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• CINECA synthetic data.Please note: This study contains synthetic data (with cohort “participants” / ”subjects” marked with FAKE) has no identifiable data and cannot be used to make any inference about cohort data or results.

  Please note: This study contains synthetic data (with cohort “participants” / ”subjects” marked with FAKE) has no identifiable data and cannot be used to make any inference about cohort data or results. The purpose of this dataset is to aid development of technical implementations for cohort data discovery, harmonization, access, and federated analysis. In support of FAIRness in data sharing, this dataset is made freely available under the Creative Commons Licence (CC-BY). Please ensure this preamble is included with this dataset and that the CINECA project (funding: EC H2020 grant 825775) is acknowledged. For any questions please contact isuru@ebi.ac.uk or cthomas@ebi.ac.uk

  Study EGAS00001002472

• High-throughput determination of the antigen specificities of T cell receptors in single cells

  High-throughput linking of T cell receptor (TCR) sequences to their binding antigens is vital for immune profiling, yet challenging. We present Tetramer associated TCR Sequencing (TetTCR-Seq) to address this challenge. Binding is determined using a library of DNA-barcoded antigen tetramers that are rapidly and inexpensively generated using an in vitro transcription/translation platform. We included CMV+ donors (CMV seropositive donors who are infected with Cytomegalovirus) to screen for CMV specific TCRs.

  Study phs001678

• Genomic_landscape_of_liver_cirrhosis

  We are interested in the genomic landscape of liver cirrhosis and its potential manifestations for liver physiology and risk for hepatocellular carcinoma. Previous work in CASM has shown that there is a strong element of clonality within cirrhotic nodules and that some recurring mutations have been identified. However how this relates to the overall liver is not known and therefore by taking geographically disparate biospies, we aim to correlate our findings and make conclusions as to the characteristics of the overall genomic landscape.

  Study EGAS00001004329

• IL-17 Signaling in Human iPSC-derived Midbrain Neurons in Parkinson's Disease

  To determine IL-17-induced global transcriptome changes in midbrain neurons derived from induced pluripotent stem cells (iPSC) from three sporadic Parkinson's disease (PD) patients and three age- and sex-machted controls, deep RNA sequencing (RNA-Seq) of IL-17-treated and untreated PD and control iPSC-dderived midbrain neurons was performed. Total RNA was isolated from untreated and IL-17-treated cells with the TruSeq RNA Sample Preparation Kit v2 (Illumina). RNA libraries were quantified using the KAPA SYBR FAST ABI Prism Library Quantification Kit (Kapa Biosystems) and cluster generation was performed on the cBot with the TruSeq SR Cluster Kit v3 (Illumina). The sequencing run was performed on a HiSeq 1000 instrument (Illumina) using the indexed, 50 cycles single read (SR) protocol and the TruSeq SBS v3 Kit (Illumina). Image analysis and base calling resulted in .bcl files that were then converted into .fastQ files by the CASAVA1.8.2 software. FastQ files were aligned to the human genome (hg19) using STAR.and annotated with gencode.v19. DESeq2 was used to determine differential expression. Criteria to determine significantly dysregulated genes were as follows: adjusted p-value below 0.05 and log2FC (fold change) of greater than one. Only genes with a mean expression value of greater than one RPKM (reads per kilobase per million mapped reads) throughout the dataset were considered. Control and PD samples were analyzed as two independent datasets. Upon IL-17 treatment only 17 genes were found to be dysregulated in controls but 125 genes were dysregulated in iPSC-derived midbrain neurons from PD patients. The 125 IL-17-dependent genes in PD iPSC-derived neurons separated the treated from untreated PD samples using an unsupervised, hierarchical clustering applying an Euclidean distance metric. More detailed study information can be found in Sommer A, Maxreiter F, Krach F, Fadler T, Grosch J, Maroni M, Graef D, Eberhardt E, Riemenschneider MJ, Yeo GW, Kohl Z, Xiang W, Gage FH, Winkler J, Prots I, Winner B. Th17 Lymphocytes Induce Neuronal Cell Death in a Human iPSC-Based Model of Parkinson's Disease. Cell Stem Cell. 2018 Jul 5;23(1):123-131.e6. doi: 10.1016/j.stem.2018.06.015. PMID: 29979986

  Study phs001686

• Illumina HumanCoreExome genotyping data from the British Society for Surgery of the Hand Genetics of Dupuytren’s Disease consortium (BSSH-GODD consortium) collection

  Dupuytren's Disease (DD - OMIM126900) is the most common heritable disorder of connective tissue. It is a fibroproliferative disorder of the palmer fascia, causing flexion contractures of affected digits. DD affects men more commonly than women. DD is an archetypal complex disease, with multiple genetic and environmental factors affecting the final expression of the disease phenotype. The incidence increases with age, and the prevalence of DD is therefore increasing as the UK population ages. Half of all patients present before age 60. DD causes significant functional impairment, and severe contractures can lead to permanent disability. The mainstay of current treatment is surgery, but recurrence and complications are both high. For severe contractures, the only suitable treatment option is amputation. The direct costs to the NHS are high, and underestimate the true economic costs to society. We collected salivary samples from adults in the UK who have had surgery for DD. We have genotyped these samples using the Illumina HumanCoreExome Beadchips. https://research.ndorms.ox.ac.uk/public/dupuytrens/what-is-dupuytrens-disease/

  Study EGAS00001001206

• RNA-sequençing of 21 inflammatory hepatocellular adenomas

  The French ICGC project on liver tumors is coordinated by Pr Jessica Zucman-Rossi and funded by Inca (French Institute for Cancer). The aim of the present project is to identify the catalog of somatic and germline mutations in liver tumors.The present series corresponds to 21 RNA-seq of IHCA samples. Aligned bam files correspond to hg38 human reference.

  Study EGAS00001003685

• Best Practices for DACs

  Best Practices Data access committees (DACs) are institutional safeguards responsible for ensuring a balance between data protection and accessibility. However, there are no procedural standards that apply across DACs, which can lead to inconsistencies in their reviews and compromise their quality and effectiveness. Standardising DAC processes can foster trust and mutual recognition, paving the way for greater coordination, collaboration, and delegation between DACs and other oversight bodies to improve the efficiency of data access without sacrificing protection. To provide guidance, the following are suggestions for actions a DAC might take upon receiving a data access request: Aim to respond to all initial requests in less than 2 weeks. Failure to do so may result in follow-up emails from the EGA, journals, and ultimately dataset withdrawal. First, check that the data/EGAD number is consistent with your data submission. In other words, ensure that the requester has contacted the correct DAC. Verify that the user will be using the data within the terms of consent by asking them to sign up to the terms within the DAA. Ensure that data users who are granted access to the data comply with the terms of a Data Access Agreement (DAA) and to use the data only in approved ways. Look for an institutional email address for the requester. Search for evidence that the requester is "appropriately qualified/bona fide" for using the data, for example on PubMed, Research Gate, LinkedIn, etc. Confirm that the affiliated organisation is real and that the requester still works there. Inquire with the requester about who should have accounts created at the EGA under the terms of the agreement. If a negative decision is made, promptly communicate it to the requester and support it with the terms of the Data Access Policy that the requester has not met or cannot meet. Keep the information in the DAC up to date. If you leave your institution and are unable to manage data access requests on their behalf, you should add your replacement as a new contact in the DAC. Assist with data access requests for any further questions related to your data. The EGA can only check information that has been deposited in the repository. If the user has a specific question that the EGA cannot answer, we will redirect the user to the DAC. All the EGA studies and datasets referenced in a publication under your DAC must be publicly searchable on the EGA website before the paper is released. If possible, describe the permitted purposes for subsequent research projects, including associated limits and conditions, for all resources hosted in a repository using a common ontology, such as the GA4GH Data Use Ontology (DUO). It is considered best practice to try and release data only to those with an institutional email address. This gives reassurance to the EGA, research participants, and the general public that an appropriate individual is accessing and using the data. To prevent potential data breaches and ensure adherence to GDPR regulations, it is essential that the European Genome-Phenome Archive (EGA) is informed via the Helpdesk team of any changes to the Data Access Committee (DAC). This should be done in addition to any changes being made on the DAC portal. Data Controllers (as per the definition in the DPA) are also responsible for notifying the previous DAC of any modifications. Without proper notification, changes might not be automatically updated in our system, leading to the risk of incorrect permissions being applied and potential data access issues. Therefore, it is imperative that all Data Controllers follow this protocol to maintain data integrity and security.

  Documentation access/data-access-committee/best-practices

• Phenotype Risk Scores Identify Patients with Unrecognized Mendelian Disease Patterns

  This study was designed to assess the phenotypic effects of rare variants. Rare variants are difficult to study in a high-throughput manner because most cohorts are underpowered to detect associations. In order to gain power to test rare variants, we use Phenotype Risk Scores (PheRS) based on features of Mendelian diseases. PheRS is calculated using claims data from an EHR that is mapped to clinical features from OMIM's clinical descriptions of Mendelian disease. We calculated PheRS for 1,204 Mendelians diseases in a cohort of 21,701 individuals genotyped on the HumanExome BeadChip. We then tested for association between these diseases and rare variants in causal genes. A phenotype risk score is calculated as the weighted sum of features for a given disease. The features are defined as a set of phecodes (consolidated ICD codes) associated with a particular Mendelian disease as described in OMIM. The weights for each phecode are calculated as the log inverse prevalence of the phecode in our cohort. For an individual, their score equals the sum of the weights for each phecode that is present in their medical record. Our study was based on a cohort of individuals with genotype data linked to de-identified electronic health records (EHR) from Vanderbilt's BioVU resource. We hope that this method will help generate phenotype-genotype correlation data on rare variants which in turn may inform rare variant interpretations use for diagnostics.

  Study phs001516

• Ischemic Stroke Genetics Study (ISGS)

  The third leading cause of death in the United States, stroke is an acute neurological event leading to death of neural tissues. Although the majority of strokes are ischemic strokes, meaning there is oxygen deprivation to the brain, almost 20% of strokes are hemorrhagic, resulting from bleeding into the brain. Stroke is a complex disorder and likely multigenic in nature, resulting from a combination of genetic and environmental factors. These well characterized risk factors that contribute to the incidence of stroke include hypertension, cardiac disease, sickle cell disease, hyperhomocysteinemia, family history of stroke and smoking. ISGS aim is to perform a prospective genetic association study of ischemic stroke focusing on the hemostatic system. ISGS is a 5-center case-control study of first-ever ischemic stroke cases and concurrent controls individually matched for age, sex and recruitment site. This data includes that from subjects both banked in the NINDS repository with biologicals publicly available, and those whose samples are not banked/not available. Important links to apply for individual-level data Data Use Certification Requirements (DUC) Apply here for controlled access to individual level data Participant Protection Policy FAQ --> This study utilized the NINDS Repository Cerebrovascular/Stroke Study, and neurologically normal controls from the sample population which are banked in the National Institute of Neurological Disorders and Stroke (NINDS Repository) collection for a first stage whole genome analysis.

  Study phs000102

• Open_Targets_020_Epigenomes_of_Cell_Lines

  There is currently a drive to establish cell based assay systems of greater human biological and disease relevance through the use of well characterised transformed cell lines, primary cells and complex cellular models (e.g. co-culture, 3D models). However, although the field is gaining valuable experience in running more non-standard & complex cell assays for target validation and compound pharmacology studies, there is the lack of a systematic approach to determine if this expansion in cell assay models is reflected in increased human biological and disease relevance. The increasing wealth of publically available transcriptomic, and epigenome (ENCODE and Epigenome Roadmap) data represents an ideal reference mechanism for determining the relationship between cell types used for target & compound studies to primary human cells and tissues from both healthy volunteers & patients. The CTTV020 epigenomes of cell line project aims to generate epigenetic and transcriptomic profiles of cell lines and compare these with existing and newly generated reference data sets from human tissue and cell types. The aim is to identify assay systems which will provide greater confidence in translating target biology and compound pharmacology to patients. Multiple cell types commonly used within research have been grouped according to biology. Examples include erythroid, lung epithelial, hepatocyte cell types and immortalised models of monocyte / macrophage biology. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001003136