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• Add Health: Longitudinal Study of a Nationally Representative Sample of Adolescents in Grades 7-12 in the United States during the 1994-95 School Year, Followed into Adulthood with Five Interviews/Surveys in 1995, 1996, 2001-02, 2008, and 2016-18

  The National Longitudinal Study of Adolescent to Adult Health [Add Health] is an ongoing longitudinal study of a nationally representative U.S. cohort of more than 20,000 adolescents in grades 7-12 (aged 12-19 years) in 1994 followed into adulthood with five interviews/surveys in 1995, 1996, 2001-02, 2008, and 2016-18. Add Health was designed to understand how social environments and behaviors in adolescence are linked to health and achievement outcomes in young adulthood. Add Health contains unprecedented environmental, behavioral, psychosocial, biological, and genetic data from early adolescence and into adulthood on a large, nationally representative cohort with unprecedented racial, ethnic, socioeconomic, and geographic diversity. Add Health has a large, multidisciplinary user base of over 50,000 researchers around the world who have published over 3,400 research articles. Add Health is housed at the Carolina Population Center of the University of North Carolina at Chapel Hill. Add Health datasets are distributed according to a tiered data disclosure plan designed to protect the data from the risk of direct and indirect disclosure of respondent identity. Add Health's large sample size, population diversity and rich longitudinal database of psychosocial, physical, and contextual data will permit investigation of an exceptionally broad range of phenotypes with known genetic variation. Prospective longitudinal measures are available to document change over time in each of these phenotypes, as well as change in the social environment and life experiences, making the Add Health sample ideal for understanding genetic linkages with health and behavior across the life course. The original design of Add Health included important features for understanding biological processes in health and developmental trajectories across the life course of young people, including an embedded genetic sample with more than 3,000 pairs of adolescents with varying biological resemblance (e.g., twins, full sibs, half sibs, and adolescents who grew up in the same household but have no biological relationship), testing of saliva and urine for sexually transmitted infections and HIV, and biomarkers of cardiovascular health, metabolic processes, immune function, renal function, and inflammation. Add Health therefore has critical objective indicators of health status and disease markers in young adulthood, well before chronic illness or its complications emerge in later adulthood. Because DNA has been collected on the full sample at Wave IV, it is possible to link genetic profiles with social, behavioral, and biological measures over time from adolescence into adulthood. Add Health sampled the multiple environments in which young people live their lives, including the family, peers, school, neighborhood, community, and relationship dyads, and provides independent and direct measurement of these environments over time. Add Health contains extensive longitudinal information on health-related behavior, including life histories of physical activity, involvement in risk behavior, substance use, sexual behavior, civic engagement, education, and multiple indicators of health status based on self-report (e.g., general health, chronic illness), direct measurement (e.g., overweight status and obesity), and biomarkers. No other data resource with this expanse of genotype and phenotype data on a large nationally representative longitudinal sample with race, ethnic, socioeconomic, and geographic diversity exists. A complete reference guide on study design and accomplishments can be found on the Add Health website: https://cdr.lib.unc.edu/concern/articles/6t053j27s.

  Study phs001367

• Privacy Notice for the Account User

  Privacy Notice for EGA User Account This Privacy Notice explains what personal data is collected by the specific service you are requesting, for what purposes, how it is processed, and how we keep it secure. Note that this service collects personal data directly provided by the user, and also collects personal data from users that is provided by other organisations. 1. Who controls your personal data and how to contact us? European Genome- Phenome Archive - EGA offers a service for permanent archiving and sharing of all types of personally identifiable genetic and phenotypic data resulting from biomedical research projects, jointly managed by European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI) and Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG). EMBL-EBI and CRG represent joint Data Controllers’ of processing of your personal data. They and their Data protection officers may be contacted for data protection queries and for exercising your rights under Section 8. You may contact EMBL-EBI, represented by dr. Thomas Keane, by: email at: tk2@ebi.ac.uk or post at EMBL-EBI, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridgeshire, UK. EMBL’s Data Protection Officer may be contacted by: telephone at +49 6221 387-8590, email at dpo@embl.org, or post at EMBL Heidelberg, Data protection officer, Meyerhofstraße 1, 69117 Heidelberg, Germany. You may contact CRG, whose EGA team is represented by dr. Jordi Rambla de Argila, by: email at jordi.rambla@crg.eu, or post at Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG), Dr.Aiguader 88, PRBB Building, 08003 Barcelona, Spain. CRG Data protection officer may be contacted by: email at dpo@crg.eu post at Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG), C/ Dr. Aiguader, 88, PRBB Building, 08003 Barcelona, Spain. 2. Which is the lawful basis for processing personal data? We process your personal data on the grounds of important public interest. For monitoring your activities on the website, we process your personal data on the grounds of important public interest. Such legal basis is found in Article 5(1)(a) of EMBL Internal Policy No 68 on General Data Protection (hereinafter IP 68), which is equivalent to Article 6 (1)(e) of the EU General Data Protection Regulation (hereinafter GDPR) and upon which personal data are processed for the achievement of the aims laid down in 1973 agreement establishing EMBL, such as the promotion of the cooperation in the fundamental research, in the development of advanced instrumentation and in advanced teaching in molecular biology and dissemination of information. 3. What personal data is collected from users of the service? How do we use this personal data? We collect the following personal data from you: Name Email address Title/Position Organisation Organisational affiliation Username and password (to authenticate access to the system) IP Address (for anonymous usage statistics) We process your personal data: to provide you with the authenticated access to the EGA service (opening and managing submission and distribution account), to publicly publish some aggregate data to facilitate scientific research (e.g. number of accounts, geographic distribution), to better understand the needs of the users and to guide future improvements of the service, to create anonymous usage statistics. If you do not provide us with your personal data we will not be able to open the user account and offer you our services or we will only provide you a subset of functionalities available within the service. 4. Who will have access to your personal data? The personal data will be disclosed to: Authorised staff in the data controller’s institutions acting on data controller`s behalf and instructions (for all user account data), Requested dataset Data access committee – DAC will have access to the name, email, organization, affiliation, title/position of the distribution user account, using it for their own purpose of granting access to their datasets. 5. Will your personal data be transferred to third countries (i.e. countries not part of EU/EEA) and/or international organisations? Distribution user account data are in the process of granting access disclosed to dataset(s) DAC, which might be a recipient in countries outside of the European Economic Area. Insofar as the second joint controller may be subject to GDPR, data transfer to and from the first joint controller (EMBL-EBI) is necessary for important reasons of public interest embedded in the aims of the EMBL and justified in the Article 9(4) of IP 68 (equivalent to Article 49(1)(d) of GDPR) read in conjunction with EMBL`s 1973 establishing agreement and Article 179(2) of the Treaty on the Functioning of the European Union. 6. How long do we keep your personal data? All data are stored for as long as you have the account open. Thereafter the storage is prolonged for as long as our service is live, even if you stop using our services. This prolongation is necessary for further scientific research, to ensure legal compliance and security and to facilitate internal and external audits it they arise. By contrast, the log files for the data categories related to anonymous usage statistics (raw web service logs) are processed only for 30 days and thereafter erased. 7. The joint Data Controllers provide these rights regarding your personal data You have the right to: Not be subject to decisions based solely on an automated processing of data (i.e. without human intervention) without you having your views taken into consideration. Request at reasonable intervals and without excessive delay or expense, information about the personal data processed about you. Under your request we will inform you in writing about, for example, the origin of the personal data or the preservation period. Request information to understand data processing activities when the results of these activities are applied to you. It must be clarified that rights under points 4 and 5 are only available whenever you need support whilst using our website. For other processing based on the grounds of important public interest you cannot exercise your rights to object, rectify or erase your personal data according to the Article 13(2)(a)(b) of IP 68 (equivalent to Article 17(3)(b)(d) and Article 21(6) of the GDPR). 8. Supervisory authority If you wish to complain against the processing of your personal data, you may do so by post at: EMBL Heidelberg, Data Protection Committee, Meyerhofstraße 1, 69117 Heidelberg, Germany, or Autoritat Catalana de Protecció de Dades (Catalan Data Protection Authority), C/Rosselló 214, Esc A, 1r 1a, Barcelona 08008, Spain. Published at: February 6, 2019

  Documentation data-protection/privacy-notice/ega-user-account

• Implementation of the GDPR

  Data Protection 1 About the EGA The European Genome-phenome Archive (EGA) was formally launched in 2008 at the European Bioinformatics Institute (EMBL-EBI), an outstation of the European Molecular Biology Laboratory (EMBL), to address an identified need for archiving and sharing the results of genome-wide association studies from the Wellcome Trust Case Control Consortium. In late 2012, with the signing of a memorandum of understanding (and subsequent formal agreement in 2016) between EMBL-EBI and the Centre for Genomic Regulation (CRG), the EGA formally became a joint project of the two institutes. The two institutes work together to support the EGA services, including supporting submissions, web site, strategic leadership, and data infrastructure developments. 2 EMBL-EBI & GDPR The EGA is co-managed by EMBL-EBI and CRG. EMBL-EBI is an international organisation established by treaty and has certain privileges and immunities (e.g. exemptions from the application of national law) and also may self-regulate its activities (e.g. establish its own institutional legal framework) within the framework of its founding act of 1973. The General Data Protection Regulation (GDPR) is a European Union (EU) regulation that legislates how organisations can share and process personal data of EU citizens. EMBL places great value in maintaining collaboration with researchers who are subject to GDPR. For that reason, it is of utmost importance for EMBL to handle data received from those collaborators in a secure and responsible manner. Mindful of its public mandate and the sensitivity of the data it handles, EMBL has always ensured a high level of data protection in its activities. Since the introduction of GDPR in May 2018, EMBL has established its internal policy on General Data Protection (IP68), exercising its right to self-regulate its operations,., IP 68 establishes a robust personal data protection framework that provides for data protection principles, enforceable data subject rights and oversight and redress mechanisms offering a level of protection comparable with GDPR. 3 CRG & GDPR The Centre for Genomic Regulation (CRG) is an international biomedical research institute of excellence, created in July 2000 and mainly participated by the Catalan Government. It is a non-profit foundation and its mission is to discover and advance knowledge for the benefit of society, public health and economic prosperity. The CRG is a CERCA center. CERCA is the collective organisation for all research centres of excellence in Catalonia. CERCA ensures these centres develop successfully by promoting synergies and strategic cooperation improving their visibility and the impact of their research and promoting the dialogue amongst both public and private stakeholders. As a legal entity based in Spain and operating within the EU, the CRG ensures the compliance with the GDPR and the legal regulations on personal data protection applicable at the national level, as well as any other legislation that may replace, modify or supplement the above-mentioned in terms of personal data protection. 4 EGA & GDPR EGA GDPR Schema 4.1 Genetic and phenotypic data Within GDPR, there are two main actors: data controllers and data processors. Data controllers are persons or entities which determine the purposes and means that the personal data may be processed, e.g. companies, researchers, or universities. For EGA, the data controller is ultimately the data producer and the submitter(s) who submit the data to EGA. The data controller also creates a Data Access Committee (DAC) who will decide on data access permissions at EGA. Data processors are the persons or entities which process the data on behalf of a data controller. With regard to GDPR, EGA is a data processor as it processes data as instructed by the data controller. GDPR applies to any organisation which accesses personal data from an individual within the EU. Under GDPR, personal data is defined as any data that is identifiable, including names and email addresses as well as health-related and genetic data. EGA does not accept personally identifiable data except genetic and phenotypic data, so all other data submitted to EGA, such as names and addresses, must be pseudonymised. GDPR requires that data controllers implement data protection principles, such as data minimisation, to minimise the risk of data leakage, and protect the rights of the data subjects. As a data processor, EGA has a set of security policies that are followed to minimise the risk of unauthorised data access or data loss. In its role as a data processor, EGA requires all submitters to sign a Data Processing Agreement (DPA) when the submission account is first created. This agreement is only required to be signed once per submitter, and will remain valid for future submissions to EGA. 4.2 Other personal data The EGA also collects personal data as part of our interactions with submitters, data access committees, and researchers accessing data distributed by EGA. The below privacy notices explain what personal data is collected by the specific service you are requesting, for what purposes, how it is processed, and how we keep it secure. Privacy Notices for EGA Title Version Last Updated EGA Data Access Committee Account Privacy Notice for EGA Data Access Committee Account 1.0 February 6, 2019 EGA User Account Privacy Notice for EGA User Account 1.0 February 6, 2019 EGA Helpdesk Service Privacy Notice for EGA Helpdesk Service 1.0 February 6, 2019 EGA Website Service Privacy Notice for EGA Website Service 1.0 February 6, 2019 Documentation Title Version Description EGA Security Overview Security Document 1.1 The EGA Security Document provides an overview of EGA’s practices in ensuring the security of data stored at EGA. EGA Data Processing Agreement Data Processing Agreement 1.5 The Data Processing Agreement must be completed and returned as part of the submission process. Please note that this document is non-negotiable. Authorised Submitters Authorised Submitters Formulary 1.0 The Authorised Submitters Form must be completed and returned as part of the submission process. Please list all those that should have access to the submission account in order to submit to the EGA should be detailed here. Dispute Resolution Any controversy or claim arising out of, or relating to, the DPA (including the enforceability or breach thereof, any question regarding its existence, validity or termination) or relating to the EGA Service shall be resolved using the internal dispute resolution mechanisms of EGA including those related to Data Protection. The EGA’s internal dispute resolution mechanism has the following procedure: EGA OPERATIONAL PHASE: Meetings between EGA staff and the Data Controller.LEGAL MANAGEMENT PHASE: Meetings between legal teams of EMBL, CRG and the Data Controller. DIRECTION MANAGEMENT PHASE: Negotiation between the legal representatives of EMBL, the CRG and the Data Controller. If the internal dispute resolution mechanism doesn’t resolve the controversy or claim the next phase is:ARBITRATION PHASE: Resolution by arbitration under the WIPO Expedited Arbitration Rules (“Rules”).

  Documentation about/privacy-notice

• National Cancer Institute (NCI) TARGET: Therapeutically Applicable Research to Generate Effective Treatments

  The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) Initiative seeks to accelerate research in novel marker and drug development, along with understanding the molecular basis of pediatric malignancy, through identification of genomic changes associated with the following childhood cancers: Acute Lymphoblastic Leukemia (ALL) - A fast-growing type of blood cancer in which too many immature white blood cells are found in the blood and bone marrow. Acute Myeloid Leukemia (AML), including AML Induction Failure cases (AML-IF) - Another type of blood cancer marked by too many myeloblasts, an alternate type of immature white blood cell, are found in the blood and bone marrow. Upon diagnosis, AML patients without high-risk genetic markers undergo standard chemotherapy regimen, called the primary induction phase, to eliminate most cancer cells and induce a remission state. Clear Cell Sarcoma of the Kidney (CCSK) - A rare type of kidney cancer, in which the inside of the cells looks clear when viewed under a microscope. Clear cell sarcoma can spread from the kidney to other organs, most commonly the bone, but also including the lungs, brain, and soft tissues of the body. Neuroblastoma (NBL) - Cancer of cells of the sympathetic nervous system. Cancer Model Systems (MDLS), including PPTP - MDLS includes acute leukemia cell lines and xenografts with matched primary and/or relapsed tumor information, NBL cell lines and xenografts with matched controls, kidney tumor cell lines, normal brain tissues, and embryonic stem cell lines that were sequenced for some TARGET disease projects. Osteosarcoma (OS) - A cancer of the bone that primarily affects children and adolescents. Rhabdoid Tumor (RT) - A rare, fast-growing cancer that usually forms in the kidney or central nervous system (the brain and spinal cord) but can also form in soft tissues in other areas of the body. Rhabdoid tumors tend to spread quickly, are hard to treat, and have a poor prognosis. Wilms' Tumor (WT) - A cancer of cells in the kidney that can spread to the liver, lung and lymph nodes. More information can be found about each TARGET subproject by following the links on the top of this page. Together these cancers account for the majority of the more than 10,000 childhood cancer cases diagnosed in the United States each year. TARGET has employed a set of advanced and complementary genome analysis technologies, including large scale 2nd and 3rd generation genome sequencing, to strategically characterize alterations in both gene expression and in genomic structure (such as deletions and amplification) that are involved in childhood cancers. The goal of this coordinated effort has been to create a comprehensive genomic and transcriptomic profile of each cancer. Integrated analysis of the TARGET data has identified those genes that are either altered in their expression level or mapped to the chromosome regions of deletion/amplification/translocation, as these genes represent strong candidates for therapeutic targeting. To learn more about the TARGET project, which is now completed, visit the website at https://www.cancer.gov/ccg/research/genome-sequencing/target. TARGET data is accessible at the NCI's Genomic Data Commons (GDC) via the GDC's Data Portal and from each of the TARGET Publication Pages at the GDC (Please see the "Supplemental Links" section of any TARGET publication's "Publication Information and Associated Data Files page" at the GDC.). Available datasets include raw sequencing data, datasets generated by the original TARGET research teams (fully annotated clinical information and higher-level/analyzed molecular characterization data - gene expression, copy number variation, epigenetics, targeted sequencing), as well as higher level data generated by the GDC. Please see the TARGET Use and Publication Guidelines for updated details on the sharing of any TARGET substudy data, including how to cite TARGET.

  Study phs000218

• The UCSD / O'Connor "TSP" (Twin/Sibling/Pedigree) Resource in Hypertension

  Study Design: The UCSD / O'Connor "TSP" (Twin/Sibling/Pedigree) Resource in Hypertension contains N=797 participants. At the time of recruitment individuals ranged from 15-84 years of age and had varying ethnic backgrounds (Whites, African-Americans and Hispanics). The cohort was developed to study the genetic basis of autonomic dysfunction in human essential hypertension (reviewed in Zhang et al. Curr Hypertens Rep. 2011 13: 36-45. PMID: 21104344) and contains cross-sectionally collected phenotypes related to blood pressure regulation.  Phenotypes: Phenotype data specifically focuses on the sympathetic branch of the autonomic system, which is a key regulator of blood pressure. Data includes circulating concentrations of the secretory "quantum" (catecholamines, neuropeptides, chromogranins) which influence vascular responses to sympatho-adrenal activation resulting in blood pressure. Other measures on the twins and a subset of family members include a standard urine panel with absolute concentrations of potassium, sodium, uric acid, phosphorous; DynaPulse acquired heart rate and blood pressure measures including systolic and diastolic blood pressure, mean arterial pressure, heart rate, Left Ventricular (LV) ejection time, LV contractility, cardiac output, stroke volume data including; and absolute concentrations of plasma aldosterone, endothelin, leptin, and insulin. Phenotype data is included for the 697 individuals sequenced here and 100 family members.  Whole Genome Sequence Data Generation: Whole genome sequencing (20X coverage) was conducted on plasma-derived DNA. The WGS was generated using the Illumina HiSeq X Ten sequencing platform with variants called by Human Longevity, Inc (HLI; Ref 1). Preparation of sample libraries was conducted by mechanical shearing of gDNA (100-800 bp), isolation of DNA fragments 300-500bp in size, followed by library PCR amplification employing high-fidelity, low-bias PCR to amplify library fragments consisting of appropriate adapter sequences on both ends. BAM files were created by alignment of sequence reads to the reference genome (GRCh38) using iSAAC-01.14.02.06 (Python2.7). Note: The user may want to realign the sequence data and recall variants. In total, the WGS dataset contains 30 monozygotic twin pairs represented by one member of the twin pair, 133 monozygotic twin pairs with both individuals sequenced, 7 dizygotic twin pairs represented by one member of the twin pair, 67 dizygotic twin pairs with both individuals sequenced, 8 twin pairs with undetermined zygosity (both individuals sequenced) and 1 triplet with mother (all individuals sequenced). The dataset also contains sequences of 260 family members of twins including their mother, father, non-twin sibling, and offspring. Table 1 describes the composition of families ("sibling" refers to a non-twin sibling in the cohort). Note: We suggest users check gender and family relatedness. Table 1: Description of the families with sequence data                                                                                                               Units with:Twin PairsTwin (no pairs)No twinSibshipsMother and Father511Mother only1222Father only113One or more siblings22133Mother and siblings132Father and siblings(s)211Offspring of twins11Non twin siblings2Sib with unknown relative1Only one member of8The UCSD/O'Connor "TSP" (Twin/Sibling/Pedigree) Resource in Hypertension is now being managed at the UCSD School of Medicine's Institute for Genomic Medicine (IGM). The TSP Resource has collected a wide range of information (phenotypic, genotypic, demographic, metabolomic) and reagents (blood, plasma, urine, DNA, human induced pluripotent stem cells). For additional information about the resource, including other data types available as well as references and links to data from studies with previous collaborators, please go to the resource website at IGM [http://igm.ucsd.edu/research/twins-resource.shtml] or contact Dr. Brinda Rana at UCSD [bkrana@ucsd.edu]. 

  Study phs002230

• How to use EGA Webin?

  EGA Webin EGA Webin serves as a platform for registering metadata for array based submissions, large scale sequence submission as well as for legacy EGA submission accounts (ega-box-XXXX). For large scale submitters of sequence data you have also the option to submit metadata via XMLS programmatic submission, while new submitters are advised to utilise the Submitter Portal for their submissions. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. WEBIN actions: Register metadata for a sequence submission Register study, samples, experiments, runs, DAC, policy and dataset/s after file upload. Register components for your array-based metadata submission Register study, samples, DAC or policy before uploading files. Edit existing submission metadata Change or update previously submitted metadata. Register metadata for a sequence submission Ensure that all sequence files have been encrypted before uploading them to your submission account using the EgaCryptor. Go to the EGA Webin and log in using your submission account name (ega-box-XXX) and password. Register components of your metadata submission Study Samples Data Access Committee (DAC) Data access policy Dataset For array-base submissions: Study, Samples, Data Access Committee (DAC) and Data access policy may all be registered BEFORE file upload and dataset registration through the array-base template. Register your Study Go to the “Studies (Projects)” box Click on “Register Study” and fill in the information related to your study. Click on “Submit”, this will save the information and generate an EGA study ID. To use the study accession number in a publication, the study has to be previously released on the EGA website, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX.Further information about EGA can be found on https://ega-archive.org and “The European Genome-phenome Archive in 202 "(10.1093/nar/gkab1059)" Register your Samples Go to the “Samples” box Click on “Register Samples” Select “Download spreadsheet to register samples” and customise your template, there is a default EGA template (EGA default checklist) but more attributes can be added if required. For the EGA default checklist, there are mandatory,recommended and optional attributes. As well custom fields which can be added if required. Mandatory attributes Field Name Description tax_id Taxonomy ID of the organism as in the NCBI Taxonomy database. Entries in the NCBI Taxonomy database have integer taxon IDs. See our tips for sample taxonomy here scientific_name Scientific name of the organism as in the NCBI Taxonomy database. Scientific names typically follow the binomial nomenclature. For example, the scientific name for humans is Homo sapiens. sample_alias Unique name of the sample. If not selected system will auto generate an unique alias sample_title Title of the sample sample_description Description of the sample phenotype *** Where possible, please use the Experimental Factor Ontology (EFO) to describe your phenotypes. Recommended attributes Field Name Description subject_id Identifier for the subject where the sample has been derived from gender * Sex Optional attributes Field Name Description sex sex of the organism from which the sample was obtained disease_site Affected organ sample type Affected organ donor_id ** Identifier of the donor where the sample has been derived from *Gender should be described as 'male', 'female' or 'unknown'. If 'unknown' due to a known sex chromosome aneuploidy, please create a user defined attribute called 'Sex chromosome karyotype' and add the appropriate value, for example, 'XXY'. **Donor id (Subject id) should be a de-identified subject handle. If unknown, please add 'unknown' to the field. ***Phenotypes should, where possible, be an Experimental Factor Ontology accession. If a term cannot be found to describe your phenotype please use free text. All sample phenotypes considered important for further analysis of the data should be provided (for example, tumour type), additional phenotype attributes can be created by defining your own attributes; use the notion 'phenotype2', 'phenotype3', etc. After you have customised the fields for the sample submission, download the template and fill in the information. Example of the sample template: Finally upload the sample template to get the EGA accession IDs for the samples. Register your Data Access Committee (DAC) Further information on the role of your DAC Go to the “Data Access” box Click on “Register Dacs” Input the information about the DAC and register at least one main DAC contact. Register your Data Access Policy Your Data Access Policy provides the terms and conditions of data use, this is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAC). Go to the “Data Access” box Click on “Register Policies” Select the DAC to which this policy will be linked to and fill in the policy information. Submitting your Runs and Analyses This section is only for sequence data submission, for array-based submission it can be skipped. Please refer to our Submitting array based metadata Runs Registration Go to the “Raw Reads (Experiments and Runs)” box Click on “Submit Reads” Select “Download spreadsheet template for Read submission” Select the template corresponding to your submission type For the templates you have the option to customise the optional fields. To check their description click on “Show Description “ Download the template and fill in the required information. Example of the runs template: We recommend that Fastq, BAM, and CRAM read files are submitted using Webin-CLI When using this interface instead of Webin-CLI, raw sequences must be uploaded in one of the supported data formats before they can be submitted. The files can be uploaded using FTP or Aspera. The study and the sequenced samples must be pre-registered before the raw reads are submitted. Please note that each individual study and sample should be registered only once. You will be asked to provide information about the sequencing libraries and instruments. Submitting your Dataset This section is only for sequence data submissions, for array based submissions it can be skipped. Please refer to our Submitting array based metadata The dataset describes the data files, defined by the run (EGARXXXXXXXXXXX) and analysis (EGAZXXXXXXXXXXX) accessions that make up the dataset and links the collection of data files to a specified Data Access Committee and Data Access Policy. As a result, you must have registered your Reads and experiments, Data Access Committee (DAC) and Data access policy before submitting your Dataset. Please consider the number of datasets that your submission consists of, for example, a case control study is likely to consist of at least two datasets. In addition, we suggest that multiple datasets should be described for studies using the same samples but different sequence technologies. Please contact EGA Helpdesk for further assistance. Go to the “Data Access” box Click on “Register Dataset” Select the Data Access Committee (DAC) and Data access policy Register your dataset After submitting your dataset you should contact the EGA Helpdesk to provide a release date for your dataset. Datasets are automatically held (i.e. not released) unless they are affiliated to a study that has already been released. Edit/update existing submission metadata Go to the “Report” section of the object you would like to edit. Locate the object and click on the arrow under action. An option menu will be displayed. Objects can be edited through their XML or with the WEBIN menu. After an object has been edited, changes would be available on the website until the submission is released again. Please contact the EGA Helpdesk if you require further assistance.

  Documentation submission/metadata/submission/EGA_webin

• Best Practices for DACs

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

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

• National Cancer Institute Cancer Genome Characterization Initiative (CGCI)

  The Office of Cancer Genomics at the National Cancer Institute sponsored a series of studies as part of the Cancer Genome Characterization Initiative (CGCI) to assess novel emerging sequencing technologies in cancer. The CGCI program included comprehensive characterization of the genetic aberrations found in different pediatric and/or adult tumors. CGCI characterized a number of B-cell non-Hodgkin lymphomas (including diffuse large B-cell lymphoma (DLBCL) from patients with and without HIV+ infection, follicular lymphoma (FL), as well as adult and pediatric Burkitt lymphomas), and additional HIV-associated tumors (including DLBCL, lung, and cervical cancers). All data from these projects is available at the NCI Genomic Data Commons. Individual project descriptions are available by disease on the substudy pages (links are included at the top of this page). Brief summaries are as follows: Non-Hodgkin Lymphoma (NHL) - CGCI investigators probed genomic alterations more deeply than had previously been possible by using state-of-the-art RNA sequencing (mRNA-seq) and whole genome shotgun sequencing (WGS) coupled with leading edge bioinformatics, data management and analysis approaches. The project sequenced tumor DNA and/or RNA from 117 NHL tumor samples and 10 cell lines. This included the genomes or exomes of 1 Follicular Lymphoma (FL) and 13 diffuse large B-cell lymphoma (DLBCL) cases, all with matched constitutional DNA sequenced to comparable depths, RNA-sequencing (mRNA-seq) of 92 DLBCL, 12 FL and 8 B-cell NHL cases with other histologies and 10 DLBCL-derived cell lines. The DLBCL cases and cell-lines are from the two major subtypes of DLBCL: germinal center B-cell (GCB) and activated B-cell (ABC). HIV+ Tumor Molecular Characterization Project (HTMCP) - This project was a joint effort of the Office of Cancer Genomics (OCG) and the Office of HIV and AIDS Malignancy (OHAM). Its goals were to characterize HIV-associated cancers (obtained from HIV-infected patients) and compare them to the same types of cancers from patients without HIV infection. Investigators performed 30X genome sequencing of 100 cases of paired tumor and germline DNA, along with transcriptome sequencing in each of 3 types of HIV+ tumors (DLBCL, lung and cervical cancers). These platforms allow discovery of mutations both in coding and non-coding genomic regions, gene expression and genomic alterations (including translocations, insertions and deletions). Comparing tumors of cancer patients both with and without HIV-infection provides insight into the potential function of this virus in certain cancers. Burkitt Lymphoma Genome Sequencing Project (BLGSP) - This project was a collaborative effort between the National Cancer Institute and the Foundation for Burkitt Lymphoma Research to develop a databank of the many alterations found in Burkitt lymphoma (BL), an uncommon type of Non-Hodgkin lymphoma that occurs most often in children and young adults. The goal of the BLGSP was to explore potential genetic changes in patients with BL that could lead to better prevention, detection and treatment of the cancer. The project characterized the alterations of the tumors' genomes (with matched normal as control) and transcriptomes by sequencing the DNA and RNA of each case. Using the data generated, the ultimate goals of the project was to discover the molecular changes that are present in BL patients and then determine how those changes correlate with treatment regimen and outcome. CGCI data is accessible at the NCI's Genomic Data Commons (GDC) via the GDC's Data Portal and from each of the CGCI Publication Pages at the GDC (Please see the "Supplemental Links" section of any CGCI publication's "Publication Information and Associated Data Files page" at the GDC.). Available datasets include raw sequencing data, datasets generated by the original CGCI research teams (fully annotated clinical information and higher-level/analyzed molecular characterization data), as well as higher level data generated by the GDC. Please see the CGCI Use and Publication Guidelines for updated details on the sharing of any CGCI substudy data, including how to cite CGCI.To learn more about the CGCI studies, please visit the CGCI Program website.

  Study phs000235

• Security Overview

  Security Overview The European Genome-phenome Archive (EGA) houses consented human data under controlled access. Access decisions are handled by the relevant Data Access Committee (DAC), which autonomously manages requests through the DAC Portal. This document provides an overview of EGA’s practices in ensuring the security of data stored at EGA. As security is a prime concern of the EGA, the EGA is a member of the Global Alliance for Genomics and Health (GA4GH) Data Security work stream. The EGA contributes and helps develop the recommendations outlined the GA4GH Security Technology Infrastructure document, which defines guidelines, best practices, and standards for building and operating an infrastructure that promotes responsible data sharing in accordance with the GA4GH Privacy and Security Policy. Explore the EGA dataflow to gain deeper insights! Accessing data in the EGA involves several steps. First, users need to create an EGA account. Once logged in, they can request access to data controlled by a DAC through the EGA website (see documentation). The DAC oversees these requests through the DAC Portal, and if approved, grants the necessary permissions to the user's EGA account, allowing them to access and download all relevant data and metadata for the requested dataset(s). The key points of EGA security strategy are: 1 Regular Risk Assessment The EGA regularly identifies and assesses risk related to the following: Breach of confidentiality,Breach of privacy or autonomy,Malicious or accidental corruption or destruction of data archived at EGA,Disruption of services provided by the EGA. 2 Risk mitigation The EGA implements and maintains safeguards to minimise the risks identified above in accordance with the 6 control objectives listed in Appendix 1 and outlined in the GA4GH Security and Infrastructure document.If a breach is discovered, the EGA applies a defined protocol to minimise damage. 3 Identity and authorisation management The EGA authenticates the identity of individuals or software accessing controlled access data held at the EGA.The EGA ensures an appropriate level of assurance (LoA) is applied to the identity consistent with the risk associated with that individual, such as multi-factor authentication for DACs.The EGA provides the minimum access rights and privileges consistent with the user’s identity, allowing access consistent with the GA4GH Privacy and Security Policy, as determined by the appropriate DAC. 4 Audit Logs The EGA maintains a set of logs recording: Changes to user access rights,Data access requests,Resource usage. 5 Cryptography, communication security, and data integrity The EGA ensures data transmission integrity using a hash function.All data transmitted to or from the EGA is end-to-end encrypted.All data at EGA is stored using strong encryption.Encryption keys are not stored in the same system as the encrypted data.All data archived at EGA must be accompanied by a signed submission statement ensuring appropriate consent or ethical approval has been obtained, and is in accordance with all applicable laws and regulations. The EGA has a defined protocol defining the response in the event of a security breach, and is continuing to work with the GA4GH Data Security Work Stream to help define best practice and associated standards for breach responses. Appendix 1 GA4GH Control Objectives Control Objective 1: Implement technology safeguards to minimise the risk of unauthorised access, use, or disclosure of confidential and private data.Control Objective 2: Implement technology safeguards to minimise the risk of discovery, access, and use of individuals’ clinical and genomic data, and individual identities, other than as authorised by applicable jurisdictional law, institutional policy, and individual consents.Control Objective 3: Implement technology safeguards to minimise the risk of accidental or malicious corruption or destruction of data.Control Objective 4: Implement technology safeguards to minimise the risk of disruption, degradation, and interruption of services enabling access to data.Control Objective 5: Implement technology safeguards to minimise the risk of potential security attacks and misuse of authorised accesses and privileges.Control Objective 6: Implement technology safeguards to promptly detect the failure to attain the above control objectives and to respond with proper countermeasures.Appendix 2Refer to the document below to learn more about EGA long-term data preservation policy and procedures at EMBL-EBI.Long-term data preservation

  Documentation about/security

• Cincinnati Children's Hospital Medical Center (CCHMC) - eMERGE Phase IIIA Data

  This submission includes genotyping or sequencing data from separate cohorts, each is described in separate paragraphs below. Extreme early onset obesity Obesity is a serious epidemic condition and on the rise in the United States. Today, nearly one out of three children is overweight or obese in this country. According to the Center for Disease Control, 35.7% of American adults and 17% of American children are obese. The medical costs associated with obesity are estimated to be in the billions. Without a doubt, interplay of additive genetic effects and common environmental effects influence this complex disease. However, despite being exposed to so-called "obesogenic environment", a large proportion of the population remains of normal weight. These observations suggest that innate, non-environmental, factors make some individuals more susceptible to obesity providing support for biological mechanisms, and thus genetic factors, to underlie the individual's response to the obesogenic environment. In young children with severe obesity the relative role of genetics and in utero programming are likely to outweigh the short duration of environmental and lifestyle exposures. This group is therefore an ideal one to study as they are likely enriched for variants that influence the risk of developing obesity. The purpose of this project is to further study and understand obesity in childhood and to develop a repository of samples for future studies into obesity. Eosinophilic Esophagitis (EoE) Eosinophilic Esophagitis (EoE) is one of the manifestations of eosinophilic gastrointestinal inflammation which have profound effects on a patient's health and development. Results of epidemiologic studies performed through our center demonstrate that eosinophil-associated gastrointestinal disease is not an uncommon entity. While the epidemiology of eosinophilic esophagitis has not been thoroughly studied until recently, there appears to be a significant increase in the diagnosis of EoE in the last decade. Based on our research, this mainly reflects increased disease recognition, but there is also a bona-fide increase in disease incidence which coincides with the increasing incidence of asthma and allergic diseases in the industrialized world. In addition, many patients with intractable symptoms thought in the past to represent atypical GERD or other disorders are now being recognized as having EoE. Diagnosis of EoE requires endoscopy and biopsies to document the characteristic histologic findings of esophageal eosinophilia. In general, this study proposed to elucidate the mechanisms underlying eosinophil growth, survival, migration, and function, and to investigate and further characterize the pathophysiology of, clinical manifestations of, and spectrum of disease severity of eosinophilic esophagitis in humans. The de-identified genotyping and genome wide association data generated as part of this research will be used for further genome research. Familial Sample Repository (FSR) and Directed Sample Repository (DSR) De novo mutations could cause many diseases, which has been demonstrated in mental retardation, autism and many rare genetic disorders. Family-based studies have a variety of advantages over case/control studies, including the elimination of analysis artifacts related to population stratification, the detection of genes that act through a recessive mechanism of inheritance and validation that the trait is not transmitted from a parent, something not possible using a case/control design. Additionally, DNA from families can be used to identify de novo mutations suggesting strong candidate causal polymorphisms. For this project, samples will be collected from families on an on-going basis. Families may be recruited because the patient either has a disease which is thought to be of genetic origin or from the general patient population to serve as controls or future identified diseases. Some phenotypes under study include fibroblastic rheumatism, diaphragmatic hernia, polymicrogyria, severe congenital neutropenia, primary sclerosing cholangitis and staph infection. CLRR-Cincinnati Lupus Registry and Repository Systemic lupus erythematosus (SLE) is a complex, partially understood autoimmune disorder. Genetic origins for SLE are supported by high heritability (> 66%), familial aggregation, increased monozygotic twin concordance, genetic linkages, and candidate gene genetic association, including HLA genes, Fc receptors, and complement components. Relevant environmental factors likely include infections (Epstein-Barr virus), therapeutics, personal habits (smoking), and diet. To continue a research resource facility for collection of well-characterized pedigrees containing a proband with systemic lupus erythematosus we develop this repository. Juvenile Idiopathic Arthritis (JIA) Juvenile Idiopathic Arthritis (JIA) is a debilitating complex genetic disorder characterized by inflammation of the joints and other tissues and shares histopathological features with other autoimmune diseases. It is considered complex genetic traits. There are more than 50,000 children with JIA in the USA, approximately 1 per 1000 births, which is about the same incidence as juvenile diabetes. It is believed that genes in the major histocompatibility complex (MHC) play a role in defining genetic risk, and it can be hypothesized that loci in other chromosomal regions are involved in conferring risk in JIA. These candidate chromosomal regions can be identified using genome-wide association analyses. The long-term goal is a comprehensive understanding of the genetic basis of these disabling arthropathies for which the molecular basis is not presently understood. These data will contribute to a national resource for the study of autoimmunity in children. Better Outcomes for Children-Cytogenetics Since 2007, more than 4000 samples, enriched with various rare or common genetic diseases as well as specific chromosomal abnormalities such as deletions and duplications have been genotyped for the purpose of subsequent GWAS and Phewas analyses and uncovering main genetic effects.

  Study phs001011