Genomic medicine and data sharing

Secondary use of genomic data: patients' decisions at point of testing and perspectives to inform international data sharing

International sharing of genomic data files arising from clinical testing of patients is essential to further improve genomic medicine. Whilst the general public are reluctant to donate DNA for research, the choices patients actually make about sharing their clinical genomic data for future re-use (research or clinical) are unknown. We ascertained the data-sharing choices of 1515 patients having genomic testing for inherited conditions or cancer treatment from clinical consent forms. To understand the experiences and preferences of these patients, surveys were administered after test consent (RR 73%). Almost all patients (98%) consented to share their data. Survey respondents' decision recall was high (90%), but poorer if English was an additional language (p < 0.001). Parents deciding on behalf of children were over-represented amongst data-sharing decliners (p = 0.047) and decliners were more likely to believe that stored data could be easily reidentified (p < 0.001). A quarter of respondents did not know if reidentification would be easy and 44% of them were concerned about this possibility. Of those willing to share data overseas (60%), 23% indicated the recipient researcher's country would affect their decision. Most respondents (89%) desired some ongoing control over research use of their data. Four preliminary data-sharing profiles emerged; their further development could inform tailored patient resources. Our results highlight considerations for establishment of systems to make clinical genomic data files available for reanalysis locally and across borders. Patients' willingness to share their data - and value of the resulting research - should encourage clinical laboratories to consider sharing data systematically for secondary uses.

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.

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.

Community-led risk analysis of direct-to-consumer whole-genome sequencing

Direct-to-consumer (DTC) genetic testing is cheaper and more accessible than ever before; however, the intention to combine, reuse, and resell this genetic information as powerful data sets is generally hidden from the consumer. This financial gain is creating a competitive DTC market, reducing the price of whole-genome sequencing (WGS) to under 300 USD. Entering this transition from single-nucleotide polymorphism-based DTC testing to WGS DTC testing, individuals looking for access to their whole-genomic information face new privacy and security risks. Differences between WGS and other methods of consumer genetic tests are left unexplored by regulation, leading to the application of legal data anonymization methods on whole-genome data, and questionable consent methods. Large representative genomic data sets are important for research and improve the standard of medicine and personalized care. However, these data can also be used by market players, law enforcement, and governments for surveillance, population analyses, marketing purposes, and discrimination. Here, we present a summary of the state of WGS DTC genetic testing and its current regulation, through a community-based lens to expose dual-use risks in consumer-facing biotechnologies.

Shariant platform: Enabling evidence sharing across Australian clinical genetic-testing laboratories to support variant interpretation

Sharing genomic variant interpretations across laboratories promotes consistency in variant assertions. A landscape analysis of Australian clinical genetic-testing laboratories in 2017 identified that, despite the national-accreditation-body recommendations encouraging laboratories to submit genotypic data to clinical databases, fewer than 300 variants had been shared to the ClinVar public database. Consultations with Australian laboratories identified resource constraints limiting routine application of manual processes, consent issues, and differences in interpretation systems as barriers to sharing. This information was used to define key needs and solutions required to enable national sharing of variant interpretations. The Shariant platform, using both the GRCh37 and GRCh38 genome builds, was developed to enable ongoing sharing of variant interpretations and associated evidence between Australian clinical genetic-testing laboratories. Where possible, two-way automated sharing was implemented so that disruption to laboratory workflows would be minimized. Terms of use were developed through consultation and currently restrict access to Australian clinical genetic-testing laboratories. Shariant was designed to store and compare structured evidence, to promote and record resolution of inter-laboratory classification discrepancies, and to streamline the submission of variant assertions to ClinVar. As of December 2021, more than 14,000 largely prospectively curated variant records from 11 participating laboratories have been shared. Discrepant classifications have been identified for 11% (28/260) of variants submitted by more than one laboratory. We have demonstrated that co-design with clinical laboratories is vital to developing and implementing a national variant-interpretation sharing effort. This approach has improved inter-laboratory concordance and enabled opportunities to standardize interpretation practices.

Genomic medicine on the frontier of precision medicine

Genomic medicine has created a great deal of hope since the completion of the Human Genome Project (HGP). Genomic medicine promises disease prevention and early diagnosis in the context of precision medicine. Precision medicine as a scientific discipline has introduced as an evolution in medicine. The rapid growth of high-development technologies permits the assessment of biological systems. Study of the integrated profiles of omics, such as genome, transcriptome, proteome and other omics information lead to significant advances in personalized and precision medicine. In the context of precision medicine, pharmacogenomics can play an important role in order to discriminate responders and non-responders to medications and avoiding toxicity and achieving the optimum dose. So precision medicine in accordance with genomic medicine will transform medicine from conventional evidence-based medicine in the diagnosis and treatment towards precision based-medicine. In this review, we have summarized the related issues for genomic medicine and precision medicine.

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

Background

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

Methods

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

Results

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

Conclusion

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

Graphic abstract

Healthcare practice strategies for integrating personalized medicine: Management of COVID-19

Personalized medicine is the tailor-made clinical treatment to the individual characteristics of each patient. It may be considered an extension of traditional approaches to knowing and treating diseases. Personalized medicine has the potential to change the way of identification and management of health problems. Coronavirus disease 2019 (COVID-19) is an infectious disease that primarily affects the patients’ lungs. The first case of pneumonia of unknown cause was reported in Wuhan, China on December 31, 2019. As thus, we are quickly approaching the era of personalized medicine. This review discusses the practices currently used in the management of COVID-19 and how they relate to personalized medicine.

Considerations for an integrated population health databank in Africa: lessons from global best practices

Background: The rising digitisation and proliferation of data sources and repositories cannot be ignored. This trend expands opportunities to integrate and share population health data. Such platforms have many benefits, including the potential to efficiently translate information arising from such data to evidence needed to address complex global health challenges. There are pockets of quality data on the continent that may benefit from greater integration. Integration of data sources is however under-explored in Africa. The aim of this article is to identify the requirements and provide practical recommendations for developing a multi-consortia public and population health data-sharing framework for Africa. Methods: We conducted a narrative review of global best practices and policies on data sharing and its optimisation. We searched eight databases for publications and undertook an iterative snowballing search of articles cited in the identified publications. The Leximancer software © enabled content analysis and selection of a sample of the most relevant articles for detailed review. Themes were developed through immersion in the extracts of selected articles using inductive thematic analysis. We also performed interviews with public and population health stakeholders in Africa to gather their experiences, perceptions, and expectations of data sharing. Results: Our findings described global stakeholder experiences on research data sharing. We identified some challenges and measures to harness available resources and incentivise data sharing.  We further highlight progress made by the different groups in Africa and identified the infrastructural requirements and considerations when implementing data sharing platforms. Furthermore, the review suggests key reforms required, particularly in the areas of consenting, privacy protection, data ownership, governance, and data access. Conclusions: The findings underscore the critical role of inclusion, social justice, public good, data security, accountability, legislation, reciprocity, and mutual respect in developing a responsive, ethical, durable, and integrated research data sharing ecosystem.

GeneBreaker: Variant simulation to improve the diagnosis of Mendelian rare genetic diseases

Mendelian rare genetic diseases affect 5%–10% of the population, and with over 5300 genes responsible for ∼7000 different diseases, they are challenging to diagnose. The use of whole‐genome sequencing (WGS) has bolstered the diagnosis rate significantly. The effective use of WGS relies on the ability to identify the disrupted gene responsible for disease phenotypes. This process involves genomic variant calling and prioritization, and is the beneficiary of improvements to sequencing technology, variant calling approaches, and increased capacity to prioritize genomic variants with potential pathogenicity. As analysis pipelines continue to improve, careful testing of their efficacy is paramount. However, real‐life cases typically emerge anecdotally, and utilization of clinically sensitive and identifiable data for testing pipeline improvements is regulated and limiting. We identified the need for a gene‐based variant simulation framework that can create mock rare disease scenarios, utilizing known pathogenic variants or through the creation of novel gene‐disrupting variants. To fill this need, we present GeneBreaker, a tool that creates synthetic rare disease cases with utility for benchmarking variant calling approaches, testing the efficacy of variant prioritization, and as an educational mechanism for training diagnostic practitioners in the expanding field of genomic medicine. GeneBreaker is freely available at http://GeneBreaker.cmmt.ubc.ca.

Biomedical Data Sharing Among Researchers: A Study from Jordan

Background

Data sharing is an encouraged practice to support research in all fields. For that purpose, it is important to examine perceptions and concerns of researchers about biomedical data sharing, which was investigated in the current study.

Methods

This is a cross-sectional survey study that was distributed among biomedical researchers in Jordan, as an example of developing countries. The study survey consisted of questions about demographics and about respondent’s attitudes toward sharing of biomedical data.

Results

Among study participants, 46.9% (n=82) were positive regarding making their research data available to the public, whereas 53.1% refused the idea. The reasons for refusing to publicly share their data included “lack of regulations” (33.5%), “access to research data should be limited to the research team” (29.5%), “no place to deposit the data” (6.5%), and “lack of funding for data deposition” (6.0%). Agreement with the idea of making data available was associated with academic rank (P=0.003). Moreover, gender (P-value=0.043) and number of publications (P-value=0.005) were associated with a time frame for data sharing (ie, agreeing to share data before vs after publication).

Conclusion

About half of the respondents reported a positive attitude toward biomedical data sharing. Proper regulations and facilitation data deposition can enhance data sharing in Jordan.

Humanizing Big Data: Recognizing the Human Aspect of Big Data

The term “big data” refers broadly to large volumes of data, often gathered from several sources, that are then analyzed, for example, for predictive analytics. Combining and mining genetic data from varied sources including clinical genetic testing, for example, electronic health records, what might be termed as “recreational” genetic testing such as ancestry testing, as well as research studies, provide one type of “big data.” Challenges and cautions in analyzing big data include recognizing the lack of systematic collection of the source data, the variety of assay technologies used, the potential variation in classification and interpretation of genetic variants. While advanced technologies such as microarrays and, more recently, next-generation sequencing, that enable testing an individual's DNA for thousands of genes and variants simultaneously are briefly discussed, attention is focused more closely on challenges to analysis of the massive data generated by these genomic technologies. The main theme of this review is to evaluate challenges associated with big data in general and specifically to bring the sophisticated technology of genetic/genomic testing down to the individual level, keeping in mind the human aspect of the data source and considering where the impact of the data will be translated and applied. Considerations in this “humanizing” process include providing adequate counseling and consent for genetic testing in all settings, as well as understanding the strengths and limitations of assays and their interpretation.

Rethinking the ethical principles of genomic medicine services

Clinical genome and exome sequencing is currently used in only a small fraction of patients, yet large scale genomic initiatives are becoming more embedded in clinical services. This paper examines the ethical principles that should guide regulatory processes regarding consent and data sharing in this context. We argue that a genomic dataset administered by the health system carries substantial societal benefits, and that the collective nature of this initiative means that at least those patients who benefit from genome sequencing have an ethical obligation to share their health information. This obligation is grounded in considerations of fairness. Furthermore, we argue that the use of genomic data for the advancement of medical knowledge should be permitted without explicit consent and that international and other bodies should be granted access to these data, provided certain conditions are satisfied.

Trust in genomic data sharing among members of the general public in the UK, USA, Canada and Australia

Trust may be important in shaping public attitudes to genetics and intentions to participate in genomics research and big data initiatives. As such, we examined trust in data sharing among the general public. A cross-sectional online survey collected responses from representative publics in the USA, Canada, UK and Australia (n = 8967). Participants were most likely to trust their medical doctor and less likely to trust other entities named. Company researchers were least likely to be trusted. Low, Variable and High Trust classes were defined using latent class analysis. Members of the High Trust class were more likely to be under 50 years, male, with children, hold religious beliefs, have personal experience of genetics and be from the USA. They were most likely to be willing to donate their genomic and health data for clinical and research uses. The Low Trust class were less reassured than other respondents by laws preventing exploitation of donated information. Variation in trust, its relation to areas of concern about the use of genomic data and potential of legislation are considered. These findings have relevance for efforts to expand genomic medicine and data sharing beyond those with personal experience of genetics or research participants.

Genomic variant sharing: a position statement

Sharing de-identified genetic variant data is essential for the practice of genomic medicine and is demonstrably beneficial to patients. Robust genetic diagnoses that inform medical management cannot be made accurately without reference to genetic test results from other patients, as well as population controls. Errors in this process can result in delayed, missed or erroneous diagnoses, leading to inappropriate or missed medical interventions for the patient and their family. The benefits of sharing individual genetic variants, and the harms of not sharing them, are numerous and well-established. Databases and mechanisms already exist to facilitate deposition and sharing of pseudonomised genetic variants, but clarity and transparency around best practice is needed to encourage widespread use, prevent inconsistencies between different communities, maximise individual privacy and ensure public trust. We therefore recommend that widespread sharing of a small number of individual genetic variants associated with limited clinical information should become standard practice in genomic medicine. Information robustly linking genetic variants with specific conditions is fundamental biological knowledge, not personal information, and therefore should not require consent to share. For additional case-level detail about individual patients or more extensive genomic information, which is often essential for clinical interpretation, it may be more appropriate to use a controlled-access model for data sharing, with the ultimate aim of making as much information as open and de-identified as possible with appropriate consent.

Genomic variant sharing: a position statement

Sharing de-identified genetic variant data is essential for the practice of genomic medicine and is demonstrably beneficial to patients. Robust genetic diagnoses that inform medical management cannot be made accurately without reference to genetic test results from other patients, as well as population controls. Errors in this process can result in delayed, missed or erroneous diagnoses, leading to inappropriate or missed medical interventions for the patient and their family. The benefits of sharing individual genetic variants, and the harms of not sharing them, are numerous and well-established. Databases and mechanisms already exist to facilitate deposition and sharing of pseudonomised genetic variants, but clarity and transparency around best practice is needed to encourage widespread use, prevent inconsistencies between different communities, maximise individual privacy and ensure public trust. We therefore recommend that widespread sharing of a small number of individual genetic variants associated with limited clinical information should become standard practice in genomic medicine. Information robustly linking genetic variants with specific conditions is fundamental biological knowledge, not personal information, and therefore should not require consent to share. For additional case-level detail about individual patients or more extensive genomic information, which is often essential for clinical interpretation, it may be more appropriate to use a controlled-access model for data sharing, with the ultimate aim of making as much information as open and de-identified as possible with appropriate consent.

A scalable, aggregated genotypic-phenotypic database for human disease variation

Next generation sequencing multi-gene panels have greatly improved the diagnostic yield and cost effectiveness of genetic testing and are rapidly being integrated into the clinic for hereditary cancer risk. With this technology comes a dramatic increase in the volume, type and complexity of data. This invaluable data though is too often buried or inaccessible to researchers, especially to those without strong analytical or programming skills. To effectively share comprehensive, integrated genotypic-phenotypic data, we built Color Data, a publicly available, cloud-based database that supports broad access and data literacy. The database is composed of 50 000 individuals who were sequenced for 30 genes associated with hereditary cancer risk and provides useful information on allele frequency and variant classification, as well as associated phenotypic information such as demographics and personal and family history. Our user-friendly interface allows researchers to easily execute their own queries with filtering, and the results of queries can be shared and/or downloaded. The rapid and broad dissemination of these research results will help increase the value of, and reduce the waste in, scientific resources and data. Furthermore, the database is able to quickly scale and support integration of additional genes and human hereditary conditions. We hope that this database will help researchers and scientists explore genotype-phenotype correlations in hereditary cancer, identify novel variants for functional analysis and enable data-driven drug discovery and development.

Genetic counselling in the era of genomic medicine

BACKGROUND

Genomic technology can now deliver cost effective, targeted diagnosis and treatment for patients. Genetic counselling is a communication process empowering patients and families to make autonomous decisions and effectively use new genetic information. The skills of genetic counselling and expertise of genetic counsellors are integral to the effective implementation of genomic medicine.

SOURCES OF DATA

Original papers, reviews, guidelines, policy papers and web-resources.

AREAS OF AGREEMENT

An international consensus on the definition of genetic counselling. Genetic counselling is necessary for implementation of genomic medicine.

AREAS OF CONTROVERSY

Models of genetic counselling.

GROWING POINTS

Genomic medicine is a growing and strategic priority for many health care systems. Genetic counselling is part of this.

AREAS TIMELY FOR DEVELOPING RESEARCH

An evidence base is necessary, incorporating implementation and outcome research, to enable health care systems, practitioners, patients and families to maximize the utility (medically and psychologically) of the new genomic possibilities.