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

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

  Study phs001991

• Smith-Lemli-Opitz Syndrome: A Longitudinal Clinical Study of Patients Receiving Cholesterol Supplementation

  Smith-Lemli-Opitz syndrome (SLOS) is a disorder of cholesterol production by the body. It is caused by changes in the DHCR7 gene, which is the blueprint for an enzyme called 7-dehydrocholesterol- delta7-reductase. This enzyme is necessary for the production of cholesterol by all cells in the body. People with SLOS often have malformations of major organs, slow growth, feeding difficulties and intellectual disability or learning problems. Because patients with SLOS cannot make enough cholesterol, it has been proposed that cholesterol supplementation (either with egg yolk or liquid suspensions of cholesterol) could help improve the symptoms of the disease. However, despite the widespread use of cholesterol supplementation, it is still not known whether it works or not. The study will try to provide an answer to this question by studying the disease and its progression while patients are receiving cholesterol. The clinical features of SLOS are thought to be related to low cholesterol and buildup of toxic cholesterol precursors (substances from which cholesterol is formed). But how exactly low cholesterol and toxic precursors contribute to the disease is poorly understood. This knowledge is critically important because it should help discover new therapeutic targets and develop treatments of the disease in the long run. The study will try to fill this gap with a comprehensive clinical and biochemical testing of the study participants over the course of several years. Last, a limitation of previous SLOS research studies has been their low number of participants. This is understandable because SLOS is a rare diseases and few research groups are working on it. However, in order to fully understand the disease, researchers need to study as many patients as possible. This study is unique in that it is run by a network of several highly specialized clinical research sites across the country. Having several sites involved increases the researcher's ability to recruit and study large number of patients, and centralize patients' information in a comprehensive SLOS clinical registry. This registry will be key to identify markers for diagnostic testing, screening and measuring outcomes in future studies of treatment. The purpose of this study is to learn as much as possible about Smith-Lemli-Opitz Syndrome (SLOS) by following a large group of affected children and adults over time. In this study, we will measure cholesterol and other similar chemicals in blood and urine, evaluate development and behavior, do limited medical evaluation, and carry out brain imaging studies.This study will help researchers: learn more about what causes SLOS and how SLOS changes with age, note differences in features of SLOS among those affected, evaluate the effect of giving extra cholesterol in this condition, and develop ways to evaluate whether treatments developed in the future will be helpful.

  Study phs001430

• PREDICT-HD Huntington Disease Study

  The purpose of this project is to make clinical measurements from the PREDICT-HD consortium available through the dbGaP mechanism. The phenotype data will first be converted into a community open standard and subsequently exported to dbGaP for archival and open access distribution of the results of the studies. This will permit members of the scientific community to utilize a permanent resource for investigating the interactions of phenotypes upon an international cohort of early Huntington Disease. In version 2 cut of the data we provided HD CAG repeat lengths for both alleles as well as enrollment age of all participants. We have also generated unique identifiers prospectively compatible with the larger initiative GWAS in Huntington's Disease project (also on DbGaP). As such, the version 1 cut of the data was mainly proof of concept and should be deprecated. Going forward, all updates will add-on to version 2 cut of the data. In version 3 cut of the data, we provided baseline or the first usable MRI T1-weighted imaging analysis subcortical and cortical segmentations and cortical parcellations based on a customized Freesurfer 5.2 pipeline developed at The University of Iowa. The customizations to the standard pipeline were mainly to improve bias field correction and image normalization such that segmentation of gray, white, internal csf, dura and surface CSF are optimized for the Freesurfer pipeline. There are 1111 subjects with results in this data release. In version 4 cut of the data, we provided all longitudinal clinical measurements for all subjects (total of 1476) assessmented or enrolled through the end of 2013. Additionally, we are providing measurements on 39 baseline FDG PET images spatially normalized by SPM5 into MNI space, relative regional metabolic values computed in 120 volumes of interest (VOI) defined in the Automated Anatomical Labeling (AAL) Atlas (Tzourio-Mazoyer et al. 2002), and global metabolic values calculated by SPM standard mean voxel value (within per image fullmean/8 mask). This project is a funded ancillary study of PREDICT-HD. In version 5 cut of the data, we provided the first of many forthcoming results from ancilliary studies of PREDICT-HD. In this data cut, we provide individual subject results derived from structural MRI data. The earliest MRI session for each subject was used. The results summarized represent source based morphometry loading coefficients for 23 components (see: "Patterns of Co-Occurring Gray Matter Concentration Loss across the Huntington Disease Prodrome", Ciarochi et al., 2016, Front Neurol. 2016; 7: 147, Published online 2016 Sep 21. doi: 10.3389/fneur.2016.00147]. In this version 6 cut of the data, we provide a full set of derived data, more than 10,000 raw MRI images, and ancillary study data sets. For sample information please link to: PREDICT-HD Biospecimen Resources

  Study phs000222

• National Institute on Aging (NIA) Long Life Family Study (LLFS)

  The Long Life Family Study (LLFS) is an international collaborative study of the genetics and familial components of exceptional survival, longevity, and healthy aging. Families were recruited through elderly probands (generally in their 90s) who self-reported on the survival history of their parents and siblings, and on the basis of this information, families which showed clustering of exceptional survival were recruited. [Specifically, a Family Longevity Selection Score (FLOSS) ≥7 was required. The FLOSS measures the average excess Observed lifespan over that Expected based upon lifetables, while adding a bonus term for still-living individuals. Thus FLOSS is a useful tool for scoring and selecting families for inclusion in a research study of exceptional survival (Sebastiani et al., 2009, PMID: 19910380)]. Probands resided in the catchment areas of four Field Centers (Boston University, Columbia University, University of Pittsburgh, and University of Southern Denmark). Recruited family members were phenotyped through extensive in-home visits by teams of technicians who traveled all over the USA and Denmark. Blood assays were centrally processed at a Laboratory Core (University of Minnesota) and protocols were standardized, monitored and coordinated through a Data Management Coordinating Center (Washington University). We examined and extensively phenotyped in all major domains of healthy aging, 4,953 individuals in 539 families through comprehensive in-home visits. Of these, 4,815 gave dbGaP sharing permission and had sufficient quantity/quality of DNA for GWAS genotyping. This large collection of families, selected on the basis of clustering for exceptional survival, is a unique resource for the study of human longevity and healthy aging. We estimate that less than 1% of the Framingham Heart Study (FHS) families (a roughly random population family sample) would meet the minimal entrance criteria for exceptional survival required in the LLFS (Sebastiani et al., 2009, PMID: 19910380). Thus, the least exceptional LLFS families show more clustering for exceptional longevity than 99% of the FHS families. Although the LLFS pedigrees were selected on the basis of longevity per se in the upper generation (and the generation above that), the children's generation have significantly lower rates of many major diseases and have better healthy aging profiles for many disease phenotypes (Newman et al., 2011, PMID: 21258136). The participants had their first in-person visit between 2006 and 2009. After that visit, they were contacted annually by telephone to update vital status, medical history, and general health. Between 2014 and 2017, willing participants completed a second in-person visit. The second visit followed the same protocols and centralized training as the first visit. During the second visit, a portable carotid ultrasound exam was added. Again, participants were continuously contacted annually for telephone follow-up during the period of the second in-person visit and after that. Annual telephone follow-ups currently ongoing, and plans for a third in-person visit are in progress.

  Study phs000397

• Coagulation and Fibrinolysis in a Pediatric Insulin Titration Trial

  Summary of the Design and Aims: The Coagulation and Fibrinolysis in a Pediatric Insulin Titration Trial (CAF-PINT) is an ancillary study to the HALF PINT (NCT01565941) randomized, controlled trial that was originally designed to study changes in inflammatory and thrombosis pathways in the setting of an interventional trial. The parent HALF PINT study was designed to study the impact of tight glycemic control (TGC) using an explicit insulin titration algorithm to safely achieve a normal glucose in target range of 80-110 mg/dL versus 150-180mg/dL on clinical outcomes among critically ill hyperglycemic children with Heart and Lung Failure. The CAF PINT study collected blood samples pre randomization and at 2 and 4 days after randomization. Blood samples collected in EDTA tubes were centrifuged at local sites to separate the plasma from cell pellets and frozen prior to shipment. In addition, 0.5 mL of whole blood collected in PAXgene tubes modified for use in pediatrics frozen 24 hours after collection prior to shipment.Population Information: Setting: Twenty-one academic children's hospitals (HALF-PINT) and 1 academic children's hospital (IIT-SBPP). Participants: Critically ill hyperglycemic children requiring mechanical ventilation/vasoactive support (n=293).Interventions and Exposures: Patients in HALF PINT were randomized to achieve a low target glucose (80-110 mg/dL) vs. a higher target (150-180 mg/dL). The primary trial was stopped early due to low likelihood of achieving a statistically significant difference in the primary outcomes. Molecular Technologies Employed: Plasma biomarkers were assayed on thawed plasma using a Luminex panel. RNA was extracted from PAXgene Blood RNA Kits modified for pediatric use. Extracted RNA was sequenced with the Novaseq S4 system (Illumina) to generate 2 x 150 base paired end reads to a target depth of 50 million read-pairs per sample.Principal Findings of the Study: We tested for heterogeneity of treatment effects according to baseline blood inflammatory proteins using logistic regression with individual biomarkers as interaction terms and Cox regression analysis using biomarker-derived latent classes. Children with higher inflammatory proteins including TNFR-1, IL-6, IL-8, and IL-10 had lower mortality when treated with insulin to a target glucose of 80-110 mg/dL as compared to 150-180 mg/dL (interaction p<0.05). Causal forest estimates ranged from a 40% mortality reduction to a 15% mortality increase with TGC, depending on each patient’s individual biomarker profile. Latent class categorization strongly interacted with TGC with respect to mortality (Cox regression interaction p=0.005). Specifically, patients with higher inflammation benefited from TGC (6% mortality with TGC vs. 48% mortality, p=0.004) whereas hypoinflamed patients showed a trend towards harm (14% mortality with TGC vs. 7% mortality, p=0.055).Data available through dbGaP: Transcriptomic (Gene Counts) and phenotype data

  Study phs003016

• The National Institute of Neurological Disorders and Stroke (NINDS) Human Genetics Resource Center: DNA and Cell Line Repository (the NINDS Repository): Motor Neuron/Amyotrophic Lateral Sclerosis (ALS) Study

  The National Institute of Neurological Disorders and Stroke (NINDS) Human Genetics Resource Center: DNA and Cell Line Repository (the NINDS Repository), banks phenotypic data and biological samples, including from individuals with motor neuron disease, in order to facilitate gene discovery in neurological disorders. Those samples are used in a number of studies, and genotyping data from studies using this resource are encouraged to be shared via dbGaP. Many studies have already shared data in this fashion, which in turn, can be linked back to the biologicals banked at the NINDS Repository. Motor Neuron Disease is characterized by selective degeneration of the motor neurons of the spinal cord, brainstem, or motor cortex. Clinical subtypes are distinguished by the major site of degeneration. In Amyotrophic Lateral Sclerosis (ALS), there is involvement of upper, lower, and brainstem motor neurons. In progressive muscular atrophy and related syndromes, the motor neurons in the spinal cord are primarily affected. With progressive bulbar palsy, the initial degeneration occurs in the brainstem. In primary lateral sclerosis, the cortical neurons are affected in isolation (Adams et al, Principles of Neurology, 6th ed, p 1089). The Motor Neuron Disease Collection of DNA and cell lines in the NINDS Repository is largely Amyotrophic Lateral Sclerosis cases (others include progressive muscular atrophy, primary lateral sclerosis, progressive bulbar palsy, Kennedy's disease). Amyotrophic lateral sclerosis (ALS) is the most common form of motor neuron disease (MND). Although ALS is the most common MND, it is still a relatively rare disease with an incidence of around 1.6 per 100,000 in the United States. It is currently incurable and treatment is largely limited to supportive care. Family history is associated with an increased risk of ALS, and many Mendelian causes have been discovered (including SOD1). However, most forms of the disease are not obviously familial. It is suspected that the sporadic forms of neurodegenerative disorders are caused by multiple genetic variants that individually make relatively weak contributions to risk. There is also an associated Control collection (see dbGaP and Coriell). Studies in motor Neuron Disease may use cases from the NINDS repository, controls from the NINDS repository, as well as cases, and controls, from other sources. A subset of subjects from The National Institute of Neurological Disorders and Stroke (NINDS) Human Genetics Resource Center: DNA and Cell Line Repository (the NINDS Repository): Motor Neuron/Amyotrophic Lateral Sclerosis (ALS) Study was utilized in the Genome-wide genotyping in amyotrophic lateral sclerosis and neurologically normal controls: first stage analysis and public release of data study. Note: The publication Chio et al., 2009 states that "Raw sample-level genotype data from the initial GWAS study ... are available for download through the dbGAP portal (phs000006.v1.p1)". Instead, please follow this link: phs000101.v1.p1.

  Study phs000006

• Submitting array based metadata

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

  Documentation submission/metadata/submission/array

• RNA-sequencing of human skin samples obtained from SSc patients and healthy controls.

  Patient information SSc patients were recruited at the Department of Rheumatology of the Leiden University Medical Center (Leiden, The Netherlands). All patients met the American Rheumatism Association classification criteria for SSc (Subcommittee for scleroderma criteria 1980), and were classified according to LeRoy and Medsger criteria as either limited or diffuse cutaneous disease (LeRoy EC, Black C, Fleischmajer R, Jablonska S, Krieg T, Medsger TA Jr, Rowell N 1988). Institutional review board approval and written informed consent was obtained before patients entered this study. Two 4 mm skin biopsies were taken from a standardized location on the most proximal part of the lower arm, distal from the elbow. In 10 patients the skin biopsy came from a clinically affected area and in 4 patients the skin was locally unaffected. One sample was used for RNA sequencing and one sample was used for immunohistochemistry. Skin biopsies from healthy individuals were commercially sourced (Tissue Solutions, UK) and collected from donors undergoing skin resection surgery and after informed consent. To match the healthy skin with patients as much as possible, skin biopsies from healthy controls were also taken from a similar position (the under-arm (for 4 controls) and leg (for 2 controls)). Healthy skin donors were selected to match the age and sex of the SSc patient cohort. Biopsies from patients and controls were equally treated and were both stored at -80°C until RNA isolation was performed. RNA from frozen skin biopsies was isolated using RNeasy kit from fibrous tissue (Qiagen, the Netherlands). RNA quantity was determined by using SimplyNano 2000 and quality was assessed on Tapestation (Agilent, the Netherlands). All samples included in the study had a RIN score above 7.0. Transcriptome characterisation and analysis RNA sequencing was performed using polyA selection and a stranded protocol using Ion Torrent next generation sequencing technology (Service XS, The Netherlands). The Ion PI Template OT2 200 Kit v3 and Ion PI Sequencing 200 Kit v3 were used according to the manufacturer’s instructions. 20 samples were run on 11 PI chips. PI chip analyses, base calling and quality checks were performed using the Torrent Server Suite. An average of 42 million 100 bp reads was generated per sample. Following quality control, reads were aligned to the human genome (Homo sapiens GRh38.78) using Bowtie2 and STAR (Dobin et al. 2013; Langmead and Salzberg 2012). Reads were first aligned with STAR. For the unmapped reads from STAR, a second alignment step was performed using bowtie2 (local very sensitive options)

  Dataset EGAD00001003832

• What is a DAC?

  What is a DAC? Given the complexity, scale, and diversity of global submitters and studies, the EGA operates a distributed data access model in which requests are made to the data controller, not to the EGA. The European Commission defines a data controller, in the General Data Protection Regulation (GDPR), as the person that determines the purposes for which and the means by which personal data is processed. A Data Access Committees, commonly referred to as DACs, comprise on or more individuals (or data controllers) that review data access requests and make decisions on who can access personally identifiable genetic, phenotypic, and clinical data deposited at the EGA. Therefore, the members of a DAC should be individuals who have the authority to approve data access requests. The animation describes how you can authorise access to your sensitive data with the help of Data Access Committee and Authorisation tool. Acknowledgement to CSC - IT Center for Finland, Elixir Finland, Elixir Europe. Frequently Asked Questions How can I create a Data Access Committee? How can I create a Data Access Committee? The members of a DAC can come from different areas of expertise, such as data management, data analysis, information technology, legal and compliance, subject matter experts, privacy and security, and representatives from the organisations or individuals that provide data to the DAC. The specific members of a DAC can vary depending on the needs of the organisation and the type of data being managed. The EGA strongly suggests checking with your organisation to align with its regulations How should a DAC be named? The chosen name must be informative to the applicant.  For example, internal identifiers, such as grant numbers, should not be used. Individual PI names should also not be used. DAC's are often named after the organisation or department of the data source.  Browse the full list of DAC names currently in the EGA. How can I become an EGA DAC contact? To register a DAC at the EGA you must create first as an EGA user. Once your EGA user has been approved by the Helpdesk team, you will be able to log in to the DAC Portal. How can I register a DAC? To register a DAC, follow the DAC Portal instructions.  You will be required to provide a DAC name, name of the individual(s) that make up your DAC and contact details for your DAC including your Institutional email(s). Wherever possible, the DAC should make sure that all points of contact are readily available and able to answer any initial data requests/queries in < 2 weeks.  Once your DAC is registered, you will have to wait upon the validation from our Helpdesk team. As soon as all the validations have been completed, your DAC will be activated. Alternatively, you can also establish a DAC at the EGA during a programmatic submission through Webin API. Which are the possible roles of a DAC contact? There are two possible roles for DAC contacts: member and admin. An admin has additional privileges compared to a member: An “admin” can manage data requests, create and edit policies, edit the content of the DAC, add or remove contacts, and decide the role of each contact. A “member” can manage data requests and create policies. However, a member does not have permission to modify DAC details, edit information from policies where they are not admins, or add/remove contacts. There is no limit to the number of admins in a DAC, and each admin is responsible for deciding who should have editing privileges. This allows for a more decentralised and democratic approach to managing the DAC. How can I modify the information of a DAC? To modify a DAC, follow the instructions here. Keep in mind that only DAC contact with an admin role can modify the information of a DAC. If your DAC was registered before the lauch of the DAC Portal, and its ID is EGAC0 (not EGAC5), you must use the programmatic submission to modify it. Please, do not hesitate to contact our Helpdesk team if you need help with this! 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. What’s the link between DAC, policy and dataset? A dataset is linked to one single policy. At the same time, one policy has a one to one relationship with a DAC. In this example, you can see that in this dataset page, we are only showing the information of one DAC (1 dataset - 1 DAC). However, the ratio of objects does not work the same in the other direction. One DAC can own multiple policy objects. And each policy object can be reused in several datasets. Thus, one DAC can manage one or more datasets. In this example, you can see that in this DAC page, we are showing all the datasets that are managed by one DAC (1 DAC - >400 datasets). EGA Data Access Committee Best Practices Which are the EGA DAC best practices? Refer to DAC Best Practices What happens if a DAC member changes institutions? EGA is committed to the protection and ownership of the data stored in our systems. We respect the institution's ownership of the data, and as such, if a DAC member changes institutions, the ownership of the data will not be transferred to the new institution. Therefore, before changing institutions, we request that the DAC contact add a new member who will replace them once they no longer work at the institution. This ensures that the data remains protected and is accessible to authorised personnel at the institution. 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. What happens if EGA detects an unresponsive DAC? EGA defines an unresponsive DAC as a DAC with one or more contacts who do not respond to data access requests. EGA has procedures in place to identify these types of DACs, escalate the issue, and attempt to reassign the DAC to a responsive contact. This is a crucial step in ensuring that data can be accessed and utilised by researchers. If EGA identifies an unresponsive DAC, the organisation will first try to resolve the issue by escalating it to the appropriate parties. This may involve attempting to reassign the DAC to a more responsive contact. Unfortunately, in situations where we cannot reassign the DAC, the dataset will be withdrawn from the public website and the files will be removed from our system. If an EGA ID is referenced in a publication, the EGA will take extra steps to ensure that the public is made aware of the data's unavailability. I don't want to receive an email notification for pending requests. How can I do that? If you are an EGA DAC with pending requests, you will always receive emails for new data access requests. However, 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! How can I manage data access requests? What documentation does the DAC need to provide? Each dataset that is submitted to the EGA must be linked to a policy object. The policy is a Data Access Agreement (DAA), which defines the terms and conditions of using the dataset, such as how the data files should be stored once downloaded or details of publication embargoes that should be observed by the approved user. As part of the Data Access Agreement, information regarding the application can be captured to help inform the DAC when making its decision. For example, requestors could be asked to provide a proposed title for their research and a proposal of how the data will be used. By asking for provision of such information the DAC can be assured that the requestor fully understands any consents associated with the data. It is important that accounts created at the EGA, are created solely for those individuals that will be downloading the data from the EGA. As part of the data access request, we strongly encourage you to identify individuals that will need an account at the EGA in order to prevent sharing of login details, which is strictly prohibited under EGA user account policy. Such information can easily be captured in the DAA. NOTICE The data access agreement template below is provided for guidance only and should be adapted as you see fit to suit your own purpose. In the interest of promoting data sharing, we suggest that if an agreement cannot be met around clause 19 in this example that both parties should agree to remain silent, and that the clause should be removed from the agreement. Example DAA How can the DAC provide the DAA? The DAC should provide their own DAA when registering a policy. Data requestors will download this document and should fill it in and send it back to the DAC. Data access decisions should be based on such documentation. The DAA can be downloaded through the request data webpage. Once it has been filled in, the signed copy of the DAA can be uploaded back to the request data webpage and sent to the DAC for review. How can I grant access to the data? Once you receive a data access request, you can login to the DAC Portal. In this portal you will see all your pending requests and will be able to grant or decline access to the requestors. I am a member of the Data Access Committee. Could I approve somebody else to deal with the requests on my behalf? If you want to delegate data access decisions to someone else, make sure that the individual's account is officially registered as a member of the DAC. Remember that a DAC contact with an "admin" role can always add new members to an existing DAC, remove members, and modify contact details through the DAC Portal. Can I automatise the process of managing data access requests? The answer is yes! You can use a programmatic approach using our DAC API! Check out the DAC API specification! Data Breach What should a DAC do if they suspect a breach? If a DAC suspects a data breach of one or more of their datasets, they should immediately contact the EGA Helpdesk team at this link. The DAC must provide the following information when contacting the EGA Helpdesk team: A list of affected datasets An estimated date of the data breach (or interval of dates) A list of unauthorised users who accessed the data (if available). Otherwise, they can provide a list of authorised users for the affected datasets Any observations they would like to raise to the EGA team Once the DAC has contacted the EGA team, we will respond within 48 hours (please allow some leeway during peak times) and activate our data breach protocol. What can I expect from the EGA if they detect a breach? Once the EGA determines that a security incident has occurred, we will notify all DAC members that a data breach has been detected, and take steps to contain the incident. Containment approaches may include: Revoking a data provider's access to the EGA resources, such as by changing passwords. Removing affected EGA datasets from distribution, such as by withdrawing a dataset. Disabling certain functions or services, such as the EGA ingestion pipeline. Shutting down the system or disconnecting it from the network. After the incident has been contained, the EGA will determine whether it is necessary to eradicate components related to the incident. Finally, the EGA will enable recovery of the service to normal operation and confirm that all services are functioning normally.

  Documentation access/data-access-committee/what-is-dac

• RNA Expression in Diverse Donor Derived Dermal Fibroblasts Correlates with Reprogramming to Pluripotency

  The cohort used in this study includes 36 African American (17 female, 19 male) and 36 White American (19 female, 17 male) individuals. The total cohort has a median age of 32 years, with a range of 20-64 years. The African American cohort has a median age of 34, with a range of 21-52 years. The White American cohort has a median age of 31, with a range of 20-64 years. Primary dermal fibroblast lines were derived from skin biopsies obtained from adult individuals at the NIEHS under institutional review board approved protocol human subjects 10-E-0063, "Sample Collection Registry for Quality Control of Biological and Environmental Specimens and Assay Development and Testing protocol" (ClinicalTrials.gov #NCT01087307). All participants gave written informed consent for tissue donation. Donor sex, age, and ancestry were voluntarily self-reported. Fibroblast cells were reprogrammed via lentiviral transduction using six transcription factors contained in three plasmids (ADDGENE/PSIN4-EF2-N2L, ADDGENE/PSIN4-EF2-O2S and ADDGENE/PSIN4-CMV-K2M). Reprogramming efficiency was determined by alkaline phosphatase staining of triplicate 10cm reprogrammed dishes containing colonies, which were then scanned. The saved images were analyzed with ImageJ 1.51h (Wayne Rasband, National Institutes of Health, USA) to count colonies, using color threshold adjusted and binary converted images of each dish. Triplicate plates were averaged and reported as colony counts or percent reprogramming efficiency ((# of colonies/250,000) x100). The African American cohort had reprogramming efficiencies ranging from 0.06-1.37%, with a median of 0.655%. The White American cohort had reprogramming efficiencies ranging from 0.02-1.13%, with a median of 0.455%. Our goal was to define transcriptomic heterogeneity that could be contributing to differences in reprogramming efficiency between individuals and between groups. Total RNA was obtained from dermal fibroblasts and matched iPSCs. For each sample, 500 ng total RNA was used as input for preparation of whole transcriptome rRNA depleted libraries. An adapter-ligated library was prepared with the KAPA HyperPrep Kit (KAPA Biosystems, Wilmington, MA) using Bioo Scientific NEXTflex&#8482; DNA Barcoded Adapters (Bioo Scientific, Austin, TX, USA) according to KAPA-provided protocol. Sequencing was performed using an Illumina HiSeq 2500 following Illumina-provided protocols for 2x150 bp paired-end sequencing. Each transcriptome was sequenced to a target depth of 125 million reads. The following mean raw reads were obtained: African American dermal fibroblasts= 129,571,450; White American dermal fibroblasts= 131,939,505; African American iPSCs= 132,501,335; White American iPSCs= 134,394,164. Raw reads were aligned to hg19 using the STAR alignment tool (https://github.com/alexdobin/STAR). The following mean aligned reads were obtained: African American dermal fibroblasts= 123,315,343; White American dermal fibroblasts= 125,178,035; African American iPSCs= 123,148,312; White American iPSCs= 123,886,992. Reprinted from L. C. Mackey et al., Epigenetic Enzymes, Age, and Ancestry Regulate the Efficiency of Human iPSC Reprogramming. Stem Cells 36, 1697-1708 (2018), with permission from Wiley. Reprinted from L. S. Bisogno et al., Ancestry-dependent gene expression correlates with reprogramming to pluripotency and multiple dynamic biological processes. Science Advances 6 (47) (2020) (PMID: 33219026), with permission from AAAS.

  Study phs002341