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• Botensilimab, an Fc-enhanced Anti-CTLA-4 Antibody, is Effective Against Tumors Poorly Responsive to Conventional Immunotherapy

  This study explores the genomic landscape associated with a novel Fc-engineered anti-CTLA-4 antibody (botensilimab) in advanced solid tumors. We seek to understand if enhancing the Fc-region affinity of anti-CTLA-4 antibodies can improve anti-tumor efficacy and expand clinical benefit to tumors poorly responsive to conventional immunotherapy. As part of an ongoing open-label, Phase 1, multicenter study, we conducted an exploratory sub-analysis of genomics data. The primary study evaluates the safety, tolerability, pharmacokinetics, and pharmacodynamics of botensilimab monotherapy and in combination with an anti-PD-1 antibody (balstilimab) in participants with advanced solid tumors. RNA from formalin-fixed paraffin-embedded (FFPE) tumor biopsies and matched normal blood samples were isolated and subjected to RNA-sequencing using the Personalis ImmunoID NeXT Platform. We demonstrated that anti-CTLA-4 engineered for enhanced FcγR affinity leverages FcγR-dependent mechanisms to potentiate T cell responsiveness, reduce intratumoral Tregs, and enhance antigen presenting cell activation. This genomic analysis contributes to our understanding of the molecular mechanisms underlying patient responses to Fc-engineered anti-CTLA-4 therapy and may inform future strategies to expand the clinical benefit of immune checkpoint blockade in cancer immunotherapy.

  Study phs003704

• Fgl2 Regulates FcγRIIB+ CD8+ T Cell Responses during Infection

  While the inhibitory receptor FcγRIIB has been shown to be upregulated on activated CD8+ T cells in both mice and humans, its impact on T cell fate during infection has not been fully elucidated. We identified an increase in FcγRIIB-expressing CD8+ T cells in COVID patients relative to healthy controls, and in mouse models of viral infection. Despite its well-known role as an Fc receptor, FcγRIIB also ligates the immunosuppressive cytokine Fgl2, resulting in CD8+ T cell apoptosis. Both chronic LCMV infection in mice and COVID in humans resulted in a significant increase in plasma Fgl2. Further, plasma Fgl2 was directly proportional to CD8+ T cell lymphopenia in COVID patients. To further understand the association of Fgl2 and CD8+ T cell lymphopenia following COVID infection, we assessed the transcriptomic profiles of PBMCs with high (≥ 9.153 ng/ml) vs low (defined as

  Study phs003870

• Data Access Committee KCL FC

  Dac EGAC00001000386

• MCF10A 12h IL6 classical signaling

  Gene expression after cell culture, 12h IL6+sgp130-Fc stimulated

  Dataset EGAD00010001970

• MCF10A 24h IL6 classical signaling

  Gene expression after cell culture, 24h IL6+sgp130-Fc stimulated

  Dataset EGAD00010001963

• Anti-TIGIT antibody tiragolumab improves PD-L1 blockade via myeloid and Treg cells

  Tiragolumab, an anti-TIGIT antibody with an active IgG1/kappa Fc, demonstrated improved outcomes in the phase 2 CITYSCAPE trial (NCT03563716) when combined with atezolizumab (anti-PD-L1) versus atezolizumab alone1. However, there remains little consensus on the mechanism(s) of response with this combination2. We found that high baseline intratumoral macrophages and Tregs were associated with better outcomes in patients treated with atezolizumab plus tiragolumab but not with atezolizumab alone. Serum sample analysis revealed that macrophage activation was associated with clinical benefit in patients receiving the combination treatment. In mouse tumor models, tiragolumab surrogate antibodies inflamed tumor-associated macrophages, monocytes, and dendritic cells through Fc gamma receptors (FcɣR), in turn driving anti-tumor CD8+ T cells from an exhausted effector-like state to a more memory-like one. These results uncover a mechanism of action by which TIGIT checkpoint inhibitors can remodel immunosuppressive tumor microenvironments, and suggest that FcɣR engagement is an important consideration in anti-TIGIT antibody development.

  Study EGAS50000000251

• High titers and low fucosylation of early phase anti-SARS-CoV-2 IgG promote hyper-inflammation by alveolar macrophages

  We show that early phase anti-Spike IgG in serum of critically ill COVID-19 patients induces hyper-inflammatory responses by human alveolar macrophages. We identified that this excessive inflammatory response is dependent on two antibody features that are specific for severe COVID-19. First, inflammation is driven by high titers of anti-Spike IgG, a hallmark of severe disease. Second, we found that anti-Spike IgG from severely ill patients is intrinsically more pro-inflammatory because of different glycosylation of the Fc tail, particularly by low fucosylation. This dataset is linked with the following ArrayExpress Experiment: E-MTAB-10431 - 2020_COVID19_FCGR_MIL10_IgGfucosylation

  Study EGAS00001005206

• Characterization of a human iPSC-derived endocrine pancreas model

  Directed differentiation of stem cells offers a scalable solution to the need for human cell models recapitulating islet biology and T2D pathogenesis. We profiled mRNA expression at six stages of an induced pluripotent stem cell (iPSC) model of endocrine pancreas development from two donors, and characterized the distinct transcriptomic profiles associated with each stage. Established regulators of endodermal lineage commitment, such as SOX17 (log2 fold change [FC] compared to iPSCs=14.2, p-value=4.9x10-5) and the pancreatic agenesis gene GATA6 (log2 FC=12.1, p-value=8.6x10-5), showed transcriptional variation consistent with their known developmental roles. However, these analyses highlighted many other genes with stage-specific expression patterns, some of which may be novel drivers or markers of islet development. For example, the leptin receptor gene, LEPR, was most highly expressed in published data from in vivo-matured cells compared to our endocrine pancreas-like cells (log2 FC=5.5, p-value=2.0x10-12), suggesting a role for the leptin pathway in the maturation process. Endocrine pancreas-like cells showed significant stage-selective expression of adult islet genes, including INS, ABCC8, and GLP1R, and enrichment of relevant GO-terms (e.g. "insulin secretion"; odds ratio=4.2, p-value=1.9x10-3): however, principal component analysis indicated that in vitro-differentiated cells were more immature than adult islets. Integration of the stage-specific expression information with genetic data from T2D genome-wide association studies revealed that 46 of 82 T2D-associated loci harbor genes present in at least one developmental stage, facilitating refinement of potential effector transcripts. Together, these data show that expression profiling in an iPSC islet development model can further understanding of islet biology and T2D pathogenesis.

  Study EGAS00001001803

• Expression profiling of a human endocrine pancreas iPSC model

  Directed differentiation of stem cells offers a scalable solution to the need for human cell models recapitulating islet biology and T2D pathogenesis. We profiled mRNA expression at six stages of an induced pluripotent stem cell (iPSC) model of endocrine pancreas development from two donors, and characterized the distinct transcriptomic profiles associated with each stage. Established regulators of endodermal lineage commitment, such as SOX17 (log2 fold change [FC] compared to iPSCs=14.2, p-value=4.9x10-5) and the pancreatic agenesis gene GATA6 (log2 FC=12.1, p-value=8.6x10-5), showed transcriptional variation consistent with their known developmental roles. However, these analyses highlighted many other genes with stage-specific expression patterns, some of which may be novel drivers or markers of islet development. For example, the leptin receptor gene, LEPR, was most highly expressed in published data from in vivo-matured cells compared to the endocrine pancreas-like cells (log2 FC=5.5, p-value=2.0x10-12), suggesting a role for the leptin pathway in the maturation process. Endocrine pancreas-like cells showed significant stage-selective expression of adult islet genes, including INS, ABCC8, and GLP1R, and enrichment of relevant GO-terms (e.g. “insulin secretion”; odds ratio=4.2, p-value=1.9x10-3): however, principal component analysis indicated that in vitro-differentiated cells were more immature than adult islets. Integration of the stage-specific expression information with genetic data from T2D genome-wide association studies revealed that 46 of 82 T2D-associated loci harbor genes present in at least one developmental stage, facilitating refinement of potential effector transcripts. Together, these data show that expression profiling in an iPSC islet development model can further understanding of islet biology and T2D pathogenesis.

  Dataset EGAD00001002148

• A Dose Escalation Study of Efmarodocokin Alfa (UTTR1147A) in Healthy Volunteers and Patients with Ulcerative Colitis

  Background: The interleukin-22 cytokine (IL-22) has demonstrated efficacy in nonclinical colitis models with a non-immunosuppressive mechanism-of-action. Efmarodocokin alfa (UTTR1147A) is a fusion protein agonist that links IL-22 to the crystallizable fragment (Fc) of human immunoglobulin (IgG4) for improved pharmacokinetic characteristics, but with a mutation to minimize Fc effector functions. Methods: This randomized, phase 1b study evaluated the safety, tolerability, pharmacokinetics, and pharmacodynamics of repeat intravenous dosing of efmarodocokin alfa in healthy volunteers (HVs; n=32) and patients with ulcerative colitis (UC; n=24) at 30–90 µg/kg doses given biweekly (Q2W) or monthly (Q4W) (6:2 active:placebo per cohort). Results: The most common adverse events (AEs) were on-target dermatological effects (dry skin, erythema, and pruritus) that were reversible. Dose-limiting non-serious dermatological AEs (severe dry skin, erythema, exfoliation, and discomfort) were seen in two HVs and one patient at 90 ug/kg Q2W. Pharmacokinetics were generally dose-proportional across the dose levels tested, but patients demonstrated lower drug exposures relative to HVs at the same dose. IL-22 serum biomarkers and IL-22 responsive genes in colon biopsies were induced with active treatment. Patients demonstrated changes in microbiota composition following active treatment, thereby reversing baseline dysbiosis. Clinical response was observed in 7/18 active- and 1/6 placebo-treated patients; clinical remission was observed in 5/18 active- and 0/6 placebo-treated patients.

  Study EGAS00001006172

• Whole exome sequencing of endometriod ovarian cancer tumours

  Whole Exome sequencing data of tumour samples for 112 patients with endometrioid ovarian carcinoma in FASTQ format. Data was derived as summarized below: Library Preparation: Libraries were prepared from each DNA sample using the Illumina TruSeq Exome Library Prep kit (#FC-150-1002) according to the provided protocol using modifications for working with FFPE sourced material. Libraries were quantified using the Qubit 2.0 Fluorometer and the Qubit DNA HS assay (#Q32854) and the size distribution of fragments was assessed using the Agilent Bioanalyser with the DNA HS Kit (#5067-4626). Library QC: Exome-captured sequencing library pools were quantified using the Qubit 2.0 Fluorometer and the Qubit DNA HS assay (#Q32854) and the size distribution of fragments was assessed using the Agilent Bioanalyser with the DNA HS Kit (#5067-4626). Fragment size and quantity measurements were used to calculate molarity for each library pool. Sequencing: Sequencing was performed using the NextSeq 500/550 High-Output v2 (150 cycle) Kit (# FC-404-2002) on the NextSeq 550 platform (Illumina Inc, #SY-415-1002).

  Dataset EGAD00001006389

• Ribo-depleted RNA-sequencing of II.3, III.1, and III.3

  This datasets contains the Ribo-depleted RNA-sequencing of patients II.3, III.1, and III.3. Libraries were sequenced to a minimum depth of 195 million read pairs (2x75nts) on an Illumina NextSeq500 Midoutput FC.

  Dataset EGAD50000002364

• Characterizing the cell-free transcriptome in a humanized DLBCL patient-derived tumor xenograft model for RNA-based liquid biopsy in a preclinical setting

  This study explores the cell-free transcriptome in a humanized DLBCL patient-derived tumor xenograft (PDTX) model. Blood plasma samples (n=171) derived from a DLBCL PDTX model including 27 humanized (HIS) PDTX, 8 HIS non-PDTX and 21 non-HIS PDTX NOD-scid IL2Rgnull mice were collected during humanization, xenografting, treatment, and sacrifice. The mice were treated with either R-CHOP, huCD20-Fc-AFN-CHOP, or phosphate-buffered saline (PBS). RNA was extracted using the miRNeasy serum/plasma kit and sequenced on the NovaSeq 6000 platform using the SMARTer Stranded Total RNA-seq pico v3 kit.

  Study EGAS50000000566

• Molecular stratification of endometrioid ovarian carcinoma predicts clinical outcome

  Endometrioid ovarian carcinoma (EnOC) is an under-investigated type of ovarian carcinoma. Here, we report the largest genomic study of EnOCs to date, performing whole exome sequencing of 112 cases following rigorous pathological assessment. High frequencies of mutation were detected in CTNNB1(43%), PIK3CA(43%), ARID1A(36%), PTEN(29%), TP53(26%) and SOX8(19%), a novel target of recurrent mutation in EnOC. POLE and mismatch repair protein-encoding genes were mutated at lower frequency (6%, 18%) with significant co-occurrence. A molecular taxonomy was constructed using a novel algorithm (PRISTINE), identifying clinically distinct EnOC subtypes: TP53m cases demonstrated greater genomic complexity, were frequently FIGO stage III/IV at diagnosis (48%) and incompletely debulked (44%), and demonstrated inferior survival; conversely, CTNNB1m cases demonstrated low complexity and excellent clinical outcome, were predominantly stage I/II at diagnosis (89%) and completely resected (87%). Tumour complexity provides further resolution within the TP53wt/CTNNB1wt group. Moreover, we identify the WNT, MAPK/RAS and PI3K pathways as good candidate targets for molecular therapeutics in EnOC.

  Study EGAS00001004366

• Genome-Wide Association Study of Schizophrenia

  The goal of the study is to find susceptibility genes for schizophrenia. Dataset versioning Version 1: European-American (EA) ancestry only Version 2: Version 1 plus African-American (AA) ancestry Consent groups and participant set General research use (GRU): 4591 cases and controls (1217 EA cases, 1442 EA controls, 953 AA cases, 979 AA controls) This consent group includes a subset of schizophrenia cases and all controls (which overlap with Bipolar study controls in Bipolar: GRU dataset). Schizophrenia and related disorders (SARC): 475 cases (187 EA cases, 288 AA cases) This consent group includes a subset of schizophrenia cases.

  Study phs000021

• Sarcopenia related to Head and Neck squamous cell carcinomas: transcriptome modifications of muscle cells induced by distant malignant cells

  The biological mechanisms of sarcopenia are still very poorly understood, particularly in the case of Head and Neck Squamous Cell Carcinomas (HNSCC). However, there is good reason to believe that diffusible factors originating from tumor cells exert a distant deleterious effect on muscle cells. To test this hypothesis we implemented a dual approach based both on experiments carried out in vitro and on the study of muscle fragments from patients.

  Study EGAS50000000669

• Whole Genome Association Study of Bipolar Disorder

  The goal of the project is to identify genes that make individuals more susceptible to bipolar disorder (manic depressive illness) and to better understand the brain pathways involved in the disease. Dataset versioning Version 1: European-American (EA) ancestry only Version 2: Version 1 plus African-American (AA) ancestry Version 3: EA and AA samples plus updated diagnostic criteria Consent groups and participant set General research use (GRU): 1767 controls (1081 EA controls, 686 AA controls) This consent group includes all controls for the Bipolar study, which are a subset of controls for the Schizophrenia study (subset of Schizophrenia: GRU). Bipolar and related disorders (BARD): 841 cases (691 EA cases, 150 AA cases) This consent group includes a subset of the Bipolar cases. Bipolar disorder only (BDO): 653 cases (388 EA cases, 265 AA cases) This consent group includes a subset of the Bipolar cases.

  Study phs000017

• Pediatric B-cell precursor acute lymphoblastic leukemia samples with very early relapse

  In this study we performed a genomic analysis of diagnosis-relapse pairs of 12 children who relapsed very early, followed by a deep-sequencing validation of all identified mutations. In addition, we included one case with a good initial treatment response and on-treatment relapse at the end of upfront therapy.

  Study EGAS00001005615

• Federated EGA

  Federated EGA Federated EGA Index Overview Map of FEGA status across the world Current status of the FEGA Network Documentation Overview Federated EGA is a global network of repositories enabling secure discovery and access to sensitive human data. Our vision is to accelerate scientific discovery and healthcare breakthroughs by creating the go-to worldwide sensitive human data resource. Our values are: Privacy & Trust: Ensuring privacy of data subjects, security of services and acting with integrity to build community trust.FAIRness: Driving reuse of data through standardisation of metadata and clear access policies.Diversity: Expanding our network and representing global diversity in the data we host to bring benefits worldwide. Federated EGA - European by name, global by nature Federated EGA collaborates with European and global initiatives (GA4GH, ELIXIR, 1+ Million Genomes Framework, GDI), demonstrating that a network for transnational discovery of and access to human data is possible. By providing a solution to emerging challenges around secure and efficient management of human omics and associated data, the FEGA Network fosters data reuse, enables reproducibility, and accelerates biomedical research. For general enquiries or to request more information about Federated EGA, please send an email to fega-info [at] lists [dot] ega-archive [dot] org. Read more about the Federated EGA in our commentary in Nature Genetics! Learn more about setting up a Federated EGA Node in our onboarding documentation. Map of FEGA status across the world Current status of the FEGA Network Node Description Joined CEGA Central EGA is managed by the Centre for Genomic Regulation (CRG) and EMBL-EBI. The EGA has been a trusted repository for sensitive data since 2008. CEGA coordinates the FEGA network, hosts central services and the EGA catalogue. 2022 Finland FEGA Finland is hosted by CSC - IT Center for Science Ltd., which is the national ELIXIR Node in Finland. CSC is a state-owned company specialising in providing high-quality IT infrastructure and services for Finnish higher education and research. 2022 Germany The German Human Genome-Phenome archive (GHGA) is part of the German National Research Data Infrastructure (NFDI). It is coordinated by the German Cancer Research Center (DKFZ) and includes over 20 academic partners. 2022 Norway FEGA Norway is a service by ELIXIR Norway, the Norwegian node of the European organisation ELIXIR. University of Oslo as a partner institution in ELIXIR Norway is hosting the service relying on the TSD infrastructure for sensitive data. ELIXIR Norway is a consortium of 5 Norwegian universities and is coordinated by University of Bergen. 2022 Spain FEGA Spain is co-hosted by the Barcelona Supercomputer Center (BSC) and the Centre for Genomic Regulation (CRG), both part of ELIXIR Spain and the Spanish National Infrastructure for Personalised Medicine associated with Science and Technology (IMPaCT). 2022 Sweden FEGA Sweden is hosted by the National Bioinformatics Infrastructure Sweden (NBIS), which legally is part of Uppsala University. NBIS forms the bioinformatics platform at SciLifeLab and constitutes the Swedish node of the European organisation ELIXIR. 2022 Poland FEGA Poland is hosted by Biobank Lodz, which is part of University of Lodz. Biobank Lodz is an element of the infrastructure of the Regional Digital Medicine Centers established by the Medical Research Agency. 2022 Portugal FEGA Portugal is managed by BioData.pt, the distributed infrastructure for Life and Health data for Portugal. This entity is a non-profit association of 15 life sciences R&I organisations spread across the country, and the home of ELIXIR Node in Portugal. 2023 Canada Canadian Genome-phenome Archive is part of the Pan Canadian Genome Library, with the mandate to allow the secure and responsible sharing and analysis of Canadian genomic information. 2024 Switzerland Swiss FEGA is hosted by SIB Swiss Institute of Bioinformatics, which is the national ELIXIR Node in Switzerland, on behalf of the Swiss FEGA Partnership which includes 4 other key partners (ETH Zurich, Health 2030 Genome Center, Swiss Data Science Center and Switch). 2025 Documentation Title Version Description Mission, vision, values Federated EGA Mission, vision, values 1.0 The official Mission-Vision-Values statement for FEGA, produced and formally approved by the FEGA Strategic Committee in September 2025. Structure and Organisation Federated EGA Structure and Organisation 2.0 The structure of the FEGA network and service expectations, responsibilities and commitments. FEGA is organised into three tiers: Central EGA, FEGA Nodes and FEGA Affiliates. Strategic Committee Federated EGA Strategic Committee 1.1 Terms of reference for the FEGA Strategic Committee describe the purpose and objectives of the committee, which is to provide direction and strategic planning for the FEGA network. The committee receives input from, and provides feedback for the EGA Strategic Committee. Operations Committee Federated EGA Operations Committee 1.1 Terms of reference for the FEGA Operations Committee describe the purpose and objectives of the committee, which is to review operational performance and coordinate technical roadmaps of FEGA Nodes and Central EGA. The committee receives advice from, and provides operational reporting to the FEGA Strategic Committee. Guidelines Node Operations guidelines 2.1 An overview of the operational areas which require resources in order to create a FEGA Node. The document is based on more than 15 years' experience of EMBL-EBI and CRG operating the EGA and initial expericences of the inaugural FEGA Nodes. The operational areas of responsability are in line with the FEGA Maturity Model, which guides establishment and operation of FEGA Nodes. Collaboration Federated EGA Node Collaboration Agreement 1.3 Nodes are welcome to make a copy of this current version of the CA to start its review with their legal teams and understand the responsibilities of joining FEGA. Nevertheless, this version (the one with a watermark) shall not be signed: the official version needs to be obtained from FEGA prior signing through its official channels.

  Documentation about/projects-and-funders/federated-ega

• DAC Ghent University - Department of Movement and Sports Sciences

  DAC of Ghent University for RNA-seq data of human skeletal muscle biopsies collected at the Department of Movement and Sports Sciences

  Dac EGAC50000000263

• Phenotype information

  This study explores the cell-free transcriptome in a humanized DLBCL patient-derived tumor xenograft (PDTX) model. Blood plasma samples (n=171) derived from a DLBCL PDTX model in-cluding 27 humanized (HIS) PDTX, 8 HIS non-PDTX and 21 non-HIS PDTX non-obese diabetic (NOD)-scid IL2Rgnull (NSG) mice were collected during humanization, xenografting, treatment, and sacrifice. The mice were treated with either rituximab, cyclophosphamide, doxorubicin, vincris-tine, and prednisone (R-CHOP), CD20-targeted human IFNα2-based AcTaferon combined with CHOP (huCD20-Fc-AFN-CHOP), or phosphate-buffered saline (PBS). RNA was extracted using the miRNeasy serum/plasma kit and sequenced on the NovaSeq 6000 platform using the using the SMARTer Stranded Total RNA-Seq Kit v3.

  Dataset EGAD50000000806

• Whole-exome sequencing of ovarian clear cell carcinoma in clinical outliers

  Whole-exome sequencing dataset for the Australian Ovarian Cancer Study (AOCS) and The Jikei University School of Medicine (JIKEI) ovarian clear cell carcinoma (OCCC) clinical outliers project, representing 10 donors. Although OCCC are commonly resistant to platinum based chemotherapy, good clinical outcomes are observed in a subset of patients. Exome sequencing was performed in 10 patients who had either unusually long survival (N=5) or had a very short time to progression (N=5).

  Study EGAS00001004248

• Barrett's and Esophageal Adenocarcinoma Genetic Susceptibility Study (BEAGESS)

  BEAGESS is a genome-wide association study (GWAS) which takes advantage of the extensive data collected by investigators in the Barrett's and Esophageal Adenocarcinoma Consortium (BEACON, http://beacon.tlvnet.net), representing many of the high-quality population-based and other epidemiologic studies of BE and EA in the world. The overall goal of our research is to evaluate the influence of genetic susceptibility on risk of EA and its main precancerous condition, BE. The primary aims of the study are to identify tagSNPs most strongly associated with risk of esophageal adenocarcinoma (EA) and Barrett's esophagus (BE). In the secondary aims we will evaluate the extent to which the most significant associations vary according to key environmental and host factors for these conditions, including gender, obesity and cigarette smoking. These results will guide additional exploratory analyses examining ways in which individual SNP results might be aggregated to shed light in the importance of specific pathways relevant to the etiology of BE and EA, and to identify possible epistatic effects.

  Study phs000869

• Molecular Genetics of Schizophrenia - nonGAIN Sample (MGS_nonGAIN)

  This study is part of the Molecular Genetics of Schizophrenia (MGS) genome wide association study (GWAS) of 3,972 cases (2,686 EA and 1,286 AA) and 3,629 controls (2,656 EA and 973 AA) (analyzed sample remaining after quality control exclusions), comprised of European ancestry (EA) and African American (AA) samples. We genotyped about half of the EA sample and almost all of the AA sample under the auspices of the Genetic Association Information Network (GAIN) with the Affymetrix 6.0 platform at the Broad Institute. The remainder of the included sample was also genotyped with the Affymetrix 6.0 platform at the Broad Institute, and we refer to this component as the nonGAIN sample. Cases met criteria for schizophrenia (SCZ) or schizoaffective disorder (SA) per the Diagnostic and Statistical Manual of Mental Disorders version IV (DSM-IV) for all three collections (SGI, MGS1, and MGS2) comprising these cases. However, for the older SGI collection, codes for the secondary diagnoses refer to the older DSM-III-R version. Controls were screened briefly and excluded if they endorsed a history of these illnesses.

  Study phs000167

• Foregut Microbiome in Development of Esophageal Adenocarcinoma

  The distal esophagus is an important anatomical area where gastric acid reflux can cause reflux esophagitis (RE), Barrett's esophagus (BE) (intestinal metaplasia), and esophageal adenocarcinoma (EA). The incidence of EA has increased 6-fold in the U.S. since the 1970s, parallel to a significant increase in the prevalence of gastroesophageal reflux diseases (GERD). Although specific host factors might predispose one to disease risk, such a rapid increase in incidence must be predominantly environmental. The cause remains unknown. Our hypothesis is that changes in the foregut microbiome are associated with EA and its precursors, RE and BE in the GERD sequence. We will conduct a case control study to characterize the microbiome in every stage of the GERD sequence as well as analyze the trend in changes in the microbiome along disease progression toward EA. Specific Aim 1. To conduct a comprehensive population survey of the foregut microbiome and demonstrate its association with GERD sequence, by a 16S rRNA gene survey. We will analyze samples of the foregut microbiome at three anatomic loci: mouth, distal esophagus, and gastric corpus. Changes of the microbiota in the distal esophagus will be correlated with the phenotypes. Spatial relationship between the esophageal microbiota and upstream (mouth) and downstream (stomach) foregut microbiotas as well as temporal stability of the microbiome-disease association will also be examined. Specific Aim 2. To define distal esophageal metagenome and demonstrate its association with GERD sequence, by shotgun metagenomic analysis. We will first classify samples of the metagenome into metagenotypes by between-sample k-mer distance and correlate the metagenotypes with the four phenotypes. Subsequent detailed analyses will include pathway-disease and gene-disease associations. DNA viruses and fungi, if identified, also will be correlated with the phenotypes. A significant association between the foregut microbiome composition and GERD sequence, if demonstrated, will be the first step for eventually testing the causal hypothesis that an abnormal microbiome is required for the development of the sequence of phenotypic changes toward EA. If EA and its precursors represent a microbial ecological disease, treating the cause of GERD might become possible, for example, by normalizing the microbiome through use of antibiotics, probiotics, or prebiotics. Causative therapy for GERD could prevent its progression and reverse the current trend of increasing incidence of EA.

  Study phs000260

• Susceptibility_genes_for_the_development_of_SLE_during_treatment_of_IBD

  A potential and very serious side effect of treating IBD with antiTNFa therapies (the currentgold standard) is the development of systemic lupus erythematosis (SLE). This side effect israre and unpredictable. Out of several thousand cases having received treatment, theUniversity of Calgary have accumulated 12 individuals with full phenotyping and novelserological antibody discovery panel data. We propose to exome sequence these samples inan effort to identify rare highly-penetrant variants that could be underlying this severephenotype.

  Study EGAS00001000387

• 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

• GEDI: A Developmental Model of Gene-Environment Interplay in SUDs: Combined Genotype Dataset from the "Great Smoky Mountains Study" and the "Caring for Children in the Community Study".

  1. Great Smoky Mountains Study (GSMS; Costello et al. 1996, 1997) Three cohorts of boys and girls, aged 9, 11, and 13 years at intake in 1993, were selected from a rural population of some 20,000 children using a household equal probability design. A two-phase procedure was used for White and African-American youth to increase power by oversampling children at risk for psychiatric and SUDs. Parents (usually mothers) of the first stage random population sample completed a questionnaire about their child's behavioral problems. Of 4,195 subjects selected, 95% (N=3,896) of parents completed the screen. All children scoring above a predetermined threshold (the top 25% of the total scores), plus a 10% random sample of the remaining 75%, were recruited for detailed interviews. Results can be back-weighted to population levels for analyses. Half of the sample consists of females, and 6% are African Americans, reflecting the population of the study area. The interviewed sample of white and African-American subjects was 1,070 (80% of those recruited). American Indian youth were oversampled (100%) because they are an understudied group known to be at high risk for stressful events, substance disorders, and mood disorders. Of 431 age-eligible children, 350 (81% boys, 49% girls) participated. Thus, the size of total GSMS sample is 1,070 + 350 = 1,420. Data collection is complete for ages 9-26, and age 30 interviews are in progress. By age 26 a total of 9,858 interviews had been completed; the average number of interviews per subject was seven, and by age 26, 97.3% completed two or more interviews. 2. The Caring for Children in the Community Study (CCC; Angold et al., 2002) This representative study of psychiatric illness and service use in African-American and White youth took place in four rural counties in the southeastern USA. The two-stage sampling design and methods are similar to those used in the GSMS. Of 4,500 youth randomly selected from the 17,117 9- to 17-year-olds in the public school's database, 3,613 (80.0%) were successfully contacted and agreed to complete the behavioral screen. Of the 1,302 selected to participate in the study, 920 (70.7%) interviews were completed. Because CCC was also the only study in GEDI to contain more than a very few African-American participants, these were omitted from the multi-site analyses. Reprinted with permission from Cambridge University Press from Costello et al., 2013: PMID: 23461817 References: Costello et al., 1996: PMID: 8956679 Costello et al., 1997: PMID: 9184514 Angold et al., 2002: PMID: 12365876

  Study phs000852

• Sporadic ALS Australia Systems Genomics Consortium (SALSA-SGC)

  In 2016 we established the Sporadic ALS Australia Systems Genomics Consortium (SALSA-SGC) funded by the Ice Bucket Challenge Grant administered by the Motor Neurone Disease Research Institute of Australia. The goals of the SALSA-SGC are to collect biological samples from clinics across Australia with matched in depth clinical and self-report phenotypes and to generate multiple levels of genetic and genomic data. In this first data generation exercise of the SALSA-SGC the majority of the samples were collected prior to the formal establishment of SALSA-SGC from clinics across Australia.Briefly, the cohort includes the University of Sydney’s Australian Motor Neuron Disease DNA Bank (MND Bank) cohort recruited April 2000 to June 2011), with study protocol approved by the Sydney South West Area Health Service Human Research Ethics Committee (HREC). Cases were recruited from around Australia via state-based MND associations with diagnosis verified by a neurologist. The remainder of the cases were recruited from clinics across Australia between 2015 and 2017 under HREC approvals from Royal Brisbane and Women’s Hospital, Macquarie University Multidisciplinary Motor Neurone Disease Clinic,  Calvary Health Care Bethlehem in Melbourne , Fiona Stanley Hospital in Perth, and from 2016 under HREC approvals at each site for the sporadic ALS Australia Systems Genomics Consortium (SALSA-SGC). The ALS cases were diagnosed with definite or probable ALS according to the revised El Escorial criteria. Some controls were recruited as either partners or friends of patients, healthy individuals free of neuromuscular diseases. We are providing GWAS and MWAS data in this dataset. Individual level GWAS data were generated using Illumina Infinium CoreExome-24 version 1.1 chips for N= 846 cases and N=665 controls. Individual MWAS data was generated using the Illumina Human methylation 450K array for N=782 cases and N=613 controls. There 1315 individuals where GWAS and MWAS data has been generated and is available. Further information on these data sets can be found: Paper 1: Restuadi, R, Garton, FC, Benyamin, B, Lin, T, et al. Amyotrophic Lateral Sclerosis Genetic Correlation with Cognitive Performance, educational attainment and schizophrenia: evidence from polygenic risk score analysis. (submitted) Paper 2: Nabais, MF, Lin, T, Benyamin, B et al. Significant out-of-sample classification from methylation profile scoring for amyotrophic lateral sclerosis. 2020. NPJ genomic medicine. 5(10). Files provided in this submission include: GWAS: This folder contains QCed genotype for the Australian ALS case-control cohort. Contains PLINK files for genotyping data (not imputed yet). The individuals selected here have: good consistency on phenotype data ethics approval registered as part of sporadic ALS studies unrelated by GRM cut-off 0.05 No ancestry QC yet MWAS: This folder contains the IDAT and post-QC normalized DNAm (beta) for the Australian ALS case-control cohort. 2019_AUS_ALS_PCTG_DNAm.tar.gz - IDATS for 1315 individuals analyzed in the MWAS study normalized_beta_values - Binary files (created with the OSCA software) containing information on the individuals, probes and the DNAm (beta) values obtained after QC phenotype_file - contains all the covariates analysed in the MWAS including: case-control status, coded 0 = Control and 1 = ALS, predicted age, predicted cell-type proportions, predicted smoking scores, slide and chip position and sex Important Notes: The DNAm data were normalized together with samples that were not part of this ALS case/control study and thus, the normalization procedure may not be 100% reproducible using only the IDAT files uploaded here. Summary data has been made publicly available and can be accessed directly: Data collection and sample processing were performed at several clinics across Australia. Genotyping and DNA methylation arrays were performed by the Human Studies Unit, at the Institute for Molecular Bioscience (University of Queensland). Quality control of the genotypic, phenotypic and DNA methylation data was done by the Program of Complex Traits Genomics, at the Institute for Molecular Bioscience (University of Queensland).

  Study phs002068

• Genetic analysis of HLA and immune escape genes in Diffuse Large B-cell Lymphoma

  Fifty percent of diffuse large B-cell lymphoma (DLBCL) cases lack cell-surface expression of the class-I major histocompatibility complex (MHC-I), thereby escaping immune recognition by cytotoxic T cells. In order to comprehensively identify the mechanisms involved in MHC-I loss, we first performed immunophenotypic analysis of both MHC class-I and -II in 657 cases across the spectrum of B-NHL, revealing that loss of MHC-I, but not MHC-II, is preferentially restricted to DLBCL. We then used whole exome and targeted deep-sequencing to examine genes involved in MHC-I expression in 74 DLBCL samples representative of MHC-I positive and negative cases. We show here that somatic biallelic or monoallelic inactivation of B2M and/or HLA-I is present in 80.9% (34/42) of MHC-I negative tumors. Furthermore, 68.8% (22/32) of MHC-I positive DLBCLs also harbored monoallelic HLA-I genetic alterations (MHC-I positivemono) that lead to allelic imbalance, suggesting allele-specific inactivation. Both MHC-I negative and MHC-I positivemono cases showed a significantly higher mutational burden as well as a higher number of inferred neo-antigens, suggesting co-selection of HLA-I loss and sustained neo-antigen production. Interestingly, the analysis of > 500.000 individuals in two databases revealed homozygosity of germline HLA-I genes in 26-38% of DLBCL patients, a frequency significantly higher than that observed in any other cancer type. In mice, germinal-center B cells lacking HLA-I expression did not progress to lymphoma and were counterselected in the context of oncogene-driven lymphomagenesis, suggesting that additional events are needed to license immune evasion. These results suggest a multi-step process of HLA-I loss including both germ-line and somatic events in DLBCL development, and have direct implications for the pathogenesis and immunotherapeutic targeting of this disease.

  Study EGAS00001005054

• Hydroxyurea to Prevent Organ Damage in Children with Sickle Cell Anemia (BABY HUG) Phase III Clinical Trial and Follow-Up Observational Studies I and II

  Sickle cell anemia is associated with substantial morbidity from acute complications and organ dysfunction beginning in the first year of life. In 1995, the Multicenter Study of Hydroxyurea (MSH) (dbGaP phs002348) demonstrated that, in adults, hydroxyurea is effective in decreasing the frequency of painful crises, hospitalizations for crises, acute chest syndrome, and blood transfusions by 50%. The phase I/II study of hydroxyurea in children (HUG KIDS) demonstrated that children have a response to hydroxyurea similar to that seen in adults in terms of increasing fetal hemoglobin levels and total hemoglobin, and decreasing complications associated with sickle cell anemia. In addition, this study demonstrated that the drug does not adversely affect growth and development between the ages of 5 and 15. A pilot study of hydroxyurea (HUSOFT) given to children between the ages of 6 months and 24 months demonstrated that the drug was well tolerated and that the fetal hemoglobin levels rose and remained elevated compared to baseline with continued hydroxyurea administration. A Special Emphasis Panel (SEP) met on April 12, 1996 to review the results of the MSH trial and the progress to date of the HUG KIDS study. The SEP recommended that NHLBI undertake the BABY HUG trial. The BABY HUG Randomized Controlled Trial concluded that hydroxyurea treatment in very young children seemed to have an acceptable safety profile and to reduce complications of sickle cell anemia. However, more data were needed on the long-term safety of hydroxyurea use in very young children. As a result, follow-up studies were initiated. The Follow-Up Study II provided longer follow-up than Follow-Up Study I, and included more assessment types than Follow-Up Study I.The BABY HUG program consisted of three related studies, each of which has associated datasets and bio-specimens. A randomized controlled trial comparing hydroxyurea to placebo in very young children with sickle cell anemia (BABY HUG Randomized Controlled Trial) The first observational follow-up study of children from the randomized controlled trial (BABY HUG Follow-Up Study I). All children in Follow-Up Study I were offered the option of taking open-label hydroxyurea, with treatment decisions made by the family and the clinical team caring for the child. The second observational follow-up study of children from BABY HUG Follow-Up Study I. All children in Follow-Up Study II were offered the option of taking open-label hydroxyurea, with treatment decisions made by the family and the clinical team caring for the child.The purpose of the Randomized Controlled Trial was to determine if hydroxyurea can safely prevent early end organ damage in very young children with sickle cell anemia. The purpose of the BABY HUG Follow-up Study I was to provide structured follow-up of the children enrolled in the BABY HUG Randomized Controlled Trial, in order to characterize the long-term toxicities and unexpected risks (if any) associated with treatment with hydroxyurea at an early age. The objective of Follow-Up Study II was to obtain additional data about the long-term safety and efficacy of hydroxyurea use in children with Sickle Cell Anemia through at least the first decade of life.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 phs002415

• Susceptibility genes for the development of SLE during treatment of IBD

  A potential and very serious side effect of treating IBD with antiTNFa therapies (the currentgold standard) is the development of systemic lupus erythematosis (SLE). This side effect israre and unpredictable. Out of several thousand cases having received treatment, theUniversity of Calgary have accumulated 12 individuals with full phenotyping and novelserological antibody discovery panel data. We propose to exome sequence these samples inan effort to identify rare highly-penetrant variants that could be underlying this severephenotype.

  Dataset EGAD00001000670

• Immunoprotective mechanisms and microbiota interplay in Salmonella Typhi infection

  Vaccine development against Salmonella enterica serovar Typhi (S. Typhi) requires a better understanding of interaction between human host and the resident microbial consortia in gastrointestinal tract. Healthy adult volunteers received either Ty21a, M01ZH09 or placebo, and underwent challenges with wt S. Typhi. Stool samples were collected at the screening interview (Baseline 1), prior to the first vaccination visit (Baseline 2), during vaccination (Day -28 and -26 for placebo and M01ZH09 groups; Day -32, -30, -28, -26 for Ty21a group), prior to challenge with S. Typhi (Day 0), and after challenge (day 0 12h, day 1, day 3, day 7, and day 10). 16S rRNA and messenger RNA were extracted from stool and sequenced on the Illumina Miseq and HiSeq 2000 platforms, respectively.

  Study phs001521

• RNAseq SCLC Cell lines MYC

  RNAseq data set (Mollaoglu et al., MYC drives progression of small cell lung cancer to a variant neuroendocrine subtype with vulnerability to Aurora kinase inhibition) RNA isolation from primary tumors and healthy lungs was performed using RNeasy Mini Kit (Qiagen) with the standard protocol. RNA was subjected to library construction with the Illumina TruSeq Stranded mRNA Sample Preparation Kit (cat# RS-122-2101, RS-122-2102) according to manufacturer’s protocol. Chemically denatured sequencing libraries (25 pM) are applied to an Illumina HiSeq v4 single read flow cell using an Illumina cBot. Hybridized molecules were clonally amplified and annealed to sequencing primers with reagents from an Illumina HiSeq SR Cluster Kit v4-cBot (GD-401-4001). Following transfer of the flowcell to an Illumina HiSeq 2500 instrument (HCSv2.2.38 and RTA v1.18.61), a 50 cycle single-read sequence run was performed using HiSeq SBS Kit v4 sequencing reagents (FC-401-4002).

  Dataset EGAD00001003099

• GA4GH

  Standards The EGA is a long-standing supporter of the Global Alliance for Genomics & Health (GA4GH) to enhance responsible sharing of human genetic data through the development of interoperable global standards for human data access. The EGA is one of the founding GA4GH Driver Projects and has contributed to the development and implementation of several GA4GH standards and APIs. Below is a list of the GA4GH standards and APIs that are currently available or planned for implementation at EGA. TechnicalStandards Purpose SpecificationVersion SupportedVersion Implementation Large Scale Genomics htsget A protocol for secure, efficient, and reliable access to sequencing read and variation data. V1.3.0 V1.0.0 Specification Documentation Endpoint Read File Formats (SAM/BAM/CRAM) Specifications for storing next-generation sequencing read data. V3.0.0 V3.0.0 Implementation Example of Usage Variation File Formats (VCF/BCF) The specifications for Variant Call Format Files (VCF) and its binary counterpart BCF. V4.0.0 V2.0.0 V4.0.0 V2.0.0 Implementation Example of Usage Crypt4GH v1.0 Enables direct byte-level compatible random access to encrypted genetic data stored in community standards (e.g. CRAM, VCF) V1.0 V1.0 Specification Documentation Endpoint refget API Enables access to reference sequences using an identifier derived from the sequence itself. V1.2.6 NA Specification RNAget API v1 Provides a means of retrieving data from several types of RNA experiments including (i) feature-level expression data from RNA-seq type measurements and (ii) coordinate-based signal/intensity data similar to a bigwig representation via a client/server model. V1.0.0 NA Documentation Discovery Beacon v2 Supports discovery of genomic variants, phenotypes, and individuals V1.0.1 V0.3 Web UI API Source Code Service Info API v1 The Service Info API is an endpoint for describing GA4GH service metadata, designed for extension and inclusion in other APIs. Service info is used to describe a single service, while Service Registry is used to describe multiple services. V1.0.0 NA Documentation Service Registry API v1 provides information about other GA4GH services, primarily for the purpose of organizing services into networks or groups and service discovery across organizational boundaries. V1.0.0 NA Documentation Data Use Researcher Identities Data Use Ontology (DUO) Allow users to semantically tag genomic datasets with usage restrictions, allowing them to become automatically discoverable based on a health, clinical, or biomedical researcher’s authorisation level or intended use. 2021-02-23 2021-02-23 Specification Documentation Endpoint Authentication & Authorization Infrastructure (AAI) The GA4GH AAI specification leverages OpenID Connect (OIDC) Servers for use in authenticating the identity of researchers desiring to access clinical and genomic resources from data holders adhering to GA4GH standards, and to enable data holders to obtain security-related attributes of those researchers. V1.2.0 V1.2.0 API URI: ega.ebi.ac.uk:8443 Documentation Repository Researcher IDs (passport, visa) Specify the collection of researchers that may access a dataset at any given time, and the credentials they must supply. V1.0.1 V1.0.1 Specification Documentation Endpoint Cloud Tool Registry Service API TRS is a standard API for exchanging tools and workflows to analyze, read, and manipulate genomic data. V2.0.1 NA Documentation Repostiory Data Repository Service API ​DRS API is a standard for building data repositories and adapting access tools to work with those repositories, works with other approved APIs from the GA4GH Cloud Work Stream to allow researchers to discover algorithms across different cloud environments and send them to datasets they wish to analyse. V1.0.3 NA Documentation Repostiory Workflow Execution Service API This API lets users run a single workflow (defined using CWL or WDL) on multiple different platforms, clouds, and environments, and be confident that it will work the same way. The API provides methods to request that a workflow be run, pass parameters to that workflow, get information about running workflows, and cancel a running workflow. V1.0.1 NA Documentation Repostiory Genomic Knowledge Standards Variation Representation v1 Provides a flexible framework of computational models, schemas, and algorithms to precisely and consistently exchange genetic variation data across communities. V1.3.0 EGA team is contributing to including it in in Beacon v2 Specification and Elixir Reference Implementation Documentation Repostiory Clin/ Pheno Data Capture Phenopackets Provides information models with different levels of complexity to enable high level clinical phenotype information as well as deep clinical phenotype information to be exchanged. V2.0.0 Included in Ongoing Submissions EGA team is contributing to including it in in Beacon v2 Specification and Elixir Reference Implementation Documentation Repostiory Driver Project The EGA, jointly coordinated by the EBI and the CRG, was announced, in 2017, to be one of the 15 Driver Projects for GA4GH. Driver Projects are international genomic data initiatives, focussed on real projects and challenges that will guide the development efforts in order to accelerate and enable completely responsible and standarised data sharing by 2022. All chosen Driver Projects make a cross-sectional effort by playing an important role across the different workstreams. Thomas Keane, Jordi Rambla, Mallory Freeberg, and Aina Jené have been named Driver Project Champions for the EGA. All Driver Project Champions will be leading this ambitious initiative for the following years.

  Documentation about/projects-and-funders/ga4gh

• Kids First: Genomic Analysis of Esophageal Atresia and Tracheoesophageal Fistulas and Associated Congenital Anomalies

  The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). All of the WGS and phenotypic data from this study are accessible through dbGaP and kidsfirstdrc.org, where other Kids First datasets can also be accessed. Esophageal atresia/tracheoesophageal fistula (EA/TEF) is a rare and complex aerodigestive congenital anomaly with an estimated incidence of 1 in 2500 to 1 in 4000 live births. We propose to elucidate the underlying genomic architecture of EA/TEF by performing whole genome sequencing to characterize new clinical syndromes associated with EA/TEF to provide more accurate clinical prognostic information.

  Study phs002161

• Somatic Mutations and Their Etiological Determinants for Breast Cancer in African American Women (B-CAUSE Study)

  The breast cancer health disparity between women of African ancestry (AA) and European ancestry (EA) remains a major public health challenge in the United States. AA women are afflicted by higher rate of triple-negative breast cancer (TNBC) and bear the highest mortality rate of all populations from the disease. Even within TNBC, some data suggest that AA women have poorer outcomes than EA women. Mounting evidence points to population differences in cancer biology, a fundamental question that remains unsettled. The disparity also manifests in representativeness in tumor sequencing projects, due to the low inclusion of AAs in genomic studies. A recent analysis of The Cancer Genome Atlas (TCGA) data revealed that the number of AA cases sequenced was sufficient only for studying frequently mutated genes (≥10%), and the power was worse for TNBC as a separate disease entity. As a result, the breast tumor mutational landscape elucidated to date is derived mostly from EA cases, and may not necessarily represent AA cancer genomes. Moreover, as discovery potential for new driver genes has come close to a plateau, cancer in AAs derived from distinct genetic and environmental contexts may provide a powerful venue for discovering mutations that are rare in EA cases. This is showcased in recent studies in colon and prostate cancer. Thus, we carried out a collaborative project, B-CAUSE, to characterize the mutational landscape of AA breast cancer genomes by pooling resources from five established studies, including the Women's Circle of Health Study (WCHS), the Black Women's Health Study (BWHS), the Southern Community Cohort Study (SCCS), the Nashville Breast Health Study (NBHS), and the Southern Tri-State Breast Health Study (STSBHS). This effort has created the largest AA-specific breast tumor mutation dataset to date in an efficient manner. The data deposited here are from the whole-exome sequencing (WES) of matched tumor-normal pairs of 462 TNBC in AA women from the B-CAUSE Study.

  Study phs003962

• Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY)

  The National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health (NIH) has sponsored a consortium of investigators to conduct a clinical treatment trial, Treatment Options for type 2 Diabetes in Adolescents and Youth (TODAY). The primary objective of the TODAY trial is to compare the efficacy of three treatment arms on time to treatment failure based on glycemic control. The secondary aims are to:compare and evaluate the safety of the three treatment arms;compare the effects of the three treatments on the pathophysiology of type 2 diabetes (T2D) with regards to beta cell function and insulin resistance, body composition, nutrition, physical activity and aerobic fitness, cardiovascular risk factors, microvascular complications, quality of life, and psychological outcomes;evaluate the influence of individual and family behaviors on treatment response; andcompare the relative cost effectiveness of the three treatment arms.The three treatment regimens are: (1) metformin alone, (2) metformin plus rosiglitazone, and (3) metformin plus an intensive lifestyle intervention called the TODAY Lifestyle Program (TLP). The study recruits patients over a three-year period and follows patients for a minimum of two years. Patients are randomized within two years of the diagnosis of T2D.

  Study phs002447

• Correlative Studies for Protocol #14-C-0059: T Cells Expressing an Anti-GD2 Chimeric Antigen Receptor in Patients with GD2+ Solid Tumors, a Collaboration with CIMAC-CIDC

  This study is a retrospective correlative analysis of immune cell phenotypes in samples from a Phase I clinical trial (NCT02107963) of GD2 CAR-T cells (GD2-CAR.OX40.28.z.iC9) in children and young adults with osteosarcoma and neuroblastoma. The study was a 3+3 dose-escalation study with four dose levels (1 x 10(5) transduced T cells/kg; 1 x 106 transduced T cells/kg; 3 x 106 transduced T cells/kg; and 1 x 107 transduced T cells/kg). Apheresis and final manufactured GD2 CAR-T product samples were analyzed by bulk RNA sequencing and ATAC sequencing. The patients were stratified into good CAR-T expanders (peak CAR-T expansion >1000 GD2 CAR copies/100ng DNA) and poor CAR-T expansion (peak CAR-T expansion Increased naïve T cell subsets in baseline apheresis are associated with patients who experienced good CAR T cell expansion A T cell exhaustion signature was observed in the manufactured GD2 CAR-T product and may have contributed to lack of efficacy observed in this trial Increased myeloid-derived suppressor cell (MDSC) signatures were observed in patients with poor CAR-T expansion CAR-T product in good expanders displayed increased AP-1 factor transcription factor motifs, such as BATF, JUN, and FOS Data provided in dbGaP for this study include: Bulk RNA-Seq data for 11 pre-treatment peripheral blood and 11 GD2 CAR-T product from 14 patients. ATAC sequencing of 22 peripheral blood samples and 5 GD2 CAR-T products from 14 patients.

  Study phs003455

• From research to data sharing: exploring EGA user's experiences

  The Cell Plasticity and Regeneration Group at the Bellvitge Biomedical Research Institute-IDIBELL focuses on the process of recruitment of macrophages that takes place in the small intestine during injury and healing. They recently published a paper titled “Mucosal Macrophages Govern Intestinal Regeneration in Response to Injury" in Gastroenterology Journal. As part of the research, some experiments were conducted using human intestinal organoid lines. These cells were processed for RNA sequencing, and the sequencing data were deposited at the EGA to be made available to the scientific community. When dealing with human genomic information, repositories must ensure the availability of the datasets while ensuring data protection. In this context, the European Genome-phenome Archive stands as a service for secure archiving and sharing of genetic, phenotypic and clinical data resulting from biomedical research. Following the recent publication of their paper, we took the opportunity to talk to Ilias Moraitis, first author, and Jordi Guiu, group leader, to find out about their experience with data sharing. Could you explain the focus of your research? In the lab, we study intestinal regeneration and how immune cells participate in this process. We use several techniques: engineered mouse models, image tracing, as well as mouse and human cells intestinal organoids. What challenges do you face regarding data management? We didn’t have a lot of problems. Always the informatic part, the data analysis, can give problems. But everything was smooth and working. We think it’s very important to deposit the data in repositories. For the mouse data it is very straightforward, but for us it was the first time depositing human data, which is sensitive because it comes from patients, and this is legally regulated. That’s why we thought about the EGA, and it was our first time. Why do you think it is important to submit data to repositories such as EGA? Because we think data is important for the science. Nowadays, we are sequencing a lot everywhere worldwide and having these resources shared with the scientific community it’s not only good for science, but it is also saving money and reducing costs. And there is so much data in there that can be used for other projects and for other questions. And it saves time! How was the process of submitting the data to the EGA? The communication worked very well but it was slower than we would like. But I think this is something we learn on the way. People usually wait until the last minute to do this before publication but if you know it in advance, you can start the process earlier. If you had to repeat the process, would you do anything differently? It’s like everything, isn’t it? For the first time you don’t know, you aren’t sure, but for the second time you know the steps, you know what to do and it’s faster. Of course, we would to do it earlier. Also, there were some things related to the control access that we didn’t know how to manage, but it was something more internal to us. Who is going to receive the communications and when, is our ethical committee going to evaluate this? All these technicalities that we didn’t know before starting this process, so we had to think about all these things. Next time it will be faster on our side. Do you have any suggestions for us to improve the submission process? Maybe we’ll need to ask our bioinformatician who did it! He was in charge of this part. Besides that, we think if the process were faster, it would be better for everyone. Also, in science a lot of times you have to do a lot of things at the very last minute, because of the nature of the experiments, or the need to accomplish for submitting a paper. Being faster in the process would be a plus. On your side, we went through the process and were able to find some information, but there were aspects we weren't aware of and weren't sure how to handle. I'm not sure if other institutes have different protocols for managing data requests or how they handle these internally. We believe the main issue lies in how the forms are filled out. Do you have any recommendations for other submitters? The main advice is to do this with enough time. Submit the data when you have the sequences, and don’t wait until the last minute. And then it can be under embargo, so you don’t need to make it public at that moment. As soon as you sequence, upload it and then it will be there for whenever you need to publish. How do you think we could encourage other researchers to submit their data? There are different layers here. One is that it is mandatory. You have to do this. The other is that we belong to a research community, to the same community and sharing this is making our research community stronger and more efficient. And then, also because this gives you visibility. The data is there, other people can analyse your data, and this also will bring citations to your papers. It has many advantages.

  Blog from-research-to-data-sharing

• RNA sequencing data of 142 samples from 142 patients with HER2+ breast cancer treated with letrozole or tamoxifen (SOLTI PAMELA trial)

  PAMELA (NCT01973660) is a non-randomized, open-label, multicentric, prospective translational research study in stage I-IIIA HER2+ breast cancer designed to evaluate the ability of the PAM50 intrinsic subtypes to predict pCR in the breast (pCRB) following 18 weeks of neoadjuvant lapatinib and trastuzumab (+/- hormonal therapy if hormone receptor [HR] positive). Patients with HR+ disease received letrozole (if postmenopausal) or tamoxifen (if pre-menopausal). The primary objective was to compare the pCRB rates of the HER2-E versus the non-HER2-E subtypes in the intent-to-treat population.

  Study EGAS00001006410

• Diabetic Retinopathy Genomics Study (DRGen) - Genetic Biomarkers of Diabetic Retinopathy

  Using a well-defined, clinically supported phenotypic strategy, we seek to better understand the role of common and rare genetic variants in the progression of diabetic retinopathy (DR). Whole exome/whole genome sequencing (WES/WGS) was used to systematically search the human genome to detect DR susceptibility genes or genes that protect people from developing advanced DR. Clinical data and bio specimens were obtained from clinically defined DR phenotypes such as, (i) No/Mild NPDR (non-proliferative DR) with a subset of extreme phenotype, ii) Diabetic macular edema (DME) alone and iii) Proliferative diabetic retinopathy (PDR) alone. In this ongoing comprehensive genomic study, we wish to i) Identify potential common and rare variants that may influence the phenotypic differentiation of DR into DME or PDR, ii) Define genetic variants stratified based on the "extreme phenotype," and iii) Characterize genetic factors that determine the differential response to anti-VEGF drugs in DME patients. Definition of Diabetic Retinopathy and classification of groups Study 1. Proliferative diabetic retinopathy (PDR). Anybody showing evidence of retinal neovascularization on the disc (NVD) or elsewhere (NVE) with or without pre-retinal and vitreous hemorrhage will be considered PDR. We will include only those "active" PDR patients. The total number of consented subjects in this study is 182. Study 2. Isolated Phenotype: Varied response with anti-VEGF treatment is a significant limiting factor for better clinical outcomes. We quantify the response to anti-VEGF treatment based on visual acuity changes and anatomic features of DME assessed by optical coherence tomography (OCT). Depending on the response at one month following three monthly intravitreal anti-VEGF injections, we have observed two phenotypes: 1) Good responders: Participants were considered "good responders" if the anatomical outcome to anti-VEGF treatment resulted in a significant reduction of baseline central retinal thickness (CRT) > 25% after three anti-VEGF injections. 2) Poor Responders: Participants were classified as "poor responders" if the outcome of anti-VEGF treatment resulted in a reduction of CRT < 20% or in any increase in CRT after three monthly injections. The total number of consented subjects in this study is 17.

  Study phs002501

• A Pharmcogenetic Study of Bipolar Disorder in a Taiwanese Han Chinese Population (TWBP)

  Objective: To identify genetic variants associated with response to lithium prophylaxis treatment. Materials: 294 bipolar I (BPI) patients receiving lithium treatment, a subset of 1761 bipolar I (BPI) patients recruited by the Taiwan Bipolar Consortium. Lithium response was assessed based on the Alda Scale. Methods: Responders to lithium treatment were defined if the patients' Alda Scale > 4, 5, 6, and 7. Genome-wide association (GWA) analysis was carried out on the 294 BPI patients for the 4 definitions of responders. The SNPs showing strongest association in the GWAS were then tested for association in a replication sample of 100 patients and further tested in a prospective follow-up series of 24 patients. Results: Two SNPs on GADL1 showed the strongest associations in the four GWA analyses and in the replication set of 100 patients. These two SNPs had a sensitivity of 0.93 for predicting lithium response and differentiated between the good and poor responders in the follow-up cohort. Summary: These genetic variants are associated with response to lithium prophylaxis treatment for BPI.

  Study phs000692

• Drug Perturbation of Primary Lymphoma Patient Samples and RNA Sequencing

  This submission contains 3'-end-based shallow-depth RNA sequencing data from 108 patients with chronic lymphocytic leukemia and 8 patients with lymphoma. Each primary patient sample was treated in vitro with DMSO as a control and with up to ten small-molecule drugs (ibrutinib, duvelisib, everolimus, trametinib, nutlin-3a, I-BET762, MK2206, selinexor, compound 26, and a combination of ibrutinib and compound 26). The samples were sequenced after 48 hours. The total data set consists of 1476 RNA sequencing samples from 6 batches (pilot, batch 1, batch 1 replicate, batch 2, batch 3, batch 4).

  Study EGAS50000001500

• Lymphoma_primary_patient_drug_perturbed_RNASeq_samples

  3'-end low-depth RNA sequencing data from 108 patients with chronic lymphocytic leukemia and 8 patients with lymphoma. Each primary patient sample was treated in vitro with DMSO as a control and with up to ten small-molecule inhibitors (ibrutinib, duvelisib, everolimus, trametinib, nutlin-3a, I-BET-762, MK2206, selinexor, compound 26, and a combination of ibrutinib and compound 26). Sequencing was performed on the Illumina HiSeq4000. The dataset contains FASTQ files.

  Dac EGAC50000000578

• Radiation-Related Genomic Profile of Papillary Thyroid Cancer after the Chernobyl Accident

  The April 26, 1986 accident at the Chernobyl nuclear power plant in northern Ukraine resulted in the release of radioactive contaminants, which were deposited in the surrounding areas in Ukraine, Belarus, and Russia. The main radiation-related health effect resulting from these exposures is the increased occurrence of thyroid cancer among individuals who were children at the time of the accident or born shortly thereafter. The purpose of this study was to conduct a comprehensive genomic landscape analysis of papillary thyroid tumors arising in individuals who were exposed as children to radioactive iodine (I-131) from the Chernobyl nuclear power plant accident.

  Study phs001134

• Impact of mutational profiles on response of primary oestrogen receptor-positive breast cancers to oestrogen deprivation

  Presurgical studies allow study of the relationship between mutations and response of estrogen receptor positive (ER+) breast cancer to aromatase inhibitors (AIs) but have been limited to small biopsies. Here in Phase I of this study, we perform exome sequencing on baseline, surgical core-cuts and blood from 60 patients (40 AI treated, 20 Controls). In poor responders (based on Ki67 change) we find significantly more somatic mutations than good responders. Subclones exclusive to baseline or surgical cores   occur in approximately 30% of tumours. In Phase II we combine targeted sequencing on another 28 treated patients with Phase I. We find six genes frequently mutated: PIK3CA, TP53, CDH1, MLL3, ABCA13 and FLG with 71% concordance between paired cores. TP53 mutations are associated with poor response. We conclude that multiple biopsies are essential for confident mutational profiling of ER+ breast cancer and TP53 mutations are associated with resistance to oestrogen deprivation therapy.

  Study EGAS00001001940

• EuCANCan: EUropean-CANadian Cancer network

  The EUropean-CANadian Cancer network (EuCANCan) is a project aiming to develop a federated network of aligned and interoperable infrastructures for the homogeneous analysis, management and sharing of genomic oncology data for Personalized Medicine. This initiative, funded by the European Commission and the Canadian Institutes of Health and coordinated by the Barcelona Supercomputing Center (BSC), is expected to process and provide the scientific community with around 30-35 thousand patient samples from different types of cancer, coming from different nodes involved in this project, during the four years of the project. Data generated as part of primary studies, often in small hospitals or research centers, is hardly reused by other projects or groups. EuCANCan is proposed to foster the homogenisation and discoverability of cancer samples from very different sources for the whole community. These efforts will help paving the way to future standardised and global genomics projects, specially for those participant countries that eventually integrate the methodology and infrastructure generated during the project. “The more data we have access to the more and better discoveries. We attempt to overcome the legal and technical limitations in order to generate, gather and exploit bigger datasets as well as answer more ambitious questions”. These were some of the words Dr. David Torrents, main coordinator of the project, shared with a regional radio during the kick-off meeting of the project, on February 11th, 2019. The EUCANCan network will be composed of reference nodes in Amsterdam, Barcelona, Berlin, Heidelberg, Paris and Toronto which have established strong research and clinical programs in the field of genomic oncology. The Centre for Genomic Regulation (CRG), specifically the European Genome-phenome Archive (EGA), is among the leading institutions. Its exhaustive experience in biomedical data archival and secure distribution is considered key for crucial developments within the project. It is also notable the recent inclusion of EUCANCan in the list of GA4GH driver projects. Further information and details can be found in the following links: InsideHPC BSC official description Techweek (Spanish)

  Blog eucancan

• Alcohol Dependence GWAS in European- and African Americans

  This study includes SSADDA (Semi Structured Assessment for Drug Dependence and Alcoholism) assessed subjects (mostly unrelated, but including some affected sibling pairs) recruited in the course of several substance dependence genetics projects. The sample includes 1889 African-American (AA) subjects and 1020 European-American (EA) subjects. Among the AAs, 1397 meet DSM-IV criteria for alcohol dependence and 491 are controls. Among the EAs, 1010 meet the criteria for alcohol dependence and 9 are controls. (One in each population meets criteria for alcohol abuse and not dependence, and is therefore counted in neither category.) Although alcohol dependence is the major focus, the sample is informative also for cocaine, nicotine, and opioid dependence.

  Study phs000425

• Exome sequencing of stroke cases with good or bad recovery three months after stroke

  With the aim to identify rare genetic variation involved in stroke recovery, this is a pilot study on extreme phenotypes of good and bad recovery. Exome sequencing was performed on 41 and 49 samples in the bad and good recovery subset, respectively. Rare Variant association Analysis was performed on the exome sequencing results.

  Study EGAS00001003463