An Icelandic saga on a centralized healthcare database and democratic decision making

Cross-population applications of genomics to understand the risk of multifactorial traits involving inflammation and immunity

The interplay between genetic and environmental factors plays a significant role in interindividual variation in immune and inflammatory responses. The availability of high-throughput low-cost genotyping and next-generation sequencing has revolutionized our ability to identify human genetic variation and understand how this varies within and between populations, and the relationship with disease. In this review, we explore the potential of genomics for patient benefit, specifically in the diagnosis, prognosis and treatment of inflammatory and immune-related diseases. We summarize the knowledge arising from genetic and functional genomic approaches, and the opportunity for personalized medicine. The review covers applications in infectious diseases, rare immunodeficiencies and autoimmune diseases, illustrating advances in diagnosis and understanding risk including use of polygenic risk scores. We further explore the application for patient stratification and drug target prioritization. The review highlights a key challenge to the field arising from the lack of sufficient representation of genetically diverse populations in genomic studies. This currently limits the clinical utility of genetic-based diagnostic and risk-based applications in non-Caucasian populations. We highlight current genome projects, initiatives and biobanks from diverse populations and how this is being used to improve healthcare globally by improving our understanding of genetic susceptibility to diseases and regional pathogens such as malaria and tuberculosis. Future directions and opportunities for personalized medicine and wider application of genomics in health care are described, for the benefit of individual patients and populations worldwide.

Application of clinical and molecular profiling data to improve patient outcomes in psoriatic arthritis

Achieving a good outcome for a person with Psoriatic Arthritis (PsA) is made difficult by late diagnosis, heterogenous clinical disease expression and in many cases, failure to adequately suppress inflammatory disease features. Single-centre studies have certainly contributed to our understanding of disease pathogenesis, but to adequately address the major areas of unmet need, multi-partner, collaborative research programmes are now required. HIPPOCRATES is a 5-year, Innovative Medicines Initiative (IMI) programme which includes 17 European academic centres experienced in PsA research, 5 pharmaceutical industry partners, 3 small-/medium-sized industry partners and 2 patient-representative organizations. In this review, the ambitious programme of work to be undertaken by HIPPOCRATES is outlined and common approaches and challenges are identified. It is expected that, when completed, the results will ultimately allow for changes in the approaches to diagnosing, managing and treating PsA allowing for better short-term and long-term outcomes.

Governing health data across changing contexts: A focus group study of citizen's views in England, Iceland, and Sweden

BACKGROUND

The governance structures associated with health data are evolving in response to advances in digital technologies that enable new ways of capturing, using, and sharing different types of data. Increasingly, health data moves between different contexts such as from healthcare to research, or to commerce and marketing. Crossing these contextual boundaries has the potential to violate societal expectations about the appropriate use of health data and diminish public trust. Understanding citizens' views on the acceptability of and preferences for data use in different contexts is essential for developing information governance policies in these new contexts.

METHODS

Focus group design presenting data sharing scenarios in England, Iceland, and Sweden.

RESULTS

Seventy-one participants were recruited. Participants supported the need for data to help understand the observable world, improve medical research, the quality of public services, and to benefit society. However, participants consistently identified the lack of information, transparency and control as barriers to trusting organisations to use data in a way that they considered appropriate. There was considerable support for fair and transparent data sharing practices where all parties benefitted.

CONCLUSION

Data governance policy should involve all stakeholders' perspectives on an ongoing basis, to inform and implement changes to health data sharing practices that accord with stakeholder views. The Findings showed that (1) data should be used for ethical purposes even when there was commercial interest; (2) data subjects and/or public institutions that provide and share data should also receive benefits from the sharing of data; (3) third parties use of data requires greater transparency and accountability than currently exists, (4) there should be greater information provided to empower data subjects.

Advances in Genomic Discovery and Implications for Personalized Prevention and Medicine: Estonia as Example

The current paradigm of personalized medicine envisages the use of genomic data to provide predictive information on the health course of an individual with the aim of prevention and individualized care. However, substantial efforts are required to realize the concept: enhanced genetic discoveries, translation into intervention strategies, and a systematic implementation in healthcare. Here we review how further genetic discoveries are improving personalized prediction and advance functional insights into the link between genetics and disease. In the second part we give our perspective on the way these advances in genomic research will transform the future of personalized prevention and medicine using Estonia as a primer.

Next-Generation Sequencing Strategies

More than a decade ago, the term "next-generation" sequencing was coined to describe what was, at the time, revolutionary new methods to sequence RNA and DNA at a faster pace and cheaper cost than could be performed by standard bench-top protocols. Since then, the field of DNA sequencing has evolved at a rapid pace, with new breakthroughs allowing capacity to exponentially increase and cost to dramatically decrease. As genome-scale sequencing has become routine, a paradigm shift is occurring in genomics, which uses the power of high-throughput, rapid sequencing power with large-scale studies. These new approaches to genetic discovery will provide direct impact to fields such as personalized medicine, evolution, and biodiversity. This work reviews recent technology advances and methods in next-generation sequencing and highlights current large-scale sequencing efforts driving the evolution of the genomics space.

Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity

Individuals differ substantially in their response to pharmacological treatment. Personalized medicine aspires to embrace these inter-individual differences and customize therapy by taking a wealth of patient-specific data into account. Pharmacogenomic constitutes a cornerstone of personalized medicine that provides therapeutic guidance based on the genomic profile of a given patient. Pharmacogenomics already has applications in the clinics, particularly in oncology, whereas future development in this area is needed in order to establish pharmacogenomic biomarkers as useful clinical tools. In this review we present an updated overview of current and emerging pharmacogenomic biomarkers in different therapeutic areas and critically discuss their potential to transform clinical care. Furthermore, we discuss opportunities of technological, methodological and institutional advances to improve biomarker discovery. We also summarize recent progress in our understanding of epigenetic effects on drug disposition and response, including a discussion of the only few pharmacogenomic biomarkers implemented into routine care. We anticipate, in part due to exciting rapid developments in Next Generation Sequencing technologies, machine learning methods and national biobanks, that the field will make great advances in the upcoming years towards unlocking the full potential of genomic data.

Electronic health records: the next wave of complex disease genetics

The combination of electronic health records (EHRs) with genetic data has ushered in the next wave of complex disease genetics. Population-based biobanks and other large cohorts provide sufficient sample sizes to identify novel genetic associations across the hundreds to thousands of phenotypes gleaned from EHRs. In this review, we summarize the current state of these EHR-linked biobanks, explore ongoing methods development in the field and highlight recent discoveries of genetic associations. We enumerate the many existing biobanks with EHRs linked to genetic data, many of which are available to researchers via application and contain sample sizes >50 000. We also discuss the computational and statistical considerations for analysis of such large datasets including mixed models, phenotype curation and cloud computing. Finally, we demonstrate how genome-wide association studies and phenome-wide association studies have identified novel genetic findings for complex diseases, specifically cardiometabolic traits. As more researchers employ innovative hypotheses and analysis approaches to study EHR-linked biobanks, we anticipate a richer understanding of the genetic etiology of complex diseases.

The development of large-scale de-identified biomedical databases in the age of genomics-principles and challenges

Contemporary biomedical databases include a wide range of information types from various observational and instrumental sources. Among the most important features that unite biomedical databases across the field are high volume of information and high potential to cause damage through data corruption, loss of performance, and loss of patient privacy. Thus, issues of data governance and privacy protection are essential for the construction of data depositories for biomedical research and healthcare. In this paper, we discuss various challenges of data governance in the context of population genome projects. The various challenges along with best practices and current research efforts are discussed through the steps of data collection, storage, sharing, analysis, and knowledge dissemination.

A Survey of U.S Adults' Opinions about Conduct of a Nationwide Precision Medicine Initiative® Cohort Study of Genes and Environment

OBJECTIVES

A survey of a population-based sample of U.S adults was conducted to measure their attitudes about, and inform the design of the Precision Medicine Initiative's planned national cohort study.

METHODS

An online survey was conducted by GfK between May and June of 2015. The influence of different consent models on willingness to share data was examined by randomizing participants to one of eight consent scenarios.

RESULTS

Of 4,777 people invited to take the survey, 2,706 responded and 2,601 (54% response rate) provided valid responses. Most respondents (79%) supported the proposed study, and 54% said they would definitely or probably participate if asked. Support for and willingness to participate in the study varied little among demographic groups; younger respondents, LGBT respondents, and those with more years of education were significantly more likely to take part if asked. The most important study incentive that the survey asked about was learning about one's own health information. Willingness to share data and samples under broad, study-by-study, menu and dynamic consent models was similar when a statement about transparency was included in the consent scenarios. Respondents were generally interested in taking part in several governance functions of the cohort study.

CONCLUSIONS

A large majority of the U.S. adults who responded to the survey supported a large national cohort study. Levels of support for the study and willingness to participate were both consistent across most demographic groups. The opportunity to learn health information about one's self from the study appears to be a strong motivation to participate.

Models of biobanks and implications for reproductive health innovation

Biobanks are designed with particular purposes in mind. These purposes are reflected in the governance frameworks that define the conditions for participation and access by researchers. In this paper, I analyse two different models: the commercially aligned deCODE biobank and the 'public good' framework of UK Biobank. These diametric models have both featured 'the public interest' as pivotal to their achievements. However, if properly understood, the public interest rhetoric of deCODE actually conflicts with any professed community interest. The reasons why are explained, and the effects this has on conditions for participation are critiqued, before reflecting on how each model might have an impact on reproductive health research.

Biobanks for cardiovascular epidemiology and prevention

Biobanks for medical research are organized collections of biological samples associated with personal data and information on their donors, to be stored for an indefinite period of time. The storage of biological samples has varied considerably over time, ranging from the informal storage of tissue specimens in a researcher's freezer in the past, to the present well-structured formal repositories. Large-scale population-related biobanks are being set up in several countries and will allow not only research into individual diseases, but also approaches to a wide range of health-related issues, such as physical activity, eating, drinking, education and pollution, among others. The purpose of this article is to discuss how biobanks have improved research in cardiovascular disease epidemiology and prevention, by describing the most relevant population-based epidemiological studies that used set-up biobanks and stored samples for research. The selection of epidemiological studies and biobanks was based on their dimensions and their contribution to the field.

Subjects matter: a survey of public opinions about a large genetic cohort study

PURPOSE

Cohort studies investigating genes, environment, and lifestyle require large study populations. To recruit and retain participants, it is important to understand the relative significance of influences on people's motivation to participate. To this end, 4659 Americans were surveyed about support for and willingness to participate in a proposed large cohort study.

METHODS

An online survey of US adults was conducted between December 2007 and January 2008. To measure the influence of study burden, compensation and receipt of individual research results on willingness to participate, respondents were randomized to one of eight different study scenarios.

RESULTS

Most respondents (84%) supported the study, and 60% would participate. Returning research results (odds ratio = 1.6, 95% confidence interval 1.3-1.8) and increasing compensation from $50 to $200 (odds ratio = 1.5, 95% confidence interval 1.2-1.7) were associated with increased willingness to participate. Decreasing study burden was less important (odds ratio = 1.2, 95% confidence interval 1.0-1.4). Three in four respondents would be less likely to participate without the return of research results. Support and willingness varied little among demographic groups; variation in influences of the three factors on willingness was observed.

CONCLUSION

Widespread support exists in the general public for a large national cohort study. Providing individual research results is a strong motivation to participate; compensating participants $200 may increase participation a similar amount. Incentives, recruitment, and return of results could be tailored to demographics groups' interests.

Genebanks: a comparison of eight proposed international genetic databases

OBJECTIVE

To identify and compare population-based genetic databases, or "genebanks", that have been proposed in eight international locations between 1998 and 2002. A genebank can be defined as a stored collection of genetic samples in the form of blood or tissue, that can be linked with medical and genealogical or lifestyle information from a specific population, gathered using a process of generalized consent.

METHODS

Genebanks were identified by searching Medline and internet search engines with key words such as "genetic database" and "biobank" and by reviewing literature on previously identified databases such as the deCode project. Collection of genebank characteristics was by an electronic and literature search, augmented by correspondence with informed individuals. The proposed genebanks are located in Iceland, the United Kingdom, Estonia, Latvia, Sweden, Singapore, the Kingdom of Tonga, and Quebec, Canada. Comparisons of the genebanks were based on the following criteria: genebank location and description of purpose, role of government, commercial involvement, consent and confidentiality procedures, opposition to the genebank, and current progress.

RESULTS

All of the groups proposing the genebanks plan to search for susceptibility genes for complex diseases while attempting to improve public health and medical care in the region and, in some cases, stimulating the local economy through expansion of the biotechnology sector. While all of the identified plans share these purposes, they differ in many aspects, including funding, subject participation, and organization. The balance of government and commercial involvement in the development of each project varies. Genetic samples and health information will be collected from participants and coded in all of the genebanks, but consent procedures range from presumed consent of the entire eligible population to recruitment of volunteers with informed consent. Issues regarding confidentiality and consent have resulted in opposition to some of the more publicized projects. None of the proposed databases are currently operational and at least one project was terminated due to opposition.

CONCLUSIONS

Ambitious genebank projects have been proposed in numerous countries and provinces. The characteristics of the projects vary, but all intend to map genes for common diseases and hope to improve the health of the populations involved. The impact of these projects on understanding genetic susceptibility to disease will be increasingly apparent if the projects become operational. The ethical, legal, and social implications of the projects should be carefully considered during their development.

Discourse ethics as an ethics of responsibility: comparison and evaluation of citizen involvement in population genomics

Population genomics seeks to better understand genomic diversity and variation at the level of populations. Numerous ethical questions are raised by large studies of human genomic diversity. First, at the level of ethical means, the confidentiality of the genetic data obtained and its handing (data banks, access, involvement of private sector, etc.) are challenging. Second, at the more fundamental level of ethical goals or ends, we find questions concerning the meaning and the interpretation of genetic knowledge. What shall we do with this knowledge? Who will decide research orientation and priorities? Ethical questions raised by population genomics imply those of “classical genomics,” but they have the additional ethical complexity of research done on populations. How should we take this particular aspect into account?

Yin-yang genetics, or the HSD deCODE controversy

Iceland has attracted world-wide attention because of a controversy surrounding the Icelandic Health Sector Database Act of 1998 and the genomics company deCODE Genetics. The controversy concerns limits to state power, bioethics and regulation of research, and civil liberties. Understanding it necessitates paying attention to the yin-yang-like character of deCODE; it is international or Icelandic, what is the relation between the HSD and deCODE, will the HSD engulf the Icelandic nation as a whole?

Ethical and methodological issues in pedigree stroke research

BACKGROUND

Stroke is a complex genetic disorder with a variable phenotype. Investigations of heritable factors in complex genetic disorders use pedigree and genetic techniques, which pose different ethical and methodological challenges than those routinely encountered in therapeutic research. Building consensus on acceptable research practices in this field is vital to the success of multicentered collaborations.

SUMMARY OF REVIEW

We review important ethical and methodological concerns related to the collection, storage, and release of pedigree research information. The human studies aspects of pedigree research are complicated methodologically because individuals can be active or passive participants and pedigrees can be proband derived, partially validated, or fully validated. Current research ethics frameworks do not work well outside of a dyadic researcher-subject relationship. Privacy and confidentiality for family members must be considered in pedigree research. Investigators should anticipate potential conflicts of interest among family members when designing a pedigree research protocol.

CONCLUSIONS

We propose a "proband-initiated contact" methodology in which the proband or the proband's designate allows identification of potential families without breaching the privacy of individuals in the family. In situations in which family history data are collected without direct contact between researchers and individuals in the proband's family, an Institutional Review Board may waive consent by family members after appropriate review of the protocol and application of rules for granting waivers of consent. Certificates of Confidentiality should be considered.

Optimum study designs

Because simplistic designs will lead to prohibitively large sample sizes, the optimization of genetic study designs is critical for successfully mapping genes for complex diseases. Creative designs are necessary for detecting and amplifying the usually weak signals for complex traits. Two important outcomes of a study design--power and resolution--are implicitly tied together by the principle of uncertainty. Overemphasis on either one may lead to suboptimal designs. To achieve optimality for a particular study, therefore, practical measures such as cost-effectiveness must be used to strike a balance between power and resolution. In this light, the myriad of factors involved in study design can be checked for their effects on the ultimate outcomes, and the popular existing designs can be sorted into building blocks that may be useful for particular situations. It is hoped that imaginative construction of novel designs using such building blocks will lead to enhanced efficiency in finding genes for complex human traits.