BackgroundInnovations in technology have contributed to rapid changes in the way that modern biomedical research is carried out. Researchers are increasingly required to endorse adaptive and flexible approaches to accommodate these innovations and comply with ethical, legal and regulatory requirements. This paper explores how Dynamic Consent may provide solutions to address challenges encountered when researchers invite individuals to participate in research and follow them up over time in a continuously changing environment.MethodsAn interdisciplinary workshop jointly organised by the University of Oxford and the COST Action CHIP ME gathered clinicians, researchers, ethicists, lawyers, research participants and patient representatives to discuss experiences of using Dynamic Consent, and how such use may facilitate the conduct of specific research tasks. The data collected during the workshop were analysed using a content analysis approach.ResultsDynamic Consent can provide practical, sustainable and future-proof solutions to challenges related to participant recruitment, the attainment of informed consent, participant retention and consent management, and may bring economic efficiencies.ConclusionsDynamic Consent offers opportunities for ongoing communication between researchers and research participants that can positively impact research. Dynamic Consent supports inter-sector, cross-border approaches and large scale data-sharing. Whilst it is relatively easy to set up and maintain, its implementation will require that researchers re-consider their relationship with research participants and adopt new procedures.

The majority of monogenic disorders cause craniofacial abnormalities with characteristic facial morphology. These disorders can be diagnosed more e ciently with the support of computer-aided nextgeneration phenotyping tools, such as DeepGestalt. These tools have learned to associate facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this "supervised" approach means that diagnoses are only possible if they were part of the training set. To improve recognition of ultra-rare diseases, we created GestaltMatcher, which uses a deep convolutional neural network based on the DeepGestalt framework. We used photographs of 21,836 patients with 1,362 rare disorders to de ne a "Clinical Face Phenotype Space". Distance between cases in the phenotype space de nes syndromic similarity, allowing test patients to be matched to a molecular diagnosis even when the disorder was not included in the training set. Similarities among patients with previously unknown disease genes can also be detected. Therefore, in concert with mutation data, GestaltMatcher could accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism.

BackgroundHealth research increasingly relies on organized collections of health data and biological samples. There are many types of sample and data collections that are used for health research, though these are collected for many purposes, not all of which are health-related. These collections exist under different jurisdictional and regulatory arrangements and include:Population biobanks, cohort studies, and genome databasesClinical and public health dataDirect-to-consumer genetic testingSocial mediaFitness trackers, health apps, and biometric data sensorsEthical, legal, and social challenges of such collections are well recognized, but there has been limited attention to the broader societal implications of the existence of these collections.DiscussionAlthough health research conducted using these collections is broadly recognized as beneficent, secondary uses of these data and samples may be controversial. We examine both documented and hypothetical scenarios of secondary uses of health data and samples. In particular, we focus on the use of health data for purposes of:Forensic investigationsCivil lawsuitsIdentification of victims of mass casualty eventsDenial of entry for border security and immigrationMaking health resource rationing decisionsFacilitating human rights abuses in autocratic regimesConclusionsCurrent safeguards relating to the use of health data and samples include research ethics oversight and privacy laws. These safeguards have a strong focus on informed consent and anonymization, which are aimed at the protection of the individual research subject. They are not intended to address broader societal implications of health data and sample collections. As such, existing arrangements are insufficient to protect against subversion of health databases for non-sanctioned secondary uses, or to provide guidance for reasonable but controversial secondary uses. We are concerned that existing debate in the scholarly literature and beyond has not sufficiently recognized the secondary data uses we outline in this paper. Our main purpose, therefore, is to raise awareness of the potential for unforeseen and unintended consequences, in particular negative consequences, of the increased availability and development of health data collections for research, by providing a comprehensive review of documented and hypothetical non-health research uses of such data.

PurposePhenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists.MethodsHere, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds.ResultsThe additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20–89% and the top 10 accuracy rate by more than 5–99% for the disease-causing gene.ConclusionImage analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis.

The majority of monogenic disorders cause craniofacial abnormalities with characteristic facial morphology. These disorders can be diagnosed more efficiently with the support of computer-aided next-generation phenotyping tools, such as DeepGestalt. These tools have learned to associate facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this “supervised” approach means that diagnoses are only possible if they were part of the training set. To improve recognition of ultra-rare diseases, we created GestaltMatcher, which uses a deep convolutional neural network based on the DeepGestalt framework. We used photographs of 21,836 patients with 1,362 rare disorders to define a “Clinical Face Phenotype Space”. Distance between cases in the phenotype space defines syndromic similarity, allowing test patients to be matched to a molecular diagnosis even when the disorder was not included in the training set. Similarities among patients with previously unknown disease genes can also be detected. Therefore, in concert with mutation data, GestaltMatcher could accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism.

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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.

Background: There is growing consensus that individual genetic research results that are scientifically robust, analytically valid, and clinically actionable should be offered to research participants. However, the general practice in European research projects is that results are usually not provided to research participants for many reasons. This article reports on the views of European experts and scholars who are members of the European COST Action CHIP ME IS1303 (Citizen's Health through public-private Initiatives: Public health, Market and Ethical perspectives) regarding challenges to the feedback of individual genetic results to research participants in Europe and potential strategies to address these challenges.Materials and Methods: A consultation of the COST Action members was conducted through an email survey and a workshop. The results from the consultation were analyzed following a conventional content analysis approach.Results: Legal frameworks, professional guidelines, and financial, organizational, and human resources to support the feedback of results are largely missing in Europe. Necessary steps to facilitate the feedback process include clarifying legal requirements to the feedback of results, developing harmonized European best practices, promoting interdisciplinary and cross-institutional collaboration, designing educational programs and cost-efficient IT-based platforms, involving research ethics committees, and documenting the health benefits and risks of the feedback process.Conclusions: Coordinated efforts at pan-European level are needed to enable equitable, scientifically sound, and socially robust feedback of results to research participants.