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• Hostage_1_phenotype_basic

  Basic phenotypes for Hostage1 cohort.

  Dataset EGAD00001007838

• Hostage_2_phenotype_lab

  Lab values for Hostage2 cohort.

  Dataset EGAD00001007841

• GEN_COVID_phenotype_lab

  Lab values for GEN_COVID cohort.

  Dataset EGAD00001007837

• GEN_COVID_phenotype_basic

  Basic phenotypes for GEN_COVID cohort.

  Dataset EGAD00001007836

• BelCovid_2_phenotype_lab

  Lab values for BelCovid2 cohort.

  Dataset EGAD00001007835

• Inherited Genetic Variation and Predisposition to Testicular Germ Cell Tumor: UPenn Local TGCT Study

  Testicular germ cell tumors (TGCT) are the most common cancer in men ages 20-40. The incidence of TGCT has more than doubled over the past forty years, without clear etiology. Both genetic effects and environmental exposures, specifically during the pre-natal period, are likely to play an important role in determining TGCT susceptibility. TGCT is known to develop from primordial germ cells (PGCs). We hypothesize that variation in genes that impact upon the differentiation and maturation of PGCs will be important determinants of TGCT susceptibility and based on this hypothesis have selected three important pathways for study, i) male germ cell development, ii) androgen and estrogen biosynthesis and metabolism, and iii) IGF signaling. The proteins involved in early male germ cell development, normally only expressed in PGCs, are markers of and are overexpressed in TGCT. Markers of increased exposure to estrogen (or relatively decreased exposure to androgen) in utero and exogenous estrogen exposures, such as endocrine disruptors, have been associated with TGCT case status in multiple studies. IGF signaling is necessary for testis differentiation and maturation in mice and interacts synergistically with the estrogen signaling pathway. Additionally, we are interested in examining genetic factors predisposing to TGCT in an unbiased fashion, and thus will conduct a Genome Wide Association Study. As well disease susceptiblity, genetics are likely to play a role in disease progression, disease outcomes and response to treatement. We will also assess association of inherited genetics with these outcomes. We will analyze the contribution of genetic variants in these pathways to TGCT risk using a population-based case-control study in the Philadelphia metropolitian area. Our goal is the collection of 550 TGCT cases and 1100 age, race and cell phone use matched controls without a history of TGCT, which will yield 500 and 1000 white cases and controls, respectively, available for final analyses. All cases will be enumerated through the New Jersey and Pennsylvania state cancer registries. We will use a two-tiered approach for case recruitment: hospital clinic-based followed by registry-based. Hospital based cases will be identified within the Univeristy of Pennsylvania Health System and the University of Pennsylvania Cancer Network. All cases identified through this mechanism will be recruited irregardless of diagnosis date. The remaining cases will be identified through the New Jersey and Pennsylvania cancer registries and contacted following their protocols. Controls will be identified through random digit dialing and address based sampling. Both cases and controls will complete a questionnaire addressing known, presumed, and hypothesized risk factors for TGCT and provide a biospecimen. Pathological slides will be reviewed to cases to confirm diagnostic sub-type of TGCT. Haplotypes and functional SNPs will be typed in the genes of interest, as well as throughout the genome. Analyses will be conducted for specific variants, common haplotypes, alone and in conjunction with each other and exposure data after appropriate adjustment for potential confounders. The findings from this study will greatly contribute to our understanding of determinants of TGCT susceptibility.

  Study phs001307

• NHLBI TOPMed: My Life Our Future (MLOF) Research Repository of Patients with Hemophilia A (Factor VIII Deficiency) or Hemophilia B (Factor IX Deficiency)

  Hemophilia A and B are X-linked bleeding disorders resulting from a deficiency in coagulation factor VIII (FVIII) or factor IX (FIX), respectively. Hemophilia affects approximately 1/5000 male births worldwide, and results in premature death and disability due to bleeding if coagulation factor replacement therapy is not used effectively. Hemophilia is clinically categorized by coagulation factor activity levels and ranges in severity from mild (6% to 30%) to moderate (1-5%) to severe (<1%). Many female "carriers" of hemophilia also have decreased factor activity and morbidity from bleeding. Hemophilia A and B are almost always caused by identifiable mutations in the F8 and F9 genes, respectively, and these mutations are found throughout the structural genes. Although the hemophilias are monogenic disorders, there are wide variations in disease severity and therapeutic outcomes which are not readily explained by the disease causing mutations alone. The My Life Our Future (MLOF) project (www.mylifeourfuture.org) is a national resource developed by a partnership of BloodworksNW (BWNW, formerly the Puget Sound Blood Center), the American Thrombosis and Hemostasis Network (ATHN), the National Hemophilia Foundation (NHF) and Bioverativ, to provide free F8 and F9 gene variant analysis to patients with hemophilia A or B, and to establish a research repository of DNA sequence, DNA, RNA, buffy coat, serum and plasma. The sequence analysis and serum samples are linked to a phenotypic database hosted by ATHN, with samples submitted and clinical data entered at ~100 hemophilia treatment centers (HTCs) nationwide. (See ATHN Research Report Brief in the resource center at www.athn.org). MLOF has become the largest hemophilia genetic project worldwide. The roles of the MLOF partners are: BWNW, to serve as the central laboratory for the project and house the research repository; ATHN, to support and provide the administrative link with HTCs, to facilitate the collection of accurate phenotypic data, to conduct research review and approval for use of the repository and with BWNW to provide samples and data for research projects; NHF, to provide consumer education and facilitate consumer input into the project; and Bioverativ, to provide financial support and scientific input. The project is governed by a Steering Committee consisting of one representative from each organization. Subject samples chosen from the MLOF parent study for TOPMed and WGS were drawn from those who gave (or parents gave) informed consent for the Research Repository and included patients of all severities and type, but with an emphasis on those with severe hemophilia and others at increased risk of neutralizing antibody (inhibitor) formation and who had samples in the Research Repository (plasma, serum, RNA) for potential additional -omic studies. Also included were samples from subjects where a likely causative variant for hemophilia was not found in the F8 or F9 coding region, intron-exon boundaries or immediate upstream and downstream regions. Since hemophilia is an X-linked disorder, the majority of subjects are male. Racial distribution is similar to the overall population distribution.

  Study phs001515

• Resuscitation Outcomes Consortium (ROC) Trauma Epidemiologic Registry (Trauma Epistry) (ROC-Trauma Epistry-BioLINCC)

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

  Study phs003809

• Best Endovascular vs. Best Surgical Therapy in Patients With Critical Limb Ischemia (BEST CLI-BioLINCC)

  Data Access NOTE Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Objectives To compare the effectiveness of surgical treatment to the effectiveness of endovascular treatment in adults with chronic limb-threatening ischemia who are eligible for both treatment options. Background Peripheral artery disease (PAD) affects more than 200 million people worldwide. A portion of those individuals develop chronic limb-threatening ischemia (CLTI). CLTI is associated with severe health outcomes. CLTI is defined by ischemic foot pain at rest, ischemic ulcerations, or gangrene. Treatment for CLTI includes revascularization to improve limb perfusion and limit the risk of amputation. However, the choice of surgery or endovascular therapy for revascularization varies greatly. The extent to which this variability affects clinical outcomes in patients with CLTI is unknown. The BEST-CLI trial was initiated to determine whether endovascular revascularization was superior to surgical revascularization in patients with CLTI. Participants A total of 1434 participants with a single segment of great saphenous vein comprised cohort 1. Of these, 718 participants were randomly assigned to the surgical treatment group and 716 participants were randomly assigned to the endovascular therapy group. A total of 396 participants needing an alternative conduit comprised cohort 2. Of these, 197 participants were randomly assigned to the surgical treatment group and 199 participants were randomly assigned to the endovascular therapy group. Design BEST-CLI was an international, randomized, open-label trial. Participants were assigned to one of two cohorts based on the availability of a single segment of great saphenous vein. Participants in both cohorts were randomly assigned to either surgery or endovascular therapy. Follow-up was performed at 30 days after the procedure, 3 months, 6 months, and every 6 months thereafter up to 84 months after randomization. Telephone visits in lieu of clinic visits were planned at 30 months and every 12 months thereafter and at the end of the trial. Participants were followed for major adverse cardiovascular events (myocardial infarction, stroke, or death from any cause), quality of life, level of pain, and performance on the six-minute walk test. The primary outcome was a composite of major adverse limb events or death from any cause. A major adverse limb event was defined as above ankle amputation of the index limb or a major index-limb reintervention (new bypass, interposition graft revision, thrombectomy, or thrombolysis). Conclusions Among patients with CLTI who had an adequate great saphenous vein for surgical revascularization (cohort 1), the incidence of a major adverse limb event or death was significantly lower in the surgical group than in the endovascular group. Among the patients who lacked an adequate saphenous vein conduit (cohort 2), the outcomes in the two groups were similar. Farber A, Menard MT, Conte MS, et al. Surgery or Endovascular Therapy for Chronic Limb-Threatening Ischemia. N Engl J Med. 2022;387(25):2305-2316. PMID 36342173

  Study phs003844

• Rapid Acceleration of Diagnostics - Digital Health Technologies (RADx-DHT): NIH Digital Health Solutions for COVID-19: SAFER-COVID - Integration of Testing and Digital Health

  SAFER-COVID provides a set of self-management tools to consumers to track symptoms, test results, vaccine record, and environmental factors, such as exposure to others. Consumers may choose to integrate data from wearables and electronic health records (EHR) for self management. These data are used to enable multiple use cases such as return-to-work, activity risk assessment and self-management within SAFER-COVID.DOI: https://rapids.ll.mit.edu/10.57895/cmt5-gh78

  Study phs002540

• Enrichment of PTPRT and JAK2 mutations in lung cancer from African Americans and evidence for increased GI and HRD in LUSC

  The goal of this study is to compare the somatic mutation landscape between European Americans and African Americans. It includes various histological forms of lung cancer. In LUAD, we find increased prevalence of PTPRT and JAK2 mutations among African Americans. These proteins function in the same pathway and as such, this may be a new actionable target for lung cancer. We have also examined the landscape of copy number profiles in African Americans and European Americans.

  Study phs001895

• A National Translational Science Network of Precision-Based Immunotherapy for Primary Liver Cancer (PLC)

  The primary objective of the NCI-CLARITY study is to establish a biospecimen repository for genomic, genetic and epigenetic analysis to study the biology of primary liver cancer (PLC) development and progression. Secondary objectives include predicting overall and progression-free survival following immunotherapy. In the retrospective phase, anonymous archival tissue from PLC patients was obtained from five participating sites. Whole exome and transcriptome sequencing was generated and used to predict overall survival in response to immunotherapy.

  Study phs003074

• Center for Common Disease Genomics (CCDG)-Cardiovascular: University of Pennsylvania Cohort

  The National Human Genome Research Institute (NHGRI) has funded the Centers for Common Disease Genomics (CCDG), a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes and to better understand the general principles of genomic architecture underlying common, complex inherited diseases. This study of early onset cardiovascular disease and metabolic risk contains data derived from the whole genome sequence data of individuals participating in Penn Medicine's BioBank.

  Study phs001502

• Generation of hypoimmunogenic induced pluripotent stem cells by CRISPR-Cas9 system and detailed evaluation for clinical application

  We established hypoimmunogenic human induced pluripotent stem cells (iPSCs) to minimize the immune rejection caused by mismatches of HLA type. Here, we optimized the protocol for good manufacturing practice (GMP)-compatible CRISPR-Cas9 genome editing to deplete the three gene locus (HLA-A, HLA-B, and CIITA genes) simultaneously in HLA homozygous iPSCs. RNA-seq and flow cytometry analyses confirmed the successful depletion of HLAs, and lineage-specific differentiation into cardiomyocytes was verified.

  Study EGAS50000001194

• Personalized analysis of ctDNA for detection of molecular residual disease and recurrence in a cohort of surgically treated patients with head and neck squamous cell carcinoma

  Despite advances in multimodal treatment, survival for patients with advanced head and neck squamous cell carcinoma (HNSCC) has improved only modestly. The recent advent of tumor-informed assays capable of detecting circulating cell-free tumor DNA (ctDNA) has enabled early detection of molecular residual disease (MRD) and recurrence following curative therapy. We assessed ctDNA in plasma from 76 and saliva from 54 HNSCC patients, respectively, who received primary curative-intent surgery.

  Study EGAS50000001018

• Allogeneic induced pluripotent stem (iPS) cell-derived cartilage transplantation for articular cartilage damage in knee joints: a phase I, first-in-human study

  Damaged articular cartilage seldom heals due to a limited capacity for self-repair, frequently leading to osteoarthritis. Regenerative interventions are expected to heal the damage. We have recently established a method to produce cartilage from induced pluripotent stem cells (iPSCs) in a clinically applicable process. The primary aim of this study was to evaluate the safety including tumorigenesis and the subsequence efficacy of allogeneic iPSC-derived cartilage transplantation into damaged areas of knee articular cartilage.

  Study EGAS50000001167

• TIRE-seq: an Integrated Sample Extraction and Transcriptomics Workflow for High Throughput Perturbation Studies

  TIRE-seq is a novel RNA sequencing method that integrates mRNA purification directly into library preparation, eliminating separate RNA extraction. The technique demonstrated utility across three biological applications, including capturing transcriptional changes in human T cells, identifying genes driving murine dendritic cell differentiation, and analyzing the dose-response effects of temozolomide on patient-derived neurospheres. With its simplified approach and high sequencing efficiency, TIRE-seq offers a cost-effective solution for large-scale gene expression studies.

  Study EGAS50000000867

• ATAC-seq iPSC cells or smNPC cells for allelic imbalance

  In order to confirm the predicted impact of the PD-associated allele of rs144814361 on BAG3 promoter, we proceeded with genome editing of the TH-REP1 cell line by using prime editing to insert the “T” allele at the position chr10:119651405 in the BAG3 promoter. 3 replicates for each condition: 1) iPSC Wild Type 2) SNP-BAG3 variant in iPSC 3) SNP-BAG3 variant in smNPC. Allelic imbalance is further assessed using read counting at this specific position.

  Study EGAS50000001579

• Fixative optimisation study for BRITROC project

  The British Translational Research Ovarian Cancer Collaborative (BriTROC), spanning more than 10 centres across UK, is a prospective observational project focused on relapsed high-grade serous ovarian cancer (HGSOC). The first study, as a part of the project, is the Fixative optimisation study, which analyses the utility for next-generation sequencing of UMFIX samples compared to NBF-fixed and fresh frozen samples. Various DNA and sequencing quality analyses were performed to compare fixation methods in bulk tumour and biopsy samples.

  Study EGAS00001001433

• Oral mucosa organoids as a potential model for personalized therapies

  Organoids were established from both tumor and matched wildtype tissue from patients diagnosed with head and neck squamous cell carcinoma (HNSCC). In order to confirm tumor status and identify oncogenic driver mutations, DNA derived of these organoids was sequenced. After validation of tumor identity, organoids were exposed to a range of (targeted) chemotherapeutics and radiotherapy. Differential responses to therapeutics were subsequently correlated to underlying genetic alterations to see if DNA mutation status could be predictive for responses to therapy.

  Study EGAS00001003628

• A Comparative Analysis of Algorithms for Somatic SNV Detection in Cancer

  Four recently published algorithms for the detection of somatic SNV sites in matched cancer-normal sequencing datasets are VarScan, SomaticSniper, JointSNVMix and Strelka. In this analysis, we apply these four SNV calling algorithms to cancer- normal Illumina exome sequencing of a chronic myeloid leukaemia (CML) patient. The candidate SNV sites returned by each algorithm are filtered to remove likely false positives, then characterised and compared to investigate the strengths and weaknesses of each SNV calling algorithm.

  Study EGAS00001000927

• Tools

  EGA Tools This page provides details of the applications available at the EGA. Submission Tool Description EgaCryptor Encrypt and create md5sum values. Crypt4gh Encrypt, decrypt or re-encrypt files. INBOX Upload your files once encrypted. FTP Upload your files once encrypted. Submitter Portal Submit and manage your submission metadata online (SEQUENCING AND PHENOTYPE ONLY) Submitter Portal API Submit and manage your submission metadata programmatically. Webin Submit and manage your submission metadata online (array data) Webin API Submit metadata using XML files. Aimed to array-based submissions. Star2xml Format conversion tool to transform tabular formats (e.g. spreadsheets) into EGA's XMLs. Used for large programmatic submissions. Data Access Tool Description DAC Portal Manage and control data access request. Download Tool Description PyEGA3 Download and decrypt datasets requested through your EGA web account. Live Outbox Download and decrypt datasets requested through your EGA web account. Fuse Client A quick and convenient way to browse through EGA files without the need for complete downloads. EGA QuickView A quick and convenient way to browse through EGA files without the need for complete downloads.

  Documentation tools

• NGS sequencing data of archival FFPE tumor tissue

  The aim of the study was to identify mutations in key ovarian cancer genes (BRCA1, BRCA2,TP53, PTEN, ATM, ATR, NF1) in archival fresh-frozen paraffin embedded (FFPE) tumor tissue from platinum-resistant ovarian cancer patients enrolled in the phase I/II GANNET53 clinical trial. From the enrolled 133 patients, archival FFPE tissue was available and DNA was extracted thereof. The DNA samples passing quality control (n=118) were NGS sequenced using the SureSeq™ Ovarian Cancer Panel and the NGS Library Preparation kit (Oxford Gene Technology), which covers all exons of 7 key ovarian cancer genes for single nucleotide variants (SNV) and indels, and the Illumina MiSeq platform. For the paired-end runs, one Read 1 (R1) and one Read 2 (R2) FASTQ file were created for each sample. FASTQ files were compressed and created with the extension \.fastq.gz.

  Dataset EGAD50000001371

• EOSC4Cancer Longitudinal Synthetic Colorectal Cancer Genomic data developed at BSC

  The synthetic genomes have been created trying to mimic real cancer data of 4 patients (Named 185,186,187 and 188). Mutations are based on real CRC patients from the PCAWG dataset. For each patient, two tumor samples at different time points and one healthy sample have been simulated. The cancer intra-tumor heterogeneity and evolution in the patients is depicted by simulating reads from tumor subclones separately and then mixing them according to their clonal proportions in each sample. For rapid use and transfer only selected chromosomes have been generated for each patient. Chromosomes per patient: -185: chr4, chr5, chr7, chr17 -186: chr1, chr7, chr12, chr17 -187: chr1, chr2, chr5, chr12, chr17 -188: chr2, chr5, chr12, chr13, chr17 Worflows used to create BAM/BAI, VCF and MAF files from FASTQ (Alignment with GRCh38): - https://usegalaxy.eu/published/workflow?id=2c3d05023c02113e - https://usegalaxy.eu/published/workflow?id=1da86d74f8535f4e

  Dataset EGAD50000000276

• Oesophageal adenocarcinoma scRNAseq from LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19)

  This dataset contains single-cell RNA-seq generated from the LUD2015-005 study (NCT02735239, EudraCT 2015-005298-19) and additional donors with Barrett's oesophagus using the 5' Single Cell Gene Expression assay from 10x Genomics. Samples were generated from oesophageal tumours, Barrett's oesophagus, and normal oesophagus and gastric tissues, with the aim of generating a reference atlas for cell types found in normal and disease-associated tissue states in the upper GI tract. This reference atlas was used as the basis for a deconvolution workflow to estimate the cell composition of bulk transcriptomes from these tissues, and to assess cell type-specific expression patterns of potential predictive biomarkers for immunochemotherapy regimens in this setting.

  Dataset EGAD00001009401

• Whole genome analyses of the childhood cancer neuroblastoma

  Neuroblastomas are tumors of peripheral sympathetic neurons and are the most common solid tumor in children. To determine the genetic basis for neuroblastoma we performed whole-genome sequencing (6 cases), exome sequencing (16 cases), genome-wide rearrangement analyses (32 cases), and targeted analyses of specific genomic loci (40 cases) using massively parallel sequencing. On average each tumor had 19 somatic alterations in coding genes (range, 3-70). Among genes not previously known to be involved in neuroblastoma, chromosomal deletions and sequence alterations of chromatin remodeling genes, ARID1A and ARID1B, were identified in 8 of 71 tumors (11%) and were associated with early treatment failure and decreased survival. Using tumor-specific structural alterations, we developed an approach to identify rearranged DNA fragments in sera, providing personalized biomarkers for minimal residual disease detection and monitoring. These results highlight dysregulation of chromatin remodeling in pediatric tumorigenesis and provide new approaches for the management of neuroblastoma patients.

  Dataset EGAD00001000282

• RNA-seq profiling of NKX6.1 reporter iPSC lines for isolation and analysis of functionally relevant neuronal and pancreas populations

  We generated human induced pluripotent stem cell (iPSC) lines with a GFP reporter inserted in the endogenous NKX6.1 locus. Characterisation of the reporter lines demonstrated faithful GFP labelling of NKX6.1 expression during pancreas and motor neuron differentiation. We performed three independent in vitro differentiations towards the pancreatic endocrine lineage. We FACS-purified GFP positive and negative cells from stage 7 cultures, and generated Smart-Seq2 RNA-sequencing libraries for the pre-sorted cells, as well as the two GFP-sorted cell populations. Gene expression profiling by RNA-sequencing reveals that the NKX6.1-positive population closely resembles mature human beta cells and the functional evaluation of purified populations shows that the glucose-responsive beta-like cells are enriched within the NKX6.1-positive population. These reporter lines provide a valuable resource to the scientific community for the derivation of functional relevant pancreas and neuronal cell subtypes.

  Dataset EGAD00001003911

• Neuroblastoma relapse trio series from the AMC

  The majority of neuroblastoma patients have tumors that initially respond to chemotherapy, but a large proportion of patients will experience therapy-resistant relapses. The molecular basis of this aggressive phenotype is unknown. Whole genome sequencing of 23 paired diagnostic and relapsed neuroblastomas showed clonal evolution from the diagnostic tumor with a median of 29 somatic mutations unique to the relapse sample. Eighteen of the 23 relapse tumors (78%) showed RAS-MAPK pathway mutations. Seven events were detected only in the relapse tumor while the others showed clonal enrichment. In neuroblastoma cell lines we also detected a high frequency of activating mutations in the RAS-MAPK pathway (11/18, 61%) and these lesions predicted for sensitivity to MEK inhibition in vitro and in vivo. Our findings provide a rationale for genetic characterization of relapse neuroblastoma and show that RAS-MAPK pathway mutations may function as a biomarker for new therapeutic approaches to refractory disease.

  Dataset EGAD00001001360

• ENU-CCK-81 cetuximab pilot project

  Cetuximab is a targeted monoclonal antibody against the epidermal growth factor receptor (EGFR) which is used therapeutically for the treatment of KRAS wild-type colorectal cancer (CRC). The Cetuximab sensitive KRAS wild-type CRC cell line NCI-H508 has been treated with a fixed concentration of ENU for 24 hours and then selected with Cetuximab until drug resistant clones were ready to be picked and grown up as sub-clones of the parental cell line. These will have genes causally implicated in cancer sequenced to identify common point mutations in multiple independently derived drug resistant clones as a forward genetic screen for mechanisms of resistance to Cetuximab in CRC.

  Dataset EGAD00001001947

• ENU-NCI-H508 cetuximab fixed concentration project

  Cetuximab is a targeted monoclonal antibody against the epidermal growth factor receptor (EGFR) which is used therapeutically for the treatment of KRAS wild-type colorectal cancer (CRC). The Cetuximab sensitive KRAS wild-type CRC cell line NCI-H508 has been treated with a fixed concentration of ENU for 24 hours and then selected with Cetuximab until drug resistant clones were ready to be picked and grown up as sub-clones of the parental cell line. These will have genes causally implicated in cancer sequenced to identify common point mutations in multiple independently derived drug resistant clones as a forward genetic screen for mechanisms of resistance to Cetuximab in CRC

  Dataset EGAD00001001948

• Functional genomics approaches to understand osteoarthritis (2019-08-01)

  The aim of this work is to apply an integrated systems approach to understand the biological underpinnings of large joint (hip and knee) osteoarthritis which culminates in the need for total joint replacement (TJR). We will obtain diseased and non-diseased cartilage as well as other disease-relevant tissue following TJR, coupled with a blood sample. We will generate genotype data and will characterise the pairs of diseased and non-diseased tissue samples in terms of methylation, transcription (RNASeq) and expression (quantitative proteomics). We will apply integrative approaches to combine information across the omics levels to characterise genes, pathways, and networks that underlie osteoarthritis progression. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2019-08-01.

  Dataset EGAD00001005215

• Whole-genome sequences of single-cell derived clonal samples and bulk blood samples from human

  Human single cells were clonally expanded by culture and whole-genome sequenced. This dataset includes 334 clonal samples and 7 blood bulks from seven individuals (DB2, DB3, DB5, DB6, DB8, DB9, DB10). We extracted genomic DNA materials from clonally expanded cells and matched peripheral blood using DNeasy Blood and Tissue kits (Qiagen) according to the protocol. DNA libraries for WGS were generated by an Accel-NGS 2S Plus DNA Library Kit (Swift Biosciences) from 1 µg of genomic DNA materials. WGS was performed on either the Illumina HiSeq X platform or the NovaSeq 6000 platform to generate mean coverage of 25.2X for 374 clonally expanded cells and 94.8X for 7 matched blood tissues.

  Dataset EGAD00001007032

• PELICAN33 Phenomic Dataset

  To better understand variation in metastatic prostate cancer behaviour, we assembled and analyzed longitudinal clinical and autopsy records in 33 men. The dataset is contained in a self-explanatory Excel Workbook, with each patient identified as A1, A2, etc. as listed in the "Combined longitudinal clinical and autopsy phenomic assessment in lethal metastatic prostate cancer: recommendations for advancing precision medicine" publication in European Urology Open Science. Please see Jasu J, Tolonen T, Antonarakis ES, Beltran H, Halabi S, Eisenberger MA, Carducci MA, Loriot Y, Van der Eecken K, Lolkema M, Ryan CJ, Taavitsainen S, Gillessen S, Högnäs G, Talvitie T, Taylor RJ, Koskenalho A, Ost P, Murtola TJ, Rinta-Kiikka I, Tammela T, Auvinen A, Kujala P, Smith TJ, Kellokumpu-Lehtinen PL, Isaacs WB, Nykter M, Kesseli J, Bova GS. Combined Longitudinal Clinical and Autopsy Phenomic Assessment in Lethal Metastatic Prostate Cancer: Recommendations for Advancing Precision Medicine. Eur Urol Open Sci. 2021 Jul 2;30:47-62. doi: 10.1016/j.euros.2021.05.011. PMID: 34337548; PMCID: PMC8317817. for more details.

  Dataset EGAD00001007800

• Bulk-tissue paired-end RNA-sequencing of anterior cingulate cortex samples derived from Lewy body disease patients

  Profiling of 24 human anterior cingulate cortex samples by bulk-tissue RNA-sequencing. Samples were derived from 5 non-neurological control individuals and 19 individuals with Lewy body disease (Parkinson’s disease = 7 individuals; Parkinson’s disease with dementia = 6 individuals; dementia with Lewy bodies = 6 individuals). Paired-end FASTQ files for each of the human samples are provided and are denoted by the suffixes R1 (read 1) and R3 (read 2). Fastp (v 0.20.0), a fast all-in-one FASTQ pre-processor, was used for adapter trimming, read filtering and base correction. Fastp default settings were used for quality filtering and base correction. Further details on parameters used are available here: https://github.com/RHReynolds/RNAseqProcessing/blob/master/QC/prealignmentQC_fastp_PEadapters.R.

  Dataset EGAD00001007698

• Rare Disease Synthetic Dataset

  The purpose of this project is to provide public human datasets for the study of rare diseases. The use of public human genomic background combined with the in-silico insertion of real disease-causing variants enable to have a representative dataset for testing purposes without facing ethical and legal issues associated with the use of human sensitive data. This project aims to help development of technical implementations for rare disease data integration, analysis, discovery, and federated access.

  Dataset EGAD00001008392

• RNA sequencing of end-stage kidney disease patients with COVID-19

  This dataset contains paired RNA sequencing data for end-stage kidney disease (ESKD) patients on dialysis. There are two cohorts. The first includes 179 samples from 51 COVID-19 patients recruited during the initial phase of the COVID-19 pandemic (April-May 2020) and 55 non-infected ESKD patients as controls. 17 patients initially recruited as controls as part of the Wave 1 cohort were later infected with COVID-19 in January-March 2021. We acquired a total of 90 samples during the acute infection and convalescent samples for 12 of the 17 patients following the acute COVID-19 episode. RNA-seq counts and full clinical metadata for these cohorts are available without restriction from Zenodo (https://doi.org/10.5281/zenodo.6497251).

  Dataset EGAD00001009752

• Postmortem Single Nuclei and Bulk RNA-seq data of the Motor Cortex and Spinal Cord for Healthy, C9ALS and sALS Patients

  This dataset contains both snRNA-seq and bulk RNA-seq data from 19 different patients, comprising of 9 healthy controls (4 Spinal Cord, 5 Motor Cortex) as well as 5 C9ALS patients and 5 sporadic ALS patients, each with paired data from the spinal cord and motor cortex. For the snRNA-seq data, fastq files containing the raw reads are provided, many of these samples were pooled for sequencing and subsequently require demultiplexing using SNPs using a tool such as freemuxlet. The paired bulk RNA-seq data (raw fastq files) can be used to acquire the ground truth per patient - a list of pooled samples can be found in map.txt. snRNA-seq data was produced using the 10X Genomics 3' v3 kit and bulk RNA-seq was produced using the Illumina TruSeq v2 kit.

  Dataset EGAD00001009686

• Platelets contain a repertoire of DNA fragments that map over the human nuclear genome, including tumour-derived DNA in patients with active malignancy.

  This dataset consists of WGS from six patients with a recent diagnosis of gastrointestinal adenocarcinoma. DNA-Seq libraries were prepared from platelet DNA (pDNA) and plasma cfDNA obtained from the same peripheral blood sample. pDNA was size selected into 2 groups (small, s-pDNA: less than 600 bp and long, b-pDNA: greater than 600 bp). The short fragments were further cleaned to remove <100 bp fragments and large platelet fragments were fragmented via sonication. Libraries were then prepared using the NEBNext Ultra II DNA Sample Preparation Kit for Illumina according to the manufacturer’s protocol and sequenced on an Illumina NextSeq 500 (300 cycle PE) at low-pass (0.1X) for all samples, and 10X for the cfDNA and s-pDNA, using four lanes in each sample.

  Dataset EGAD00001009856

• CCA ChIP-seq data (63 CCA, 8 normal, 19 cell-line)

  Fastq files for H3K27ac ChIP-seq (with matched input-DNA control) for 63 CCAs, 8 normal bile duct tissues, 16 CCA cell-lines (including replicates), and 3 normal cholangiocyte cell-lines (including replicates). Library prep was performed with the NEBNext ChIP-seq library preparation kit. Each library (including matching input DNA) was sequenced to an average depth of 20 to 30 million raw reads on Illumina HiSeq4000 sequencer, with paired-end sequencing (except for 3 normal bile tissues done with single-end sequencing).

  Dataset EGAD00001012103

• Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease

  A comprehensive clinical and multi-omic profiling for 199 patients with acute coronary syndrome (ACS), an acute subcategory of CAD, that we recruited in two major Israeli hospitals. We demonstrate that ACS has distinct serum metabolome and gut microbial signatures, as compared to a control cohort. Metabolic aberrations linked with microbiome and diet show a gradual trend with significant metabolite deviations in control participants with metabolic impairment suggesting their involvement in earlier dysmetabolic phases preceding clinically overt CAD.

  Study EGAS00001005342

• HBCC Postmortem Psychiatric Molecular Studies

  This postmortem study examines molecular, genetic and epigenetic signatures in the brains of hundreds of subjects with or without mental disorders conducted by the DIRP NIMH Human Brain Collection Core (HBCC). The brain tissues are obtained under protocols approved by the CNS IRB (NCT00001260), with the permission of the next-of-kin (NOK) through the Offices of the Chief Medical Examiners (MEOs) in the District of Columbia, Northern Virginia and Central Virginia. Additional samples were obtained from the University of Maryland Brain and Tissue Bank (contracts NO1-HD-4-3368 and NO1-HD-4-3383) (http://www.medschool.umaryland.edu/btbank/ and the Stanley Medical Research Institute: http://www.stanleyresearch.org/brain-research/). Clinical characterization, neuropathological screening, toxicological analyses, and dissections of various brain regions were performed as previously described (Lipska et al. 2006; PMID: 16997002). All patients met DSM-IV criteria for a lifetime Axis I diagnosis of psychiatric disorders including schizophrenia or schizoaffective disorder, bipolar disorder and major depression. Controls had no history of psychiatric diagnoses or addictions. SNP array: Array-based genotyping was performed on most samples published in this collection. The number of SNPs assayed via Illumina chips varied between 650,000 and 5 Million. Cerebellar tissue was generally used for genotyping studies. # Diagnosis SNP Array 1 Anxiety Disorder 1 2 Autism Spectrum Disorder 13 3 Bipolar Disorder 114 4 Control 387 5 Eating Disorder (ED) 2 6 Major Depressive Disorder (MDD) 186 7 Obsessive Compulsive Disorder (OCD) 5 8 Post-Traumatic Stress Disorder (PTSD) 0 9 Schizophrenia 220 10 Other 7 11 Tic Disorder 3 12 Undetermined 1 13 Williams Syndrome 2 Table: Numbers of samples in each diagnostic category. DNA extraction: 45-80 mg of cerebellar tissue was pulverized for DNA extractions. The QIAamp DNA mini Kit (Qiagen) method was employed for tissue DNA extraction. The tissue was initially lysed using Tissue Lyser (Qiagen) and extractions were accomplished according to manufacturer's protocol. The DNA was captured in 500uL elution buffer. The concentrations were measured using Thermo Scientific's NanoDrop 1000/NanoDrop ONE. The mean yield was 128.85 uG (+/- 79.48), the mean ratio of 260/280 was 1.87 (+/- 0.105), and the mean ratio of 260/230 was 2.48 (+/-1.75). Genotyping methods: Three types of Illumina Beadarray chips were used: HumanHap650Y, Human1M-Duo, and HumanOmni5M-Quad (San Diego, California). The genotyping was done according to the manufacturer's protocol (Illumina Proprietary, Catalog # WG-901-5003, Part # 15025910 Rev.A, June 2011). Approximately, 400ng DNA was used and each DNA sample was QC tested for 260/280 ratio by nanodrop and DNA band intactness on 2% agarose gel. Briefly, the samples were whole-genome amplified, fragmented, precipitated and resuspended in appropriate hybridization buffer. Denatured samples were hybridized on prepared Bead Array Chips. After hybridization, the Bead Chip oligonucleotides were extended by a single fluorescent labeled base, which was detected by fluorescence imaging with an Illumina Bead Array Reader, iScan. Normalized bead intensity data obtained for each sample were loaded into the Illumina Genome Studio (Illumina, v.2.0.3) with cluster position files provided by Illumina, and fluorescence intensities were converted into SNP genotypes. Microarray: We generated RNA expression data using array technology for psychiatric subjects compared to non-psychiatric subjects as controls. We used tissues from three different brain regions i.e. hippocampus, dorsolateral prefrontal cortex (DLPFC), and dura mater for a large cohort of individuals (total number 552 subjects for hippocampus, 800 for DLPFC and 146 for dura). Total RNA was extracted from ~100 mg of tissue using the RNeasy kit (Qiagen) according to the manufacturer's protocol. RNA quality and quantity were examined using the Bioanalyzer (Agilent, Inc) and NanoDrop (Thermo Scientific, Inc), respectively. Samples with RNA integrity number (RIN) # Diagnosis DLPFC Hippo Dura 1 Anxiety Disorder 1 0 0 2 Autism Spectrum Disorder 14 6 0 3 Bipolar Disorder 90 49 0 4 Control 336 270 75 5 Eating Disorder (ED) 2 1 0 6 Major Depressive Disorder (MDD) 144 87 0 7 Obsessive Compulsive Disorder (OCD) 5 3 0 8 Post-Traumatic Stress Disorder (PTSD) 6 0 0 9 Schizophrenia 192 125 71 10 Other 5 6 0 11 Tic Disorder 3 3 0 12 Undetermined 1 1 0 13 Williams Syndrome 2 1 0 Table: Numbers of samples in each diagnostic category. RNA-Seq of Dorso-lateral prefrontal cortex: All brains were collected and the dorsolateral prefrontal cortical (DLPFC) samples dissected at the HBCC, DIRP, NIMH. Dorsolateral prefrontal cortex (DLPFC) specimens were dissected from right or left hemisphere of frozen coronal slabs. The study was funded by the DIRP, NIMH under contract (#HHSN 271201400099C) with Icahn School of Medicine at Mount Sinai,1106402 One Gustave L. Levy Place, Box 3500, New York NY 10029-6574. RNA extraction, library preparation and sequencing were performed under contract at Icahn School of Medicine. The Common Mind Consortium (CMC) provided project management support. RNA isolation: Total RNA from 468 HBCC samples was isolated from approximately 100 mg homogenized tissue from each sample by TRIzol/chloroform extraction and purification with the Qiagen RNeasy kit (Cat#74106) according to manufacturer's protocol. Samples were processed in randomized batches of 12. The order of extraction for schizophrenia, bipolar, and MDD disorders and control samples was assigned randomly with respect to diagnosis and all other sample characteristics. The mean total RNA yield was 24.2 ug (+/- 9.0). The RNA Integrity Number (RIN) was determined by 4200 Agilent TapeStation System. Samples with RIN DLPFC RNA-Seq quantified expression data are provided for 364 samples. Data were generated, QC'd, processed and quantified as follows: RNA library preparation and sequencing: All samples submitted to the New York Genome Center for RNAseq were prepared for sequencing in randomized batches of 94. The sequencing libraries were prepared using the KAPA Stranded RNAseq Kit with RiboErase (KAPA Biosystems). rRNA was depleted from 1ug of RNA using the KAPA RiboErase protocol that is integrated into the KAPA Stranded RNAseq Kit. The insert size and DNA concentration of the sequencing library was determined on Fragment Analyzer Automated CE System (Advanced Analytical) and Quant-iT PicoGreen (ThermoFisher) respectively. Schizophrenia Bipolar Control 89 65 210 Table: Numbers of samples in each diagnostic category. RNA-Seq of subgenual anterior cingulate cortex (sgACC): All the 200 post-mortem brain samples (61 controls; 39 bipolar disorder; 46 schizophrenia; 54 major depressive disorder) were collected by the HBCC, DIRP, NIMH. RNA Extraction and Quality Assessment: Tissue from sgACC was pulverized and stored at -80°C. Total RNA was extracted from 50-80 mg of the tissue using QIAGEN RNeasy Lipid Tissue Mini Kit (QIAGEN, Cat. # 74804) with DNase treatment (QIAGEN, Cat. # 79254). The RNA Integrity Number (RIN) for each sample was assessed with high-resolution capillary electrophoresis on the Agilent Bioanalyzer 2100 (Agilent Technologies, Palo Alto, California). The concentration of RNA and their 260/280 ratio (2.1+/- 0.032 SD) were determined with NanoDrop (Thermo Scientific). RNA sequencing: Stranded RNA-Seq libraries were constructed after rRNA depletion using Ribo-Zero GOLD (Illumina). RNA sequencing was performed at National Institute of Health Intramural Sequencing Center (NISC). Schizophrenia Bipolar Control MDD 46 39 61 54 Table: Numbers of samples in each diagnostic category. Whole Genome Sequencing: All brains were collected and dissected at the HBCC, DIRP, NIMH. This study generates whole genome sequencing data using sequencing of DNA in the dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex (ACC) or cerebellum of 443 individuals with schizophrenia, bipolar disorder and major depressive disorder and non-psychiatric controls. The study was funded by the DIRP, NIMH under contract (#HHSN 271201400099C) with Icahn School of Medicine at Mount Sinai,1106402 One Gustave L. Levy Place, Box 3500, New York NY 10029-6574. DNA extraction, library preparation and sequencing were performed under contract at Icahn School of Medicine. The Common Mind Consortium (CMC) provided project management support. All specimens were dissected from right or left hemisphere of frozen coronal slabs. DNA Library Preparation and Sequencing: All samples submitted to the New York Genome Center for WGS were prepared for sequencing in randomized batches of 95. The sequencing libraries were prepared using the Illumina PCR-free DNA sample preparation Kit. The insert size and DNA concentration of the sequencing library was determined on Fragment Analyzer Automated CE System (Advanced Analytical) and Quant-iT PicoGreen (ThermoFisher) respectively. A quantitative PCR assay (KAPA), with primers specific to the adapter sequence, was used to determine the yield and efficiency of the adaptor ligation process. Performed on the Illumina HiSeqX with 30X coverage. Schizophrenia Bipolar Control 115 78 230 Table: Numbers of samples in each diagnostic category. ChIP-Seq: All brains were collected and the dorsolateral prefrontal cortical (DLPFC) samples dissected at the HBCC, DIRP, NIMH. This study generates epigenetic data using sequencing of DNA after chromatin immunoprecipitation (ChIP-Seq) for marks H3K4me3 and H3K27ac in the dorsolateral prefrontal cortex (DLPFC). Dorsolateral prefrontal cortex (DLPFC) specimens were dissected from right or left hemisphere of frozen coronal slabs. The study was funded by the DIRP, NIMH under contract (#HHSN 271201400099C) with Icahn School of Medicine at Mount Sinai,1106402 One Gustave L. Levy Place, Box 3500, New York NY 10029,6574. Chromatin precipitation, library preparation and sequencing were performed under contract at Icahn School of Medicine. The Common Mind Consortium (CMC) provided project management support. Chromatin immunoprecipitation (ChIP) assays for histone marks H3K4me3 and H3K27ac were carried out using Native ChIP. Micrococcal Nuclease (MNase) (Sigma, N3755) treatment was used to digest chromatin into mononucleosomes. The following antibodies were used for chromatin pull-down: anti-H3K4me3 (Cell Signaling, Cat# 9751BC, lot 7) and anti-H3K27ac (Active Motif, Cat# 39133, Lot # 31814008). Histone modification-enriched genomic DNA fragments were recovered using Protein A/G magnetic beads (Thermo Scientific, 88803-88938 or Millipore 16-663), and then washed, eluted, and treated with RNAse A and proteinase K. Final ChIP DNA products were isolated using phenol-chloroform extraction followed by ethanol precipitation. The efficiency of each ChIP assay was validated using Qubit concentration measurement and qPCR for positive (GRIN2B, DARPP32) and negative (HBB) control genomic regions. Only ChIP assays that passed quality control were further processed for library preparation and sequencing; this included ChIP DNA that was not detectable on Qubit but showed a good signal and expected enrichment patterns in qPCR. HISTONE_MARK H3K27ac H3K4me3 Input Bipolar 56 4 7 Control 158 11 24 Schizophrenia 79 11 12 Table: Numbers of individuals in each assay grouped by histone mark or input.Long-Read Whole-Genome Sequencing (WGS) Cohort Description: Brain specimens were obtained from the Human Brain Collection Core (HBCC), part of the NIH NeuroBioBank. Samples were collected under protocols approved by the NIH CNS Institutional Review Board (IRB) (NCT03092687), with informed consent from next-of-kin (NOK). Collection was coordinated through the Offices of the Chief Medical Examiners (MEOs) in Washington, D.C., Northern Virginia, and Central Virginia. Clinical metadata and documentation are publicly available via the NIMH Data Archive (NDA) (Collection #3151) https://nda.nih.gov/edit_collection.html?id=3151 Eligibility Criteria No clinical diagnosis of major neuropsychiatric or neurodegenerative diseaseNo diagnosis of cognitive impairment during life All individuals were confirmed to be neurologically normal at time of deathDemographics Initial cohort size: 155 individuals Ancestry: All individuals self-identified as African or African-admixed Mean age at death: 44.2 years (range: 18–85 years) Sex distribution: 36.4% femaleSample Processing: Frozen frontal cortex tissue was dissected and processed according to the public protocol: https://www.protocols.io/view/processing-human-frontal-cortex-brain-tissue-for-p-kxygxzmmov8j/v2. High-molecular-weight DNA was extracted and libraries were prepared using the Oxford Nanopore Technologies (ONT) LSK-114 kit. Sequencing was performed using ONT PromethION flow cells (R10.4.1 chemistry) Data Processing and Quality Control: Basecalling: Conducted using Guppy v6.38 Read Alignment: Reads were aligned to the GRCh38 reference genome using minimap2 Sample Identity Verification: Sample identity was validated by comparing ONT-derived SNP calls with matched short-read WGS genotypes to ensure concordance and prevent sample swaps Variant Calling and Phasing: Reads were base-called with Guppy v6.38. Reads were aligned to GRCh38 using minimap2. We verified sample identity by cross-checking ONT SNV calls with the existing short-read WGS genotypes, confirming no sample switches. The napu pipeline (https://github.com/nanoporegenomics/napu_wf) produced; haplotype-resolved assemblies, joint small-variant (SNV/indel) calls, and multi-caller structural-variant sets, all reported on GRCh38 and phased where possible. Raw signal data were basecalled to obtain 5-methyl-cytosine (5mC) status; methylation tags were added to the phased BAM files. Genome-wide methylation summaries are provided in BED format.Dataset Filtering and Exclusions: All 155 samples underwent sequencing and SNP-based ancestry inference 8 samples were excluded due to ancestry inconsistent with African or African-admixed background 1 sample was excluded due to insufficient sequencing quality Final Sample Set: 146 high-quality samples from individuals of African or African-admixed ancestry were retained for downstream analyses See PMID: 39764002 for further analysis detailsDiagnosis#SamplesControl155Table: Diagnostic Summary.Note: The data derived from HBCC resources were removed from dbGAP and are now available in the NIMH Data Archive (NDA). They include genotypes, short read whole genome sequencing (WGS), epigenetics (DNA methylation, ChIP-seq for histones), RNA expression (qPCR, microarray, RNA-seq, single nucleus RNA-seq) of various brain regions in cases with schizophrenia, bipolar disorder, major depression, substance use disorders and normative controls. Please access our NDA collection (https://nda.nih.gov/edit_collection.html?id=3151) for further detail.

  Study phs000979

• DAC for Whole Genome Sequencing Expands Diagnostic Utility and Improves Clinical Management in Pediatric Medicine

  Dac EGAC00001000419

• The data access committee for High Grade Serous Ovarian Carcinomas Originate in the Fallopian Tube

  Dac EGAC00001000755

• Data Access Committee for the UCSF Chordoid Glioma of the Third Ventricle Genome Project Dataset

  Dac EGAC00001000776

• Data access committee handling data access requests for biomarker data from the clinical trial IMmotion150.

  Dac EGAC00001000946

• The data access committee for genome-wide cell-free DNA fragmentation in patients with cancer

  Dac EGAC00001001180

• Data access policy for PDTX Breast Cancer RNA-seq data from Ros et al (2020)

  Dac EGAC00001001620

• DAC for "Detection of Gene Fusions using Targeted Next-Generation Sequencing – a Comparative Evaluation"

  Dac EGAC00001001905

• Data Access Committee for study Genetics of Preeclampsia at High Altitudes

  Dac EGAC00001002097

• DAC Committee for the "PIK3CA mutation in a case of CTNNB1 mutant sinonasal glomangiopericytoma" study

  Dac EGAC00001002371

• NPY methylated ctDNA is a promising biomarker for treatment response monitoring in metastatic colorectal cancer

  Dac EGAC00001003001

• DAC for YCC Sarcoma

  Access can be granted by contacting Hyo Song Kim (hyosong77@yuhs.ac).

  Dac EGAC50000000046

• DAC Endoresist

  Data access committee for the Endoresist project of panel sequencing: Professor Johan Hartman

  Dac EGAC50000000174

• Identification of IQCH as a calmodulin-associated protein required for sperm motility in humans

  Study EGAS00001007423

• DAC - A standardised framework for robust fragmentomic feature extraction from cell-free DNA sequencing data

  Dac EGAC00001003530

• CReATe Fertility Centre DAC

  DAC for the management of dataset own by CReATe Fertility Centre.

  Dac EGAC50000000646

• Human glioblastoma single cell DAC (Linnarsson)

  DAC for human glioblastoma single cell sequencing samples

  Dac EGAC50000000575

• Human developing meninges single cell DAC (Linnarsson)

  DAC for human developing meninges single cell sequencing

  Dac EGAC50000000576

• Asan Medical Center Data Access Committee

  Asan Medical Center Data Access Committee for BRISK study.

  Dac EGAC50000000733

• Hepatocellular Carcinoma Spatial transcriptomics Data Access Committee

  This is the DAC for study ST HCC

  Dac EGAC50000000635

• Pan Prostate Cancer Group Data Access Control Committee

  DACC for the PanProstate Cancer Group projects

  Dac EGAC50000000602

• DAC SysMed

  Data access control for datasets submitted by SysMed

  Dac EGAC50000000089

• MOSAIC Window DAC

  Data Access Committee for the MOSAIC Window initiative.

  Dac EGAC50000000398

• DAC for Genetic and Microenvironmental Analysis of PTCL at Department of Hematology, University of Tsukuba

  Dac EGAC50000000600

• DAC for "H3K27me3-mediated epigenetic repression regulates neuroblastoma development and contributes to biological heterogeneity"

  Dac EGAC00001003620

• CD3 bispecific antibody-induced cytokine release is dispensable for cytotoxic T cell activity

  Study EGAS00001003734

• Circulating tumor cells for comprehensive and multiregional non-invasive genetic characterization of multiple myeloma

  Study EGAS00001004288

• A method for multiplexed full-length single-molecule sequencing of the human mitochondrial genome

  Study EGAS00001006280

• Genome sequencing in monozygotic twins discordant for Mayer-Rokitansky-Küster-Hauser syndrome

  Study EGAS00001006371

• EGAD00010000052

  Monozygotic twins that are discordant for schizophrenia (Genotyping)

  Dataset EGAD00010000052

• EGAD00000000027

  eQTL data for European newborns

  Dataset EGAD00000000027

• Exome reads

  Exome sequencing for 2 infertile brothers

  Dataset EGAD00001003193

• Exome data for PDXs

  Exome data of PDX models.

  Dataset EGAD00001001863

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Multimodal Developmental Neurogenetics of Females

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants, both in coding and noncoding regions, will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases, pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002043

• Center for Common Disease Genomics [CCDG] - Neuropsychiatric: Genomics of Autism Spectrum Disorder (GASD)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants, both in coding and noncoding regions, will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of New York Genome Center (NYGC). WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal.An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases, pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002509

• Center for Common Disease Genomics [CCDG] - Neuropsychiatric: Simons Searchlight

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants, both in coding and noncoding regions, will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of New York Genome Center (NYGC). WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal.An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases, pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002512

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Autism Center of Excellence (ACE II)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants, both in coding and noncoding regions, will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases, pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002042

• Genome Wide Association for Asthma and Lung Function

  The SNP Typing for Association with Multiple Phenotypes from Existing Epidemiologic (STAMPEED) asthma project includes subjects with asthma and controls from the Chicago Asthma Genetics Study (CAG), NHLBI multicenter Severe Asthma Research Program (SARP) and NHLBI Collaborative Studies on the Genetics of Asthma CSGA (Wake Forest). All studies included European American and African American children and adults with asthma ranging from mild to severe and adult controls. CAG participants were collected at the University of Chicago. SARP participants were recruited at the NHLBI SARP sites with an emphasis on recruiting severe asthmatics (Moore et al, Am J Respir Crit Care Med, 2010, PMID: 19892860). CSGA cases and controls collected by the Wake Forest investigators were also included. Asthma status was based on both a physician's diagnosis and either bronchodilator reversibility or hyper-responsiveness to methacholine as well as less than 5 pack years of smoking. Genotyping was performed on the Illumina 1Mv1 platform, with individual genotypes called using clustering algorithms as implemented in the BeadStudio software by Illumina. The total number of markers following standard QC was 1,025,129. Imputation was performed using the HapMap phase 2, release 21 SNPs using MACH with the phased HapMap CEU and YRI haplotypes as a reference. Case/control association tests for asthma status were performed using logistic regression in R (http://CRAN.R-project.org/) on genotype dosages, and adjusting for the first principal component from EIGENSTRAT.

  Study phs000355

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Study of Autism Genetics Exploration (SAGE)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs001740

• Center for Common Disease Genomics (CCDG)-Neuropsychiatric: A Study of the Genetic Causes of Complex Pediatric Disorders (CAG)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002004

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Autism Genetic Resource Exchange

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs001766

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: The Autism Simplex Collection (TASC)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs001741

• Center for Sub-Cellular Genomics

  A cell is a highly complex system with distributed molecular physiologies in structured sub-cellular compartments whose interplay with the nuclear genome determine the functional characteristics of the cell. A classic example of distributed genomic processes is found in neurons. Learning and memory require modulation of individual synapses through RNA localization, localized translation, and localized metabolites, such as those from dendritic mitochondria. Dendrites of neurons integrate distributed synaptic signals into both electrical and nuclear transcriptional response. Dysfunction of these distributed genomic functions in neurons can result in a broad spectrum of neuropsychiatric diseases such as bipolar and depressive disorders, autism, among others. The Center for Sub-Cellular Genomics' (CSG) mission is to develop new technologies to enable the dissection of complex genomic interactions within a single cell. New technologies include methods to target DNA and RNA sampling at sub-cellular resolution in live cells; high-throughput isolation and microfluidics chemistry for sub-cellular fractions; sub-cellular resolution mass-spectrometry; and computational tools for analysis of sub-cellular genomics data. The new technologies developed by CSG will enable the broader genomics community to associate sub-cellular processes to cell function and dysfunction. These technologies will be utilized in the center to generate a comprehensive atlas of genomic signatures of sub-cellular compartments in the mammalian neuron system. The technologies and neuron-focused data obtained here will be relevant to neurodegenerative and neuropsychiatric diseases including schizophrenia, depression, autism spectrum disorder, traumatic brain injury, and Alzheimer's.

  Study phs002120

• NHLBI TOPMed: Pulmonary Fibrosis Whole Genome Sequencing

  This is a set of cases diagnosed with idiopathic pulmonary fibrosis, a fatal interstitial lung disease. These cases were included in the TOPMed phase three studies. The planned study will compare these cases to within-TOPMed controls for genome-wide association studies.

  Study phs001607

• Bulk RNA Sequencing of 86 Human Donor Lungs

  The goal of this study was to characterize the effect of age on gene expression in normal human lung. We collected distal lung samples from 86 human donors distributed in age between 16 and 76 years and extracted RNA for Illumina-based sequencing.

  Study phs002484

• Whole-Genome and Exome Sequencing in clear-cell Renal Cell Carcinoma (ccRCC)

  Study to evaluate the molecular genetics of ccRCC through integrative analyses of tumors and tumorgrafts using multiple platforms in clinically annotated samples. Genome and exome data for patients giving consent to have the information available on a public, secure, database is being submitted.

  Study phs000491

• Phase 2 Study of Pembrolizumab in Combination with Gemcitabine and Cisplatin as Neoadjuvant Therapy

  The purpose of the clinical trial (NCT02690558) is to evaluate whether adding pembrolizumab (Keytruda) to the combination of gemcitabine and cisplatin will improve shrinkage of the tumor before having a cystectomy, for people with muscle-invasive bladder cancer (MIBC).

  Study phs003452

• Transplant Outcomes in Aplastic Anemia (TOAA): GWAS and Whole Exome Sequence Data

  The TOAA study is a research collaboration between the National Cancer Institute and the Center for International Blood and Marrow Transplant Research. The study aims to identify molecular predictors of outcomes after hematopoietic cell transplant in patients with severe aplastic anemia.

  Study phs001710

• COVID_Methyl_scRNA

  DAC for the study of whole-genome enzymatic DNA methylation sequencing and single-cell RNA sequencing of nasal cells from COVID-19 patients. Nasal swabs were collected from COVID-19 patients during the acute phase of infection as well as 3 and 12-months post-infection. Cells isolated from nasal swabs were subjected to whole-genome enzymatic DNA methylation sequencing (n=33) and scRNA-seq (n=24). For n=12 former COVID-19 pateints, follow-up samples were collected at 3 and 12 months post-infection and subjected to scRNA-seq.

  Dac EGAC50000000197

• Comprehensive molecular profiling with whole-exome sequencing (WES) of PDX tumors

  To identify biomarkers for the antitumor effects of molecularly targeted therapy, we performed whole-exome sequencing on 795 patient-derived xenograft (PDX) samples established from 269 patients with solid tumors and 125 frozen tissue samples obtained at the time of surgery.

  Study JGAS000853

• Target sequencing of 8 hereditary prostate cancer genes in Japanese

  We performed a case control association study for variants in coding regions of 8 hereditary prostate cancer genes in 7,636 male unselected prostate cancer patients, 12,366 male controls of Japanese ancestry. The 8 genes were ATM, BRCA1, BRCA2, BRIP1, CHEK2, HOXB13, NBN, and PALB2.

  Study JGAS000203

• Phase II clinical trial of adult Philadelphia chromosome-negative precursor B-cell acute lymphocytic leukemia with combination chemotherapy

  Adults with B-ALL showed extremely inferior outcomes when compared to children. Genetic basis undelying adult B-ALL is largely unknown. We performed integrated analysis of RNA-seq and target DNA-seq for the patients enrolled in BALL213 study.

  Study JGAS000278

• Single cell RNA sequencing of human umbilical cord blood lymphoid progenitors

  We performed single cell RNA sequencing of human umbilical cord blood. To explore the differentiation pathway for human lymphoid lineage deeply, our target cells contain CD34+CD38+CD45RA+CD19-CD7-CD10+ cells (CD10SP). All these populations are evaluated by single cell RNA sequencing.

  Study JGAS000551

• DAC for STimage project

  The STimage Data Access Committee (DAC) oversees the access and distribution of datasets associated with the STimage project. This project focuses on the development and benchmarking of deep learning frameworks for spatial transcriptomics and digital pathology. The datasets managed by this DAC typically comprise paired histology images (H&E) and spatial gene expression data (e.g., 10x Genomics Visium) derived from human tissue samples. Our primary objective is to facilitate bona fide biomedical research while strictly protecting participant privacy and ensuring data is used in accordance with the original consent and ethical approvals.

  Dac EGAC50000000867

• single cell RNA sequencing and ATAC sequencing, and Whole Genome sequencing of ALS patients

  To investigate ALS pathology at the cellular level, we conducted a multiome (single-nucleus RNA sequencing and assay for transposase-accessible chromatin using sequencing) analysis using the motor cortex and spinal cord of patients with ALS and control individuals.

  Study JGAS000852

• SNU_PROSPECTIVE

  This study includes 5 paired samples of Korean AML paitients. 5 AML tumor samples are paired with each normal(Saliva or BM derived) sequencing data.The results of this WES study were analyzed by Mutect and GATK for ranking germline variants and cancer variants to make mutational profile matrix.

  Study EGAS00001002154

• WGS data for ctDNA monitoring for NSCLC in TRACERx

  Ultrasensitive circulating tumour DNA (ctDNA) detection can stratify clinical risk in early-stage non-small cell lung cancer (NSCLC). Here, we applied a whole-genome, tumour-informed approach to study ctDNA in more than 400 NSCLC of the TRACERx project

  Study EGAS50000001187

• Clonal expansion and epigenetic reprogramming following deletion or amplification of mutant IDH

  Initially IDH1 mutant gliomas are shown to have IDH1 is deletions or amplifications at recurrence yielding a higher-grade tumor with a reprogrammed epigenome. We also report systematic selection for cells with IDH1 CNA in vitro and in vivo.

  Study EGAS00001002618

• A Novel APP p.V742L variant in a patient with ischemic small vessel disease enhances FE65 signalling

  We identified a novel variant in the APP gene associated with cerebral ischemic strokes, which is not seen before with APP variants. This results in increased affinity of APP for FE65, APP’s most important intracellular interactor that regulates gene transcription.

  Study EGAS50000001283

• Yemen_and_Chad_Genotyping

  HumanOmni2.5-8 data from Chad and Yemen.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001001231