Assessing the privacy risks of data sharing in genomics

Best practices for data management and sharing in experimental biomedical research

Effective data management is crucial for scientific integrity and reproducibility, a cornerstone of scientific progress. Well-organized and well-documented data enable validation and building on results. Data management encompasses activities including organization, documentation, storage, sharing, and preservation. Robust data management establishes credibility, fostering trust within the scientific community and benefiting researchers' careers. In experimental biomedicine, comprehensive data management is vital due to the typically intricate protocols, extensive metadata, and large datasets. Low-throughput experiments, in particular, require careful management to address variations and errors in protocols and raw data quality. Transparent and accountable research practices rely on accurate documentation of procedures, data collection, and analysis methods. Proper data management ensures long-term preservation and accessibility of valuable datasets. Well-managed data can be revisited, contributing to cumulative knowledge and potential new discoveries. Publicly funded research has an added responsibility for transparency, resource allocation, and avoiding redundancy. Meeting funding agency expectations increasingly requires rigorous methodologies, adherence to standards, comprehensive documentation, and widespread sharing of data, code, and other auxiliary resources. This review provides critical insights into raw and processed data, metadata, high-throughput versus low-throughput datasets, a common language for documentation, experimental and reporting guidelines, efficient data management systems, sharing practices, and relevant repositories. We systematically present available resources and optimal practices for wide use by experimental biomedical researchers.

Assessing Privacy Vulnerabilities in Genetic Data Sets: Scoping Review

BACKGROUND

Genetic data are widely considered inherently identifiable. However, genetic data sets come in many shapes and sizes, and the feasibility of privacy attacks depends on their specific content. Assessing the reidentification risk of genetic data is complex, yet there is a lack of guidelines or recommendations that support data processors in performing such an evaluation.

OBJECTIVE

This study aims to gain a comprehensive understanding of the privacy vulnerabilities of genetic data and create a summary that can guide data processors in assessing the privacy risk of genetic data sets.

METHODS

We conducted a 2-step search, in which we first identified 21 reviews published between 2017 and 2023 on the topic of genomic privacy and then analyzed all references cited in the reviews (n=1645) to identify 42 unique original research studies that demonstrate a privacy attack on genetic data. We then evaluated the type and components of genetic data exploited for these attacks as well as the effort and resources needed for their implementation and their probability of success.

RESULTS

From our literature review, we derived 9 nonmutually exclusive features of genetic data that are both inherent to any genetic data set and informative about privacy risk: biological modality, experimental assay, data format or level of processing, germline versus somatic variation content, content of single nucleotide polymorphisms, short tandem repeats, aggregated sample measures, structural variants, and rare single nucleotide variants.

CONCLUSIONS

On the basis of our literature review, the evaluation of these 9 features covers the great majority of privacy-critical aspects of genetic data and thus provides a foundation and guidance for assessing genetic data risk.

The five pillars of computational reproducibility: bioinformatics and beyond

Computational reproducibility is a simple premise in theory, but is difficult to achieve in practice. Building upon past efforts and proposals to maximize reproducibility and rigor in bioinformatics, we present a framework called the five pillars of reproducible computational research. These include (1) literate programming, (2) code version control and sharing, (3) compute environment control, (4) persistent data sharing and (5) documentation. These practices will ensure that computational research work can be reproduced quickly and easily, long into the future. This guide is designed for bioinformatics data analysts and bioinformaticians in training, but should be relevant to other domains of study.

Federated Analysis for Privacy-Preserving Data Sharing: A Technical and Legal Primer

Continued advances in precision medicine rely on the widespread sharing of data that relate human genetic variation to disease. However, data sharing is severely limited by legal, regulatory, and ethical restrictions that safeguard patient privacy. Federated analysis addresses this problem by transferring the code to the data-providing the technical and legal capability to analyze the data within their secure home environment rather than transferring the data to another institution for analysis. This allows researchers to gain new insights from data that cannot be moved, while respecting patient privacy and the data stewards' legal obligations. Because federated analysis is a technical solution to the legal challenges inherent in data sharing, the technology and policy implications must be evaluated together. Here, we summarize the technical approaches to federated analysis and provide a legal analysis of their policy implications.

Canadians' opinions towards COVID-19 data-sharing: a national cross-sectional survey

OBJECTIVES

COVID-19 research has significantly contributed to pandemic response and the enhancement of public health capacity. COVID-19 data collected by provincial/territorial health authorities in Canada are valuable for research advancement yet not readily available to the public, including researchers. To inform developments in public health data-sharing in Canada, we explored Canadians' opinions of public health authorities sharing deidentified individual-level COVID-19 data publicly.

DESIGN/SETTING/INTERVENTIONS/OUTCOMES

A national cross-sectional survey was administered in Canada in March 2022, assessing Canadians' opinions on publicly sharing COVID-19 datatypes. Market research firm Léger was employed for recruitment and data collection.

PARTICIPANTS

Anyone greater than or equal to 18 years and currently living in Canada.

RESULTS

4981 participants completed the survey with a 92.3% response rate. 79.7% were supportive of provincial/territorial authorities publicly sharing deidentified COVID-19 data, while 20.3% were hesitant/averse/unsure. Datatypes most supported for being shared publicly were symptoms (83.0% in support), geographical region (82.6%) and COVID-19 vaccination status (81.7%). Datatypes with the most aversion were employment sector (27.4% averse), postal area (26.7%) and international travel history (19.7%). Generally supportive Canadians were characterised as being ≥50 years, with higher education, and being vaccinated against COVID-19 at least once. Vaccination status was the most influential predictor of data-sharing opinion, with respondents who were ever vaccinated being 4.20 times more likely (95% CI 3.21 to 5.48, p=0.000) to be generally supportive of data-sharing than those unvaccinated.

CONCLUSIONS

These findings suggest that the Canadian public is generally favourable to deidentified data-sharing. Identifying factors that are likely to improve attitudes towards data-sharing are useful to stakeholders involved in data-sharing initiatives, such as public health agencies, in informing the development of public health communication and data-sharing policies. As Canada progresses through the COVID-19 pandemic, and with limited testing and reporting of COVID-19 data, it is essential to improve deidentified data-sharing given the public's general support for these efforts.

Sharing GWAS summary statistics results in more citations

A review of citation rates from genomic studies in the GWAS Catalog suggests that sharing summary statistics results, on average, in ~81.8% more citations, highlighting a benefit of publicly sharing GWAS summary statistics.

Generating evidence on privacy outcomes to inform privacy risk management: A way forward?

Effective and efficient privacy risk management (PRM) is a necessary condition to support digitalization in health care and secondary use of patient data in research. To reduce privacy risks, current PRM frameworks are rooted in an approach trying to reduce undesired technical/organizational outcomes such as broken encryption or unintentional data disclosure. Comparing this with risk management in preventive or therapeutic medicine, a key difference becomes apparent: in health-related risk management, medicine focuses on person-specific health outcomes, whereas PRM mostly targets more indirect, technical/organizational outcomes. In this paper, we illustrate and discuss how a PRM approach based on evidence of person-specific privacy outcomes might look using three consecutive steps: i) a specification of undesired person-specific privacy outcomes, ii) empirical assessments of their frequency and severity, and iii) empirical studies on how effectively the available PRM interventions reduce their frequency or severity. After an introduction of these three steps, we cover their status quo and outline open questions and PRM-specific challenges in need of further conceptual clarification and feasibility studies. Specific challenges of an outcome-oriented approach to PRM include the potential delays between concrete threats manifesting and the resulting person/group-specific privacy outcomes. Moreover, new ways of exploiting privacy-sensitive information to harm individuals could be developed in the future. The challenges described are of technical, legal, ethical, financial and resource-oriented nature. In health research, however, there is explicit discussion about how to overcome such challenges to make important outcome-based assessments as feasible as possible. This paper concludes that it might be the time to have this discussion in the PRM field as well.

Toward a Framework for Assessing Privacy Risks in Multi-Omic Research and Databases

While the accumulation and increased circulation of genomic data have captured much attention over the past decade, privacy risks raised by the diversification and integration of omics have been largely overlooked. In this paper, we propose the outline of a framework for assessing privacy risks in multi-omic research and databases. Following a comparison of privacy risks associated with genomic and epigenomic data, we dissect ten privacy risk-impacting omic data properties that affect either the risk of re-identification of research participants, or the sensitivity of the information potentially conveyed by biological data. We then propose a three-step approach for the assessment of privacy risks in the multi-omic era. Thus, we lay grounds for a data property-based, 'pan-omic' approach that moves away from genetic exceptionalism. We conclude by inviting our peers to refine these theoretical foundations, put them to the test in their respective fields, and translate our approach into practical guidance.

Controlling my genome with my smartphone: first clinical experiences of the PROMISE system

Background

The development of Precision Medicine strategies requires high-dimensional phenotypic and genomic data, both of which are highly privacy-sensitive data types. Conventional data management systems lack the capabilities to sufficiently handle the expected large quantities of such sensitive data in a secure manner. PROMISE is a genetic data management concept that implements a highly secure platform for data exchange while preserving patient interests, privacy, and autonomy.

Methods

The concept of PROMISE to democratize genetic data was developed by an interdisciplinary team. It integrates a sophisticated cryptographic concept that allows only the patient to grant selective access to defined parts of his genetic information with single DNA base-pair resolution cryptography. The PROMISE system was developed for research purposes to evaluate the concept in a pilot study with nineteen cardiomyopathy patients undergoing genotyping, questionnaires, and longitudinal follow-up.

Results

The safety of genetic data was very important to 79%, and patients generally regarded the data as highly sensitive. More than half the patients reported that their attitude towards the handling of genetic data has changed after using the PROMISE app for 4 months (median). The patients reported higher confidence in data security and willingness to share their data with commercial third parties, including pharmaceutical companies (increase from 5 to 32%).

Conclusion

PROMISE democratizes genomic data by a transparent, secure, and patient-centric approach. This clinical pilot study evaluating a genetic data infrastructure is unique and shows that patient’s acceptance of data sharing can be increased by patient-centric decision-making.

Graphic abstract

'It’s not something you can take in your hands'. Swiss experts’ perspectives on health data ownership: an interview-based study

Objectives

The evolution of healthcare and biomedical research into data-rich fields has raised several questions concerning data ownership. In this paper, we aimed to analyse the perspectives of Swiss experts on the topic of health data ownership and control.

Design

In our qualitative study, we selected participants through purposive and snowball sampling. Interviews were recorded, transcribed verbatim and then analysed thematically.

Setting

Semi-structured interviews were conducted in person, via phone or online.

Participants

We interviewed 48 experts (researchers, policy makers and other stakeholders) of the Swiss health-data framework.

Results

We identified different themes linked to data ownership. These include: (1) the data owner: data-subjects versus data-processors; (2) uncertainty about data ownership; (3) labour as a justification for data ownership and (4) the market value of data. Our results suggest that experts from Switzerland are still divided about who should be the data owner and also about what ownership would exactly mean. There is ambivalence between the willingness to acknowledge patients as the data owners and the fact that the effort made by data-processors (eg, researchers) to collect and manage the data entitles them to assert ownership claims towards the data themselves. Altogether, a tendency to speak about data in market terms also emerged.

Conclusions

The development of a satisfactory account of data ownership as a concept to organise the relationship between data-subjects, data-processors and data themselves is an important endeavour for Switzerland and other countries who are developing data governance in the healthcare and research domains. Setting clearer rules on who owns data and on what ownership exactly entails would be important. If this proves unfeasible, the idea that health data cannot truly belong to anyone could be promoted. However, this will not be easy, as data are seen as an asset to control and profit from.

Attitudes of Costa Rican individuals towards donation of personal genetic data for research

Aim: We explore attitudes from the public in Costa Rica regarding willingness to donate DNA data for research. Materials & methods: A total of 224 Costa Rican individuals answered the anonymous online survey 'Your DNA, Your Say'. It covers attitudes toward DNA and medical data donation, trust in research professionals and concerns about consequences of reidentification. Results & conclusion: Most individuals (89%) are willing to donate their information for research purposes. When confronted with different potential uses of their data, participants are significantly less likely to donate data to for-profit researchers (34% willingness to donate). The most frequently cited concerns regarding donation of genetic data relate to possible discrimination by health/life insurance companies and employers. For the participants in the survey, the most trusted professionals are their own medical doctor and nonprofit researchers from their country. This is the first study regarding attitudes toward genetic data donation in Costa Rica.

A semi-automatic methodology for analysing distributed and private biobanks

Privacy issues limit the analysis and cross-exploration of most distributed and private biobanks, often raised by the multiple dimensionality and sensitivity of the data associated with access restrictions and policies. These characteristics prevent collaboration between entities, constituting a barrier to emergent personalized and public health challenges, namely the discovery of new druggable targets, identification of disease-causing genetic variants, or the study of rare diseases. In this paper, we propose a semi-automatic methodology for the analysis of distributed and private biobanks. The strategies involved in the proposed methodology efficiently enable the creation and execution of unified genomic studies using distributed repositories, without compromising the information present in the datasets. We apply the methodology to a case study in the current Covid-19, ensuring the combination of the diagnostics from multiple entities while maintaining privacy through a completely identical procedure. Moreover, we show that the methodology follows a simple, intuitive, and practical scheme.

A deliberative study of public attitudes towards sharing genomic data within NHS genomic medicine services in England

Whole genome (DNA) sequencing is becoming part of routine care healthcare in England. Genomic data are most useful when pooled with other patients' data, meaning that clinicians may need to share data to effectively treat patients. We ran deliberative focus groups to explore views among 44 patients and members of the public about proposals for wider genomic data sharing for clinical care. Participants were briefed about genomic medicine and engaged in group and individual exercises to deliberate on the benefits and risks of using genomic data. Findings showed that participants supported wider sharing of genomic data within health services and naturally linked care and research activities. Nonetheless, they were concerned about managing flows of information to protect patient confidentiality and guard against unauthorised uses, now and over the long-term. Ongoing conversations with the public are needed to determine appropriate uses of genomic data and safeguards to inform service development.

Ethical, Legal, and Social Issues (ELSI) of Responsible Data Sharing Involving Children in Genomics: A Systematic Literature Review of Reasons

BACKGROUND

Progress in precision medicine relies on the access to, use of, and exchange of genomic and associated clinical data, including from children. The ethical, legal, and social issues (ELSI) of such data access, use, and exchange may be accentuated in the pediatric context due in part to the highly sensitive nature of genomic data, children's consent-related vulnerabilities, and uncertain risks of reidentification. Systematic analyses of the ELSI and scientific reasons for why and how genomic data may be shared responsibly are, however, limited. Methods: We conducted a modified systematic review of reasons according to Sofaer and Strech to examine the ELSI and scientific reasons for "responsible" sharing of children's genomic and associated clinical data. Empirical articles, commentaries, and data-sharing policies indexed in Medline, Scopus, Web of Science, and BIOSIS were included in the analysis if they discussed ELSI and were published between 2003 and 2017 in English. Results: One hundred and fifty-one records met our inclusion criteria. We identified 11 unique reasons and 8 subreasons for why children's genomic data should or should not be shared. Enhancing the prospect of direct and indirect benefits and maximizing the utility of children's data were top reasons why data should be shared. Inadequate data privacy protection was the leading reason why it should not. We furthermore identified 8 reasons and 30 subreasons that support conditional data sharing, in which recontact for the continued use of children's data once they reach the age of majority was the most frequently endorsed condition. Conclusions: The complete list of ELSI reasons and responsible conditions provides an evidentiary basis upon which institutions can develop data-sharing policies. Institutions should encourage the sharing of children's data to advance genomic research, while heeding special reconsent and data protection mechanisms that may help mitigate uncertain longitudinal risks for children and families.

Privacy challenges and research opportunities for genomic data sharing

The sharing of genomic data holds great promise in advancing precision medicine and providing personalized treatments and other types of interventions. However, these opportunities come with privacy concerns, and data misuse could potentially lead to privacy infringement for individuals and their blood relatives. With the rapid growth and increased availability of genomic datasets, understanding the current genome privacy landscape and identifying the challenges in developing effective privacy-protecting solutions are imperative. In this work, we provide an overview of major privacy threats identified by the research community and examine the privacy challenges in the context of emerging direct-to-consumer genetic-testing applications. We additionally present general privacy-protection techniques for genomic data sharing and their potential applications in direct-to-consumer genomic testing and forensic analyses. Finally, we discuss limitations in current privacy-protection methods, highlight possible mitigation strategies and suggest future research opportunities for advancing genomic data sharing.

Ethical, legal, and social issues (ELSI) in rare diseases: a landscape analysis from funders

Recent interest in personalized medicine has highlighted the importance of research in ethical, legal, and social issues (ELSI). Issues in ELSI research may be magnified in the rare diseases population (i.e., small numbers of affected individuals, challenges in maintaining confidentiality, and paucity of treatments for diseases where natural history information may be limited). More than other areas of research, potential barriers include the lack of funding opportunities and appropriate review processes for applications to funding agencies. The ELSI Working Group of the International Rare Diseases Research Consortium (IRDiRC) performed an informal survey on ELSI funding initiatives to learn more about different funding mechanisms and to identify potential gaps in funding opportunities. The Working Group discusses these challenges and highlights the role of funding agencies and partners such as patient advocacy groups, specialists in social sciences and humanities, and clinicians to advance ELSI research in rare diseases.

Openness in Big Data and Data Repositories: The Application of an Ethics Framework for Big Data in Health and Research

There is a growing expectation, or even requirement, for researchers to deposit a variety of research data in data repositories as a condition of funding or publication. This expectation recognizes the enormous benefits of data collected and created for research purposes being made available for secondary uses, as open science gains increasing support. This is particularly so in the context of big data, especially where health data is involved. There are, however, also challenges relating to the collection, storage, and re-use of research data. This paper gives a brief overview of the landscape of data sharing via data repositories and discusses some of the key ethical issues raised by the sharing of health-related research data, including expectations of privacy and confidentiality, the transparency of repository governance structures, access restrictions, as well as data ownership and the fair attribution of credit. To consider these issues and the values that are pertinent, the paper applies the deliberative balancing approach articulated in the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019) to the domain of Openness in Big Data and Data Repositories. Please refer to that article for more information on how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end.

Model consent clauses for rare disease research

Background

Rare Disease research has seen tremendous advancements over the last decades, with the development of new technologies, various global collaborative efforts and improved data sharing. To maximize the impact of and to further build on these developments, there is a need for model consent clauses for rare diseases research, in order to improve data interoperability, to meet the informational needs of participants, and to ensure proper ethical and legal use of data sources and participants’ overall protection.

Methods

A global Task Force was set up to develop model consent clauses specific to rare diseases research, that are comprehensive, harmonized, readily accessible, and internationally applicable, facilitating the recruitment and consent of rare disease research participants around the world. Existing consent forms and notices of consent were analyzed and classified under different consent themes, which were used as background to develop the model consent clauses.

Results

The IRDiRC-GA4GH MCC Task Force met in September 2018, to discuss and design model consent clauses. Based on analyzed consent forms, they listed generic core elements and designed the following rare disease research specific core elements; Rare Disease Research Introductory Clause, Familial Participation, Audio/Visual Imaging, Collecting, storing, sharing of rare disease data, Recontact for matching, Data Linkage, Return of Results to Family Members, Incapacity/Death, and Benefits.

Conclusion

The model consent clauses presented in this article have been drafted to highlight consent elements that bear in mind the trends in rare disease research, while providing a tool to help foster harmonization and collaborative efforts.

Electronic supplementary material

The online version of this article (10.1186/s12910-019-0390-x) contains supplementary material, which is available to authorized users.

Trust and Expectations of Researchers and Public Health Departments for the Use of HIV Molecular Epidemiology

Background: Molecular epidemiology (ME) is a technique used to study the dynamics of pathogen transmission through a population. When used to study HIV infections, ME generates powerful information about how HIV is transmitted, including epidemiologic patterns of linkage and, potentially, transmission direction. Thus, ME raises challenging questions about the most responsible way to protect individual privacy while acquiring and using these data to advance public health and inform HIV intervention strategies. Here, we report on stakeholders' expectations for how researchers and public health agencies might use HIV ME. Methods: We conducted in-depth semistructured interviews with 40 key stakeholders to find out how these individuals respond to the proposed risks and benefits of HIV ME. Transcripts were coded and analyzed using Atlas.ti. Expectations were assessed through analysis of responses to hypothetical scenarios designed to help interviewees think through the implications of this emerging technique in the contexts of research and public health. Results: Our analysis reveals a wide range of imagined responsibilities, capabilities, and trustworthiness of researchers and public health agencies. Specifically, many respondents expect researchers and public health agencies to use HIV ME carefully and maintain transparency about how data will be used. Informed consent was discussed as an important opportunity for notification of privacy risks. Furthermore, some respondents wished that public health agencies were held to the same form of oversight and accountability represented by informed consent in research. Conclusions: To prevent HIV ME from becoming a barrier to testing or a source of public mistrust, the sense of vulnerability expressed by some respondents must be addressed. In research, informed consent is an obvious opportunity for this. Without giving specimen donors a similar opportunity to opt out, public health agencies may find it difficult to adopt HIV ME without deterring testing and treatment.

Privacy and ethical challenges in next-generation sequencing

INTRODUCTION

Next-generation sequencing (NGS) is expected to revolutionize health care. NGS allows for sequencing of the whole genome more cheaply and quickly than previous techniques. NGS offers opportunities to advance medical diagnostics and treatments, but also raises complicated ethical questions that need to be addressed.

AREAS CONSIDERED

This article draws from the literature on research and clinical ethics, as well as next-generation sequencing, in order to provide an overview of the ethical challenges involved in next-generation sequencing. This article includes a discussion of the ethics of NGS in research and clinical contexts.

EXPERT OPINION

The use of NGS in clinical and research contexts has features that pose challenges for traditional ethical frameworks for protecting research participants and patients. NGS generates massive amounts of data and results that vary in terms of known clinical relevance. It is important to determine appropriate processes for protecting, managing and communicating the data. The use of machine learning for sequencing and interpretation of genomic data also raises concerns in terms of the potential for bias and potential implications for fiduciary obligations. NGS poses particular challenges in three main ethical areas: privacy, informed consent, and return of results.

Model framework for governance of genomic research and biobanking in Africa - a content description

Genomic research and biobanking are expanding globally, with a promise to fast-track the research needed to improve approaches to disease treatment and prevention through scientific collaborations such as the Human Heredity and Health in Africa (H3Africa) initiative. Integral to this type of research is the availability of samples and data for research. The need for broad access brings along a host of ethical concerns, including those related to privacy and confidentiality, as well as fairness and equity in access and capacity to utilise these samples between scientists from the high income and low income countries. Addressing these concerns while promoting genomic research, especially in Africa, requires the implementation of a sound governance framework. In this paper, we describe the contents of a Framework for Best Practice for Genomics Research and biobanking in Africa that was developed, under the auspices of the H3Africa initiative. This framework is broad enough to be used and adapted by African countries to facilitate the development of country-specific guidelines and to help improve the conduct and governance of genomics research.

Model framework for governance of genomic research and biobanking in Africa - a content description

Genomic research and biobanking are expanding globally, with a promise to fast-track the research needed to improve approaches to disease treatment and prevention through scientific collaborations such as the Human Heredity and Health in Africa (H3Africa) initiative. Integral to this type of research is the availability of samples and data for research. The need for broad access brings along a host of ethical concerns, including those related to privacy and confidentiality, as well as fairness and equity in access and capacity to utilise these samples between scientists from the high income and low income countries. Addressing these concerns while promoting genomic research, especially in Africa, requires the implementation of a sound governance framework. In this paper, we describe the contents of a Framework for Best Practice for Genomics Research and biobanking in Africa that was developed, under the auspices of the H3Africa initiative. This framework is broad enough to be used and adapted by African countries to facilitate the development of country-specific guidelines and to help improve the conduct and governance of genomics research.

The Bermuda Triangle: The Pragmatics, Policies, and Principles for Data Sharing in the History of the Human Genome Project

The Bermuda Principles for DNA sequence data sharing are an enduring legacy of the Human Genome Project (HGP). They were adopted by the HGP at a strategy meeting in Bermuda in February of 1996 and implemented in formal policies by early 1998, mandating daily release of HGP-funded DNA sequences into the public domain. The idea of daily sharing, we argue, emanated directly from strategies for large, goal-directed molecular biology projects first tested within the "community" of C. elegans researchers, and were introduced and defended for the HGP by the nematode biologists John Sulston and Robert Waterston. In the C. elegans community, and subsequently in the HGP, daily sharing served the pragmatic goals of quality control and project coordination. Yet in the HGP human genome, we also argue, the Bermuda Principles addressed concerns about gene patents impeding scientific advancement, and were aspirational and flexible in implementation and justification. They endured as an archetype for how rapid data sharing could be realized and rationalized, and permitted adaptation to the needs of various scientific communities. Yet in addition to the support of Sulston and Waterston, their adoption also depended on the clout of administrators at the US National Institutes of Health (NIH) and the UK nonprofit charity the Wellcome Trust, which together funded 90% of the HGP human sequencing effort. The other nations wishing to remain in the HGP consortium had to accommodate to the Bermuda Principles, requiring exceptions from incompatible existing or pending data access policies for publicly funded research in Germany, Japan, and France. We begin this story in 1963, with the biologist Sydney Brenner's proposal for a nematode research program at the Laboratory of Molecular Biology (LMB) at the University of Cambridge. We continue through 2003, with the completion of the HGP human reference genome, and conclude with observations about policy and the historiography of molecular biology.

Direct-to-Consumer Genetic Testing: Finding a Clear Path Forward

Regulatory compliance in the direct-to-consumer genetic testing market is highly sophisticated as there are numerous federal, state, and ethical barriers to entry. In 2010, amid an underregulated market, 23andMe sought to do what few companies in the medical industry had attempted before-disregard the guidance and requests of the US Food and Drug Administration. This regulatory strategy effectively destined the company's Personal Genome Service for failure; however, the company changed course and has been granted several regulatory clearances. This exemplifies the importance of a healthy relationship with regulatory agencies, although challenges remain. The DTC industry continues to have a perplexing regulatory framework at both the federal and state level. There are also ethical concerns with the monetization of deidentified genetic health information, as genetic data have an inherent level of identifiability and are not fully protected by the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule in these scenarios. Although some DTC tests are now cleared in the United States, the concern that a learned intermediary is needed for interpretation of results remains. The regulatory oversight of this market will need to continue to develop to ensure the protection of consumer health and privacy.

Preanalytical Processing and Biobanking Procedures of Biological Samples for Metabolomics Research: A White Paper, Community Perspective (for "Precision Medicine and Pharmacometabolomics Task Group"-The Metabolomics Society Initiative)

BACKGROUND

The metabolome of any given biological system contains a diverse range of low molecular weight molecules (metabolites), whose abundances can be affected by the timing and method of sample collection, storage, and handling. Thus, it is necessary to consider the requirements for preanalytical processes and biobanking in metabolomics research. Poor practice can create bias and have deleterious effects on the robustness and reproducibility of acquired data.

CONTENT

This review presents both current practice and latest evidence on preanalytical processes and biobanking of samples intended for metabolomics measurement of common biofluids and tissues. It highlights areas requiring more validation and research and provides some evidence-based guidelines on best practices.

SUMMARY

Although many researchers and biobanking personnel are familiar with the necessity of standardizing sample collection procedures at the axiomatic level (e.g., fasting status, time of day, "time to freezer," sample volume), other less obvious factors can also negatively affect the validity of a study, such as vial size, material and batch, centrifuge speeds, storage temperature, time and conditions, and even environmental changes in the collection room. Any biobank or research study should establish and follow a well-defined and validated protocol for the collection of samples for metabolomics research. This protocol should be fully documented in any resulting study and should involve all stakeholders in its design. The use of samples that have been collected using standardized and validated protocols is a prerequisite to enable robust biological interpretation unhindered by unnecessary preanalytical factors that may complicate data analysis and interpretation.

Balancing the local and the universal in maintaining ethical access to a genomics biobank

BACKGROUND

Issues of balancing data accessibility with ethical considerations and governance of a genomics research biobank, Generation Scotland, are explored within the evolving policy landscape of the past ten years. During this time data sharing and open data access have become increasingly important topics in biomedical research. Decisions around data access are influenced by local arrangements for governance and practices such as linkage to health records, and the global through policies for biobanking and the sharing of data with large-scale biomedical research data resources and consortia.

METHODS

We use a literature review of policy relevant documents which apply to the conduct of biobanks in two areas: support for open access and the protection of data subjects and researchers managing a bioresource. We present examples of decision making within a biobank based upon observations of the Generation Scotland Access Committee. We reflect upon how the drive towards open access raises ethical dilemmas for established biorepositories containing data and samples from human subjects.

RESULTS

Despite much discussion in science policy literature about standardisation, the contextual aspects of biobanking are often overlooked. Using our engagement with GS we demonstrate the importance of local arrangements in the creation of a responsive ethical approach to biorepository governance. We argue that governance decisions regarding access to the biobank are intertwined with considerations about maintenance and viability at the local level. We show that in addition to the focus upon ever more universal and standardised practices, the local expertise gained in the management of such repositories must be supported.

CONCLUSIONS

A commitment to open access in genomics research has found almost universal backing in science and health policy circles, but repositories of data and samples from human subjects may have to operate under managed access, to protect privacy, align with participant consent and ensure that the resource can be managed in a sustainable way. Data access committees need to be reflexive and flexible, to cope with changing technology and opportunities and threats from the wider data sharing environment. To understand these interactions also involves nurturing what is particular about the biobank in its local context.

Clarifying the concept of the "Social" in risk assessments for human subjects research

International guidelines for the conduct of research with human participants, such as those put forth by the Council for International Organizations of Medical Sciences (CIOMS, 2002), recommend that research review committees account for social risk and benefits to society in their review of proposed research. What do the concepts of the "social" and "society" mean in the context of the review of human participants research? Here we analyze concepts of social and society to define the terms: social harm, social risk, social benefit, and benefits to society. We argue that use of these terms invite more questions than answers and beg for difficult empirical research to determine the nature, likelihood, and magnitude of this category of risk and benefit. Until more research is done and these questions are answered, we advise reviewers to adopt an attitude of provisionalism and caution in their review of specifically "social" risks and benefits and "benefits to society."

Motivations of participants in the citizen science of microbiomics: data from the British Gut Project

PURPOSE

The establishment of databases for research in human microbiomics is dependent on the recruitment of sufficient numbers and diversity of participants. Factors that support or impede participant recruitment in studies of this type have not yet been studied.

METHODS

We report the results of a survey aimed at establishing the motivations of participants in the British Gut Project, a research project that relies on volunteers to provide samples and to help fund the project.

RESULTS

The two most frequently reported motivations for participation were altruism and solidarity. Low education levels appeared to be a recruitment obstacle. More than half of our 151 respondents said they would participate in further citizen-science projects; 38% said they would not participate in a similar project if it was for-profit or in a project that did not release data sets in repositories accessible to scientists (30%).

CONCLUSIONS

The desire to take part in research was reported as a key motivation for participation in the British Gut Project (BGP). Such prosocial motivations can be mobilized for the establishment of large data sets for research.Genet Med advance online publication 26 January 2017.

An "Ethical Moment" in Data Sharing

This study draws on interviews with forty-nine members of a biomedical research community in the UK that is involved in negotiating data sharing and access. During an interview, an interviewee used the words "ethical moment" to describe a confrontation between collaborators in relation to data sharing. In this article, I use this as a lens for thinking about relations between "the conceptual and the empirical" in a way that allows both analyst and actor to challenge the status quo and consider other ethical possibilities. Drawing on actor network theory (ANT), I approach "the empirical" using the concepts of controversy and ontological uncertainty as methodological tools to tackle the problem of ethics. I suggest that these concepts also provide a bridge for understanding the ontological structure of the virtual and the actual, as described in Deleuze's Difference and Repetition. While other science and technology studies scholars have sought to draw on Deleuze, this article addresses the integration of ethics and empirical research. It arises as a critical reaction to existing treatments of this problem as found in empirical ethics, especially in the sociology of bioethics, and indirectly in ANT texts.

Big Data in medical research and EU data protection law: challenges to the consent or anonymise approach

Medical research is increasingly becoming data-intensive; sensitive data are being re-used, linked and analysed on an unprecedented scale. The current EU data protection law reform has led to an intense debate about its potential effect on this processing of data in medical research. To contribute to this evolving debate, this paper reviews how the dominant 'consent or anonymise approach' is challenged in a data-intensive medical research context, and discusses possible ways forwards within the EU legal framework on data protection. A large part of the debate in literature focuses on the acceptability of adapting consent or anonymisation mechanisms to overcome the challenges within these approaches. We however believe that the search for ways forward within the consent or anonymise paradigm will become increasingly difficult. Therefore, we underline the necessity of an appropriate research exemption from consent for the use of sensitive personal data in medical research to take account of all legitimate interests. The appropriate conditions of such a research exemption are however subject to debate, and we expect that there will be minimal harmonisation of these conditions in the forthcoming EU Data Protection Regulation. Further deliberation is required to determine when a shift away from consent as a legal basis is necessary and proportional in a data-intensive medical research context, and what safeguards should be put in place when such a research exemption from consent is provided.

Discussion of "Combining Health Data Uses to Ignite Health System Learning"

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Combining Health Data Uses to Ignite Health System Learning" written by John D. Ainsworth and Iain E. Buchan [1]. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the paper of Ainsworth and Buchan. In subsequent issues the discussion can continue through letters to the editor. With these comments on the paper "Combining Health Data Uses to Ignite Health System Learning", written by John D. Ainsworth and Iain E. Buchan [1], the journal seeks to stimulate a broad discussion on new ways for combining data sources for the reuse of health data in order to identify new opportunities for health system learning. An international group of experts has been invited by the editor of Methods to comment on this paper. Each of the invited commentaries forms one section of this paper.

Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine

As research laboratories and clinics collaborate to achieve precision medicine, both communities are required to understand mandated electronic health/medical record (EHR/EMR) initiatives that will be fully implemented in all clinics in the United States by 2015. Stakeholders will need to evaluate current record keeping practices and optimize and standardize methodologies to capture nearly all information in digital format. Collaborative efforts from academic and industry sectors are crucial to achieving higher efficacy in patient care while minimizing costs. Currently existing digitized data and information are present in multiple formats and are largely unstructured. In the absence of a universally accepted management system, departments and institutions continue to generate silos of information. As a result, invaluable and newly discovered knowledge is difficult to access. To accelerate biomedical research and reduce healthcare costs, clinical and bioinformatics systems must employ common data elements to create structured annotation forms enabling laboratories and clinics to capture sharable data in real time. Conversion of these datasets to knowable information should be a routine institutionalized process. New scientific knowledge and clinical discoveries can be shared via integrated knowledge environments defined by flexible data models and extensive use of standards, ontologies, vocabularies, and thesauri. In the clinical setting, aggregated knowledge must be displayed in user-friendly formats so that physicians, non-technical laboratory personnel, nurses, data/research coordinators, and end-users can enter data, access information, and understand the output. The effort to connect astronomical numbers of data points, including ‘-omics’-based molecular data, individual genome sequences, experimental data, patient clinical phenotypes, and follow-up data is a monumental task. Roadblocks to this vision of integration and interoperability include ethical, legal, and logistical concerns. Ensuring data security and protection of patient rights while simultaneously facilitating standardization is paramount to maintaining public support. The capabilities of supercomputing need to be applied strategically. A standardized, methodological implementation must be applied to developed artificial intelligence systems with the ability to integrate data and information into clinically relevant knowledge. Ultimately, the integration of bioinformatics and clinical data in a clinical decision support system promises precision medicine and cost effective and personalized patient care.

Stakeholders' perspectives on biobank-based genomic research: systematic review of the literature

The success of biobank-based genomic research is widely dependent on people's willingness to donate their tissue. Thus, stakeholders' opinions should be considered in the development of best practice guidelines for research and recruiting participants. We systematically analyzed the empirical literature describing different stakeholders' views towards ethical questions with regard to type of consent, data sharing and return of incidental findings. Patients are more open to one-time general consent than the public. Only a small proportion desires recontact if the research aim changed. A broad consent model would prevent only a small proportion of patients from participating in research. Although professionals are concerned about a risk of reidentification, patients and the public support data sharing and find that the benefit of research outweighs the potential risk of reidentification. However, they desire detailed information about the privacy protection measures. Regarding the return of incidental findings, the public and professionals focus on clinically actionable results, whereas patients are interested in receiving as much information as possible. For professionals, concrete guidelines that help managing the return of incidental findings should be warranted. For this it would be helpful addressing the different categories - actionable, untreatable and inheritable diseases - upfront with patients and public.

Genetic research on biospecimens poses minimal risk

Genetic research on human biospecimens is increasingly common. However, debate continues over the level of risk that this research poses to sample donors. Some argue that genetic research on biospecimens poses minimal risk; others argue that it poses greater than minimal risk and therefore needs additional requirements and limitations. This debate raises concern that some donors are not receiving appropriate protection or, conversely, that valuable research is being subject to unnecessary requirements and limitations. The present paper attempts to resolve this debate using the widely-endorsed 'risks of daily life' standard. The three extant versions of this standard all suggest that, with proper measures in place to protect confidentiality, most genetic research on human biospecimens poses minimal risk to donors.

DataSHIELD: an ethically robust solution to multiple-site individual-level data analysis

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.

Size matters: how population size influences genotype-phenotype association studies in anonymized data

OBJECTIVE

Electronic medical records (EMRs) data is increasingly incorporated into genome-phenome association studies. Investigators hope to share data, but there are concerns it may be "re-identified" through the exploitation of various features, such as combinations of standardized clinical codes. Formal anonymization algorithms (e.g., k-anonymization) can prevent such violations, but prior studies suggest that the size of the population available for anonymization may influence the utility of the resulting data. We systematically investigate this issue using a large-scale biorepository and EMR system through which we evaluate the ability of researchers to learn from anonymized data for genome-phenome association studies under various conditions.

METHODS

We use a k-anonymization strategy to simulate a data protection process (on data sets containing clinical codes) for resources of similar size to those found at nine academic medical institutions within the United States. Following the protection process, we replicate an existing genome-phenome association study and compare the discoveries using the protected data and the original data through the correlation (r(2)) of the p-values of association significance.

RESULTS

Our investigation shows that anonymizing an entire dataset with respect to the population from which it is derived yields significantly more utility than small study-specific datasets anonymized unto themselves. When evaluated using the correlation of genome-phenome association strengths on anonymized data versus original data, all nine simulated sites, results from largest-scale anonymizations (population ∼100,000) retained better utility to those on smaller sizes (population ∼6000-75,000). We observed a general trend of increasing r(2) for larger data set sizes: r(2)=0.9481 for small-sized datasets, r(2)=0.9493 for moderately-sized datasets, r(2)=0.9934 for large-sized datasets.

CONCLUSIONS

This research implies that regardless of the overall size of an institution's data, there may be significant benefits to anonymization of the entire EMR, even if the institution is planning on releasing only data about a specific cohort of patients.

ARX--A Comprehensive Tool for Anonymizing Biomedical Data

Collaboration and data sharing have become core elements of biomedical research. Especially when sensitive data from distributed sources are linked, privacy threats have to be considered. Statistical disclosure control allows the protection of sensitive data by introducing fuzziness. Reduction of data quality, however, needs to be balanced against gains in protection. Therefore, tools are needed which provide a good overview of the anonymization process to those responsible for data sharing. These tools require graphical interfaces and the use of intuitive and replicable methods. In addition, extensive testing, documentation and openness to reviews by the community are important. Existing publicly available software is limited in functionality, and often active support is lacking. We present ARX, an anonymization tool that i) implements a wide variety of privacy methods in a highly efficient manner, ii) provides an intuitive cross-platform graphical interface, iii) offers a programming interface for integration into other software systems, and iv) is well documented and actively supported.

Informed consent form challenges for genetic research in a developing Arab country with high risk for genetic disease

The prevalence of genetic disease is high in the Middle East, particularly in the United Arab Emirates. Our study assesses the information provided in, and the readability ease of, informed consent forms (ICF) for genetic research studies. A multicenter retrospective cross-sectional review of 54 ICFs was conducted to assess compliance by comparing them with standard good clinical practice guidelines for developing consent forms. Readability of the forms was determined using the Flesch-Kincaid scale. Overall Good Clinical Practice compliance for the ICFs averaged at 63 %. Information regarding privacy, confidentiality, specimen collection and storage were absent from the majority of the ICFs. Readability ease score was low (36.7 ± 4.6) and required college-level (11.8 ± 1.4) reading skills to understand the information. Our study highlights the need to improve the readability and information contained in the ICFs for genetic research studies in our setting. Our findings may be generalized to similar cultures in the Middle East and Asia.

Knowing who to trust: exploring the role of 'ethical metadata' in mediating risk of harm in collaborative genomics research in Africa

BACKGROUND

The practice of making datasets publicly available for use by the wider scientific community has become firmly integrated in genomic science. One significant gap in literature around data sharing concerns how it impacts on scientists' ability to preserve values and ethical standards that form an essential component of scientific collaborations. We conducted a qualitative sociological study examining the potential for harm to ethnic groups, and implications of such ethical concerns for data sharing. We focused our empirical work on the MalariaGEN Consortium, one of the first international collaborative genomics research projects in Africa.

METHODS

We conducted a study in three MalariaGEN project sites in Kenya, the Gambia, and the United Kingdom. The study entailed analysis of project documents and 49 semi-structured interviews with fieldworkers, researchers and ethics committee members.

RESULTS

Concerns about how best to address the potential for harm to ethnic groups in MalariaGEN crystallised in discussions about the development of a data sharing policy. Particularly concerning for researchers was how best to manage the sharing of genomic data outside of the original collaboration. Within MalariaGEN, genomic data is accompanied by information about the locations of sample collection, the limitations of consent and ethics approval, and the values and relations that accompanied sample collection. For interviewees, this information and context were of important ethical value in safeguarding against harmful uses of data, but is not customarily shared with secondary data users. This challenged the ability of primary researchers to protect against harmful uses of 'their' data.

CONCLUSION

We identified three protective mechanisms--trust, the existence of a shared morality, and detailed contextual understanding--which together might play an important role in preventing the use of genomic data in ways that could harm the ethnic groups included in the study. We suggest that the current practice of sharing of datasets as isolated objects rather than as embedded within a particular scientific culture, without regard for the normative context within which samples were collected, may cause ethical tensions to emerge that could have been prevented or addressed had the 'ethical metadata' that accompanies genomic data also been shared.

Biobanking and translation of human genetics and genomics for infectious diseases

Biobanks are invaluable resources in genomic research of both the infectious diseases and their hosts. This article examines the role of biobanks in basic research of infectious disease genomics, as well as the relevance and applicability of biobanks in the translation of impending knowledge and the clinical uptake of knowledge of infectious diseases. Our research identifies potential fields of interaction between infectious disease genomics and biobanks, in line with global trends in the integration of genome-based knowledge into clinical practice. It also examines various networks and biobanks that specialize in infectious diseases (including HIV, HPV and Chlamydia trachomatis), and provides examples of successful research and clinical uptake stemming from these biobanks. Finally, it outlines key issues with respect to data privacy in infectious disease genomics, as well as the utility of adequately designed and maintained electronic health records. We maintain that the public should be able to easily access a clear and detailed outline of regulations and procedures for sample and data utilization by academic or commercial investigators, and also should be able to understand the precise roles of relevant governing bodies. This would ultimately facilitate uptake by researchers and clinics. As a result of the efforts and resources invested by several networks and consortia, there is an increasing awareness of the prospective uses of biobanks in advancing infectious disease genomic research, diagnostics and their clinical management.

•

The role of biobanks in research of host genomic factors and infectious diseases.

•

Examples of translation of HIV, HPV and Chlamydia research results into clinics.

•

Lack of published overviews of infectious disease biobanks, result is low visibility.

•

Regulations and sample utilization procedures should be more easily accessible.

Understanding consumer evaluations of personalised nutrition services in terms of the privacy calculus: a qualitative study

BACKGROUND

Personalised nutrition (PN) may provide major health benefits to consumers. A potential barrier to the uptake of PN is consumers' reluctance to disclose sensitive information upon which PN is based. This study adopts the privacy calculus to explore how PN service attributes contribute to consumers' privacy risk and personalisation benefit perceptions.

METHODS

Sixteen focus groups (n = 124) were held in 8 EU countries and discussed 9 PN services that differed in terms of personal information, communication channel, service provider, advice justification, scope, frequency, and customer lock-in. Transcripts were content analysed.

RESULTS

The personal information that underpinned PN contributed to both privacy risk perception and personalisation benefit perception. Disclosing information face-to-face mitigated the perception of privacy risk and amplified the perception of personalisation benefit. PN provided by a qualified expert and justified by scientific evidence increased participants' value perception. Enhancing convenience, offering regular face-to face support, and employing customer lock-in strategies were perceived as beneficial.

CONCLUSION

This study suggests that to encourage consumer adoption, PN has to account for face-to-face communication, expert advice providers, support, a lifestyle-change focus, and customised offers. The results provide an initial insight into service attributes that influence consumer adoption of PN.

Identifying genetic relatives without compromising privacy

The development of high-throughput genomic technologies has impacted many areas of genetic research. While many applications of these technologies focus on the discovery of genes involved in disease from population samples, applications of genomic technologies to an individual's genome or personal genomics have recently gained much interest. One such application is the identification of relatives from genetic data. In this application, genetic information from a set of individuals is collected in a database, and each pair of individuals is compared in order to identify genetic relatives. An inherent issue that arises in the identification of relatives is privacy. In this article, we propose a method for identifying genetic relatives without compromising privacy by taking advantage of novel cryptographic techniques customized for secure and private comparison of genetic information. We demonstrate the utility of these techniques by allowing a pair of individuals to discover whether or not they are related without compromising their genetic information or revealing it to a third party. The idea is that individuals only share enough special-purpose cryptographically protected information with each other to identify whether or not they are relatives, but not enough to expose any information about their genomes. We show in HapMap and 1000 Genomes data that our method can recover first- and second-order genetic relationships and, through simulations, show that our method can identify relationships as distant as third cousins while preserving privacy.

A flexible approach to distributed data anonymization

Sensitive biomedical data is often collected from distributed sources, involving different information systems and different organizational units. Local autonomy and legal reasons lead to the need of privacy preserving integration concepts. In this article, we focus on anonymization, which plays an important role for the re-use of clinical data and for the sharing of research data. We present a flexible solution for anonymizing distributed data in the semi-honest model. Prior to the anonymization procedure, an encrypted global view of the dataset is constructed by means of a secure multi-party computing (SMC) protocol. This global representation can then be anonymized. Our approach is not limited to specific anonymization algorithms but provides pre- and postprocessing for a broad spectrum of algorithms and many privacy criteria. We present an extensive analytical and experimental evaluation and discuss which types of methods and criteria are supported. Our prototype demonstrates the approach by implementing k-anonymity, ℓ-diversity, t-closeness and δ-presence with a globally optimal de-identification method in horizontally and vertically distributed setups. The experiments show that our method provides highly competitive performance and offers a practical and flexible solution for anonymizing distributed biomedical datasets.

Research participants' attitudes towards the confidentiality of genomic sequence information

Respecting the confidentiality of personal data contributed to genomic studies is an important issue for researchers using genomic sequencing in humans. Although most studies adhere to rules of confidentiality, there are different conceptions of confidentiality and why it is important. The resulting ambiguity obscures what is at stake when making tradeoffs between data protection and other goals in research, such as transparency, reciprocity, and public benefit. Few studies have examined why participants in genomic research care about how their information is used. To explore this topic, we conducted semi-structured phone interviews with 30 participants in two National Institutes of Health research protocols using genomic sequencing. Our results show that research participants value confidentiality as a form of control over information about themselves. To the individuals we interviewed, control was valued as a safeguard against discrimination in a climate of uncertainty about future uses of individual genome data. Attitudes towards data sharing were related to the goals of research and details of participants' personal lives. Expectations of confidentiality, trust in researchers, and a desire to advance science were common reasons for willingness to share identifiable data with investigators. Nearly, all participants were comfortable sharing personal data that had been de-identified. These findings suggest that views about confidentiality and data sharing are highly nuanced and are related to the perceived benefits of joining a research study.