Search Results - EGA European Genome-Phenome Archive

INCLUDE: Genetic Underpinnings of the Multifactorial Phenotype of Trisomy 21 Patients Unveiled by Multi-Omics Approaches

The INCLUDE (INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE) Project is an NIH-wide collaboration that seeks to improve health and quality-of-life for people with Down syndrome. The INCLUDE Project Data Coordinating Center and partners created the INCLUDE Data Hub, a centralized data resource that allows access to large-scale clinical and multi-omics datasets specific to Down syndrome and supports collaborative, cloud-based analysis to accelerate scientific discoveries related to Down syndrome and its co-occurring conditions.Down syndrome (DS) is the most common chromosomal abnormality in livebirths with an incidence of 1 in 700 in the US. To better understand the pathophysiology of DS, this proposal will generate and analyze sequence data on 777 pediatric DS patients from the Children's Hospital of Philadelphia (CHOP), as well as 321 mothers and 148 fathers. We anticipate that the information derived from this deeply-phenotyped cohort will allow for improved understanding of the pathophysiology and molecular mechanisms underlying DS-associated comorbidities, which may inform on new practices for treatment or innovative future therapies.

Study phs002983

Fetal Genomics Consortium (FGC)

Many of the congenital anomalies and associated genomic variants identified during prenatal care are rare, requiring aggregation and harmonization to assure their appropriate clinical interpretation and to support the expanding utility of genomics in perinatal medicine. The primary step in accomplishing this is the creation of interoperability allowing collection, collaborative storage, dissemination and use of harmonized and vetted genomic and phenotypic data. The overall goal of this project is to generate a prenatal repository for both clinical care and research ensuring data are Findable; Accessible; Interoperable; and Reusable. The specific goals are: (1) collect and organize data contributions from multiple sites using the cloud-based Terra platform; (2) establish a governance structure for data contributions across sites; (3) develop and implement a pipeline for phenotype driven analysis and implementation of fetal specific Phenopackets on Terra; (4) generate a searchable callset of genomic variants and phenotype features on Terra; and (5) explore methods for facilitating broad access and sharing of longitudinal phenotype and genomic data facilitating genomic analysis in the prenatal space.

Study phs003193

Genomic Copy-Number Variants Drive Apoptotic Resistance and Relapses on Immune Checkpoint Inhibitors

We investigated the genomic mechanisms underlying acquired resistance (AR) to immune checkpoint inhibitors (ICIs) across tumor types, specifically by examining how copy number variant (CNV)-driven subclonal evolution contributes to clinical relapses in melanoma patients. We analyzed longitudinal clinical melanoma samples from patients who experienced relapses on ICI therapy, complemented by isogenic human melanoma cell line models (M486) and murine melanoma tumor systems. Single-cell whole-genome sequencing (scWGS) was employed to resolve subclonal CNV patterns in AR tumors. The scWGS data revealed that AR-dominant subclones systematically deleted pro-apoptotic genes while amplifying anti-apoptotic genes and innate resistance pathway components. Notably, shared and private AR subclonal CNVs preexisted selective pressure, with convergent evolution targeting apoptosis-regulatory pathways and resulting in downregulated apoptotic/mitochondrial priming that enables ICI resistance. scWGS datasets from isogenic human melanoma cell lines, patient-matched tumor samples, and murine melanoma models will be made available through dbGaP, enabling researchers to access high-resolution subclonal CNV profiles that define the genomic landscape of acquired ICI resistance evolution.

Study phs004102

Comprehensive analyses of somatic TP53 mutation in tumors with variable mutant allele frequency

Somatic mutation of the tumor suppressor gene TP53 is reported in at least 50% of human malignancies. Most high-grade serous ovarian cancers (HGSC) have a mutant TP53 allele. Accurate detection of these mutants in heterogeneous tumor tissue is paramount as therapies emerge to target mutant p53. We used a Fluidigm Access Array™ System with Massively Parallel Sequencing (MPS) to analyze DNA extracted from 76 serous ovarian tumors. This dataset has been made available to researchers through the European Genome-phenome Archive (EGA). Herein, we present detailed analyses of this dataset using HaplotypeCaller and MuTect2 through the Broad Institute’s Genome Analysis Toolkit (GATK). We also present comparisons of the ability to detect TP53 mutation by MPS and Sanger sequencing in these tumors. We anticipate that this TP53 mutation dataset will be useful to researchers developing and testing new software to accurately determine high and low frequency variant alleles in heterogeneous aneuploid tumor tissue. Further, the analysis pipeline we present provides a valuable framework for determining somatic variants more broadly in tumor tissue.

Study EGAS00001002200

TGF-β attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells.

We examined tumours from a large cohort of patients with metastatic urothelial bladder cancer (mUC) treated with an anti–PD-L1 agent (atezolizumab) and identified major determinants of clinical response and immune escape. Whole transcriptome profiles were generated for 368 patients using TruSeq RNA Access technology (Illumina). Somatic mutations were determined via whole exome sequencing for a subset of these patients. - Response was associated with CD8+ T effector cell phenotype and, to an even greater extent, high neoantigen or tumour mutation burden (TMB). On the other hand, lack of response was associated with a transforming growth factor β (TGF-β) signature, particularly in patients with CD8+ T cells preferentially residing in collagen-rich matrix surrounding tumours. Integration of these three independent biological features provided the best basis for understanding outcome in this setting. -

Study EGAS00001002556

RNASeq profiles of ROBUST clinical trial and processed WGS mutation calls output

The dataset contains RNAseq profiles of 1040 patients from the ROBUST clinical trial (NCT02285062). The Allprep DNA/RNA FFPE kit was used to simultaneously purify genomic DNA and total RNA from formalin-fixed, paraffin embedded (FFPE) tissue sections. RNAseq libraries (75PE, 50M) were constructed using Illumina TruSeq RNA Access method. Fastq files are included. Also includes processed WGS mutation calls output.

Dataset EGAD50000000482

EGAC00001001864 - Data Access Committee Princess Maxima Center

Dac EGAC00001001864

snRNA-seq in white matter post-mortem tissue from MS and controls

This Dataset is currently hosted by the European Nucleotide Archive. To access the data contained within the Dataset please follow the link below: https://www.ebi.ac.uk/ena/browser/view/PRJEB39323 Dataset consists of 20 snRNA-seq bam files from 10X v2. 5 samples from postmortem white matter tissue from non-neurological controls and15 samples from different MS lesions from the white matter tissue of 4 postmortem progressive MS patients.

Dataset EGAD00001004544

Breast Cancer in Blacks: Impact of Genomics, Healthcare Use and Lifestyle on Outcomes (BRIGHT)

Young Black females bear a disproportionate burden of breast cancer (BC) mortality compared to White females and are underrepresented in clinical studies. It remains critical to understand factors that contribute to the high mortality from BC among young Black females in order to improve outcomes. The higher BC mortality rate among young Black females with breast cancer is partially attributed to the disproportionately higher proportions who develop the aggressive triple-negative breast cancer (TNBC). In addition to biologic factors, timely access to care, and lifestyle factors contribute to this disparity. Consequently, we sought to evaluate the interplay between biologic and non-biologic contributors to BC outcomes (i.e., BC disease-free survival (DFS)), and to better characterize the highly aggressive TNBC among young Black females based on distinct molecular subtypes. Ultimately, it is critical to assess both biologic and non-biologic factors to fully understand and address existing health disparities in this population. Through prior and subsequent recruitment of a population-based study of young Black females diagnosed with invasive breast cancer spanning 2009-2016, we: 1) assessed the contribution of biologic and non-biologic factors on BC-DFS observed among young Black females with BC, and 2) investigated molecular features of the subgroup with TNBC. Through this study, consented participants were asked to provide a saliva sample from which DNA was extracted for germline analyses, including germline inherited cancer genetic testing and Infinium Expanded Multi-Ethnic Genotyping Array. Breast cancer tumor specimens were also collected, and tumor RNA was extracted from viable FFPE tumor samples. Tumor RNA analyses included gene expression profiling through NanoString PAM50.Data that will be available through dbGaP for this study include SNP array data and PAM50 data.

Study phs003466

Resuscitation Outcomes Consortium (ROC) Trauma Epidemiologic Registry (Trauma Epistry) (ROC-Trauma Epistry-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.Available Data: This dataset only includes traumatic injury participants. For ROC cardiac arrest Epistry data please see: ROC-Cardiac Epistry 1 and 2 and ROC-Cardiac Epistry 3.Objectives: To build a prospective population-based registry of participants with out-of-hospital traumatic injury responded to by Emergency Medical Services (EMS). Specific aims included: to establish whether the results of Resuscitation Outcomes Consortium (ROC) trials can be generalized to the larger population of people that experience traumatic injury; to more fully establish the burden of traumatic injury; and to examine the relationships between variation in EMS structure and process, regional and periodic factors, and participant outcomes.Background: Traumatic injury is a major public health problem generating substantial morbidity, mortality, and economic burden on society. The majority of seriously injured persons are initially evaluated and cared for by prehospital providers, however the effect of EMS systems, EMS clinical care, and EMS interventions on trauma patient outcomes is largely unknown. EMS factors such as service level, number of responding providers, use of procedures or drugs in the field, training, quality assurance/feedback, and response time intervals also vary significantly by region.The Resuscitation Outcomes Consortium (ROC) was established in 2004 to conduct clinical research in the areas of cardiopulmonary arrest and life-threatening traumatic injury with the overall goal of improving resuscitation outcomes. Previous trauma registries have generally focused primarily on hospitalized patients with limited prehospital information. Registries may also exclude trauma cases at far ends of the spectrum, such as those who die in the field or in a non-trauma center and/or patients that are treated and released. These limitations do not allow for detailed, outcome-based assessments of EMS system factors necessary to define prehospital resuscitation best practices. Therefore there was a need for standardized data collection of out-of-hospital traumatic injuries matched to hospital-based outcomes in diverse geographic locations in order to identify the independent effects of prognostic or treatment factors accounting for variations in survival. Participants: The registry included 13,830 traumatic events from 264 EMS agencies transporting to 287 acute care hospitals from the following regional centers: Birmingham, Alabama; Dallas, Texas; Iowa City, Iowa; Milwaukee, Wisconsin; Pittsburgh, Pennsylvania; Portland, Oregon; San Diego, California; Seattle/King County, Washington; Ottawa, Ontario; Toronto, Ontario; and Vancouver, British Columbia.Design: ROC Epistry collected standardized data regarding episode-specific factors, participant demographics, clinical information, pre-hospital interventions and disposition, hospital information, and participant outcome for all out-of-hospital traumatic injuries in the ROC regions. Cases were identified through review of emergency response system records including dispatch centers, EMS ground agencies, and air medical services. Out-of-hospital data were extracted from existing databases whenever possible and augmented with targeted review of EMS reports. Hospital data were abstracted directly from the hospital file in most cases. Sites submitted data using a web-based interface or batch uploads (Newgard, et al., 2008, PMID: 18482792).

Study phs003809

Resuscitation Outcomes Consortium Trial of Continuous Compressions Versus Standard CPR in Patients With out-of-Hospital Cardiac Arrest (ROC CCC-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 compare the rate of survival to hospital discharge after continuous chest compressions versus standard American Heart Association recommended cardiopulmonary resuscitation with interrupted chest compressions in patients with out-of-hospital cardiac arrest.Background: Standard cardiopulmonary resuscitation (CPR) consists of manual chest compressions to maintain blood flow and positive-pressure ventilation to maintain oxygenation until spontaneous circulation is restored. Chest compressions during standard CPR are interrupted frequently by ventilations. These interruptions reduce blood flow and potentially reduce the effectiveness of CPR. In animals with cardiac arrest, the interruption of chest compressions has been associated with decreased survival. Observational studies involving humans with out-of-hospital cardiac arrest of presumed cardiac cause have suggested that continuous compressions are associated with higher rates of survival than interrupted compressions.Participants: Of 35,904 patients with out-of-hospital cardiac arrest who were screened, 26,148 were eligible for participation in the trial and were enrolled in the trial during either the run-in phase or the active-enrollment phase. The active-enrollment phase included 23,711 patients, of whom 12,653 were assigned to the intervention group and 11,058 to the control group.Design: There were 114 participating EMS agencies across the eight participating Resuscitation Outcomes Consortium (ROC) sites, which were grouped into 47 clusters. Clusters of agencies were randomly assigned, in a 1:1 ratio, to perform continuous chest compressions or interrupted chest compressions during all the out-of-hospital cardiac arrests to which they responded. Each cluster was required to begin enrolling patients in a run-in phase to demonstrate adherence to the protocol before entering the active-enrollment phase of the trial. Twice per year, each cluster was crossed over to the other resuscitation strategy.Patients assigned to the group that received continuous chest compressions (intervention group) were to receive continuous chest compressions at a rate of 100 compressions per minute, with asynchronous positive-pressure ventilations delivered at a rate of 10 ventilations per minute. Patients assigned to the group that received interrupted chest compressions (control group) were to receive compressions that were interrupted for ventilations at a ratio of 30 compressions to two ventilations; ventilations were to be given with positive pressure during a pause in compressions of less than 5 seconds in duration. The primary outcome was the rate of survival to hospital discharge. Secondary outcomes included neurologic function at discharge, which was measured with the use of the modified Rankin scale, and adverse events. Conclusions: During the active-enrollment phase, 1129 of 12,613 patients in the continuous chest compression group and 1072 of 11,035 patients in the interrupted chest compressions group survived to hospital discharge.In patients with out-of-hospital cardiac arrest, continuous chest compressions during CPR performed by EMS providers did not result in significantly higher rates of survival or favorable neurologic function than did interrupted chest compressions.

Study phs003901

Assessment of Health-related Quality of Life in Rare Kidney Stones

The purpose of this study is to learn about Health-related Quality Of Life (HRQoL) in patients enrolled in the Rare Kidney Stone Consortium (RKSC) registries. Quality of life studies look at different aspects of both physical and mental components of people's lives. HRQoL data for people affected by kidney stones are not currently available for people followed over time. All participants enrolled in the RKSC registries will be invited, via email or postal mail, to participate in this study. Health-related quality of life in people with rare kidney stones enrolled in the RKSC registries will be measured. Instruments used in this study are the SF-10 for pediatric participants and SF-36v2 for adult participants. The surveys will be completed online. Participants without a computer or internet access will participate using a paper version of the survey. These surveys will be done at enrollment and annually thereafter. The SF-36v2 includes 36 questions and is estimated to take 15 minutes for its completion, while the SF-10 includes 10 questions and could be completed in 5 minutes. The research questions are: Assessment of HRQoL in patients with rare kidney stones. Comparison of HRQoL results between rare kidney stone formers, general population and other populations with relevant chronic diseases. Comparison of HRQoL results in the same populations affected by different stones and analyzed with attention to the effects of various factors included in the disease registry, such as stone activity, burden of urologic interventions, medication and other treatments. Evaluation of HRQoL and its trends over time in patients undergoing treatment for rare kidney stones. About this study: The plan is to have 400 participants from all four registries take part in this multi-center study. The assessment of your HRQoL will be done annually for 5 years.

Study phs001770

Blood Somatic Mutations in TCGA Donors Suffering from Solid Tumors

Blood somatic mutations identified across 8,530 solid tumor patients in TCGA cohort. We reasoned that low-coverage whole-genome sequencing of blood samples routinely carried out in cancer genomics projects may be repurposed to detect clonal hematopoiesis (CH). Thus, inspired by a previous approach to identify early mutations in the development of the hematopoietic system, we implemented a pipeline to systematically carry out this "reverse" somatic mutation calling on the paired blood/tumor samples. Blood somatic mutations were called using a 'reverse' approach, in which the tumor sample taken from each patient was used as control of their germline genome. Blood somatic mutations are thus detected as variants in the blood (with respect to the human reference genome) that are absent in the tumor sample. The variant calling was carried out in our in house cluster on downloaded blood/tumor BAM files. The matched blood and tumoral BAM files -- masked and deduplicated using GATK -- of 8,530 whole-exome patients were obtained as described above. The variant calling was carried out using Strelka2 (employing default parameters) with the blood sample as the tumoral input and the tumor sample as control (reverse calling). All variants with two or more supporting reads matching the caller PASS filter and with VAF<0.5 were kept. Mutations in lowly mappable regions as defined by the DUST algorithm (k=30) and UMAP68 (36-kmers) were excluded. Contiguous variants were merged into double-base substitutions. Variants with greater frequency across the cohorts than the DNMT3A R882H or JAK2 V617F hotspot in a cohort-specific Panel of Normals and in gnomAD v2.1 were removed. This was equivalent to discarding variants present in these datasets with an allele frequency greater than 0.008 in PoN TCGA and 0.0003 in gnomAD v2.1. Additionally, common SNPs as defined by the dbSNP 151 Common UCSC track and dbSNP were excluded. Mutations within segmental duplications, simple repeats and masked regions as defined in UCSC tracks were also removed. Finally, samples with the mutation count above the 97.5 percentile of the mutation burden across the cohort were deemed unreliable and excluded for further analyses. We call the set of variants obtained after the application of these filters the full set. A more conservative subset of somatic mutations was generated from the full set of blood somatic mutations. To this end, we applied MosaicForecast, a software designed to phase mutations to polymorphisms with the aim of identifying somatic mutations in a small number of cells and also predict mosaicism for the unphased ones with a random forest classifier. As a result, we obtained a subset of mosaic-phased mutations, and a subset of mutations likely to be somatic (mosaic set).In order to obtain access to this study, please first obtain access to TCGA study (phs000178).

Study phs002867

FEGA FAQs: a summary of the Q+A session from the FEGA workshop at ELIXIR AHM 2024

The ELIXIR All Hands Meeting (AHM) was held in Uppsala, Sweden this June. We at EGA always look forward to this meeting as a chance to see many of our partners and colleagues face-to-face. The Federated EGA (FEGA) is one initiative that always benefits from the AHM, and this year FEGA was in the spotlight, with several questions related to the advances we've made recently. We held this year's FEGA workshop a little differently, with most time for discussions on various “hot topics”. Organised in different breakouts, over 2 rounds, participants could choose which session they wanted to attend. One breakout was a general Q+A about FEGA and the path to join. We had a diverse range of participants, including experts from Central EGA (CEGA) and FEGA inaugural nodes, people from up and coming nodes at various stages of readiness, as well as participants not directly a part of FEGA but curious to learn more. It made for a great discussion! In this blog post, we highlight some of the questions for those who didn’t have a chance to be there. What is the FEGA Collaboration Agreement? It is a legal agreement between the node and Central EGA (CEGA) where both parties agree to certain responsibilities, like what services will be provided, sharing of public metadata with CEGA, interoperable technologies and more. The agreement is for 4 years, with the expectation of long-term sustainability. If a node is made up of multiple institutes, one institute must be the signatory. This could require a legal agreement within the node. CEGA is composed of CRG and EMBL-EBI (EGA itself has its own legal agreement between these institutes) Check the FEGA Collaboration Agreement What about purely medical data? Is this accepted in FEGA? CEGA only accepts data approved for secondary research use. We do accept medical data collected in this context, as do some nodes. Nodes are able to accept any data they like, as long as it is sensitive, thus in need of controlled access. What about other data types other than genomics? Like proteomics or images? They can be accepted, although CEGA is currently not designed with those formats in mind, so they might not fit with precision in the data model. The FEGA nodes are welcome to accept any data they want. Proteomic studies generating sensitive data are increasing, and specific work will likely be developed in the near future to adress this need. Does CEGA require a certain amount or type of data to be shared by the node? No, we do not and it's up to each node to decide what specific data types they accept and in what format. Nonetheless, a FEGA node has the duty to be open to data submission from any institution of their country, and can't be limited by design to a single or limited data source. Does CEGA and FEGA require a certain amount or type of details and description on the data to be submitted to the node? Data description (technical and biological), usually called metadata in this field, is crucial for the findability of the submitted data. Thus, we highly recommend including as much information as available to maximise the discoverability of the data. Specifical minimal metadata is defined in the submission schema, and thresholds are specified by the nodes. We are currently working towards a unified FEGA metadata model. What are the geographical borders of FEGA? FEGA should make the E for Everyone, because any institute worldwide could join and we indeed do have countries and use-cases worldwide. Some of them are very close to officially joining the federation. What are the main differences between FEGA and GDI? The two networks have a lot in common and also some critical differences. You can read all about this in one of our latest blog post. If you have more questions, please consider having a look at the FEGA onboarding webpage and, of course, don't miss our workshop at the ELIXIR AHM next year!

Blog fega-faqs-elixir-ahm-2024

Idiopathic Pulmonary Fibrosis Network AntiCoagulant Effectiveness in Idiopathic Pulmonary Fibrosis (IPFNet-ACE-IPF-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: The ACE-IPF trial tested the hypothesis that treatment with warfarin at recognized therapeutic doses would reduce rates of mortality, hospitalization, and declines in Forced Vital Capacity (FVC) in subjects with Idiopathic pulmonary fibrosis (IPF).Background: IPF is a chronic, progressive lung disease of unknown cause characterized by the histopathologic pattern of usual interstitial pneumonia. The median survival of patients with IPF after the onset of symptoms is 2 to 5 years. Prior animal and human studies in pulmonary fibrosis provide a compelling rationale to examine anticoagulation as a therapeutic approach in IPF. Large epidemiologic studies link IPF with thrombosis-related clinical events, such as an increased risk of acute coronary syndrome and deep vein thrombosis. The suspected causal relationship may extend beyond simple coagulation cascade-induced thrombus formation, as procoagulant enzymes may directly stimulate fibrosis via cell surface receptor - mediated responses.Participants: Patients aged 35 to 80 years with progressive IPF were potentially eligible. Progressive IPF was defined as a history of (1) worsening of dyspnea, or (2) physiologic deterioration defined as an absolute decline of either FVC greater than or equal to 10% or DlCO greater than or equal to 15%, a reduction in arterial oxygen saturation of greater than or equal to 5%, or progression of radiographic findings. Between December 14, 2009 and April 1, 2011, 145 subjects were enrolled: 72 in the warfarin group and 73 in the placebo group. The mean age for the population was 67 years. 27% of the subjects were women and 92% were white.Participants were excluded if they met any of the following criteria: current indication for, or treatment with, warfarin, prasugrel, or clopidogrel combined with aspirin; the presence of an increased risk of bleeding; a recent cerebral vascular accident or gastrointestinal bleeding; any current signs or symptoms of severe, progressive, or uncontrolled comorbid illness; and their presence on the active list for lung transplantation. Design: ACE-IPF was a double-blind, randomized, placebo-controlled trial of warfarin targeting an international normalized ratio (INR) of 2.0 to 3.0 in patients with IPF. Subjects were randomized in a 1:1 ratio to warfarin or matching placebo for a planned treatment period of 48 weeks. Study subjects were provided two strengths of warfarin tablets (1 mg and 2.5 mg) or matching placebos. Subjects measured their INR with encrypted meters at least weekly. Participants were seen at screening, baseline, and at 16, 32, and 48 weeks after enrollment. Home monitoring was validated by plasma INR measurement at the week 1 and 16 visits.The primary outcome was a composite endpoint based on the time to all-cause mortality; nonelective, nonbleeding hospitalization; or a decrease in the absolute FVC greater than or equal to 10% from baseline value. Secondary outcome measures included rates of mortality, hospitalization, respiratory-related hospitalization, acute exacerbation, bleeding, cardiovascular events, and changes over time in FVC, 6-minute walk test distance, DlCO, plasma fibrin D-dimer levels, and quality of life (QOL).Conclusions: The study did not show a benefit for warfarin in the treatment of patients with progressive IPF and was terminated due to excess mortality in the warfarin treatment group. Treatment with warfarin was associated with an increased risk of mortality in an IPF population who lacked other indications for anticoagulation.Reported causes of death indicated 11 of the 14 were respiratory-related in the warfarin group versus three of the three in the placebo group. There were also three cardiovascular deaths in the warfarin group versus none in the placebo group. No deaths were attributed to bleeding. The warfarin group also demonstrated an increased rate of combined all-cause hospitalization and all-cause mortality. (Am J Respir Crit Care Med. 2012 Jul 1;186(1):88-95.)

Study phs004070

SweGen genetic variation from the Swedish Twin Registry

The dataset contains files with single nucleotide variants in VCF format for a total of 942 DNA samples, selected to represent a cross-section of the Swedish population. The samples originate from the Swedish Twin Registry (STR) and have been obtained from different geographical regions. For each of the 942 individuals, DNA was extracted from a blood sample and subject to whole genome sequencing (WGS). The WGS was performed using 2x150 bp paired-end chemistry on Illumina HiSeq X Ten instrumentation at the SciLifeLab National Genomics Infrastructure (NGI) in Stockholm and Uppsala. FASTQ files generated by WGS were analyzed using the nf-core pipeline Sarek, which includes pre-processing, alignment to the human GRCh38 reference genome, and germline variant calling. All participants gave their written informed consent and the TwinGene study was approved by the regional ethics committee (Regionala Etikprövningsnämnden, Stockholm, dnr 2007-644-31, dnr 2014/521-32). Access to phenotypic information can be requested from the Swedish Twin Registry (http://ki.se/en/research/the-swedish-twin-registry).

Dataset EGAD50000001323

SweGen whole-genome sequencing from the Swedish Twin Registry

The dataset contains whole-genome sequencing data (aligned read files) in CRAM-format (lossless compression) for a total of 942 DNA samples, selected to represent a cross-section of the Swedish population. The samples originate from the Swedish Twin Registry (STR) and have been obtained from different geographical regions. For each of the 942 individuals, DNA was extracted from a blood sample and subject to whole genome sequencing (WGS). The WGS was performed using 2x150 bp paired-end chemistry on Illumina HiSeq X Ten instrumentation at the SciLifeLab National Genomics Infrastructure (NGI) in Stockholm and Uppsala. FASTQ files generated by WGS were analyzed using the nf-core pipeline Sarek, which includes pre-processing, alignment to the human GRCh38 reference genome, and germline variant calling. All participants gave their written informed consent and the TwinGene study was approved by the regional ethics committee (Regionala Etikprövningsnämnden, Stockholm, dnr 2007-644-31, dnr 2014/521-32). Access to phenotypic information can be requested from the Swedish Twin Registry (http://ki.se/en/research/the-swedish-twin-registry).

Dataset EGAD50000001326

Long-Term Oxygen Treatment Trial (LOTT-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 evaluate the efficacy of long-term treatment with supplemental oxygen in people with stable chronic obstructive pulmonary disease (COPD) and resting or exercise-induced moderate desaturation. Background: COPD is the fourth leading cause of death in the United States, with more than twelve million people currently diagnosed with the disease. In 2011, Medicare reimbursements for oxygen-related costs for people with COPD exceeded two billion dollars. The quality of life of a person with COPD decreases as the disease progresses, making treating and managing COPD in the moderate stages important. The benefits of oxygen supplementation were studied in the 1970s, but these benefits were specific to people with COPD who have severe resting hypoxemia. The LOTT trial was designed to address the effectiveness of supplemental oxygen therapy in treating people with COPD who have moderately low blood oxygen levels at rest or who have normal blood oxygen levels at rest, but have low or very low blood oxygen levels during exercise. Participants: There were a total of 1759 people screened for eligibility for the LOTT trial. Of the 1759 screened, 738 people with COPD were selected for randomization with 368 randomly assigned to the supplemental-oxygen group and 370 to the no-supplemental-oxygen group. Of the 738 people with COPD who underwent randomization, 133 (18%) had resting desaturation only, 319 (43%) had exercise-induced desaturation only, and 286 (39%) had both types of desaturation. Furthermore, in the supplemental-oxygen group, 220 people were prescribed 24-hour oxygen and 148 were prescribed oxygen during exercise and sleep only. Design: The LOTT trial was originally designed to test whether the use of supplemental oxygen would result in a longer time to death for people with COPD and moderate resting desaturation. After seven months, the trial design was judged to be infeasible; therefore, the trial was redesigned to include people with exercise-induced desaturation with a primary composite outcome of death or first hospitalization for any cause, whichever occurs first. Other outcomes measured included: death, health care utilization, COPD exacerbation, quality of life measurements, anxiety, depression, and measures of functional status. Potential participants were screened using questionnaires, a breathing test, a brief physical exam, a blood draw, and measurements of resting and walking blood oxygen levels. Based on those results, eligible participants returned for a second screening visit for further evaluations. At the end of the second visit, eligible participants were assigned randomly to supplemental oxygen therapy or no oxygen therapy. Participants assigned to supplemental oxygen therapy received stationary and/or portable oxygen systems. Participants were required to return for a one hour visit to determine how much oxygen to use while walking and to learn how to use the equipment. Participants who had low blood oxygen levels during rest were instructed to use supplemental oxygen 24 hours per day. Participants with normal resting blood oxygen levels, but low or very low blood oxygen levels during exercise were instructed to use it during physical activity and sleep. Throughout the treatment period, participants were asked to keep records of the number of oxygen tanks emptied or pounds of oxygen delivered, meter readings, and changes in equipment. Study officials contacted participants weekly for the first month, monthly for the next five months, and then every two months until the Year 1 study visit. Participants assigned to receive no oxygen treatment were contacted one week after assignment for a check-up. All participants returned for study visits once a year for up to seven years. At each of these visits, participants completed some of the same tests and questionnaires from the screening visit. Participants underwent a blood draw during the one year study visit. Participants in both treatment groups received two phone calls each year to check on status and use of oxygen. In addition, participants in both groups were asked to complete a quality of life questionnaire by mail at four months and sixteen months. Medicare claims were collected for the duration of each participant's enrollment in the study. Conclusions: In participants with stable COPD and resting or exercise-induced moderate desaturation, the prescription of long-term supplemental oxygen did not result in a longer time to death or first hospitalization than no long-term supplemental oxygen, nor did it provide sustained benefit with regard to any of the other measured outcomes. Long-Term Oxygen Treatment Trial Research Group, Albert RK, Au DH, Blackford AL, Casaburi R, Cooper JA Jr, Criner GJ, Diaz P, Fuhlbrigge AL, Gay SE, Kanner RE, MacIntyre N, Martinez FJ, Panos RJ, Piantadosi S, Sciurba F, Shade D, Stibolt T, Stoller JK, Wise R, Yusen RD, Tonascia J, Sternberg AL, Bailey W. A Randomized Trial of Long-Term Oxygen for COPD with Moderate Desaturation. N Engl J Med. 2016 Oct 27;375(17):1617-1627.

Study phs003933

ARC Data Access Committee

Dac EGAC00001003045

cfDNA sWGS BAM — Healthy donors

Paired-end shallow whole-genome sequencing (sWGS) of plasma cfDNA from healthy donors recruited at EFS Montpellier (France). Libraries: DSP (NEBNext Ultra II) and/or SSP (IDT/Swift Accel-NGS 1S Plus). Sequencing by IntegraGen (France, 2022–2024). Reads aligned to GRCh38 (hg38 canonical) with BWA-MEM; BAMs coordinate-sorted and indexed; submission contains primary mapped reads only (unmapped/secondary/supplementary/duplicates removed; typical filter “-F 3332”, MAPQ threshold as noted in analysis records). Each BAM/BAI pair is linked to its Sample and Experiment. Data Use: controlled access.

Dataset EGAD50000001880

NAR-GAB 2025 deposit data

Paired-end whole-exome sequencing data derived from Illumina hybrid-capture experiments (Agilent SureSelect Human All Exon kits). Six human probands with rare diseases; one sample per subject. Sequencing reads from each individual were processed into analysis-ready gVCF files following standard best-practice pipelines, including alignment, duplicate marking, base quality calibration, and variant calling. All gVCF files are encrypted using Crypt4GH and are available under controlled access through our DAC in accordance with a strict DUO-based policy.

Dataset EGAD50000002100

Targeted Next-Generation Sequencing Data of IDH1 Exon 4 in Intrahepatic Cholangiocarcinoma Samples

This dataset contains targeted next-generation sequencing data from 76 FFPE tumor tissues focusing on IDH1 exon 4 and codon 132. Genomic DNA was isolated using the QIAamp DNA FFPE Tissue Kit and sequenced on the Illumina MiSeq platform with 200x minimum coverage. The results are provided as encrypted somatic variant call files (VCF) generated through the Nextera XT library preparation protocol. This collection represents the specific somatic mutation profiles of the intrahepatic cholangiocarcinoma cohort for controlled access.

Dataset EGAD50000002345

CEITEC DAC

A CEITEC Data Access Committee (DAC) is a body of one or more named individuals who are responsible for data release to external requesters. The members of DAC are selected members of research groups and their respective leaders. Multiple datasets may be affiliated to a single DAC. DAC continues communication with requester to understand the purpose of use, i.e. research setting the dataset is envisioned for by requester, to identify if data sharing is possible directly under current conditions for the dataset, or actions such as re-consenting is needed and desirable to accommodate the proposed research setting/data use.

Dac EGAC50000000049

Whole exome sequencing of young onset Primary Sclerosing Cholangitis

Primary sclerosing chloangitis is a rare autoimmune disease of the liver (prevalence =10/100,000) with a mean age of onset of 40 years. We are currently undertaking GWASand immunochip experiments to identify loci underlying PSC susceptibility. Through ourcollaborators at the University of Calgary we have access to DNA from three parent-offspringtrios where the children required liver transplants due to PSC before the age of 9. These areextremely rare cases indeed and we believe that exome-sequencing represents a powerfulmeans of identifying the causal mutation underlying this severe phenotype.

Dataset EGAD00001000671

Pervasive H3K27 acetylation and ERV expression in H3.3K27M gliomas present a therapeutic vulnerability

We sequenced a total of 2 H3.3K27WT (pcGBM2, G477; 3 replicates total) and 2 H3.3K27M (DIPGVI, DIPGXIII; 6 replicates total) patient-derived cell lines as well as Crispr/Cas9 H3.3K27M-KO clones for one of the cell lines (3 replicates total; DIPGXIII-KO) using ATAC-Seq. P-XX designates passages of replicates. These samples can be found at GEO under accession number GSE128744. This repository contains 1 replicate of G477 to be released under controlled access.

Dataset EGAD00001004961

Implementation of the GDPR

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

Documentation about/privacy-notice

International Standards for Cytogenomic Arrays

The International Standards for Cytogenomic Arrays (ISCA) Consortium is a rapidly growing group of clinical cytogenetics and molecular genetics laboratories committed to improving quality of patient care related to clinical genetic testing using new molecular cytogenetic technologies including array comparative genomic hybridization (aCGH) and quantitative SNP analysis by microarrays or bead chip technology. The ISCA Consortium is now working with the sequencing community to extend the goals of standardization, collaboration, and data sharing. To reflect this combined effort, we are becoming ICCG, or the International Collaboration for Clinical Genomics. Efforts of the Consortium include: Clinical Utility: The ISCA Consortium has made recommendations regarding the appropriate clinical indications for cytogenetic array testing (Miller et al. AJHG 2010, PMID: 20466091). Currently, discussions are focused on pediatric applications for children with unexplained developmental delay, intellectual disability, autism and other developmental disabilities. A separate committee has been developed to address appropriate cancer genetic applications (http://www.urmc.rochester.edu/ccmc/). Evidence-based standards for cytogenomic array design: The Consortium will develop recommendations for standards for the design, resolution and content of cytogenomic arrays using an evidence-based process and an international panel of experts in clinical genetics, clinical laboratory genetics (cytogenetics and molecular genetics), genomics and bioinformatics. This design is intended to be platform and vendor-neutral (common denominator is genome sequence coordinates), and is a dynamic process with input from the broader genetics community and evidence-based review by the expert panel (a Steering Committee with international representation). Public Database for clinical and research community: It is essential that a publicly available database be created and maintained for cytogenetic array data generated in clinical testing laboratories. This will be integrated into the current dbGaP database at NCBI/NIH and released through dbVar, and curated by a committee of clinical genetics laboratory experts. The very high quality of copy number data (i.e., deletions and duplications) coming from clinical laboratories combined with expert curation will produce an invaluable resource to the clinical and research communities. Standards for interpretation of cytogenetic array results: Using the ISCA Database, along with other genomic and genetics databases, the Consortium will develop recommendations for the interpretation and reporting of pathogenic vs. benign copy number changes as well as imbalances of uncertain clinical significance. Membership in the ISCA Consortium is open to all individuals and laboratories involved in cytogenetic array testing who are committed to free data sharing and to participation in a process to develop evidence-based standards and guidelines to improve patient care. ISCA is available through dbVar: http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd37/ http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd75/ http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd45/ http://www.ncbi.nlm.nih.gov/dbvar/studies/nstd101/

Study phs000205

Type 1 Diabetes Genetics Consortium (T1DGC): Genome-Wide Association Study in Type 1 Diabetes, 2008

Cases with Type 1 Diabetes (T1D) in the UK, were part of the Wellcome Trust Case Control Consortium (WTCCC) - http://www.wtccc.org.uk - that first reported in 2007: Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447, 661-678. [PubMed: 17554300] In this genome-wide association study (GWAS), funded by the NIH and JDRF, and sponsored by the Type 1 Diabetes Genetics Consortium (T1DGC), we were able to extend the case and control groups used in the WTCCC, with the intention of performing a well-powered meta-analysis. The study is written up as: Barrett, J.C., Clayton, D.G., Concannon, P., Akolkar, B., Cooper, J.D., Erlich, H.A., Julier, C., Morahan, G., Nerup, J., Nierras, C., Plagnol, V., Pociot, F., Schuilenburg, H., Smyth, D.J., Stevens, H., Todd, J.A., Walker, N.M., Rich, S.S. and The Type 1 Diabetes Genetics Consortium Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nature Genetics, PMID: 19430480. Resources - data: * Case data from this study is deposited here (i.e. in dbGaP) * Control data from this experiment - with subjects from the 1958 British Birth Cohort - is deposited with the European Genotype Archive (EGA): http://www.ebi.ac.uk/ega/ from where the WTCCC data is also available. * A complete description of how to request all components of the meta-analysis is available at: http://www.t1dbase.org/page/PosterView/display/poster_id/324 * Additional genetic data on the same case subjects, including some HLA types, are available from the Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory (JDRF/WT-DIL): http://www-gene.cimr.cam.ac.uk/todd/ Resources - samples: Case and control DNA samples are also available: * Case DNA samples are available from the JDRF/WT-DIL, as above, and will be available from the NIDDK Genetics Repository at Rutgers: https://www.niddkrepository.org * Control DNA samples are available from the 1958 British Birth Cohort (aka National Child Development Study): http://www.cls.ioe.ac.uk/studies.asp?section=000100020003 Use restrictions: There are access limitations to both data and samples, in order to keep their use in line with subjects' consent.

Study phs000180

Altered oligodendrocyte heterogeneity in Multiple sclerosis revealed by single nuclei RNA sequencing

This study is currently hosted by the European Nucleotide Archive. To access the data contained within the Study please follow the link below: https://www.ebi.ac.uk/ena/browser/view/PRJEB39323 Oligodendrocyte (OL) pathology is increasingly implicated in neurodegenerative diseases, as they are involved in metabolic support of axons and functional cross-talk with other brain cells. Rodent OLs are heterogeneous, with developmental and biological differences, but the extent of heterogeneity in the normal human brain and its contribution to any changes to disease remains unknown. Here we performed single nuclei RNA-sequencing (snRNA-seq) from white matter (WM) areas of post mortem human brain both in control (Ctr) and multiple sclerosis (MS) patients. We identified several sub-clusters of oligodendroglia in the Ctr human WM, some similar to those in mouse, and defined new markers for these cell states. Strikingly, some of these sub-clusters were under-represented in MS tissue, while others were more prevalent than in controls. We found a lack of OL precursor cells (OPCs) and an OL subcluster in an intermediate stage of differentiation in MS lesions and in normal appearing white matter (NAWM), suggesting either depletion by the disease or by a regenerative response. The differences in mature OL sub-clusters indicate different functional states of OLs in MS tissue and, as this is similar in NAWM to lesions, that MS is a more diffuse brain disease than the focal demyelinating lesions suggest. We were also able to identify new putative markers of different MS lesion subtypes. Our findings of an altered heterogeneity of oligodendroglia in MS may have an important contribution to our understanding of disease progression and may alter therapeutic approaches to MS.

Study EGAS00001003412

Urethral Microbiome of Adolescent Males

Urethral microbiome of adolescent males is designed to characterize the microbial communities resident in the urethra of young men, to identify differences in these communities as a function of race/ethnicity, circumcision status, sexual exposures, and uro-genital symptoms. We collect detailed sexual behavior and symptoms data using cellular telephones with Internet access. Specimens are routinely collected at monthly intervals, and intermittently following reported symptoms, specific sexual exposures, or identification of a sexually transmitted infection. We also collect periodic samples from the penile coronal sulcus to better characterize its relationship to the urethral micriobial communities. Participants are ages 14 - 17 at enrollment, and prior history of sexual exposure is not required for participation. Parental permission is obtained for each participant. The planned duration of followup is up to 4 years allowing for prospective observation of both physical and behavioral maturation from middle adolescence into young adulthood. The overall objectives of the project are to better characterize the healthy male urethral microbiome, and to use this information to better understand acquisition of urethritis and sexually transmitted infections, as well as chronic genital pain and prostatitis syndromes that become common among young adults.

Study phs000259

eMERGE Phase III Clinical Center at Partners HealthCare

The Partners HealthCare Biobank is a large research data and sample repository working within the framework of Partners Personalized Medicine. It provides researchers access to high quality, consented samples to help foster research, advance understanding of the causes of common diseases, and advance the practice of medicine. The Partners Biobank provides banked samples (plasma, serum and DNA) collected from consented patients. These samples are available for distribution to Partners HealthCare investigators with appropriate approval from the Partners Institutional Review board (IRB). They are linked to clinical data that originates in the Electronic Medical Record (EMR), as well as additional health information collected in a self-reported survey. The Partners Biobank will be genotyping 25,000 subjects with the Illumina Multiethnic Beadchip 1.6 million SNPs with exome and custom content (> 60,000 LoFs). Of the participants genotyped so far, 4929 of 4962 (99.3%) individuals have genotype data that passed the default quality thresholds for the Infinium array (call rate >= 0.99). We are submitting the genotype data to dbGaP for 4929 subjects with 12 phenotypes (based on icd9 codes). We will do annual releases until we reach the full 25,000 genotyped subjects.

Study phs000944

The proliferative history shapes the DNA methylome of B-cell tumor and predicts clinical outcome

Here, we provide access to CLL and DLBCL DNA methylation and gene expression data in the context of a systematic analysis of the DNA methylation variability in 1,595 samples of normal cell subpopulations and 14 tumor subtypes spanning the entire human B-cell lineage. This approach showed that differential methylation among tumor entities relates to differences in cellular origin and to de novo epigenetic alterations, which allowed us to build an accurate machine learning-based diagnostic algorithm. We identify extensive patient-specific methylation variability in silenced chromatin associated with the proliferative history of normal and neoplastic B cells. Mitotic activity generally leaves both hyper- and hypomethylation imprints, but some B-cell neoplasms preferentially gain or lose DNA methylation. Subsequently, we construct a DNA methylation-based mitotic clock called epiCMIT, whose lapse magnitude represents a strong independent prognostic variable in B-cell tumors and is associated with particular driver genetic alterations. Our findings reveal DNA methylation as a holistic tracer of B-cell tumor developmental history, with implications in the differential diagnosis and prediction of clinical outcome.

Study EGAS00001004640

An Atlas of Cells in the Human Tonsil

Palatine tonsils are secondary lymphoid organs representing the first line of immunological defense against inhaled or ingested pathogens. Here, we provide a comprehensive census of cell types forming the human tonsil by applying single cell transcriptome, epigenome, proteome and adaptive immune repertoire as well as spatial transcriptomics, resulting in an atlas of >350,000 cells. We provide a glossary of over one hundred annotated cell types and states, and disentangle gene regulatory mechanisms that drive cells through specialized lineage trajectories. Exemplarily, we stratify multiple tonsil-resident myeloid slancyte subtypes, establish a distant BCL6 superenhancer as locally active in both follicle-associated T and B cells, and describe SIX5 as a potentially novel transcriptional regulator of plasma cell maturation. Further, our atlas is a reference map to understand alterations observed in disease, here we discover immune-phenotype plasticity in tumoral cells and microenvironment shifts of mantle cell lymphomas (MCL). To facilitate such reference-based analysis, we develop HCATonsilData and SLOcatoR, a computational framework that provides programmatic and modular access to our dataset; and allows the straightforward annotation of future single-cell profiles from secondary lymphoid organs.

Study EGAS00001006375

Resuscitation Outcomes Consortium (ROC) Cardiac Epidemiologic Registry (Cardiac Epistry) Version 3 (ROC-Cardiac Epistry 3-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. Available Data The ROC Cardiac Epistry version 3 include all cardiac arrest cases entered into the ROC database from April 1, 2011 to June 30, 2015. This is separate from versions 1 and 2 because of the major changes in how and which data were collected. Version 3 represents the following major changes to the structure and data collection: separate ED Arrival and Hospital Admits forms, mandatory ED and Hospital elements and a revamped CPR Process form. For ROC traumatic injury Epistry data please see: ROC-Trauma Epistry Objectives To build a prospective population-based registry of participants with out-of-hospital cardiac arrest responded to by Emergency Medical Services (EMS). Specific aims included: to establish whether the results of Resuscitation Outcomes Consortium (ROC) trials can be generalized to the larger population of participants that experience cardiac arrest; to more fully establish the burden of cardiac arrest; and to examine the relationships between variation in EMS structure and process, regional and periodic factors, and participant outcomes. Background Cardiac arrest is a common, serious, debilitating and costly public health problem. Although there has been a steady decline in morbidity and mortality from most cardiovascular diseases, high mortality rates for out-of-hospital cardiac arrests continue to pose a challenge for healthcare providers and a significant public health burden. The Resuscitation Outcomes Consortium (ROC) was established in 2004 to conduct clinical research in the areas of cardiopulmonary arrest and life-threatening traumatic injury with the overall goal of improving resuscitation outcomes. Participant and care characteristics can predict favorable outcomes in cardiac arrests, but there is still a wide variation in outcomes that is not well understood. EMS factors such as service level, number of responding providers, use of procedures or drugs in the field, training, quality assurance/feedback, and response time intervals also vary significantly by region. Variations in geographic, socioeconomic and periodic factors may also be associated with differences in outcomes. Prior to ROC Cardiac Epistry, there were no North American population-based registries for out-of-hospital cardiac arrests. Therefore there was a need for standardized data collection of out-of-hospital cardiac arrests in diverse geographic locations in order to identify the independent effects of prognostic or treatment factors accounting for variations in survival. Participants There were 120,306 participants. Design ROC Epistry collected standardized data regarding episode-specific factors, participant demographics, clinical information, pre-hospital interventions and disposition, hospital information, and participant outcome for all out-of-hospital cardiac arrests in the ROC regions. Each ROC site had to ensure capture of all eligible cases within the EMS service areas. Out-of-hospital data were extracted from existing databases whenever possible and augmented with targeted review of EMS reports. Hospital data were abstracted directly from the hospital file in most cases. Alternative methods included linkage to death registries and obituaries if the death occurred within 30 days. Sites submitted data using a web-based interface or batch uploads. Participants were not contacted directly.

Study phs003726

Resuscitation Outcomes Consortium (ROC) Cardiac Epidemiologic Registry (Cardiac Epistry) Versions 1 and 2 (ROC-Cardiac Epistry 1 and 2-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. Available Data: The ROC Cardiac Epistry versions 1 and 2 include all cardiac arrest cases entered into the ROC database from December 1, 2005 to April 1, 2011. ROC Cardiac Epistry 3 includes cardiac arrest cases captured between 2011 and 2015, and introduced significant changes in how and which data were collected.For ROC traumatic injury Epistry data, please see: ROC-Trauma Epistry Objectives: To build a prospective population-based registry of participants with out-of-hospital cardiac arrest responded to by Emergency Medical Services (EMS). Specific aims: to establish whether the results of Resuscitation Outcomes Consortium (ROC) trials can be generalized to the larger population of participants that experience cardiac arrest;to more fully establish the burden of cardiac arrest; and to examine the relationships between variation in EMS structure and process, regional and periodic factors, and participant outcomes.Background: Cardiac arrest is a common, serious, debilitating and costly public health problem. Although there has been a steady decline in morbidity and mortality from most cardiovascular diseases, high mortality rates for out-of-hospital cardiac arrests continue to pose a challenge for healthcare providers and a significant public health burden. The Resuscitation Outcomes Consortium (ROC) was established in 2004 to conduct clinical research in the areas of cardiopulmonary arrest and life-threatening traumatic injury with the overall goal of improving resuscitation outcomes. Participant and care characteristics can predict favorable outcomes in cardiac arrests, but there is still a wide variation in outcomes that is not well understood. EMS factors such as service level, number of responding providers, use of procedures or drugs in the field, training, quality assurance/feedback, and response time intervals also vary significantly by region. Variations in geographic, socioeconomic and periodic factors may also be associated with differences in outcomes.Prior to ROC Cardiac Epistry, there were no North American population-based registries for out-of-hospital cardiac arrests. Therefore there was a need for standardized data collection of out-of-hospital cardiac arrests in diverse geographic locations in order to identify the independent effects of prognostic or treatment factors accounting for variations in survival. Participants: The registry included 109,326 cardiac arrest events from 264 EMS agencies transporting to 287 acute care hospitals from the following regional centers: Birmingham, Alabama; Dallas, Texas; Iowa City, Iowa; Milwaukee, Wisconsin; Pittsburgh, Pennsylvania; Portland, Oregon; San Diego, California; Seattle/King County, Washington; Ottawa, Ontario; Toronto, Ontario; and Vancouver, British Columbia.Design: ROC Epistry collected standardized data regarding episode-specific factors, participant demographics, clinical information, pre-hospital interventions and disposition, hospital information, and participant outcome for all out-of-hospital cardiac arrests in the ROC regions. Each ROC site had to ensure capture of all eligible cases within the EMS service areas. Out-of-hospital data were extracted from existing databases whenever possible and augmented with targeted review of EMS reports. Hospital data were abstracted directly from the hospital file in most cases. Alternative methods included linkage to death registries and obituaries if the death occurred within 30 days. Sites submitted data using a web-based interface or batch uploads. Participants were not contacted directly.

Study phs003803

National Institute on Aging (NIA) Late-Onset Alzheimer's Disease Genetics Initiative: The Multiplex Family Study

Multiplex Family StudyThe purpose of the NIA Genetics Initiative: Multiplex Family Study is to identify families with multiple members diagnosed with late-onset Alzheimer's Disease. Families will be characterized clinically and blood samples will be collected to establish cell lines. If a blood sample is not available, autopsy samples will be collected for DNA extraction and storage. Our goal is to recruit 1,000 families over the course of the study. Clinical and demographic data from these families will be collected at the local site and coded data, without identifiers, will be sent and included in a national database of families with Alzheimer's Disease. This database, along with the biological samples, will be housed at the National Cell Repository for Alzheimer's Disease (NCRAD) at Indiana University. The Center or provider and the Cell Repository must sign a Material Transfer Agreement for shipment of biological samples and phenotypic data to NCRAD. The biological samples and data from these families will be available to qualified researchers, who must sign a Material Transfer Agreement before receiving DNA and data. An oversight committee known as the Cell Bank Advisory Committee (CBAC) will review and monitor the process of family identification and enrollment, data collection, the establishment of cell lines and access to samples. LOAD CIDR ProjectThe first 362 families of the Multiplex Family Study (including 2,105 family members) were included in a 6K SNP genome-wide scan at the NIH-supported, Center for Inherited Disease Research (CIDR). An additional 297 unrelated, healthy controls were also genotyped at CIDR. The average age of onset of AD in the genotyped sample is 74 years and 62% of the sample is women. While primarily Caucasian, 3% are African-American, and 3% are of Hispanic ancestry. 72% of the families had at least 2 affected siblings, whereas 21% had 3 or more sampled affected individuals and 7% had 4 or more genotyped, affected family members.

Study phs000160

RNA-Seq Profiles from the CheckMate-577 Clinical Trial

The dataset contains RNAseq profiles of 386 patients from the CheckMate-577 (CA209-577) clinical trial whose ICF allows data deposition into a public repository. The Allprep DNA/RNA FFPE kit was used to simultaneously purify genomic DNA and total RNA from formalin-fixed, paraffin embedded (FFPE) tissue sections. RNA was analyzed using the Illumina TruSeq RNA Access method for library preparation, followed by sequencing on the Illumina NovaSeq platform with a 50bp paired-end strategy and a target read depth of 50M per sample. Fastq files are included.

Dataset EGAD50000002394

Query AML data

Shallow targeted sequencing with 462 mRNA and 97 antibodies of AML patient’s bone marrow mononuclear cells from iliac crest aspirations from. Please note raw and integrated gene expression data, cell type annotation, metadata and dimensionality reduction are available as Seurat v3 objects through figshare. Access link is https://doi.org/10.6084/m9.figshare.14780127.v1 AMLQ4_SMK1 AML314 male AMLQ1_SMK2 AML116 female AMLQ3_SMK3 AML127 female AMLQ6_SMK4 AML183 male AMLQ2_SMK5 AML327 female AMLQ5_SMK6 AML334 male APLQ5_SMK7 APL124 male APLQ3_SMK8 APL142 male APLQ6_SMK9 APL218 female APLQ4_SMK10 APL147 male APLQ2_SMK11 APL223 female APLQ1_SMK12 APL224 female

Dataset EGAD00001008185

UNITO_Molpheno_Closed

This dataset contains Whole Genome Sequencing, RNA-sequencing and ATAC-sequencing data obtained from PBMCs derived from blood samples of one patient with complex genomic rearrangements and the biological parents. The patient has multiple congenital anomalies and delayed development. Data access is closed.

Dataset EGAD00001004863

UMCU Molpheno Closed

This dataset contains Whole Genome Sequencing and, if available, RNA-sequencing and/or ATAC-sequencing data obtained from PBMCs derived from blood samples of two patients with intellectual disability and/or multiple congenital anomalies and eight parents included in the University Medical Center Utrecht (The Netherlands). Data access is closed.

Dataset EGAD00001004864

Incentives and Case Management to Improve Cardiac Care: Healthy Lifestyle Program (HeLP)

Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this study.Objectives: The HeLP study assessed how early case management, financial incentives contingent on cardiac-rehabilitation attendance, or both impacted adherence to cardiac rehabilitation among patients with lower socioeconomic status (SES) who had a cardiac-rehabilitation-qualifying diagnosis. Background:Cardiovascular disease (CVD) is the leading cause of death in the United States. Regular attendance at cardiac rehabilitation (CR) is the number one recommendation for patients who have experienced a recent cardiac event. CR is a secondary prevention outpatient program involving an individually tailored structured exercise program along with counseling and encouragement regarding lifestyle changes to reduce the recurrence of major cardiac events.Despite the benefits of CR attendance, enrollment and adherence to the program are low. This is particularly true of individuals of lower socioeconomic status (SES), who also generally suffer a higher burden from CVD on account of higher-risk cardiac profiles. As these profiles are the result of modifiable behavior targeted by CR (i.e., smoking, obesity, lack of physical activity), methods of increasing CR attendance among this population are necessary.Case management and financial incentives contingent on CR attendance are two potential strategies for addressing this issue. Previous studies have shown how trained case managers can support and assist patients with attending CR and improving their cardiac health, and how financial incentives can promote various types of health-related behavior. The purpose of the present study was to evaluate how these two interventions, individually and in tandem, fared at improving CR adherence among individuals of lower SES specifically. Participants:A total of 314 participants were screened, and 209 were randomized. All consented to have their data shared for research purposes. Design:Eligible patients were approached either while still in the hospital or during initial visits to CR. After obtaining informed consent, clinical and demographic characteristics (i.e., age, sex, education, race and ethnicity, smoking status, and CR-qualifying diagnosis) and depressive symptoms were collected. Participants then were randomized 2:3:3:3 to usual care (UC), case management starting in hospital (CM), financial incentives for attending CR (FI), or financial incentives plus case management (FICM). For all participants regardless of condition, an appropriate referral for CR was confirmed after randomization. Over the course of the study participants completed three assessments – one at intake, one after four months, and one after one year. Clinical (i.e., cardiorespiratory fitness, body composition, and cardiac-related quality of life) and sociocognitive (i.e., Beck Depression Inventory, Achenbach System of Empirically Based Assessment, and Behavior Rating Inventory of Executive Function) measures were obtained at each assessment. Participants randomized to the UC condition were contacted weekly by study staff for adverse event checks, but received no additional intervention. Participants randomized to the CM condition were immediately assigned a case manager. Case managers met with their participants, usually within 24 hours of consent while they were still in the hospital, to introduce themselves during an initial check-in. Within the first week of consent they completed a longer, more thorough initial needs assessment with their participant and established behavioral goals. For the next 16 weeks, case managers met with their participants at least once a week, typically over the phone, to discuss progress on these goals and address any problems or barriers that had arisen. Case managers also were available via phone outside of these regular meetings during normal working hours and Saturday mornings. Participants randomized to the FI condition could earn money for completing an initial CR orientation and attending CR sessions. Twenty dollars could be earned for completing the CR orientation session. CR attendance was reinforced on an escalating schedule starting at $10. Payments increased by $2 for each successive session attended, and capped out at a maximum of $40 per session. Unexcused absences resulted in the incentives for that session not being earned, and the potential earnings for the next scheduled session being reset to $10. Successful participation of two consecutive sessions following the reset resulted in the incentives being returned to the amount prior to the reset. In total, participants could earn $1220 for attending the initial orientation and all 36 sessions of CR. The primary outcome of interest was the percentage of participants who completed at least 30 sessions of CR. Secondary outcomes included the number of sessions completed, cardiorespiratory fitness (peak VO2 and estimated metabolic equivalents (METs)), body composition (waist circumference and BMI), depression scores, and both general and cardiac-related qualify of life. Conclusions:The two interventions involving financial incentives (i.e., financial incentives alone and financial incentives plus case management) significantly improved CR adherence compared to usual care. Subsequent analyses revealed that the combination of financial incentives and case management led to greater improvements in CR attendance than financial incentives alone.

Study phs003737

Cooperative Study of Sickle Cell Disease (CSSCD)

The Cooperative Study of Sickle Cell Disease was initiated in 1977 to determine the natural history of sickle cell disease (SCD) from birth to death in order to identify those factors contributing to the morbidity and mortality of the disease. Specific objectives included: 1) to study the effect of sickle cell disease on growth and development from birth through adolescence 2) to study the conditions or events that may be related to the onset of painful crises 3) to obtain data on the nature, duration, and outcome of major complications of SCD 4) determine the nature, prevalence, and age- related incidence of organ damage due to SCD, and 5) study the role of SCD and its interaction with selected health events.Phases 2 and 3 of the study involved followup of the infant cohort. A total of 709 infants (age less than 6 months) were enrolled during Phase 1 of the Cooperative Study of Sickle Cell Disease (CSSCD), and Phases 2 and 3 of the CSSCD was designed to follow these children for an additional 10 years. The study objectives included: 1) define prospectively the natural history of sickle cell disease; 2) determine the relationships between cognitive and academic functioning and brain status as determined by MRI; 3) determine the cognitive or behavioral markers of silent infarct; 4) determine the relationship of family functioning on the Family Environment Scale (FES) to brain status, cognitive functioning, and social and demographic factors; 5) continue studies that will enhance the state of knowledge on the influence of sickle cell disease on the psychosocial adjustment of children and adolescents. Phase 2A of the study sought to examine the progression of organ damage in the heart, lung, kidney, and liver in adult cohort patients (born before 1/1/56) enrolled in phase 1 of the study between 3/79 and 5/81. A total of 620 patients from 11 centers were eligible for phase 2A.Instructions for requesting individual-level data are available on BioData Catalyst at https://biodatacatalyst.nhlbi.nih.gov/resources/data/. Apply for data access in dbGaP. Upon approval, users may begin accessing requested data in BioData Catalyst. For questions about availability, you may contact the BioData Catalyst team at https://biodatacatalyst.nhlbi.nih.gov/contact.

Study phs002362

NINDS-Genome-Wide Genotyping in Parkinson's Disease: First Stage Analysis and Public Release of Data

Epidemiological studies have estimated a cumulative prevalence of PD of greater than 1 per thousand. When prevalence is limited to senior populations, this proportion increases nearly 10-fold. The estimated genetic risk ratio for PD is approximately 1.7 (70% increased risk for PD if a sibling has PD) for all ages, and increases over 7-fold for those under age 66 years. The role for genes contributing to the risk of PD is therefore significant. This study utilized the well characterized collection of North American Caucasians with Parkinson's disease, and neurologically normal controls from the sample population which are banked in the National Institute of Neurological Disorders and Stroke (NINDS Repository) collection for a first stage whole genome analysis. Genome-wide, single nucleotide polymorphism (SNP) genotyping of these publicly available samples was originally done in 267 Parkinson's disease patients and 270 controls, and this has been extended to include genome wide genotyping in 939 Parkinson's disease cases and 802 controls. The NINDS repository was established in 10-2001 towards the goal of developing standardized, broadly useful diagnostic and other clinical data and a collection of DNA and cell line samples to further advances in gene discovery of neurological disorders. All samples, phenotypic, and genotypic data are available to the research community including to academics and industry scientists. In addition, well characterized neurologically normal control subjects are a part of the collection. This collection formed the basis of this first stage study by Fung et al., and the expanded study by Simon-Sanchez et al. The genotyping data was generated and provided by the laboratory of Dr. Andrew Singleton NIA, and Dr. John Hardy NIA (NIH Intramural, funding from NIA and NINDS). Important links to apply for individual-level data Data Use Certification Requirements (DUC) Apply here for controlled access to individual level data Participant Protection Policy FAQ

Study phs000089

PyEGA3 download client

PyEGA3 Checks prior to download - Is the dataset available for download? Most datasets are available using the PyEGA3 Download client, except for specific legacy datasets. For these ones, refer to Live Outbox distribution, an alternative way to download these datasets.If the dataset you've been granted access to begins with the ID EGAD5XXXXXXX, please refer to the Live Outbox distribution. If the dataset you have been granted access to contains encrypted data (filename ending in .gpg), contact EGA Helpdesk for detailed information on how to download these files.General Information The PyEGA3 download client is available at its GitHub repository. In its README you will find a step by step guide on how to use it to download files you have been granted access to. PyEGA3 implements the GA4GH-compliant HTSGET protocol supporting requests over genomic ranges. This enables the download of specific regions of interest rather than the entire file. HTSGET is not possible for all the files held by EGA: if the dataset does not contain index files (e.g. filename ending in *.crai), then genomic range requests cannot be performed. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Check out our video tutorial! Frequently Asked Questions General questions How do I download the datasets to which I have been granted access? After setting up your EGA download account, you can proceed to download using the EGA download client – PyEGA3. The pyEGA3 download client is a python-based tool for viewing and downloading files from authorised EGA datasets. This download client is continuously being developed for more user-friendly download experiences. Can I download the dataset’s metadata via the download client? No, the download client cannot be used to download metadata. Registered EGA users can download metadata of an authorised dataset by logging into the EGA webpage and navigating to the dataset of choice. Approximately two thirds down the page you will find the option to download the metadata as a zip file. How do I download large datasets as quickly as possible? The main EGA download client is pyEGA3 download client, a python-based tool for viewing and downloading files from authorised EGA datasets.We could exceptionally consider the option of an Aspera download for users experiencing very slow download rates or facing particular issues.Contact the EGA Helpdesk for further information. Pre-requirement and Installation What are the major requirements for the PyEGA3 download client installation and usage? The PyEGA3 client is compatible with any OS with Python 3.6+ installed. The client requires a connection to the internet, sufficient space on the destination drive, and the EGA download account credentials. Where can I find the PyEGA3 download client installation and download instructions? We strongly recommend checking our video tutorial demonstrating the usage of PyEGA3 from installation to file download. More detailed information is available at PyEGA3 GitHub. What ports are required for the PyEGA3 download client to function? PyEGA3 makes HTTPS calls to the EGA Data API (https://ega.ebi.ac.uk:8443). This port 8443 must be reachable from the location where PyEGA3 is executed to avoid timeouts. PyEGA3 download client How to update the PyEGA3 download client to be on the latest version? Updating the client to the latest version can be achieved by running the following command: pip3 install pyega3 --upgrade I do not have an active EGA account. Can I test the download with the PyEGA3 download client? An EGA download test account (ega-test-data@ebi.ac.uk) has been created for troubleshooting and training purposes. The test account does not require an EGA username and password because it contains publicly accessible files from the 1000 Genomes Project. More information about the use of this account and log in details are available at pyEGA3. I lost my connection while the download was in progress. Does this mean that I have to download the file again from the start? The PyEGA3 download client supports automatic resumption of downloads. To enable quicker download speeds, the API breaks files into up to four segments and downloads them in parallel. With the resume feature, downloads will automatically resume if you encounter any errors or if the connection is interrupted. How can I improve the overall download speed of the PyEGA3 download client? Download speeds can be optimised using the --connections parameter which will parallelise download at the file level. If the --connections parameter is provided, all files >100Mb will be downloaded using the specified number of parallel connections. It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Why is my file taking a long time to be saved? Please note that when a file is being saved, it goes through two processes. First, the downloaded file "chunks" are pieced back together to reconstruct the original file. Secondly, PyEGA3 calculates the checksum of the file to confirm that the file was downloaded successfully. Larger files will take more time to reconstruct and validate the checksum. Why is the HTSGET protocol not working with my dataset of interest? PyEGA3 implements the GA4GH-compliant htsget protocol for supporting requests over genomic ranges. This exciting new feature means that for data files with accompanying index files (e.g. .crai for CRAM files) users can download specific regions of interest rather than the entire file, saving both time and storage space. Please note that in order for the genomic range requests to work for a BAM, CRAM or VCF file there must be an associated BAI, CRAI or TBI file, respectively. If the dataset does not contain these index files then genomic range requests cannot be performed. After download Do I need to decrypt data files downloaded through the pyEGA3 download client? Files are transferred over secure HTTPS connections and received unencrypted, thus there is no need for decryption after data download. What is the purpose of an MD5 file? After the download completes, file integrity is verified using checksums. The PyEGA3 download client automatically verifies each file’s unencrypted md5 after download to ensure that the file was downloaded correctly from the EGA. Errors Why do I get a “400 Client Error” while trying to access the download client? Ensure that your credentials are formatted correctly. Please contact the EGA Helpdesk if you are unsure about your account’s username (i.e. your email address). Why do I get a “500 Server Error”? It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Otherwise, 500 server errors are internal server errors from EGA’s end. Retry after some time and, if the 500 error persists: check our homepage or Twitter for any outage information or report it to EGA Helpdesk. Why do I get a “slice error”? This type of error occurs when one slice download was prematurely interrupted. Please re-start the same download. The download client will pick up any partial download and download any missing slices. Why do I get the error “Dataset' EGADXXXXXXXXXXX' is not in the list of your authorised datasets”? Please note that the data access was given to the email (username) provided to the Data Access Committee (DAC) in your official request. Therefore, you shall check that the account that you are using is the same one that you sent to the DAC. Why do I get “ERROR:root:Failed to obtain IP address”? This might mean that the connection ports are blocked. Check that the port 8443 is reachable.In case the port is reachable and the error persists, please try to modify the file pyega3.py on line 44 in the python module installation: Replace endpoint = 'https://ipinfo.io/json' with endpoint = 'https://api.myip.com' Why do I get the error “Invalid username, password or secret key - please check and retry”? Make sure that you are using the correct credentials (username and password) and there is no typo. Contact EGA Helpdesk if the problem persists.

Documentation access/download/files/pyega3

Data Access Committee of CiRA Foundation

The iPS Cell Stock Project promoted by the CiRA Foundation is intended to generate and distribute iPS cells for medical use so they can be effectively utilized for research and development of cell transplantation therapy and contribute to the advancement of regenerative medicine that may become a novel treatment for patients suffering from intractable diseases or trauma who are not expected to improve with conventional therapies. In order to achieve this objective, CiRA Foundation establish the DAC based on the basic policies and rules for handling analysis information including iPS cell stocks for the purpose of proper sharing of samples and analyzed data collected directly or indirectly from the donors of iPS cell stocks.

Dac EGAC50000000128

MIBC NAC2020 cohort RNA sequencing

The dataset contains RNA sequencing data from N=48 transurethral resections of bladder tumours (TURBTs) from N=48 patients with muscle-invasive bladder cancer who were treated with neoadjuvant chemotherapy followed by radical cystectomy. All TURBTs originate from formalin-fixed paraffin embedded tissue. We used the Illumina TruSeq RNA exome kit, previously known as the TruSeq RNA Access Library Prep Kit. This kit converts total RNA into template molecules of known strand origin, followed by sequence-specific capture of coding RNA. All samples were run in an Illumina NovaSeq6000 instrument. Paired FASTQ files from all 48 patients are provided.

Dataset EGAD50000001025

Whole Exome Sequencing data of glioma from the Chinese Glioma Genome Atlas (CGGA) project

Glioma is the most common and aggressive brain cancer in adults. While primary glioma has been widely studied, molecular characterization of recurrent glioma is still rare. The high-quality sequencing data that we generated provides a useful resource for the community. The CGGA project contains over 2,000 samples from Chinese cohorts. It totally includes the whole-exome sequencing (286), DNA methylation (159), mRNA sequencing (1,018), mRNA microarray (301) and microRNA microarray (198) and matched clinical data. CGGA removes the barriers to researchers, providing rapid and convenient access to high-quality functional genomic data resources for biological research and clinical applications.

Dataset EGAD00001006445

PRDM9 loss of function follow up from Born-in-Bradford Autozygosity sequencing

In the autozygosity exome sequencing of Born-in-Bradford samples of Pakistani origin there is a mother who is homozygous for an apparent truncating stop codon in PRDM9, the gene responsible for localising recombination during meiosis. We plan to deep sequence mother and child with X10, and physically phase the mother with PacBio sequencing. We will use this data to identify recombination locations, and test whether these are consistent with the known fine scale recombination map. Data Access is controlled by the Wellcome Trust Sanger Institute DAC and the Born In Bradford Executive Group.

Dataset EGAD00001001686

Test dataset with ligh-weight files

This is a test dataset derived from public data of the 1000 Genomes Project. Its purpose is not to allow for any inference about cohort data or results, but to aid bioinformaticians in the technical development and testing of tools, as well as data consumers in learning how to access information. This dataset consists of 3 pairs of light-weight (sliced) files: BAM + BAI, CRAM + CRAI and VCF + TBI. These files can be downloaded directly through the EGA-download-client PyEGA3 (https://github.com/EGA-archive/ega-download-client). For any further questions, please contact the DAC (Helpdesk - email: helpdesk [at] ega-archive [dot] org).

Dataset EGAD00001009826

Genetic regulation of RNA splicing in human pancreatic islets

Here we provide access to newly generated RNA-seq data for 101 human islet samples used to map genetic effects on gene expression and alternative splicing (eQTLs and sQTLs) in a total of 399 human islets. We also make publicly available genotyping array data for 128 human islets, including the fraction of 101 human islet samples with existing RNA-seq data.

Dataset EGAD00001009102

A Genome-Wide Association Study of Sporadic ALS in an Irish Population (SIALS) and Sequencing and Analysis of an Irish Human Genome

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by loss of motor neurons. Epidemiological studies indicate that 2-5% of cases are familial, usually with an autosomal dominant pattern of inheritance. For a portion of these, causative genes have been identified. The remaining 95% of ALS cases are described as sporadic, and believed to result from a combination of genetic and environmental factors. This study utilized samples from Irish patients with sporadic ALS and Irish control individuals. The aim of the study was to identify susceptibility alleles for ALS in the Irish population. Genome-wide, single-nucleotide polymorphism (SNP) genotyping was undertaken using Illumina 550K version 3 chips. Genotyping data are available on 221 SALS patients and 211 controls. Future work will aim to examine ALS genetic risk at individual sequence level. As an initial step, as of September 2010 we have made available the human genome sequence from a single anonymous male control individual from the SIALS collection. Recent studies generating complete human sequences from Asian, African and European subgroupings have revealed population specific variation and illuminated disease susceptibility loci. The Irish population is of interest because of its location at the periphery of Europe and also the impact its emigrants have had on the genetics of populations in a number of other countries. We have identified sequence variants that may be specific to this population and have identified potentially novel disease associated variants. We describe a novel method for improving SNP calling accuracy at low genome coverage using haplotype information. Overall we demonstrate utility in generating whole genome sequences to test general principles and reveal specific instances of human biology. With increasing access to low cost sequencing we would predict that similar studies will emerge.

Study phs000127

A Genome-Wide Association Study of Heroin Dependence

This collaboration of Australian and American investigators aims to identify genes associated with liability for heroin dependence. The project uses a case-control design in which cases met lifetime DSM-IV criteria for heroin dependence. Controls included assessed individuals who did not meet DSM-IV heroin dependence criteria and unassessed general population controls. Cases and controls were obtained from the several large investigations including: The Comorbidity and Trauma Study, Heroin Dependence in Western Australia, the OZ-ALC Study, a Twin Study of Mole Development in Adolescence, and ongoing genetic studies of substance dependence conducted by investigators at Yale and collaborating institutions. These projects are briefly described below. The Comorbidity and Trauma Study (PI: Elliot Nelson), a retrospective case-control study examining genetic and environmental factors contributing to heroin dependence liability. The study was funded by the National Institute on Drug Abuse (NIDA), and was run in collaboration with Washington University, the Queensland Institute of Medical Research (QIMR), and the National Drug and Alcohol Research Centre (NDARC), University of New South Wales. Case participants were recruited from maintenance clinics in the greater Sydney area. Control participants were recruited from employment centres and community centres, open street malls, and local press servicing the same geographical area as the opioid maintenance treatment clinics and either denied recreational use of opioids or had used these drugs recreationally fewer than 11 times lifetime. The prevalence in these individuals of non-opioid licit drug dependence and illicit drug dependence as well as childhood trauma exposure and other psychiatric disorders is elevated considerably versus estimates of similar measures in Australian general population samples. Participants provided blood samples as a source of DNA and completed a comprehensive psychiatric diagnostic interview based on the Semi-Structured Assessment of the Genetics of Alcoholism - Australia (SSAGA-OZ) augmented with sections drawn from other instruments assessing childhood trauma exposure, family history, and screening for borderline personality disorder. Heroin Dependence in Western Australia (PI: Sybille Schwab) is a study focusing both on genetic contributions to heroin dependence and response to naltrexone treatment of the disorder. Participants completed a clinical assessment and provided blood samples during their treatment at the Perth Naltrexone Clinic now name as the Fresh Start Recovery Programme. Funding for the project was provided by the Australia Government's National Health and Medical Research Council (Grant # 513862; PI: Sybille Schwab) Affected subjects from ongoing genetic studies of substance dependence conducted by investigators at Yale (PI: Joel Gelernter) and collaborating institutions were collected in the course of several NIDA-funded studies. Those included in the current set were assessed by means of the SSADDA (Semi-Structured Assessment for Drug Dependence and Alcoholism). All are opioid dependent European-Americans, and all list heroin as the opioid must used. Most were collected at Yale University School of Medicine or University of CT School of Medicine under the supervision of Drs Joel Gelernter and Henry Kranzler. Control subjects were also collected in the course of several NIDA- and NIAAA-funded studies. Those included in the current set were assessed by means of the SSADDA (Semi-structured Assessment for Drug Dependence and Alcoholism). Most were collected at Yale University School of Medicine or University of Connecticut School of Medicine under the supervision of Drs Joel Gelernter and Henry Kranzler. The OZ-ALC Study (PI: Andrew Heath) consists of a large group of twins and their family members ascertained from the general population Australian Twin Registry who have participated in ongoing research projects. For the control investigation, we have selected individuals who do meet criteria for illicit drug dependence who have had GWAS genotyping with the Illumina Human CNV370-Quad. Inclusion of individuals with alcohol dependence or nicotine dependence was minimized. For a more detailed description of the study, please see: http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000181.v1.p1 The Twin Study of Mole Development in Adolescence (PI: Nick Martin) is an ongoing investigation of melanocytic naevi funded by the Australian Government's National Health and Medical Research Council (Grant # 389891; PI: Nick Martin). For the current project, unassessed parents of these twins will serve as a control group. These individuals will either have been previously genotyped with the Illumina Human 610-Quad BeadChip or will be genotyped as part of the current project. Parents have largely survived the period of risk for heroin dependence and, by virtue of their participation in this research, are very likely to have a prevalence of heroin dependence lower than that in the general population (i.e., <0.7%). In order to understand the immune modulatory effect of opioids across peripheral blood immune populations, a second study (PI: Christine S. Cheng) analyzed single cell RNA-seq profiles from the PBMCs of a subset of case participants and control participants from the Comorbidity and Trauma Study (PI: Elliot Nelson). The study assessed and identified transcriptional changes between opioid dependent and control samples across naive and LPS stimulated immune populations.

Study phs000277

Data Use Ontology (DUO)

Data Use Ontology Data Use Ontology at EGA The EGA is committed to its involvement in the work of GA4GH. In an effort to enhance data discoverability and streamline data access, EGA have implemented the use of the Data Use Ontology (DUO), based on consent codes as described in Dyke et al. 2017. The Data Use Ontology codes will be displayed on the live dataset page of your submission to advise any would be requestor on how the data can be used and also to enhance data discoverability as users will be able to search on these codes to find applicable datasets. DUO can be browsed online via the Ontology Lookup Service Learn more reading the Data Use Ontology publication and the GA4GH Machine-Readable Consent Guidance! Check our DAC Portal Take The Tour and learn how to add DUO codes to your policy! Term Shorthand Label Description DUO:0000004 NRES no restriction This data use permission indicates there is no restriction on use. DUO:0000042 GRU general research use This data use permission indicates that use is allowed for general research use for any research purpose. DUO:0000006 HMB health or medical or biomedical research This data use permission indicates that use is allowed for health/medical/biomedical purposes; does not include the study of population origins or ancestry. DUO:0000007 DS disease specific research This data use permission indicates that use is allowed provided it is related to the specified disease. DUO:0000011 POA population origins or ancestry research only This data use permission indicates that use of the data is limited to the study of population origins or ancestry. DUO:0000012 RS research specific restrictions This data use modifier indicates that use is limited to studies of a certain research type. DUO:0000015 NMDS no general methods research This data use modifier indicates that use does not allow methods development research (e.g., development of software or algorithms). DUO:0000016 GSO genetic studies only This data use modifier indicates that use is limited to genetic studies only (i.e., studies that include genotype research alone or both genotype and phenotype research, but not phenotype research exclusively) DUO:0000018 NPUNCU not for profit, non commercial use only This data use modifier indicates that use of the data is limited to not-for-profit organizations and not-for-profit use, non-commercial use. DUO:0000019 PUB publication required This data use modifier indicates that requestor agrees to make results of studies using the data available to the larger scientific community. DUO:0000020 COL collaboration required This data use modifier indicates that the requestor must agree to collaboration with the primary study investigator(s). DUO:0000021 IRB ethics approval required This data use modifier indicates that the requestor must provide documentation of local IRB/ERB approval. DUO:0000022 GS geographical restriction This data use modifier indicates that use is limited to within a specific geographic region. DUO:0000024 MOR publication moratorium This data use modifier indicates that requestor agrees not to publish results of studies until a specific date. DUO:0000025 TS time limit on use This data use modifier indicates that use is approved for a specific number of months. DUO:0000026 US user specific restriction This data use modifier indicates that use is limited to use by approved users. DUO:0000027 PS project specific restriction This data use modifier indicates that use is limited to use within an approved project. DUO:0000028 IS institution specific restriction This data use modifier indicates that use is limited to use within an approved institution. DUO:0000029 RTN return to database or resource This data use modifier indicates that the requestor must return derived/enriched data to the database/resource. DUO:0000043 CC clinical care use This data use modifier indicates that use is allowed for clinical use and care. DUO:0000044 NPOA population origins or ancestry research prohibited This data use modifier indicates use for purposes of population, origin, or ancestry research is prohibited. DUO:0000045 NPU not for profit organisation use only This data use modifier indicates that use of the data is limited to not-for-profit organisations. DUO:0000046 NCU non-commercial use only This data use modifier indicates that use of the data is limited to not-for-profit use. Point to Notice: For the consent code DUO:0000007 where data is restricted to use on a specific disease, please accompany it with an appropriate ontology from MONDO e.g., If the data is restricted to the use of research into juvenile idiopathic arthritis the code should be displayed as DUO:0000007; MONDO:0011429.

Documentation access/data-access-committee/data-use-ontology

The Genomic Landscape of Childhood and Adolescent Melanoma

Despite remarkable advances in the genomic characterization of adult melanoma, the molecular pathogenesis of pediatric melanoma remains largely unknown. We analyzed 15 conventional melanomas (CM), 3 melanomas arising in congenital nevi (CNM), and 5 spitzoid melanomas (SM), using various platforms, including whole genome or exome sequencing, molecular inversion probe assay, and/or targeted sequencing. CM demonstrated a high burden of somatic single nucleotide variations (SNV), with each case containing a TERT promoter (TERT-p) mutation, 13/15 containing an activating BRAF V600 mutation, and >80% of the identified SNV consistent with UV damage. By contrast, the 3 CNM contained an activating NRAS Q61 mutation and no TERT-p mutations. SM were characterized by chromosomal rearrangements resulting in activated kinase signaling in 40%, and an absence of TERT-p mutations except for the one SM that succumbed to hematogenous metastasis. We conclude that pediatric CM has a very similar UV-induced mutational spectrum to that found in the adult counterpart, emphasizing the need to promote sun protection practices in early life and to improve access to therapeutic agents being explored in adults in young patients. By contrast, the pathogenesis of CNM appears to be distinct. TERT-p mutations may identify the rare subset of spitzoid melanocytic lesions prone to disseminate.

Study EGAS00001000901

Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness (ESCAPE-BioLINCC)

Accessing Data Please refer to “Authorized Access” below regarding accessing data through the BioData Catalyst ecosystem. The data from this accession is not available for download through dbGaP. Objectives To test whether pulmonary artery catheter use was safe and could improve clinical outcomes in participants hospitalized with recurrent heart failure. Background Pulmonary artery catheters have been used to guide adjustment of therapy in multiple settings, but recent studies have raised concern that pulmonary artery catheters may lead to increased mortality in hospitalized participants. Participants A total of 433 participants at 26 sites were enrolled, and randomly assigned to receive therapy guided by clinical assessment and the pulmonary artery catheter or clinical assessment alone. Patients with acute decompensation in which the attending heart failure physician considered pulmonary artery catheterization (PAC) was required or likely to be required within the next 24 hours were entered into a PAC registry. A total of 439 patients were added to the registry. Conclusions Therapy to reduce volume overload during hospitalization for heart failure led to marked improvement in signs and symptoms of elevated filling pressures, with or without the pulmonary artery catheter. Addition of the pulmonary artery catheter to careful clinical assessment did not impact overall mortality and hospitalization. Future trials should test noninvasive assessments with specific treatment strategies that could be used to better tailor therapy for both survival time and survival quality as valued by participants. (Binanay, C. et al., JAMA, 2005)

Study phs003782

Deep single-cell RNA sequencing data for 11138 T cells from tumour, adjacent normal tissue and peripheral blood of treatment-naive CRC patients. The DATA ACCESS AGREEMENT is provided at https://github.com/zhangyybio/single-T-cell-data-access. Applicants can request access to the data by directly downloading it or by sending an email to cancerpku@pku.edu.cn. The process that is used to approve an application includes verifying the institution, participants and research purposes of the application. In general this process will take about two weeks. In principal, any scientific research program complying with the laws and bioethic regulation policies of China will be approved.

T cells are central players in cancer immunotherapy1, yet some of their fundamental properties such as development and migration within tumours remain elusive. The enormous T cell receptor (TCR) repertoire, required for recognising foreign and self-antigens2,3, could serve as lineage tags to track these T cells in tumours4. Here, we obtained transcriptomes of 11,138 single T cells from 12 colorectal cancer (CRC) patients and developed STARTRAC (Single T-cell Analysis by Rna-seq and Tcr TRACking) indices to quantitatively analyse dynamic relationships among 20 identified T cell subsets with distinct functions and clonalities. While both CD8+ effector and “exhausted” T cells exhibited high clonal expansion, they were independently connected with tumour-resident CD8+ effector memory cells, implicating a TCR-based fate decision. Of the CD4+ T cells, the majority of tumour-infiltrating Tregs showed clonal exclusivity, whereas certain Treg clones were developmentally linked to multiple TH clones. Notably, we identified two IFNG+ TH1-like clusters in tumours, the GZMK+ TEM and CXCL13+ TH1-like clusters, which were associated with distinct IFN-γ-regulating transcription factors, EOMES/RUNX3 and BHLHE40, respectively. Only CXCL13+BHLHE40+ TH1-like cells were preferentially enriched in tumours of microsatellite-instable (MSI) patients, which might explain their favourable response rates to immune-checkpoint blockade. Furthermore, we found IGFLR1 to be highly expressed in both CXCL13+BHLHE40+ TH1-like and CD8+ exhausted T cells and possessed co-stimulatory functions. Our integrated STARTRAC analyses provides a powerful avenue to comprehensively dissect the T cell properties in CRC, which could shed new insights into the dynamic relationships of T cells in other cancers

Study EGAS00001002791

Targeted sequencing of healthy individuals, aged individuals and AML patients

Targeted sequencing using BD Rhapsody with 462 mRNA and 97 antibodies of healthy young and aged adult as well as AML bone marrow mononuclear cells from iliac crest aspirations. Please note raw and integrated gene expression data, cell type annotation, metadata and dimensionality reduction are available as Seurat v3 objects through figshare. Access link is https://figshare.com/s/0fda29b169c719223ee3.

Dataset EGAD00001008188

Polycystic Ovary Syndrome (PCOS) Genetics

PCOS is a complex genetic disease reflecting the interplay of susceptibility genes and environmental factors. The cardinal reproductive feature of the syndrome, hyperandrogenemia, appears to play a direct role in the pathogenesis of the associated metabolic abnormalities. Male as well as female first-degree relatives have reproductive and metabolic phenotypes including increased prevalence rates of type 2 diabetes (T2D), metabolic syndrome (MBS) and other risk factors for cardiovascular disease (CVD). Northwestern University (NU) investigators lead a team that has extensive experience in phenotyping PCOS and in the genetic analysis of complex diseases including genome-wide association study (GWAS). Together with an expert group of collaborators from the Hershey Medical Center, and The University of Chicago, we have conducted a GWAS to identify PCOS susceptibility alleles using a large cohort of extensively and consistently phenotyped PCOS cases. Population controls for this study come from the NUgene project described below. NUgene project: In 2002, Northwestern committed to the development of a DNA repository to serve as a platform for the identification and validation of genotype-phenotype associations that will impact healthcare. The NUgene Project is a repository with longitudinal medical information from participating patients at affiliated hospitals and outpatient clinics from the Northwestern University Medical Center. Participants' DNA samples are coupled with data from a questionnaire (2 versions were used, 1 before and 1 after February 2006, both are included) and continuously updated data from our Electronic Medical Record (EMR) representing actual clinical care events. Northwestern has a state-of-the art, comprehensive inpatient and outpatient EMR system of over 2 million patients. NUgene has broad access to participant data for all outpatient visits as well as inpatient data via a consolidated data warehouse. NUgene participants consent to distribution and use of their coded DNA samples and data for a broad range of genetic research by third-party investigators.

Study phs000368

Single-Gene vs. Panel Sequencing in Advanced HR+/HER2− Breast Cancer

Background: Hormone receptor-positive (HR+)/HER2-negative (HER2−) breast cancer is the most common subtype of breast cancer, with biomarker-driven therapies improving outcomes. Liquid biopsies, which analyze circulating tumor DNA (ctDNA), are increasingly essential in clinical management, offering a minimally invasive approach to assess somatic alterations and guide therapy decisions. However, the choice of assay directly affects clinical utility and global access to validated testing platforms remains inconsistent. To address these challenges, this study aimed to compare the performance of single-gene and panel-based sequencing for detecting PIK3CA mutations in HR+/HER2− breast cancer and evaluate the added value of broader genomic profiling in guiding treatment decisions. Patients and methods: We conducted a prospective, multicenter study comparing our previously established SiMSen-Seq (SSS) assay, a single-gene method targeting PIK3CA hotspot mutations, with the AVENIO ctDNA Expanded assay, enriching for 77 clinically relevant cancer genes. Additionally, tumor fractions were estimated with mFAST-SeqS by assessing chromosomal aneuploidy. We analyzed 161 plasma samples collected from 146 patients with HR+/HER2− advanced breast cancer before starting a new line of palliative therapy. Results: High concordance (92.6%) was observed between SSS and AVENIO for PIK3CA mutation detection, with accurate variant allele frequency (VAF) quantification confirmed by Bland-Altman analysis and linear mixed-effects modeling. AVENIO identified additional PIK3CA mutations in 19% of samples as well as other actionable alterations, including ESR1 mutations (17.5%) and PI3K pathway alterations (40.6%). Panel sequencing improved the interpretation of negative liquid biopsy results by integrating tumor fraction and VAF metrics. Conclusions: Panel sequencing assays such as AVENIO provide broader genomic coverage, enhance the detection of actionable mutations and refine the interpretation of negative liquid biopsy findings. These findings support the integration of comprehensive ctDNA profiling into routine clinical practice for HR+/HER2− advanced breast cancer while highlighting the need for improved assay accessibility and expanded genomic targets.

Study EGAS00001008200

Providing safe access to sensitive human data across borders: Federated EGA becomes a reality

Enabling discovery and access to sensitive data across national boundaries is vital for improving human health. It enables more powerful and efficient research by increasing the volume and diversity of data available for analysis. It allows us to better understand the causes of diseases – cancer, rare diseases, infectious diseases like COVID-19 – and develop new medicines and treatments. Sensitive human omics data are typically generated by research initiatives and shared using specialist repositories which provide services for data submission, discovery, and access. The EGA is one such repository. Established in 2008 at EMBL’s European Bioinformatics Institute (EMBL-EBI) in the UK, since 2012 the EGA (“Central EGA”) has been jointly managed by EMBL-EBI and the Centre for Genomic Regulation (CRG) in Spain. Many countries have emerging personalised medicine programmes which generate data from national initiatives. These programmes are driving a transition in human genomics from being research-driven to receiving funding through healthcare. Data generated in a clinical context are subject to stricter governance than research data and must follow national data protection legislation. To solve these challenges, the Federated EGA provides a network of connected resources to enable transnational discovery of and access to human omics data for research, while also respecting jurisdictional data protection regulations. In this way, the Federated EGA infrastructure supports the goals of European initiatives such as the 1+ Million Genomes initiative (1+MG), the European Health Data Space, and a number of EU-funded 1+MG implementation projects including Beyond 1 Million Genomes. The Federated EGA is made up of “Nodes” – typically nationally funded and operated – which store and manage data locally while allowing global discovery within the Federated EGA network. Since 2016, multiple parallel efforts, supported by ELIXIR and other transnational and national initiatives, have created technical and legal frameworks for establishing the Federated EGA. In 2022, the first five Nodes – Finland, Germany, Norway, Spain, and Sweden – officially joined the Federated EGA by signing Federated EGA Collaboration Agreements with Central EGA. The Finnish FEGA Node is operated by CSC - IT Center for Science and provides data management services according to national laws and the requirements of the EU General Data Protection Regulation (GDPR). These services provide tools and support for the whole life-cycle of sensitive research data from collating to analysis, publication, and authorised re-use. The development of the services has been a joint effort with the other Nordic nodes within NeIC's Tryggve and Heilsa projects, and funded by Finnish Ministry of Education and Culture and projects coordinated by ELIXIR Finland. Read more about Finnish FEGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. In Norway, a key component of the infrastructure is the services of The Sensitive Data Service (TSD) offered by USIT at University of Oslo. The Federated EGA Norway Node is developed by ELIXIR Norway and operated by the University of Oslo as the responsible legal entity. Core software modules are developed jointly with the other Nordic nodes in the NeIC Tryggve and Heilsa projects. Read more about FEGA Norway signing. The Spanish FEGA (es-FEGA) is a national service for storing sensitive biomedical data in Spain. Supported by the Spanish Institute of Bioinformatics (INB) in collaboration with Central EGA, sensitive research datasets are primarily hosted at the Barcelona Supercomputing Centre facilities. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. By providing a solution for secure and efficient management of human omics data, the Federated EGA aims to foster data reuse, enable reproducibility, accelerate biomedical research, and improve human health. Find out more Interested in setting up your own Federated EGA Node? Check out the FEGA Onboarding Knowledge Base for more information. The ELIXIR Federated Human Data Community is a great entry point for anyone interested in learning more about the Federated EGA. You can: Join the ELIXIR Federated Human Data Community mailing list (select “Human Data”) Attend the ELIXIR Federated Human Data Community calls

Blog safe-access-to-sensitive-human-data-federated-ega

Framingham Heart Study-Cohort (FHS-Cohort) - Imaging

The Framingham Heart Study (FHS) is a population-based, observational cohort study initiated in 1948 to prospectively investigate the determinants of cardiovascular disease to guide public health prevention. The FHS began by recruiting an Original Cohort of 5,209 men and women between the ages of 30 and 62 from the town of Framingham, Massachusetts, who had not yet developed overt symptoms of cardiovascular disease or suffered a heart attack or stroke. The Original cohort Exam 1 took place between 1948 and 1953. Since that time the cohort has had a total of 32 biennial exams (ending in 2014) and event follow-up through 2022. In 1971, the FHS added the Offspring cohort, comprising 5124 children whose parents were enrolled in the Original cohort and the spouses of the children. This cohort on average has been examined every three to four years. However, there was an eight year gap between Exam 1 and Exam 2 and a seven year gap between Exam 7 and Exam 8. The latest exam (10) was completed in 2022. In 2002, the transgenerational FHS design was facilitated with the recruitment of 4095 children of the Offspring cohort (Third Generation), and 103 spouses of the Offspring who were not previously enrolled in the study (New Offspring Spouses, NOS). These cohorts have completed 3 exams through 2019. To reflect the changing demographic characteristics of the greater Framingham community, the FHS additionally recruited and enrolled 2 cohorts comprising racial and ethnic minority groups, termed Omni-1 and Omni-2, (n = 506 and 410, respectively) in 1995 and 2002, respectively. These cohorts included individuals of African American, Hispanic, Asian, Indian, Native American, and Pacific Islander descent. The OMNI-1 cohort have completed 5 exams through 2022. The OMNI-2 cohort have completed 3 exams through 2019. Data available for request include Echocardiogram images, available from the following exams in each cohort. Original Cohort: exams 18-32; Offspring cohort: exams 3-10; Third Generation, NOS and OMNI-2 cohorts: exams 1-3; OMNI-1 cohort: exams 1-5.CT image data at one or two timepoints were added for 4427 participants in the offspring, third generation, and OMNI1 and OMNI2 cohorts.Summary level phenotypes for the Framingham Cohort study participants can be viewed at the top-level study page phs000007 Framingham Cohort. Individual level phenotype data and molecular data for all Framingham top-level study and substudies are available by requesting Authorized Access to the Framingham Cohort study phs000007.

Study phs003593

Bulk RNAseq FASTQ files of three PDAC organoid lines treated with LGK974 or DMSO for 24h

This dataset contains raw FASTQ files from bulk RNA sequencing of three patient-derived pancreatic ductal adenocarcinoma (PDAC) organoid lines (P28, P40, P47). Each organoid line was treated with either the WNT secretion inhibitor LGK974 or DMSO for 24 hours, with three biological replicates per treatment condition. RNA was extracted using the Qiagen QiaSymphony SP system, and libraries were prepared with the Illumina Truseq stranded polyA kit. Sequencing was performed on an Illumina NextSeq2000 platform using single-end 50bp reads. Quality control was conducted with FastQC, and reads were trimmed with TrimGalore as appropriate. Data are controlled-access and intended for transcriptomic analysis of WNT pathway perturbation in PDAC organoid models.

Dataset EGAD50000002219

Germline and somatic variants in myelodysplastic syndrome and therapy-related myeloid neoplasms

Somatic mutation frequencies in patients with therapy-related myeloid neoplasms (129 patients, 181 samples including bone marrow, mesenchymal stromal cells and hair DNA) and primary myelodysplastic syndrome (108 patients, 215 samples including bone marrow, mesenchymal stromal cells and hair DNA) is assessed by deep sequencing of selected genes, using a Fluidigm Access Array, a Nimblegen capture panel and an Ion AmpliSeq panel. The dataset consists of paired fastq files obtained by either Hiseq (2x101bp) or Nextseq (2x150bp) Illumina sequencing. The mutational burden is found to be similar in both, however the distribution of variants is different. Correlation of the mutational spectrum with prognosis is also observed.

Dataset EGAD00001004861

Genetic Markers of Lipids in Indians: A Validation Study of Most Relevant Findings of Genome-Wide Association Studies

We aimed to validate the GWAS loci (identified in European populations) related to serum lipid levels (total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and very low-density lipoprotein cholesterol (VLDL-C)) in the Indian population originally recruited to examine the 20-year trend of cardiometabolic risk factors in rural and urban areas around National Capital region in India. Isolated DNA was processed for genotyping of 12 established GWAS variants using multiplex Sequenom Mass ARRAY technology. We examined the association of these genetic variants with different lipid levels and found association of eight variants for lipid levels. We also derived weighted genetic risk scores (wGRAS) using the validated variants. The wGRS for triglycerides and VLDL-C was derived based on four associated variants (rs174546 at FADS1, rs17482753 at LPL, rs2293889 at TRPS1, rs4148005 at ABCA8, rs4420638 at APOC1) which was associated with 36.31 mg/dL elevated triglycerides and VLDL-C (β=0.95, SE=0.16, pTRPS1 and rs4147536 at ADH1B) was associated with 40.62 mg/dL higher total cholesterol (β=1.01, SE=0.23, pTRPS1, rs4147536 at ADH1B, rs4420638 at APOC1 and rs660240 at CELSR2). The wGRS derived from five associated variants (rs174546 and FADS1, rs17482753 at LPL, rs4148005 at ABCA8, rs4420638 at APOC1 and rs7832643 at PLEC) was associated with 10.64 mg/dL lower HDL-C (β= -0.87, SE =0.14, pThe phenotypic and genotypic data will be made available on the dbGaP portal for access.

Study phs003469

Human genomic and phenotypic synthetic data for the study of rare diseases

The purpose of this dataset is to facilitate development of technical implementations for rare disease data integration, analysis, discovery, and federated access. This synthetic dataset includes clinical and genomic data from 6 rare disease cases. It consists of 18 whole genomes (6 index cases with their parents) which have genetic background based on public human data sequenced in the context of the Illumina Platinum initiative (Eberle, MA et al. (2017)) and made available by the HapMap project (https://www.genome.gov/10001688/international-hapmap-project). In each of the cases, real causative variants correlating with the phenotypic data provided were spiked-in. The cases included in this synthetic dataset correspond to the following type of disorders: CASE 1- Congenital myasthenic syndrome (Autosomal Dominant -de novo variant) CASE 2- Macular dystrophy (Autosomal Dominant) CASE 3- Muscular dystrophy (Autosomal Recessive-compound heterozygous variants) CASE 4- Mitochondrial disorder (Autosomal Recessive-consanguineous case - homozygous variant) CASE 5- Breast cancer (Autosomal Dominant) CASE 6- Similar as case 1 for patient matchmaking tests: Congenital myasthenic syndrome (Autosomal Dominant-de novo variant) For each case you will be able to download the following data: clinical information (phenopackets per individual and pedigree per family), raw genomic data (FASTQ and BAMs) and processed genomic data (vcfs). When using the data, the following should be acknowledged: the RD-Connect GPAP (https://platform.rd-connect.eu/), EC H2020 project EJP-RD (grant # 825575), EC H2020 project B1MG (grant # 951724) and Generalitat de Catalunya VEIS project (grant # 001-P-001647).

Study EGAS00001005702

Research Program on Genes, Environment and Health (RPGEH)

The Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH) is a resource developed to facilitate research on genetic and environmental factors on common diseases and healthy aging. The RPGEH resource links biospecimens, health surveys, and comprehensive electronic medical records on broadly consented adult members of Kaiser Permanente Medical Care Plan, Northern California Region (KPNC). KPNC is an integrated health care delivery system with a membership of approximately 3.3 million people in northern California. The membership of KPNC is representative of the general population in the 14 county area in which facilities are located, although extremes of income are underrepresented. At the end of 2013, the RPGEH resource included: (1) demographic and behavioral surveys from over 430,000 participants; (2) biospecimens (DNA, serum, plasma, and/or saliva) from over 204,000 participants, including over 13,000 pregnant women; (3) genome-wide genotype data (70 billion SNP genotypes) on over 100,000 participants, including the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort; and (4) the longitudinal electronic medical records of the participants. The RPGEH was developed beginning in 2005 at the Division of Research of Kaiser Permanente Northern California by Catherine Schaefer (Director), Neil Risch (Co-Director), Lisa Croen, Eric Jorgenson, Lawrence Kushi, Charles Quesenberry, Sarah Rowell, Carol Somkin, Stephen Van den Eeden, Larry Walter, and Rachel Whitmer. Funding of the RPGEH was provided to C. Schaefer (PI) and N. Risch (co-PI) by the Wayne and Gladys Valley Foundation, The Ellison Medical Foundation, the Robert Wood Johnson Foundation, Kaiser Permanente Northern California, and the Kaiser Permanente National and Regional Community Benefit Programs. The GERA cohort was funded by a grant from NIH to RPGEH and UCSF (RC2 AG036607; C. Schaefer and N. Risch, PIs). At the time of the award of the RC2 project in late 2009, the RPGEH had established a cohort of about 140,000 individuals who had answered a detailed survey, provided saliva samples for extraction of DNA, and given broad consent for the use of their data in studies of health and disease. Survey and Cohort Recruitment. Initially, the RPGEH developed electronic disease registries to enable identification of phenotypes, using algorithms applied to EMR data. In 2007, the RPGEH mailed a four page survey to 1.9 million adult (≥ 18 years old) members of KPNC who had been members for two years or more, to obtain data on demographic and behavioral factors complementary to the clinical data in the EMR. The survey materials included a cover letter introducing the RPGEH, a two page list of Frequently Asked Questions, and the survey, which included questions on demographic factors such as education, race-ethnicity, income and marital status, dietary factors, physical activity, smoking, and alcohol consumption, as well as reproductive history and reproductive health. Members whose electronic medical records indicated a preference for written communications in Chinese or Spanish received survey materials both in English and a Chinese or Spanish translation. Approximately 400,000 completed surveys were returned. Saliva Sample Collection. Beginning in July 2008, respondents to the survey were asked to sign and return a consent form and authorization for use and disclosure of protected health information. The consent form authorized broad use of biospecimens, survey data, and data from participants' electronic health records for use in studies of genetic and environmental influences on health and disease. Respondents who returned completed consent forms were mailed (Oragene) saliva collection kits; more than 132,000 saliva samples were collected in two years. Completed saliva kits were scanned and archived in a temporary biorepository at the KPNC Division of Research. In late 2009, the RPGEH began collection of saliva samples from the California Men's Health Study (CMHS), a cohort that had been previously assembled in 2002-2003 and had been excluded from the RPGEH survey mailing with the intent of later adding CMHS participants to the assembled RPGEH cohort. The CMHS was developed to facilitate research on prostate cancer and other conditions in older men; the study protocol is described in Enger, et al., 2006. It enrolled and surveyed more than 40,000 men in KPNC, ages 45-69 years, who were members of KPNC during 2002-2003. CMHS men completed two mailed surveys with demographic and behavioral data similar to that of the RPGEH. The data on analogous variables were reconciled and integrated with the data derived from the RPGEH cohort for use in the RPGEH resource. By 2011, RPGEH collected approximately 15,400 saliva samples from men participating in the CMHS. RPGEH Access and Collaborations Website and Procedures. The RPGEH maintains a web portal for inquiries and applications for collaboration and access to data. The url is: https://rpgehportal.kaiser.org/. RPGEH has an application process and an Access Review Committee that reviews applications for collaboration and use. For more information, please contact RPGEH through the website.

Study phs000788

Drug screening of patient-derived organoids from colorectal peritoneal metastases

Colorectal cancer (CRC) patients with peritoneal metastases (CRPM) have limited treatment options and the lowest CRC survival rates. We trialed the possibility of organoid directed precision treatment for CRPM patients. CRPM organoids (peritonoids) isolated from patients underwent next-generation sequencing and medium-throughput drug panel testing ex vivo to identify specific drug sensitivities for each patient. We measured the utility of such a service including: success of peritonoid generation, time to cultivate peritonoids, reproducibility of the medium-throughput drug testing, and documented changes to clinical therapy as a result of the testing. Peritonoids were successfully generated and validated from 68% (19/28) of patients undergoing standard care. Genomic and drug profiling was completed within 8 weeks and a formal report ranking drug sensitivities was provided to the medical oncology team upon failure of standard care treatment. This resulted in a treatment change for 2 patients, one of whom had a partial response despite previously progressing on multiple rounds of standard care chemotherapy. The barrier to implementing this technology in Australia is the need for drug access and funding for off-label indications. In conclusion, our approach is feasible, reproducible and can guide novel therapeutic choices in this poor prognosis cohort, where new treatment options are urgently needed. This platform is relevant to many solid organ malignancies.

Study phs002023

eMERGE Geisinger eGenomic Medicine (GeM) - MyCode Project Controls

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 participants provided written informed consent and HIPAA authorization. Consenting patient agreed to provide blood samples for broad biomedical research use, and permission to access data in their Geisinger electronic medical record for research. The enrollment rate was 90% of patients approached. The demographics of the cohort approximate those of the Geisinger Clinic outpatient population. Research blood samples were collected during an outpatient clinical phlebotomy encounter. Research blood samples are coded and stored in a central biorepository. Samples are linkable to clinical data in a de-identified manner for research via an IRB-approved data broker process. For genomic analysis, DNA is extracted from EDTA-anticoagulated whole blood. For the initial eMERGE Geisinger eGenomic Medicine (GeM) genotyping project, a subset of 1,232 unique samples were genotyped using Illumina HumanOmniExpress-12 v1.0 arrays, and used as population controls for other Geisinger Clinic case cohorts, including abdominal aortic aneurysm and gastric bypass surgery cases. These samples were selected from a larger subset of approximately 6,000 MyCode DNA samples using a partial matching algorithm that included age, sex, and body mass index as variables.

Study phs000381

Serrated Colorectal Cancer: An Emerging Disease Subtype

This dataset represents two combined study populations. Serrated Colorectal Cancer: An Emerging Disease Subtype (called the Advanced Colorectal Cancer of Serrated Subtype Study or ACCESS Study) was a grant awarded to investigate a newly-recognized, biologically-distinct subtype of colorectal cancer (CRC) called “serrated CRC.” The objective of this project was to characterize factors related to the genetic predisposition, clinical presentation, and prognosis of serrated CRC. The study recruited incident invasive CRC cases diagnosed between April 2016 and December 2018, aged 20-74 years at diagnosis. Cases were identified through the Surveillance, Epidemiology and End Results (SEER) cancer registry serving 13 counties in western Washington State. Eligibility for all individuals was limited to those who were English-speaking and could consent. Participation included completing a baseline epidemiologic questionnaire shortly after diagnosis, optional donation of a saliva sample for genetic analysis, and optional consent to release of medical records and tissue specimens related to their diagnosis. Tumor specimens were tested for serrated CRC-defining molecular characteristics. Further, we have vital status on all participants and cause of death on those that have died since enrollment. Hormones and Colon Cancer: Epigenetic Subtypes, Risks, and Survival (called the Post-Menopausal Hormones Study or PMH Study) was a grant awarded to investigate the impact of post-menopausal hormone use on colon cancer risk, tumor molecular characteristics, and outcomes. Eligible cases were females, newly diagnosed with invasive colorectal adenocarcinoma between October 1998 and February 2002, aged 50 to 74 years. Cases were residents of 10 out of the 13 counties in western Washington State served by the Surveillance, Epidemiology and End Results (SEER) cancer registry. Eligibility for all individuals was limited to those who were English-speaking with available telephone numbers, in which they could be contacted. Unrelated population-based controls were randomly selected according to age distribution (in 5-year age intervals) of the eligible cases by using lists of licensed drivers from the Washington State Department of Licensing (for individuals aged 50 to 64 years) and rosters from the Health Care Financing Administration (now the Centers for Medicare and Medicaid, for individuals older than 64 years). Participation included completing a baseline epidemiologic questionnaire, optional donation of a saliva sample for genetic analysis, and (for cases only) optional consent to release of medical records and tissue specimens related to their diagnosis. Tumor specimens were tested for epigenetic and other molecular characteristics. The ACCESS study was supported by funding from the National Cancer Institute of the National Institutes of Health (NCI/NIH) (R01CA196337, PI: Newcomb, PA), as was the PMH Study (R01CA076366, PI: Newcomb, PA). Additional support for the PMH Study came from the Seattle site of the Colon Cancer Family Registry (SCCFR) (U01CA167551, PI: Jenkins, M, and U01/U24CA074794, PI: Newcomb, PA). Additional support for case ascertainment was provided by the Cancer Surveillance System of the Fred Hutchinson Cancer Center, which is funded by Contract Number HHSN261201300012I; NCI Control Number: N01 PC-2013-00012; Contract Number HHSN261201800004I; and NCI Control Number: N01 PC-2018-00004 from the Surveillance, Epidemiology and End Results (SEER) Program of the National Cancer Institute with additional support from the Fred Hutchinson Cancer Center and the State of Washington. This research was also supported by the Genomics and Bioinformatics, Comparative Medicine, Specialized Pathology, Collaborative Data Services, and Experimental Histopathology Shared Resources of the Fred Hutch/University of Washington Cancer Consortium (P30 CA015704).Tumor marker testing was performed using formalin-fixed paraffin-embedded diagnostic tumor tissue specimens, and DNA extracted from those specimens. Testing for microsatellite instability (MSI) was based on either a 10-gene panel (BAT25, BAT26, BAT40, MYCL, D5S346, D17S250, ACTC, D18S55, D10S197, BAT34C4) or a 4-marker immunohistochemistry panel of DNA mismatch repair proteins (MLH1, MSH2, MSH6, PMS2). CpG island methylator phenotype (CIMP) testing was based on a validated quantitative DNA methylation assay using a five-gene panel (CACNA1G, IGF2, NEUROG1, RUNX3, SOCS1) or eight-gene panel (CACNA1G, IGF2, NEUROG1, RUNX3, SOCS1, MLH1, CRABP1, CDKN2A). Somatic p.V600E BRAF mutation status was tested for using a fluorescent allele-specific PCR assay. KRAS mutations in codons 12 and 13 were also assessed through forward and reverse sequencing of amplified tumor DNA. DNA was extracted from blood/saliva samples using conventional methods. The genotyping panel completed was the Build37 OncoArray500K-C, including 1%-6% blinded duplicates to monitor the quality of the genotyping. Quality control procedures were performed to 1) make sure that there were no patterns of missing data by batch, study, or plate, 2) check for gender discrepancies and kinship, 3) complete Principal Component Analysis, and 4) test for Hardy-Weinberg equilibrium (HWE). Samples were excluded based on call rate, heterozygosity, unexpected duplicates, gender discrepancy, and unexpectedly high identity-by-descent or unexpected genotypic concordance (>65%) with another individual. In addition, variants were excluded based on call rate (98%), lack of HWE in controls (P

Study phs002171

Framingham Cohort

Startup of Framingham Heart Study. Cardiovascular disease (CVD) is the leading cause of death and serious illness in the United States. In 1948, the Framingham Heart Study (FHS) -- under the direction of the National Heart Institute (now known as the National Heart, Lung, and Blood Institute, NHLBI) -- embarked on a novel and ambitious project in health research. At the time, little was known about the general causes of heart disease and stroke, but the death rates for CVD had been increasing steadily since the beginning of the century and had become an American epidemic. The objective of the FHS was to identify the common factors or characteristics that contribute to CVD by following its development over a long period of time in a large group of participants who had not yet developed overt symptoms of CVD or suffered a heart attack or stroke. Design of Framingham Heart Study. In 1948, the researchers recruited 5,209 men and women between the ages of 30 and 62 from the town of Framingham, Massachusetts, and began the first round of extensive physical examinations and lifestyle interviews that they would later analyze for common patterns related to CVD development. Since 1948, the subjects have returned to the study every two years for an examination consisting of a detailed medical history, physical examination, and laboratory tests, and in 1971, the study enrolled a second-generation cohort -- 5,124 of the original participants' adult children and their spouses -- to participate in similar examinations. The second examination of the Offspring cohort occurred eight years after the first examination, and subsequent examinations have occurred approximately every four years thereafter. In April 2002 the Study entered a new phase: the enrollment of a third generation of participants, the grandchildren of the original cohort. The first examination of the Third Generation Study was completed in July 2005 and involved 4,095 participants. Thus, the FHS has evolved into a prospective, community-based, three generation family study. The FHS is a joint project of the National Heart, Lung and Blood Institute and Boston University. Research Areas in the Framingham Heart Study. Over the years, careful monitoring of the FHS population has led to the identification of the major CVD risk factors -- high blood pressure, high blood cholesterol, smoking, obesity, diabetes, and physical inactivity -- as well as a great deal of valuable information on the effects of related factors such as blood triglyceride and HDL cholesterol levels, age, gender, and psychosocial issues. Risk factors have been identified for the major components of CVD, including coronary heart disease, stroke, intermittent claudication, and heart failure. It is also clear from research in the FHS and other studies that substantial subclinical vascular disease occurs in the blood vessels, heart and brain that precedes clinical CVD. With recent advances in technology, the FHS has enhanced its research capabilities and capitalized on its inherent resources by the conduct of high resolution imaging to detect and quantify subclinical vascular disease in the major blood vessels, heart and brain. These studies have included ultrasound studies of the heart (echocardiography) and carotid arteries, computed tomography studies of the heart and aorta, and magnetic resonance imaging studies of the brain, heart, and aorta. Although the Framingham cohort is primarily white, the importance of the major CVD risk factors identified in this group have been shown in other studies to apply almost universally among racial and ethnic groups, even though the patterns of distribution may vary from group to group. In the past half century, the Study has produced approximately 1,200 articles in leading medical journals. The concept of CVD risk factors has become an integral part of the modern medical curriculum and has led to the development of effective treatment and preventive strategies in clinical practice. In addition to research studies focused on risk factors, subclinical CVD and clinically apparent CVD, Framingham investigators have also collaborated with leading researchers from around the country and throughout the world on projects involving some of the major chronic illnesses in men and women, including dementia, osteoporosis and arthritis, nutritional deficiencies, eye diseases, hearing disorders, and chronic obstructive lung diseases. Genetic Research in the Framingham Heart Study. While pursuing the Study's established research goals, the NHLBI and the Framingham investigators has expanded its research mission into the study of genetic factors underlying CVD and other disorders. Over the past two decades, DNA has been collected from blood samples and from immortalized cell lines obtained from Original Cohort participants, members of the Offspring Cohort and the Third Generation Cohort. Several large-scale genotyping projects have been conducted in the past decade. Genome-wide linkage analysis has been conducted using genotypes of approximately 400 microsatellite markers that have been completed in over 9,300 subjects in all three generations. Analyses using microsatellite markers completed in the original cohort and offspring cohorts have resulted in over 100 publications, including many publications from the Genetics Analysis Workshop 13. Several other recent collaborative projects have completed thousands of SNP genotypes for candidate gene regions in subsets of FHS subjects with available DNA. These projects include the Cardiogenomics Program of the NHLBI's Programs for Genomics Applications, the genotyping of ~3000 SNPs in inflammation genes, and the completion of a genome-wide scan of 100,000 SNPs using the Affymetrix 100K Genechip. Framingham Cohort Phenotype Data. The phenotype database contains a vast array of phenotype information available in all three generations. These will include the quantitative measures of the major risk factors such as systolic blood pressure, total and HDL cholesterol, fasting glucose, and cigarette use, as well as anthropomorphic measures such as body mass index, biomarkers such as fibrinogen and CRP, and electrocardiography measures such as the QT interval. Many of these measures have been collected repeatedly in the original and offspring cohorts. Also included in the SHARe database will be an array of recently collected biomarkers, subclinical disease imaging measures, clinical CVD outcomes as well as an array of ancillary studies. The phenotype data is located here in the top-level study phs000007 Framingham Cohort. To view the phenotype variables collected from the Framingham Cohort, please click on the "Variables" tab above. The Framingham Cohort is utilized in the following dbGaP substudies. To view genotypes, analysis, expression data, other molecular data, and derived variables collected in these substudies, please click on the following substudies below or in the "Substudies" section of this top-level study page phs000007 Framingham Cohort. phs000342 Framingham SHARe phs000282 Framingham CARe phs000363 Framingham SABRe phs000307 Framingham Medical Resequencing phs000401 Framingham ESP Heart-GO phs000651 Framingham CHARGE-S phs000724 Framingham DNA Methylation phs001610 Framingham T2D-GENES phs002558 Framingham ADSP phs002559 Framingham BRIDGET phs002560 Framingham Cholesterol phs002611 Framingham Post-Mortem Brain Tissue phs002938 Framingham Molecular QTL The unflagging commitment of the research participants in the NHLBI FHS has made more than a half century of research success possible. For decades, the FHS has made its data and DNA widely available to qualified investigators throughout the world through the Limited Access Datasets and the FHS DNA Committee, and the SHARe database will continue that tradition by allowing access to qualified investigators who agree to the requirements of data access. With the SHARe database, we continue with an ambitious research agenda and look forward to new discoveries in the decades to come.

Study phs000007

A Single-Cell and Spatial Atlas of Early Human Olfactory Development

The human nasal region is a complex structure derived from neural crest and placodal lineages, but its development remains poorly understood due to limited fetal tissue access and its intricate architecture. To address this, we created a single-nucleus and spatial transcriptomic atlas of the human fetal nasal region using tissue from 10 fetuses between 7 and 12 post-conceptional weeks. Single-nucleus RNA sequencing (snRNA-seq) revealed 34 distinct cell types, including epithelial, cartilaginous, immune, neuronal, glial, muscular, and vascular cells. By integrating snRNA-seq with multiplexed error-robust fluorescence in situ hybridization (MERFISH), we tracked dynamic changes in cell composition and gene expression over time in the olfactory epithelium (OE) and surrounding nasal regions. We identified novel markers of olfactory sensory neuron development and key pathways regulating epithelial patterning and OE morphogenesis. Notably, we observed early molecular signatures consistent with the "one neuron-one receptor" model, with spatially organized expression of 171 olfactory receptor genes. Together, this study presents the first integrated molecular and spatial framework of early human nasal development. It offers an unprecedented molecular blueprint of olfactory system formation and serves as a foundational resource for embryologists and developmental biologists studying sensory neurogenesis, epithelial patterning, and congenital disorders.

Dataset EGAD50000001712

Genome-Wide Predictors of Treatment-Related Toxicities in SWOG S0221 Trial

When undergoing treatment for breast cancer, many women experience severe side effects, some of which result in treatment-related death and some that can persist for years. Little is understood regarding factors that may predict drug toxicities. Pharmacogenetics, the investigation of variants in candidate genes in drug metabolism pathways, has been used to determine susceptibility to treatment-related toxicities, as well as to cancer recurrence. Although there have been some strong and important findings using this approach, such as identification of single nucleotide polymorphisms (SNPs) that predispose to side effects associated with thiopurines and irinotecan, there has been less progress in assessment of genetic variants that predispose to toxicities resulting from the multi-drug regimen commonly used to treat breast cancer, anthracyclines (A), cyclophosphamide (C), and taxanes (T). These difficulties in identification of key gene variants may be due to the complex metabolic pathways of these drugs, the lack of rate limiting enzymes in the processes, or the limitations of single SNP analysis, rather than capturing all of the variability across the genes. In addition to drug metabolism pathways, however, there may be a number of constitutional factors or other processes that affect damage to normal tissues in the course of chemotherapy, some known or hypothesized, such as DNA repair and oxidative stress pathways, and others not yet discovered. However, there have been no clear candidate genes that account for a large part of the variation in drug or treatment response, and there are likely important genes that influence sensitivity of cells to chemotherapy through unknown pathways which have not yet been identified or hypothesized. The present technological capabilities to screen the entire genome for variants that discriminate populations allows the opportunity to identify these as yet unknown pathways, and open the doors to exciting new avenues of research into mechanisms that had not been previously considered. We conducted a genome-wide scan (GWAS) in a clinical trial (S0221), in which women with high risk breast cancer were treated with different dosing schedules of C, A and T. Blood specimens were collected and banked, and DNA extracted for genotyping on the Illumina OMNI 1M platform. The GWAS data were used to examine genetic variants significantly associated with grades 3 and 4 toxicities, including peripheral neuropathy recorded during the T segment. In S0221, toxicities were graded according to the NCI Common Toxicity Criteria for Adverse Events (CTCAE). The neurotoxicity is predominantly a distal sensory neuropathy, which is characterized by pain, numbness, tingling, and reduced functional capacity in the extremities. Other symptoms include parasthesias, ataxia, impaired vibration and joint position sense, and loss of tendon reflexes. By using a GWAS approach, it is likely that important pathways not previously considered can be revealed as important in susceptibility to treatment-related toxicities, identifying those at greatest risk for alternate drugs or dose reduction, and opening new areas of research for prevention of taxanes-related neuropathy among patients receiving chemotherapy for breast cancer.

Study phs001428

Bulk RNAseq FASTQ files of WNT7B reporter PDAC organoids (P28 and P40) sorted by mNeonGreen high and low

This dataset contains raw FASTQ files from bulk RNA sequencing of two patient-derived PDAC organoid lines (P28 and P40) carrying a WNT7B reporter knock-in. Organoids were sorted into mNeonGreen high (WNT-high) and low (WNT-low) populations, with three biological replicates per sorted population. RNA was extracted using the Qiagen QiaSymphony SP system, and libraries were prepared with the Illumina Truseq stranded polyA kit. Sequencing was performed on an Illumina NextSeq2000 platform using single-end 50bp reads. Quality control was conducted with FastQC, and reads were trimmed with TrimGalore as appropriate. Data are controlled-access and intended for transcriptomic analysis of WNT7B expression-dependent transcriptional programs in PDAC organoids.

Dataset EGAD50000002218

Single cell RNAseq FASTQ files of three PDAC organoid lines (P28, P40, P47) using SORT-seq

This dataset contains raw FASTQ files from single cell RNA sequencing of three patient-derived pancreatic ductal adenocarcinoma (PDAC) organoid lines (P28, P40, P47) cultured under standard conditions. For each line, single cells were FACS sorted into 384-well capture plates, with each well containing a 50nl droplet of barcoded primers. Plates were processed following an adapted SORT-seq protocol, and cDNA libraries were generated using CEL-Seq2 with TruSeq small RNA primers (Illumina). Sequencing was performed on an Illumina NextSeq500 platform using paired-end reads (read 1: 26 cycles, index read: 6 cycles, read 2: 60 cycles). Data are controlled-access and intended for single cell transcriptomic analysis of PDAC organoid heterogeneity and WNT pathway activity.

Dataset EGAD50000002220

DAC for Central African ancient demography processes NGS dataset

Title DAC Ancient tree-topologies and gene-flow processes among human lineages in Africa Contacts: Paul Verdu +33144057317 paul.verdu@mnhn.fr CNRS-MNHN-Université de Paris UMR7206 Eco-anthropology Short description The Data Access Committee for acess to genotype and sample information used in the article "Ancient tree-topologies and gene-flow processes among human lineages in Africa" by Gwenna Breton et al. 2025 ensures that data-access requests comply with the Informed Consent provided by donors, and with ethical and deontological guidelines with respect to privacy. DNA samples from individuals were collected with the subjects’ informed consent following the Declaration of Helsinki guidelines and approved by the following Ethics committees: Institut de Recherche pour le Développement (IRD, France); Cameroon Ministry of Scientific Research and Technology; Central African Republic Ministère de l’Enseignement Supérieur et de la Recherche Scientifique; France Ministère de l’Enseignement Supérieur, de la Recherche et de l’Innovation (Comité de Protection des Personnes DC-2016-2740); French Commission Nationale Informatique et Liberté CNIL (declaration n°1972648); Uganda National Council for Science and Technology; Institutional Review Board of the University of Chicago Biological Sciences Division and the Pritzker School of Medicine University of Chicago Hospitals (protocol number 16986A); Committee for the Protection of Human Subjects of the Trustees of Dartmouth College and the Dartmouth-Hitchcock Medical Center (protocol number 22410); Human Research Ethics Committee (Medical) at the University of the Witwatersrand, Johannesburg (Protocol Numbers: M980553, M050902, M090576, M10270, M180654); Working Group of Indigenous Minorities in Southern Africa (WIMSA); South African San Council (SASC); and the Swedish Ethical Review Authority (Dnr 2019-05174). Requests for accessing all or part of the Central African data used in the article "Ancient tree-topologies and gene-flow processes among human lineages in Africa", by Gwenna Breton et al. 2024 (doi - PENDING), should be addressed to the DAC providing: 1) Data can only be used to explore human genetic evolution, evolutionary ecology and population history 2) The research institution where data will be stored and were analyses will be conducted. 3) The detailed coordinates of the researcher responsible for data storage and analyses. 4) A description (max 20 sentences) of the scientific finality and context of the project and classes of methodological analyses that will be performed to achieve it using the requested data. 5) When applicable, mention also other previously published data sets that will be used jointly with the requested datas et For accessing any or all the Central African genetic data used in the article "Ancient tree-topologies and gene-flow processes among human lineages in Africa", by Gwenna Breton et al. 2024 (doi - PENDING), the requester hereby ensures that: 1) Individual donor privacy should be respected at all times. Data cannot be made publicly available under any circumstances. 2) Data cannot be shared nor transferred to a third party without re-applying for eGA access to the DAC. 3) No direct or indirect commercial use of the data can be made 4) No individual medical genetics or disease diagnostics can be performed with the data 5) No medical or legal forensic investigations can be conducted with the data 6) No genealogical research can be conducted with the data (e.g. no paternity nor maternity testing, no genetic identification, no search of relatives using other datasets) 7) Appropriate citation of the article and data set used for any scientific communication using the data 8) Data is destroyed after the completion of the project. Any further re-use of the data is subject to a novel application to the DAC.

Dac EGAC50000000447

The Finland-United States Investigation of NIDDM Genetics (FUSION) Tissue Biopsy Study

The Finland-United States Investigation of NIDDM Genetics (FUSION) study is a long-term effort to identify genetic variants that predispose to type 2 diabetes (T2D) or that impact the variability of T2D-related quantitative traits (QTs). Most of the variants associated with T2D and related traits (glucose and insulin, anthropometrics, lipids) through genome-wide association studies (GWAS) occur in non-coding regions, suggesting a strong regulatory component to disease susceptibility. Regulatory element activity is often tissue-specific, which further complicates discovery of the causal/functional variation. Therefore, there is a critical need to identify the appropriate cell type, regulatory elements, target genes, and causal variants(s) in T2D-relevant tissues. We hypothesize that a subset of T2D and related variants alter gene expression regulation in skeletal muscle and adipose tissue - two major insulin target tissues and play key roles in insulin resistance. To that end, our study contains a comprehensive survey of genomics, epigenomics and transcriptomics in skeletal muscle and adipose tissue from individuals with glucose tolerance categories ranging from normal to T2D.For this FUSION Tissue Biopsy Study, we obtained RNA-Seq, microRNA (miRNA)-Seq, and DNA methylation (methyl)-Seq data on biopsy samples from 331 individuals from across the range of glucose tolerance: 124 normal glucose tolerance (NGT), 77 impaired glucose tolerance (IGT), 44 impaired fasting glucose (IFG), and 86 newly-diagnosed T2Ds. Participants completed two study visits, two weeks apart. First visits comprised most of the clinical phenotyping, including four-point OGTT (fasting, and 30, 60, and 120 minute post-load); BMI, WHR; lipids; blood pressure; and many other variables. Participants also completed FUSION health history, medication, and lifestyle questionnaires. On the second visit, we obtained ~250mg vastus lateralis skeletal muscle, ~750mg abdominal subcutaneous adipose, and a ~5x15mm section of abdominal skin. Visits were completed in March 2013. RNA isolation is ongoing in the Collins laboratory at the NIH, RNA and miRNA sequencing at the NIH Intramural Sequencing Center (NISC), and genotyping at the Center for Inherited Disease Research (CIDR). Individual-level data is available here for the 306 individuals who consented to data deposit. To focus on evaluation of gene expression and its regulation in skeletal muscle, we analyzed mRNA extracted from vastus lateralis skeletal muscle obtained from 271 of the 331 individual subjects from Finland, along with genome-wide genotypes. Individual-level data is available here for the 250 subjects who consented to the use of their data.Release phs001048.v2.p1 adds muscle data for an additional 42 subjects and data from adipose tissue for 276 subjects. Total RNA was isolated using Trizol extraction in the Collins laboratory at the NIH. The mRNA was poly-A selected, 24-plex libraries were generated using the Illumina TruSeq directional mRNA-seq library protocol and RNA sequencing was performed on HiSeq2000 sequencers using 101bp paired-end reads at NISC. miRNA libraries were prepared from total RNA from 296 muscle and 270 adipose samples, pooled and sequenced 50bp single-end reads on Illumina HiSeq2500. Data for 272 muscle and 251 adipose samples are available here for individuals with consent for data deposit. DNA was extracted from blood in the Collins laboratory, and genotyping on the Illumina Omni2.5M array was performed at CIDR. Genotypes were imputed using the HRC 2016 reference panel. In order to assess regions of open chromatin in skeletal muscle, we obtained muscle tissue from a commercial provider to perform ATAC-seq; these samples were sequenced at the University of Michigan DNA Sequencing Core.Release phs001048.v3.p1 adds single-nucleus (sn) RNA-seq and ATAC-seq data in 287 skeletal muscle samples out of the original 331 individuals. Individual-level data is available here for the 265 subjects who consented to the use of their data. The frozen tissue biopsy samples were processed in ten batches, each consisting of 40-41 samples. These batches were organized using a randomized block design to protect against experimental contrasts of interest including cohort, age, sex, BMI among others. Samples in each batch were pulverized, pooled together followed by nuclei isolation. The nuclei were processed on the 10X Genomics Chromium platform separately for snATAC-seq and snRNA-seq (v. 3.1 chemistry for snRNA-seq).Release phs001048.v4.p1 adds additional phenotypes and provides some corrections to several previous phenotypes.

Study phs001048

Disease Variant Landscape of a Large Multiethnic Population of Moyamoya Patients by Exome Sequencing

We report a significant advance in Moyamoya disease (MMD) research through our unique access to a large MMD patient population combined with high depth exome sequencing and bioinformatics. We confirm a reported RNF213 founder mutation (FM), provide evidence for its ethnic specificity and identify novel variants in other genes that associate with Caucasian and non-RNF213 FM MMD. This work has a broader impact in vascular research by highlighting novel genetic aspects of cerebrovascular disease. Moyamoya Disease (MMD) is a rare cerebrovascular disorder characterized by unilateral or bilateral progressive stenosis or occlusion of the internal carotid artery, with frequent involvement of the anterior cerebral artery and the middle cerebral artery (MCA). Fragile collateral vessels subsequently develop and are particularly prone to hemorrhage. MMD patients are usually diagnosed angiographically following presentation of developmental delays, seizures, migraines, ischemia, and/or hemorrhage. The most common approach to re-establish normal blood flow is bypass surgery. While some progress has been made in identifying disease-associated genes, the underlying disease biology is not well understood. Moyamoya disease (MMD) is a rare cerebrovascular disease. Our unique access to a large MMD patient population combined with high depth exome sequencing and bioinformatics has led to a significant advance in the field. We confirm a reported RNF213 founder mutation (FM), provide evidence for its ethnic specificity and identify novel variants in other genes that associate with Caucasian and non-RNF213 FM MMD. This work has a broader impact in vascular research by highlighting novel genetic aspects of cerebrovascular disease. The 125 ethnically diverse, unrelated MMD patients were matched based on sex and broad ethnic category to 125 controls. Control DNA from the 1000 Genomes project was used to design a control Library by Personalis. 125 controls were selected from this Personalis Control DNA Library for our study. (The 1000 Genomes Project Consortium 2012). Genomic DNA was extracted using the Gentra Puregene kit (Qiagen, Valencia, CA). Libraries were prepared from approximately 3 µg of high quality genomic DNA (50-200 ng/µl) using Illumina TruSeq Genomic DNA High throughput Sample Prep Kits (Illumina, San Diego, CA) and exome enrichment (targeting 62Mb) was accomplished using the TruSeq Exome Target Enrichment kit (Illumina, San Diego, CA), all according to manufacturer's protocols. Target enrichment validation was confirmed by determining the concentration of the library by PicoGreen-based quantitation. Library yields ranged from 100-1000ng of DNA, a portion of which was run on the Bioanalyzer HS DNA chip (Agilent, Santa Clara, CA), with an average size of 300-550nt for DNA fragments. Exome Sequencing. Sequencing was performed using Illumina Hiseq2000 or HiSeq2500 sequencers with single lane, paired-end 2X100bp reads. DNA fragments were generated and amplified using Clonal Single Molecule Array technology (Illumina, San Diego, CA). The sequences were determined using the Clonal Single Molecule Array and Sequencing-by-Synthesis using Illumina's instrumentation and Reversible Terminator Chemistry. Each sequencing lane interrogated the DNA sequences of a pool of 3 individual sample libraries each carrying a unique index. Sequencing reads of at least 2x100bp in length for a total of approximately 8 Gb of sequence data were generated for each sample.

Study phs001700

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

The International Consortium for Prostate Cancer Genetics Genome Wide Association Study of Familial Prostate Cancer

Initially, the ICPCG focused largely on high-risk prostate cancer pedigrees using linkage analysis. However, association studies have also been informative to find prostate cancer susceptibility variants in the genome. We hypothesize that genome-wide (GW) association studies of familial cases of prostate cancer will have the power to identify rarer and perhaps less penetrant risk variants associated with prostate cancer than have been identified to date. Our approach will be to examine evidence for association in selected, independent cases from high-risk pedigrees; we hypothesize that they represent a more homogeneous set of cases with potentially different associations. Use of the 5M plus exome SNP platform will allow us to include in our search more rare variants in our search than have ever been investigated for prostate cancer. Aim 1. Identify a set of independent prostate cancer cases selected from ICPCG high-risk prostate cancer pedigrees that have 3 or more related prostate cancer cases and have an average age at diagnosis ≤ 75 years. Identify appropriate matched controls from participating ICPCG groups/ populations. Aim 2. Perform GW SNP genotyping with the Illumina 5M plus exome SNP set on the set of independent prostate cancer cases and controls described in Aim 1. Aim 3. Perform association analyses appropriately matching controls for different ICPCG populations and groups. There will be a two-stage design for confirmation. Aim 4. Test reported associations using the extensive sample set available to us by collaboration with the PRACTICAL consortium that has access to 50 000 DNA samples from 34 groups internationally (over 7,000 of whom are positive for prostate cancer family history).

Study phs000733

Department of Human Genetics, Radboud University Nijmegen Medical Centre, Data Access Committee

Dac EGAC00001000076

MOSAIC Window DLBCL Data

This dataset will include Spatial Transcriptomics from the 10x Genomics Visium SD technology, Single-Cell RNA-Seq from the 10x Genomics Chromium Flex protocol, Bulk RNA-Seq (sequenced on Illumina), WES (sequenced on Illumina), H&E, and Clinical data from 10 DLBCL patients. For Visium and Chromium data we are sharing the outputs of respectively Space Ranger and Cell Ranger. For Bulk RNA-Seq we are including processed files such as counts.tsv, FPKM.tsv, fusion_genes.tsv, TPM count matrix.tsv, DESeq2 count matrix.tsv, multiqc_report. For WES, we are sharing the presence or absence of single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number variants (CNVs).* Researchers from private or public institutions outside the MOSAIC Consortium will be able to apply to access this data and, pending approval, use the data for their research.

Dataset EGAD50000001702

MOSAIC Window Ovarian Data

This dataset will include Spatial Transcriptomics from the 10x Genomics Visium SD technology, Single-Cell RNA-Seq from the 10x Genomics Chromium Flex protocol, Bulk RNA-Seq (sequenced on Illumina), WES (sequenced on Illumina), H&E, and Clinical data from 15 Ovarian patients. For Visium and Chromium data we are sharing the outputs of respectively Space Ranger and Cell Ranger. For Bulk RNA-Seq we are including processed files such as counts.tsv, FPKM.tsv, fusion_genes.tsv, TPM count matrix.tsv, DESeq2 count matrix.tsv, multiqc_report. For WES, we are sharing the presence or absence of single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number variants (CNVs).* Researchers from private or public institutions outside the MOSAIC Consortium will be able to apply to access this data and, pending approval, use the data for their research.

Dataset EGAD50000001704

MOSAIC Window Mesothelioma Data

This dataset will include Spatial Transcriptomics from the 10x Genomics Visium SD technology, Single-Cell RNA-Seq from the 10x Genomics Chromium Flex protocol, Bulk RNA-Seq (sequenced on Illumina), WES (sequenced on Illumina), H&E, and Clinical data from 10 Mesothelioma patients. For Visium and Chromium data we are sharing the outputs of respectively Space Ranger and Cell Ranger. For Bulk RNA-Seq we are including processed files such as counts.tsv, FPKM.tsv, fusion_genes.tsv, TPM count matrix.tsv, DESeq2 count matrix.tsv, multiqc_report. For WES, we are sharing the presence or absence of single nucleotide variants (SNVs), small insertions and deletions (indels), and copy number variants (CNVs).* Researchers from private or public institutions outside the MOSAIC Consortium will be able to apply to access this data and, pending approval, use the data for their research.

Dataset EGAD50000001706

Reference Standards for Mosaic Variant Detection

After conception, postzygotic mutations continuously occur throughout life in humans, causing somatic mosaicism in an individual. The variant type, time of origination, and locations of the mosaic mutations result in unique mosaic patterns in a combinatorial manner and further affect phenotypes, including various noncancerous diseases. Several efforts have, thus, been made to identify the mutational landscape and mechanisms underlying the mosaic mutations. From a technical aspect, the accurate detection of mutations is at the core of the mosaicism research, and the construction of a standard reference is a critical first step to serve as the basis for analytical validation and benchmarks for germline and somatic mutation detection. Furthermore, a reference standard for mosaic mutations is urgently needed to enable more advanced research.Herein, we generated robust, large-scale, and cell line mixture-based reference standards that contain abundant mosaic SNVs and INDELs along with wild-type sites and germ line variants. Moreover, the whole reference standard set mimics the cumulative aspect of mosaic variant acquisition in the early developmental stage through a progressive cell line mixing with confirmed genotypes. It can be used for optimizing the current use of mosaic variant detection strategies, as well as developing algorithms for future improvements.We previously generated an NCBI BioProject (PRJNA758606) to release much of these data. Only the data that are subject to authorized access constraints have been included in this submission to dbGaP. The two data sources are thus, complementary to each other.

Study phs003399

Exploration of CNV’s and SNV’s in cancers with well-known genetic rearrangements: Identification of additional genetic changes in rearrangements-driven cancer

Specific cancers are well known to have specific gene rearrangements. Representative cancer is chronic myeloid leukemia (CML) which is famous for BCR/ABL gene rearrangements. By the way, also genetic rearrangements are not infrequently found in acute leukemia, and those include CBFB/MYH11 rearrangements, PML/RARA rearrangements, AML/ETO rearrangements, and BCR/ABL rearrangements. The situation is same is for lung adenocarcinoma of non-smokers and prostate cancers, and ALK and RET gene rearrangements are recently found. However, not all these rearrangements are sufficient to develop cancer, and it is suggested that some genetic aberrations are required for the development and progression of cancer in addition to these genetic rearrangements. In this study, we have a plan to explore the existence of additional genetic changes including single nucleotide variants (SNV’s) and copy number variations (CNV’s) in cancers with specific genetic rearrangements so as to define additional genetic changes that confer full oncogenic potential to cancer cells. So as to look at SNV’s and small indels, we are going to use whole exome sequencing (WES) data. And as for CNV’s, we are going to use array-CGH data. Also, to check the functionality of these genetic changes, we are going to use whole transcriptome data (WTS). In addition, in samples with whole genome sequencing (WGS ) data, we are going to explore genetic changes in intronic regions also. In fact, we sequenced 10 AML samples which are known to have specific genetic rearrangements. Analysis of these WGS, WES and WTS data are under the way. If we have a chance to access WES, array-CGH and WTS data of other cancer samples with specific genetic rearrangements, we would be able to compare additional genetic changes required for oncogenesis in cancer cells with various genetic rearrangements. We hope this result will enlighten our understanding of cancer genetics further.

Study EGAS00001000673

Single cell transcriptional characterization of human megakaryocyte lineage commitment and maturation

Single cell RNA-sequencing of sternal bone marrow reciding Hematopoietic Stem Cells (HSCs) and Megakaryocytes (MKs) from individuals undergoing elective open heart valve replacement. HSCs were defined as Lineage-, CD34+, CD38-, CD45RA-, CD90+, CD49f+ cells. MKs where CD41a+, CD42b+ and ploidy was determined with Hoechst. A sternal bone marrow scraping was taken directly following median sternotomy using a Volkmann’s spoon. The sample was collected into an EDTA Vacutainer tube containing 1.8mg/ml EDTA. 4mL of Dulbecco’s phosphate buffered saline (PBS, Sigma) containing 10% human serum albumin (HSA, Gemini Bio Products) was added and the whole volume was resuspended by pipetting 2-3 times. The sample was then put on metallic thermal beads (ThermoFisher Scientific) at a temperature between 0-4°C and transported to the University of Cambridge for further processing. For HSC isolation the cells were stained with the following antibody cocktail: PECy5 conjugated anti-lineage specific antibodies: CD2 (BD), CD3 (BD), CD10 (BD), CD11b (BD), CD11c (BD), CD19 (BD), CD20 (BD), CD56 (BD), biotinylated CD42b (Pab5, NHS Blood and Transplant, International Blood Group Reference Laboratory [IBGRL]), biotinylated GP6 (Pab5, NHS Blood and Transplant, International Blood Group Reference Laboratory [IBGRL]) used in combination with PECy5 conjugated streptavidin (Biolegend). Alexa Fluor 700 conjugated anti-CD34 (BD), PerCP-Cy5.5 conjugated anti-CD38 (BD), Pacific Blue conjugated anti-CD45RA (Invitrogen), PECy7 conjugated anti-CD90 (BD),PE conjugated anti-CD49f (BD). After staining cells were kept at 4°C before sorting using a FACS Aria Fusion flow sorter (BD). Single HSCs defined as Lineage-, CD34+, CD38-, CD45RA-, CD90+, CD49f+ cells were sorted by FACS directly into individual wells of a 96-well plate. Index sort data was collected for each single cell. For MK isolation the cells were stained for surface MK markers with mouse anti-human CD41a APC conjugated antibody (BD) and mouse anti-human CD42b PE conjugated antibody (BD) and for ploidy analysis with 1ug/ml Hoechst 33342 (Invitrogen). After incubation at 37°C for 30 minutes, the cells were kept at 4°C before sorting using a FACS Aria Fusion flow sorter (BD). Single cells and MK pools of 20 cells were sorted by FACS according to ploidy level using a 100uM nozzle directly into individual wells of a 96-well plate. cDNA synthesis and poly(A) enrichment was performed following the G&T-seq protocol (Macaulay et al. 2015), a variation of the Smart-seq2 protocol1. ERCC spike-in RNA (Ambion) was added to the lysis buffer in a dilution of 1:4,000,000.

Dataset EGAD00001006677

Guiding Evidence Based Therapy Using Biomarker Intensified Treatment in Heart Failure (GUIDE-IT-BioLINCC)

Accessing Data: Please refer to “Authorized Access” below regarding accessing data through the BioData Catalyst ecosystem. The data from this accession is not available for download through dbGaP.Objective: To determine whether an amino-terminal pro-B-type natriuretic peptide (NT-pro-BNP)-guided treatment strategy improves clinical outcomes vs usual care in high-risk patients with heart failure (HF) and reduced ejection fraction (HFrEF).Background: Heart failure is a common disorder. Standard treatment for HF includes diuretics to control fluid, and drugs called "neurohormonal antagonists" (such as beta-blockers and ACE-inhibitors) to help the heart work more efficiently. The natriuretic peptides, specifically NT-pro-BNP, are biomarkers that reflect HF severity and are significantly associated with adverse outcomes in HF. Smaller studies have evaluated adjusting HF therapy based on natriuretic peptide levels (“guided therapy”) with inconsistent results.Participants: A total of 894 participants were enrolled at 45 sites in the United States and Canada, with 446 randomized to the NT-pro-BNP-guided strategy treatment group and 448 randomized to the usual care treatment group.Design: GUIDE-IT was a multicenter randomized clinical trial with participants randomized to either the NT-pro-BNP-guided therapy strategy or usual care. Given the nature of the study intervention, treatment assignment was not blinded. For participants randomized to the NT-pro-BNP-guided strategy, clinicians were instructed to titrate HF therapy to target an NT-pro-BNP level After an initial visit at 2 and 6 weeks, visits occurred every 3 months throughout the remainder of the study. After therapy adjustment for HF (whether driven by NT-pro-BNP levels or clinical reasons), participants had a 2-week follow-up visit for reassessment. All participants in either group also had blinded NT-pro-BNP concentrations measured in a core laboratory at each study visit. The primary end point was the composite of time-to-first HF hospitalization or cardiovascular mortality. Secondary end points included all-cause mortality, total hospitalizations for HF, days alive and not hospitalized for cardiovascular reasons, the individual components on the primary end point, and adverse events. Conclusions: The guided strategy intervention was stopped in July of 2016 (median follow-up of 15 months) after the study met prespecified inefficacy criteria. Compared to usual care, a strategy of NT-pro-BNP-guided therapy was not more effective in improving time-to-first HF hospitalization or cardiovascular mortality in high-risk participants with HFrEF.

Study phs003621

Privacy Notice for Data Access Committee Account

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

Documentation data-protection/privacy-notice/ega-dac

A panel of reference haplotypes for imputing complement component 4 (C4) gene structural variation

Neuropsychiatric and autoimmune disorders have substantial epidemiological correlations (Benros et al., JAMA Psychiatry 2013, PMID: 23760347) and broad, genome-wide patterns of shared genetic risk (Pouget et al., Hum Mol Genet, PMID: 31211845; Tylee et al., Am J Med Genet B Neuropsychiatr Genet 2018, PMID: 30325587). Some cases of psychosis involve autoantibodies against the NMDA receptor, AMPA receptor, or other synaptic proteins (reviewed in Dalmau et al, Lancet Neurol 2011, PMID: 21163445). The related autoimmune conditions lupus and Sjogren's Syndrome also involve the development of autoantibodies. The possibility that neuropsychiatric disorders can have molecular mechanisms in common with autoimmune disorders - for example, that psychosis involves an inflammatory or autoimmune component in some patients, or that immune molecules are re-used in the brain to underlie other important biological activities (Stevens et al., Cell 2007, PMID: 18083105) - could open novel therapeutic possibilities for neuropsychiatric disorders. At a genetic level, the strongest genetic associations of schizophrenia, lupus, and Sjogren's Syndrome to common genetic variation involve associations to genetic markers in the Major Histocompatibility Complex (MHC) locus. Bipolar disorder in some studies also associates with variation in or near the MHC locus, though less strongly than schizophrenia does. Intriguingly, the same specific SNPs appear to associate strongly with schizophrenia, lupus, and Sjogren's; these strongly associating SNPs span a genomic segment that includes the HLA class II genes (which have an important role in antibody production) and the complement component 4 (C4) genes. The specific genes and alleles responsible for these associations need to be completely defined, and the extent to which they represent shared or distinct genetic influences in neuropsychiatric and autoimmune illnesses needs to be clarified. The complement component 4 (C4A and C4B) genes are present in the MHC locus, between the class I and class II HLA genes. C4A and C4B commonly vary in genomic copy number and encode complement proteins with distinct affinities for molecular targets. The complex genetic variation at C4 - arising from many alleles with different numbers of C4A and C4B genes - has been challenging to analyze in large cohorts. We recently developed an approach to this problem based on imputation: people share long haplotypes with the same combinations of SNP and C4 alleles, such that C4A and C4B gene copy numbers can be imputed from SNP data (Sekar et al., Nature 2016, PMID: 26814963). In the current work, to analyze C4 in large cohorts, we developed a way to identify C4 alleles from whole-genome sequence (WGS) data, then analyzed WGS data from 1,234 individuals to create a large multi-ancestry panel of 2,530 reference haplotypes of MHC SNPs and C4 alleles that can then be imputed into still-larger cohorts for which SNP data are available. With this dbGaP submission, we make this reference panel available for other studies. Protocols and software for imputing C4 alleles into genome-wide SNP data, and for performing molecular analyses on the C4 genes (such as direct measurement of copy number from genomic DNA), can be found on the McCarroll Lab web site (http://mccarrolllab.org/resources). We are also working to create additional reference panels for imputation of C4 alleles that will be based on still-larger and more diverse population samples; links to these will also be available on the McCarroll Lab web site as we create and validate them.

Study phs001992

Cell and Circuit-Specific Exploration of HIV Neurogenomics in Context of Opiate and Cocaine Misuse

Utilizing frozen postmortem brain tissue from the Manhattan HIV Brain Bank, we performed cell-type-specific, genome-wide sequencing of three different types: 1) single-nucleus RNA-sequencing (snRNA-seq), 2) in situ Hi-C, and 3) HIV integration site sequencing (IS-seq). We assayed tissue from three different donor types: 1) HIV-uninfected control (HIV-), HIV-infected (HIV+), and HIV-infected with HIV encephalitis (HIVE), who served as a positive control for active CNS viral infection. The diagnosis of HIVE was made by a board-certified neuropathologist on routine histopathology, based on the presence of a microglial nodule encephalitis with characteristic multinucleated syncytial cells and staining for HIV p24 antigen. Other HIV-infected individuals (the HIV+ group) did not have this histologic evidence of active HIV brain replication, and were variable with regard to cART status and virologic control prior to death. Donor characteristics, including ante-mortem plasma HIV loads, CD4 counts, and cART status, were documented. Quality controls were used for each assay: For snRNA-seq we applied filters to remove low quality nuclei from analysis and performed experiments in which mouse tissue was mixed with human tissue to determine a reliable HIV read count threshold. For Hi-C, we used paired HIV- and HIVE samples and processed all samples in parallel, at the same time. Correlation analysis was used to ensure biological replicates were aligned. For IS-seq, each time the assay was run, a positive and negative control were included. This allowed us to ensure a lack of contamination in IS-seq libraries and that, when a particular sample failed to yield library, this was due to low levels of HIV rather than a failure of the assay. Altogether, we built an integrative dataset from 79 sequencing files, including snRNA-seq, and cell type specific Hi-C, IS-seq, and ChIP-seq datasets. Human autopsy brain samples were collected by the Manhattan HIV Brain Bank (MHBB, U24MH100931), using protocols under the supervision of the Icahn School of Medicine at Mount Sinai (ISMMS) Institutional Review Board. Written informed consent was obtained either from decedents or their primary next of kin. Major Findings: Reorganization of open/repressive (A/B) compartment structures in HIVE microglia encompassing 6.4% of the genome was linked to transcriptional activation of interferon (IFN) signaling and cell migratory pathways and was partially recapitulated by IFN-gamma stimulation of cultured microglia. In contrast, decreased expression and repressive compartmentalization of genes regulating neuronal health and signaling was seen in both HIVE and HIV+ microglia. IS-seq recovered 1,221 integration sites in the brain that displayed distinct genomic patterns as compared to peripheral lymphocyte integration and were enriched for chromosomal domains newly mobilized into a permissive chromatin environment in HIVE microglia. Viral transcription occurred in a subset of highly activated microglia comprising 0.003% of all nuclei in HIVE brain. Our findings point to a disruption of microglia-neuronal interactions in the HIV infected brain and an interrelation of retroviral integration and expression with interferon-associated remodeling of the microglial 3D genome during progression to HIVE. Search terms: HIV, postmortem brain, microglia, substance abuse, viral integration, chromosomal conformation capture, Hi-C, chromatin, single nuclei RNA-seq, Irf8, NeuN, HIV encephalitis, neuron-microglia interaction, cytokine

Study phs003080

The EGA Helpdesk team: 2025 in review and what we are building next

Behind every dataset submitted, every access request processed, and every technical question answered, there is a team working quietly to keep things moving: the Helpdesk (HD). With the onset of 2026, this feels like the right moment to look back on what 2025 has been like for the HD team: the challenges we faced, how we adapted, and where we're heading next. Why the Helpdesk matters to EGA The EGA Helpdesk is more than a support channel. It plays a key role in maintaining trust in the EGA ecosystem. By supporting data submitters, researchers, Data Access Committees (DACs), and institutional partners, the HD helps ensure that data can flow securely, efficiently, and reliably. When issues arise, the Helpdesk is often the first place where their impact is felt and addressed. In that sense, the HD sits at the intersection of technology, policy, and people. One Helpdesk, two locations, one shared mission The EGA Helpdesk is a joint, distributed team working closely across two locations: CRG (Barcelona, Spain) and EMBL-EBI (Cambridge, UK). Although we are based in different institutions, we operate as a single Helpdesk, with shared workflows, priorities, and responsibility towards users. At CRG, the HD team is formed by: Andrea Max Àlex and me At EMBL-EBI, we work closely with: Silvia Coline Aravind What defines us as a team is simple: we work user-first, even under pressure. In a highly technical environment, clarity, empathy, and consistency matter just as much as tools and processes. A close collaboration across sites is essential to making that happen. What does the EGA Helpdesk do? The HD supports users across the full lifecycle of data in EGA. This includes: Data submissions, uploads, and encryption workflows. Data access requests and permissions. Questions around policies, consent, and data usage. Technical and system-related issues. Coordination between users, internal teams, and external partners. 2025: growth, change, and recalibration 2025 was a year of growth, but not always a predictable one. Early in the year, several technical and system-related challenges required us to adjust our original plans. Priorities shifted, timelines changed, and some improvements had to be rethought. For the HD team, this is often the hardest part of the job: we see delays through the eyes of users and understand the real impact they can have on ongoing research. One of the key lessons from 2025 was that stability is not only a technical challenge, but also an organisational one. Teamwork proved to be essential: anticipating peak periods, sharing context early, and coordinating closely across teams made a tangible difference. When things became complex, working together across roles and locations was what allowed us to keep moving forward. In 2025, the Helpdesk received 5.313 tickets and resolved 5.511 requests, reflecting both increased adoption of EGA and the team’s ability to absorb higher demand. At the same time, demand continued to grow. Compared to 2024, ticket creation increased by over 6%, while resolution capacity grew by more than 11%. The team not only kept up with incoming requests but also resolved part of the accumulated backlog, finishing the year having solved more tickets than were created. The real challenge of 2025 was not overall performance, but how the workload was concentrated during peak months. Seasonality and demand spikes placed pressure on the system, even while overall efficiency remained strong. On a team level, 2025 was also a year of transition. I joined the HD leadership role in January 2025, stepping into a period of change and rapid learning. Later in the year, in October, we said goodbye to Raül, and in January 2026, we welcomed Àlex, strengthening the team for the next phase. What users needed most in 2025 While requests vary widely, some themes stood out throughout the year: Support with data submissions and uploads Data access requests and permissions Technical and system-related issues As EGA matures, day-to-day operations have become more complex. Many long-running tickets are not delayed due to a lack of follow-up, but because they depend on external approvals, cross-institutional coordination, or multi-step processes. Understanding these patterns helps us focus not just on resolving tickets, but on improving how work flows through the system. Looking ahead to 2026 With a reinforced team and clearer insights from 2025, our focus for 2026 shifts from throughput to flow. Key priorities include: Strengthening our web content and documentation Reducing structural backlog Improving cross-team and cross-system coordination Anticipating peak demand earlier and planning capacity accordingly Challenges will continue to arise in 2026, as they always do. However, 2025 reinforced something important: a stable, empathetic, and well-aligned Helpdesk team is essential to supporting EGA's mission at scale. Supporting users well means supporting research, and that remains at the core of what we do.

Blog ega-helpdesk-team-2025-in-review-and-upcoming-improvements

REDS-IV-P Epidemiology, Surveillance and Preparedness of the Novel SARS-CoV-2 Epidemic (RESPONSE)

Leveraging access to the blood supply and blood donors, the REDS-IV-P program began conducting the RESPONSE study (REDS-IV-P Epidemiology, Surveillance and Preparedness of the Novel SARS-CoV-2 Epidemic) in early 2020 in order to 1) evaluate if SARS-CoV-2 RNA was found in blood donations in the U.S. using an assay that could potentially be used to screen the blood supply if evidence of SARS-CoV-2 transfusion-transmission became apparent 2) conduct serosurveys using optimized assays/algorithms to monitor antibody reactivity in blood donor populations over time, 3) enroll SARS-CoV-2 positive donors and others into a longitudinal cohort study to answer fundamental questions about the evolution of viremia and immune responses, and 4) establish a sharable biorepository that includes specimens collected early on in the infection and potentially large volumes of plasma from infected/convalescent donors.Screening for SARS-CoV-2 RNAemia was completed using a SARS-CoV-2 nucleic acid test (NAT) performed on retained blood donor minipool samples from six geographic regions in the US. The study also included serosurveillance (i.e. testing for antibody directed against the SARS-CoV2 spike protein) of donations from the same six regions to document accruing seroincidence in blood donor populations and to project these rates in the general population. To enrich for donors with acute SARS-CoV-2 infection, another part of the study focused on donors reporting post-donation information (PDI) consistent with COVID-19 by testing plasma from all available PDI donations for SARS-CoV-2 RNA by NAT. Subjects who were diagnosed with COVID-19 based on PDI reports or who tested positive by SARS-CoV-2 NAT on index donation plasma were enrolled into a longitudinal follow-up study which collected multiple samples for up to one-year post-infection. The longitudinal follow-up study also enrolled community members who reported a new positive SARS-CoV-2 NAT test in the prior 7-14 days. The specific aims of the RESPONSE study were to: Establish the incidence of SARS-CoV-2 RNAemia in blood donations from the American Red Cross (ARC) regions in Los Angeles, Boston, and Minneapolis metropolitan areas, Bloodworks Northwest (BWNW), New York Blood Center (NYBC), and Vitalant San Francisco Bay Area, monthly between March and September of 2020 Conduct serosurveys to study antibody reactivity in same six areas as above monthly for March to August 2020 Document rates of Post Donation Information (PDI) reports to determine PDI rates relevant to SARS-CoV-2 clinical disease and test index donation plasma from PDI donors for SARS-CoV-2 RNA Enroll SARS-CoV-2 infected subjects into a longitudinal cohort study to answer fundamental questions on the evolution of viremia, early immune responses and waning of immunity over 3-12 months of follow-up Establish a sharable biorepository of samples from all of the above Aims for future research The data from Aims 3 and 4 above is being made available in BioData Catalyst.Biospecimens for these Aims are managed by Vitalant Research Institute (VRI). More information on biospecimens is available.

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Jackson Heart Study (JHS) Cohort

The Jackson Heart Study (JHS) is a large, community-based, observational study whose participants were recruited from urban and rural areas of the three counties (Hinds, Madison and Rankin) that make up the Jackson, MS metropolitan statistical area (MSA). Participants were enrolled from each of 4 recruitment pools: (1) random, 17%, (2) volunteer, 30%, (3) currently enrolled in the Atherosclerosis Risk in Communities (ARIC) Study, 31%, and (4) secondary family members, 22%. The final cohort of 5,306 participants included 6.59% of all African American Jackson MSA residents, aged 35-84, during the baseline exam (N-76,426, US Census 2000). Among these, approximately 3,600 gave consent that allows genetic research and deposition of data into dbGaP. Major components of three clinic examinations (see study history) include medical history, physical examination, blood/urine analytes, and interview questions on areas such as physical activity, stress, coping and spirituality, racism and discrimination, socioeconomic position, and access to health care. Extensive clinical phenotyping includes anthropometrics, electrocardiography, carotid ultrasound, ankle-brachial blood pressure index, echocardiography, CT chest and abdomen for coronary and aortic calcification, liver fat, and subcutaneous and visceral fat measurements, and cardiac MRI. At 12-month intervals after the baseline clinic visit (Exam 1), participants have been contacted by telephone to update information, confirm vital statistics, document interim medical events, hospitalizations and functional status, and obtain additional sociocultural information. Questions about medical events, symptoms of cardiovascular disease and functional status are repeated annually. Ongoing cohort surveillance includes abstraction of medical records and death certificates for relevant International Classification of Diseases (ICD) codes and adjudication of nonfatal events and deaths. CMS data are currently being incorporated into the dataset. Note: Regarding date variables warehoused in the Jackson Heart Study (JHS) Cohort on dbGaP, the coordinating center has developed an algorithm that will systematically review and de-identify any of the (nearly 100) date-related variable types stored in the data package. To simultaneously minimize (i) de-identifiability of the data and (ii) impact on analyses utilizing sensitive data elements, a participant-level random number was generated to avoid the necessity of sharing any potentially sensitive data. The coordinating center maintains an archived linkage of these data in their raw form and regularly reviews ad hoc requests to utilize the raw data on a project-by-project basis. The JHS Cohort is utilized in the following dbGaP substudies. To view genotypes, other molecular data, and derived variables collected in these substudies, please click on the following substudies below or in the "Substudies" section of this top-level study page phs000286 JHS Cohort. phs000402 ESP Heart GO JHS phs000498 JHS Allelic Spectrum Seq phs000499 JHS CARephs001069 MI Gen JHS phs001098 T2D GENES Exome Seq phs001356 Exome Chip phs002256 Cardiometabolic Renal Proteomics phs002369 Metabolomics Insulin Resistance

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Center for Craniofacial and Dental Genetics: Exome Sequencing of Orofacial Cleft Families

Orofacial Clefts (OFCs) are genetically complex structural birth defects caused by genetic factors, environmental exposures, and their interactions. OFCs are the most common craniofacial anomalies in humans. Affect about 1 in 800 newborns, OFCs are also one of the most common structural birth defects worldwide. OFCs can impose a substantial personal cost resulting from the associated emotional and financial burden. On average a child undergoes 6 surgeries, spends 30 days in hospital, receives 5 years of orthodontic treatment, and participates in ongoing speech therapy, leading to an estimated total lifetime treatment cost of about $200,000. Individuals born with an OFC have higher infant mortality (especially in developing countries with limited access to care), higher mortality rates at all other stages of life, increased incidence of mental health problems, and higher risk for other disorders (notably including breast, brain, and colon cancers). Prior genetic studies have focused on the contribution of common genetic variants through genome-wide linkage and association studies. These studies have revealed a significant etiologic heterogeneity. One promising approach to dissect the etiology of OFCs into focus on subclinical phenotypic features present in the non-cleft family members of affected individuals. These subclinical features are subtle phenotypic features that may represent mild manifestations of the same risk genes; thus, their inclusion in family-based studies may clarify the inheritance patterns in OFC families. In this project, we conducted whole exome sequencing in multiplex families with both overt OFCs and subclinical phenotypes to identify high-impact genes causing OFCs.

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Human Skin Cancer Atlas, Medical University of Vienna Data Access Policy

Cancer-associated fibroblasts (CAFs) play a key role in cancer progression and treatment outcome. Here, we elucidate the yet unresolved intra-tumoral CAF variety in three skin cancer types at molecular and spatial single-cell resolution in a large cohort. We show that two out of three CAF subtypes contribute to tumor immune surveillance with distinct mechanisms. Matrix CAFs (mCAFs) ensheath tumor nests and synthesize extracellular-matrix to prevent T cell invasion. Immunomodulatory CAFs (iCAFs), which express proinflammatory and immunomodulatory factors, are only detected in high abundance in aggressive tumors. Strikingly, iCAFs but not tumor cells are (apart from immune cells) the exclusive cell type producing chemokines and, thus, play a key role in immune cell recruitment and activation. Mechanistically, we show that cancer cells transform adjacent healthy fibroblasts into cytokine-expressing iCAFs, which subsequently recruit immune cells and modulate the immune response. In conclusion, targeting CAF variants holds promise for improved efficacy of immunotherapy.

Dac EGAC50000000154

Genetic Discovery and Application in a Clinical Setting: Continuing a Partnership (eMERGE Phase II)

The electronic Medical Records and Genomics (eMERGE) Network is a consortium of ten participating sites funded by the NHGRI to investigate the use of electronic medical record systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 19,000 individuals using EMR-derived phenotypes and DNA from linked Biorepositories. The eMERGE Network brings together researchers with a wide range of expertise in genomics, statistics, ethics, informatics, and clinical medicine from leading medical research institutions across the country. Each center participating in the consortium is uniquely situated to provide critical resources to this highly collaborative and productive network. Each site combines a biobank or study cohort with extensive genomic data and access to clinical data derived from electronic medical records. Sites are geographically dispersed and have diverse patient populations, including two sites focusing specifically on pediatrics. The eMERGE Network is comprised of 9 sites and one coordinating center. Each site maintains its own biorepository where DNA specimens are linked to phenotypic data contained within EMRs. Children's Hospital of Pennsylvania, PI: Hakon Hakonarson, MD, PhD Cincinnati Children's Medical Center with Boston Children's Hospital, PI: John Harley, MD, PhD - CCHMC and Ingrid Holm, MD, MPH - BCH Geisinger Health System, PI: David Carey, PhD and Marc Williams, MD Group Health Cooperative with University of Washington, PI: Eric Larson, MD, MPH - GHC and Gail Jarvik, MD, PhD - UW Marshfield Clinic with Essentia Institute of Rural Health, PI: Catherine McCarty, PhD, MPH - Essentia and Murray Brilliant, PhD - Marshfield Mayo Clinic, PI: Christopher Chute, MD, DrPH and Iftikhar Kullo, MD Icahn School of Medicine at Mount Sinai, PI: Erwin Bottinger, MD Northwestern University, PI: Rex Chisholm, PhD and Maureen Smith, MS Vanderbilt University, PI: Dan Roden, MD eMERGE Coordinating Center - Vanderbilt University, PI: Paul Harris, PhD Using electronic phenotyping methods, the consortium used DNA samples from all participating sites to explore the genetic determinants of red cell indices, white blood count (WBC) differential, diabetic retinopathy, height, serum lipid levels, specifically total cholesterol, HDL (high density lipoprotein), LDL (low density lipoprotein), and triglycerides, and autoimmune hypothyroidism as well as using the phenome-wide association study (PheWAS) paradigm to replicate and discover relationships between targeted genotypes with multiple phenotypes.

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Capturing the Genetic Diversity of the Himba Population

The Himba people are a semi-nomadic, Bantu-speaking pastoralist group from the northwestern Kunene region of Namibia. Because of their limited population size, restricted geographic distribution, and endogamy, founder populations represent an ideal system for genetic analyses due to reduction of the confounding environmental and cultural variables. The original study used genotype data and demographic interview data to understand family dynamics and ancestry in a population of Himba pastoralists. HLA allele calls and genome-wide methylation array data were later generated for a subset of the initial dataset for 2 additional studies. These 3 molecular datasets, available through dbGaP, are relevant to broader studies of ancestry, allele frequencies and the relationship of Himba to other ethnic groups in sub-Saharan Africa. The Himba population provides a unique opportunity to study questions regarding extra-pair paternity and mate selection because Himba culture allows both men and women, married and not, to take multiple partners. Previous estimates of extra-pair paternity rates in human populations have been very low, but these estimates lack global and cultural diversity in population representation. Thus, investigating the rate of extra-pair paternity in the Himba greatly aids in understanding the global variation in non-paternity rates among human populations. Saliva samples were collected from parents and children, and were genotyped on Illumina SNP array platforms (MEGAex and H3Africa). In total, 678 individuals' genotype array data are deposited. Paternity results were de-identified and protected by a double blind protocol (no researcher has full knowledge of individual paternity results). Due to the sensitive nature of this study, paternity assertions are not available for access. For a subset of 360 individuals, HLA class 1 (A, B, C) and class 2 (DPA1, DPB1, DQA1, DQB1, DRB1) genes were targeted for DNA sequencing using a probe-based capture method. HLA alleles at the four field resolution were determined from the sequence capture using the consensus calls obtained from two algorithms: NGSengine® 2.10.0 (GenDX, Utrecht, the Netherlands) and HLA Explore™ (Omixon Biocomputing Ltd. Budapest, Hungary). These data were used to investigate evidence for mate selection based on HLA dissimilarity in the Himba. Himba cultural norms permit concurrent arranged marriages and chosen sexual partnerships allowing us to directly test for evidence of disassortative mate selection based on HLA genotype. Due to the sensitive nature of this study, partnership data are not available for access.For a subset of 51 Himba individuals methylation array data was generated from the saliva-derived DNA. These data were generated in order to investigate the accuracy of epigenetic age predictors in this population and to construct population-specific epigenetic age predictors in a subsequent publication. Chronological ages are available for these 51 individuals.

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eMERGE: Northwestern (NUgene) WGS

The NUgene Project is a biorepository with longitudinal medical information from participating patients at Northwestern Medicine affiliated hospitals and outpatient clinics. Participants' DNA samples are coupled with data from a self-reported questionnaire (2 versions were used, 1 before and 1 after February 2006, both are included) and continuously updated data from our Electronic Medical Record (EMR) representing actual clinical care events. Northwestern has a state-of-the art, comprehensive inpatient and outpatient EMR system of over 3 million patients. NUgene has broad access to participant data for all outpatient visits as well as inpatient data via a consolidated enterprise data warehouse. NUgene participants consent to distribution and use of their coded DNA samples and data for a broad range of genetic research by third-party investigators. The electronic MedicalRecords and Genomics (eMERGE) Network is a consortium of 9 clinical sites with EMR linked DNA biobanks, including Northwestern University and its NUgene biobank, funded by the NHGRI (National Human Genome Research Institute) to investigate the use of electronic medical record systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 100,000 individuals using EMR-derived phenotypes and DNA from linked biorepositories. Using electronic phenotyping methods, the consortium has been and is using DNA samples from all participating sites to explore the genetic determinants of approximately 80 phenotypes, including both diseases and traits, for which the electronic phenotyping algorithms have or are being published on PheKB.org. Thus, for the eMERGE network, ~900 NUgene subjects were selected, as described in the study inclusion/exclusion criteria section below, to be genotyped using whole genome sequencing (WGS), for use in the eMERGE network as eMERGE subjects. The criteria used below were selected so that these subjects will likely meet the criteria to be cases and/or controls, of diverse races and/or ethnicities, for at least 1 of the ~80 phenotypes being studied within eMERGE, and could be recontacted for future research if needed.

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Hematopoietic Cell Transplant for Sickle Cell Disease (HCT for SCD)

The Center for International Blood and Marrow Transplant Research (CIBMTR) is a hematopoietic cell transplant registry that was established in 1972 at the Medical College of Wisconsin. The overarching goal of the registry is to study trends in transplantations and to advance the understanding and application of allogeneic hematopoietic cell transplantation for malignant and non-malignant diseases. Included in this dataset are children, adolescents and young adults with severe sickle cell disease who received an allogeneic hematopoietic cell transplant in the United States and provided written informed consent for research.Hematopoietic cell transplant for sickle cell disease is curative. Offering this treatment for patients with severe disease is challenging as only about 20-25% of patients expected to benefit have an HLA-matched sibling. Consequently, several transplantations have utilized an HLA-matched or mismatched unrelated adult donor and HLA-mismatched relative. Transplantation strategies have also evolved over time that has included transplant conditioning regimens of varying intensity, grafts other than bone marrow and novel approaches to overcome the donor-recipient histocompatibility barrier and limit graft-versus-host disease. The data that is available for sickle cell disease transplants have been utilized to report on outcomes after transplantation and compare outcomes after transplantation of grafts HLA-matched related, HLA-mismatched related, HLA-matched and HLA-mismatched unrelated donors. Collectively, these data have advanced our knowledge and understanding of hematopoietic cell transplant for this disease. These data can also serve as "contemporaneous controls" for comparison with other more recent curative treatments like gene therapy and gene editing.Data available for request include allogeneic hematopoietic cell transplants for sickle cell disease (Hb SS and Hb Sβ thalassemia) in the United States from 1991 to 2019. Follow-up data through December 2020 are available.Instructions for requesting individual-level data are available on BioData Catalyst at https://biodatacatalyst.nhlbi.nih.gov/resources/data/. Apply for data access in dbGaP. Upon approval, users may begin accessing requested data in BioData Catalyst. For questions about availability, you may contact the BioData Catalyst team at https://biodatacatalyst.nhlbi.nih.gov/contact.

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Northwestern NUgene Project: Type 2 Diabetes

The ability to correlate genetic variation with disease susceptibility and response to drug therapy depends on genotype or sequence analysis of large numbers of richly characterized DNA samples. Eight years agoWe are a part of NHGRI's electronic Medical Records and Genomics (eMERGE) Network, whose goal is to conduct genome-wide association studies in thousands of individuals using EMR-derived phenotypes and DNA from linked biorepositories. For eMERGE, Northwestern University (NU) is studying type 2 diabetes as a phenotype. In addition, in order to explore race differences in the prevalence of type 2 diabetes, NU collaborated with Vanderbilt University to study a mix of both Caucasian and African-Americans. Northwestern University: In 2002, Northwestern committed to the development of a DNA repository to serve as a platform for the identification and validation of genotype-phenotype associations that will impact healthcare. The NUgene Project is a repository with longitudinal medical information from participating patients at affiliated hospitals and outpatient clinics from the Northwestern University Medical Center. Participants' DNA samples are coupled with data from a questionnaire (2 versions were used, 1 before and 1 after February 2006, both are included) and continuously updated data from our Electronic Medical Record (EMR) representing actual clinical care events. Northwestern has a state-of-the art, comprehensive inpatient and outpatient EMR system of over 2 million patients. NUgene has broad access to participant data for all outpatient visits as well as inpatient data via a consolidated data warehouse. NUgene participants consent to distribution and use of their coded DNA samples and data for a broad range of genetic research by third-party investigators. Vanderbilt University: BioVU, Vanderbilt's DNA databank, is an enabling resource for exploration of the relationships among genetic variation, disease susceptibility, and variable drug responses, and represents a key first step in moving the emerging sciences of genomics and pharmacogenomics from research tools to clinical practice. BioVU acquires DNA from discarded blood samples collected from routine patient care. The biobank is linked to de-identified clinical data extracted from Vanderbilt's EMR, which forms the basis for phenotype definitions used in genotype-phenotype correlations.

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14308 results for ""Pennsylvania+German"+ACS+study+'Socius'+open+access"

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