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• BipEx_Ouwehand_Cambridge

  "BipEx_EGAS000010058454_Ouwehand_Cambridge Bipolar Disorder Exomes. This research project was a collaboration between Cambridge University, UK and the Stanley Center at the Broad Institute. In this project we sequenced and analyzed the whole exomes of 2,873 Bipolar case/control samples from collaborators in UK. Genomic DNA from each sample was sequenced to a mean depth of 20x."

  Dac EGAC50000000138

• BipEx_Posthuma_Amsterdam

  "BipEx_EGAS00001005857_Posthuma_Amsterdam Bipolar Disorder Exomes. This research project was a collaboration between VU and the Stanley Center at the Broad Institute. In this project we sequenced and analyzed the whole exomes of 943 Bipolar case/control samples from collaborators in the Netherlands. Genomic DNA from each sample was sequenced to a mean depth of 20x."

  Dac EGAC50000000143

• scMultiome analysis of human tongue cancer organoids

  We performed single-cell Multiome analysis (10X Genomics) of a uniquely established human tongue cancer organoids, including both chemo-sensitive organoids and chemotherapy resistant organoids.

  Study JGAS000606

• BipEx_Owen_Cardiff

  "BipEx_EGAS000010058455_Owen_Cardiff Bipolar Disorder Exomes. This research project was a collaboration between Cardiff University, UK and the Stanley Center at the Broad Institute. In this project we sequenced and analyzed the whole exomes of 1,106 Bipolar case/control samples from collaborators in UK. Genomic DNA from each sample was sequenced to a mean depth of 20x."

  Dac EGAC50000000139

• Single-nucleus RNA-sequencing of normal adrenal cortex and adrenocortical tumors

  This study reports a single-nucleus transcriptome atlas of steroid, stromal and immune cells in 38 human normal adrenals and adrenocortical tumors.

  Study EGAS50000000585

• Nanopore sequencing of FSHD, BAMS and healthy control fibroblast cell lines

  The goal was to resolve the complex genetic and epigenetic structures of D4Z4 alleles, a macrosatellite whose desilencing results in FSHD.

  Study EGAS50000001065

• Long-read methylation analysis of breast cancer using the enzymatic base conversion and the nanopore sequencing

  Long-read methylation analysis by a nanopore sequnecer PromethION was performed using breast samples collected from breast cacner patients.

  Study JGAS000265

• BipEx_Adolfsson_Umea

  "BipEx_EGAS00001005842_Umea University Bipolar Disorder Exomes. This research project was a collaboration between Umea University, Sweden and the Stanley Center at the Broad Institute. In this project we sequenced and analyzed the whole exomes of 924 Bipolar case/control samples from collaborators in Sweden. Genomic DNA from each sample was sequenced to a mean depth of 20x."

  Dac EGAC50000000133

• WHOLE GENOME SEQUENCING OF CLEAR CELL RENAL CELL CARCINOMA IN VHL PATIENT

  This submission contains Whole Genome Sequencing data of six ccRCCs, four cysts and one blood DNA sample of a VHL patient.

  Study EGAS50000000295

• Sequence variation of rs774984872G>T

  This dataset contains the genotypes status of rs774984872G>T, a loss of function variant in ANGPTL8. 8 individuals had the reference allele (G/G), 5 were heterozygous (G/T) and 1 was a knock out (T/T).

  Dataset EGAD50000002135

• Capture-based NGS

  Capture-based NGS obtained using the "all-CLL" panel (also known as SOPHiA DDM (TM) Community CLL Clonality Solution). Library preparation was performed following SOPHiA GENETICS recommendations using 200 ng genomic DNA. Libraries were sequenced on a MiSeq instrument (2x300 bp, Illumina) aiming at a mean coverage of 1,000x.

  Dataset EGAD00001011151

• Synchronous Colorectal Cancer genome sequencing

  Here, we performed a characterisation of 12 tumours and matched normal samples from 3 syCRC patients by whole genome sequencing: Patient A (tumours A1 and A2), Patient B (tumours B1-B5), and Patient C (tumours C1-C5). Somatic SNVs, indels and stuructural variants were called.

  Dataset EGAD00001006131

• WGS and RNA-Seq data from a GBM patient PT-PV2594

  WGS and RNA-Seq data from a GBM patient PT-PV2594

  Dataset EGAD00001008526

• WGS and WES data for manuscript titled: ctDNA as a biomarker of progression in oesophageal adenocarcinoma

  WGS and WES data for manuscript titled: ctDNA as a biomarker of progression in oesophageal adenocarcinoma

  Dataset EGAD00001008554

• RNA-Seq data for manuscript titled: A sporadic Alzheimers blood-brain barrier model for developing ultrasound-mediated delivery of Aducanumab and anti-Tau antibodies

  RNA-Seq data for manuscript titled: A sporadic Alzheimers blood-brain barrier model for developing ultrasound-mediated delivery of Aducanumab and anti-Tau antibodies

  Dataset EGAD00001008670

• Variants from a subset of genes from WES of adult AML patient samples

  Dataset comprises one vcf file containing variants from a list of genes (DNA repair and metabolism associated genes) subset from WES of an adult AML cohort. The cohort contains 145 patient samples. WES was performed using Illumina platform.

  Dataset EGAD00001008700

• HV31 - De novo assembly of eight immune system regions

  De novo assembly of eight immune system regions for individual HV31, generated using a multi-platform pipeline. A full description of the generation of these assemblies can be found at https://doi.org/10.1101/2021.02.03.429586.

  Dataset EGAD00001007050

• Genetics of Microcephalic Osteodysplatics Primordial Dwarfism

  This project aims to find causal variants in 50 patients diagnosed with Microcephalic Osteodysplastic Primordial Dwarfism (MOPD), of presumed recessive inheritance performing whole exome sequencing to ~50x mean depth.This is a collaboration with Prof A. Jackson, MRC Human Genetics Unit, Edinburgh

  Dataset EGAD00001000342

• Cancer-Normal chronic myeloid leukaemia sequencing data for "A comparative analysis of algorithms for somatic SNV detection in cancer", Bioinformatics 29 (2013) 2223

  This dataset contains the fastq sequencing data collected from bone marrow DNA of a chronic myeloid leukaemia patient at time of diagnosis.

  Dataset EGAD00001000967

• Whole genome sequencing of ATCWGS42

  WGS of patient ATCWGS42, a patient who responded to BRAF/MEK targeted therapy but relapsed. This folder includes multi-region sequencing from the patient.

  Study EGAS50000001489

• NHLBI TOPMed: Defining the Time-Dependent Genetic and Transcriptomic Responses to Cardiac Injury Among Patients with Arrhythmias

  The UMMS miRhythm Study is an ongoing study of adult patients undergoing an elective electrophysiology study or arrhythmia ablation procedure for a supraventricular or ventricular arrhythmia, including atrial fibrillation (AF). Atrial fibrillation is a major clinical and public health problem that is related to atrial pathologic remodeling. Few tools are available to quantify the activity or extent of this remodeling, rendering it difficult to identify individuals at risk for AF. Previous studies have suggested an important role for miRNA in cardiovascular disease through gene expression regulation, making this a promising avenue for studying AF mechanisms. The aim of the study is to determine the time-dependent changes to key circulating miRNAs in a model of planned atrial injury and remodeling via ablation. Such knowledge might provide additional insight into the biology and activity of the acute atrial injury response, and furthermore, inform new targets for development of preventative interventions or allow for better AF risk stratification. To assess pathways regulating atrial pathological remodeling, patient blood samples are collected prior to their ablation procedures and also at a regularly scheduled 1-month follow-up appointment. Plasma expression of miRNA is measured using high-throughput quantitative reverse transcriptase polymerase chain reaction (RT-qPCR), providing novel insights into the regulatory processes underlying AF, as well as acute atrial injury in vivo. Additionally, data collected from whole-genome sequencing (WGS) is used to supplement miRNA analyses and further explore new relations between genes and abnormal heart rhythm.

  Study phs001434

• eMERGE: Genetics of Complex Pediatric Disorders from the Center for Applied Genomics

  The Center for Applied Genomics (CAG) is a specialized Center of Emphasis at the Children's Hospital of Philadelphia (CHOP) with the primary goal of translating basic research findings to medical innovations. The mission of CAG is to develop new and better ways to diagnose and treat children affected by rare and complex medical disorders. We aim to discover genetic causes for the most prevalent diseases of childhood including asthma, autism, diabetes, epilepsy, obesity, schizophrenia, pediatric cancer, and a range of rare diseases. Ultimately, our objective is to generate new diagnostic tests and to guide physicians to the most appropriate therapies. The CAG is one of the world's largest genetics research programs in pediatrics, and the only center at a pediatric hospital to have established a large-scale biobank of genotyped samples. The electronic Medical Records and Genomics (eMERGE) Network is a consortium of 9 clinical sites with EMR linked DNA biobanks, including Northwestern University and its NUgene biobank, funded by the NHGRI (National Human Genome Research Institute) to investigate the use of electronic medical record systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 100,000 individuals using EMR-derived phenotypes and DNA from linked biorepositories. Using electronic phenotyping methods, the consortium has been and is using DNA samples from all participating sites to explore the genetic determinants of approximately 80 phenotypes, including both diseases and traits, for which the electronic phenotyping algorithms have or are being published on PheKB.org.

  Study phs001165

• Rare Diseases Clinical Research Network (RDCRN) Vasculitis Clinical Research Consortium (VCRC) Longitudinal Protocol for Polyarteritis Nodosa

  The development and validation of accurate biological markers and predictors of disease activity and outcome in Polyarteritis Nodosa (PAN), a form a idiopathic vasculitis, would have a major positive impact on the clinical care of patients with this rare disease, be an important advance in the design of clinical trials and feasibility of new drug development, and provide important insight into the pathogenesis of this condition. Current measures of disease activity, including erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP), while helpful in selected settings, are mostly considered highly flawed and markers whose poor correlation with disease flares makes them inadequate to help guide therapy. Similarly, ESR and CRP are clearly unhelpful as diagnostic tests for vasculitis. The current state of clinical investigation for vasculitis relies heavily upon determination of disease status solely by clinical presentation and investigator opinion. Thus, discovery and validation of more precise markers that quantifiably measure activity, predict disease course, and correlate with an important biological process, would be of great importance. Knowledge to be gained from this study could potentially be highly important. Discovery of effective biomarkers of vasculitis and assessment of long term disease course and complications by the collection of clinical and diagnostic imaging studies proposed could lead to better care and less toxic treatment regimens. Furthermore, insight into the pathophysiology of vasculitis could be gained and this might lead to better treatments and improved outcomes in terms of reduction of vascular and treatment related complications. Ideally, excellent biomarkers reflect a sophisticated understanding of the pathophysiology of a disease.

  Study phs000590

• Non-Coding Autoimmune Risk Variant Defines Role for ICOS in T Peripheral Helper Cell Development

  Our fine-mapping of 76 non-MHC loci associated with risk for rheumatoid arthritis (RA, 11,475 cases, 15,870 controls) has recently identified rs117701653, a non-coding single nucleotide polymorphism (SNP) in the CTLA4/CD28/ICOS locus, as the variant most likely to modulate RA risk within this region, with the minor allele C showing disease protection compared to the major allele A. We found that this SNP exhibits allele-specific protein binding and transcriptional modulation, further supporting its regulatory nature. Here, we aimed to determine whether the functional non-coding variant rs117701653 modulates the expression of genes within the CD28/CTLA4/ICOS locus. To address this question, we employed low-input RNA sequencing using the Illumina SmartSeq2 platform on resting total CD4+ T cells from the blood of 24 healthy subjects (8 AA, 8 AC, 8 CC) recruited based on genotype from the Mass General Brigham biobank. We performed a targeted cis-eQTL mapping analysis for protein coding genes with transcription start sites (TSS) within a 1MB window of SNP rs117701653. These genes included WDR12, NBEAL1, CYP20A1, ABI2, RAPH1, CD28, CTLA4, ICOS, and PARD3B. Surprisingly, allelic variation at rs117701653 correlated strongly with expression of ICOS. This suggests that individuals carrying the protective allele C at rs117701653 express lower levels of ICOS, implicating ICOS as a plausible target gene influenced by this protective allele. The raw sequence data from the 24 healthy individuals are available through dbGAP. Gene expression matrices have been deposited to GEO accession GSE235868.

  Study phs003448

• NCI Cancer Model Development for the Human Cancer Model Initiative (HCMI)

  The Cancer Model Development Center (CMDC) project is part of the Human Cancer Model Initiative (HCMI) and is managed by the Office of Cancer Genomics within the Center for Cancer Genomics at the National Cancer Institute (NCI). The goal of the HCMI is to generate approximately 1000 novel human "next generation" tumor-derived models as a community resource. The models aim to faithfully recapitulate the cellular complexity and heterogeneity of human tumors. The CMDCs are tasked by the NCI with producing a proportion of the cancer models. Wellcome Sanger Institute (WSI) is a contributing member of the HCMI. Models developed by HCMI are being derived from a number of tumor types and subtypes (see below), including rare adult, pediatric and understudied cancers. The number of models and the clinical sites from which patient samples are acquired is increasing. Normal tissue, originating tumor, and tumor-derived models are sequenced. The next generation cancer models are associated with rich clinical and genomic data through the NCI Genomic Data Commons for all researchers to advance cancer research. The CMDC and HCMI models will serve as excellent tools in research to provide insight into the pathways that influence tumor growth and progression, how tumors respond to therapy, etc. Ultimately, the data generated using these models will support the development of personalized oncology. There is a delay between registered IDs and model availability. The current model count is found in The HCMI Searchable Catalog which is updated when new models are available for distribution to the research community.

  Study phs001486