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• Cerebrospinal Fluid Analysis of HIV-1 Viral Burden in HIV-1 Infected Subjects: Response to Antiretroviral Therapy

  The purpose of this study is to provide a reference profile of small extracellular RNAs in body fluids. These samples were originally obtained in a study that had a different purpose. The purpose of the original study was to collect information on the changes in cerebrospinal fluid (CSF) related to HIV infection, including the viral burden (amount of virus) and the body responses of the infected individual, such as the number and types of lymphocytes in the CSF. In addition to looking at the number and types of lymphocytes in the CSF, this original study also examined the presence of markers of inflammation and other chemical changes, and the relationship of those changes to nervous system dysfunction in AIDS.

  Study phs001694

• Genomic Regions Associated with Susceptibility to Barrett's Esophagus and Esophageal Adenocarcinoma in African Americans: The Cross BETRNet Admixture Study

  Differences in rates of diseases between different populations with similar environments are presumed to be secondary to variability in population frequencies of disease causing alleles. Recently admixed populations such as African Americans (AAs) provide a natural opportunity to identify disease-causing variants by examining the ancestry of long chromosomal haplotypes. Admixture mapping approach can be successful in locating genes for diseases such as EAC whose rates varies markedly between the ancestral populations. This is a case only study with 54 African American cases and also on a subset of 28 cases with high genotyping quality. We seek to identify chromosome regions that have excess European ancestry and contrasting it to excess African ancestry.

  Study phs001454

• Urothelial Cancer - Genomic Analysis to Improve Patient Outcomes and Research (UC-GENOME): a Bladder Cancer Advocacy Network (BCAN)-Led Collaborative Research Pilot Study - for Samples Collected at Johns Hopkins

  UC-GENOME is a real-world cohort of patients with metastatic urothelial carcinoma (UC). The study consisted of two co-equal aims: 1) to provide targeted DNA sequencing for clinical decision making at no cost to patients; and 2) to create a resource for collaborative translational science including a clinically annotated biobank. The submitted data represents the foundational analysis of 218 patients accrued at 7 academic medical centers. The analysis includes 176 patients with both targeted DNA sequencing and RNA sequencing. The genomic data was combined with clinical variables to model response to chemotherapy and immune checkpoint inhibitors (ICI).The larger dataset for this study can be found in dbGaP study phs003066.

  Study phs003094

• Mapping Genotypes to Chromatin Accessibility Profiles in Single Cells

  In somatic tissue differentiation, chromatin accessibility changes govern priming and precursor commitment towards cellular fates1–3. Therefore, somatic mutations likely alter chromatin accessibility patterns, as they disrupt differentiation topologies leading to abnormal clonal outgrowth. However, defining the impact of somatic mutations on the epigenome in human samples is challenging due to admixed mutated and wild-type cells. To chart how somatic mutations disrupt epigenetic landscapes in human clonal outgrowths, we developed Genotyping of Targeted loci with single-cell Chromatin Accessibility (GoT-ChA). This high-throughput platform links genotypes to chromatin accessibility at single-cell resolution, across thousands of cells within a single assay. We applied GoT-ChA to CD34+ cells from myeloproliferative neoplasm (MPN) patients with JAK2V617F-mutated hematopoiesis.

  Study EGAS50000000164

• Heterogeneous metabolic signatures are linked to cancer cell differentiation in colorectal cancer

  Colorectal cancer (CRC) is characterized by functional intratumor heterogeneity that shares many similarites with the hierarchical organization of the normal intestinal epithelium. In order to relate transcriptional subtypes to functional tumor cell heterogeneity we applied scRNA-seq to 12 patient-derived CRC spheroid cultures. We identified shared expression programs that relate to intestinal lineages and revealed metabolic signatures that are linked to cancer cell differentiation. In addition, we validated and complemented sequencing results by quantitative microscopy using live-dyes and multiplexed RNA fluorescence in situ hybridization, thereby revealing metabolic compartmentalization and potential cell-cell interactions. FInally, we demonstrate functional differences between metabolically distinct lineage subtypes that might have strong implications for future treatment strategies of CRC.

  Study EGAS00001004064

• Use of new methodologies to achieve a thorough molecular characterization in pediatric acute leukemia

  In childhood leukemia, a better genetic characterization is needed to define new clinic-biological entities, refine the risk-stratification of patients and help to reduce the toxicity by applying a risk-directed therapy. This project aims to obtain information that will allow us to personalize the management of pediatric patients with leukemia as much as possible, so that we can offer a complete diagnosis, a sensitive follow-up and a more targeted treatment to the real risk of each case. The use of massive genome analysis technologies such as transcriptome sequencing (RNA-Sequencing) will allow a comprehensive biological characterization of pediatric leukemia patients, especially those cases that we have not been able to characterize by conventional diagnostic techniques. We believe that the RNA-Seq technique will allow us to identify new biomarkers that will be useful for: 1) defining new biological entities with their own clinical behavior; 2) refining the stratification of patients into risk groups; 3) finding targets for a better follow-up of MRD and 4) identifying therapeutic targets for the application of targeted therapies to reduce toxicity. PI: Camós-Guijosa, Mireia; Vega-García, Nerea Funding: PI21/00213 Instituto de Salud Carlos III (ISCIII)

  Study EGAS50000000701

• Detection of cancers three years prior to diagnosis using plasma cell-free DNA

  A major goal of early cancer detection is to identify subclinical disease when the tumor burden is low, so that treatments are more effective. But how early can cancers be detected prior to clinical signs or symptoms? This question can be answered only through the evaluation of participants whose clinical course has not been altered by the study itself. We here describe such an evaluation, performed on prospectively collected plasma samples from the Atherosclerosis Risk in Communities (ARIC) study, including 26 participants diagnosed with cancer and 26 matched controls. At the index time point, eight of these 52 participants scored positively with a multicancer early detection (MCED) blood test. All eight of these participants were diagnosed with cancer within 4 months after blood collection. In six of these 8 participants, we were able to assess an earlier plasma sample collected 3.1 to 3.5 years prior to clinical diagnosis. In four of these six participants, the same mutations detected by the MCED test could be identified, but at 8.6 to 79-fold lower levels. These results demonstrate that it is possible to detect circulating tumor DNA (ctDNA) more than three years prior to clinical diagnosis, and provide benchmark sensitivities required for the success of ctDNA-based tests for this purpose.

  Study EGAS00001008068

• About

  About What's the EGA? The European Genome-phenome Archive (EGA) is a global network for permanent archiving and sharing of personally identifiable genetic, phenotypic, and clinical data generated for the purposes of biomedical research projects or in the context of research-focused healthcare systems. Jointly managed by the European Bioinformatics Institute (EMBL-EBI) in Cambridge (UK) and the Centre for Genomic Regulation (CRG) in Barcelona, we aim to advance biomedical research and promote personalised medicine worldwide by enabling discovery of and access to human genomic and health research data. The EGA contains data collected from individuals whose consent agreements authorise data release for specific research use to bona fide researchers. We ensure strict security measures to control access to the data and maintain patient confidentiality. With expertise in data management and technical infrastructure, we promote FAIR data reuse and enable researchers to share their data securely. By leveraging public funding and our strategic partnerships, the EGA provides a free service for permanent data storage, data discovery, and secure data access. In addition, we foster a federated network to provide transnational access to human research data in compliance with legal frameworks. For additional information about the EGA, please contact: Helen Parkinson and Mallory Freeberg EMBL European Bioinformatics Institute Arcadi Navarro, Roderic Guigó, and Jordi Rambla Center for Genomic Regulation History The European Genome-phenome Archive was launched in 2008 at the European Bioinformatics Institute (EMBL-EBI), an outstation of the European Molecular Biology Laboratory (EMBL), to address an identified need for archiving and sharing the results of genome-wide association studies from the Wellcome Trust Case Control Consortium. With the signing of a memorandum of understanding in 2013 and a formal agreement in 2016, the EGA became a joint project of EMBL-EBI and the Centre for Genomic Regulation (CRG). The two institutes work together to support the EGA services, including supporting submissions, website, strategic leadership, and data infrastructure developments. In 2022, the Federated EGA was officially launched with the signature of the first five countries: Finland, Germany, Norway, Spain, and Sweden. With more than 20 additional nodes worldwide preparing to join, the Federated EGA aims to become the largest human omics data sharing initiative towards understanding human health and disease. EGA overview If you're a researcher you may need to deposit, manage, or access genomic data in a secure and regulated way. The European Genome-phenome Archive (EGA) is a platform that facilitates these processes, ensuring that sensitive data is stored and shared in accordance with legal and ethical regulations. Submission process To start a submission, you need to become an EGA submitter. For that, you’ll need to sign a Data Processing Agreement (DPA) with us, that defines the terms and conditions under which your data will be processed and shared within the EGA system. The access to each study is controlled by its Data Access Committee (DAC). The DAC is responsible for managing data access requests and ensuring that the release of data is in accordance with the General Data Protection Regulation (GDPR). Please, note that once your data is released, all public metadata related to your study and dataset(s) will be searchable on the EGA website. However, the files are only accessible under controlled access, which means that a DAC has to agree to a previous data access request. Request process It is possible to submit an access request to data stored at the EGA. The DAC assigned to the study will assess the request and, if approved, grant access to the data. Requesters must provide sufficient justification for your request and comply with the intended data usage in order to get access to it. Each dataset is covered by a Data Access Agreement (DAA) that defines the terms and conditions of use for the specified dataset/s. The DAA is created and provided by the DAC, and must be signed by the individual requesting access to the given dataset/s. Download process Once the request for access is approved, the data and metadata can be downloaded. The EGA offers various download options to fit the needs: it is possible to preview files without downloading them, download specific files of interest, or even download terabytes of data. Overall, the EGA provides a secure and regulated environment for depositing, managing, and accessing human data. Regardless of the role - submitter, DAC member, or requester - the EGA provides assistance for each while ensuring that sensitive data is managed in an ethical and responsible manner. More information on how EGA handles data is available in the EGA dataflow.

  Documentation about/ega

• Access to shallow whole genome sequencing data from DETECT

  Access to this dataset is subject to aceptance of the terms and conditions. Please summarise your project and sign the following document:

  Dac EGAC50000000553

• Primary T-cell differentiation to T helper subtypes

  human CD4+ T cells differentiated to diverse T helper subtypes

  Study EGAS50000000818

• Genome-Wide Association Study of Celiac Disease

  Celiac disease (gluten-sensitive enteropathy, celiac sprue) is a common disease with significant morbidity and mortality. It is caused by sensitivity to the dietary protein gluten, resulting in a chronic enteropathy in the small intestine. Celiac disease is now recognized to be a common disease, with reports that the disease frequency is 1:133 in the United States, similar to European estimates. There is recent evidence to suggest that the incidence of the disease is rising. Occult disease is frequently present with minimal classic symptoms or signs. The ratio of symptomatic to asymptomatic celiac disease is estimated to be 1:7. Some complications of celiac disease include lymphoma, osteoporosis, anemia, miscarriages, seizures, vitamin deficiencies, and co-occurrence of other autoimmune diseases. The only treatment is a gluten-free diet, so that recurrence of symptoms and complications may occur after minor dietary indiscretions. Identifying the underlying genetic causes of celiac disease may allow us to identify susceptible individuals, as well as advance new ways to prevent the disease and to treat it once it occurs. Several genome-wide linkage studies and one genome-wide association study of celiac disease have been conducted. Other than the common locus at HLA, few putative celiac disease loci have been replicated between studies, suggesting that the disease is heterogeneous and complex. A portion of the genetic predisposition can be attributed to HLA, but the etiology is likely due to a number of rare, high penetrant genes (as would be identified in linkage analysis) and to more common, low penetrant genes (as would be identified in association studies). The objective of this study is to identify new loci that increase risk to develop celiac disease. We will capitalize on existing North American resources, including three large collections of celiac cases and controls (Aim 1) and an independent set of celiac cases and their families (Aim 3). We propose a comprehensive multi-stage approach, with the following specific aims. In Aim 1, we will conduct a genome-wide association (GWA) study of single nucleotide polymorphisms (SNPs) and copy number variants (CNVs). The GWA will include1900 disease cases and 3400 matched controls, genotyped using the Illumina Human 610-quad chip. Statistical analyses will be performed to test for associations with celiac disease. In Aim 2, we will conduct a combined analysis of our GWA dataset from Aim 1 and a previous GWA dataset to identify additional low-penetrance loci. In Aim 3, we will attempt to replicate significant findings from the GWA study in two independent sample sets. At the conclusion of this study, we expect to have validated a number of SNPs and CNPs from regions in the genome that alter the risk of developing celiac disease. These SNPs may prove useful at both the clinical and research level. The data from the study will be shared with the scientific community.

  Study phs000274

• dbGaP Collection: NHLBI Heart Failure Related dbGaP Data (No IRB requirement)

  The National Heart, Lung, and Blood Institute (NHLBI) Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research has concluded. The NHLBI will continue to make this collection of data available to the research community.Past information regarding the Data Collection for the NHLBI Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research is below.The National Heart, Lung, and Blood Institute (NHLBI), part of the National Institutes of Health (NIH), is inviting novel Solutions for the NHLBI Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research. The goal of the challenge is to foster innovation in computational analysis and machine learning approaches utilizing large-scale NHLBI-funded datasets to identify new paradigms in heart failure research. The challenge aims to address the need for new open source disease models that can define sub-categorizations of adult heart failure to serve as a springboard for new research hypotheses and tool development in areas of heart failure research from basic to clinical settings. NHLBI has a history of making considerable investments in the creation of deep data resources including: long-standing, deeply-phenotyped epidemiological cohorts, innovative clinical trials, and large-scale precision medicine efforts that have generated whole genome sequencing and "other omics" data for more than one-hundred thousand individuals. To provide challenge participants with streamlined access to data across NHLBI's numerous studies containing heart failure data, the NHLBI has created this dbGaP study collection. Data access for this collection is controlled by NHLBI's Data Access Committee. Challenge participants are required to follow the use restrictions and acknowledgement instructions from the original dataset(s). Please note that the data in this collection are not harmonized across studies or otherwise altered from the original study. The NHLBI Big Data Analysis Challenge: Heart Failure Data Collection contains all NHLBI studies currently in dbGaP that contain data that may be relevant to research on heart failure. The studies in this collection are approved for General Research Use (GRU), Health/Medical/Biomedical Use (HMB), or Disease-Specific Use (DS) that permits research on heart failure. These studies span a variety of study designs, inclusion and exclusion criteria, sample sizes, and provide a wide breadth and depth of phenotype data on study participants. The available genomic data in these studies also varies, including genotyping arrays, sequencing (targeted, exome, whole genome), and additional -omic data (e.g., RNA or metabolite profiles). Please refer to each study's individual accession page to learn more about how study data were collected. Challenge participants are reminded that in addition to dbGaP, NHLBI's Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) contains other studies that have collected data relevant to heart failure and may wish to utilize BioLINCC data in the development of their Solution. Please refer to the individual accession page for each study in this collection to learn more about the history of each study.

  Study phs001991

• INCLUDE Data Hub: NDA GUIDs for Down Syndrome Research

  The INCLUDE (INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE) Project is an NIH-wide collaboration that seeks to improve health and quality of life for people with Down syndrome. The INCLUDE Project Data Coordinating Center (DCC) and partners created the INCLUDE Data Hub, a centralized data resource that allows access to large-scale clinical and multi-omics datasets specific to Down syndrome and supports collaborative, cloud-based analysis to accelerate scientific discoveries related to Down syndrome and its co-occurring conditions. The INCLUDE Data Hub includes an innovative researcher data repository, a portal, and a cloud-based platform for data analysis. The portal provides access to de-identified demographics and clinical metadata, as well as multi-omics datasets, including whole genome sequences, transcriptomes, proteomes, and metabolomes. The INCLUDE Data Hub uses a 'registered access' model, whereby users can access the portal and associated data after agreeing to terms of use. From the Data Hub, users can also reach 'controlled access' data (e.g., whole genome sequences) only for those studies for which they have obtained approval by an NIH Data Access Committee through the corresponding dbGaP study. A thoughtful linkage strategy to identify duplicate participants across studies enables researchers to merge multiple datasets and data types to create richer datasets and reduce costly redundancies in large-scale data generation (e.g., -omics) while mitigating potential risks introduced by linkage.Based on recommendations from the NICHD Office of Data Science and Sharing, which completed a comprehensive assessment of existing Privacy Protecting Record Linkage (PPRL) and associated data governance solutions, the INCLUDE DCC and the associated NIH Steering Committee have implemented NIMH Data Archive Global Unique Identifiers (NDA GUIDs) in the INCLUDE Data Hub. The NDA GUID Tool is a government-owned and operated PPRL technology that enables researchers to link data from multiple sources to the same individual without revealing personally identifiable information (PII). With the NDA GUID Tool, the same participant information will return the same GUID whenever or wherever it is entered. The GUID itself is not personally identifiable information or protected health information. Sharing data with NDA GUIDs allows approved researchers to link together data on a single participant, even if the data were collected at different locations or through different studies or are distributed from different data repositories. This dbGaP study enables researchers to access an INCLUDE GUID Mapping File that includes all available NDA GUIDs and their associations to study-specific Participant IDs across studies in the INCLUDE Data Hub. This dbGaP study only distributes NDA GUIDs associated with the INCLUDE Data Hub datasets. To access the associated controlled-access datasets, add the corresponding dbGaP studies to your Data Access Request. A list of these studies is available on the INCLUDE Data Hub Portal: https://portal.includedcc.org/studies.

  Study phs003678

• Submitting array based metadata

  Submitting array based metadata For further information please check our Submission FAQs, submission quickguide as well as submission terms! The submission metadata required for Array-based submission must be submitted using EGA programmatic submission and by completing the Array-based format (AF) template. The guidelines for this workflow are described on this page. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA Array-based-Format (AF) template. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA-Array-based-Format (AF) template. Metadata Model Registering Metadata Use the EGA programmatic submission to register your Study, Samples, Data Access Committee (DAC) and Policy. This online interface enables you to create new and edit existing submissions. Do not use the Submitter Portal to register the metadata objects for array submissions. Please, go to the EGA programmatic submission. Registering Study Go to the EGA programmatic submission Submit an study XML Take a note of your study accession number (EGASXXXXXXXXXXX) To use the study accession number in a publication, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"(https://doi.org/10.1093/nar/gkab1059 ). Registering Samples Go to the EGA programmatic submission Submit a sample XML Save the list of accession numbers. These will be needed for the AF template. Registering Data Access Committee Further information on the role of your DAC. Go to the EGA programmatic submission Submit a DAC XML Take a note of the DAC accession number (EGACXXXXXXXXXXX) Registering Policy Your Data Access Policy provides the terms and conditions of data use. This is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAA) Go to the EGA programmatic submission Submit a Policy XML Take a note of the Policy accession number (EGAPXXXXXXXXXXX) Complete the Array-based format (AF) spreadsheet Once you have completed the registration of your Study, DAC and Policy using the programmatix submission, you must then complete and return the AF spreadsheet The AF spreadsheet consists on four components: Do not use EGA IDs registered using the Submitter Portal. You can easily identify objects registered in the Submitter Portal by their EGA ID pattern: EGA[A-Z]5{10 more digits} (e.g. for a sample EGAN50000002506). For populating an AF spreadsheet, please exclusively use EGA IDs following this specific pattern: EGA[A-Z]0{10 more digits} (e.g. for a sample EGAN00001691542). Tab1) Webin accessions : Provide the accession numbers for your study, DAC and policy. Please, also add your ega-box number. Tab2) Sample & phenotypes: Please, leave this tab blank. Tab3) Dataset: Describe the dataset to be created Tab4) Data files: Define how your data is going to be organised into datasets and packets for distribution (linkage between samples and files). Should further assistance be required after going through the guide below; please do not hesitate to contact the EGA helpdesk Once the AF spreadsheet is populated, please send it to our EGA helpdesk for further validation. AF spreadsheet Should your submission require multiple DAC's or policies, use ' ; ' to separate the accession numbers. Accessions AF spreadsheet: Samples & phenotypes Samples and phenotypes AF spreadsheet: Datasets We suggest that each dataset consists of a common set of data. The example below consists of two datasets, grouped according to shared data type, technology and by case/control. We also like to capture the number of unique samples that make up the dataset and the Data Access Committee (DAC) responsible for providing the named dataset and their policy (EGAP). Datasets AF spreadsheet: Data files What follows is an example of how to map your samples to the array based files added to your upload account (4th tab). Data Files Please, find below some practical examples on how to register the linkage between samples-files Case 1) 1 sample or list of samples in different datasets: Data Files In case you have a list of samples that belong to different datasets, please, repeat the samples accession number/s in the first column and link the sample to the corresponding dataset each time (each row). Each row is one linkage between sample-file-dataset. Case 2) 1 sample links to several files: Data Files In order to add multiple files to one sample you MUST use “ ; “ between filenames. Example: file1.gpg;file2.gpg;file3.gpg In case that you want to add an extra file to the sample (phenotype or .Rdata), please use “Additional files” column. Important note: You MUST upload the encrypted and unencrypted md5sum values of all files uploaded to your submission account using the filename nomenclature (file.gpg, file.md5,file.md5.gpg). Your submission will not be processed without md5values supplied for all files in the CORRECT format. What happens after the submission of a dataset? All datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitted or DAC contact instructs our EGA helpdesk for the study to be released. Finally, your data is archived within our databases and prepared for encrypted distribution upon the request of permitted EGA account holders. We strongly advise you NOT to delete your data until we confirm that your data has been successfully archived.

  Documentation submission/metadata/submission/array

• Wistar PDX Development and Trial Center

  Overall the outcomes of patients with metastatic melanoma have improved dramatically over the last decade due to an improved understanding of the molecular drivers of this disease. In particular, multiple targeted therapy regimens have been approved for patients with a BRAFV600E/K mutation, which are present in ~50% of cutaneous melanomas. These treatments achieve clinical responses in ~80% of patients with a BRAFV600E/K mutation, thus providing proof-of-concept of the therapeutic potential for personalized therapeutic strategies. However, most of the patients will progress within 2 years of starting those therapies. Further, currently there are no targeted therapies that have been shown to be effective in patients with a wild-type BRAF. Thus, there are unmet clinical needs to develop treatments that prevent or overcome resistance to existing therapies for patients with a BRAFV600E/K mutation, and that are effective in patients without a BRAF mutation. In order to facilitate the development of new therapeutic strategies, over the last 5 years we have led a major effort to develop a broad collection of PDX models to reflect the clinical, histological, and genetic heterogeneity of this disease. Our collection of PDX models represents one of the largest collections for any human malignancy, and our initial testing demonstrates that the collection accurately recapitulates the oncogenic drivers and molecular heterogeneity that is observed in patients. This collection also includes a subset of PDX established from patients with acquired resistance to targeted therapies that have been maintained on those agents in vivo to sustain their resistant phenotype. Together these efforts have generated a robust resource to develop, refine, and prioritize new personalized combinatorial therapies for patients. Thus, we propose to establish a multi-disciplinary and multi-institutional PDTC Program focused on the use and continued expansion of our robust melanoma PDX collection to identify new therapeutic approaches that fill important clinical gaps in this disease.

  Study phs002432

• Phenotypic and Genotypic Study of Keratoconus

  Keratoconus, a disease in which the cornea is abnormally steep and thin, affects approximately 1 in 2000 individuals and usually begins in the second decade of life. Keratoconus is a progressive disorder that results in loss of vision that cannot be alleviated by contact lenses or glasses alone and is a leading indication for corneal transplantation in developed countries (Udar et al., 2006, PMID: 16877401). Keratoconus has a strong genetic component, and while several causative genes are clearly suspected, the underlying pathogenic processes have not yet been identified. The goal of our research is to identify genes that harbor mutations contributing to keratoconus and underlying biochemical processes relevant to keratoconus pathogenesis and to develop a better understanding of the etiology of keratoconus, leading to improved detection, treatment, and management. We will enroll isolated keratoconus along with familial cases and unaffected relatives when available. The subject population will consist of individuals diagnosed with keratoconus and unaffected relatives in families. Individuals/families will be ascertained through support groups and clinical collaborators. The identities of the study participants will be known only to Dr. Chakravarti, the study coordinator, physicians in the team and the post-doctoral fellow(s) working directly on the project. DNA, lymphocytes, and lymphoblastoid cell lines may be prepared from the blood samples for future use. Molecular analyses using markers and sequencing and statistical analyses of these data will be used to identify regions of human chromosomes where putative keratoconus disease genes reside. Analyses of coding DNA sequences within genes from keratoconus subjects will be compared to published control sequences to identify disease specific variants. Additionally, to understand disease pathogenesis, subsets of patients may be asked to provide tear samples. Study subjects will not directly benefit from participation and results will not be disclosed to participants nor their health care providers, unless medically relevant.

  Study phs003168

• Advancing Precision Oncology in a Humanized, Fully Autologous Mouse Model

  Progress in the early detection and treatment of cancer requires accurate model systems to further evaluate new, promising discoveries. Small animal and, in particular, mouse model systems are attractive to researchers for numerous reasons, including their ease of use and well-described platforms. The ability to grow human tumors in immune-deficient mice (so-called patient-derived xenografts, or PDXs) allows researchers to work directly with human cancer tissue in a controlled setting. However, PDX models are limited by their lack of an intact immune system. Immunotherapy has revolutionized clinical oncology, but lacks pre-clinical models of the human immune system and human cancer to investigate new modalities and limitations/toxicities of treatment regimens. The broad objective of this study is to validate an in vivo model to evaluate human tumors in the context of a complete and intact human immune system in a completely personalized and autologous fashion to study cancer immunotherapy. Herein, we will: (1) validate the ability of our humanized system to serve as a model for cancer immunotherapy treatment response and toxicity in patients with melanoma, including immunotherapy checkpoint blockade and vaccine strategies, and (2) extend our current work in melanoma by validating the ability to establish humanized mice and evaluate tumor growth and leukocyte development in autologous human pancreatic and colorectal tumors established in humanized mice. In each of these areas, we will leverage our multi-institutional team's individual expertise along with our institutional infrastructure to maximize the success of the experimental aims. The results from this project will be widely available to the general research community for future, hypothesis-driven research. Taken together, our study will meet multiple goals and address several unmet needs in cancer immunotherapy, thus significantly enhancing the applicability of a fully autologous and immunocompetent precision model system for use in translational oncology research.

  Study phs003090

• 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

• Longitudinal study of whole blood gene expression in Kenyan children exposed to malaria

  We have collected RNA samples from whole blood of Kenyan children exposed to malaria in the Kilifi region of Kenya. Collections were performed each year from 2015 until 2018. This is a follow-up study to that described in Bediako et al. (in preparation). The SIMS consortium is seeking to identifying the underlying reasons why some children are more susceptible to malaria than others. In this study we hope to track changes in children’s immune systems over time which relate to the number of malaria episodes they experience. 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/

  Dataset EGAD00001015405

• North Carolina Clinical Genomic Evaluation by NextGen Exome Sequencing (NCGENES)

  North Carolina Clinical Genomic Evaluation by Next-generation Exome Sequencing This study is part of a larger consortium project investigating the validity and best use of next-generation sequencing (in particular, whole exome sequencing, or WES) in clinical care. Participants are patients who were either seen in the UNC Cancer and Adult Genetics Clinic or referred to the study by their physician. They will be approached by their physician or a genetic counselor for recruitment. Once enrolled, a clinical geneticist or genetic counselor will obtain consent and collect blood samples to be analyzed using WES. Results may include information related to a diagnosis and incidental information. Medically actionable incidental findings will be CLIA-certified and returned to participants in a routine genetic counseling session, along with diagnostic findings. Eligible adult participants will be randomized to have the opportunity to choose to get certain types of non-medically actionable incidental findings, as well. Their decisions will be investigated, as will psychosocial and behavioral responses to sequencing and receiving sequencing information. This is a longitudinal, mixed methods study (i.e., multiple assessments pre- and post-return of results, with both quantitative and qualitative methods used to gather data). Because only the quantitative component of the study uses randomization, only measures and procedures associated with that component are included here. The third study release includes data of additional n=189 subjects.

  Study phs000827

• GWAS for Genetic Determinants of Bone Fragility in European-American Premenopausal Women

  Osteoporotic fractures are largely due to an increased propensity to fall with aging and a reduction in bone strength. Although skeletal architecture contributes to fracture risk, bone mineral density (BMD) is the most important determinant of bone strength and fracture risk. Between 60 and 80% of the variance of BMD of adult Caucasian women is due to heritable factors. Final BMD is a function of peak bone mass attained during young adulthood and the subsequent rate of bone loss, which occurs as a result of both post-menopausal estrogen loss and aging. The evidence for a genetic contribution to rate of loss in BMD is substantially weaker than that for peak BMD. Therefore, we have focused our sample collection on the recruitment of premenopausal women, in whom we have sought to identify the genes influencing peak BMD at the spine and hip, the two major skeletal sites of osteoporotic fracture. The primary goal of this study is to identify genes that affect peak BMD in premenopausal women. Identification of these genes may: 1) lead to molecular tests that predict risk of osteoporosis and allow institution of early preventive measures; 2) provide insight into basic bone cell biology and other factors that affect peak BMD; and 3) provide molecular targets for therapeutic agents to increase BMD.

  Study phs000138

• MAESTRO-Pool Enables Highly Parallel and Specific Mutation-Enrichment Sequencing for Minimal Residual Disease Detection in Cohort Studies

  MAESTRO-Pool builds upon the technology MAESTRO (Minor Allele Enriched Sequencing Through Recognition Oligonucleotides) by pooling tumor-specific MAESTRO probes from multiple patients and applying these to all samples from all patients. In this study, MAESTRO-Pool was applied to a cohort of 9 melanoma patients where 98 plasma samples were screened for 22,333 mutations. Additionally, a new dynamic minimal residual disease (MRD) caller was used to estimate the likelihood of MRD detection based on the number mutations and cell-free DNA molecules interrogated which allowed each bespoke MRD test to be properly calibrated against false detection. These developments led to sensitive detection in patient-matched samples down to 0.78 parts per million (ppm) while limiting false detection in negative controls to 1/784 (tumor fraction=2.7 ppm). MAESTRO-Pool and the dynamic MRD caller enable a unique blend of sensitivity, specificity, and efficiency, which will improve the ability to detect MRD.The data contained in this repository consists of:Tumor and normal whole genome sequencing data used to call patient-specific tumor mutations and design the MRD Tracker and MAESTRO-Pool fingerprintsMAESTRO-Pool data (enriched duplex sequencing via hybrid capture) of tumor gDNA, normal gDNA and cfDNA MRD Tracker data (duplex sequencing via hybrid capture) of tumor gDNA, normal gDNA and cfDNA

  Study phs003447

• This study generated Oxford Nanopore long read sequencing data of cancer cell line mixtures for validating long-read variant calls in cancer genomics

  Long-read sequencing (LRS) improves genome alignment and facilitates resolving variants in complex genomic regions, making it a promising approach for cancer variant detection and biomarker discovery. Here, we evaluate the performance of LRS to detect somatic variants across different tumour purities and sequencing depths by comparing to somatic variants from short-read sequencing. We generated experimental mixtures of cancer cell lines and matched normal cell lines to simulate 10 tumour purities (ranging from 0 to 100% tumour content). This resulted in 22 samples which were sequenced using whole genome LRS to a targeted depth of 60x and somatic variants were identified. We downsampled sequencing data to explore the optimal read depth for somatic variant detection. Our results show that long-read variant calling tools achieve recall rates comparable to short-read gold standards. While a tumour sequencing depth of 30x to 60× is generally sufficient for detecting common variants, particularly structural variants (SVs), sequencing the matched normal sample at adequate depth is crucial for accuracy. Notably, we found variants unique to LRS that may represent real and previously undetected events. This study highlights key factors for optimising cancer genome sequencing with LRS and provides a comprehensive dataset of cell line mixtures for future research and tool development.

  Study EGAS00001008107

• Genomic Factors Involved in Chromosome Rearrangements

  Approximately 15-20% of children referred for chromosomal microarray analysis (CMA) testing have a clinically relevant CNV that in many cases explains their phenotype. The goal of this study is to discover the genomic factors that mediate chromosome rearrangements and the phenotypic effects of chromosome rearrangements. We hypothesize that particular DNA sequences are susceptible to breakage and rearrangement. To this end, we fine-map chromosome breakage sites and analyze the DNA motifs that underlie double-strand breaks (DSBs). Our studies will also capture the genomic structure of breakpoint junctions, which will allow us to determine the mechanisms of DNA repair that shape different types of chromosome rearrangements. We also correlate chromosome rearrangements with clinical features to establish genotype-phenotype correlations. Defining critical regions of deletion or duplication is the first step to identifying candidate genes responsible for particular phenotypes.

  Study phs000845

• Genome-Wide Association Study of Relapse of Childhood Acute Lymphoblastic Leukemia

  Using risk-directed therapy for childhood acute lymphoblastic leukemia (ALL), outcome has improved dramatically in the last 40 years. However, a substantial portion of patients experience relapse, many of whom have no known risk factors. Taking a genome-wide approach, we sought to evaluate the relationships between germline SNP genotypes and the risk of relapse in 2,535 children with newly diagnosed ALL after adjusting for genetic ancestry and treatment regimen. We examine prognostic value of selected SNPs in the context of known relapse risk factors (molecular subtypes, minimal residual disease, age and leukocyte count at diagnosis). Associations of relapse-related SNPs with pharmacokinetic and pharmacodynamics of antileukemic drugs offer plausible mechanism by which they are linked to treatment outcome. Finally, we aim to identify SNPs that are related to both genetic ancestry and relapse which are likely to contribute to racial disparities in ALL survival.

  Study phs000638

• Single-cell analysis reveals different age-related somatic mutation profiles between stem and differentiated cells in human liver

  Highly metabolic liver is exposed to many sources of spontaneous mutagenesis resulting in increased genome instability that, potentially, causes age-related functional liver failure. Here we comparatively characterize the somatic mutational landscape in single human liver cells from individuals of various ages. Mutation frequency is very high in normal human hepatocytes, increasing dramatically with age as compared to other tissues and cell types, e.g., B-cells, neurons, and clonal surrogates of various adult stem cells. Moreover, differentiated hepatocytes tend to accumulate more mutations compared to age-matched proliferating liver stem cells (LSCs). Mutation spectra also differ dramatically between different cell types. Elevated oxidative hallmarks are observed in mouse liver and ex vivo cultured LSCs, but not during liver aging. Overall, human liver is prone to high age-dependent genome instability, compared to other tissues, and reveals functional difference between LSCs and hepatocytes.

  Study phs001956

• PDAC Prognosis Biomarkers in Genomic and Transcriptomic Molecular Data

  Our study was designed to identify biomarkers related to the prognosis of Pancreatic Ductal AdenoCarcinoma (PDAC) patients using genomic variants and gene expression in coding regions of the human genome (exome). PDAC is the 7th cause of cancer deaths and is among types of cancer having the poorest prognosis. To improve treatment outcomes of PDAC, biomarkers related to PDAC prognosis are needed. The goals of our study are to find genomic and transcriptomic biomarkers, which can classify prognosis subtype of PDAC patients, and eventually to predict subtypes or prognosis for each patient. In this study, we generated Whole Exome Sequencing (WES) and exome capture RNA sequencing data of 450 tumor and matched normal samples from 150 PDAC patients. Our data may be useful for understanding genomic and transcriptomic features, prognosis prediction, and precision medicine for PDAC.

  Study phs002347

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Genetic Epidemiology of COPD Study (COPDGene)

  Cohort Description The Genetic Epidemiology of COPD (COPDGene) study is a non-interventional, multicenter, longitudinal, case-control study at 21 US sites of smokers with a ≥10 pack-year history of smoking, with and without COPD, and healthy never smokers. The initial goal was to characterize disease-related phenotypes and explore associations with susceptibility genes. Data Being Submitted Wave 1 questionnaire data includes 397 variables for up to 3683 COPDGene participants in C4R. Wave 2 questionnaire data includes 447 variables for up to 2191 COPDGene participants in C4R. Dried Blood Spot/Serosurvey data includes 7 variables for up to 1692 COPDGene participants in C4R. Derived data includes 43 variables for up to 4082 COPDGene participants in C4R. Phenotype data includes 113 variables for up to 4082 COPDGene participants in C4R.

  Study phs002910

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Mediators of Atherosclerosis in South Asians Living in America Study (MASALA)

  Cohort Description The Mediators of Atherosclerosis in South Asians Living in America (MASALA) study is a prospective cohort of South Asians that aims to identify risk factors for heart disease in a large, growing Asian American subgroup. MASALA enrolled 906 South Asians in 2010-2013 and then added a new wave of 258 South Asian participants from 2017-2018, for a full cohort size of 1,164. Data Being Submitted Wave 1 questionnaire data includes 3967 variables for up to 431 MASALA participants in C4R. Wave 2 questionnaire data includes 448 variables for up to 313 MASALA participants in C4R. Dried Blood Spot/Serosurvey data includes 7 variables for up to 248 MASALA participants in C4R. Derived data includes 43 variables for up to 528 MASALA participants in C4R. Phenotype data includes 113 variables for up to 429 MASALA participants in C4R.

  Study phs002980

• Sequencing Lymphoma

  This repository contains sequencing data to describe the genomic profiles of several B-cell lymphomas including follicular lymphoma and Hodgkin lymphoma. We have used several different high throughput sequencing technologies to interrogate genomic DNA and RNA from tumor cells obtained from lymph node biopsies and normal cells obtained from skin biopsies and peripheral blood mononuclear cells. A number of sequencing platforms for DNA, RNA, and protein were used to generate the data. These platforms include exome sequencing and a custom capture panel (NimbleGen) that targets 7.05 MB corresponding to the exons and splice sites of 1716 genes related to lymphoma biology (WUSM-LP). Other platforms include single-cell RNA-seq and Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), which was used to describe transcriptional and proteomic changes in immune cell subsets within lymphoma patients pre- and post-therapy.

  Study phs001229

• Pediatric Preclinical Testing Consortium (PPTC)

  The Pediatric Preclinical Testing Consortium (PPTC) is addressing the unmet need of streamlining the development of new therapies for childhood cancers. The PPTC seeks to develop robust biomarkers of anticancer drug activity, and the majority of these are predicted to be genetic mutations that can be detected in tumor DNA and/or RNA. In order to design the most impactful experiments that can be rapidly translated to the clinic, PPTC investigators require a complete genomic characterization of the patient-derived xenograft tumor models that are utilized across the consortium. This will not only allow for the most robust experimental design, but also will increase the engagement of industry partners who seek collaborators poised to provide the proof-of-concept necessary for drugs in their development pipelines. All data and models will be made available to academically qualified investigators.

  Study phs001437

• Transcriptomic profiles of tumor samples from patients with stage I-III TNBC treated with anthracycline-taxane chemotherapy plus fasting-mimicking diet plus/minus metformin in the context of the BREAKFAST trial (NCT04248998)

  BREAKFAST is a randomized, non-comparative, phase II trial enrolling stage I-III (cT>1cm) TNBC pts treated with neoadjuvant doxorubicin-cyclophosphamide q3w for 4 cycles, followed by weekly paclitaxel for 12 cycles. Pts were randomized 1:1 to receive: CT + triweekly 5-day FMD cycles (arm A), or CT + FMD + daily metformin (1700 mg) (arm B). The primary study objective was to investigate if one or both experimental treatments were able to increase pCR rates when compared to anthracycline-taxane CT alone according to historical data. Secondary/exploratory endpoints included biomarker analyses based on tumor transcriptomics analyses. Specifically, we collected fresh-frozen tumor samples at baseline and 14-21 days after the first treament cycle and we analyzed the RNA-seq profiles to identify early predictors of treatment activity.

  Study EGAS50000000690

• Validation_of_a_Haloplex_platform_for_targeted_re_sequencing_of_the_exons_of_25_genes

  We recently used the Agilent SureSelect platform to re-sequence a set of genes known to bemutated in human AML. The results from 10 AML DNA samples were very satisfactory, butthe effort required was significant.Thus, we decided to re-sequence the same genes using the Haloplax system for targetenrichment in 48 AML samples. We planned to do this using MiSeq and have data from apilot of 3 samples. The data is promising but coverage appears pathcy so far.However, in order to get a better understanding of the data we will need deeper sequencing. Wewill need two lanes of HiSeq to get the same degree coverage as Sureselect.his 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 EGAS00001000285

• Patient-derived organoids model treatment response of metastatic gastrointestinal cancers (targeted and whole-genome sequencing)

  Patient-derived organoids (PDOs) have recently emerged as robust pre-clinical models, however, their potential to predict patient clinical outcomes remain unclear. We report a living biobank of PDOs from metastatic, heavily-pretreated colorectal and gastroesophageal cancer patients recruited in phase I/II clinical trials. Phenotypic and genotypic profiling of PDOs showed a high-degree of similarity to the original patient tumor. Molecular profiling of tumor organoids was matched to drug screening results, suggesting PDOs could complement existing approaches in defining cancer vulnerabilities and improving treatment responses. We compared ex vivo organoid responses to anticancer agents, and PDO-based orthotopic mouse tumor xenograft models to the response of the patient in clinical trials. Our data suggest that PDOs can recapitulate patient responses in the clinic, and have the potential to be implemented in personalized medicine programs.

  Study EGAS00001002784

• Genotyping_of_additional_Inflammatory_Bowel_Disease_cases___2014

  Both internal and external funding has enabled 5000 inflammatory bowel disease cases to be whole genome sequenced (CD @4X, UC @2X). The Anderson and Barrett groups are currently generating genotypes across these samples for comparison to 4000 population controls sequenced as part of UK10K. The UK10K project has shown that imputing sequenced genetic variation into previously GWASed samples greatly increases power to detect association. Given that our study has been designed to detect association to low frequency variation (0.5% and above) these gains in power are especially important. Overall, the UKIBDGC has GWAS data for around 1800 CD samples (Affy 500K) and 3000 UC samples (Affy6), though some of these samples have also been whole genome sequenced. Here, we apply to get all remaining non-GWAS and non-WGS IBD cases in the UKIBDGC (N=X) genotyped genotyped on the Illumina Core Exome Array

  Study EGAS00001000924

• Colorectal advanced adenomas NKI-AvL TGO COCOS series

  Colorectal cancer (CRC) develops from normal epithelium, through a benign precursor lesion called adenoma, by accumulation of genetic alterations affecting oncogenes and tumour suppressor genes. About 5% of colorectal adenomas are estimated to progress to CRC. However, it is important to identify which adenomas actually carry a high-risk of progression, because these serve as intermediate endpoints for e.g. screening programs. In clinical practice, adenomas with a size of ≥10 mm, villous component and/or high-grade dysplasia, called advanced adenomas, are considered high-risk, although solid evidence for this classification is lacking. Specific DNA copy number changes are associated with adenoma-to-carcinoma progression.For this tissue dataset, we applied low-pass whole genome sequencing to 96 advanced adenomas. Advanced adenomas were classified as lesions with low-risk or high-risk of progression, according to the presence of specific DNA copy number changes (Carvalho et al, CancerPrevRes, 2018).

  Study EGAS00001002952

• Investigation_of_mutational_signatures_associated_with_DNMT3A_deficiency_

  Mutational signatures have been shown to be attributable to specific genetic contexts, such as mutations in DNA repair genes. DNMT3A is a DNA methyltransferase that helps maintain the DNA methylation pattern in a site-specific manner and may participate in DNA repair or the stress response. We have identified an adult individual who is a germline mosaic for a DNMT3A mutation. We have obtained clonal lymphoblastoid cells (LCLs) from the subject representing both WT and mutant lines grown in the same individual for >50 years. These clones represent a unique opportunity to examine the mutational impact of the DNMT3A mutation in a well-controlled setting. Our goal is to perform WGS on whole blood, representing the pool, as well as several WT and several mutant clones, in order to investigate the contribution of DNMT3A to mutation rates and signatures.

  Study EGAS00001002329

• Screening_for_human_epigenetic_variation_at_CpG_islands

  Many human characteristics, including susceptibility to disease, are determined genetically. An unexplored alternative to such genetic determination concerns epigenetic mechanisms such as DNA methylation. CpG islands (CGIs) are generally constitutively hypomethylated, however there are circumstances in which they become heavily methylated and, when coincident with a gene promoter, this invariably causes transcriptional silencing. CGI methylation occurs in normal tissues during processes such as X-inactivation, but abnormal patterns of methylation have also been implicated in disease. The vast majority of evidence relates to cancer, where silencing of multiple genes in this way appears to be a causal contributor to the cancer state. To address the role and extent of CGI methylation in ‘normal’ and diseased cells we applied MBD-affinity purification in conjunction with next generation sequencing in a panel of human brain autopsy samples. These samples represent a panel of individuals as well as specific brain regions and neurological pathologies.

  Study EGAS00001000074

• Vento_Placental_Cell_Atlas

  The Human Cell Atlas initiative is an ambitious global research effort which aims to describe every cell in the human body across all developmental stages to generate a reference map to accelerate progress in science and medicine particularly relating to human development and disease. The initiative is led by investigators based at the Wellcome Sanger Institute and the Broad Institute of MIT and Harvard and funding support from the Wellcome and NIH. This specific project will assess human development. We aim to interrogate the cellular composition and molecular regulators underpinning development and maturation through single cell RNA-sequencing/spatial transcriptomics and computational algorithms to predict cellular developmental trajectories and cell-cell interactions. We will subsequent validate these findings in situ using allied imaging technologies e.g. immunohistochemistry and RNAscope and in vitro culture of human intestinal organoids (HIOs).

  Study EGAS00001004187

• Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Objectives The Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT) was designed to evaluate the hypothesis that amiodarone or a conservatively programmed shock-only, single-lead implantable cardioverter defibrillator (ICD) would decrease the risk of death from any cause in a broad population of patients with mild-to-moderate heart failure. Background Patients with congestive heart failure (CHF) can die suddenly and unpredictably from arrhythmia despite the use of proven medical therapies, such as beta-blockade. Two approaches have been developed to prevent sudden death among patients with CHF: therapy with amiodarone and therapy with an ICD. However, most of the mortality data on amiodarone and ICD therapy at the time of the study had been obtained in clinical trials performed after myocardial infarction in patients without CHF or those with ventricular arrhythmias. Thus, more data was needed to guide these therapies in patients who did not meet these criteria. Participants There were a total of 2521 participants. 847 were randomly assigned to placebo, 845 to amiodarone, and 829 to ICD therapy. Design Before randomization, participants underwent electrocardiography, a 6-minute walk test, 24-hour ambulatory electrocardiography, liver and thyroid function studies, and chest radiography. Participants were required, if clinically reasonable, to receive treatment with a beta-blocker and an angiotensin-converting-enzyme inhibitor, as well as an aldosterone antagonist, aspirin, and statins, when appropriate. Participants were randomly assigned in equal proportions to receive placebo, amiodarone, or a single-chamber ICD programmed to shock-only mode. Participants assigned to amiodarone or matching placebo began therapy as outpatients immediately after randomization. Placebo and amiodarone were administered as a loading dose of 800 mg/day for one week, 400 mg/day for three weeks, and then as a daily maintenance dosage according to weight and bradycardia status. Participants assigned to ICD therapy received their device a median of three days after randomization. The goal of ICD therapy was to treat only rapid, sustained ventricular tachycardia or ventricular fibrillation. Dual-chamber or biventricular devices, or antitachycardia or rate-response pacing therapies were not permitted. The ICD was uniformly programmed to have a detection rate of 187 beats per minute or more. Antibradycardia pacing was initiated only if the intrinsic rate decreased to less than 34 beats per minute. Median follow-up was 45.5 months. The primary end point was death from any cause. Secondary outcome measures included cardiac mortality, arrhythmic mortality, morbidity, quality of life, and incremental cost-effectiveness of the interventions. Each active treatment arm was compared to the placebo drug arm. Conclusions In participants with NYHA class II or III CHF and LVEF of 35% or less, amiodarone had no favorable effect on survival, whereas single-lead, shock-only ICD therapy reduced overall mortality by 23%.

  Study phs003654

• Netherlands Cancer Institute (NKI-AVL) general DAC

  Data is available under reasonable request. All data requests will be reviewed by the NKI IRB and must be supported by the Principal Investigator of the study. The researcher will need to sign a data access agreement with the NKI after approval. Go to https://ega.nki.nl to fill out the form to request access.

  Dac EGAC50000000055

• DNA sequencing of sgRNAs enriched from the CRISPR-Cas9 screened HCC organoids

  There are 12 samples. The project aims to find enriched or depleted sgRNAs to identify functional genes in HCC progression and lenvatinib resistance

  Study EGAS50000001130

• Whole exome sequencing, RNA sequencing and single-cell RNA sequencing of 4 melanoma patients

  We perfomed whole exome sequencing and RNA sequencing of 4 melanoma patients to assess the detailed biological mechanisms of resistance to immune checkpoint inhibitors.

  Study JGAS000285

• Kidney_Single_Cell_Study

  We intend to use single cell transcriptome analysis to explore the heterogenity of different cell types within the kidney.

  Study EGAS00001002325

• The landscape of LAM disease

  Tumor biopsies from LAM disease were retrospectively analyzed by multiple techniques to characterize the alterations in patients ,to elucidate the landscape of genetic/genomic alterations.

  Study EGAS00001003534

• Sequencing data for CLL patients

  Single-cell RNA-sequencing and other genome-wide approaches were applied to a cohort of CLL patients receiving Venetoclax treatment to study mechanisms of resistance.

  Study EGAS00001005815

• Exome and transcriptome sequencing of Desmoplastic Small Round Cell Sarcoma

  To conduct exome and transcriptome profiling to identify secondary recurrent mutations, multi-site phylogenetic analyse and immune characterization of Desmoplastic Small Round Cell Sarcoma.

  Study EGAS00001004575

• Genomics analysis of mucinous tumours of the ovary and related neoplasms

  To perform genetic and gene expression analyses on mucinous ovarian tumours to determine a progression model, cell of origin and novel therapeutic targets.

  Study EGAS00001003545

• Whole exome sequencing study of cholesteatoma patients from affected families

  This study used whole exome sequencing on 21 patients with cholesteatoma from 10 families in order to identify variants that may attribute to cholesteatoma aetiology.

  Study EGAS00001006147

• Rifaximin stimulates nitrogen detoxification by PXR-independent mechanisms in human small intestinal organoids

  To study the effects of rifaximin on human small intestinal (hSI) organoids, we performed RNAseq in PXR knockdown hSI compared to control hSI.

  Study EGAS00001006857