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

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

  Study phs000180

• Copy-Number Analysis of Understudied Black Women Ovarian Cancers

  Two general types of genetic alterations drive cancer progression: mutations and copy number alterations (CNAs). With the exception of p53, single nucleotide variant mutations (here, simply "mutations") are not drivers of 80% of tumors within the most frequent and deadly histotype of ovarian cancer: serous ovarian cancer (SOC). Rather, CNAs are more prevalent in SOC than any other cancer type studied by phs000178 The Cancer Genome Atlas (TCGA). Fully two-thirds of genes are altered by CNAs in the average SOC. Bioinformatic methods have shown that these CNAs modulate specific molecular pathways, such as autophagy, and this finding enabled effective pathway-targeted therapies1. Unfortunately, TCGA ovarian cancer data lacks racial diversity. Only 6% of SOC tumors (n=32) studied are from African origin. Black women diagnosed with ovarian cancer are more likely to die from their disease than white women. Five-year survival after diagnosis is 31% among blacks versus 42% among whites, and this disparity is seen in every age group and tumor stage distribution. Dr. Kelemen examined data from 365 white and 95 black ovarian cancer patients from the Hollings Cancer Center (HCC) Cancer Registry between 2000 and 2015 and found that black women had an 81% higher risk of death after diagnosis compared to white women (HR 1.81, 95% CI 1.35-2.43), independent of diagnosis center (98% were treated at HCC but half were diagnosed elsewhere), tumor stage, histology, extent of residual tumor remaining following surgery, insurance status, smoking, type of treatment for ovarian cancer, and age-adjusted Charlson comorbidity index. This disparity remained even when restricted to women who received the standard of care, surgery-chemotherapy sequence (HR 1.79, 95% CI 1.10-2.89). Despite receiving the same treatment, black race was still associated with inferior survival. A similar disparity has been reported by others, indicating that the HCC experience is representative of national data. Here, an additional set of high-grade serous ovarian cancer primary tumors were acquired from biorepositories with a focus on expanding data originating from African American women. Data were analyzed for copy-number alterations using exome sequencing and control-FREEC software.

  Study phs002313

• RNA-Seq of Whole Blood from Patients with Intracranial Aneurysms

  Rupture of intracranial aneurysms (IAs) causes intracranial hemorrhaging that leads to high rates of neurological deficits and death. Although rupture rates are low, clinicians must decide whether to treat or monitor these potentially dangerous lesions. In the current clinical practice, the most common metric to measure risk of rupture is aneurysm size. However, clinical data show that small aneurysms can also rupture. As a result, alternative clinical stratification scores have been proposed, including the PHASES (Population, Hypertension, Age, Size, Earlier subarachnoid hemorrhage, and Site) score based on patient demographics and IA characteristics to stratify ruptured and unruptured IAs, and the Rupture Resemblance Score (RRS) that stratifies ruptured and unruptured IAs based on hemodynamic and morphological properties. However, all metrics require imaging on digital subtraction angiography (DSA), which is invasive, expensive, requires the use of X-rays, and is associated with transient or permanent neurological and non-neurological complications. We hypothesize that individuals with dangerous IAs have detectable gene expression differences in their blood that could be used as biomarkers to determine rupture risk. We propose to use whole blood transcriptomes to develop a "one-stop" diagnostic test that can detect the presence of IAs and determine the risk of rupture based on circulating RNA expression biomarkers using our prototype AneuScreenTM platform. We aim to develop and validate biomarkers to predict risk, as calculated by the currently-used metric of aneurysm size, the clinical PHASES score, and the RRS. Here, we collected blood samples from consented individuals who were receiving cerebral imaging at the Gates Vascular Institute (Buffalo, NY) for intracranial aneurysm. RNA extracted from blood samples (n=43) was subjected to RNA-sequencing and added to our existing database (n=44). When combined with our previous data, we had transcriptome data from 68 aneurysms (after additional sample filtering). This dataset was stratified into low- and high-risk according to IA size, PHASES score, and RRS score. Differentially expressed genes were identified and used to construct predictive models. Fastq files and basic demographic information for the 43 newly-sequenced intracranial aneurysm samples on this project will be available through dbGaP.

  Study phs003072

• Centers for Common Disease Genomics (CCDG) - Whole Genome Sequencing in Type 1 Diabetes (T1DGC)

  The Type 1 Diabetes Genetics Consortium (T1DGC) was established to collect resources (biological samples and data) and conduct research to better understand the genetic basis of type 1 diabetes (T1D). Collection was initiated by ascertaining affected sib-pair families (both parents, two affected siblings and, when available, an unaffected sibling), collected from five geographic regions through four recruitment networks (Asia-Pacific, Europe, North America, United Kingdom). In addition, the T1DGC collected trio families (both parents and affected child) and cases and controls from low-prevalence populations (African-American, with four grandparents self-reporting as African ancestry; Mexican-American, with four grandparents self-reporting as ancestry from Mexico). The T1DGC also served as a repository for contributed collections from other studies, all meeting the broad data-sharing policy of the T1DGC, for inclusion in the genetic studies. These collections include T1D case samples ascertained from the UK Genetic Resource Investigating Diabetes (UK GRID) cohort, SEARCH for Diabetes in Youth (SEARCH), The Genetics of Kidneys in Diabetes (GoKinD), and control samples obtained from the British 1958 Birth Cohort, the UK National Blood Services collection, CLEAR (Consortium for the Longitudinal Evaluation of African-Americans with Early Rheumatoid Arthritis), the New York Cancer Project (NYCP), and other cohorts. For the NHGRI-funded Centers for Common Disease Genomics (CCDG) project, participants with T1D and ancestry-matched controls were identified through the T1DGC, either through direct ascertainment or by contribution from other sources to the T1DGC. As the CCDG has focused initially on non-Caucasian populations for whole genome sequencing, T1DGC participants of African, Mexican and Asian ancestry (targeting ~1200 cases and ~1200 controls in each ancestral group) and a small group of participants of Northern European ancestry (~100 cases, ~100 controls) were to be contributed to the study. Whole genome sequencing of T1DGC samples would be conducted at Washington University McDonnell Genome Institute and based upon matching case-control status within an ancestry group and prioritization by the CCDG.

  Study phs001222

• NIDDK IBD Genetics Consortium Ulcerative Colitis Genome-Wide Association Study

  This dataset contains data for 1,028 white, non-Hispanic, European ancestry individuals with ulcerative colitis who were included in a genome-wide association study published by Silverberg et al. (2009). These individuals were ascertained in North America and selected to have either left-sided or extensive disease (i.e., individuals with proctitis only were excluded). Genotyping was performed using the Illumina HumanHap300v2 (n = 540) and HumanHap550v3 (n = 488) Genotyping BeadChips at the Feinstein Institute for Medical Research. Control data (not included) were obtained from the NIDDK IBD Genetics Consortium's Crohn's Disease GWAS (available from dbGaP) and from studies 64 and 65 deposited in the Illumina iControlDB. Seven hundred eighty individuals in this dataset were taken from the NIDDK IBD Genetics Consortium cell line repository (http://www.niddkrepository.org). These individuals are identified in the file dbGaP_SubjectDS.txt. The subject IDs for these individuals may be used to request corresponding samples for follow-up research through the repository. In addition, complete phenotype data for these individuals are included, collected using the Consortium's forms and phenotyping manual (both included). The remaining 248 individuals were identified from pre-existing collections ascertained by members of the Consortium or their collaborators. For these samples, several of the items in the phenotype file are incomplete. Those who wish to replicate the results in Silverberg et al. should note that 6 individuals with missing genotype rates > 0.07 were excluded from that analysis (leaving 1,022 affected samples total). In addition, the minor allele frequencies (MAFs) reported in the publication were calculated using only those individuals who were included in the allelic association tests (n = 977 for SNPs included in the HumanHap300 and n = 476 for SNPs included only in the HumanHap550). These tests were performed using conditional logistic regression on gender-ancestry strata; individuals who were not placed in a stratum (using the procedure described in the supplementary information for Silverberg et al.) were excluded. The indicator variables hh300 and hh550 in the file dbGaP_PhenotypeDS.txt identify the samples included in the allelic association tests, and may be used to replicate the published MAFs among affected individuals.

  Study phs000345

• Mutational evolution in a lobular breast tumour profiled at single nucleotide resolution

  We have used next generation sequencing to sequence genomes (>43-fold coverage) and transcriptomes of an oestrogen-receptor-alpha-positive metastatic lobular breast cancer at depth. We found 32 somatic non-synonymous coding mutations present in the metastasis, and measured the frequency of these somatic mutations in DNA from the primary tumour of the same patient.

  Study EGAS00000000054

• Paired-end RNA-Seq Dataset of 72 Brain Organoid Samples: Sequencing and Gene Expression Analysis

  72 fastq sequencing files of 72 brain-organoid paired-end sequenced samples (R1/R2). RNA sequencing libraries were prepared using the NEBNext Ultra II RNA Library Prep Kit for Illumina following the manufacturer’s instructions (New England Biolabs, USA). mRNAs were first enriched with Oligo(dT) beads. The samples were sequenced using a 2x150 Pair-End configuration (ver. 1.5) on an Illumina NovaSeq 6000 The raw sequence reads were aligned to the human reference genome GRCh38 using the STAR aligner (Dobin et al., 2013). Gene expression levels were quantified using the FeatureCounts tool (Liao et al., 2014) (file: "All_Counts_final.xlsx") For more information, see "Study Description"

  Dataset EGAD50000000935

• Methylation sequencing dataset for "Molecular counting enables accurate and precise quantification of methylated ctDNA"

  Methylation sequencing dataset for "Molecular counting enables accurate and precise quantification of methylated ctDNA." This dataset was created by running samples through Northstar Response, where bisulfite converted samples are amplified in a multiplex PCR targeting more than 500 genomic regions. Samples in this dataset include contrived samples made from 84 different tumors DNA, each mixed with their matched buffy coat DNA at varying ratios, as well as cfDNA and buffy coat DNA samples collected from 67 cancer patients undergoing treatment. All samples were sequenced on Illumina NextSeq 2000 with single end 100 cycle sequencing; files are in fastq.gz format.

  Dataset EGAD50000001015

• CLUSTER Consortium RNAseq PBMC Dataset of pre-methotrexate UK JIA patients cohort

  This is the RNAseq PBMC dataset (paired FASTQ files, n = 82 paired files) generated from pre-methotrexate treatment JIA patients cohort samples. Total PBMC were isolated from pre-treated blood of JIA patients and clinical metadata is also included in this dataset. Samples were sequenced on the NovaSeq6000 instrument (Illumina, San Diego, US) at 300pM, using a 100bp paired read run with corresponding 8bp dual sample index and 5bp or 8bp unique molecular index reads. Run data were demultiplexed and converted to FASTQ files using Illumina’s BCL Convert Software v3.75.

  Dataset EGAD50000001457

• CLUSTER Consortium RNAseq CD8 Dataset of pre-methotrexate UK JIA patients cohort

  This is the RNAseq CD8 dataset (paired FASTQ files, n = 85 paired files) generated from pre-methotrexate treatment JIA patients cohort samples. CD8 cells were sorted from isolated total PBMC by cell sorter (BD FACSAriaTM III, BD Biosciences). Clinical metadata is also included in this dataset. Samples were sequenced on the NovaSeq6000 instrument (Illumina, San Diego, US) at 300pM, using a 100bp paired read run with corresponding 8bp dual sample index and 5bp or 8bp unique molecular index reads. Run data were demultiplexed and converted to FASTQ files using Illumina’s BCL Convert Software v3.75.

  Dataset EGAD50000001456

• CLUSTER Consortium RNAseq CD4 Dataset of pre-methotrexate UK JIA patients cohort

  This is the RNAseq CD4 dataset (paired FASTQ files, n = 86 paired files) generated from pre-methotrexate treatment JIA patients cohort samples. CD4 cells were sorted from isolated total PBMC by cell sorter (BD FACSAriaTM III, BD Biosciences). Clinical metadata is also included in this dataset. Samples were sequenced on the NovaSeq6000 instrument (Illumina, San Diego, US) at 300pM, using a 100bp paired read run with corresponding 8bp dual sample index and 5bp or 8bp unique molecular index reads. Run data were demultiplexed and converted to FASTQ files using Illumina’s BCL Convert Software v3.75.

  Dataset EGAD50000001455

• CLUSTER Consortium RNAseq CD14 Dataset of pre-methotrexate UK JIA patients cohort

  This is the RNAseq CD14 dataset (paired FASTQ files, n = 80 paired files) generated from pre-methotrexate treatment JIA patients cohort samples. CD14 cells were sorted from isolated total PBMC by cell sorter (BD FACSAriaTM III, BD Biosciences). Clinical metadata is also included in this dataset. Samples were sequenced on the NovaSeq6000 instrument (Illumina, San Diego, US) at 300pM, using a 100bp paired read run with corresponding 8bp dual sample index and 5bp or 8bp unique molecular index reads. Run data were demultiplexed and converted to FASTQ files using Illumina’s BCL Convert Software v3.75.

  Dataset EGAD50000001458

• CLUSTER Consortium RNAseq CD19 Dataset of pre-methotrexate UK JIA patients cohort

  This is the RNAseq CD19 dataset (paired FASTQ files, n = 83 paired files) generated from pre-methotrexate treatment JIA patients cohort samples. CD19 cells were sorted from isolated total PBMC by cell sorter (BD FACSAriaTM III, BD Biosciences). Clinical metadata is also included in this dataset. Samples were sequenced on the NovaSeq6000 instrument (Illumina, San Diego, US) at 300pM, using a 100bp paired read run with corresponding 8bp dual sample index and 5bp or 8bp unique molecular index reads. Run data were demultiplexed and converted to FASTQ files using Illumina’s BCL Convert Software v3.75.

  Dataset EGAD50000001459

• SCANDARE HNSCC DNA targeted panel sequencing

  SCANDARE (NCT03017573) is a multicentric biobanking study, enrolling adult patients with newly diagnosed head and neck squamous cell carcinoma (HNSCC), triple negative breast cancer (TNBC), ovarian and cervical cancer. Tumor tissue and blood samples are collected at several time points during patient's journey, including at diagnosis, post-neoadjuvant chemotherapy in case of neoadjuvant treatment, at surgery, at recurrence and at disease progression following treatment initiated at recurrence. Since its launch in 2017 at Institut Curie, SCANDARE has enabled the longitudinal collection and preservation of samples for in-depth analyses. Here, we describe molecular alterations in 232 FFPE samples at baseline from 83 HNSCC patients using DNA targeted panel sequencing, with FASTQ files deposited in the dataset.

  Dataset EGAD50000001654

• Analysis of chromosomal background of cancerous mutations using a long-read sequencer

  In our previous study detecting cancerous complex SVs, we performed whole-genome sequencing of lung cancer clinical samples using PromethION. In this study, its depth and quality was substantially enriched and we conducted the phasing analysis in human lung cancer genomes. In addition, long read and short read RNA-seq was performed for transcriptome analysis.

  Study JGAS000349

• Comparison between b2m KO and US2 expressing iPSC lines

  This dataset contains bulk RNA-seq transcriptomic profiles from a human induced pluripotent stem cell (hiPSC) line and three engineered derivatives: B2M knockout, EGFP overexpression, and US2 overexpression, alongside the parental line. Total RNA integrity was assessed using the Experion RNA StdSens 1K Analysis Kit on the Bio-Rad Experion Automated Electrophoresis System prior to library preparation and sequencing. Libraries were sequenced on an Illumina NovaSeq 6000 generating ~20 million reads per sample. Raw read quality was assessed with FastQC, reads were aligned to the human reference genome hg38 (Ensembl release 109) using STAR, and alignment/library QC metrics (e.g., orientation, composition, coverage) were evaluated with Picard.

  Dataset EGAD50000002321

• scRNAseq data of human innate lymphoid cells (ILCs) and natural killer (NK) cells from fresh umbilical cord blood

  scRNA-seq was performed on the BD Rhapsody platform. ILCs (ILC1, ILC2, ILC3) and NK cells were bulk sorted from fresh cord bloods of 3 donors. The 4 samples containing ILCs and NK cells from each donor were stained with Sampletag antibodies (12 samples in total) before Abseq staining with the BD Immune Discovery panel and 6 additional antibodies was performed to better classify immune cell subsets. The sorted population were pooled before loading onto the BD Rhapsody cartridge. The manufacturer's protocol was followed for library construction and samples were sequenced on a Illumina NovaSeq 6000.

  Dataset EGAD50000001713

• Archive growth Statistics

  EGA Statistics Bibliography Growth Community Archive Distribution Catalogue In this section, we expose the overall volume of data available to download and the different archived file types. EGA Archive growth in size and number of files The below figure represents the EGA archive growth in size (TB) and number of files. EGA Archive Growth per year lineChart('ega-archive-growth', 'https://stats.ega-archive.org/growth/archive', ['Volume (TB)'], ['T']) Archive files The below figure represents on a initial level, the percentage of archived files by data technology. The second level, acessible by clicking in the required extension, displays the number of files by extension archived in the ega archive. Please click over the extension to learn more about the files. EGA Archived Files barChart('ega-archived-files', 'https://stats.ega-archive.org/extensions', false ,'', true)

  Documentation about/statistics/archive

• Preeclampsia InterPregGen Consortium: Whole Genome Sequencing of 100 unrelated Kazakhs (DNA samples from the Scientific Center of Obstetrics, Gynecology and Perinatology, Almaty, Kazakhstan; Gulnara Svyatova, Principal Investigator

  Whole genome sequencing of 100 unrelated Kazakhs in order to impute genotypes into PE cases and controls from Kazakhstan and to provide genetic data and infrastructure for future genetic studies in Kazakhstan and Central Asia more generally and to fill a gap in worldwide information as Central Asia is not adequately represented in available genomic data. This dataset is one component of the InterPregGen FP7 project. DNA samples for this component were collected by InterPregGen Consortium collaborators at the Scientific Center of Obstetrics, Gynecology and Perinatology, Almaty, Kazakhstan (Gulnara Svyatova, Principal Investigator)

  Dataset EGAD00001005467

• Colon Cancer Organoid Cultures and Tumors Whole Genome Sequencing Data

  Five hundred nanograms of genomic DNA was fragmented by Covaris S2, the fragmented DNAs were performed end-repair, A-tailing at the 3 prime end, adaptors ligation with an IDT dual-indexed UMI adaptor system at the terminal ends. The adapter ligated library with size range 300-750bp were selected by dual-SPRI method. Twenty percent of the size selected PCR-free libraries were enriched by 5 PCR cycles prior to library size assessment by Bioanalyzer Fragment Analyzer. The PCR-free libraries were quantified by qPCR.The PCR-free libraries were denatured and diluted to optimal concentration. Illumina NovaSeq 6000 was used for Pair-End 151bp sequencing.

  Dataset EGAD00001005759

• Polymorphisms in the Mitochondrial Genome associated with Bullous Pemphigoid in Germans

  This study is the first to interrogate the whole mtDNA in BP patients and controls and to implicate multiple novel mtDNA variants in disease susceptibility. Whole mtDNA of German BP patients (n=180) and age- and sex-matched healthy controls (n=188) were sequenced using next generation sequencing (NGS) technology, followed by the replication study using Sanger sequencing of an additional independent BP (n=89) and control cohort (n=104). While the BP and control groups showed comparable mitochondrial haplogroup distributions, the haplogroup T exhibited a tendency of higher frequency in BP patients suffering from neurodegenerative diseases (ND) compared to BP patients without ND (p= 0.1448, Fisher’s exact test)

  Dataset EGAD00001005379

• TS and WGS data

  The dataset contains the targeted sequencing (TS) and the whole genome low pass (WGS) BAM files of the study. For the TS: Samples: TS_XXX There are normal and tumor DNA samples. Each DNA strand has been sequenced independently (PoolA and PoolB). A TruSeq Custom Amplicon panel of 20 genes frequently mutated in ILC and/or ER-positive BC in general was designed using DesignStudio from Illumina: AKT1, ARID1A, CDH1, ERBB2, ERBB3, ESR1, FOXA1, GATA3, IGF1R, JAK2, MAP2K4, MAP3K1, NF1, PIK3CA, PTEN, RB1, RUNX1, STAT3, TBX3, and, TP53. For the WGS: Samples: WGS_XXX There are normal and tumor DNA samples. Samples were sequenced to an average target coverage of 0.5X.

  Dataset EGAD00001006393

• Capture-based Methods for Transcriptomic Profiling of FFPE material

  This dataset includes all FASTQ files for 11 samples where different capture-based methods for transcriptome profiling have been tested. Specifically, we have the 'traditional' RNA-seq experiment with fresh frozen (FF) material, and 3 different capture methods for the matching formalin-fixed paraffin-embedded samples: Agilent (AGI), IDT, and Twist Biosciences (TBS). In total, there are 43 samples with paired-end FASTQ files (1 sample did not have sufficient material to test all methods). Samples are identified by the R01-R11 IDs with a suffix that indicates the capture method used (or FF for fresh frozen).

  Dataset EGAD00001007655

• RNA-seq data from 27 glioblastoma samples

  27 Fresh frozen tissue specimens were crushed by mortar and pestle, homogenized using the QIAShredder kit (Qiagen), and genomic DNA and total RNA were extracted using the AllPrep DNA/RNA Mini kit (Qiagen), according to the manufacturer’s instructions. RNA libraries were synthesized using 200 ng of total RNA using the Ilumina TruSeq Stranded RNA LT Sample Prep Kit (Illumina), and subsequently sequenced on the NextSeq550 platform to a read depth of 80 million clusters and 160 million paired end reads (75 bp X 75 bp) using V2 chemistry.

  Dataset EGAD00001008362

• Induction Failure in Childhood and Young Adult T-cell Acute Lymphoblastic Leukemia

  We carried out WGS and RNAseq on a cohort of 48 children and young adults with induction failure in T-cell Acute Lymphoblastic Leukemia (T-ALL) to identify genomic drivers of treatment resistance. The study includes WGS for 33 tumour/normal pairs and 15 tumour-only samples. In addition, there is RNAseq data for 37 cases.

  Dataset EGAD00001009058