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Summary Human biomedical datasets that are critical for research and clinical studies to benefit human health also often contain sensitive or potentially identifying information of individual participants. Thus, care must be taken when they are processed and made available to comply with ethical and regulatory frameworks and informed consent data conditions. To enable and streamline data access for these biomedical datasets, the Global Alliance for Genomics and Health (GA4GH) Data Use and Researcher Identities (DURI) work stream developed and approved the Data Use Ontology (DUO) standard. DUO is a hierarchical vocabulary of human and machine-readable data use terms that consistently and unambiguously represents a dataset’s allowable data uses. DUO has been implemented by major international stakeholders such as the Broad and Sanger Institutes and is currently used in annotation of over 200,000 datasets worldwide. Using DUO in data management and access facilitates researchers’ discovery and access of relevant datasets. DUO annotations increase the FAIRness of datasets and support data linkages using common data use profiles when integrating the data for secondary analyses. DUO is implemented in the Web Ontology Language (OWL) and, to increase community awareness and engagement, hosted in an open, centralized GitHub repository. DUO, together with the GA4GH Passport standard, offers a new, efficient, and streamlined data authorization and access framework that has enabled increased sharing of biomedical datasets worldwide., Graphical abstract, Highlights Biomedical advances depend on the efficient and compliant re-use of sensitive human data The Data Use Ontology standardizes terms and definitions for consented data uses The Data Use Ontology facilitates discovery of, request for, and access to datasets Over 200,000 datasets worldwide have been annotated using the Data Use Ontology, The GA4GH Data Use Ontology (DUO) provides unambiguous, machine-readable standard language for consent forms and the data sharing policies they represent. Lawson et al. describe the DUO standard and implementations throughout the data access workflow to expedite data access while maintaining or improving compliant processes.

The Global Alliance for Genomics and Health (GA4GH) aims to accelerate biomedical advances by enabling the responsible sharing of clinical and genomic data through both harmonized data aggregation and federated approaches. The decreasing cost of genomic sequencing (along with other genome-wide molecular assays) and increasing evidence of its clinical utility will soon drive the generation of sequence data from tens of millions of humans, with increasing levels of diversity. In this perspective, we present the GA4GH strategies for addressing the major challenges of this data revolution. We describe the GA4GH organization, which is fueled by the development efforts of eight Work Streams and informed by the needs of 24 Driver Projects and other key stakeholders. We present the GA4GH suite of secure, interoperable technical standards and policy frameworks and review the current status of standards, their relevance to key domains of research and clinical care, and future plans of GA4GH. Broad international participation in building, adopting, and deploying GA4GH standards and frameworks will catalyze an unprecedented effort in data sharing that will be critical to advancing genomic medicine and ensuring that all populations can access its benefits., B.P.C. acknowledges funding from Abigail Wexner Research Institute at Nationwide Children’s Hospital; T.H. Nyrönen acknowledges funding from Academy of Finland grant #31996; A.M.-J., K.N., T.F.B., O.M.H., and Z.S. acknowledge funding from Australian Medical Research Future Fund; M.S. acknowledges funding from Biobank Japan; D. Bujold and S.J.M.J. acknowledge funding from Canada Foundation for Innovation; L.J.D. acknowledges funding from Canada Foundation for Innovation Cyber Infrastructure grant #34860; D. Bujold and G.B. acknowledge funding from CANARIE; L.J.D. acknowledges funding from CANARIE Research Data Management contract #RDM-090 (CHORD) and #RDM2-053 (ClinDIG); K.K.-L. acknowledges funding from CanSHARE; T.L.T. acknowledges funding from Chan Zuckerberg Initiative; T. Burdett acknowledges funding from Chan Zuckerberg Initiative grant #2017-171671; D. Bujold, G.B., and L.D.S. acknowledge funding from CIHR; L.J.D. acknowledges funding from CIHR grant #404896; M.J.S.B. acknowledges funding from CIHR grant #SBD-163124; M. Courtot and M. Linden acknowledge funding from CINECA project EU Horizon 2020 grant #825775; D. Bujold and G.B. acknowledge funding from Compute Canada; F.M.-G. acknowledges funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – NFDI 1/1 “GHGA – German Human Genome-Phenome Archive; R.M.H.-S. acknowledges funding from Duke-Margolis Center for Health Policy; S.B. and A.J.B. acknowledge funding from EJP-RD EU Horizon 2020 grant #825575; A. Niewielska, A.K., D.S., G.I.S., J.A.T., J.R., M.A.K., M. Baudis, M. Linden, S.B., S.S., T.H. Nyrönen, and T.M.K. acknowledge funding from ELIXIR; A. Niewielska acknowledges funding from EOSC-Life EU Horizon 2020 grant #824087; J.-P.H. acknowledges funding from ETH Domain Strategic Focal Area “Personalized Health and Related Technologies (PHRT)” grant #2017-201; F.M.-G. acknowledges funding from EUCANCan EU Horizon 2020 grant #825835; B.M.K., D. Bujold, G.B., L.D.S., M.J.S.B., N.S., S.E.W., and Y.J. acknowledge funding from Genome Canada; B.M.K., M.J.S.B., S.E.W., and Y.J. acknowledge funding from Genome Quebec; F.M.-G. acknowledges funding from German Human Genome-Phenome Archive; C. Voisin acknowledges funding from Google; A.J.B. acknowledges funding from Health Data Research UK Substantive Site Award; D.H. acknowledges funding from Howard Hughes Medical Institute; S.B. acknowledges funding from Instituto de Salud Carlos III; S.-S.K. and K.T. acknowledge funding from Japan Agency for Medical Research and Development (AMED); S. Ogishima acknowledges funding from Japan Agency for Medical Research and Development (AMED) grant #20kk0205014h0005; C.Y. and K. Kosaki acknowledge funding from Japan Agency for Medical Research and Development (AMED) grant #JP18kk0205012; GEM Japan acknowledges funding from Japan Agency for Medical Research and Development (AMED) grants #19kk0205014h0004, #20kk0205014h0005, #20kk0205013h0005, #20kk0205012h0005, #20km0405401h0003, and #19km0405001h0104; J.R. acknowledges funding from La Caixa Foundation under project #LCF/PR/GN13/50260009; R.R.F. acknowledges funding from Mayo Clinic Center for Individualized Medicine; Y.J. and S.E.W. acknowledge funding from Ministère de l’Économie et de l’Innovation du Québec for the Can-SHARE Connect Project; S.E.W. and S.O.M.D. acknowledge funding from Ministère de l’Économie et de l’Innovation du Québec for the Can-SHARE grant #141210; M.A.H., M.C.M.-T., J.O.J., H.E.P., and P.N.R. acknowledge funding from Monarch Initiative grant #R24OD011883 and Phenomics First NHGRI grant #1RM1HG010860; A.L.M. and E.B. acknowledge funding from MRC grant #MC_PC_19024; P.T. acknowledges funding from National University of Singapore and Agency for Science, Technology and Research; J.M.C. acknowledges funding from NHGRI; A.H.W. acknowledges funding from NHGRI awards K99HG010157, R00HG010157, and R35HG011949; A.M.-J., K.N., D.P.H., O.M.H., T.F.B., and Z.S. acknowledge funding from NHMRC grants #GNT1113531 and #GNT2000001; D.L.C. acknowledges funding from NHMRC Ideas grant #1188098; A.B.S. acknowledges funding from NHMRC Investigator Fellowship grant #APP177524; J.M.C. and L.D.S. acknowledge funding from NIH; A.A.P. acknowledges funding from NIH Anvil; A.V.S. acknowledges funding from NIH contract #HHSN268201800002I (TOPMed Informatics Research Center); S.U. acknowledges funding from NIH ENCODE grant #UM1HG009443; M.C.M.-T. and M.A.H. acknowledge funding from NIH grant #1U13CA221044; R.J.C. acknowledges funding from NIH grants #1U24HG010262 and #1U2COD023196; M.G. acknowledges funding from NIH grant #R00HG007940; J.B.A., S.L., P.G., E.B., H.L.R., and L.S. acknowledge funding from NIH grant #U24HG011025; K.P.E. acknowledges funding from NIH grant #U2C-RM-160010; J.A.E. acknowledges funding from NIH NCATS grant #U24TR002306; M.M. acknowledges funding from NIH NCI contract #HHSN261201400008c and ID/IQ Agreement #17X146 under contract #HHSN2612015000031 and #75N91019D00024; R.M.C.-D. acknowledges funding from NIH NCI grant #R01CA237118; M. Cline acknowledges funding from NIH NCI grant #U01CA242954; K.P.E. acknowledges funding from NIH NCI ITCR grant #1U24CA231877-01; O.L.G. acknowledges funding from NIH NCI ITCR grant #U24CA237719; R.L.G. acknowledges funding from NIH NCI task order #17X147F10 under contract #HHSN261200800001E; A.F.R. acknowledges funding from NIH NHGRI grant #RM1HG010461; N.M. and L.J.Z. acknowledge funding from NIH NHGRI grant #U24HG006941; R.R.F., T.H. Nelson, L.J.B., and H.L.R. acknowledge funding from NIH NHGRI grant #U41HG006834; B.J.W. acknowledges funding from NIH NHGRI grant #UM1HG009443A; M. Cline acknowledges funding from NIH NHLBI BioData Catalyst Fellowship grant #5118777; M.M. acknowledges funding from NIH NHLBI BioData Catalyst Program grant #1OT3HL142478-01; N.C.S. acknowledges funding from NIH NIGMS grant #R35-GM128636; M.C.M.-T., M.A.H., P.N.R., and R.R.F. acknowledge funding from NIH NLM contract #75N97019P00280; E.B. and A.L.M. acknowledge funding from NIHR; R.G. acknowledges funding from Project Ris3CAT VEIS; S.B. acknowledges funding from RD-Connect, Seventh Framework Program grant #305444; J.K. acknowledges funding from Robertson Foundation; S.B. and A.J.B. acknowledge funding from Solve-RD, EU Horizon 2020 grant #779257; T.S. and S. Oesterle acknowledge funding from Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN), supported by the Swiss State Secretariat for Education, Research and Innovation SERI; S.J.M.J. acknowledges funding from Terry Fox Research Institute; A.E.H., M.P.B., M. Cupak, M.F., and J.F. acknowledge funding from the Digital Technology Supercluster; D.F.V. acknowledges funding from the Australian Medical Research Future Fund, as part of the Genomics Health Futures Mission grant #76749; M. Baudis acknowledges funding from the BioMedIT Network project of Swiss Institute of Bioinformatics (SIB) and Swiss Personalized Health Network (SPHN); B.M.K. acknowledges funding from the Canada Research Chair in Law and Medicine and CIHR grant #SBD-163124; D.S., G.I.S., M.A.K., S.B., S.S., and T.H. Nyrönen acknowledge funding from the EU Horizon 2020 Beyond 1 Million Genomes (B1MG) Project grant #951724; P.F., A.D.Y., F.C., H.S., I.U.L., D. Gupta, M. Courtot, S.E.H., T. Burdett, T.M.K., and S.F. acknowledge funding from the European Molecular Biology Laboratory; Y.J. and S.E.W. acknowledge funding from the Government of Canada; P.G. acknowledges funding from the Government of Canada through Genome Canada and the Ontario Genomics Institute (OGI-206); J.Z. acknowledges funding from the Government of Ontario; C.K.Y. acknowledges funding from the Government of Ontario, Canada Foundation for Innovation; C. Viner and M.M.H. acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (grant #RGPIN-2015-03948 to M.M.H. and Alexander Graham Bell Canada Graduate Scholarship to C.V.); K.K.-L. acknowledges funding from the Program for Integrated Database of Clinical and Genomic Information; J.K. acknowledges funding from the Robertson Foundation; D.F.V. acknowledges funding from the Victorian State Government through the Operational Infrastructure Support (OIS) Program; A.M.L., R.N., and H.V.F. acknowledge funding from Wellcome (collaborative award); F.C., H.S., P.F., and S.E.H. acknowledge funding from Wellcome Trust grant #108749/Z/15/Z; A.D.Y., H.S., I.U.L., M. Courtot, H.E.P., P.F., and T.M.K. acknowledge funding from Wellcome Trust grant #201535/Z/16/Z; A.M., J.K.B., R.J.M., R.M.D., and T.M.K. acknowledge funding from Wellcome Trust grant #206194; E.B., P.F., P.G., and S.F. acknowledge funding from Wellcome Trust grant #220544/Z/20/Z; A. Hamosh acknowledges funding from NIH NHGRI grant U41HG006627 and U54HG006542; J.S.H. acknowledges funding from National Taiwan University #91F701-45C and #109T098-02; the work of K.W.R. was supported by the Intramural Research Program of the National Library of Medicine, NIH. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript version arising from this submission. H.V.F. acknowledges funding from Wellcome Grant 200990/A/16/Z ‘Designing, developing and delivering integrated foundations for genomic medicine'.

We have generated an improved assembly and gene annotation of the pig X chromosome, and a first draft assembly of the pig Y chromosome, by sequencing BAC and fosmid clones, and incorporating information from optical mapping and fibre-FISH. The X chromosome carries 1,014 annotated genes, 689 of which are protein-coding. Gene order closely matches that found in Primates (including humans) and Carnivores (including cats and dogs), which is inferred to be ancestral. Nevertheless, several protein-coding genes present on the human X chromosome were absent from the pig (e.g. the cancer/testis antigen family) or inactive (e.g. AWAT1), and 38 pig-specific X-chromosomal genes were annotated, 22 of which were olfactory receptors. The pig Y chromosome assembly focussed on two clusters of male-specific low-copy number genes, separated by an ampliconic region including the HSFY gene family, which together make up most of the short arm. Both clusters contain palindromes with high sequence identity, presumably maintained by gene conversion. The long arm of the chromosome is almost entirely repetitive, containing previously characterised sequences. Many of the ancestral X-related genes previously reported in at least one mammalian Y chromosome are represented either as active genes or partial sequences. This sequencing project has allowed us to identify genes - both single copy and amplified - on the pig Y, to compare the pig X and Y chromosomes for homologous sequences, and thereby to reveal mechanisms underlying pig X and Y chromosome evolution.

Model organisms are becoming increasingly important for the study of complex diseases such as type 1 diabetes (T1D). The non-obese diabetic (NOD) mouse is an experimental model for T1D having been bred to develop the disease spontaneously in a process that is similar to humans. Genetic analysis of the NOD mouse has identified around 50 disease loci, which have the nomenclature Idd for insulin-dependent diabetes, distributed across at least 11 different chromosomes. In total, 21 Idd regions across 6 chromosomes, that are major contributors to T1D susceptibility or resistance, were selected for finished sequencing and annotation at the Wellcome Trust Sanger Institute. Here we describe the generation of 40.4 mega base-pairs of finished sequence from 289 bacterial artificial chromosomes for the NOD mouse. Manual annotation has identified 738 genes in the diabetes sensitive NOD mouse and 765 genes in homologous regions of the diabetes resistant C57BL/6J reference mouse across 19 candidate Idd regions. This has allowed us to call variation consequences between homologous exonic sequences for all annotated regions in the two mouse strains. We demonstrate the importance of this resource further by illustrating the technical difficulties that regions of inter-strain structural variation between the NOD mouse and the C57BL/6J reference mouse can cause for current next generation sequencing and assembly techniques. Furthermore, we have established that the variation rate in the Idd regions is 2.3 times higher than the mean found for the whole genome assembly for the NOD/ShiLtJ genome, which we suggest reflects the fact that positive selection for functional variation in immune genes is beneficial in regard to host defence. In summary, we provide an important resource, which aids the analysis of potential causative genes involved in T1D susceptibility. Database URLs: http://www.sanger.ac.uk/resources/mouse/nod/; http://vega-previous.sanger.ac.uk/info/data/mouse_regions.html#Idd

Zebrafish have become a popular organism for the study of vertebrate gene function1,2. The virtually transparent embryos of this species, and the ability to accelerate genetic studies by gene knockdown or overexpression, have led to the widespread use of zebrafish in the detailed investigation of vertebrate gene function and increasingly, the study of human genetic disease3–5. However, for effective modelling of human genetic disease it is important to understand the extent to which zebrafish genes and gene structures are related to orthologous human genes. To examine this, we generated a high-quality sequence assembly of the zebrafish genome, made up of an overlapping set of completely sequenced large-insert clones that were ordered and oriented using a high-resolution high-density meiotic map. Detailed automatic and manual annotation provides evidence of more than 26,000 protein-coding genes6, the largest gene set of any vertebrate so far sequenced. Comparison to the human reference genome shows that approximately 70% of human genes have at least one obvious zebrafish orthologue. In addition, the high quality of this genome assembly provides a clearer understanding of key genomic features such as a unique repeat content, a scarcity of pseudogenes, an enrichment of zebrafish-specific genes on chromosome 4 and chromosomal regions that influence sex determination.