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The Catalogue Of Somatic Mutations In Cancer (COSMIC), https://cancer.sanger.ac.uk/cosmic, is an expert-curated knowledgebase providing data on somatic variants in cancer, supported by a comprehensive suite of tools for interpreting genomic data, discerning the impact of somatic alterations on disease, and facilitating translational research. The catalogue is accessed and used by thousands of cancer researchers and clinicians daily, allowing them to quickly access information from an immense pool of data curated from over 29 thousand scientific publications and large studies. Within the last 4 years, COSMIC has substantially expanded its utility by adding new resources: the Mutational Signatures catalogue, the Cancer Mutation Census, and Actionability. To improve data accessibility and interoperability, somatic variants have received stable genomic identifiers that are associated with their genomic coordinates in GRCh37 and GRCh38, and new export files with reduced data redundancy have been made available for download., (© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Ensembl (https://www.ensembl.org) has produced high-quality genomic resources for vertebrates and model organisms for more than twenty years. During that time, our resources, services and tools have continually evolved in line with both the publicly available genome data and the downstream research and applications that utilise the Ensembl platform. In recent years we have witnessed a dramatic shift in the genomic landscape. There has been a large increase in the number of high-quality reference genomes through global biodiversity initiatives. In parallel, there have been major advances towards pangenome representations of higher species, where many alternative genome assemblies representing different breeds, cultivars, strains and haplotypes are now available. In order to support these efforts and accelerate downstream research, it is our goal at Ensembl to create high-quality annotations, tools and services for species across the tree of life. Here, we report our resources for popular reference genomes, the dramatic growth of our annotations (including haplotypes from the first human pangenome graphs), updates to the Ensembl Variant Effect Predictor (VEP), interactive protein structure predictions from AlphaFold DB, and the beta release of our new website., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Ensembl (https://www.ensembl.org) is unique in its flexible infrastructure for access to genomic data and annotation. It has been designed to efficiently deliver annotation at scale for all eukaryotic life, and it also provides deep comprehensive annotation for key species. Genomes representing a greater diversity of species are increasingly being sequenced. In response, we have focussed our recent efforts on expediting the annotation of new assemblies. Here, we report the release of the greatest annual number of newly annotated genomes in the history of Ensembl via our dedicated Ensembl Rapid Release platform (http://rapid.ensembl.org). We have also developed a new method to generate comparative analyses at scale for these assemblies and, for the first time, we have annotated non-vertebrate eukaryotes. Meanwhile, we continually improve, extend and update the annotation for our high-value reference vertebrate genomes and report the details here. We have a range of specific software tools for specific tasks, such as the Ensembl Variant Effect Predictor (VEP) and the newly developed interface for the Variant Recoder. All Ensembl data, software and tools are freely available for download and are accessible programmatically., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

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.

Maximizing the personal, public, research, and clinical value of genomic information will require the reliable exchange of genetic variation data. We report here the Variation Representation Specification (VRS, pronounced "verse"), an extensible framework for the computable representation of variation that complements contemporary human-readable and flat file standards for genomic variation representation. VRS provides semantically precise representations of variation and leverages this design to enable federated identification of biomolecular variation with globally consistent and unique computed identifiers. The VRS framework includes a terminology and information model, machine-readable schema, data sharing conventions, and a reference implementation, each of which is intended to be broadly useful and freely available for community use. VRS was developed by a partnership among national information resource providers, public initiatives, and diagnostic testing laboratories under the auspices of the Global Alliance for Genomics and Health (GA4GH)., Competing Interests: DECLARATION OF INTERESTS H.L.R. is a member of the advisory board for Cell Genomics.

The Ensembl project (https://www.ensembl.org) annotates genomes and disseminates genomic data for vertebrate species. We create detailed and comprehensive annotation of gene structures, regulatory elements and variants, and enable comparative genomics by inferring the evolutionary history of genes and genomes. Our integrated genomic data are made available in a variety of ways, including genome browsers, search interfaces, specialist tools such as the Ensembl Variant Effect Predictor, download files and programmatic interfaces. Here, we present recent Ensembl developments including two new website portals. Ensembl Rapid Release (http://rapid.ensembl.org) is designed to provide core tools and services for genomes as soon as possible and has been deployed to support large biodiversity sequencing projects. Our SARS-CoV-2 genome browser (https://covid-19.ensembl.org) integrates our own annotation with publicly available genomic data from numerous sources to facilitate the use of genomics in the international scientific response to the COVID-19 pandemic. We also report on other updates to our annotation resources, tools and services. All Ensembl data and software are freely available without restriction., (© The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The Ensembl (https://www.ensembl.org) is a system for generating and distributing genome annotation such as genes, variation, regulation and comparative genomics across the vertebrate subphylum and key model organisms. The Ensembl annotation pipeline is capable of integrating experimental and reference data from multiple providers into a single integrated resource. Here, we present 94 newly annotated and re-annotated genomes, bringing the total number of genomes offered by Ensembl to 227. This represents the single largest expansion of the resource since its inception. We also detail our continued efforts to improve human annotation, developments in our epigenome analysis and display, a new tool for imputing causal genes from genome-wide association studies and visualisation of variation within a 3D protein model. Finally, we present information on our new website. Both software and data are made available without restriction via our website, online tools platform and programmatic interfaces (available under an Apache 2.0 license) and data updates made available four times a year., (© The Author(s) 2019. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The Ensembl project (https://www.ensembl.org) makes key genomic data sets available to the entire scientific community without restrictions. Ensembl seeks to be a fundamental resource driving scientific progress by creating, maintaining and updating reference genome annotation and comparative genomics resources. This year we describe our new and expanded gene, variant and comparative annotation capabilities, which led to a 50% increase in the number of vertebrate genomes we support. We have also doubled the number of available human variants and added regulatory regions for many mouse cell types and developmental stages. Our data sets and tools are available via the Ensembl website as well as a through a RESTful webservice, Perl application programming interface and as data files for download.

The Ensembl project has been aggregating, processing, integrating and redistributing genomic datasets since the initial releases of the draft human genome, with the aim of accelerating genomics research through rapid open distribution of public data. Large amounts of raw data are thus transformed into knowledge, which is made available via a multitude of channels, in particular our browser (http://www.ensembl.org). Over time, we have expanded in multiple directions. First, our resources describe multiple fields of genomics, in particular gene annotation, comparative genomics, genetics and epigenomics. Second, we cover a growing number of genome assemblies; Ensembl Release 90 contains exactly 100. Third, our databases feed simultaneously into an array of services designed around different use cases, ranging from quick browsing to genome-wide bioinformatic analysis. We present here the latest developments of the Ensembl project, with a focus on managing an increasing number of assemblies, supporting efforts in genome interpretation and improving our browser., (© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.)

The major goal of sequencing humans and many other species is to understand the link between genomic variation, phenotype and disease. There are numerous valuable and well-established variation resources, but collating and making sense of non-homogeneous, often large-scale data sets from disparate sources remains a challenge. Without a systematic catalogue of these data and appropriate query and annotation tools, understanding the genome sequence of an individual and assessing their disease risk is impossible. In Ensembl, we substantially solve this problem: we develop methods to facilitate data integration and broad access; aggregate information in a consistent manner and make it available a variety of standard formats, both visually and programmatically; build analysis pipelines to compare variants to comprehensive genomic annotation sets; and make all tools and data publicly available.

Background: Interleukin-2 (IL-2) has an essential role in the expansion and function of CD4+ regulatory T cells (Tregs). Tregs reduce tissue damage by limiting the immune response following infection and regulate autoreactive CD4+ effector T cells (Teffs) to prevent autoimmune diseases, such as type 1 diabetes (T1D). Genetic susceptibility to T1D causes alterations in the IL-2 pathway, a finding that supports Tregs as a cellular therapeutic target. Aldesleukin (Proleukin; recombinant human IL-2), which is administered at high doses to activate the immune system in cancer immunotherapy, is now being repositioned to treat inflammatory and autoimmune disorders at lower doses by targeting Tregs., Methods and Findings: To define the aldesleukin dose response for Tregs and to find doses that increase Tregs physiologically for treatment of T1D, a statistical and systematic approach was taken by analysing the pharmacokinetics and pharmacodynamics of single doses of subcutaneous aldesleukin in the Adaptive Study of IL-2 Dose on Regulatory T Cells in Type 1 Diabetes (DILT1D), a single centre, non-randomised, open label, adaptive dose-finding trial with 40 adult participants with recently diagnosed T1D. The primary endpoint was the maximum percentage increase in Tregs (defined as CD3+CD4+CD25highCD127low) from the baseline frequency in each participant measured over the 7 d following treatment. There was an initial learning phase with five pairs of participants, each pair receiving one of five pre-assigned single doses from 0.04 × 106 to 1.5 × 106 IU/m2, in order to model the dose-response curve. Results from each participant were then incorporated into interim statistical modelling to target the two doses most likely to induce 10% and 20% increases in Treg frequencies. Primary analysis of the evaluable population (n = 39) found that the optimal doses of aldesleukin to induce 10% and 20% increases in Tregs were 0.101 × 106 IU/m2 (standard error [SE] = 0.078, 95% CI = -0.052, 0.254) and 0.497 × 106 IU/m2 (SE = 0.092, 95% CI = 0.316, 0.678), respectively. On analysis of secondary outcomes, using a highly sensitive IL-2 assay, the observed plasma concentrations of the drug at 90 min exceeded the hypothetical Treg-specific therapeutic window determined in vitro (0.015-0.24 IU/ml), even at the lowest doses (0.040 × 106 and 0.045 × 106 IU/m2) administered. A rapid decrease in Treg frequency in the circulation was observed at 90 min and at day 1, which was dose dependent (mean decrease 11.6%, SE = 2.3%, range 10.0%-48.2%, n = 37), rebounding at day 2 and increasing to frequencies above baseline over 7 d. Teffs, natural killer cells, and eosinophils also responded, with their frequencies rapidly and dose-dependently decreased in the blood, then returning to, or exceeding, pretreatment levels. Furthermore, there was a dose-dependent down modulation of one of the two signalling subunits of the IL-2 receptor, the β chain (CD122) (mean decrease = 58.0%, SE = 2.8%, range 9.8%-85.5%, n = 33), on Tregs and a reduction in their sensitivity to aldesleukin at 90 min and day 1 and 2 post-treatment. Due to blood volume requirements as well as ethical and practical considerations, the study was limited to adults and to analysis of peripheral blood only., Conclusions: The DILT1D trial results, most notably the early altered trafficking and desensitisation of Tregs induced by a single ultra-low dose of aldesleukin that resolves within 2-3 d, inform the design of the next trial to determine a repeat dosing regimen aimed at establishing a steady-state Treg frequency increase of 20%-50%, with the eventual goal of preventing T1D., Trial Registration: ISRCTN Registry ISRCTN27852285; ClinicalTrials.gov NCT01827735., Competing Interests: FWL has received fees for consulting and speaking on type 1 diabetes and immunotherapeutics from Epidarex Capital, GlaxoSmithKline, Novo Nordisk, Eli Lilly, and Hoffmann-La Roche. LSW has received funds to support research from Hoffmann-La Roche and has received consultancy fees from Kymab Access Limited. JAT has received ad hoc consultancy fees from GlaxoSmithKline, AstraZeneca, Pfizer, Janssen and Kymab Limited and is Director of the JDRF/Wellcome Trust Diabetes and Inflammation Laboratory that has received research grant funds from F Hoffmann-La Roche, AstraZeneca and Eli Lilly. SB has a non-financial competing interests as a member of the board for the NIHR Efficacy and Mechanism Evaluation Programme board. APM has received payments for consulting with GSK, Danone and grants from EU IMI scheme, MRC, BHF and CRUK. SN has had his research salary partly funded by Senseonics.

Aims/hypothesis: Type 1 diabetes results from the autoimmune destruction of insulin-secreting pancreatic beta cells by T cells. Despite the established role of T cells in the pathogenesis of the disease, to date, with the exception of the identification of islet-specific T effector (Teff) cells, studies have mostly failed to identify reproducible alterations in the frequency or function of T cell subsets in peripheral blood from patients with type 1 diabetes., Methods: We assessed the production of the proinflammatory cytokines IL-21, IFN-γ and IL-17 in peripheral blood mononuclear cells from 69 patients with type 1 diabetes and 61 healthy donors. In an additional cohort of 30 patients with type 1 diabetes and 32 healthy donors, we assessed the frequency of circulating T follicular helper (Tfh) cells in whole blood. IL-21 and IL-17 production was also measured in peripheral blood mononuclear cells (PBMCs) from a subset of 46 of the 62 donors immunophenotyped for Tfh., Results: We found a 21.9% (95% CI 5.8, 40.2; p = 3.9 × 10(-3)) higher frequency of IL-21(+) CD45RA(-) memory CD4(+) Teffs in patients with type 1 diabetes (geometric mean 5.92% [95% CI 5.44, 6.44]) compared with healthy donors (geometric mean 4.88% [95% CI 4.33, 5.50]). Consistent with this finding, we found a 14.9% increase in circulating Tfh cells in the patients (95% CI 2.9, 26.9; p = 0.016)., Conclusions/interpretation: These results indicate that increased IL-21 production is likely to be an aetiological factor in the pathogenesis of type 1 diabetes that could be considered as a potential therapeutic target.

Background: Killer Immunoglobulin-like Receptors (KIRs) are surface receptors of natural killer cells that bind to their corresponding Human Leukocyte Antigen (HLA) class I ligands, making them interesting candidate genes for HLA-associated autoimmune diseases, including type 1 diabetes (T1D). However, allelic and copy number variation in the KIR region effectively mask it from standard genome-wide association studies: single nucleotide polymorphism (SNP) probes targeting the region are often discarded by standard genotype callers since they exhibit variable cluster numbers. Quantitative Polymerase Chain Reaction (qPCR) assays address this issue. However, their cost is prohibitive at the sample sizes required for detecting effects typically observed in complex genetic diseases., Results: We propose a more powerful and cost-effective alternative, which combines signals from SNPs with more than three clusters found in existing datasets, with qPCR on a subset of samples. First, we showed that noise and batch effects in multiplexed qPCR assays are addressed through normalisation and simultaneous copy number calling of multiple genes. Then, we used supervised classification to impute copy numbers of specific KIR genes from SNP signals. We applied this method to assess copy number variation in two KIR genes, KIR3DL1 and KIR3DS1, which are suitable candidates for T1D susceptibility since they encode the only KIR molecules known to bind with HLA-Bw4 epitopes. We find no association between KIR3DL1/3DS1 copy number and T1D in 6744 cases and 5362 controls; a sample size twenty-fold larger than in any previous KIR association study. Due to our sample size, we can exclude odds ratios larger than 1.1 for the common KIR3DL1/3DS1 copy number groups at the 5% significance level., Conclusion: We found no evidence of association of KIR3DL1/3DS1 copy number with T1D, either overall or dependent on HLA-Bw4 epitope. Five other KIR genes, KIR2DS4, KIR2DL3, KIR2DL5, KIR2DS5 and KIR2DS1, in high linkage disequilibrium with KIR3DL1 and KIR3DS1, are also unlikely to be significantly associated. Our approach could potentially be applied to other KIR genes to allow cost effective assaying of gene copy number in large samples.

Objective: IKZF1 encoding Ikaros, an essential regulator of lymphopoiesis and immune homeostasis, has been implicated in the development of childhood acute lymphoblastic leukemia (C-ALL). Because recent genome-wide association (GWA) studies have linked a region of the 3'-UTR of IKZF1 with C-ALL susceptibility, we tested whether IKZF1 is associated with the autoimmune disease type 1 diabetes., Research Design and Methods: rs10272724 (T>C) near IKZF1 at 7p12 was genotyped in 8,333 individuals with type 1 diabetes, 9,947 control subjects, and 3,997 families of European ancestry. Association was tested using logistic regression in the case-control data and by the transmission disequilibrium test in the families. Expression data for IKZF1 by rs10272724 genotype were obtained using quantitative PCR of mRNA/cDNA generated from peripheral blood mononuclear cells from 88 individuals, whereas expression data for five other neighboring genes were obtained from the online Genevar dataset., Results: The minor allele of rs10272724 (C) was found to be protective from type 1 diabetes (odds ratio 0.87 [95% CI 0.83-0.91]; P = 1.1 × 10(-11)). rs10272724 was not correlated with levels of two transcripts of IKZF1 in peripheral blood mononuclear cells., Conclusions: The major susceptibility genotype for C-ALL confers protection from type 1 diabetes. Our finding strengthens the link between autoimmunity and lymphoid cancers. Further investigation is warranted for the genetic effect marked by rs10272724, its impact on IKZF1, and the role of Ikaros and other family members, Ailios (IKZF3) and Eos (IKZF4), in autoimmunity.

Copy number variants (CNVs) account for a major proportion of human genetic polymorphism and have been predicted to have an important role in genetic susceptibility to common disease. To address this we undertook a large, direct genome-wide study of association between CNVs and eight common human diseases. Using a purpose-designed array we typed approximately 19,000 individuals into distinct copy-number classes at 3,432 polymorphic CNVs, including an estimated approximately 50% of all common CNVs larger than 500 base pairs. We identified several biological artefacts that lead to false-positive associations, including systematic CNV differences between DNAs derived from blood and cell lines. Association testing and follow-up replication analyses confirmed three loci where CNVs were associated with disease-IRGM for Crohn's disease, HLA for Crohn's disease, rheumatoid arthritis and type 1 diabetes, and TSPAN8 for type 2 diabetes-although in each case the locus had previously been identified in single nucleotide polymorphism (SNP)-based studies, reflecting our observation that most common CNVs that are well-typed on our array are well tagged by SNPs and so have been indirectly explored through SNP studies. We conclude that common CNVs that can be typed on existing platforms are unlikely to contribute greatly to the genetic basis of common human diseases.

T1DBase (http://T1DBase.org) is a public website and database that supports the type 1 diabetes (T1D) research community. The site is currently focused on the molecular genetics and biology of T1D susceptibility and pathogenesis. It includes the following datasets: annotated genome sequence for human, rat and mouse; information on genetically identified T1D susceptibility regions in human, rat and mouse, and genetic linkage and association studies pertaining to T1D; descriptions of NOD mouse congenic strains; the Beta Cell Gene Expression Bank, which reports expression levels of genes in beta cells under various conditions, and annotations of gene function in beta cells; data on gene expression in a variety of tissues and organs; and biological pathways from KEGG and BioCarta. Tools on the site include the GBrowse genome browser, site-wide context dependent search, Connect-the-Dots for connecting gene and other identifiers from multiple data sources, Cytoscape for visualizing and analyzing biological networks, and the GESTALT workbench for genome annotation. All data are open access and all software is open source.

The genetic dissection of complex disease remains a significant challenge. Sample-tracking and the recording, processing and storage of high-throughput laboratory data with public domain data, require integration of databases, genome informatics and genetic analyses in an easily updated and scaleable format. To find genes involved in multifactorial diseases such as type 1 diabetes (T1D), chromosome regions are defined based on functional candidate gene content, linkage information from humans and animal model mapping information. For each region, genomic information is extracted from Ensembl, converted and loaded into ACeDB for manual gene annotation. Homology information is examined using ACeDB tools and the gene structure verified. Manually curated genes are extracted from ACeDB and read into the feature database, which holds relevant local genomic feature data and an audit trail of laboratory investigations. Public domain information, manually curated genes, polymorphisms, primers, linkage and association analyses, with links to our genotyping database, are shown in Gbrowse. This system scales to include genetic, statistical, quality control (QC) and biological data such as expression analyses of RNA or protein, all linked from a genomics integrative display. Our system is applicable to any genetic study of complex disease, of either large or small scale.