Search Results - EGA European Genome-Phenome Archive

SCRNA10X_SA_CHIP0080_002

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0080_002 1 samples; filetype=fastq

Dataset EGAD00001006462

SCRNA10X_SA_CHIP0006_001

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0006_001 1 samples; filetype=fastq

Dataset EGAD00001006460

SCRNA10X_SA_CHIP0141_004

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0141_004 1 samples; filetype=fastq

Dataset EGAD00001006463

SCRNA10X_SA_CHIP0142_001

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0142_001 1 samples; filetype=fastq

Dataset EGAD00001006464

SCRNA10X_SA_CHIP0142_003

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0142_003 1 samples; filetype=fastq

Dataset EGAD00001006465

SCRNA10X_SA_CHIP0146_002

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0146_002 1 samples; filetype=fastq

Dataset EGAD00001006466

SCRNA10X_SA_CHIP0149_001

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0149_001 1 samples; filetype=fastq

Dataset EGAD00001006467

SCRNA10X_SA_CHIP0149_002

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0149_002 1 samples; filetype=fastq

Dataset EGAD00001006468

SCRNA10X_SA_CHIP0150_001

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0150_001 1 samples; filetype=fastq

Dataset EGAD00001006469

SCRNA10X_SA_CHIP0150_002

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0150_002 1 samples; filetype=fastq

Dataset EGAD00001006470

SCRNA10X_SA_CHIP0152_001

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0152_001 1 samples; filetype=fastq

Dataset EGAD00001006471

SCRNA10X_SA_CHIP0152_002

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0152_002 1 samples; filetype=fastq

Dataset EGAD00001006472

SCRNA10X_SA_CHIP0163_001

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0163_001 1 samples; filetype=fastq

Dataset EGAD00001006473

SCRNA10X_SA_CHIP0163_002

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0163_002 1 samples; filetype=fastq

Dataset EGAD00001006474

SCRNA10X_SA_CHIP0172_001

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0172_001 1 samples; filetype=fastq

Dataset EGAD00001006475

SCRNA10X_SA_CHIP0172_002

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0172_002 1 samples; filetype=fastq

Dataset EGAD00001006476

TENX062

Transcriptome profiling by high-throughput sequencing for single cells for library TENX062 1 samples; filetype=fastq

Dataset EGAD00001006477

SCRNA10X_SA_CHIP0063_000

Transcriptome profiling by high-throughput sequencing for single cells for library SCRNA10X_SA_CHIP0063_000 1 samples; filetype=fastq

Dataset EGAD00001006478

TENX064

Transcriptome profiling by high-throughput sequencing for single cells for library TENX064 1 samples; filetype=fastq

Dataset EGAD00001006479

TENX065

Transcriptome profiling by high-throughput sequencing for single cells for library TENX065 1 samples; filetype=fastq

Dataset EGAD00001006480

TENX066

Transcriptome profiling by high-throughput sequencing for single cells for library TENX066 1 samples; filetype=fastq

Dataset EGAD00001006481

TENX068

Transcriptome profiling by high-throughput sequencing for single cells for library TENX068 1 samples; filetype=fastq

Dataset EGAD00001006482

Reference epigenome DB31_N_Alpha_mRNA-Seq data generated from KEP study

A DB31_N_Alpha_mRNA-Seq paired end data for alpha cells(PSA-NCAM(-), pancreas)

Dataset EGAD00001003485

Reference epigenome DB31_N_Alpha_smRNA-Seq data generated from KEP study

A DB31_N_Alpha_smRNA-Seq single end data for alpha cells(PSA-NCAM(-), pancreas)

Dataset EGAD00001003494

Reference epigenome ADMSC01_smRNA-Seq data generated from KEP study

A ADMSC01_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

Dataset EGAD00001003867

Reference epigenome ADMSC02_smRNA-Seq data generated from KEP study

A ADMSC02_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

Dataset EGAD00001003868

Reference epigenome ADMSC03_smRNA-Seq data generated from KEP study

A ADMSC03_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

Dataset EGAD00001003869

Reference epigenome KNIH005 WGBS data generated from KEP study

A KNIH005 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for islet cells

Dataset EGAD00001002753

Reference epigenome KNIH006 WGBS data generated from KEP study

A KNIH006 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for beta cells

Dataset EGAD00001002754

Reference epigenome KNIH007 WGBS data generated from KEP study

A KNIH007 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for adipocytes

Dataset EGAD00001002755

Reference epigenome KNIH009 WGBS data generated from KEP study

A KNIH009 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for preadipocytes

Dataset EGAD00001002757

Reference epigenome KNIH001 WGBS data generated from KEP study

A KNIH001 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for islet cells

Dataset EGAD00001002749

Reference epigenome KNIH002 WGBS data generated from KEP study

A KNIH002 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for islet cells

Dataset EGAD00001002750

Reference epigenome KNIH003 WGBS data generated from KEP study

A KNIH003 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for islet cells

Dataset EGAD00001002751

Reference epigenome KNIH004 WGBS data generated from KEP study

A KNIH004 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for islet cells

Dataset EGAD00001002752

CYP2C19 long-read sequencing

We have assessed the added value of long-read sequencing for PGx focusing on the clinically important and highly polymorphic CYP2C19 gene within 48 samples.

Dataset EGAD00001009883

Hi-C of cohesin-mutated and wildtype adult AMLs

High-throughput chromosome conformation capture (Hi-C) data generated for cohesin-mutated (STAG2 or RAD21) and cohesin-wildtype AMLs.

Dataset EGAD00001011198

(ChIP-seq) Shifted assembly and function of mSWI/SNF family subcomplexes underlie targetable dependencies in endometriod carcinomas

ChIP-seq data of 18 samples from the VOA1066 undifferentiated endometrial carcinoma cell-line with various pulldowns: BRG1 (abcam, ab110642, GR3255117-11), BRD9 (CST, E4Q3F, Lot 1), ARID1A (CST, D2A8U, Lot 4), SS18 (CST, D6I4Z, Lot 1), PBRM1 (CST, D3F70, Lot 3), GLTSCR1 (CST, E6I3A, Lot 1), H3K27ac (CST, C36B11, Lot 16), or none. Sample were either untreated or received doxycycline-induced ARID1A treatment. Samples were sequenced on Illumina NextSeq 500 using 37 bp single-end sequencing parameters. Raw fastq files, along with processed bigwig files containing normalized genomic coverage, and called narrow peaks, are included for each sample.

Dataset EGAD50000001473

Genomic Architecture of Progression and Treatment Response in AMD

Numerous studies have identified common and rare genetic variation associated with risk of advanced age-related macular degeneration (AMD). However, risk is just one facet of AMD disease architecture. Both disease progression and response to treatment are two critical elements that may also be influenced by genetic variation. The primary aim of this study was to identify genetic variation influencing progression and response to treatment for AMD, with a secondary aim of further elucidating the genetic etiology of AMD risk. We ascertained AMD cases and controls of European ancestry from Vanderbilt Eye Institute (VEI) and the Bascom Palmer Eye Institute (BPEI) and obtained blood samples from all participants. Eyes were examined using standard ophthalmological methods, and graded according the modified Age-Related Eye Disease Study scale (AREDS grades 1-5) using fundus photography. Individuals were re-examined in follow-up exams at regular intervals to assess any change in AMD grade. Eyes with advanced neovascular AMD that were examined using ocular coherence tomography imaging (OCT), treated with anti-angiogenesis therapy and monitored for response to treatment. All individuals ascertained for the study were exome-chipped for genome-wide single nucleotide variation coverage. To target rare genetic variation, whole exome sequencing was performed on individuals at the phenotypic extremes based on 1) AMD grade, 2) rate of progression from intermediate to advanced AMD and 3) response to anti-VEGF treatment for eyes with advanced neovascular AMD.

Study phs001046

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

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

Genetic Determinants of Viral Clearance in HCV-Infected Populations

The World Health Organization estimates there are 170 million persons with HCV infection worldwide, and an estimated 4 million persons are infected in the United States. Persistent HCV infection can cause cirrhosis and hepatocellular cancer. Long term disease consequences do not occur in persons who spontaneously recover. It is not known why 10-40% recover from hepatitis C (and are not at increased risk of cirrhosis and cancer), but that knowledge would contribute to efforts to treat and/or prevent HCV infection. The overall purpose of this study is to investigate the host genetic basis for recovery from hepatitis C virus (HCV) infection. There is strong evidence that host genetic differences determine HCV recovery. After accidental exposure to the same HCV inoculum, some persons recover while others develop persistent infection. Recovery from HCV infection is an outstanding phenotype for genetic studies since both viral exposure and viral recovery can be ascertained with high sensitivity and specificity and since there are no known viral or environmental determinants. The study group is comprised of an ethnically and geographically diverse international population. All cohorts comprising this study group of 3350 individuals are well characterized. This case-control study is comprised of approximately 40% clearance individuals who are matched by age and HIV status within their respective cohorts at a ratio of 1:1 or 1:2 with HCV chronically infected individuals.

Study phs000248

Genomic Diagnosis and Individualized Therapy of Highly Penetrant Genetic Diabetes

The most common and well-known causes of diabetes are type 1 and type 2 diabetes; however, there are many other etiologies of diabetes. At least 1-2% of diabetes cases result from high penetrance single gene defects, most commonly in a gene encoding either a transcription factor involved in beta cell development and function or the enzyme glucokinase (GCK). Others have mutations in transcription factors necessary for beta cell development and function. This subset of monogenic diabetes cases is autosomal dominant and is known as "Maturity-Onset Diabetes of the Young" (MODY). Patients with type 1 diabetes are insulin dependent, meaning they lack insulin production and therefore require multiple daily injections of insulin, as well as multiple times a day blood glucose monitoring. This can be quite burdensome. Type 2 diabetes is predominantly an insulin resistance problem and can often be managed with pills initially, but there are many who also require insulin either at diagnosis or later on in their disease course. Usually, monogenic diabetes is misdiagnosed as type 1 or type 2 diabetes and thus treated suboptimally. Those with monogenic diabetes, due to a defect in a gene encoding a transcription factor, respond extremely well to a class of oral diabetes medications called sulfonylureas. Those with GCK-MODY often have a mild, stable, fasting, nonprogressive hyperglycemia for which pharmacological treatment is often not necessary. It is important to accurately diagnose our patients with the correct etiology to their diabetes for a multitude of reasons. Accurately diagnosing a patient with MODY, for example, can predict their clinical course and we can tailor treatment accordingly. If diagnosed with a transcription factor MODY, we can try switching the patient from insulin to an oral sulfonylurea, and if diagnosed with a GCK-MODY, we can consider discontinuing pharmacological treatment altogether. With specific tailoring of treatment, we would hope to improve quality of life by discontinuing perhaps multiple daily injections of insulin and glucose checking as these may not be necessary. In addition, we would hope to improve glycemic control with an eventual decreased cost to society by decreasing the complication rate of diabetes, resulting in less specialty referrals, lab tests, supplies and procedures. Further, given MODY is an autosomal dominant condition with a 50% chance of inheritance to first-degree family members, we could offer testing to asymptomatic family members which could lead to prevention and/or early diagnosis and treatment. Overall, testing for monogenic diabetes is underutilized clinically. Testing is being done in the U.K., but in the U.S. we are lagging behind. This is most predominantly due to lack of knowledge and concern over cost. In addition, it is difficult to differentiate the relatively small number of monogenic diabetes cases from the larger number with type 1 and type 2 diabetes as each of these etiologies have overlapping characteristics. In fact, many patients with monogenic diabetes are being misdiagnosed with type 1 and type 2 diabetes. There are recommendations set forth by different societies on who to consider screening for monogenic diabetes. However, these recommendations are difficult to employ clinically as they are general recommendations, there is no specific algorithm, and they are based on varying levels of evidence. The purpose of this study is to implement a personalized diabetes medicine program to enhance the identification of individuals and families affected by highly penetrant diabetes gene mutations through a combination of systematic screening and genetic testing at the University of Maryland Center for Diabetes and Endocrinology, the Baltimore VA Medical Center and additional partner centers at Geisinger Health System and Bay West Endocrinology Associates, and other UM outpatient clinics. Our hope is to develop an approach that can be eventually implemented across the United States so that we can more often uncover the correct etiology to diabetes, tailor treatment accordingly, and test asymptomatic family members.

Study phs001771

Atezolizumab Plus Chemotherapy With or Without Bevacizumab in Advanced Biliary Tract Cancer: Clinical and Biomarker Data From the Randomized Phase II IMbrave151 Trial

PURPOSE Biliary tract cancers (BTCs) harbor an immunosuppressed tumor microenvironment (TME) and respond poorly to PD-1/PD-L1 inhibitors. Bevacizumab (anti-VEGF) plus chemotherapy can promote anticancer immunity, augmenting response to PD-L1 inhibition. PATIENTS AND METHODS This randomized, double-blind, proof-of-concept Phase II study enrolled patients (n=162) with previously untreated advanced BTC (IMbrave151; NCT04677504). Patients were randomized 1:1 to receive 3-weekly cycles of atezolizumab (1200 mg) plus bevacizumab (15 mg/kg) or atezolizumab plus placebo until disease progression or unacceptable toxicity. All patients received cisplatin (25 mg/m2) plus gemcitabine (1000 mg/m2; CisGem) on Days 1 and 8 every 3 weeks for up to 8 cycles. Stratification of patients was by disease status, geographic region, and primary tumor location. The primary endpoint was progression-free survival (PFS). No formal hypothesis testing was performed. Exploratory correlative biomarker analysis was undertaken using transcriptome analysis (n=95) and mutation profiling (n=102) on baseline tumor samples. RESULTS Between February and September 2021, 162 patients were enrolled. Median PFS was 8.3 months in the bevacizumab arm and 7.9 months in the placebo

Study EGAS50000000387

Idiopathic Pulmonary Fibrosis Network AntiCoagulant Effectiveness in Idiopathic Pulmonary Fibrosis (IPFNet-ACE-IPF-BioLINCC)

Data Access NOTE: Please refer to the "Authorized Access" section below for information about how access to the data from this accession differs from many other dbGaP accessions. Objectives: The ACE-IPF trial tested the hypothesis that treatment with warfarin at recognized therapeutic doses would reduce rates of mortality, hospitalization, and declines in Forced Vital Capacity (FVC) in subjects with Idiopathic pulmonary fibrosis (IPF).Background: IPF is a chronic, progressive lung disease of unknown cause characterized by the histopathologic pattern of usual interstitial pneumonia. The median survival of patients with IPF after the onset of symptoms is 2 to 5 years. Prior animal and human studies in pulmonary fibrosis provide a compelling rationale to examine anticoagulation as a therapeutic approach in IPF. Large epidemiologic studies link IPF with thrombosis-related clinical events, such as an increased risk of acute coronary syndrome and deep vein thrombosis. The suspected causal relationship may extend beyond simple coagulation cascade-induced thrombus formation, as procoagulant enzymes may directly stimulate fibrosis via cell surface receptor - mediated responses.Participants: Patients aged 35 to 80 years with progressive IPF were potentially eligible. Progressive IPF was defined as a history of (1) worsening of dyspnea, or (2) physiologic deterioration defined as an absolute decline of either FVC greater than or equal to 10% or DlCO greater than or equal to 15%, a reduction in arterial oxygen saturation of greater than or equal to 5%, or progression of radiographic findings. Between December 14, 2009 and April 1, 2011, 145 subjects were enrolled: 72 in the warfarin group and 73 in the placebo group. The mean age for the population was 67 years. 27% of the subjects were women and 92% were white.Participants were excluded if they met any of the following criteria: current indication for, or treatment with, warfarin, prasugrel, or clopidogrel combined with aspirin; the presence of an increased risk of bleeding; a recent cerebral vascular accident or gastrointestinal bleeding; any current signs or symptoms of severe, progressive, or uncontrolled comorbid illness; and their presence on the active list for lung transplantation. Design: ACE-IPF was a double-blind, randomized, placebo-controlled trial of warfarin targeting an international normalized ratio (INR) of 2.0 to 3.0 in patients with IPF. Subjects were randomized in a 1:1 ratio to warfarin or matching placebo for a planned treatment period of 48 weeks. Study subjects were provided two strengths of warfarin tablets (1 mg and 2.5 mg) or matching placebos. Subjects measured their INR with encrypted meters at least weekly. Participants were seen at screening, baseline, and at 16, 32, and 48 weeks after enrollment. Home monitoring was validated by plasma INR measurement at the week 1 and 16 visits.The primary outcome was a composite endpoint based on the time to all-cause mortality; nonelective, nonbleeding hospitalization; or a decrease in the absolute FVC greater than or equal to 10% from baseline value. Secondary outcome measures included rates of mortality, hospitalization, respiratory-related hospitalization, acute exacerbation, bleeding, cardiovascular events, and changes over time in FVC, 6-minute walk test distance, DlCO, plasma fibrin D-dimer levels, and quality of life (QOL).Conclusions: The study did not show a benefit for warfarin in the treatment of patients with progressive IPF and was terminated due to excess mortality in the warfarin treatment group. Treatment with warfarin was associated with an increased risk of mortality in an IPF population who lacked other indications for anticoagulation.Reported causes of death indicated 11 of the 14 were respiratory-related in the warfarin group versus three of the three in the placebo group. There were also three cardiovascular deaths in the warfarin group versus none in the placebo group. No deaths were attributed to bleeding. The warfarin group also demonstrated an increased rate of combined all-cause hospitalization and all-cause mortality. (Am J Respir Crit Care Med. 2012 Jul 1;186(1):88-95.)

Study phs004070

Transcriptome Sequencing PPGL

RNA library preparation of samples with RNA Integrity Number (RIN) > 5.5 (median 7.1; range 5.6-8.9) was performed as described in the TruSeq Stranded mRNA Library Prep Kit (Illumina, RS-122-2101). Briefly, 0.7-1 µg total RNA PolyA+ fraction was purified and randomly fragmented, converted to double stranded cDNA and processed through subsequent enzymatic treatments of end-repair, dA-tailing, and ligation to adapters. Adapter-ligated library was completed by PCR with Illumina PE primers. The resulting purified cDNA library was applied to an Illumina flow cell for cluster generation and sequenced on an Illumina HiSeq2500 on a 51bp single-read format following manufacturer's protocols. cDNA libraries from FFPE tumors and low integrity RNA’s (RIN<5.5) from FF tumors (median 2.4; range 1-5.5) but % of fragments > 200 nucleotides higher than 20% (median 57.5; range 20-96) were prepared using QuantSeq 3’ mRNA-Seq Library Prep Kit FWD for Illumina (Lexogen, 015) with a UMI Second Strand Synthesis Module for QuantSeq FWD (Lexogen, 081), vendor’s protocol for low input/ low quality/ FFPE RNA using 200-500 ng of total RNA, according to availability, was used as starting material. PCR Add-on Kit for Illumina (Lexogen, 020) was used to adjust the library amplification number of cycles. Libraries were applied to an Illumina flow cell for cluster generation and sequenced on NovaSeq6000. Image analysis, per-cycle basecalling and quality score assignment was performed with Illumina Real Time Analysis software. Demultiplexing of BCL files to FASTQ format was performed with the bcl2fastq Software (Illumina).

Study EGAS00001006044

HV31 - Illumina PCR-free sequencing

Illumina PCR-free sequencing data for individual HV31 generated using DNA from peripheral blood mononuclear cells, to a sequencing depth of ~44×. Sequencing was performed at the Wellcome Centre for Human Genetics on the Illumina Novaseq platform.

Dataset EGAD00001007042

Sequencing data for oesophageal / related samples - Kazachenka et al (RNA)

Data supporting: "The transcriptional landscape of endogenous retroelements delineates esophageal adenocarcinoma subtypes" RNAseq for 279 samples

Dataset EGAD00001011076

Sequencing data for oesophageal and related samples - Ganguli et al (sWGS)

Data supporting: "TBC" Ganguli et al (sWGS for 75 samples)

Dataset EGAD00001011189

Processed DNA methylome sequencing data

The dataset contains methylation values of all SNP-filtered CpG sites for all samples from the air pollution study (total n=60). Nasal lavage samples were collected from n=29 moderately exposed (residing in Stuttgart) and n=31 lowly exposed (residing in Simmerath) individuals. For methods and study details, please see PMID 37343754.

Dataset EGAD00001011208

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