Between Openness and Privacy in Genomics

Sync fast and solve things-best practices for responsible digital health

Digital health innovation is expected to transform healthcare, but it also generates ethical and societal concerns, such as privacy risks, and biases that can compound existing health inequalities. While such concerns are widely recognized, existing regulatory principles, oversight methods and ethical frameworks seem out of sync with digital health innovation. New governance and innovation best practices are thus needed to bring such principles to bear with the reality of business, innovation, and regulation.To grant practical insight into best practices for responsible digital health innovation, we conducted a qualitative study based on an interactive engagement methodology. We engaged key stakeholders (n = 46) operating at the translational frontier of digital health. This approach allowed us to identify three clusters of governance and innovation best practices in digital health innovation: i) inclusive co-creation, ii) responsive regulation, and iii) value-driven innovation. Our study shows that realizing responsible digital health requires diverse stakeholders' commitment to adapt innovation and regulation practices, embracing co-creation as the default modus operandi for digital health development. We describe these collaborative practices and show how they can ensure that innovation is neither slowed by overregulation, nor leads to unethical outcomes.

The Buzz Surrounding Precision Medicine: The Imperative of Incorporating It into Evidence-Based Medical Practice

Precision medicine (PM), through the integration of omics and environmental data, aims to provide a more precise prevention, diagnosis, and treatment of disease. Currently, PM is one of the emerging approaches in modern healthcare and public health, with wide implications for health care delivery, public health policy making formulation, and entrepreneurial endeavors. In spite of its growing popularity and the buzz surrounding it, PM is still in its nascent phase, facing considerable challenges that need to be addressed and resolved for it to attain the acclaim for which it strives. In this article, we discuss some of the current methodological pitfalls of PM, including the use of big data, and provide a perspective on how these challenges can be overcome by bringing PM closer to evidence-based medicine (EBM). Furthermore, to maximize the potential of PM, we present real-world illustrations of how EBM principles can be integrated into a PM approach.

Open Data in the Era of the GDPR: Lessons from the Human Cell Atlas

The Human Cell Atlas (HCA) is striving to build an open community that is inclusive of all researchers adhering to its principles and as open as possible with respect to data access and use. However, open data sharing can pose certain challenges. For instance, being a global initiative, the HCA must contend with a patchwork of local and regional privacy rules. A notable example is the implementation of the European Union General Data Protection Regulation (GDPR), which caused some concern in the biomedical and genomic data-sharing community. We examine how the HCA's large, international group of researchers is investing tremendous efforts into ensuring appropriate sharing of data. We describe the HCA's objectives and governance, how it defines open data sharing, and ethico-legal challenges encountered early in its development; in particular, we describe the challenges prompted by the GDPR. Finally, we broaden the discussion to address tools and strategies that can be used to address ethical data governance.

An iterative regulatory process for robot governance

There is an increasing gap between the policy cycle’s speed and that of technological and social change. This gap is becoming broader and more prominent in robotics, that is, movable machines that perform tasks either automatically or with a degree of autonomy. This is because current legislation was unprepared for machine learning and autonomous agents. As a result, the law often lags behind and does not adequately frame robot technologies. This state of affairs inevitably increases legal uncertainty. It is unclear what regulatory frameworks developers have to follow to comply, often resulting in technology that does not perform well in the wild, is unsafe, and can exacerbate biases and lead to discrimination. This paper explores these issues and considers the background, key findings, and lessons learned of the LIAISON project, which stands for “Liaising robot development and policymaking,” and aims to ideate an alignment model for robots’ legal appraisal channeling robot policy development from a hybrid top-down/bottom-up perspective to solve this mismatch. As such, LIAISON seeks to uncover to what extent compliance tools could be used as data generators for robot policy purposes to unravel an optimal regulatory framing for existing and emerging robot technologies.

Genomic privacy, identity and dignity

Significant advancements towards a future of big data genomic medicine, associated with large-scale public dataset repositories, intensify dilemmas of genomic privacy. To resolve dilemmas adequately, we need to understand the relative force of the competing considerations that make them up. Attitudes towards genomic privacy are complex and not well understood; understanding is further complicated by the vague claim of 'genetic exceptionalism'. In this paper, we distinguish between consequentialist and non-consequentialist privacy interests: while the former are concerned with harms secondary to exposure, the latter represent the interest in a private sphere for its own sake, as an essential component of human dignity. Empirical studies of attitudes towards genomic privacy have almost never targeted specifically this important dignitary component of the privacy interest. In this paper we first articulate the question of a non-consequentialist genomic privacy interest, and then present results of an empirical study that probed people's attitudes towards that interest. This was done via comparison to other non-consequentialist privacy interests, which are more tangible and can be more easily assessed. Our results indicate that the non-consequentialist genomic privacy interest is rather weak. This insight can assist in adjudicating dilemmas involving genomic privacy.

Genetic data sharing and artificial intelligence in the era of personalized medicine based on a cross-sectional analysis of the Saudi human genome program

The success of the Saudi Human Genome Program (SHGP), one of the top ten genomic programs worldwide, is highly dependent on the Saudi population embracing the concept of participating in genetic testing. However, genetic data sharing and artificial intelligence (AI) in genomics are critical public issues in medical care and scientific research. The present study was aimed to examine the awareness, knowledge, and attitude of the Saudi society towards the SHGP, the sharing and privacy of genetic data resulting from the SHGP, and the role of AI in genetic data analysis and regulations. Results of a questionnaire survey with 804 respondents revealed moderate awareness and attitude towards the SHGP and minimal knowledge regarding its benefits and applications. Respondents demonstrated a low level of knowledge regarding the privacy of genetic data. A generally positive attitude was found towards the outcomes of the SHGP and genetic data sharing for medical and scientific research. The highest level of knowledge was detected regarding AI use in genetic data analysis and privacy regulation. We recommend that the SHGP’s regulators launch awareness campaigns and educational programs to increase and improve public awareness and knowledge regarding the SHGP’s benefits and applications. Furthermore, we propose a strategy for genetic data sharing which will facilitate genetic data sharing between institutions and advance Personalized Medicine in genetic diseases’ diagnosis and treatment.

Public willingness to participate in personalized health research and biobanking: A large-scale Swiss survey

This paper reports survey findings on the Swiss public's willingness, attitudes, and concerns regarding personalized health research participation by providing health information and biological material. The survey reached a sample of 15,106 Swiss residents, from which we received 5,156 responses (34.1% response rate). The majority of respondents were aware of research using human biological samples (71.0%) and held a positive opinion towards this type of research (62.4%). Of all respondents, 53.6% indicated that they would be willing to participate in a personalized health research project. Willingness to participate was higher in younger, higher educated, non-religious respondents with a background in the health sector. Respondents were more willing to provide 'traditional' types of health data, such as health questionnaires, blood or biological samples, as opposed to social media or app-related data. All respondents valued the return of individual research results, including risk for diseases for which no treatment is available. Our findings highlight that alongside general positive attitudes towards personalized health research using data and samples, respondents have concerns about data privacy and re-use. Concerns included potential discrimination, confidentiality breaches, and misuse of data for commercial or marketing purposes. The findings of this large-scale survey can inform Swiss research institutions and assist policymakers with adjusting practices and developing policies to better meet the needs and preferences of the public. Efforts in this direction could focus on research initiatives engaging in transparent communication, education, and engagement activities, to increase public understanding and insight into data sharing activities, and ultimately strengthen personalized health research efforts.

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.

Using Personal Genomic Data within Primary Care: A Bioinformatics Approach to Pharmacogenomics

One application of personalized medicine is the tailoring of medication to the individual, so that the medication will have the highest chance of success. In order to individualize medication, one must have a complete inventory of all current pharmaceutical compounds (a detailed formulary) combined with pharmacogenetic datasets, the genetic makeup of the patient, their (medical) family history and other health-related data. For healthcare professionals to make the best use of this information, it must be visualized in a way that makes the most medically relevant data accessible for their decision-making. Similarly, to enable bioinformatics analysis of these data, it must be prepared and provided through an interface for controlled computational analysis. Due to the high degree of personal information gathered for such initiatives, privacy-sensitive implementation choices and ethical standards are paramount. The Personal Genetic Locker project provides an approach to enable the use of personal genomic data in primary care. In this paper, we provide a description of the Personal Genetic Locker project and show its utility through a use case based on open standards, which is illustrated by the 4MedBox system.

Integrative Biology and Big-Data-Centrism: Mapping out a Bioscience Ethics Perspective with a S.W.O.T. Matrix

In current biomedicine, omics technologies drive systems-oriented modes of research to achieve a more holistic and personalized view of health and disease. This shift in scientific approach co-occurs with an era of biocapitalism characterized by markets for biomaterial (e.g., DNA, cells, and tissues) as exploitable resources, high-throughput technologies as tools, and "Big Data" as currency. Prediagnostics and genomics-based analyses successfully entered the public domain more or less unfiltered, offering numerous business opportunities envisioning individuals to contribute to the health sector by providing biomaterial and data as well as by using technology, thus becoming participants and informed coproducers of health. Exploring strengths and weaknesses, as well as opportunities and threats by S.W.O.T. analysis, we highlight some chances, pitfalls, and biases of this sector from a bioscience ethics stance. We conclude that the shift from diagnostic to predictive interpretation of data that comes along with integrative biology seems to escape the general and sometimes the experts' awareness. Moreover, rapid translation into products for the global health market is based on marketable views on health and disease that in turn affect basic research through, for example, funding policies and the research questions being asked. Along with this, biological reductionism is revived fuelling simplified understandings of the genotype phenotype relationship in terms of biology and the human dimension in a broader sense, as well as visions of achieving human perfection through novel biotechnologies.

Motivations for data sharing-views of research participants from four European countries: A DIRECT study

The purpose of this study was to explore and compare different countries in what motivated research participants' decisions whether to share their de-identified data. We investigated European DIRECT (Diabetes Research on Patient Stratification) research project participants' desire for control over sharing different types of their de-identified data, and with who data could be shared in the future after the project ends. A cross-sectional survey was disseminated among DIRECT project participants. The results found that there was a significant association between country and attitudes towards advancing research, protecting privacy, and beliefs about risks and benefits to sharing data. When given the choice to have control, some participants (<50% overall) indicated that having control over what data is shared and with whom was important; and control over what data types are shared was less important than respondents deciding who data are shared with. Danish respondents indicated higher odds of desire to control data types shared, and Dutch respondents showed higher odds of desire to control who data will be shared with. Overall, what research participants expect in terms of control over data sharing needs to be considered and aligned with sharing for future research and re-use of data. Our findings show that even with de-identified data, respondents prioritise privacy above all else. This study argues to move research participants from passive participation in biomedical research to considering their opinions about data sharing and control of de-identified biomedical data.

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.

Sharing data for future research-engaging participants' views about data governance beyond the original project: a DIRECT Study

PURPOSE

Biomedical data governance strategies should ensure that data are collected, stored, and used ethically and lawfully. However, research participants' preferences for how data should be governed is least studied. The Diabetes Research on Patient Stratification (DIRECT) project collected substantial amounts of health and genetic information from patients at risk of, and with type II diabetes. We conducted a survey to understand participants' future data governance preferences. Results will inform the postproject data governance strategy.

METHODS

A survey was distributed in Denmark, Sweden, The Netherlands, and the United Kingdom.

RESULTS

In total 855 surveys were returned. Ninety-seven percent were supportive of sharing data postproject, and 90% were happy to share data with universities, and 56% with commercial companies. The top three priorities for data sharing were highly secure database, DIRECT researchers to monitor data used by other researchers, and researchers cannot identify participants. Respondents frequently suggested that a postproject Data Access Committee should involve a DIRECT researcher, diabetes clinician, patient representative, and a DIRECT participant.

CONCLUSION

Preferences of how data should be governed, and what data could be shared and with whom varied between countries. Researchers are considered as key custodians of participant data. Engaging participants aids in designing governance to support their choices.

A comparison of views regarding the use of de-identified data

Data sharing of large genomic databases and biorepositories provides researchers adequately powered samples to advance the goals of precision medicine. Data sharing may also introduce, however, participant privacy concerns including possible reidentification. This study compares views of research participants, genetic researchers, and institutional review board (IRB) professionals regarding concerns about the use of de-identified data. An online survey was completed by cancer patients, their relatives, and controls from the Northwest Cancer Genetics Registry (n = 450) querying views about potential harms with the use of de-identified data. This was compared to our previous online national survey of human genetic researchers (n = 351) and IRB professionals (n = 208). Researchers were less likely to feel that participants would be personally identified or harmed from a study involving de-identified data or feel that a federal agency might compel researchers to disclose information about research participants. Compared to genetic researchers, IRB professionals and participants were significantly more likely to express that personal identification or harm was likely or that researchers might be forced to disclose information by a federal agency. An understanding of the differences in views regarding possible harm from the use of de-identified data between these three important stakeholder groups is necessary to move forward with genomic research.

Pulling the Strands Together: MEGA Steps to Drive European Genomics and Personalised Medicine

The increasing understanding of the genome is recognised as being one of the main determinants of future improvement in healthcare. The availability of genetic data from a large number of individuals increases the ability to investigate questions across many rare and common diseases and in different populations, and also provides more information for understanding clinical care outcomes for an individual. A number of large scale genome sequencing initiatives have been launched in the last few years to try and capitalise on this potential. Within Europe, the UK has led the way with the 100,000 Genomes Project. This project looks at the genome sequences of patients with rare diseases or cancer. More recently France announced plans to invest EUR 670 million in a genomics and personalised medicine programme. In the US, the Precision Medicine Initiative aims at large-scale research by gathering one million or more volunteers to extend precision medicine to all diseases. Meanwhile, China has announced plans to invest nearly USD 10 billion in its own precision medicine initiative. These projects demonstrate the commitment at a national level and raise the question "What benefits would be realised by undertaking a million genome initiative in a coordinated effort across European countries?" A coordinated, pan-European MEGA project would garner crucial genetic information that could have an immeasurable benefit when it comes to the health of current and future EU citizens.

How Healthcare Can Refocus on Its Super-Customers (Patients, n =1) and Customers (Doctors and Nurses) by Leveraging Lessons from Amazon, Uber, and Watson

Healthcare is transforming with data-intensive omics technologies and Big Data. The "revolution" has already happened in technology, but the bottlenecks have shifted to the social domain: Who can be empowered by Big Data? Who are the users and customers? In this review and innovation field analysis, we introduce the idea of a "super-customer" versus "customer" and relate both to 21st century healthcare. A "super-customer" in healthcare is the patient, sample size of n = 1, while "customers" are the providers of healthcare (e.g., doctors and nurses). The super-customers have been patients, enabled by unprecedented social practices, such as the ability to track one's physical activities, personal genomics, patient advocacy for greater autonomy, and self-governance, to name but a few. In contrast, the originally intended customers-providers, doctors, and nurses-have relatively lagged behind. With patients as super-customers, there are valuable lessons to be learned from industry examples, such as Amazon and Uber. To offer superior quality service, healthcare organizations have to refocus on the needs, pains, and aspirations of their super-customers by enabling the customers. We propose a strategic solution to this end: the PPT-DAM (People-Process-Technology empowered by Data, Analytics, and Metrics) approach. When applied together with the classic Experiment-Execute-Evaluate iterative methodology, we suggest PPT-DAM is an extremely powerful approach to deliver quality health services to super-customers and customers. As an example, we describe the PPT-DAM implementation by the Benchmarking Improvement Program at the Seattle Children's Hospital. Finally, we forecast that cognitive systems in general and IBM Watson in particular, if properly implemented, can bring transformative and sustainable capabilities in healthcare far beyond the current ones.

How Sensitive Is Genetic Data?

The rising demand to use genetic data for research goes hand in hand with an increased awareness of privacy issues related to its use. Using human genetic data in a legally compliant way requires an examination of the legal basis as well as an assessment of potential disclosure risks. Focusing on the relevant legal framework in the European Union, we discuss open questions and uncertainties around the handling of genetic data in research, which can result in the introduction of unnecessary hurdles for data sharing. First, we discuss defining features and relative disclosure risks of some DNA-related biomarkers, distinguishing between the risk for disclosure of (1) the identity of an individual, (2) information about an individual's health and behavior, including previously unknown phenotypes, and (3) information about an individual's blood relatives. Second, we discuss the European legal framework applicable to the use of DNA-related biomarkers in research, the implications of including both inherited and acquired traits in the legal definition, as well as the issue of “genetic exceptionalism”—the notion that genetic information has inherent characteristics that require different considerations than other health and medical information. Finally, by mapping the legal to specific technical definitions, we draw some initial conclusions concerning how sensitive different types of “genetic data” may actually be. We argue that whole genome sequences may justifiably be considered “exceptional” and require special protection, whereas other genetic data that do not fulfill the same criteria should be treated in a similar manner to other clinical data. This kind of differentiation should be reflected by the law and/or other governance frameworks as well as agreed Codes of Conduct when using the term “genetic data.”

Ethical sharing of health data in online platforms - which values should be considered?

Intensified and extensive data production and data storage are characteristics of contemporary western societies. Health data sharing is increasing with the growth of Information and Communication Technology (ICT) platforms devoted to the collection of personal health and genomic data. However, the sensitive and personal nature of health data poses ethical challenges when data is disclosed and shared even if for scientific research purposes.With this in mind, the Science and Values Working Group of the COST Action CHIP ME 'Citizen's Health through public-private Initiatives: Public health, Market and Ethical perspectives' (IS 1303) identified six core values they considered to be essential for the ethical sharing of health data using ICT platforms. We believe that using this ethical framework will promote respectful scientific practices in order to maintain individuals' trust in research.We use these values to analyse five ICT platforms and explore how emerging data sharing platforms are reconfiguring the data sharing experience from a range of perspectives. We discuss which types of values, rights and responsibilities they entail and enshrine within their philosophy or outlook on what it means to share personal health information. Through this discussion we address issues of the design and the development process of personal health data and patient-oriented infrastructures, as well as new forms of technologically-mediated empowerment.

Assessment of knowledge about biobanking among healthcare students and their willingness to donate biospecimens

Background

Biobanks and biospecimen collections are becoming a primary means of delivering personalized diagnostics and tailoring individualized therapeutics. This shift towards precision medicine (PM) requires interactions among a variety of stakeholders, including the public, patients, healthcare providers, government, and donors. Very few studies have investigated the role of healthcare students in biobanking and biospecimen donations. The main aims of this study were (1) to evaluate the knowledge of senior healthcare students about biobanks and (2) to assess the students’ willingness to donate biospecimens and the factors influencing their attitudes.

Methods

A cross-sectional study was conducted among senior healthcare students at King Abdulaziz University (KAU), Saudi Arabia. The data were obtained using a self-administered questionnaire in English. In addition to the respondents’ biographical data section, the questionnaire assessed the respondents’ general knowledge about biobanking, the factors influencing their willingness to donate biospecimens to biobanks and their general attitudes towards biomedical research.

Results

A total of 597 senior healthcare students were included in the study. The general knowledge score was 3.2 (±1.6) out of 7. Only approximately 44% and 27% of students were aware of the terms “Human Genome Project” (HGP) and “biobank,” respectively. The majority of the students (89%) were willing to donate biospecimens to biobanks. Multiple factors were significantly associated with their willingness to donate, including their perceived general health (p < 0.001), past experience with both tissue testing (p < 0.04) and tissue donation (p < 0.001), biobanking knowledge score (p < 0.001) and biomedical research attitude score (p < 0.001). The main reasons for students’ willingness to donate were advancement of medical research and societal benefits, whereas misuse of biospecimens and confidentiality breaches were the main reasons for a reluctance to donate.

Conclusion

Despite their strong willingness to donate biospecimens, students exhibited a notable lack of knowledge about biobanking and the HGP. To expedite the transition towards PM, it is highly recommended to enhance healthcare curricula by including more educational and awareness programmes to familiarize students with OMICs technologies in addition to the scope of research and clinical applications.

Power to the People: Data Citizens in the Age of Precision Medicine

Twentieth-century bioethics celebrated individual autonomy but framed autonomy largely in terms of an individual's power to make decisions and act alone. The most pressing challenges of big data science in the twenty-first century can only be resolved through collective action and common purpose. This Article surveys some of these challenges and asks how common purpose can ever emerge on the present bioethical and regulatory landscape. The solution may lie in embracing a broader concept of autonomy that empowers individuals to protect their interests by exercising meaningful rights of data citizenship. This Article argues that twentieth-century bioethics was a paternalistic, top-down affair in which self-proclaimed ethics experts set standards to protect research subjects portrayed as autonomous yet too vulnerable and disorganized to protect themselves. The time may be ripe for BioEXIT, a popular uprising of regular people seeking a meaningful voice in establishing citizen-led ethical and privacy standards to advance big-data science while addressing the concerns people feel about the privacy of their health data.

The dynamics of big data and human rights: the case of scientific research

In this paper, we address the complex relationship between big data and human rights. Because this is a vast terrain, we restrict our focus in two main ways. First, we concentrate on big data applications in scientific research, mostly health-related research. And, second, we concentrate on two human rights: the familiar right to privacy and the less well-known right to science. Our contention is that human rights interact in potentially complex ways with big data, not only constraining it, but also enabling it in various ways; and that such rights are dynamic in character, rather than fixed once and for all, changing in their implications over time in line with changes in the context we inhabit, and also as they interact among themselves in jointly responding to the opportunities and risks thrown up by a changing world. Understanding this dynamic interaction of human rights is crucial for formulating an ethic tailored to the realities-the new capabilities and risks-of the rapidly evolving digital environment.This article is part of the themed issue 'The ethical impact of data science'.

Reconciling evidence-based medicine and precision medicine in the era of big data: challenges and opportunities

This era of groundbreaking scientific developments in high-resolution, high-throughput technologies is allowing the cost-effective collection and analysis of huge, disparate datasets on individual health. Proper data mining and translation of the vast datasets into clinically actionable knowledge will require the application of clinical bioinformatics. These developments have triggered multiple national initiatives in precision medicine—a data-driven approach centering on the individual. However, clinical implementation of precision medicine poses numerous challenges. Foremost, precision medicine needs to be contrasted with the powerful and widely used practice of evidence-based medicine, which is informed by meta-analyses or group-centered studies from which mean recommendations are derived. This “one size fits all” approach can provide inadequate solutions for outliers. Such outliers, which are far from an oddity as all of us fall into this category for some traits, can be better managed using precision medicine. Here, we argue that it is necessary and possible to bridge between precision medicine and evidence-based medicine. This will require worldwide and responsible data sharing, as well as regularly updated training programs. We also discuss the challenges and opportunities for achieving clinical utility in precision medicine. We project that, through collection, analyses and sharing of standardized medically relevant data globally, evidence-based precision medicine will shift progressively from therapy to prevention, thus leading eventually to improved, clinician-to-patient communication, citizen-centered healthcare and sustained well-being.

Barbarians at the Gate: Consumer-Driven Health Data Commons and the Transformation of Citizen Science

"The expression 'barbarians at the gate' was … used by the Romans to describe foreign attacks against their empire." ¹ "[It] is often used in contemporary English within a sarcastic, or ironic context, when speaking about a perceived threat from a rival group of people, often deemed to be less capable or somehow 'primitive.'" ².

An overview of human genetic privacy

The study of human genomics is becoming a Big Data science, owing to recent biotechnological advances leading to availability of millions of personal genome sequences, which can be combined with biometric measurements from mobile apps and fitness trackers, and of human behavior data monitored from mobile devices and social media. With increasing research opportunities for integrative genomic studies through data sharing, genetic privacy emerges as a legitimate yet challenging concern that needs to be carefully addressed, not only for individuals but also for their families. In this paper, we present potential genetic privacy risks and relevant ethics and regulations for sharing and protecting human genomics data. We also describe the techniques for protecting human genetic privacy from three broad perspectives: controlled access, differential privacy, and cryptographic solutions.

Sharing health-related data: a privacy test?

Greater sharing of potentially sensitive data raises important ethical, legal and social issues (ELSI), which risk hindering and even preventing useful data sharing if not properly addressed. One such important issue is respecting the privacy-related interests of individuals whose data are used in genomic research and clinical care. As part of the Global Alliance for Genomics and Health (GA4GH), we examined the ELSI status of health-related data that are typically considered 'sensitive' in international policy and data protection laws. We propose that 'tiered protection' of such data could be implemented in contexts such as that of the GA4GH Beacon Project to facilitate responsible data sharing. To this end, we discuss a Data Sharing Privacy Test developed to distinguish degrees of sensitivity within categories of data recognised as 'sensitive'. Based on this, we propose guidance for determining the level of protection when sharing genomic and health-related data for the Beacon Project and in other international data sharing initiatives.