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• 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

• Circulating RNAs in Acute Heart Failure (CRUCIAL)

  The purpose of this American Heart Association-funded and NIH-funded study is to examine circulating RNAs in the acute congestive heart failure (CHF) setting, and how they change with decongestive therapy, and their function in vitro and in vivo. The investigators are testing the hypothesis that ex-RNA levels change significantly during decongestion therapy and can be used as a marker of those individuals who respond to CHF therapy (in terms of cardiac structure or outcome). Additionally, the translational research design allows the investigators to assay the effects of these RNAs on tissue phenotypes in vitro.

  Study phs003403

• Cell-free DNA Methylome Analysis Enables Early Preeclampsia Prediction

  This dataset contains in solution target-enrichment bisulfite sequencing of placental tissue, buffy coat and plasma DNA from pregnant women. Blood samples were taken for cell-free DNA (cfDNA) DNA extraction from 64 women at the time of early-onset preeclampsia (PE) diagnosis, or from 38 controls (uncomplicated pregnancies) at a similar gestational age that did not develop preeclampsia subsequently. Among these subjects, plasma samples from 7 PE patients and 6 controls were also subjected to oxidative bisulfite sequencing. Placental tissues from 11 PE and 26 control subjects after delivery, and buffy coat from 16 PE and 16 control subjects at the same time of cfDNA sampling were profiled. A discovery cohort for early PE assessment in the first trimester was collected. In this cohort, cfDNA from 75 pregnancies that went on to develop early-onset PE and from 124 matched controls were collected and methylome sequencing were carried out. An independent validation cohort to validate early PE assessment with methylome profiling was collected as well. This validation cohort includes cfDNA samples from 61 PE and 136 control pregnancies.

  Dataset EGAD00001010159

• Virginia PrIMeD Study

  The goal of this study was to determine whether screening children (2-16 yrs) in pediatric waiting rooms for genetic risk of type 1 diabetes (T1D) could be applied to diverse (by geography, genetic ancestry) communities, and for those at "high genetic risk," if barriers prevent subsequent screening for presence of islet autoantibodies. A total of 3,818 children were recruited from eight general pediatric and specialty clinics across Virginia. Clinical Research Coordinators were stationed at each clinic and obtained informed consent/assent, a brief medical history, and a saliva sample for DNA extraction and determination of their genetic risk using a T1D Genetic Risk Score (T1D GRS). Children were present in the clinic for a "healthy" visit, although children with and without a history of T1D were recruited into the study. Age, sex, self-reported ancestry/race, T1D history, and other medical history information was obtained. DNA was extracted from the saliva samples and genotyping with a T1D-focused array using 74 SNPs (including 26 SNPs in the HLA region) provided data to generate a T1D GRS for each individual. Of the 3,818 children recruited, there was a slight excess of males (1,959, 51.3%), a majority of white and Hispanic/Latino ancestry (82.8%), and 91 (2.4%) with prevalent T1D. A total of 542 (14.2%) had "high T1D genetic risk" (T1D GRS > 5). Although the T1D GRS included non-HLA genetic variants, 80% of those with "high genetic risk" would have been classified using only those SNPs in the HLA region alone. Of children with "high genetic risk" and without pre-existing T1D (n=494), 7.0% (34/494) consented for islet autoantibody screening. Among children with pre-existing T1D (n=91), 52% (n=48) also had a "high genetic risk." The HLA-focused T1D GRS was not significantly associated with age at onset of T1D. Of those at high genetic risk that consented for autoantibody testing (n=34) and completed the blood collection (n=28), two (2/28, 7.1%) tested positive for multiple autoantibodies and were at significant risk of developing T1D. Follow up of 55% of parents/guardians identified 2 (of 2,096, 0.095%) participants who developed T1D, one at "high genetic risk" and one with a first-degree relative with T1D. A major factor in obtaining islet autoantibody testing was concern over SARS-Cov-2 exposure and an independent non-scheduled blood collection.Individual-level data at each SNP (genotyped and imputed) for T1D genetic risk and affiliated data are provided in dbGaP.

  Study phs003609

• Identification of rare germline variants in familial multiple myeloma

  The risk of developing multiple myeloma (MM) in first-degree relatives of affected individuals is 2 to 4 fold higher than in the general population, implying the existence of inherited susceptibility. While over 20 common, low-penetrance variants have been associated with MM risk, less is known about rare, high-penetrance germline predisposition. We aimed to identify and characterize high/moderated penetrance germline variants in MM families using whole genome sequencing. For that purpose we identified families with at least two individuals diagnosed with MM or its precursors MGUS and smoldering MM. Also, patients with solitary plasmacytoma and AL amyloidosis were enrolled. We also recruited unaffected family members, and they were screened to exclude undetected MM or its precursor stages. After whole genome sequencing of blood DNA and filtering for rare variants (minor allele frequency &lt;0.1%) and family segregation, variants are prioritized by a range of published in silico tools and literature search. The most promising variants are subjected to experimental validation to evaluate their functional relevance. Our data will provide guidance to worldwide efforts to unravel germline predisposition in MM and validation of the proposed variants and genes.

  Study EGAS00001004734

• Genetic Modifiers of Huntington's Disease

  Huntington&#39;s disease (HD) is a neurodegenerative disorder typically diagnosed in mid-life that is caused by expansion of an otherwise polymorphic CAG trinucleotide repeat. The mutation causes a gain-of-function of the huntingtin protein to trigger a pathogenic process that produces detectable phenotypic differences many years before the traditional diagnosis, which is based upon characteristic motor symptoms. The prediagnosis phase of pathogenesis is now the subject of intense scrutiny by an NINDS-funded longitudinal study, PREDICT-HD, in which undiagnosed gene carriers are followed longitudinally and subjected to detailed phenotyping (PREDICT-HD Huntington Disease Study -- dbGaP Study Accession: phs000222). This powerful approach, which offers the potential for moving the focus of therapeutic development to the decades prior to neurological disease diagnosis, is enabled by the fact that all individuals with HD have the same type of mutation, which can be determined at any time in life by a single HD CAG repeat PCR amplification assay. The precise length of the HD CAG repeat differs between individuals. There is a strong negative correlation between the number of CAG repeats and the age at onset of diagnostic neurological abnormalities in HD, such that the CAG repeat accounts for ~50% of the variation in age at diagnosis. Analysis of the remaining variance not explained by the length of the CAG repeat has shown that it is highly heritable, being due to genetic variation, elsewhere in the genome. The intent of the Genetic Modifiers of Huntington&#39;s Disease study is to identify genetic modifiers of HD pathogenesis by using genomewide association techniques in diagnosed HD individuals to identify genetic factors associated with the residual variance in age at onset not explained by the CAG repeat, and to extend these analyses to pre-diagnosis phenotypes, for example, those defined in the PREDICT-HD study. Identification of modifier genes is a top priority for HD research (and an example of an approach that can be applied in other late-onset genetic disorders), as it could provide clues to developing rational treatments that delay or prevent the pathogenic process from causing the ravages of the disease that ensue in the ~15 years of inexorable decline to ultimate death that now follows clinical diagnosis. To further that goal, the release of study version 2 makes available whole exome sequencing data of n=221 study participants.

  Study phs000371

• NINDS Family-Based Whole-Genome Sequencing to Find Modifiers of Age of Onset in Huntington's Disease

  The goal of our studies is to identify genetic modifiers of neurodegeneration in Huntington&#39;s disease (HD). HD is caused by expansion of CAG repeats in the huntingtin (Htt) gene, with longer stretches often leading to more rapid disease onset and progression. Yet, for a given number of repeats, the age of symptom onset can be variable, differing by up to decades. Thus, the age of onset of motor symptoms in HD is only partly explained by the length of the CAG expansion. Available evidence suggests that genetic modifiers contribute to the variation in HD onset. Identifying genetic modifiers is important because they may provide critical insights into HD pathogenesis and reveal key pathways that could be targeted by novel HD therapeutics. This is important since there are no disease-modifying therapies for HD, and mHtt is an unattractive small-molecule drug target. We recruited 21 HD families with varying characteristics of disease progression and age of onset and obtained medical histories, clinical records and DNA samples that were subjected to whole-genome sequencing (WGS). These WGS data describe families of 104 subjects, including HD patients and their unaffected family members. These individuals were selected based on individual clinical histories and family structures that best fit our criteria for expressing potential genetic modifiers. We are testing the hypothesis that novel, rare genetic variants contribute to HD and those genetic modifiers can be identified by WGS.

  Study phs001071

• UK10K NEURO GURLING

  In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. This sample set consists of DNA from multiply affected schizophrenia families. The families have been diagnosed using the SADS-L clinical instrument which gives diagnoses at the probable level of the research diagnostic criteria (RDC). In addition all diagnoses are available using DSMIIIR criteria. These criteria are widely accepted as being valid and reliable for the diagnosis of schizophrenia. All families have been collected to ensure that they are uni-lineal for transmission of schizophrenia, i.e. they have only one affected parent with schizophrenia, or a relative of only one transmitting or obligate carrier parent with schizophrenia. Families with bi-lineal transmission of schizophrenia (i.e. with both parents being affected) were not sampled for this study. All families have multiple cases of schizophrenia and related disorders. The families have been selected to ensure there are no cases of bipolar disorder within them and that they do not contain bipolar disorder in any relatives on either side of the family.For further information on this cohort please contact Hugh Gurling (h.gurling@ucl.ac.uk).

  Study EGAS00001000225

• Genetic Analysis of Normal Human Facial Variation

  The purpose of this project is to identify genes associated with normal human quantitative facial variation. The motivation for this project stems from the fact that very little is known about how variation in specific genes relates to the diversity of facial forms commonly observed in humans. Viable candidates for these morphogenes originate from a number of sources: tissue expression studies, animal models with targeted or spontaneous mutations, and genetic syndromes with craniofacial manifestations. Importantly, understanding the genetic basis for normal facial variation also has important implications for health-related research. For example, this work has the potential to shed light on the factors influencing liability to common craniofacial anomalies such as orofacial clefts. There is now ample evidence that certain facial features (e.g., increased midfacial retrusion) characterize individuals genetically at-risk for orofacial clefts (e.g., biological relatives of affected cases). While these predisposing facial features are statistically over-represented in at-risk groups, they are also common in the general population. Since many of the current candidate genes for clefting are thought to play a critical role in facial morphogenesis, variation in these genes may also underlie normal variation in these facial features. These candidate genes, however, probably represent only a small fraction of the total number of loci influencing normal human facial variation. Phenotypes for this project were obtained from over 3000 healthy Caucasian subjects recruited through three separate studies. The majority of the subjects were recruited as part of the 3D Facial Norms Project, which is described in extensive detail here: (https://www.facebase.org/facial_norms/notes). The provided dbGaP phenotypes include a series of anthropometric craniofacial measurements (linear distances) primarily derived from 3D photographic facial surface scans (see previous hyperlink). The specific genotyping requested is described elsewhere in this document. Our analysis team is pursuing a variety of different analytic approaches to derive genetically informative phenotypes, including various shape-based morphometric methods. For those interested in pursuing more advanced phenotypic approaches, the original 3D surface scans and additional phenotypic traits are available to researchers through the FaceBase Consortium. This dataset has the potential to facilitate the discovery of new genetic loci with an important role in both normal and abnormal facial development. It may also serve as a dataset to test hypotheses regarding specific SNP associations (e.g. as a replication dataset) or as part of a larger meta-analysis.

  Study phs000949

• NHLBI and NIA The New England Centenarian Study (NECS)

  Supercentenarians (age 110+ years old) generally delay or escape age-related diseases and disability well beyond the age one hundred and this exceptional survival is likely to be influenced by a genetic predisposition that includes both common and rare genetic variants. In this report, we describe the complete genomic sequences of male and female supercentenarians, both age &gt;114 years old. We show that: (1) the sequence variant spectrum of these two individuals&#39; DNA sequences is largely comparable to existing non-supercentenarian genomes; (2) the two individuals do not appear to carry most of the well-established human longevity enabling variants already reported in the literature; (3) they have a comparable number of known disease-associated variants relative to most human genomes sequenced to date; (4) approximately 1% of the variants these individuals possess are novel and may point to new genes involved in exceptional longevity; and (5) both individuals are enriched for longevity-associated variants that we discovered through a large genome-wide association study. These analyses suggest that there are both common and rare longevity associated variants that may counter the effects of disease predisposing variants and extend lifespan. The continued analysis of the genomes of these rare individuals who have survived to extremely old ages should provide insight into the processes that contribute to the maintenance of health during extreme ageing.

  Study phs000451