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in 14.73 milliseconds.

• Lipid Research Clinics - Prevalence Study (LRC-PS-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 LRC Program began in 1971 under the sponsorship of the National Heart, Lung, and Blood Institute, National Institutes of Health. Part of this program was the Lipid Research Clinics Prevalence Study, a standardized series of cross-sectional surveys of various North American populations designed to determine the prevalence of dyslipidemias and to describe the distributions of lipids and lipoproteins in major ethnic and social groups. In addition to contributing to the aggregate analysis, each independent population-based study was designed with capabilities for separate analyses of lipid and lipoprotein distributions. The participating populations were not selected to be a probability sample representative of the North American population per se, but by virtue of their size and economic and geographical diversity, they provide a useful cross-sectional group. The Family Study was the third phase of the Lipids Research Clinics North American Population Studies. The Family Study was designed to obtain knowledge of the distribution of lipids and lipoproteins among family members and of the association of familial and genetic attributes to dyslipoproteinemias. Background: An association between serum cholesterol and coronary heart disease is well established. A system was developed for classifying hyperlipoproteinemias into six types of patterns, as a basis for characterizing lipoprotein disorders. The Lipid Research Clinics was thus created to improve the detection of and clinical management of hyperlipoproteinemias. The three primary objectives were: 1) to acquire data on the prevalence of different types of hyperlipoproteinemia in various age and ethnic groups, with special emphasis on the nature and frequency of genetic forms; 2) to collect reliable data on the prevalence and incidence of atherosclerosis in different types of hyperlipoproteinemia; and 3) to conduct an intervention trial to determine if lowering plasma lipid levels would reduce the risk of CHD. Participants: There were 60,495 participants. Conclusions: Data from this study confirm findings from earlier studies in developed countries, showing age-related differences in plasma lipid levels. However, for overall distributions, the LRC data showed slightly lower cholesterol and markedly higher trigylceride values than those previously reported for North America. Some variation in plasma lipid values was evident among the clinic populations. The large number of participants within most subgroups permitted a variety of analytic and comparative studies. For example, data from the large pediatric population revealed a drop in plasma cholesterol levels in adolescent males and females. Males aged 20-50 years had higher cholesterol levels than females in the same age group, and higher trigylceride levels between ages 20-70 years. Numbers were also significient for meaningful comparisons between lipid distributions of females who were taking sex hormones and those who were not; in females taking sex hormones, cholesterol and triglyceride levels were higher for subjects younger than 45 years, but slightly lower after age 45, than lipid levels in females not taking hormones (Circulation 1979; 60(2):427-439).

  Study phs003995

• Studying Glioblastoma in a Human Organoid Tumor Transplantation Model

  This study comprises two complementary investigations that leverage innovative human model systems and cutting-edge molecular profiling to dissect glioblastoma (GBM) heterogeneity and tumor-microenvironment interactions. The first study uses a novel Human Organoid Tumor Transplantation (HOTT) model, in which freshly dissociated GBM tumor cells from five patients were transplanted into human embryonic stem cell-derived cortical organoids. This system enables the interrogation of tumor and microenvironment interactions in a human-specific, developmentally relevant context. Using single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA-seq (snRNA-seq), the authors profiled over 216,000 cells, analyzing tumor heterogeneity across multiple media conditions. Ligand-receptor analysis (CellChat), immunofluorescence, and RNAScope were used to validate findings. The study revealed that the HOTT model faithfully recapitulates primary GBM cell types and microenvironmental components, and uncovered a novel bidirectional signaling axis mediated by PTPRZ1 that influences both tumor migration and cell fate in a context-dependent manner. PTPRZ1 is identified to be important for intercellular communication between tumor and microenvironmental cells, while PDGFRB, NOTCH3, HOPX, FAM107A, CLDN5, RGS5, FOXC1, SOX18 are key markers of the neurovascular progenitor that we identify to be a multipotent, restricted progenitor in GBM. Related datasets include the inclusion of immune cells in the HOTT system, termed “iHOTT”. The second study focuses on identifying and characterizing a rare but functionally potent neurovascular progenitor (NVP) population within GBM tumors. By generating a meta-atlas from seven publicly available GBM scRNA-seq datasets, the authors identified a unique tumor-derived cell population co-expressing neural progenitor and vascular markers. This population was validated in situ and in vivo through immunostaining and lineage tracing via DNA barcoding in the HOTT system. Functional knockdown of NVPs in a murine GBM model resulted in reduced cycling cells, altered tumor composition, and increased survival. Clonal analysis showed NVPs have dual potency to give rise to both neuronal and vascular-like tumor cells, contributing significantly to tumor cell diversity. Molecular technologies used across the studies include: Single-cell and single-nucleus RNA sequencing DNA barcoding and clonal lineage tracing (CellTag) Population information: Tumor samples were obtained directly from multiple GBM patients (all IDH1 wild-type, adult), including core and peripheral tumor regions. Human cortical organoids derived from human embryonic stem cells (hESCs) served as the microenvironment in the HOTT model. Principal findings include: HOTT recapitulates the heterogeneity and cell type fidelity of GBM, while enabling the study of tumor–microenvironment signaling. PTPRZ1 has opposing roles in tumor and microenvironment, influencing GBM cell migration and fate via non-catalytic mechanisms. A novel GBM neurovascular progenitor population (NVP) was identified, validated, and shown to contribute clonally to distinct tumor cell types. Elimination of NVPs in vivo reduces tumor aggressiveness and improves survival. Data to be available through dbGaP: Raw and processed single-cell and single-nucleus RNA-seq data (HOTT model and NVP studies) CellTag barcode-labeled scRNA-seq clonal lineage data Associated metadata including patient of origin, experimental condition, and sample annotations.

  Study phs003936

• Transcriptomic analysis of liver CD8+ T cells

  Sorted single CD8+T cells expressing CD14 from human liver for SMARTSeq2. Livers processed: Kucykowicz et al STAR Prot 2022:pubmed.ncbi.nlm.nih.gov/35516846/ Published: Pallett et al Nature 2022 Tissue CD14+CD8+T-cells are reprogrammed by myeloid cells and modulated by LPS The liver is bathed in bacterial products including LPS transported from the intestinal portal vasculature, but maintains a state of tolerance exploited by persistent pathogens and tumours1,2,3,4. The cellular basis mediating this tolerance, yet allowing a switch to immunity or immunopathology, needs to be better understood for successful immunotherapy of liver diseases. Here we show that a variable proportion of CD8+T-cells compartmentalised in the human liver co-stain for CD14 and other prototypic myeloid membrane proteins; CD14+CD8+T-cells preferentially accumulate within the donor pool in liver allografts, amongst hepatic virus-specific and tumour-infiltrating responses, and in cirrhotic ascites. CD14+CD8+T-cells exhibit increased turnover, activation and constitutive immunomodulatory features with high homeostatic IL-10/IL-2 production directly ex vivo, and an enhanced capacity to rapidly produce antiviral/anti-tumour cytokines upon TCR engagement. This CD14+CD8+T-cell profile can be recapitulated by acquisition of membrane proteins, including the LPS receptor complex, from mononuclear phagocytes, resulting in augmented tumour killing by TCR-redirected T-cells in vitro. CD14+CD8+T-cells express integrins and chemokine receptors favouring interactions with the local stroma, which can promote their induction through CXCL12. LPS can also increase the frequency of CD14+CD8+T-cells in vitro and in vivo, and skew their function towards the production of chemotactic and regenerative cytokines. Thus, bacterial products in the gut-liver axis and tissue stromal factors can tune liver immunity by driving myeloid instruction of CD8+T-cells with immunomodulatory capacity. For the analysis of the single cell transcriptome two intrahepatic perfusate samples were isolated and frozen at -80 'C for storage prior to transportation to Newcastle, UK. Once thawed in pre-warmed HBSS containing 0.001% DNaseI, intrahepatic leukocytes were sorted by FACS to index classical mononuclear phagocytes (MNP) defined as Singlets, CD3-CD56-CD19-CD14hiHLA-DR+ or CD14± CD8+T-cells as follows: Singlets, CD3+CD56-CD19-CD4-CD8+V7.2- and split in to CD14± based on an isotype control. Single cells were sorted into 96-well skirted LoBind plates (Eppendorf) containing 5 µL lysis buffer (TCL buffer [Qiagen], supplemented with 1% b-mercaptoethanol [Sigma Aldrich; Merck]) on a BD Bioscience FACSFusion. The sorted plates were subsequently immediately sealed, centrifuged, and placed on dry ice to flash-freeze the cell lysate and stored at -80 'C. A modified SMART-seq2 protocol57 was performed on the single flow cytometry sorted-cells as previously described58. After cDNA generation, libraries were prepared (384 cells per library) using the Illumina Nextera XT kit (Illumina). Each library was sequenced to achieve a minimum depth of 1-2 million raw reads per cell using an Illumina HiSeq 4000 using v. 4 SBS chemistry to generate 75-bp paired-end reads.

  Study EGAS00001006885

• Diagnostic Genomic Analysis is Prognostic in AYA ALL Patients Treated on a MRD-Stratified Paediatric Protocol

  Background: Over the last decade, next generation sequencing has enabled classification of multiple new recurrent genomic drivers of acute lymphoblastic leukemia (ALL). We provide a comprehensive genomic survey of adolescent and young adult (AYA) patients treated on the Australasian Leukaemia and Lymphoma Group (ALLG) ALL06 study. ALL06 clinical trial described the efficacy and tolerability of AYA ALL patients aged 15-39 years treated on a single minimal residual disease (MRD)-stratified platform. In this study we have taken the ALL06 cohort and retrospectively performed comprehensive genomic analyses assessing response of AYA patients with various genomic subtypes treated with a paediatric approach. Methods: Genomic risk classification of B- and T-ALL patients (n=57) enrolled to the study was based on mRNA-Sequencing, MLPA, immunophenotyping and cytogenetics. Results: Using this approach, 36/40 (90%) B- and 13/17 (76.5%) T-ALL patients were classified according to genomic risk. We have linked outcome data with each patient’s genomic risk and found a strong correlation between adverse genomic risk and MRD at the end of consolidation, translating to inferior overall and relapse free survival. Patients with adverse risk genomics who achieved negative MRD status had improved responses compared to those with persistent MRD. Patients with standard risk genomics had excellent responses regardless of their MRD status. Conclusion: Our data argue strongly for incorporation of a genomic risk classification into future ALL treatment paradigms at the time of diagnosis.

  Study EGAS50000000752

• Genetic variability in exon 1 of the glucocorticoid receptor gene NR3C1 is associated with postoperative complications

  Patients having major surgery experience inflammation post-operatively, which may contribute to post-operative morbidity. Endogenous glucocorticoids (GCs) are an essential part of the stress response, but this response varies between individuals, which may in turn affect clinical outcome in general, and post-operative inflammation, specifically. Exon 1 of the NR3C1 gene, encoding the glucocorticoid receptor, contains an established region of differential regulation. DNA methylation patterns in this region have been found to differ between individuals. We investigated the methylation status and genotype in the Cytosine-phosphate-Guanine (CpG) island in exon 1 of NR3C1 in 24 patients who underwent major abdominal surgery and correlated it with post-operative complications. DNA was extracted from peripheral blood leukocytes and underwent targeted bisulfite sequencing of the CpG island. Complications were graded according to the Clavien-Dindo classification and 14 out of 24 had post-operative complications. Multifactorial and partial least square methods were used to analyse the data. A homogenous demethylated pattern was observed in all patients and no single CpG methylation was associated with post-operative complications. Four single nucleotide polymorphisms (SNPs) were significantly correlated with higher Clavien-Dindo scores. Genetic variability in the chr5:143,402,505-143,405,805 region of exon 1 of the glucocorticoid receptor gene NR3C1, but not DNA methylation, was associated with more severe post-operative complications in patients having major abdominal surgery. This indicates that the response to GCs may be of clinical importance for inflammatory conditions.

  Study EGAS00001005737

• NIPT samples for systematic evaluation of BinDel, a software tool for detecting clinically significant microdeletions in low-coverage WGS-based NIPT samples

  This dataset contains three sets of samples. The first sample set contains euploid fetus pregnancies reported by NIPTIFY screening test and postnatal evaluation. Dataset was processed similarly to previously published guidelines from KU Leuven, with modifications [1]. Briefly, peripheral blood samples were collected in cell-free DNA BCT tubes (Streck, USA), and plasma was separated with standard dual centrifugation. Cell-free DNA was extracted from 3 ml plasma using MagMAX Cell-Free DNA Isolation Kit (ThermoFisher Scientific). Whole-genome libraries were prepared using the FOCUS (Fragmented DNA Compact Sequencing Assay, Competence Centre on Health Technologies, Estonia) NIPT method protocol with 12 cycles for the final PCR enrichment step. In the following quantification, equal amounts of 36 samples were pooled, and the quality and quantity of the pool were assessed on Agilent 2200 TapeStation (Agilent Technologies, USA). Whole genome sequencing was performed on the NextSeq 550 instrument (Illumina Inc.) with an average coverage of 0.32× (minimum 0.08 and maximum 0.42) and producing 85 bp single-end reads. The second sample set contains a single NIPT sample postnatally diagnosed with Prader-Willi syndrome. The sample was sequenced with Illumina NextSeq 500 platform, producing 85 bp single-end reads with an average per-sample coverage of 0.32× at the University of Tartu, Institute of Genomics Core Facility, according to the manufacturer’s standard protocols, as described previously [2]. The third sample set contains samples SC005 (SeraCare Life Sciences Inc lot #10446565), SC0042 (#10571706), and SC016 (#10560229). These are SeraCare Life Sciences Inc circulating cell-free DNA (ccfDNA) like mixture of human genomic DNA that consists of matched maternal and fetus. SC005 and SC0042 consist of matched DNA of maternal and fetus with DiGeorge Syndrome. SC016 is a custom-ordered DNA Mix with fetus DNA having a pathogenic loss of the terminal region of 20p13 and a pathogenic 3q29 duplication. SC016 was processed as the first sample set was processed, and SC0042 was processed as the second sample set was processed. Sample SC005 was processed once as was sample set 1 and once as was sample set 2 processed. This study was performed with the approval of the Research Ethics Committee of the University of Tartu (#352/M-12). 1. Bayindir B, Dehaspe L, Brison N, Brady P, Ardui S, Kammoun M, et al. Noninvasive prenatal testing using a novel analysis pipeline to screen for all autosomal fetal aneuploidies improves pregnancy management. Eur J Hum Genet. 2015;23: 1286– 1293. doi:10.1038/ejhg.2014.282 2. Žilina O, Rekker K, Kaplinski L, Sauk M, Paluoja P, Teder H, et al. Creating basis for introducing noninvasive prenatal testing in the Estonian public health setting. Prenat Diagn. 2019;39: 1262–1268. doi:10.1002/pd.5578

  Dataset EGAD00001009512

• DAC Portal documentation

  DAC Portal Welcome to the DAC Portal documentation! If you are involved in governance or legal aspects, technical or operational aspects, or serving as a data steward, this page will be helpful for you. By exploring these materials, you can define your own Data Access Committee (DAC) and policies, understand the minimal requirements for a DAC and a policy object, and comprehend how the EGA data access requests are managed. DAC Portal Index Setting up your account DACs and Policies Pending Request table Manage Data Requests History page Audit your DACs, policies, and datasets Deprecation User Preferences DAC API - A programmatic approach Setting up your account Register yourself as an EGA user. The Helpdesk team will validate your account (this could take up to 48 hours). After validation, you will receive an email with a link to verify your email - Make sure you click on the link to verify your account! If you don’t receive the email, please check your spam folder. Once your account is active you can login to the DAC Portal. We recommend you to check out the Take The Tour! DACs and Policies Create a DAC Click on Create a DAC Add the title and description to your DAC. Once ready, click on the ‘Create’ button. You will have to wait until the Helpdesk Team validates the creation of your DAC. Once approved by Helpdesk, your DAC will be assigned with a persistent identifier (EGAC). Edit a DAC Once your DAC is registered and approved, you can edit it and add contacts. Remember, you can add as many contacts as needed. And, you can also remove/add contacts anytime. To add a contact, you must write the username or email of a registered EGA user in the Members field. Once you have typed the whole username or email, a drop-down menu will appear where you can select the contact. Select a contact from the drop-down menu. Make sure you write the full username or email of the contact so it appears as an option. Make sure that you select one main contact. Set up the role you want to grant to the new contact of the DAC. We recommend you to check out the FAQ to learn more about the different roles! After adding all the necessary contacts and assigning roles, click on Update. This will send an email notification to the new DAC member, letting them know about the invitation to managing the DAC. Once they log into the DAC Portal, they will be able to either accept or turn down the invitation. Create a policy First, please note that all information registered in a policy metadata object will be publicly available on the EGA website. For each released dataset, all users will be able to read the policy under the “Data Access” tab. Select Create a Policy from the Policies tab. Select the DAC that you want your policy to depend on. Add the title and terms and conditions for accessing and using your data. If you already have an external website for data access requests, you can add the URL directly here (see example here). You can find a template of a Data Access Agreement (DAA) document in our Policy documentation page. The DAA template is provided for guidance only and should be adapted to suit your own purpose depending on the security policies, terms for publication or embargoes, and restrictions on data use or sharing. You can also add Data Use Ontologies (DUO) to your policy. These DUO codes are used to semantically tag the terms and conditions of using the data linked to the policy (example). For more information, you can refer to the Data Uso Ontology documentation. Once completed, make sure to click the Create button in order to register your policy. A persistent identifier (EGAP) will be assigned automatically. Edit a policy Please, note that you will not be able to edit the policy if it shows in orange. Meaning that you are a member of the DAC linked to that policy. Members don’t have edit rights. Only admins can edit objects. You can check your registered policies on My policies, available at the menu in the top-right corner. Select your policy. It will then display the information of that policy, allowing you to edit the information. Once you are done with the modifications, click Update. Pending Requests Table Table Settings Upon selecting a specific DAC, you will be presented with a customisable table showcasing all pending data access requests. This table allows you to tailor the displayed information according to your preferences by selecting desired columns. Available Columns (Displayed with the Eye Icon): Date: The date when the data access request was submitted. Full Name: Name and surname of the user submitting the request. Email: Email address associated with the user's request. Username: The username linked to the EGA account of the user submitting the request. Organisation: Affiliated organisation of the user submitting the request. Dataset: EGA accession ID for the requested EGA dataset. Dataset Title: Title of the requested dataset. DAC Comment: Space provided for internal comments related to the request. Expiration: Option to specify an expiration date for granted permissions. Check more information on the pending request table here! Table Filters By default, all data access requests are displayed. However, you have the option to apply filters to refine your view. Follow these steps to apply filters: Click on the More button. This action will reveal the different columns available for applying filters. As you select columns, the checked fields will be displayed at the top of the request table. Choose the specific values within the selected columns that you want to filter for. Click the Search button to apply the selected filters. Do you find yourself frequently applying the same filters? You can save multiple combinations of filters for easy access at any time! Here's how: Click the Save button. Provide a descriptive name for the filter combination. Click Save to confirm. To load saved filters, simply select the saved filter from the dropdown menu to apply it automatically. Check more information on filters here! Advanced Search For users requiring more granular control over their data access requests, the DAC Portal offers an Advanced Search feature using JIRA Query Language (JQL). With JQL, you can craft precise and complex queries to filter and retrieve specific data access requests based on various criteria such as date, user details, dataset attributes, and more. Using JQL's flexibility, you can create custom queries to meet your specific needs, allowing for advanced filtering options beyond the standard filters provided. You can find the Advanced Search feature following these steps: Click the … button Select Advanced To view the allowed fields for filtering and explore all available options, simply click on the info icon. Once you have written the filtering values, click the Search button to apply. Check more information on filters here! Manage Data Requests Upon logging into the DAC Portal, you'll notice a sand clock icon next to the DAC ID, indicating the number of pending requests. Click on the DAC to review these requests. Accept requests Click on the right side of the toggle button to grant access to the user. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with granting permissions. Deny requests Click on the left side of the toggle button to deny access to the user. Provide a reason for the denial. Note that the user will receive an email with this reason. Click on the Done button. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with denying permissions. Tips! Apply a filter to view all requests to be managed at once. Use the toggle in the row with column names to grant or deny permissions for multiple requests simultaneously. You can grant and deny permissions in the same action, simplifying the process. The confirmation box will provide a summary of all actions, including grants, denials, DAC comments, and expiration dates. History Page The History page serves as a dedicated space to view information regarding all requests managed by all DAC members. Here, you can review active permissions and revoke them as needed. Go to the History page by clicking on the "HISTORY" button from a DAC page in the DAC Portal. Here are the different row types you may find: Current permissions: row in green with a toggle button to revoke permissions on the right. Approved requests: row in green, with no toggle button. Request denied: row in red with “request denied” on the right. Permission revoked: row in red with “permission revoked” on the right. Distinctions to Note! Between Request Denied and Permission Revoked: Request Denied: Refers to requests that were rejected from the outset, indicating that access to the dataset was never granted. Permission Revoked: Indicates that permissions were previously granted but have since been revoked. Users with permissions revoked have previously accessed the dataset. Between Approved Request and Current Permissions: Approved Request: Represents an entry when a data access request has been approved in the past. Current Permissions: Denotes ongoing permissions where a user has present access to the dataset. An approved request may now appear as Permission Revoked in the present. By observing these different rows, users can gain insight into the complete history of a user's interactions and permissions regarding a dataset over time. To revoke access to a specific user for a dataset, follow these steps: Go to History page Look for the row with the specific permissions. You can use the filters! Click on the toggle button to revoke access. Add a denial reason. Bear in mind that the requester will receive the denial reason! Click on the Apply button. Check more information on the Hisotry page here! Audit your metadata objects In the DAC Portal, you can efficiently manage and audit various metadata objects pertinent to your role as a Data Controller. Upon accessing the DAC Portal, you will encounter three primary tabs on the homepage: DAC: Contains information about Data Access Committees (DACs). Policies: Provides insights into linked policies. Datasets: Displays datasets and relevant details. Within each tab, you'll find a comprehensive list of the objects you manage, these being grouped by type (DAC, Policies, Datasets), as well as by your role (member/admin). Whilst the lists give you a quick look, to check how things are connected, we've added a table at the bottom DACs and policies. DACs Let’s check which policies are linked to a specific DAC: Go to "My DACs." Select a DAC. Click on "EDIT" to see more details. You are now on this page: Scroll down to see the linked policies. Click on “List of linked policies of this DAC” and you will see a list of all policies linked to your selected DAC. Policies Do you want to view a list of datasets connected to a particular policy? Follow the same steps mentioned earlier, but head to the policy tab this time. In the policy tab, you'll find a list of all your policies. Here, you may notice two different icons next to the policy ID (EGAP): DAC Icon (): This represents the DAC. Hover your mouse over the icon to see the DAC ID. Dataset icon (): This indicates datasets falling under that policy. The number next to the icon tells you how many datasets are linked to the policy. For example, if you see "2" next to the icon, it means there are two datasets linked to that policy. If you want to check the linked datasets and their relevant information, simply click on a specific policy. You'll then find the "List of linked datasets of this policy" at the bottom of the page. Datasets Finally, in the dataset tab, by default you will see a list of all the datasets you can manage with all your DACs and policies. Yet, we've included two handy ways to organise them: DAC vs. policy: You can group by datasets, either by DAC or policy. Released vs. unreleased: You can sort out datasets based on their release status. Feel free to experiment with both options! For instance, if you want to see which DACs have unreleased datasets, simply select DAC and unreleased, and you'll get the details you need! Deprecation Do you have a bunch of metadata objects like DACs and policies that you don’t need anymore? This section shows you how to get rid of them! But what does "deprecation" mean for EGA? It's basically changing the status of a metadata object to "deprecated," which means we won't be using it in the future. In simple terms, it's like saying these objects are no longer useful. However, because we believe in making metadata FAIR, once an object has a persistent identifier, we can't just delete it. So, instead of deleting, we deprecate it. Here's a helpful tip! If you want to make a metadata object disappear from the DAC Portal, deprecate it. You won’t see it in the portal anymore! Let’s say you want to deprecate a DAC. Let’s do it! Go to "My DACs." Select a DAC. Click "EDIT" to see more details. You are now in this page: Click on the Deprecate button It will then appear a message. There are two options here: Your DAC is not linked to any policy, hence it’s ready to be deprecated. Click on “Yes. Confirm” to deprecate your DAC object! Your DAC is linked to at least one policy, and you need to either: Deprecate the linked policy first, or Link the policy to a different DAC Now, let’s say you don’t want to get rid of the policy altogether, but you want to change the DAC it’s linked to. Here's how: Go to the policy tab. Find the policy you want to change (for example, EGAP50000000019). Choose a new DAC to link it to. Click on Update. See the Edit Policy section for more details. After ensuring that the DAC we want to deprecate isn’t linked to any policy, return to the "My DACs" section and follow the steps outlined previously. This will lead you to a confirmation message. You can only deprecate DACs and Policies. To deprecate a dataset, please contact our Helpdesk team. Check more information on how deprecation works here! User Preferences We have implemented email notifications in the DAC Portal. Here’s the complete list: DACs: Pending requests Approved by Helpdesk Rejected by Helpdesk DAC Invitation Requesters: Data access request approved Data access request denied Permissions revoked Upcoming expiration date As a DAC member, you will be able to decide whether you want to receive the DAC notifications or not. For that, go to the top-right corner menu, select User Preference. You will be able to decide whether you want to receive notifications for: Approved by Helpdesk Rejected by Helpdesk DAC Invitation For pending requests, you will be able to select how often do you want to receive the notification: Daily Weekly Fortnightly As a DAC member, you are responsible for managing data access requests. Consequently, you will receive notifications for pending requests. If you prefer not to manage these requests, please arrange to be removed as a contact for your DAC. Here's a tip! Do you have a pending request that you don’t want to receive a notification? Add a DAC comment! The EGA understands that a request resolution can take some time, for this reason, if you add a comment (make sure you save it by clicking the APPLY button!) we will filter those requests at the time of sending the notification! DAC API - A programmatic approach In addition to the new DAC Portal, we are excited to announce the release of the DAC API. This enables users to programmatically manage permissions. If you are interested in learning more about the technical specifications, you can click the button below. Check out the DAC API specification!

  Documentation access/data-access-committee/dac-portal

• The Environmental Determinants of Diabetes in the Young Study (TEDDY)

  The Environmental Determinants of Diabetes in the Young (TEDDY) Study investigates genetic and genetic-environmental interactions, including gestational infection or other gestational events, childhood infections and other environmental factors after birth, in relation to the development of pre-diabetic islet autoimmunity and type 1 diabetes (T1D). Beginning in 2002, a consortium of six centers assembled to participate in the development and implementation of studies to identify environmental factors that trigger the development of islet autoimmunity and T1D in genetically susceptible individuals. The TEDDY study screened around 400,000 newborns and recruited 7,749 neonates from the general population with a pre-determined T1D risk of 3% and 919 neonates with first degree relatives who have T1D and who have a pre-determined T1D risk of 10%. Thus, TEDDY proposes to follow over 8,000 participants across six clinical centers worldwide (Finland, Germany, Sweden and three in the United States) until the age of 15. Participants are followed every three months for islet autoantibody measurements with blood sampling until four years of age and then at least every six months until the age of 15. After the age of four, autoantibody positive subjects continue to be followed at 3 month intervals and autoantibody negative subjects are followed at six month intervals. In addition to the analysis of autoantibodies, additional data and sample collection is performed at each visit. The parents collect monthly stool samples in early childhood. The parents also fill out questionnaires at regular intervals in connection with study visits and record information about diet and health status in the child's TEDDY Book between visits. Continued long-term follow-up of the currently active TEDDY participants will provide important scientific information on early childhood diet, reported and measured infections, vaccinations, and psychosocial stressors that may contribute to the development of type 1 diabetes and islet autoimmunity. DNA extracted from the 9-month TEDDY blood sample was used for SNP genotyping. Samples were available for 7,082 participants. SNPs were genotyped by the Center for Public Health Genomics at the University of Virginia, using the Illumina ImmunoChip SNP microarray of around 196,000 SNPs selected from 186 regions associated with 12 autoimmune diseases (including T1D) (Hadley et al., 2015). "Data quality control (QC) steps included eliminating subjects with a low call rate (> 5% SNPs missing) and discrepancies between reported gender and prior genotyping at the HLA laboratory. Secondly, SNPs were removed from analysis due to low call rate, Hardy-Weinberg equilibrium P value < 10-6, except for chromosome 6 due to HLA eligibility requirements)" (Törn et al., 2015). QC resulted in a total of 7,023 subjects with data on 176,586 SNPs. Additional information on the TEDDY study are available in the following articles: The Environmental Determinants of Diabetes in the Young (TEDDY) Study. TEDDY Study Group. Annals of the New York Academy of Science, 2008 and TEDDY - The Environmental Determinants of Diabetes in the Young - An Observational Clinical Trial. Annals of the New York Academy of Science, 2006 Details of the TEDDY protocol can be found in The Environmental Determinants of Diabetes in the Young (TEDDY): Genetic Criteria and International Diabetes Risk Screening of 421,000 infants. Pediatric Diabetes, 2011

  Study phs001037

• Genome-Wide Association Study of Anorexia Nervosa (Price Foundation, Klarman Family Foundation, Center for Applied Genomics at the Children's Hospital of Philadelphia, Scripps Translational Sciences Institute Clinical Translational Science Award)

  Twin studies are consistent with substantial heritability in anorexia nervosa (AN), although detecting and confirming individual risk alleles has been difficult, due to small effects of single alleles and limited samples in prior studies. Methods: 1105 AN DNA samples obtained in a multisite study and 3733 pediatric patients of normal body weight were genotyped using the Illumina 610Q chip at The Center for Applied Genomics at The Children's Hospital of Philadelphia. Cases and controls were of European ancestry. Single nucleotide polymorphism (SNP) genotypes were subjected to quality control procedures, including elimination of SNPs with: call rates < 95%, minor allele frequency < 5%, and if Hardy-Weinberg equilibrium testing yielded p < 0.0001. Samples were removed if not of European ancestry or if showing cryptic relatedness. Eight AN DNA samples were deleted for low call rate, and 51 AN samples were deleted for cryptic relatedness. The statistical program, PLINK, was used to calculate a P value for each SNP passing quality control using the Cochran-Armitage trend test. The PennCNV program was used to define copy number variation (CNV). Results: We performed a genome-wide association study (GWAS) using with 1033 AN cases and 3733 controls (genomic inflation factor = 1.08). There were 11 SNPs with P<1X10-5 [(rsID, chromosome location, nearest gene, distance to nearest gene (base-pair), P value) rs6959888, 7q21.13, ZNF804B, 0, 1.63E-06; rs17725255, 20p11.23, CSRP2BP, 39396, 1.72E-06; rs10494067, 1p13.3, NTNG1, 0, 5.83E-06; rs2383378, 14q12, AKAP6, 0, 6.41E-06; rs410644, 5q14.1, SSBP2, 40462, 6.74E-06; rs4479806, 5p14.1, CDH9, 156926, 7.79E-06; rs957788, 13q33.3, FAM155A, 0, 8.11E-06; rs830998, 2q31.1, LRP2, 0, 8.68E-06; rs6782029, 3p25.3, VGLL4, 0, 9.04E-06; rs512089, 9p21.3, ELAVL2, 47984, 9.85E-06; rs3808986, 11q24.3, APLP2, 29217, 9.92E-6]. CNV analyses were performed on 1015 AN cases and 3532 controls. Overall frequencies of common or rare CNVs were similar between cases and controls. Specific rare and large (>500 kb, <1%) CNVs of interest included deletion of a large (~1.4Mbp) region of chr13q12 found in 2 AN samples and at a 10 fold lower frequency in a large sample (72,918) of subjects containing the genes SGCG, SACS, TNFRSF19, MIPEP, PCOTH, C1QTNF9B, SPATA13, MIR2276, and C1QTNF9, and an overlapping deletion and a duplication observed in 3 AN samples overlapping the CNTN6 and CNTN4 genes. Partially overlapping ~ 30 kb duplications were seen in 12 AN cases and in 1 control at the CDC5L gene on chromosome 6. Partially overlapping deletion of the 3' half of the PSTPIP1 gene was seen in 5 AN cases and no controls. Finally, a ~ 60 kb deletion of the PLEC1 gene was observed in 5 AN cases and in none of 3532 controls. Discussion: This is the first GWAS of AN. These preliminary results are consistent with absence of a major gene effect in AN. These results are consistent with genetic risk conveyed by multiple common alleles of small effect, as well as some uncommon CNVs of potentially larger effect. None of the implicated genes has a clear role in eating behavior. It must be emphasized that these results are preliminary and an additional ~ 400 AN cases will be analyzed as time and resources permit. These results require confirmation through study of independent populations of AN women.

  Study phs000679

• Rapid Acceleration of Diagnostics - Underserved Populations (RADx-UP): COVID-19 Testing and Prevention in Correctional Settings

  This study will work with diverse correctional facilities across MN, RI, WA, and FL to characterize the incidence of COVID-19 disease progression and related outcomes. The goal is to identify barriers and facilitators to develop strategies to improve testing in these under-served populations by: Increasing the reach, access, uptake, and impact of COVID-19 testing among incarcerated people and staff in prison and jails. Individuals in congregate settings have been disproportionately affected by COVID-19, with correctional settings accounting for 39 of the 50 largest cluster outbreaks in the country to date. Schema of Correctional Facilities: Correctional facilities are amplifiers of infectious diseases for structural reasons: 1) Dormitory-style living and overcrowded conditions make social distancing nearly impossible 2) They have a focus primarily on security not health 3) Their medical departments are under-resourced and often disconnected from local public health agencies. Correctional facilities must be the focus of urgent intervention to understand the incidence of COVID-19, how it spreads in correctional facilities, and durable long-term strategies to reduce transmission, including testing and vaccines. However, our work from the COVID Prison Project shows that correctional systems have implemented a range of testing strategies, without coordination, such that synthesizing the information on COVID-19 incidence, outcomes and optimal testing strategies (including repeat testing and contact tracing) has been challenging. Incarcerated people decline testing due to concerns about confidentiality, implications for release, and fear of placement in solitary confinement. Through interviews we will identify ethical concerns and potential solutions for COVID-19 testing and vaccine strategies in correctional facilities using a community-engaged strategy. The project's work from the COVID Prison Project shows that correctional systems have implemented a range of testing strategies, without coordination, such that synthesizing the information on COVID-19 incidence, outcomes and optimal testing strategies (including repeat testing and contact tracing) has been challenging. Incarcerated people decline testing due to concerns about confidentiality, implications for release, and fear of placement in solitary confinement. Through interviews we will Identify ethical concerns and potential solutions for COVID-19 testing and vaccine strategies in correctional facilities using a community-engaged strategy. Goals: 1. Identify ethical concerns and potential solutions for COVID-19 testing and vaccine strategies in correctional facilities using a community-engaged strategy. 2. Characterize incidence of COVID-19, disease progression, and related-outcomes and effectiveness of testing and contact tracing in correctional facilities. Participant Selection: a. Potential subjects must be currently incarcerated at key facilities in Washington, Rhode Island, Minnesota, and Florida. These facilities are Everglades Correctional Institution and Homestead Correctional Institution in Florida, Shakopee and Stillwater correctional facilities in Minnesota, the Rhode Island Department of Corrections, and Yakima County Jail in Washington. b. Participating correctional facilities in Florida, Minnesota, Rhode Island, and Washington will provide de-identified data on approximately 16,859 incarcerated adults and correctional staff. Data sources include deidentified data from medical records, administrative data, and policy documents pertaining to COVID-19. c. 3 series of focus groups (6, 12, 18 months) x 3 distinct groups (incarcerated people, medical providers, and correctional leaders) x 4 sites=36 total focus groups over the study period. d. Will utilize 74,812 COVID-19 tests to test and re-test a total of 16,859 incarcerated people and correctional staff for active infection over a six-month period in the FL, MN, and RI state prison systems and the Yakima County Jail in WA.

  Study phs003361