Search Results - EGA European Genome-Phenome Archive

ChIP-Seq data for the paper titled "Orthotopic Patient-Derived Xenografts of Pediatric Solid Tumors"

Dataset EGAD00001003432

WGS files for double minute brain tumor paper.

Whole Genome Sequencing files accompanying the paper titled "Structure and evolution of double minutes in diagnosis and relapse brain tumors". Please read the paper for more details.

Dataset EGAD00001004337

Exome sequencing data for Mesothelioma

Dataset EGAD00001001913

RNA-Seq data for Mesothelioma.

Dataset EGAD00001001915

NHLBI TOPMed: Pediatric Cardiac Genomics Consortium (PCGC)'s Congenital Heart Disease Biobank

The Pediatric Cardiovascular Genetics Consortium (PCGC) proposes to define genetic causes for congenital heart defects (CHD) as part of the TOPMed Program are the most common form of heart disease in childhood and are also the most prevalent form of birth defects, occurring in 2-3% of live births. The PCGC has recruited and clinically characterized = 10,000 CHD probands and parents (CHD trios). From whole exome sequencing (WES) of >2800 CHD trios, we identified a substantial enrichment of damaging de novo mutations in genes important for cardiogenesis, particularly implicating histone modifier enzyme gene defects. Analysis of whole genome sequencing (WGS) of 350 probands with CHD unexplained by WES and their parents preliminarily implicated de novo mutations in enhancers of genes previously shown to cause CHD in mouse knock out models. Sequencing of RNA (RNAseq) from discarded cardiac tissues from CHD probands has revealed likely causal allele-specific expression (ASE) as well as biallelic loss of expression (LOE). We have also discovered de novo epimutations, differentially methylated regions (DMRs), some with underlying de novo DNA variation, that are detectable in peripheral blood leukocytes and appear to underlie 10% of CHD. Of note, these assorted 'omic' approaches have enabled one another, both for attributing causality and assessing functional impact. Based on these extensive preliminary data, we hypothesize that PCGC probands with uninformative exomic analyses (WES-negative) harbor de novo genetic and/or epigenetic mutations in critical regulatory elements that participate in developmental expression of cardiac genes. To identify these etiologies, we propose analyses of WGS in 1000 WES-negative CHD trios, prioritizing those with probands with banked CHD tissues (n=78), one damaging variant in a recessive CHD gene, and older fathers (age>45). We also request WGS for 230 probands, for whom we have cardiac tissues but not parental DNAs. We request RNAseq for 308 cardiac tissues. For DNA methylation, which TOPMed will offer through the Illumina 850k array platform, we are requesting analysis of DNAs from peripheral blood leukocytes for all probands for whom WGS will be performed (1000 from trios, 230 singletons) as well as DNAs from cardiac tissues (n=308) to pair with the WGS, RNAseq and blood DNA methylation data. We will use existing resources and capabilities of the PCGC to confirm relevant mutations and those of its companion consortium in the Bench to Bassinet Program, the Cardiovascular Development Consortium, to inform analyses of non-coding mutations as well as to perform confirmatory functional genomics studies using cell and animal models. We expect that the studies resulting from data generated through TOPMed will provide novel insights into the molecular basis for CHD and fundamental knowledge about genes and pathways involved in cardiac development. Aside from being relevant to CHD, we anticipate that our findings will inform the understanding of later-onset cardiovascular diseases, including some arising in adulthood.

Study phs001735

Understanding the genetic risk underlying racial disparities in uterine fibroids

Fibroids affect 77% of women by onset of menopause in the U.S. and account for $2.1 billion in healthcare costs each year. Fibroids negatively impact reproductive health causing heavy and painful menses, pelvic pain and pressure, pregnancy complications, and interventions including myomectomy and hysterectomy. Until recently, tumor tissue and cell culture studies investigating fibroid growth have been the primary sources for understanding fibroid pathophysiology. Genetic analysis can provide a powerful and cost effective tool to identify etiological and causal factors, especially since a genetic predisposition to fibroids has already been documented from twin studies. As much as 69% of risk is explained by genetic factors. Racial disparities also support a role for genetics with fibroid risk. African American women have earlier age of onset, more numerous and larger fibroids with a greater lifetime incidence compared to Caucasians. We propose to identify genetic markers for risk of fibroids through a genome-wide association study (GWAS) of African American and Caucasian participants, leveraging ancestral differences to narrow down genomic regions for targeted follow- up analyses. To accomplish this we will take advantage of a unique Vanderbilt resource, the BioVU DNA databank. BioVU currently has over 122,470 adults linked to electronic medical records. From BioVU we have already identified 2,902 African American and Caucasian subjects who meet our stringent inclusion criteria to conduct a GWAS of fibroids, including pelvic imaging. Available imaging is critical, because many women with fibroids are asymptomatic and without imaging, studies may misclassify as many as 51% of women. We have also defined definitive controls who reached menopause without fibroids. We have a strong group of nationally known fibroid researchers who will provide over 10,000 samples for replication. Our first Specific Aim is to conduct a GWAS for association between common single nucleotide polymorphisms (SNPs) and fibroid risk. Using a case-control design we will perform a GWAS in 2,902 (1,451 fibroid cases and 1,451 controls) women from BioVU stratified by African American and Caucasian race. Secondary admixture mapping (AM) analyses will also be performed to identify chromosomal regions of interest to prioritize for replication. Our second Aim is to resequence chromosome regions identified from GWAS and AM to discover rare variants. Finally, in Aim 3 we will replicate SNPs selected from Aim 1 and 2 in independent samples of at least 3,230 fibroid cases and 7,097 controls. We propose an efficient and cost-effective approach to identify genetic risk factors for fibroids, by taking advantage of imaging information and DNA available through BioVU. This study represents the largest GWAS of uterine fibroids and the first among African Americans leveraging emerging technologies and new statistical approaches to conduct this study. Our proposed study will fundamentally change knowledge about fibroids and lay the ground work for breakthroughs in understanding mechanisms of fibroid formation and in identifying novel therapeutic approaches.

Study phs001409

FHS-Net Social Networks

Note: Data Use Restrictions: The data must only be used for genetic research projects solely in the area of health-related social-network research. This study compiles detailed information on family and social ties linked to participants in the offspring cohort of the Framingham Heart Study. The investigators took computerized information from self-report data used by the FHS for over 30 years to facilitate health exam scheduling. These tracking sheets asked participants to identify people close to them, which were assumed by the investigators to indicate a social tie. Individuals are listed as "egos" (if the ties are from their perspective) and/or "alters" (if they are linked as a friend or family members to one of the other participants). All egos were FHS offspring participants while alters could be FHS participants in any cohort or non-participants. This information was combined with internal FHS pedigree data on family ties to list family member alters (as well as the nature of their relationship). The resultant dataset therefore includes each ego-alter tie, the nature of their relationship and the start and end dates for their ties for each of seven waves for the offspring study. Due to privacy concerns, all exam dates for individuals are listed relative to the initial FHS exam date. The study investigators chose a random, confidential date, to which all initial exam dates are linked (by number of months from random date to initial exam date). With this information, researchers can have access to the temporal relationships among participants' exam dates while remaining unaware of the actual exam dates for individual participants. During each clinic exam cycle, the participants undergo a detailed examination including physical examination, medical history, laboratory testing, and electrocardiogram. Over the years, other tests (that may not be performed at every exam cycle) have included pulmonary function, lifestyle, physical function, cognitive function questionnaires, and various noninvasive cardiovascular tests including echocardiograms. This study involves phenotypic data from these exams including (when available) basic body measurements (height, weight, blood pressure), laboratory values (blood sugar levels, LDL levels), information on smoking and alcohol use, and tests of depression and cognitive functioning. Important links to apply for individual-level data Data Use Certification Requirements (DUC) Instructions to Request Authorized Access Apply here for controlled access to individual level data Participant Protection Policy FAQ In 1948, researchers recruited men and women from the town of Framingham, Massachusetts, beginning the first round of extensive physical examinations and lifestyle interviews that would later be analyzed for common patterns related to CVD development. Initially, the Framingham Heart Study enrolled 5,209 men and women from the Framingham area who were between the ages of 28 and 62 years. Beginning in 1971, the Framingham Heart Study enrolled 5,124 men and women, who were either offspring of the original cohort or spouses of those offspring. In 2002, 4,095 third generation participants (men and women) were enrolled.

Study phs000153

About

About What's the EGA? The European Genome-phenome Archive (EGA) is a global network for permanent archiving and sharing of personally identifiable genetic, phenotypic, and clinical data generated for the purposes of biomedical research projects or in the context of research-focused healthcare systems. Jointly managed by the European Bioinformatics Institute (EMBL-EBI) in Cambridge (UK) and the Centre for Genomic Regulation (CRG) in Barcelona, we aim to advance biomedical research and promote personalised medicine worldwide by enabling discovery of and access to human genomic and health research data. The EGA contains data collected from individuals whose consent agreements authorise data release for specific research use to bona fide researchers. We ensure strict security measures to control access to the data and maintain patient confidentiality. With expertise in data management and technical infrastructure, we promote FAIR data reuse and enable researchers to share their data securely. By leveraging public funding and our strategic partnerships, the EGA provides a free service for permanent data storage, data discovery, and secure data access. In addition, we foster a federated network to provide transnational access to human research data in compliance with legal frameworks. For additional information about the EGA, please contact: Helen Parkinson and Mallory Freeberg EMBL European Bioinformatics Institute Arcadi Navarro, Roderic Guigó, and Jordi Rambla Center for Genomic Regulation History The European Genome-phenome Archive was launched in 2008 at the European Bioinformatics Institute (EMBL-EBI), an outstation of the European Molecular Biology Laboratory (EMBL), to address an identified need for archiving and sharing the results of genome-wide association studies from the Wellcome Trust Case Control Consortium. With the signing of a memorandum of understanding in 2013 and a formal agreement in 2016, the EGA became a joint project of EMBL-EBI and the Centre for Genomic Regulation (CRG). The two institutes work together to support the EGA services, including supporting submissions, website, strategic leadership, and data infrastructure developments. In 2022, the Federated EGA was officially launched with the signature of the first five countries: Finland, Germany, Norway, Spain, and Sweden. With more than 20 additional nodes worldwide preparing to join, the Federated EGA aims to become the largest human omics data sharing initiative towards understanding human health and disease. EGA overview If you're a researcher you may need to deposit, manage, or access genomic data in a secure and regulated way. The European Genome-phenome Archive (EGA) is a platform that facilitates these processes, ensuring that sensitive data is stored and shared in accordance with legal and ethical regulations. Submission process To start a submission, you need to become an EGA submitter. For that, youll need to sign a Data Processing Agreement (DPA) with us, that defines the terms and conditions under which your data will be processed and shared within the EGA system. The access to each study is controlled by its Data Access Committee (DAC). The DAC is responsible for managing data access requests and ensuring that the release of data is in accordance with the General Data Protection Regulation (GDPR). Please, note that once your data is released, all public metadata related to your study and dataset(s) will be searchable on the EGA website. However, the files are only accessible under controlled access, which means that a DAC has to agree to a previous data access request. Request process It is possible to submit an access request to data stored at the EGA. The DAC assigned to the study will assess the request and, if approved, grant access to the data. Requesters must provide sufficient justification for your request and comply with the intended data usage in order to get access to it. Each dataset is covered by a Data Access Agreement (DAA) that defines the terms and conditions of use for the specified dataset/s. The DAA is created and provided by the DAC, and must be signed by the individual requesting access to the given dataset/s. Download process Once the request for access is approved, the data and metadata can be downloaded. The EGA offers various download options to fit the needs: it is possible to preview files without downloading them, download specific files of interest, or even download terabytes of data. Overall, the EGA provides a secure and regulated environment for depositing, managing, and accessing human data. Regardless of the role - submitter, DAC member, or requester - the EGA provides assistance for each while ensuring that sensitive data is managed in an ethical and responsible manner. More information on how EGA handles data is available in the EGA dataflow.

Documentation about/ega

Genome-Wide Approach to Measure Variant-Based Heritability of Drug Outcome Phenotypes

Current pharmacogenetic approaches often use a few large-effect alleles to predict drug outcomes. However, many drug outcome phenotypes are determined by complex pathways that may be influenced by common single-nucleotide variants (SNVs) across the genome. We ascertained how much variation in drug response is explained by common SNVs for pharmacokinetic (drug levels) and pharmacodynamic phenotypes (highest creatinine) for 4 drugs: vancomycin, gentamicin, cyclosporine and tacrolimus.

Study phs002506

Cellular Heterogeneity in Early Human Development at Stage CS16

In this study, we applied 10x Genomics Multiome assays for single-nucleus profiling of gene expression and chromatin accessibility simultaneously to a Carnegie stage 16 (37-42 days) specimen. These data can be used to identify and characterize diverse human cell types and annotate them with respect to marker genes, expression, and regulatory modules. These data comprise a rich resource for the exploration of human biology.

Study phs003532

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