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• STAMPEED: Northern Finland Birth Cohort 1966 (NFBC1966)

  The Northern Finland Birth Cohorts program (NFBC) was initiated in the 1960s in the two northernmost provinces of Finland to study risk factors involved in pre-term birth and intrauterine growth retardation, and the consequences of these early adverse events on subsequent morbidity and mortality. The uniqueness of NBFCs is that the data of the cohorts were obtained from early fetal life (including maternal health during pregnancy) to adulthood. The NFBC1966 includes 12,058 live births to mothers in the two northern-most provinces of Finland. Two decades later, a second cohort of 9432 births was obtained (NFBC1986). In NFBC1966 pregnancies were followed prospectively from the first antenatal contact (10-16th week). After birth, the offspring were examined and then again underwent clinical evaluation at ages 1y, 7y, 14-16y and 31y. At each visit, a wide range of phenotypic, lifestyle and demographic data were gathered by questionnaires and clinical examinations. For the most part, NFBC1986 has undergone similar evaluations to NFBC1966. Linkage to national registries includes hospitalization, deaths, education, medication, pensions, and provides up-to-date demographic and clinical information for members of both cohorts. DNA samples were obtained from 5,923 subjects from NFBC1966 and 6688 subjects from NFBC1986. Data coverage, 96% of all births in 1966 and 99% in 1986, is highly representative for the whole population. The NFBC program comprises more than 20 different projects coordinated by the Center of Lifecourse Disease studies in Northern Finland (COLD) at Oulu University. The prospective data collected from the NFBCs form a unique resource, allowing the study of disease emergence, and of the importance of genetic, biological, social and behavioral risk factors. The genome-wide association (GWA) study sponsored through the STAMPEED program of NHLBI employed genomic DNA samples previously collected by the NFBC1966 study and stored in the DNA repository of the National Institute for Health and Welfare, Finland. This NHLBI sponsored RO1 project aimed to identify genetic variants contributing to metabolic and cardiovascular diseases (CVD). In addition to de-identified genome wide genotypic data, a selected list of phenotypic data related to CVD including weight, height, BMI, HDL, LDL, total cholesterol, triglyceride, glucose, insulin and fasting status, are also available in dbGaP. A summary of the GWAS for the NFBC1966 cardiovascular risk traits can be found in Sabatti et al., Nature Genetics 41: 35-46, 2009, PMID: 19060910. The version 2 release of this study contains sequence data from seventeen loci associated with levels of triglyceride, HDL-C, LDL-C, total cholesterol, fasting plasma glucose, and fasting plasma insulin (Kathiresan et al. 2008, Willer et al. 2008, Sabatti et al. 2009, Dupuis et al. 2010, Teslovich et al. 2010). At each locus, protein-coding regions and 5' and 3' untranslated regions of genes nearest to single nucleotide polymorphisms showing genome-wide significant association with metabolic syndrome-related traits, were sequenced. Targeted Illumina sequencing of 78 genes (~270kb) using 150bp probes was performed on 4943 subjects of the Northern Finland Birth Cohort 1966 (NFBC1966). Whole exome sequencing on the Illumina platform was carried out on 586 of those participants. The sequencing study is part of a larger project that is funded by the National Human Genome Research Institute's Allelic Spectrum in Common Disease Initiative, and comprises sequence data from more than 7000 individuals in two Finnish cohorts: NFBC1966 and the Finland-United States Investigation of NIDDM Genetics (FUSION) study.

  Study phs000276

• NHLBI TOPMed: Best ADd-on Therapy Giving Effective Response (BADGER)

  BADGER is a 56-week randomized, double-blind, three-treatment, three-period cross-over trial that will evaluate the differential improvement in control that is achieved following three separate treatment interventions in children whose asthma is not acceptably controlled on a low dose of ICS (per NAEPP guidelines). All participants will enter an 8-week run-in period during which time they will receive a dose of 1x ICS (fluticasone 200 μg/day). During this 8-week time period, running 2-week averages to establish the lack of acceptable asthma control will be calculated. Thus, a child could qualify for randomization at any time during this 8-week run-in period. This approach should maximize both patient safety and successful enrollment. Children will continue to receive 1x ICS during the entire treatment phase. During each period of the treatment phase, they also will receive one add-on therapy in the form of LABA, LTRA or additional 1x ICS. The order of the add-on therapy assignment will be determined by randomization into one of six treatment sequences (order determined randomly). Each treatment period will be 16 weeks in length; the initial 4 weeks of each period will be considered to be the washout period for the previous treatment. The primary outcome measures will be frequency of asthma exacerbations, asthma control days, and FEV1.

  Study phs001728

• NHLBI TOPMed: The Cleveland Family Study (CFS)

  The Cleveland Family Study (CFS) is one cohort involved in the WGS project. The CFS was designed to provide fundamental epidemiological data on genetic and non-genetic risk factors for sleep disordered breathing (SDB). In brief, the CFS is a family-based study that enrolled a total of 2284 individuals from 361 families between 1990 and 2006. The sample was selected by identifying affected probands who had laboratory diagnosed obstructive sleep apnea. All first degree relatives, spouses and available second degree relatives of affected probands were studied. In addition, during the first 5 study years, neighborhood control families were identified through a neighborhood proband, and his/her spouses and first degree relatives. Each exam, occurring at approximately 4 year intervals, included new enrollment as well as follow up exams for previously enrolled subjects. For the first three visits, data, including an overnight sleep study, were collected in the participants' homes while the last visit occurred in a general clinical research center (GCRC). Phenotypic characterization of the entire cohort included overnight sleep apnea studies, blood pressure, spirometry, anthropometry and questionnaires. The GCRC exam (n=735 selected individuals) included more comprehensive phenotype data on a focused subsample of the larger cohort, to permit linking SDB phenotypes with cardio-metabolic phenotypes, with an interest in identifying genetic loci that are associated with these related phenotypes. In this last round of data collection, a subset of 735 individuals was selected based on expected genetic informativity by choosing pedigrees where siblings had extremes of the apnea hypopnea index (AHI). Participants underwent detailed phenotyping including laboratory polysomnography (PSG), ECG, spirometry, nasal and oral acoustic reflectometry, vigilance testing, and blood and urine collection before and after sleep and after an oral glucose tolerance test. A wide range of biochemical measures of inflammation and metabolism were assayed by a Core Laboratory at the University of Vermont. 994 individuals were sequenced as part of TOPMed Phase 1, including 507 African-Americans and 487 European-Americans. Among the sequenced individuals, 156 were probands with diagnosed sleep apnea, an additional 706 were members of families with probands, and 132 were from neighborhood control families. 298 individuals were sequenced as part of TOPMed Phase 3.5, including 169 African-Americans and 129 European-Americans. Among the newly sequenced individuals, 33 were probands with diagnosed sleep apnea, an additional 214 were members of families with probands, and 51 were from neighborhood control families. Please note: Phenotype and pedigree data are available through "NHLBI Cleveland Family Study (CFS) Candidate Gene Association Resource (CARe)", phs000284.

  Study phs000954

• Download Metadata

  Metadata Distribution Welcome to the realm of Metadata Distribution within the EGA ecosystem! Our Metadata REST API empowers you to effortlessly retrieve metadata from the expansive landscape of EGA. By utilising this API, you gain access to publicly available insights across various EGA domains, including studies, samples, experiments, runs, analyses, policies, DACs, and datasets. Furthermore, this API facilitates cross-referencing of objects, enabling you to gather, for example, all the datasets associated with a specific DAC, seamlessly. In addition, we have added the ability to query private data using the metadata API. If you possess the necessary permissions, you can access behind-the-login private data for a specified list of datasets. Metadata Distribution Index Identifiers Dataset Mappings Website Download Metadata API - Private Identifiers At the core of EGA's organisational structure are unique accessions that serve as essential tags for our diverse objects. Here's a quick overview of the accessions and their corresponding object types: EGA Accession ID EGA Object description EGAS EGA Study Accession ID EGAC EGA DAC Accession ID EGAP EGA Policy Accession ID EGAN EGA Sample Accession ID EGAR EGA Run Accession ID EGAX EGA Experiment ID EGAZ EGA Analysis Accession ID EGAD EGA Dataset Accession ID EGAB EGA Submission ID EGAF EGA File Unique Accession ID For further information check our metadata schema documentation. Dataset Mappings For authorised datasets, comprehensive mappings reveal meaningful connections: Sample_file: This file presents information about the linkage between samples and files available in the dataset. Study_experiment_run_sample: This file presents information about the linkage between studies, experiments, runs, and samples within the dataset. Study_analysis_sample: This file presents information about the linkage between studies, analyses, and samples contained within the dataset. Run_sample: This file presents information about the linkage between runs and samples within the dataset. Analysis_sample: This file presents information about the linkage between analyses and samples within the dataset. An empty file indicates the absence of corresponding information. Website Download Our website serves as your gateway to downloading metadata. Simply navigate to the dataset page, and you'll find a blue Metadata button. Once authenticated, you can click this button for authorised datasets. If you lack permissions for a particular dataset, request access by clicking the 'Request Access' button. For authorised datasets, choose your preferred metadata format: CSV, TSV, or JSON. Metadata API - Private Leverage the power of programmatic metadata downloads! Start by authenticating yourself with your credentials to obtain an access token. With this token, programmatically query private information. Queries mirror the structure of the Public Metadata API. However, behind the login, you can delve into specific mapping information (as mentioned above in dataset mappings) alongside object-level exploration. Authentication An active session is required to work with the API. Each time you log in with your credentials a new session is started, which is identified by an access_token. Below an example on how to obtain one using curl: curl https://idp.ega-archive.org/realms/EGA/protocol/openid-connect/token \ -d 'client_id=metadata-api' \ -d 'username=...' \ --data-urlencode 'password=...' \ -d 'grant_type=password' All responses from the API are in JSON format. A successful response should include a new token to be used for the session: {"access_token":"eyJhbGciOiJSUzI1NiIsInR5cCIgOiA...TNw", "expires_in":300, "refresh_expires_in":1800, "refresh_token":"eyJhbGciOiJIUzI1NiIsInR5cCIgOiA...pTX10", "token_type":"Bearer", ... } Save the access_token value and include it in the API call headers. Example query usage Below you can find some example of queries available behing authentication and authorisation. Querying study-experiment-run-sample mappings: curl https://metadata.ega-archive.org/datasets/{datasetID}/mappings/study_experiment_run_sample \ -H 'Authorization: Bearer access_token' Querying run-sample mappings: curl https://metadata.ega-archive.org/datasets/{datasetID}/mappings/run_sample \ -H 'Authorization: Bearer access_token' Querying study-analysis-sample mappings: curl https://metadata.ega-archive.org/datasets/{datasetID}/mappings/study_analysis_sample \ -H 'Authorization: Bearer access_token' Querying analysis-sample mappings: curl https://metadata.ega-archive.org/datasets/{datasetID}/mappings/analysis_sample \ -H 'Authorization: Bearer access_token' Querying sample-file mappings: curl https://metadata.ega-archive.org/datasets/{datasetID}/mappings/sample_file \ -H 'Authorization: Bearer access_token' You can get a different output format by adding one of these options to the curl command: -H 'Accept: text/tsv' -H 'Accept: application/json' -H 'Accept: text/csv' For more detailed information, refer to the Metadata API Specification.

  Documentation access/download/metadata

• The Environmental Determinants of Diabetes in the Young Study (TEDDY)

  The Environmental Determinants of Diabetes in the Young (TEDDY) Study investigates genetic and genetic-environmental interactions, including gestational infection or other gestational events, childhood infections and other environmental factors after birth, in relation to the development of pre-diabetic islet autoimmunity and type 1 diabetes (T1D). Beginning in 2002, a consortium of six centers assembled to participate in the development and implementation of studies to identify environmental factors that trigger the development of islet autoimmunity and T1D in genetically susceptible individuals. The TEDDY study screened around 400,000 newborns and recruited 7,749 neonates from the general population with a pre-determined T1D risk of 3% and 919 neonates with first degree relatives who have T1D and who have a pre-determined T1D risk of 10%. Thus, TEDDY proposes to follow over 8,000 participants across six clinical centers worldwide (Finland, Germany, Sweden and three in the United States) until the age of 15. Participants are followed every three months for islet autoantibody measurements with blood sampling until four years of age and then at least every six months until the age of 15. After the age of four, autoantibody positive subjects continue to be followed at 3 month intervals and autoantibody negative subjects are followed at six month intervals. In addition to the analysis of autoantibodies, additional data and sample collection is performed at each visit. The parents collect monthly stool samples in early childhood. The parents also fill out questionnaires at regular intervals in connection with study visits and record information about diet and health status in the child's TEDDY Book between visits. Continued long-term follow-up of the currently active TEDDY participants will provide important scientific information on early childhood diet, reported and measured infections, vaccinations, and psychosocial stressors that may contribute to the development of type 1 diabetes and islet autoimmunity. DNA extracted from the 9-month TEDDY blood sample was used for SNP genotyping. Samples were available for 7,082 participants. SNPs were genotyped by the Center for Public Health Genomics at the University of Virginia, using the Illumina ImmunoChip SNP microarray of around 196,000 SNPs selected from 186 regions associated with 12 autoimmune diseases (including T1D) (Hadley et al., 2015). "Data quality control (QC) steps included eliminating subjects with a low call rate (> 5% SNPs missing) and discrepancies between reported gender and prior genotyping at the HLA laboratory. Secondly, SNPs were removed from analysis due to low call rate, Hardy-Weinberg equilibrium P value < 10-6, except for chromosome 6 due to HLA eligibility requirements)" (Törn et al., 2015). QC resulted in a total of 7,023 subjects with data on 176,586 SNPs. Additional information on the TEDDY study are available in the following articles: The Environmental Determinants of Diabetes in the Young (TEDDY) Study. TEDDY Study Group. Annals of the New York Academy of Science, 2008 and TEDDY - The Environmental Determinants of Diabetes in the Young - An Observational Clinical Trial. Annals of the New York Academy of Science, 2006 Details of the TEDDY protocol can be found in The Environmental Determinants of Diabetes in the Young (TEDDY): Genetic Criteria and International Diabetes Risk Screening of 421,000 infants. Pediatric Diabetes, 2011

  Study phs001037

• Single-Cell Mitochondrial Mutation Lineage Tracing of Non-Dysplastic and Dysplastic Barrett's Esophagus

  This study addresses a critical knowledge gap in understanding Barrett's esophagus (BE), a common metaplastic condition and precursor to esophageal adenocarcinoma, by identifying lineage connections and cell states underlying its development and malignant transformation. We collected biopsies from 13 Barrett's esophagus patients undergoing routine surveillance endoscopy, sampling metaplastic tissue alongside normal squamous esophagus and gastric cardia tissues when clinically appropriate. Our novel approach integrated single-cell RNA sequencing of nearly 180,000 cells with mitochondrial DNA mutation analysis for lineage tracing, coupled with spatial transcriptomics and comprehensive whole-exome sequencing. For one patient with high-grade dysplasia, we performed detailed whole-exome sequencing analysis at 100x coverage on dissociated Barrett's esophagus cells alongside matched normal esophagus and gastric cardia tissues. This genomic profiling revealed critical genetic drivers of malignant transformation, including a truncating mutation in the mutation cluster region of APC (a negative WNT regulator), mutations in the tumor suppressor genes CDKN2A and TP53, and significant copy number alterations including loss of CDKN2A on chromosome 9. These genetic alterations corresponded with the transcriptional changes observed in our single-cell data, particularly WNT pathway dysregulation and the expansion of LGR5-expressing stem cells, providing molecular evidence for how single clones can initiate the dysplastic transformation. Data deposited in dbGaP consist specifically of the whole-exome sequencing raw files, processed variant calls, and copy number analyses from the single patient with high-grade dysplasia and matched normal esophagus and stomach control tissue. Whole exome sequencing was performed on an Illumina NovaSeq 6000. Sequencing data was preprocessed following the Genome Analysis Toolkit's (GATK) Best Practices. Sequencing data was analyzed with Mutect2 (GATK, v4.2.5.0) to identify short somatic mutations including single-nucleotide polymorphisms and insertions and deletions. The Barrett's esophagus sample was run with the two matched normals, as well as the publicly available germline resource somatic-hg38_af-only-gnomad.hg38.vcf.gz (GATK). Somatic variant calls were filtered using FilterMutectCalls (GATK) and loaded into the Integrative Genomics Viewer (v2.12.3) for identification of high-quality variants in known esophageal adenocarcinoma regulators. Copy number alterations in the Barrett's esophagus sample were estimated using CNVkit (v0.9.9). CNV, Mutect2, and SVC analyses will be made available through dbGAP. Copy number alterations in the Barrett's esophagus sample were estimated using CNVkit (v0.9.9) with the default run settings; a pooled reference was generated from the normal esophagus and stomach samples. Copy number results were plotted directly within CNVkit using the function scatter.

  Study phs003949

• New set of services for all users unveiled at the EGA

  The European Genome-phenome Archive (EGA) releases today a brand-new website as well as a set of improved services for users, comprising a revamped Submitter Portal with an improved API and data brokering functionality for submitters, as well as a new DAC Portal for Data Access Committee members to easily manage data access requests. The EGA team has been working hard over the past months on these improvements that will provide a better experience for the users. Discover them below! Visit our new EGA website and check the new functionalities and updated guides Our new website is live, with a fresh look and updated features to discover all the novelties. The homepage now includes three pathways to guide our users, depending on whether they want to deposit, download or manage data within the Archive. One of the most outstanding developments is the introduction of a streamlined process for requesting access to data: EGA Users can now directly request access to their preferred datasets through the website using their EGA accounts. Additionally, users can customise their profile, oversee active sessions, and manage the public keys, essential for our latest array of services, alongside a range of other features. Another major change is our new Help Desk contact method: a built-in website form tailored for user inquiries. This system, called "Need Help?", can be easily located on the top right-hand side of the website. It will encompass both EGA and Federated EGA node users' requests. We will detail its use soon, so stay tuned. Register your metadata to the EGA easily with our new Submitter Portal The new Submitter Portal makes the submission process quicker and more intuitive. The process is currently divided into sections that the user will know beforehand by taking the tour that the EGA team has created to guide users through the Portal before starting the first submission. Do not complete your data submissions alone! Our Submitter Portal now includes a collaboration mode so that every submission can be completed by authorised collaborators with an EGA Profile. There are a lot more features that we will be detailing from now on in incoming posts. A new platform for Data Access Committee Members The new DAC Portal brings novelties for individuals engaged in governance, legal, technical, operational roles, or those acting as data stewards. DAC members will be able to easily manage DACs, policies, and data access requests with the new Portal. Take the tour now to check them out! Help us improve the services completing the EGA Survey As part of our ongoing journey to improve your experience, we have created an EGA User Survey. This questionnaire covers a wide range of aspects such as user experience, support and data accessibility, among others. Participate now and take the opportunity to suggest your own improvements. Help us enhance your experience in this new era for the EGA. Subscribe to the EGA Mailing List and stay tuned In the next weeks we will share more information about the possibilities of the new services. You can easily join the EGA Mailing List to receive all the updates.

  Blog new-set-of-services-at-EGA

• Genome-Wide Association Studies of Prematurity and Its Complications

  Preterm labor resulting in the delivery of a premature child is a complex problem with an enormous impact on individuals, families and society. An estimated 500,000 children are born prematurely in the U.S. each year, and 5 million worldwide die annually of prematurity and its complications. Prematurity is also the single largest contributor to disability-adjusted life years, a measure of the lifetime impact of a disease. Despite the importance of the problem and its disproportionate occurrence in poor and minority populations, its underlying etiology (or etiologies) remains unknown; the single best predictor for preterm delivery is a previous preterm birth. The largest single cause of prematurity is spontaneous preterm labor, and suspected triggers for this include infection, stress, poor nutrition and genetic factors. Family and twin studies provide strong evidence that genetic factors underlie about 40% of the risk for prematurity. A major challenge in studying genetic factors in prematurity is maternal/uterine factors, fetal/placental factors, or both may influence risk. Thus, any approach to studying preterm birth should account for both infant and maternal risk, environmental covariates and interactions. The Danish National Birth Cohort (DNBC) is a well-established, prospective cohort that enrolled women early in pregnancy, prior to any adverse pregnancy outcomes, to minimize bias in data collection and sampling. See details at: http://www.ssi.dk/English. The DNBC followed over 100,000 pregnancies beginning in the first trimester and has extensive biological material and epidemiologic data on health outcomes in both mother and child. The current study posted on dbGaP contains data from a genome-wide case/control study using approximately 1,000 preterm mother-child pairs from the DNBC most with spontaneous onset of labor or preterm premature rupture of membranes (PPROM), along with 1,000 control pairs where the child was born at ~40 weeks' gestation. After data cleaning some small changes in case/control status and other variables resulted in minor changes in numbers of cases or controls in certain categories. Environmental variables are being used as covariates in the analysis. To replicate positive findings, we are using additional mother-child pairs from the DNBC and from the general Danish population, as well as 2200 samples coming primarily from an African-American population known to have high rates of preterm labor and delivery. This group includes over 1000 mostly very low birthweight infants, over 500 infant term controls, 326 term mother/baby pairs and 180 preterm mother/baby pairs. The data from these additional cohorts will be available in separate dbGaP postings. The study is expected to yield a better understanding of the biology of parturition, identify common genetic factors that play a role in preterm birth and its complications and suggest environmental modifications that can prolong gestation, with the goal of improving both neonatal and adult outcomes. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to prematurity and its complications through large-scale genome-wide association studies of a well-characterized cohort of Danish mothers and babies. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000103

• Characterization of individual foci of multicentric/multifocal breast cancer using targeted next generation sequencing

  Multifocality or multicentricity in breast cancer may be defined as the presence of two or more tumor foci within a single quadrant of the breast or within different quadrants of the same breast, respectively. This original classification of the breast cancer as multicentric or multifocal was based on the assumption that cancers arising in the same quadrant were more likely to arise from the same ductal structures than those occurring in separate areas of the breast. The problem with these definitions is that the ?quadrants? of the breast are arbitrary external designations, as no internal boundaries do exist. This project will therefore focus both on synchronous multifocal and multicentric tumors. The incidence of multifocal and multicentric breast cancers was reported to be between 13 and 75% depending on the definition used, the extent of the pathologic sampling of the breast and whether in situ disease is considered evidence of multicentricity (1). Although this incidence is variable, those figures show that it is a frequent phenomenon. Multiple (multifocal/multicentric) breast carcinomas, especially when occurring in the same breast, represent a real challenge for both pathologists and clinicians in terms of identifying the cellular origin and the best therapeutic management of the cancer. Multifocality or multicentricity has been associated with a number of more aggressive features including an increased rate of regional lymph node metastases and adverse patient outcome when compared with unifocal tumors (2-3), and a possible increased risk of local recurrence following breast conserving surgery (4). For the moment, the literature is divided on whether there is a corresponding impact on survival outcomes. Today, the current convention to stage and to treat multifocal and multicentric tumors is the classical tumor-node-metastasis (TNM) staging guidelines with which tumor size is assessed by the largest tumor focus without taking other foci of disease into consideration. If some papers, as the recent one from Lynch and colleagues, support the current staging convention (3), others, however, as Boyages et al. suggested that aggregate size and not the size of the largest lesion should be considered in order to refine the prognostic assessment of those tumors (5). On the top of that, the question whether multifocal/multicentric carcinomas are due to the spread of a single carcinoma throughout the breast or is due to multiple carcinomas arising simultaneously has been a matter of debate. Some studies suggested that multifocal breast cancer may result from either intramammary spread from a single primary tumor or multiple synchronous primary tumors; whereas others suggest that multiple breast carcinomas always arise from the same clone (6-8). Recently, Pietri and colleagues analyzed the biological characterization of a series of 113 multifocal/multicentric breast cancers (8) which were diagnosed over a 5-year period. The expression of estrogen (ER) and progesterone (PgR) receptors, Ki-67 proliferative index, expression of HER2 and tumor grading were prospectively determined in each tumor focus, and mismatches among foci were recorded. Mismatches in ER status were present in 5 (4.4%) cases and PgR in 18 (15.9%) cases. Mismatches in tumor grading were present in 21 cases (18.6%), proliferative index (Ki-67) in 17 (15%) cases and HER2 status in 11 (9.7%) cases. Interestingly, this heterogeneity among foci has led to 14 (12.4%) patients receiving different adjuvant treatments compared with what would have been indicated if we had only taken into account the biologic status of the primary tumor. This study therefore showed that differences in biological characteristics of multifocal/multicentric lesions play a crucial role in the adjuvant treatment decision making process. In this study, we will concentrate on a larger series of patients with multifocal invasive ductal breast cancer lesions. We aim at: 1. Evaluating the incidence of multifocality according to the different breast cancer molecular subtypes (ER-/HER2-, HER2+, ER+/HER2-). 2. Evaluating the incidence of multifocality in patients with hereditary breast cancer disease (presence of germline BRCA1 or BRCA2 mutations). Moreover, we would like to investigate if multifocal lesions with BRCA1 or BRCA2 mutations exhibit a characteristic combination of substitution mutation signatures and a distinctive profile of deletions as demonstrated recently by Nik-Zainal and colleagues (9). 3. Correlating multifocality with clinical information in order to define its influence on patients? survival (DFS and OS). 4. Carrying high coverage targeted gene sequencing of driver cancer genes and genes whose mutation is of therapeutic importance in order to compare clinically-relevant genetic differences between several multifocal breast cancer lesions. 5. Evaluating the impact of the distance between the different lesions on the clinical outcome but also on the genetic differences. 6. Comparing gene expression patterns between several multifocal breast cancer lesions and correlate them with the results of the targeted genes screen. 7. Characterizing the genomic and transcriptomic status of cancer related genes in metastatic lesions (local recurrence, positive lymph node or distant metastatic sites) from the same multifocal invasive ductal breast cancer patients in order to evaluate the consequence of genomic and transcriptomic heterogeneity of multifocal lesions on metastatic lesions. Multiple (multifocal/multicentric) breast carcinomas, especially when occurring in the same breast, represent a real challenge for both pathologists and clinicians in terms of identifying the cellular origin and the best therapeutic choice. This project has the potential to identify genetic/transcriptomic differences existing between several lesions constituting multifocal breast cancers, which in the routine clinical practice are usually considered to be homogeneous among them. We foresee validating significant results in a larger series of patients and this, in turn, could have a remarkable impact on the treatment and clinical management of multifocal breast cancers. Indeed, we hope to provide some evidence whether or not each focus matters in multifocal and multicentric breast cancer to define the adequate therapeutic approach, especially in the context of targeted therapies. The work to be done at Sanger will be target gene screen pooling of 1400 samples.

  Dataset EGAD00001000624

• Impact of Respiratory Virus Infections and Bacterial Microbiome Shifts on Lymphocyte and Respiratory Function in Infants Born Prematurely or Full Term

  Public health importance: Babies born preterm, approximately 1 out of every 9 live births in the United States, have significant respiratory morbidity over the first two years of life, exacerbated by respiratory viral infections. Many (<50%) return to pediatricians, emergency rooms and pulmonologists with symptoms of respiratory dysfunction (SRD): intermittent or chronic wheezing, poor growth and an excess of upper and lower respiratory tract infections (LRTI). SRD correlate inversely with gestational age and weight at birth and is more common in those with chronic lung disease of prematurity, yet its incidence and severity varies widely among both the prematurely born and those born at term. There is evidence from clinical studies and animal models that risks of LRTI and recurrent wheezing is influenced by gut and respiratory flora and by T cell responses to infection. Information gained from this study will be used to identify characteristics, risk factors and potential mechanisms for early and persistent lung disease in children born at term and born preterm. This Clinical Research Study will investigate the relationships between sequential respiratory viral infections, patterns of intestinal and respiratory bacterial colonization, and adaptive cellular immune phenotypes which are associated with increased susceptibility to respiratory infections and long term respiratory morbidity in preterm and full term infants. We hypothesize that the timing and acquisition of specific viral infections and bacterial species are directly related to respiratory morbidity in the first year of life as defined by SRD and by measures of pulmonary function. We hypothesize that cellular and molecular immuno-maturity are altered due to factors presented by premature birth in such a way as to promote chronic inflammatory and cytotoxic damage to the lung, with subsequent enhanced, damaging responses to infectious agents and environmental irritants. Our preliminary studies demonstrate both feasibility and expertise in mutiparameter immunophenotyping of small volume peripheral blood samples obtained from premature infants including gene expression arrays of flow cytometry sorted cells. We will use new technologies for known viral identification, as well as high-throughput metagenome sequencing of RNA and DNA virus like particles (VLP) to be used for viral discovery in infant respiratory sample and use of high-throughput pyrosequencing (454T) of bacterial 16S rRNA to determine shifts in bacterial community structure, occurring in pre-term (PT) as compared to full term (FT) infants, over the first year of life. Finally, we present statistical approaches to stratify disease risk predictors using multivariate logistic regression modeling approaches. We propose to evaluate T cell phenotypic and functional profiles relative to viral and predominant bacterial exposures according to highly complementary, but independent, Specific Objectives. Objective 1: To determine if viral respiratory infections and patterns of respiratory and gut bacterial community structure (microbiome) in prematurely born babies predict the rate and degree of immunologic maturation, and pulmonary dysfunction, measured from birth to 36 weeks corrected gestational age (CGA). Objective 2: To determine the relationship between respiratory viral infections and disease severity up to one year CGA, and the lymphocyte (Lc) phenotypes documented at term gestation (birth for term infants and 36 wks/NICU discharge in preterm infants) and at one year CGA. Three secondary outcomes of this objective will be to a) relate the quantity, type and severity of viral infections with pulmonary function at one and three years of life, b) relate the viral community structure to severity of viral infections and c) to seek evidence of modulation of viral susceptibility by bacterial respiratory and gut community structure (microbiome). The relationship of colonization with known and non-identified bacterial species in both the respiratory tract and the gut will be evaluated. Flow cytometry data corresponding to this study can be found within Immport study SDY1302. Positive and negative controls for microbiome samples are uploaded under SRA bioproject PRJNA474485. Microbiome samples corresponding to PRISM2 are distinguished from PRISM1 via "_PRISM2" appended to the sample name. Within the positive and negative controls, PRISM1 controls are uploaded as bam files and PRISM2 controls are uploaded as paired fastq. Samples ending in -08 correspond to TLDA qPCR results for a given sample. There is a column for each pathogen tested and a column to indicate where that pathogen was bacteria or virus.

  Study phs001347

• Sickle Cell Disease Implementation Consortium Registry (SCDIC Registry-BioLINCC)

  Data Access NOTE: Please refer to the "Authorized Access" section below for information about how access to the data from this accession differs from many other dbGaP accessions.Objectives: To understand the barriers to care and other factors related to reduced healthcare utilization after the transition from pediatric to adult SCD care which may contribute to increased morbidity and mortality.Background: Fifty years ago, it was rare for individuals with SCD to live beyond childhood. Advances in care delivery and treatment have more than doubled the life expectancy of individuals with sickle cell disease (SCD) since 1972. Beginning in the 1970s, measures such as newborn screening, prophylactic administration of penicillin, and immunization against bacterial infections decreased complications and morbidity, increasing the length and quality of life of children with SCD. Blood transfusions are currently the only proven way to prevent some of the major complications of SCD, especially recurrent stroke. They are also used frequently to help manage some of the acute complications of SCD. More recently, the use of hydroxyurea (HU) as a therapeutic agent to increase fetal hemoglobin has been shown to further reduce the debilitating symptoms of and improve survival in SCD. L-glutamine and crizanlizumab are additional treatments to reduce pain crises and voxelotor is approved to lower the risk of anemia and improve blood flow. In the absence of a widely accessible cure, treatment for SCD is usually aimed at avoiding crises, relieving symptoms, and preventing complications. Many of the advances in treatment have not translated into an increase in longevity or quality of life for adolescents and adults because of disparities in access to routine primary health care.Individuals with SCD experience a markedly increased mortality beginning in the second decade of life. The third and later decades of life are frequently associated with severe chronic pain progressive organ damage and frequent hospitalizations. The provision of evidence-based and expert opinion-based care in SCD is complicated by the difficulties that many patients experience in obtaining access to the health care system and in receiving long-term care from knowledgeable providers. A registry of 2,400 people with SCD was established to provide a rich resource to study the natural history of SCD and to understand barriers to care during the transition from pediatric to adult care. Participants: 2,438 participants with a confirmed diagnosis of SCD were enrolled into the registry over an 18-month period. At the time of enrollment, all participants were 15-45 years of age, English speaking, and without a bone marrow transplant.Design: The SCDIC Registry is a longitudinal observational cohort study. At enrollment, data collection included a medical record abstraction of clinical history, the most recent lab results, and a participant survey which included several patient reported outcomes from PROMIS, ASCQ-Me and NeuroQoL. Similar surveys were administered at annual follow-up. Four to five years after enrollment, a second record abstraction was completed with all clinical events that occurred since enrollment and another set of recent labs. There were many research questions proposed to be answered with the study data thus there was no primary outcome.Conclusions: As noted previously, there were no primary outcomes determined at the start of the study. Research questions included examination of co-morbidities, healthcare utilization, and patient reported outcomes. Key findings from the Registry include:Executive dysfunction, learning difficulties, and poor comprehension significantly associated with poor activities of daily living skills. Executive dysfunction also associated with HU non-adherence. Continuing HU after conception increased odds of miscarriage or stillbirth compared to women who were not using HU. Continuing HU after conception increased odds of low birth weight in infants. Compared to adolescents, young adults had significantly more severe pain, organ dysfunction, mental health disorders, sleep problems, and barriers to medical care. Young adults were significantly less likely to see a sickle cell specialist in the past 2 years. Females had higher pain frequency and severity, more pain episodes, anxiety, depression, hospitalizations, and higher fetal hemoglobin levels. Males had more respiratory, musculoskeletal, genitourinary, and cardiovascular complications, more skin ulcers, and more use of HU. Among 128 subjects that died during the study period, iron overload, pulmonary hypertension, and depression, were significant predictors of the risk of death in multivariate analyses.

  Study phs004203

• Acute Respiratory Distress Network Early Versus Delayed Enteral Feeding to Treat People with Acute Lung Injury or Acute Respiratory Distress Syndrome (ARDSNet EDEN-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Biospecimens: Access to Biospecimens is through the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). Biospecimens from ARDSNet-EDEN include bronchial lavage, plasma, DNA, and urine. Please note that use of biospecimens in genetic and non-genetic research is subject to a tiered consent. Objectives: To determine if initial lower-volume trophic enteral feeding would increase ventilator-free days (VFDs) and decrease gastrointestinal intolerances compared with initial full enteral feeding. Background: Mechanically ventilated patients cannot eat normally and if not fed for long periods become malnourished. Because malnutrition is associated with poor outcomes in critically ill patients, artificial nutrition is often provided, especially in those with acute lung injury (ALI) and with expected longer duration of mechanical ventilation. When feasible, enteral nutrition targeting full caloric needs has been advocated over parenteral nutrition. However, feeding intolerance and common care practices often serve as practical barriers to reaching recommended goals. Although confounded by indication and severity of illness, several observational studies have shown improved clinical outcomes, including fewer infections, shorter duration of mechanical ventilation, and lower mortality for patients receiving a higher percentage of calculated caloric needs. Nonetheless, the best timing, formulation, and amount of enteral nutrition remain unknown. Participants: A total of 1,000 participants were enrolled. Design: Participants were randomized, stratified by site and presence of shock at enrollment, to receive either trophic or full enteral feeding for the first 6 days of mechanical ventilation. The initial 272 patients were also simultaneously randomized to a separate trial (the OMEGA study) comparing a nutritional supplement containing omega-3 fatty acids and antioxidants with an isocaloric, isovolemic control in a 2 × 2 factorial design.The designated feeding strategy was initiated within 6 hours of randomization and continued until death, extubation, or day 6. The care of mechanically ventilated patients still receiving enteral feedings after day 6 was managed according to the full feeding strategy in both groups. In extubated patients who then required reintubation, enteral nutrition was restarted and managed according to the study protocol.In the full-feeding group, enteral nutrition was initiated at 25 mL/h and advanced to goal rates as quickly as possible. Gastric residual volumes were checked every 6 hours while enteral feeding was increased. Patients randomized to the initial trophic-feeding group had enteral nutrition initiated at 10 mL/h (10-20 kcal/h) for the first 272 patients who also received the omega-3 or control supplement (240 mL volume per day). After the data and safety monitoring board stopped the OMEGA portion of the factorial design, the initial trophic feeding rate was changed to 20 kcal/h to approximate the calories that had been delivered in the OMEGA study. Gastric Residual Volumes (GRVs) were checked every 12 hours during trophic feeding. In patients randomized to trophic feeding, enteral nutrition was advanced to full-energy feeding rates following the same protocol used for the full-feeding group if they were still receiving mechanical ventilation at 144 hours. Conclusions: There was no difference between groups with regard to the primary end point, VFDs to day 28. There also were no differences in 60-day mortality, organ failure−free days, ICU-free days, or the incidence of infection between groups. Similarly, there were no differences between groups in VFDs or survival when analyzed by body mass index category or when subsets of patients with shock or more severe lung injury (acute respiratory distress syndrome) were examined.JAMA. 2012 Feb 22;307(8):795-803

  Study phs004168

• T2D-GENES Project 2: San Antonio Mexican American Family Studies

  The Type 2 Diabetes (T2D) Genetic Exploration by Next-generation sequencing in Ethnic Samples (T2D-GENES) Consortium is a collaborative international effort to identify genes influencing susceptibility to T2D in multiple ethnic groups using next generation sequencing. T2D-GENES Project 2 is a complex pedigree-based study designed to identify low frequency or rare variants influencing susceptibility to T2D, using whole genome sequence (WGS) information from 1,043 individuals in 20 Mexican American T2D-enriched pedigrees from San Antonio, Texas. The major objectives of this study are to identify low frequency or rare variants in and around known common variant signals for T2D, as well as to find novel low frequency or rare variants influencing susceptibility to T2D. The sampled individuals are obtained from two studies: the San Antonio Family Heart Study (SAFHS) and the San Antonio Family Diabetes/Gallbladder Study (SAFDGS), collectively referred to as the San Antonio Mexican American Family Studies (SAMAFS). The strategy is to sequence approximately 600 individuals at an average of 50x coverage across the entire genome, then impute genome wide genotypes for about 440 additional family members. The 600 sequenced individuals are specifically chosen for their value in imputing sequence information into other family members. By studying large pedigrees, we expect to find multiple individuals carrying each genetic variant, even if this variant is very rare in the population at large. Thus, a pedigree-based approach provides an excellent opportunity for identifying rare novel variants influencing risk of T2D and quantitative variation in T2D-related phenotypes. The whole genome sequencing has been done commercially by Complete Genomics, Inc. (CGI). The final data set includes whole genome sequence data for 607 individuals. After quality control, 585 sequenced individuals provide data for family based imputation, using Merlin linkage analysis software, into approximately 440 additional family members for whom chip based genotypes are available to indicate which parental haplotype is transmitted. Extensive phenotype data is provided for 1048 individuals. These include 5 sequenced individuals who do not belong to any of the 20 large pedigrees. Phenotype information was collected between 1991 and 2011 in the two contributing longitudinal studies. SAFHS participants may have information from up to 5 visits, and SAFDGS participants may have up to 4 visits. The clinical variables reported are coordinated with T2D-GENES Project 1 (multi-ethnic exome sequencing) and include T2D status and age at diagnosis, glycemic traits (fasting and 2 hour glucose and insulin), blood pressure, blood lipids (total cholesterol, HDL cholesterol, calculated LDL cholesterol and triglycerides), clinical chemistry (cystatin c, glutamic acid decarboxylase antibody titer (GadAb), creatinine, adiponectin and leptin). Glycated hemoglobin (HbA1c) was not measured for these individuals and insulin C-peptide is not included in this data set. Additional phenotype data include the medication status at each visit, classified in four categories as any current use of diabetes, hypertension or lipid-lowering medications, and, for females, current use of female hormones. Anthropometric measurements include age, sex, height, weight, hip circumference, waist circumference and derived ratios. Each phenotype variable has an initial summary column containing the most recent non-missing measurement for each individual, followed by the five potential time points for each individual, the number of non-missing measurements, and the age and year for the most recent non-missing measurement. For historical reasons, the order in which variables are presented on the dbGaP web site differs from their order in the data download file. When reading the comment fields for each variable, please note that commas are omitted to support data exchange in .csv format.

  Study phs000462

• eMERGE Network's Multi-Center Pilot of Pharmacogenetic Sequencing in Clinical Practice

  eMERGE-PGx is a multi-site test of the concept that sequence information can be coupled to electronic medical records (EMRs) for use in healthcare. The promise of personalized medicine - health care guided by each individual's biological characteristics - is being fostered by increasingly powerful and economical methods to acquire clinically relevant biomarkers from large numbers of people. One therapeutic area that seems especially ripe for an early test of the personalized medicine concept is pharmacogenomics (PGx) - the idea that individual variation in drug response includes a genomic component. Drug response variation is an accepted feature of virtually all drug treatments, and contemporary molecular biologic tools continue to identify key genes mediating drug metabolism, transport, and targets. Importantly, common variation in these genes is an increasingly well-recognized contributor, sometimes with large effects, to variation in drug responses. As a result, recommendations for genotype-guided therapy are increasing. These evidence-based recommendations, if implemented in health care practice, could reduce adverse drug events and improve time to therapeutic response. Through eMERGE-PGx, we are developing strategies for the optimal implementation of genetic sequence data into the clinical environment with the ultimate goal of improving patient care. Site and participants include: Children's Hospital of Pennsylvania (CHOP): The Center for Applied Genomics (CAG) at the Children's Hospital of Philadelphia (CHOP) is a high-throughput, highly automated genotyping and sequencing facility equipped with state-of-the-art genotyping and sequencing platforms. Children who are treated at the Children's Hospital Healthcare Network and their parents may be eligible to take part in a major initiative to collect more than 100,000 blood samples, covering a wide range of pediatric diseases. The PGx population selected for sequencing with the PGRNseq panel at CHOP is 1,650 children from CAG's biorepository with well-documented drug-related severe adverse events (SAEs) or EHR-based drug response profiles. SAEs were extracted from EPIC records and from CHOP's Adverse Event (AE) database, which documents every AE at CHOP. These AEs are classified by a medical review panel according to the causal relationship with the suspected drug into 'doubtful', 'possible', and 'probable'. Individuals with events classified as probable, severe and objective, were selected for sequencing. The drugs more frequently associated with adverse events are antibiotics, antineoplastics, immunosuppressants and psychotropic drugs. This cohort constitutes 50% of the target population. The remaining subjects were selected using EHR-based algorithms that we have developed and validated at CAG for identifying patients not responding to ADHD medication (primarily atomoxetine) and patients refractory to antiepileptic treatment from responders. Cincinnati Children's Hospital Medical Center/Boston's Children's Hospital (CCHMC/BCH): 811 CCHMC samples were obtained from children, adolescents or young adults exposed to medication or at risk for needing medication of study interest. 55% of participants were exposed to one or more opioids and their DNA source was a CCHMC study-specific biobank; while 27% of participants were at risk for needing an opioid for surgical pain management and were newly recruited. The remainder of the cohort was exposed to methylphenidate and their DNA samples were obtained from a CCHMC study-specific biobank. The focus of Boston Children's Hospital eMERGE PGx project is on individuals with epilepsy. Samples were taken from a current pharmacogenomics study already in place through which DMET analysis was run and used as confirmation for PGRN-Seq results. A total of 109 samples were sent for PGRN-Seq analysis at University of Washington. The remaining 141 epilepsy samples were from Children's Hospital of Philadelphia and underwent testing with PGRN-Seq at CHOP. Geisinger Health System: A research cohort of adult Geisinger Clinic patients was enrolled from community-based primary care clinics of the Geisinger Health System. Patients were eligible for enrollment if they were a primary care patient of a Geisinger Clinic physician and were scheduled for a non-emergent clinic visit. All data are from Geisinger patients who consent to participate in the MyCode project. MyCode participants agree to provide biological samples for broad research use, including genomic analysis, and for linking of sample data to information in the participant's Geisinger health record. The consent also permits sharing of de-identified data for research purposes. Group Health(GH)/University of Washington (UW): Potential GH participants for the PGx project were enrolled in the eMERGE Network through the Northwest Institute of Genetic Medicine (NWIGM) biorepository, and provided the appropriate consent to receive clinically relevant genetic results (N~6300). Participants were eligible if aged 50 - 65 years old at the time of their enrollment into the NWIGM repository, living, enrolled in GH's integrated group practice, and had completed an online Health Risk Appraisal. The selection algorithm was based on several data sources from the EHR at Group Health: 1. Demographics - participants with self-reported race as Asian or African ancestry were prioritized and selected to enrich for non-European ancestry; 2. Diagnosis and procedure codes - participants were selected if found to have a history of hypertension, atrial fibrillation (AF,) or congestive heart failure (CHF). Participants with a history of arrhythmia were added if the entire selection algorithm did not generate 900 individuals. We also enriched for participants with EHR evidence of actionable indications related to PGRNSeq genes. Participants were selected if found to have an ICD9 code for malignant hyperthermia, hypertension, atrial fibrillation, congestive heart failure or long QT syndrome (LQTS); 3. Laboratory values - if a participant had any laboratory event of creatine kinase (CK) > 1000, and were dispensed statins within 6 months of the event, then they were selected; and 4. Medications - participants were excluded if ever on carbamazepine or had a current regimen of warfarin. Essentia Institute of Rural Health, Marshfield Clinic, Pennsylvania State University (Marshfield): For this study, 750 subjects were selected and enrolled into PGx based on Vanderbilt's algorithm designed to enrich for patients who are most likely to receive one of three common drugs (Clopidogrel, Warfarin or Simvastatin) in the next 2-3 years. These patients were sent a letter of invitation and description of the PGx project. Follow-up phone calls were made, and interested subjects came in for a one time meeting to discuss the project and go through the informed consent with the research coordinator. If they were interested they signed the consent and HIPAA forms and gave blood. Subjects were chosen and enrolled into PGx independently of previous biobank participation. Mayo Clinic: The Right Drug, Right Dose, Right Time - Using Genomic Data to Individualize Treatment (The RIGHT Protocol) enrolled 1013 patients to test the hypothesis that prescribers could deliver genome-guided drug therapy at the point-of-care by using pharmacogenomic data preemptively integrated in the electronic medical record. Complete details regarding the study population have been previously described (Bielinski et al., 2014). Icahn School of Medicine at Mount Sinai School (Mt Sinai): Our study site is the Primary Care Associates (PCA) practice group of the Mount Sinai Faculty Practice Associates (FPA) of the Mount Sinai Medical Center in New York City. This practice has 12 physician providers. All patient encounters are documented and managed with EpicCare ambulatory electronic medical record. Active PCA Patients eligible for enrollment fulfilled the following criteria: a) age 50 or older receiving clinical care at Mount Sinai FPA PCA practice with at least one practice encounter within 18 months prior to commencement of enrollment; b) no history or current use of clopidogrel, warfarin, or simvastatin. Eligible patients were invited to participate through de novo recruitment by letter sent by their provider. Interested patients were screened for eligibility and enrolled to participate in the eMERGE PGX study on site by a dedicated research coordinator. In addition to de novo enrollment from clinical practice, patients of FPA PCA who had previously enrolled in Mount Sinai's BioMe Biobank program AND fulfilled eligibility criteria as stated under a) and b) were identified by chart review and samples sequenced at CIDR using PGRNseq platform (N=300). PGRNseq data from 291 samples passed stringent quality control and are included in the current data set. Furthermore, 56 of these patients carrying known and validated 'actionable' variants affecting prescribing of clopidogrel, warfarin, and/or simvastatin were enrolled in the eMERGE PGX study following invitation through recontacting by the Principal Investigator of the BioMe Program. Northwestern University: Participants for this study were recruited from the General Internal Medicine (GIM) clinic at Northwestern Medical Group (NMG). Patients were selected for invitation to participate if they had been seen a minimum of two times over the last four years, having a high likelihood to receive a prescription for warfarin, Plavix, or a statin, and are seeing a physician who has agreed to allow their patients to be contacted for the study. We utilized an algorithm developed at Vanderbilt and tailored to our population which uses our EHR to estimate the probability that individuals will receive a prescription for warfarin, Plavix, or a statin in the next three years. Participants were sent a letter explaining the study prior to their GIM appointment and offered participation at the time of their visit. Participants were consented on-site and blood drawn after consent was obtained. The GIM clinic consists of 39 primary care physicians who provide approximately 80,000 patient encounters per year. As with any large primary care clinic, a significant proportion of patients in GIM clinic suffer from a variety of chronic health conditions, such as diabetes, hypertension, and coronary artery disease. Over 50,000 individuals have been seen by GIM doctors in the past 5 years; 11,562 of these patients have evidence of a statin prescription in the EHR, 3,436 have evidence of a warfarin prescription, and 1,872 have evidence of a Plavix prescription. Vanderbilt University: The more than 1000 participants enrolled into Vanderbilt's eMERGE PGx study were newly recruited from the Cardiology and Internal Medicine Clinics and the Hillsboro Medical Group within Vanderbilt University Medical Center (VUMC). Patients were selected based on a predictive algorithm estimating the patient's likelihood of receiving Clopidogrel, Warfarin, and/or Simvastatin. The algorithm identifies primarily older middle-aged patients, and the mean age of the study group is 74. The cohort is approximately 45% female with 75% of subjects self-identified as EA and 24% as AA. Subjects were consented in person by study personnel following a routine clinic visit and an introduction to the study staff by their doctor. VUMC is a comprehensive health care facility dedicated to patient care, research, and the education of health care professionals. Translational research into the causes and treatment of disease as well as studying fundamental biological properties is the primary focus of discovery at Vanderbilt. Clinical research is conducted in Vanderbilt University Hospital, the Nashville Veterans Administration Hospital, Meharry General Hospital and in their associated outpatient clinics. These hospitals and clinics, all associated with the Vanderbilt system, each have full time Vanderbilt faculty and medical housestaff and provide clinical care and participate in research programs. The Vanderbilt Clinic is comprised of more than 95 adult outpatient specialty practices and received over 1.5 million ambulatory visits in 2012-13. The Vanderbilt Heart and Vascular Institute offers a comprehensive heart program offering diagnosis, medical treatment, minimally invasive therapies, surgical intervention and disease management, tailored to each individual's unique needs. All programs within the Vanderbilt Clinic have survival figures that surpass the national average.

  Study phs000906

• Systolic Blood Pressure Intervention Trial (SPRINT-Imaging)

  Available DataSystolic Blood Pressure Intervention Trial, SPRINT-Imaging, provides access to ECG signals data from the SPRINT clinical trial. The clinical phenotyping and outcomes data from the trial are associated with SPRINT-BioLINCC, phs003483.ObjectiveThe Systolic Blood Pressure Trial (SPRINT) was conducted to test the hypothesis that treating systolic blood pressure to a target of less than 120 mm Hg, as compared to a target of less than 140 mm Hg, would reduce the incidence of cardiovascular disease. BackgroundHypertension is a highly prevalent condition among adults and is a leading risk factor for myocardial infarction and stroke. Further, isolated systolic hypertension is the most common form of hypertension in adults over 50 years of age. Observational studies have shown a monotonic increase in cardiovascular risk with systolic blood pressures above 115 mm Hg; however, general population clinical trials have only documented the benefits of lowering systolic blood pressure to a target of 150 mm Hg. A 2007 expert panel sponsored by the National Heart, Lung, and Blood Institute designated the hypothesis that lowering the systolic blood pressure goal to a level SubjectsA total of 9361 participants were enrolled, with 4,678 randomized to the intensive-treatment group and 4,683 randomized to the standard-treatment group. DesignSPRINT was a randomized, single blinded (outcome adjudicators were blinded to treatment assignment) treatment trial with participants randomized to a systolic blood-pressure target of either less than 140 mm Hg (the standard-treatment group) or less than 120 mm Hg (the intensive-treatment group). Following randomization, baseline hypertensive regimens were adjusted in accordance with study treatment algorithms established for each group. The study formulary included all major classes of antihypertensive agents. Investigators could prescribe other antihypertensive medications, but the use of drug classes with the strongest evidence for reduction in cardiovascular outcomes was encouraged. This included thiazide-type diuretics as the first-line agent, loop diuretics for participants with advanced chronic kidney disease, and beta-adrenergic blockers for participants with coronary artery disease. Medications for participants in the intensive-treatment group were adjusted on a monthly basis to target a systolic blood pressure of less than 120 mm Hg. Medications for participants in the standard-treatment group were adjusted to target a systolic blood pressure of 135 to 139 mm Hg, and the dose was reduced if systolic blood pressure was less than 130 mm Hg on a single visit or less than 135 mm Hg on two consecutive visits. Lifestyle modification was encouraged as part of the management strategy. Participants were seen monthly for the first 3 months and every 3 months thereafter. Demographic data were collected at baseline. Clinical and laboratory data were obtained at baseline and every 3 months thereafter. A structured interview was used in both groups every 3 months to obtain self-reported cardiovascular disease outcomes. Medical records and electrocardiograms were obtained for documentation of events. Incidences of hypotension, syncope, injurious falls, electrolyte abnormalities, and bradycardia that were evaluated in an emergency department were included in adverse event reporting. Occurrences of acute kidney injury or acute renal failure requiring hospitalization were also monitored. The primary outcome was a composite outcome of myocardial infarction, other acute coronary syndromes, stroke, heart failure, or death from cardiovascular causes. ConclusionsThe blood pressure intervention was stopped in August of 2015 (median follow-up of 3.26 years) after the cardiovascular outcome results exceeded the boundary for efficacy at two consecutive time points. Compared with a systolic blood pressure target of less than 140 mm Hg, an intensive systolic blood pressure target of 120 mm Hg resulted in lower rates of fatal and nonfatal major cardiovascular events and death from any cause. Significantly higher rates of some adverse events were observed in the intensive-treatment group.

  Study phs003566

• Everything you need to know about Federated EGA Affiliates: Q&A

  Addressing common questions about Federated EGA (FEGA) Affiliates and providing answers. FEGA Affiliates have recently been defined as the third tier within the newly approved “Federated EGA Structure and Organisation” document, alongside Central EGA (CEGA) and the FEGA Nodes. In this blog post, we aim to answer the questions that may arise on this topic. For more general information about FEGA Affiliates, you can refer to this blog post. Let's now start with the questions and answers! What is the difference between a FEGA Affiliate and a FEGA Node? FEGA Nodes A FEGA Node is a national-level service that accepts submissions and provides archiving and access services within a single country's jurisdiction. Nodes provide end-to-end services for submitters in that jurisdiction, including submission support, processing, permissions management, and distribution. FEGA Affiliates A FEGA Affiliate is not a national submission service. Instead, it archives data from its own legal entity and/or selected partner institutions (for example, consortium partners). Affiliates are designed for organisations and consortia that need to keep data stewardship and access decisions within their own governance model, while still enabling discovery through the broader FEGA ecosystem. What do Affiliates commit to? Here is a practical checklist of the core expectations for a FEGA Affiliate. FEGA Affiliate checklist Discovery and metadata Submit non-personal metadata to CEGA to obtain EGA accessions and enable discovery (with metadata validation handled by the CEGA Helpdesk). Operations and resilience Maintain a data storage system and a defined backup/recovery approach. Ensure operational capacity (personnel and infrastructure) to deliver Affiliate services. Maintain systems and practices that support local data security and align with EGA best-practices. Access and user support Provide data distribution or access (e.g in a trusted research environment) to authorised users to datasets hosted at the Affiliate. Provide permissions management for datasets archived by the Affiliate (the CEGA DAC (Data Access Committee) Portal is always at your disposal to use). Provide a minimal helpdesk with sufficient capacity to support relevant DACs and approved users. Membership term Commit to a minimum membership term of four years. Governance and participation (lightweight by design) Affiliates integrate into the FEGA network operationally, but without the committee obligations associated with Nodes. When is becoming an Affiliate the right fit? Consider the Affiliate tier if: You host (or plan to host) controlled-access datasets that must be managed under your organisation’s or consortium’s governance, you want studies to be discoverable through EGA, but need access control and/or distribution to remain local, and you are not aiming to operate a national submission service tier.. What if a FEGA Node already exists in my jurisdiction? We encourage any potential Affiliate to reach out to their national FEGA Node to explore collaboration at the national level. Why? Federated models within a country already exist in FEGA, where a national Node operates like a Central EGA for their country. FEGA Nodes can also represent the interests of any Affiliates within their jurisdiction at the FEGA Committees. FEGA Nodes have experience and can support you on your FEGA journey. Ready to join as a FEGA Affiliate? We’re excited to open this formally defined pathway for organisations and consortia to participate in the federation. If you think the FEGA Affiliate tier fits your needs, start by assessing: Your scope (organisation/consortium-held data rather than public external submissions), Your ability to provide the core services (permissions, distribution/access, helpdesk, monitoring), and Your readiness to submit non-personal metadata to CEGA for accessioning and discovery. To express interest, please contact us here, and we'll help you understand the onboarding steps and how Affiliates integrate into the FEGA network.

  Blog fega-affiliates-q&a

• Systolic Blood Pressure Intervention Trial (SPRINT-BioLINCC)

  Available DataThe available data include all elements of the previously released SPRINT Primary Outcome Paper (SPRINT-POP) data, the full SPRINT clinical data including the MRI and MIND data, and select ancillary study data (Ambulatory Blood Pressure Monitoring, APOL1, Acute Kidney Injury, ASK, Heart, FAST, PWV, Renal Resistance, Biomarkers, Plasma AD).ObjectiveThe Systolic Blood Pressure Trial (SPRINT) was conducted to test the hypothesis that treating systolic blood pressure to a target of less than 120 mm Hg, as compared to a target of less than 140 mm Hg, would reduce the incidence of cardiovascular disease.BackgroundHypertension is a highly prevalent condition among adults and is a leading risk factor for myocardial infarction and stroke. Further, isolated systolic hypertension is the most common form of hypertension in adults over 50 years of age. Observational studies have shown a monotonic increase in cardiovascular risk with systolic blood pressures above 115 mm Hg; however, general population clinical trials have only documented the benefits of lowering systolic blood pressure to a target of 150 mm Hg. A 2007 expert panel sponsored by the National Heart, Lung, and Blood Institute designated the hypothesis that lowering the systolic blood pressure goal to a level SubjectsA total of 9361 participants were enrolled, with 4,678 randomized to the intensive-treatment group and 4,683 randomized to the standard-treatment group.DesignSPRINT was a randomized, single blinded (outcome adjudicators were blinded to treatment assignment) treatment trial with participants randomized to a systolic blood-pressure target of either less than 140 mm Hg (the standard-treatment group) or less than 120 mm Hg (the intensive-treatment group). Following randomization, baseline hypertensive regimens were adjusted in accordance with study treatment algorithms established for each group. The study formulary included all major classes of antihypertensive agents. Investigators could prescribe other antihypertensive medications, but the use of drug classes with the strongest evidence for reduction in cardiovascular outcomes was encouraged. This included thiazide-type diuretics as the first-line agent, loop diuretics for participants with advanced chronic kidney disease, and beta-adrenergic blockers for participants with coronary artery disease. Medications for participants in the intensive-treatment group were adjusted on a monthly basis to target a systolic blood pressure of less than 120 mm Hg. Medications for participants in the standard-treatment group were adjusted to target a systolic blood pressure of 135 to 139 mm Hg, and the dose was reduced if systolic blood pressure was less than 130 mm Hg on a single visit or less than 135 mm Hg on two consecutive visits. Lifestyle modification was encouraged as part of the management strategy.Participants were seen monthly for the first 3 months and every 3 months thereafter. Demographic data were collected at baseline. Clinical and laboratory data were obtained at baseline and every 3 months thereafter. A structured interview was used in both groups every 3 months to obtain self-reported cardiovascular disease outcomes. Medical records and electrocardiograms were obtained for documentation of events. Incidences of hypotension, syncope, injurious falls, electrolyte abnormalities, and bradycardia that were evaluated in an emergency department were included in adverse event reporting. Occurrences of acute kidney injury or acute renal failure requiring hospitalization were also monitored. The primary outcome was a composite outcome of myocardial infarction, other acute coronary syndromes, stroke, heart failure, or death from cardiovascular causes.ConclusionsThe blood pressure intervention was stopped in August of 2015 (median follow-up of 3.26 years) after the cardiovascular outcome results exceeded the boundary for efficacy at two consecutive time points. Compared with a systolic blood pressure target of less than 140 mm Hg, an intensive systolic blood pressure target of 120 mm Hg resulted in lower rates of fatal and nonfatal major cardiovascular events and death from any cause. Significantly higher rates of some adverse events were observed in the intensive-treatment group.

  Study phs003483

• 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

• DAC Portal documentation

  DAC Portal Welcome to the DAC Portal documentation! If you are involved in governance or legal aspects, technical or operational aspects, or serving as a data steward, this page will be helpful for you. By exploring these materials, you can define your own Data Access Committee (DAC) and policies, understand the minimal requirements for a DAC and a policy object, and comprehend how the EGA data access requests are managed. DAC Portal Index Setting up your account DACs and Policies Pending Request table Manage Data Requests History page Audit your DACs, policies, and datasets Deprecation User Preferences DAC API - A programmatic approach Setting up your account Register yourself as an EGA user. The Helpdesk team will validate your account (this could take up to 48 hours). After validation, you will receive an email with a link to verify your email - Make sure you click on the link to verify your account! If you don’t receive the email, please check your spam folder. Once your account is active you can login to the DAC Portal. We recommend you to check out the Take The Tour! DACs and Policies Create a DAC Click on Create a DAC Add the title and description to your DAC. Once ready, click on the ‘Create’ button. You will have to wait until the Helpdesk Team validates the creation of your DAC. Once approved by Helpdesk, your DAC will be assigned with a persistent identifier (EGAC). Edit a DAC Once your DAC is registered and approved, you can edit it and add contacts. Remember, you can add as many contacts as needed. And, you can also remove/add contacts anytime. To add a contact, you must write the username or email of a registered EGA user in the Members field. Once you have typed the whole username or email, a drop-down menu will appear where you can select the contact. Select a contact from the drop-down menu. Make sure you write the full username or email of the contact so it appears as an option. Make sure that you select one main contact. Set up the role you want to grant to the new contact of the DAC. We recommend you to check out the FAQ to learn more about the different roles! After adding all the necessary contacts and assigning roles, click on Update. This will send an email notification to the new DAC member, letting them know about the invitation to managing the DAC. Once they log into the DAC Portal, they will be able to either accept or turn down the invitation. Create a policy First, please note that all information registered in a policy metadata object will be publicly available on the EGA website. For each released dataset, all users will be able to read the policy under the “Data Access” tab. Select Create a Policy from the Policies tab. Select the DAC that you want your policy to depend on. Add the title and terms and conditions for accessing and using your data. If you already have an external website for data access requests, you can add the URL directly here (see example here). You can find a template of a Data Access Agreement (DAA) document in our Policy documentation page. The DAA template is provided for guidance only and should be adapted to suit your own purpose depending on the security policies, terms for publication or embargoes, and restrictions on data use or sharing. You can also add Data Use Ontologies (DUO) to your policy. These DUO codes are used to semantically tag the terms and conditions of using the data linked to the policy (example). For more information, you can refer to the Data Uso Ontology documentation. Once completed, make sure to click the Create button in order to register your policy. A persistent identifier (EGAP) will be assigned automatically. Edit a policy Please, note that you will not be able to edit the policy if it shows in orange. Meaning that you are a member of the DAC linked to that policy. Members don’t have edit rights. Only admins can edit objects. You can check your registered policies on My policies, available at the menu in the top-right corner. Select your policy. It will then display the information of that policy, allowing you to edit the information. Once you are done with the modifications, click Update. Pending Requests Table Table Settings Upon selecting a specific DAC, you will be presented with a customisable table showcasing all pending data access requests. This table allows you to tailor the displayed information according to your preferences by selecting desired columns. Available Columns (Displayed with the Eye Icon): Date: The date when the data access request was submitted. Full Name: Name and surname of the user submitting the request. Email: Email address associated with the user's request. Username: The username linked to the EGA account of the user submitting the request. Organisation: Affiliated organisation of the user submitting the request. Dataset: EGA accession ID for the requested EGA dataset. Dataset Title: Title of the requested dataset. DAC Comment: Space provided for internal comments related to the request. Expiration: Option to specify an expiration date for granted permissions. Check more information on the pending request table here! Table Filters By default, all data access requests are displayed. However, you have the option to apply filters to refine your view. Follow these steps to apply filters: Click on the More button. This action will reveal the different columns available for applying filters. As you select columns, the checked fields will be displayed at the top of the request table. Choose the specific values within the selected columns that you want to filter for. Click the Search button to apply the selected filters. Do you find yourself frequently applying the same filters? You can save multiple combinations of filters for easy access at any time! Here's how: Click the Save button. Provide a descriptive name for the filter combination. Click Save to confirm. To load saved filters, simply select the saved filter from the dropdown menu to apply it automatically. Check more information on filters here! Advanced Search For users requiring more granular control over their data access requests, the DAC Portal offers an Advanced Search feature using JIRA Query Language (JQL). With JQL, you can craft precise and complex queries to filter and retrieve specific data access requests based on various criteria such as date, user details, dataset attributes, and more. Using JQL's flexibility, you can create custom queries to meet your specific needs, allowing for advanced filtering options beyond the standard filters provided. You can find the Advanced Search feature following these steps: Click the … button Select Advanced To view the allowed fields for filtering and explore all available options, simply click on the info icon. Once you have written the filtering values, click the Search button to apply. Check more information on filters here! Manage Data Requests Upon logging into the DAC Portal, you'll notice a sand clock icon next to the DAC ID, indicating the number of pending requests. Click on the DAC to review these requests. Accept requests Click on the right side of the toggle button to grant access to the user. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with granting permissions. Deny requests Click on the left side of the toggle button to deny access to the user. Provide a reason for the denial. Note that the user will receive an email with this reason. Click on the Done button. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with denying permissions. Tips! Apply a filter to view all requests to be managed at once. Use the toggle in the row with column names to grant or deny permissions for multiple requests simultaneously. You can grant and deny permissions in the same action, simplifying the process. The confirmation box will provide a summary of all actions, including grants, denials, DAC comments, and expiration dates. History Page The History page serves as a dedicated space to view information regarding all requests managed by all DAC members. Here, you can review active permissions and revoke them as needed. Go to the History page by clicking on the "HISTORY" button from a DAC page in the DAC Portal. Here are the different row types you may find: Current permissions: row in green with a toggle button to revoke permissions on the right. Approved requests: row in green, with no toggle button. Request denied: row in red with “request denied” on the right. Permission revoked: row in red with “permission revoked” on the right. Distinctions to Note! Between Request Denied and Permission Revoked: Request Denied: Refers to requests that were rejected from the outset, indicating that access to the dataset was never granted. Permission Revoked: Indicates that permissions were previously granted but have since been revoked. Users with permissions revoked have previously accessed the dataset. Between Approved Request and Current Permissions: Approved Request: Represents an entry when a data access request has been approved in the past. Current Permissions: Denotes ongoing permissions where a user has present access to the dataset. An approved request may now appear as Permission Revoked in the present. By observing these different rows, users can gain insight into the complete history of a user's interactions and permissions regarding a dataset over time. To revoke access to a specific user for a dataset, follow these steps: Go to History page Look for the row with the specific permissions. You can use the filters! Click on the toggle button to revoke access. Add a denial reason. Bear in mind that the requester will receive the denial reason! Click on the Apply button. Check more information on the Hisotry page here! Audit your metadata objects In the DAC Portal, you can efficiently manage and audit various metadata objects pertinent to your role as a Data Controller. Upon accessing the DAC Portal, you will encounter three primary tabs on the homepage: DAC: Contains information about Data Access Committees (DACs). Policies: Provides insights into linked policies. Datasets: Displays datasets and relevant details. Within each tab, you'll find a comprehensive list of the objects you manage, these being grouped by type (DAC, Policies, Datasets), as well as by your role (member/admin). Whilst the lists give you a quick look, to check how things are connected, we've added a table at the bottom DACs and policies. DACs Let’s check which policies are linked to a specific DAC: Go to "My DACs." Select a DAC. Click on "EDIT" to see more details. You are now on this page: Scroll down to see the linked policies. Click on “List of linked policies of this DAC” and you will see a list of all policies linked to your selected DAC. Policies Do you want to view a list of datasets connected to a particular policy? Follow the same steps mentioned earlier, but head to the policy tab this time. In the policy tab, you'll find a list of all your policies. Here, you may notice two different icons next to the policy ID (EGAP): DAC Icon (): This represents the DAC. Hover your mouse over the icon to see the DAC ID. Dataset icon (): This indicates datasets falling under that policy. The number next to the icon tells you how many datasets are linked to the policy. For example, if you see "2" next to the icon, it means there are two datasets linked to that policy. If you want to check the linked datasets and their relevant information, simply click on a specific policy. You'll then find the "List of linked datasets of this policy" at the bottom of the page. Datasets Finally, in the dataset tab, by default you will see a list of all the datasets you can manage with all your DACs and policies. Yet, we've included two handy ways to organise them: DAC vs. policy: You can group by datasets, either by DAC or policy. Released vs. unreleased: You can sort out datasets based on their release status. Feel free to experiment with both options! For instance, if you want to see which DACs have unreleased datasets, simply select DAC and unreleased, and you'll get the details you need! Deprecation Do you have a bunch of metadata objects like DACs and policies that you don’t need anymore? This section shows you how to get rid of them! But what does "deprecation" mean for EGA? It's basically changing the status of a metadata object to "deprecated," which means we won't be using it in the future. In simple terms, it's like saying these objects are no longer useful. However, because we believe in making metadata FAIR, once an object has a persistent identifier, we can't just delete it. So, instead of deleting, we deprecate it. Here's a helpful tip! If you want to make a metadata object disappear from the DAC Portal, deprecate it. You won’t see it in the portal anymore! Let’s say you want to deprecate a DAC. Let’s do it! Go to "My DACs." Select a DAC. Click "EDIT" to see more details. You are now in this page: Click on the Deprecate button It will then appear a message. There are two options here: Your DAC is not linked to any policy, hence it’s ready to be deprecated. Click on “Yes. Confirm” to deprecate your DAC object! Your DAC is linked to at least one policy, and you need to either: Deprecate the linked policy first, or Link the policy to a different DAC Now, let’s say you don’t want to get rid of the policy altogether, but you want to change the DAC it’s linked to. Here's how: Go to the policy tab. Find the policy you want to change (for example, EGAP50000000019). Choose a new DAC to link it to. Click on Update. See the Edit Policy section for more details. After ensuring that the DAC we want to deprecate isn’t linked to any policy, return to the "My DACs" section and follow the steps outlined previously. This will lead you to a confirmation message. You can only deprecate DACs and Policies. To deprecate a dataset, please contact our Helpdesk team. Check more information on how deprecation works here! User Preferences We have implemented email notifications in the DAC Portal. Here’s the complete list: DACs: Pending requests Approved by Helpdesk Rejected by Helpdesk DAC Invitation Requesters: Data access request approved Data access request denied Permissions revoked Upcoming expiration date As a DAC member, you will be able to decide whether you want to receive the DAC notifications or not. For that, go to the top-right corner menu, select User Preference. You will be able to decide whether you want to receive notifications for: Approved by Helpdesk Rejected by Helpdesk DAC Invitation For pending requests, you will be able to select how often do you want to receive the notification: Daily Weekly Fortnightly As a DAC member, you are responsible for managing data access requests. Consequently, you will receive notifications for pending requests. If you prefer not to manage these requests, please arrange to be removed as a contact for your DAC. Here's a tip! Do you have a pending request that you don’t want to receive a notification? Add a DAC comment! The EGA understands that a request resolution can take some time, for this reason, if you add a comment (make sure you save it by clicking the APPLY button!) we will filter those requests at the time of sending the notification! DAC API - A programmatic approach In addition to the new DAC Portal, we are excited to announce the release of the DAC API. This enables users to programmatically manage permissions. If you are interested in learning more about the technical specifications, you can click the button below. Check out the DAC API specification!

  Documentation access/data-access-committee/dac-portal

• Programmatic submission based on XML

  Programmatic submissions (XML based) For further information please check our Submission FAQs, submission quickguide as well as submission terms! Introduction Besides the Submitter Portal tool, EGA supports programmatic sequence and clinical data metadata submissions. If you are not sure what this means, you may want to explore our brief metadata introduction. Programmatic submissions are recommended for array-based submission. Moreove, it may be of help if your submission is recurrent or it is difficult to manage manually due to its sheer size. Otherwise, we highly recommend using the Submitter Portal to perform submissions. In this page we will guide you through the required steps to programmatically submit data to the EGA. Programmatic submissions require your metadata to be structured for an easy and straightforward validation and archival. It basically consists in formatting your metadata as Extensible markup language (XML) files and submitting them to the EGA using the WEBIN Before submitting metadata to the EGA, it is important to ensure that the information in your XML files is compliant with our standards. You can see further details on how these standards are maintained at EGA at our EGA Schemas documentation page. Using WEBIN, you can validate your XML files against EGA's schemas to ensure that your metadata is compliant before submission. WEBIN services WEBIN production service WEBIN test service We advise you to submit your metadata to the test service when submitting to the production service for the first time. The test service is identical to the production service except that all submissions will be discarded in the following 24 hours. This allows you to learn about the submission process without having to worry about data being submitted. Authentication Authentication is required each time a submission is made. The submission service uses HTTPS protocol for metadata encryption and identification to provide a secure submission environment. Data file upload Both Runs and Analyses reference files (e.g. FASTQ need to be uploaded to the EGA before these metadata objects are submitted. In other words, if you submit a Run that references a file that we cannot find associated with your account, the metadata submission will fail. See further details on how to upload your files in our File Upload documentation. Metadata model of the EGA Our metadata model is formed by multiple metadata objects. Check further details in our documentation at our EGA Schema documentation page. Working with EGA XMLs files Now that the basic concepts of the EGA metadata have been described, you can start preparing your programmatic submission through XML. Here you will find the guidance on how to prepare the XML files. Programmatic Submission Tutorial Video Take a look at the Programmatic Submission Tutorial Video, which explains the workflow of a programmatic submission and goes over an example metadata submission. Programmatic Submission Tutorial Video. When building your XML files, we recommend using text editors (e.g.Sublime Text or VisualStudio) that allow you to visualise the structure of the XML with ease. Furthermore, these editors constantly check the consistency of the XML structure. Alternatively, and if the submission consists of a big number of objects (specially analyses), you may find the tool star2xml handy. This tool allows for a direct conversion between metadata in a tabular format (e.g. a spreadsheet) into XMLs. Identifying objects: Aliases and center names Every EGA object must be uniquely identified within the submission account using their alias attribute. The aliases can be used in submissions to make references between EGA objects. Let us dig into EGA's use of aliases and center names: alias: every object should have a name that is unique within your submission account. Once submitted successfully, every alias will be assigned a unique and permanent accession (EGA ID). refname: when an object references another by its alias, the alias of the referenced object goes into the "refname" attribute of the referencing object. For example, if a sample has the alias "sample1", and an experiment uses this sample, then the experiment's "EXPERIMENT/SAMPLE/refname" attribute should be "sample1". center_name: The "center_name" attribute is required within the submission XML and, if not provided when the object is submitted, it will be automatically filled using your default EGA account center_name. This element is the "controlled vocabulary acronym or abbreviation that is provided to the account holder when the account is first generated". If the submitter is brokering a submission for another institute, the submitter should use their special broker account name in broker_name while the data centre acronym remains in center_name. Log-in details should have been provided when you requested a submission account. Please contact our Helpdesk team if you have any questions. run_center: Many submitting centers contract out the actual sample sequencing to another center. In these cases, the sequencing center should be acknowledged in the run_center attribute. Again, this is controlled vocabulary and the acronym should be sought from EGA helpdesk before submitting. Please contact our Helpdesk team if you have any questions. Prepare your XMLs The goal of this section is to provide sufficient information to be able to create the metadata XML documents required for programmatic submissions. Please note, the EGA utilises the XML schemas maintained at the European Nucleotide Archive (ENA). It is important due to the fact that by using a similar system, some pieces of documentation from the ENA's programmatic submission can also help you with your programmatic submission to the EGA. For example, you can submit programmatically without using a Submission XML by following the steps at Submission actions without submission XML. A submission does not have to contain all different types of XMLs. For example, it is possible to submit only a few samples; or a study that is later to be referenced. You can submit each object one by one, or submit all in a batch: you choose what method of submission works best for you. We do recommend, nevertheless, that you submit the objects to be referenced (e.g. samples or studies) first, and the objects that reference these (e.g. experiments or datasets) afterwards. You can see a graphical view of these objects and their relationships at our EGA Schemas page. Independently of the submission scenario, you will always require a Dataset XML. The entity of a dataset is what is used to control access to the given data, in the form of runs or analyses. In other words, when a requester is granted access, it is through the dataset and the objects (e.g. runs or analyses) that the dataset contains, granting access to them in one go. Given the nature of the EGA, a dataset XML will always be required for the data access. First, we will differentiate between submissions of "raw" and "processed" data: Runs and Analyses, respectively. Run data submissions Raw data derives from instruments "as is". For example, a plain sequence file (e.g. FASTQ or unaligned BAM files) would be considered raw data. A typical raw (unaligned) sequence read submission consists of 8 XMLs: Submission Study Sample Experiment Run DAC Policy Dataset When technical reads (e.g. barcodes, adaptors or linkers) are included in the submitted raw sequences, a spot descriptor must be submitted to describe the position of the technical reads so that they can be removed. The following data files can be submitted without providing spot descriptor information in the experiment/run XML: BAM files (single reads) SFF files (single reads without barcodes) FastQ files (single reads without any technical reads) Complete Genomics files Analysis data submissions Processed data is, in some way, refined raw data. This includes raw data that has been processed by some form of analysis method (e.g. alignment, noise reduction, etc.). For example, an aligned sequence (e.g. BAM file), that was created using raw FASTQ files, would be a processed file. This category includes most types of data: sequence alignment files (e.g. BAM or CRAM), clinical data (e.g. phenopackets), sequence variation files (e.g. VCF), sequence annotation, etc. A typical EGA analysis data submission consists of 7 EGA XML: Submission Study Sample Analysis DAC Policy Dataset We accept three different types of analysis data submissions: BAM files (for multiple read alignments) VCF files (for sequence variations) Phenotype files (in any format) In anycase, keep in mind that samples must be created in order to be referenced in the analyses. In other words, the provenance of the information within the BAM, VCF and phenotype files Example XMLs Below you can find a non-extensive list of example XMLs with descriptive fields (i.e. explaining what to provide in each field). Furthermore, you can also find real examples (i.e. the true value of the provided fields) in our GitHub repository. Submission XML The submission XML is used to validate, submit or update any number of other objects. The submission XML refers to other XMLs. New submissions use the ADD action to submit new objects. Object updates are done using the MODIFY action and objects can be validated using the VERIFY action. Descriptive submission XML example True values submission XML example Study XML The study XML is used to describe the study containing a title, a study type and abstract as it would appear in a publication. Descriptive study XML example True values study XML example Please use the following notation within the property "STUDY_LINKS" when including PubMed citations in the Study XML: <STUDY_LINKS> <STUDY_LINK> <XREF_LINK> <DB>PUBMED</DB> <ID>18987735</ID> </XREF_LINK> </STUDY_LINK> </STUDY_LINKS> Sample XML The sample XML is used to describe the samples used to obtain the data, whether they were sequenced, measured in any other way, or have an associated phenotype. The mandatory fields include information about the taxonomy of the sample, sex, subject ID and phenotype. For example, the mandatory attribute fields for each sample would look like these, within the array of "SAMPLE_ATTRIBUTES": <SAMPLE_ATTRIBUTES> <SAMPLE_ATTRIBUTE> <TAG>subject_id</TAG> <VALUE>free text!</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>sex</TAG> <VALUE>female/male/unknown</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>phenotype</TAG> <VALUE>Free text, EFO terms (e.g. EFO:0000574) are recommended</VALUE> </SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTES> Sample is one of the most important objects to be described biologically, it is highly recommended that “TAG-VALUE” pairs are generated as SAMPLE_ATTRIBUTES to describe the sample in as much detail as possible. For example, were we to give the population ancestry of the sample, we could add a new attribute to the array, in which, for example, we would indicate that the sample derives from an individual of "Mende in Sierra Leone" (MSL), with an african ancestry: <SAMPLE_ATTRIBUTE> <TAG>Population</TAG> <VALUE>MSL</VALUE> </SAMPLE_ATTRIBUTE> Given that VALUE and TAG are free text, the combinations are limitless in order to give you full flexibility on the information you want to provide. We recommend you use the Experimental Factor Ontology (EFO) to describe the phenotypes of your samples. You can provide more than one phenotype by adding more items to the array of SAMPLE_ATTRIBUTES. Phenotypes considered essential for understanding the data submission should be provided. Each phenotype described should be listed as a separate sample attribute <SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTE>. There is no limit to the number of phenotypes that can be submitted. If a suitable EFO accession cannot be found for your phenotype attribute, please consider using another controlled ontology database (e.g. HPO, MONDO, etc.) before using free text. Descriptive sample XML example True values sample XML example Experiment XML The experiment XML is used to describe the experimental setup, including instrument platform and model details, library preparation details, and any additional information required to correctly interpret the submitted data. Where any of these values differ between runs, a new experiment object must exist, since runs are grouped by experiments. Each experiment references a study and a sample by alias, or if previously-submitted, by accession. Pooled data must be demultiplexed by barcode for submission. Descriptive experiment ( Illumina paired read ) XML example True values experiment ( Illumina paired read ) XML example Run XML The run XML is used to associate data files with experiments and typically comprises a single data file (e.g. a FASTQ file). Please note that pooled samples should be de-multiplexed prior submission and submitted as different runs. Descriptive run XML example True values run XML example Analysis XML Given that an analysis can be used to submit any type of processed data to the EGA, we will list below an example of each of the three most common types of analysis XMLs submitted to the EGA: sequence alignments (e.g. BAM files); sequence variation (e.g. VCF files); and clinical metadata or phenotypes (e.g. phenopackets). Regardless of the type of processed data submitted in the analysis, the analysis must be associated with a Study and can reference multiple types of other objects, from samples to experiments, if they are available at the EGA. Just like with Runs, whenever a file is submitted to the EGA through an analysis object, the file MD5 checksums must be present, in order for the EGA to validate file integrity upon transfer. This also includes index files when applicable (e.g. .bai.md5 files). Ideally, any analysis that uses a reference sequence for some kind of alignment (e.g. BAM, CRAM or VCF files), would contain metadata about the alignment, such as INSDC reference assemblies and sequences, by either using accessions (e.g. CM000663.1) or common labels (e.g. GRCh37). Read alignment (BAM) Analysis XML The Analysis can be used to submit BAM alignments to EGA. Only one BAM file can be submitted in each analysis and the samples used within the BAM read groups must be associated with Samples. Descriptive bam alignments XML example True values bam alignments XML example Sequence variation (VCF) Analysis XML The Analysis can be used to submit VCF files to EGA. Only one VCF file can be submitted in each analysis and the samples used within the VCF files must be associated with Samples. Download analysis XML (VCF) Phenotype files The Analysis XML can be used to submit phenotype files to the EGA. Only one phenotype file can be submitted in each analysis and the samples used within the phenotype files must be associated with EGA Samples. Download analysis XML (Phenotype) DAC XML The DAC XML describes the Data Access Committee (DAC) affiliated to the data submission. The DAC may consist of a group or a single individual and is responsible for the data access decisions based on the application procedure described in the POLICY.XML. As with any other object, if it was already submitted to the EGA, there is no need to submit it again: you can reference an existing object within the EGA. Hence, A DAC XML does not need to be provided if your submission is affiliated to an existing EGA DAC.. Further information on DACs can be found here, and you can always contact our Helpdesk team if you have further inquiries. Descriptive dac XML example True values dac XML example Policy XML The Policy XML describes the Data Access Agreement (DAA) to be affiliated to the named Data Access Committee. Descriptive policy XML example True values study XML example Dataset XML The dataset XML describes the data files, defined by the Run.XML and Analysis.XML, that make up the dataset and links the collection of data files to a specified Policy. The dataset xml is commonly the last metadata object to be submitted, since it references multiple other entities. 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. Descriptive dataset XML example True values dataset XML example Validating and submitting your EGA Validating EGA's XMLs through Webin After you have ensured that the XMLs are properly formatted and contain all the required information. You can proceed to validate and submit your data. Use the curl command to validate your XML file: Once you have prepared your XML file and asserted you have access to Webin, you can validate your XML file programmatically against EGA's schemas using the curl command. There are multiple ways in which you can validate your XMLs. This variety has to do with the fact that: (1) there are 2 instances of Webin (test and production); and (2) that validation is a default step during submission. In other words, any time that you submit your data through Webin, it will be validated automatically before being accepted. This allows for 4 possible routes of validation, all having the same validation result: validating or submitting to either the production service or the test service of Webin. For example, directly validating a "study" object XML in the testing service (wwwdev…) would look like the following: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" In this command, you would need to replace <USERNAME> and <PASSWORD> with your EGA account username and password, respectively. You would also replace <INPUT_FILE> with the path to your XML file. A mock example would look like the following: curl -u ega-test-data@ebi.ac.uk:egarocks -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" The validation attempt can have different results depending on the given arguments: If your XML file is valid according to EGA's schemas, you will see a message indicating that your XML file is compliant. For example, see below for our mock example, where the "success" was "true" (i.e. no validation errors found). Nevertheless, notice how the "<STUDY accession=" is empty: it is because we were simply validating, so the study did not get an accession or ID. <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:19:28.850+01:00" submissionFile="submission-EBI-TEST_1681222768850.xml" success="true"> <STUDY accession="" alias="Mock example" status="PRIVATE"/> <SUBMISSION accession="" alias="SUBMISSION-11-04-2023-15:19:28:840"/> <MESSAGES> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If there are any errors or warnings, the tool will display them, allowing you to correct them before submitting your data to EGA. For example, in the following response, it is said that the object we were trying to submit was already existing, and therefore the "success" was "false". <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:12:35.609+01:00" submissionFile="submission-EBI-TEST_1681222355609.xml" success="false"> <STUDY alias="Example!_Human Microbiome Project SP56J" status="PRIVATE" holdUntilDate="2023-03-11Z"/> <SUBMISSION alias="SUBMISSION-11-04-2023-15:12:35:576"/> <MESSAGES> <ERROR>In study, alias: "Example!_Human Microbiome Project SP56J". The object being added already exists in the submission account with accession: "ERP127584".</ERROR> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If the curl command retrieves no response at all, please double check if your username and password are correctly provided. Also notice the "ACTION=..." argument passed to the Curl command. This specifies the action to take during the call to Webin, so we do not need a "Submission" XML just for a validation attempt. See more at submission actions without submission XML. Furthermore, validation of multiple files or objects (e.g. sample, experiment, study…) can be done in a single command by adding more arguments (i.e. '-F'). For example: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" -F "SAMPLE=@sample.xml" -F "DATASET=@dataset.xml" As mentioned above, beside "validate" action in the test environment, you can also validate your metadata by three other methods: "Validate" in the production server. From our example above, you simply need to take the "dev" away from the URL. curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the development server. From our example above, you would simply need to replace the action: from "validate" to "add". Whatever is submitted to this service will be discarded in 24h, so whether something gets submitted or not would not matter in the long run. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the productionserver. A combination of the previous two methods, which would render this attempt into a submission. This path is just to be taken when you are sure your metadata is compliant and what you want to submit. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" What happens after the submission of a dataset XML? Once you have completed the registration of your dataset/s please contact our Helpdesk Team to provide a release date for your study. Please note that all datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitter or DAC contact contact the EGA Helpdesk for the study to be released. We strongly advise you not to delete your data until EGA Helpdesk confirms that your data has been successfully archived.

  Documentation submission/metadata/submission/programmatic-submission-xml

• Center for Common Disease Genomics [CCDG] - Inflammatory Bowel Disease (IBD) - Global Microbiome Conservancy Host Exomes

  The human body is a complex ecosystem, hosting tens of billions of bacteria, primarily in the gut. This community, known as the &#39;gut microbiome&#39;, is vital to human health, affecting functions ranging from metabolism, immunity, and development, to behavior. Conversely, reduced microbiome diversity and other imbalances of the gut community (dysbiosis) are associated with many diseases of the industrialized world, such as metabolic syndrome, asthma, and inflammatory bowel diseases among others. Microbiome-based interventions thus hold the potential to revolutionize our understanding of, and therapeutic approaches to, many aspects of human health and disease. Despite many advances, current understanding of the human microbiome is largely limited to majority ethnic groups in industrialized nations, and does not reflect the full diversity of human commensal microbiota. In addition to the scientific ramifications of minority underrepresentation, there are significant translational and ethical implications as well: First, failing to capture the full diversity of healthy human microbiota may limit our ability to understand and generate effective therapeutics for many microbiome-associated diseases. Second, underrepresented groups are less likely to benefit from microbiome-based medical advances tailored to well-studied populations, propagating healthcare inequities. The window of opportunity to characterize human microbiome diversity is closing rapidly. Human gut microbial diversity is diminishing around the globe, due to the spread of Westernized diets and lifestyles, which are associated with reduced microbiome biodiversity. The Global Microbiome Conservancy (GMbC) is a non-profit collaboration between scientists and communities around the world that aims to 1) preserve the full biodiversity of the human gut microbiome, 2) characterize its evolution across populations and host lifestyles and 3) investigate its interactions with the human host. We are collecting gut microbiome and human DNA samples from diverse populations around the world, from isolated groups to urbanized populations, to culture and study this essential biodiversity and to preserve it in a living "seed bank" as a long-term safeguard against future extinctions. The National Human Genome Research Institute (NHGRI) has funded a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes. Called the Centers for Common Disease Genomics (CCDG), this initiative will explore a range of diseases with the ultimate goal of: Undertaking variant discovery for enough different examples of disease architectures and study designs to better understand the general principles of the genomic architecture underlying common, complex inherited diseases. Understanding how best to design rare variant studies for common disease. Developing resources, informatics tools, and innovative approaches and technologies for multiple disease research communities and the wider biomedical research community. The initial focus of the CCDGs will be in cardiovascular disease (early-onset coronary artery disease, atrial fibrillation, hemorrhagic stroke), neuropsychiatric disease (epilepsy, autism), and autoimmune/inflammatory disease (type 1 diabetes, inflammatory bowel disease). The Broad Institute is one of four selected CCDG project centers. Both exome and genotyping information of GMbC participants will help understanding how recruited human populations are structured and genetically related. This human genetic structure will be analyzed in light of gut microbiome compositional and genomic differences to illuminate associations between specific microbiome traits and human groups and the evolutionary processes involved in the co-evolution of human hosts and their gut microbiome. It will also provide variant information for critical genes shaping the composition of microbiomes, such as genes involved in innate and adaptive immunity (e.g. antimicrobial peptides) or in the regulation of the immune response (e.g. Nod2). Association studies between specific variants in these genes and microbiome features will be performed, which will then be validated with in vitro and in vivo experiments. Human data from GMbC cohorts will also increase the genetic diversity of human populations, providing additional variant information for many susceptible loci that were found to be associated with Crohn&#39;s disease and ulcerative colitis. The whole genome sequencing data generated is comprised of samples from diverse and indigenous populations. Raw sequencing data, metadata, vcf and phenotype data at the individual level for a subset of the cohort&#39;s samples are available at https://anvilproject.org/data. For questions about availability, contact help@lists.anvilproject.org. Microbiome data for the samples sequenced in this study can be found in phs002235 Global Microbiome Conservancy Sequence Data.

  Study phs002205

• Prevention and Early Treatment of Acute Lung Injury (PETAL) Crystalloid Liberal or Vasopressors Early Resuscitation in Sepsis (CLOVERS) (PETAL CLOVERS-BioLINCC)

  Data Access NOTE: Please refer to the "Authorized Access" section below for information about how access to the data from this accession differs from many other dbGaP accessions. Access to Biospecimens is through the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). Biospecimens from PETAL CLOVERS include plasma, and whole blood. Please note that use of biospecimens in genetic research is subject to a tiered consent. Available Data: The data available for request now include Long Term Outcome data.Objectives: To compare the effects of a restrictive fluid strategy (with early use of vasopressors) to a liberal fluid strategy in participants with sepsis-induced hypotension.Background: Intravenous fluid resuscitation is a common therapy used in the initial treatment of participants with septic shock and sepsis-induced hypotension. The goal of initial fluid therapy is to increase depleted or functionally reduced intravascular volume that occurs in sepsis due to a vasodilated vascular network. However, intravenous fluid resuscitation can create dilutional coagulopathy, fluid overload, and pathogenic edema in the lungs and other organs. Vasopressor agents are also commonly used to treat hypoperfusion by inducing constriction of arterioles and venules and increasing cardiac contractility. Vasopressor therapy also comes with risks that include vasoconstriction resulting in tissue ischemia, increased cardiac workload, and arrhythmias. Clinicians have used these strategies, typically in combination, to provide supportive care for participants with sepsis-induced hypoperfusion. However, at the time of the CLOVERS study, there was limited data to guide specific use of these therapies, including fluid volumes, in the early care of participants with sepsis-induced hypotension. The CLOVERS study hypothesized that a restrictive fluid strategy used during the first 24-hours of resuscitation for sepsis-induced hypotension would lead to lower mortality before discharge home by day 90 than a liberal fluid strategy.Participants: A total of 1,563 participants, from 60 medical centers, of the planned 2,230 participants were enrolled, with 782 assigned to the restrictive fluid group and 781 to the liberal fluid group. Enrollment in the trial was ended after the second interim analysis due to a lack of significant difference observed between the two 24-hour strategies.Design: This study was a multi-center, prospective, phase 3 randomized non-blinded interventional trial of fluid treatment strategies in the first 24 hours for participants with sepsis-induced hypotension. Participants were randomly assigned in a 1:1 ratio to either a restrictive fluid strategy (with early vasopressor use) or a liberal fluid strategy. In each group, the assigned protocol was followed for a period of 24 hours. The restrictive fluid protocol prioritized vasopressors as the primary treatment for sepsis-induced hypotension, with 'rescue fluids' being permitted for prespecified indications that suggested severe intravascular volume depletion. The liberal fluid protocol consisted of a recommended initial 2000-ml intravenous infusion of isotonic crystalloid, followed by fluid boluses administered on the basis of clinical triggers (e.g., tachycardia) with 'rescue vasopressors' permitted for prespecified indications. A protocol amendment implemented in October, 2019, allowed for limiting the initial infusion to 1,000 ml if the participant's blood pressure and heart rate had stabilized and the clinical assessment was that the participant was unlikely to benefit from additional intravenous fluid administration. The clinical team could override the protocol-specified care instructions at any time if it was judged to be in the best interest of the participant.The primary outcome was death from any cause before discharge home by day 90. Secondary outcomes included 28-day measures of the number of days free from ventilator use, days free from renal-replacement therapy, days free from vasopressor use, days out of the ICU, and days out of the hospital. Conclusions: Among participants with sepsis-induced hypotension, the restrictive fluid strategy that was used in this trial did not result in significantly lower (or higher) mortality, or other measures of recovery such as length of hospital stay, before discharge home by day 90 than the liberal fluid strategy.

  Study phs004080

• 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

• Upcycling and merging data to challenge the dogma and identify new therapeutic targets for Glioblastoma

  Glioblastoma is the most common brain tumour. Characterised by a poor prognosis and its recurrence after multimodal treatments, the search for preventable risk factors has been mainly inconclusive up to date. Recently, the data merge from datasetsdeposited at the EGA allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. Challenges in glioblastoma research As for virtually all cancer types, many efforts have been made to unveil the molecular features responsible of the disease, and several cellular pathways have indeed been identified as being frequently mutated in glioblastoma. Nonetheless, targeted therapies based on identified genes have so far failed to improve outcome, thus survival mostly relies on a standard treatment unchanged since 2005. This is a frustrating situation for both the scientific and medical communities, and above all for the patients, still facing a dreadful path. Some researchers hypothesised that this may be due to the inability to efficiently target cancer stem cells, the originators of the other cell types, thus inducing cancer relapse. In 2019 Charles P. Couturier and colleagues sequenced RNA from single cells of freshly excised glioblastomas of 16 patients, and demonstrated that glioblastoma cells replicate normal brain cell development with a conserved neural cancer cell hierarchy centered around glial progenitor-like cells. In this way, they helped identify the possible target cells to improve efficacy and durability of treatment. Data upcycling at the EGA: the glioblastoma case study Single cell RNA sequencing is generated with a laborious and expensive protocol. The quality of the starting material is crucial (cells sample freshly extracted from the patients) and often several attempts are needed before producing reliable quality sequencing results. Collecting big numbers of patients is also challenging. The sequencing data produced by Kevin Petrecca’s group in Montreal, Canada and deposited at the EGA (EGAS00001004422) was recently upcycled by Lin Wang in San Francisco, California and pooled with their freshly produced ones, and then deposited at the EGA as Dataset EGAS00001004909 The data merge allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. They profiled 86 primary-recurrent patient-matched paired glioblastoma samples with single-nucleus RNA, among other techniques. These very comprehensive results lead the team to challenge the findings from several other cancer fields where chemotherapy standard chemo-radiation therapy selection pressure at the level of genomic alterations; this is indeed not the case for glioblastoma, where the pressure results in phenotypic transition between cellular states. Several technical controls were made to ensure that the merge of the data was not introducing a bias in the results, and an Inter-table analysis demonstrated nearly equal contribution to overall variance from each of the studies included, indicating that the findings were not due to inter-laboratory technical effect. A novel principal-component analysis showed that the largest contribution to variation in primary glioblastoma neoplastic cells was an axis between MES (mesenchymal) and proneural expression programs. In summary, treating glioblastoma often makes a MES, as commented by Lucy Stead about this remarkable work. Check Aaron Diaz’s team Nature Cancer paper, with strong technical tools, state of the art analysis and data from different sources converging to the same outcomes that made possible a significant step toward a better handling of a frightful cancer. References Couturier, C.P., Ayyadhury, S., Le, P.U. et al. Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy. Nat Commun 11, 3406 (2020). Stead, L.F. Treating glioblastoma often makes a MES. Nat Cancer 3, 1446–1448 (2022). Wang, L., Jung, J., Babikir, H. et al. A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. Nat Cancer 3, 1534–1552 (2022). Related links: Kevin Petrecca’s group dataset deposited at the EGA: EGAS00001004422 Lin Wang dataset deposited at the EGA: EGAS00001004909

  Blog upcycling-and-merging-data-to-challenge-glioblastoma

• Submission FAQ

  Submission FAQ Before Submission Is the EGA the right archive for my data? The most suitable archive for your data is dependent on the type of data you are wishing to submit and whether the data require public or controlled access. Public access is defined as complete and open access to all submitted data. On the contrary, controlled access, exerted by the EGA, requires formal applications to be made to access the submitted data files and metadata. EGA only accepts human-derived data subject to controlled access. If your submission contains other types of data, please choose the appropriate repository for it (see image below): ENA, EVA, ArrayExpress, BioSD and GWAS catalog.  Should your submission be subjected to controlled access? Data access conditions are normally defined in the original informed consent agreements signed by the participants involved in your study. All data submitted to the EGA is subject to controlled access. These consents prevent the derived data files, potentially identifiable, from being dispersed by open and public access. Controlled access data often refers to human data derived from medical research and consortium projects. If in doubt, consult the informed consent agreements that apply to your study The EGA enables you to hold a submission before publication. What data types can be submitted to the EGA? Data types accepted by the EGA can be split into three categories: Sequences: both in generic and platform-specific formats. Array-based: from raw signal files to processed matrices. Phenotypes: all possible phenotype formats are accepted. All manufacturer-specific raw data formats derived from major next generation sequencing platforms are accepted. Also generic sequence formats: flat reads in a FASTQ file, aligned sequences (BAM or CRAM files) as well as sequence variation files in VCF format. All array-based technologies are accepted, including raw data, intensity and analysis files, without any restriction on data formats accepted. We also accept and distribute phenotype data (associated to the samples) in almost any format: from an image to a README file. How long does a submission take? Submissions to EGA come in a variety of formats and sizes, thus it is difficult for us to exactly predict how long a submission will take. We, therefore, advise all of our submitters to allow as much time as possible to make a submission. Based on previous records, we anticipate that the submission process may take at least one month. The submitter’s familiarity with the procedures, possible technical issues that may arise during submission and the amount of data that is being submitted are the main factors that will affect the length of the submission process. However, each step of a regular submission should be considered when estimating the time it would take: Encryption of the files Upload of the files Metadata submission Archival of the files Release of the study and datasets to EGA webpage For example, the upload of the files depends on the submission size, while metadata submission mainly relies on each submitter’s expertise. Further, some steps (e.g. answering to inquiries) depend on the EGA Helpdesk team, which may take some days to be processed during busy times Is data deposited in the EGA secure? The EGA set-up consists of a secure computing facility for data processing, a shared EBI set-up for data submissions and distribution of data via data requests made through the EGA website. Data is also copied in the Barcelona Supercomputing Center (BSC) infrastructure, where all stored and distributed data is encrypted Data is encrypted along the submission process and stored securely, granting its access to authorised users exclusively. During the download process, through our Python Client or Aspera, all requested data is downloaded over secure https connections. All data at the EGA is encrypted, and only accessible (for log-in and download) through secure protocols. For further information please, visit our security overview. What documentation do I need to provide? All submissions require policy’s documentation: 'Data Access Agreement (DAA)', 'Data Processing Agreement (DPA)' and 'Authorized Submitters Formulary'. The data processors (EGA) and the data owners will also sign the DPA. Will all metadata be public? Among the submitted metadata we need to make the distinction between identifiable and unidentifiable metadata: (1) the former may allow the identification of the human the sample derived from (e.g. detailed geographical providence, personal name, family ancestry…); (2) while the latter can be used to interpret the data without compromising the anonymity of the patients. The majority of the metadata submitted to the EGA corresponds to the unidentifiable category (e.g. sequencer's model). This type of metadata is publicly available on the EGA website and other EBI resources/partners’ websites. On the other hand, some parts samples’ metadata are subject to being identifiable, and thus only accessible by authorized data requesters, with the exception of: 5 submitter-defined attributes of the sample: alias, title, subject_id, gender and phenotype. It is the submitter’s responsibility not to submit sensitive metadata in these public fields. 3 anonymised fields that pinpoint the sample record in archivals: sample’s EGA stable ID (EGAN…), BioSample ID (SAMEA…) and submitter’s center name. During Submission Are there any sample specific requirements for EGA? All samples submitted to the EGA must include the attributes of biological sex, subject ID (anonymised individual identifier) and phenotype information. These are critical for data findability and its analysis, and we highly recommend using controlled ontology terms where applicable. For example: defining tumour and non-tumour samples and/or defining disease state. The EGA recommends using the Experimental Factor Ontology Database to find ontologized terms that describe your sample phenotypes. How do I get an accession number to use in my publication? You will receive your study accession number (EGAS…) upon complete your submission, either: Programmatically. As soon as the metadata is submitted and validated your study will be assigned an accession number that will be given in the submission’s response. Manually registering your study and relevant metadata using the online metadata submission tool: the EGA submitter Portal. How are files uploaded to the EGA? Data files are uploaded into private submission drop boxes (i.e. environments to which you are granted access and where you can transfer your files) using INBOX or FTP. These spaces are provided as part of the submission procedure. Before uploading any file, you must encrypt your files, . Only encrypted files shall be uploaded to the drop boxes. Why does data need to to encrypted for my submitted files? It is one of the security steps the EGA has implemented. In case of a security breach, people without the proper encryption key will not be able to read or use the information that could have been leaked. This measure is essential when working with sensitive data, such as controlled access human data. All submitters must use crypt4gh to create EGA compliant files prior to uploading them. This encryption is GPG-based, using EGA’s public key. Why are my files not available if I see them in the INBOX? There exists a time window between the data upload and the availability of such files via the Submitter Portal. For this reason, some metadata (run and analysis objects) cannot be registered until at least 24 hours after the files have been uploaded to your box. Why are MD5 sum values generated for my submitted files? We require pre- and post- encryption MD5 (message-digest) checksum values to be provided for all submitted files. These 128-bit values are computed using the content of each file, creating unique sequences that allow us to ensure that file integrity has been maintained during the transfer process. In other words, if the MD5 checksums we generate and those you generated match, we infer that the content of the transferred files is correct (i.e. files are not corrupted or truncated). MD5 checksums are computed automatically using the crypt4gh tool provided. Your submission will not be accepted and may be significantly delayed if you do not provide MD5 checksum values for all data files in the required format. How can I check if my files are correctly uploaded to the inbox? It is important to check the status pf your file so you know whether your files are in the inbox, being processed, or if there is any issue with one of them. In order to check this, you should: Look for the file locally. Drag and drop it to the file table. Then bars will appear on the table, which means that we are processing it. Green: The files checksum are correct and your file will move to “ingested files”. No further actions are needed from your end.. Red: The files checksum does not match and your file needs to be re-uploaded. Please re-upload relevant files to your inbox using the same path. How can I add my dataset to the cBioPortal instance? In order to link your data to the cBioPortal, you must register it as a normal dataset in the Submitter Portal. For more information, please refer to the cBioPortal documentation. After Submission How do I use my accession number in my publication? We suggest the use of the below template, using your study accession ID (EGAS…) : 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 at https://ega-archive.org and "The European Genome-phenome Archive of human data consented for biomedical research" Your study ID will be the one that groups your whole submission, and thus its usage is recommended as such. Nevertheless, all metadata submitted to EGA hold a unique and persistent identifier (starting with EGA…) that can be used to identify specific records. For example, you could reference a specific dataset (EGAD…) or sample (EGAN…) in your publications (see full list of identifiers). How do I make my data searchable? Once you have finalized your submission, you can schedule the data release. Please take into account that the release process needs time for the files to be archived in our system, and for the Helpdesk team to validate your submission. Can I withdraw (meta)data from the EGA? We have methods in place for the secure removal of deposited (meta)data. Contact EGA-helpdesk for further details. EGA complies with FAIRness of (meta)data, and thus, even when the data is removed we keep an entry for their identifiers in our system. In other words, we execute a soft delete on canceled objects (e.g. a study): metadata is still stored in our systems, but it loses all links, cannot be queried and data files cannot be retrieved anymore. The reason behind this behaviour is so that queries using withdrawn data properly respond back (see example of a canceled study). What happens to the data once it has been submitted to the EGA? When the data is submitted, the submitter can choose either keep their data private or schedule the release of their data.

  Documentation submission/metadata/submission/FAQ