Search Results - EGA European Genome-Phenome Archive

NHGRI GREGoR Consortium: Genomics Research to Elucidate the Genetics of Rare Disease

The NHGRI GREGoR (Genomics Research to Elucidate the Genetics of Rare Disease) Consortium was established in June 2021 with the goal of developing novel tools and approaches to advance the discovery of the genetic basis of rare conditions. Numerous types of molecular data are generated in GREGoR and available on the AnVIL cloud platform via dbGaP application, including short- and long-read genome and exome sequencing, transcriptomics, metabolomics, methylomics, and proteomics. De-identified clinical and demographic data is obtained, with a focus on standardized ontologies.Visit the GREGoR Consortium data webpage for summary information about the GREGoR Dataset, including numbers of participants and data types, methods documentation, and Release Notes. The Consortium comprises five Research Centers (RCs - see below), a Data Coordinating Center (DCC), and various partner members and external collaborators.Baylor College of Medicine Research Center (BCM-GREGoR) The Baylor College of Medicine GREGoR program, which is part of the GREGoR consortium, enrolls individuals, families, and cohorts with suspected rare disease across a range of syndromic and non-syndromic phenotypes. Subjects are enrolled from national and international collaborating physician referrals. Subjects provide written informed consent for future re-contact. Data generated and shared include family structure, detailed phenotypes, exome or short-read genome data, and in some cases long-read genome or RNA-sequencing, and these are shared upon completion of standard quality control checks and annotation. Broad Institute (Broad) The Broad Center for Mendelian Genomics, part of the GREGoR consortium uses next-generation sequencing (exome, genome, transcriptome, and long read sequencing), computational approaches, and functional studies to discover the variants and genes that underlie Mendelian conditions with a particularly focus on neuromuscular, neurodevelopmental, and syndromic phenotypes. Samples come from collaborators and direct enrollment through the Rare Genomes Project and we are committed to rapid data sharing without an embargo period. University of California, Irvine (UCI-GREGoR) To accelerate the pace of Mendelian disease gene discovery and clinical implementation, we propose a Mendelian Genomics Research Center, part of the GREGoR Consortium, leveraging the broad pediatric and adult clinical and research expertise at Children's National Hospital and University of California, Irvine. Our goal is to develop best practices to increase the diagnostic yield of rare diseases, engage the community to reduce health disparities for complex diagnoses, while creating a dataset accessible to all. Our Center will unite world class experts combining basic and translational research with innovative approaches to phenotyping, variant identification and functional investigation of both coding and non-coding sequence changes with the goals of discovering novel Mendelian gene variations and identifying variants not detected on current sequencing pipelines, disambiguating uncertain variants into disease-causing versus benign categorizations, and sharing information by working collaboratively with the GREGoR community.GREGoR Stanford Site (GSS) The goal of the GREGoR Stanford Site (GSS) is to provide a platform for functional genomics research and validation to improve diagnosis in Mendelian disease. Participants included individuals with undiagnosed suspected Mendelian disease who had non-diagnostic exome sequencing and their immediate family members. Participants and their family members provided written, informed consent and biological samples from which DNA, RNA, plasma, fibroblasts, PBMCs and other cell types were generated and stored. Samples from research participants and their immediate family members may have undergone short and long-read genome sequencing, transcriptome sequencing, metabolomics and/or lipidomics profiling, methyl-capture-sequencing and ATAC-sequencing. De-identified clinical data extracted from participant medical records are linked to the samples. University of Washington Center for Rare Disease Research (UW-CRDR) The goals of the University of Washington Center for Rare Disease Research are to: (1) maximize novel gene discovery for Mendelian conditions by recruitment, short- and long-read whole genome sequencing, transcriptome sequencing and analysis of families with rare conditions for which the gene is either unknown or the gene is known but no pathogenic variant can be identified via clinical testing; (2) develop new strategies for gene discovery for Mendelian conditions caused by variants that are difficult to detect using conventional testing strategies, variants of unknown function effect (e.g., regulatory, structural variants) or have unusual modes of inheritance; and (3) implement high-throughput screening and targeted follow-up functional studies to prioritize and validate candidate non-coding variants. De-identified data and phenotypic information are shared via MyGene2, ClinVar, and AnVIL.

Study phs003047

Rapid Acceleration of Diagnostics - Digital Health Technologies (RADx-DHT): NIH Digital Health Solutions for COVID-19: Team SAE

The goal of this project is to develop a smartphone-based platform to monitor and support individuals with COVID-19 symptoms (who may need testing) and those who have already tested positive. The app will integrate a Bluetooth-enabled thermometer and pulse oximeter into an approach uniquely designed for low-resource settings and underserved populations. This project will focus on the American Indian/Alaskan Native (AI/AN) underserved population and will determine the feasibility of the digital health solution to collect symptom survey data and vital data including temperature, pulse, and oxygen saturation level. The complete study will include 300 participants 100% medically underserved with at least 51% from the AI/AN community. A combination of hard and soft data will be collected consisting of vital data, medical history and questionnaire data, and participant COVID-19 viral and antibody testing results collected during the study.DOI: https://rapids.ll.mit.edu/10.57895/wv88-by98

Study phs002534

Characterization of genomic landscape of Peripheral T-cell Lymphomas, not otherwise specified (PTCL-NOS) (2018-10-30)

Peripheral T-cell lymphomas not otherwise specified (PTCL-NOS) represent a heterogeneous group of nodal and extra-nodal mature T-cell lymphomas, with a low prevalence in Western countries. PTCL-NOSs account for about 25% of all PTCLs and are currently diagnosed based on exclusion criteria, as this lymphomas lack unifying morphological, phenotypic and genomic features. Cytogenetic and FISH analysis of PTCL-NOS samples have not revealed recurrent pathogenetic abnormalities, while gene expression profiling has shown only partial ability to segregate cases representing homogeneous clinic-pathological entities. This underscores the need to look at PTCL-NOS with innovative and high-throughput approaches to identify recurrent genetic lesions that could further our understanding of the biology of this heterogeneous group of diseases, provide better diagnostic tools and perhaps new targets for innovative treatments. Our aim is to study ~15 patients affected by PTCL-NOS. Out study will be funded by a private, non-profit Italian cancer research fund (Associazione Italiana per la Ricerca sul Cancro, www.airc.it) based on a grant owned by Anna Dodero and Cristiana Carniti, hematologists at INT. Samples will be analysed by whole genome sequencing using Illumina X10 machines, on a 150bp-PE protocol. Data will be analysed using the pipeline available in Team 78, under the supervision of Peter Campbell, the WTSI faculty who will oversee the project, and by Francesco Maura, visiting scientist at the WTSI. . This dataset contains all the data available for this study on 2018-10-30.

Dataset EGAD00001004428

RNA-Seq in Patients with Primordial Dwarfism

Transcriptome studies in patients with rare genetic diseases can potentially aid in the interpretation of likely causal genetic variation through identification of altered transcript abundance and/or structure. RNA-Seq is the most sensitive assay for both investigating transcript structure and abundance The primary aim of this pilot project is to investigate to what degree integrating exome-Seq and RNA-Seq data on the same individual can accelerate the identification of causal alleles for rare genetic diseases. There are two main strands to this: (i) identifying which variants discovered in exome-seq appear to be having a functional impact on transcripts, and (ii) identifying transcript outliers, especially among known causal genes, that may not necessarily have a causal variant identified from exome sequencing. The latter may identify the presence of causal variants that lie far from coding regions (e.g. the formation of cryptic splice sites deep within introns, or loss of long range regulatory elements), which can be confirmed with further targeted genetic assays. Just over 50% of all disease-causing variants recorded in the Human Gene Mutation Database (HGMD) affect transcript structure and abundance (e.g. nonsense SNVs, essential splice site SNVs, frameshifting indels, CNVs). This pilot project will study RNA from lymphoblastoid cell-lines from 12 patients with primordial dwarfism syndromes, for 10 of these samples we have previously generate exome data as part of our collaboration with the group of Prof Andrew Jackson. The two remaining samples are positive controls where the causal mutation is known, and is known to affect transcript structure and/or abundance. Primordial dwarfism is a prime candidate for these RNA-seq studies because all known causal mutations to date have key roles in DNA replication and thus, unsurprisingly, the products of the causal genes are typically ubiquitously expressed. Each RNA will be sequenced, with two technical replicates (independent RT-PCR and libraries) per sample, and each replicate run in 1/2 of a HiSeq lane using 100bp paired reads. Samples preparation was as follows :The cells were grown to confluency, then pellets frozen at -80. RNA samples were prepared using the Qiagen RNeasy kit, then nanodropped and analyzed using the bioanalyzer to determine concentration and purity. 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/

Dataset EGAD00001000640

Single-cell RNA-seq of human kidney tumors

Fresh nephrectomy samples were collected from a total of 5 untreated ccRCC patients. Out of these 5 patients, 7 samples were obtained. Two samples consisted of fresh versus frozen single cells from the primary tumor site of one patient. Two other samples consisted of matched primary and distant thrombus sites (the vena cava) of a second patient. The three remaining samples came from the primary tumor sites of three distinct ccRCC patients. Single-cells were captured into 10x barcoded gel beads and RNA-sequencing library preparation was done using Chromium Single Cell 3' v2 chemistry. Sequencing was performed on a Illumina HiSeq 4000 sequencer.

Dataset EGAD00001009306

Lung Tissue Research Consortium (LTRC-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. Biospecimens: Access to Biospecimens is through the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). Biospecimens from LTRC include DNA, Plasma, Serum, Tissue - FFPE Cassettes, Tissue – RNALater Frozen, and Tissue - Snap Frozen. Please note that use of biospecimens in genetic research is subject to a tiered consent. Objectives: The LTRC was a biobank resource established by the NHLBI to collect and distribute lung tissue, blood samples, clinical data, and radiographic studies from participants with chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), other related idiopathic interstitial pneumonias (IIP) and interstitial pneumonias associated with connective tissue diseases who undergo medically-indicated lung resection. All tissue and blood specimens and clinical data were banked centrally and stored for distribution to external investigators who have approved study proposals to investigate the pathogenesis or management of lung diseases. The ultimate goal of this program was to enable research that illuminates the pathobiology of lung diseases and leads to novel interventional treatments for these conditions. Background: Chronic lung diseases are a main cause of death and disability in the United States. COPD affects over 14 million individuals in the United States and represents the third leading cause of mortality. Cigarette smoking is a major risk factor. However, only one of six individuals who smoke develops COPD. This could imply either an individual susceptibility or an additional immunologic or infectious injury to lung cells. Current treatments offer symptomatic relief, but do not prevent disease progression. Better understanding of disease pathogenesis, including the potential roles of lung parenchymal cell apoptosis, immunologic injury, and inflammation may lead to therapies that improve survival and quality of life. Interstitial pneumonias, including IPF and those associated with connective tissue disease, are less common than COPD, but for many of these diseases there are poor outcomes. For example, IPF has a 50% survival rate 2-3 years following diagnosis, and currently no treatment exists which prolongs survival. The prevalence of IPF is approximately 28 cases per 100,000. The underlying histology of IPF is usual interstitial pneumonia (UIP), which can also occur in connective tissue diseases. The incidences of other interstitial pneumonias such as non-specific interstitial pneumonia (NSIP) or acute interstitial pneumonia (AIP) are less frequent but also occur as an expression of interstitial lung disease in the connective tissue diseases. Moreover, there is significant crossover of these three interstitial pneumonias so that cases of IPF/UIP may also reveal fibrotic NSIP and be complicated by episodes of AIP. This implies common injuries but dissimilar histological responses. All of these processes are characterized by epithelial injury, uncontrolled fibroproliferation and the deposition of collagen, irrespective of the histology. It is clear that a better understanding of the genesis of the interstitial pneumonias is required before effective interventions can be developed. Participants: A total of 4,486 participants were enrolled, and lung tissue was obtained from 3,333 of these participants.Design: Written informed consent of each participant was required before any LTRC procedure was performed. Phenotypic data were then obtained that included recording of relevant medical information, a limited exposure history, radiological evaluation, and pulmonary physiological and lung function testing. Questionnaires were administered to determine the extent of symptoms, associated medical illnesses, smoking, environmental and occupational exposures, and quality of life. Laboratory testing included pulmonary function testing, a six-minute walk test, and chest x-ray CT. Blood specimens were collected both for defining the clinical phenotype of donors and to obtain serum, plasma, and DNA for later investigative purposes. At the time of surgery, lung tissues were collected and processed for long-term storage. The LTRC collected only the 'non-tumorous' portions of lung tissue from surgical procedures performed for primary or metastatic lung tumors and received those specimens only after the local pathologist had procured all tissue required for clinical care. Samples of appropriate size were cut and placed in formalin, RNAlater, glutaraldehyde, or liquid nitrogen within 30 minutes of excision (approximately 5% of cases exceeded this target time). Blood and tissue specimens were subsequently shipped to a central Tissue Repository for further processing and long-term storage. A Radiology Center provided quality control and quality assessment of CT data. A Data Coordinating Center managed study operations and maintained a repository of study data. Conclusions: LTRC established a biospecimen collection that is unique in its size, diseases included, standardization of methods, and extent of phenotypic data, serving as a valuable resource to facilitate research on the pathobiology of lung diseases.

Study phs003913

Latency and interval therapy affect the evolution in metastatic colorectal cancer

While comparison of primary tumor and metastases has highlighted genomic heterogeneity in colorectal cancer (CRC), previous studies have focused on a single metastatic site or limited genomic testing. Combining data from whole exome and ultra-deep targeted sequencing, we explored possible evolutionary trajectories beyond the status of these mutations, particularly among patient-matched metastatic tumors. Our findings confirm the persistence of known clinically-relevant mutations (e.g. those of RAS family of oncogenes) in CRC primary and metastases, yet reveal that latency and interval systemic therapy affect the course of evolutionary events within metastatic lesions. Specifically, our analysis of patient-matched primary and multiple metastatic lesions, developed over time, showed a similar genetic composition for liver metastatic tumors, which were 21-months apart. This genetic makeup was different from those identified in lung metastases developed before manifestation of the second liver metastasis. These results underscore the role of dormancy in the evolutionary path of metastatic CRC.

Study EGAS00001003646

Clinical Proteomic Tumor Analysis Consortium (CPTAC) Proteogenomic Confirmatory Study of Breast, Colon, Lung, and Ovarian Tumors

Recently, significant progress has been made in characterizing and sequencing the genomic alterations in statistically robust numbers of samples from several types of cancer. For example, The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and other similar efforts are identifying genomic alterations associated with specific cancers (e.g., copy number aberrations, rearrangements, point mutations, epigenomic changes, etc.). The availability of these multi-dimensional data to the scientific community sets the stage for the development of new molecularly targeted cancer interventions. Understanding the comprehensive functional changes in cancer proteomes arising from genomic alterations and other factors is the next logical step in the development of high-value candidate protein biomarkers. Hence, proteomics can greatly advance the understanding of molecular mechanisms of disease pathology via the analysis of changes in protein expression, their modifications and variations, as well as protein-protein interaction, signaling pathways and networks responsible for cellular functions such as apoptosis and oncogenesis. Realizing this great potential, the NCI launched the second phase of the CPTC initiative in September 2011. Renamed the Clinical Proteomic Tumor Analysis Consortium, CPTAC is beginning to leverage its analytical outputs from Phase I to define cancer proteomes on genomically-characterized biospecimens. The purpose of this integrative approach is to provide the broad scientific community with knowledge that links genotype to proteotype and ultimately phenotype. The data contained in this dataset are derived from samples designed to confirm CPTAC findings from the TCGA samples. These confirmatory samples contain breast, ovarian, colon, and lung tumors collected via a protocol optimized for proteomics. Specifically, ischemic time of the sample was controlled and restricted to less than 30 minutes. ACGT, Inc. produced whole exome, mRNAseq, and miRNAseq for these samples. Corresponding proteomic data are available at: https://cptac-data-portal.georgetown.edu/cptacPublic/ The study design was to profile colon, breast, ovarian, and lung tumors both genomically and proteomically. Germline DNA was obtained from blood. Normal control samples for proteomics varied by organ site: adjacent colon tissue for colon cases, contralateral breast tissue for some breast cases, and Fallopian tube fimbria for some ovarian cases. Lung cases had no normal control for proteomic analysis. All cancer samples were derived from primary and untreated tumors.

Study phs000892

Epigenetic Profiling of Human Colorectal Cancer

DNA methylation, together with chromatin modifications, constitute the epigenome that functions to regulate gene expression and genome integrity. DNA methylation alterations are ubiquitous in human cancers, as many genes acquire DNA methylation in a cancer-specific manner. DNA methylation at these sites in the genome of cancer cells not only serves as a marker for tumor identification, but together with gene mutation and gene expression data, can also be used to describe subsets of tumors of the same organ source. We have previously shown distinct human colorectal cancer subtypes based on DNA methylation differences. Correlating these DNA methylation differences with clinical co-variates will serve to further understand how these distinct subtypes are generated. We have collected 100 colorectal tumor tissues (for which clinical information is known) and have obtained (unprotected) genome-wide DNA methylation and chromatin modification information for each sample for the purposes of identifying and classifying unique tumor subtypes of colorectal cancers. In addition, we have determined mutations of key genes relevant to colorectal cancer as well as gene expression profiles. We will use the clinical data for each de-identified sample to correlate with the DNA methylation, mutation and gene expression information so as to understand the driving forces behind these distinct colorectal subtypes. We have selected the most promising tumors for whole-genome bisulfite sequencing using next-generation sequencing technology to obtain complete maps of colon cancer methylomes. Researchers will be unable to identify the subjects because the samples and associated information have been de-identified and anonymized by the tissue source site. In addition, upon receipt by the USC Epigenome Center, we have assigned new random identifiers for each sample. The data generated using these new codes are not traceable to the patient identity.

Study phs000385

CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) Consortium Summary Results from Genomic Studies

GWAS have successfully identified genetic loci associated with a variety of conditions such as type 2 diabetes and coronary disease. The large number of statistical tests required in GWAS has posed a special challenge because few studies that have DNA and high-quality phenotype data are sufficiently large to provide adequate statistical power for detecting small to modest effect sizes. Even before the era of GWAS, the requirement for large sample sizes and the importance of replication have served as powerful incentives for collaboration. Meta-analyses combining summary data from multiple sources have improved the ability to detect new loci. The Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium was formed to facilitate GWAS meta-analyses and replication among multiple large and well-phenotyped cohort studies. The design of the CHARGE Consortium was formed initially from 5 prospective cohort studies from the United States and Europe: the Age, Gene/Environment Susceptibility (AGES) - Reykjavik Study, the Atherosclerosis Risk in Communities (ARIC) Study, the Cardiovascular Health Study (CHS), the Framingham Heart Study (FHS), and the Rotterdam Study (RS). Additional studies have expanded the CHARGE consortium based upon the phenotypes and willingness to share information across the research community. In order to facilitate investigators across the world to examine relationships between phenotypes and genetic markers within CHARGE published reports, an open site is made available on dbGaP that provides the rsID and the p-value for inspection. Access to detailed summary statistics (including minor allele frequency, odds ratio/effect size) requires approval of a Data Access Request (DAR).

Study phs000930

Genetic Risk for Subsequent Neoplasms among Long-term Survivors of Childhood Cancer in the St. Jude Lifetime Cohort

While childhood cancer survivors are known to be at increased risk of subsequent neoplasms (SNs), the contribution of germline mutations in cancer predisposition genes to the risk of SN is largely unknown. Whole-genome sequencing (30X) was performed on 3,006 5+ year survivors of childhood cancer (median age, 35.8 [range: 7.1-69.8] years) from the St. Jude Lifetime Cohort. Survivors underwent a comprehensive clinical assessment. Germline mutations in 156 cancer predisposition genes (SJCPG156), including a subset of 60 (SJCPG60) autosomal dominant genes with moderate to high penetrance, were classified for their pathogenicity. Piecewise exponential regression, stratified by radiation exposure, was used to evaluate the relative rate (RR) and 95% confidence interval (95% CI) of SN occurrence by mutation status. Pathogenic or likely pathogenic mutations in SJCPG156 and SJCPG60 were identified in 11.7% and 5.8% of survivors, respectively. The most frequently mutated SJCPG60 genes included RB1 (n=43), NF1 (n=22), BRCA2 (n=14), BRCA1 (n=12) and TP53 (n=10). Mutations in SJCPG60 were associated with the rate of subsequent sarcoma (RR, 10.3; 95% CI, 4.2 to 25.6) and breast cancer (RR, 13.3; 95% CI, 5.8 to 30.5) among irradiated survivors, and the rate of developing any SN (RR, 4.8; 95% CI, 2.4 to 9.4) and breast cancer (RR, 7.8; 95% CI, 2.5 to 24.0) among non-irradiated survivors. The rate of developing ≥2 histologically distinct SNs was increased 17.8-fold (95% CI, 3.5 to 89.8) among SJCPG60 mutation carriers treated without radiotherapy. Similar but attenuated associations were observed for mutations in SJCPG156. Survivors who develop a SN without prior radiotherapy or who are diagnosed with a subsequent sarcoma or breast cancer within a site of prior radiotherapy should be referred to genetic counseling.

Study EGAS00001002499

Privacy Notice for EGA Web user

Privacy Notice for EGA Public Website This Privacy Notice explains what personal data is collected by the specific service you are requesting, for what purposes, how it is processed, and how we keep it secure. 1. Who controls your personal data and how to contact us? European Genome- Phenome Archive - EGA offers a service for permanent archiving and sharing of all types of personally identifiable genetic and phenotypic data resulting from biomedical research projects, jointly managed by European Molecular Biology Laboratory – European Bioinformatics Institute (EMBL-EBI) and Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG). EMBL-EBI and CRG represent joint Data Controllers’ of processing of your personal data. They and their Data protection officers may be contacted for data protection queries and for exercising your rights under Section 8. You may contact EMBL-EBI, represented by Mallory Freeberg, by: email at: mfreeberg@ebi.ac.uk , or post at EMBL-EBI, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridgeshire, UK. EMBL’s Data Protection Officer may be contacted by: telephone at +49 6221 387-8590, email at dpo@embl.org , or post at EMBL Heidelberg, Data protection officer, Meyerhofstraße 1, 69117 Heidelberg, Germany. You may contact CRG, whose EGA team is represented by dr. Jordi Rambla de Argila, by: email at jordi.rambla@crg.eu, or post at Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG), Dr.Aiguader 88, PRBB Building, 08003 Barcelona, Spain. CRG Data protection officer may be contacted by: email at dpo@crg.eu post at Fundació Centre de Regulació Genòmica - Centre for Genomic Regulation (CRG), C/ Dr. Aiguader, 88, PRBB Building, 08003 Barcelona, Spain. 2. Which is the lawful basis for processing personal data? For providing you access to the website and for offering support whilst using the website, we need your consent under Article 5 (2) of the IP 68 (equivalent to Article 6 (1)(a) of the GDPR). For monitoring your activities on the website, we process your personal data on the grounds of important public interest. Such legal basis is found in Article 5(1)(a) of EMBL Internal Policy No 68 on General Data Protection (hereinafter IP 68), which is equivalent to Article 6 (1)(e) of the EU General Data Protection Regulation (hereinafter GDPR) and upon which personal data are processed for the achievement of the aims laid down in 1973 agreement establishing EMBL, such as the promotion of the cooperation in the fundamental research, in the development of advanced instrumentation and in advanced teaching in molecular biology and dissemination of information. 3. What personal data is collected from users of the service? How do we use this personal data? We collect the following personal data from you: IP addressesDate and time of a visit to the service website Operating systemAmount of data transmittedBrowserEmail address (only when support is requested by the user) The data controller will use your personal data for the following purposes: To provide the user access to the serviceTo better understand the needs of the users and guide future improvements of the serviceTo monitor website activities according to and in compliance with the Terms of UseTo better understand the needs of the website visitors and guide future improvements of the serviceTo conduct and monitor website security activitiesTo create anonymous usage statistics 4. Who will have access to your personal data? The personal data will be disclosed to: Authorised staff in the data controller’s institutions acting on data controller`s behalf and instructions. 5. Will your personal data be transferred to third countries (i.e. countries not part of EU/EEA) and/or international organisations? There are no personal data transfers to third countries or international organisations. 6. How long do we keep your personal data? Any personal data directly obtained from you will be retained as long as the service is live. Such duration serves the purpose of enabling scientific research and ensures legal compliance and facilitates internal and external audits if they arise. By contrast, the log files for the data categories related to anonymous usage statistics (raw web service logs) are processed only for 30 days and thereafter erased. 7. The joint Data Controllers provide these rights regarding your personal data You have the right to: Not be subject to decisions based solely on an automated processing of data (i.e. without human intervention) without you having your views taken into consideration.Request at reasonable intervals and without excessive delay or expense, information about the personal data processed about you. Under your request we will inform you in writing about, for example, the origin of the personal data or the preservation period.Request information to understand data processing activities when the results of these activities are applied to you.Object at any time to the processing of your personal data unless we can demonstrate that we have legitimate reasons to process your personal data.Request free of charge and without excessive delay rectification or erasure of your personal data if we have not been processing it respecting the data protection policies of the respective controllers. It must be clarified that rights under points 4 and 5 are only available whenever you need support whilst using our website. For other processing based on the grounds of important public interest you cannot exercise your rights to object, rectify or erase your personal data according to the Article 13(2)(a)(b) of IP 68 (equivalent to Article 17(3)(b)(d) and Article 21(6) of the GDPR). 8. Supervisory authority If you wish to complain against the processing of your personal data, you may do so by post at: EMBL Heidelberg, Data Protection Committee, Meyerhofstraße 1, 69117 Heidelberg, Germany, or Autoritat Catalana de Protecció de Dades (Catalan Data Protection Authority), C/Rosselló 214, Esc A, 1r 1a, Barcelona 08008, Spain. 9. CookiesWe use cookies and similar technologies that are strictly necessary for the correct functioning and security of the EGA website (for example, to enable core site features and to help protect the service). These cookies do not store information that directly identifies you.You can set your browser to block or delete cookies. Please note that if you disable strictly necessary cookies, parts of the website may not function properly.Published on February 6, 2019

Documentation data-protection/privacy-notice/ega-website

Optimizing Primary Stroke Prevention in Children with Sickle Cell Anemia (STOP II)

The STOP II trial evaluated whether prophylactic transfusion in patients with sickle cell disease and high risk of stroke can be safely halted after 30 months of treatment during which patients became low risk for stroke.Stroke causes substantial morbidity in children with sickle cell disease. To prevent first strokes, the Stroke Prevention Trial in Sickle Cell Anemia (STOP) used prophylactic transfusions in children who were identified by transcranial Doppler (TCD) ultrasonography as being at high risk for stroke. This strategy reduced the incidence of stroke among such children from 10% per year to less than 1% per year, leading to recommendations for TCD screening and prophylactic transfusion for children with abnormal velocities on ultrasonography. Despite the reduced risk of stroke, long-term use of transfusions can cause adverse side effects, such as iron overload or alloimmunization. However, cessation of transfusions is associated with recurrence of stroke, and at the time of the STOP II trial, there were no clinical or laboratory indicators to guide the duration of prophylaxis. Therefore the STOP II trial was initiated to determine whether transfusions could be limited by monitoring patients with TCD examinations after transfusions were halted and resuming transfusions if the examination indicated a high risk of stroke.The trial was halted for safety concerns after 79 of a planned 100 children were randomized. Discontinuation of transfusion for the prevention of stroke in children with sickle cell disease resulted in a high rate of reversion to abnormal blood-flow velocities on Doppler studies and stroke incidence.Instructions for requesting individual-level data are available on BioData Catalyst at https://biodatacatalyst.nhlbi.nih.gov/resources/data/. Apply for data access in dbGaP. Upon approval, users may begin accessing requested data in BioData Catalyst. For questions about availability, you may contact the BioData Catalyst team at https://biodatacatalyst.nhlbi.nih.gov/contact.

Study phs002386

Light at Night and Prostate Cancer in the Health Professionals Follow-Up Study

The Health Professionals Follow-Up Study (HPFS) is a prospective cohort study of men designed to evaluate hypotheses about men's health related to diet and lifestyle to the incidence of serious illnesses, such as cancer, heart disease, and other vascular diseases. It consisted of 51,529 U.S. male health professionals who were 40-75 years old at baseline in 1986 and completed a mailed six-page baseline questionnaire. Information on age, height, weight, ancestry, medications, disease history, physical activity, lifestyle factors, and diet were gathered, with follow-up questionnaires sent biennially to update information and record health outcomes with every four years to update their diet. Light at Night and Prostate Cancer project uses HPFS participants to conduct research assessing the impact of light at night on prostate cancer incidence, with follow-up from baseline in 1986 to 2016. Average annual outdoor light at night (exposure) was derived from satellite imagery data from the US Defense Meteorological Satellite Program's Operational Linescan System (maintained by the National Oceanic and Atmospheric Administration's Earth Observation Group). Prostate cancer cases (outcome) were self-reported by study participants, and then confirmed by the HPFS team through medical record and pathology report review. The data for this project are arranged in a long format, meaning that there are multiple rows for each subject ID. The various rows correspond to different questionnaire periods. Because the exposure in this dataset, light at night, and several important covariates are time-varying variables, the format of this dataset captures the variation in exposure status by questionnaire period. The outcome variables are incidence of prostate cancer, including incidence of prostate cancer clinical subtypes, such as advanced, aggressive, high- and low-grade prostate cancer. For the published study, please see Chowdhury-Paulino et al.'s 2023 publication in Cancer Epidemiology Biomarkers and Prevention entitled, “Association between Outdoor Light at Night and Prostate Cancer in the Health Professionals Follow-up Study.” (PMID: 37462694).

Study phs003538

Multicenter Study of Hydroxyurea (MSH)

This study aimed to determine whether or not treatment with hydroxyurea titrated to maximum tolerated doses would reduce the frequency of vaso-occlusive (painful) crises by at least 50% in 299 men and women between 18 and 50 years old with a diagnosis of sickle cell anemia by gel electrophoresis conducted by a Core Laboratory. A secondary objective investigated correlations of fetal hemoglobin (HbF) levels and other patient or treatment characteristics with the occurrence of vaso-occlusive (painful) crises, and the effect of treatment on the quality of life.This controlled trial made hydroxyurea the first drug of proven benefit in preventing vaso-occlusive pain crisis and acute chest syndrome caused by sickle cell disease, with additional findings including reduced mortality in adult patients taking hydroxyurea for frequent painful sickle cell episodes after 9 of years follow-up. No significant side-effects of hydroxyurea therapy were noted.Instructions for requesting individual-level data are available on BioData Catalyst at https://biodatacatalyst.nhlbi.nih.gov/resources/data/. Apply for data access in dbGaP. Upon approval, users may begin accessing requested data in BioData Catalyst. For questions about availability, you may contact the BioData Catalyst team at https://biodatacatalyst.nhlbi.nih.gov/contact.

Study phs002348

The Federated EGA network

In the last 10 years, most human omics data has been generated in the context of research consortia while recently there has been an emergence of large cohorts of human data generated by healthcare initiatives. Many countries in Europe now have nascent personalised medicine programmes. Thus, human genomics is shifting from being predominantly research-driven to being funded through healthcare systems. Genetic data generated in a healthcare context is subject to more stringent information governance than research data and nonetheless all data must adhere to national data protections laws. In this context, EGA identified the need for the development of a federated network to enable secure sharing of data whilst enabling genetic data to remain within the jurisdiction in which it was generated. The Federated EGA is designed to support national data management requirements for genomic and clinical data collected from citizens as part of healthcare or biomedical research projects. It includes a secure authorised access mechanism to support research use of these data across Europe and worldwide. We have engaged with over 14 ELIXIR countries to develop the federation model. The EGA federated configuration is composed of Central EGA, Federated EGA nodes and Community EGA nodes: Central EGA offers international submissions and helpdesk support, currently EGA co-managed by EMBL-EBI and CRG, Federated EGA nodes offer EGA services to researchers within their national jurisdiction, Community EGA nodes are individual institutions or initiatives with human genetic data intended to be shared with the research community. Key services available in the Federated EGA structure are: Central EGA Federated EGA node Community EGA nodes Data submission Offers international submissions service Offers submission service in a particular jurisdiction Does not offer an external submissions service Helpdesk support Provides external international helpdesk Provides helpdesk support for submitters in its jurisdiction and for approved users of data managed at its facilities Provides helpdesk support only for approved users of data managed at its facilities Data distribution Manages worldwide distribution for data hosted at Central EGA Manages worldwide distribution for data hosted at Federated EGA node Distribution for data hosted at Community EGA node EMBL-EBI and CRG have prepared a series of documents describing the overall governance and coordination framework. These describe the Federated EGA structure, the rights and responsibilities of the different parties and governing committees, and node operation guidelines. The federation governance proposal is under review by the first countries invited to join as Federated nodes, with further interested countries likely to join in the coming years. The Central EGA team has been working closely with many ELIXIR partners, including ELIXIR Finland, Luxembourg, Germany, Norway, Spain and Sweden.

Blog the-federated-ega-network

BRCA1 secondary splice-site mutations

BRCA1 splice isoforms d11 and d11q can contribute to PARP inhibitor (PARPi) resistance by splicing-out mutation-containing exons, producing truncated, partially-functional proteins. However, the clinical impact and underlying drivers of BRCA1 exon skipping remain undetermined. We analyzed nine ovarian and breast cancer patient derived xenografts (PDX) with BRCA1 exon 11 frameshift mutations for splice isoform expression and therapy response.

Study EGAS50000000022

Luminal progenitor cell line iHBEC(CD117)

Basal-like breast cancer originates in luminal progenitors, frequently with an altered PI3K pathway, and focally in close association with genetically altered myoepithelial cells at the site of tumor initiation. The exact trajectory behind this bi-lineage phenomenon remains poorly understood. Here we used a breast cancer relevant transduction protocol including hTERT, shp16, shp53, and PIK3CA(H1047R) to immortalize FACS isolated luminal cells, and we identified a candidate multipotent progenitor. We found that the apparent luminal phenotype of these oncogene transduced progenitors was metastable giving rise to basal-like cells dependent on culture conditions. After culturing the cells for more than 60 passages, cells were subjected to scRNA-seq as well as bulk RNA sequencing of two subpopulations (CD271+ and CD271-).

Dataset EGAD50000000723

NHLBI GO-ESP: Heart Cohorts Exome Sequencing Project (Ischemic Stroke Genetic Study, ISGS)

The NHLBI "Grand Opportunity" Exome Sequencing Project (GO-ESP), a signature project of the NHLBI Recovery Act investment, was designed to identify genetic variants in coding regions (exons) of the human genome (the "exome") that are associated with heart, lung and blood diseases. These and related diseases that are of high impact to public health and individuals from diverse racial and ethnic groups will be studied. These data may help researchers understand the causes of disease, contributing to better ways to prevent, diagnose, and treat diseases, as well as determine whether to tailor prevention and treatments to specific populations. This could lead to more effective treatments and reduce the likelihood of side effects. GO-ESP is comprised of five collaborative components: 3 cohort consortia - HeartGO, LungGO, and WHISP - and 2 sequencing centers - BroadGO and SeattleGO. The Ischemic Stroke Genetics Study (ISGS) is a study of newly onset cases (~600) with ischemic stroke (a stroke due to sudden interruption of blood flow to a part of the brain) compared with sex- and age-matched non-stroke participants. The study was conducted to determine the genes and their variants that contribute to an individual's risk of developing an ischemic stroke. The coordination of the recruitment and flow of the samples occurred at the Mayo Clinic, Jacksonville, FL, under the direction of James F. Meschia, MD. The University of Virginia (Stephen S. Rich, PhD) served as the analytic site for the genetic data. All GWAS data on ISGS participants have been deposited into dbGaP. As part of the NHLBI Exome Sequencing Project, DNA from a subset of ISGS participants will undergo exome sequencing. For the NHLBI ESP, a subset of 92 individuals with lacunar (small vessel) or atherosclerotic (large vessel) TOAST subtypes were selected from among all ISGS participants, excluding those individuals with TOAST subtypes of stroke of other etiology or of stroke with undetermined etiology. All 92 samples pass initial quality control metrics and 89 samples completed exome sequencing. A total of 75 participants with appropriate consent and variant calls had their genetic and phenotypic data deposited into dbGaP.

Study phs000546

Prognostic and therapeutic significance of leukemia subtypes and minimal residual disease measurements in pediatric acute lymphoblastic leukemia treated with contemporary risk-directed trial: a cohort study

Background Genomic analyses to identify leukemia subtypes and refinement of minimal residual disease (MRD) determination are two recent advances that would have substantial impact on the management of children with acute lymphoblastic leukemia (ALL). However, the combined application of these two approaches to determine the prognostic and therapeutic significance of MRD measurement and full spectrum of leukemia subtypes in the context of contemporary risk-directed therapy has not been studied comprehensively.MethodsBetween October 29, 2007 and March 26, 2017, 598 consecutive children with newly diagnosed ALL were enrolled in a clinical trial at St. Jude Children’s Research Hospital. Patients were provisional classified according to the presenting clinical and laboratory features including immunophenotype and cytogenetics. Patients with ETV6-RUNX1 and high hyperdiploid B-progenitor ALL were provisionally included in the low-risk groups; those with TCF3-PBX1, hypodiploidy and T-cell ALL in the standard (intermediate)-risk group; and patients with BCR-ABL1, KMT2A and early T-cell precursor ALL in the high-risk group. Final risk assignment was based primarily on MRD levels measured by flow cytometry on Day 15 (≥1%) and Day 42 (<0.01% or ≥1%) of remission induction. Using genomic classification based on cytogenetics and genomic analysis (single nucleotide polymorphism array analysis and transcriptome, exome, and genome sequencing), patients were classified into 17 subtypes. The primary aim was to determine the event-free survival, overall survival and cumulative risk of relapse according to specific leukemia subtypes and MRD levels on Days 8, 15 and 42 of remission induction. This clinical trial was registered at ClinicalTrials.gov, number NCT00549848.FindingsPatients with ETV6-RUNX1, high hyperdiploidy and DUX4-rearranged B-ALL all received low- or standard-risk therapy, and had the highest 5-year event-free survival rates: 98.4% (95% CI, 95.9-100), 95.3% (91.2-99.4), and 95.0% (84.2-100), respectively. All 142 patients with Day 8 MRD<0.01%, except two with KMT2A-rearranged ALL and one with TCF3-PBX1, were in continuous complete remission. The TCF3-PBX1, PAX5alt, T-cell, early T-cell precursor, iAMP21 and hypodiploid ALL had intermediate outcome with 5-year event-free survival rates ranging from 80.0% (39.4-100) to 88.2% (71.7-100). Patients with KMT2A-rearranged, BCR-ABL1, BCR-ABL1-like and ETV6-RUNX1-like ALL had the worst outcomes, with 5-year event-free survival rates between 64.1% (43.9-84.3) and 76.2% (51.9-100), despite most patients receiving standard- or high-risk therapy. Treatment intensification based on MRD and genotype improved outcome. Relapse did not occur in any of the patients with ETV6-RUNX1, DUX4-rearranged, TCF3-PBX1, iAMP21, ZNF384-rearranged, hypodiploid, or BCR-ABL1-like B-ALL who had day 15 MRD≥1% and received standard-risk therapy. However, day 42 MRD<0.01% did not preclude relapse in patients with intermediate-risk or unfavorable subtypes including TCF3-PBX1, PAX5alt, T-cell, iAMP21, BCR-ABL1, BCR-ABL1-like, ETV6-RUNX1-like, KMT2A-rearranged, and MEF2D-rearranged ALL, despite intensified treatment in the standard- or high-risk arms. InterpretationContemporary risk-directed treatment based on genotypes and MRD can cure virtually all patients with ETV6-RUNX1, hyperdiploid>50 and DUX4-rearranged ALL. While intensified treatment improve outcome for patients with high MRD during induction, substantial proportions of patients with intermediate-risk or unfavorable subtypes would still relapse even achieving negative MRD status at the end of induction and require novel therapeutics to improve outcome.

Study EGAS00001004739

The British Autozygosity Populations BioResource (2022-04-26)

This data set contains whole exome sequences of individuals from 8086 (mostly British Pakistani/British Bangladeshi, mostly self-reported parentally related) individuals from the following studies: 1. 5236 British Pakistani/British Bangladeshi adults from East London Genes & Health, now known as Genes & Health 2. 2624 British South Asian mothers from Born in Bradford (mostly Pakistani) 3. 1061 British South Asian adults from Birmingham (mostly Pakistani) This dataset contains all the exome sequence data available for this study on 2022-04-26

Dataset EGAD00001008736

Clonal selection after gene therapy in sickle cell disease

While gene therapy (GT) provides a potentially curative treatment option for patients with sickle cell disease (SCD), the occurrence of myeloid malignancies in clinical trials has prompted concern. To interrogate potential mechanisms underlying increased cancer risk, we used hematopoietic stem cell (HSC) clonal tracking by whole genome sequencing (WGS) to map the somatic mutation and clonal landscape of 2,592 gene modified as well as unmodified single stem and progenitor cells from six SCD patients undergoing gene therapy (7-26 years old, average 12.7× depth). Pre-GT phylogenetic trees in SCD were highly polyclonal and mutation burdens per cell were elevated in some, but not all, patients. Post-GT, no clonal expansions were identified. However, an increased frequency of driver mutations associated with myeloid neoplasms or clonal hematopoiesis (DNMT3A- and EZH2-mutated clones in particular) were seen in both genetically modified and unmodified cells suggested positive selection of mutant clones during gene therapy. This work sheds light on the mutation landscape and HSC clonal dynamics in gene therapy for SCD and highlights enhanced fitness of some HSCs harboring pre-existing driver mutations following gene therapy. Future studies should define the long-term fate of mutant clones including any contribution to expansions associated with myeloid neoplasms.

Dataset EGAD00001010913

Transcriptional Profiling of Macrophages In Situ in Metastatic Melanoma Reveals Localization-Dependent Phenotypes and Function

Therapeutic interventions modulating immune function at the tumor site could improve outcomes in cancer. Here we analyzed metastatic melanoma, a tumor type highly responsive to T-cell based therapies, and found that the tumor-infiltrating T cells are localized closely to CD14+ monocytes/macrophages rather than to melanoma cells. Using novel immunofluorescence-guided laser capture micro-dissection, we analyzed transcriptomes of CD3+ T cells, CD14+ monocytes/macrophages and melanoma cells in non-dissociated tissue. CD14+ cells localized in tumors beds displayed specific transcriptional signatures distinct from CD14+ cells that were localized in tumor stroma.The 8 distinct metastatic melanoma samples are from the Cooperative Human Tissue Network (CHTN). 64 RNA-seq files are available from 16 intraCD14+, 16 stromaCD14+, 16 cancer cells, 8 intraCD3+ and 8 stromaCD3+ cells. 2 LCM harvests were completed for the CD14+ and cancer cells on two sections of the samples. One harvest was done for the CD3+ samples.The processed data (TPM/raw counts tables) are available on GEO under GSE180124. 

Study phs002564

Genomic Profiling of Sequentially Acquired Metastatic Sites from an "Exceptional Responder" Lung Adenocarcinoma Patient Reveals Extensive Genomic Heterogeneity and Novel Somatic Variants

We employed next-generation sequencing to identify somatic alterations in multiple metastatic sites from an "exceptional responder" lung adenocarcinoma patient during his seven year course of ERBB2-directed therapies. The degree of heterogeneity was unprecedented, with ~1% similarity between somatic alterations of the lung and lymph nodes. One novel translocation, PLAG1-ACTA2, present in both sites, up-regulated ACTA2 expression. ERBB2, the predominant driver oncogene, was amplified in both sites, more pronounced in the lung, and harbored an L869R mutation in the lymph node. Functional studies demonstrated increased proliferation, migration, metastasis, and resistance to ERBB2-directed therapy due to L869R mutation and increased migration due to ACTA2 overexpression. Within the lung, a nonfunctional CDK12, due to a novel G879V mutation, correlated with down-regulation of DNA damage response genes, causing genomic instability, and sensitivity to chemotherapy. We propose a model whereby a sub-clone metastasized early from the primary site and evolved independently in lymph nodes.

Study phs001159

WES in HCCOs with varying Doxorubicin resistance

We aimed to identify somatic mutations that could explain the resistance to Doxorubicin. Therefor, we purified Exomes from liver biopsies and HCC organoids using Agilent SureSelectXT (clinical research exome, HS2 V8, or Human all Exon V6 Cosmic) and performed WES. Matched non-tumoral liver biopsies served as the control for germline mutations. 12 of these samples were previously reported. Sequence reads were aligned to the reference human genome GRCh38 using Burrows-Wheeler Aligner (BWA, v0.7.17). Duplicate removal, base quality adjustment, mutation calling (Mutect2, intervals: SureSelectXT_HS_V8_S33266340, germline-recourse: gnomad.exomes.r2.1.1.sites.liftover_grch38, Panel-of-Normal: 1000g_pon.hg38), filtering of Mutect calls, and functional annotation (Funcotator, funcotator_dataSources.v1.7.20200521g) were performed using the Genome Analysis Toolkit (GATK, v4.4) according to the Broad Institute best practices in Somatic short variant discovery workflows. Mutations with a functional annotation of Deletion, Insertion, Non-stop, Splice-Site, Start-Codon and Missense mutations were kept. A list of 43 commonly mutated genes in HCC was analyzed in the HCC organoids [TCGA, Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma. Cell, 2017. 169(7): p. 1327-1341.e23].

Study EGAS50000000043

Epigenetic and metabolomic data from type 2 diabetes adolescents

Objective: Rates of type 2 diabetes (T2D) among adolescents are on the rise. Epigenetic changes could be associated with the metabolic alterations in adolescents with T2D. Methods: We performed a cross-sectional integrated analysis of DNA methylation data from peripheral blood mononuclear cells with serum metabolomic data from First Nation adolescents with T2D and controls participating in the Improving Renal Complications in Adolescents with type 2 diabetes through REsearch (iCARE) cohort study, to explore the molecular changes in adolescents with T2D. Results: Our analysis showed that 43 serum metabolites and 36 differentially methylated regions (DMR) were associated with T2D. Several DMRs were located near the transcriptional start site of genes with established roles in metabolic disease and associated with altered serum metabolites (e.g. glucose, leucine, and gamma-glutamylisoleucine). These included the free fatty acid receptor-1 (FFAR1), upstream transcription factor-2 (USF2), and tumor necrosis factor-related protein-9 (C1QTNF9), among others. Conclusions: We identified DMRs and metabolites that merit further investigation to determine their significance in controlling gene expression and metabolism which could define T2D risk in adolescents.

Study EGAS00001003816

An Isogenic Cell Line Panel for Sequence-based Screening of Targeted Anti-cancer Drugs

We describe the creation and characterization of an isogenic cell line panel representing common cancer pathways, with multiple features optimized for high-throughput screening. More than 1,800 cell lines from three normal human cells were generated using CRISPR-technologies. Surprisingly, we discovered most of these lines did not result in complete gene inactivation, despite integration of sgRNA at the desired genomic site. However, a subset of the lines harbored true, biallelic disruptions of the targeted tumor suppressor gene, yielding a final panel of 100 well-characterize lines covering 19 pathways frequently subject to loss of function in cancers. This panel included genetic markers optimized for sequence-based ratiometric assays for drug-based screening assays. To illustrate the potential utility of this panel, we developed a multiplexed high-throughput screen that identified Wee1 inhibitor MK-1775 as a selective growth inhibitor of cells with inactivation of TP53. These cell lines and screening approach should prove useful for researchers studying a variety of cellular and biochemical phenomena

Study EGAS00001005974

Prevention and Early Treatment of Acute Lung Injury Network - Vitamin D to Improve Outcomes by Leveraging Early Treatment (PETAL VIOLET-BioLINCC)

Data Access NOTE: Please refer to the "Authorized Access" section below regarding accessing data through the BioData Catalyst ecosystem. The data from this accession is not available for download through dbGaP. Biospecimens: Access to Biospecimens is through the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). Biospecimens from PETAL VIOLET include Plasma and Whole Blood. Please note that use of biospecimens in genetic research is subject to a tiered consent. Specimens may not be used to produce commercial products.Objectives: To evaluate the effect of short-term vitamin D supplementation on mortality among critically ill patients with a vitamin D deficiency.Background: Observational data indicate that vitamin D deficiency is common among critically ill patients and constitutes a potentially modifiable risk factor associated with longer lengths of stay in the hospital and intensive care unit (ICU), lung and other organ injury, prolonged mechanical ventilation, and death. In a previous phase 2 trial, vitamin D supplementation administered to vitamin D-deficient, critically ill patients was associated with lower observed mortality than placebo at 28 days and at 6 months. Because of the need for a larger, phase 3 trial, the PETAL-VIOLET study was initiated to determine if early administration of high-dose vitamin D3 would reduce all-cause mortality among critically ill patients with a vitamin D deficiency.Participants: 1360 patients underwent randomization, 690 were assigned to the vitamin D group and 668 were assigned to the placebo group. Of the 1078 patients confirmed to have a vitamin D deficiency by liquid chromatography-tandem mass spectrometry (LC-MS-MS), 538 had been assigned to the vitamin D group and 540 had been assigned to the placebo group.Design: PETAL-VIOLET was a multicenter, double-blind, placebo-controlled, phase 3 trial. Patients were enrolled within 12 hours after the clinician's decision to admit the patient to the ICU from the emergency department, hospital ward, operating room, or outside facility. Patients were tested for vitamin D deficiency, with a threshold of plasma 25-hydroxyvitamin D level of less than 20 ng per milliliter. Patients were randomly assigned in a 1:1 ratio, stratified according to site, to receive either a single enteral (administered orally or through a nasogastric or orogastric tube) dose of 540,000 IU of vitamin D3 or matched placebo, in liquid form, administered within 2 hours after randomization.Conclusions: After the first interim analysis, the data and safety monitoring board recommended that the trial be stopped for futility.A single 540,000 IU enteral dose of vitamin D3 administered early during critical illness rapidly corrected vitamin D deficiency but did not provide an advantage over placebo with respect to mortality or other clinically important end points.

Study phs003879

eIMPACT Trial: Modernized Collaborative Care to Reduce the Excess CVD Risk of Older Depressed Patients

Cardiovascular disease (CVD) is the #1 killer of American men and women, and its economic burden is substantial and on the rise. Adults with depression are at elevated risk of CVD events and poor CVD prognosis. Unfortunately, past clinical trials of depression treatments have not observed the anticipated cardiovascular benefits. A novel explanation for these null results is that the depression interventions in these trials, which all involved patients with preexisting CVD, were delivered too late in the natural history of CVD. Accordingly, the objective of this single-center, parallel-group, assessor-blinded randomized controlled trial was to determine whether successful depression treatment before, versus after, the onset of clinical CVD reduces CVD risk in depression. Primary care patients with depression and elevated CVD risk from a safety net healthcare system (N = 216, Mage = 59 years, 78% female, 50% Black, 46% with income <$10,000/year) were randomized 1:1 to 12 months of the eIMPACT intervention (a modernized collaborative stepped care program involving internet cognitive-behavioral therapy [CBT], telephonic CBT, and/or select antidepressants) or usual primary care for depression (primary care providers supported by embedded behavioral health clinicians and psychiatrists). The primary outcome was endothelial dysfunction (brachial flow-mediated dilation) at 12 months, and the secondary outcomes were self-reported depressive symptoms (Hopkins Symptom Checklist-20), autonomic dysfunction (high-frequency heart rate variability), systemic inflammation (interleukin-6 and high-sensitivity C-reactive protein), and platelet activation (β-thromboglobulin and platelet factor 4) at 12 months. The central hypothesis was that the eIMPACT intervention would improve endothelial dysfunction in depressed adults by decreasing depressive symptoms, autonomic dysfunction, systemic inflammation, and platelet activation. Primary results are posted to ClinicalTrials.gov at NCT02458690. The de-identified limited dataset available on BDC consists of only the individual phenotypic data needed to confirm this trial’s primary results – i.e., randomization status, primary and secondary outcome variables, and participant characteristics utilized in the hypothesis-testing models (age, sex, race, education, income, and systolic blood pressure). We also provide the SAS code to replicate the results of the hypothesis-testing models reported in Table 4 and Supplemental Tables 1-3 in the main outcomes paper (#2 in Selected Publications). Of note, the user will need to specify the file path for the dataset to run the SAS code. Instructions for requesting individual-level data are available on BDC at https://biodatacatalyst.nhlbi.nih.gov/resources/data/. Apply for data access in dbGaP. Upon approval, users may begin accessing requested data in BDC. For questions about availability, you may contact the BDC team at https://biodatacatalyst.nhlbi.nih.gov/contact.

Study phs003283

CCDG CVD: VIRGO - Variation in Recover-Role of Gender on Outcomes of Young Acute Myocardial Infarction (AMI) Patients

The VIRGO study is a four year research project funded by the NHLBI to study young women and men with heart attacks. A total of 2,000 women who are 18 - 55 years of age and a smaller comparison group of 1,000 similarly aged men will be enrolled in this multi-site observational study from approximately 120 different hospitals across the country. Young women have a much greater risk of dying after an acute myocardial infarction (AMI) than similarly aged men. Their mortality risk is markedly higher than that of young men, and limited data on young minority women suggest that they may have the highest risk of any young subgroup. Findings from the small number of published studies of young women with ischemic heart disease suggest that the biology, epidemiology, care, and outcomes of this group are distinct from those of men. To date, there have been no large studies of this population, even as the death toll is comparable to that from breast cancer in this country. This study will help researchers identify key factors that influence recovery from a heart attack and potentially improve the care and outcomes of young women and men with AMI. Specific Aims: To investigate the excess risk in young women with AMI and to identify key demographic, clinical, metabolic, psychosocial, health care delivery, and biological determinants of prognosis, we propose a national, prospective, observational study of 2,000 women who are 55 years or younger with AMI and a smaller comparison group of 1,000 men. Our prior work and preliminary studies indicate that the sex differences are most prominent in this age group and suggest that by 1 year after discharge, there are substantial sex differences in outcomes. We will also develop risk stratification tools that will help clinicians to identify young women with the highest and lowest risk. Specific Aim 1: Determine sex differences in outcomes following AMI. Specific Aim 2: Determine sex differences in the prevalence and prognostic importance of demographic, clinical, and psychosocial risk factors. Specific Aim 3:Determine sex differences in quality of care. Specific Aim 4: Determine sex differences in the prevalence and prognostic importance of selected biochemical biomarkers following AMI. These samples have been whole genome sequenced as part of the NHGRI's Center for Common Disease Genetics at the Broad Institute.

Study phs001259

Heart Failure Network Oral Iron Repletion Effects on Oxygen Uptake in Heart Failure (HFN IRONOUT-BioLINCC)

ObjectivesTo test the hypothesis that, compared to placebo, oral iron repletion in heart failure patients with iron deficiency improves exercise capacity after 16 weeks of therapy.Background Iron deficiency affects approximately one half of patients with symptomatic heart failure. The presence of iron deficiency in patients with heart failure is associated with reduced functional capacity, poorer quality of life, and increased mortality. Despite growing recognition of the significance of iron deficiency, randomized multicenter trials exploring the utility of oral iron supplementation, a therapy that is inexpensive, readily available, and safe, have not been performed in patients with heart failure. Moreover, patient characteristics and biochemical profiles that may influence responsiveness to oral iron in patients with heart failure have not been defined. Results of intravenous iron repletion trials have been favorable, but regularly treating patients with intravenous iron products is expensive and poses logistical challenges for outpatients. Therefore, the HFN-IRONOUT study was initiated to investigate the efficacy of oral iron in patients with heart failure with reduced ejection fraction (HFrEF). ParticipantsA total of 225 participants were enrolled, 111 randomized to receive oral iron and 114 randomized to receive the placebo. Design The HFN-IRONOUT study was a phase 2, double-blind, placebo-controlled randomized clinical trial that enrolled participants at 23 sites in the United States. Participants were randomly assigned, in a 1:1 ratio, to receive either oral iron polysaccharide or placebo with the use of an automated web-based system. A permuted block randomization method (with 4 participants per block) was stratified by enrolling site and anemia status (defined as hemoglobin At baseline, 8 weeks, and 16 weeks participants underwent studies including history and physical examination, cardiopulmonary exercise testing (CPET), Kansas City Cardiomyopathy Questionnaire (KCCQ), and 6-minute walk test. CPETs were performed using a 10 Watt/minute incremental ramp protocol and breath-by-breath measures of oxygen uptake were uniformly analyzed by the CPET Core Lab. Participants also underwent phlebotomy for biomarkers. Iron studies, including iron, total iron binding capacity, and ferritin, were measured at baseline and after 16 weeks to determine the extent to which oral iron led to iron repletion. The primary end point was the change in peak oxygen uptake (peak VO2) after 16 weeks of therapy. Conclusions Among participants with HFrEF with iron deficiency, high-dose oral iron did not improve exercise capacity over 16 weeks.

Study phs003557

Stratifying and Targeting Pediatric Medulloblastoma through Genomics

In this project, genomic analyses of pediatric medulloblastoma samples, obtained through the international medulloblastoma consortium, will be performed. RNA and miRNA expression profiles of 1000 samples, representing all four subgroups (Wnt, Shh, Group C, and D), will be studied to identify novel subtypes within each subgroup. The resulting subtype-specific expression profiles will support the development of reliable and robust biomarkers to more accurately and reliably classify medulloblastomas for treatment in clinical trials. For that purpose, two assays will be developed: an antibody-based immunohistochemical assay and an orthogonal nucleic acid-based hybridization assay. Additional genomic DNA analysis of the 300 high risk subgroup cases will support the discovery of subgroup specific somatic mutations in order to inform current clinical trials of targeted therapies, and to identify genes and pathways already targeted in other diseases. Such therapies could be rapidly transitioned to Phase II trials in medulloblastoma. Furthermore, the discovery of somatic mutations could be used for developing as well as validating specific biomarkers. The project team will also try to identify risk factors that predispose children to this type of cancer.

Study EGAS00001000273

Paired Biopsy Project: West Coast Dream Team

This project aims to analyze the paired metastatic biopsy samples from patients with metastatic castration-resistant prostate cancer (mCRPC), collected before the initiation of the first-line androgen receptor signaling inhibitor (ARSI) for mCRPC and after radiographic disease progression on that ARSI, to understand the resistance mechanisms. We generated both whole genome sequencing (WGS) and RNA-seq data on these samples. A majority of these samples have been deposited in the public domain along with prior publications, and this project contains the 16 WGS samples that have not yet been deposited.

Study EGAS50000000327

Best Practices for DACs

Best Practices Data access committees (DACs) are institutional safeguards responsible for ensuring a balance between data protection and accessibility. However, there are no procedural standards that apply across DACs, which can lead to inconsistencies in their reviews and compromise their quality and effectiveness. Standardising DAC processes can foster trust and mutual recognition, paving the way for greater coordination, collaboration, and delegation between DACs and other oversight bodies to improve the efficiency of data access without sacrificing protection. To provide guidance, the following are suggestions for actions a DAC might take upon receiving a data access request: Aim to respond to all initial requests in less than 2 weeks. Failure to do so may result in follow-up emails from the EGA, journals, and ultimately dataset withdrawal. First, check that the data/EGAD number is consistent with your data submission. In other words, ensure that the requester has contacted the correct DAC. Verify that the user will be using the data within the terms of consent by asking them to sign up to the terms within the DAA. Ensure that data users who are granted access to the data comply with the terms of a Data Access Agreement (DAA) and to use the data only in approved ways. Look for an institutional email address for the requester. Search for evidence that the requester is "appropriately qualified/bona fide" for using the data, for example on PubMed, Research Gate, LinkedIn, etc. Confirm that the affiliated organisation is real and that the requester still works there. Inquire with the requester about who should have accounts created at the EGA under the terms of the agreement. If a negative decision is made, promptly communicate it to the requester and support it with the terms of the Data Access Policy that the requester has not met or cannot meet. Keep the information in the DAC up to date. If you leave your institution and are unable to manage data access requests on their behalf, you should add your replacement as a new contact in the DAC. Assist with data access requests for any further questions related to your data. The EGA can only check information that has been deposited in the repository. If the user has a specific question that the EGA cannot answer, we will redirect the user to the DAC. All the EGA studies and datasets referenced in a publication under your DAC must be publicly searchable on the EGA website before the paper is released. If possible, describe the permitted purposes for subsequent research projects, including associated limits and conditions, for all resources hosted in a repository using a common ontology, such as the GA4GH Data Use Ontology (DUO). It is considered best practice to try and release data only to those with an institutional email address. This gives reassurance to the EGA, research participants, and the general public that an appropriate individual is accessing and using the data. To prevent potential data breaches and ensure adherence to GDPR regulations, it is essential that the European Genome-Phenome Archive (EGA) is informed via the Helpdesk team of any changes to the Data Access Committee (DAC). This should be done in addition to any changes being made on the DAC portal. Data Controllers (as per the definition in the DPA) are also responsible for notifying the previous DAC of any modifications. Without proper notification, changes might not be automatically updated in our system, leading to the risk of incorrect permissions being applied and potential data access issues. Therefore, it is imperative that all Data Controllers follow this protocol to maintain data integrity and security.

Documentation access/data-access-committee/best-practices

San Francisco Bay Area Latina Breast Cancer Study

The genome-wide association study (GWAS) includes participants enrolled into two different studies. The first study, the San Francisco Bay Area Cancer Study (SFBCS) is a population-based case-control study of breast cancer conducted in the San Francisco Bay Area and included women ages 35-79 years from three racial/ethnic groups: Non-Hispanic whites, African Americans, and Hispanics/Latinas. For the GWAS, only Hispanic/Latina women were included. Women diagnosed with invasive breast cancer between 1995 and 2002 were identified through the Greater Bay Area Cancer Registry. Controls were identified by random digit dialing and were frequency-matched to cases by age in 5 year increments and by race/ethnicity. Hispanic/Latina ethnicity was assessed by self-report. 175 Hispanic/Latina cases and 307 Hispanic/Latina controls from the SFBCS had given adequate consent and provided biospecimens that were used in the GWAS to be included in this data submission. The second study is the Northern California site of the Breast Cancer Family Registry (NC-BCFR). This population-based family study recruited breast cancer cases ages 18-64 years diagnosed from 1995-2009 that were identified through the Greater Bay Area Cancer Registry. Cases included all women at increased genetic susceptibility for breast cancer who met one or more of the following criteria: (a) being diagnosed with breast cancer at age <35 years; b) having a personal history of ovarian cancer or childhood cancer; (c) being diagnosed with two different breast cancers (bilateral breast cancers), with the first one diagnosed at age <50 years; and d) having one or more first-degree relatives with breast cancer, ovarian cancer or childhood cancer. Cases not meeting these criteria were randomly sampled and racial/ethnic minorities were oversampled. Controls were recruited by random digit dialing and were matched by 5-year age increments and by race/ethnicity. For the current GWAS only Latina/Hispanic cases and controls were included. Latina/Hispanic ethnicity was assessed by self-report. 631 Hispanic/Latina cases and 61 Hispanic/Latina controls from the NC-BCFR had given adequate consent and provided biospecimens that were used in the GWAS to be included in this data submission.

Study phs000912

Losartan Effects on Emphysema Progression (LEEP-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 To evaluate the efficacy of losartan, an angiotensin receptor blocker, to reduce emphysema progression in patients with COPD and mild to moderate emphysema. Background Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease comprising chronic bronchitis and emphysema. COPD is a major cause of morbidity and mortality. Smoking cessation slows the progression of the disease, and bronchodilators can provide sustained improvement in lung function, but there are no pharmacologic agents that clearly modify emphysema progression. Angiotensin receptor blockers (ARBs) have been suggested as potential drugs to slow the progression of COPD. A few trials have shown that patients treated with ARBs had slower progression of radiographic emphysema. The LEEP study was initiated to test the hypothesis that the ARB losartan would reduce the progression of emphysema in patients with COPD with mild to moderate emphysema.ParticipantsA total of 220 participants were enrolled. 108 participants were randomized to receive losartan, and 112 participants were randomized to receive the placebo.Design LEEP was a randomized, placebo-controlled multicenter trial. Participants were randomly assigned (1:1) to receive losartan or placebo. The dose was 50 mg losartan or placebo given as one capsule daily for 2 weeks, and, if well tolerated and the systolic blood pressure was >90 mm Hg, it was increased to two capsules. Participants and site investigators were masked to treatment assignment. Participant responses to the St George's Respiratory Questionnaire, the modified Medical Research Council dyspnea scale, the COPD Assessment Test, and the Physical Function–Short Form 20a were collected. The number and severity of COPD exacerbations were recorded. COPD exacerbations were defined as two or more new or worse symptoms for ≥3 days and classified by treatment as mild (neither antibiotics nor oral steroids), moderate (an antibiotic or oral steroid), or severe (hospitalization). The primary outcome measure was the change in quantitative whole-lung emphysema score after 48 weeks measured by the percentage of lung voxels less than -950 Hounsfield units (PCT950) on full inspiratory HRCT. Conclusions Losartan does not prevent progression of emphysema in patients with COPD who have mild to moderate pulmonary emphysema.

Study phs004313

Kids First: Whole Genome Sequencing in Recessive Structural Brain Defects

Almost 5% of all live births in the US (1:20 births) display an inborn defect, including both structural and functional/metabolic abnormalities. These are among the most common causes of Infant mortality in the developed world and underlie nearly half of hospitalizations in the first 3 years of life. Of the 35 major defects observable at birth from the International Clearinghouse for Birth Defects Surveillance Program (www.icbdsr.org, external link), about half involve the nervous system. Many of these result in lifelong neurodevelopmental disorders. Structural Brain Defects (SBDs) result from errors in development of the central nervous system, including defects in the forebrain, midbrain and hindbrain. Many SBDs arise as the consequence of a single gene bi-allelic mutation, and for this reason, occur more commonly in populations or communities with elevated consanguinity.Our lab has identified dozens of novel SBD genes using WES/WGS in consanguineous SBD families. Importantly, the genes that we and others have identified in these unique families are then used to advance diagnosis in pediatric SBDs around the world. We have built an enormous cohort of SBD families, including newly recruited families not yet studied genetically, and previous families that were negative for cause following WES analysis. Here we propose to collaborate with the Gabriella Miller Kids First Pediatric Research Program (X01) to have sequencing performed in individuals from a total of 200 families with genetically undiagnosed SBDs. The Gleeson Lab team of researchers is dedicated to the field of SBDs, with an outstanding track record of high-impact science, and a collaborative approach to discovery. We have 150 newly recruited families that we propose to study by WES by sequencing blood-derived DNA from two affected patients, or the parents and one affected patient. We also have 50 families in which WES was negative, which we propose to study in a multi-omics approach combining WGS from blood-derived DNA. We anticipate that this study will lead to the identification of many new molecular causes of SBDs, as well as uncover new genotype-phenotype correlations and new disease mechanisms, paving the way for future breakthroughs in detection, treatment and prevention.

Study phs002590

Quick Guide for data submission

Quick Guide This is a quick guide to submit data to the EGA. Please select data type to display the right detailed instructions. Sequence data Array-based data Get your submission account You first need to create your EGA user. Then, once your account has been verified by the Help Desk team, request your submission account and provide details of the data type and platform(s) in your submission. Register Study/DAC Use Submitter Portal to register your study, samples, Data Access Committee (DAC) and Policy. Upload data Encrypt your data files using the and upload it to your inbox using SFTP. Register experiments and runs Associate each data file to a registered sample and study by Linking files to samples. Details of the experimental procedure you followed must be provided. Finalise your submission Group your runs/analysis into datasets and link them to your new or existing DAC and policy using DAC Portal. Data request are done at a dataset level, thus files within a datasets must share release conditions. Release and admin When finalising the submission, set a released date to instruct our Helpdesk to release your study. All registered studies are automatically placed on hold until the named submission or DAC contact instructs our Helpdesk for the study to be released. Get your submission account Fill the submission form and provide details of the data type and platform(s) in your submission. Register Study/DAC Use EGA Programmatic submission to register your study, samples, Data Access Committee (DAC) and Policy. Upload data Encrypt your data files using the EgaCryptor and upload it using default FTP clients or Aspera. Complete the Array-Format (AF) spreadsheet Download the AF spreadsheet and complete all four sections. Return the spreadsheet to the EGA helpdesk. Release and admin Instruct our Helpdesk to release your study. All registered studies are automatically placed on hold until the named submission or DAC contact instructs our Helpdesk for the study to be released.

Documentation submission/quickguide

Genetic coupling of enhancer activity and connectivity in gene expression control

In this study, we investigated whether the genetic effects of eQTLs on enhancer activity and enhancer-promoter contacts (connectivity) are inherently coupled or distinct. To address this, we performed Capture Hi-C targeting ~1100 eQTL loci, along with ATAC-seq and RNA-seq, in monocytes from 34 healthy male donors. Using single-modality and multi-modality Bayesian statistical approaches, we demonstrated that eQTL effects are predominantly shared across enhancer activity, connectivity, and gene expression. However, we also identified a locus where an eQTL disrupts a CTCF binding site at a putative insulator, facilitating contacts between the PCK2 gene promoter and downstream enhancers. To validate these findings, we used 4C-seq to confirm the allelic effects of eQTLs on enhancer-promoter connectivity at the PCK2 locus and another eQTL region associated with THBS1 expression, both of which showed consistent eQTL effects across all three modalities. Additionally, we conducted Hi-C on monocytes from two donors to map reference TADs and performed ChIP-seq to confirm the allelic effects of the PCK2 eQTL on CTCF binding. Together, these data provide comprehensive insights into the genetic coupling of enhancer activity, connectivity, and gene expression.

Study EGAS50000000756

Treatment-mediated selection of lethal prostate cancer clones defined by copy number architectures

Despite initial responses to hormone treatment, metastatic prostate cancer invariably evolves to a lethal state. To characterize the intra-patient relationships of metastases that evade treatment, we performed genome- wide copy number profiling and bespoke approaches targeting the androgen receptor (AR) on 142 metastatic regions from 10 organs harvested post-mortem from nine men who died from prostate cancer. We identified diverse and patient-unique alterations clustering around the AR in metastases from every patient with evidence of independent acquisition of related genomic changes within an individual and, in some patients, the co-existence of AR-neutral clones. Using the genomic boundaries of pan-autosome copy number change, we confirmed a common clone of origin across metastases and diagnostic biopsies; and identified in individual patients, clusters of metastases occupied by dominant clones with diverged autosomal copy number alterations. Autosome-defined clusters were characterized by cluster-specific AR gene architectures that in two index cases were topologically more congruent than by chance (p-values 0.03, 3.07x10-8). Integration with anatomical site suggested patterns of spread and points of genomic divergence. Copy number boundaries identified treatment-selected clones with putatively distinct lethal trajectories.

Study EGAS00001006598

A Genome-Wide Association Study in Patients Experiencing Musculoskeletal Adverse Events on NCIC CTG Trial MA.27 Evaluating Aromatase Inhibitors as Adjuvant Therapy in Early Breast Cancer. A Collaboration Between the NIH Pharmacogenetics Research Network and the RIKEN Yokohama Institute Center for Genomic Medicine.

Source of Patients The source of the patients for this genome-wide case control study was MA.27, which was conducted as a multi-cooperative group effort under the auspices of the NCI Breast Cancer Intergroup of North America. The NCIC Clinical Trials Group (CTG) serves as the coordinating group, with participation by the NCI-sponsored North Central Cancer Treatment Group, Eastern Cooperative Oncology Group (ECOG), Southwest Oncology Group, and Cancer and Leukemia Group B (CALGB). MA.27 involved postmenopausal women with histologically confirmed and completely resected invasive breast cancer with surgical margins clear of invasive carcinoma and DCIS in the following TMN categories (AJCC Version 6): pT1, pT2, pT3; pNx, pN0, pN1, pN2, pN3 (only when the sole basis is presence of 10 or more involved axillary nodes); MO. The primary tumor must have been ER and/or progesterone receptor positive. Patients were stratified by lymph node status at diagnosis, prior adjuvant chemotherapy, and trastuzumab use and were randomized to 5 years of adjuvant therapy with anastrozole or exemestane. The trial was activated on May 26, 2003, and reached its accrual objectives on July 31, 2008, after the randomization of 6827 North American patients, with the majority (79%) providing DNA and consent for genetic testing. Non-North American patients were also entered by the International Breast Cancer Study Group but they did not contribute DNA. From 2003 to December 21, 2004, patients also underwent a second randomization to celecoxib 400 mg twice daily or placebo but, after the entry of 1,622 patients, this treatment was discontinued because of reports of increased cardiovascular risk associated with celecoxib. Case Definition for Musculoskeletal Adverse Events Cases were defined to have had at least one of the following six MSAE): 1. pain - joint, 2. pain - muscle, 3. pain - bone, 4. arthritis, 5. joint - function, 6. musculoskeletal - other. Cases were required to have at least grade 3 toxicity, according to the NCI Common Terminology Criteria for Adverse Events v3.0, or go off treatment for any grade of the six MSAEs; and were required to have had an MSAE within the first two years (i.e., a MSAE occurring after 2 years was not be considered a case). Subjects were excluded from the case group who met the case definition while on celecoxib or in the three months after stopping celecoxib but subjects were allowed to be cases if they developed their first MSAE more than three months after stopping celecoxib. Study Design The design was a nested matched case-control study with two controls selected for each case. Random matching from the potential control group (i.e., risk-set sampling such that controls had at least the same length of followup as the timing of the case event) was done by matching the case on the following factors: 1. treatment arm (exemestane vs. anastrozole, blinded), 2. prior adjuvant chemotherapy (yes vs. no), 3. age at start of treatment (+/- 5 years), 4. Celecoxib allocation (yes vs. no). Attempts were made to exactly match each case with two controls according to the above criteria. If, however, controls could not be exactly matched, "close" matching was used to define a distance between each case and all potential controls in an optimal matching algorithm. Race is a potential confounding factor, and 94% of the MA.27 population is Caucasian, 3% are Black, 1% Asian, 1% American Indian, and 1% are of unknown race. Because there are so few non-Caucasian participants, was restricted to only Caucasians. Following identification of cases and controls, the list was sent to the CALGB Pathology Coordinating Office (PCO) at Ohio State University Medical Center for plating of the samples. Other patient factors that might influence the association of SNP genotypes with a MSAE, either as confounding factors or as effect modifiers, were included as investigational covariates. These include 1. body mass index (BMI), 2. bisphosphonate use, 3. fractures in past 10 years, 4. baseline ECOG performance status, 5. prior hormone replacement therapy, 6. prior adjuvant radiotherapy, and 7. prior taxane treatment. An Honest Broker Process was utilized to ensure patient confidentiality. Genotype Assays and Quality Control A GWAS was performed with the Illumina Human610-Quad at the RIKEN Center for Genomic Medicine. The DNA for all of the samples were adjusted to a concentration of 50 to 100 ng/µL in a final volume of 50 µL. The 96-well plates were designed to have a unique distribution pattern with x duplicates per plate to evaluate genotype concordance. Samples with <98% call rate were removed from analyses. SNPs with ambiguous call will be excluded using the following criteria: 1. low signal intensity of each allele, 2. low cluster separation score, 3. SNP call rate <0.98, 4. SNPs with Hardy Weinberg Equilibrium p-value <0.001 were excluded. X-chromosome SNPs were used to validate female sex and quality control measures were examined across the different submitting centers. For all pairs of subjects, the percentage of matching genotypes were computed to screen for unexpected duplicate samples. All SNPs found to be associated with MSAE were subjected to visual inspection of the cluster plots to verify that the genotype clustering was adequate.

Study phs000210

PyEGA3 download client

PyEGA3 Checks prior to download - Is the dataset available for download? Most datasets are available using the PyEGA3 Download client, except for specific legacy datasets. For these ones, refer to Live Outbox distribution, an alternative way to download these datasets.If the dataset you've been granted access to begins with the ID EGAD5XXXXXXX, please refer to the Live Outbox distribution. If the dataset you have been granted access to contains encrypted data (filename ending in .gpg), contact EGA Helpdesk for detailed information on how to download these files.General Information The PyEGA3 download client is available at its GitHub repository. In its README you will find a step by step guide on how to use it to download files you have been granted access to. PyEGA3 implements the GA4GH-compliant HTSGET protocol supporting requests over genomic ranges. This enables the download of specific regions of interest rather than the entire file. HTSGET is not possible for all the files held by EGA: if the dataset does not contain index files (e.g. filename ending in *.crai), then genomic range requests cannot be performed. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Check out our video tutorial! Frequently Asked Questions General questions How do I download the datasets to which I have been granted access? After setting up your EGA download account, you can proceed to download using the EGA download client – PyEGA3. The pyEGA3 download client is a python-based tool for viewing and downloading files from authorised EGA datasets. This download client is continuously being developed for more user-friendly download experiences. Can I download the dataset’s metadata via the download client? No, the download client cannot be used to download metadata. Registered EGA users can download metadata of an authorised dataset by logging into the EGA webpage and navigating to the dataset of choice. Approximately two thirds down the page you will find the option to download the metadata as a zip file. How do I download large datasets as quickly as possible? The main EGA download client is pyEGA3 download client, a python-based tool for viewing and downloading files from authorised EGA datasets.We could exceptionally consider the option of an Aspera download for users experiencing very slow download rates or facing particular issues.Contact the EGA Helpdesk for further information. Pre-requirement and Installation What are the major requirements for the PyEGA3 download client installation and usage? The PyEGA3 client is compatible with any OS with Python 3.6+ installed. The client requires a connection to the internet, sufficient space on the destination drive, and the EGA download account credentials. Where can I find the PyEGA3 download client installation and download instructions? We strongly recommend checking our video tutorial demonstrating the usage of PyEGA3 from installation to file download. More detailed information is available at PyEGA3 GitHub. What ports are required for the PyEGA3 download client to function? PyEGA3 makes HTTPS calls to the EGA Data API (https://ega.ebi.ac.uk:8443). This port 8443 must be reachable from the location where PyEGA3 is executed to avoid timeouts. PyEGA3 download client How to update the PyEGA3 download client to be on the latest version? Updating the client to the latest version can be achieved by running the following command: pip3 install pyega3 --upgrade I do not have an active EGA account. Can I test the download with the PyEGA3 download client? An EGA download test account (ega-test-data@ebi.ac.uk) has been created for troubleshooting and training purposes. The test account does not require an EGA username and password because it contains publicly accessible files from the 1000 Genomes Project. More information about the use of this account and log in details are available at pyEGA3. I lost my connection while the download was in progress. Does this mean that I have to download the file again from the start? The PyEGA3 download client supports automatic resumption of downloads. To enable quicker download speeds, the API breaks files into up to four segments and downloads them in parallel. With the resume feature, downloads will automatically resume if you encounter any errors or if the connection is interrupted. How can I improve the overall download speed of the PyEGA3 download client? Download speeds can be optimised using the --connections parameter which will parallelise download at the file level. If the --connections parameter is provided, all files >100Mb will be downloaded using the specified number of parallel connections. It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Why is my file taking a long time to be saved? Please note that when a file is being saved, it goes through two processes. First, the downloaded file "chunks" are pieced back together to reconstruct the original file. Secondly, PyEGA3 calculates the checksum of the file to confirm that the file was downloaded successfully. Larger files will take more time to reconstruct and validate the checksum. Why is the HTSGET protocol not working with my dataset of interest? PyEGA3 implements the GA4GH-compliant htsget protocol for supporting requests over genomic ranges. This exciting new feature means that for data files with accompanying index files (e.g. .crai for CRAM files) users can download specific regions of interest rather than the entire file, saving both time and storage space. Please note that in order for the genomic range requests to work for a BAM, CRAM or VCF file there must be an associated BAI, CRAI or TBI file, respectively. If the dataset does not contain these index files then genomic range requests cannot be performed. After download Do I need to decrypt data files downloaded through the pyEGA3 download client? Files are transferred over secure HTTPS connections and received unencrypted, thus there is no need for decryption after data download. What is the purpose of an MD5 file? After the download completes, file integrity is verified using checksums. The PyEGA3 download client automatically verifies each file’s unencrypted md5 after download to ensure that the file was downloaded correctly from the EGA. Errors Why do I get a “400 Client Error” while trying to access the download client? Ensure that your credentials are formatted correctly. Please contact the EGA Helpdesk if you are unsure about your account’s username (i.e. your email address). Why do I get a “500 Server Error”? It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Otherwise, 500 server errors are internal server errors from EGA’s end. Retry after some time and, if the 500 error persists: check our homepage or Twitter for any outage information or report it to EGA Helpdesk. Why do I get a “slice error”? This type of error occurs when one slice download was prematurely interrupted. Please re-start the same download. The download client will pick up any partial download and download any missing slices. Why do I get the error “Dataset' EGADXXXXXXXXXXX' is not in the list of your authorised datasets”? Please note that the data access was given to the email (username) provided to the Data Access Committee (DAC) in your official request. Therefore, you shall check that the account that you are using is the same one that you sent to the DAC. Why do I get “ERROR:root:Failed to obtain IP address”? This might mean that the connection ports are blocked. Check that the port 8443 is reachable.In case the port is reachable and the error persists, please try to modify the file pyega3.py on line 44 in the python module installation: Replace endpoint = 'https://ipinfo.io/json' with endpoint = 'https://api.myip.com' Why do I get the error “Invalid username, password or secret key - please check and retry”? Make sure that you are using the correct credentials (username and password) and there is no typo. Contact EGA Helpdesk if the problem persists.

Documentation access/download/files/pyega3

16S-based fecal microbiota composition

The dataset reports the 16S rRNA gene sequencing of the fecal microbiota of donors from the Milieu Intérieur Cohort. The Milieu Intérieur cohort includes a total of 1,000 healthy individuals of western European ancestry, recruited in France as part of the Milieu Intérieur project. To assess their fecal microbiota composition, 16S rRNA profiles were generated from stool samples of 863 of the 1,000 donors. Human stool samples were produced at home no more than 24 hours before the scheduled medical visit and collected in a double-lined sealable bag maintaining strict anaerobic conditions. Upon reception at the clinical site, the fresh stool samples were aliquoted and stored immediately at -80°C. DNA was extracted from stool and barcoding PCR was carried out using indexed primers targeting the V3-V5 region of the 16S rRNA gene. Equal volumes of normalized PCR reaction were pooled and thoroughly mixed. The amplicon libraries were sequenced on Illumina MiSeq.

Dataset EGAD00001004979

DNA whole-exome sequencing data from patients with metastatic basal cell carcinoma

Whole-exome sequencing data in fastq format of matched tumour and germline DNA from 8 patients with metastatic basal cell carcinoma. Samples are labeled as Primary, Local or Metastasis: Primary: Sample was obtained from primary tumour. Local: Sample was obtained from local recurrence of primary tumour. Metastasis: Sample was obtained from a metastatic site. Germline: Sample was obtained from normal adjacent tissue. DNA was isolated from FFPE tissue sections of the tumor biopsies using the AllPrep DNA/RNA FFPE Kit (Qiagen) and quality controls conducted using the Qubit fluorometer (Thermo Fisher Scientific). Library preparation was performed using the Agilent SureSelect Human All Exon v7 XTHS2 probes and sequenced on a NovaSeq 6000 S2 2x100bp

Dataset EGAD00001008675

eMERGE Network Phase III: HRC SNV and 1000 Genomes SV Imputed Array Data of 105,000 Participants

The Electronic Medical Records and Genomics (eMERGE) Network is a National Institutes of Health (NIH)-organized and funded consortium of U.S. medical research institutions. The primary goal of the eMERGE Network is to develop, disseminate, and apply approaches to research that combine biorepositories with electronic medical record (EMR) systems for genomic discovery and genomic medicine implementation research. eMERGE was announced in June 2007 and began its third phase in September 2015. eMERGE Phase III (June 2015 - June 2020) consists of 10 study sites, two central sequencing and genotyping facilities, and a coordinating center. Included in this study are: Human Reference Consortium (HRC) single nucleotide variants and 1000 Genomes structural variants imputed array data of 105,108 eMERGE participants from nine Phase III study sites and three Phase II study site collaborators. Corresponding demographics, body mass index measurements. Case/control status for the following phenotypes: Abdominal aortic aneurysm; Ace-Inhibitor/Cough; Attention Deficit Hyperactivity Disorder; Age-related macular disease; Appendicitis; Asthma; Atopic Dermatitis; Autism; Benign Prostatic Hyperplasia; Carotid artery disease as a Quantitative Measure; caMRSA; Cataract; Clostridium difficile colitis; Extreme Obesity; Chronic Kidney Disease; Chronic Kidney Disease and Type 2 Diabetes; Chronic Kidney Disease, Type 2 Diabetes and Hypertension; Colon Polyps; Cardiorespiratory Fitness; Dementia; Diverticulosis; Diabetic retinopathy; Gastroesophageal Reflux Disease; Glaucoma; Height; Heart failure; Hypothyroidism; Lipids; Ocular hypertension; Peripheral Arterial Disease; QRS duration; Red blood cell indices; Remission of Diabetes after ROUX-EN-Y gastric bypass surgery; Resistant hypertension; MACE while on Statins; Type 2 Diabetes; Venous Thromboembolism; White blood cell indices; and Zoster virus infection. Study sites and participants include: Boston Children's Hospital: The Gene Partnership (TGP) is a prospective longitudinal registry at Boston Children's Hospital (BCH) to study the genetic and environmental contributions to childhood health and disease, collect genetic information on a large number of children who have been phenotyped, and implement the Informed Cohort and the Informed Cohort Oversight Board (ICOB). The term "The Gene Partnership" reflects a partnership between researchers and participants. Children seen at BCH are offered enrollment, as are their parents and siblings. DNA is collected on all enrollees. BCH has a comprehensive EMR system, and virtually all inpatient and outpatient data are captured electronically. Clinical data in the BCH EMR is loaded in the i2b2 data warehouse which is available to investigators. Cases, phenotypes, and covariates are ascertained using the i2b2 database. Participants at BCH in TGP have consented to receive any research result and/or incidental finding that arises from studies using TGP that is approved by the Informed Cohort Oversight Board (ICOB) and is in accordance with the participants'preferences;results are returned through the Personally Controlled Health Record (PCHR). BCH and Cincinnati Children's Hospital Medical Center (CCHMC) have partnered as the Pediatric Alliance for Genomic and Electronic Medical Record (EMR) Research (PAGER) site for the eMERGE Phase II network for pediatric institutions, and the cohort for eMERGE at BCH is TGP. Children's Hospital of Pennsylvania (CHOP): The Center for Applied Genomics (CAG) at the Children's Hospital of Philadelphia (CHOP) is a high-throughput, highly automated genotyping and sequencing facility equipped with state-of-the-art genotyping and sequencing platforms. Children who are treated at the Children's Hospital Healthcare Network and their parents may be eligible to take part in a major initiative to collect more than 100,000 blood samples, covering a wide range of pediatric diseases. A large majority of participants consenting to prospective genomic analyses also consent to analysis of their de-identified electronic health records (EHRs). EHRs are longitudinal, with a mean duration of 6.5 years. Cincinnati Children's Hospital Medical Center/Boston's Children's Hospital (CCHMC/BCH): Cincinnati Children's Hospital Medical Center (CCHMC) is a pediatric institution dedicated to improving health and welfare of children and to the discovery and practical application of new genomic information to the ordinary care of children. CCHMC brings an extraordinary faculty to eMERGE III who are committed to gain a better understanding of the genesis of disease and to elucidate the mechanisms of diseases that afflict children, specifically pediatric disease phenotypes that will leverage the available eMERGE adult genomic data and electronic medical records (EMRs) to discover meaningful use results. Generation of EMR phenotype algorithms, informed by natural language processing, using heuristic and machine learning methods is ongoing. CCHMC has developed tools to evaluate adolescent return of results preferences, examined the ethical and legal obligations and potential to reanalyze results, and developed clinical decision support for phenotyping, test ordering, and returning sequencing results. The success of these eMERGE III studies is enhanced by the ongoing institutional investment made in the CCHMC Biobank, the comprehensive EMR (EPIC), the i2b2 de-identified medical record data warehouse, and hundreds of faculty and senior staff who make genomics or informatics an active focus of their research. Columbia University: Columbia University Medical Center/New York Presbyterian (CUMC/NYP) Hospital system is one of the nation's largest and most comprehensive hospital systems with over 2 million inpatient and outpatient yearly visits that serves a racially and ethnically diverse urban patient population. The Columbia University GENomic Integration with EHR (GENIE) research study contributed and shared phenotype and genotype data for individuals who were recruited as part of a diverse array of initiatives within the hospital, including Northern Manhattan Study (NOMAS), Pediatric Cardiac Genomic Consortium (PCGC), Caribbean Hispanics with Familial and Sporadic Late Onset Alzheimer's disease (AD), Alzheimer's Disease Sequencing Project (ADSP), and Genetics of Chronic Kidney Disease study. Some of these individuals had kidney or neurological problems, some were healthy adult volunteers with self-reported health status information from the medically underserved Northern Manhattan community, and others were pediatric patients with cardiac conditions. For the kidney disease cohort, patients with the diagnosis of Chronic Kidney Disease (CKD) and healthy controls were recruited to the Columbia University CKD biobank. For the NOMAS cohort, eligible participants were stroke-free, were 40 years old, and resided for at least 3 months in a Northern Manhattan household with a telephone. The PCGC study recruited parent-offspring trios with pediatric probands diagnosed with congenital heart defects (CHD). For the Caribbean Hispanics with Alzheimer's disease project, individuals from families affected by AD and with sporadic AD were recruited, along with unrelated controls. Samples for the ADSP study have been selected from well-characterized cohorts of individuals with AD diagnosis. Geisinger Health System: A research cohort of adult Geisinger Clinic patients was enrolled from community-based primary care clinics of the Geisinger Health System. Patients were eligible for enrollment if they were a primary care patient of a Geisinger Clinic physician and were scheduled for a non-emergent clinic visit. All participants provided written informed consent and HIPAA authorization. Consenting patients agreed to provide blood samples for broad biomedical research use, and permission to access data in their Geisinger electronic medical record for research. The enrollment rate was 90% of patients approached. The demographics of the cohort approximate those of the Geisinger Clinic outpatient population. Research blood samples were collected during an outpatient clinical phlebotomy encounter. Research blood samples are coded and stored in a central biorepository. Samples are linkable to clinical data in a de-identified manner for research via an IRB-approved data broker process. For genomic analysis, DNA is extracted from EDTA-anticoagulated whole blood. Partners Health Care (Harvard): The Partners HealthCare Biobank is a large research program designed to help researchers understand how people's health is affected by their genes, lifestyle, and environment. This large research data and sample repository provides access to high-quality, consented blood samples to help foster research, advance our understanding of the causes of common diseases, and advance the practice of medicine. For the Partners research community (Massachusetts General Hospital and Brigham and Women's Hospital), the Biobank provides: Banked samples (plasma, serum, and DNA) collected from consented patients Blood samples that were discarded after clinical testing in the Crimson Cores maintained in the Brigham and Women's Hospital and Massachusetts General Hospital Pathology Departments Sample handling and preparation services Link to the biobank data to the Partners Research Patient Data Registry (RPDR) a research instance of our electronic clinical chart Data access through our research portal. To date, over 60,000 Partners patients have given their consent to enroll, give a blood sample, receive research results and agreed to be re-contacted for additional research studies. The Biobank has enabled Partners investigators to compete for nationally recognized grants in personalized medicine such as a clinical electronic Medical Records and Genomics network (eMERGE) site and the national All of US program. The Biobank currently supports over 120 Partners investigators and over 100 million dollars in NIH research. Kaiser Permanente Washington with the University of Washington and the Fred Hutchinson Cancer Research Center: KPWA participants were enrolled in the eMERGE Network through the Northwest Institute of Genetic Medicine (NWIGM) biorepository, and provided the appropriate consent to receive clinically relevant genetic results (N~8,073.) NWIGM is based at the University of Washington and co-managed by the University of Washington and KPWA. The purpose of the NWIGM biorepository is to build infrastructure and resources to carry out a broad range of future genetic research. KPWA members enrolled in the biorepository are asked to provide informed consent to providing a DNA sample for storage in the NWIGM biorepository. The consent is purposefully broad to serve the dual purpose of reducing the burden on researchers who wish to use this biorepository and the IRB committees who will be responsible for reviewing these requests in the future. Participants were eligible if aged 50 - 65 years old at the time of their enrollment into the NWIGM repository, living, enrolled in KPWA's integrated group practice, and had completed an online Health Risk Appraisal. The selection algorithm was based on several data sources from the EHR at KPWA. 1) Demographics - participants with self-reported race as Asian ancestry were prioritized and selected to enrich for non-European ancestry. The KPWA eMERGE cohort includes n=1,245 members of Asian ancestry. 2) Participants were also selected for a history of colorectal cancer (n=1,002), in order to allow us to enrich germline pathogenic variants. Essentia Institute of Rural Health, Marshfield Clinic, Pennsylvania State University (Marshfield): The Marshfield Clinic Personalized Medicine Research Project is a population-based biobank in central Wisconsin with more than 20,000 adult subjects who provided written, informed consent to access their medical records and provided a blood sample from which DNA was extracted and plasma and serum stored. In addition to an average of 30 years of medical history data, a questionnaire about environmental exposures, including a detailed food frequency questionnaire, is available to facilitate gene/environment studies. Mayo Clinic: The Mayo Vascular Disease Biorepository is a disease-specific biobank for vascular diseases including peripheral arterial disease (PAD). PAD patients were identified from individuals referred to the non-invasive vascular laboratory for lower extremity arterial evaluation. Since 1997, laboratory findings have been recorded into an electronic database employing an in-house software package for data archiving and retrieval;this data becomes part of the Mayo EMR. Patients referred to the center with suspected PAD undergo a comprehensive non-invasive evaluation including the ankle-brachial index (ABI) - the ratio of blood pressure measured in the upper arms divided by blood pressure measured at the ankles. Controls subjects are identified from patients referred to the Cardiovascular Health Clinic for stress ECG. The prevalence of PAD in patients with normal exercise capacity who do not have inducible ischemia on the stress ECG , was <1%. Data regarding risk factors for atherosclerosis such as diabetes, dyslipidemia, hypertension, and smoking are ascertained from the EMR. Case control study of venous thromboembolism (PI John Heit) Controls from a case control study of pancreatic cancer (PI Gloria Petersen) Mayo Clinic Biobank. Icahn School of Medicine at Mount Sinai School (Mt. Sinai): The Institute for Personalized Medicine (IPM) Biobank Project is a consented, EMR-linked medical care setting biorepository of the Mount Sinai Medical Center (MSMC) drawing from a population of over 70,000 inpatients and 800,000 outpatient visits annually. MSMC serves diverse local communities of upper Manhattan, including Central Harlem (86% African American), East Harlem (88% Hispanic Latino), and Upper East Side (88% Caucasian/white) with broad health disparities. IPM Biobank populations include 28% African American (AA), 38% Hispanic Latino (HL) predominantly of Caribbean origin, 23% Caucasian/White (CW). IPM Biobank disease burden is reflective of health disparities with broad public health impact: average body mass index of 28.9 and frequencies of hypertension (55%), hypercholesterolemia (32%), diabetes (30%), coronary artery disease (25%), chronic kidney disease (23%), among others. Biobank operations are fully integrated in clinical care processes, including direct recruitment from clinical sites, waiting areas and phlebotomy stations by dedicated Biobank recruiters independent of clinical care providers, prior to or following a clinician standard of care visit. Recruitment currently occurs at a broad spectrum of over 30 clinical care sites. Northwestern University: The NUgene Project is a repository with longitudinal medical information from participating patients at affiliated hospitals and outpatient clinics from the Northwestern University Medical Center. Participants'DNA samples are coupled with data from a self-reported questionnaire and continuously updated data from our Electronic Medical Record (EMR) representing actual clinical care events. Northwestern has a state-of-the art, comprehensive inpatient and outpatient EMR system of over 2 million patients. NUgene has broad access to participant data for all outpatient visits as well as inpatient data via a consolidated data warehouse. NUgene participants consent to distribution and use of their coded DNA samples and data for a broad range of genetic research by third-party investigators. Vanderbilt University Medical Center: BioVU, Vanderbilt's DNA databank, was designed as an enabling resource for exploration of the relationships among genetic variation, disease susceptibility, and variable drug responses. BioVU acquires DNA from discarded blood samples collected from routine patient care. The biobank is linked to de-identified clinical data extracted from Vanderbilt's EMR, which forms the basis for phenotype definitions used in genotype-phenotype correlations. BioVU is currently the largest single site DNA collection world-wide, at >235,000 samples as of spring 2017.

Study phs001584

CIDR: The Role of Rare Coding Variation in Prostate Cancer in Men of African Ancestry - RESPOND Project 2

In RESPOND Project 2, we seek to identify rare genetic factors that are associated with prostate cancer (PCa) risk and aggressiveness in men of African ancestry (AA). We will conduct exome sequencing of 15,000 prostate cancer cases and 5,000 controls from the RESPOND cohort and the African Ancestry Prostate Cancer Consortium (AAPC) with cases selected based on risk categories: high-risk (stage T3/T4 or Gleason 8+ or PSA>20 ng/ml), intermediate-risk (stage T2b/T2c or Gleason 7 or PSA 10-20 ng/ml) and low-risk disease (stage T1/T2a and Gleason ≤ 6 and PSA<10 ng/ml). We expect the findings from this Project to significantly advance knowledge of susceptibility to aggressive PCa and racial/ethnic disparities in PCa risk, and to guide the development of future preventive, early detection and prognostic measures for AA men. The first phase of the study will include exome sequence data for approximately 7,500 cases and 5,000 controls from AAPC. The second phase of the study will include exome data for 5,000 cases from RESPOND and AAPC.ACKNOWLEDGMENTS and CONTRIBUTING SITES Multiethnic Cohort (MEC): The MEC and the genotyping in this study were supported by National Institutes of Health (NIH) grants CA63464, CA54281, CA1326792, CA148085, and HG004726. Cancer incidence data for the MEC and Los Angeles Study of Aggressive Prostate Cancer (LAAPC) studies have been collected by the Los Angeles Cancer Surveillance Program of the University of Southern California with federal funds from the National Cancer Institute (NCI)/NIH/Department of Health and Human Services (DHHS) under Contract No. N01-PC-35139, and the California Department of Health Services as part of the state-wide cancer reporting program mandated by California Health and Safety Code Section 103885, and grant 1U58DP000807-3 from the Centers for Disease Control and Prevention (CDC). Ghana Prostate Study (GPS): The Ghana Prostate Study was funded by the Intramural Program of the NCI/NIH/DHHS under Contract No. HHSN261200800001E. Men of African Descent and Carcinoma of the Prostate (MADCaP): We thank all MADCaP study participants. This work is a product of the MADCaP network. This work was supported by NCI/NIH grant U01CA184374 to Timothy Rebbeck and National Institute of General Medical Sciences (NIGMS) MIRA grant R35GM133727 to Joseph Lachance. Additional funding includes a seed grant from the Integrated Cancer Research Center at Georgia Institute of Technology. UGANDA: The UGANDA study was supported by NIH grant R01CA165862. Southern Community Cohort (SCCS) is funded by NIH grant CA092447. SCCS sample preparation was conducted at the Epidemiology Biospecimen Core Lab that is supported in part by the Vanderbilt Ingram Cancer Center (CA68485). Data on SCCS cancer cases were provided by Alabama Statewide Cancer Registry, Kentucky Cancer Registry, Office of Cancer Surveillance at Tennessee Department of Health, Florida Cancer Data System, Central Cancer Registry at North Carolina Division of Public Health, Georgia Comprehensive Cancer Registry, Louisiana Tumor Registry, Mississippi Cancer Registry, South Carolina Central Cancer Registry, Virginia Cancer Registry at Virginia Department of Health, and Cancer Registry at Arkansas Department of Health. The Arkansas Central Cancer Registry is fully funded by a grant from National Program of Cancer Registries (NPCR)/Centers for Disease Control and Prevention (CDC). Data on SCCS cancer cases from Mississippi were collected by the Mississippi Cancer Registry which participates in the NPCR/CDC. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or the Mississippi Cancer Registry. Karuprostate: The Karuprostate study was supported by the French National Health Directorate and by the Association pour la Recherche sur les Tumeurs de la Prostate. Séverine Ferdinand, Marc Romana. The North Carolina - Louisiana Prostate Cancer Project (PCaP) is carried out as a collaborative study supported by the Department of Defense contract DAMD 17-03-2-0052. The authors thank the staff, advisory committees and research subjects participating in the PCaP study for their important contributions. We would also like to acknowledge the UNC BioSpecimen Facility and the LSUHSC Pathology Lab for our DNA extractions, blood processing, storage and sample disbursement (https://genome.unc.edu/bsp). Gene-Environment Interaction in Prostate Study (GECAP) was supported by NIH grant ES011126. King County Prostate Cancer Study (KCPCS) was supported by NIH grants CA056678, CA082664, and CA092579, with additional support from the Fred Hutchinson Cancer Research Center. We thank the participants in these studies, and Ms. Suzanne Kolb for help with study management. The Los Angeles Study of Aggressive Prostate Cancer (LAAPC) was funded by grant 99-00524V-10258 from the Cancer Research Fund, under Interagency Agreement #97-12013 (University of California contract #98-00924V) with the Department of Health Services Cancer Research Program. Cancer incidence data for the MEC and LAAPC studies have been collected by the Los Angeles Cancer Surveillance Program of the University of Southern California with Federal funds from the NCI/NIH/DHHS under Contract No. N01-PC-35139, and the California Department of Health Services as part of the state-wide cancer reporting program mandated by California Health and Safety Code Section 103885, and grant 1U58DP000807-3 from the CDC. Prostate Cancer Studies at MD Anderson (MDA) was supported by grants CA68578, ES007784, DAMD W81XWH-07-1-0645, and CA140388.

Study phs002637

NHLBI TOPMed - NHGRI CCDG: The BioMe Biobank at Mount Sinai

The IPM BioMe Biobank, founded in September 2007, is an ongoing, broadly-consented electronic health record (EHR)-linked clinical care biobank that enrolls participants non-selectively from the Mount Sinai Medical Center patient population. BioMe currently comprises >42,000 participants from diverse ancestries, characterized by a broad spectrum of longitudinal biomedical traits. Participants are enrolled through an opt-in process and consent to be followed throughout their clinical care (past, present, and future) in real-time, allowing us to integrate their genomic information with their EHRs for discovery research and clinical care implementation. BioMe participants consent for recall, based on their genotype and/or phenotype, permitting in-depth follow-up and functional studies for selected participants at any time. Phenotypic and genomic data are stored in a secure database and made available to investigators, contingent on approval by the BioMe Governing Board. BioMe uses a "data-broker" system to protect confidentiality. Ancestral diversity - BioMe participants represent a broad racial, ethnic and socioeconomic diversity with a distinct and population-specific disease burden. Specifically, BioMe participants are of African (AA), Hispanic/Latino (HL), European (EA) and other/mixed ancestry. BioMe participants are predominantly of African (AA, 24%), Hispanic/Latino (HL, 35%), European (EA, 32%), and other ancestry (OA, 10%). Participants who self-identify as Hispanic/Latino further report to be of Puerto Rican (39%), Dominican (23%), Central/South American (17%), Mexican (5%) or other Hispanic (16%) ancestry. More than 40% of European ancestry participants are genetically determined to be of Ashkenazi Jewish ancestry. With this broad ancestral diversity, BioMe is uniquely positioned to examine the impact of demographic and evolutionary forces that have shaped common disease risk. Phenotypes available in BioMe - BioMe has a high-quality and validated set of fully implemented clinical phenotype data that has been culled by a multi-disciplinary team of experienced investigators, clinicians, information technologists, data-managers, and programmers who apply advanced medical informatics and data mining tools to extract and harmonize EHRs. BioMe, as a cohort, offers a great versatility for designing nested case-control sample-sets, particularly for studying longitudinal traits and co-morbidity in disease burden. Biomedical and clinical outcomes: The BioMe Biobank is linked to Mount Sinai's system-wide Epic EHR, which captures a full spectrum of biomedical phenotypes, including clinical outcomes, covariate and exposure data from past, present and future health care encounters. As such, the BioMe Biobank has a longitudinal design as participants consent to make all of their EHR data from past (dating back as far as 2003), present and future inpatient or outpatient encounters available for research, without restriction. The median number of outpatient encounters is 21 per participant, reflecting predominant enrollment of participants with common chronic conditions from primary care facilities. Environmental data: The clinical and EHR information is complemented by detailed demographic and lifestyle information, including ancestry, residence history, country of origin, personal and familial medical history, education, socio-economic status, physical activity, smoking, dietary habits, alcohol intake, and body weight history, which is collected in a systematic manner by interview-based questionnaire at time of enrollment. The IPM BioMe Biobank contributed ~10,600 DNA samples for whole genome sequencing to the TOPMed program. Samples were selected for the Coronary Artery Disease (CAD) and the Chronic Obstructive Pulmonary Disease (COPD) working groups. Using a Case-Definition-Algorithm (CDA), we identified ~4,100 individuals with CAD (~50% women) and ~3,000 individuals as controls (65% women). In addition, we identified ~800 individuals with COPD (62% women) and 1800 individuals as controls (72% women). Another 600 BioMe participants with Atrial Fibrillation, all of African ancestry, were included.

Study phs001644

Center for Craniofacial and Dental Genetics (CCDG): Genetics of Orofacial Clefts and Related Phenotypes

The purpose of this study is to investigate the genetics of orofacial clefts (OFCs) in a large study population, and importantly, to incorporate subclinical phenotypic features into these studies. Orofacial clefts (OFCs) comprise a significant fraction of human birth defects (about 1/700 live births (Rahimov et al. 2012) and represent a major public health challenge, as individuals with these anomalies require surgical, nutritional, dental, speech, medical and behavioral interventions, thus imposing a substantial economic and personal burden (Berk and Marazita 2002*). The most common forms include OFCs of the lip alone (CL, Figure 1A), CL plus cleft palate (CL+CP, Figure 1B) or of the palate only (CP, Figure 1C). Individuals born with OFC may have their first surgical repair at age 3 months, but this initial surgery is just the beginning of a lifetime of health burdens. An individual born with an OFC has a hospital use rate increased for most ages (up to 233% increase for children ages 0-10 years and 16% for middle aged adults (Wehby et al. 2012). Healthcare costs for children with OFCs are estimated to be 800% greater compared with their unaffected peers (Boulet et al. 2009). Data from Denmark show that people born with CL with or without CP (CL/P) have an increased mortality up to age 55, which may be attributed to an increased risk of suicide and/or certain cancers (Christensen et al. 2004). The focus of most OFC genetic research has been CL and/or CP. Furthermore, the majority of OFC, i.e. about 70% of CL/P and 50% of CP is considered "nonsyndromic" (Jones 1988), i.e. isolated anomalies with no other apparent cognitive or structural abnormalities. Figure 1 Sample OFC Types A: Bilateral Cleft Lip; B:Cleft Lip plus Cleft Palate; C:Cleft Palate Alone The factors leading to the majority of nonsyndromic OFCs are still unclear, particularly at an individual family level. As is true for many complex traits, substantial progress in gene identification has occurred in the OFC field in the last two years (Dixon et al. 2011; Marazita 2012). Genome Wide Association Studies (GWAS) and sequencing studies to date by our research team and others have focused on genetic risk factors for overt CL/P and CPO-and have been very successful. A major finding from this work is that OFCs exhibit significant genetic heterogeneity, i.e., multiple genetic regions have been implicated (Beaty et al. 2010; Ludwig et al. 2012). Thus, approaches are needed to understand this genetic heterogeneity. Are there GxG interactions at work? Are there subsets of families, each due to a different gene? Our research group has shown that a promising approach to dissect the etiology of OFC is to focus on subclinical phenotypic features within entire cleft families (not just in affected cases, but also in their non-cleft relatives). These subtle features are believed to represent mild manifestations of the same underlying genetic susceptibility responsible for OFCs; as such, their inclusion in case-control and family-based genetic studies can help to clarify and refine the relationship between genotype and phenotype. The study population comprises a large number of families and individuals (~12,000 individuals) from multiple populations worldwide (Caucasians from the US and Europe, Asians from China and the Philippines, Mixed Native American/Caucasians from South America, and Africans from Nigeria and Ethiopia). There are cases, case families (nuclear families and extended kindreds), as well as controls with no history of OFC nor other developmental defects. *Berk NW, Marazita ML (2002) Costs of Cleft lip and Palate: Personal and Societal Implications. In: Wyszynski DF (ed) Cleft Lip and Palate: From Origin to Treatment. Oxford University Press, Inc., New York, pp 458-467.

Study phs000774

Distribution of data using Live Distribution

Live Distribution Welcome to the documentation for using the Live Distribution feature for distributing data files securely through the EGA platform. This guide will walk you through the process of downloading encrypted files and decrypting them using Crypt4GH. Please follow the steps below to ensure a smooth experience. Before Downloading Create an EGA user. Make sure that you have the permissions to download the dataset of interest. In case you don’t have access, request access to the dataset. Add your Crypt4GH-compatible public key to your EGA account. Please allow a few hours for your public key to be synced with your profile. Afterwards, you will be able to connect to your EGA outbox using the SFTP protocol. Download Graphical User Interface (GUI) You can use any GUI that supports SFTP connections, such as FileZilla, an open-source FTP client. For Filezilla as your GUI, follow these steps to download files: Open FileZilla and access Site Manager (File > Site Manager). Create a new connection with the following settings (Figure 1): Protocol: SFTP - SSH File Transfer Protocol Host: __EGA_OUTBOX_DOMAIN__ Logon Type: Key file User: your EGA username Key file: path/to/your/private_key Figure 1: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using a key file as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the EGA outbox, ensuring seamless data transfers. Click Connect to access your Outbox. This folder serves as your storage space within the EGA cloud, containing files accessible for download in a secure way. Browse the remote directory on the right side of the FileZilla screen. Select the files you wish to download, right-click, and choose Download (Figure 2). Figure 2: Step-by-step process of manually downñoad files from __EGA_OUTBOX_DOMAIN__ using FileZilla, with FileZilla version 3.52.2 operating on IOS v11.2.3. The figure demonstrates how users can downñoad data from the EGA outbox to their local storage by following the depicted steps SFTP command line You can also use the SFTP command line to securely download files from the EGA Outbox. Using SFTP command line client in Linux/Unix Open a terminal window Enter the following command to connect: sftp username@hostname Enter your EGA password To see a list of available sftp commands type help sftp> put – Upload file sftp> get – Download file sftp> cd path – Change remote directory to ‘path’ sftp> pwd – Display remote working directory sftp> lcd path – Change the local directory to ‘path’ sftp> lpwd – Display local working directory sftp> ls – Display the contents of the remote working directory sftp> lls – Display the contents of the local working directory Type get command to download files. For example: get encrypted_file.c4gh Use the bye command to close the connection (SFTP session). Convenient SSH settings Include the following settings in your SSH config file, located in ~/.ssh/config Host __EGA_OUTBOX_DOMAIN__ EGA-outbox hostname __EGA_OUTBOX_DOMAIN__ User username IdentityFile path/to/the/private/key Replace username and path/to/the/private/key with the appropriate settings, and you will be able to connect to the __EGA_OUTBOX_DOMAIN__ simply using sftp EGA-outbox. How to decrypt Files archived at the EGA are encrypted based on Crypt4GH. Hence, to decrypt the files you need to install Crypt4GH. You can install a python implementation of it, with pip install crypt4gh or directly from the Github repository pip install git+https://github.com/EGA-archive/crypt4gh.git After installing Crypt4GH, decrypt files using the following command: crypt4gh decrypt --sk /path/private/key < encrypted_file.c4gh > decrypted_filename The command reads the encrypted file from stdin (with <) and output the decrypted version to stdout (with >). Replace encrypted_file.c4gh and decrypted_filename with the appropriate filenames but make sure to not use the same filename for both reading and writing because your SHELL would then truncate both files before you even read or write. Frequently Asked Questions What username should I use to log in to my outbox? The authentication process for logging in to the EGA website, as well as accessing your inbox and outbox, requires the use of your username, not your email address. Therefore, if you registered a username different from your email address when creating your EGA account, you must use that username to log in. If you have forgotten your registered username, please, contact our Helpdesk team for assistance. I see that some files in my dataset have 'unavailable' as extension. What should I do? Within your Outbox, you'll find a list of all the datasets available for download. Occasionally, certain files may be marked as "unavailable". These unavailable files can be identified by the "unavailable" extension added to their filenames (e.g. filename.fastq.gz.unavailable.c4gh). If you encounter an unavailable file that you need, please reach out to our Helpdesk. We'll promptly work on making the file accessible for download as soon as possible. Specific to using keys Can I access one EGA account from different devices? Yes, you can access your account from different devices by linking several public keys to your EGA account. Each device can generate a unique public-private key pair, and the corresponding public keys can be linked to the same account. This way, you can use different public keys on different devices and still have access to the same account and data. I have several keys and I don't remember which one is which When generating SSH keys, it's a good practice to add a comment using the -C flag. This will allow you to add a descriptive tag to your key, making it easier to identify later on. Here's an example command that generates an SSH key with a comment: ssh-keygen -t ed25519 -C work-pass In this example, we're generating an ed25519 SSH key with the comment work-pass. Once you have multiple keys with different comments, you can use the comments to easily identify each key. To view the comments for your existing SSH keys, you can use the following command: ssh-keygen -l -f /path/to/key This will display the key fingerprint and the associated comment. By checking the comments, you should be able to identify which key is which. What if I can't find my SSH keys for uploading files with a key file, and how can I use new keys? If you can't find your SSH keys, don't worry - you can make new ones. To do this, open your terminal or command prompt and type a command to make a new SSH key. You can pick a name for the key, and choose a password to keep it safe. After making the key, you can add the new key to your account or server where you want to upload files using the key file. This usually involves copying and pasting the key's "public" (e.g. file.pub) part to the right place. If you lose track of the key again, just make a new one and add it again. Keep in mind that SSH keys belong to you and your computer, so if you switch computers or accounts, you'll need to make new keys. I don't want to type the passphrase every time I use the key. What can I do? You can use an ssh-agent to avoid typing the passphrase every time you use the key. An ssh-agent is a program that stores your private keys in memory and provides them to ssh when needed. You can add your key to the ssh-agent using the command ssh-add followed by the path to your key file.Here's an example of the steps to follow: Open a terminal window.Start the ssh-agent by typing the command eval $(ssh-agent).Add your key to the ssh-agent by typing the command ssh-add [key filepath]. For instance, if your key file is located in the home directory with the name mykey, the command will look like this: ssh-add ~/mykey After adding your, key to the ssh-agent, you should be able to use ssh without having to enter your passphrase every time. Can I use my password for authentication (without my private key)? If you prefer to use your username and password for authentication instead of your private key, you can still do so. When using a Graphical User Interface (GUI) such as FileZilla, you can select Ask for password as your Logon Type (Figure 3). This option will prompt you to enter your password when you click Connect, instead of using your private key. Figure 3: This option will prompt you to enter your password when you click "Connect", instead of using your private key. Figure 3: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using your password as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers. It's worth noting that using a password for authentication can be less secure than using an SSH key, as passwords can be more easily compromised through various means. However, if you choose to use your password for authentication, selecting "Ask for password" as your Logon Type is a good way to do so securely via a GUI. Why is it better to use my key and not my password? SSH keys for authentication is generally considered to be more secure and convenient than using passwords. SSH keys are more difficult to crack than passwords, and they can be restricted to specific users and machines, giving you more control over access. Once you set up your SSH keys, you can use them to authenticate quickly and easily, without having to enter a password every time. This makes automation of tasks, such as uploading encrypted files, much simpler. Additionally, SSH keys provide better logging, allowing you to keep track of who is accessing your systems and when. All in all, using SSH keys is a good practice for improving security and convenience in your authentication process.

Documentation access/download/files/live-outbox

ALCHEMIST Study

The ALCHEMIST study will accrue patients that are potentially eligible for the Intergroup adjuvant studies and perform central EGFR and ALK genotyping using a central reference laboratory certified by the Clinical Laboratory Improvement Amendments of 1988 (CLIA). Patients may either present prior to surgery with resectable NSCLC, may present following complete resection (before or after adjuvant chemotherapy). Eligibility is limited to those with NSCLC of a non-squamous histological subtype and those with adequate performance status and organ function for future trial eligibility. All subjects must submit tissue for central EGFR and ALK genotyping, as well as additional tissue for advanced genomics at the CCG. Subjects may have had local genotyping done prior to registration, however if these results shows no targetable EGFR or ALK alterations (or if it shows a KRAS mutation) then the patient will not be eligible for this screening protocol given the primary aim is to facilitate accrual to the adjuvant studies. All subjects will provide peripheral blood for matched normal DNA. All subjects (including those known to have EGFR or ALK alterations with local genotyping) will have formalin-fixed tissue collected for central genotyping. The testing will be performed at Response Genetics (Los Angeles, CA), a commercial CLIA-certified laboratory. ALK FISH will be performed using the Vysis break-apart probe and EGFR sequencing will be performed of exons 18-21. It is preferred that a full tumor block be submitted, but if unavailable, 15 unstained slides can be submitted. Genotyping results are expected to be provided to the treating clinician within 14 business days of submission so they can be used to determine eligibility for the randomized adjuvant studies, or to confirm the local results. Results will also be reported at intervals to the study team for update into the Alliance database. In addition to the commercial genotyping at Response Genetics, tissue will be collected for research genomics by CCG. For those patients with a block available, this will be forwarded to the CCG after clinical testing at Response Genetics. For patients without a block available, 15 unstained slides should be submitted to the CCG BCR for exploratory analysis. A peripheral blood specimen will also be collected and sent to the CCG BCR to use as matched normal. Specimens will be coded. Over the course of the study, the CCG will perform advanced genomic analysis of the resected lung cancer specimens in a research, non-CLIA environment. Following completion of the genomic analysis, the results can be matched with the clinical follow-up results using a link between the samples codes and the patient identifiers for correlative analyses. Subjects participating in the follow-up portion of the ALCHEMIST study, as well as those participating in the adjuvant therapeutic studies, are expected to undergo a standard-of-care diagnostic biopsy to confirm recurrence. At least two core biopsies, minimum, should be obtained as part of this recurrence biopsy. Paraffin embedded tissue from this biopsy will be used for confirmation of recurrence, for CLIA genomics to plan subsequent clinical trials, and for additional research genomics by the CCG. Plasma will additionally be collected at recurrence and sent to CCG for study of circulating free DNA (cfDNA). Data for the ALCHEMIST study is not yet available to be accessed by the public at this time; when ready it will be made available at the GDC Data Portal.

Study phs001140

6257 results for "fc 26 ultimate team coins buy Buyfc26coins.com is FC 26 coins official site..E4Pd"

in 39.98 milliseconds.