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• Stratifying and Targeting Pediatric Medulloblastoma through Genomics

  In this project, genomic analyses of pediatric medulloblastoma samples, obtained through the international medulloblastoma consortium, will be performed. RNA and miRNA expression profiles of 1000 samples, representing all four subgroups (Wnt, Shh, Group C, and D), will be studied to identify novel subtypes within each subgroup. The resulting subtype-specific expression profiles will support the development of reliable and robust biomarkers to more accurately and reliably classify medulloblastomas for treatment in clinical trials. For that purpose, two assays will be developed: an antibody-based immunohistochemical assay and an orthogonal nucleic acid-based hybridization assay. Additional genomic DNA analysis of the 300 high risk subgroup cases will support the discovery of subgroup specific somatic mutations in order to inform current clinical trials of targeted therapies, and to identify genes and pathways already targeted in other diseases. Such therapies could be rapidly transitioned to Phase II trials in medulloblastoma. Furthermore, the discovery of somatic mutations could be used for developing as well as validating specific biomarkers. The project team will also try to identify risk factors that predispose children to this type of cancer.

  Study EGAS00001000273

• Loss of astrocytic markers and impaired metabolic function in spinocerebellar ataxia type 7 patient-derived neural cultures

  Spinocerebellar ataxia type 7 (SCA7) is a rare neurodegenerative disorder caused by a CAG repeat expansion in the ATXN7 gene. This repeat expansion results in an abnormally long polyglutamine (PolyQ) tract in the Ataxin-7 protein. This ultimately leads to the degeneration of most notably Purkinje cells and retinal cells. Because no treatment exist that can halt or slow disease progression, there is a critical need for patient-specific disease models to uncover new pathogenic mechanisms and enable therapeutic testing. In this study, induced human pluripotent stem cells (hiPSCs) derived from healthy controls and individuals with SCA7 were differentiated into a mixed neural cell population consisting of neurons and astrocytes. Although control and SCA7 neurons appeared morphologically similar, SCA7-derived astrocytes exhibited a pronounced loss of the astrocyte-specific markers GFAP and S100B. Transcriptome analysis revealed substantial alterations in genes related to glial differentiation, cellular metabolism and oxygen handling, protein homeostasis, and neuronal differentiation and neuronal signaling. Mitochondrial stress assays further confirmed a mitochondrial phenotype in SCA7 neural cells. Together, these findings demonstrate that hiPSC-derived neural cells provide a robust platform that can be used for studying disease mechanisms and testing potential therapies for SCA7.

  Study EGAS50000001869

• FASTQ files of total RNA-Seq data from the POPS PET (pre-eclamptic) samples

  All the samples were obtained from the Pregnancy Outcome Prediction–a prospective cohort study of nulliparous women attending the Rosie Hospital, Cambridge (UK) for their dating ultrasound scan between January 14, 2008, and July 31, 2012. Ethical approval for the study was given by the Cambridgeshire 2 Research Ethics Committee (reference number 07/H0308/163) and all participants provided written informed consent. Cases of preeclampsia (PET) were defined on the basis of the 2013 ACOG criteria and cases of small for gestational age (SGA)infants were confined to severe SGA, i.e. a customized birth weight <5th percentile. Chorionic villi from the corresponding placentas (free from decidua, visible infarction, calcification, hematoma, or damage) were collected and processed within 30 minutes of separation from the uterus. After repeated washes in chilled phosphate buffered saline, the samples were placed in RNA later (Applied Biosystems) and stored at -80°C. Total placental RNA was extracted using mirVana Isolation Kit (Ambion). For each placenta, approximately 5 mg of tissue were homogenized in the Lysis/Binding solution for 20 sec at 6 m/s using a bead beater (FastPrep24) and Lysing Matrix D Tubes (MP Biomedicals). The samples were then spun at 13,000 rpm for 5 min at 4°C and the supernatants recovered. Afterwards, the manufacturer's instructions were followed. Immediately after the RNA extraction, placental RNA samples were DNase-treated using DNA-free DNA Removal Kit (Ambion), aliquoted, and stored in -80°C. Quantity and quality of the RNA samples were assessed using the Agilent 2100 Bioanalyzer, the Agilent RNA 6000 Nano Kit (Agilent Technologies), and Qubit fluorometer. Libraries were prepared starting with 300-500 ng of good quality total RNA (RIN ≥7.5) using the TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero Human/Mouse/Rat (Illumina), according to the manufacturer's instructions. The kit contains 96 uniquely indexed adapter combinations in order to allow pooling of multiple samples prior to sequencing. After determining their size (with the Agilent 2100 Bioanalyzer and the Agilent High Sensitivity DNA Kit by Agilent Technologies) and concentration (by qPCR with the KAPA Illumina ABI Prism Library Quantification Kit, Kapa Biosystems), libraries have been pooled and sequenced (single-end, 125 bp) using a Single End V4 Cluster Kit and an Illumina HiSeq2500 or HiSeq4000 instrument.

  Dataset EGAD00001003508

• Single-cell level characterization of B cell depletion and repopulation following rituximab in systemic lupus erythematosus

  Rituximab, a CD20+ B cell depletion therapy, is frequently used in the treatment of systemic lupus erythematosus (SLE). However, variability in patient response highlights the need for a deeper understanding of the underlying immune cell dynamics of B cell depletion and repopulation. In this study, we conducted longitudinal single-cell profiling of nine SLE patients treated with rituximab from pretreatment to up to 15 months post-treatment. These were compared to eight healthy controls. We profiled PBMCs via 10X Genomics single-cell RNA, surface protein (CITE-seq), B cell receptor (BCR), and T cell receptor (TCR) sequencing and sequenced bulk BCR repertoires in parallel. For single cell sequencing, 10 pools were created with an equal number of cells from 4 samples each. Samples collected at different timepoints from the same patient were distributed across different pools to allow demultiplexing of individuals using genotypes captured from the scRNA-seq data. Libraries were pooled using a ratio of GEX:CITE:TCR:BCR=9:2:1:1 and were sequenced across two lanes of the NovaSeq 6000. For bulk BCR sequencing, individual samples were demultiplexed using primer barcodes and paired-end BCR amplicon reads were merged prior to submission; data are provided as unmapped single-end reads.

  Study EGAS00001006798

• 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

• Institute for Refractory Cancer Research (IRCR) Data Access Committee

  Dac EGAC00001000272

• DAC for study Hominin-specific NOTCH2 paralogs expand human cortical neurogenesis through regulation of Delta/Notch interactions.

  Dac EGAC00001000816

• Wargo Group Data Access Committee for Neodjuvant Immune Checkpoint Blockade in High-Risk Resectable Melanoma

  Dac EGAC00001001008

• DAC for study the impact of urbanization and diet on innate immune responses in healthy Tanzanians

  Dac EGAC00001001531

• Patient-derived tumor organoids for personalized medicine in a rare case of hepatocellular carcinoma with neuroendocrine differentiation.

  Dac EGAC00001002458

• Ultra-deep targeted sequencing for studying the accumulation of somatic mutations in cancer-free human skin

  Study EGAS00001004279

• Karolinska Institutet - Susanne Schlisio Lab

  DAC for Susanne Schlisio Lab, Department of Oncology-Pathology, Karolinska Institutet, Sweden

  Dac EGAC50000000603

• MutationalPatterns2: The one stop shop for the analysis of mutational processes: DNA repair deletions

  Study EGAS00001004789

• Preliminary Results from the Initiative for Molecular Profiling and Advanced Cancer Therapy 2 (IMPACT 2) Study

  Study EGAS00001004964

• Molecular evolution and clinical trajectories of prostate cancer identifies novel markers for risk stratification - additional data

  Study EGAS00001004884

• High coverage sequencing of a single sample can account for the problem of intratumor heterogeneity

  Study EGAS00001004200

• A capture-based next-generation sequencing panel for the molecular characterization of chronic lymphocytic leukemia

  Study EGAS00001006975

• CUPiD, a cfDNA methylation-based tissue-of-origin classifier for Cancers of Unknown Primary

  Study EGAS00001007445

• HC_genotyping

  HC genotyping data for lead SNPs using Illuminia Global Array V2.0

  Dataset EGAD00010002475

• WES of TGA

  Whole exome sequencing data for 381 TGA probands

  Dataset EGAD00001005809

• Genomic Sequencing of Triple Negative Breast Cancer - CUTseq data

  CUTseq data for study EGAS00001008261

  Dataset EGAD00001015684

• Genome-Wide Association Study of Breast Cancer in the African Diaspora - the ROOT study

  The paucity of data on the genetic epidemiology of breast cancer for racial/ethnic groups other than those of European ancestry hinders the development of innovative interventions to reduce health disparities. Women in the African Diaspora experience a disproportionate burden of pre-menopausal breast cancer in comparison to all other races for reasons that remain unknown and understudied. This higher proportion of early-onset breast cancer might suggest a stronger genetic component in these populations. Genome-wide association studies (GWAS) have revealed several genetic loci that confer risk of breast cancer. Because all GWAS started the discovery stage in women of European ancestry and replicated mainly in women of European ancestry, we propose a novel approach for a GWAS in indigenous African women to identify alleles associated with breast cancer risk which will then be replicated in other populations. This innovative design builds on our current understanding of the etiologic heterogeneity in breast cancer and the distribution of breast cancer molecular subtypes which differ between women of African ancestry and women of European ancestry. The major objective of the proposed studies is to get to the "root" causes of breast cancer by identifying breast cancer risk alleles in a pooled sample of women of African ancestry and to replicate our findings in other populations. To achieve this objective, we conducted a case control study of breast cancer in women of African ancestry, including Africans living in Nigeria, African Americans and African Barbadians. We will genotype ~3800 individuals using the Illumina HumanOmni2.5-Quad platform. We will conduct both standard and novel genetic analyses of the data to map genes associated with breast cancer susceptibility, verify genotyping and carry out fine-mapping studies in genes or regions showing association with breast cancer risk, and replicate in other African American and non-African American populations. By pooling unique resources from studies throughout the African Diaspora, this study has the potential to identify risk alleles in several genes that contribute to increased breast cancer risk and may have implications for early detection, prognosis and treatment of breast cancer in ALL women. This should ultimately lead to improved outcomes for those who suffer a disproportionate burden of early-onset breast cancer.

  Study phs000383

• Cooperative Study of Sickle Cell Disease (CSSCD)

  The Cooperative Study of Sickle Cell Disease was initiated in 1977 to determine the natural history of sickle cell disease (SCD) from birth to death in order to identify those factors contributing to the morbidity and mortality of the disease. Specific objectives included: 1) to study the effect of sickle cell disease on growth and development from birth through adolescence 2) to study the conditions or events that may be related to the onset of painful crises 3) to obtain data on the nature, duration, and outcome of major complications of SCD 4) determine the nature, prevalence, and age- related incidence of organ damage due to SCD, and 5) study the role of SCD and its interaction with selected health events.Phases 2 and 3 of the study involved followup of the infant cohort. A total of 709 infants (age less than 6 months) were enrolled during Phase 1 of the Cooperative Study of Sickle Cell Disease (CSSCD), and Phases 2 and 3 of the CSSCD was designed to follow these children for an additional 10 years. The study objectives included: 1) define prospectively the natural history of sickle cell disease; 2) determine the relationships between cognitive and academic functioning and brain status as determined by MRI; 3) determine the cognitive or behavioral markers of silent infarct; 4) determine the relationship of family functioning on the Family Environment Scale (FES) to brain status, cognitive functioning, and social and demographic factors; 5) continue studies that will enhance the state of knowledge on the influence of sickle cell disease on the psychosocial adjustment of children and adolescents. Phase 2A of the study sought to examine the progression of organ damage in the heart, lung, kidney, and liver in adult cohort patients (born before 1/1/56) enrolled in phase 1 of the study between 3/79 and 5/81. A total of 620 patients from 11 centers were eligible for phase 2A.Instructions for requesting individual-level data are available on BioData Catalyst at https://biodatacatalyst.nhlbi.nih.gov/resources/data/. Apply for data access in dbGaP. Upon approval, users may begin accessing requested data in BioData Catalyst. For questions about availability, you may contact the BioData Catalyst team at https://biodatacatalyst.nhlbi.nih.gov/contact.

  Study phs002362

• Mayo-Perlegen LEAPS (Linked Efforts to Accelerate Parkinson's Solutions) Collaboration

  We performed a two-tiered, whole-genome association study of Parkinson disease (PD). For tier 1, we individually genotyped 198,345 uniformly spaced and informative single-nucleotide polymorphisms (SNPs) in 443 sibling pairs discordant for PD. For tier 2a, we individually genotyped 1,793 PD-associated SNPs (P<.01 in tier 1) and 300 genomic control SNPs in 332 matched case-unrelated control pairs. We identified 11 SNPs that were associated with PD (P<.01) in both tier 1 and tier 2 samples and had the same direction of effect. For these SNPs, we combined data from the case-unaffected sibling pair (tier 1) and case-unrelated control pair (tier 2) samples and employed a liberalization of the sibling transmission/disequilibrium test to calculate odds ratios, 95% confidence intervals, and P values. A SNP within the semaphorin 5A gene (SEMA5A) had the lowest combined P value (P=7.62E-6). The protein encoded by this gene plays an important role in neurogenesis and in neuronal apoptosis, which is consistent with existing hypotheses regarding PD pathogenesis. A second SNP tagged the PARK11 late-onset PD susceptibility locus (P=1.70E-5). In tier 2b, we also selected for genotyping additional SNPs that were borderline significant (P<.05) in tier 1 but that tested a priori biological and genetic hypotheses regarding susceptibility to PD (n=941 SNPs). In analysis of the combined tier 1 and tier 2b data, the two SNPs with the lowest P values (P=9.07E-6; P=2.96E-5) tagged the PARK10 late-onset PD susceptibility locus. Independent replication across populations will clarify the role of the genomic loci tagged by these SNPs in conferring PD susceptibility. Note: The following instruments were used: 1)clinical assessments form and manual; 2) sibling screening form and manual; 3) unrelated control screening and manual; and 4) risk factors questionnaire and manual. All cases underwent #1, as did sibling controls screening positive (see below). Unrelated controls were not examined. All siblings underwent #2. All unrelated controls underwent #3. All subjects (cases, sibling controls, and unrelated controls) underwent #4. Any publications using the data are to cite the original American Journal of Human Genetics article (Maraganore et al., 2005). Investigators using the data collection instruments are to acknowledge Drs. Maraganore and Rocca, and cite grants ES-10751 and NS-33978.

  Study phs000048

• Effect of Crohn's Disease Risk Alleles on Enteric Microbiota

  This UH2/UH3 demonstration project entitled "Effects of Crohn's disease risk alleles on enteric microbiota" is focused on characterizing intestinal associated microbiota in patients with ileal Crohn's disease (ileal CD), ulcerative colitis (UC) and control patients without inflammatory bowel diseases (non-IBD). We hypothesize that genetic factors that affect Paneth cell function, contribute to compositional changes in intestinal microbiota. These changes in microbiota may lead to reduction of protective commensal organisms and increased numbers of aggressive organisms that incite intestinal inflammation. This hypothesis is being tested by high throughput 16S rRNA sequence analysis of de-identified ileal and colonic tissues that have been archived at Washington University St. Louis, University of North Carolina, Mount Sinai Hospital and the Cleveland Clinic. Multivariate analysis of the metagenomic data will be conducted with genotyping metadata (highly reproducible CD risk alleles, including NOD2 and ATG16L1) and phenotyping metadata (e.g. age, gender, race, body mass index, medications and smoking.) Shotgun sequencing will be performed on selected fecal specimens linked to ileal tissues to identify additional, or auxiliary, or synergistic pathogenic factors or other functional changes in the microbiome. Because members of this research team have observed that a chronic viral infection is required for the Paneth cell defect in Atg16l1 hypomorphic mice, a major focus of these studies will be towards identifying potential viral triggers for the defective Paneth cell phenotype in individuals harboring the ATG16L1 risk allele. Novel genetic probes for protective and aggressive organisms will be developed by mining bacterial genome and shotgun sequencing data. Genomic sequences will be produced for candidate protective and aggressive strains (e.g. adherent-invasive strains of E. coli) isolated from human intestinal tissues where there is limited existing genome information. Quantitative qPCR assays using the novel as well as established genetic probes will be conducted to test the hypothesis that an imbalance between protective and aggressive organisms is associated with genetic factors that affect Paneth cell function. Our combined expertise in multiple disciplines across multiple institutions, our demonstrated ability to collect a large number of well-phenotyped samples with longitudinal clinical information that will be linked to host response and morphologic studies, and our consortium's capacity for high-throughput sequencing will be used to investigate how alterations in human microbiome relate to CD risk alleles and CD pathogenesis.

  Study phs000255

• Nicotine Addiction Genetics and Correlates

  Ascertainment: Families were ascertained through panels of adult Australian twins, and a sample of spouses of Australian twins. Families with at least one offspring who was identified as a current or former smoker in an earlier survey. Screening telephone interviews were conducted with index cases to provide confirmation of a history of heavy cigarette smoking, to obtain confirmation of a history of heavy cigarette smoking, and to obtain additional information on the history of cigarette use and the survival status of other family members, and their full siblings and parents. Index cases were the affected spouse of a twin, or the affected twin from pairs discordant for a history of heavy smoking, or a randomly chosen twin from pairs where both have a history of regular smoking (i.e., has smoked at least 100 cigarettes lifetime), and at least one live biological parent to identify sibships with at least one affected sib pair (ASP) concordant for heavy smoking, and at least one living parent. If permission was granted by the index case, eligible family members were contacted and invited to provide a blood sample and to complete a telephone diagnostic interview. In families with just one available biological parent, and additional unaffected sibling who had never smoked on a regular basis (i.e., has smoked fewer than 100 cigarette lifetime, but has experimented with cigarettes once or twice) was included (when available) to help compensate for the loss of information due to missing parental phenotypes. (Target N=400 Australian families with approximately 600 ASPs; and 600 TDT trios comprised of a nicotine dependent index case and both biological parents). Diagnostic Assessment: The telephone diagnostic interview assessed DSM-IV and modified DSM-IIIR diagnoses (i.e., without time clustering) for nicotine dependence, alcohol and other drug dependence and abuse, major depression, and childhood conduct disorder. Most diagnostic assessments were based on the SSAGA/SSAGA-II, developed for the multi-site gene-mapping alcoholism study (the Collaborative Study on the Genetics of Alcohol. An exception was the tobacco section, which was developed directly from the CIDI and DIS (as the original SSAGA did not include a diagnostic section on nicotine dependence). In version 2 of this study, a text file containing 7704 SNPs with large allele frequency discrepancy as compared to 1000 Genomes is included. Users have the option to exlude these 7704 SNPs.

  Study phs001299

• Genetic Epidemiology of COPD (COPDGene) Funded by the National Heart, Lung, and Blood Institute

  Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in the United States and the only leading cause of death that is steadily increasing in frequency. This project will establish a racially diverse cohort that is sufficiently large and appropriately designed for genome-wide association analysis of COPD. A total of 10,000 subjects will be recruited, including control smokers, definite COPD cases (GOLD Stage 2 to 4), and subjects not included in either group (GOLD 1 or GOLD-Unclassified). This cohort will be used for cross-sectional analysis, although long-term longitudinal follow-up will be a future goal. The primary focus of the study will be genome-wide association analysis to identify the genetic risk factors that determine susceptibility for COPD and COPD-related phenotypes. Detailed phenotyping of both cases and controls, including chest CT scan assessment of emphysema and airway disease, will allow identification of genetic determinants for the heterogeneous components of the COPD syndrome. The hypotheses to be studied are: 1) Precise phenotypic characterization of COPD subjects using computed tomography, as well as clinical and physiological measures, will provide data that will enable the broad COPD syndrome to be decomposed into clinically significant subtypes. 2) Genome-wide association studies will identify genetic determinants for COPD susceptibility that will provide insight into clinically relevant COPD subtypes. 3) Distinct genetic determinants influence the development of emphysema and airway disease. The initial phase of genome-wide association analysis included 500 COPD cases and 500 control subjects (all non-Hispanic White) genotyped with the Illumina Omni-1 chip. The second phase genotyped the entire study cohort using the Illumina Omni-Express chip. Unique aspects of the study include: 1) Inclusion of large numbers of African American subjects (approximately 1/3 of the cohort); 2) Obtaining chest CT scans (including inspiratory and expiratory images); and 3) Inclusion of the full range of disease severity. The COPDGene_v6 Cohort is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in these sub-studies, please click on the following sub-studies below or in the "Sub-studies" box located on the right hand side of this top-level study page phs000179 COPDGene_v6 Cohort. phs000296 ESP LungGO COPDGene phs000765 COPDGene_Geno

  Study phs000179

• Overcoming Clinical Resistance to EZH2 Inhibition Using Rational Epigenetic Combination Therapy

  Essential epigenetic dependencies have become evident in many cancers. Based on functional antagonism between BAF/SWI/SNF and PRC2 in SMARCB1-deficient sarcomas, we and colleagues recently completed the clinical trial of the EZH2 inhibitor tazemetostat, leading to its FDA approval. However, the principles of tumor response to epigenetic therapy in general and tazemetostat in particular remain unknown. Using functional genomics of patient tumors and diverse experimental models, we defined molecular mechanisms of tazemetostat resistance. We found distinct classes of acquired mutations that converge on the RB1/E2F axis, decoupling EZH2-dependent differentiation and cell cycle control. This allows tumor cells to escape tazemetostat-induced G1 arrest despite EZH2 inhibition and suggests a general mechanism for effective EZH2 therapy. Using RNA-sequencing of patient tumor specimens, we identified specific genes as markers of a deficient RB1/E2F axis, which we propose as prospective biomarkers for therapy stratification. This offers a paradigm for rational epigenetic combination therapy suitable for immediate translation to clinical trials.This study was performed on archived tumor tissue collected as part of the above clinical trial at Memorial Sloan Kettering Cancer Center. Patient tumors were classified into "Response" or "Progression" groups based on RECIST 1.1 criteria. "Response" included tumors exhibiting a complete response, partial response, or stable disease. All other tumors were classified under the "Progression" group. Archived frozen tumor samples were weighed and up to 20-30mg were homogenized in RLT buffer, followed by extraction using the AllPrep DNA/RNA Mini Kit (QIAGEN catalog 80204) according to the manufacturer's instructions. RNA was eluted in 13 μL nuclease-free water. After RiboGreen quantification and quality control by Agilent BioAnalyzer, 1 μg of total RNA with DV200 percentages varying from 78% to 100% underwent ribosomal depletion and library preparation using the TruSeq Stranded Total RNA LT Kit (Illumina catalog RS-122-1202) according to instructions provided by the manufacturer with 8 cycles of PCR. Samples were barcoded and sequenced using NovaSeq 6000 in a PE150 mode, with the NovaSeq 6000 S4 Reagent Kit (Illumina). On average, 84 million paired reads were generated per sample and 70% of the data mapped to mRNA. For analysis, read adaptors were trimmed and quality filtered using 'trim_galore' and mapped to GRCh38/hg19 reference genome using STAR v2.7.9 with default parameters.

  Study phs003188

• Kids First: The Genomic Basis of Structural Birth Defects Associated with Chromosome 18 Copy Number Changes

  Chromosome abnormalities are a common cause of structural birth defects. Both childhood cancers and structural birth defects are critical and costly conditions associated with substantial morbidity and mortality. Elucidating the underlying genetic etiology of these diseases has the potential to profoundly improve preventative measures, diagnostics, and therapeutic interventions.However, because these genomic copy number changes involve multiple genes and because most of these conditions are individually rare, defining the specific causative genes behind specific phenotypes has been a challenge. We met that challenge by spending the last 28 years enrolling and evaluating anyone with a chromosome 18 abnormality. The cohort now includes over 700 individuals with a wide variety of chromosome 18 copy number changes (CNV), as well as their parents. Because the vast majority of these participants have individually unique CNVs, we have been able to perform extensive genotype-phenotype correlations resulting in 58 publications. Of the 263 genes on chromosome 18, 28 are linked to specific hemizygous phenotypes. However, most have low penetrance. Genomic sequence data could identify variants in the extant allele that are hypermorphic and compensate for hemizygosity, or are hypomorphic and exacerbate hemizygosity. Additionally, there are phenotypes within this cohort that are rare, or an extreme version of one of the more common phenotypes. Genomic sequence data could help discover new biallelic conditions by revealing functional sequence variants of the extant allele. In both of these cases, there could also be variants in other genes on other chromosomes that may be associated with the phenotype that confer susceptibility or resilience. Inclusion of this unique cohort in the Gabriella Miller Kids First Pediatric Research Program can advance the goals of the program in several ways. First, by adding known susceptibility loci to the existing Kids First cohorts for the structural birth defects that are also found in our cohort. Second, for those structural birth defects known to be polygenetic, this cohort has a single defined risk factor which can simplify the search for secondary factors. Third, these studies can also bring clarity to people with chromosome 18 conditions and help to make the genotype more accurately predict phenotype. This approach could be a model for understanding the many other rare chromosome abnormalities which collectively are a common cause of structural birth defects.

  Study phs002627

• Whole Blood Transcriptomics of Patients With Melioidosis

  Melioidosis is a neglected tropical infection caused by the Gram negative bacterium Burkholderia pseudomallei that is associated with high mortality rates in southeast Asia. The goal of this study was to a) identify human blood leukocyte transcriptomic features and pathways specific to melioidosis (vs other etiologies of infection), b) identify transcriptomic features and pathways specific to fatal melioidosis, and c) identify transcriptomic predictors of death in patients with melioidosis. This study used clinical data and whole blood leukocyte samples from a subset of participants in the Ubon Sepsis Study, a prospective single-center cohort study conducted at Sunpasitthiprasong Hospital, Ubon Ratchathani, Thailand between 2013 and 2017. Participants in the Ubon Sepsis Study had suspected or documented infection with accompanying systemic manifestations of infection and were enrolled, with blood sampling, within 24 hours of admission to the study hospital. Enrolled patients were followed over time and vital status was determined at 28 days after enrollment. The subset of participants from the parent Ubon Sepsis Study cohort selected for this study was comprised of 164 patients with melioidosis (defined as having a culture of clinical sample positive for B. pseudomallei), 35 patients with Gram negative bacteremia due to either Escherichia coli or Klebsiella pneumoniae, 16 patients with Gram positive bacteremia due to Staphylococcus aureus, and 19 patients who had negative blood cultures and no clinical sample culture-positive for B. pseudomallei. Fifty uninfected control patients with or without diabetes were recruited from the outpatient department and blood donation center at Udon Thani hospital, Udon Thani, Thailand from 2018 to 2019. Blood was collected at the time of enrollment in Tempus Blood RNA tubes. RNA was extracted using the Tempus Spin RNA Isolation kit. RNA sequencing (RNA-Seq) was performed at the University of Washington Northwest Genomics Center. Analysis was performed using a variety of statistical methods. The main finding of the study was that the transcriptomes of melioidosis patients, compared to patients with other infections, were characterized by upregulation of interferon signaling. The transcriptomes of patients with fatal melioidosis were notable for upregulation of inflammatory and type 2 immune responses and downregulation of genes related to T-cell function. A five-gene (EPHB1, SYCP2L, MERTK, IL5RA, and SPOCK1) predictive signature for death in melioidosis was developed.

  Study phs003724

• Singapore Adult Metabolism Study - Phase 2 (SAMS2)

  Singapore Adult Metabolism Study - Phase 2 (SAMS2) was an interventional study where hundreds of donors aged 21-45 were recruited to participate in a 16-week weight loss program. Study individuals selected (see below for more detailed selection standards) were sedentary (exercise 1 or fewer times a week), obese or overweight with a body fat mass greater than 24% and a BMI between 23-35 kg/m2. For this study, we adjusted BMI definition for Asian population, based on the WHO Consultation 2002, and the BMI cut-off is 23 kg/m2 for overweight and 27.5 kg/m2 for obese. The weight loss program included a combination of dietary interventions, structured exercise sessions, and additional physical activity performed in participants' own time. Energy and protein requirements were calculated based on each participant's weight, height, and physical activity level, with the goal of achieving a 40% calorie deficit. Participants' calorie intake was tracked using food recalls and questionnaires. Additionally, subjects attended structured exercise sessions at least three times per week, supervised by a coach. Each session consisted of 90 minutes of aerobic and strength training exercises, designed to burn approximately 500 kcal per session. To monitor daily physical activity, participants wore pedometers throughout the study. In total, the exercise sessions (500 kcal per session) and daily physical activity (targeting an additional 500 kcal) were aimed at achieving a total caloric expenditure of 2000 kcal per week. We collected clinical data and skeletal muscle biopsies from 54 overweight/obese Asian individuals before and after a 16-week lifestyle intervention, which resulted in an average ~10% weight loss, accompanied by a ~30% increase in insulin-stimulated glucose uptake. Improvements were observed in 118 of 252 clinical traits and six blood lipids. Transcriptomic analysis of paired skeletal muscle biopsies identified 505 differentially expressed genes enriched in mitochondrial function and insulin sensitivity. Thousands of muscle-specific e/sQTLs were detected pre- and post- intervention, including hundreds of lifestyle-responsive e/sQTLs. Notably, approximately 4.2% of eQTLs and 7.3% of sQTLs showed Asian specificity. Joint analysis with GWAS identified 16 putative metabolic risk genes. Our study reveals gene-by-lifestyle interactions and how lifestyle modulates gene regulation in skeletal muscle.

  Study phs004078

• Exome Sequencing of Alcohol-Associated Hepatitis

  Alcoholic hepatitis (AH) is a clinically severe, acute disease that only afflicts a fraction of patients with alcohol use disorder (AUD). Although recent genomic studies of alcoholic cirrhosis (AC) and steatosis have revealed that several genes of large effect are shared between these disorders, the genetic and environmental sources of variation that lead to AH, AC or alcohol fatty liver disease (AFLD) in some AUD patients remain largely unknown. In order to investigate the genetic determinants of AH, we sequenced a 58 Mb exome target to high genotype accuracy (0.996) and completeness (0.941) in 1,735 patients with AH (N=784) and heavy drinking controls without alcohol-associated liver disease (ALD) (N=951), detecting 1,085,982 unique single nucleotide variants (SNVs). Analysis of common SNPs that comprised 17.5% of those SNVs, we observed exome-wide significant association of PNPLA3 (rs738409-G allele: odds ratio [OR]=1.73, 95% confidence interval [CI]: 1.44-2.08, p =3.75e-9), as had previously been seen for AH in candidate gene contexts, and as previously observed for PNPLA3 to AC and nonalcohol steatohepatitis in GWAS. Other genes previously implicated in AC did not strongly contribute to AH, and the only prominently implicated (but not exome wide significant) gene overlapping with AUD was ADH1B, also a known liver disease gene. However, SNPs at ICOSLG (Chr 21) and TOX4/RAB2B (Chr 14), were also exome-wide significant and of large effect-size (ICOSLG rs13052840-C allele: OR=1.67, 95% CI: 1.39-2.02, p = 4.59e-8; TOX4/RAB2B rs17516362-C allele: OR = 0.61, 95% CI: 0.50-0.74, p=1.25e-7). ICOSLG encodes a co-stimulatory signal for T-cell proliferation and cytokine secretion and induces B-cell proliferation and differentiation, and thus could be tied to the inflammatory component of AH. TOX4 was previously implicated in diabetes and immune system and its linked SNP is a cis eQTL for TOX4 and RAB2B. A polygenic signal was visible on the exome-wide quantile-quantile graph, where there was modest p value inflation (lambda = 1.040). Ranking p values of all 18,705 genes evaluated versus a list of 227 genes objectively precompiled for relationship to liver diseases (via GWAS), we observed a strong (pThe data generated here also represents a resource that can be used cumulatively or meta-analytically with future datasets.

  Study phs003659

• Clinical outcomes in ctDNA-positive urothelial carcinoma patients treated with adjuvant immunotherapy

  Minimally invasive approaches to detect residual disease after surgery are urgently needed to select patients at highest risk for metastatic relapse for additional therapies. Circulating tumour DNA (ctDNA) holds promise as a biomarker for molecular residual disease (MRD) and relapse,1-3 but its clinical value has yet to be demonstrated in a randomised clinical trial. We evaluated outcomes in post-surgical ctDNA-positive (+) patients in a randomised phase III trial of adjuvant atezolizumab versus observation. IMvigor010 enrolled 809 patients with muscle-invasive urothelial carcinoma and did not meet its primary endpoint of disease-free survival (DFS) in the intent-to-treat population. Within the study, an exploratory planned analysis of prospectively collected plasma was performed, which tested the utility of ctDNA to identify patients who may benefit from adjuvant atezolizumab treatment. ctDNA was measured at the start of therapy (cycle 1 day 1; C1D1) and at week 6 (cycle 3 day 1; C3D1), and 581 patients were evaluable for ctDNA. The prevalence of ctDNA positivity at C1D1 was 37% (n=214), and ctDNA positivity identified patients with poor prognosis (observation arm DFS HR= 6.19 (4.29, 8.91), p<0.0001). Here we show that ctDNA(+) patients had improved DFS and overall survival (OS) with atezolizumab versus observation (DFS HR= 0.56 (0.41-0.77); p=0.0003 and OS HR= 0.58 (0.4-0.86); p=0.0063). No difference in DFS or OS between arms was noted for ctDNA-negative patients. The rate of ctDNA clearance was higher with atezolizumab (18%) versus observation (4%) (p=0.0041). Transcriptomic analysis revealed that tumours from ctDNA(+) patients had higher expression of cell cycle and keratin genes. Within the ctDNA(+) patient population in the atezolizumab arm, non-relapsing patients were further enriched in prominent immune response signatures including PD-L1, IFNG, CXCL9, and high tumour mutational burden, whereas relapse was associated with angiogenesis and fibroblast-transforming growth factor-beta signatures (F-TBRS). TCGA molecular subset analysis revealed increased efficacy of atezolizumab in patients with basal-squamous tumours, consistent with underlying tumour-immune contexture. Together these findings suggest that adjuvant atezolizumab may be associated with improved outcomes compared with observation in this high-risk ctDNA(+) population. These findings, if validated in other settings, would shift approaches to post-operative cancer care.

  Study EGAS00001004997

• CIDR, NCI, NIDA Sequencing of Targeted Genomic Regions Associated with Smoking

  This study includes samples from two projects: Collaborative Genetic Study of Nicotine Dependence (COGEND; PI: Laura Bierut) and Genetic Study of Nicotine Dependence in African Americans (AAND; PI: Laura Bierut and Eric Johnson). The majority of the COGEND subjects included in the current study overlap with the two datasets already available on dbGaP. GWAS data are available for COGEND subjects through the Study of Addiction: Genetics and Environment (SAGE), dbGaP study accession phs000092. It should be noted that the case definition in the SAGE study is DSM-IV alcohol dependence. GWAS data are available for additional COGEND subjects through The Genetic Architecture of Smoking and Smoking Cessation, dbGaP study accession phs000404. The overall goal of this project is to apply deep sequencing to key genomic regions associated with nicotine dependence in order to accelerate the discovery of variation in molecular pathways that govern the development of nicotine dependence. The sample includes unrelated cases and controls of European American and African American descent. Cases are defined by a commonly used definition of nicotine dependence, a current score of 4 or more (maximum score of 10) on the Fagerstrom Test for Nicotine Dependence (FTND). Control status is defined as an individual who smoked at least 100 cigarettes during their lifetime, yet never became dependent (lifetime FTND<2). By selecting controls who smoked cigarettes, we focus on those genetic effects that are specific to the development of nicotine dependence. COGEND: COGEND was initiated in 2001 as a three-part program project grant funded through the National Cancer Institute (NCI; PI: Laura Bierut). The three projects included a study of the familial transmission of nicotine dependence, a genetic study of nicotine dependence, and a study of the relationship of nicotine dependence with nicotine metabolism. The primary goal was to detect, localize, and characterize genes that predispose or protect an individual with respect to heavy tobacco consumption, nicotine dependence, and related phenotypes and to integrate these findings with the family transmission and nicotine metabolism findings. The primary design was a community based case-control study. Nicotine dependent cases and non-dependent, smoking controls were identified and recruited from Detroit and St. Louis. More than 54,000 subjects aged 25-44 years were screened by telephone; more than 3,100 subjects were personally interviewed; and more than 2,900 subjects donated blood samples for genetic studies. AAND: AAND was initiated in 2009 to identify and characterize genetic determinants of nicotine dependence in a large African American population. Community-based recruitment of approximately 100,000 people was conducted to ascertain 1,000 African American nicotine dependent cases and 1,000 African American non-dependent, smoking controls. All subjects were between the ages of 25-44. Subjects were screened by telephone; if they qualified as a case or control, they completed the same interview that was used in COGEND and donated a blood sample. Both studies (COGEND and AAND) included measures of basic socio-demographic variables, including age, sex, race/ethnicity, family income, and educational attainment using the Semi-Structured Assessment for the Genetics of Nicotine Dependence. Information on nicotine dependence, as assessed by the Fagerstrom Test for Nicotine Dependence (FTND) is available for all subjects. In addition, participants also completed the Nicotine Dependence Syndrome Scale (NDSS; Shiffman et al., 2004) and the Wisconsin Inventory of Smoking Dependence Motives (WISDM-68; Piper et al, 2004). All subjects were assessed in person by trained research assistants.

  Study phs000813

• Cell and Circuit-Specific Exploration of HIV Neurogenomics in Context of Opiate and Cocaine Misuse

  Utilizing frozen postmortem brain tissue from the Manhattan HIV Brain Bank, we performed cell-type-specific, genome-wide sequencing of three different types: 1) single-nucleus RNA-sequencing (snRNA-seq), 2) in situ Hi-C, and 3) HIV integration site sequencing (IS-seq). We assayed tissue from three different donor types: 1) HIV-uninfected control (HIV-), HIV-infected (HIV+), and HIV-infected with HIV encephalitis (HIVE), who served as a positive control for active CNS viral infection. The diagnosis of HIVE was made by a board-certified neuropathologist on routine histopathology, based on the presence of a microglial nodule encephalitis with characteristic multinucleated syncytial cells and staining for HIV p24 antigen. Other HIV-infected individuals (the HIV+ group) did not have this histologic evidence of active HIV brain replication, and were variable with regard to cART status and virologic control prior to death. Donor characteristics, including ante-mortem plasma HIV loads, CD4 counts, and cART status, were documented. Quality controls were used for each assay: For snRNA-seq we applied filters to remove low quality nuclei from analysis and performed experiments in which mouse tissue was mixed with human tissue to determine a reliable HIV read count threshold. For Hi-C, we used paired HIV- and HIVE samples and processed all samples in parallel, at the same time. Correlation analysis was used to ensure biological replicates were aligned. For IS-seq, each time the assay was run, a positive and negative control were included. This allowed us to ensure a lack of contamination in IS-seq libraries and that, when a particular sample failed to yield library, this was due to low levels of HIV rather than a failure of the assay. Altogether, we built an integrative dataset from 79 sequencing files, including snRNA-seq, and cell type specific Hi-C, IS-seq, and ChIP-seq datasets. Human autopsy brain samples were collected by the Manhattan HIV Brain Bank (MHBB, U24MH100931), using protocols under the supervision of the Icahn School of Medicine at Mount Sinai (ISMMS) Institutional Review Board. Written informed consent was obtained either from decedents or their primary next of kin. Major Findings: Reorganization of open/repressive (A/B) compartment structures in HIVE microglia encompassing 6.4% of the genome was linked to transcriptional activation of interferon (IFN) signaling and cell migratory pathways and was partially recapitulated by IFN-gamma stimulation of cultured microglia. In contrast, decreased expression and repressive compartmentalization of genes regulating neuronal health and signaling was seen in both HIVE and HIV+ microglia. IS-seq recovered 1,221 integration sites in the brain that displayed distinct genomic patterns as compared to peripheral lymphocyte integration and were enriched for chromosomal domains newly mobilized into a permissive chromatin environment in HIVE microglia. Viral transcription occurred in a subset of highly activated microglia comprising 0.003% of all nuclei in HIVE brain. Our findings point to a disruption of microglia-neuronal interactions in the HIV infected brain and an interrelation of retroviral integration and expression with interferon-associated remodeling of the microglial 3D genome during progression to HIVE. Search terms: HIV, postmortem brain, microglia, substance abuse, viral integration, chromosomal conformation capture, Hi-C, chromatin, single nuclei RNA-seq, Irf8, NeuN, HIV encephalitis, neuron-microglia interaction, cytokine

  Study phs003080

• Ongoing Replication Stress Tolerance and Clonal T Cell Responses Distinguish Liver and Lung Recurrence and Patient Outcomes in Pancreatic Ductal Adenocarcinoma

  Patients with metastatic pancreatic ductal adenocarcinoma (PDAC) survive longer when disease spreads to the lung but not to the liver. We generated overlapping, multi-omic datasets to identify molecular and cellular features that distinguish patients whose disease develops liver metastasis (liver cohort) from those whose disease progression results in lung metastasis without liver metastases (lung cohort). Lung cohort patients generally survived longer than liver cohort patients, independent of tumor subtype. We developed a pORG gene signature that distinguishes primary tumors in the liver and lung cohorts. We identified ongoing replication stress (RS) response pathways in high pORG/liver cohort tumors, while low pORG/lung cohort tumors had greater densities of lymphocytes and shared T cell clonal responses. Our study demonstrates that liver-avid PDAC is associated with tolerance to ongoing RS, limited tumor immunity, and less favorable outcomes; whereas low RS, lung-avid/liver-averse tumors are associated with active tumor immunity that may account for favorable outcomes. As expected, we found high frequencies of KRAS, TP53, CDKN2A, and SMAD4 gene alterations in our tumor samples. High pORG activity (GSVA scores) in our primary tumors appear to be positively correlated with alteration frequencies in both TP53 and CDKN2A. We did not see a correlation with alterations in KRAS as almost all the tumor samples have KRAS alterations. From a de-identified dataset of 1,873 patients diagnosed with and/or treated for PDAC at our institution between 2004 and 2020, we identified 422 patients for which we had specimens with sequencing data (N=374) and/or specific evidence of disease metastasis site(s) from the OHSU cancer registry or disease-relevant computed tomography (CT) scans to allow cohort classification. Note that our study includes RNA-Seq, DNA-Seq panel, and TCR-Seq (T-cell Receptor Sequencing) data, but that this dbGaP submission just includes the patients/samples with RNA-Seq and/or DNA-Seq panel data. Therefore, this submission includes 290 samples from 278 patients. TCR sequence data is available on the Adaptive Biotechnologies platform. Clinical course timepoints, patient demographics, stage, grade, nodal involvement, resection margins, and angiolymphatic invasion were provided as deidentified data by the OHSU cancer registry with quality control data verification in a subset by pathologists (BB and TM). We reviewed all available computed tomography (CT) scans for all patients with primary tumor resection dates recorded by the cancer registrar, with tumor samples analyzed by RNA-seq, DNA-seq, or TCR-seq, and/or with additional information indicating metastatic spread (e.g., metastatic samples received for related studies). We abstracted the site of all lesions proven to be metastatic by biopsy and/or that clearly increased in size during progression or decreased in size during treatment as long as a radiologist described the lesion as “likely”, “suspicious for”, “concerning for”, or “favor” metastasis. Clinical imaging was reviewed by a radiologist (AG) to validate patient assignments to the liver, lung, and neither liver nor lung (other recurrence site) cohorts. Time to recurrence was calculated from the earliest of either the recurrence date provided by the OHSU cancer registry, or the date of earliest lesion abstracted from CT reports. The subject attributes reported in this submission were up to date at the time of our data freeze, which occurred July 2021.

  Study phs003597

• Heart Failure Network - Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in Diastolic Heart Failure (HFN RELAX-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. Related StudyEcho images are available through HFN RELAX-ImagingObjectivesThe RELAX trial tested the hypothesis that chronic phosphodiesterase type-5 inhibitor therapy with sildenafil would improve exercise capacity and clinical status in heart failure patients with normal ejection fraction, as compared to placebo. Background Heart failure (HF) with preserved ejection fraction (HFpEF) is a common and highly morbid condition that is characterized by chronic exercise intolerance, progressive functional decline and a high rate of readmission. At the time of the RELAX trial, clinical trials of renin-angiotensin system antagonists had not demonstrated improvement in outcomes or clinical status in HFpEF, and effective therapies were needed. Phosphodiesterase type-5 (PDE-5) metabolizes the nitric oxide (NO) and natriuretic peptide (NP) systems' second messenger cyclic guanosine monophosphate (cGMP), and thus may limit beneficial NO and NP actions in the heart, vasculature and kidney. Pre-clinical studies suggest that inhibition of PDE-5 reverses adverse cardiac structural and functional remodeling and enhances vascular, neuroendocrine and renal function. In clinical studies, PDE-5 inhibitor therapy improved exercise tolerance and clinical status in patients with idiopathic pulmonary arterial hypertension and in patients with HF and reduced ejection fraction (HFrEF). A small, single-center study in HFpEF observed improved hemodynamics, left ventricular (LV) diastolic function, right ventricular (RV) systolic function, LV hypertrophy and lung function with chronic PDE-5 inhibition as compared to placebo. In aggregate, these studies suggested the potential for PDE-5 inhibition to ameliorate several key pathophysiological perturbations in HFpEF, and thus improve exercise capacity and clinical status. ParticipantsA total of 216 participants were enrolled in the trial with 113 in the Sildenafil group and 103 in the placebo group. Design Participants who met screening criteria underwent baseline studies including a history and physical examination, cardiopulmonary exercise test (CPXT), six-minute walk distance, Minnesota Living with Heart Failure Questionnaire (MLWHFQ), echocardiography, cardiac magnetic resonance imaging, and phlebotomy for biomarkers. Subjects were then randomly assigned, in a 1:1 ratio, to either the sildenafil or placebo intervention group. The study drug was administered orally at 20 mg three times daily (TID) for 12 weeks. If the dose was well tolerated at 12 weeks, it was increased to 60 mg TID for another 12 weeks. If side effects developed, study staff could recommend discontinuation or return to a lower or previously tolerated dose of study drug. Sildenafil levels 2 hours after a scheduled dose of study drug were obtained at 12 and 24 weeks. The primary endpoint was exercise capacity determined by change in peak oxygen consumption during the CPXT after 24 weeks of therapy. Secondary endpoints included change in six-minute walk distance at 12 and 24 weeks, change in peak oxygen consumption at 12 weeks, and a three tier score reflective of clinical status where patients were ranked based on time to death (lowest tier), time to cardiovascular or cardiorenal hospitalization (middle tier), and change in the MLWHFQ for patients alive without cardiovascular or cardiorenal hospitalization after 24 weeks (highest tier). Conclusions Chronic phosphodiesterase type-5 inhibitor therapy with sildenafil for 24 weeks did not alter exercise capacity or clinical status compared to placebo in patients with heart failure and preserved ejection fraction.

  Study phs003565

• Resuscitation Outcomes Consortium (ROC) Cardiac Epidemiologic Registry (Cardiac Epistry) Versions 1 and 2 (ROC-Cardiac Epistry 1 and 2-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. Available Data: The ROC Cardiac Epistry versions 1 and 2 include all cardiac arrest cases entered into the ROC database from December 1, 2005 to April 1, 2011. ROC Cardiac Epistry 3 includes cardiac arrest cases captured between 2011 and 2015, and introduced significant changes in how and which data were collected.For ROC traumatic injury Epistry data, please see: ROC-Trauma Epistry Objectives: To build a prospective population-based registry of participants with out-of-hospital cardiac arrest responded to by Emergency Medical Services (EMS). Specific aims: to establish whether the results of Resuscitation Outcomes Consortium (ROC) trials can be generalized to the larger population of participants that experience cardiac arrest;to more fully establish the burden of cardiac arrest; and to examine the relationships between variation in EMS structure and process, regional and periodic factors, and participant outcomes.Background: Cardiac arrest is a common, serious, debilitating and costly public health problem. Although there has been a steady decline in morbidity and mortality from most cardiovascular diseases, high mortality rates for out-of-hospital cardiac arrests continue to pose a challenge for healthcare providers and a significant public health burden. The Resuscitation Outcomes Consortium (ROC) was established in 2004 to conduct clinical research in the areas of cardiopulmonary arrest and life-threatening traumatic injury with the overall goal of improving resuscitation outcomes. Participant and care characteristics can predict favorable outcomes in cardiac arrests, but there is still a wide variation in outcomes that is not well understood. EMS factors such as service level, number of responding providers, use of procedures or drugs in the field, training, quality assurance/feedback, and response time intervals also vary significantly by region. Variations in geographic, socioeconomic and periodic factors may also be associated with differences in outcomes.Prior to ROC Cardiac Epistry, there were no North American population-based registries for out-of-hospital cardiac arrests. Therefore there was a need for standardized data collection of out-of-hospital cardiac arrests in diverse geographic locations in order to identify the independent effects of prognostic or treatment factors accounting for variations in survival. Participants: The registry included 109,326 cardiac arrest events from 264 EMS agencies transporting to 287 acute care hospitals from the following regional centers: Birmingham, Alabama; Dallas, Texas; Iowa City, Iowa; Milwaukee, Wisconsin; Pittsburgh, Pennsylvania; Portland, Oregon; San Diego, California; Seattle/King County, Washington; Ottawa, Ontario; Toronto, Ontario; and Vancouver, British Columbia.Design: ROC Epistry collected standardized data regarding episode-specific factors, participant demographics, clinical information, pre-hospital interventions and disposition, hospital information, and participant outcome for all out-of-hospital cardiac arrests in the ROC regions. Each ROC site had to ensure capture of all eligible cases within the EMS service areas. Out-of-hospital data were extracted from existing databases whenever possible and augmented with targeted review of EMS reports. Hospital data were abstracted directly from the hospital file in most cases. Alternative methods included linkage to death registries and obituaries if the death occurred within 30 days. Sites submitted data using a web-based interface or batch uploads. Participants were not contacted directly.

  Study phs003803

• Study on the Genetics of Alcoholism (COGA): African American Family GWAS

  COGA is a family study of alcoholism, in which the subjects have been drawn from the Collaborative Study on the Genetics of Alcoholism (COGA), a large, ongoing family-based study that includes subjects from seven sites around the US. COGA has gathered detailed, standardized data on study participants, including diagnostic and neurophysiological assessments. This project has already proved successful in identifying several genes that influence the risk for alcoholism and neurophysiological endophenotypes, which have been independently replicated. COGA data were included as part of two Genetic Analysis Workshops, and the phenotypes are familiar to the genetics community. Alcoholic probands were recruited from treatment facilities, assessed by personal interview, and after securing permission, other family members were also assessed. A set of comparison families was drawn from the same communities as the families recruited through an alcoholic proband. Assessment involved a detailed personal interview developed for this project, the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA), which gathers detailed information on alcoholism related symptoms along with other drugs and psychiatric symptoms. Many participants also came to the laboratories for electroencephalographic studies. Neurophysiological features that have been shown to be useful endophenotypes for which we have linkage and in some cases association results are included on a subset of the case-control sample: the beta power of the resting electroencephalogram (EEG), the P3(00) amplitude of the visual event-related potential (ERP), and the theta and delta event-related oscillations (EROs) underlying the P3. As part of COGA, a set of informative African American families were selected to have Genome-Wide Association data obtained within families. Genotyping was performed using the Illumina Omni2.5_080814_1 chip to genotype 3,438 subjects selected from densely affected families. Genotyping was performed at CIDR. This sample complements a set of densely affected European American families previously made available under dbGaP study accession phs000763. In addition, exome sequencing data on a subset of individuals with GWAS were added in version 2.For version 3, a subset had 30X Whole Genome Sequencing (WGS) as part of the NIDA Sequencing Initiative. The subset contained two distinct sets: Sibling pairs where one sibling had at least two dependence diagnoses in the set (alcohol, cannabis, cocaine, and opioid), and the other had none, and non-related Case-Control pairs matched for age and ethnicity where the cases had alcohol and at least 2 other dependence diagnoses and controls had none. After sequencing, some sibling pairs are re-classified as half siblings, Three VCF files (small variants, structural variants, and copy number variations) are provided. Additional substance abuse variables are made available in version 3. We note that the full sample data are deposited in four dbGaP submissions and the sequenced samples are split across all four: CIDR: Collaborative Study on the Genetics of Alcoholism Case Control Study (phs000125). GWAS data on cases (primarily probands) and controls drawn from the families. Families with highest density of alcohol dependence and/or extreme event-related oscillation data (phs000763). GWAS data on 119 extended families of European descent are available here, along with extensive documentation. Study on the Genetics of Alcoholism (COGA): African American Family GWAS (phs000976). GWAS data on all available COGA families of African descent are available. COGA: Smokescreen GWAS (phs001208). GWAS data on all remaining COGA DNA samples, primarily of other racial background, were genotyped on the Smoke Screen array. A listing of all sequenced pairs is provided in the documentation to facilitate the merging of these samples.

  Study phs000976

• FEGA and European GDI: working together to improve human health

  The Federated EGA is a global resource for discovery of and access to sensitive human omics and associated data consented for secondary use, through a network of human data repositories to accelerate biomedical research and improve human health. The Federated EGA network was launched in September 2022 with five inaugural nodes, and since 2023 seven operational nodes can share data across national borders in adherence to European and national laws. A few weeks after FEGA's official launch, in November 2022, the European Genomic Data Infrastructure (GDI) project was kicked-off . This European Commission co-funded project, coordinated by ELIXIR, is aimed to deliver federated, sustainable and secure data infrastructure to access genomic and related phenotypic and clinical data across Europe. This project supports the aim of the 1+MG initiative (25 EU countries, Norway and UK) to enable personalised medicine and health through a shared framework and infrastructure for securely accessing and integrating high quality genomic data and other health data across borders.1+MG will be an integral component of the European Health Data Space (EHDS) for secondary use (Healthdata@EU) as an authorised participant. How are Federated EGA and GDI similar? Given their shared visions, the Federated EGA and European GDI networks have a lot in common. To begin, they share the same overall goal of establishing networks of “nodes” that host sensitive human data within a jurisdiction and connecting these nodes in a global network to support data discovery and promote human genome data access for research and healthcare. FEGA and GDI are both initially focused on enabling access to human genomic data, while FEGA ambition is to expand the scope to clinical research data and other omics data in the future. FEGA and GDI are both built on open and interoperable software solutions, a subset of which are based on the LocalEGA components. FEGA and GDI implementation solutions are based on international community standards, for example those developed by the Global Alliance for Genomics and Health, which contributes to making them interoperable. Both FEGA and GDI allow for the nodes to make use of any solution that is fully compatible. As illustrated in Figure 1, a final key point is the significant overlap of institutions involved in envisioning and operating FEGA and GDI nodes, with the clear wish to keep them interoperable in the future. What distinguishes Federated EGA from GDI? Despite being largely similar, there are some differences between the FEGA and GDI networks, which we aim to clarify in this post. The first difference is the governance model that the nodes will operate to comply with national laws and GDPR. In the FEGA network, nodes have taken inspiration from the EGA data access model, where the infrastructure is a data processor of the hosted data and data controllership remains with the originating Data Access Committees (i.e. Data controllers) for each dataset. On the other hand, in the GDI network the controllership of datasets will be transferred to a 1+MG European Digital Infrastructure Consortium (EDIC) legal entity created by the Member States (MS), who will make data access decisions, with data holders having veto powers for their datasets. Importantly, FEGA nodes have the flexibility to choose another model to fit with their data protection framework, including becoming data controllers for their datasets. The second difference is the inclusion criteria for data. While FEGA nodes are designed to accept almost any type of omics data in need of control access (e.g. genomics, transcriptomics, genotyping, single cells sequencing, patient-tracked metagenomics), GDI nodes are initially, but not only, focused on accepting whole genome and exome sequencing data and affiliated data from sources such as (i) the Genome of Europe use case of the 1+MG initiative which specifically aims to fulfil the mission of building “a European network of national genomic reference cohorts of at least 500,000 citizens”) (ii) data collection of other types of genomic data identified by countries through the 1+MG dashboard and (iii) genomic data coming from data holders which would need to fulfil EHDS requirements. The third difference is the maturity of the software stacks. The FEGA network provides a set of software for data and metadata submission, storage, permissions management, and file distribution (the LocalEGA package). GDI is building a complete set of open source reference for the five functionalities covering the full data life cycle (a few more compared to the LocalEGA) including federated processing (analytics, AI/ML), which is still under active development. Notably, one of the GDI functionalities - storage and interfaces - can be satisfied by using the LocalEGA storage solution, highlighting the ability of FEGA and GDI to be interoperable. In both FEGA and GDI, nodes are allowed to use alternative solutions as long as they are fully interoperable with the networks. Figure 1 provides a simple overview of these described commonalities and differences. Figure1: schematic overview of the main commonalities and difference among a FEGA and a GDI node Can the same institute run a FEGA and a GDI node at the same time? The answer is Yes! We believe this is entirely possible and encouraged. As long as 1+MG requirements are continuously fulfilled in the implementation. The same trained personnel could operate the infrastructure and leverage the same national funding to run a FEGA and a GDI node. Several European nodes, especially GDI vanguard nodes like Norway, Sweden, Finland and Spain are following this model. The idea is that a lot of the work can be reused, given the overlapping scope and the interoperable technology. Thus, the hosted datasets can be discovered via the two catalogues, perhaps accessible under the same or different governance models. From a FEGA node perspective, the GDI datasets could simply be a subset of the datasets hosted in the FEGA node, which have their specific governance as any other. What's next? So, the amazing teams of people building the Federated EGA network and the European GDI network are working together to create infrastructures able to provide secure access to human genomic and associated data around Europe. Because we all know how much human genomics can improve healthcare and precision medicine. And we all want to collaborate to make it happen. This article has been reviewed collectively by members of the EGA and the GDI coordination team.

  Blog fega-and-gdi

• DAC - Comparison of sequencing assays for sensitive detection of circulating tumour DNA in stage IA-IV breast cancer

  Dac EGAC00001002549

• Data Access Committee for data from EGAS00001002864: Genome analysis of oesophageal cancer and Barretts oesophagus

  Dac EGAC00001000863

• This DAC is to control access to data contained within dataset EGAS00001001147, for Myeloma XI clinical trial patients.

  Dac EGAC00001000307

• Centre for Genomic Regulation - Beta Cell Genome Regulation Lab

  Dac EGAC00001000853

• DAC for Clonal somatic copy number altered driver events inform drug sensitivity in high-grade serous ovarian cancer

  Dac EGAC00001002644

• ITER-FIISC Data Access Committee (microbiome)

  DAC for Metagenomic characterization of tracheal aspirates from non-pulmonary sepsis patients

  Dac EGAC50000000230

• DAC Fondazione Michelangelo

  This is the DAC responsible for granting access to sequencing data generated by Fondazione Michelangelo

  Dac EGAC50000000179

• Uni_Wue_Chair of Microbiology

  Dual RNA seq datasets of M2 sequenced macrophages after chlamydia infection for 30 hours.

  Dac EGAC50000000577

• South African Blood Regulatory (SABR) Resource Committee

  Data access committee for the South African Blood Regulatory (SABR) Resource.

  Dac EGAC50000000501

• OICR-DAC, Ontario Institute for Cancer Research; Biliary Tract Cancer

  Next Gen Sequencing data from Biliary Tract Cancers

  Dac EGAC50000000528