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• Profiling Chromatin Accessibility in Humans Using Adenine Methylation and Long-Read Sequencing

  This dataset includes raw nanopore, base-called, and 6mA frequency data for EcoGII-treated NA12878 and MCF7 chromatin samples. It also includes raw nanopore data for the HG002 EcoGII-treated DNA.

  Dataset EGAD00001015374

• DNA WGS Long Read Sequence (PromethION) for manuscript titled: "Performance of Somatic Structural Variant Calling in Lung Cancer using Oxford Nanopore Sequencing Technology"

  DNA WGS Long Read Sequence (PromethION) for manuscript titled: "Performance of Somatic Structural Variant Calling in Lung Cancer using Oxford Nanopore Sequencing Technology"

  Dataset EGAD00001015400

• Illumina genome sequencing data for HICF2 craniosynostosis families (Genome Medicine)

  BAM files for two families recruited to the HICF2 genome sequencing project due to craniosynostosis. One family is a singleton and the other is an affected mother-daughter duo.

  Dataset EGAD00001011373

• Dataset for "CutNRun" sequencing of 12 samples of patients with colorectal cancer

  This dataset contains 12 "CutNRun" sequencing samples of patients with colorectal cancer. The sequencing was performed on Illumina NextSeq 550 using Cell Signaling TechnologyDNA Library Prep _Kit for Illumina Systems. The sequencing was always paired.

  Dataset EGAD00001015694

• Thymic epithelial transplantation for complete DiGeorge syndrome Spatial (2026-01-19)

  Spatial transcriptome analysis of thymic cells from children with syndromic diseases such as Trisomy 21 and inflammatory/autoimmune diseases such as myasthenia gravis. . This dataset contains all the data available for this study on 2026-01-19.

  Dataset EGAD00001015798

• Dataset for transcriptome sequencing of chordoma cells

  This dataset contains transcriptome sequencing for 15 samples from chordoma cells. The sequencing was performed on Illumina HiSeq 4000 and Illumina NovaSeq 6000. The sequencing was always paired.

  Dataset EGAD00001015642

• Whole exome sequencing data for patients with Bosma arhinia microphthalmia syndrome (BAMS)

  Whole exome sequencing data for patients with Bosma arhinia microphthalmia syndrome (BAMS). The dataset includes 21 samples from 7 families with BAMS; see Gordon et al, Nature Genetics, 2017.

  Dataset EGAD00001003130

• Transcriptomic profiles of neuroblastoma PDXs and primary tumors

  This dataset contains bam files for RNA-seq experiments for 6 neuroblastoma PDXs (Patient Derived Xenograft) and 3 pairs of neuroblastoma tumors at diagnosis and at relapse.

  Dataset EGAD00001003393

• Whole Genome Sequencing for the paper titled "Orthotopic Patient-Derived Xenografts of Pediatric Solid Tumors"

  Whole Genome Sequencing for the paper titled "Orthotopic Patient-Derived Xenografts of Pediatric Solid Tumors"

  Dataset EGAD00001003435

• Exome and Transcriptome for pan-cancer gene-drug analysis

  Total of 584 tumor specimens and/or patient-derived cells across 14 cancer types were subjected for whole-exome/targeted-exome and/or whole-transcriptome sequencing.

  Dataset EGAD00001003441

• Exome sequencing of patients with acute promyelocytic leukemia

  The dataset consists in 64 fastq files from 23 patients with acute promyelocytic leukemia. Exome sequencing was conducted on several stages (Diagnosis, Remission, Relapse) for each patient. For 5 patients, only Diagnosis and Relapse samples are available.

  Dataset EGAD00001004043

• A Hematogenous Route for Medulloblastoma Leptomeningeal Metastases

  A Hematogenous Route for Medulloblastoma Leptomeningeal Metastases

  Dataset EGAD00001003907

• Oslo University Hospital Data Access Committee no 2 for Celiac Disease datasets submitted to FEGA Norway

  This is the Data Access Committee appointed by Oslo University Hospital for governing access to Celiac Disease datasets submitted to FEGA Norway, under the Regional Ethical Committee approval 6544, with main contact Rasmus Iversen.

  Dac EGAC50000001040

• BLUEPRINT release January 2015, DNase-Hypersensitivity for monocyte

  DNase-Hypersensitivity data for 2 monocyte sample(s). 4 run(s), 2 experiment(s), 2 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_dnaseseq_analysis_20140811

  Dataset EGAD00001001185

• Whole Genome Sequencing (WGS) for St. Jude High Grade Glioma (HGG) study

  Whole Genome Sequencing (WGS) for St. Jude High Grade Glioma (HGG) study

  Dataset EGAD00001000806

• Whole Exome Sequencing (WES) for St. Jude High Grade Glioma (HGG) study

  Whole Exome Sequencing (WES) for St. Jude High Grade Glioma (HGG) study

  Dataset EGAD00001000807

• GENCORD2_GENOTYPES

  204 individuals were genotyped with the Illumina 2.5M Omni chip. Filtered genotypes were imputed into the 1000 genomes project European panel SNPs. Beagle R2 is indicated in VCF files for further filtering. See Materials and Methods in publication for details.

  Dataset EGAD00001000428

• BLUEPRINT September 2016, ATAC-seq for bone marrow, on Genome GRCh38

  ATAC-seq data for 4 sample(s) from bone marrow, on Genome GRCh38. 4 run(s), 4 experiment(s), 4 alignment(s). Part of BLUEPRINT (September 2016).

  Dataset EGAD00001002710

• Whole exome sequencing of human and mouse sarcoma samples for personalized therapy

  Whole exome sequencing of human and mouse sarcoma samples for creation of personalized therapy options. Tissues were sequenced directly; no interventions or alterations were made to the tissue samples

  Dataset EGAD00001004885

• Exome sequencing of Non-syndromic Congenital Heart Defects (2019-04-24)

  Exome sequencing of ~1,800 patients with non-syndromic Congenical Heart Defects (CHD) to identify genes enriched for damaging rare variants that increase risk of CHD . This dataset contains all the data available for this study on 2019-04-24.

  Dataset EGAD00001004975

• RNAseq files for Mullighan_GL_reALL RNASEQ2

  RNAseq files for Mullighan_GL_reALL RNASEQ2 paper titled "Mutational landscape and patterns of clonal evolution in relapsed pediatric acute lymphoblastic leukemia"

  Dataset EGAD00001005510

• RNASeq files for Mullighan_GL_reALL RNASEQ1

  RNASeq files for Mullighan_GL_reALL RNASEQ1 paper titled "Mutational landscape and patterns of clonal evolution in relapsed pediatric acute lymphoblastic leukemia"

  Dataset EGAD00001005511

• Reference exome data for a Northern Brazilian population

  Here we provide a catalogue of variants called after sequencing the exomes of 45 babies from the State of Rio Grande do Nord in Brazil. Our data set provides a useful reference point for diagnosis of rare diseases in Brazil.

  Dataset EGAD00001006407

• Exome and Targeted sequencing of GZL

  Tumor and matching normal exomes for 28 GZL cases (n=56) and targeted capture sequencing for 42 GZL cases with 3 matching normals and 2 pooled normals (n=47)

  Dataset EGAD00001006328

• Single cell karyotype sequencing of 7 samples from colorectal cancer (CRC) patients

  Contains data for all cells sequenced for this study. Data is organized as one bam-file per sample. Individual cells can be identified through the CB tag in the bam-files.

  Dataset EGAD00001006438

• Glioblastoma stem cell lines WGS data

  WGS data for 20 Glioblastoma stem cell (GSC) lines and matched blood samples. Fastq files are available. For 10 GSC samples and the 10 matched blood samples the reads are on 3 fastq files per sample

  Dataset EGAD00001006488

• Cram files for study entitled Compound heterozygous variants in LAMC3 in association with posterior periventricular nodular heterotopia

  This dataset contains three cram files for paired end sequencing of a trio, sequenced with Illumina Hiseq 2500

  Dataset EGAD00001006578

• Data for the genome‐wide association study of cutaneous leishmaniasis in Brazil

  DNAs were genotyped on Illumina Infinium HumanCoreExome Beadchips (Illumina Inc., San Diego, CA, USA) with probes for 551,004 single nucleotide variants (SNVs): 282,373 informative across ancestries; 268,631 exome-focused. Human genome build 37 (hg19) was used.

  Dataset EGAD00001006681

• A95646B

  Whole genome sequencing for single cells for library A95646B 507 cells; filetype=bam

  Dataset EGAD00001007306

• PPIL4 is essential for brain angiogenesis and implicated in intracranial aneurysms in humans

  Patients in IA cohort with PPIL4 mutations (Please see Supplementary Table 3 for clinical characteristics of the patients)

  Dataset EGAD00001008106

• RNA-Seq data for manuscript titled: CBL0137 impairs homologous recombination repair and sensitizes high-grade serous ovarian carcinoma to PARP inhibitors Sequencing

  RNA-Seq data for manuscript titled: CBL0137 impairs homologous recombination repair and sensitizes high-grade serous ovarian carcinoma to PARP inhibitors Sequencing

  Dataset EGAD00001009799

• KiCS WGS data for academic use only

  Data from samples that are marked for academic use only

  Dataset EGAD00001009698

• Population Architecture Using Genomics and Epidemiology (PAGE): Epidemiologic Architecture for Genes Linked to Environment (EAGLE) - BioVU Cancer Project

  As part of Population Architecture using Genomics and Epidemiology PAGE study (Phase I), the Epidemiologic Architecture using Genomics and Epidemiology (EAGLE I) project accessed both epidemiologic- and clinic-based collections. The epidemiologic-based collection of EAGLE I included the National Health and Nutritional Examination Surveys (NHANES), ascertained between 1991-1994 (NHANES III), 1999-2002, and 2007-2008. NHANES is a population-based cross-sectional survey now conducted every year in the United States to assess the health status of Americans at the time of ascertainment and to assess trends over the years of survey. Genetic NHANES consists of 19,613 DNA samples linked to thousands of variables including demographics, health and lifestyle variables, physical examination variables, laboratory variables, and exposures. NHANES is diverse with almost one-half of the samples (46.4%) coming from self-reported Mexican Americans and non-Hispanic blacks. In contrast to NHANES, BioVU is a clinic-based collection of >150,000 DNA samples from Vanderbilt University Medical Center linked to de-identified electronic medical records (EMRs). Approximately 12% of BioVU's overall DNA sample collection is from African American, Hispanic, and Asian patients. The overall goals of PAGE I and EAGLE I were broad and several-fold: Replicate genome-wide association study (GWAS)- identified variants in European Americans; Identify population-specific and trans-population genotype-phenotype associations; Identify genetic and environmental modifiers of these associations. NHANES is an excellent resource for the study of quantitative traits associated with common human diseases. However, given that the age range of NHANES spans childhood to late adulthood and not all diseases are surveyed, NHANES is less useful for the study of adult-onset diseases such as major cancers. Therefore, under American Recovery and Reinvestment Act (ARRA) funding, EAGLE as part of PAGE I defined eight major cancers sites for genetic analysis in BioVU, Vanderbilt's biorepository linked to de-identified EMRs. The eight major cancers defined for this study included melanoma, breast, ovarian, prostate, colorectal, lung, endometrial, and Non-Hodgkin's lymphoma (NHL). Cancer cases were defined using a combination of ICD-9 codes and tumor registry entries. Controls include BioVU participants without cancer and encompassing the age and gender distributions of cancer cases. Targeted genotyping of GWAS-identified variants for these diseases (124 SNPs) and ancestry informative markers (128 AIMs) was performed by the Center for Human Genetics Research Vanderbilt DNA Resources Core. After quality control, a total of 116 cancer-associated SNPs and 122 AIMs were available for downstream analyses.

  Study phs000559

• 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

• NIDA Genetic Epidemiology of Opioid Dependence in Bulgaria (GEODB)

  The study was conducted in Bulgaria as a collaboration between the Molecular Medicine Center (Medical University, Sofia), the Bulgarian Addictions Institute (Sofia), the Initiative for Health Foundation (Sofia) and Washington University School of Medicine (Saint Louis, MO, USA). This is a case-population control study. Participants (N=2,499 interviews; N=1,995 with GWAS data) in our study are primarily active heroin users (78%) aged 18 and over, with a median 8.9 years of daily heroin use (minimum of 12 months). Virtually all participants (99.1%) qualified for a lifetime DSM-IV diagnosis of dependence, symptom endorsement probabilities ranging from 77% to 99.3%; and 61% endorsing all symptoms. 85% were heroin injectors and 97% reported using (or having used) heroin daily. Anonymized, population-representative control samples (no phenotypic data, N = 1,062 with GWAS data) were drawn from the MMC repository. Unlike other studies, where samples are clinically ascertained (e.g., from ORT programs), our participants are primarily untreated. PTSD, major depression, suicidality, mania, alcohol, nicotine and substance dependence (separating heroin and opioids) were assessed using the MAGIC (Todd et al., 2003, PMID: 14627881), a broad semi-structured psychiatric assessment tool with good reliability and prospective stability. The onset of (any) illicit drug use in this sample is not particularly remarkable (age~16), but the age of onset of heroin use is much lower than in the USA: 25% by age 16; 50% by age 19, with no differences between ethnicities or gender. This is consistent with findings from other studies in Bulgaria. In contrast, in 2008, SAMHSA estimated an average age of 23.4 for new heroin initiates in the USA. Further, progression from first use to regular use appears to be more rapid, with the majority of participants reporting onset of dependence problems within one year of initiation. Another unique aspect of this study population are the comparatively low levels of polysubstance use (outside of nicotine and alcohol). This early-use, fast-transition and monosubstance heroin pattern stands in sharp contrast to what is reported in many studies in the Western World. In our study, 47% of Ethnic Bulgarians and 66% of Roma used only opioids, even years after the onset of drug use. 15.4% of Ethnic Bulgarians and 66.2% of Roma reported heroin as the very first illicit drug used. Lifetime, 12.5% of Ethnic Bulgarians and 52.8% of Roma report never having used another illicit drug other than heroin more than five times. While both Roma and Ethnic Bulgarians certainly experiment with other drugs, abuse/dependence rates for these drugs are low in Ethnic Bulgarians (from 8.4% for inhalants to 28% for stimulants) and in the Roma population (from 1.3% for hallucinogens to 11.2% for stimulants).

  Study phs001804

• Pathogenesis and Immunity in Endemic Burkitt Lymphoma

  This genomic landscape of Burkitt lymphoma represents a multimodal sequencing of tumors and control tissues and individuals to better understand the etiology, and molecular pathogenesis of Burkitt lymphoma including the roles of the associated Plasmodium falciparum malaria and EBV infections. Comprehensive sequencing set includes genomic, transcriptomic, and epigenomic datasets in concert with variable clinical phenotypes and outcome information such as anatomical presentation site, in-hospital survival rates, and EBV genome type. This deposit includes additional small RNA-seq data from endemic BL (eBL) tumors and RNA-sequencing data from sorted NK cell subsets from the peripheral blood of eBL patients. The additional small RNA-seq data are from tumor specimens from 17 eBL cases, of the previously deposited polyA RNA-seq data from 28 eBL cases (phs1282.V1). The additional RNAseq data from Fluorescence-activated cell sorting (FACS) of NK cell subsets, isolated from the peripheral blood of 7 eBL cases. The additional RNAseq data are from eBL tumor samples of 5 eBL cases, BL cell lines established from these tumor samples and also NSG mouse tumors created by implanting the established BL cell lines into NSG mice. The dataset also includes RNAseq data of the NSG mouse tumors after treatment with Rituximab (RIT) and Phosphate buffered saline (PBS). The fifth batch of data includes mRNA-seq from different passages of a single eBL cell line that have variable EBV phenotypes (low passage with lytic EBV and high passage with latent EBV). This batch also includes lytic and latent lymphoblastoid cell line (LCL) clones from a single blood donor generated through infection with the strain of EBV isolated from the aforementioned eBL cell line. For initial transcriptome analysis see: Kaymaz et al. "Comprehensive Transcriptome and Mutational Profiling of Endemic Burkitt Lymphoma Reveals EBV Type-specific Difference." Molecular Cancer Research, 2017, PMID: 28465297. For initial microRNA analysis see: Oduor et al. "Human and Epstein-Barr Virus miRNA Profiling as Predictive Biomarkers for Endemic Burkitt Lymphoma." Frontiers in Microbiology 8, 2017, PMID: 28400759. For initial NK subsets RNA expression analysis see: Forconi et al. "A new hope for CD56negCD16pos NK cells as unconventional cytotoxic mediators: an adaptation to chronic diseases." Frontiers in Cellular and Infection Microbiology, 2020, PMID: 32373555.For initial eBL cell line and mouse avatar RNA expression analysis see: Saikumar et al. "Endemic Burkitt lymphoma avatar mouse models for exploring inter-patient tumor variation and testing targeted therapies." Life Science Alliance, 2023, PMID: 36878637. For general overview of cohort study see: Buckle et al. "Factors influencing survival among Kenyan children diagnosed with endemic Burkitt lymphoma between 2003 and 2011: A historical cohort study." Int J Cancer. 15:1231, 2016, PMID: 27136063.

  Study phs001282

• Sickle Cell Disease Natural History Data Resource (SCD NHDR)

  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. BackgroundThe NHLBI initiated the Sickle Cell Disease Natural History Data Resource (SCD NHDR) to leverage existing SCD studies to build a data resource to provide phenotypic data on contemporaneous control cohorts for gene therapy trials and other studies. The potential uses of the NHDR are broad, including providing matched controls for clinical trials, observing a cohort of untreated patients as part of a natural history study, or conducting comparative effectiveness research on a subpopulation with selected co-morbidities or drug treatment profiles. The SCD NHDR includes two multi-center SCD longitudinal cohort studies with extensive annual clinical data and patient surveys, the Sickle Cell Disease Implementation Consortium (SCDIC-I and SCDIC-II) and the Globin Research Network for Data and Discovery (GRNDaD). The SCDIC-I, initiated in 2017 and concluded in June 2022, enrolled over 2400 patients with SCD from 8 US treatment centers. The SCDIC-II reconsented 1220 of the SCDIC-I patients as well as enrolling an additional 450 new patients from the same 8 centers between September 2022 and October 2023, for a total of 1670 in SCDIC-II. Only two of the many GRNDaD centers are submitting data to the SCD NHDR for deposit into BDC (approximately 122 patients). Data submissions from the SCD NHDR to BDC will be annually starting in early 2024. Description of the Study Data All data obtained for the SCD NHDR are considered common data elements (CDEs) important for studies of SCD. These CDEs were identified from several workgroups of SCD experts convened under the NHLBI's Cure-SC Initiative (https://curesickle.org/cde-catalog). The following types of data elements were abstracted from the medical record – variables that describe the SCD diagnosis, SCD co-morbidities and other clinical complications, elements from the physical examination, medications and transfusions, insurance type, laboratory measurements, healthcare utilization, history of SCD-related procedures, and measurements from cardiac procedures. The following types of data were self-reported on the patient survey – demographics, diagnosis information, pain experience, social and mental health information, other patient reported outcome domains (e.g., sleep, fatigue), and alcohol and smoking history. Standard PRO measures were used from ASCQ-Me, PROMIS and Neuro-QoL. Patient surveys were completed approximately annually. Under SCDIC-I, two medical record abstractions and labs were reported at enrollment and approximately 3 years later. Under SCDIC-II and GRNDaD, all data are collected annually. Patients may refuse to complete the survey.

  Study phs003529

• 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

• 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

• APCDR Uganda GWAS: Genome-wide sequence variation and susceptibility loci for cardiometabolic traits in a sub-Saharan African population (UGWAS component)

  Genomic studies in African populations provide unique opportunities to understand disease aetiology, human genetic diversity and population history in a regional and a global context. To leverage the relative benefits of different strategies, we undertook a combined approach of genotyping and whole-genome sequencing (WGS) in a population-based study of 6,400 individuals from a geographically defined rural community in South-West Uganda. We present data from 4,778 individuals with genotypes for ~2.2 million SNPs from the Uganda GWAS resource (UGWAS), and sequence data on up to 1,978 individuals spanning 41.5M SNPs and 4.5M indels (UG2G); 343 individuals overlap between the two datasets. We highlight the value of the largest sequence panel from Africa to date as a global resource for variant discovery, imputation and understanding the mutational spectrum and its clinical relevance in African populations. Alongside phenotype data, we provide a rich new genomic resource for researchers in Africa and globally.

  Study EGAS00001001558

• Sequencing of an organoid biobank for childhood kidney cancers that captures disease and tissue heterogeneity.

  Kidney tumors are among the most common solid tumors in children, comprising several distinct subtypes differing in many aspects, including cell-of-origin, genetics, and pathology. Pre-clinical cell models capturing the disease heterogeneity are currently lacking. Here, we describe the first pediatric cancer organoid biobank. It contains tumor and matching normal organoids from over 50 children with different subtypes of kidney cancer, including Wilms tumors, malignant rhabdoid tumors, renal cell carcinomas, and congenital mesoblastic nephromas. The malignant rhabdoid tumor organoids represent the first organoid model for tumors of non-epithelial origin. The tumor organoids retain key properties of native tumors, useful for revealing patient specific drug vulnerabilities. We further demonstrate that organoid cultures derived from Wilms tumors consist of multiple different cell types, including epithelial, stromal and blastemal-like. Our organoid biobank captures the cellular heterogeneity of pediatric kidney tumors, providing a representative collection of well-characterized models for basic cancer research, drug-screening, and personalized medicine.

  Study EGAS00001003853

• Reference exome data for Australian Aboriginal populations to support health-based research

  Whole exome sequencing (WES) is a popular and successful technology which is widely used in both research and clinical settings. However, there is a paucity of reference data for Aboriginal Australians to underpin the translation of health-based genomic research. Here we provide a catalogue of variants called after sequencing the exomes of 50 Aboriginal individuals from the Northern Territory (NT) of Australia and compare these to 72 previously published exomes from a Western Australian (WA) population of Martu origin. Sequence data for both NT and WA samples were processed using an ‘intersect-then-combine’ (ITC) approach, using GATK and SAMtools to call variants. A total of 289,829 variants were identified in at least one individual in the NT cohort and 248,374 variants in at least one individual in the WA cohort. Of these, 166,719 variants were present in both cohorts, whilst 123,110 variants were private to the NT cohort and 81,655 were private to the WA cohort. Our data set provides a useful reference point for genomic studies on Aboriginal Australians.

  Study EGAS00001003745

• The ITCC-P4 PDX platform of pediatric cancers for preclinical testing

  Cancer is the leading cause of disease-related deaths among children in high-income countries. Tumor heterogeneity and lack of mechanism-of-action-based therapeutic options are key challenges to overcome in order to improve pediatric cancer patients’ survival. Here, we report the EU-IMI-2 funded public-private partnership “ITCC-Pediatric Preclinical Proof-of-Concept Platform” (ITCC-P4), which has built a large repertoire of patient-derived xenograft (PDX) models, representing all major solid pediatric cancer types, for in vivo drug testing. Three-hundred-fifty-three PDX models from diagnostic and relapsed pediatric cancers have been established and molecularly characterized, together with matched germline/tumor samples. As proof-of-concept, we present in vivo drug screening data in neuroblastoma and rhabdomyosarcoma models. PDX data, accessible at http://r2platform.com/itcc-p4, allow the selection of models based on oncogenic drivers and/or potential biomarkers for preclinical testing. Operated by a non-profit entity (www.itccp4.com), this sustainable platform aids academic and industrial researchers in developing and prioritizing innovative therapies for pediatric cancer.

  Study EGAS00001008330

• CIDR: NINDS High Throughput Genotyping Resource Access for Structural Hindbrain Disorders

  Recessive forms for many structural brain disorders (SBDs), including cerebellar hypoplasia (CBH), corpus callosum hypoplasia (CCH), cobblestone lissencephaly (COB), classical lissencephaly (LIS), microcephaly (MIC), and pontocerebellar hypoplasia (PCH), have been described. Patients usually present to the clinic with neurodevelopmental disorders (NDDs), including autism, epilepsy, intellectual disability, and cerebral palsy. Subsequent brain MRI usually leads to the diagnosis of an SBD. While a handful of genes that cause these diseases have been identified, most patients do not have mutations in these genes, suggesting that there is still much work to be done in identifying the genetic causes of these disorders. We have recruited a cohort of families with inherited SBDs, most from families with documented parental consanguinity with more than one affected member. Current approaches utilizing exome sequencing of SBD affected members still leave most cases unsolved, in part because there are too many possible variants to consider. Linkage analysis will allow us to extract all the useful genetic information from the family, taking advantage of each genetically informative meiosis in each family. We propose to analyze up to 100 SBD families for 500 samples per year for 4 years, through the CIDR SNP Genotyping Service, in parallel with already-funded exome sequencing of affects, leveraging off an already funded Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) effort to identify new causes of SBDs. Understanding the pathogenetic mechanisms underlying recessive pediatric SBDs will lead to better diagnostic and therapeutic opportunities for patients, and may shed light on complex NDDs. Ultimately, this work will identify new genetic disease loci to provide insight into pediatric SBD, with the hope to mitigate the impact of SBD worldwide.Public Health Significance: NDDs affect approximately 4-­6% of the general population, most notably children, and are evident in disorders, such as intellectual disability, epilepsy, and autism, with estimated yearly costs of $51.2B for intellectual disability, $11.5B for cerebral palsy, and $2.5B for visual impairment, making up >5% of total health care costs. Recessive NDDs impose enormous personal, social, and economic costs because of the early onset and the lifetime of medical dependence that often ensues. Genetic testing is critical to the diagnosis and treatment of these diseases. Many of the diseases under study have no treatment or cure. Structural brain diseases (SBDs) underlie a large percentage of these diseases, and are defined loosely as any condition in which defects in the structure or volume of a key brain component. SBDs are divided into major categories based upon the anatomical brain location. The major types involve the anatomical structures of the developing neural tube, forebrain, hindbrain, cerebral cortex, and those affecting overall brain size. For the purposes of this application, we have excluded SBDs resulting from environmental causes (i.e. prematurity-­induced), those below the brainstem, or those unlikely to be due to a germline or de novo mutation (i.e. focal SBDs or simplex disease).

  Study phs002621

• Submitting array based metadata

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

  Documentation submission/metadata/submission/array

• Human Companion Data for 'A Transcriptomic Atlas of Mouse Cerebellar Cortex Reveals Novel Cell Types'

  In this study, we used single-nucleus RNA-seq to comprehensively define the cell specialization of the mouse cerebellar cortex and vermis. We focused in particular on two interneuron populations—the unipolar brush cells (UBCs) and the molecular layer interneurons (MLIs), identifying continuous variation within the former population, and a new discrete axis of variation in the latter. Human postmortem tissue was used to demonstrate that these specializations are evolutionarily conserved in primates. Together, these findings provide the first comprehensive cellular atlas of the cerebellar cortex, and outline a methodological and conceptual framework for the integration of molecular, morphological, and physiological ontologies for defining brain cell types.

  Study phs002414

• Transcriptomic Profiling of Peripheral Immune Cells in a Trial of Ruxolitinib with Nivolumab for Anti-PD1 Non-Responsive cHL

  Treatment with checkpoint inhibitors is effective in multiple cancer types. However, even in cancers with high response rates such as Hodgkin lymphoma, some patients fail to respond or relapse following an initial response to checkpoint inhibitors. In this study, we examined patients enrolled in a clinical trial of ruxolitinib with nivolumab in relapsed or refractory Hodgkin lymphoma (NCT03681561). Patients relapsed or refractory to anti-PD1 therapy received ruxolitinib for 8 days followed by nivolumab every 4 weeks and continued ruxolitinib twice daily. For this transcriptomic study, peripheral blood mononuclear cells (PBMCs) were collected at baseline and 8 days of ruxolitinib therapy and analyzed by single-cell and bulk RNA-sequencing.

  Study phs003601

• University of Zurich (UZH) - Snedeker lab

  Authorised Personnel: The individuals at the User Institution to whom University of Zurich (UZH) grants access to the Data. This includes the User, the individuals listed in Appendix II and any other individuals for whom the User Institution subsequently requests access to the Data. Details of the initial Authorised Personnel are set out in Appendix II. Data: The managed access datasets to which the User Institution has requested access. Data Producers: UZH and the collaborators listed in Appendix I responsible for the development, organisation, and oversight of these Data. External Collaborator: A collaborator of the User, working for an institution other than the User Institution. Project: The project for which the User Institution has requested access to these Data. A description of the Project is set out in Appendix II. Publications: Includes, without limitation, articles published in print journals, electronic journals, reviews, books, posters and other written and verbal presentations of research. Research Participant: An individual whose data form part of these Data. Research Purposes: Shall mean research that is seeking to advance the understanding of genetics and genomics, including the treatment of disorders, and work on statistical methods that may be applied to such research. User: The principal investigator for the Project. User Institution(s): The Institution that has requested access to the Data. UZH: University of Zurich, Raemistrasse 71, 8001 Zurich, Switzerland

  Dac EGAC50000000817

• Ultra-deep Error-corrected Sequencing of Peripheral Blood for Clonal Hematopoiesis in Patients Undergoing AAA Surgery

  Patients undergoing elective abdominal aortic aneurysm (AAA) surgery were enrolled to investigate the prevalence and characteristics of clonal hematopoiesis. Peripheral blood was collected at the time of surgical admission using PAXgene DNA tubes. Genomic DNA was extracted and subjected to ultra-deep error-corrected targeted sequencing using a custom gene panel covering recurrent clonal hematopoiesis-associated genes. Sequencing libraries were prepared following established protocols and sequenced on an Illumina NovaSeq X system. The resulting dataset includes paired-end FASTQ files for each participant, enabling high-sensitivity detection of somatic variants down to low variant allele frequencies. This study provides a comprehensive sequencing resource for evaluating clonal hematopoiesis in AAA patients.

  Study JGAS000864

• A Combined Omics and Tissue Biobank for Paediatric Cancers

  The Swedish Childhood Tumour Biobank (BTB) is a nonprofit national infrastructure for samples and genomic data collection from pediatric patients diagnosed with central nervous system and other solid tumours in Sweden. Fresh frozen tumour tissue and patient’s matched blood are collected from affected patients and genomic data is continuously generated and made available for approved medical research projects. We expect that the accessibility of biological samples and/or data to the research community will contribute to further advances in the understanding of tumour biology, which in turn will impact many aspects of clinical care of children with cancer, by refining diagnosis and/or identifying oncogenic drivers and therapeutic targets.

  Study EGAS50000000209

• 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

• 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.

  Study phs003578

• Lipid Research Clinics - Prevalence Study (LRC-PS-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 LRC Program began in 1971 under the sponsorship of the National Heart, Lung, and Blood Institute, National Institutes of Health. Part of this program was the Lipid Research Clinics Prevalence Study, a standardized series of cross-sectional surveys of various North American populations designed to determine the prevalence of dyslipidemias and to describe the distributions of lipids and lipoproteins in major ethnic and social groups. In addition to contributing to the aggregate analysis, each independent population-based study was designed with capabilities for separate analyses of lipid and lipoprotein distributions. The participating populations were not selected to be a probability sample representative of the North American population per se, but by virtue of their size and economic and geographical diversity, they provide a useful cross-sectional group. The Family Study was the third phase of the Lipids Research Clinics North American Population Studies. The Family Study was designed to obtain knowledge of the distribution of lipids and lipoproteins among family members and of the association of familial and genetic attributes to dyslipoproteinemias. Background: An association between serum cholesterol and coronary heart disease is well established. A system was developed for classifying hyperlipoproteinemias into six types of patterns, as a basis for characterizing lipoprotein disorders. The Lipid Research Clinics was thus created to improve the detection of and clinical management of hyperlipoproteinemias. The three primary objectives were: 1) to acquire data on the prevalence of different types of hyperlipoproteinemia in various age and ethnic groups, with special emphasis on the nature and frequency of genetic forms; 2) to collect reliable data on the prevalence and incidence of atherosclerosis in different types of hyperlipoproteinemia; and 3) to conduct an intervention trial to determine if lowering plasma lipid levels would reduce the risk of CHD. Participants: There were 60,495 participants. Conclusions: Data from this study confirm findings from earlier studies in developed countries, showing age-related differences in plasma lipid levels. However, for overall distributions, the LRC data showed slightly lower cholesterol and markedly higher trigylceride values than those previously reported for North America. Some variation in plasma lipid values was evident among the clinic populations. The large number of participants within most subgroups permitted a variety of analytic and comparative studies. For example, data from the large pediatric population revealed a drop in plasma cholesterol levels in adolescent males and females. Males aged 20-50 years had higher cholesterol levels than females in the same age group, and higher trigylceride levels between ages 20-70 years. Numbers were also significient for meaningful comparisons between lipid distributions of females who were taking sex hormones and those who were not; in females taking sex hormones, cholesterol and triglyceride levels were higher for subjects younger than 45 years, but slightly lower after age 45, than lipid levels in females not taking hormones (Circulation 1979; 60(2):427-439).

  Study phs003995

• Whole Transcriptome Sequencing of Human Tumor Cells and Hematopoietic Stem and Progenitor Cells During Aging and Bone Marrow Disorders

  Whole transcriptome sequencing analyses were performed on human hematopoietic stem and progenitor cells during aging and bone marrow disorders. For this purpose, normal and diseased bone marrow or peripheral blood-derived mononuclear cells were FACS-purified into hematopoietic stem (CD34+CD38-Lin-) or hematopoietic progenitor (CD34+CD38+Lin-) populations. Primary human samples from healthy individuals were used along with comparative analyses in some pre-malignant and malignant conditions, including myelodysplastic syndrome (MDS), acute myeloid leukemia (AML) and plasma cell dyscrasias (e.g., multiple myeloma). Studies were also performed on total mononuclear cell fractions from patients with newly-diagnosed or relapsed multiple myeloma, or plasma cell leukemia. Briefly, RNA was extracted from all samples and quality was assessed using a Bioanalyzer instrument. Only samples with RNA integrity (RIN) values >7 were subjected to library preparation for RNA-sequencing. The SMARTer v4 cDNA amplification kit was used along with library construction by the NEB Ultra DNA kit for Illumina. Paired-end sequencing was performed on Illumina NextSeq instruments (Scripps Next Generation Sequencing Core), with all samples run over two separate flow-cells (to generate additional sequencing reads); >95M reads were generated in total for each sample. The RNA-sequencing data are available as FASTQ files.

  Study phs002291

• The National Heart, Lung, and Blood Institute (NHLBI)-funded Next Generation Genetic Association Studies (NextGen) Consortium: Phenotyping Lipid traits in iPS derived hepatocytes Study (PhLiPS Study)

  The goal of the PhLiPS study is to create a library of induced pluripotent stem cell (iPSC) lines and iPSC-derived hepatocytes of diverse genotypes for use in metabolic profiling and interrogating lipid phenotypes. These cell lines were created as a part of the Next Generation Genetic Association Studies (Next Gen) Program, which was a five-year, $80 million program to investigate functional genetic variation in humans by assessing cellular profiles that are surrogates for disease phenotypes. To achieve this, researchers from multiple institutions across the U.S. were awarded grants to derive iPSC lines from more than 1,500 individuals representing various conditions as well as healthy controls for use in functional genomic ("disease in a dish") research. This extensive panel includes a diverse set of age, gender, and ethnic backgrounds, and therefore will be an invaluable tool for evaluations across demographics. Further enhancing the utility of these cell lines are data sets such as phenotyping, GWAS, genome sequencing, gene expression and -omics analyses (e.g., lipidomic, proteomic, methylomic) that can be matched to the cell lines. The PhLiPS Study focuses on individuals free of cardiovascular disease or with lipoprotein metabolism disorders in the community served by the Hospital of the University of Pennsylvania.

  Study phs001341

• CPTAC: Proteogenomic Studies of Ovarian Tumor Responses to Agents Targeting the DNA Damage Response

  There is a critical unmet need for predictors of chemo-refractory High-grade serous ovarian cancer (HGSOC). Despite over 3 decades of research on platinum responses in cancer, no predictive biomarker has been translated into clinical use. Predictors of refractory disease could spare these patients the unnecessary toxicity of a platinum-based regimen and provide a means to triage these patients to clinical trials to identify effective therapies for refractory disease. This study has approached the development of a predictor in a novel way enabling detection of a multi-analyte panel biomarker capable of detecting a subset of refractory HGSOCs with high specificity and implicating potential treatment vulnerabilities for refractory disease. We reported the first proteogenomic analysis of platinum refractoriness in HGSOC and made the novel dataset available as a resource to the community, including a ProTrack portal: http://ptrc.cptac-data-view.org/. Detailed proteogenomic profiles were generated for this study from a retrospective cohort of 242 treatment-naive HGSOCs with enriched representation of "exceptional non-responders" (i.e., patients with chemo-refractory disease). Whole genome sequence (WGS) and RNA seq FASTQ files are available through dbGaP. Proteomic data are available through the NCI Proteomic Data Commons (PDC): https://pdc.cancer.gov/pdc/.

  Study phs003152

• Genetic Epidemiology of Lung Cancer Consortium GWAS of Familial Lung Cancer

  Familial lung cancer cases were collected by the Genetic Epidemiology of Lung Cancer Consortium (GELCC) recruitment sites: University of Cincinnati, Karmanos Cancer Institute at Wayne State University, Louisiana State University Health Sciences Center-New Orleans, Mayo Clinic, and Medical College of Ohio. Familial cases for this study came from three sources: 1) one case each from families (at least 3 affected lung cancer cases in the family) included in the linkage analysis; 2) one case from linkage-eligible families (at least 3 affected lung cancer cases in the family) but where there were insufficient biospecimens available to make them informative for linkage analysis; and 3) one case from families not eligible for the linkage study with a family history of at least one first or second degree relative affected with lung cancer. Unrelated controls were selected from 1) among the spouses of family members, thus matching on socio-economic status (SES) and ethnicity (typically) of the cases or 2) from case-control studies of lung cancer conducted in the same location in which the linkage families were collected and matched on age, sex, and race. All cases and controls self-reported as European American. Biospecimens used for normal DNA extraction included blood, saliva or mouthwash. Some samples underwent whole genome amplication.

  Study phs000629

• Longitudinal Study of Bone and Endocrine Disease in Children with MPS I, II, and VI: A Multicenter Study of the Lysosomal Disease Network

  Mucopolysaccharidoses (MPS) I, II, and VI are a group of rare, lysosomal storage diseases in which certain enzymes are missing that are normally required for breaking down specific complex carbohydrates. Deficiencies in these enzymes result in the build-up of toxic substances in various tissues and cause damage to the body's organs. In the past, children diagnosed with MPS I, II, or VI frequently died in early to late childhood. Recent advances in treatments including hematopoietic cell transplantation (HCT) and enzyme replacement therapy (ERT) have been helping children with these disorders live into adulthood. The long term-course of the disease is now unknown. Unfortunately, severe skeletal disease persists for most individuals despite current therapies. This is a longitudinal study of 50 children and adults with MPS I, II, and VI. Study participants will be evaluated every year for up to 3 years. The purpose of this study is to document the progression of skeletal disease and identify biomarkers that either predict disease severity or could be used as therapeutic targets. This information is needed to: determine long-term benefits and limitations of current treatments including HCT or ERT direct development of further treatment options improve clinical care for children affected by the disorder

  Study phs001576

• Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA)

  The OPPERA project has two sub-studies investigating effects of genetic, physiological, psychological and clinical factors on the risk of developing painful temporomandibular disorder (TMD). OPPERA I baseline case-control study of chronic TMJD (dbGaP study accession phs000762). This sub-study uses a case-control study design to investigate characteristics associated with chronic TMD. The parent OPPERA phenotype file contains an indicator variable (PHASE1CASECONTROL) identifying participants in this sub-study. Data users should use the relevant case-classification variable (CHRNONICTMDCASE) and apply statistical methods appropriate for case-control studies when analyzing TMJD in this sub-study. OPPERA I prospective cohort for first onset of TMJD (dbGaP study accession phs000850). This sub-study uses a prospective cohort study design to investigate characteristics that predict risk of developing first-onset TMD. The parent OPPERA phenotype file contains an indicator variable (PROSPECTIVECOHORT) identifying participants in this sub-study. Data users should use the relevant case-classification variable (INCIDENTCASE) and the relevant time-in-study variable (FOLLOWUPYEARS) when analyzing TMJD in this sub-study. It is recommended that data users apply statistical methods appropriate for cohort studies of a dynamic population (for example, Cox regression models, where INCIDENTCASE is the censoring variable and FOLLOWUPYEARS is time in study).

  Study phs000761

• PIEZO1 Loss of Function Compound Heterozygous Mutation in the Rare Congenital Human Disorder Prune Belly Syndrome

  Prune Belly Syndrome (PBS) is a rare congenital myopathy that is incompletely understood genetically. In this IRB approved study, one white Non-Hispanic 35 year-old male PBS proband was prospectively enrolled and phenotyped. Blood lymphocyte DNA from the proband underwent paired-end Whole Exome Sequencing using the Illumina SureSelect kit and HiSeq2500 sequencer with data pipeline analysis. We aligned the subject's sequence against the GRCh38/hg19 assembly and variant calling was performed using data training sets from the 1000 Genomes Project, Omni 2.5 M SNP microarray, and HapMap phase 3.3. These variants were filtered for rare minor allele frequency ( 0.9, CADD score > 20, GERP > 4, MutationTaster = “DC”, Mutation Assessor = “M” or “NE”, and Vest > 3.00. Rare and novel candidate functional variants were screened in the ClinVar database for prior association with smooth muscle myopathy (SMM). Only variants that met these criteria were included for further analysis. Confirmed variants had inheritance analysis performed on available samples. Our WES analysis identified novel compound heterozygous variants in the PIEZO1 gene. We conducted an extensive functional analysis of the PIEZO1 PBS variants that revealed loss-of-function characteristics. Thus, PIEZO1 mutations may be causal for PBS. Our WES data will be available through dbGaP.

  Study phs003475

• Tumor detection by analysis of both symmetric- and hemi-methylation of plasma cell-free DNA

  Aberrant DNA methylation plays a critical role in tumorigenesis. While DNA methylation has been used for cancer detection and classification, DNA hemi-methylation, a novel epigenetic mark has not been analyzed extensively in cancer epigenomes. Here we report a strand-specific (ss) sequencing method (MeDIP-Seq) for plasma cell free (cf) DNA (sscf-MeDIP-Seq), which can analyze both symmetrically methylated DNA regions (DMRs) as well as hemi-methylated regions (DHMRs). Using the sscf-MeDIP-Seq method, we analyzed plasma cfDNA methylomes of 271 samples from subjects with liver cancer and brain cancer and from individuals without cancer (controls). Among them, 215 samples were chosen as the discovery cohort for the identification of DMRs and DHMRs specific in each subject group and for the training of machine learning models of multi-cancer detection (MCD) using DMRs, DHMRs and DMRs+DHMRs as inputs. These models were then used to predict the 56 samples in the validation cohort. We found that models trained with DMRs+DHMRs as inputs in general outperformed models trained with DMRs or DHMRs alone, with AUROC being 0.971, 0.981, and 0.99 in predicting control, liver and brain cancer samples in the validation cohort, respectively, by the DMR+DHMR-trained models.

  Study phs003462

• Single-Cell Genomic and Transcriptomic Analysis of the Aging Human Brain

  We studied human brain aging using single-cell whole genome sequencing and single-nucleus RNA sequencing of the pre-frontal cortex from 19 donors ranging in age from 0.4 years to 104 years. All donors were free of neuropathology and none received a clinical neurological diagnosis prior to death. snRNA-seq libraries were prepared in batches that always included old and young donors to mitigate batch effects in the preparations. Neurons for single-cell WGS were collected from the same tissue region as the snRNA-seq for each donor but represent independent cells. Multiplexed error-robust fluorescent in-situ hybridization (MERFISH) data was also collected from pre-frontal cortex samples of a subset of the 19 donors. Transcriptomic results identified infant-specific cell clusters, and common-down regulation of cell-essential genes involved in ribosomes, transport, homeostasis and metabolism during aging, across all cell types. scWGS identified two-age associated mutational signatures, as well as gene-length and expression-level dependent somatic mutation rates in neurons. Raw sequencing data in fastq format for single-nucleus RNA-seq and single-cell DNA-seq, as well as cell and gene count matrix, transcript metadata, and cell metadata for MERFISH will be available in dbGaP.

  Study phs003445

• BHD-associated kidney cancer

  Background Birt-Hogg-Dub? (BHD) syndrome is a hereditary kidney cancer syndrome, which predisposes patients to develop kidney cancer, cutaneous fibrofolliculomas and pulmonary cysts. The responsible gene, FLCN is a tumor suppressor for kidney cancer which plays an important role for energy homeostasis through the regulation of mitochondrial oxidative metabolism. However, how FLCN-deficiency leads to renal tumorigenesis is unclear. Results Whole-exome sequencing analysis of BHD-associated kidney cancer revealed that copy number variations (CNV) of BHD-associated kidney cancer are considerably different from those already reported in sporadic cases. Somatic mutation analysis revealed BHD-associated kidney cancer has quite few common mutations in specific genes; however, chromatin remodeling genes in total were frequently mutated in BHD-associated kidney cancer (17/29 tumors, 59%). Metabolite analysis revealed that glycolysis, pentose phosphate pathway and lipid metabolism were upregulated whereas the rate of beta oxydation and TCA cycle is decreased in BHD-associated kidney cancer compared to normal adjacent kidney. Conclusions BHD-associated kidney cancer displays a unique molecular characteristics which is totally different from that of sporadic kidney cancer, providing the mechanistic insight into tumorigenesis under FLCN deficiency as well as the foundation for the development of novel therapeutics for kidney cancer.

  Study JGAS000115

• Assessing gene expression profiling from FFPE Patient Samples: A Comparison of Two Library Preparation Approaches and Recommendations

  Next-Generation Sequencing (NGS) has transformed cancer research and clinical practice, with Whole Exome Sequencing (WES) driving advances in mutational profiling and personalized oncology. Yet, transcriptomic signatures remain essential for understanding disease mechanisms, including therapy resistance pathways. RNA sequencing (RNA-seq), however, faces unique challenges when dealing with low-input or degraded RNA, as often found in archival formalin-fixed paraffin-embedded (FFPE) tissues. Although previous studies have compared library preparation protocols, rapidly evolving technologies call for updated evaluations. Here, we present a direct comparison of two FFPE-compatible stranded RNA-seq library preparation kits: TaKaRa™ SMARTer® Stranded Total RNA-Seq Kit v2 (Kit A) and Illumina Stranded Total RNA Prep Ligation with Ribo-Zero Plus (Kit B). Both kits generated high-quality RNA-seq data, yet important differences emerged. Notably, Kit A achieved comparable gene expression quantification to Kit B while requiring 20-fold less RNA input, a crucial advantage for limited samples, albeit with increased sequencing depth. We critically discuss these results in relation to RNA availability, technical performance, cost-effectiveness, processing time, and automation potential, offering practical guidance for selecting optimal RNA-seq strategies in clinical and translational research settings

  Study EGAS50000001066

• IMPRESS: Improved methylation profiling using restriction enzymes and smMIP sequencing, combined with a new biomarker panel, creating a multi-cancer detection assay

  Background Despite the worldwide progress in cancer diagnostics, more sensitive diagnostic biomarkers are needed. The methylome has been extensively investigated in the last decades, but a low-cost, bisulfite-free detection method for multiplex analysis is still lacking. Methods We developed a methylation detection technique called IMPRESS, which combines methylation-sensitive restriction enzymes and single-molecule Molecular Inversion Probes. We used this technique for the development of a multi-cancer detection assay for eight of the most lethal cancer types worldwide. We selected 1791 CpG sites that can distinguish tumor from normal tissue based on DNA methylation. These sites were analysed with IMPRESS in 35 blood, 111 tumor and 114 normal samples. Finally, a classifier model was built. Results We present the successful development of IMPRESS and validated it with ddPCR. The final classifier model discriminating tumor from normal samples was built with 358 CpG target sites and reached a sensitivity of 0.95 and a specificity of 0.91. Moreover, we provide data that highlight IMPRESS’s potential for liquid biopsies. Conclusions We successfully created an innovative DNA methylation detection technique. By combining this method with a new multi-cancer biomarker panel, we developed a sensitive and specific multi-cancer assay, with potential use in liquid biopsies.

  Study EGAS50000000624

• Whole-Genome sequencing of hepatocellular carcinomas

  The French ICGC project on liver tumors is coordinated by Pr Jessica Zucman-Rossi and funded by Inca (French Institute for Cancer). The aim of the present project is to identify the catalog of somatic and germline mutations in liver tumors using whole genome and whole exome sequencing together with CGH-SNP, methylome and transcriptomic profiling. For this purpose, a series of 500 liver tumors are collected through the French National Liver Collection and these samples will be analyzed using the different omics technologies. The data will be deposited in the ICGC and EGA database to be publically available for the scientific community. Hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers, and is a major health problem. It is the 3rd cause of cancer-related mortality. Advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. Thus, the development of next-generation sequencing technologies has made it possible to generate more comprehensive catalogues of somatic alteration events (single nucleotide substitutions, structural variations, and epigenetic changes) in liver cancer genome than ever before. The dataset will include 50 whole genome sequencing tumor/germline pairs, of which 6 are deposited in February 2014.

  Study EGAS00001000706

• WGS of 32 paired SRCC samples

  Signet-ring cell carcinoma (SRCC) has specific epidemiology and oncogenesis in gastric cancer, however with no systematical investigation for prognostic genomic features. We systematically investigated the clinical characteristics of 1,868 Chinese gastric cancer patients, and found that signet-ring cell content was significantly related to multiple clinical characteristics and treatment outcomes. Next, we performed whole genome sequencing for 32 SRCC patients with > 80% signet-ring cells in tumor, and identified frequent5 CLDN18-ARHGAP26/6 fusion (25%). With additional 797 patients for validation, we notice that prevalence of CLDN18-ARHGAP26/6 fusion is positively associated with signet-ring cell content (P = 4.1 x 10 -9 ), younger age at diagnosis (P = 4.2 x 10 -10 ), female/male ratio (P = 1.7 x 10 -9 ), and advanced TNM stage (P = 1.7 x 10 -5 ). Importantly, patients with CLDN18-ARHGAP26/6 fusion had a worse survival outcomes (P = 0.03), and got no benefit0 from platinum/fluoropyrimidines based chemotherapy (P = 0.92) compared to fusion-free patients (P = 0.001), which is consistent with the fact of oxaliplatin/fluoropyrimidine resistance acquired in CLDN18-ARHGAP26 introduced gastric cancer cell lines. Overall, this study provides new insights into the clinical and genomic features of SRCC, and highlights the importance of frequent CLDN18-ARHGAP26/6 fusions in chemotherapy response for SRCC.

  Study EGAS00001002668

• Direct Detection of Early-stage Cancers Using Circulating Tumor DNA

  Early detection and intervention are likely to be the most effective means for reducing morbidity and mortality of human cancer. However, development of methods for noninvasive detection of early-stage tumors has remained a challenge. We have developed an approach called targeted error correction sequencing (TEC-Seq) that allows ultrasensitive direct evaluation of sequence changes in circulating cell-free DNA using massively parallel sequencing. We have used this approach to examine 58 cancer-related genes encompassing 81 kb. Analysis of plasma from 44 healthy individuals identified genomic changes related to clonal hematopoiesis in 16% of asymptomatic individuals but no alterations in driver genes related to solid cancers. Evaluation of 200 patients with colorectal, breast, lung, or ovarian cancer detected somatic mutations in the plasma of 71, 59, 59, and 68%, respectively, of patients with stage I or II disease. Analyses of mutations in the circulation revealed high concordance with alterations in the tumors of these patients. In patients with resectable colorectal cancers, higher amounts of preoperative circulating tumor DNA were associated with disease recurrence and decreased overall survival. These analyses provide a broadly applicable approach for noninvasive detection of early-stage tumors that may be useful for screening and management of patients with cancer.

  Study EGAS00001002577

• Reconstituting the transcriptome and DNA methylome landscapes of human implantation at single-cell resolution

  Implantation is a milestone event during mammalian embryogenesis. Due to the extreme difficulty of obtaining in vivo human early post-implantation embryos, the gene regulatory network and epigenetics controlling human embryo implantation remains elusive. Here, combining an in vitro culture system for human post-implantation development and single-cell omics sequencing technologies, over 10,000 single cells at five representative stages of pre/post-implantation development were systematically analyzed. Unsupervised dimensionality reduction and clustering algorithm of the transcriptome data show stepwise implantation routes for the epiblast, primitive endoderm, and trophectoderm lineages, suggesting preparation for the establishment of a mother-to-offspring connection after implantation. Female embryos showed asynchronous progress of dosage loss of X chromosomes during implantation. Furthermore, using the single cell trio-Seq (scTrio-Seq) strategy, re-methylation of the genomes of all the three lineages was unambiguously revealed. Surprisingly, the genome re-methylation of PE lineage were much slower than both EPI and TE lineages during the implantation process, indicating distinct methylome features between EPI and PE although both of which were derived from ICM. Collectively, our work paves the way for understanding the complex molecular mechanisms that regulate human embryo implantation, informing new insights and future efforts in early embryonic development and reproductive medicine.

  Study EGAS00001003443

• RNA-seq study of human long-term and short-term hematopoietic stem cells from umblical cord blood with lentiviral overexpression of S1PR3

  To investigate the molecular and biological pathways altered by S1PR3OE in human hematopoietic stem cells (HSC), we performed RNA-sequencing (RNA-seq) of LT- and ST-HSC 3 days after transduction with control or S1PR3 overexpression (OE) lentiviral vectors. LT-HSC and ST-HSC from 3 pool of CB lin- were FACS-purified, cells were prestimulated for 4 hours and transduced with lentiviral vectors. At day 3, 2000-5300 BFP+ cells were FACS-purified for RNA isolation with a PicoPure kit. We were able to isolate only 1600-1800 BFP+ cells from LT-HSC control samples as opposed to 4000-5400 BFP+ cells from S1PR3OE samples. Thus, we pooled all control BFP+ LT-HSC cells into one sample for RNA-seq analysis. BFP- LT-HSC from control vector transduction were purified from CB1 as an additional LT-HSC control. Nextera libraries generated from 10 ng RNA from 5 LT-HSC samples (2 controls, 3 S1PR3OE) and 6 ST-HSC samples (3 controls, 3 S1PR3OE) were subjected to 125 bp, paired-end RNA-sequencing on the Illumina HiSeq 2500 with an average of 50 million reads/sample at the Center for Applied Genomics, Sick Kids Hospital.

  Study EGAS00001004798

• Nanopore_and_Illumina_sequencing_of_human_glioblastomas

  Liquid biopsy analysis refers to methods designed to detect tumour-specific material (e.g., circulating tumour cells or tumour DNA) in body fluids, such as urine or blood samples. A widely-used liquid biopsy approach consists of genotyping the circulating tumour DNA (ctDNA) through sequencing of plasma/serum DNA. Although useful in the context of metastatic disease (where the concentration of ctDNA is high), current liquid biopsy technologies show limited sensitivity of detection for the early detection of cancer, and low specificity, as cancer-related mutations accumulate in healthy tissues as part of the ageing process, thus making it difficult to distinguish these from tumour mutations, and because sequencing errors and true mutations show overlapping profiles. Therefore, developing liquid biopsy protocols with increased sensitivity and specificity represents an urgent clinical need. Here we harness extrachromosomal circular DNA (eccDNA) elements, which are circular DNA structures physically separated from the chromosomes of up to several Mbp long pervasive in human cancers, for liquid biopsy analysis. In this pilot study we will focus on the analysis of glioblastomas, because there is strong evidence for the presence of eccDNA in these tumour types, and because developing liquid biopsy approaches for brain tumours to reduce the invasiveness of brain tumour biopsies remains an unmet clinical need.

  Study EGAS00001005812

• 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

• Cancer Discovery Hub (CDH), National Cancer Center Singapore

  This is a DAC for data deposited into EGA generated by Cancer Discover Hub, NCCS. The use of datasets in this portal is solely for research and academic use only, uses other than the have to be approved by the relevant DAC members.

  Dac EGAC50000000039

• Data access committee for "Detection of brain cancer using genome-wide cell-free DNA fragmentomes"

  The data access committee for “ Detection of brain cancer using genome-wide cell-free DNA fragmentomes”. The DAC is comprised of Drs. Dimitrios Mathios, Jillian Phallen, Victor Velculescu, Robert Scharpf, Noushin Niknafs, and Shashikant Koul, and can be contacted at skoul3@jh.edu.

  Dac EGAC50000000605

• Fluctuating DNA methylation tracks cancer evolution at clinical scale

  Long-read Nanopore data of fCpGs from Gabbutt & Duran-Ferrer (2025) for developing the EVOFLUx method to study cancer evolution via DNA methylation fluctuations.

  Study EGAS50000001192

• Spontaneously differentiatiated iPSCs to EBs

  SUM-seq data (single-cell chromatin accessibility and gene expression) for spontaneously differentiatiated iPSCs. This data is the representative data in the Nature Protocols publication.

  Study EGAS50000001094

• WGS of primary neuroblastoma data

  Study of ecDNA structure heterogeneity for 13 primary neuroblastoma patients from shallow long-read whole-genome sequencing (MinION platform, WGS mean-coverage 5-10X).

  Study EGAS50000000348

• RNA-seq dataset for two NUP98-Rearranged Acute Myeloid Leukemia PDX samples

  This dataset includes RNA-seq of two patient-derived xenograft (PDX) samples with NUP98-Rearranged Acute Myeloid Leukemia. Bam files are provided for each sample.

  Dataset EGAD50000001564

• Bulk WES Fastq Files for 103 Samples of Cornell-NCI DLBCL Genomic Project

  Paired-read fastq files were derived from standard Illumina WES NGS sequencing for 103 DLBCL biopsy samples. This is one of three datasets associated with the multi-platform NGS sequencing efforts of the Cornell-NCI DLBCL genomic study.

  Dataset EGAD50000001747

• BAM files from whole-exome sequencing of 5 agressive B-cell lymphoma tumour samples

  This dataset contains BAM files from whole-exome sequencing (WES) of 5 agressive B-cell lymphoma tumour samples for cases 2, 3, 4 and 5. For case 2, non-tumor whole-exome sequencing is also available.

  Dataset EGAD50000000803

• FMT metagenomics triads and plasma metabolomics

  These are the metagenomic datasets of 29 FMT traids with corresponding plasma metabolomics. Data for each triad consists of metagenomics for baseline, post FMT and corresponding donor. Clinical data concerning the glucose rate of disappearance and blood pressure are available as phenotypes.

  Dataset EGAD50000000599

• scRNA-seq from lung cancer organoids and immune cells

  15 scRNA-seq samples were multiplexed, and 5' 10X Chromium scRNA-seq was performed. This dataset provides the FASTQ files for the multiplexed data, as well as the BAM files for the demultiplexed samples.

  Dataset EGAD50000000845

• WES data for genomic determinants of response and resistance to inotuzumab in B-ALL

  Whole exome sequencing data for 69 samples with B-cell acute lymphoblastic leukemia

  Dataset EGAD50000000097

• Array data for oesophageal and related samples – sj_paper_methyl_barretts_release

  Array data for oesophageal and related samples – sj_paper_methyl_barretts_release

  Dataset EGAD00010001838

• Exome sequencing for paired tumor/normal samples from patients with corticotropin-independnet Cushing's syndrome

  Exome sequencing was performed for paired tumor/normal samples from patients with corticotropin-independnet Cushing's syndrome. Tumor DNA was extracted from adrenocortical adenomas and normal DNA was extracted from adjacent adrenal tissues or periphral blood.

  Dataset EGAD00001000715

• Oslo University Hospital Data Access Committee for Celiac Disease datasets submitted to FEGA Norway

  This is the Data Access Committee appointed by Oslo University Hospital for governing access to Celiac Disease datasets submitted to FEGA Norway, under the Regional Ethical Committee approval 6544, with main contact Ludvig M. Sollid.

  Dac EGAC50000001032

• TraIT Cell Line use case

  Experimental data collected from a set of cell lines. To be used as a test for pipelines and infrastructure.

  Study EGAS00001001476

• Whole_genome_sequencing_of_in_vitro_colonies

  In this study we will perform whole genome sequencing on in vitro colonies. Colonies of various sizes (100-300 cells) will be collected for somatic mutation analysis.

  Study EGAS00001003112

• Res1_HT29_exp1_MC_02_03_22

  1 sample is pure plasmid DNA and 10 samples are cell pellets for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kozuke primers.

  Study EGAS00001006092

• Res1_H23_exp1_MC_04_03_22

  1 sample is pure plasmid DNA and 8 samples are cell pellets for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kozuke primers.

  Study EGAS00001006091

• Res1_HT29_exp2_MC_03_03_22

  1 sample is pure plasmid DNA and 10 samples are cell pellets for genomic DNA extraction. CRISPR PCR1 and PCR2 indexing - Please use standard Kozuke primers.

  Study EGAS00001006093

• Prognostic markers of DNA methylation and NGS sequencing in progressive glioblastoma from the EORTC-26101 trial

  These dataset contains NGS data from 380 samples from the EORTC-26101 clinical trial for patients with progressive glioblastoma.

  Study EGAS00001007421

• WGS files for Klco-NUP98 data

  WGS files for paper titled "Fusion oncoproteins and cooperating mutations define disease phenotypes in NUP98-rearranged leukemia" PMID: 39974131, PMCID: PMC11838931, DOI: 10.1101/2025.01.21.25320683

  Dataset EGAD00001015443

• WXS files for Klco-NUP98 data

  WXS files for paper titled "Fusion oncoproteins and cooperating mutations define disease phenotypes in NUP98-rearranged leukemia" PMID: 39974131, PMCID: PMC11838931, DOI: 10.1101/2025.01.21.25320683

  Dataset EGAD00001015444

• RNASeq files for Klco-NUP98 data

  RNASeq files for paper titled "Fusion oncoproteins and cooperating mutations define disease phenotypes in NUP98-rearranged leukemia" PMID: 39974131, PMCID: PMC11838931, DOI: 10.1101/2025.01.21.25320683

  Dataset EGAD00001015445

• scRNASeq files for Klco-NUP98 data

  scRNASeq files for paper titled "Fusion oncoproteins and cooperating mutations define disease phenotypes in NUP98-rearranged leukemia" PMID: 39974131, PMCID: PMC11838931, DOI: 10.1101/2025.01.21.25320683

  Dataset EGAD00001015479

• DNA WGS Short Read Sequence (Illumina NovaSeq) for manuscript titled: "Performance of Somatic Structural Variant Calling in Lung Cancer using Oxford Nanopore Sequencing Technology"

  DNA WGS Short Read Sequence (Illumina NovaSeq) for manuscript titled: "Performance of Somatic Structural Variant Calling in Lung Cancer using Oxford Nanopore Sequencing Technology"

  Dataset EGAD00001015399

• Dataset for EGAS00001007937

  WGS, RNAseq, Methylation, variant calling analysis for a Novel paediatric case of a spinal high-grade astrocytoma with piloid features in a patient with Noonan Syndrome

  Dataset EGAD00001015412

• Whole Exome Sequencing of healthy Spanish individuals - VCF file

  The need for a detailed catalogue of local variability for the study of rare diseases within the context of the Medical Genome Project motivated the whole exome sequencing of 267 unrelated individuals, representative of the healthy Spanish population.

  Dataset EGAD00001003101

• V4 Colorectal panel test (2018-03-07)

  Targeted gene screen of cell line tumours for testing the new V4 Colorectal gene panel. . This dataset contains all the data available for this study on 2018-03-07.

  Dataset EGAD00001004000