Data sharing in large research consortia: experiences and recommendations from ENGAGE

Australian public perspectives on genomic data governance: responsibility, regulation, and logistical considerations

Genomic sequencing generates huge volumes of data, which may be collected or donated to form large genomic databases. Such information can be stored for future use, either for the data donor themselves or by researchers to help improve our understanding of the genetic basis of disease. Creating datasets of this magnitude and diversity is only possible if patients, their families, and members of the public worldwide share their data. However, there is no consensus on the best technical approach to data sharing that also minimises risks to individuals and exploration of stakeholders' views on aspects of genomic data governance models-the ways genomic data is stored, managed, shared and used-has been minimal. To address this need, we conducted focus groups with 39 members of the Australian public exploring their views and preferences for different aspects of genomic data governance models. We found that consent and control were essential to participants, as they wanted the option to choose who had access to their data and for what purposes. Critically, participants wanted a trustworthy body to enforce regulation of data storage, sharing and usage. While participants recognised the importance of data accessibility, they also expressed a strong desire for data security. Finally, financial responsibility for data storage raised concerns for inequity as well as organisations and individuals using data in ethically contentious ways to generate profit. Our findings highlight some of the trade-offs that need to be considered in the development of genomic data governance systems.

Sharing Medical Big Data While Preserving Patient Confidentiality in Innovative Medicines Initiative: A Summary and Case Report from BigData@Heart

Sharing individual patient data (IPD) is a simple concept but complex to achieve due to data privacy and data security concerns, underdeveloped guidelines, and legal barriers. Sharing IPD is additionally difficult in big data-driven collaborations such as Bigdata@Heart in the Innovative Medicines Initiative, due to competing interests between diverse consortium members. One project within BigData@Heart, case study 1, needed to pool data from seven heterogeneous data sets: five randomized controlled trials from three different industry partners, and two disease registries. Sharing IPD was not considered feasible due to legal requirements and the sensitive medical nature of these data. In addition, harmonizing the data sets for a federated data analysis was difficult due to capacity constraints and the heterogeneity of the data sets. An alternative option was to share summary statistics through contingency tables. Here it is demonstrated that this method along with anonymization methods to ensure patient anonymity had minimal loss of information. Although sharing IPD should continue to be encouraged and strived for, our approach achieved a good balance between data transparency while protecting patient privacy. It also allowed a successful collaboration between industry and academia.

Towards trust-based governance of health data research

Developments in medical big data analytics may bring societal benefits but are also challenging privacy and other ethical values. At the same time, an overly restrictive data protection regime can form a serious threat to valuable observational studies. Discussions about whether data privacy or data solidarity should be the foundational value of research policies, have remained unresolved. We add to this debate with an empirically informed ethical analysis. First, experiences with the implementation of the General Data Protection Regulation (GDPR) within a European research consortium demonstrate a gap between the aims of the regulation and its effects in practice. Namely, strictly formalised data protection requirements may cause routinisation among researchers instead of substantive ethical reflection, and may crowd out trust between actors in the health data research ecosystem; while harmonisation across Europe and data sharing between countries is hampered by different interpretations of the law, which partly stem from different views about ethical values. Then, building on these observations, we use theory to argue that the concept of trust provides an escape from the privacy-solidarity debate. Lastly, the paper details three aspects of trust that can help to create a responsible research environment and to mitigate the encountered challenges: trust as multi-agent concept; trust as a rational and democratic value; and trust as method for priority setting. Mutual cooperation in research-among researchers and with data subjects-is grounded in trust, which should be more explicitly recognised in the governance of health data research.

Australian public perspectives on genomic data storage and sharing: Benefits, concerns and access preferences

Diagnostic genomic sequencing generates unprecedented amounts of data. In addition to its primary use, this data could be used for a wide range of secondary purposes, including research and informing future healthcare for the data donor. These opportunities may require data to be shared with third parties. Although effective data sharing relies on public support, there are barriers which may prevent people from choosing to donate their genomic data and surprisingly few studies explore these barriers in depth. To address this need, this study aimed to qualitatively explore the Australian public's views and preferences for storing and sharing genomic data. Online focus groups were recorded, transcribed, and analysed using inductive content analysis. A total of 7 focus groups were conducted with 39 members of the Australian public ranging from 18 to 67 years of age. Participants were mostly supportive of genomic data being stored and shared for secondary purposes, recognising the potential benefits for individual health and wider medical research. However, some concerns were identified. Participants felt genomic data was particularly sensitive information, and raised the potential for discrimination, stigma, and other malicious uses of such data. Concerns for privacy and security of the data were also prevalent. Trustworthiness of data users was important when considering who genomic data should be shared with. Although participants were supportive of data being freely available to health professionals and researchers, they were opposed to insurance companies and employers accessing the data. There was greater controversy around sharing data with law enforcement and pharmaceutical companies. Participants recognised both benefits and harms to sharing with law enforcement. They were also cognizant of the dual purpose of pharmaceutical companies as both research and profit-driven organisations. Finally, participants expressed varying perspectives about sharing genomic data with family members, yet most agreed that explicit consent from the data donor should be required to share their information with relatives. This study highlighted several of the Australian public's perceived barriers and motivators for the storage and sharing of genomic data. Participants recognised both the benefits of collecting, storing and sharing such data widely but also the potential for harm from data misuse. While public acceptance of such endeavours is required to maximise the volume of data made available, the concerns around data access and security need to be addressed before this can occur. These findings also highlight the nuance and ethical complexity of decisions about who we should allow to access donated genomic data. These perspectives will be essential in helping to shape the way large-scale genomic data storage and sharing is developed and implemented in Australia, and internationally.

Clinical predictors of antipsychotic treatment resistance: Development and internal validation of a prognostic prediction model by the STRATA-G consortium

INTRODUCTION

Our aim was to, firstly, identify characteristics at first-episode of psychosis that are associated with later antipsychotic treatment resistance (TR) and, secondly, to develop a parsimonious prediction model for TR.

METHODS

We combined data from ten prospective, first-episode psychosis cohorts from across Europe and categorised patients as TR or non-treatment resistant (NTR) after a mean follow up of 4.18 years (s.d. = 3.20) for secondary data analysis. We identified a list of potential predictors from clinical and demographic data recorded at first-episode. These potential predictors were entered in two models: a multivariable logistic regression to identify which were independently associated with TR and a penalised logistic regression, which performed variable selection, to produce a parsimonious prediction model. This model was internally validated using a 5-fold, 50-repeat cross-validation optimism-correction.

RESULTS

Our sample consisted of N = 2216 participants of which 385 (17 %) developed TR. Younger age of psychosis onset and fewer years in education were independently associated with increased odds of developing TR. The prediction model selected 7 out of 17 variables that, when combined, could quantify the risk of being TR better than chance. These included age of onset, years in education, gender, BMI, relationship status, alcohol use, and positive symptoms. The optimism-corrected area under the curve was 0.59 (accuracy = 64 %, sensitivity = 48 %, and specificity = 76 %).

IMPLICATIONS

Our findings show that treatment resistance can be predicted, at first-episode of psychosis. Pending a model update and external validation, we demonstrate the potential value of prediction models for TR.

Identifying Datasets for Cross-Study Analysis in dbGaP using PhenX

Identifying relevant studies and harmonizing datasets are major hurdles for data reuse. Common Data Elements (CDEs) can help identify comparable study datasets and reduce the burden of retrospective data harmonization, but they have not been required, historically. The collaborative team at PhenX and dbGaP developed an approach to use PhenX variables as a set of CDEs to link phenotypic data and identify comparable studies in dbGaP. Variables were identified as either comparable or related, based on the data collection mode used to harmonize data across mapped datasets. We further added a CDE data field in the dbGaP data submission packet to indicate use of PhenX and annotate linkages in the future. Some 13,653 dbGaP variables from 521 studies were linked through PhenX variable mapping. These variable linkages have been made accessible for browsing and searching in the repository through dbGaP CDE-faceted search filter and the PhenX variable search tool. New features in dbGaP and PhenX enable investigators to identify variable linkages among dbGaP studies and reveal opportunities for cross-study analysis.

Reproducibility of prediction models in health services research

The field of health services research studies the health care system by examining outcomes relevant to patients and clinicians but also health economists and policy makers. Such outcomes often include health care spending, and utilization of care services. Building accurate prediction models using reproducible research practices for health services research is important for evidence-based decision making. Several systematic reviews have summarized prediction models for outcomes relevant to health services research, but these systematic reviews do not present a thorough assessment of reproducibility and research quality of the prediction modelling studies. In the present commentary, we discuss how recent advances in prediction modelling in other medical fields can be applied to health services research. We also describe the current status of prediction modelling in health services research, and we summarize available methodological guidance for the development, update, external validation and systematic appraisal of prediction models.

Leveraging fine-mapping and multipopulation training data to improve cross-population polygenic risk scores

Polygenic risk scores suffer reduced accuracy in non-European populations, exacerbating health disparities. We propose PolyPred, a method that improves cross-population polygenic risk scores by combining two predictors: a new predictor that leverages functionally informed fine-mapping to estimate causal effects (instead of tagging effects), addressing linkage disequilibrium differences, and BOLT-LMM, a published predictor. When a large training sample is available in the non-European target population, we propose PolyPred+, which further incorporates the non-European training data. We applied PolyPred to 49 diseases/traits in four UK Biobank populations using UK Biobank British training data, and observed relative improvements versus BOLT-LMM ranging from +7% in south Asians to +32% in Africans, consistent with simulations. We applied PolyPred+ to 23 diseases/traits in UK Biobank east Asians using both UK Biobank British and Biobank Japan training data, and observed improvements of +24% versus BOLT-LMM and +12% versus PolyPred. Summary statistics-based analogs of PolyPred and PolyPred+ attained similar improvements.

Invited Commentary: Standards, Inputs, and Outputs-Strategies for Improving Data-Sharing and Consortia-Based Epidemiologic Research

Data-sharing improves epidemiologic research, but the sharing of data frustrates epidemiologic researchers. The inefficiencies of current methods and options for data-sharing are increasingly documented and easily understood by any study group that has shared its data and any researcher who has received shared data. In this issue of the Journal, Temprosa et al. (Am J Epidemiol. 2021;191(1):147-158) describe how the Consortium of Metabolomics Studies (COMETS) developed and deployed a flexible analytical platform to eliminate key pain points in large-scale metabolomics research. COMETS Analytics includes an online tool, but its cloud computing and technology are the supporting rather than the leading actors in this script. The COMETS team identified the need to standardize diverse and inconsistent metabolomics and covariate data and models across its many participating cohort studies, and then developed a flexible tool that gave its member studies choices about how they wanted to meet the consortium's analytical requirements. Different specialties will have different specific research needs and will probably continue to use and develop an array of diverse analytical and technical solutions for their projects. COMETS Analytics shows how important-and enabling-the upstream attention to data standards and data consistency is to producing high-quality metabolomics, consortia-based, and large-scale epidemiology research.

Using imputation to provide harmonized longitudinal measures of cognition across AIBL and ADNI

To improve understanding of Alzheimer’s disease, large observational studies are needed to increase power for more nuanced analyses. Combining data across existing observational studies represents one solution. However, the disparity of such datasets makes this a non-trivial task. Here, a machine learning approach was applied to impute longitudinal neuropsychological test scores across two observational studies, namely the Australian Imaging, Biomarkers and Lifestyle Study (AIBL) and the Alzheimer's Disease Neuroimaging Initiative (ADNI) providing an overall harmonised dataset. MissForest, a machine learning algorithm, capitalises on the underlying structure and relationships of data to impute test scores not measured in one study aligning it to the other study. Results demonstrated that simulated missing values from one dataset could be accurately imputed, and that imputation of actual missing data in one dataset showed comparable discrimination (p < 0.001) for clinical classification to measured data in the other dataset. Further, the increased power of the overall harmonised dataset was demonstrated by observing a significant association between CVLT-II test scores (imputed for ADNI) with PET Amyloid-β in MCI APOE-ε4 homozygotes in the imputed data (N = 65) but not for the original AIBL dataset (N = 11). These results suggest that MissForest can provide a practical solution for data harmonization using imputation across studies to improve power for more nuanced analyses.

Implementing the FAIR Data Principles in precision oncology: review of supporting initiatives

Compelling research has recently shown that cancer is so heterogeneous that single research centres cannot produce enough data to fit prognostic and predictive models of sufficient accuracy. Data sharing in precision oncology is therefore of utmost importance. The Findable, Accessible, Interoperable and Reusable (FAIR) Data Principles have been developed to define good practices in data sharing. Motivated by the ambition of applying the FAIR Data Principles to our own clinical precision oncology implementations and research, we have performed a systematic literature review of potentially relevant initiatives. For clinical data, we suggest using the Genomic Data Commons model as a reference as it provides a field-tested and well-documented solution. Regarding classification of diagnosis, morphology and topography and drugs, we chose to follow the World Health Organization standards, i.e. ICD10, ICD-O-3 and Anatomical Therapeutic Chemical classifications, respectively. For the bioinformatics pipeline, the Genome Analysis ToolKit Best Practices using Docker containers offer a coherent solution and have therefore been selected. Regarding the naming of variants, we follow the Human Genome Variation Society's standard. For the IT infrastructure, we have built a centralized solution to participate in data sharing through federated solutions such as the Beacon Networks.

The ENIGMA Brain Injury working group: approach, challenges, and potential benefits

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium brings together researchers from around the world to try to identify the genetic underpinnings of brain structure and function, along with robust, generalizable effects of neurological and psychiatric disorders. The recently-formed ENIGMA Brain Injury working group includes 10 subgroups, based largely on injury mechanism and patient population. This introduction to the special issue summarizes the history, organization, and objectives of ENIGMA Brain Injury, and includes a discussion of strategies, challenges, opportunities and goals common across 6 of the subgroups under the umbrella of ENIGMA Brain Injury. The following articles in this special issue, including 6 articles from different subgroups, will detail the challenges and opportunities specific to each subgroup.

Governance of research consortia: challenges of implementing Responsible Research and Innovation within Europe

Responsible Research and Innovation ('RRI') is a cross-cutting priority for scientific research in the European Union and beyond. This paper considers whether the way such research is organised and delivered lends itself to the aims of RRI. We focus particularly on international consortia, which have emerged as a common model to organise large-scale, multi-disciplinary research in contemporary biomedical science. Typically, these consortia operate through fixed-term contracts, and employ governance frameworks consisting of reasonably standard, modular components such as management committees, advisory boards, and data access committees, to co-ordinate the activities of partner institutions and align them with funding agency priorities. These have advantages for organisation and management of the research, but can actively inhibit researchers seeking to implement RRI activities. Conventional consortia governance structures pose specific problems for meaningful public and participant involvement, data sharing, transparency, and 'legacy' planning to deal with societal commitments that persist beyond the duration of the original project. In particular, the 'upstream' negotiation of contractual terms between funders and the institutions employing researchers can undermine the ability for those researchers to subsequently make decisions about data, or participant remuneration, or indeed what happens to consortia outputs after the project is finished, and can inhibit attempts to make project activities and goals responsive to input from ongoing dialogue with various stakeholders. Having explored these challenges, we make some recommendations for alternative consortia governance structures to better support RRI in future.

Harmonizing behavioral outcomes across studies, raters, and countries: application to the genetic analysis of aggression in the ACTION Consortium

BACKGROUND

Aggression in children has genetic and environmental causes. Studies of aggression can pool existing datasets to include more complex models of social effects. Such analyses require large datasets with harmonized outcome measures. Here, we made use of a reference panel for phenotype data to harmonize multiple aggression measures in school-aged children to jointly analyze data from five large twin cohorts.

METHODS

Individual level aggression data on 86,559 children (42,468 twin pairs) were available in five European twin cohorts measured by different instruments. A phenotypic reference panel was collected which enabled a model-based phenotype harmonization approach. A bi-factor integration model in the integrative data analysis framework was developed to model aggression across studies while adjusting for rater, age, and sex. Finally, harmonized aggression scores were analyzed to estimate contributions of genes, environment, and social interaction to aggression. The large sample size allowed adequate power to test for sibling interaction effects, with unique dynamics permitted for opposite-sex twins.

RESULTS

The best-fitting model found a high level of overall heritability of aggression (~60%). Different heritability rates of aggression across sex were marginally significant, with heritability estimates in boys of ~64% and ~58% in girls. Sibling interaction effects were only significant in the opposite-sex twin pairs: the interaction effect of males on their female co-twin differed from the effect of females on their male co-twin. An aggressive female had a positive effect on male co-twin aggression, whereas more aggression in males had a negative influence on a female co-twin.

CONCLUSIONS

Opposite-sex twins displayed unique social dynamics of aggressive behaviors in a joint analysis of a large, multinational dataset. The integrative data analysis framework, applied in combination with a reference panel, has the potential to elucidate broad, generalizable results in the investigation of common psychological traits in children.

Genetic Epidemiology in Latin America: Identifying Strong Genetic Proxies for Complex Disease Risk Factors

Epidemiology seeks to determine the causal effects of exposures on outcomes related to the health and wellbeing of populations. Observational studies, one of the most commonly used designs in epidemiology, can be biased due to confounding and reverse causation, which makes it difficult to establish causal relationships. In recent times, genetically informed methods, like Mendelian randomization (MR), have been developed in an attempt to overcome these disadvantages. MR relies on the association of genetic variants with outcomes of interest, where the genetic variants are proxies or instruments for modifiable exposures. Because genotypes are sorted independently and at random at the time of conception, they are less prone to confounding and reverse causation. Implementation of MR depends on, among other things, a strong association of the genetic variants with the exposure, which has usually been defined via genome-wide association studies (GWAS). Because GWAS have been most often carried out in European populations, the limited identification of strong instruments in other populations poses a major problem for the application of MR in Latin America. We suggest potential solutions that can be realized with the resources at hand and others that will have to wait for increased funding and access to technology.

Crowd-Sourced Chemistry: Considerations for Building a Standardized Database to Improve Omic Analyses

Mass spectrometry (MS) is used in multiple omics disciplines to generate large collections of data. This data enables advancements in biomedical research by providing global profiles of a given system. One of the main barriers to generating these profiles is the inability to accurately annotate omics data, especially small molecules. To complement pre-existing large databases that are not quite complete, research groups devote efforts to generating personal libraries to annotate their data. Scientific progress is impeded during the generation of these personal libraries because the data contained within them is often redundant and/or incompatible with other databases. To overcome these redundancies and incompatibilities, we propose that communal, crowd-sourced databases be curated in a standardized fashion. A small number of groups have shown this model is feasible and successful. While the needs of a specific field will dictate the functionality of a communal database, we discuss some features to consider during database development. Special emphasis is made on standardization of terminology, documentation, format, reference materials, and quality assurance practices. These standardization procedures enable a field to have higher confidence in the quality of the data within a given database. We also discuss the three conceptual pillars of database design as well as how crowd-sourcing is practiced. Generating open-source databases requires front-end effort, but the result is a well curated, high quality data set that all can use. Having a resource such as this fosters collaboration and scientific advancement.

Attitudes of publics who are unwilling to donate DNA data for research

With the use of genetic technology, researchers have the potential to inform medical diagnoses and treatment in actionable ways. Accurate variant interpretation is a necessary condition for the utility of genetic technology to unfold. This relies on the ability to access large genomic datasets so that comparisons can be made between variants of interest. This can only be successful if DNA and medical data are donated by large numbers of people to 'research', including clinical, non-profit and for-profit research initiatives, in order to be accessed by scientists and clinicians worldwide. The objective of the 'Your DNA, Your Say' global survey is to explore public attitudes, values and opinions towards willingness to donate and concerns regarding the donation of one's personal data for use by others. Using a representative sample of 8967 English-speaking publics from the UK, the USA, Canada and Australia, we explore the characteristics of people who are unwilling (n = 1426) to donate their DNA and medical information, together with an exploration of their reasons. Understanding this perspective is important for making sense of the interaction between science and society. It also helps to focus engagement initiatives on the issues of concern to some publics.

The governance structure for data access in the DIRECT consortium: an innovative medicines initiative (IMI) project

Biomedical research projects involving multiple partners from public and private sectors require coherent internal governance mechanisms to engender good working relationships. The DIRECT project is an example of such a venture, funded by the Innovative Medicines Initiative Joint Undertaking (IMI JU). This paper describes the data access policy that was developed within DIRECT to support data access and sharing, via the establishment of a 3-tiered Data Access Committee. The process was intended to allow quick access to data, whilst enabling strong oversight of how data were being accessed and by whom, and any subsequent analyses, to contribute to the overall objectives of the consortium.

MONTRA: An agile architecture for data publishing and discovery

BACKGROUND AND OBJECTIVE

Data catalogues are a common form of capturing and presenting information about a specific kind of entity (e.g. products, services, professionals, datasets, etc.). However, the construction of a web-based catalogue for a particular scenario normally implies the development of a specific and dedicated solution. In this paper, we present MONTRA, a rapid-application development framework designed to facilitate the integration and discovery of heterogeneous objects, which may be characterized by distinct data structures.

METHODS

MONTRA was developed following a plugin-based architecture to allow dynamic composition of services over represented datasets. The core of MONTRA's functionalities resides in a flexible data skeleton used to characterize data entities, and from which a fully-fledged web data catalogue is automatically generated, ensuring access control and data privacy.

RESULTS

MONTRA is being successfully used by several European projects to collect and manage biomedical databases. In this paper, we describe three of these applications scenarios.

CONCLUSIONS

This work was motivated by the plethora of geographically scattered biomedical repositories, and by the role they can play altogether for the understanding of diseases and of the real-world effectiveness of treatments. Using metadata to expose datasets' characteristics, MONTRA greatly simplifies the task of building data catalogues. The source code is publicly available at https://github.com/bioinformatics-ua/montra.

Ethical concerns on sharing genomic data including patients' family members

Background

Platforms for sharing genomic and phenotype data have been developed to promote genomic research, while maximizing the utility of existing datasets and minimizing the burden on participants. The value of genomic analysis of trios or family members has increased, especially in rare diseases and cancers. This article aims to argue the necessity of protection when sharing data from both patients and family members.

Main text

Sharing patients’ and family members’ data collectively raises an ethical tension between the value of datasets and the rights of participants, and increases the risk of re-identification. However, current data-sharing policies have no specific safeguards or provisions for familial data sharing. A quantitative survey conducted on 10,881 general adults in Japan indicated that they expected stronger protection mechanisms when their family members’ clinical and/or genomic data were shared together, as compared to when only their data were shared. A framework that respects decision-making and the right of withdrawal of participants, including family members, along with ensuring usefulness and security of data is needed. To enable this, we propose recommendations on ancillary safeguards for familial data sharing according to the stakeholders, namely, initial researchers, genomic researchers, data submitters, database operators, institutional review boards, and the public and participants.

Conclusions

Families have played significant roles in genetic research, and its value is re-illuminated in the era of genomic medicine. It is important to make progress in data sharing while simultaneously protecting the privacy and interests of patients and families, and return its benefits to them.

Society and personal genome data

Genomic data offer a goldmine of information for understanding the contribution of genetic variation makes to health and disease. The potential of genomic medicine, to predict, diagnose, manage and treat genetic disease, is underpinned by accurate variant interpretation. This in itself hinges on the ability to access large and varied genomic databases. There is now recognition that international collaboration between research and healthcare systems are paramount to delivering the scale of genomic data required. No single research group, institute or country will liberate our understanding, it is only through global cooperation, together with super computing power, will we truly make sense of how genotype and phenotype correlate. Whilst it is logistically possible to create computing systems that talk to each other and aggregate datasets ready to reveal novel correlations, the bottom line is that this will only happen if people (whether they be scientists, clinicians, patients, research participants, policy makers, politicians, law makers) support the principle that we should be donating, accessing and sharing our DNA data in this way. And in order to make the most sense of genomics, given the geographical and ancestral variation between us, such people are likely to be the majority of society. Within this review, a perspective is proffered on the human story that underpins genomic 'big data' access and how we are at a tipping point as a society-we need to decide collectively, are we in? and if so, what needs to be in place to protect us? or are we out?

The challenges of the expanded availability of genomic information: an agenda-setting paper

Rapid advances in microarray and sequencing technologies are making genotyping and genome sequencing more affordable and readily available. There is an expectation that genomic sequencing technologies improve personalized diagnosis and personalized drug therapy. Concurrently, provision of direct-to-consumer genetic testing by commercial providers has enabled individuals' direct access to their genomic data. The expanded availability of genomic data is perceived as influencing the relationship between the various parties involved including healthcare professionals, researchers, patients, individuals, families, industry, and government. This results in a need to revisit their roles and responsibilities. In a 1-day agenda-setting meeting organized by the COST Action IS1303 "Citizen's Health through public-private Initiatives: Public health, Market and Ethical perspectives," participants discussed the main challenges associated with the expanded availability of genomic information, with a specific focus on public-private partnerships, and provided an outline from which to discuss in detail the identified challenges. This paper summarizes the points raised at this meeting in five main parts and highlights the key cross-cutting themes. In light of the increasing availability of genomic information, it is expected that this paper will provide timely direction for future research and policy making in this area.

Multicenter Research Studies in Radiology

RATIONALE AND OBJECTIVES

Here we review the current state of multicenter radiology research (MRR), and utilize a survey of experienced researchers to identify common advantages, barriers, and resources to guide future investigators.

MATERIALS AND METHODS

The Association of University Radiologists established a Radiology Research Alliance task force, Multi-center Research Studies in Radiology, composed of 12 society members to review MRR. A REDCap survey was designed to gain more insight from experienced researchers. Recipients were authors identified from a PubMed database search, utilizing search terms "multicenter" or "multisite" and "radiology." The survey included investigator background information, reasons why, barriers to, and resources that investigators found helpful in conducting or participating in MRR.

RESULTS

The survey was completed by 23 of 80 recipients (29%), the majority (76%) of whom served as a primary investigator on at least one MRR project. Respondents reported meeting collaborators at national or international (74%) and society (39%) meetings. The most common perceived advantages of MRR were increased sample size (100%) and improved generalizability (91%). External funding was considered the most significant barrier to MRR, reported by 26% of respondents. Institutional funding, setting up a central picture archiving and communication system, and setting up a central database were considered a significant barrier by 30%, 22%, and 22% of respondents, respectively. Resources for overcoming barriers included motivated staff (74%), strong leadership (70%), regular conference calls (57%), and at least one face-to-face meeting (57%).

CONCLUSIONS

Barriers to MRR include funding and establishing a central database and a picture archiving and communication system. Upon embarking on an MRR project, forming a motivated team who meets and speaks regularly is essential.

Legal issues in governing genetic biobanks: the Italian framework as a case study for the implications for citizen's health through public-private initiatives

This paper outlines some of the challenges faced by regulation of genetic biobanking, using case studies coming from the Italian legal system. The governance of genetic resources in the context of genetic biobanks in Italy is discussed, as an example of the stratification of different inputs and rules: EU law, national law, orders made by authorities and soft law, which need to be integrated with ethical principles, technological strategies and solutions. After providing an overview of the Italian legal regulation of genetic data processing, it considers the fate of genetic material and IP rights in the event of a biobank’s insolvency. To this end, it analyses two case studies: a controversial bankruptcy case which occurred in Sardinia, one of the first examples of private and public partnership biobanks. Another case study considered is the Chris project: an example of partnership between a research institute in Bolzano and the South Tyrolean Health System. Both cases seem to point in the same direction, suggesting expediency of promoting and improving public-private partnerships to manage biological tissues and biotrust to conciliate patent law and public interest.

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.”

Big and disparate data: considerations for pediatric consortia

PURPOSE OF REVIEW

Increasingly, there is a need for examining exposure disease associations in large, diverse datasets to understand the complex determinants of pediatric disease and disability. Recognizing that children's health research consortia will be important sources of big data, it is crucial for the pediatric research community to be knowledgeable about the challenges and opportunities that they will face. The present review will provide examples of existing children's health consortia, highlight recent pooled analyses conducted by children's health research consortia, address common challenges of pooled analyses, and provide recommendations to advance collective research efforts in pediatric research.

RECENT FINDINGS

Formal consortia and other collective-science initiatives are increasingly being created to share individual data from a set of relevant epidemiological studies to address a common research topic under the concept that the joint effort of many individual groups can accomplish far more than working alone. There are practical challenges to the participation of investigators within consortia that need to be addressed in order for them to work.

SUMMARY

Researchers who access consortia with data centers will be able to go far beyond their initial hypotheses and potentially accomplish research that was previously thought infeasible or too costly.

Improving the informed consent process in international collaborative rare disease research: effective consent for effective research

The increased international sharing of data in research consortia and the introduction of new technologies for sequencing challenge the informed consent (IC) process, adding complexities that require coordination between research centres worldwide. Rare disease consortia present special challenges since available data and samples may be very limited. Thus, it is especially relevant to ensure the best use of available resources but at the same time protect patients' right to integrity. To achieve this aim, there is an ethical duty to plan in advance the best possible consent procedure in order to address possible ethical and legal hurdles that could hamper research in the future. Therefore, it is especially important to identify key core elements (CEs) to be addressed in the IC documents for international collaborative research in two different situations: (1) new research collections (biobanks and registries) for which information documents can be created according to current guidelines and (2) established collections obtained without IC or with a previous consent that does not cover all CEs. We propose here a strategy to deal with consent in these situations. The principles have been applied and are in current practice within the RD-Connect consortia - a global research infrastructure funded by the European Commission Seventh Framework program but forward looking in terms of issues addressed. However, the principles established, the lessons learned and the implications for future research are of direct relevance to all internationally collaborative rare-disease projects.

StemBANCC: Governing Access to Material and Data in a Large Stem Cell Research Consortium

This paper makes the case for implementing an internal governance framework for sharing materials and data in stem cell research consortia. A governance framework can facilitate a transparent and accountable system while building trust among partner institutions. However, avoiding excessive bureaucracy is essential. The development and implementation of a governance framework for materials and data access in the Stem cells for Biological Assays of Novel drugs and prediCtive toxiCology (StemBANCC) consortium is presented as a practical example. The StemBANCC project is a multi-partner European research consortium, which aims to build a resource of 1,500 well characterised induced pluripotent stem cell (iPSC) lines for in vitro disease modelling and toxicology studies. The project governance framework was developed in two stages. A small working group identified key components of a framework and translated the project legal agreements into a draft policy document. The second phase allowed input from all consortium partners to shape the iterative development of a final policy document that could be agreed by all parties. Careful time management strategies were needed to manage the duration of this component. This part of the process also served as an exploratory space where different options could be proposed, potential gaps in planning identified, and project co-ordination activities specified.

Sweat, Skepticism, and Uncharted Territory: A Qualitative Study of Opinions on Data Sharing Among Public Health Researchers and Research Participants in Mumbai, India

Efforts to internalize data sharing in research practice have been driven largely by developing international norms that have not incorporated opinions from researchers in low- and middle-income countries. We sought to identify the issues around ethical data sharing in the context of research involving women and children in urban India. We interviewed researchers, managers, and research participants associated with a Mumbai non-governmental organization, as well as researchers from other organizations and members of ethics committees. We conducted 22 individual semi-structured interviews and involved 44 research participants in focus group discussions. We used framework analysis to examine ideas about data and data sharing in general; its potential benefits or harms, barriers, obligations, and governance; and the requirements for consent. Both researchers and participants were generally in favor of data sharing, although limited experience amplified their reservations. We identified three themes: concerns that the work of data producers may not receive appropriate acknowledgment, skepticism about the process of sharing, and the fact that the terrain of data sharing was essentially uncharted and confusing. To increase data sharing in India, we need to provide guidelines, protocols, and examples of good practice in terms of consent, data preparation, screening of applications, and what individuals and organizations can expect in terms of validation, acknowledgment, and authorship.

Developing an Ethical and Legal Interoperability Assessment Process for Retrospective Studies

The past decade has witnessed the creation of major international research consortia, aiming to facilitate the sharing of data from different studies to maximize health benefits. However, combining heterogeneous data across existing studies requires addressing issues related to both data harmonization and ethical and legal interoperability. This article proposes a rigorous interoperability assessment process to assess whether different data sets are sufficiently ethically and legally interoperable to allow for a given proposed research use. The methodology used to develop this process is based on a comprehensive analysis of the international ethical and legal framework governing the use of retrospective data in research, and includes the following steps: (I) finding existing processes; (II) comparing processes to identify similarities and differences and determining the limits of the "consistent whole"; (III) establishing common principles and procedures; and, (IV) changing or removing processes that do not contribute to the consistent whole. Each of these four steps were examined using step-specific methodologies, including (a) literature and policy reviews; (b) consultations with international ethical, legal and social implications (ELSI) experts; and (c) a case study piloting the proposed framework in an actual international research consortium. This assessment process takes into account key legal and ethical components such as consent, recontact, and waiver of consent. As a result, this analysis allows the development of a comprehensive filter used to verify the legal and ethical restrictions pertaining to a data set. This in turns helps in determining whether the given data set can to be used for a proposed research project, or is ethically and legally interoperable for use in research collaborations. By integrating this filter to the regular data access processes used by cohorts, not only will researchers be able to create virtual "mega-cohorts" of research participants, but it will also ensure that these cohorts respect basic legal and ethical precepts.

Improving the informed consent process in international collaborative rare disease research: effective consent for effective research

The increased international sharing of data in research consortia and the introduction of new technologies for sequencing challenge the informed consent (IC) process, adding complexities that require coordination between research centres worldwide. Rare disease consortia present special challenges since available data and samples may be very limited. Thus, it is especially relevant to ensure the best use of available resources but at the same time protect patients' right to integrity. To achieve this aim, there is an ethical duty to plan in advance the best possible consent procedure in order to address possible ethical and legal hurdles that could hamper research in the future. Therefore, it is especially important to identify key core elements (CEs) to be addressed in the IC documents for international collaborative research in two different situations: (1) new research collections (biobanks and registries) for which information documents can be created according to current guidelines and (2) established collections obtained without IC or with a previous consent that does not cover all CEs. We propose here a strategy to deal with consent in these situations. The principles have been applied and are in current practice within the RD-Connect consortia - a global research infrastructure funded by the European Commission Seventh Framework program but forward looking in terms of issues addressed. However, the principles established, the lessons learned and the implications for future research are of direct relevance to all internationally collaborative rare-disease projects.

Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research

A wealth of biospecimen samples are stored in modern globally distributed biobanks. Biomedical researchers worldwide need to be able to combine the available resources to improve the power of large-scale studies. A prerequisite for this effort is to be able to search and access phenotypic, clinical and other information about samples that are currently stored at biobanks in an integrated manner. However, privacy issues together with heterogeneous information systems and the lack of agreed-upon vocabularies have made specimen searching across multiple biobanks extremely challenging. We describe three case studies where we have linked samples and sample descriptions in order to facilitate global searching of available samples for research. The use cases include the ENGAGE (European Network for Genetic and Genomic Epidemiology) consortium comprising at least 39 cohorts, the SUMMIT (surrogate markers for micro- and macro-vascular hard endpoints for innovative diabetes tools) consortium and a pilot for data integration between a Swedish clinical health registry and a biobank. We used the Sample avAILability (SAIL) method for data linking: first, created harmonised variables and then annotated and made searchable information on the number of specimens available in individual biobanks for various phenotypic categories. By operating on this categorised availability data we sidestep many obstacles related to privacy that arise when handling real values and show that harmonised and annotated records about data availability across disparate biomedical archives provide a key methodological advance in pre-analysis exchange of information between biobanks, that is, during the project planning phase.

Sharing and Reuse of Sensitive Data and Samples: Supporting Researchers in Identifying Ethical and Legal Requirements

Availability of and access to data and biosamples are essential in medical and translational research, where their reuse and repurposing by the wider research community can maximize their value and accelerate discovery. However, sharing human-related data or samples is complicated by ethical, legal, and social sensitivities. The specific ethical and legal requirements linked to sensitive data are often unfamiliar to life science researchers who, faced with vast amounts of complex, fragmented, and sometimes even contradictory information, may not feel competent to navigate through it. In this case, the impulse may be not to share the data in order to safeguard against unintentional misuse. Consequently, helping data providers to identify relevant ethical and legal requirements and how they might address them is an essential and frequently neglected step in removing possible hurdles to data and sample sharing in the life sciences. Here, we describe the complex regulatory context and discuss relevant online tools-one which the authors co-developed-targeted at assisting providers of sensitive data or biosamples with ethical and legal questions. The main results are (1) that the different approaches of the tools assume different user needs and prior knowledge of ethical and legal requirements, affecting how a service is designed and its usefulness, (2) that there is much potential for collaboration between tool providers, and (3) that enriched annotations of services (e.g., update status, completeness of information, and disclaimers) would increase their value and facilitate quick assessment by users. Further, there is still work to do with respect to providing researchers using sensitive data or samples with truly 'useful' tools that do not require pre-existing, in-depth knowledge of legal and ethical requirements or time to delve into the details. Ultimately, separate resources, maintained by experts familiar with the respective fields of research, may be needed while-in the longer term-harmonization and increase in ease of use will be very desirable.

MARK-AGE data management: Cleaning, exploration and visualization of data

Databases are an organized collection of data and necessary to investigate a wide spectrum of research questions. For data evaluation analyzers should be aware of possible data quality problems that can compromise results validity. Therefore data cleaning is an essential part of the data management process, which deals with the identification and correction of errors in order to improve data quality. In our cross-sectional study, biomarkers of ageing, analytical, anthropometric and demographic data from about 3000 volunteers have been collected in the MARK-AGE database. Although several preventive strategies were applied before data entry, errors like miscoding, missing values, batch problems etc., could not be avoided completely. Such errors can result in misleading information and affect the validity of the performed data analysis. Here we present an overview of the methods we applied for dealing with errors in the MARK-AGE database. We especially describe our strategies for the detection of missing values, outliers and batch effects and explain how they can be handled to improve data quality. Finally we report about the tools used for data exploration and data sharing between MARK-AGE collaborators.

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.

Building a data sharing model for global genomic research

Data sharing models designed to facilitate global business provide insights for improving transborder genomic data sharing. We argue that a flexible, externally endorsed, multilateral arrangement, combined with an objective third-party assurance mechanism, can effectively balance privacy with the need to share genomic data globally.

A human rights approach to an international code of conduct for genomic and clinical data sharing

Fostering data sharing is a scientific and ethical imperative. Health gains can be achieved more comprehensively and quickly by combining large, information-rich datasets from across conventionally siloed disciplines and geographic areas. While collaboration for data sharing is increasingly embraced by policymakers and the international biomedical community, we lack a common ethical and legal framework to connect regulators, funders, consortia, and research projects so as to facilitate genomic and clinical data linkage, global science collaboration, and responsible research conduct. Governance tools can be used to responsibly steer the sharing of data for proper stewardship of research discovery, genomics research resources, and their clinical applications. In this article, we propose that an international code of conduct be designed to enable global genomic and clinical data sharing for biomedical research. To give this proposed code universal application and accountability, however, we propose to position it within a human rights framework. This proposition is not without precedent: international treaties have long recognized that everyone has a right to the benefits of scientific progress and its applications, and a right to the protection of the moral and material interests resulting from scientific productions. It is time to apply these twin rights to internationally collaborative genomic and clinical data sharing.

Big data, open science and the brain: lessons learned from genomics

The BRAIN Initiative aims to break new ground in the scale and speed of data collection in neuroscience, requiring tools to handle data in the magnitude of yottabytes (10²⁴). The scale, investment and organization of it are being compared to the Human Genome Project (HGP), which has exemplified “big science” for biology. In line with the trend towards Big Data in genomic research, the promise of the BRAIN Initiative, as well as the European Human Brain Project, rests on the possibility to amass vast quantities of data to model the complex interactions between the brain and behavior and inform the diagnosis and prevention of neurological disorders and psychiatric disease. Advocates of this “data driven” paradigm in neuroscience argue that harnessing the large quantities of data generated across laboratories worldwide has numerous methodological, ethical and economic advantages, but it requires the neuroscience community to adopt a culture of data sharing and open access to benefit from them. In this article, we examine the rationale for data sharing among advocates and briefly exemplify these in terms of new “open neuroscience” projects. Then, drawing on the frequently invoked model of data sharing in genomics, we go on to demonstrate the complexities of data sharing, shedding light on the sociological and ethical challenges within the realms of institutions, researchers and participants, namely dilemmas around public/private interests in data, (lack of) motivation to share in the academic community, and potential loss of participant anonymity. Our paper serves to highlight some foreseeable tensions around data sharing relevant to the emergent “open neuroscience” movement.

An ethics safe harbor for international genomics research?

BACKGROUND

Genomics research is becoming increasingly globally connected and collaborative, contesting traditional ethical and legal boundaries between global and local research practice. As well, global data-driven genomics research holds great promise for health discoveries. Yet, paradoxically, current research ethics review systems around the world challenge potential improvements in human health from such research and thus undermine respect for research participants. Case reports illustrate that the current system is costly, fragmented, inefficient, inadequate, and inconsistent. There is an urgent need to improve the governance system of ethics review to enable secure and seamless genomic and clinical data sharing across jurisdictions.

DISCUSSION

Building on the international privacy 'safe harbor' model that was developed following the adoption of the European Privacy Directive, we propose an international infrastructure. The goal is to create a streamlined and harmonized ethics governance system for international, data-driven genomics research projects. The proposed 'Safe Harbor Framework for International Ethics Equivalency' would consist in part of an agency supporting an International Federation for Ethics Review (IFER), formed by a voluntary agreement among countries, granting agencies, philanthropies, institutions, and healthcare, patient advocacy, and research organizations. IFER would be both a central ethics review body and also a forum for review and follow-up of policies concerning ethics norms for international genomics research projects. It would be built on five principle elements: (1) registration; (2) compliance review; (3) recognition; (4) monitoring and enforcement; and (5) public participation.

SUMMARY

A Safe Harbor Framework for International Ethics Equivalency would create many benefits for researchers, countries, and the general public, and may eventually have application beyond genomics to other areas of biomedical research that increasingly engage in secondary use of data and present only negligible risks. Among the benefits, research participants and patients would have uniform adequate protection, while researchers would be ensured expert ethics review with a reduction in cost, time, administrative hassle, and redundant regulatory hurdles. Most importantly, society would enjoy the maximization of the potential benefits of genomics research.

Data harmonization and federated analysis of population-based studies: the BioSHaRE project

Abstracts