Search Results - EGA European Genome-Phenome Archive

Incentives and Case Management to Improve Cardiac Care: Healthy Lifestyle Program (HeLP)

Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this study.Objectives: The HeLP study assessed how early case management, financial incentives contingent on cardiac-rehabilitation attendance, or both impacted adherence to cardiac rehabilitation among patients with lower socioeconomic status (SES) who had a cardiac-rehabilitation-qualifying diagnosis. Background:Cardiovascular disease (CVD) is the leading cause of death in the United States. Regular attendance at cardiac rehabilitation (CR) is the number one recommendation for patients who have experienced a recent cardiac event. CR is a secondary prevention outpatient program involving an individually tailored structured exercise program along with counseling and encouragement regarding lifestyle changes to reduce the recurrence of major cardiac events.Despite the benefits of CR attendance, enrollment and adherence to the program are low. This is particularly true of individuals of lower socioeconomic status (SES), who also generally suffer a higher burden from CVD on account of higher-risk cardiac profiles. As these profiles are the result of modifiable behavior targeted by CR (i.e., smoking, obesity, lack of physical activity), methods of increasing CR attendance among this population are necessary.Case management and financial incentives contingent on CR attendance are two potential strategies for addressing this issue. Previous studies have shown how trained case managers can support and assist patients with attending CR and improving their cardiac health, and how financial incentives can promote various types of health-related behavior. The purpose of the present study was to evaluate how these two interventions, individually and in tandem, fared at improving CR adherence among individuals of lower SES specifically. Participants:A total of 314 participants were screened, and 209 were randomized. All consented to have their data shared for research purposes. Design:Eligible patients were approached either while still in the hospital or during initial visits to CR. After obtaining informed consent, clinical and demographic characteristics (i.e., age, sex, education, race and ethnicity, smoking status, and CR-qualifying diagnosis) and depressive symptoms were collected. Participants then were randomized 2:3:3:3 to usual care (UC), case management starting in hospital (CM), financial incentives for attending CR (FI), or financial incentives plus case management (FICM). For all participants regardless of condition, an appropriate referral for CR was confirmed after randomization. Over the course of the study participants completed three assessments – one at intake, one after four months, and one after one year. Clinical (i.e., cardiorespiratory fitness, body composition, and cardiac-related quality of life) and sociocognitive (i.e., Beck Depression Inventory, Achenbach System of Empirically Based Assessment, and Behavior Rating Inventory of Executive Function) measures were obtained at each assessment. Participants randomized to the UC condition were contacted weekly by study staff for adverse event checks, but received no additional intervention. Participants randomized to the CM condition were immediately assigned a case manager. Case managers met with their participants, usually within 24 hours of consent while they were still in the hospital, to introduce themselves during an initial check-in. Within the first week of consent they completed a longer, more thorough initial needs assessment with their participant and established behavioral goals. For the next 16 weeks, case managers met with their participants at least once a week, typically over the phone, to discuss progress on these goals and address any problems or barriers that had arisen. Case managers also were available via phone outside of these regular meetings during normal working hours and Saturday mornings. Participants randomized to the FI condition could earn money for completing an initial CR orientation and attending CR sessions. Twenty dollars could be earned for completing the CR orientation session. CR attendance was reinforced on an escalating schedule starting at $10. Payments increased by $2 for each successive session attended, and capped out at a maximum of $40 per session. Unexcused absences resulted in the incentives for that session not being earned, and the potential earnings for the next scheduled session being reset to $10. Successful participation of two consecutive sessions following the reset resulted in the incentives being returned to the amount prior to the reset. In total, participants could earn $1220 for attending the initial orientation and all 36 sessions of CR. The primary outcome of interest was the percentage of participants who completed at least 30 sessions of CR. Secondary outcomes included the number of sessions completed, cardiorespiratory fitness (peak VO2 and estimated metabolic equivalents (METs)), body composition (waist circumference and BMI), depression scores, and both general and cardiac-related qualify of life. Conclusions:The two interventions involving financial incentives (i.e., financial incentives alone and financial incentives plus case management) significantly improved CR adherence compared to usual care. Subsequent analyses revealed that the combination of financial incentives and case management led to greater improvements in CR attendance than financial incentives alone.

Study phs003737

NINDS-Genome-Wide Genotyping in Parkinson's Disease: First Stage Analysis and Public Release of Data

Epidemiological studies have estimated a cumulative prevalence of PD of greater than 1 per thousand. When prevalence is limited to senior populations, this proportion increases nearly 10-fold. The estimated genetic risk ratio for PD is approximately 1.7 (70% increased risk for PD if a sibling has PD) for all ages, and increases over 7-fold for those under age 66 years. The role for genes contributing to the risk of PD is therefore significant. This study utilized the well characterized collection of North American Caucasians with Parkinson's disease, and neurologically normal controls from the sample population which are banked in the National Institute of Neurological Disorders and Stroke (NINDS Repository) collection for a first stage whole genome analysis. Genome-wide, single nucleotide polymorphism (SNP) genotyping of these publicly available samples was originally done in 267 Parkinson's disease patients and 270 controls, and this has been extended to include genome wide genotyping in 939 Parkinson's disease cases and 802 controls. The NINDS repository was established in 10-2001 towards the goal of developing standardized, broadly useful diagnostic and other clinical data and a collection of DNA and cell line samples to further advances in gene discovery of neurological disorders. All samples, phenotypic, and genotypic data are available to the research community including to academics and industry scientists. In addition, well characterized neurologically normal control subjects are a part of the collection. This collection formed the basis of this first stage study by Fung et al., and the expanded study by Simon-Sanchez et al. The genotyping data was generated and provided by the laboratory of Dr. Andrew Singleton NIA, and Dr. John Hardy NIA (NIH Intramural, funding from NIA and NINDS). Important links to apply for individual-level data Data Use Certification Requirements (DUC) Apply here for controlled access to individual level data Participant Protection Policy FAQ

Study phs000089

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

Data Access Committee of CiRA Foundation

The iPS Cell Stock Project promoted by the CiRA Foundation is intended to generate and distribute iPS cells for medical use so they can be effectively utilized for research and development of cell transplantation therapy and contribute to the advancement of regenerative medicine that may become a novel treatment for patients suffering from intractable diseases or trauma who are not expected to improve with conventional therapies. In order to achieve this objective, CiRA Foundation establish the DAC based on the basic policies and rules for handling analysis information including iPS cell stocks for the purpose of proper sharing of samples and analyzed data collected directly or indirectly from the donors of iPS cell stocks.

Dac EGAC50000000128

MIBC NAC2020 cohort RNA sequencing

The dataset contains RNA sequencing data from N=48 transurethral resections of bladder tumours (TURBTs) from N=48 patients with muscle-invasive bladder cancer who were treated with neoadjuvant chemotherapy followed by radical cystectomy. All TURBTs originate from formalin-fixed paraffin embedded tissue. We used the Illumina TruSeq RNA exome kit, previously known as the TruSeq RNA Access Library Prep Kit. This kit converts total RNA into template molecules of known strand origin, followed by sequence-specific capture of coding RNA. All samples were run in an Illumina NovaSeq6000 instrument. Paired FASTQ files from all 48 patients are provided.

Dataset EGAD50000001025

Test dataset with ligh-weight files

This is a test dataset derived from public data of the 1000 Genomes Project. Its purpose is not to allow for any inference about cohort data or results, but to aid bioinformaticians in the technical development and testing of tools, as well as data consumers in learning how to access information. This dataset consists of 3 pairs of light-weight (sliced) files: BAM + BAI, CRAM + CRAI and VCF + TBI. These files can be downloaded directly through the EGA-download-client PyEGA3 (https://github.com/EGA-archive/ega-download-client). For any further questions, please contact the DAC (Helpdesk - email: helpdesk [at] ega-archive [dot] org).

Dataset EGAD00001009826

Whole Exome Sequencing data of glioma from the Chinese Glioma Genome Atlas (CGGA) project

Glioma is the most common and aggressive brain cancer in adults. While primary glioma has been widely studied, molecular characterization of recurrent glioma is still rare. The high-quality sequencing data that we generated provides a useful resource for the community. The CGGA project contains over 2,000 samples from Chinese cohorts. It totally includes the whole-exome sequencing (286), DNA methylation (159), mRNA sequencing (1,018), mRNA microarray (301) and microRNA microarray (198) and matched clinical data. CGGA removes the barriers to researchers, providing rapid and convenient access to high-quality functional genomic data resources for biological research and clinical applications.

Dataset EGAD00001006445

Genetic regulation of RNA splicing in human pancreatic islets

Here we provide access to newly generated RNA-seq data for 101 human islet samples used to map genetic effects on gene expression and alternative splicing (eQTLs and sQTLs) in a total of 399 human islets. We also make publicly available genotyping array data for 128 human islets, including the fraction of 101 human islet samples with existing RNA-seq data.

Dataset EGAD00001009102

PRDM9 loss of function follow up from Born-in-Bradford Autozygosity sequencing

In the autozygosity exome sequencing of Born-in-Bradford samples of Pakistani origin there is a mother who is homozygous for an apparent truncating stop codon in PRDM9, the gene responsible for localising recombination during meiosis. We plan to deep sequence mother and child with X10, and physically phase the mother with PacBio sequencing. We will use this data to identify recombination locations, and test whether these are consistent with the known fine scale recombination map. Data Access is controlled by the Wellcome Trust Sanger Institute DAC and the Born In Bradford Executive Group.

Dataset EGAD00001001686

A Genome-Wide Association Study of Sporadic ALS in an Irish Population (SIALS) and Sequencing and Analysis of an Irish Human Genome

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by loss of motor neurons. Epidemiological studies indicate that 2-5% of cases are familial, usually with an autosomal dominant pattern of inheritance. For a portion of these, causative genes have been identified. The remaining 95% of ALS cases are described as sporadic, and believed to result from a combination of genetic and environmental factors. This study utilized samples from Irish patients with sporadic ALS and Irish control individuals. The aim of the study was to identify susceptibility alleles for ALS in the Irish population. Genome-wide, single-nucleotide polymorphism (SNP) genotyping was undertaken using Illumina 550K version 3 chips. Genotyping data are available on 221 SALS patients and 211 controls. Future work will aim to examine ALS genetic risk at individual sequence level. As an initial step, as of September 2010 we have made available the human genome sequence from a single anonymous male control individual from the SIALS collection. Recent studies generating complete human sequences from Asian, African and European subgroupings have revealed population specific variation and illuminated disease susceptibility loci. The Irish population is of interest because of its location at the periphery of Europe and also the impact its emigrants have had on the genetics of populations in a number of other countries. We have identified sequence variants that may be specific to this population and have identified potentially novel disease associated variants. We describe a novel method for improving SNP calling accuracy at low genome coverage using haplotype information. Overall we demonstrate utility in generating whole genome sequences to test general principles and reveal specific instances of human biology. With increasing access to low cost sequencing we would predict that similar studies will emerge.

Study phs000127

Data Use Ontology (DUO)

Data Use Ontology Data Use Ontology at EGA The EGA is committed to its involvement in the work of GA4GH. In an effort to enhance data discoverability and streamline data access, EGA have implemented the use of the Data Use Ontology (DUO), based on consent codes as described in Dyke et al. 2017. The Data Use Ontology codes will be displayed on the live dataset page of your submission to advise any would be requestor on how the data can be used and also to enhance data discoverability as users will be able to search on these codes to find applicable datasets. DUO can be browsed online via the Ontology Lookup Service Learn more reading the Data Use Ontology publication and the GA4GH Machine-Readable Consent Guidance! Check our DAC Portal Take The Tour and learn how to add DUO codes to your policy! Term Shorthand Label Description DUO:0000004 NRES no restriction This data use permission indicates there is no restriction on use. DUO:0000042 GRU general research use This data use permission indicates that use is allowed for general research use for any research purpose. DUO:0000006 HMB health or medical or biomedical research This data use permission indicates that use is allowed for health/medical/biomedical purposes; does not include the study of population origins or ancestry. DUO:0000007 DS disease specific research This data use permission indicates that use is allowed provided it is related to the specified disease. DUO:0000011 POA population origins or ancestry research only This data use permission indicates that use of the data is limited to the study of population origins or ancestry. DUO:0000012 RS research specific restrictions This data use modifier indicates that use is limited to studies of a certain research type. DUO:0000015 NMDS no general methods research This data use modifier indicates that use does not allow methods development research (e.g., development of software or algorithms). DUO:0000016 GSO genetic studies only This data use modifier indicates that use is limited to genetic studies only (i.e., studies that include genotype research alone or both genotype and phenotype research, but not phenotype research exclusively) DUO:0000018 NPUNCU not for profit, non commercial use only This data use modifier indicates that use of the data is limited to not-for-profit organizations and not-for-profit use, non-commercial use. DUO:0000019 PUB publication required This data use modifier indicates that requestor agrees to make results of studies using the data available to the larger scientific community. DUO:0000020 COL collaboration required This data use modifier indicates that the requestor must agree to collaboration with the primary study investigator(s). DUO:0000021 IRB ethics approval required This data use modifier indicates that the requestor must provide documentation of local IRB/ERB approval. DUO:0000022 GS geographical restriction This data use modifier indicates that use is limited to within a specific geographic region. DUO:0000024 MOR publication moratorium This data use modifier indicates that requestor agrees not to publish results of studies until a specific date. DUO:0000025 TS time limit on use This data use modifier indicates that use is approved for a specific number of months. DUO:0000026 US user specific restriction This data use modifier indicates that use is limited to use by approved users. DUO:0000027 PS project specific restriction This data use modifier indicates that use is limited to use within an approved project. DUO:0000028 IS institution specific restriction This data use modifier indicates that use is limited to use within an approved institution. DUO:0000029 RTN return to database or resource This data use modifier indicates that the requestor must return derived/enriched data to the database/resource. DUO:0000043 CC clinical care use This data use modifier indicates that use is allowed for clinical use and care. DUO:0000044 NPOA population origins or ancestry research prohibited This data use modifier indicates use for purposes of population, origin, or ancestry research is prohibited. DUO:0000045 NPU not for profit organisation use only This data use modifier indicates that use of the data is limited to not-for-profit organisations. DUO:0000046 NCU non-commercial use only This data use modifier indicates that use of the data is limited to not-for-profit use. Point to Notice: For the consent code DUO:0000007 where data is restricted to use on a specific disease, please accompany it with an appropriate ontology from MONDO e.g., If the data is restricted to the use of research into juvenile idiopathic arthritis the code should be displayed as DUO:0000007; MONDO:0011429.

Documentation access/data-access-committee/data-use-ontology

A Genome-Wide Association Study of Heroin Dependence

This collaboration of Australian and American investigators aims to identify genes associated with liability for heroin dependence. The project uses a case-control design in which cases met lifetime DSM-IV criteria for heroin dependence. Controls included assessed individuals who did not meet DSM-IV heroin dependence criteria and unassessed general population controls. Cases and controls were obtained from the several large investigations including: The Comorbidity and Trauma Study, Heroin Dependence in Western Australia, the OZ-ALC Study, a Twin Study of Mole Development in Adolescence, and ongoing genetic studies of substance dependence conducted by investigators at Yale and collaborating institutions. These projects are briefly described below. The Comorbidity and Trauma Study (PI: Elliot Nelson), a retrospective case-control study examining genetic and environmental factors contributing to heroin dependence liability. The study was funded by the National Institute on Drug Abuse (NIDA), and was run in collaboration with Washington University, the Queensland Institute of Medical Research (QIMR), and the National Drug and Alcohol Research Centre (NDARC), University of New South Wales. Case participants were recruited from maintenance clinics in the greater Sydney area. Control participants were recruited from employment centres and community centres, open street malls, and local press servicing the same geographical area as the opioid maintenance treatment clinics and either denied recreational use of opioids or had used these drugs recreationally fewer than 11 times lifetime. The prevalence in these individuals of non-opioid licit drug dependence and illicit drug dependence as well as childhood trauma exposure and other psychiatric disorders is elevated considerably versus estimates of similar measures in Australian general population samples. Participants provided blood samples as a source of DNA and completed a comprehensive psychiatric diagnostic interview based on the Semi-Structured Assessment of the Genetics of Alcoholism - Australia (SSAGA-OZ) augmented with sections drawn from other instruments assessing childhood trauma exposure, family history, and screening for borderline personality disorder. Heroin Dependence in Western Australia (PI: Sybille Schwab) is a study focusing both on genetic contributions to heroin dependence and response to naltrexone treatment of the disorder. Participants completed a clinical assessment and provided blood samples during their treatment at the Perth Naltrexone Clinic now name as the Fresh Start Recovery Programme. Funding for the project was provided by the Australia Government's National Health and Medical Research Council (Grant # 513862; PI: Sybille Schwab) Affected subjects from ongoing genetic studies of substance dependence conducted by investigators at Yale (PI: Joel Gelernter) and collaborating institutions were collected in the course of several NIDA-funded studies. Those included in the current set were assessed by means of the SSADDA (Semi-Structured Assessment for Drug Dependence and Alcoholism). All are opioid dependent European-Americans, and all list heroin as the opioid must used. Most were collected at Yale University School of Medicine or University of CT School of Medicine under the supervision of Drs Joel Gelernter and Henry Kranzler. Control subjects were also collected in the course of several NIDA- and NIAAA-funded studies. Those included in the current set were assessed by means of the SSADDA (Semi-structured Assessment for Drug Dependence and Alcoholism). Most were collected at Yale University School of Medicine or University of Connecticut School of Medicine under the supervision of Drs Joel Gelernter and Henry Kranzler. The OZ-ALC Study (PI: Andrew Heath) consists of a large group of twins and their family members ascertained from the general population Australian Twin Registry who have participated in ongoing research projects. For the control investigation, we have selected individuals who do meet criteria for illicit drug dependence who have had GWAS genotyping with the Illumina Human CNV370-Quad. Inclusion of individuals with alcohol dependence or nicotine dependence was minimized. For a more detailed description of the study, please see: http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000181.v1.p1 The Twin Study of Mole Development in Adolescence (PI: Nick Martin) is an ongoing investigation of melanocytic naevi funded by the Australian Government's National Health and Medical Research Council (Grant # 389891; PI: Nick Martin). For the current project, unassessed parents of these twins will serve as a control group. These individuals will either have been previously genotyped with the Illumina Human 610-Quad BeadChip or will be genotyped as part of the current project. Parents have largely survived the period of risk for heroin dependence and, by virtue of their participation in this research, are very likely to have a prevalence of heroin dependence lower than that in the general population (i.e., <0.7%). In order to understand the immune modulatory effect of opioids across peripheral blood immune populations, a second study (PI: Christine S. Cheng) analyzed single cell RNA-seq profiles from the PBMCs of a subset of case participants and control participants from the Comorbidity and Trauma Study (PI: Elliot Nelson). The study assessed and identified transcriptional changes between opioid dependent and control samples across naive and LPS stimulated immune populations.

Study phs000277

Evaluation Study of Congestive Heart Failure and Pulmonary Artery Catheterization Effectiveness (ESCAPE-BioLINCC)

Accessing Data Please refer to “Authorized Access” below regarding accessing data through the BioData Catalyst ecosystem. The data from this accession is not available for download through dbGaP. Objectives To test whether pulmonary artery catheter use was safe and could improve clinical outcomes in participants hospitalized with recurrent heart failure. Background Pulmonary artery catheters have been used to guide adjustment of therapy in multiple settings, but recent studies have raised concern that pulmonary artery catheters may lead to increased mortality in hospitalized participants. Participants A total of 433 participants at 26 sites were enrolled, and randomly assigned to receive therapy guided by clinical assessment and the pulmonary artery catheter or clinical assessment alone. Patients with acute decompensation in which the attending heart failure physician considered pulmonary artery catheterization (PAC) was required or likely to be required within the next 24 hours were entered into a PAC registry. A total of 439 patients were added to the registry. Conclusions Therapy to reduce volume overload during hospitalization for heart failure led to marked improvement in signs and symptoms of elevated filling pressures, with or without the pulmonary artery catheter. Addition of the pulmonary artery catheter to careful clinical assessment did not impact overall mortality and hospitalization. Future trials should test noninvasive assessments with specific treatment strategies that could be used to better tailor therapy for both survival time and survival quality as valued by participants. (Binanay, C. et al., JAMA, 2005)

Study phs003782

The Genomic Landscape of Childhood and Adolescent Melanoma

Despite remarkable advances in the genomic characterization of adult melanoma, the molecular pathogenesis of pediatric melanoma remains largely unknown. We analyzed 15 conventional melanomas (CM), 3 melanomas arising in congenital nevi (CNM), and 5 spitzoid melanomas (SM), using various platforms, including whole genome or exome sequencing, molecular inversion probe assay, and/or targeted sequencing. CM demonstrated a high burden of somatic single nucleotide variations (SNV), with each case containing a TERT promoter (TERT-p) mutation, 13/15 containing an activating BRAF V600 mutation, and >80% of the identified SNV consistent with UV damage. By contrast, the 3 CNM contained an activating NRAS Q61 mutation and no TERT-p mutations. SM were characterized by chromosomal rearrangements resulting in activated kinase signaling in 40%, and an absence of TERT-p mutations except for the one SM that succumbed to hematogenous metastasis. We conclude that pediatric CM has a very similar UV-induced mutational spectrum to that found in the adult counterpart, emphasizing the need to promote sun protection practices in early life and to improve access to therapeutic agents being explored in adults in young patients. By contrast, the pathogenesis of CNM appears to be distinct. TERT-p mutations may identify the rare subset of spitzoid melanocytic lesions prone to disseminate.

Study EGAS00001000901

Deep single-cell RNA sequencing data for 11138 T cells from tumour, adjacent normal tissue and peripheral blood of treatment-naive CRC patients. The DATA ACCESS AGREEMENT is provided at https://github.com/zhangyybio/single-T-cell-data-access. Applicants can request access to the data by directly downloading it or by sending an email to cancerpku@pku.edu.cn. The process that is used to approve an application includes verifying the institution, participants and research purposes of the application. In general this process will take about two weeks. In principal, any scientific research program complying with the laws and bioethic regulation policies of China will be approved.

T cells are central players in cancer immunotherapy1, yet some of their fundamental properties such as development and migration within tumours remain elusive. The enormous T cell receptor (TCR) repertoire, required for recognising foreign and self-antigens2,3, could serve as lineage tags to track these T cells in tumours4. Here, we obtained transcriptomes of 11,138 single T cells from 12 colorectal cancer (CRC) patients and developed STARTRAC (Single T-cell Analysis by Rna-seq and Tcr TRACking) indices to quantitatively analyse dynamic relationships among 20 identified T cell subsets with distinct functions and clonalities. While both CD8+ effector and “exhausted” T cells exhibited high clonal expansion, they were independently connected with tumour-resident CD8+ effector memory cells, implicating a TCR-based fate decision. Of the CD4+ T cells, the majority of tumour-infiltrating Tregs showed clonal exclusivity, whereas certain Treg clones were developmentally linked to multiple TH clones. Notably, we identified two IFNG+ TH1-like clusters in tumours, the GZMK+ TEM and CXCL13+ TH1-like clusters, which were associated with distinct IFN-γ-regulating transcription factors, EOMES/RUNX3 and BHLHE40, respectively. Only CXCL13+BHLHE40+ TH1-like cells were preferentially enriched in tumours of microsatellite-instable (MSI) patients, which might explain their favourable response rates to immune-checkpoint blockade. Furthermore, we found IGFLR1 to be highly expressed in both CXCL13+BHLHE40+ TH1-like and CD8+ exhausted T cells and possessed co-stimulatory functions. Our integrated STARTRAC analyses provides a powerful avenue to comprehensively dissect the T cell properties in CRC, which could shed new insights into the dynamic relationships of T cells in other cancers

Study EGAS00001002791

Targeted sequencing of healthy individuals, aged individuals and AML patients

Targeted sequencing using BD Rhapsody with 462 mRNA and 97 antibodies of healthy young and aged adult as well as AML bone marrow mononuclear cells from iliac crest aspirations. Please note raw and integrated gene expression data, cell type annotation, metadata and dimensionality reduction are available as Seurat v3 objects through figshare. Access link is https://figshare.com/s/0fda29b169c719223ee3.

Dataset EGAD00001008188

Polycystic Ovary Syndrome (PCOS) Genetics

PCOS is a complex genetic disease reflecting the interplay of susceptibility genes and environmental factors. The cardinal reproductive feature of the syndrome, hyperandrogenemia, appears to play a direct role in the pathogenesis of the associated metabolic abnormalities. Male as well as female first-degree relatives have reproductive and metabolic phenotypes including increased prevalence rates of type 2 diabetes (T2D), metabolic syndrome (MBS) and other risk factors for cardiovascular disease (CVD). Northwestern University (NU) investigators lead a team that has extensive experience in phenotyping PCOS and in the genetic analysis of complex diseases including genome-wide association study (GWAS). Together with an expert group of collaborators from the Hershey Medical Center, and The University of Chicago, we have conducted a GWAS to identify PCOS susceptibility alleles using a large cohort of extensively and consistently phenotyped PCOS cases. Population controls for this study come from the NUgene project described below. NUgene project: In 2002, Northwestern committed to the development of a DNA repository to serve as a platform for the identification and validation of genotype-phenotype associations that will impact healthcare. 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 questionnaire (2 versions were used, 1 before and 1 after February 2006, both are included) 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.

Study phs000368

Single-Gene vs. Panel Sequencing in Advanced HR+/HER2− Breast Cancer

Background: Hormone receptor-positive (HR+)/HER2-negative (HER2−) breast cancer is the most common subtype of breast cancer, with biomarker-driven therapies improving outcomes. Liquid biopsies, which analyze circulating tumor DNA (ctDNA), are increasingly essential in clinical management, offering a minimally invasive approach to assess somatic alterations and guide therapy decisions. However, the choice of assay directly affects clinical utility and global access to validated testing platforms remains inconsistent. To address these challenges, this study aimed to compare the performance of single-gene and panel-based sequencing for detecting PIK3CA mutations in HR+/HER2− breast cancer and evaluate the added value of broader genomic profiling in guiding treatment decisions. Patients and methods: We conducted a prospective, multicenter study comparing our previously established SiMSen-Seq (SSS) assay, a single-gene method targeting PIK3CA hotspot mutations, with the AVENIO ctDNA Expanded assay, enriching for 77 clinically relevant cancer genes. Additionally, tumor fractions were estimated with mFAST-SeqS by assessing chromosomal aneuploidy. We analyzed 161 plasma samples collected from 146 patients with HR+/HER2− advanced breast cancer before starting a new line of palliative therapy. Results: High concordance (92.6%) was observed between SSS and AVENIO for PIK3CA mutation detection, with accurate variant allele frequency (VAF) quantification confirmed by Bland-Altman analysis and linear mixed-effects modeling. AVENIO identified additional PIK3CA mutations in 19% of samples as well as other actionable alterations, including ESR1 mutations (17.5%) and PI3K pathway alterations (40.6%). Panel sequencing improved the interpretation of negative liquid biopsy results by integrating tumor fraction and VAF metrics. Conclusions: Panel sequencing assays such as AVENIO provide broader genomic coverage, enhance the detection of actionable mutations and refine the interpretation of negative liquid biopsy findings. These findings support the integration of comprehensive ctDNA profiling into routine clinical practice for HR+/HER2− advanced breast cancer while highlighting the need for improved assay accessibility and expanded genomic targets.

Study EGAS00001008200

Accurate mutation detection in leukemia by re-sequencing a cancer gene set

With the advent of whole-genome and whole-exome sequencing, high-quality catalogs of recurrently mutated cancer genes are becoming available for many cancer types. Increasing access to sequencing technology, including bench-top sequencers, provide the opportunity to re-sequence a limited set of cancer genes across a patient cohort with limited processing time. Here, we re-sequenced a set of cancer genes in T-cell acute lymphoblastic leukemia (T-ALL) using the Roche/454 technology coupled with Nimblegen sequence capture. First, we investigated how a maximal sensitivity and specificity of mutation detection can be achieved through a benchmark study. We tested nine combinations of different mapping and variant-calling methods, varied the variant calling parameters, and compared the predicted mutations with a large independent validation set obtained by capillary re-sequencing. We found that the combination of two mapping algorithms, namely BWA-SW and SSAHA2, coupled with the variant calling algorithm Atlas-SNP2 yields the highest sensitivity (95%) and the highest specificity (93%). Next, we applied this analysis pipeline to identify mutations in a set of 58 cancer genes, in a panel of 18 T-ALL cell lines and 15 T-ALL patient samples. We confirmed mutations in known T-ALL drivers, including PHF6, NF1, FBXW7, NOTCH1, KRAS, NRAS, PIK3CA, and PTEN. Interestingly, we also found mutations in several cancer genes that had not been linked to T-ALL before, including JAK3. Finally, we re-sequenced a small set of 39 candidate genes and identified recurrent mutations in TET1, SPRY3 and SPRY4. In conclusion, we established an optimized analysis pipeline for Roche/454 data that can be applied to accurately detect gene mutations in cancer, which led to the identification of several new candidate T-ALL driver mutations.

Study EGAS00001000268

Framingham Heart Study-Cohort (FHS-Cohort) - Imaging

The Framingham Heart Study (FHS) is a population-based, observational cohort study initiated in 1948 to prospectively investigate the determinants of cardiovascular disease to guide public health prevention. The FHS began by recruiting an Original Cohort of 5,209 men and women between the ages of 30 and 62 from the town of Framingham, Massachusetts, who had not yet developed overt symptoms of cardiovascular disease or suffered a heart attack or stroke. The Original cohort Exam 1 took place between 1948 and 1953. Since that time the cohort has had a total of 32 biennial exams (ending in 2014) and event follow-up through 2022. In 1971, the FHS added the Offspring cohort, comprising 5124 children whose parents were enrolled in the Original cohort and the spouses of the children. This cohort on average has been examined every three to four years. However, there was an eight year gap between Exam 1 and Exam 2 and a seven year gap between Exam 7 and Exam 8. The latest exam (10) was completed in 2022. In 2002, the transgenerational FHS design was facilitated with the recruitment of 4095 children of the Offspring cohort (Third Generation), and 103 spouses of the Offspring who were not previously enrolled in the study (New Offspring Spouses, NOS). These cohorts have completed 3 exams through 2019. To reflect the changing demographic characteristics of the greater Framingham community, the FHS additionally recruited and enrolled 2 cohorts comprising racial and ethnic minority groups, termed Omni-1 and Omni-2, (n = 506 and 410, respectively) in 1995 and 2002, respectively. These cohorts included individuals of African American, Hispanic, Asian, Indian, Native American, and Pacific Islander descent. The OMNI-1 cohort have completed 5 exams through 2022. The OMNI-2 cohort have completed 3 exams through 2019. Data available for request include Echocardiogram images, available from the following exams in each cohort. Original Cohort: exams 18-32; Offspring cohort: exams 3-10; Third Generation, NOS and OMNI-2 cohorts: exams 1-3; OMNI-1 cohort: exams 1-5.CT image data at one or two timepoints were added for 4427 participants in the offspring, third generation, and OMNI1 and OMNI2 cohorts.Summary level phenotypes for the Framingham Cohort study participants can be viewed at the top-level study page phs000007 Framingham Cohort. Individual level phenotype data and molecular data for all Framingham top-level study and substudies are available by requesting Authorized Access to the Framingham Cohort study phs000007.

Study phs003593

Providing safe access to sensitive human data across borders: Federated EGA becomes a reality

Enabling discovery and access to sensitive data across national boundaries is vital for improving human health. It enables more powerful and efficient research by increasing the volume and diversity of data available for analysis. It allows us to better understand the causes of diseases – cancer, rare diseases, infectious diseases like COVID-19 – and develop new medicines and treatments. Sensitive human omics data are typically generated by research initiatives and shared using specialist repositories which provide services for data submission, discovery, and access. The EGA is one such repository. Established in 2008 at EMBL’s European Bioinformatics Institute (EMBL-EBI) in the UK, since 2012 the EGA (“Central EGA”) has been jointly managed by EMBL-EBI and the Centre for Genomic Regulation (CRG) in Spain. Many countries have emerging personalised medicine programmes which generate data from national initiatives. These programmes are driving a transition in human genomics from being research-driven to receiving funding through healthcare. Data generated in a clinical context are subject to stricter governance than research data and must follow national data protection legislation. To solve these challenges, the Federated EGA provides a network of connected resources to enable transnational discovery of and access to human omics data for research, while also respecting jurisdictional data protection regulations. In this way, the Federated EGA infrastructure supports the goals of European initiatives such as the 1+ Million Genomes initiative (1+MG), the European Health Data Space, and a number of EU-funded 1+MG implementation projects including Beyond 1 Million Genomes. The Federated EGA is made up of “Nodes” – typically nationally funded and operated – which store and manage data locally while allowing global discovery within the Federated EGA network. Since 2016, multiple parallel efforts, supported by ELIXIR and other transnational and national initiatives, have created technical and legal frameworks for establishing the Federated EGA. In 2022, the first five Nodes – Finland, Germany, Norway, Spain, and Sweden – officially joined the Federated EGA by signing Federated EGA Collaboration Agreements with Central EGA. The Finnish FEGA Node is operated by CSC - IT Center for Science and provides data management services according to national laws and the requirements of the EU General Data Protection Regulation (GDPR). These services provide tools and support for the whole life-cycle of sensitive research data from collating to analysis, publication, and authorised re-use. The development of the services has been a joint effort with the other Nordic nodes within NeIC's Tryggve and Heilsa projects, and funded by Finnish Ministry of Education and Culture and projects coordinated by ELIXIR Finland. Read more about Finnish FEGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. The German Human Genome-Phenome Archive (GHGA) strives to provide a national infrastructure as well as an ethical and legal framework that balances FAIR omics data usage and data protection needs for Germany. As a Germany-wide consortium funded by the German Research Foundation under the umbrella of the NFDI association, GHGA combines the expertise of 21 universities and research institutions to form a federated national infrastructure. Read more about GHGA signing. In Norway, a key component of the infrastructure is the services of The Sensitive Data Service (TSD) offered by USIT at University of Oslo. The Federated EGA Norway Node is developed by ELIXIR Norway and operated by the University of Oslo as the responsible legal entity. Core software modules are developed jointly with the other Nordic nodes in the NeIC Tryggve and Heilsa projects. Read more about FEGA Norway signing. The Spanish FEGA (es-FEGA) is a national service for storing sensitive biomedical data in Spain. Supported by the Spanish Institute of Bioinformatics (INB) in collaboration with Central EGA, sensitive research datasets are primarily hosted at the Barcelona Supercomputing Centre facilities. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. The Swedish Sensitive Data Archive is a secure data archive and sharing platform for sensitive datasets. It was developed by the National Bioinformatics Infrastructure Sweden (NBIS) in collaboration with other Nordic ELIXIR Nodes through the Tryggve and Heilsa projects funded by NeIC and coordinated with Central EGA through ELIXIR. Read more about the Swedish Node signing. By providing a solution for secure and efficient management of human omics data, the Federated EGA aims to foster data reuse, enable reproducibility, accelerate biomedical research, and improve human health. Find out more Interested in setting up your own Federated EGA Node? Check out the FEGA Onboarding Knowledge Base for more information. The ELIXIR Federated Human Data Community is a great entry point for anyone interested in learning more about the Federated EGA. You can: Join the ELIXIR Federated Human Data Community mailing list (select “Human Data”) Attend the ELIXIR Federated Human Data Community calls

Blog safe-access-to-sensitive-human-data-federated-ega

Bulk RNAseq FASTQ files of three PDAC organoid lines treated with LGK974 or DMSO for 24h

This dataset contains raw FASTQ files from bulk RNA sequencing of three patient-derived pancreatic ductal adenocarcinoma (PDAC) organoid lines (P28, P40, P47). Each organoid line was treated with either the WNT secretion inhibitor LGK974 or DMSO for 24 hours, with three biological replicates per treatment condition. RNA was extracted using the Qiagen QiaSymphony SP system, and libraries were prepared with the Illumina Truseq stranded polyA kit. Sequencing was performed on an Illumina NextSeq2000 platform using single-end 50bp reads. Quality control was conducted with FastQC, and reads were trimmed with TrimGalore as appropriate. Data are controlled-access and intended for transcriptomic analysis of WNT pathway perturbation in PDAC organoid models.

Dataset EGAD50000002219

Germline and somatic variants in myelodysplastic syndrome and therapy-related myeloid neoplasms

Somatic mutation frequencies in patients with therapy-related myeloid neoplasms (129 patients, 181 samples including bone marrow, mesenchymal stromal cells and hair DNA) and primary myelodysplastic syndrome (108 patients, 215 samples including bone marrow, mesenchymal stromal cells and hair DNA) is assessed by deep sequencing of selected genes, using a Fluidigm Access Array, a Nimblegen capture panel and an Ion AmpliSeq panel. The dataset consists of paired fastq files obtained by either Hiseq (2x101bp) or Nextseq (2x150bp) Illumina sequencing. The mutational burden is found to be similar in both, however the distribution of variants is different. Correlation of the mutational spectrum with prognosis is also observed.

Dataset EGAD00001004861

Genetic Markers of Lipids in Indians: A Validation Study of Most Relevant Findings of Genome-Wide Association Studies

We aimed to validate the GWAS loci (identified in European populations) related to serum lipid levels (total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and very low-density lipoprotein cholesterol (VLDL-C)) in the Indian population originally recruited to examine the 20-year trend of cardiometabolic risk factors in rural and urban areas around National Capital region in India. Isolated DNA was processed for genotyping of 12 established GWAS variants using multiplex Sequenom Mass ARRAY technology. We examined the association of these genetic variants with different lipid levels and found association of eight variants for lipid levels. We also derived weighted genetic risk scores (wGRAS) using the validated variants. The wGRS for triglycerides and VLDL-C was derived based on four associated variants (rs174546 at FADS1, rs17482753 at LPL, rs2293889 at TRPS1, rs4148005 at ABCA8, rs4420638 at APOC1) which was associated with 36.31 mg/dL elevated triglycerides and VLDL-C (β=0.95, SE=0.16, pTRPS1 and rs4147536 at ADH1B) was associated with 40.62 mg/dL higher total cholesterol (β=1.01, SE=0.23, pTRPS1, rs4147536 at ADH1B, rs4420638 at APOC1 and rs660240 at CELSR2). The wGRS derived from five associated variants (rs174546 and FADS1, rs17482753 at LPL, rs4148005 at ABCA8, rs4420638 at APOC1 and rs7832643 at PLEC) was associated with 10.64 mg/dL lower HDL-C (β= -0.87, SE =0.14, pThe phenotypic and genotypic data will be made available on the dbGaP portal for access.

Study phs003469

Human genomic and phenotypic synthetic data for the study of rare diseases

The purpose of this dataset is to facilitate development of technical implementations for rare disease data integration, analysis, discovery, and federated access. This synthetic dataset includes clinical and genomic data from 6 rare disease cases. It consists of 18 whole genomes (6 index cases with their parents) which have genetic background based on public human data sequenced in the context of the Illumina Platinum initiative (Eberle, MA et al. (2017)) and made available by the HapMap project (https://www.genome.gov/10001688/international-hapmap-project). In each of the cases, real causative variants correlating with the phenotypic data provided were spiked-in. The cases included in this synthetic dataset correspond to the following type of disorders: CASE 1- Congenital myasthenic syndrome (Autosomal Dominant -de novo variant) CASE 2- Macular dystrophy (Autosomal Dominant) CASE 3- Muscular dystrophy (Autosomal Recessive-compound heterozygous variants) CASE 4- Mitochondrial disorder (Autosomal Recessive-consanguineous case - homozygous variant) CASE 5- Breast cancer (Autosomal Dominant) CASE 6- Similar as case 1 for patient matchmaking tests: Congenital myasthenic syndrome (Autosomal Dominant-de novo variant) For each case you will be able to download the following data: clinical information (phenopackets per individual and pedigree per family), raw genomic data (FASTQ and BAMs) and processed genomic data (vcfs). When using the data, the following should be acknowledged: the RD-Connect GPAP (https://platform.rd-connect.eu/), EC H2020 project EJP-RD (grant # 825575), EC H2020 project B1MG (grant # 951724) and Generalitat de Catalunya VEIS project (grant # 001-P-001647).

Study EGAS00001005702

14388 results for ""Pennsylvania+German"+ACS+study+'Socius'+open+access"

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