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BACKGROUND: The U.K. 100,000 Genomes Project is in the process of investigating the role of genome sequencing in patients with undiagnosed rare diseases after usual care and the alignment of this research with health care implementation in the U.K. National Health Service. Other parts of this project focus on patients with cancer and infection.METHODS: We conducted a pilot study involving 4660 participants from 2183 families, among whom 161 disorders covering a broad spectrum of rare diseases were present. We collected data on clinical features with the use of Human Phenotype Ontology terms, undertook genome sequencing, applied automated variant prioritization on the basis of applied virtual gene panels and phenotypes, and identified novel pathogenic variants through research analysis.RESULTS: Diagnostic yields varied among family structures and were highest in family trios (both parents and a proband) and families with larger pedigrees. Diagnostic yields were much higher for disorders likely to have a monogenic cause (35%) than for disorders likely to have a complex cause (11%). Diagnostic yields for intellectual disability, hearing disorders, and vision disorders ranged from 40 to 55%. We made genetic diagnoses in 25% of the probands. A total of 14% of the diagnoses were made by means of the combination of research and automated approaches, which was critical for cases in which we found etiologic noncoding, structural, and mitochondrial genome variants and coding variants poorly covered by exome sequencing. Cohortwide burden testing across 57,000 genomes enabled the discovery of three new disease genes and 19 new associations. Of the genetic diagnoses that we made, 25% had immediate ramifications for clinical decision making for the patients or their relatives.CONCLUSIONS: Our pilot study of genome sequencing in a national health care system showed an increase in diagnostic yield across a range of rare diseases. (Funded by the National Institute for Health Research and others.).

A fundamental problem in rare-disease diagnostics is the lack of consensus as to which genes have sufficient evidence to attribute causation. To address this issue, we have created PanelApp ( https://panelapp.genomicsengland.co.uk ), a publicly available knowledge base of curated virtual gene panels.

Background Discrete Choice Experiment (DCE) is a well-established technique to elicit individual preferences, but it has rarely been used to elicit governance preferences for health data sharing. Objectives The aim of this article was to describe the process of identifying attributes for a DCE study aiming to elicit preferences of citizens in Sweden, Iceland and the UK for governance mechanisms for digitally sharing different kinds of health data in different contexts. Methods A three-step approach was utilised to inform the attribute and level selection: 1) Attribute identification, 2) Attribute development and 3) Attribute refinement. First, we developed an initial set of potential attributes from a literature review and a workshop with experts. To further develop attributes, focus group discussions with citizens (n = 13), ranking exercises among focus group participants (n = 48) and expert interviews (n = 18) were performed. Thereafter, attributes were refined using group discussion (n = 3) with experts as well as cognitive interviews with citizens (n = 11). Results The results led to the selection of seven attributes for further development: 1) level of identification, 2) the purpose of data use, 3) type of information, 4) consent, 5) new data user, 6) collector and 7) the oversight of data sharing. Differences were found between countries regarding the order of top three attributes. The process outlined participants’ conceptualisation of the chosen attributes, and what we learned for our attribute development phase. Conclusions This study demonstrates a process for selection of attributes for a (multi-country) DCE involving three stages: Attribute identification, Attribute development and Attribute refinement. This study can contribute to improve the ethical aspects and good practice of this phase in DCE studies. Specifically, it can contribute to the development of governance mechanisms in the digital world, where people's health data are shared for multiple purposes.

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.

Kidney stone disease is a complex disorder with a strong genetic component. We conducted a genome-wide association study of 28.3 million sequence variants detected through whole-genome sequencing of 2,636 Icelanders that were imputed into 5,419 kidney stone cases, including 2,172 cases with a history of recurrent kidney stones, and 279,870 controls. We identify sequence variants associating with kidney stones at ALPL (rs1256328[T], odds ratio (OR)=1.21, P=5.8 × 10−10) and a suggestive association at CASR (rs7627468[A], OR=1.16, P=2.0 × 10−8). Focusing our analysis on coding sequence variants in 63 genes with preferential kidney expression we identify two rare missense variants SLC34A1 p.Tyr489Cys (OR=2.38, P=2.8 × 10−5) and TRPV5 p.Leu530Arg (OR=3.62, P=4.1 × 10−5) associating with recurrent kidney stones. We also observe associations of the identified kidney stone variants with biochemical traits in a large population set, indicating potential biological mechanism., Kidney stone formation is influenced by genetic factors and recurrent stone formation places a significant burden on health care systems. Here Oddsson et al. perform a large-scale genome-wide association study and uncover new genetic variants associated with kidney stone susceptibility and associated biochemical traits.