Search Results - EGA European Genome-Phenome Archive

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

NIDDK IBD Genetics Consortium Crohn's Disease Genome-Wide Association Study

This dataset contains data from a genome-wide association study performed with 968 Inflammatory Bowel Disease (IBD) affected cases and 995 unrelated controls using the Illumina HumanHap300 Genotyping BeadChip. Cases were selected to have Crohn's disease with ileal involvement, and controls were matched to cases based on sex and year of birth. Subjects were drawn from two cohorts: (1) persons with non-Jewish, European ancestry (561 cases and 563 controls), and (2) persons with Jewish ancestry (407 cases and 432 controls). Genotyping was performed at the Feinstein Institute for Medical Research. Seven-hundred fifty-four of the samples (468 cases and 286 controls) were taken from the NIDDK IBD Genetics Consortium cell line repository. These samples are identified in the IBD_Sample file. 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 available, together with the Consortium's phenotyping manual and the forms used to collect the data. The remaining 1,209 samples were obtained from pre-existing collections ascertained through Cedars-Sinai Medical Center, Johns Hopkins University, University of Chicago, University of Montreal, University of Pittsburgh, University of Toronto, and the New York Health project (controls only). For these samples, only sex, cohort (Jewish vs. non-Jewish), and age at diagnosis (cases only) are available. Two-hundred three individuals from among the pre-existing samples did not provide consent to release their genotype data (designated as consent group 2 in the file IBD_Subject). Thus, individual genotype data are only provided for 1,760 samples. To compensate for this, we have provided summary results for each SNP. These are based on a stratified analysis testing case/control association. Fifty-one samples had a call rate less than 93% and were therefore excluded from this analysis, leaving an overall sample size of 1,963 - 51 = 1,912. X Chromosome Heterozygosity Nine samples have X chromosome heterozygosity that is neither consistent nor inconsistent with their phenotypic sex. One of these samples was found to have Turner Syndrome. The remaining 8 samples have heterozygosity ranging from 35-76%.

Study phs000130

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

B cell receptor silencing reveals origin and dependencies of high-grade B cell lymphomas with MYC and BCL2 rearrangements

High-grade B cell lymphomas with MYC and BCL2 rearrangements (HGBCL-DH-BCL2) represent highly aggressive Germinal center (GC) derived B cell malignancies with dismal prognosis and limited therapeutic options. In the present study, we investigated the expression of BCR complex components, including immunoglobulin (IG) heavy (H) and light (L) chains, and CD79A and CD79B signaling subunits in HGBCL-DH-BCL2, scoring 65% of cases with undetectable IGH immunoreactivity (IGHUND). Transcriptomics (bulk RNA-seq) and genetic (whole exome/genome sequencing) analyses indicated that IGHUND and IGH+ HGBCL-DH-BCL2 lymphomas represent distinct biological entities. Specifically, IGHUND HGBCL-DH-BCL2 exhibit a GC DZ-like molecular program, and select subset-specific gene mutations pointing to distinct evolutionary trajectories respect to the IGH+ counterparts. IG profiling revealed that IGHUND HGBCL-DH-BCL2 preserved potentially productive, hypermutated IGH variable (V) gene rearrangements, indicating a selective advantage conferred to the malignant B cells by residual IGH molecules. Accordingly, disruption of the productively rearranged IGHG1 gene in BCRnull HGBCL-DH-BCL2 cell line model confirmed the dependency on IGH expression for optimal fitness. Using transcriptomics and in situ RNA-scope analysis, we reported that IGHUND HGBCL-DH-BCL2 originated from IG-class switched GC B cells, while IGH-expressing tumors were selected for continuous IGM expression. Moreover, while IGH+ HGBCL-DH-BCL2 favor IGM/IG-Kappa expression, IGHUND counterparts complete IG-Lambda light chain rearrangements. Notably, BCR silencing, caused by accelerated IGH turnover and reduced IGH expression, precedes HGBCL-DH-BCL2 onset, inducing RAG1/2-dependent IG light chain editing and eventually facilitating t(8;22)/IGL::MYC translocations. Finally, IGH silencing observed in HGBCL-DH-BCL2 cell models, which leads to intracellular retention/silencing of CD79B, results in reduced sensitivity to the CD79B-targeting antibody-drug conjugate Polatuzumab-vedotin. Collectively, HGBCL-DH-BCL2 originates from isotype-switched t(14;18)+ GC B cells that undergo IG light chain editing fuelling intra-clonal diversification, BCR silencing, and the acquisition of t(8;22) translocation, while preserving IGH dependency.

Study EGAS50000001047

Automated machine-learning approach for next generation profiling of sequence alterations, mutation burden, microsatellite instability, and structural variants in human cancers

Sequence and structural alterations together with tumor mutation burden (TMB) and microsatellite instability (MSI) have been identified as biomarkers for the determination of response to targeted and immune checkpoint inhibitor therapies. However, widespread clinical adoption of these biomarkers has historically been limited due to barriers such as evidence of clinical utility and reimbursement. We have developed 2.2 Mb targeted NGS system and an automated machine-learning analysis approach (PGDx elio™ tissue complete, ETC) that has been FDA cleared for examination of 500+ cancer-related genes and 68 mononucleotide repeats for identification of sequence and structural alterations, TMB, and MSI in solid cancers in a clinical setting. We designed and trained this approach using sequence data from 4,174 cancers and &gt;124,000 in silico alterations and evaluated the methodology in &gt;2,550 tumor or non-cancerous normal samples. Independent analyses of ETC sequence changes in 440 formalin fixed paraffin embedded (FFPE) tumor or cell line samples using MSK-IMPACT™, FoundationOne®, and ddPCR revealed a positive percent agreement (PPA) &gt;97% with high sensitivity as low as 3% mutant allele fraction. We observed high concordance between panel-wide and whole-exome TMB for 307 pan-cancer FFPE tumors (Pearson r=0.95, p &lt; 0.0001) using samples with ≥20% tumor cellularity. Comparison of the mutation context and repeat-based MSI approach in ETC with a multiplex MSI PCR assay in 223 samples revealed a PPA of 99% and negative predictive agreement (NPA) &gt;99%. We confirmed the accuracy and precision of TMB and MSI measurements across three independent laboratories (CV of &lt;5% and average PPA &gt;99%, respectively). Finally, evaluation of amplifications and translocations against DNA and RNA-based approaches exhibited &gt;98% NPA and PPA of 86% and 82% respectively. These results demonstrate high analytical performance for determination of sequence and structural changes, TMB, and MSI using a targeted NGS panel and provide a scalable and facile approach for evaluating these biomarkers in a clinical laboratory.

Study EGAS00001005556

Chromatin profiles classify castration-resistant prostate cancers suggesting therapeutic targets

Untreated prostate cancers rely on androgen receptor (AR) signaling for growth and survival, forming the basis for the initial efficacy of androgen deprivation therapy (ADT). Yet, the disease can relapse and progress to a lethal stage termed castration-resistant prostate cancer (CRPC). Reactivation of AR signaling represents the most common driver of CRPC growth and next generation AR signaling inhibitors (ARSIs) are now used in combination with ADT as a first line therapy. However, ARSIs can result in selective pressure generating AR-independent tumors. The transition from AR-dependence frequently accompanies a change in phenotype resembling developmental trans-differentiation or ‘lineage plasticity’. Neuroendocrine prostate cancer, which lacks a defined pathologic classification, is the most studied type of lineage plasticity. However, most AR-null tumors do not exhibit neuroendocrine features and are classified as ‘double-negative prostate cancer’, the drivers of which are poorly defined. Lineage plasticity studies in CRPC are limited by the lack of genetically defined patient-derived models that recapitulate the disease spectrum. To address this, we developed a biobank of organoids generated from patient biopsies to study the landscape of metastatic CRPC and allow for functional validation assays. Proteins called transcription factors (TFs) are drivers of tumor lineage plasticity. To identify the key TFs that drive the growth of AR-independent tumors, we integrated epigenetic and transcriptomic data generated from CRPC models. By presenting a map of the chromatin accessibility and transcriptomic landscape of CRPC using a diverse biobank of organoids, PDXs and cell lines that recapitulate the heterogeneity of metastatic prostate cancer, we validate CRPC-AR and CRPC-NE subtypes and reveal two subtypes of AR-negative/low samples as well as their respective masterTFs. Additional analysis revealed a model in which YAP, TAZ, TEAD and AP-1 function together and drive oncogenic growth in CRPC-SCL samples. Overall, we show here how an approach to stratify CRPC patients into four subtypes using their transcriptomic signatures can potentially inform upon appropriate clinical decisions.

Study EGAS00001006059

Influence of Genomic Landscape on Cancer Immunotherapy for Newly Diagnosed Ovarian Cancer: Biomarker Analyses from the IMagyn050 Randomized Clinical Trial

The IMagyn050 trial (NCT03038100), which evaluated the efficacy of Atezo vs placebo (Pla) with carboplatin, paclitaxel and bevacizumab (CPB) in front line ovarian cancer patient (pts), did not meet its co-primary endpoints of PFS in ITT or PD-L1+ (Moore et al. JCO 2021). The aim of this exploratory study was to evaluate whether patients with BRCA1/2-mutated or homologous recombination-deficient (HRD) ovarian cancers benefitted from atezolizumab in the phase III IMagyn050 (NCT03038100) trial. Methods: Patients with newly diagnosed ovarian cancer were randomized to either atezolizumab or placebo with standard chemotherapy and bevacizumab. PD-L1 status of tumor-infiltrating immune cells was determined centrally (VENTANA SP142 assay). Genomic alterations, including deleterious BRCA1/2 alterations, genomic loss of heterozygosity (gLOH), tumor mutation burden (TMB), and microsatellite instability (MSI), were evaluated using the FoundationOne assay. HRD was defined as gLOH ≥16%, regardless of BRCA1/2 mutation status. Potential associations between progression-free survival (PFS) and genomic biomarkers were evaluated using standard correlation analyses and log-rank of Kaplan-Meier estimates. Results Among biomarker-evaluable samples, 22% (234/1050) harbored BRCA1/2 mutations and 46% (446/980) were HRD. Median TMB was low irrespective of BRCA1/2 or HRD. Only 3% (29/1024) had TMB ≥10 mut/Mb and 0.3% (3/1022) were MSI-high. PFS was better in BRCA2-mutated versus BRCA2-non-mutated tumors and in HRD versus proficient tumors. PD-L1 positivity (≥1% expression on immune cells) was associated with HRD but not BRCA1/2 mutations. PFS was not improved by adding atezolizumab in BRCA2-mutated or HRD tumors; there was a trend toward enhanced PFS with atezolizumab in BRCA1-mutated tumors. Conclusion Most ovarian tumors have low TMB despite BRCA1/2 mutations or HRD. Neither BRCA1/2 mutation nor HRD predicted enhanced benefit from atezolizumab. This is the first randomized double-blind trial in ovarian cancer demonstrating that genomic instability triggered by BRCA1/2 mutation or HRD is not associated with improved sensitivity to immune checkpoint inhibitors.

Study EGAS00001006838

Transcriptional profiling of tauopathies in human IPS-derived neurons (2019-08-21)

This project is a pilot study, in collaboration with Maria Grazia Spillantini and Mariangela Iovino (Cambridge Centre for Brain Repair), to investigate the utility of IPS-derived neurons for the study of neurodegenerative disorders. Our aim is to characterise the transcriptional consequences of tauopathies using neurons derived from differentiated IPSCs as a model system. We will use IPS cells derived from six individuals, four with known mutations in the tau protein, 2 without. RNA will be extracted at Day 0 and Day 65 of differentiation by which time the neuronal tauopathy is apparent. RNA will be extracted and the transcriptome of each line characterised using RNAseq. We will then search for genes that are differentially expressed between the transcriptomes of individuals with tau mutations versus those in controls. My lab will analyse the RNAseq data, comparing both affected and controls and both time-points, to establish candidate genes. Darren Logan’s lab, along with our collaborators, will experimentally verify and further investigate these genes in additional lines and animal models. From this analysis we will generate a list of candidate genes that are differentially expressed between cases and controls. This study will not only help us understand the molecular basis of tauopathies, but also identify gene candidates for biomarkers of neurodegenerative disease. It will serve as a proof of principle for future planned studies into generating and transcriptomically analysing an allelic series of Tau mutations in IPSCs with a controlled genetic background. 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 2019-08-21.

Dataset EGAD00001005277

Comprehensive cancer predisposition gene testing in an adult multiple primary tumor series shows a broad range of deleterious variants and atypical tumor phenotypes

Multiple primary tumors (MPT) affect a substantial proportion of cancer survivors and may result from various causes including inherited predisposition. Currently, germline genetic testing of MPT cases for cancer predisposition gene (CPG) variants is mostly targeted by tumor type. We ascertained pre-assessed MPT cases from genetics centers (defined as ≥2 primaries by age 60 years or ≥3 by 70) and performed whole genome sequencing (WGS) on 460 individuals from 440 families. Despite previous negative genetic assessment/molecular investigations, pathogenic variants in moderate and high-risk CPGs were detected in 67/440 (15.2%) of probands. WGS detected variants that would not be (or were not) detected by targeted resequencing strategies including structural variants at low frequency (6/440 (1.4%) of probands). In most individuals with a germline variant assessed as pathogenic or likely pathogenic (P/LP), at least one of their tumor types was characteristic of variants in the relevant CPG. However, in 29 probands (42.2% of those with a P/LP variant) the tumor phenotype appeared discordant. The frequency of individuals with truncating or splice site CPG variants and at least one discordant tumor type was significantly higher than a control population (χ2=43.642 P=<0.0001). 2/67 (3%) of probands with P/LP variants had evidence of multiple inherited neoplasia allele syndrome (MINAS) with deleterious variants in two CPGs. Summing together variant detection rates from a similarly ascertained previous MPT case series, the present results suggest that first-line comprehensive CPG analysis in a clinical genetics referral-based MPT cohort would detect a deleterious variant in about a third of cases.

Dataset EGAD00001004088

All you need to know about the new Submitter Portal

In September we launched new services for all EGA users, including a new version of the former Submitter Portal. Our main objective with this Portal is to transform the metadata submission in a simplified and user-friendly method. Although it is possible to find documentation about the Submitter Portal on our website, we wanted to highlight the new features in this article. Enjoy! Credentials: who needs to create a new user to access the Submitter Portal and who doesn't? Users with an old submission account (ega-box) can already access the new Submitter Portal. If it is the first time that you want to access, it is time to request a submitter role linked to the EGA User. In the form, it is mandatory to explain which type of data you want to deposit, the size of your submission, add any comment you’d like us to read, and manage the EGA Data Processing Agreement (DPA) document. This document should be checked by the legal department of your institution and signed by a person with authority to sign contracts on behalf of the institution. Note that we provide a DPA template for new Users; it can be found and downloaded in the Submitter Request section. With the DPA signed and the Submitter Request sent, it is time to wait for our Helpdesk team to validate the request. Metadata submission now can be collaborative It is possible to add collaborator(s) to your submissions. Only registered EGA Users will appear on the search bar. When adding a collaborator, it is mandatory to define the permission granted: Read only: collaborators with this permission can check if the information of the submission is correct but cannot modify anything. Read & write: collaborators with this label can register new objects and modify the registered ones. We wanted to save time for submitters that usually rely on the same collaborators. For that, we have implemented the possibility of adding the same collaborators from previous submissions; all you need to do is to search the submission, and the exact set of EGA users will have access to your submission. You can check the collaborators and their permissions at any point of the submission by going to the collaborators section. Even adding more people to complete the submission is allowed by following the same steps: looking for the EGA User and defining the permission granted (please, note that during the submission it is not possible to add collaborators from previous submissions). Use external links to complement the study information When filling the information about the study we recommend users to enrich the submission with complementary fields such as PubMeds IDs, custom tags and external links. External links are used to connect a study to an external repository. We have implemented an external link to Euro-Bioimaging for a European Project (EuCanImage). Now, we are looking forward to keeping increasing the list of external repositories! Choose the expected release date for your metadata to be searchable on our website Once all the metadata objects are registered it is time to finalise the submission so that the Helpdesk team can approve it. In the new version of the Submitter Portal users can choose the expected release date for the metadata to be searchable on the EGA website. Please, note that the selected date will have a minimum of one week margin from the day of finalisation. This time is necessary for your files to be archived and for the submission to be validated by the Helpdesk Team. Are you looking for the Array submission within the Submitter Portal? Our Submitter Portal does not support array submissions. The submission of Array-based metadata must be done using the EGA programmatic submission. You will find our Array-based format template and all the necessary information on our website, and you can find all the steps in our submission quickguide! More information about the submission process at the EGA Our Team has developed an extended documentation related to metadata submission. A good first step is to take a closer look at the Submission FAQ. To have a general overview of all the metadata submission process we recommend taking a glimpse of our take-the-tour or watching the tutorial available on our YouTube Channel. Don't miss our section on EGA Schemas, to learn more about how the EGA is built. It can be useful to fully ensure that your submissions comply with the EGA's standards and contribute to a valuable and accessible genomic resource.

Blog new-submitter-portal

999 results for "1993+article+mouse+line+professor"

in 8.49 milliseconds.