data protection law, at least not when personal data are used for research purposes, and consider some of the other ways in which the research context can qualify an individual’s ability to exercise control over processing operations. We explain how the process of pseudonymization is to be understood in the context of transfer of genomic data to third parties, as well as how adequacy of data protection in a third country is to be determined in general terms. We conclude with reflections on the future direction of UK data protection law post Brexit with the reclassification of the UK itself as a third country., In the United Kingdom (UK), transfer of genomic data to third countries is regulated by data protection legislation. This is a composite of domestic and European Union (EU) law, with EU law to be adopted as domestic law when Brexit takes place. In this paper we consider the content of data protection legislation and the likely impact of Brexit on transfers of genomic data from the UK to other countries. We examine the advice by regulators not to rely upon consent as a lawful basis for processing under data protection law, at least not when personal data are used for research purposes, and consider some of the other ways in which the research context can qualify an individual's ability to exercise control over processing operations. We explain how the process of pseudonymization is to be understood in the context of transfer of genomic data to third parties, as well as how adequacy of data protection in a third country is to be determined in general terms. We conclude with reflections on the future direction of UK data protection law post Brexit with the reclassification of the UK itself as a third country.

Motivation: The data that put the ‘evidence’ into ‘evidence-based medicine’ are central to developments in public health, primary and hospital care. A fundamental challenge is to site such data in repositories that can easily be accessed under appropriate technical and governance controls which are effectively audited and are viewed as trustworthy by diverse stakeholders. This demands socio-technical solutions that may easily become enmeshed in protracted debate and controversy as they encounter the norms, values, expectations and concerns of diverse stakeholders. In this context, the development of what are called ‘Data Safe Havens’ has been crucial. Unfortunately, the origins and evolution of the term have led to a range of different definitions being assumed by different groups. There is, however, an intuitively meaningful interpretation that is often assumed by those who have not previously encountered the term: a repository in which useful but potentially sensitive data may be kept securely under governance and informatics systems that are fit-for-purpose and appropriately tailored to the nature of the data being maintained, and may be accessed and utilized by legitimate users undertaking work and research contributing to biomedicine, health and/or to ongoing development of healthcare systems. Results: This review explores a fundamental question: ‘what are the specific criteria that ought reasonably to be met by a data repository if it is to be seen as consistent with this interpretation and viewed as worthy of being accorded the status of ‘Data Safe Haven’ by key stakeholders’? We propose 12 such criteria. Contact: paul.burton@bristol.ac.uk

Background: Data from individual collections, such as biobanks and cohort studies, are now being shared in order to create combined datasets which can be queried to ask complex scientific questions. But this sharing must be done with due regard for data protection principles. DataSHIELD is a new technology that queries nonaggregated, individual-level data in situ but returns query data in an anonymous format. This raises questions of the ability of DataSHIELD to adequately protect participant confidentiality. Methods: An ethico-legal analysis was conducted that examined each step of the DataSHIELD process from the perspective of UK case law, regulations, and guidance. Results: DataSHIELD reaches agreed UK standards of protection for the sharing of biomedical data. All direct processing of personal data is conducted within the protected environment of the contributing study; participating studies have scientific, ethics, and data access approvals in place prior to the analysis; studies are clear that their consents conform with this use of data, and participants are informed that anonymisation for further disclosure will take place. Conclusion: DataSHIELD can provide a flexible means of interrogating data while protecting the participants' confidentiality in accordance with applicable legislation and guidance.

Contemporary bioscience is seeing the emergence of a new data economy: with data as its fundamental unit of exchange. While sharing data within this new 'economy' provides many potential advantages, the sharing of individual data raises important social and ethical concerns. We examine ongoing development of one technology, DataSHIELD, which appears to elide privacy concerns about sharing data by enabling shared analysis while not actually sharing any individual-level data. We combine presentation of the development of DataSHIELD with presentation of an ethnographic study of a workshop to test the technology. DataSHIELD produced an application of the norm of privacy that was practical, flexible and operationalizable in researchers' everyday activities, and one which fulfilled the requirements of ethics committees. We demonstrated that an analysis run via DataSHIELD could precisely replicate results produced by a standard analysis where all data are physically pooled and analyzed together. In developing DataSHIELD, the ethical concept of privacy was transformed into an issue of security. Development of DataSHIELD was based on social practices as well as scientific and ethical motivations. Therefore, the 'success' of DataSHIELD would, likewise, be dependent on more than just the mathematics and the security of the technology. Copyright © 2012 S. Karger AG, Basel.

International audience; Background Contemporary bioscience sometimes demands vast sample sizes and there is often then no choice but to synthesise data across several studies and to undertake an appropriate pooled analysis. This same need is also faced in health-services and socio-economic research. When a pooled analysis is required, analytic efficiency and flexibility are often best served by combining the individual-level data from all sources and analysing them as a single large data set. But ethico-legal constraints, including the wording of consent forms and privacy legislation, often prohibit or discourage the sharing of individual-level data, particularly across national or other jurisdictional boundaries. This leads to a fundamental conflict in competing public goods: individual-level analysis is desirable from a scientific perspective, but is prevented by ethico-legal considerations that are entirely valid. Methods DataSHIELD (Data Aggregation Through Anonymous Summary-statistics from Harmonized Individual-levEL Databases), provides a simple approach to analysing pooled data that circumvents this conflict. This is achieved via parallelized analysis and modern distributed computing and, in one key setting, takes advantage of the properties of the updating algorithm for generalized linear models (GLMs). Results The conceptual use of DataSHIELD is illustrated in two different settings. Conclusions As the study of the aetiological architecture of chronic diseases advances to encompass more complex causal pathways - e.g. to include the joint effects of genes, life-style and environment - sample size requirements will increase further and the analysis of pooled individual-level data will become ever more important. An aim of this conceptual paper is to encourage others to address the challenges and opportunities that DataSHIELD presents, and to explore potential extensions, for example to its use when different data sources hold different data on the same individuals.

Unfortunately, the same cannot be said of its impact in other regions, where Pap screening has not worked as ex-pected despite being broadly adopted or has only benefit-ed a small, privileged segment of society. Cervical cancer is a disease of inequity: it affects mostly women of low socio-economic status and of low educational attain-ment, who had many children and no or little access to Pap screening. The high-fertility, resource-poor coun-tries of sub-Saharan Africa, Latin America and Southeast Asia bear the highest burden from cervical cancer, both directly and indirectly. As it strikes around the pre-meno-pausal years when these women are still the primary caregivers for their children, their death leaves a vacuum and contributes to the high rates of marginalization in these countries.I n light of this sad state of affairs, it is thus with justi-fiable optimism that the public health community ob-serves the emerging science on the aforementioned 2 fronts of cervical cancer prevention. Universal HPV vac-cination of young, adolescent girls before they are sexu-ally active and improved screening with HPV-based tech-nologies provide new hopes for primary and secondary prevention of cervical cancer, respectively. The oncologi-cal paradigm of preventing cervical cancer via Pap screen-ing has changed into one of dealing with a sexually trans-mitted disease and in rooting out its upstream causes. Cervical cancer is now firmly recognized as the last step in a series of carcinogenetic events that begins with sexu-ally acquired infection of the uterine cervix with one or I n its inaugural year

Recent projects conducted by the International Cancer Genome Consortium (ICGC) have raised the important issue of distinguishing quality assurance (QA) activities from research in the context of genomics. Research was historically defined as a systematic effort to expand a shared body of knowledge, whereas QA was defined as an effort to ascertain whether a specific project met desired standards. However, the two categories increasingly overlap due to advances in bioinformatics and the shift toward open science. As few ethics review policies take these changes into account, it is often difficult to determine the appropriate level of review. Mislabeling can result in unnecessary burdens for the investigators or, conversely, in underestimation of the risks to participants. Therefore, it is important to develop a consistent method of selecting the review process for genomics and bioinformatics projects. This paper begins by discussing two case studies from the ICGC, followed by a literature review on the distinction between QA and research and a comparative analysis of ethics review policies from Canada, the United States, the United Kingdom, and Australia. These results are synthesized into a novel two-step decision tool for researchers and policymakers, which uses traditional criteria to sort clearly defined activities while requiring the use of actual risk levels to decide more complex cases.

Background Although smoking prevalence in England has declined, one in five adults smoke. Smokers are at increased risk of a number of diseases, including COPD which affects an estimated 1.5 million people in England alone. This study aimed to explore issues relating to smoking behaviour and intention to quit that might be used to inform the development of cessation interventions. Issues explored included knowledge of smoking related disease, with a particular emphasis on Chronic Obstructive Pulmonary Disease (COPD). Understanding around risk of disease, including genetic risk was explored, as were features of appropriate and accessible cessation materials and support. Methods Semi-structured interviews and focus groups were conducted with a total of 30 individuals of which 17 were smoking cessation clients and 13 were professionals working within health care settings relevant to supporting smokers to quit. A largely purposive approach was taken to sampling, and data were analysed using the constant comparative method. Results Knowledge of the smoking related disease COPD was limited. Smokers’ concerns around risk of disease were influenced by their social context and were more focussed on how their smoking might impact on the health of their family and friends, rather than how it might impact on them as individuals. Participants felt the provision of genetic risk information may have a limited impact on motivation to quit. Genetic risk was considered to be a difficult concept to understand, particularly as increased risk does not mean an individual will definitely develop disease. In terms of cessation approaches, the use of visual media was consistently supported, as was the use of materials that linked directly with life experiences. Images of children inhaling second hand smoke for example, had a particular impact. Conclusions Public health messages around the risks of smoking and approaches to quitting should continue to have an emphasis on the dangers that an individual’s smoking has on the lives of the people around them. More work also needs to be done to raise awareness around both the risk of COPD in smokers and the impact this disease has on quality of life and life expectancy.

Unprecedented progress in sequencing technologies and decreasing cost have brought genomic testing into the clinical setting. At the same time, the debate in the literature concerning the return of incidental findings (IFs) has made this an important issue internationally. These developments reflect a shift in genetics that will also affect smaller countries, such as Greece, that are just starting to implement these technologies and may look to other countries for examples of good practice. Ten in-depth interviews were conducted with Greek experts in clinical sequencing. Previous experiences and attitudes toward IFs and clinical sequencing were investigated as well as views on the existing policy regarding managing genetic information generated through testing. . Interviews were analysed using thematic analysis. All participants reported the lack of any legal or other supportive mechanism. IFs are currently managed at a “local” level, i.e. within the clinic or the laboratory in an ad hoc way. All participants thought that clinically valid and actionable IFs should be returned, but always with caution and in respect to patients’ wishes, although several experts reported returning IFs according to their clinical discretion. Experts reported that most patients ask for all tests available but they felt that more counselling is needed to understand and manage genetic information. Due to the lack of any supporting mechanisms, professionals in Greece, even those with established experience in the field of genetic and genomic testing, have difficulties dealing with IFs. All experts agreed that it is now time, before the full integration of genomic testing into everyday clinical practice, for guidance to help Greek physicians work with patients and their families when IFs are discovered.

Background: DataSHIELD (Data Aggregation Through Anonymous Summary-statistics from Harmonised Individual levEL Databases) has been proposed to facilitate the co-analysis of individual-level data from multiple studies without physically sharing the data. In a previous paper, we investigated whether DataSHIELD could protect participant confidentiality in accordance with UK law. In this follow-up paper, we investigate whether DataSHIELD addresses a broader range of ethics-related data-sharing concerns. Methods: Ethics-related data-sharing concerns of Institutional Review Boards, ethics experts, international research consortia and research participants were identified through a literature search and systematically examined at a multidisciplinary workshop to determine whether DataSHIELD proposes mechanisms which can address these concerns. Results: DataSHIELD addresses several ethics-related data-sharing concerns related to privacy, confidentiality, and the protection of the research participant's rights while sharing data and after the data have been shared. The data remain entirely under the direct management of the study that collected them. Data processing commands are strictly supervised, and the data are queried in a protected environment. Issues related to the return of individual research results when data are shared are eliminated; the responsibility for return remains at the study of origin. Conclusion: DataSHIELD can provide an innovative and robust solution for addressing commonly encountered ethics-related data-sharing concerns.

Gaye, Amadou Marcon, Yannick Isaeva, Julia LaFlamme, Philippe Turner, Andrew Jones, Elinor M Minion, Joel Boyd, Andrew W Newby, Christopher J Nuotio, Marja-Liisa Wilson, Rebecca Butters, Oliver Murtagh, Barnaby Demir, Ipek Doiron, Dany Giepmans, Lisette Wallace, Susan E Budin-Ljosne, Isabelle Oliver Schmidt, Carsten Boffetta, Paolo Boniol, Mathieu Bota, Maria Carter, Kim W deKlerk, Nick Dibben, Chris Francis, Richard W Hiekkalinna, Tero Hveem, Kristian Kvaloy, Kirsti Millar, Sean Perry, Ivan J Peters, Annette Phillips, Catherine M Popham, Frank Raab, Gillian Reischl, Eva Sheehan, Nuala Waldenberger, Melanie Perola, Markus van den Heuvel, Edwin Macleod, John Knoppers, Bartha M Stolk, Ronald P Fortier, Isabel Harris, Jennifer R Woffenbuttel, Bruce H R Murtagh, Madeleine J Ferretti, Vincent Burton, Paul R eng MC_UP_A540₁021/Medical Research Council/United Kingdom MR/K006525/1/Medical Research Council/United Kingdom Medical Research Council/United Kingdom Wellcome Trust/United Kingdom Research Support, Non-U.S. Gov't England 2014/09/30 06:00 Int J Epidemiol. 2014 Dec;43(6):1929-44. doi: 10.1093/ije/dyu188. Epub 2014 Sep 26.; International audience; BACKGROUND: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK's proposed 'care.data' initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data. METHODS: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC. RESULTS: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach. CONCLUSIONS: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property-the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.

There is a noticeable lack of international regulation on personal data exchange and management in research. This article sheds light in this area by describing how the International Cancer Genome Consortium is developing policies and procedures to address the ethical and legal issues raised by the international transfer of data and results. These policies and procedures aim, first and most importantly, to safeguard the interests of the research participants and other involved stakeholders and, secondly, to facilitate the sharing of data and results to realize greater benefits from this kind of internationally collaborative genetic research.

A key element in the success of a biobank is the trust and support of the public. Building this trust is a difficult process. The field of population genomics raises many ethical and societal issues that must be addressed in order for the public to see participation as a trustworthy activity. The Public Population Project in Genomics (P3G) is an international collaboration dedicated to bringing members of the population biobanking community together to share their expertise for the advancement of population genomics research. Through its fundamental principles of promotion of the common good, responsibility, mutual respect, accountability and proportionality, P3G seeks to create tools and resources to assist those involved in biobanks to conduct their research in such a way as to inspire the trust and participation of the public.

The number of population-based and disease-based biobanks being created for research purposes is increasing. These collections of samples and associated data are being used to discover the links between genes and disease, and the genetic, lifestyle and environmental factors behind common complex diseases. In order to make a decision as to whether or not to provide their consent, potential participants in both types of biobanks need to be informed of the requirements and implications of participation. This comparative examination of clauses contained in consent materials from disease biobanks and population biobanks points to the factors that are specific to each type of biobank and highlights the issues that should be taken into consideration when creating consent materials for biobanking activities.

Introduction It is well recognised that it can be difficult for researchers to ensure that proper consent provisions are in place for their research project. If this can be difficult for those conducting a single site study, those difficulties can be compounded when that study becomes a member of an international scientific research consortium. These consortia bring together often diverse groups of researchers who, while working on a common topic, may represent different countries, cultures and scientific methodologies. Harmonising consent information and processes across these studies can be a complicated task. At the local level, participants need to be informed of the details of the study to which they are being recruited, and informed of the implications of the study’s inclusion in the consortium. Likewise, the international consortium needs to make certain that member studies have met appropriate consent requirements so that participants’ samples and data can be shared with and used by the consortium for agreed purposes. A considerable amount of time and effort is needed to ensure an international consortium is running consistently across its constituent parts. And as the consortium grows, so do the complexities of dealing with the different regulatory and cultural norms of its members, while staying within the organisational requirements of the consortium itself. But a level of consistency, in terms of consent to use of data and samples, must be achieved across the consortium members in order for its work to proceed while respecting the consent provisions agreed to by participants. The question of how to arrive at this benchmark is a vital one. This paper will present issues raised as a result of an examination of consent materials used by International Cancer Genome Consortium (ICGC) members.

Returning individual results to participants in research studies is gaining acceptance and policy guidance is now available for investigators to develop a plan for returning results at the local level. However, returning results discovered through the work of an international scientific research consortium presents additional ethical and procedural difficulties. No general guidance is available for international consortia that wish to consider this issue, but there are examples of internal policies that are being used by consortia such as the International Cancer Genome Consortium (ICGC) and the Type 1 Diabetes Genetics Consortium (T1DGC). This paper presents the policy stance these studies have adopted regarding returning individual research results and their reasons behind it, and gives specific examples from their policy documents and project consent materials. Finally, it suggests an oversight mechanism these and other international consortia can use to ensure that this important issue is addressed appropriately.

The regulation of stem cell research is an issue that has drawn much comment, criticism and even judicial arbitration in recent years. An emerging issue, addressed in this article, is how the fruits of that research-stem cell medicine-are likely to be regulated en route from lab to market. Taking account of the ethical, legal, social and safety issues raised by stem cell medicine and the goals of governance, the article explains the relevant regulatory instruments (e.g. the draft UK Stem Cell Bank Code, the EU Directive on Human Tissue, the EU Directives on medical products and devices, and the Human Tissue Act 2004) and critically examines the framework they provide.

Very large sample sizes are required for estimating effects which are known to be small, and for addressing intricate or complex statistical questions. This is often only achievable by pooling data from multiple studies, especially in genetic epidemiology where associations between individual genetic variants and phenotypes of interest are generally weak. However, the physical pooling of experimental data across a consortium is frequently prohibited by the ethico-legal constraints that govern agreements and consents for individual studies. Study level meta-analyses are frequently used so that data from multiple studies need not be pooled to conduct an analysis, though the resulting analysis is necessarily restricted by the available summary statistics. The idea of maintaining data security is also of importance in other areas and approaches to carrying out ‘secure analyses’ that do not require sharing of data from different sources have been proposed in the technometrics literature. Crucially, the algorithms for fitting certain statistical models can be manipulated so that an individual level meta-analysis can essentially be performed without the need for pooling individual-level data by combining particular summary statistics obtained individually from each study. DataSHIELD (Data Aggregation Through Anonymous Summary-statistics from Harmonised Individual levEL Databases) is a tool to coordinate analyses of data that cannot be pooled. In this paper, we focus on explaining why a DataSHIELD approach yields identical results to an individual level meta-analysis in the case of a generalised linear model, by simply using summary statistics from each study. It is also an efficient approach to carrying out a study level meta-analysis when this is appropriate and when the analysis can be pre-planned. We briefly comment on the IT requirements, together with the ethical and legal challenges which must be addressed.

BACKGROUND: Genealogical research and ancestry testing are popular recreational activities but little is known about the impact of the use of these services on clients' biological and social families. Ancestry databases are being enriched with self-reported data and data from deoxyribonucleic acid (DNA) analyses, but also are being linked to other direct-to-consumer genetic testing and research databases. As both family history data and DNA can provide information on more than just the individual, we asked whether companies, as a part of the consent process, were informing clients, and through them clients' relatives, of the potential implications of the use and linkage of their personal data.METHODS: We used content analysis to analyse publically-available consent and informational materials provided to potential clients of ancestry and direct-to-consumer genetic testing companies to determine what consent is required, what risks associated with participation were highlighted, and whether the consent or notification of third parties was suggested or required.RESULTS: We identified four categories of companies providing: 1) services based only on self-reported data, such as personal or family history; 2) services based only on DNA provided by the client; 3) services using both; and 4) services using both that also have a research component. The amount of information provided on the potential issues varied significantly across the categories of companies. 'Traditional' ancestry companies showed the greatest awareness of the implications for family members, while companies only asking for DNA focused solely on the client. While in some cases companies included text recommending clients inform their relatives, showing they recognised the issues, often it was located within lengthy terms and conditions or privacy statements that may not be read by potential clients.CONCLUSIONS: We recommend that companies should make it clearer that clients should inform third parties about their plans to participate, that third parties' data will be provided to companies, and that that data will be linked to other databases, thus raising privacy and issues on use of data. We also suggest investigating whether a 'generational consent' should be created that would include more than just the individual in decisions about participating in genetic investigations.