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in 36.18 milliseconds.

• 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

• DAC for "Establishment and characterization of circulating tumor cells-derived organoids from metastatic breast cancer patients."

  Dac EGAC00001003514

• Breast Cancer Association Consortium Data Access Coordinating Committee

  Data access committee for the Breast Cancer Association Consortium (BCAC).

  Dac EGAC50000000824

• Synthetic modeling reveals HOXB genes are critical for the initiation and maintenance of human leukemia

  Study EGAS00001003627

• Evaluation of protocols for rRNA depletion-based RNA sequencing of nanogram inputs of mammalian total RNA

  Study EGAS00001003849

• Oncogenic and immunological targets for matched therapy of pediatric blood cancer patients: Dutch iTHER study experience

  Study EGAS00001008218

• Methylation data for Genomic landscapes of endometrioid and mucinous ovarian cancers and morphologically similar tumor types

  Study EGAS00001008175

• Molecular Classification of Lymph Node Metastases Subtypes Predict for Survival in Head and Neck Cancer

  Study EGAS00001003233

• The INFORM Precision Medicine Study for High-Risk Pediatric Malignancies

  Study EGAS00001005112

• DNA methylation and Panel sequencing for pancreatic neuroendocrine carcinomas (PanNECs) and pancreatic neuroendocrine tumors (PanNETs)

  Study EGAS00001005731

• Shallow nanopore RNA sequencing enables transcriptome profiling for precision cancer medicine (Hipo_021)

  Study EGAS00001006317

• The genomic diversity of Taiwanese Austronesian groups: implications for the ‘Into and Out of Taiwan’ models

  Study EGAS00001006911

• CLUC complete genomics dataset

  Complete genomics data for VCaP and PC346c.

  Dataset EGAD00001002069

• GFD viral enrichment sequencing

  sequencing of libraires enriched for viral particles

  Dataset EGAD00001007638

• 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

• 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

• DATA ACCESS AGREEMENT CGNT- GROUP COMMITTEE for project "Exome sequencing of 22 Pheochromocytoma/paraganglioma tumors" at Gothenburg University.

  Dac EGAC00001000411

• DAC for Greenland Studies of University of Copenhagen and University of Southern Denmark.

  Dac EGAC00001000736

• The data access committee for Evolution of neoantigen landscape during immune checkpoint blockade in non-small cell lung cancer.

  Dac EGAC00001000757

• Data Access Committee for the study "Somatic chronology of treatment-resistant prostate cancer via deep whole-genome ctDNA sequencing"

  Dac EGAC00001002479

• Access Committee for Separation, characterization, and identification of individuals from multi-person blood mixtures

  Dac EGAC00001002646

• ASTAR Skin Research Laboratory

  Data Access Committee for A*STAR Skin Research Laboratory, A*STAR Research Institutes, Singapore.

  Dac EGAC50000000109

• Data for paper Mold, Weissman et al. 'Clonally heritable gene expression imparts a layer of diversity within cell types'

  Dac EGAC50000000102

• DAC - organotypic co-cultures @IEO

  DAC for RNAseq data on patient-derived organotypic models (Battistini et al, CDDis 2024)

  Dac EGAC50000000238

• eQTL-CHiC DAC

  The Data Access Committee for eQTL-CHiC and related data from Ray-Jones et al., 2024.

  Dac EGAC50000000445

• DAC for Transcriptomic and genomic profiling of fragile X syndrome unmethylated full mutation carriers

  Dac EGAC50000000416

• DKFZ-HIPO DACO for "Patient-derived tumoroids from CIC::DUX4 rearranged sarcoma identify MCL1 as a therapeutic target"

  Dac EGAC00001003559

• Data Access Committee for Patient-derived tumoroids from CIC::DUX4 rearranged sarcoma identify MCL1 as a therapeutic target

  Dac EGAC00001003558

• DAC_Circadian_Neuroendocrinology

  DAC for projects of the group Circadian Neuroendocrinology at the Department of Endocrinology and Metabolism at Amsterdam UMC.

  Dac EGAC50000000692

• Distinct Phenotypes of Human Intrahepatic and Extrahepatic Bile duct Organoids and their Applications for Biliary Disease Modeling

  Study EGAS00001003792

• Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

  Study EGAS00001005115

• Patient WGS for #198

  Whole genome sequencing of patient tumour and blood

  Dataset EGAD50000000217

• Genotypes_343_Japanese

  Genotype data for 343 Japanese subjects obtained with Infinium Asian Screening Array.

  Dataset EGAD00010002449

• TumorSNP

  SNP data for 473 tumor samples

  Dataset EGAD00010002038

• WGS dataset

  4 WGS bam files for 4 cases with fusion

  Dataset EGAD00001004359

• PDAC

  ctDNA and protein markers for earlier detection of pancreatic cancers

  Dataset EGAD00001004399

• Nimblegen SeqCap Custom Panel Sequencing

  Nimblegen SeqCap Custom Panel Sequencing for pNet

  Dataset EGAD00001005494

• Genomic Sequencing of Triple Negative Breast Cancer - Exome data

  Exome data for study EGAS00001008261

  Dataset EGAD00001015687

• 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

• 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

• 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

• Durvalumab Plus Tremelimumab Alone or in Combination with Low-Dose or Hypofractionated Radiotherapy in Metastatic Non-Small-Cell Lung Cancer Refractory to Previous PD(L)-1 Therapy: an Open-Label, Multicentre, Randomised, Phase 2 Trial

  Brief SummaryThis randomized phase II trial studies the side effects of durvalumab and tremelimumab and to see how well they work with or without high or low-dose radiation therapy in treating patients with colorectal or non-small cell lung cancer that has spread to other parts of the body (metastatic). Immunotherapy with durvalumab and tremelimumab, may induce changes in body's immune system and may interfere with the ability of tumor cells to grow and spread. Radiation therapy uses high energy x-rays to kill tumor cells and shrink tumors. Giving durvalumab and tremelimumab with radiation therapy may work better in treating patients with colorectal or non-small cell lung cancer.Detailed DescriptionPRIMARY OBJECTIVES:I. To assess safety and tolerability of combined checkpoint blockade with MEDI4736 (durvalumab) and tremelimumab alone or with high or low-dose radiation in non-small cell lung cancer (NSCLC). (NSCLC Cohort) II. To compare the overall response (excluding the irradiated lesion[s]) between combined checkpoint blockade with MEDI4736 and tremelimumab alone or combined checkpoint blockade with low or high dose radiation. (NSCLC Cohort) III. To assess safety and tolerability of combined checkpoint blockade with MEDI4736 and tremelimumab with high or low-dose radiation. (Colorectal Cohort) IV. To determine the overall response rate (excluding the irradiated lesion[s]) with combined checkpoint blockade with MEDI4736 and tremelimumab with either low or high dose radiation. (Colorectal Cohort)SECONDARY OBJECTIVES:I. To estimate median progression-free survival and overall survival. (NSCLC Cohort) II. To determine local control within the irradiated field(s) and abscopal response rates. (NSCLC Cohort) III. To evaluate associations between PD-L1 expression as well as levels of infiltrating CD3+, CD8+ T-cells and overall response. (NSCLC Cohort) IV. To explore changes in PD-L1 expression, circulating T-cell populations, T-cell infiltration, ribonucleic acid (RNA) expression, spatial relationship of immune markers, and mutational burden as a result of low or high dose radiation. (NSCLC Cohort) V. To estimate median progression-free survival and overall survival. (Colorectal Cohort) VI. To determine local control within the irradiated field and abscopal response rates. (Colorectal Cohort) VII. To evaluate associations between PD-L1 expression as well as levels of infiltrating CD3+ CD8+ T-cells and overall response. (Colorectal Cohort) VIII. To evaluate changes between PD-L1 expression as well as levels of infiltrating CD3+, CD8+ T-cells induced by targeted low or high dose radiation. (Colorectal Cohort) IX. To explore changes in circulating T-cell populations, T-cell infiltration, RNA expression, spatial relationship of immune markers, and mutational burden as a result of low or high dose radiation. (Colorectal Cohort)OUTLINE: Patients are assigned to 1 of 2 cohorts. NOTE: This Study is only for Cohort 1. Please see phs003294 for Cohort 2COHORT 1: Patients with NSCLC are randomized to 1 of 3 arms.ARM A: Patients receive tremelimumab and durvalumab and as in Arm C. Beginning at week 2, patients receive high dose radiation therapy once per day (QD) over 10 days for up to 3 fractions.ARM B: Patients receive tremelimumab and durvalumab and as in Arm C. Beginning at week 2, patients receive low dose radiation therapy every 6 hours twice per day (BID) on weeks 2, 6, 10 and 14.ARM C: Patients receive tremelimumab intravenously (IV) and durvalumab IV over 60 minutes every 4 weeks for up to 16 weeks in the absence of disease progression or unacceptable toxicity. Patients then receive durvalumab IV over 60 minutes 4 weeks after last combination dose for up to 9 additional doses.COHORT 2: Patients with CRC are randomized to 1 of 2 arms.ARM A: Patients receive tremelimumab intravenously (IV) and durvalumab IV over 60 minutes every 4 weeks for up to 16 weeks in the absence of disease progression or unacceptable toxicity. Patients then receive durvalumab IV over 60 minutes 4 weeks after last combination dose for up to 9 additional doses. Beginning at week 2, patients receive high dose radiation therapy once per day (QD) over 10 days for up to 3 fractions.ARM B: Patients receive tremelimumab intravenously (IV) and durvalumab IV over 60 minutes every 4 weeks for up to 16 weeks in the absence of disease progression or unacceptable toxicity. Patients then receive durvalumab IV over 60 minutes 4 weeks after last combination dose for up to 9 additional doses. Beginning at week 2, patients receive low dose radiation therapy every 6 hours twice per day (BID) on weeks 2, 6, 10 and 14.After completion of study treatment, patients are followed for up to 12 weeks.Official TitleA Phase 2 Study of MEDI4736 (Durvalumab) and Tremelimumab Alone or in Combination With High or Low-Dose Radiation in Metastatic Colorectal and NSCLC Conditions Metastatic Colorectal CarcinomaMetastatic Lung Non-Small Cell CarcinomaStage IV Colorectal Cancer AJCC v7Stage IV Lung Non-Small Cell Cancer AJCC v7Stage IVA Colorectal Cancer AJCC v7Stage IVB Colorectal Cancer AJCC v7 Intervention/Treatment Biological: DurvalumabRadiation: Radiation TherapyBiological: Tremelimumab Other Study ID Numbers NCI-2016-01325NCI-2016-01325 ( Registry Identifier ) (REGISTRY: CTRP (Clinical Trial Reporting Program))17-71910021 ( Other Identifier ) (OTHER: Dana-Farber - Harvard Cancer Center LAO)10021 ( Other Identifier ) (OTHER: CTEP) Molecular Technologies / AssaysCyTOFIHCmIFOlinkRNAseqWES

  Study phs003295

• Whole Genome Sequencing of Two Family Trios with 22q.11.2 Deletion Syndrome

  Two subjects with 22q11.2 deletion syndromes (22q11DS) and their parents were recruited for a whole genome sequencing study to identify candidate genetic modifiers of the various phenotypes seen in 22q11DS. Both probands had a typical 3 megabase deletion on chromosome 22q11.2 but discordant phenotypes.

  Study phs000837

• Understanding the Biology of Language Impairment through Whole Genome Sequencing

  This study involves the whole genome sequencing of a cohort of 390 individuals assessed for language ability, IQ, and behavior as schoolchildren. The primary goal of the study is to identify genes, regulatory elements, and pathways which may underlie variation in language ability within an average population. 

  Study phs002255

• Human Responses to Influenza Vaccination

  The data consist of the DNA sequences of antibody gene rearrangements from peripheral blood human B cells of subjects vaccinated with trivalent seasonal influenza or monovalent pandemic H1N1 vaccine. Multiple replicate libraries of immunoglobulin heavy chain gene rearrangements were prepared from each subject for each time point.

  Study phs000760

• Genetics of Fuchs Corneal Dystrophy

  This grant proposal continues to expand our understanding of the clinical presentation of Fuchs corneal dystrophy, and its underlying genetic basis, which will lead to the development of better therapeutic models and treatment for a common corneal dystrophy that affects 4% of the US population over age 40.

  Study phs001834

• Genome-Wide Association Study on Calcific Aortic Valve Stenosis in Quebec (QUEBEC-CAVS)

  Calcific aortic valve stenosis (CAVS) is a common and life-threatening heart disease with no drug that can stop or delay its progression. A genome-wide association study (GWAS) on 1,009 cases and 1,017 ethnically-matched controls was performed to identify susceptibility genes for CAVS.

  Study phs001492

• Bulgarian Trio Sequencing Study to Identify de Novo Mutations in Schizophrenia

  The primary aim of this study is to whole exome sequence complete Bulgarian trios to identify de novo mutations in schizophrenic probands. We are investigating the rate of de novo mutations in probands as well as looking for enrichment among previously implicated genes and synaptic gene sets.

  Study phs000687

• RNAseq of Sjögren's Syndrome and Healthy Volunteers' Salivary Glands

  Mechanistic genetic and inflammatory endpoints were studied to gain insight into the molecular mechanisms of Sjögren's syndrome. Transcriptomic studies allowed the exploration of molecular alterations that might cause or influence the studied salivary gland pathology and potentially elucidate druggable pathways for intervention.

  Study phs001842

• NHLBI TOPMed: Genetic Causes of Complex Pediatric Disorders - Asthma (GCPD-A)

  The Center for Applied Genomics (CAG) at the Children's Hospital of Philadelphia (CHOP) is focused on addressing the roles of both single nucleotide variants and structural copy number variants, and their functional impact, together with gene-environment interactions and their influence on asthma drug response.

  Study phs001661

• Genomic analysis of high-risk prostate cancer.

  Whole genome sequencing analysis of high risk localised primary prostate cancer samples including metastatic samples was performed across 153 tumour samples with matching germline sequencing (whole blood). Samples were all fresh frozen frozen section confirmed tumour specimens and associated clinical and pathological metadata is available for these specimens.

  Study EGAS00001003088

• Whole exome sequence analysis in sporadic amyotrophic lateral sclerosis

  To identify a susceptible gene for amyotrophic lateral sclerosis, exome sequence analysis was conducted in patients with amyotrophic lateral sclerosis and control subjects to perform whole genome association studies. In this data set, exome sequencing data of 11 patients with amyotrophic lateral sclerosis was included.

  Study JGAS000013

• Single cell RNA sequencing of human cord Blood CD34 Cells

  We performed single cell RNA sequencing and ATAC e valuation of human umbilical cord blood. To explore the differentiation pathway for human lymphoid lineage, our target cells contain CD34-positive cord blood. That population is evaluated by single cell RNA sequencing and single cell ATAC.

  Study JGAS000528

• IBD_Whole_Genome_Sequencing

  Whole genome sequences at 15X depth of patients with Inflammatory Bowel Disease. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002754

• Repeated_clinical_malaria_episodes_are_associated_with_modification_of_the_immune_system_in_children_

  Repeated clinical malaria episodes are associated with modification of the immune system in children.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/.

  Study EGAS00001003167

• Analysis of transcriptomic landscape of iPSC-derived neurons in Williams Syndrome

  To analyse the transcriptomic changes in iPSC-derived neuronal cells derived from individuals with Williams syndrome vs. controls at different stages of neuronal maturation. This was done to better understand how the deletion responsible for WS alters neuronal development thereby resulting in the characteristic WS phenotype

  Study EGAS50000001214

• Resolving_the_Genetic_Architecture_of_Aseptic_Loosening_After_Total_Hip_Replacement

  This is a genome-wide association scan of aseptic loosening after total hip replacement in a Norwegian population. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001001883

• Molecular characterisation of paediatric PDX cells before and after 3D RASTRUM bioprintin

  This dataset is a part of the publication and contains source data for comparative analysis of the molecular (TSO500, RNAseq) characteristics between patient-derived xenograft (PDX) cells from high-risk paediatric neuroblastoma and sarcoma models before and after 3D bioprinting using RASTRUM Bioprinter.

  Study EGAS00001008220

• CD79B expression in DLBCL

  This study proposes a novel association between CD79B expression and cell-of-origin subtypes of DLBCL. Integrative single-cell analyses also revealed a distinct association between germinal center B-cell physiology and the pattern of CD79B expression, which is potentially a key explanation for the variable CD79B expression in DLBCL.

  Study EGAS50000000363

• Genome-wide array data from Eivissan and Menorcan Individuals

  22 and 20 genome-wide array data for Eivissan and Menorcan healthy unrelated volunteers, respectively. The affymetrix Human Origins array was used to genotype these individuals. The variant set was lifted over to the hg38 human genome reference. A single gziped VCF file can be found.

  Study EGAS50000000423

• ESGI___Molecular_diagnosis_for_mitochondrial_disorders_

  The MITOEXME project aims to improve protocols for molecular diagnosis of patients with OXPHOS disorders with a focus on a next generation sequencing methods and to increase the knowledge of pahtophysiological mechanisms by identification of new targets and cellular studies. In this project we will sequence the exomes fo 120 patients.

  Study EGAS00001000164

• Spatial and temporal transcriptome analysis on human skeletal muscle regeneration

  Skeletal muscle carries a unique ability for adaptation as well as regeneration that highly depends on a supportive cellular microenvironment. Here we show that spatial and single-cell transcriptomic analysis in human skeletal muscle is a unique approach to illuminate cellular interactions in the microenvironment.

  Study EGAS50000000182

• Multi-omics analysis of CUD in the VS

  The aim was to molecularly characterize the human ventral striatum in CUD. For this, we generated epigenomic, transcriptomic, and proteomic datasets at the bulk-level, performed integrative multi-omics analyses and further analyzed CUD-associated transcriptomic changes at a cell type-specific level using snRNA-seq.

  Study EGAS50000000623

• An exome sequencing pilot study of HIV elite-long term non progressors and rapid progressors

  A pilot study examining genetic determinants associated with elite long term non progression and rapid progression using exome sequencing of individuals at the extremes of the distribution of HIV control and progression. Data can only be used for HIV related research.

  Study EGAS00001000057

• Exomes of High-risk Prostate cancer

  DNA extracted from tumoral tissue of 39 patients was used for exome sequencing. As a comparison, DNA extracted from blood was used as a non-tumoral control. DNA was captured using the EZ SeqCap v3 exome kit from Roche and sequenced using an Illumina HiSeq instrument.

  Study EGAS00001001015

• Cohort A spatial transcriptomics sequencing

  Cohort A includes glioma tumors of varied grading and pathology from male and female individuals. Fresh frozen glioma tissues for each sample in cohort A were analyzed with the 10X Genomics Visium Spatial Gene Expression protocol. Processed data is available on the NCBI Gene Expression Omnibus database with accession GSE242352.

  Study EGAS50000000954

• Cohort B spatial transcriptomics sequencing

  Cohort B includes IDH-wild type, EGFR amplified, surgically derived glioblastoma tissue samples from male and female individuals. FFPE tissues for each sample in cohort B were analyzed with the 10X Genomics Visium CytAssist protocol. Processed data is available on the NCBI Gene Expression Omnibus database with accession GSE242352.

  Study EGAS50000000956

• Whole_Genome_Sequencing_of_INTERVAL

  15x Whole Genome Sequencing of 15,000 individuals from the INTERVAL study cohort, phase II. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002461

• Tetralogy of Fallot Exome Trios

  Exome sequencing of 30 parent-offspring trios to >50X mean depth, where the offspring has sporadic TOF, to identify potential causal de novo mutations. We will use the exome plus design for pulldown that incorporates ~6.8Mb of additional regulatory sequences in addition to the ~50Mb GENCODE exome.

  Study EGAS00001000071

• Whole_Genome_Sequencing_of_INTERVAL

  15x Whole Genome Sequencing of 15,000 individuals from the INTERVAL study cohort, phase III. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002787

• 2018_ETO_WGS

  This study includes 12 paired samples of Korean AML-ETO paitients.12 AML-ETO tumor samples are paired with each normal(Saliva or BM derived) sequencing data.The results of this WGS study were analyzed by Mutect and GATK for ranking germline variants and cancer variants to make mutational profile matrix.

  Study EGAS00001002804

• Clones derived from early passage tumoroids of colorectal cancer

  We obtained different clones from early passage CRC tumoroids to study mutational signatures specific for truncal or private somatic alterations. These whole genome sequences are part of a larger study on the heterogeneity and evolution of DNA mutation rates in microsatellite-stable colorectal cancer.

  Study EGAS50000000107

• Non-invasive prediction of immunotherapy response (NIPIT) project

  This project aims to find transcriptomic biomarkers for immunotherapy response prediction in several cancer types. So far, we have performed poly-A RNA-seq using whloe blood and peripheral blood mononuclear cells from 33 non-small cell lung cancer patients, collected before and after the start of immunotherapy.

  Study EGAS50000000266

• RNA-sequencing of adult T-cell leukemia/lymphoma samples

  Adult T-cell leukemia/lymphoma (ATL) is a highly aggressive hematological malignancy derived from mature CD4+ T-lymphocytes. In this study, we performed RNA-sequencing (RNA-seq) analysis for ATL samples including 9 primary tumors and 1 ATL cell line to clarify gene expression profiles in this disease.

  Study EGAS00001003575

• Post-zygotic germline mutations in sperm

  These data from sperm were generated to demonstrate detection of post-zygotic mutations in the male germline. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001001700

• Australia_and_New_Guinea_haplotype_phasing_

  Whole-genome sequencing and phasing of admixed Aboriginal Australian genomes and Papua New Guinean genomes using 10x Genomics Chromium technology. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001001853

• Bottleneck_Sequencing_Of_Human_Tissue__Wgs_

  Bottleneck sequencing of human tissue including neurons, cord blood, sperm This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001004066

• Keratinocyte_CRISPR_screens

  Immortalised HaCaT keratinocytes were transduced with Cas9 and the CRISPR-KO v1.1 genome-wide gRNA library. The gRNA library was prepared from genomic DNA isolated 14 days post library transduction. gRNA representation will be compared to the original CRISPR-KO v1.1 library to reveal genes essential for HaCaT survival and growth.

  Study EGAS00001002714

• 2015_AML_ETO

  This study includes 10 paired samples of 2015 Korean AML-ETO paitients.10 AML-ETO tumor samples are paired with each normal(Saliva or BM derived) sequencing data.The results of this WGS study were analyzed by Mutect and GATK for ranking germline variants and cancer variants to make mutational profile matrix.

  Study EGAS00001002897

• 2015_AML_ETO_WGS_additional

  This study includes 2 paired samples of 2015 Korean AML-ETO paitients. 2 AML-ETO tumor samples are paired with each normal(Saliva or BM derived) sequencing data.The results of this WGS study were analyzed by Mutect and GATK for ranking germline variants and cancer variants to make mutational profile matrix.

  Study EGAS00001002898

• The genomic landscape of lung adenocarcinoma in East Asians

  We performed exome sequencing of 210 and transcriptome sequencing of 181 LUADs of Chinese patients. For more details, please see publication "Genomic landscape of lung adenocarcinoma in East Asians" by Chen J. et. al. in Nature Genetics 2020. https://www.nature.com/articles/s41588-019-0569-6.

  Study EGAS00001002941