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• 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

• National Sleep Research Resource (NSRR): Cleveland Family Study (CFS)

  The Cleveland Family Study (CFS) is a family-based study of sleep apnea, consisting of 2,284 individuals (46% African American) from 361 families studied on up to 4 occasions over a period of 16 years. The study began in 1990 with the initial aims of quantifying the familial aggregation of sleep apnea. National Institutes of Health (NIH) renewals provided expansion of the original cohort, including increased minority recruitment, and longitudinal follow-up, with the last exam occurring in February 2006. The CFS was designed to provide fundamental epidemiological data on risk factors for sleep disordered breathing (SDB). The sample was selected by identifying affected probands who had laboratory diagnosed obstructive sleep apnea. All first-degree relatives, spouses and available second-degree relatives of affected probands were studied. In addition, during the first 5 study years, neighborhood control families were identified through a neighborhood proband, and his/her spouses and first-degree relatives. Each exam, occurring at approximately 4-year intervals, included new enrollment as well as follow up exams for previously enrolled subjects. For the first three visits, data, including an overnight sleep study, were collected in participants' homes while the last visit occurred in a general clinical research center (GCRC). Phenotypic characterization of the entire cohort included overnight sleep apnea studies, blood pressure, spirometry, anthropometry and questionnaires. Currently, data of 710 individuals are available for use through BioData Catalyst (with genotype data available through dbGaP).The National Sleep Research Resource (NSRR) is a NIH-supported sleep data repository that offers free access to large collections of de-identified physiological signals and related clinical data from a large range of cohort studies, clinical trials and other data sources from children and adults, including healthy individuals from the community and individuals with known sleep or other health disorders. The goals of NSRR are to facilitate rigorous research that requires access to large or more diverse data sets, including raw physiological signals, to promote a better understanding of risk factors for sleep and circadian disorders and/or the impact of sleep disturbances on health-related outcomes. Data from over 15 data sources and more than 40,000 individual sleep studies, many linked to dozens if not hundreds of clinical data elements, are available (as of Feb. 2022). Query tools are available to identify variables of interest, and their meta-data and provenance.

  Study phs002715

• Alzheimer's Disease Sequencing Project (ADSP)

  NOTICE OF CHANGE IN LOCATION FOR ALZHEIMER'S DISEASE SEQUENCING PROJECT (ADSP) GENETIC AND PHENOTYPIC DATA: ADSP whole exome and whole genome sequence data that are shared through dbGaP were mapped to the Genome Reference Consortium human genome GRCh37 (build 37). These data are from the Discovery Phase of the project (described below) and will continue to be available at this site. Please see the ADSP Design page for the complete study description. All data that are mapped to GRCh38 (hg38) are being shared through the NIA Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) Data Sharing Service (DSS). For instructions on how access the ADSP Build 38 data that are shared through NIAGADS DSS, visit the Application Instructions page. STUDY DESCRIPTION FOR dbGaP BUILD 37 ADSP DATA: The overarching goals of the Alzheimer's Disease Sequencing Project (ADSP) are to: (1) identify new genomic variants contributing to increased risk of developing Alzheimer's Disease (AD), (2) identify new genomic variants contributing to protection against developing AD, and (3) provide insight as to why individuals with known risk factor variants escape from developing AD. These factors will be studied in multi-ethnic populations in order to identify new pathways for disease prevention. Such a study of human genomic variation and its relationship to health and disease requires examination of a large number of study participants and needs to capture information about common and rare variants (both single nucleotide and copy number) in well phenotyped individuals. Using existing samples from NIH funded and other studies, three NHGRI funded Large Scale Sequencing and Analysis Centers (LSAC) - Broad, Baylor, and Washington University - produced the DNA sequence data. Variant call data are being made available to the scientific community through NIH-approved data repositories. Statistical analysis of the sequence data is anticipated to identify new genetic risk and protective factors. The ADSP will conduct and facilitate analysis of sequence data to extend previous discoveries that may ultimately result in new directions for AD therapeutics. Analysis of ADSP data will be done in two phases. The Discovery Phase analysis (2014-2018) is funded under PAR-12-183. The entire Discovery dataset contains whole-genome sequencing data on 584 subjects from 113 families, and pedigree data for > 4000 subjects; whole exome sequencing data on 5096 cases 4965 controls; and whole exome sequence data on an additional 853 (682 Cases [510 Non-Hispanic, 172 Hispanic]), and 171 Hispanic Control subjects from families that are multiply affected with AD. The Replication Phase (2016-2021) analysis will be funded under RFA-AG-16-001 and RFA-AG-16-002 and is expected to include a combination of genotyping and sequencing approaches on at least 30,000 subjects. Targeted sequencing will be done by the LSACs. GRCh37 Data Releases The first ADSP data release occurred on November 25, 2013. It included the whole-genome sequencing data in BAM file format on 410 individuals. The second ADSP data release occurred on March 31, 2014, and included the whole-genome sequencing data in BAM file format for an additional 168 individuals. The third ADSP data release occurred on November 03, 2014 and included whole-exome sequencing data in BAM file format for 10,939 individuals. The fourth ADSP data release occurred on February 13, 2015 and included revised ethnic data for subjects with whole-exome sequencing data. The fifth ADSP data release occurred on July 13, 2015 and included whole-genome genotypes and updated phenotypes as well as changes to pedigree structures and sample IDs. The sixth ADSP data release occurred on December 8, 2015, and included whole-exome genotypes and updated phenotypes as well as changes to subject IDs. This seventh ADSP data release on April 12, 2016 includes: (1) WES and WGS SNV VCF files (2) WES and WGS Indel PLINK files ADSP Data Available through dbGaP: ADSP - Whole Genome Sequencing ADSP - Whole Exome Sequencing Comments DNA-Seq (BAM) n=578 n=10913 Sequence data available (plus n=38 replications w/out genotype data) Concordant SNV Genotypes (PLINK format) N/A n=10913 QC'ed genotypes that are concordant between the Atlas (Baylor's) and GATK (Broad's) calling pipelines (a subset of the consensus genotype set) Consensus Genotypes (PLINK and VCF format) n=578 n=10913 QC'ed genotypes that are concordant between Atlas and GATK pipelines as well as those that that were called uniquely by Atlas or GATK Concordant Indel Genotypes (PLINK format) n=578 n=10913 QC'ed genotypes that are concordant between the Atlas and GATK calling pipelines Phenotype Data n=4735 n=10913 Data of n=53 phenotype variables available (plus administrative data), including APOE genotype. WGS phenotypes include data of connecting family members. Please use the release notes provided by dbGaP to obtain detailed information about study release updates. The ADSP data portal provides a customized interface for users to quickly identify and retrieve files by covariates, phenotypes, and data properties such as sequencing facility or coverage. For more information about the ADSP study and the data portal, please visit https://www.niagads.org/adsp/.

  Study phs000572

• Out with the Java, in with the Python - new EGA data download client unveiled

  We will shortly be deprecating the Java-based download client also known as API V2. The Java client began its life at the back end of 2014, and during its time has seen many developments that have served our users well. During its tenure, the Java client enabled distribution of over 6.5 million files totalling nearly 15 petabytes of data to researchers around the globe. Recent increases in genomic and phenotypic data file sizes, the number of datasets archived in the EGA, the number of users downloading data, and the diversity of download locations has meant that at times the Java client struggled to distribute data efficiently. In response, our team of developers have built a new download client with more robust features and better functionality and reliability. Enter stage left our shiny new API V3.1! The new API V3.1 will replace both API V2 and API V3. The download client for the new API V3.1 is written in Python, and the code is openly available on Github. This Python client makes https calls to the EGA AAI (https://ega.ebi.ac.uk:8443/) and to the EGA Data API (https://ega.ebi.ac.uk:8052), both of which must be accessible from the client location. In addition to general updates to improve performance, the API V3.1 has three new features enhancing the EGA distributions services. First, requested data are now downloaded over secure https connections rather than http, meaning that data can be delivered unencrypted. Instead of separately requesting, downloading, and decrypting data using the Java client, data can now be requested and downloaded using a single command without the need for decrypting. The API V3.1 also automatically verifies each file’s unencrypted md5 after download to ensure the file has downloaded correctly. Second, the new API supports segmenting and automatic resuming. To enable quicker download speeds, the API breaks files into up to four segments and downloads them in parallel. With the new resume feature, downloads - both segmented and continuous stream - will automatically resume if they encounter any errors or the connection is interrupted. These features result in both a faster and more reliable download experience. Finally, the new API V3.1 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. The new features of the Python-based API V3.1 represent significant improvements in security, speed, reliability, and flexibility that are sure to provide a better data download experience. To take full advantage of these features, users must update to Python 3.X. Considering the changes in the data structures with the new API, the upgrade to a new version of the Python client is also mandatory. Updating the client to the latest version can be achieved by running the following command: pip3 install pyega3 -U We are excited for users to try out the new features and take advantage of the multifariously growing EGA distribution services for human biomedical omics data. If you have any questions or feedback, please email the EGA Helpdesk team. We'd love to hear from you!

  Blog new-ega-data-download-client

• Discover Cancer Image Europe, the first release of the EUCAIM platform to fuel cancer research and data sharing

  The EUCAIM consortium and the European Commission have announced the release of Cancer Image Europe, a platform to fuel collaborative innovation and data sharing across Europe. This constitutes a major milestone in EUCAIM's development and an exciting step towards achieving the project's vision and goals. The platform aims to accelerate the pace of AI development and other data-intensive cancer research activities, constituting the basis for a fully-fledged infrastructure for sharing, reusing, and exploiting cancer imaging data, especially using AI techniques. A platform paving the way for the future of cancer diagnosis and treatment Cancer Image Europe brings benefits to researchers, clinicians, and AI innovators across Europe as it addresses the fragmentation of existing cancer image repositories. The platform gathers a public catalogue of cancer imaging datasets from the repositories of the EU-funded AI for Health Imaging projects. A distributed Atlas of Cancer Imaging with over 60 million anonymised cancer image data from over 100,000 patients will be established as part of future updates. The first version of Cancer Image Europe also includes a public catalogue of cancer imaging datasets, a federated searching tool to understand the information available in the federated providers, and full integration with Life Science Login. Moreover, it reuses and adds value to key components of EU-funded research projects and infrastructure in the field of cancer. The Public Catalogue When we talk about data re-use, data discovery is key. The EUCAIM public catalogue is the result of a systematic and collaborative process to which the EGA-CRG has actively contributed. This browsing interface gathers the metadata of the available cancer imaging datasets. This descriptive information will help researchers in their data retrieval. Our contribution to the Public Catalogue comes from our experience gained in the deployment of our federated network, the Federated EGA (FEGA), and as partners in projects such as EuCanImage (AI4HI initiative), in which we worked on the definition of common data models, ontologies and data standards. The EUCAIM Project EUCAIM is a cornerstone of the European Commission-initiated European Cancer Imaging Initiative, a flagship of the Europe's Beating Cancer Plan, which aims to foster innovation and deployment of digital technologies in cancer treatment and care, to achieve more precise and faster clinical decision-making, diagnostics, treatments, and predictive medicine for cancer patients. For more information about the first release of the Cancer Image Europe platform and the EUCAIM project, please visit cancerimage.eu.

  Blog discover-cancer-image-europe

• NHLBI TOPMed: The Cleveland Family Study (CFS)

  The Cleveland Family Study (CFS) is one cohort involved in the WGS project. The CFS was designed to provide fundamental epidemiological data on genetic and non-genetic risk factors for sleep disordered breathing (SDB). In brief, the CFS is a family-based study that enrolled a total of 2284 individuals from 361 families between 1990 and 2006. The sample was selected by identifying affected probands who had laboratory diagnosed obstructive sleep apnea. All first degree relatives, spouses and available second degree relatives of affected probands were studied. In addition, during the first 5 study years, neighborhood control families were identified through a neighborhood proband, and his/her spouses and first degree relatives. Each exam, occurring at approximately 4 year intervals, included new enrollment as well as follow up exams for previously enrolled subjects. For the first three visits, data, including an overnight sleep study, were collected in the participants' homes while the last visit occurred in a general clinical research center (GCRC). Phenotypic characterization of the entire cohort included overnight sleep apnea studies, blood pressure, spirometry, anthropometry and questionnaires. The GCRC exam (n=735 selected individuals) included more comprehensive phenotype data on a focused subsample of the larger cohort, to permit linking SDB phenotypes with cardio-metabolic phenotypes, with an interest in identifying genetic loci that are associated with these related phenotypes. In this last round of data collection, a subset of 735 individuals was selected based on expected genetic informativity by choosing pedigrees where siblings had extremes of the apnea hypopnea index (AHI). Participants underwent detailed phenotyping including laboratory polysomnography (PSG), ECG, spirometry, nasal and oral acoustic reflectometry, vigilance testing, and blood and urine collection before and after sleep and after an oral glucose tolerance test. A wide range of biochemical measures of inflammation and metabolism were assayed by a Core Laboratory at the University of Vermont. 994 individuals were sequenced as part of TOPMed Phase 1, including 507 African-Americans and 487 European-Americans. Among the sequenced individuals, 156 were probands with diagnosed sleep apnea, an additional 706 were members of families with probands, and 132 were from neighborhood control families. 298 individuals were sequenced as part of TOPMed Phase 3.5, including 169 African-Americans and 129 European-Americans. Among the newly sequenced individuals, 33 were probands with diagnosed sleep apnea, an additional 214 were members of families with probands, and 51 were from neighborhood control families. Please note: Phenotype and pedigree data are available through "NHLBI Cleveland Family Study (CFS) Candidate Gene Association Resource (CARe)", phs000284.

  Study phs000954

• Efficacy of a Therapeutic Treatment Trial in Angelman Syndrome

  This study has been designed to evaluate the possible therapeutic benefits of L-5-methyltetrahydrofolate (Metafolin), vitamin B12, creatine and betaine in children with Angelman Syndrome (AS). This study is based on the hypotheses 1) that dietary manipulation may increase global DNA methylation; 2) that increased methylation of the paternal chromosome in AS participants may increase expression of the Angelman gene causing clinical benefit; and 3) that dietary interventions with Metafolin in conjunction with creatine, betaine, and vitamin B12 would represent little risk, but some chance for benefit. The dietary regimen is felt to be quite benign and safe. The clinical response will be measured by recording: changes in seizure activity, changes in communication skills, changes in developmental skills and changes in behavioral patterns as indicated by formal developmental assessments and clinical evaluations. We will assess the progress of the participants by monitoring their changes from baseline throughout their participation in the study and measure their improvement in particular in the developmental parameters. As a secondary aim, we plan to measure the biochemical response to these compounds by obtaining levels of homocysteine, methionine, S-adenosyl-methionine (SAM), and S-adenosyl-homocysteine (SAH). The molecular response will be measured by global DNA methylation analysis. As an exploratory aim, we hope to use control data we have previously obtained from a double blind placebo-controlled trial done in the past using betaine and folic acid to compare the development of those children who have and those who have not received medications. More specifically, we wish to compare differences in the rate of development for those children who have never received study medications (from the first trial) with those who have received medications from the present trial. Developmental parameters used in the previous trial will be consistent with those used in the present trial in order to permit these comparisons. The previous trial was conducted at Baylor College of Medicine, Rady San Diego Children's Hospital and Boston Children's Hospital whereas a group of AS participants received folic acid and betaine or placebo in a randomized double-blind basis. All the participants for the study will include patients who have a documented molecular diagnosis of AS. The participants will be seen at Baylor College of Medicine, Rady Children's Hospital of San Diego, Boston Children's Hospital and the Greenwood Genetics Center at 0 months and at 12 months for follow-up. Clinical and relevant historical data will be collected during the clinical visit and examination and also by reviewing medical and laboratory records. This information will then be entered into a HIPAA compliant clinical research database.

  Study phs000701

• 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

• Atherosclerosis Risk in Communities Study (ARIC-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. At present, the ARIC sIRB has determined that ARIC data cannot be shared with for-profit entities.Related Studies Other ARIC data available include: Imaging studies (ARIC-Imaging), Genetics and genomics (ARIC, phs000280.v8.p2), Collaborative Cohort of Cohorts for COVID-19 Research (C4R): ARIC (phs002988.v1.p1), and as a component of the Sleep Heart Health Study (SHHS-BioLINCC, phs003637.v1.p1). Available Data Data available for request include ARIC v1-v8 examination cycles, collated annual follow-up communication data for contact years 2-32, and follow-up for mortality, heart disease, and stroke events through 2019. Also included are data from ancillary studies. Objectives The objectives of ARIC are to: 1) investigate associations of factors, including those not previously measured in cohort studies, with prevalence of atherosclerosis and incidence of CHD, clinical stroke and other cardiovascular diseases; and 2) measure cardiovascular disease occurrence and trends and relate these to community levels of, and changes in, risk factors, medical care and atherosclerosis. Background At the time of project initiation, the NHLBI had long recognized the need for longitudinal studies to identify the biochemical and physiological markers and specific environmental factors which place individuals at high risk for the major atherosclerosis diseases. The development of reliable ultrasound examination of peripheral arteries enhanced the expected benefit of such studies. Community surveillance planning began for ARIC in response to recommendations of the 1978 NHLBI Workshop on the Decline in CHD Mortality and has been extended in its purpose to evaluate the large geographic differences in U.S. mortality. Participants Black and white men and women, age 45-64 at entry; sample size: 15,792. Design ARIC is a large-scale, long-term prospective study that measures associations of established and suspected coronary heart disease risk factors with both atherosclerosis and new CHD events in men and women from four geographically diverse communities. The project has two components: community surveillance of morbidity and mortality; and repeated examinations of a representative cohort of men and women in each community. The community surveillance involves abstracting hospital records and death certificates and investigating out-of-hospital deaths. The representative cohorts include approximately 4,000 persons from each community. Community surveillance data includes detailed hospital record abstraction, ECG tracings, and event adjudication. Data from out-of-hospital events in the community include physician, informant, and coroner questionnaires as well as death certificate data and event adjudication. Community surveillance ended in 2014.Cohort participants were examined four times at three year intervals between 1987 and 1998, and have been continuously contacted annually to update their medical histories. Atherosclerosis was measured by carotid ultrasonography. Risk factors studied include: blood lipids, lipoprotein cholesterols, and apolipoproteins; plasma hemostatic factors; blood chemistries and hematology; sitting, supine and standing blood pressures; anthropometry; fasting blood glucose and insulin levels; ECG findings; cigarette and alcohol use; physical activity levels; dietary aspects; and family history. Clinic visits were restarted in 2011 with ARIC visit 5.

  Study phs003738

• Biological Determinants of Peritoneal Dialysis Outcomes

  Peritoneal Dialysis (PD) is a technique for treating kidney failure where fluid is instilled into the body's peritoneal cavity. Fluid and solutes travel across the peritoneum, and this is critical to successful PD. Studies have shown that certain demographic and clinical variables explain a small part of the variability in baseline peritoneal solute transfer rate and water transfer function across the peritoneum. This study sought to identify the common genetic variants associated with the baseline peritoneal function in patients starting treatment with PD and change in function upon treatment with PD. This study has two specific aims: Aim 1: To identify and validate genetic loci that influence the peritoneal solute transfer rate (PSTR) at start of PD. Aim 2: To identify and validate genetic loci in pre-specified biologic pathways with change in peritoneal ultrafiltration capacity. This study incorporates data from participants' peritoneal solute transfer rate in their first ever peritoneal equilibrium test (PET) and changes in the transfer of water across the peritoneal membrane over time in a sub-group of participants, demographic information, and results from laboratory analysis of DNA, blood, and dialysate. This study comprises patient populations from pre-existing biorepositories in Europe, and prospectively enrolled participants in United States, Canada, United Kingdom, and Australia. Clinical data related to the participants' peritoneal solute transfer rate from their first ever Peritoneal Equilibrium Test (PET), demographic information, and change in ultrafiltration capacity over time, are correlated with various genetic markers of interest. For individuals enrolled prospectively, blood and dialysate were collected at the first study visit. Annually, subjects either underwent a PET as standard of care or perform an additional 4-hour dwell as part of the study. These subsequent measures were utilized to determine change in ultrafiltration capacity over time. Inclusion criteria included adults over the age of 20 who are able to provide consent and had a record of a PET within 6 months of starting PD. Subjects with missing outcome data and that did not pass genotyping QC were excluded. Genotyping was performed in two batches using the Illumina InfiniumOmni2-5-8v1-3_A1 array with 2,372,784 single nucleotide variants (SNVs) in the first batch (n=1957). The second genotyping batch (n=1053) was on the InfiniumOmni2-5-8v1-4_A1 array with 2,382,209 SNVs. Imputation was performed using the Michigan Imputation Server and the HRC1.1 genotype reference. The Institutional Review Board of the University of Washington has “dbGaP-certified” the consent forms used to enroll participants in Australia, Canada, and United States and the genotype-phenotype data from the participants (n=827) deposited in the public repository. Furthermore, it has been determined that under the EU-GDPR regulations, depositing data of participants enrolled in UK, Belgium, and Sweden (n=2,023) into dbGaP “serves no lawful purpose” as the consent form used for enrolling participants did not obtain explicit permission for depositing data in a public repository.

  Study phs002996

• Jackson Heart Study (JHS-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.Related Studies: Other Jackson Heart Study data available include: Imaging studies (JHS-Imaging), Genetics and genomics (The Jackson Heart Study, phs000286.v7.p2), and Collaborative Cohort of Cohorts for COVID-19 Research (C4R-JHS, phs002907.v1.p1): Jackson Heart Study (phs002907.v1.p1).Available Data: Data available for request include Jackson Heart Study visit 1-3 examination cycles, collated annual follow-up communication data through 2016, and follow-up for mortality, heart disease, and stroke events through 2014.Objectives: The objectives of the Jackson Heart Study are to: 1) investigate the associations of biological, psychosocial, and behavioral factors with the incidence atherosclerotic events and health outcomes in an African American cohort; and 2) increase access to and the participation of African American populations and scientists in biomedical research and professions.Background: It has long been recognized that African Americans share a disproportionate burden of deleterious health outcomes including diabetes, hypertension, kidney disease and early onset of cardiovascular disease. The Jackson Heart Study was initiated in 2000 to explore potential mechanisms and mediators of health outcomes in a large African American cohort. In addition, the JHS conducts a variety of community education and outreach activities to promote healthy lifestyles to reduce disease risk burden and student training programs to promote and support public health research.Participants: African American men and women, age 35-84 at entry. Of the 5306 cohort members enrolled in the study, the data repository contains data from 3,883 that provided informed consent to share their data with investigators not affiliated with the study.Design: Participants were enrolled in the study from 2000-2004 from urban and rural areas of three counties (Hinds, Madison and Rankin) in the Jackson, MS metropolitan statistical area. Participants were enrolled from each of 4 recruitment pools: a random sample component (17%), volunteer component (30%), currently enrolled in the Atherosclerosis Risk in Communities (ARIC) Study (31%), and secondary family members (22%). Recruitment was limited to non-institutionalized adult African Americans age 35-84 years, except in the family cohort where those age ≥21 years were eligible. The final cohort of 5,306 participants includes 6.59% of all African American residents aged 35-84 (N=76,426, US Census 2000). Data collection at the baseline exam included a medical history, physical examination, blood/urine analytes and interview questions on areas such as: physical activity; stress, coping and spirituality; racism and discrimination; socioeconomic status; and health care access.The current release of the Jackson Heart Study includes data collected at baseline, exam 2 (2005-2008), and exam 3 (2009-2013). Annual follow-up and surveillance of clinical cardiovascular events is ongoing.

  Study phs003740

• Development and Use of Network Infrastructure for High-Throughput GWA Studies

  The "Development and Use of Network Infrastructure for High-Throughput GWA Studies" project is collaboration between Group Health Cooperative (GH), the University of Washington (UW), and the Fred Hutchinson Cancer Research Center (FHCRC). It is one of 5 sites participating in The Electronic Medical Records and Genomics (eMERGE) Network funded by the National Human Genome Research Institute (NHGRI) with additional funding from the National Institute of General Medical Sciences (NIGMS). The overall eMERGE project is designed to assess whether linking biorepositories of patients in healthcare delivery systems with electronic medical records (EMRs) is an efficient strategy for high-throughput genome wide association (GWA) studies and whether information can be pooled across sites. The primary phenotype in the Group Health/UW Aging and Dementia eMERGE study project is dementia. Phenotyped participants originated from four population-based sources. Seattle-area members of GH (a large integrated health care system in Washington State) consented and enrolled in 1) the University of Washington Alzheimer's Disease Patient Registry (ADPR) and 2) the Adult Changes in Thought (ACT) study, 3) Marshfield Clinic Personalized Medicine Research Project (PMRP), a population-based DNA, plasma and serum biobank of 20,000 adults, 4) Vanderbilt's BioVU, a de-identified DNA biobank. The ADPR (PI: Eric B. Larson; NIH/NIA U01 AG 006781) is a population-based registry of incident dementia cases designed to identify all new dementia cases within GH from 1987 to 1996. Potential dementia cases were identified through referrals from primary care physicians, mental health services, and neurologists, as well as review of CT scans, neurology, emergency room, geriatrician, and mental health clinic logs, and hospital discharge diagnoses. Medical history, physical, laboratory testing, and neuropsychological testing were performed on all consenting potential cases. These data were reviewed by a consensus conference including a neuropsychologist, study physicians, and an epidemiologist. After discussion of the case, dementia status is assigned using DSM-III-R criteria. Alzheimer's disease status was assigned using the National Institute of Neurological and Communicative Disorders and Stroke-Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA) criteria. The study base of the ADPR population was stable with an attrition rate of less than 1%/year. The ACT study (PI: Eric B. Larson; NIH/NIA U01 AG 006781) is an ongoing community-based cohort study designed to determine the incidence of Alzheimer's Disease (AD), other types of dementia, and cognitive impairment and to determine risk factors for these conditions. The original cohort of 2,581 was enrolled in 1994-1996. An expansion cohort of 811 was enrolled in 2000-2002. Continuous enrollment to keep 2,000 persons enrolled and at-risk for dementia outcomes was begun in 2005. To date the study has accrued more than 4,000 participants. Dementia-free participants are enrolled in this cohort and evaluated every two years by study personnel with many evaluations including an assessment of cognitive functioning using the Cognitive Abilities Screening Instrument (CASI), which is an extended version of the Mini-Mental State Examination that has been widely used in epidemiological studies of the elderly. Persons with CASI scores lower than 86 or in whom interviewers suspect unusual cognitive decline are followed up with dementia diagnostic evaluations which include physical and neurological examinations and neuropsychological tests. Medical records are reviewed, and laboratory tests and neuroimaging are obtained if not available. The dementia diagnoses and dementia subtypes are determined during a consensus conference attended by the examining clinicians and other study physicians, a neuropsychologist, and research nurse. After discussion of the case, dementia status is assigned using DSM-IV criteria. Alzheimer's disease status was assigned using NINCDS-ADRDA criteria. Individuals diagnosed with dementia are seen again one year following diagnosis using the same procedures. Individuals not diagnosed with dementia are returned to the pool of at-risk individuals and are seen at their next routinely scheduled biennial visit. The ACT sub-sample is stable; for the original cohort, median enrollment in GH was 19 years prior to joining the ACT study, and 85% of the cohort has ≥ 10 years of GH enrollment. DNA for the ADPR participants were obtained through a companion study, Genetic Differences in Cases and Controls (PI: Walter Kukull; NIH/NIA R01 AG007584). DNA obtained through both studies were extracted from blood using Gentra Systems Puregene methods. DNA concentration is determined by UV optical density. All samples are checked for quality by OD 260/280 ratio. For long-term storage, samples are aliquoted and stored at -70°C. The Marshfield study, Personalized Medicine Research Project (PMRP), a population-based DNA, plasma and serum biobank of 20,000 adults. PMRP was started in 2002 as a 3 Phase project. Completed in April 2004, the objectives of Phase I were to build and develop a large population-based biobank with DNA, plasma and serum samples to facilitate genomics research. Phase 1 was also to educate, inform and consult with the Marshfield Epidemiologic Study Area population and communities concerning potential studies, create the DNA foundation of the personalized medicine database and build the bioinformatics tools to store securely and analyze genotypic and phenotypic data. Phase I tasks included operation of the ethics and security advisory board, scientific advisory board and community advisory group. More than 18,000 residents aged 18 and above from 19 different zip codes surrounding Marshfield, WI were invited to participate in Phase I. After providing written informed consent, participants completed brief questionnaires that included questions about demographics, some environmental exposures, family history of disease, and adverse drug reactions, as well as family members living in the area. Participants provided 50ml of blood from which DNA was extracted and plasma and serum samples were stored. The informed consent process allowed access to electronic medical records and included language about non-disclosure of personal research results. A tick-off box was included so that participants could either allow or decline subsequent recontact for future research studied. The objectives of Phase II are to create the phenotypic database, establish the scientific and administrative infrastructure to support genetic mapping of the DNA and the initial discovery projects and genotype a sufficient portion of the genetic material to support these discovery projects. The objectives of Phase III are to expand the discovery projects, complete the genotyping of the genetic database and expand physician/health care provider education and community consultation. The Vanderbilt BioVU model uses discarded blood leftover from clinical care and de-identified clinical information, both of which are tracked with a Research Unique Identifier that is permanently disconnected from the medical record number used to generate it. The Vanderbilt IRB determined that the project does not qualify as "human subject" research under §46.102(f)(1)(2). The program has been reviewed by OHRP twice (the latest immediately prior to launch), and the determination of the Vanderbilt IRB that the project is consistent with the 8/10/2004 guidance has been validated. The model includes a simple opt out mechanism developed for patients who do not wish to have their samples included. Planning (including community involvement, ethics and IRB discussions, focus groups, development of operating procedures and pilot studies to assess them) began in May 2004, and the resource commenced sample collection in February 2007 and as of May 2009 contains approximately 56,000 samples. The current resource will be available to all Vanderbilt investigators who sign a Data Use Agreement. The resource represents Vanderbilt's broad patient population and thus provides a potential resource for research in a range of common and rare diseases as well as drug response or medication safety. BioVU patient protection model: There has been extensive involvement by the ethics community and the IRB to put in place procedures to provide privacy protection. The overall protection plan is multifaceted and includes a combination of technology and policy: (1) creation of the SD described above; (2) return of only the specific clinical data items requested by investigators (rather than the complete de-identified chart); (3) submission of any specific proposed project to the IRB and to a separate protocol review committee, both of which must approve; (4) implementing a Data Use Agreement that permits only approved queries and data analyses, specifically prohibits attempts at re-identification (at the risk of institutional sanctions), and mandates redeposit of all genetic information generated in a research study back into a genomic database; and (5) tracking and auditing all use.

  Study phs000234

• Heart Failure Network - Nitrate's Effect on Activity Tolerance in Heart Failure with Preserved Ejection Fraction (HFN NEAT-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. Related StudiesEcho images are available with HFN-NEAT Imaging.ObjectivesTo determine the effect of isosorbide mononitrate on daily activity in patients with heart failure and preserved ejection fraction.Background Nitrates are commonly prescribed for symptom relief in patients with heart failure. Early studies in patients with heart failure with a reduced ejection fraction concluded that long-acting nitrates improve activity tolerance; however, approximately half of heart failure patients have preserved ejection fraction. The effects of nitrates in patients with heart failure and a preserved ejection fraction have not been extensively studied and the overall effect of nitrates on activity tolerance in such patients is uncertain. ParticipantsThere were a total of 110 participants enrolled with 51 participants assigned to receive isosorbide mono-nitrate first and placebo second, and 59 participants assigned to receive placebo first and isosorbide mononitrate second. Design Enrolled participants underwent baseline assessments, including echocardiography, quality-of-life scores, 6-minute walk test distance, and NT-proBNP levels. participants were also supplied with two kinetic activity monitors containing high-sensitivity triaxis accelerometers, to be worn 24 hours per day. The accelerometer measurements were expressed as arbitrary accelerometer units and stored every 15 minutes equaling 96 data points per day. The 15-minute cumulative accelerometer units were totaled over a 24-hour period to provide daily accelerometer units. Participants were assigned to one of two treatment groups: 6 weeks of placebo first with crossover to 6 weeks of isosorbide mononitrate, or 6 weeks of isosorbide mononitrate first with crossover to 6 weeks of placebo. The study drugs were prepared as 30-mg tablets of isosorbide mononitrate and matching placebo. During each 6-week period, participants were instructed to take no study drug for the first 2 weeks followed by one tablet (30 mg daily) for 1 week, two tablets (60 mg once daily) for 1 week, and four tablets (120 mg once daily) until the next study visit, for a treatment duration of at least 2 weeks and up to 4 weeks. After each 6-week period, participants returned to the study center to repeat end-point assessments. The primary outcome for the study was the comparison of average daily accelerometer units during the period in which participants were receiving the 120-mg dose of isosorbide mononitrate compared to the period in which participants received the placebo. Other secondary end points included the 6-minute walk distance and the post-walk Borg dyspnea score, scores on the Kansas City Cardiomyopathy Questionnaire and the Minnesota Living with Heart Failure Questionnaire, and NT-proBNP levels. In addition, participants completed a questionnaire indicating in which period (first, second, no preference) they felt better. Conclusions Participants were active for fewer hours of the day during the 120-mg phase of receipt of isosorbide mononitrate as compared with placebo. Furthermore, during all study-drug regimens combined (30 mg to 120 mg), participants were less active during receipt of isosorbide mononitrate as compared with placebo. There was no significant effect of isosorbide mononitrate on secondary outcomes.

  Study phs003548

• Heart Failure Network - Imaging from Nitrate's Effect on Activity Tolerance in Heart Failure with Preserved Ejection Fraction (HFN NEAT-Imaging)

  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 determine the effect of isosorbide mononitrate on daily activity in patients with heart failure and preserved ejection fraction.Background: Nitrates are commonly prescribed for symptom relief in patients with heart failure. Early studies in patients with heart failure with a reduced ejection fraction concluded that long-acting nitrates improves activity tolerance; however, approximately half of heart failure patients have preserved ejection fraction. The effects of nitrates in patients with heart failure and a preserved ejection fraction have not been extensively studied and the overall effect of nitrates on activity tolerance in such patients is uncertain.Subjects: There were a total of 110 patients enrolled with 51 patients assigned to receive isosorbide mono-nitrate first and placebo second, and 59 patients assigned to receive placebo first and isosorbide mononitrate second.Design: Enrolled subjects underwent baseline assessments, including echocardiography, quality-of-life scores, 6-minute walk test distance, and NT-proBNP levels. Subjects were also supplied with two kinetic activity monitors containing high-sensitivity triaxis accelerometers, to be worn 24 hours per day. The accelerometer measurements were expressed as arbitrary accelerometer units and stored every 15 minutes equaling 96 data points per day. The 15-minute cumulative accelerometer units were totaled over a 24-hour period to provide daily accelerometer units.Subjects were assigned to one of two treatment groups: 6 weeks of placebo first with crossover to 6 weeks of isosorbide mononitrate, or 6 weeks of isosorbide mononitrate first with crossover to 6 weeks of placebo. The study drugs were prepared as 30-mg tablets of isosorbide mononitrate and matching placebo. During each 6-week period, patients were instructed to take no study drug for the first 2 weeks followed by one tablet (30 mg daily) for 1 week, two tablets (60 mg once daily) for 1 week, and four tablets (120 mg once daily) until the next study visit, for a treatment duration of at least 2 weeks and up to 4 weeks. After each 6-week period, patients returned to the study center to repeat end-point assessments. The primary outcome for the study was the comparison of average daily accelerometer units during the period in which patients were receiving the 120-mg dose of isosorbide mononitrate compared to the period in which patients received the placebo. Other secondary end points included the 6-minute walk distance and the post-walk Borg dyspnea score, scores on the Kansas City Cardiomyopathy Questionnaire and the Minnesota Living with Heart Failure Questionnaire, and NT-proBNP levels. In addition, subjects completed a questionnaire indicating in which period (first, second, no preference) they felt better. Conclusions: Patients were active for fewer hours of the day during the 120-mg phase of receipt of isosorbide mononitrate as compared with placebo. Furthermore, during all study-drug regimens combined (30 mg to 120 mg), patients were less active during receipt of isosorbide mononitrate as compared with placebo. There was no significant effect of isosorbide mononitrate on secondary outcomes.

  Study phs004254

• T2D-GENES Project 2: San Antonio Mexican American Family Studies

  The Type 2 Diabetes (T2D) Genetic Exploration by Next-generation sequencing in Ethnic Samples (T2D-GENES) Consortium is a collaborative international effort to identify genes influencing susceptibility to T2D in multiple ethnic groups using next generation sequencing. T2D-GENES Project 2 is a complex pedigree-based study designed to identify low frequency or rare variants influencing susceptibility to T2D, using whole genome sequence (WGS) information from 1,043 individuals in 20 Mexican American T2D-enriched pedigrees from San Antonio, Texas. The major objectives of this study are to identify low frequency or rare variants in and around known common variant signals for T2D, as well as to find novel low frequency or rare variants influencing susceptibility to T2D. The sampled individuals are obtained from two studies: the San Antonio Family Heart Study (SAFHS) and the San Antonio Family Diabetes/Gallbladder Study (SAFDGS), collectively referred to as the San Antonio Mexican American Family Studies (SAMAFS). The strategy is to sequence approximately 600 individuals at an average of 50x coverage across the entire genome, then impute genome wide genotypes for about 440 additional family members. The 600 sequenced individuals are specifically chosen for their value in imputing sequence information into other family members. By studying large pedigrees, we expect to find multiple individuals carrying each genetic variant, even if this variant is very rare in the population at large. Thus, a pedigree-based approach provides an excellent opportunity for identifying rare novel variants influencing risk of T2D and quantitative variation in T2D-related phenotypes. The whole genome sequencing has been done commercially by Complete Genomics, Inc. (CGI). The final data set includes whole genome sequence data for 607 individuals. After quality control, 585 sequenced individuals provide data for family based imputation, using Merlin linkage analysis software, into approximately 440 additional family members for whom chip based genotypes are available to indicate which parental haplotype is transmitted. Extensive phenotype data is provided for 1048 individuals. These include 5 sequenced individuals who do not belong to any of the 20 large pedigrees. Phenotype information was collected between 1991 and 2011 in the two contributing longitudinal studies. SAFHS participants may have information from up to 5 visits, and SAFDGS participants may have up to 4 visits. The clinical variables reported are coordinated with T2D-GENES Project 1 (multi-ethnic exome sequencing) and include T2D status and age at diagnosis, glycemic traits (fasting and 2 hour glucose and insulin), blood pressure, blood lipids (total cholesterol, HDL cholesterol, calculated LDL cholesterol and triglycerides), clinical chemistry (cystatin c, glutamic acid decarboxylase antibody titer (GadAb), creatinine, adiponectin and leptin). Glycated hemoglobin (HbA1c) was not measured for these individuals and insulin C-peptide is not included in this data set. Additional phenotype data include the medication status at each visit, classified in four categories as any current use of diabetes, hypertension or lipid-lowering medications, and, for females, current use of female hormones. Anthropometric measurements include age, sex, height, weight, hip circumference, waist circumference and derived ratios. Each phenotype variable has an initial summary column containing the most recent non-missing measurement for each individual, followed by the five potential time points for each individual, the number of non-missing measurements, and the age and year for the most recent non-missing measurement. For historical reasons, the order in which variables are presented on the dbGaP web site differs from their order in the data download file. When reading the comment fields for each variable, please note that commas are omitted to support data exchange in .csv format.

  Study phs000462

• Study of Melanoma Risk in Australia and the United Kingdom

  The results from a small number of melanoma GWAS have been published. Initial studies identified several pigmentation- and nevus-associated loci that mediate an effect on melanoma risk, however, these studies were somewhat limited in that sample number (hence power) was low, they used first-generation low density SNP arrays, or used pools of DNA samples. Subsequently, additional melanoma GWAS have been completed, including a "Phase 2" study by the International Melanoma Genetics Consortium (GenoMEL), a full GWAS of the Australian sample resource previously used in a pooled-DNA GWAS, and study conducted through the MD Anderson Cancer Center and the Brigham and Women's Hospital. The groups who performed these studies have recently shared data and replicated a number of new melanoma susceptibility loci. Aside from the identification of novel loci, there are two additional notable outcomes of these studies. Firstly, an examination of the observed versus expected test statistics (Q-Q plot) from the Australian study, even after removing data from SNPs within known replicated susceptibility loci, reveals that there remains an excess of positive results. Similarly, the latest GenoMEL study finds almost three times as many SNPs reaching p-values between 10-4 and 10-5 as would be expected by chance. In the light of other studies of complex traits [1], these data suggest that studies based on additional melanoma samples from the same population are likely to identify an even larger "polygenic tail" and should prove invaluable in identifying and characterizing the underlying loci. A second observation, is that the effect size for replicated associations differs very little between GWAS. Given the strong effect of ultraviolet radiation (UVR) on risk of melanoma this might be expected to affect penetrance, particularly in the Australian and English samples, which are ethnically similar, but have very different sun exposures. We hypothesize that those SNPs whose effects are strongly mediated by UVR exposure may have smaller marginal effects on risk and are therefore harder to detect initially, suggesting a whole category of genetic effects that are unexplored.Indeed, most melanoma GWAS performed to date used samples from both European and Australian populations in discovery and or replication, and thus, existing studies have been poorly powered to detect associations with a wide difference in effect between these low- and high-UVR populations. Here, we wish to pursue this hypothesis further and plan to genotype additional cases drawn from the English and Australian populations. This will serve two complementary objectives: firstly, it will greatly increase our collective power to detect additional new melanoma risk alleles; secondly, it will highlight associations that are stronger specifically within the context of either low-UVR or high-UVR exposure environments. We posit that these data will be invaluable, not just for identifying novel melanoma-associated loci, but also for addressing gene-environment interactions. We have available a collection of valuable resources to help fulfill our aims. These include a considerable number of well-characterized and ungenotyped Australian and UK melanoma cases, as well as existing genotype data from sets of Australian and UK cases and population-based controls. We propose the following Specific Aims: To extend our current collective melanoma GWAS by genotyping a large number of additional UK and Australian cases. To jointly analyze these data and identify new melanoma risk loci. To determine whether pre-existing and new melanoma risk alleles show different degrees of association between the English and Australian populations, and assess whether these differences might be due to differences in sunlight exposure.

  Study phs000519

• Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) Lipidomics Study

  The GOLDN study was initiated to assess how genetic factors interact with environmental (diet and drug) interventions to influence blood levels of triglycerides and other atherogenic lipid species and inflammation markers (registered at clinicaltrails.gov, number NCT00083369). The study recruited Caucasian participants primarily from three-generational pedigrees from two NHLBI Family Heart Study (FHS) field centers (Minneapolis, MN and Salt Lake City, UT). Only families with at least two siblings were recruited and only participants who did not take lipid-lowering agents (pharmaceuticals or nutraceuticals) for at least 4 weeks prior to the initial visit were included. A total of 1048 GOLDN participants were included in the diet intervention. The diet intervention followed the protocol of Patsch et al. (1992). The whipping cream (83% fat) meal had 700 Calories/m2 body surface area (2.93 MJ/m2 body surface area): 3% of calories were derived from protein (instant nonfat dry milk) and 14% from carbohydrate (sugar). The ratio of polyunsaturated to saturated fat was 0.06 and the cholesterol content of the average meal was 240 mg. The mixture was blended with ice and flavorings. Blood samples were drawn immediately before (fasting) and at 3.5 and 6 hours after consuming the high-fat meal. For the GOLDN lipidomics study, sterols and fatty acids were measured from stored plasma (-80 degrees Celsius) collected at fasting and 3.5 hours after the diet intervention using TrueMass Panels from Lipomics (West Sacramento, CA). A total of 11 sterols were quantified in nmols/gram of sample including total cholesterol, 7-dehydrocholesterol, desmosterol, lanosterol, lathasterol, cholestanol, coprostanol, beta-sitosterol, campesterol, stigmasterol, and 7alpha-hydroxycholesterol. A total of 35 fatty acids were quantified in nmols/gram of sample inlcuding myristic acid (14:0); pentadecanoic acid (15:0); palmitic acid (16:0); stearic acid (18:0); arachidic acid (20:0); behenic acid (22:0); lignoceric acid (24:0); myristoleic acid (14:1n5); palmitoleic acid (16:1n7); palmitelaidic acid (t16:1n7); oleic acid (18:1n9); elaidic acid (t18:1n9); vaccenic acid (18:1n7); linoleic acid (18:2n6); gamma-linolenic acid (18:3n6); alpha-linolenic acid (18:3n3); stearidonic acid (18:4n3); eicosenoic acid (20:1n9); eicosadienoic acid (20:2n6); mead acid (20:3n9); di-homo-gamma-linolenic acid (20:3n6); arachidonic acid (20:4n6); eicsoatetraenoic acid (20:4n3); eicosapentaenoic acid (20:5n3); erucic acid (22:1n9); docosadienoic acid (22:2n6); adrenic acid (22:4n6); docosapentaenoic acid (22:5n6); docosapentaenoic acid (22:5n3); docosahexaenoic acid (22:6n3); nervonic acid (24:1n9); and plasmalogen derivatives of 16:0, 18:0, 18:1n9, and 18:1n7.

  Study phs000741

• FEGA FAQs: a summary of the Q+A session from the FEGA workshop at ELIXIR AHM 2024

  The ELIXIR All Hands Meeting (AHM) was held in Uppsala, Sweden this June. We at EGA always look forward to this meeting as a chance to see many of our partners and colleagues face-to-face. The Federated EGA (FEGA) is one initiative that always benefits from the AHM, and this year FEGA was in the spotlight, with several questions related to the advances we've made recently. We held this year's FEGA workshop a little differently, with most time for discussions on various “hot topics”. Organised in different breakouts, over 2 rounds, participants could choose which session they wanted to attend. One breakout was a general Q+A about FEGA and the path to join. We had a diverse range of participants, including experts from Central EGA (CEGA) and FEGA inaugural nodes, people from up and coming nodes at various stages of readiness, as well as participants not directly a part of FEGA but curious to learn more. It made for a great discussion! In this blog post, we highlight some of the questions for those who didn’t have a chance to be there. What is the FEGA Collaboration Agreement? It is a legal agreement between the node and Central EGA (CEGA) where both parties agree to certain responsibilities, like what services will be provided, sharing of public metadata with CEGA, interoperable technologies and more. The agreement is for 4 years, with the expectation of long-term sustainability. If a node is made up of multiple institutes, one institute must be the signatory. This could require a legal agreement within the node. CEGA is composed of CRG and EMBL-EBI (EGA itself has its own legal agreement between these institutes) Check the FEGA Collaboration Agreement What about purely medical data? Is this accepted in FEGA? CEGA only accepts data approved for secondary research use. We do accept medical data collected in this context, as do some nodes. Nodes are able to accept any data they like, as long as it is sensitive, thus in need of controlled access. What about other data types other than genomics? Like proteomics or images? They can be accepted, although CEGA is currently not designed with those formats in mind, so they might not fit with precision in the data model. The FEGA nodes are welcome to accept any data they want. Proteomic studies generating sensitive data are increasing, and specific work will likely be developed in the near future to adress this need. Does CEGA require a certain amount or type of data to be shared by the node? No, we do not and it's up to each node to decide what specific data types they accept and in what format. Nonetheless, a FEGA node has the duty to be open to data submission from any institution of their country, and can't be limited by design to a single or limited data source. Does CEGA and FEGA require a certain amount or type of details and description on the data to be submitted to the node? Data description (technical and biological), usually called metadata in this field, is crucial for the findability of the submitted data. Thus, we highly recommend including as much information as available to maximise the discoverability of the data. Specifical minimal metadata is defined in the submission schema, and thresholds are specified by the nodes. We are currently working towards a unified FEGA metadata model. What are the geographical borders of FEGA? FEGA should make the E for Everyone, because any institute worldwide could join and we indeed do have countries and use-cases worldwide. Some of them are very close to officially joining the federation. What are the main differences between FEGA and GDI? The two networks have a lot in common and also some critical differences. You can read all about this in one of our latest blog post. If you have more questions, please consider having a look at the FEGA onboarding webpage and, of course, don't miss our workshop at the ELIXIR AHM next year!

  Blog fega-faqs-elixir-ahm-2024

• eMERGE Network's Multi-Center Pilot of Pharmacogenetic Sequencing in Clinical Practice

  eMERGE-PGx is a multi-site test of the concept that sequence information can be coupled to electronic medical records (EMRs) for use in healthcare. The promise of personalized medicine - health care guided by each individual's biological characteristics - is being fostered by increasingly powerful and economical methods to acquire clinically relevant biomarkers from large numbers of people. One therapeutic area that seems especially ripe for an early test of the personalized medicine concept is pharmacogenomics (PGx) - the idea that individual variation in drug response includes a genomic component. Drug response variation is an accepted feature of virtually all drug treatments, and contemporary molecular biologic tools continue to identify key genes mediating drug metabolism, transport, and targets. Importantly, common variation in these genes is an increasingly well-recognized contributor, sometimes with large effects, to variation in drug responses. As a result, recommendations for genotype-guided therapy are increasing. These evidence-based recommendations, if implemented in health care practice, could reduce adverse drug events and improve time to therapeutic response. Through eMERGE-PGx, we are developing strategies for the optimal implementation of genetic sequence data into the clinical environment with the ultimate goal of improving patient care. Site and participants include: 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. The PGx population selected for sequencing with the PGRNseq panel at CHOP is 1,650 children from CAG's biorepository with well-documented drug-related severe adverse events (SAEs) or EHR-based drug response profiles. SAEs were extracted from EPIC records and from CHOP's Adverse Event (AE) database, which documents every AE at CHOP. These AEs are classified by a medical review panel according to the causal relationship with the suspected drug into 'doubtful', 'possible', and 'probable'. Individuals with events classified as probable, severe and objective, were selected for sequencing. The drugs more frequently associated with adverse events are antibiotics, antineoplastics, immunosuppressants and psychotropic drugs. This cohort constitutes 50% of the target population. The remaining subjects were selected using EHR-based algorithms that we have developed and validated at CAG for identifying patients not responding to ADHD medication (primarily atomoxetine) and patients refractory to antiepileptic treatment from responders. Cincinnati Children's Hospital Medical Center/Boston's Children's Hospital (CCHMC/BCH): 811 CCHMC samples were obtained from children, adolescents or young adults exposed to medication or at risk for needing medication of study interest. 55% of participants were exposed to one or more opioids and their DNA source was a CCHMC study-specific biobank; while 27% of participants were at risk for needing an opioid for surgical pain management and were newly recruited. The remainder of the cohort was exposed to methylphenidate and their DNA samples were obtained from a CCHMC study-specific biobank. The focus of Boston Children's Hospital eMERGE PGx project is on individuals with epilepsy. Samples were taken from a current pharmacogenomics study already in place through which DMET analysis was run and used as confirmation for PGRN-Seq results. A total of 109 samples were sent for PGRN-Seq analysis at University of Washington. The remaining 141 epilepsy samples were from Children's Hospital of Philadelphia and underwent testing with PGRN-Seq at CHOP. 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 data are from Geisinger patients who consent to participate in the MyCode project. MyCode participants agree to provide biological samples for broad research use, including genomic analysis, and for linking of sample data to information in the participant's Geisinger health record. The consent also permits sharing of de-identified data for research purposes. Group Health(GH)/University of Washington (UW): Potential GH participants for the PGx project 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~6300). Participants were eligible if aged 50 - 65 years old at the time of their enrollment into the NWIGM repository, living, enrolled in GH'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 Group Health: 1. Demographics - participants with self-reported race as Asian or African ancestry were prioritized and selected to enrich for non-European ancestry; 2. Diagnosis and procedure codes - participants were selected if found to have a history of hypertension, atrial fibrillation (AF,) or congestive heart failure (CHF). Participants with a history of arrhythmia were added if the entire selection algorithm did not generate 900 individuals. We also enriched for participants with EHR evidence of actionable indications related to PGRNSeq genes. Participants were selected if found to have an ICD9 code for malignant hyperthermia, hypertension, atrial fibrillation, congestive heart failure or long QT syndrome (LQTS); 3. Laboratory values - if a participant had any laboratory event of creatine kinase (CK) > 1000, and were dispensed statins within 6 months of the event, then they were selected; and 4. Medications - participants were excluded if ever on carbamazepine or had a current regimen of warfarin. Essentia Institute of Rural Health, Marshfield Clinic, Pennsylvania State University (Marshfield): For this study, 750 subjects were selected and enrolled into PGx based on Vanderbilt's algorithm designed to enrich for patients who are most likely to receive one of three common drugs (Clopidogrel, Warfarin or Simvastatin) in the next 2-3 years. These patients were sent a letter of invitation and description of the PGx project. Follow-up phone calls were made, and interested subjects came in for a one time meeting to discuss the project and go through the informed consent with the research coordinator. If they were interested they signed the consent and HIPAA forms and gave blood. Subjects were chosen and enrolled into PGx independently of previous biobank participation. Mayo Clinic: The Right Drug, Right Dose, Right Time - Using Genomic Data to Individualize Treatment (The RIGHT Protocol) enrolled 1013 patients to test the hypothesis that prescribers could deliver genome-guided drug therapy at the point-of-care by using pharmacogenomic data preemptively integrated in the electronic medical record. Complete details regarding the study population have been previously described (Bielinski et al., 2014). Icahn School of Medicine at Mount Sinai School (Mt Sinai): Our study site is the Primary Care Associates (PCA) practice group of the Mount Sinai Faculty Practice Associates (FPA) of the Mount Sinai Medical Center in New York City. This practice has 12 physician providers. All patient encounters are documented and managed with EpicCare ambulatory electronic medical record. Active PCA Patients eligible for enrollment fulfilled the following criteria: a) age 50 or older receiving clinical care at Mount Sinai FPA PCA practice with at least one practice encounter within 18 months prior to commencement of enrollment; b) no history or current use of clopidogrel, warfarin, or simvastatin. Eligible patients were invited to participate through de novo recruitment by letter sent by their provider. Interested patients were screened for eligibility and enrolled to participate in the eMERGE PGX study on site by a dedicated research coordinator. In addition to de novo enrollment from clinical practice, patients of FPA PCA who had previously enrolled in Mount Sinai's BioMe Biobank program AND fulfilled eligibility criteria as stated under a) and b) were identified by chart review and samples sequenced at CIDR using PGRNseq platform (N=300). PGRNseq data from 291 samples passed stringent quality control and are included in the current data set. Furthermore, 56 of these patients carrying known and validated 'actionable' variants affecting prescribing of clopidogrel, warfarin, and/or simvastatin were enrolled in the eMERGE PGX study following invitation through recontacting by the Principal Investigator of the BioMe Program. Northwestern University: Participants for this study were recruited from the General Internal Medicine (GIM) clinic at Northwestern Medical Group (NMG). Patients were selected for invitation to participate if they had been seen a minimum of two times over the last four years, having a high likelihood to receive a prescription for warfarin, Plavix, or a statin, and are seeing a physician who has agreed to allow their patients to be contacted for the study. We utilized an algorithm developed at Vanderbilt and tailored to our population which uses our EHR to estimate the probability that individuals will receive a prescription for warfarin, Plavix, or a statin in the next three years. Participants were sent a letter explaining the study prior to their GIM appointment and offered participation at the time of their visit. Participants were consented on-site and blood drawn after consent was obtained. The GIM clinic consists of 39 primary care physicians who provide approximately 80,000 patient encounters per year. As with any large primary care clinic, a significant proportion of patients in GIM clinic suffer from a variety of chronic health conditions, such as diabetes, hypertension, and coronary artery disease. Over 50,000 individuals have been seen by GIM doctors in the past 5 years; 11,562 of these patients have evidence of a statin prescription in the EHR, 3,436 have evidence of a warfarin prescription, and 1,872 have evidence of a Plavix prescription. Vanderbilt University: The more than 1000 participants enrolled into Vanderbilt's eMERGE PGx study were newly recruited from the Cardiology and Internal Medicine Clinics and the Hillsboro Medical Group within Vanderbilt University Medical Center (VUMC). Patients were selected based on a predictive algorithm estimating the patient's likelihood of receiving Clopidogrel, Warfarin, and/or Simvastatin. The algorithm identifies primarily older middle-aged patients, and the mean age of the study group is 74. The cohort is approximately 45% female with 75% of subjects self-identified as EA and 24% as AA. Subjects were consented in person by study personnel following a routine clinic visit and an introduction to the study staff by their doctor. VUMC is a comprehensive health care facility dedicated to patient care, research, and the education of health care professionals. Translational research into the causes and treatment of disease as well as studying fundamental biological properties is the primary focus of discovery at Vanderbilt. Clinical research is conducted in Vanderbilt University Hospital, the Nashville Veterans Administration Hospital, Meharry General Hospital and in their associated outpatient clinics. These hospitals and clinics, all associated with the Vanderbilt system, each have full time Vanderbilt faculty and medical housestaff and provide clinical care and participate in research programs. The Vanderbilt Clinic is comprised of more than 95 adult outpatient specialty practices and received over 1.5 million ambulatory visits in 2012-13. The Vanderbilt Heart and Vascular Institute offers a comprehensive heart program offering diagnosis, medical treatment, minimally invasive therapies, surgical intervention and disease management, tailored to each individual's unique needs. All programs within the Vanderbilt Clinic have survival figures that surpass the national average.

  Study phs000906

• Systolic Blood Pressure Intervention Trial (SPRINT-BioLINCC)

  Available DataThe available data include all elements of the previously released SPRINT Primary Outcome Paper (SPRINT-POP) data, the full SPRINT clinical data including the MRI and MIND data, and select ancillary study data (Ambulatory Blood Pressure Monitoring, APOL1, Acute Kidney Injury, ASK, Heart, FAST, PWV, Renal Resistance, Biomarkers, Plasma AD).ObjectiveThe Systolic Blood Pressure Trial (SPRINT) was conducted to test the hypothesis that treating systolic blood pressure to a target of less than 120 mm Hg, as compared to a target of less than 140 mm Hg, would reduce the incidence of cardiovascular disease.BackgroundHypertension is a highly prevalent condition among adults and is a leading risk factor for myocardial infarction and stroke. Further, isolated systolic hypertension is the most common form of hypertension in adults over 50 years of age. Observational studies have shown a monotonic increase in cardiovascular risk with systolic blood pressures above 115 mm Hg; however, general population clinical trials have only documented the benefits of lowering systolic blood pressure to a target of 150 mm Hg. A 2007 expert panel sponsored by the National Heart, Lung, and Blood Institute designated the hypothesis that lowering the systolic blood pressure goal to a level SubjectsA total of 9361 participants were enrolled, with 4,678 randomized to the intensive-treatment group and 4,683 randomized to the standard-treatment group.DesignSPRINT was a randomized, single blinded (outcome adjudicators were blinded to treatment assignment) treatment trial with participants randomized to a systolic blood-pressure target of either less than 140 mm Hg (the standard-treatment group) or less than 120 mm Hg (the intensive-treatment group). Following randomization, baseline hypertensive regimens were adjusted in accordance with study treatment algorithms established for each group. The study formulary included all major classes of antihypertensive agents. Investigators could prescribe other antihypertensive medications, but the use of drug classes with the strongest evidence for reduction in cardiovascular outcomes was encouraged. This included thiazide-type diuretics as the first-line agent, loop diuretics for participants with advanced chronic kidney disease, and beta-adrenergic blockers for participants with coronary artery disease. Medications for participants in the intensive-treatment group were adjusted on a monthly basis to target a systolic blood pressure of less than 120 mm Hg. Medications for participants in the standard-treatment group were adjusted to target a systolic blood pressure of 135 to 139 mm Hg, and the dose was reduced if systolic blood pressure was less than 130 mm Hg on a single visit or less than 135 mm Hg on two consecutive visits. Lifestyle modification was encouraged as part of the management strategy.Participants were seen monthly for the first 3 months and every 3 months thereafter. Demographic data were collected at baseline. Clinical and laboratory data were obtained at baseline and every 3 months thereafter. A structured interview was used in both groups every 3 months to obtain self-reported cardiovascular disease outcomes. Medical records and electrocardiograms were obtained for documentation of events. Incidences of hypotension, syncope, injurious falls, electrolyte abnormalities, and bradycardia that were evaluated in an emergency department were included in adverse event reporting. Occurrences of acute kidney injury or acute renal failure requiring hospitalization were also monitored. The primary outcome was a composite outcome of myocardial infarction, other acute coronary syndromes, stroke, heart failure, or death from cardiovascular causes.ConclusionsThe blood pressure intervention was stopped in August of 2015 (median follow-up of 3.26 years) after the cardiovascular outcome results exceeded the boundary for efficacy at two consecutive time points. Compared with a systolic blood pressure target of less than 140 mm Hg, an intensive systolic blood pressure target of 120 mm Hg resulted in lower rates of fatal and nonfatal major cardiovascular events and death from any cause. Significantly higher rates of some adverse events were observed in the intensive-treatment group.

  Study phs003483

• Systolic Blood Pressure Intervention Trial (SPRINT-Imaging)

  Available DataSystolic Blood Pressure Intervention Trial, SPRINT-Imaging, provides access to ECG signals data from the SPRINT clinical trial. The clinical phenotyping and outcomes data from the trial are associated with SPRINT-BioLINCC, phs003483.ObjectiveThe Systolic Blood Pressure Trial (SPRINT) was conducted to test the hypothesis that treating systolic blood pressure to a target of less than 120 mm Hg, as compared to a target of less than 140 mm Hg, would reduce the incidence of cardiovascular disease. BackgroundHypertension is a highly prevalent condition among adults and is a leading risk factor for myocardial infarction and stroke. Further, isolated systolic hypertension is the most common form of hypertension in adults over 50 years of age. Observational studies have shown a monotonic increase in cardiovascular risk with systolic blood pressures above 115 mm Hg; however, general population clinical trials have only documented the benefits of lowering systolic blood pressure to a target of 150 mm Hg. A 2007 expert panel sponsored by the National Heart, Lung, and Blood Institute designated the hypothesis that lowering the systolic blood pressure goal to a level SubjectsA total of 9361 participants were enrolled, with 4,678 randomized to the intensive-treatment group and 4,683 randomized to the standard-treatment group. DesignSPRINT was a randomized, single blinded (outcome adjudicators were blinded to treatment assignment) treatment trial with participants randomized to a systolic blood-pressure target of either less than 140 mm Hg (the standard-treatment group) or less than 120 mm Hg (the intensive-treatment group). Following randomization, baseline hypertensive regimens were adjusted in accordance with study treatment algorithms established for each group. The study formulary included all major classes of antihypertensive agents. Investigators could prescribe other antihypertensive medications, but the use of drug classes with the strongest evidence for reduction in cardiovascular outcomes was encouraged. This included thiazide-type diuretics as the first-line agent, loop diuretics for participants with advanced chronic kidney disease, and beta-adrenergic blockers for participants with coronary artery disease. Medications for participants in the intensive-treatment group were adjusted on a monthly basis to target a systolic blood pressure of less than 120 mm Hg. Medications for participants in the standard-treatment group were adjusted to target a systolic blood pressure of 135 to 139 mm Hg, and the dose was reduced if systolic blood pressure was less than 130 mm Hg on a single visit or less than 135 mm Hg on two consecutive visits. Lifestyle modification was encouraged as part of the management strategy. Participants were seen monthly for the first 3 months and every 3 months thereafter. Demographic data were collected at baseline. Clinical and laboratory data were obtained at baseline and every 3 months thereafter. A structured interview was used in both groups every 3 months to obtain self-reported cardiovascular disease outcomes. Medical records and electrocardiograms were obtained for documentation of events. Incidences of hypotension, syncope, injurious falls, electrolyte abnormalities, and bradycardia that were evaluated in an emergency department were included in adverse event reporting. Occurrences of acute kidney injury or acute renal failure requiring hospitalization were also monitored. The primary outcome was a composite outcome of myocardial infarction, other acute coronary syndromes, stroke, heart failure, or death from cardiovascular causes. ConclusionsThe blood pressure intervention was stopped in August of 2015 (median follow-up of 3.26 years) after the cardiovascular outcome results exceeded the boundary for efficacy at two consecutive time points. Compared with a systolic blood pressure target of less than 140 mm Hg, an intensive systolic blood pressure target of 120 mm Hg resulted in lower rates of fatal and nonfatal major cardiovascular events and death from any cause. Significantly higher rates of some adverse events were observed in the intensive-treatment group.

  Study phs003566

• The Environmental Determinants of Diabetes in the Young Study (TEDDY)

  The Environmental Determinants of Diabetes in the Young (TEDDY) Study investigates genetic and genetic-environmental interactions, including gestational infection or other gestational events, childhood infections and other environmental factors after birth, in relation to the development of pre-diabetic islet autoimmunity and type 1 diabetes (T1D). Beginning in 2002, a consortium of six centers assembled to participate in the development and implementation of studies to identify environmental factors that trigger the development of islet autoimmunity and T1D in genetically susceptible individuals. The TEDDY study screened around 400,000 newborns and recruited 7,749 neonates from the general population with a pre-determined T1D risk of 3% and 919 neonates with first degree relatives who have T1D and who have a pre-determined T1D risk of 10%. Thus, TEDDY proposes to follow over 8,000 participants across six clinical centers worldwide (Finland, Germany, Sweden and three in the United States) until the age of 15. Participants are followed every three months for islet autoantibody measurements with blood sampling until four years of age and then at least every six months until the age of 15. After the age of four, autoantibody positive subjects continue to be followed at 3 month intervals and autoantibody negative subjects are followed at six month intervals. In addition to the analysis of autoantibodies, additional data and sample collection is performed at each visit. The parents collect monthly stool samples in early childhood. The parents also fill out questionnaires at regular intervals in connection with study visits and record information about diet and health status in the child's TEDDY Book between visits. Continued long-term follow-up of the currently active TEDDY participants will provide important scientific information on early childhood diet, reported and measured infections, vaccinations, and psychosocial stressors that may contribute to the development of type 1 diabetes and islet autoimmunity. DNA extracted from the 9-month TEDDY blood sample was used for SNP genotyping. Samples were available for 7,082 participants. SNPs were genotyped by the Center for Public Health Genomics at the University of Virginia, using the Illumina ImmunoChip SNP microarray of around 196,000 SNPs selected from 186 regions associated with 12 autoimmune diseases (including T1D) (Hadley et al., 2015). "Data quality control (QC) steps included eliminating subjects with a low call rate (> 5% SNPs missing) and discrepancies between reported gender and prior genotyping at the HLA laboratory. Secondly, SNPs were removed from analysis due to low call rate, Hardy-Weinberg equilibrium P value < 10-6, except for chromosome 6 due to HLA eligibility requirements)" (Törn et al., 2015). QC resulted in a total of 7,023 subjects with data on 176,586 SNPs. Additional information on the TEDDY study are available in the following articles: The Environmental Determinants of Diabetes in the Young (TEDDY) Study. TEDDY Study Group. Annals of the New York Academy of Science, 2008 and TEDDY - The Environmental Determinants of Diabetes in the Young - An Observational Clinical Trial. Annals of the New York Academy of Science, 2006 Details of the TEDDY protocol can be found in The Environmental Determinants of Diabetes in the Young (TEDDY): Genetic Criteria and International Diabetes Risk Screening of 421,000 infants. Pediatric Diabetes, 2011

  Study phs001037

• STAMPEED: Northern Finland Birth Cohort 1966 (NFBC1966)

  The Northern Finland Birth Cohorts program (NFBC) was initiated in the 1960s in the two northernmost provinces of Finland to study risk factors involved in pre-term birth and intrauterine growth retardation, and the consequences of these early adverse events on subsequent morbidity and mortality. The uniqueness of NBFCs is that the data of the cohorts were obtained from early fetal life (including maternal health during pregnancy) to adulthood. The NFBC1966 includes 12,058 live births to mothers in the two northern-most provinces of Finland. Two decades later, a second cohort of 9432 births was obtained (NFBC1986). In NFBC1966 pregnancies were followed prospectively from the first antenatal contact (10-16th week). After birth, the offspring were examined and then again underwent clinical evaluation at ages 1y, 7y, 14-16y and 31y. At each visit, a wide range of phenotypic, lifestyle and demographic data were gathered by questionnaires and clinical examinations. For the most part, NFBC1986 has undergone similar evaluations to NFBC1966. Linkage to national registries includes hospitalization, deaths, education, medication, pensions, and provides up-to-date demographic and clinical information for members of both cohorts. DNA samples were obtained from 5,923 subjects from NFBC1966 and 6688 subjects from NFBC1986. Data coverage, 96% of all births in 1966 and 99% in 1986, is highly representative for the whole population. The NFBC program comprises more than 20 different projects coordinated by the Center of Lifecourse Disease studies in Northern Finland (COLD) at Oulu University. The prospective data collected from the NFBCs form a unique resource, allowing the study of disease emergence, and of the importance of genetic, biological, social and behavioral risk factors. The genome-wide association (GWA) study sponsored through the STAMPEED program of NHLBI employed genomic DNA samples previously collected by the NFBC1966 study and stored in the DNA repository of the National Institute for Health and Welfare, Finland. This NHLBI sponsored RO1 project aimed to identify genetic variants contributing to metabolic and cardiovascular diseases (CVD). In addition to de-identified genome wide genotypic data, a selected list of phenotypic data related to CVD including weight, height, BMI, HDL, LDL, total cholesterol, triglyceride, glucose, insulin and fasting status, are also available in dbGaP. A summary of the GWAS for the NFBC1966 cardiovascular risk traits can be found in Sabatti et al., Nature Genetics 41: 35-46, 2009, PMID: 19060910. The version 2 release of this study contains sequence data from seventeen loci associated with levels of triglyceride, HDL-C, LDL-C, total cholesterol, fasting plasma glucose, and fasting plasma insulin (Kathiresan et al. 2008, Willer et al. 2008, Sabatti et al. 2009, Dupuis et al. 2010, Teslovich et al. 2010). At each locus, protein-coding regions and 5' and 3' untranslated regions of genes nearest to single nucleotide polymorphisms showing genome-wide significant association with metabolic syndrome-related traits, were sequenced. Targeted Illumina sequencing of 78 genes (~270kb) using 150bp probes was performed on 4943 subjects of the Northern Finland Birth Cohort 1966 (NFBC1966). Whole exome sequencing on the Illumina platform was carried out on 586 of those participants. The sequencing study is part of a larger project that is funded by the National Human Genome Research Institute's Allelic Spectrum in Common Disease Initiative, and comprises sequence data from more than 7000 individuals in two Finnish cohorts: NFBC1966 and the Finland-United States Investigation of NIDDM Genetics (FUSION) study.

  Study phs000276

• Prevention and Early Treatment of Acute Lung Injury (PETAL) Network - Functional, Imaging, and Respiratory Evaluation in CORAL (PETAL FIRE CORAL-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 PETAL FIRE CORAL include plasma and whole blood. Please note that use of biospecimens in genetic research is subject to a tiered consent and biospecimens may not be used to produce commercial products.Objectives: To examine the recovery from COVID-19 disease following acute hospitalization with an emphasis on functional, imaging, and respiratory evaluation, and assess the feasibility of conducting a larger study to evaluate variables associated with differential recovery.Background: The BLUE CORAL study, dbGaP phs003419, was intended to address the knowledge gaps and provide critical data to help guide clinical care, public health, and scientific efforts regarding SARS-CoV-2 (COVID-19) recovery. The understanding and treatment of long-term sequelae of COVID-19, referred to variously as post-acute sequelae of COVID-19 (PASC), post-acute COVID, long COVID, or long-haul COVID, is fundamental to patient recovery. These long-term consequences are common and increasingly recognized, although heterogeneous in manifestation, and can result in a failure to return to baseline level of health. At the time of the FIRE CORAL study, there were limited studies that collected objective assessments following hospital discharge for a wide spectrum of patients surviving COVID-19.Participants: FIRE CORAL enrolled adult participants as a subset of those enrolled in the BLUE CORAL study with a recent COVID-19 hospitalization and that completed the 1- or 3-month post-hospital telephone long-term outcomes assessment. The target enrollment for this pilot study cohort was 80 participants.Design: FIRE CORAL was a multicenter prospective cohort study of participants recovering from COVID-19 disease with in-person follow-up, as an extension of the BLUE CORAL study. The study consisted of a battery of assessments objectively measuring pulmonary function, abnormalities on lung imaging, and functional status. Participants were to attend and perform initial in-person testing at 3 to 9 months after hospital discharge. Participants with abnormal findings on pulmonary function testing or chest imaging during their initial assessment or those with persistent respiratory symptoms were eligible to repeat study procedures 3 months later with a subsequent visit at 12 months. All participants were invited to return for in-person follow up at 12 months after hospital discharge. Assessments included high-resolution chest CT scan without contrast, spirometry, lung volume assessment, diffusing capacity measured by single breath measurement, Six Minute Walk Test, Short Physical Performance Battery, St. George's Respiratory Questionnaire, and Functional Assessment of Chronic Illness Therapy-Fatigue scale. Demographic and hospitalization data are available as part of the BLUE CORAL study.The primary aim of the FIRE CORAL study was to examine the feasibility of conducting rigorous in-person follow-up testing of participants discharged from the hospital following COVID-19 illness to assist with planning for a larger study to evaluate variables associated with differential recovery. The secondary aim was to describe the pulmonary, imaging, and functional recovery following COVID-19 hospitalization in a diverse population of patients.

  Study phs004130

• Jackson Heart Study (JHS) Cohort

  The Jackson Heart Study (JHS) is a large, community-based, observational study whose participants were recruited from urban and rural areas of the three counties (Hinds, Madison and Rankin) that make up the Jackson, MS metropolitan statistical area (MSA). Participants were enrolled from each of 4 recruitment pools: (1) random, 17%, (2) volunteer, 30%, (3) currently enrolled in the Atherosclerosis Risk in Communities (ARIC) Study, 31%, and (4) secondary family members, 22%. The final cohort of 5,306 participants included 6.59% of all African American Jackson MSA residents, aged 35-84, during the baseline exam (N-76,426, US Census 2000). Among these, approximately 3,600 gave consent that allows genetic research and deposition of data into dbGaP. Major components of three clinic examinations (see study history) include medical history, physical examination, blood/urine analytes, and interview questions on areas such as physical activity, stress, coping and spirituality, racism and discrimination, socioeconomic position, and access to health care. Extensive clinical phenotyping includes anthropometrics, electrocardiography, carotid ultrasound, ankle-brachial blood pressure index, echocardiography, CT chest and abdomen for coronary and aortic calcification, liver fat, and subcutaneous and visceral fat measurements, and cardiac MRI. At 12-month intervals after the baseline clinic visit (Exam 1), participants have been contacted by telephone to update information, confirm vital statistics, document interim medical events, hospitalizations and functional status, and obtain additional sociocultural information. Questions about medical events, symptoms of cardiovascular disease and functional status are repeated annually. Ongoing cohort surveillance includes abstraction of medical records and death certificates for relevant International Classification of Diseases (ICD) codes and adjudication of nonfatal events and deaths. CMS data are currently being incorporated into the dataset. Note: Regarding date variables warehoused in the Jackson Heart Study (JHS) Cohort on dbGaP, the coordinating center has developed an algorithm that will systematically review and de-identify any of the (nearly 100) date-related variable types stored in the data package. To simultaneously minimize (i) de-identifiability of the data and (ii) impact on analyses utilizing sensitive data elements, a participant-level random number was generated to avoid the necessity of sharing any potentially sensitive data. The coordinating center maintains an archived linkage of these data in their raw form and regularly reviews ad hoc requests to utilize the raw data on a project-by-project basis. The JHS Cohort is utilized in the following dbGaP substudies. To view genotypes, other molecular data, and derived variables collected in these substudies, please click on the following substudies below or in the "Substudies" section of this top-level study page phs000286 JHS Cohort. phs000402 ESP Heart GO JHS phs000498 JHS Allelic Spectrum Seq phs000499 JHS CARephs001069 MI Gen JHS phs001098 T2D GENES Exome Seq phs001356 Exome Chip phs002256 Cardiometabolic Renal Proteomics phs002369 Metabolomics Insulin Resistance

  Study phs000286

• Single Cell Analysis Program - Transcriptome (SCAP-T)

  This initiative is part of the Single Cell Analysis Program (SCAP) and is funded through the NIH Common Fund (See http://nihroadmap.nih.gov/), which supports cross-cutting programs that are expected to have exceptionally high impact. Common Fund initiatives address key roadblocks in biomedical research that impede basic scientific discovery and its translation into improved human health. In addition, these programs capitalize on emerging opportunities to catalyze progress across multiple biomedical fields. Single cell analysis has recently emerged as an important field of research because technologies have improved in sensitivity and throughput sufficiently to begin measuring and understanding heterogeneity in complex biological systems and correlating it with changes in biological function and disease processes. By profiling individual cells it is possible to resolve rare cells, transient cell states, and the influence of organization and environment on such cells and states, which cannot be described by ensemble measurements. The long-term goal of the SCAP is to accelerate this move towards personalizing health to the cellular level by understanding the link between cell heterogeneity, tissue function and emergence of disease through the discovery, development and translation of innovative approaches which will dramatically change the way cells are characterized. The SCAP will focus on research, which will systematically measure, analyze and model cell-to-cell variation, and identify crucial differences and rare biological states, which may have important functional consequences. Under SCAP, there are three studies to evaluate the cellular heterogeneity using transcriptional profiling of single cells (U01): University of Pennsylvania: Role of single cell mRNA variation in systems associated electrically excitable cells University of Southern California: Evaluation of cellular heterogeneity using patchclamp and RNA-seq of single cells University of California at San Diego: Single cell sequencing and in situ mapping of RNA transcripts in human brains The SCAP has been designed as a five-year program with several components: (1) the collection, analysis and sharing of comprehensive expression datasets to understand the role of heterogeneity in tissues and systemically and identify critical parameters and states; (2) the discovery of new, innovative tools for spatiotemporal imaging, manipulation, analysis and modeling of a biologically relevant population of cells with minimal perturbation; (3) milestone-driven validation and translation of technologies for characterizing single cells in situ meeting the needs of end-users; and (4) development and coordination of a multidisciplinary research community through workshops and other collective endeavors. Further details about the NIH SCAP-T program can be found here http://commonfund.nih.gov/singlecell/. The SCAP-T data sets include the detailed phenotype information, experimental protocols, QC information, RNA-sequencing data and NGS results from human heart and human brain cells. The first data release includes 697 single cells from human brain and heart. The second data release includes an additional 978 single cells from human brain and heart. The third data release includes an additional 1631 single cells from human brain and heart. The fourth data release is a correction to this study, but adds no new cells. The fifth data release includes an additional 2984 single cells from human brain and heart. The sixth data release includes an additional 944 single cells from human brain and heart. The seventh data release includes an additional 1977 single cells from human brain and heart. The SCAP-T data portal provides a customized interface for users to quickly identify and retrieve files by phenotypes, and data properties such as sequencing facility or coverage for all of these 9231 single cells. For more information about the SCAP-T study and the data portal, please visit http://www.scap-t.org.

  Study phs000833

• Federated EGA

  Federated EGA Federated EGA Index Overview Map of FEGA status across the world Current status of the FEGA Network Documentation Overview Federated EGA is a global network of repositories enabling secure discovery and access to sensitive human data. Our vision is to accelerate scientific discovery and healthcare breakthroughs by creating the go-to worldwide sensitive human data resource. Our values are: Privacy & Trust: Ensuring privacy of data subjects, security of services and acting with integrity to build community trust.FAIRness: Driving reuse of data through standardisation of metadata and clear access policies.Diversity: Expanding our network and representing global diversity in the data we host to bring benefits worldwide. Federated EGA - European by name, global by nature Federated EGA collaborates with European and global initiatives (GA4GH, ELIXIR, 1+ Million Genomes Framework, GDI), demonstrating that a network for transnational discovery of and access to human data is possible. By providing a solution to emerging challenges around secure and efficient management of human omics and associated data, the FEGA Network fosters data reuse, enables reproducibility, and accelerates biomedical research. For general enquiries or to request more information about Federated EGA, please send an email to fega-info [at] lists [dot] ega-archive [dot] org. Read more about the Federated EGA in our commentary in Nature Genetics! Learn more about setting up a Federated EGA Node in our onboarding documentation. Map of FEGA status across the world Current status of the FEGA Network Node Description Joined CEGA Central EGA is managed by the Centre for Genomic Regulation (CRG) and EMBL-EBI. The EGA has been a trusted repository for sensitive data since 2008. CEGA coordinates the FEGA network, hosts central services and the EGA catalogue. 2022 Finland FEGA Finland is hosted by CSC - IT Center for Science Ltd., which is the national ELIXIR Node in Finland. CSC is a state-owned company specialising in providing high-quality IT infrastructure and services for Finnish higher education and research. 2022 Germany The German Human Genome-Phenome archive (GHGA) is part of the German National Research Data Infrastructure (NFDI). It is coordinated by the German Cancer Research Center (DKFZ) and includes over 20 academic partners. 2022 Norway FEGA Norway is a service by ELIXIR Norway, the Norwegian node of the European organisation ELIXIR. University of Oslo as a partner institution in ELIXIR Norway is hosting the service relying on the TSD infrastructure for sensitive data. ELIXIR Norway is a consortium of 5 Norwegian universities and is coordinated by University of Bergen. 2022 Spain FEGA Spain is co-hosted by the Barcelona Supercomputer Center (BSC) and the Centre for Genomic Regulation (CRG), both part of ELIXIR Spain and the Spanish National Infrastructure for Personalised Medicine associated with Science and Technology (IMPaCT). 2022 Sweden FEGA Sweden is hosted by the National Bioinformatics Infrastructure Sweden (NBIS), which legally is part of Uppsala University. NBIS forms the bioinformatics platform at SciLifeLab and constitutes the Swedish node of the European organisation ELIXIR. 2022 Poland FEGA Poland is hosted by Biobank Lodz, which is part of University of Lodz. Biobank Lodz is an element of the infrastructure of the Regional Digital Medicine Centers established by the Medical Research Agency. 2022 Portugal FEGA Portugal is managed by BioData.pt, the distributed infrastructure for Life and Health data for Portugal. This entity is a non-profit association of 15 life sciences R&I organisations spread across the country, and the home of ELIXIR Node in Portugal. 2023 Canada Canadian Genome-phenome Archive is part of the Pan Canadian Genome Library, with the mandate to allow the secure and responsible sharing and analysis of Canadian genomic information. 2024 Switzerland Swiss FEGA is hosted by SIB Swiss Institute of Bioinformatics, which is the national ELIXIR Node in Switzerland, on behalf of the Swiss FEGA Partnership which includes 4 other key partners (ETH Zurich, Health 2030 Genome Center, Swiss Data Science Center and Switch). 2025 Documentation Title Version Description Mission, vision, values Federated EGA Mission, vision, values 1.0 The official Mission-Vision-Values statement for FEGA, produced and formally approved by the FEGA Strategic Committee in September 2025. Structure and Organisation Federated EGA Structure and Organisation 2.0 The structure of the FEGA network and service expectations, responsibilities and commitments. FEGA is organised into three tiers: Central EGA, FEGA Nodes and FEGA Affiliates. Strategic Committee Federated EGA Strategic Committee 1.1 Terms of reference for the FEGA Strategic Committee describe the purpose and objectives of the committee, which is to provide direction and strategic planning for the FEGA network. The committee receives input from, and provides feedback for the EGA Strategic Committee. Operations Committee Federated EGA Operations Committee 1.1 Terms of reference for the FEGA Operations Committee describe the purpose and objectives of the committee, which is to review operational performance and coordinate technical roadmaps of FEGA Nodes and Central EGA. The committee receives advice from, and provides operational reporting to the FEGA Strategic Committee. Guidelines Node Operations guidelines 2.1 An overview of the operational areas which require resources in order to create a FEGA Node. The document is based on more than 15 years' experience of EMBL-EBI and CRG operating the EGA and initial expericences of the inaugural FEGA Nodes. The operational areas of responsability are in line with the FEGA Maturity Model, which guides establishment and operation of FEGA Nodes. Collaboration Federated EGA Node Collaboration Agreement 1.3 Nodes are welcome to make a copy of this current version of the CA to start its review with their legal teams and understand the responsibilities of joining FEGA. Nevertheless, this version (the one with a watermark) shall not be signed: the official version needs to be obtained from FEGA prior signing through its official channels.

  Documentation about/projects-and-funders/federated-ega

• National Cancer Institute (NCI) TARGET: Therapeutically Applicable Research to Generate Effective Treatments

  The Therapeutically Applicable Research to Generate Effective Treatments (TARGET) Initiative seeks to accelerate research in novel marker and drug development, along with understanding the molecular basis of pediatric malignancy, through identification of genomic changes associated with the following childhood cancers: Acute Lymphoblastic Leukemia (ALL) - A fast-growing type of blood cancer in which too many immature white blood cells are found in the blood and bone marrow. Acute Myeloid Leukemia (AML), including AML Induction Failure cases (AML-IF) - Another type of blood cancer marked by too many myeloblasts, an alternate type of immature white blood cell, are found in the blood and bone marrow. Upon diagnosis, AML patients without high-risk genetic markers undergo standard chemotherapy regimen, called the primary induction phase, to eliminate most cancer cells and induce a remission state. Clear Cell Sarcoma of the Kidney (CCSK) - A rare type of kidney cancer, in which the inside of the cells looks clear when viewed under a microscope. Clear cell sarcoma can spread from the kidney to other organs, most commonly the bone, but also including the lungs, brain, and soft tissues of the body. Neuroblastoma (NBL) - Cancer of cells of the sympathetic nervous system. Cancer Model Systems (MDLS), including PPTP - MDLS includes acute leukemia cell lines and xenografts with matched primary and/or relapsed tumor information, NBL cell lines and xenografts with matched controls, kidney tumor cell lines, normal brain tissues, and embryonic stem cell lines that were sequenced for some TARGET disease projects. Osteosarcoma (OS) - A cancer of the bone that primarily affects children and adolescents. Rhabdoid Tumor (RT) - A rare, fast-growing cancer that usually forms in the kidney or central nervous system (the brain and spinal cord) but can also form in soft tissues in other areas of the body. Rhabdoid tumors tend to spread quickly, are hard to treat, and have a poor prognosis. Wilms' Tumor (WT) - A cancer of cells in the kidney that can spread to the liver, lung and lymph nodes. More information can be found about each TARGET subproject by following the links on the top of this page. Together these cancers account for the majority of the more than 10,000 childhood cancer cases diagnosed in the United States each year. TARGET has employed a set of advanced and complementary genome analysis technologies, including large scale 2nd and 3rd generation genome sequencing, to strategically characterize alterations in both gene expression and in genomic structure (such as deletions and amplification) that are involved in childhood cancers. The goal of this coordinated effort has been to create a comprehensive genomic and transcriptomic profile of each cancer. Integrated analysis of the TARGET data has identified those genes that are either altered in their expression level or mapped to the chromosome regions of deletion/amplification/translocation, as these genes represent strong candidates for therapeutic targeting. To learn more about the TARGET project, which is now completed, visit the website at https://www.cancer.gov/ccg/research/genome-sequencing/target. TARGET data is accessible at the NCI's Genomic Data Commons (GDC) via the GDC's Data Portal and from each of the TARGET Publication Pages at the GDC (Please see the "Supplemental Links" section of any TARGET publication's "Publication Information and Associated Data Files page" at the GDC.). Available datasets include raw sequencing data, datasets generated by the original TARGET research teams (fully annotated clinical information and higher-level/analyzed molecular characterization data - gene expression, copy number variation, epigenetics, targeted sequencing), as well as higher level data generated by the GDC. Please see the TARGET Use and Publication Guidelines for updated details on the sharing of any TARGET substudy data, including how to cite TARGET.

  Study phs000218

• The Finland-United States Investigation of NIDDM Genetics (FUSION) Tissue Biopsy Study

  The Finland-United States Investigation of NIDDM Genetics (FUSION) study is a long-term effort to identify genetic variants that predispose to type 2 diabetes (T2D) or that impact the variability of T2D-related quantitative traits (QTs). Most of the variants associated with T2D and related traits (glucose and insulin, anthropometrics, lipids) through genome-wide association studies (GWAS) occur in non-coding regions, suggesting a strong regulatory component to disease susceptibility. Regulatory element activity is often tissue-specific, which further complicates discovery of the causal/functional variation. Therefore, there is a critical need to identify the appropriate cell type, regulatory elements, target genes, and causal variants(s) in T2D-relevant tissues. We hypothesize that a subset of T2D and related variants alter gene expression regulation in skeletal muscle and adipose tissue - two major insulin target tissues and play key roles in insulin resistance. To that end, our study contains a comprehensive survey of genomics, epigenomics and transcriptomics in skeletal muscle and adipose tissue from individuals with glucose tolerance categories ranging from normal to T2D.For this FUSION Tissue Biopsy Study, we obtained RNA-Seq, microRNA (miRNA)-Seq, and DNA methylation (methyl)-Seq data on biopsy samples from 331 individuals from across the range of glucose tolerance: 124 normal glucose tolerance (NGT), 77 impaired glucose tolerance (IGT), 44 impaired fasting glucose (IFG), and 86 newly-diagnosed T2Ds. Participants completed two study visits, two weeks apart. First visits comprised most of the clinical phenotyping, including four-point OGTT (fasting, and 30, 60, and 120 minute post-load); BMI, WHR; lipids; blood pressure; and many other variables. Participants also completed FUSION health history, medication, and lifestyle questionnaires. On the second visit, we obtained ~250mg vastus lateralis skeletal muscle, ~750mg abdominal subcutaneous adipose, and a ~5x15mm section of abdominal skin. Visits were completed in March 2013. RNA isolation is ongoing in the Collins laboratory at the NIH, RNA and miRNA sequencing at the NIH Intramural Sequencing Center (NISC), and genotyping at the Center for Inherited Disease Research (CIDR). Individual-level data is available here for the 306 individuals who consented to data deposit. To focus on evaluation of gene expression and its regulation in skeletal muscle, we analyzed mRNA extracted from vastus lateralis skeletal muscle obtained from 271 of the 331 individual subjects from Finland, along with genome-wide genotypes. Individual-level data is available here for the 250 subjects who consented to the use of their data.Release phs001048.v2.p1 adds muscle data for an additional 42 subjects and data from adipose tissue for 276 subjects. Total RNA was isolated using Trizol extraction in the Collins laboratory at the NIH. The mRNA was poly-A selected, 24-plex libraries were generated using the Illumina TruSeq directional mRNA-seq library protocol and RNA sequencing was performed on HiSeq2000 sequencers using 101bp paired-end reads at NISC. miRNA libraries were prepared from total RNA from 296 muscle and 270 adipose samples, pooled and sequenced 50bp single-end reads on Illumina HiSeq2500. Data for 272 muscle and 251 adipose samples are available here for individuals with consent for data deposit. DNA was extracted from blood in the Collins laboratory, and genotyping on the Illumina Omni2.5M array was performed at CIDR. Genotypes were imputed using the HRC 2016 reference panel. In order to assess regions of open chromatin in skeletal muscle, we obtained muscle tissue from a commercial provider to perform ATAC-seq; these samples were sequenced at the University of Michigan DNA Sequencing Core.Release phs001048.v3.p1 adds single-nucleus (sn) RNA-seq and ATAC-seq data in 287 skeletal muscle samples out of the original 331 individuals. Individual-level data is available here for the 265 subjects who consented to the use of their data. The frozen tissue biopsy samples were processed in ten batches, each consisting of 40-41 samples. These batches were organized using a randomized block design to protect against experimental contrasts of interest including cohort, age, sex, BMI among others. Samples in each batch were pulverized, pooled together followed by nuclei isolation. The nuclei were processed on the 10X Genomics Chromium platform separately for snATAC-seq and snRNA-seq (v. 3.1 chemistry for snRNA-seq).Release phs001048.v4.p1 adds additional phenotypes and provides some corrections to several previous phenotypes.

  Study phs001048

• eMERGE Network Imputed GWAS for 41 Phenotypes

  The electronic Medical Records and Genomics (eMERGE) Network is a consortium of ten participating sites (Cincinnati Children's Hospital Medical Center/Boston Children's Hospital, Children's Hospital of Philadelphia, Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University, Geisinger Clinic, Group Health Cooperative/University of Washington, Mayo Clinic, Icahn School of Medicine at Mount Sinai, Northwestern University, Vanderbilt University Medical Center) funded by the NHGRI to investigate the use of electronic medical record (EMR) systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 55,000 individuals using EMR-derived phenotypes and DNA from linked Biorepositories. Using electronic phenotyping methods, the consortium used DNA samples from all participating sites to explore the genetic determinants of over forty phenotypes, including 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, as well as using the phenome-wide association study (PheWAS) paradigm to replicate and discover relationships between targeted genotypes with multiple phenotypes. Sites and participants include: 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 medical records (EMRs). EMRs 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) and Boston Children's Hospital (BCH) are pediatric institutions dedicated to improving health and welfare of children and to the shared purpose of discovery and practical application of new genomic information to the ordinary care of children. The CCHMC/BCH site has been built on a five-year history of collaboration, particularly in patient electronic record (ERM)-related informatics, the basis of much of eMERGE II. CCHMC and BCH together bring an extraordinary faculty to eMERGE II who are committed to diseases that afflict children, specifically phenotypes that focus upon diseases of children in ways that will leverage the available eMERGE adult GWAS and EMRs to discover meaningful use results. CCHMC/BCH plans to demonstrate real-time execution of phenotypic selection across their two distinct pediatric institutions as a model for ensuring phenotypic standardization and for national scalability. They will also look carefully at parents' responses to results and use of their children's research results and better understand the factors that influence their decisions about learning incidental findings. In addition to patient and parent perceptions CCHMC/BCH will also explore clinician perceptions of pharmacogenetic research results after EMR integration. 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. Group Health(GH)/University of Washington (UW): GH participants for the PGx project 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~6300.) Participants were eligible if aged 50 - 65 years old at the time of their enrollment into the NWIGM repository, living, enrolled in GH'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 Group Health: 1. Demographics - participants with self-reported race as Asian or African ancestry were prioritized and selected to enrich for non-European ancestry; 2. Diagnosis and procedure codes - participants were selected if found to have a history of hypertension, atrial fibrillation (AF), or congestive heart failure (CHF). Participants with a history of arrhythmia were added if the entire selection algorithm did not generate 900 individuals. We also enriched for participants with EHR evidence of actionable indications related to PGRNSeq genes. Participants were selected if found to have an ICD9 code for malignant hyperthermia, hypertension, atrial fibrillation, congestive heart failure or long QT syndrome (LQTS); 3. Laboratory values - if participants had any laboratory event of creatine kinase (CK) >1000, and were dispensed statins within 6 months of the event, then they were selected; and 4. Medications - participants were excluded if ever on carbamazepine or had a current regimen of warfarin. 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 biobank 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. 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: BioVU, Vanderbilt's DNA databank, is an enabling resource for exploration of the relationships among genetic variation, disease susceptibility, and variable drug responses, and represents a key first step in moving the emerging sciences of genomics and pharmacogenomics from research tools to clinical practice. 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.

  Study phs000888

• Women's Health Initiative Clinical Trial and Observational Study

  The Women's Health Initiative (WHI) is a long-term national health study that has focused on strategies for preventing heart disease, breast and colorectal cancer, and osteoporotic fractures in postmenopausal women. The original WHI study included 161,808 postmenopausal women enrolled between 1993 and 1998. The Fred Hutchinson Cancer Research Center in Seattle, WA serves as the WHI Clinical Coordinating Center for data collection, management, and analysis of the WHI. The WHI has two major parts: a partial factorial randomized Clinical Trial (CT) and an Observational Study (OS); both were conducted at 40 Clinical Centers nationwide. The CT enrolled 68,132 postmenopausal women between the ages of 50-79 into trials testing three prevention strategies. If eligible, women could choose to enroll in one, two, or all three of the trial components. The components are: Hormone Therapy Trials (HT): This double-blind component examined the effects of combined hormones or estrogen alone on the prevention of coronary heart disease and osteoporotic fractures, and associated risk for breast cancer. Women participating in this component with an intact uterus were randomized to estrogen plus progestin (conjugated equine estrogens [CEE], 0.625 mg/d plus medroxyprogesterone acetate [MPA] 2.5 mg/d] or a matching placebo. Women with prior hysterectomy were randomized to CEE or placebo. Both trials were stopped early, in July 2002 and March 2004, respectively, based on adverse effects. All HT participants continued to be followed without intervention until close-out. Dietary Modification Trial (DM): The Dietary Modification component evaluated the effect of a low-fat and high fruit, vegetable and grain diet on the prevention of breast and colorectal cancers and coronary heart disease. Study participants were randomized to either their usual eating pattern or a low-fat dietary pattern. Calcium/Vitamin D Trial (CaD): This double-blind component began 1 to 2 years after a woman joined one or both of the other clinical trial components. It evaluated the effect of calcium and vitamin D supplementation on the prevention of osteoporotic fractures and colorectal cancer. Women in this component were randomized to calcium (1000 mg/d) and vitamin D (400 IU/d) supplements or a matching placebo. The Observational Study (OS) examines the relationship between lifestyle, environmental, medical and molecular risk factors and specific measures of health or disease outcomes. This component involves tracking the medical history and health habits of 93,676 women not participating in the CT. Recruitment for the observational study was completed in 1998 and participants were followed annually for 8 to 12 years. Extension Studies: The original protocol allowed for follow-up until March 2005, after which participants were invited to enroll in the first WHI Extension Study for follow-up through 2010. Participants were invited again to participate in the second WHI Extension Study with continued follow up from 2010 to at least 2015. As of March 31, 2011 there were 93,122 women enrolled in the second extension. In Extension Study 2, the overall WHI study population was divided into two new subsamples, the Medical Records Cohort (MRC) and the Self-Report Cohort (SRC). The MRC consists of all former hormone trial participants and all African American and Hispanic participants from all study components. The SRC consists of the remaining participants. The extent of outcome information collected differs between the two cohorts, with more extensive outcomes information collection on the MRC. As part of Extension Study 2, selected older WHI participants were invited to participate in an In Person Visit (a.k.a., Long Life Study) at their homes during which additional blood samples were collected and various measurements were taken (such as blood pressure, height, weight, waist circumference, grip strength, etc.). In October 2015, Extension Study 2 was renewed with continued follow-up planned through October 2020, pending annual contract review and renewal. Additional Information: The WHI website, https://www.whi.org/about/SitePages/About%20WHI.aspx has much more information about the study. For WHI data collection forms used over the years, please see https://www.whi.org/researchers/studydoc/SitePages/Forms.aspx. For additional dataset documentation, see https://www.whi.org/researchers/data/Pages/Available%20Data.aspx. For data preparation and use, please refer to 'WHI dbGaP Cohort Data Release Data Preparation Guide May 2018' for additional details about the WHI data. The WHI Cohort is utilized in the following dbGaP substudies. To view genotypes, analysis, expression data, other molecular data, and derived variables collected in these substudies, please click on the following substudies below or in the "Substudies" box located on the right hand side of this top-level study page phs000200 WHI Cohort. phs000386 WHI SHARe phs000281 GO-ESP WHISP phs000315 WHI GARNET phs000503 WHISE phs000227 PAGE WHI phs000675 WHIMS+ phs000746 WHI Harmonized and Imputed GWAS phs001334 WHI Metabolomics of CHD phs001335 WHI BA23 phs001614 WHI LLS Phase III GWAS

  Study phs000200

• Cardiovascular Cell Therapy Research Network (CCTRN): A Phase II, RCT of Mesenchymal Stem Cells & Cardiac Stem Cells in Subjects With Ischemic HF (CONCERT HF-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. ObjectivesTo assess the feasibility, safety, and efficacy of autologous mesenchymal stromal cells (MSCs) and c-kit positive cardiac cells (CPCs), alone or in combination, in participants with ischemic heart failure.Background The prognosis of heart failure (HF) caused by chronic ischemic cardiomyopathy (coronary artery disease and prior myocardial infarction), hereby referred to as “ischemic HF”, remains poor. Many studies have explored the use of various types of stem or progenitor cells in participants with chronic ischemic HF, with encouraging results. Several clinical trials have suggested that MSCs and CPCs are both safe and beneficial in participants with ischemic HF. At the time of this study, no information was available on the efficacy of MSCs and CPCs in humans, however, both cell types have been shown to attenuate left ventricle (LV) dysfunction in animal models. Preclinical models indicate that combining MSCs and CPCs increases the therapeutic effects, but this had not yet been tested in humans. The CONCERT-HF study was initiated to assess whether autologous MSCs and CPCs, alone or in combination, can be manufactured and delivered to participants with ischemic HF; are well-tolerated; and improve LV function, quality of life, and functional capacity, and/or reduce scar size. Participants A total of 125 participants were randomized with 33 participants randomized to the MSCs and CPCs group, 29 participants to the MSCs alone group, 31 participants to the CPCs alone group, and 32 participants to the placebo group. Design The CONCERT-HF study was a multi-center Phase II, double-blind, randomized, placebo-controlled trial designed to evaluate the feasibility, safety, and efficacy of MSCs alone, CPCs alone, and their combination compared with placebo as well as each other in patients with ischemic HF. In Stage 1 (open label, lead-in study) participants were randomized 1:1 to either a standard-of-care control group (i.e., they did not undergo harvest, mapping, or injection procedures) or combination therapy (MSCs + CPCs, as described for Stage 2) to complete safety assessments. Once approval was granted for Stage 2, participants were randomized (1:1:1:1) to one of four treatments: placebo, autologous MSCs (target dose, 150 × 106 cells), autologous CPCs (target dose, 5 × 106 cells), or a combination of autologous MSCs and CPCs. At the harvest visit, right ventricular endocardial biopsy (EMB) was performed in participants randomized to receive CPCs alone or a combination of MSCs and CPCs. Participants randomized to receive MSCs alone or placebo had a sham procedure (right heart catheterization without EMB). All participants underwent a bone marrow aspiration and approximately 14 weeks later had transendocardial, electromechanically-guided injections of study product. Visits occurred at 1 day, 1 week, and 1, 3, 6, and 12 months after study product injection. A telephone contact took place at 24 months to assess the participant's current medications, as well as morbidity and mortality. Study endpoints included measures of safety, feasibility, and efficacy. Safety outcomes included all adverse events grade 2 and higher, including major adverse cardiac events (MACE) related to HF (death, hospitalization for worsening HF, and HF exacerbation not requiring hospitalization). Efficacy endpoints included quality of life, MRI measures of LV function and structure, functional capacity, and N-terminal pro-brain natriuretic peptide (NT-proBNP) levels. Conclusions In patients with ischemic HF, autologous MSCs and CPCs, alone or in combination, are safe and feasible; CPCs were associated with a reduction in the incidence of MACE related to HF compared to placebo; MSCs, either alone or in combination with CPCs, were associated with improved quality of life; these seemingly beneficial effects of CPCs and MSCs on clinical outcome were not associated with changes in LV function or structure; and combination therapy with MSCs and CPCs was associated with the best clinical outcomes with respect to both MACE related to HF and quality of life.

  Study phs004055

• 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

• Privacy Notice for Data Access Committee Account

  Privacy Notice for EGA Data Access Committee Account 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. Note that this service collects personal data directly provided by the user, and also collects personal data from users that is provided by other organisations. 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 , orpost at EMBL-EBI, Wellcome Genome Campus, CB10 1SD Hinxton, Cambridgeshire, UK. EMBL’s Data Protection Officer may be contacted by: email at dpo@embl.org, orpost 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, orpost 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.eupost 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? We process your personal data on the grounds of important public interest. 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: NameEmail addressTitle/PositionOrganisationOrganisational affiliationBusiness addressTelephone numberIP addressesDate and time of a visit to the service websiteOperating systemAmount of data transmittedBrowserUsernamePassword The data controller will use your personal data for the following purposes: To provide DAC user account and authenticated access to the service,To publicly publish some information to facilitate scientific research,To better understand the needs of the data subjects and guide future improvements of the service,To create anonymous usage statistics (from number of DACs, datasets per DAC). 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 (for all user account data),The general public via Internet will get access to your name, email address, business address, telephone number and organisation you belong to. 5. Will your personal data be transferred to third countries (i.e. countries not part of EU/EEA) and/or international organisations? Data categories ‘name`, `email address`, `telephone number`, `business address`; `organisation’ of the DAC user are published on the Internet. They thus become accessible to recipients in countries outside the European Economic Area. Insofar as the second joint controller may be subject to GDPR, data transfer to and from the first joint controller (EMBL-EBI), is necessary for important reasons of public interest embedded in the aims of EMBL and justified in the Article 9(4) of IP 68 (equivalent to Article 49(1)(d) of GDPR) read in conjunction with EMBL`s 1973 establishing agreement and Article 179(2) of the Treaty on the Functioning of the European Union 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. 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. Published at: February 6, 2019

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

• Unrelated Donor Reduced Intensity Bone Marrow Transplant for Children with Severe Sickle Cell Disease (BMT CTN-0601-BioLINCC)

  ObjectivesThe primary objective is to determine event-free survival (EFS) at 1 year after unrelated donor (URD) hematopoietic stem cell transplantation (HCT) using bone marrow (BM) in patients with sickle cell disease (SCD).BackgroundSickle cell disease (SCD), also known as sickle cell anemia, is an inherited blood disease that can cause organ damage, stroke, and intense pain episodes. Children with sickle cell disease experience organ damage, impaired quality of life, and premature mortality. A blood stem cell transplant is a treatment option for someone with a severe form of the disease. Prior to undergoing a transplant, people typically receive a conditioning regimen of high doses of chemotherapy and other medications to prepare the body to accept the transplant. This type of conditioning regimen is known as a myeloablative conditioning regimen, but it can result in toxicities and sterility. A conditioning regimen that uses lower doses of chemotherapy and medications may be safer for transplant recipients. This type of regimen is known as reduced intensity conditioning (RIC) regimen. RIC has a more favorable toxicity profile but is associated with higher rates of graft rejection (GR), especially with graft sources such as umbilical cord blood This study evaluated the safety and effectiveness of blood stem cell transplants, using bone marrow from unrelated donors, in children with severe SCD who receive a RIC regimen prior to the transplant.SubjectsPatients 3.0-19.75 years old with symptomatic SCD AND one or more of the following complications: (1)-(i) a clinically significant neurologic event (stroke) or any neurologic defect lasting > 24 hours and accompanied by an infarct on cerebral magnetic resonance imaging (MRI); OR, (ii) patients who have a Transcranial Doppler (TCD) velocity that exceeds 200 cm/sec by the non-imaging technique (or TCD measurement of >185 cm/sec by the imaging technique) measured at a minimum of 2 separate occasions one month or more apart; OR, (2) Minimum of two episodes of acute chest syndrome within the preceding 2-year period defined as new pulmonary alveolar consolidation involving at least one complete lung segment (associated with acute symptoms including fever, chest pain, tachypnea, wheezing, rales, or cough that is not attributed to asthma or bronchiolitis) despite adequate supportive care measures; OR, (3) History of 3 or more severe pain events (defined as new onset of pain that lasts for at least 2 hours for which there is no other explanation) per year in the 2 years prior to enrollment despite adequate supportive care measures (if patients are receiving hydroxyurea and compliant with therapy, being symptomatic is an indication for transplantation; however, if patients decline hydroxyurea or non-compliant with this therapy, they would still remain eligible for study if pain criteria as described above are met). Lansky/Karnofsky performance score must be ≥ 40. Hemoglobin S must be ≤ 45%. Patients must have an unrelated adult bone marrow donor who is HLA-matched at 8 of 8 HLA-A, -B, -C and -DRB1 at high resolution using DNA-based typing. Patients with bridging fibrosis or cirrhosis of the liver, with uncontrolled bacterial, viral, or fungal infection in the past month, or seropositivity for HIV are excluded. Patients with HLA-matched family donors, or who have received prior HCT, and females who are pregnant or breast feeding are excluded. Thirty patients were enrolled on this study and of these, 29 patients met the criteria and proceeded to the study transplant.DesignParticipants attended a study visit prior to the transplant to undergo a blood collection, neurocognitive testing to measure learning and brain function, magnetic resonance angiogram (MRA) and magnetic resonance imaging (MRI) scans. Questionnaires to assess quality of life were also completed. All patients received erythrocyte transfusions before transplant. Twenty-two days (-22) before the transplant, participants began receiving a reduced intensity conditioning regimen of chemotherapy and medications. On days -21, -20, and -19 participants weighing 10 kg or more received 10 mg, 15 mg, and then 20 mg of Alemtuzumab intravenously (IV) followed by 30 mg/m2/day IV on days -8 through -4 of Fludarabine. Eight days (-8) before the transplant, participants were admitted to the hospital to continue the conditioning regimen which included 140 mg/m2 IV of Melphalan on day -3. Participants received the bone marrow transplant on day 0. Prophylaxis for GVHD consisted of a calcineurin inhibitor (tacrolimus or cyclosporine) administered from day -3 through day 100 after graft infusion, with subsequent taper through day 180; methotrexate 7.5 mg/m2 on days 1, 3, and 6; and methylprednisolone 1 mg/kg per day from days 7 through 28, with subsequent taper by 20% per week. One week after the transplant continuing until the WBC is normal, participants received granulocyte-colony-stimulating factor (G-CSF). After leaving the hospital, participants attended study visits weekly during weeks 1 to 8, at day 60, weekly during weeks 9 to 14, at Day 100, at month 6, and at years 1 and 2. At all study visits, a blood collection, medical history review, and physical exam occurred. In addition, at day 100, month 6, and years 1 and 2, questionnaires to assess quality of life were completed. At select visits the following procedures were conducted: lung function testing, heart function testing, MRA and MRI scans, and neurocognitive testing.The primary outcome was 1-year EFS. Death, disease recurrence or graft rejection by 1 year were considered events for this endpoint.ConclusionsThe trial met its prespecified 1-year EFS, and significantly improved HRQL was reported posttransplant. However, although the Reduced-intensity conditioning (RIC) provided successful engraftment in most patients, the regimen cannot be considered safe for widespread adoption without modification due to the regimen-related toxicity (RRT) and high rate of chronic GVHD, which was the predominant cause of mortality.

  Study phs003470

• How to encrypt files with EGACryptor

  File preparation Due to the processes used at the EGA for file archival the use of non-alphanumeric characters in a filename will cause issues in archival. By convention whitespaces in filenames are to be avoided and should be replaced with the underscore character (_). Before encrypting your files please make sure that any files that will be uploaded to EGA do not use special characters such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | &  Crypt4gh EGACryptor Files encrypted with EGACryptor must be uploaded via FTP EGACryptor The EGACryptor v.2.0.0 is a JAVA-based application which enables submitters to produce EGA compliant encrypted files along with files for the encrypted and unencrypted md5sum for each file to be submitted. The application will generate an output folder that will by default mirror the directory structure containing the original files. This output folder can subsequently be uploaded to the EGA FTP staging area via an FTP or Aspera client. Download EgaCryptor Download EgaCryptor Using the EgaCryptor Using the EgaCryptor Encrypting single file Encrypting multiple files Encrypting all files in folder Points to note Troubleshooting Troubleshooting Download EGACryptor The required jar files can be obtained by downloading EgaCrytptor jar file After the file has been downloaded, extraction of the zipped archive is required. The EGACryptor has been built to work with Java Runtime Environments from version 6 and above and with the OpenJDK Environment. Please refer to the relevant resources for installation guidance. Installing the latest version of the OpenJDK will include the JCE files. If your installation of Java JRE is less than 1.8.0_151 will require the manual installation of the JCE Policy Files. You can verify the version of the Java SE Runtime Environment (JRE) installed by using the command: $ java -version If you need to install the JCE please follow the instructions below: Installing the JCE policy files (due to licensing terms and conditions the required policy files must be downloaded direct from the ORACLE website) : Download the unlimited strength JCE policy files (JRE 6 / JRE 7/ JRE 8) Uncompress and extract the downloaded file. This will create a subdirectory called JCE. This directory contains the following files: README.txt, COPYRIGHT.html, local_policy.jar and US_export_policy.jar Install the two policy JAR files by replacing the existing ones in your java home directory. Install the two policy JAR files by replacing the existing ones in your directory. The standard place for JCE jurisdiction policy JAR files is: /lib/security [Unix] or \lib\security [Win32] Notes: refers to the directory where the Java SE Runtime Environment (JRE) was installed. Additional performance enhancements that have been included in the EGACryptor V2.0.0: The ability to parallelise the processing of datasets through the use of the resources on a system. Multicore systems will allow the user to specify n-1 cores for an n-core system. The use of this feature on clusters may speed up the processing of datasets that have large file numbers but consult your local cluster guide to ensure that there are not monopolising resources that are needed by other system users. The default for this process remains single threaded. 3 levels of system usage can be specified. Full usage within the limits detailed above. A limited mode that will ensure that 50% of the system resources are available for other tasks. Maximum mode is limited to 75% of system resources, this allows encryption to be prioritised but allows for the system to be usable for light alternate tasks. Finally there is a throttling mode that allows you to specify the exact number of computational threads to be used. the EGACryptor is able to ingest a structured directory and will output a directory with the same structure containing the encrypted files along with the md5checksums for the plain and encrypted files. The entire output directory can then be uploaded to the EGA for archival. as with the input path, it is now possible to specify the output path. the options have been updated inline with the upgraded functionality. The tool can only be used via the command line. The EGACryptor is designed to perform a single task, encrypting your data, for upload of these files please refer to our uploading guide Using the EgaCryptor Below are the three ways on how the EGACryptor tool can be used: Encrypting a single file : java -jar ../EGA-Cryptor_2_0_0.jar -i example1.bam Encrypting multiple files : java -jar ../EGA-Cryptor_2_0_0.jar -i "example1.bam,example2.bam" Encrypting all the files within a folder java -jar ../EGA-Cryptor_2_0_0.jar -i path/to/target/directory By default the EGACryptor v2 will create a new output directory containing all encrypted files and the relevant checksums within the target directory. If a specific directory is desired this can be specified by using the -o flag. This can be achieved in a similar manner to the following example: java -jar ../EGA-Cryptor_2_0_0.jar -i /path/to/target/directory -o /path/to/output/directory The tool will output three files per input file: file.gpg ( encrypted file ) file.md5( file md5 sum value file ) file.gpg.md5 ( encrypted file md5 sum value file ) All output files must then be uploaded to your submission account using Aspera or FTP. Further documentation on how to upload files: FTP and Aspera. Points to note Remember to provide the path to EgaCryptor.jar and run the command from within the directory the file/s are located. ECryptor writes files to the source directory of your local file system, as a result you must have write-access permissions for the source directory. Troubleshooting If in doubt about the function of the EGACryptor it is recommended to first consult the built-in documentation. This can be accessessed by using the -h flag as stated in the following table. Built-in Commands Table: list of the command line options built into EGA-Cryptor v2.0.0. Command line Option Action --------------------- ----------- -f Set this option to allow application to create maximum threads to utilise full capacity of cores/processors available on machine -h Use this option to view the bult-in help menu * -i File(s) to encrypt. Provide file/folder path or comma separated file path if multiple files in double quotes -l Set this option to allow application to create maximum threads equals to 50% capacity of cores/processors available on machine -m Set this option to allow application to create maximum threads equals to 75% capacity of cores/processors available on machine -o Path of the output file. This is optional. If not provided then output files will be generated in the same path as that of source file (default: output- files) -t Set this option if user wants to control application to create maximum threads as specified. Application will calculate no. of cores/processors available on machine & will create threads accordingly Encryption Errors UnixFileSystem.createFileExclusively (Native Method) The error is thrown by UNIX ("UnixFileSystem.createFileExclusively(Native Method)"). It appears that the user does not have write-access to the file system where the file to be uploaded is located. EGACryptor always writes MD5 checksums into files before uploading them to the server, and these files are created in the same location where the uploaded file itself resides.Solution: address your directory permission issue and re-run the command. Install JCE Unlimited Strength Jurisdiction Policy files The JCE policy unlimited strength jurisdiction files should be installed according to your current java version If you are still facing difficulty with the EGACryptor v.2 after having consulted the documentation please contact the EGA Helpdesk.

  Documentation submission/data/file-preparation/egacryptor

• National Cancer Institute Cancer Genome Characterization Initiative (CGCI)

  The Office of Cancer Genomics at the National Cancer Institute sponsored a series of studies as part of the Cancer Genome Characterization Initiative (CGCI) to assess novel emerging sequencing technologies in cancer. The CGCI program included comprehensive characterization of the genetic aberrations found in different pediatric and/or adult tumors. CGCI characterized a number of B-cell non-Hodgkin lymphomas (including diffuse large B-cell lymphoma (DLBCL) from patients with and without HIV+ infection, follicular lymphoma (FL), as well as adult and pediatric Burkitt lymphomas), and additional HIV-associated tumors (including DLBCL, lung, and cervical cancers). All data from these projects is available at the NCI Genomic Data Commons. Individual project descriptions are available by disease on the substudy pages (links are included at the top of this page). Brief summaries are as follows: Non-Hodgkin Lymphoma (NHL) - CGCI investigators probed genomic alterations more deeply than had previously been possible by using state-of-the-art RNA sequencing (mRNA-seq) and whole genome shotgun sequencing (WGS) coupled with leading edge bioinformatics, data management and analysis approaches. The project sequenced tumor DNA and/or RNA from 117 NHL tumor samples and 10 cell lines. This included the genomes or exomes of 1 Follicular Lymphoma (FL) and 13 diffuse large B-cell lymphoma (DLBCL) cases, all with matched constitutional DNA sequenced to comparable depths, RNA-sequencing (mRNA-seq) of 92 DLBCL, 12 FL and 8 B-cell NHL cases with other histologies and 10 DLBCL-derived cell lines. The DLBCL cases and cell-lines are from the two major subtypes of DLBCL: germinal center B-cell (GCB) and activated B-cell (ABC). HIV+ Tumor Molecular Characterization Project (HTMCP) - This project was a joint effort of the Office of Cancer Genomics (OCG) and the Office of HIV and AIDS Malignancy (OHAM). Its goals were to characterize HIV-associated cancers (obtained from HIV-infected patients) and compare them to the same types of cancers from patients without HIV infection. Investigators performed 30X genome sequencing of 100 cases of paired tumor and germline DNA, along with transcriptome sequencing in each of 3 types of HIV+ tumors (DLBCL, lung and cervical cancers). These platforms allow discovery of mutations both in coding and non-coding genomic regions, gene expression and genomic alterations (including translocations, insertions and deletions). Comparing tumors of cancer patients both with and without HIV-infection provides insight into the potential function of this virus in certain cancers. Burkitt Lymphoma Genome Sequencing Project (BLGSP) - This project was a collaborative effort between the National Cancer Institute and the Foundation for Burkitt Lymphoma Research to develop a databank of the many alterations found in Burkitt lymphoma (BL), an uncommon type of Non-Hodgkin lymphoma that occurs most often in children and young adults. The goal of the BLGSP was to explore potential genetic changes in patients with BL that could lead to better prevention, detection and treatment of the cancer. The project characterized the alterations of the tumors' genomes (with matched normal as control) and transcriptomes by sequencing the DNA and RNA of each case. Using the data generated, the ultimate goals of the project was to discover the molecular changes that are present in BL patients and then determine how those changes correlate with treatment regimen and outcome. CGCI data is accessible at the NCI's Genomic Data Commons (GDC) via the GDC's Data Portal and from each of the CGCI Publication Pages at the GDC (Please see the "Supplemental Links" section of any CGCI publication's "Publication Information and Associated Data Files page" at the GDC.). Available datasets include raw sequencing data, datasets generated by the original CGCI research teams (fully annotated clinical information and higher-level/analyzed molecular characterization data), as well as higher level data generated by the GDC. Please see the CGCI Use and Publication Guidelines for updated details on the sharing of any CGCI substudy data, including how to cite CGCI.To learn more about the CGCI studies, please visit the CGCI Program website.

  Study phs000235

• The Environmental Determinants of Diabetes in the Young Study (TEDDY)

  Study Overview The Environmental Determinants of Diabetes in the Young (TEDDY) Study is a longitudinal study that investigates genetic and genetic-environmental interactions, including gestational events, childhood infections, dietary exposures, and other environmental factors after birth, in relation to the development of islet autoimmunity and type 1 diabetes (T1D). A consortium of six clinical centers assembled to participate in the development and implementation of the study to identify environmental triggers for the development of islet autoimmunity and T1D in genetically susceptible individuals. Beginning in 2004, the TEDDY study screened over 400,000 newborns for high-risk HLA-DR, DQ genotypes from both the general population and families already affected by T1D. The TEDDY study enrolled around 8,676 participants across six clinical centers worldwide (Finland, Germany, Sweden and three in the United States) in the 15-year prospective follow-up. Participants are followed every three months for islet autoantibody (IA) measurements with blood sampling until four years of age and then at least every six months until the age of 15. After the age of four, autoantibody positive participants continue to be followed at three month intervals and autoantibody negative participants are followed at six-month intervals. In addition to the analysis of autoantibodies, additional data and sample collection are performed at each visit. Parents collect monthly stool samples in early childhood. The parents also fill out questionnaires at regular intervals in connection with study visits and record information about diet and health status in the child's TEDDY Book between visits. Continued long-term follow-up of the currently active TEDDY participants will provide important scientific information on early childhood diet, reported and measured infections, vaccinations, and psychosocial stressors that may contribute to the development of type 1 diabetes and islet autoimmunity. Additional information on the TEDDY study is available in the following articles: Rewers et al., 2008, PMID: 19120261 and Hagopian et al., 2006, PMID: 17130573. Details of the TEDDY protocol can be found in Hagopian et al., 2011, PMID: 21564455. TEDDY data currently available in dbGaP include: gene expression, SNPs, exome, microbiome (gut, nasal, and plasma), RNA sequencing, and whole genome sequencing. For more information on TEDDY Study version history please refer to TEDDY Study dbGaP README File. ImmunoChip SNP DNA from whole blood samples on study participants and their family members (mothers, fathers, and siblings) was obtained and used for SNP genotyping. Genotyping was performed by the Center for Public Health Genomics at the University of Virginia using the Illumina ImmunoChip SNP array, which contains around 196,000 SNPs from 186 regions associated with 12 autoimmune diseases (Hadley et al., 2015, PMID: 26010309). Data cleaning and validation included the removal of subjects with a low call rate (&lt; 5% SNPs missing) and differences in reported sex and prior genotyping at the TEDDY HLA laboratory. Additionally, SNPs with a low call rate or Hardy-Weinberg equilibrium P value &lt; 10-6, except for chromosome 6 due to HLA eligibility requirements, were removed from the final dataset (T&#246;rn et al., 2015, PMID: 25422107).TEDDY-T1DExome ArrayDNA from whole blood samples on study participants and their family members (mothers, fathers, and siblings) was obtained and used for genotyping. Genotyping was performed by the University of Virginia using the Illumina TEDDY-T1DExome array. The TEDDY-T1DExome array is a custom chip that contains 550,601 markers from the Infinium CoreExome-24 v1.1 BeadChip and an additional 90,214 tagSNPs specifically selected by the TEDDY investigators based on their associations with nutrients, vitamins, type 2 diabetes, autoimmune diseases, body-mass index, or other exposures and phenotypes measured by TEDDY study.The Illumina GenTrain2 algorithm was used for genotype calling. Sample quality control metrics included sample call rate, heterozygosity rate and concordance of gender between the information reported and genotyped. Gene Expression The TEDDY study collected peripheral blood for the extraction of total RNA from enrolled children starting at 3 months of age, and then at 3 month intervals up to 48 months and then biannually. Total RNA was extracted using a high throughput (96-well format) extraction protocol using magnetic (MagMax) beads technology at the TEDDY RNA Laboratory, Jinfiniti Biosciences in Augusta, GA. Purified RNA (200 ng) was further used for cRNA amplification and labeling with biotin using Target Amp cDNA synthesis kit (Epicenter catalog no. TAB1R6924). Labeled cRNA was hybridized to the Illumina HumanHT-12 Expression BeadChips based on the manufacturer's instructions. The HumanHT-12 Expression BeadChip provides coverage for more than 47,000 transcripts and known splice variants across the human transcriptome. Microbiome The TEDDY microbiome study aimed to characterize the longitudinal development of the microbiome, including bacteria, viruses and other microorganisms in the gut, plasma, and nasal cavity of prediabetic and diabetic subjects compared to autoantibody negative non-diabetic subjects. Stool samples used were collected monthly from 3 to 48 months, after which stool samples were collected every 3 months. Nasal swab samples were collected every 3 months starting at 9 months of age until 48 months, after which nasal swabs were collected every 6 months. Plasma samples were collected every 3 months starting at 3 months of age until 48 months, after which plasma samples were collected every 6 months. If the subject was autoantibody positive at 48 months then they remained on the 3 month collection interval for nasal swab and plasma samples. Samples underwent 16s rRNA gene sequencing, DNA and viral RNA metagenomics shotgun sequencing, and sequencing of the internal transcribed spacer (ITS) regions. Additional information on the TEDDY microbiome data is available in the following articles: Vatanen et al., 2018, PMID: 30356183, Stewart et al., 2018, PMID: 30356187, and Vehik et al., 2020, PMID: 31792456. RNA Sequencing The TEDDY study aimed to characterize the transcriptome in subjects with islet autoimmunity and type 1 diabetes compared to matched control subjects. Peripheral blood was collected to extract total RNA from enrolled children starting at 3 months of age, and then at 3 month intervals up to 48 months and then biannually. Total RNA was extracted using a high throughput (96-well format) extraction protocol using magnetic (MagMax) beads technology at the TEDDY RNA Laboratory, Jinfiniti Biosciences in Augusta, GA. Purified RNA was then sent to the Broad Institute for the generation of the TEDDY RNA sequencing (RNA-Seq) data. The RNA samples were prepped using Superscript III reverse transcriptase and Illumina's TruSeq Stranded mRNA Sample Prep Kit. The TruSeq libraries were run on the Illumina HiSeq2500 platform. Whole Genome Sequencing The TEDDY study aimed to conduct deep whole genome sequencing and examine the genomic variations in subjects with islet autoimmunity and type 1 diabetes compared to matched autoantibody negative and non-diabetic children. DNA from whole blood was obtained from TEDDY children for whole genome sequencing. The WGS data were generated on the Illumina HiSeq X Ten system.

  Study phs001442

• Women's Health Initiative Clinical Trial and Observational Study - Imaging

  Objectives: The clinical trial assessed the safety and efficacy of three interventions. Specifically, it evaluated (1) the major health benefits and risks of estrogen plus progestin and estrogen alone, (2) the effects of a low-fat eating pattern on risk of colorectal cancer, and (3) the efficacy of calcium with vitamin D supplementation for preventing hip and other fractures. The objective of the memory study was to determine whether estrogen plus progestin therapy protects global cognitive function, and evaluate the therapy's effect on the incidence of dementia and mild cognitive impairment.The observational study is examining the relationship between lifestyle, socioeconomic, health, and other risk factors with cardiovascular, breast cancer, colorectal cancer and osteoporotic fracture outcomes. Secondary objectives include providing more reliable estimates of the extent to which known risk factors predict disease, more precise estimates of new occurrences of disease, and to provide a future resource for the identification of new or novel risk factors especially factors found in blood. Background: The Women's Health Initiative (WHI) is a long-term national health study that has focused on strategies for preventing the major causes of death, disability, and frailty in postmenopausal women, specifically heart disease, cancer, and osteoporotic fractures. The WHI is primarily composed of an observational study (OS), as well a clinical trial (CT) with three components: Hormone Replacement Therapy (HT), Dietary Modification, (DM) and Calcium/Vitamin D supplementation (CaD).Prior to the WHI, observational studies suggested that postmenopausal hormone therapy was associated with a decreased risk of coronary heart disease (CHD). Potential cardioprotection was based on generally supportive data on lipid levels in intermediate outcome clinical trials, trials in nonhuman primates, and a large body of observational studies suggesting a 40% to 50% reduction in risk among users of either estrogen alone or, less frequently, combined estrogen and progestin. Observational studies primarily examining unopposed estrogen preparations have suggested a 30% to 50% reduction in coronary events, and an 8% to 30% increase in breast cancer with extended use. Other research findings indicated that hormone therapy was also associated with a decreased risk of osteoporosis and increased bone density. The WHI HT trials were designed to test the effects of postmenopausal hormone therapy on risk for coronary heart disease and assess overall risks and benefits in predominantly healthy women. The Women's Health Initiative Memory Program (WHIMS) consists of a suite of studies which include cohorts of women who participated in the WHI HT trials. Postmenopausal women have a greater risk than men of developing Alzheimer's disease, but studies of the effects of estrogen therapy on Alzheimer's disease have been inconsistent. Additionally, observational studies have suggested that postmenopausal hormone treatment may improve cognitive function, but data from randomized clinical trials have been sparse and inconclusive. International comparisons and migration studies have suggested that countries with 50% lower fat intake than the US population had approximately one third the risk of colorectal cancer. Additionally, fairly consistent evidence existed for an effect of dietary fat, vegetables and fruits, and grains on colorectal cancer risk from within-country observational studies, although the protective effect of lower fat intake was no longer clear after adjusting for energy intake. The WHI DM trial was the first randomized trial to directly address the health effects of a low-fat eating pattern in predominantly healthy postmenopausal women from diverse racial/ethnic, geographic, and socioeconomic backgrounds. Osteoporosis is a major cause of injury, loss of independence, and death, and contributes to hip fractures. Observational evidence and data from previous randomized clinical trials suggest that calcium and/or vitamin D supplements may slow bone loss and reduce the risk of falls in postmenopausal and elderly women. However, evidence from trials, observational studies, and meta-analyses of calcium and vitamin D supplementation with respect to hip and other fractures was limited at the time the WHI was initiated. In two prior randomized trials, calcium plus vitamin D supplements did not reduce the risk of nonvertebral fractures among older women. When the WHI CaD trial was designed, guidelines recommended daily intakes of 800 to 1200 mg of calcium with 400 IU of vitamin D for the prevention of osteoporosis, which was not met by many American women. Therefore, the WHI CaD trial was designed to test the primary hypothesis that postmenopausal women randomly assigned to calcium plus vitamin D supplementation would have a lower risk of hip fracture and, secondarily, of all fractures than women assigned to placebo. Subjects: Postmenopausal women ages 50 to 79 were eligible to participate. A woman was considered postmenopausal if she had experienced no vaginal bleeding for 6 months (12 months for women under 55 years of age), had had a hysterectomy, or had ever used postmenopausal hormones. Recruitment was carried out in 40 US clinical centers in 1993-1998. The clinical trial components had additional specific inclusion or exclusion criteria.A total of 68,132 women were randomized into at least one component of the clinical trial. 27,347 women were enrolled in the hormone therapy component with 16,608 in the estrogen plus progestin trial and 10,739 in the unopposed estrogen trial, 48,835 women were enrolled in the diet modification component, and 36,282 women were enrolled in the calcium/vitamin D component. 7,479 women 65 years of age and older at baseline and that participated in the HT trial component were enrolled in the ancillary memory study. Women who were either ineligible or unwilling to participate in the clinical trial component were enrolled in the observational study. For example, many potential participants to the clinical trial component of the study were already undertaking a low fat diet or were using hormone replacement therapy. The effect of the selection process was that women enrolled in the observational study tended to have healthier lifestyles compared to those enrolled in the clinical trial. In total, 93,676 subjects were enrolled in WHI OS, with over 16% being members of a racial/ethnic minority group. The first WHI Extension Study enrolled 115,407 consenting participants from all components of the original WHI study for an additional five years of follow-up, from 2005 to 2010. In 2010, 93,567 women consented to continued follow-up. Design: The clinical trial component of the WHI included three randomized comparisons: hormone therapy, dietary modification, and calcium/vitamin D supplementation. Women could have been randomized into one, two or all three trials.The hormone therapy trial enrolled women to one of two double-blinded trials: estrogen (0.625 mg of conjugated equine estrogens daily) plus progestin (2.5 mg of medroxyprogesterone acetate daily) or estrogen alone. Women with a prior hysterectomy were eligible for the trial of unopposed estrogen. Women with an intact uterus at screening were initially also eligible for unopposed estrogen, but were reassigned to the trial of combined postmenopausal hormones beginning in 1995. Both trials randomized participants 1:1 to either hormone therapy or placebo. A 3-month washout period was required before baseline evaluation of women using postmenopausal hormones at initial screening. Study participants were contacted by telephone 6 weeks after randomization to assess symptoms and reinforce adherence. Follow-up contacts by telephone or clinic visit occurred every 6 months, with clinic visits required annually. The estrogen plus progestin trial was halted in July 2002 after a mean 5.2 years of follow-up because health risks, including increased risk of breast cancer and cardiovascular disease, exceeded benefits. The estrogen alone trial was stopped early in March 2004, because an increased risk of stroke was found with no benefit for coronary heart disease. The primary outcome was coronary heart disease (CHD) (nonfatal myocardial infarction and CHD death), with invasive breast cancer as the primary adverse outcome. The dietary modification trial evaluated the effect of a low-fat, high fruit, vegetable, and grain diet on preventing cardiovascular disease and cancer. Participants were randomly assigned to an intervention or a comparison group in the ratio of 2:3 for cost-efficiency. The intervention was an intensive behavioral modification program, using 18 group sessions in the first year and quarterly sessions thereafter, led by specially trained and certified nutritionists. The program was designed to promote dietary change with the goals of reducing total fat to 20% of energy intake, increasing vegetables and fruits to at least 5 servings daily and grains to at least 6 servings daily. The intervention did not include total energy reduction or weight loss goals. Comparison group participants received a copy of the US Department of Health and Human Services' Dietary Guidelines for Americans and other health-related materials but were not asked to make dietary changes. Dietary intake was monitored using the WHI food frequency questionnaire at 1 year and in a rotating one-third subsample every year thereafter. Women completed a medical update questionnaire every 6 months, and medical records were sought for all women reporting colorectal cancer. The primary outcome was invasive colorectal cancer incidence. Participants in the calcium/vitamin D trial were randomized 1:1 to either supplements or placebo. Active tablets contained 500 mg of elemental calcium (as calcium carbonate) and 200 IU of vitamin D3, to be taken twice daily with meals. The presence and severity of symptoms, safety concerns, and outcomes were ascertained at annual clinic visits and telephone or clinic visits at intervening six-month intervals. Risk factors for fracture were assessed by questionnaire, interview, and clinical examination. The primary outcome was incidence of hip fracture. Participants in the observational study attended a baseline examination and were re-examined three years later. Participants completed annual updates of exposures and clinical outcomes by mail. Final data were collected by mail during the close-out period in April 2004 to March 2005. The major clinical outcomes of interest were coronary heart disease, stroke, breast cancer, colorectal cancer, endometrial cancer, ovarian cancer, osteoporotic fractures, diabetes, and total mortality. Most outcomes were initially ascertained by self-report on an annual questionnaire and documented by hospital and related records. Charts with potential cardiovascular, cancer, and fracture outcomes were sent to the local physician adjudicator for evaluation and classification. Staff at the Clinical Coordinating Center coded and adjudicated all cancers of major interest in the study using standardized SEER guidelines. In 2005, WHI participants were invited to join the Extension Study for an additional five years of follow-up in order to collect long-term outcomes. Participants completed annual data collection forms primarily by mail, similar to the OS follow-up. Women reporting study outcomes were contacted by WHI field center staff to obtain additional details and medical records, which were evaluated by physician adjudicators. In 2010, the woman remaining were invited to join the next Extension Study. In the second extension, women were divided into two groups, one of which would have outcomes documented with medical records (the Medical Records Cohort, MRC), and the other would just be followed by self-report (the Self-Report Cohort, SRC). The MRC consists of women who were in the hormone therapy trials, and all African-American and Hispanic women. In 2012-2013, a subset of the MRC was identified for a potential in-home visit to collect blood and several objective measures of physical functioning. Conclusions: Overall health risks exceeded benefits from use of combined estrogen plus progestin after an average 5.2 year follow-up among healthy postmenopausal US women (Rossouw et al., 2002, PMID:12117397). Among postmenopausal women aged 65 years or older, estrogen plus progestin did not improve cognitive function when compared with placebo (Rapp et al., 2003, PMID: 12771113), increased the risk for probable dementia, and did not prevent mild cognitive impairment (Shumaker, et al., 2003, PMID: 12771112). The use of conjugated equine estrogen increased the risk of stroke, decreased the risk of hip fracture, and did not affect CHD incidence in postmenopausal women with prior hysterectomy after an average of 6.8 years of follow-up (Anderson et al., 2004, PMID: 15082697). Over approximately 8 years of follow-up, a low-fat dietary pattern did not reduce the risk of colorectal cancer (Beresford, et al., PMID: 16467233). Calcium with vitamin D supplementation resulted in a small but significant improvement in hip bone density; however, no significant difference was observed in hip fractures (Jackson, et al., 2006, PMID: 16481635). A recent review summarizes the conclusions from the WHI clinical trials with a focus on clinical practice (Manson, et al., 2024, PMID: 38691368).Description of ECG Imaging Data: Electric cardiograms (ECGs) were given to all clinical trial participants at baseline and in years 3, 6, and 9 of the original WHI study.EKG data consist of 12 lead 10 seconds ECGS sampled at 500Hz via GE ECG machines and process via GE MUSE system. The ECG waveform were directly exported from GE MUSE using MUSE export function in XML format, which include EKG waveform data as well as other ECG characteristics. Waveform data is in base64 encoded format, when it is decoded, it is a binary data that can be used to draw waveform graph. Many programming languages and data tools have built in functions to decode base64 strings. All the other necessary information is included in the LeadData section, total byte size, total sample size etc. (usually 1 sample is 2 bytes). See example below: encoded-data (base64 encoded string) JwAoAC0AKAAiACIAJAAkACQAIwAiACIAHgAcABwAGwAZABgAGAAYABcAEwAQABAAEAAL^/AAsADAAM... decoded-binary-data (1 sample is 2 bytes) 270028002D002800220022002400240024002300220022001E001C001C001B00 1900180018001800170013001000100010000B000B000C000C000D000D000D00 0A000A000A0009000600040004000700070005000500020... These binary values are integers (Y axis data of the graph), hence it is a straightforward process to draw the waveform graph. Acquisition dates have been redacted from this ECG data to comply with WHI policy. All acquisition dates within files and in file names have been set to January 1, 1900 (19000101) to comply with this policy.

  Study phs003824

• 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&#39;s Hospital: The Gene Partnership (TGP) is a prospective longitudinal registry at Boston Children&#39;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&#39;preferences;results are returned through the Personally Controlled Health Record (PCHR). BCH and Cincinnati Children&#39;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&#39;s Hospital of Pennsylvania (CHOP): The Center for Applied Genomics (CAG) at the Children&#39;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&#39;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&#39;s Hospital Medical Center/Boston&#39;s Children&#39;s Hospital (CCHMC/BCH): Cincinnati Children&#39;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&#39;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&#39;s disease (AD), Alzheimer&#39;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&#39;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&#39;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&#39;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&#39;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&#39;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 &#60;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&#39;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&#39;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&#39;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 &#62;235,000 samples as of spring 2017.

  Study phs001584

• 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

• Genome-Wide Association Study of HCC in Non-Asian USA Population

  This GWAS of HCC relied on existing biological and data resources from 7 USA sites and one Canadian site which allows for the integration of genetic and environmental data. Total of 2199 case patients and 1103 non cancer controls were genotyped. Case patients are defined as patients with pathological or radiological diagnosis of HCC with and without chronic hepatitis C virus infection. Study was restricted to Caucasian population without prior infection with chronic HBV infection. All DNA samples were extracted from peripheral blood. Study population of the participated sites are: Site-1: University of Texas MD Anderson Cancer Center A hospital based-case-control study was initiated at UT MD Anderson Cancer Center in 2000 and approved by the Institutional Review Board of The University of Texas M. D. Anderson Cancer Center. Study design was previously described in detailed (1-3). Written informed consent for an interview and for a biological sample was obtained from each participant. At time of diagnosis prior to treatment exposure. Case patients were recruited from the population of patients with newly diagnosed HCC who were evaluated and treated at the institution's gastrointestinal medical and surgical oncology outpatient clinics. Inclusion criteria were as follows: a pathologically confirmed diagnosis of HCC, U.S. residency, and the ability to communicate in English. Patients with concurrent or past history of other types of cancers were excluded. Control subjects were healthy spouses of patients with cancers other than liver, gastrointestinal, lung, or head and neck (smoking-related cancers) undergoing treatment at our institution. Eligibility criteria for control subjects were the same as those for patients, except that control subjects could not have ever had cancer. A short, structured questionnaire was used to screen potential control subjects on the basis of eligibility criteria. Control subjects and patients were recruited simultaneously. Case patients and control subjects were interviewed by well-trained interviewers who followed a written protocol to guide ascertainment and reduce surveillance, interviewer, and recall bias. No proxy interviews were conducted. The interviewers used a structured and validated questionnaire to collect information about demographic characteristics and potential risk factors for HCC such as personal smoking history, alcohol use, medical history, occupational history, and family history of cancer. Blood samples from cases and controls were tested for HBV and HCV. HCV antibodies, hepatitis B surface antigen, and antibodies to hepatitis B core antigen were detected by use of a third-generation enzyme-linked immunosorbent assay (ELISA) (Abbott Laboratories, North Chicago, IL). Important clinical information were retrieved from patients' medical records. Total of 1188 case patients and 278 controls from MD Anderson study were genotyped. Site-2: Mayo Clinic Case patients and controls included in the study from Mayo clinic were recruited as part of Mayo International Hepatobiliary Neoplasia Register and Biorepository. Only USA patients and controls were participated. All cases and controls signed informed consent indicating their willingness to participate in genetic studies. Epidemiological and clinical data were collected from participants and retrieved from medical records. Total of 522 HCC case patients and 182 controls were genotyped in this study. Site-3: Toronto University Molecular Epidemiology of Hepatobiliary Tumors (HBT study, CAPCR 09-0289) is a hospital-based study that includes 1710 patients diagnosed with liver, bile duct or gallbladder adenocarcinoma (Hepatocellular carcinoma - HCC; cholangiocarcinoma - CCA; and gall bladder adenocarcinoma -GBCa) recruited at University Health Network (UHN) between 2009 and 2018. HBT study is a prospective study of cancer patients that collects self-reported demographic and epidemiological data, medical records/clinical information and blood samples for research purposes. Eligible patients recruited between 2009 and March 15 2016 were selected to participate in the Genome-Wide Association Study in Patients with Hepatocellular Carcinoma (GWAS in HCC, CAPCR 15-9943-CE), a sub-study of the HBT study. Eligible prospective patients recruited after March 15 2016 signed the Addendum to the HBT consent form and became eligible to participate in the GWAS in HCC study. Total of 271 cases and 21 controls were genotyped from Toronto University Site-4: University of Pittsburgh The design of the study has been described previously (4-6). Briefly, this population-based study enrolled 120 HCC patients and 230 matched controls from black, Hispanic, and non-Hispanic white residents of Los Angeles County who were between 18 and 74 years of age at diagnosis from January 1995 through December 2001. HCC Cases were identified through the Los Angeles County Cancer Surveillance Program. We sought to recruit up to two control subjects per case from the neighborhoods where HCC patients resided at the time of diagnosis, who were matched to the index case by sex, age (within 5 years), and race (Hispanic white, non-Hispanic white, black). Blood samples (plasma and buffy coat), medical and lifestyle factors were collected from all consenting participants. Total of 65 cases and 60 controls were genotyped in this study Site-5: South Western Dallas Under IRB approval, HCC Cases and controls were prospectively collected since 2015 from the outpatient Liver clinics from Parkland Health and Hospital System and UT Southwestern Medical Center, two large health systems in Dallas TX. A Total of 31 cases and 29 controls were genotyped from site 5. Site-6: Columbia University HCC patients from Columbia are those recruited as part of the Herbert Irving Comprehensive Cancer Center Database Shared Resource which seeks to recruit all cancer patients for potential future studies. The PI is Dr. Katherine Crew AAAL5871. This resource collects sociodemographic, lifestyle and clinical data on patients as well as a blood sample. Those who indicated on their consent form that they would participate in genetic studies were included. Total of 79 case patients with HCC were genotyped from Columbia University. Site-7: University of Michigan The study population included from University of Michigan were included from a prospective study of with chronic HCV infection recruited in Ann Arbor Patients who had under- gone liver transplantation, known coinfection with HIV, life expectancy &lt;12 months due to extra-hepatic illnesses, or were receiving HCV treatment at enrolment, were excluded. Protocol, surveys, and data forms were developed where each enrolled patient completed the same questionnaire All patients provided written informed consent before enrolment in the study. The study was approved by the institutional review board or ethics committee at the University of Michigan. Detailed study description was previously published (7). Total of 44 cases and 347 controls were genotyped Site-8: Veteran Administration Medical Center in Houston This research including written informed consent form was jointly approved by Institutional Review Boards for the Baylor College of Medicine and the Michael E. DeBakey VA Medical Center in Houston, Texas. Study details have been previously published (8). Briefly, we prospectively recruited consecutive HCV-infected veterans prior to their previously scheduled HCV clinic visit at a large tertiary care VA medical center between May 1, 2009 and December 31, 2012. Patients completed a research assistant (RA) administered survey interrogating medical and risk factor history including lifetime alcohol use, had anthropometric measurements taken, and completed a fasting venipuncture for performance of the FibroSure-ActiTest as a measure of hepatic pathology. We restricted our current analysis to individuals who were: (1) White male veterans between 18 and 70 years; (2) had no history of HCC, liver transplant, decompensated liver disease including ascites, dementia, or psychosis; (3) were serologically-confirmed to have chronic HCV and to be negative for both HIV and active HBV infection; (4) were not currently receiving anti-HCV pharmacotherapy; and (5) had FibroSURE testing shown F3/F4 fibrosis consistent with cirrhosis.

  Study phs001744

• ELLIPSE Prostate Cancer Meta-Analysis and Genotyping

  The ELLIPSE Consortium is an international effort to discover risk loci for prostate cancer. It includes the meta-analysis of existing GWAS data as well as novel GWAS, exome, and iCOGS genotyping. The GWAS meta-analysis includes the following cases and controls from studies of European ancestry: UK GWAS stage 1 (Illumina Infinium HumanHap 550 Array: 1854 cases and 1894 controls), UK GWAS stage 2 (Illumina iSELECT: 3706 cases and 3884 controls), CAPS1 (Affymetrix GeneChip 500K: 474 cases and 482 controls), CAPS2 (Affymetrix GeneChip 5.0K: 1458 cases and 512 controls), BPC3 (Illumina Human610 Illumina: 2068 cases and 3011 controls), PEGASUS (HumanOmni2.5: 4600 cases and 2941 controls). The OMNI 2.5M genotyping was conducted for 977 prostate cancer cases from UKGPCS. The Exome SNP array genotyping was conducted for 4741 subjects from UKGPCS. The iCOGs genotyping was conducted for 10366 subjects which includes the Multiethnic Cohort (n=1648) and UKGPCS (n=8718). Below is a description of each study that contributed to the meta-analysis of men of European ancestry. Information about the studies that contributed to the multiethnic meta-analysis can be found on the associated study page and also in Conti et al (Nature Genetics, PMID:33398198).&nbsp;&nbsp; UK GWAS Stage 1 (UK1) and Stage 2 (UK2): The UK Genetic Prostate Cancer Study (UKGPCS) was first established in 1993 and is the largest prostate cancer study of its kind in the UK, involving nearly 189 hospitals. We are based at The Institute of Cancer Research in Sutton, Surrey, and collaborate with the Royal Marsden NHS Foundation Trust. Our aim is to find genetic changes which are associated with prostate cancer risk. Our target is to recruit 26,000 gentlemen into the UKGPCS by 2017. Men are eligible to take part if they fit into at least one of the following groups: They have been diagnosed with prostate cancer at 60 years of age or under (up to their 61st birthday). They have been diagnosed with prostate cancer and a first, second or third degree relative where at least one of these men were diagnosed with prostate cancer at 65 years of age or under. They are affected and have 3 or more cases of prostate cancer on one side of their family. They are a prostate cancer patient at the Royal Marsden NHS Foundation Trust. We have to date recruited around 16,000 men on whom we have germline DNA and clinical data at diagnosis. The UK GWAS is based on genotyping of 541,129 SNPs in 1,854 individuals with clinically detected (non-PSA-screened) prostate cancer (cases) and 1,894 controls. 43,671 SNPs showing strong evidence of association in stage 1 were followed up by genotyping a further 3,268 cases and 3,366 controls from UK and Melbourne in stage2. CAPS1 and CAPS2: The CAPS (Cancer of the Prostate in Sweden) study represents a large Swedish population-based cancer study, comprising 3,161 cases and 2,149 controls, recruited between 2001 and 2003. Biopsy confirmed prostate cancer cases were identified and recruited from four out of six regional cancer registries in Sweden, diagnosed between July 2001 and October 2003. Clinical data including TNM stage, Gleason grade and PSA levels at time for diagnosis were retrieved through record linkage to the National Prostate Cancer Registry. Control subjects, who were recruited concurrently with case subjects, were randomly selected from the Swedish Population Registry and matched according to the expected age distribution of cases (groups of 5-year intervals) and geographic region. Whole blood was collected from all individuals for extraction of genomic DNA. A GWAS was conducted in two parts. In the first phase (CAPS1) 498 cases and 502 controls were genotyped, in the second phase 1,483 cases and 519 controls were genotyped. Genotyping was performed using the GeneChip Human Mapping 500K (CAPS1) and 5.0K (CAPS2) Array Set from Affymetrix (Santa Clara, CA). The National Cancer Institute Breast and Prostate Cancer Cohort Consortium, BPC3: BPC3 was a consortium of prospective cohort studies investigating genetic and gene-environmental risk factors for breast and prostate cancer. Each study selected cases and controls for this study as described below. The clinical criteria defining advanced prostate cancer (Gleason = 8 or stage C/D) were either obtained from medical records or cancer registries. The Gleason score source was either surgical specimens (radical prostatectomy or autopsy) or the diagnostic biopsy (needle biopsy or TURP). When multiple Gleason scores were available the surgical value was used. PLCO was removed from the analysis as the samples were included in the Pegasus GWAS described below. In total 2,473 advanced prostate cancer cases and 3,534 controls were included in the analysis following QC. ATBC, Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study: ATBC was a randomized, placebo-controlled primary prevention trial to investigate whether &#945;-tocopherol or &#223;-carotene supplementation reduced the incidence of lung or other cancers in male smokers. Between 1985 and 1988, 29,133 men ages 50 to 69 years were enrolled in the trial from Finland and randomized to supplementation (50 mg &#945;-tocopherol, 20mg &#223;-carotene, or both) or placebo. Men with a prior history of cancer, other than non-melanoma skin cancer or carcinoma in situ, were excluded from participating. Incident cancer cases are identified through linkage with the Finnish Cancer Registry, which has ~100% ascertainment of cancer cases nationwide. Cases included 249 men diagnosed with advanced prostate cancer (Gleason = 8 or stage C/D) from 1985 to 2003 with DNA available. Controls were 1,271 men selected previously for a GWAS of lung cancer in ATBC without a diagnosis of prostate cancer. CPSII, Cancer Prevention Study II: CPSII is a cohort study started in 1982 to investigate the relationship between dietary, lifestyle and other etiologic factors and cancer mortality. Approximately 1.2 million men and women enrolled in the study from 50 states in the U.S. In 1992, a subset of these participants (n= ~184,000) were enrolled in the CPSII Nutrition Cohort to examine the relationship between dietary and other exposures and cancer incidence. Blood samples were drawn from approximately 39,376 members of the Nutritional Cohort from 1998 to 2001, and buccal cells were collected from 69,467 members from 2001 to 2002. Cancer cases are identified by self-report through follow-up questionnaires followed by verification through medical records and/or linkage to state cancer registries as well as death certificates. A total of 660 advanced prostate cancer cases (Gleason = 8 or stage III/IV) with a source of DNA were identified for this study. Controls were 660 men matched on ethnicity, date of birth, sample collection date and DNA type. EPIC, European Prospective Investigation into Cancer and Nutrition: EPIC is a prospective study designed to investigate both genetic and non-genetic risk factors for different forms of cancer. Study participants were almost all white Europeans. Approximately 500,000 individuals (150,000 men) in EPIC were recruited between 1992 and 2000, from 23 centers in 10 European countries. Overall approximately 400,000 subjects also provided a blood sample at recruitment. The methods of recruitment and details of the study design are described in detail elsewhere. In brief, study participants completed an extensive questionnaire on both dietary and nondietary data at recruitment. The present study includes subjects from advanced prostate cancer cases (Gleason = 8 or stage III/IV) matched to controls based on study center, length of follow-up, age at enrollment (&#177; 6 months), fasting and time of day of blood collection (&#177; 1 hour). The advanced prostate cancer subjects were from 8 of the 10 participating countries: Denmark, Germany, Greece, Italy, the Netherlands, Spain, Sweden and the United Kingdom (UK). France and Norway were not included in the current study because these cohorts only included female subjects. All participants gave written consent for the research and approval for the study was obtained from the ethical review board from all local institutions in the regions where participants had been recruited for the EPIC study. HPFS, Health Professionals Follow-up Study: HPFS began in 1986 and is an ongoing prospective cohort study of 51,529 United States male dentists, optometrists, osteopaths, podiatrists, pharmacists, and veterinarians 40 to 75 years of age. The baseline questionnaire provided information on age, marital status, height and weight, ancestry, medications, smoking history, disease history, physical activity, and diet. At baseline the cohort was 97% white, 2% Asian American, and 1% African American. The median follow-up through 2005 was 10.5 years (range 2-19 years). Self-reported prostate cancer diagnoses were confirmed by obtaining medical and/or pathology records. Prostate cancer deaths are either reported by family members in response to follow-up questionnaires, discovered by the postal system, or the National Death Index. Questionnaires are sent every two years to surviving men to update exposure and medical history. In 1993 and 1994, a blood specimen was collected from 18,018 men without a prior diagnosis of cancer. Prostate cancer cases are matched to controls on birth year (+/-1) and ethnicity. Controls are selected from those who are cancer-free at the time of the case&#8217;s diagnosis, and had a prostate-specific antigen test after the date of blood draw.&nbsp;&nbsp; MEC, Multiethnic Cohort: The Multiethnic Cohort Study is a population-based prospective cohort study that was initiated between 1993 and 1996 and includes subjects from various ethnic groups - African Americans and Latinos primarily from Californian (great Los Angeles area) and Native Hawaiians, Japanese-Americans, and European Americans primarily from Hawaii. State drivers&#8217; license files were the primary sources used to identify study subjects in Hawaii and California. Additionally, in Hawaii, state voter&#8217;s registration files were used, and, in California, Health Care Financing Administration (HCFA) files were used to identify additional African American men. All participants (n=215,251) returned a 26-page self-administered baseline questionnaire that obtained general demographic, medical and risk factor information. In the cohort, incident cancer cases are identified annually through cohort linkage to population-based cancer Surveillance, Epidemiology, and End Results (SEER) registries in Hawaii and Los Angeles County as well as to the California State cancer registry. Information on stage and grade of disease are also obtained through the SEER registries. Blood sample collection in the MEC began in 1994 and targeted incident prostate cancer cases and a random sample of study participants to serve as controls for genetic analyses. PHS, Physicians Health Study:PHS was a randomized trial of aspirin and &#223; carotene for cardiovascular disease and cancer among 22,071 U.S. male physicians ages 40-84 years at randomization; none had a cancer diagnosis at baseline. The original trial ended, but the men are followed. From 1982 to 1984, blood samples were collected from 14,916 physicians before randomization. Participants are sent yearly questionnaires to ascertain endpoints. Whenever a physician reports cancer, we request permission to obtain the medical records, and cancers are confirmed by pathology report. We obtain death certificates and pertinent medical records for all deaths. Follow-up for nonfatal outcomes in PHS is over 97% complete, and for mortality, over 99%. PLCO, Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial:PLCO is a multicenter, randomized trial to evaluate screening methods for the early detection of prostate, lung, colorectal and ovarian cancer. Between 1993 and 2001, over 150,000 men and women ages 55-74 years were recruited from ten centers in the United States (Birmingham, AL; Denver, CO; Detroit, MI; Honolulu, HI; Marshfield, WI; Minneapolis, MN; Pittsburgh, PA; Salt Lake City, UT; St. Louis, MO; and Washington, D.C.). Men randomized to the screening arm underwent prostate cancer screening with prostate-specific antigen (PSA) annually for six years and digital rectal exam annually for four years. Blood specimens were collected from participants randomized to the screening arm of the trial, and buccal cell specimens were obtained from participants randomized to the control arm. Cases included 754 men diagnosed with advanced prostate cancer (Gleason = 8 or stage III/IV) from either arm of the trial. Of these cases, 317 were genotyped previously as part of Cancer Genetic Markers of Susceptibility (CGEMS), a GWAS for prostate cancer. Controls included 1,491 men without a diagnosis of prostate cancer from the screening arm of the PLCO trial. All subjects provided informed consent to participate in genetic etiology studies of cancer and other traits. This study was approved by the institutional review boards at the ten centers and the National Cancer Institute. PLCO was removed from the meta-analysis of the BPC3 studies as a consequence of PEGASUS below. PEGASUS, Prostate cancer Genome-wide Association Study of Uncommon Susceptibility loci: Pegasus is a genome-wide association nested within the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. PLCO is a multicenter, randomized trial to evaluate screening methods for the early detection of prostate, lung, colorectal and ovarian cancer. Between 1993 and 2001, over 150,000 men and women ages 55-74 years were recruited from ten centers in the United States (Birmingham, AL; Denver, CO; Detroit, MI; Honolulu, HI; Marshfield, WI; Minneapolis, MN; Pittsburgh, PA; Salt Lake City, UT; St. Louis, MO; and Washington, D.C.). Men randomized to the screening arm underwent prostate cancer screening with prostate-specific antigen annually for six years and digital rectal exam annually for four years. Blood specimens were collected from participants randomized to the screening arm of the trial, and buccal cell specimens were obtained from participants randomized to the control arm. Cases included 4,598 men of European ancestry diagnosed with prostate cancer from either arm of the trial and controls included 2,941 men of European ancestry without a diagnosis of cancer from the screening arm, matched on age and year of randomization. All subjects provided informed consent, and the study approved by the institutional review board at the National Cancer Institute. Funding:This work was supported by the GAME-ON U19 initiative for prostate cancer (ELLIPSE): U19 CA148537. The BPC3 was supported by the U.S. National Institutes of Health, National Cancer Institute (cooperative agreements U01-CA98233, U01-CA98710, U01-CA98216, and U01-CA98758, and Intramural Research Program of NIH/National Cancer Institute, Division of Cancer Epidemiology and Genetics). The ATBC study and PEGASUS was supported in part by the Intramural Research Program of the NIH and the National Cancer Institute. Additionally, this research was supported by U.S. Public Health Service contracts N01-CN-45165, N01-RC-45035, N01-RC-37004 and HHSN261201000006C from the National Cancer Institute, Department of Health and Human Services. CAPS: The Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden was supported by the Cancer Risk Prediction Center (CRisP; www.crispcenter.org), a Linneus Centre (Contract ID 70867902) financed by the Swedish Research Council, Swedish Research Council (grant: K2010-70X-20430-04-3), the Swedish Cancer Foundation (grant: 09-0677), the Hedlund Foundation, the S&#246;derberg Foundation, the Enqvist Foundation, ALF funds from the Stockholm County Council. Stiftelsen Johanna Hagstrand och Sigfrid Linn&#233;r&#8217;s Minne, Karlsson&#8217;s Fund for urological and surgical research. We thank and acknowledge all of the participants in the Stockholm-1 study. We thank Carin Cavalli-Bj&#246;rkman and Ami R&#246;nnberg Karlsson for their dedicated work in the collection of data. Michael Broms is acknowledged for his skillful work with the databases. KI Biobank is acknowledged for handling the samples and for DNA extraction. Hans Wallinder at Aleris Medilab and Sven Gustafsson at Karolinska University Laboratory are thanked for their good cooperation in providing historical laboratory results. UKGPCS would like to acknowledge the NCRN nurses and Consultants for their work in the UKGPCS study. We thank all the patients who took part in this study. This work was supported by Cancer Research UK (grants: C5047/A7357, C1287/A10118, C1287/A5260, C5047/A3354, C5047/A10692, C16913/A6135 and C16913/A6835). We would also like to thank the following for funding support: Prostate Research Campaign UK (now Prostate Cancer UK), The Institute of Cancer Research and The Everyman Campaign, The National Cancer Research Network UK, The National Cancer Research Institute (NCRI) UK. We are grateful for support of NIHR funding to the NIHR Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. The MEC was supported by NIH grants CA63464, CA54281 and CA098758.&nbsp;

  Study phs001120

• A Study to Assess the Cardiovascular, Cognitive, and Subjective Effects of Atomoxetine in Combination with Intravenous Methamphetamine

  The Study to Assess the Cardiovascular, Cognitive, and Subjective Effects of Atomoxetine in Combination with Intravenous Methamphetamine started in 2007 and ended in 2011. It was a small pilot study that included methamphetamine abusing individuals and healthy control participants aged 18-50 years. Participants who qualified over the telephone were scheduled to visit the London Laboratory at the UCLA Semel Institute for Neuroscience and Human Behavior for the In-Person Screening Phase. The screening procedures, usually scheduled over two visits, determined if participants were eligible to complete the subsequent study visits. Both groups completed identical screening procedures, including a SCID diagnostic interview to rule out any major psychiatric disorders. Methamphetamine users also answered questionnaires specific to their drug use. Urine toxicology for marijuana, opiates, cocaine, methamphetamine, and benzodiazepines were monitored at the beginning and throughout the study. Additionally, participants had blood samples to test for Rapid Plasma Reagin (exposure to syphilis), purified protein derivative (PPD), HIV and Hepatitis-C (HCV). Positive test results were relayed by a study physician and resulted in termination from continuing in the study. The inpatient portion of the study lasted a total of 24 days. To confirm compliance with abstinence from illicit and prescription drug use for the duration of the study, urine and breath alcohol testing was performed for both inpatient (methamphetamine users) and outpatient (healthy control) groups. Each subject was randomized to receive either atomoxetine (80 mg daily) or placebo. During inpatient days of the study, a study physician oversaw research subjects daily and experimental procedures were conducted in the General Clinical Research Center (GCRC). During drug administration sessions, heart rate and blood pressure were assessed at frequent intervals. Experimental sessions lasted around three hours and were conducted at approximately the same time of day for a given participant. Lunch was provided to participants at 11:30am with the prohibition of caffeine on study days involving methamphetamine administration. Participants were provided with smoke breaks as needed to assure that acute nicotine withdrawal did not negatively impact performance on cognitive testing. The following outlines the day to day procedures for a methamphetamine inpatient: Phase I Inpatient Study (15 days total) Days 1-3: On the day of admission, participants received blood tests and an EKG in order to determine that they had no occult medical conditions which would make their participation in the study medically contraindicated. A three day washout period was provided at the beginning of the study to establish an appropriate methamphetamine-free baseline for testing. Days 4-6: Participants completed cognitive test batteries to assess inhibitory control including self-report measures and cognitive testing, as well as up to three MRI scanning sessions. Day 7: On day 7, at 9AM and 1PM, participants received methamphetamine in a single-blinded condition. It was administered as two IV infusions of 15mg separated by a 60min break. The total dose of IV methamphetamine was 30mg. Participants were clinically evaluated by continuous cardiac telemetry, serial EKG and measurement of vital signs, during and after infusions, in order to determine the medical safety of methamphetamine infusion in the absence of study medication. Day 8: On day 8 we evaluated the safety and residual effects of the methamphetamine administered on the previous day. Days 9-10: After demonstrating medical and psychiatric stability of the initial methamphetamine infusion, participants were then randomized to receive placebo or atomoxetine (0 or 40mg) under double-blind conditions, which would be given at 8AM on both study days. Days 11-12: On days 11 and 12, participants remained in the GCRC and took two doses of test compound atomoxetine (0 or 40mg) at 8AM and 8PM. On the morning of day 12, participants completed a series of cognitive testing. Day 13: On day 13, participants remained in the GCRC, and took two doses of the test compound atomoxetine (0 or 40mg) at 8AM and 8PM. At 9AM and noon, they received methamphetamine under double-blind conditions administered non-contingently as two infusions of 0 or 15mg by IV (intravenously), with each infusion separated by 60min. The total dose of IV methamphetamine that each participant received was 30mg. As before, participants were clinically evaluated by continuous cardiac telemetry, serial EKG and measurement of vital signs, during and after infusions, in order to determine the medical safety of methamphetamine infusion in those participants receiving study medication. Day 14: On day 14 at 8AM, participants received their final dosing of the first test compound (atomoxetine 0 or 40mg). At 9AM and 1PM, participants selected between methamphetamine infusion or money using double-blind conditions in a multiple-choice self-administration paradigm. Participants were able to take up to two infusions of 0 or 15mg of methamphetamine, separated by 60min. The total dose of IV methamphetamine that each participant received on day 14 was 30mg. Participants were clinically evaluated by continuous cardiac telemetry, serial EKG and measurement of vital signs, during and after infusions, in order to determine the medical safety of methamphetamine infusion in those participants receiving study medication. At 5pm on day 14 discharge laboratory studies were drawn. Day 15: On day 15, a final EKG was performed as well as a review of discharge laboratory studies, including review of the EKG and continued medical and psychiatric stability. Upon clearance from a physician, participants were discharged on day 15. Participants spent at least 2 weeks (14 days) out of the hospital prior to returning to the UCLA GCRC for inpatient Phase II. Phase II Inpatient Study (9 days total) Day 1-2: On the day of re-admission, participants received blood tests and an EKG. A two day washout period was provided at the beginning of the study to establish an appropriate methamphetamine free condition for safe administration of the second test compound condition. Days 3-4: On days 3 and 4, given medical stability, participants were then switched to the opposite test compound (atomoxetine 0 or 40mg) under double-blind conditions, in the same manner as in Days 9-10 in Phase I. Days 5-6: Same procedure as in Days 11-12 in Phase I but with opposite medication condition. Day 7: Same procedure as in Day13 in Phase I but with opposite medication condition. Day 8: Same procedure as in Day14 in Phase I but with opposite medication condition. Day 9: Same procedure as in Day14 in Phase I but with opposite medication condition. For healthy control participants, the study consisted of 30 days or fewer of out-patient testing. Healthy control participants had an extensive screening, including a history and physical exam performed by a study physician, an EKG, and laboratory studies performed to assess for medical or psychiatric conditions. Control participants meeting criteria were invited back to UCLA to undergo cognitive testing, and some participants received fMRI scans. Participants were required to refrain from illicit and prescription drug use for the duration of the study, which was confirmed with urine toxicology and breath alcohol level testing on examination days. Caffeine was restricted on study days for least 2 hours prior to cognitive testing and MRI scans. Participants were permitted to drink their normal daily intake of caffeine, given the caveat above. On study days, cigarette smoking was permitted to match conditions used with methamphetamine users. The incorporation of smoke breaks during the test day assured that acute nicotine withdrawal did not negatively impact performance on cognitive tests or neuroimaging. The following outlines procedures for healthy control participants: For normal controls, participation involved one day of outpatient screening, and one day of baseline cognitive testing and possible MRI scans. After these procedures were completed, participants were randomized to the first test compound (atomoxetine 40mg or placebo), to be taken once daily in the morning for 2 days. It was then increased to twice daily for the next 3 days, followed by a final dose (40mg or placebo) on the sixth day of study medication. Participants were also asked to bring their pill packages to assess compliance with the study medication. Additionally, control participants completed a series of repeated-measures cognitive testing (on placebo or atomoxetine, respectively) and possibly two fMRI sessions (separated by a short break). After at least a four-day washout period, control participants were instructed to start the opposite test compound (atomoxetine 40mg or placebo), again to be taken once daily in the morning for two days, then twice daily for 3 days, reaching the final dose (40mg or placebo) of the second test compound in the morning. Participants were again asked to bring their pill packages to assess compliance and had blood drawn to assess atomoxetine levels. Control participants then completed another series of cognitive testing and fMRI testing sessions. They were subsequently discharged from the study. Normal control participants returned two weeks after completion of both study phases in order to complete assessments to their physical and psychological status following treatment.

  Study phs001195

• Submission FAQ

  Submission FAQ Before Submission Is the EGA the right archive for my data? TThe most suitable archive for your data is dependent on the type of data you are wishing to submit and whether the data require public or controlled access. Public access is defined as complete and open access to all submitted data. On the contrary, controlled access, exerted by the EGA, requires formal applications to be made to access the submitted data files and metadata. EGA only accepts human-derived data subject to controlled access. If your submission contains other types of data, please choose the appropriate repository for it (see image below): ENA, EVA, ArrayExpress, BioSD and GWAS catalog.  Should your submission be subjected to controlled access? Data access conditions are normally defined in the original informed consent agreements signed by the participants involved in your study. All data submitted to the EGA is subject to controlled access. These consents prevent the derived data files, potentially identifiable, from being dispersed by open and public access. Controlled access data often refers to human data derived from medical research and consortium projects. If in doubt, consult the informed consent agreements that apply to your study The EGA enables you to hold a submission before publication. What data types can be submitted to the EGA? Data types accepted by the EGA can be split into three categories: Sequences: both in generic and platform-specific formats. Array-based: from raw signal files to processed matrices. Phenotypes: all possible phenotype formats are accepted. All manufacturer-specific raw data formats derived from major next generation sequencing platforms are accepted. Also generic sequence formats: flat reads in a FASTQ file, aligned sequences (BAM or CRAM files) as well as sequence variation files in VCF format. All array-based technologies are accepted, including raw data, intensity and analysis files, without any restriction on data formats accepted. We also accept and distribute phenotype data (associated to the samples) in almost any format: from an image to a README file. How long does a submission take? Submissions to EGA come in a variety of formats and sizes, thus it is difficult for us to exactly predict how long a submission will take. We, therefore, advise all of our submitters to allow as much time as possible to make a submission. Based on previous records, we anticipate that the submission process may take at least one month. The submitter’s familiarity with the procedures, possible technical issues that may arise during submission and the amount of data that is being submitted are the main factors that will affect the length of the submission process. However, each step of a regular submission should be considered when estimating the time it would take: Encryption of the files Upload of the files Metadata submission Archival of the files Release of the study and datasets to EGA webpage For example, the upload of the files depends on the submission size, while metadata submission mainly relies on each submitter’s expertise. Further, some steps (e.g. answering to inquiries) depend on the EGA Helpdesk team, which may take some days to be processed during busy times Is data deposited in the EGA secure? The EGA set-up consists of a secure computing facility for data processing, a shared EBI set-up for data submissions and distribution of data via data requests made through the EGA website. Data is also copied in the Barcelona Supercomputing Center (BSC) infrastructure, where all stored and distributed data is encrypted Data is encrypted along the submission process and stored securely, granting its access to authorised users exclusively. During the download process, through our Python Client or Aspera, all requested data is downloaded over secure https connections. All data at the EGA is encrypted, and only accessible (for log-in and download) through secure protocols. For further information please, visit our security overview. What documentation do I need to provide? All submissions require policy’s documentation: 'Data Access Agreement (DAA)', 'Data Processing Agreement (DPA)' and 'Authorized Submitters Formulary'. The data processors (EGA) and the data owners will also sign the DPA. Will all metadata be public? Among the submitted metadata we need to make the distinction between identifiable and unidentifiable metadata: (1) the former may allow the identification of the human the sample derived from (e.g. detailed geographical providence, personal name, family ancestry…); (2) while the latter can be used to interpret the data without compromising the anonymity of the patients. The majority of the metadata submitted to the EGA corresponds to the unidentifiable category (e.g. sequencer's model). This type of metadata is publicly available on the EGA website and other EBI resources/partners’ websites. On the other hand, some parts samples’ metadata are subject to being identifiable, and thus only accessible by authorized data requesters, with the exception of: 5 submitter-defined attributes of the sample: alias, title, subject_id, gender and phenotype. It is the submitter’s responsibility not to submit sensitive metadata in these public fields. 3 anonymised fields that pinpoint the sample record in archivals: sample’s EGA stable ID (EGAN…), BioSample ID (SAMEA…) and submitter’s center name. During Submission Are there any sample specific requirements for EGA? All samples submitted to the EGA must include the attributes of biological sex, subject ID (anonymised individual identifier) and phenotype information. These are critical for data findability and its analysis, and we highly recommend using controlled ontology terms where applicable. For example: defining tumour and non-tumour samples and/or defining disease state. The EGA recommends using the Experimental Factor Ontology Database to find ontologized terms that describe your sample phenotypes. How do I get an accession number to use in my publication? You will receive your study accession number (EGAS…) upon complete your submission, either: Programmatically. As soon as the metadata is submitted and validated your study will be assigned an accession number that will be given in the submission’s response. Manually registering your study and relevant metadata using the online metadata submission tool: the EGA submitter Portal. How are files uploaded to the EGA? Data files are uploaded into private submission drop boxes (i.e. environments to which you are granted access and where you can transfer your files) using INBOX or FTP. These spaces are provided as part of the submission procedure. Before uploading any file, you must encrypt your files, . Only encrypted files shall be uploaded to the drop boxes. Why does data need to to encrypted for my submitted files? It is one of the security steps the EGA has implemented. In case of a security breach, people without the proper encryption key will not be able to read or use the information that could have been leaked. This measure is essential when working with sensitive data, such as controlled access human data. All submitters must use crypt4gh to create EGA compliant files prior to uploading them. This encryption is GPG-based, using EGA’s public key. Why are my files not available if I see them in the INBOX? There exists a time window between the data upload and the availability of such files via the Submitter Portal. For this reason, some metadata (run and analysis objects) cannot be registered until at least 24 hours after the files have been uploaded to your box. Why are MD5 sum values generated for my submitted files? We require pre- and post- encryption MD5 (message-digest) checksum values to be provided for all submitted files. These 128-bit values are computed using the content of each file, creating unique sequences that allow us to ensure that file integrity has been maintained during the transfer process. In other words, if the MD5 checksums we generate and those you generated match, we infer that the content of the transferred files is correct (i.e. files are not corrupted or truncated). MD5 checksums are computed automatically using the crypt4gh tool provided. Your submission will not be accepted and may be significantly delayed if you do not provide MD5 checksum values for all data files in the required format. How can I check if my files are correctly uploaded to the inbox? It is important to check the status pf your file so you know whether your files are in the inbox, being processed, or if there is any issue with one of them. In order to check this, you should: Look for the file locally. Drag and drop it to the file table. Then bars will appear on the table, which means that we are processing it. Green: The files checksum are correct and your file will move to “ingested files”. No further actions are needed from your end.. Red: The files checksum does not match and your file needs to be re-uploaded. Please re-upload relevant files to your inbox using the same path. After Submission How do I use my accession number in my publication? We suggest the use of the below template, using your study accession ID (EGAS…) : Data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found at https://ega-archive.org and "The European Genome-phenome Archive of human data consented for biomedical research" Your study ID will be the one that groups your whole submission, and thus its usage is recommended as such. Nevertheless, all metadata submitted to EGA hold a unique and persistent identifier (starting with EGA…) that can be used to identify specific records. For example, you could reference a specific dataset (EGAD…) or sample (EGAN…) in your publications (see full list of identifiers). How do I make my data searchable? Once you have finalized your submission, you can schedule the data release. Please take into account that the release process needs time for the files to be archived in our system, and for the Helpdesk team to validate your submission. Can I withdraw (meta)data from the EGA? We have methods in place for the secure removal of deposited (meta)data. Contact EGA-helpdesk for further details. EGA complies with FAIRness of (meta)data, and thus, even when the data is removed we keep an entry for their identifiers in our system. In other words, we execute a soft delete on canceled objects (e.g. a study): metadata is still stored in our systems, but it loses all links, cannot be queried and data files cannot be retrieved anymore. The reason behind this behaviour is so that queries using withdrawn data properly respond back (see example of a canceled study). What happens to the data once it has been submitted to the EGA? When the data is submitted, the submitter can choose either keep their data private or schedule the release of their data.

  Documentation submission/metadata/submission/FAQ

• How to upload Crypt4GH files

  Uploading files Users who hold an ega-box-XXX account can upload files using either INBOX or FTP. Users who have a Submitter role associated with their email will only be able to upload files using INBOX. Before uploading your files, please make sure that any files that will be uploaded to EGA do not use special characters in their naming convention, such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | &. This can cause issues with the archiving process, leading to problems for end users. The EGA is a shared, public service with limited storage. To manage the available resources, we enforce a limit of 10TB per submission account at any one time. If you exceed this limit, a “permission denied” message will be displayed. This will prevent you from uploading more files, but connecting to your inbox.For submissions larger than 10TB, please perform uploads in 10TB batches: register all the metadata and then finalise the submission. Upload the next batch of files and repeat the same metadata registration and finalisation process until you have completed the file upload. Further information can be found in the SP documentation. INBOX FTP The INBOX is only compatible with files encrypted using the Crypt4gh tool Before uploading If you are not a registered EGA user, you will first need an EGA user account. Please note that it may take a few days for your account to be activated, as it needs to be vouched for by the EGA Helpdesk. Once your account is validated, you will be able to request a submitter role. [Optional] Meanwhile, you can create and add your public key to your EGA account profile. This option is not available for old submission accounts (e.g., ega-box-NNN). As soon as you have been granted a submitter role, you will be able to connect with your username and password to the EGA inbox using the SFTP protocol. If you have also registered a public key in your profile, you can also connect using this key. To upload files to your account, you can use the graphical user interface (GUI) or the command line. Graphical User Interface (GUI)We recommend using FileZilla, a free, open-source FTP client. However, you can use any other GUI that allows connecting over the SFTP protocol. For FileZilla as your GUI, follow these steps to upload files: Create a new connection in Site Manager (File > Site Manager) and select the following options (Figure 1): Protocol: SFTP - SSH File Transfer ProtocolHost: __EGA_INBOX_DOMAIN__Logon Type: Key fileUser: your EGA usernameKey file: Path/to/your/private_keyFigure 1: Process of establishing a new connection to __EGA_INBOX_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 inbox, ensuring seamless data transfers.Click Connect, and you will log in remotely to your home directory. You can think of this folder as a storage "in the EGA cloud" in which you will add your files for the EGA. The uploading area has three folders:To-encrypt: Files uploaded in this folder will be encrypted automatically on the fly.Encrypted: Files uploaded in this folder must already be encrypted with Crypt4gh. Upload your files here if your connection is unstable or you have problems completing the upload into-encrypt.Etc: This folder contains two files that allow the server to show you your username and group instead of some internal numbers. Please do not upload files here; otherwise, you will obtain a permission denied error. Find the files you want to upload by browsing your local storage (left side of your screen in FileZilla). Select all the files you want to upload, then right-click on them and select Upload (Figure 2). Figure 2: Step-by-step process of manually uploading files to __EGA_INBOX_DOMAIN__ using FileZilla, with FileZilla version 3.52.2 operating on IOS v11.2.3. The figure demonstrates how users can transfer data from their local storage to the "EGA cloud" by following the depicted steps Please note that regardless of which folder you upload your files in, both folders (to-encrypt, encrypted) will point to the same path (/) (Figure 3). Therefore, you will see your files in both folders. Figure 3: Both folders, to-encrypt and encrypted, point to the same path (/)" If your connection is unstable, please encrypt your files first using Crypt4gh. Then upload them to the ‘encrypted’ folder. The example above shows how to connect to __EGA_INBOX_DOMAIN__ using the private key. However, if you prefer to log in using your credentials, you can do so. Please go to the Frequently Asked Questions (FAQs)  for more information. SFTP command line To upload files securely to your private area of the EGA, you can use SFTP(Secure File Transfer Protocol) with your favorite FTP client. Here's what you need to know to get started: Connect to the target host __EGA_INBOX_DOMAIN__. This is the new hostname for the EGA SFTP service. Log in with your EGA username and key files (or password). Upload files to your private EGA inbox to ensure that only you can access the files. By following these steps, you can securely upload your files to the EGA for safe storage and sharing. Using the SFTP command line client in Linux/Unix Open a terminal and type sftp username@hostnameEnter your EGA passwordTo see a list of available SFTP commands, type helpsftp> put – Upload filesftp> get – Download filesftp> cd path – Change remote directory to ‘path’sftp> pwd – Display remote working directorysftp> lcd path – Change the local directory to ‘path’sftp> lpwd – Display local working directorysftp> ls – Display the contents of the remote working directorysftp> lls – Display the contents of the local working directoryType the "put" command to upload files. For example: put *.bamUse the bye command to close the connection (SFTP session). After uploading- Once you have uploaded files to the inbox, please bear in mind that the checksum needs to be calculated, which can take up to two days. You will only be able to link your files to a run/analysis once the encrypted checksum has been calculated.- When linking your files to the 'Run' or 'Analysis', ensure that the file name matches the file path '/name' in the INBOX folder.- Please delete the files from your SFTP INBOX after all the runs/analyses have been registered and files are ingested (SP > Files > Files ingested). This will clear your inbox space an allow you to upload more files. This will also prevent the files from reappearing in your Submitter Portal inbox. Frequently Asked Questions Specific to the inbox What username should I use to log in to my inbox? The authentication process for logging in to the EGA website, as well as accessing your inbox and outbox, requires the use of your username.  If you have forgotten your registered username, please contact our Helpdesk team for assistance. How are checksums calculated in your inbox? If you encrypt the file beforehand and upload it to the "encrypted" folder, the unencrypted checksum will not be calculated until the file is ingested (i.e., until it is used in a run/analysis). If the file is uploaded to the "to-encrypt" folder, then both checksums are calculated.Please bear in mind that after files have been uploaded to the inbox, the checksum must be calculated, which can take from a few hours to two days.  Specific to using keys to authenticate 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_INBOX_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 submission/data/uploading-files/inbox

• Oncoarray Consortium - Lung Cancer Studies

  The study was conducted under the auspices of the Transdisciplinary Research In Cancer of the Lung (TRICL) Research Team, which is a part of the Genetic Associations and MEchanisms in ONcology (GAME-ON) consortium, and associated with the International Lung Cancer Consortium (ILCCO). Ethics: All participants provided written informed consent. All studies were reviewed and approved by institutional ethics review committees at the involved institutions. Sequencing data are derived from four substudies. The substudies that contributed include Harvard, Liverpool, Toronto, and IARC. The Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study is a randomized primary prevention trial including 29,133 male smokers enrolled in Finland between 1985 and 1993. Participants ranged between ages of 50 to 69 at enrollment and were randomized in a factorial design to take either 50 milligrams of d-alpha tocopheryl acetate (Vitamin E), 20 mg of all-trans-beta-carotene, both or placebo. The study continued to monitor cancer incidence through 2012 and total mortality through December 2013. The CAncer de PUlmon en Asturias Study (CAPUA) is a hospital-based case-control study conducted in Asturias, Spain by the University of Oviedo. Lung cancer cases were recruited in three main hospitals of Asturias, following an identical protocol from 2002 to 2012. Eligible cases were incident cases of histologically confirmed lung cancer between 30 and 85 years of age and residents in the geographical area of each participating hospital. Controls were selected from patients admitted to those hospitals with diagnoses unrelated to the exposures of interest and individually matched by ethnicity, gender, age (&#177; 5 years) and hospital. Epidemiologic data were collected personally through computer-assisted questionnaires by trained interviewers during the first hospital admission. Structured questionnaires collected information on sociodemographic characteristics, recent and prior tobacco use, environmental exposure (air pollution and passive smoking), diet, personal and family history of cancer, and occupational history from each participant. Peripheral blood samples (or mouthwash samples when they refused to donate blood) were collected from all participants. Coding of histology was based on 2001 WHO/IASLC. Genomic DNA was extracted based on standard protocol. The Canadian Screening Study includes the nested case-control samples from 3 screening programs: IELCAP-Toronto: Ever smokers of more than 10 pack-years age 50 and above were eligible for the I-ELCAP screening program since 2003, and a total of 4782 individuals have been enrolled in the Greater Toronto Area. Participants were administered a LDCT scan along with a standard study questionnaire at baseline. Blood samples were systematically collected at baseline since 2006. Participants who had an abnormality in a CT scan were followed up every 1 to 2 years. The screening program was organized by the Princess Margaret Hospital. PanCan: Ever smokers between the ages of 50-75 with no previous history of invasive cancer are eligible to participate in the study. The study was carried out across Canada in Vancouver, Calgary, Hamilton, Toronto, Ottawa, Quebec, Halifax, and St. John's. A total of 2537 smokers have been screened from 2008 to 2011. All study participants completed a detailed questionnaire, spirometry, collection of blood specimens for biomarker measurement and LDCT at baseline. All participants are followed for a minimum of 3 years. On yearly follow up, an updated shorter questionnaire is administered, blood is collected and CT scans are performed. Blood samples are available from all 2537 individuals. BCCA Screening Program: From 1990 to 2007, 4274 smokers above 40 years old who had smoked 20 pack-years or more were enrolled at BCCA. Upon enrollment, subjects completed a questionnaire for their lifestyle and medical history. Baseline spirometry was conducted using a flow-sensitive spirometer in accordance with the American Thoracic Society recommendations. Since 2000, a LDCT was obtained in 2440 individuals. The participants were followed prospectively to determine whether they developed lung cancer. A total of 9759 individuals participated in the CT screening program in Canada from these 3 programs. The samples included in this project is based on a subset of nested lung cancer case-control pairs based on 1:2 ratio. The Carotene and Retinol Efficacy Trial (CARET) was a randomized, double-blind, placebo-controlled trial of the cancer prevention efficacy and safety of a daily combination of 30 mg of beta-carotene and 25,000 IU of retinyl palmitate in 18,314 persons at high risk for lung cancer. CARET began in 1985, and the intervention was halted in January 1996, 21 months ahead of schedule, with the twin conclusions for definitive evidence of no benefit and substantial evidence of a harmful effect of the intervention on both lung cancer incidence and total mortality. CARET continued to follow and collect endpoints on their participants through 2005. Pathology reports and medical records were reviewed to confirm cancer endpoints, and death certificates obtained to capture cause of death. During the active intervention phase of CARET, serum, plasma, whole blood, and lung tissue specimens were collected on participants. These biospecimens make up the CARET Biorepository. For the OncoArray Project, CARET provided DNA extracted from whole blood of lung cancer cases and controls matched on age at baseline (&#177; 4 years), sex, race, baseline smoking status, history of occupational asbestos exposure (asbestos vs heavy smoker), and year of enrollment (2-year intervals). The European Prospective Investigation into Cancer and Nutrition (EPIC) study is a multi-center cohort study involving 521,000 study participants from 10 European countries. The current study involved EPIC participants from 7 countries (Greece, Netherlands, UK, France, Germany, Spain, and Italy), including 1223 incident lung cancer cases and 1249 smoking matched controls. The Kentucky Lung Cancer Research Initiative is a study conducted by the Markey Cancer Center Cancer Center and the University of Kentucky using a population-based, case-control framework to study the extraordinarily high rates of lung cancer in Southeastern, Appalachian Kentucky. Cancer cases were recruited from the Kentucky Cancer Registry at the time of diagnosis and controls were recruited from a random digit dialing process from the same region. Study accrual began in January 5, 2012 and completed on September 5, 2014 and 520 subjects were recruited in a 4:1 ratio of controls: cases from Appalachian Kentucky. Of the 520 subjects recruited, 231 are included in the OncoArray analysis, including all 93 cancer cases, and 123 controls. Newly diagnosed lung cancer cases and controls underwent blood, toenail (for trace element analysis), urine, buffy coat, water, soil, and radon collection, residence GPS mapping, as well as an extensive epidemiologic, occupational, and health history questionnaire (Clinical Trials.gov Identifier: NCT01648166). The Harvard Lung Cancer Study (HLCS) is a case-control study based at Mass General Hospital (MGH) in Boston, Massachusetts from 1992 to 2004. Details of the study were described previously. Briefly, eligible cases included any person over the age of 18 years with a diagnosis of primary lung cancer that was further confirmed by an MGH lung pathologist. Controls were recruited from the friends or spouses of cancer patients or the friends or spouses of other surgery patients in the same hospital. Potential controls were excluded from participation if they had a diagnosis of any cancer (other than non-melanoma skin cancer). Interviewer-administered questionnaires, a modified version of the standardized American Thoracic Society respiratory questionnaire, collected information on demographics, medical history, family history of cancer, smoking history, and a detailed work history, including job titles and tasks. Genome-wide genotype data were first generated using Illumina Human 610-Quad BeadChips and then imputed by MACH against the 1000 Genome Project dataset (http://browser.1000genomes.org/index.html). The Institutional Review Board of MGH and the Human Subjects Committee of the Harvard School of Public Health approved the study. The Israel study (NICCC-LCA) is an ongoing case-control study of newly diagnosed lung cancer cases of any histology and population age/sex/ethnicity-matched "healthy" controls. All participants undergo face-to-face interviews, provide a venous blood sample (separated into DNA, Sera, lymphocytes) after signing an IRB-approved form. Histology reports, FFPE blocks and clinical follow-up are available for most cancer cases. The MD Anderson Cancer Center (MDACC) Study. Lung cancer cases and frequency-matched controls were ascertained from a large ongoing case-control study at the University of Texas MD Anderson Cancer Center (UTMDACC) since 1991. Detailed study description was provided previously (Spitz et al 2007). In brief, cases were newly-diagnosed and histologically confirmed lung cancer patients recruited from UTMDACC. Controls were healthy individuals without a history of cancer (except for nonmelanoma skin cancer) and recruited from the Kelsey-Seybold Clinics, the largest private multispecialty physician group in the Houston metropolitan area. Controls were frequency-matched to cases on age (&#177;5 years), sex, and race/ethnicity. After providing written informed consent, each study participants completed an in-person interview by staff interviewers to collect information on demographics, smoking status, etc. Blood samples were also drawn from all the study participants. This study was approved by institutional review boards of UTMDACC and Kelsey-Seybold Clinics. The Malm&#246; Diet and Cancer Study (MDCS) is a population-based prospective cohort study that recruited men and women aged at 44 to 74 years old of living in Malm&#246;, Sweden between 1991 and 1996. The main goal of the MDCS is to study the impact of diet on cancer incidence and mortality. It consists of a baseline examination including dietary assessment, a self-administered questionnaire, anthropometric measurements and collection of blood samples. A total of 165 incident lung cancer cases and 174 individually smoking-matched controls were available for this analysis. The Multiethnic Cohort (MEC) Study includes 215,251 men and women aged 45-74 years at recruitment, primarily from five ethnic/racial groups - African Americans and Latinos mostly recruited from CA (mainly from Los Angeles County) and Japanese Americans, Native Hawaiians and whites (mostly recruited from HI). The cohort was assembled in 1993-1996 by mailing a self-administered questionnaire to persons identified primarily through driver's license files. The baseline questionnaire obtained information on demographics, anthropometry, smoking history, medical and reproductive histories, family history of cancer, diet and physical activity. Incident cancer cases are identified by regular linkage with the State of California Cancer Registry and the Hawaii Tumor Registry, both members of the SEER Program of the NCI. In 2001-2006, a prospective biorepository was assembled by collecting a pre-diagnostic blood specimen from 67,594 surviving MEC members. At the time of blood collection a short questionnaire was administered that included information on smoking during the previous 15 days. For this study, cases were all lung cancer cases incident to blood draw and diagnosed before December 2012. For each case, a control was selected among unaffected MEC participants who were alive at time of the case's diagnosis and matched on study site, sex, race/ethnicity, age (age at diagnosis for cases; age at blood collection for controls), and date of blood collection. The Mount-Sinai Hospital-Princess Margaret Study (MSH-PMH) was conducted in the greater Toronto area from 2008 to 2013. Lung cancer cases were recruited at the hospitals in the network of the University of Toronto. Controls were selected randomly from individuals registered in the family medicine clinics databases and were frequency matched with cases on age and sex. All subjects were interviewed, and information on lifestyle risk factors, occupational history and medical and family history was collected using a standard questionnaire. Tumors were centrally reviewed by the reference pathologist, a member of the International Association for the Study of Lung Cancer (IASLC) committee, and a second pathologist in the University Health Network. If the reviews conflicted, a consensus was arrived at after discussion. Coding of histology was based on 2001 WHO/IASLC. Genomic DNA was extracted based on standard protocol. The New England Lung Cancer Study (NELCS) is a population-based case-control study of lung cancer among residents of Northern and Central New Hampshire counties and the bordering region of Vermont. Cases with histologically confirmed primary incident lung cancer were identified from 2005 to 2007 using the New Hampshire State Cancer Registry and the Dartmouth-Hitchcock Medical Center (DHMC) Tumor Registry. Control participants were identified using a commercial database and matched to lung cancer cases within 5-year age groups, sex and county. Genomic DNA was isolated from blood or buccal specimens provided by consenting participants. The study complied with requirements of the Dartmouth College's Committee for Protection of Human Subjects. The Nijmegen Lung Cancer Study. The Netherlands patients with lung cancer were identified through the population-based cancer registry of the Netherlands Comprehensive Cancer Organisation in Nijmegen, the Netherlands. Patients who were diagnosed in one of three hospitals (Radboud University Medical Center, Canisius Wilhelmina Hospital in Nijmegen, and Rijnstate Hospital in Arnhem) since 1989 and who were still alive at April 15th, 2008 were recruited for a study on gene-environment interactions in lung cancer. 458 patients gave informed consent and donated a blood sample. This case series was expanded with 94 patients to a total of 552 by linking three other studies to the population-based cancer registry in order to identify new occurrences of lung cancer among the participants of these other studies. All three other studies (i.e., POLYGENE, the Nijmegen Biomedical Study, and the Radboudumc Urology Outpatient Clinic Epidemiology Study) were initiated to study genetic risk factors for disease and participants to these studies gave general informed consent for DNA-related research and linkage with disease registries. Information on histology, stage of disease, and age at diagnoses was obtained through the cancer registry. Lifestyle information was collected through a structured questionnaire and whole blood for DNA isolation was collected by the regional thrombosis services. The cancer-free controls (46% males) were selected from participants of the "Nijmegen Biomedical Study" (NBS), an age- and sex-stratified random sample of the general population of the municipality of Nijmegen, The Netherlands. All participants provided extensive lifestyle information by structured questionnaires and blood samples for DNA isolation, serum and plasma. All controls are of self-reported European descent. The study protocols of the NBS were approved by the Institutional Review Board of the Radboudumc and all study subjects signed a written informed consent form. The Northern Sweden Health and Disease Study (NSHDS) encompasses several prospective cohorts. The current study involves participants from the V&#228;sterbotten Intervention Project (VIP), a sub-cohort within NSHDS. VIP is an ongoing prospective cohort and intervention study intended for health promotion of the general population of the V&#228;sterbotten County in northern Sweden. VIP was initiated in 1985 and all residents in the V&#228;sterbotten County were invited to participate by attending a health check-up at 40, 50 and 60 years of age. Participants were asked to complete a self-administered questionnaire including various demographic factors such as education, smoking habits, physical activity and diet. In addition, height and weight were measured and participants were asked to donate a fasting blood sample for future research. A total of 243 incident lung cancer cases and 266 individually smoking-matched controls were available for this analysis. Norway National Institute of Occupational Health Study. Early-stage NSCLC cases and healthy controls at the time of enrollment were Caucasians of Norwegian origin and were recruited from the same geographical region (Western Norway). The patients were enrolled in the study, whenever practically feasible among patients admitted for lung cancer at the Haukeland University Hospital in Bergen, Norway. The informed written consents covering analysis of molecular and genetic markers was signed by the patients prior to surgery. Only patients with histologically confirmed early-stage NSCLC were included in our study. The subjects included in this project are a subgroup recruited into the project "lung cancer genetics" at NIOH. The controls were recruited from the same geographical region of Western Norway and frequency-matched with cases on cumulative smoking dose (pack-years). Pack-years smoked [( 20 cigarettes per day) x years smoked] were calculated to indicate the cumulative smoking dose. The Cases and controls were interviewed using similar questionnaires and were categorized as never smokers, ex-smokers or current smokers. Never smokers are subjects indicating having smoked less than 100 cigarettes in their life time. Ex-smokers were defined as those having quitted at least 1 year before sampling, and current smokers were those indicating that they were smokers at the time of sampling. The project has been approved by the Regional Committee for Medical and Health Research Ethics in Southern Norway in accordance with the WMA Declaration of Helsinki. The ethical approval covered access to the NSCLC databank. The Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO) Study, a randomized trial aimed at evaluating the efficacy of screening in reducing cancer mortality, recruited approximately 155,000 men and women age 55 to 74 years from 1992 to 20014. Screening for lung cancer among participants in the intervention arm included a chest x-ray at baseline followed by either three annual x-rays (for current or former smokers at enrollment) or two annual x-rays (for never smokers); participants in the control arm received routine health care. Screening-arm participants provided data on sociodemographic factors, smoking behavior, anthropometric characteristics, medical history, and family history of cancer, as well as blood samples annually for the first 6 years of the study (baseline T0 and T1 through T5). Lung cancers were ascertained through annual questionnaires mailed to the participants, and positive reports were followed up by abstracting medical records or death certificates. Follow-up in the trial as of July 2009 was 96.7%. Patients were excluded because of missing baseline questionnaire, previous history of any cancer, diagnosis of multiple cancers during follow-up, missing smoking information at baseline, missing consent for utilization of biologic specimens for etiologic studies, or unavailability/insufficient quantity of serum or DNA specimens. The Resource for the Study of Lung Cancer Epidemiology in North Trent (ReSoLuCENT) is an ongoing study conducted in Sheffield from 2006 and due to complete recruitment in 2016. The study recruited pathologically confirmed lung cancer cases diagnosed at age 60 years or younger and family matched controls. Lung cancer cases diagnosed at ages older than 60 years were recruited if they reported a family history of lung cancer. The cases and matched controls were recruited through several major cancer treatment centers, however, the majority were recruited in North Trent. All participants completed a detailed lifestyle questionnaire which included questions about occupational exposures, education, medical history and family history of cancer and lung disease. Participants also donated blood samples for DNA extraction. The ReSoLuCENT study has been funded by the Sheffield Hospitals Charity, Sheffield ECMC and Weston Park Hospital Cancer Charity. First degree relatives were removed from the sample deposited to dbGaP. The Roy Castle Lung Study of Liverpool Lung Project (LLP) is a case-control and cohort study which has recruited over 11,500 individuals since 1996 from the Liverpool region in the UK. Detailed epidemiological and clinical data is collected with associated specimens (i.e. tumor tissue, blood, plasma, sputum, bronchial lavage and oral brushings). The participants have completed a detailed lifestyle questionnaire at recruitment, with repeat questionnaires at intervals; updated data on clinical outcome and hospital events are collected through the Health and Social Care Information Center (including Office of National Statistics mortality data, Cancer Registry and Health Episode Statistics). The project is registered on the UK National Institute for Health Research (NIHR) lung cancer portfolio and has all the required ethical approvals and sponsorship arrangements in place. The lung tumors were reviewed by the reference pathologist. The Seoul Bundang Lung Cancer Study was conducted between 2005 and 2010 to discover genetic and environmental factors related with lung cancer development. Lung cancer cases were recruited at the Seoul National University Hospital in Bundang. Controls were selected randomly from individuals participated in health check-up program and were frequency matched with cases on age and sex. All subjects were interviewed, and information on lifestyle risk factors, occupational history and medical and family history was collected using a standard questionnaire. Tumors were reviewed by the pathologists in the hospital. If the reviews conflicted, a consensus was arrived at after discussion. Coding of histology was based on 2001 WHO/IASLC. Genomic DNA was extracted based on standard protocol. The Shanghai Cohort Study (SCS) consisted of 18,244 men in Shanghai, China, who were 45-64 years old at the time of enrollment during 1986-1989. Approximately 80% of eligible men participated in the study. At the time of recruitment, each cohort subject was interviewed in-person by a trained nurse interviewer using a structured questionnaire that included background information, history of tobacco and alcohol use, current diet, and medical history. At the completion of the interview, the nurse collected a 10 ml blood and a single void urine specimen from the study participant. The buccal cell samples were collected from all surviving cohort members (~15,000) in the 2001-2002 follow-up interviews. The cohort has been followed for the occurrence of cancer and death through routine ascertainment of new cases from the population-based Shanghai Cancer Registry and Shanghai Vital Statistics Units. To maximize the cancer findings and minimize the loss of follow-up, we contacted each surviving cohort member annually. Retired nurses visit the last known address of each living cohort member and record details of the interim health history of the cohort member. As of December 31, 2014, cumulatively 612 (3.4%) original subjects were lost to follow-up, and 574 (3.1%) refused to our continued follow-up interview. A nested case-control study of incident lung cancer cases within the Shanghai Cohort Study was used to examine the association between serum levels of vitamin B6 and other compounds in the one-carbon metabolism pathway and risk of lung cancer. Briefly, 516 lung cancer cases were identified among cohort participants with available serum samples as of 12/31/2006. For each case, we randomly selected one control subject from all cohort members who were free of cancer and alive at the time of cancer diagnosis of the index case. Controls were matched to the index case by age at enrollment (&#177;2 years), date of biospecimen collection (&#177;1 month) and neighborhood of residence at recruitment, and smoking status (current, former and never smokers) as established previously for other studies. For former smokers, cases and controls were further matched by years since quitting smoking (&lt;10 vs &#8805;10 years). One serum vial per subject was retrieved from biorepository and all serum samples were sent to the laboratory (B-vital) for measurements. DNA samples of 250 lung cancer cases and 250 matched controls were available for the present study. The Singapore Chinese Health Study (SCHS) cohort consisted of 63,257 Chinese men and women in Singapore when they were 45-74 years old at the time of enrollment between April 1993 and December 1998. At recruitment, each study subject was interviewed in person by a trained interviewer using a structured questionnaire that emphasized current diet assessed via a validated, 165-item food frequency questionnaire. The questionnaire also requested information on demographics, lifetime use of tobacco, incense use, current physical activity, usual sleep duration, reproductive history (women only), occupational exposure, medical history, and family history of cancer. Blood or buccal cell, and spot urine samples were collected first from a random 3% sample of cohort participants in April 1994, and extended to all surviving cohort participants starting in January 2000. Overall approximately 60% of eligible cohort participants donated biospecimens. The cohort has been passively followed for death and cancer occurrence through regular record linkage with the population-based Singapore Cancer Registry and the Singapore Registry of Births and Deaths. Migration out of Singapore, especially among housing estate residents, was negligible. As of latest update, only 55 individuals from this cohort were known to be lost to follow-up due to migration and other reason. A nested case-control study of incident lung cancer cases within the Singapore Chinese Health Study was used to examine the association between serum levels of vitamin B6 and other compounds in the one-carbon metabolism pathway and risk of lung cancer. As of 12/31/2011, 422 lung cancer cases were identified among cohort participants with available prediagnostic plasma samples. For each case, one control subject was randomly selected from all eligible cohort members who were alive and free of cancer on the date of cancer diagnosis of the index case. The control subject was individually matched to the index case by gender, dialect group (Hokkien, Cantonese), age at enrollment (&#177;3 years), date of baseline interview (&#177;2 year), date of biospecimen collection (&#177;6 months), and smoking status (current, former, and never smokers). For current smokers, cases and controls were further matched by number of cigarettes per day (&lt;15, &#8805;15 cigarettes/day). For former smokers, cases and controls were further matched by years since quitting smoking (&lt;10, &#8805;10 years). One plasma aliquot per subject was retrieved from the biorepository and all plasma samples were sent to the laboratory (B-vital) for measurements, and one aliquot of DNA per subject for the present study. The International Agency for Research on Cancer (IARC) L2 Study. Lung cancer cases and controls were recruited through a multicentric case-control study coordinated by the IARC in Russia, Poland, Serbia, Czech Republic, and Romania from 2005 to 2013. Cases were incident cancer patients collected from general hospitals. Controls were recruited from individuals visiting general hospitals and out-patient clinics for disorders unrelated to lung cancer and/or its associated risk factors, or from the general population. Information on lifestyle risk factors, medical and family history was collected from subjects by interview using a standard questionnaire. All study participants provided written informed consent. The current study included 1,133 lung cancer cases and 1,117 controls genotyped on the Oncoarray. The Washington State University Lung Cancer Study is a hospital case-control study of 511 subjects with newly-diagnosed (within 1 year of diagnosis) lung cancer and 820 race-, sex- and age-matched controls. Lung cancer cases were recruited from lung cancer clinics within the H. Lee Moffitt Cancer Center while controls were recruited from the Lifetime Cancer Screening Center, a H. Lee Moffitt Cancer Center affiliate. None of the controls were diagnosed with any form of cancer at the time of screening. Detailed questionnaire data and oral buccal cells were collected for all subjects. The Total Lung Cancer (TLC) Study is a hospital-based study that included 458 lung cancer patients recruited for Moffitt Cancer Center's Total Cancer Care&#8482; protocol between April 2006 and August 2010. Total Cancer Care&#8482; is a multi-institutional observational study of cancer patients that prospectively collects self-reported demographic and clinical data, medical record information and blood samples for research purposes. All patients used in this cohort were recruited from the Thoracic Oncology Clinic at the Moffitt Cancer Center. The Vanderbilt Lung Cancer Study (BioVU) is a case-control study nested within the Vanderbilt University Medical Center biobank, BioVU. BioVU is a biorepository of DNA extracted from blood drawn from patients seeking routine clinical care at Vanderbilt University Medical Center and linked to de-identified electronic health records for research purposes. Lung cancer cases and controls were identified from BioVU participants in February 2014. Lung cancer cases were identified from the Vanderbilt tumor registry. All specimens undergo pathologic review for determination of morphology. Coding of histology was based on SEER Program Coding Guidelines. Controls were randomly selected from BioVU participants, excluding cancer patients, and were matched to cases on age (&#177; 5 years), sex, and race. Relevant covariates were identified from electronic health records using natural language processing. Genomic DNA was extracted based on a standard protocol.

  Study phs001273

• eMERGE Network Phase III Clinical Sequencing: eMERGEseq Panel

  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 September 2007 and began its third phase in September 2015. eMERGE III consists of nine study sites, two central sequencing and genotyping facilities, and a coordinating center. eMERGE Phase III aims to: 1) sequence and assess the phenotypic implication of rare variants in a custom designed eMERGEseq panel consisting of 109 genes (including 56 ACMG actionable finding list genes and the top 6 genes from each site relevant to their specific aims), as well as approximately 1400 SNPs; 2) assess the phenotypic implications of these variants by developing, validating and implementing new phenotype algorithms, 3) integrate genetic variants into EMRs to inform clinical care; and 4) create community resources. Included in this study are: ~24,000 eMERGE participants from 10 eMERGE III study sites. Corresponding demographics, body mass index measurements. Top PheWAS codes generated from a collated list of ICD codes from all study sites. Study sites and participants include: Cincinnati Children&#39;s Hospital Medical Center (CCHMC): Cincinnati Children&#39;s Hospital Medical Center (CCHMC) is a not-for-profit hospital and research center pioneering breakthrough treatments, providing outstanding family-centered patient care and training healthcare professionals for the future, and dedicated to improving health and welfare of children and to the shared purpose of discovery and practical application of new genomic information to the ordinary care of children. We bring a comprehensive electronic health record (EPIC), a deidentified i2b2 data warehouse of 680K patient records, a biobank with &#62;261,000 consents that allow return of results to &#62;84,000 patients and guardians who have provided DNA samples, and hundreds of faculty and senior staff who make genomics or informatics an active focus of their research. CCHMC will help the eMERGE III Steering Committee identify genes for the eMERGE III targeted sequencing panel, provide 3,000 DNA samples from CCHMC patients to be sequenced, review targeted gene panels from clinical care at CCHMC for somatic mosaicism and reinterpretation, and further develop and disseminate a software workflow suite for sequence analysis. We will also extend our work generating phenotype algorithms using heuristic and machine learning methods to many new childhood diseases. We will develop tools to evaluate adolescent return of results preferences, examine the ethical and legal obligations and potential to reanalyze results, and develop clinical decision support for phenotyping, test ordering, and returning sequencing results. Children&#39;s Hospital of Philadelphia (CHOP): The Center for Applied Genomics (CAG) is a specialized Center of Emphasis at the Children&#39;s Hospital of Philadelphia (CHOP), and one of the world&#39;s largest genetics research programs, with to state-of-the-art high-throughput sequencing and genotyping technology. Our primary goal is to translate basic research findings to medical innovations. We aim to develop new and better ways to diagnose and treat children affected by rare and complex medical disorders, including asthma, autism, epilepsy, pediatric cancer, learning disabilities, and a range of rare diseases. Ultimately, our objective is to generate new diagnostic tests and to guide physicians to the most appropriate therapies. Participants were recruited from the CAG biorepository (n&#62;450,000), specifically from &#62;100,000 CHOP pediatric patients and family members, which is enriched for rare-diseases (n&#62;12,000). Center for Applied Genomics, The Children&#39;s Hospital of Philadelphia We gratefully thank all the children and their families who enrolled in this study, and all individuals who donated blood samples for research purposes. Genotyping for this project was performed at the Center for Applied Genomics and supported by an Institutional Development Award from The Children&#39;s Hospital of Philadelphia. Sequencing was supported by the National Institutes of Health through an award from the National Human Genome Research Institute&#39;s Electronic Medical Records and Genomics (eMERGE) program (U01HG008684). Columbia University: The goal of the Columbia eMERGE III project is to develop methods for integrating genomic data in EHRs and to study the impact of such genomic informatics interventions on the health of a diverse, underserved urban adult English- and Spanish-speaking patient population in Northern Manhattan served by Columbia University Medical Center/New York-Presbyterian Hospital system. The study group is 2500 patients recruited from diverse clinics and community outreach centers of self-reported White (~61%), Asian (~11%), African-American (~11%), American Indian/Alaska Native (&#60;1%) racial and Hispanic (~33%) ethnic backgrounds. There are two subgroups in the study cohort - a retrospective group (N=1052) that includes patients from oncology and nephrology clinics, and a prospective one (N=1448) that includes healthy individuals as well as participants with diverse medical conditions. Confirmed pathogenic variants in 70 selected genes will be returned to participants and their healthcare providers through the EHR integration. Participants are able to choose the results they receive and will have the freedom to meet with a genetic counselor and a geneticist to review results. The impact of genetic testing on clinical care is determined by periodic monitoring of EHRs. Geisinger: Samples and phenotype data in this study were provided by the Geisinger MyCode&#174; Community Health Initiative. Participants are recruited across the Geisinger System via online consents or in-person consents at a hospital or clinic visit. Enrollment is ongoing with over 100,000 individuals currently consented. Partners Healthcare (Harvard University): The Partners HealthCare Biobank is a large research program designed to help researchers understand how people&#39;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&#39;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&#39;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 70,000 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 130 million dollars in NIH research. Kaiser Permanente Washington/ (KPWA) / University of Washington (UW): 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=2,500.) 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&#39;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=1,255), in order to allow us to enrich germline pathogenic variants. Mayo Clinic: The Return of Actionable Variants Empirical (RAVE) Study was approved by the Mayo Clinic IRB. We recruited 2537 participants from Mayo Clinic biobanks in Rochester, MN, who had hypercholesterolemia or colon polyps, thereby enriching for Familial hypercholesterolemia (FH) and monogenic causes of colorectal cancer (CRC). Additional eligibility criteria were: 1) residents of Southeast MN who were alive and aged 18-70 years; 2) LDL-C level &#62;155 or &#62;120 mg/dl while on lipid-lowering therapy; 3) no known cause of secondary hyperlipidemia; and 4) no cognitive impairment or dementia that would compromise their ability to give written informed consent. Based on these criteria, we identified 5270 eligible patients and obtained informed consent from 3030 participants. Recruitment was conducted in waves and utilized mailed recruitment packets consisting of a study brochure, a written informed consent form, a baseline psychosocial questionnaire, and a return postage-paid envelope. DNA of 2537 participants was sent for CLIA-certified targeted sequencing of 109 genes including genes associated with FH and CRC. Targeted sequencing and genotyping was performed in a Central Laboratory Improvement Amendment (CLIA)-certified laboratory. Northwestern University: Samples and data used in this study were obtained from patients from Northwestern Medicine, an integrated healthcare system, formed through a partnership of Northwestern Memorial HealthCare and Northwestern University Feinberg School of Medicine. Participants include a retrospective cohort from the Northwestern Pharmacogenomics Study, funded through the eMERGE II project, NHGRI (3U01HG006388-02S1) and a prospective cohort from the Genetic Testing and Your Health Study, funded through the eMERGE III project, NHGRI (U01HG008673). Patients were eligible to participate if they were18 years or older and see a physician at Northwestern Medicine. Patients consented to genetic testing and to allow their results to be placed in their electronic medical record. Vanderbilt University Medical Center: Vanderbilt University Medical Center (VUMC) participants were enrolled in the eMERGE Network through the Vanderbilt Genome-Electronic Records (VGER) project. Patients were provided the appropriate consent to receive clinically relevant genetic results (N=2,700). Participants were eligible if aged 21 or over, had a healthcare provider at VUMC, and visited the provider at least 3 times in the past 3 years. Meharry Medical College: Inclusion of ethnic groups in genomic research is critical to identify possible reasons for health disparities. African-Americans are being enrolled in various outpatient clinics of Nashville General Hospital at Meharry, an inner city hospital primary serving a poorer patient group. A total of 500 African Americans with four cancer types demonstrating health disparities in this population - prostate, colon, breast, lung are identified and approached by clinical research coordinators. The purpose of the study is to determine if any genetic information can be identified from these patients who have or are at high risk of one of these disparate cancers. All participants provide written informed consent and HIPAA authorization to provide blood samples for broad research use and permission to access data in their hospital electronic medical record for research now and in the future. An extensive demographic profile is obtained and entered into a REDCap database. Blood samples are obtained for a panel of alleles from extracted DNA at Baylor. In addition, de-identified coded samples are processed and stored in a central biorepository for further DNA, RNA and proteomic analyses. The survey and phlebotomy are performed at the time of the initial contact and agreement to participate. Nearly all patients approached willingly agree to participate for potential benefit to themselves, family members, or humankind. Little concern is voiced of providing samples for genetic analysis. Study investigators will share results with the participants and providers if testing does not indicate high risk. Results indicating increased risk or actionable alleles for the patient and/or family will be returned by a genetic counselor. Monitoring of the patients&#39; health in this cohort will continue to be followed in the EMR to identify any future associations that might explain health disparities in African Americans. Proposals will be reviewed from investigators to study the genetic or proteomic samples as well as the clinical and demographic information in the repository. Please note that this version of the dataset has a handful of mismatches between genotyped and provided sex. Data with the following IDs should be removed prior to analysis: 420252874213744142412243424569384245694642672223

  Study phs001616

• OncoArray: Prostate Cancer

  Original description of the study: From ELLIPSE (linked to the PRACTICAL consortium), we contributed ~78,000 SNPs to the OncoArray. A large fraction of the content was derived from the GWAS meta-analyses in European ancestry populations (overall and aggressive disease; ~27K SNPs). We also selected just over 10,000 SNPs from the meta-analyses in the non-European populations, with a majority of these SNPs coming from the analysis of overall prostate cancer in African ancestry populations as well as from the multiethnic meta-analysis. A substantial fraction of SNPs (~28,000) were also selected for fine-mapping of 53 loci not included in the common fine-mapping regions (tagging at r2&#62;0.9 across &#177;500kb regions). We also selected a few thousand SNPs related with PSA levels and/or disease survival as well as SNPs from candidate lists provided by study collaborators, as well as from meta-analyses of exome SNP chip data from the Multiethnic Cohort and UK studies. The Contributing Studies: Aarhus: Hospital-based, Retrospective, Observational. Source of cases: Patients treated for prostate adenocarcinoma at Department of Urology, Aarhus University Hospital, Skejby (Aarhus, Denmark). Source of controls: Age-matched males treated for myocardial infarction or undergoing coronary angioplasty, but with no prostate cancer diagnosis based on information retrieved from the Danish Cancer Register and the Danish Cause of Death Register. AHS: Nested case-control study within prospective cohort. Source of cases: linkage to cancer registries in study states. Source of controls: matched controls from cohort ATBC: Prospective, nested case-control. Source of cases: Finnish male smokers aged 50-69 years at baseline. Source of controls: Finnish male smokers aged 50-69 years at baseline BioVu: Cases identified in a biobank linked to electronic health records. Source of cases: A total of 214 cases were identified in the VUMC de-identified electronic health records database (the Synthetic Derivative) and shipped to USC for genotyping in April 2014. The following criteria were used to identify cases: Age 18 or greater; male; African Americans (Black) only. Note that African ancestry is not self-identified, it is administratively or third-party assigned (which has been shown to be highly correlated with genetic ancestry for African Americans in BioVU; see references). Source of controls: Controls were identified in the de-identified electronic health record. Unfortunately, they were not age matched to the cases, and therefore cannot be used for this study. Canary PASS: Prospective, Multi-site, Observational Active Surveillance Study. Source of cases: clinic based from Beth Israel Deaconness Medical Center, Eastern Virginia Medical School, University of California at San Francisco, University of Texas Health Sciences Center San Antonio, University of Washington, VA Puget Sound. Source of controls: N/A CCI: Case series, Hospital-based. Source of cases: Cases identified through clinics at the Cross Cancer Institute. Source of controls: N/A CerePP French Prostate Cancer Case-Control Study (ProGene): Case-Control, Prospective, Observational, Hospital-based. Source of cases: Patients, treated in French departments of Urology, who had histologically confirmed prostate cancer. Source of controls: Controls were recruited as participating in a systematic health screening program and found unaffected (normal digital rectal examination and total PSA &#60; 4 ng/ml, or negative biopsy if PSA &#62; 4 ng/ml). COH: hospital-based cases and controls from outside. Source of cases: Consented prostate cancer cases at City of Hope. Source of controls: Consented unaffected males that were part of other studies where they consented to have their DNA used for other research studies. COSM: Population-based cohort. Source of cases: General population. Source of controls: General population CPCS1: Case-control - Denmark. Source of cases: Hospital referrals. Source of controls: Copenhagen General Population Study CPCS2: Source of cases: Hospital referrals. Source of controls: Copenhagen General Population Study CPDR: Retrospective cohort. Source of cases: Walter Reed National Military Medical Center. Source of controls: Walter Reed National Military Medical Center ACS_CPS-II: Nested case-control derived from a prospective cohort study. Source of cases: Identified through self-report on follow-up questionnaires and verified through medical records or cancer registries, identified through cancer registries or the National Death Index (with prostate cancer as the primary cause of death). Source of controls: Cohort participants who were cancer-free at the time of diagnosis of the matched case, also matched on age (&#177;6 mo) and date of biospecimen donation (&#177;6 mo). EPIC: Case-control - Germany, Greece, Italy, Netherlands, Spain, Sweden, UK. Source of cases: Identified through record linkage with population-based cancer registries in Italy, the Netherlands, Spain, Sweden and UK. In Germany and Greece, follow-up is active and achieved through checks of insurance records and cancer and pathology registries as well as via self-reported questionnaires; self-reported incident cancers are verified through medical records. Source of controls: Cohort participants without a diagnosis of cancer EPICAP: Case-control, Population-based, ages less than 75 years at diagnosis, H&#233;rault, France. Source of cases: Prostate cancer cases in all public hospitals and private urology clinics of d&#233;partement of H&#233;rault in France. Cases validation by the H&#233;rault Cancer Registry. Source of controls: Population-based controls, frequency age matched (5-year groups). Quotas by socio-economic status (SES) in order to obtain a distribution by SES among controls identical to the SES distribution among general population men, conditionally to age. ERSPC: Population-based randomized trial. Source of cases: Men with PrCa from screening arm ERSPC Rotterdam. Source of controls: Men without PrCa from screening arm ERSPC Rotterdam ESTHER: Case-control, Prospective, Observational, Population-based. Source of cases: Prostate cancer cases in all hospitals in the state of Saarland, from 2001-2003. Source of controls: Random sample of participants from routine health check-up in Saarland, in 2000-2002 FHCRC: Population-based, case-control, ages 35-74 years at diagnosis, King County, WA, USA. Source of cases: Identified through the Seattle-Puget Sound SEER cancer registry. Source of controls: Randomly selected, age-frequency matched residents from the same county as cases Gene-PARE: Hospital-based. Source of cases: Patients that received radiotherapy for treatment of prostate cancer. Source of controls: n/a Hamburg-Zagreb: Hospital-based, Prospective. Source of cases: Prostate cancer cases seen at the Department of Oncology, University Hospital Center Zagreb, Croatia. Source of controls: Population-based (Croatia), healthy men, older than 50, with no medical record of cancer, and no family history of cancer (1st &#38; 2nd degree relatives) HPFS: Nested case-control. Source of cases: Participants of the HPFS cohort. Source of controls: Participants of the HPFS cohort IMPACT: Observational. Source of cases: Carriers and non-carriers (with a known mutation in the family) of the BRCA1 and BRCA2 genes, aged between 40 and 69, who are undergoing prostate screening with annual PSA testing. This cohort has been diagnosed with prostate cancer during the study. Source of controls: Carriers and non-carriers (with a known mutation in the family) of the BRCA1 and BRCA2 genes, aged between 40 and 69, who are undergoing prostate screening with annual PSA testing. This cohort has not been diagnosed with prostate cancer during the study. IPO-Porto: Hospital-based. Source of cases: Early onset and/or familial prostate cancer. Source of controls: Blood donors Karuprostate: Case-control, Retrospective, Population-based. Source of cases: From FWI (Guadeloupe): 237 consecutive incident patients with histologically confirmed prostate cancer attending public and private urology clinics; From Democratic Republic of Congo: 148 consecutive incident patients with histologically confirmed prostate cancer attending the University Clinic of Kinshasa. Source of controls: From FWI (Guadeloupe): 277 controls recruited from men participating in a free systematic health screening program open to the general population; From Democratic Republic of Congo: 134 controls recruited from subjects attending the University Clinic of Kinshasa KULEUVEN: Hospital-based, Prospective, Observational. Source of cases: Prostate cancer cases recruited at the University Hospital Leuven. Source of controls: Healthy males with no history of prostate cancer recruited at the University Hospitals, Leuven. LAAPC: Subjects were participants in a population-based case-control study of aggressive prostate cancer conducted in Los Angeles County. Cases were identified through the Los Angeles County Cancer Surveillance Program rapid case ascertainment system. Eligible cases included African American, Hispanic, and non-Hispanic White men diagnosed with a first primary prostate cancer between January 1, 1999 and December 31, 2003. Eligible cases also had (a) prostatectomy with documented tumor extension outside the prostate, (b) metastatic prostate cancer in sites other than prostate, (c) needle biopsy of the prostate with Gleason grade &#8805;8, or (d) needle biopsy with Gleason grade 7 and tumor in more than two thirds of the biopsy cores. Eligible controls were men never diagnosed with prostate cancer, living in the same neighborhood as a case, and were frequency matched to cases on age (&#177; 5 y) and race/ethnicity. Controls were identified by a neighborhood walk algorithm, which proceeds through an obligatory sequence of adjacent houses or residential units beginning at a specific residence that has a specific geographic relationship to the residence where the case lived at diagnosis. Malaysia: Case-control. Source of cases: Patients attended the outpatient urology or uro-onco clinic at University Malaya Medical Center. Source of controls: Population-based, age matched (5-year groups), ascertained through electoral register, Subang Jaya, Selangor, Malaysia MCC-Spain: Case-control. Source of cases: Identified through the urology departments of the participating hospitals. Source of controls: Population-based, frequency age and region matched, ascertained through the rosters of the primary health care centers MCCS: Nested case-control, Melbourne, Victoria. Source of cases: Identified by linkage to the Victorian Cancer Registry. Source of controls: Cohort participants without a diagnosis of cancer MD Anderson: Participants in this study were identified from epidemiological prostate cancer studies conducted at the University of Texas MD Anderson Cancer Center in the Houston Metropolitan area. Cases were accrued in the Houston Medical Center and were not restricted with respect to Gleason score, stage or PSA. Controls were identified via random-digit-dialing or among hospital visitors and they were frequency matched to cases on age and race. Lifestyle, demographic, and family history data were collected using a standardized questionnaire. MDACC_AS: A prospective cohort study. Source of cases: Men with clinically organ-confined prostate cancer meeting eligibility criteria for a prospective cohort study of active surveillance at MD Anderson Cancer Center. Source of controls: N/A MEC: The Multiethnic Cohort (MEC) is comprised of over 215,000 men and women recruited from Hawaii and the Los Angeles area between 1993 and 1996. Between 1995 and 2006, over 65,000 blood samples were collected from participants for genetic analyses. To identify incident cancer cases, the MEC was cross-linked with the population-based Surveillance, Epidemiology and End Results (SEER) registries in California and Hawaii, and unaffected cohort participants with blood samples were selected as controls MIAMI (WFPCS): Prostate cancer cases and controls were recruited from the Departments of Urology and Internal Medicine of the Wake Forest University School of Medicine using sequential patient populations as described previously (PMID:15342424). All study subjects received a detailed description of the study protocol and signed their informed consent, as approved by the medical center&#39;s Institutional Review Board. The general eligibility criteria were (i) able to comprehend informed consent and (ii) without previously diagnosed cancer. The exclusion criteria were (i) clinical diagnosis of autoimmune diseases; (ii) chronic inflammatory conditions; and (iii) infections within the past 6 weeks. Blood samples were collected from all subjects. MOFFITT: Hospital-based. Source of cases: clinic based from Moffitt Cancer Center. Source of controls: Moffitt Cancer Center affiliated Lifetime cancer screening center NMHS: Case-control, clinic based, Nashville TN. Source of cases: All urology clinics in Nashville, TN. Source of controls: Men without prostate cancer at prostate biopsy. PCaP: The North Carolina-Louisiana Prostate Cancer Project (PCaP) is a multidisciplinary population-based case-only study designed to address racial differences in prostate cancer through a comprehensive evaluation of social, individual and tumor level influences on prostate cancer aggressiveness. PCaP enrolled approximately equal numbers of African Americans and Caucasian Americans with newly-diagnosed prostate cancer from North Carolina (42 counties) and Louisiana (30 parishes) identified through state tumor registries. African American PCaP subjects with DNA, who agreed to future use of specimens for research, participated in OncoArray analysis. PCMUS: Case-control - Sofia, Bulgaria. Source of cases: Patients of Clinic of Urology, Alexandrovska University Hospital, Sofia, Bulgaria, PrCa histopathologically confirmed. Source of controls: 72 patients with verified BPH and PSA&#60;3,5; 78 healthy controls from the MMC Biobank, no history of PrCa PHS: Nested case-control. Source of cases: Participants of the PHS1 trial/cohort. Source of controls: Participants of the PHS1 trial/cohort PLCO: Nested case-control. Source of cases: Men with a confirmed diagnosis of prostate cancer from the PLCO Cancer Screening Trial. Source of controls: Controls were men enrolled in the PLCO Cancer Screening Trial without a diagnosis of cancer at the time of case ascertainment. Poland: Case-control. Source of cases: men with unselected prostate cancer, diagnosed in north-western Poland at the University Hospital in Szczecin. Source of controls: cancer-free men from the same population, taken from the healthy adult patients of family doctors in the Szczecin region PROCAP: Population-based, Retrospective, Observational. Source of cases: Cases were ascertained from the National Prostate Cancer Register of Sweden Follow-Up Study, a retrospective nationwide cohort study of patients with localized prostate cancer. Source of controls: Controls were selected among men referred for PSA testing in laboratories in Stockholm County, Sweden, between 2010 and 2012. PROGReSS: Hospital-based, Prospective, Observational. Source of cases: Prostate cancer cases from the Hospital Cl&#237;nico Universitario de Santiago de Compostela, Galicia, Spain. Source of controls: Cancer-free men from the same population ProMPT: A study to collect samples and data from subjects with and without prostate cancer. Retrospective, Experimental. Source of cases: Subjects attending outpatient clinics in hospitals. Source of controls: Subjects attending outpatient clinics in hospitals ProtecT: Trial of treatment. Samples taken from subjects invited for PSA testing from the community at nine centers across United Kingdom. Source of cases: Subjects who have a proven diagnosis of prostate cancer following testing. Source of controls: Identified through invitation of subjects in the community. PROtEuS: Case-control, population-based. Source of cases: All new histologically-confirmed cases, aged less or equal to 75 years, diagnosed between 2005 and 2009, actively ascertained across Montreal French hospitals. Source of controls: Randomly selected from the Provincial electoral list of French-speaking men between 2005 and 2009, from the same area of residence as cases and frequency-matched on age. QLD: Case-control. Source of cases: A longitudinal cohort study (Prostate Cancer Supportive Care and Patient Outcomes Project: ProsCan) conducted in Queensland, through which men newly diagnosed with prostate cancer from 26 private practices and 10 public hospitals were directly referred to ProsCan at the time of diagnosis by their treating clinician (age range 43-88 years). All cases had histopathologically confirmed prostate cancer, following presentation with an abnormal serum PSA and/or lower urinary tract symptoms. Source of controls: Controls comprised healthy male blood donors with no personal history of prostate cancer, recruited through (i) the Australian Red Cross Blood Services in Brisbane (age range 19-76 years) and (ii) the Australian Electoral Commission (AEC) (age and post-code/ area matched to ProsCan, age range 54-90 years). RAPPER: Multi-centre, hospital based blood sample collection study in patients enrolled in clinical trials with prospective collection of radiotherapy toxicity data. Source of cases: Prostate cancer patients enrolled in radiotherapy trials: CHHiP, RT01, Dose Escalation, RADICALS, Pelvic IMRT, PIVOTAL. Source of controls: N/A SABOR: Prostate Cancer Screening Cohort. Source of cases: Men &#62;45 yrs of age participating in annual PSA screening. Source of controls: Males participating in annual PSA prostate cancer risk evaluations (funded by NCI biomarkers discovery and validation grant), recruited through University of Texas Health Science Center at San Antonio and affiliated sites or through study advertisements, enrolment open to the community SCCS: Case-control in cohort, Southeastern USA. Prospective, Observational, Population-based. Source of cases: SCCS entry population. Source of controls: SCCS entry population SCPCS: Population-based, Retrospective, Observational. Source of cases: South Carolina Central Cancer Registry. Source of controls: Health Care Financing Administration beneficiary file SEARCH: Case-control - East Anglia, UK. Source of cases: Men &#60; 70 years of age registered with prostate cancer at the population-based cancer registry, Eastern Cancer Registration and Information Centre, East Anglia, UK. Source of controls: Men attending general practice in East Anglia with no known prostate cancer diagnosis, frequency matched to cases by age and geographic region SNP_Prostate_Ghent: Hospital-based, Retrospective, Observational. Source of cases: Men treated with IMRT as primary or postoperative treatment for prostate cancer at the Ghent University Hospital between 2000 and 2010. Source of controls: Employees of the University hospital and members of social activity clubs, without a history of any cancer. SPAG: Hospital-based, Retrospective, Observational. Source of cases: Guernsey. Source of controls: Guernsey STHM2: Population-based, Retrospective, Observational. Source of cases: Cases were selected among men referred for PSA testing in laboratories in Stockholm County, Sweden, between 2010 and 2012. Source of controls: Controls were selected among men referred for PSA testing in laboratories in Stockholm County, Sweden, between 2010 and 2012. PCPT: Case-control from a randomized clinical trial. Source of cases: Randomized clinical trial. Source of controls: Randomized clinical trial SELECT: Case-cohort from a randomized clinical trial. Source of cases: Randomized clinical trial. Source of controls: Randomized clinical trial TAMPERE: Case-control - Finland, Retrospective, Observational, Population-based. Source of cases: Identified through linkage to the Finnish Cancer Registry and patient records; and the Finnish arm of the ERSPC study. Source of controls: Cohort participants without a diagnosis of cancer UGANDA: Uganda Prostate Cancer Study: Uganda is a case-control study of prostate cancer in Kampala Uganda that was initiated in 2011. Men with prostate cancer were enrolled from the Urology unit at Mulago Hospital and men without prostate cancer (i.e. controls) were enrolled from other clinics (i.e. surgery) at the hospital. UKGPCS: ICR, UK. Source of cases: Cases identified through clinics at the Royal Marsden hospital and nationwide NCRN hospitals. Source of controls: Ken Muir&#39;s control- 2000 ULM: Case-control - Germany. Source of cases: familial cases (n=162): identified through questionnaires for family history by collaborating urologists all over Germany; sporadic cases (n=308): prostatectomy series performed in the Clinic of Urology Ulm between 2012 and 2014. Source of controls: age-matched controls (n=188): age-matched men without prostate cancer and negative family history collected in hospitals of Ulm WUGS/WUPCS: Cases Series, USA. Source of cases: Identified through clinics at Washington University in St. Louis. Source of controls: Men diagnosed and managed with prostate cancer in University based clinic. Acknowledgement Statements: Aarhus: This study was supported by the Danish Strategic Research Council (now Innovation Fund Denmark) and the Danish Cancer Society. The Danish Cancer Biobank (DCB) is acknowledged for biological material. AHS: This work was supported by the Intramural Research Program of the NIH, National Cancer Institute, Division of Cancer Epidemiology and Genetics (Z01CP010119). ATBC: This research was supported in part by the Intramural Research Program of the NIH and the National Cancer Institute. Additionally, this research was supported by U.S. Public Health Service contracts N01-CN-45165, N01-RC-45035, N01-RC-37004, HHSN261201000006C, and HHSN261201500005C from the National Cancer Institute, Department of Health and Human Services. BioVu: The dataset(s) used for the analyses described were obtained from Vanderbilt University Medical Center&#39;s BioVU which is supported by institutional funding and by the National Center for Research Resources, Grant UL1 RR024975-01 (which is now at the National Center for Advancing Translational Sciences, Grant 2 UL1 TR000445-06). Canary PASS: PASS was supported by Canary Foundation and the National Cancer Institute&#39;s Early Detection Research Network (U01 CA086402) CCI: This work was awarded by Prostate Cancer Canada and is proudly funded by the Movember Foundation - Grant # D2013-36.The CCI group would like to thank David Murray, Razmik Mirzayans, and April Scott for their contribution to this work. CerePP French Prostate Cancer Case-Control Study (ProGene): None reported COH: SLN is partially supported by the Morris and Horowitz Families Endowed Professorship COSM: The Swedish Research Council, the Swedish Cancer Foundation CPCS1 &#38; CPCS2: Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev Ringvej 75, DK-2730 Herlev, DenmarkCPCS1 would like to thank the participants and staff of the Copenhagen General Population Study for their important contributions. CPDR: Uniformed Services University for the Health Sciences HU0001-10-2-0002 (PI: David G. McLeod, MD) CPS-II: The American Cancer Society funds the creation, maintenance, and updating of the Cancer Prevention Study II cohort. CPS-II thanks the participants and Study Management Group for their invaluable contributions to this research. We would also like to acknowledge the contribution to this study from central cancer registries supported through the Centers for Disease Control and Prevention National Program of Cancer Registries, and cancer registries supported by the National Cancer Institute Surveillance Epidemiology and End Results program. EPIC: The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by the Danish Cancer Society (Denmark); the Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany); the Hellenic Health Foundation, Greek Ministry of Health; Greek Ministry of Education (Greece); the Italian Association for Research on Cancer (AIRC) and National Research Council (Italy); the Dutch Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF); the Statistics Netherlands (The Netherlands); the Health Research Fund (FIS), Regional Governments of Andaluc&#237;a, Asturias, Basque Country, Murcia and Navarra, Spanish Ministry of Health ISCIII RETIC (RD06/0020), Red de Centros RCESP, C03/09 (Spain); the Swedish Cancer Society, Swedish Scientific Council and Regional Government of Sk&#229;ne and V&#228;sterbotten, Fundacion Federico SA (Sweden); the Cancer Research UK, Medical Research Council (United Kingdom). EPICAP: The EPICAP study was supported by grants from Ligue Nationale Contre le Cancer, Ligue d&#233;partementale du Val de Marne; Fondation de France; Agence Nationale de s&#233;curit&#233; sanitaire de l&#39;alimentation, de l&#39;environnement et du travail (ANSES). The EPICAP study group would like to thank all urologists, Antoinette Anger and Hasina Randrianasolo (study monitors), Anne-Laure Astolfi, Coline Bernard, Oriane Noyer, Marie-H&#233;l&#232;ne De Campo, Sandrine Margaroline, Louise N&#39;Diaye, and Sabine Perrier-Bonnet (Clinical Research nurses). ERSPC: This study was supported by the DutchCancerSociety (KWF94-869,98-1657,2002-277,2006-3518, 2010-4800), The Netherlands Organisation for Health Research and Development (ZonMW-002822820, 22000106, 50-50110-98-311, 62300035), The Dutch Cancer Research Foundation (SWOP), and an unconditional grant from Beckman-Coulter-HybritechInc. ESTHER: The ESTHER study was supported by a grant from the Baden W&#252;rttemberg Ministry of Science, Research and Arts. The ESTHER group would like to thank Hartwig Ziegler, Sonja Wolf, Volker Hermann, Heiko M&#252;ller, Karina Dieffenbach, Katja Butterbach for valuable contributions to the study. FHCRC: The FHCRC studies were supported by grants R01-CA056678, R01-CA082664, and R01-CA092579 from the US National Cancer Institute, National Institutes of Health, with additional support from the Fred Hutchinson Cancer Research Center. FHCRC would like to thank all the men who participated in these studies. Gene-PARE: The Gene-PARE study was supported by grants 1R01CA134444 from the U.S. National Institutes of Health, PC074201 and W81XWH-15-1-0680 from the Prostate Cancer Research Program of the Department of Defense and RSGT-05-200-01-CCE from the American Cancer Society. Hamburg-Zagreb: None reported HPFS: The Health Professionals Follow-up Study was supported by grants UM1CA167552, CA133891, CA141298, and P01CA055075. HPFS are grateful to the participants and staff of the Physicians&#39; Health Study and Health Professionals Follow-Up Study for their valuable contributions, as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, and WY. IMPACT: The IMPACT study was funded by The Ronald and Rita McAulay Foundation, CR-UK Project grant (C5047/A1232), Cancer Australia, AICR Netherlands A10-0227, Cancer Australia and Cancer Council Tasmania, NIHR, EU Framework 6, Cancer Councils of Victoria and South Australia, and Philanthropic donation to Northshore University Health System. We acknowledge support from the National Institute for Health Research (NIHR) to the Biomedical Research Centre at The Institute of Cancer Research and Royal Marsden Foundation NHS Trust. IMPACT acknowledges the IMPACT study steering committee, collaborating centres, and participants. IPO-Porto: The IPO-Porto study was funded by Funda&#231;&#228;o para a Ci&#234;ncia e a Tecnologia (FCT; UID/DTP/00776/2013 and PTDC/DTP-PIC/1308/2014) and by IPO-Porto Research Center (CI-IPOP-16-2012 and CI-IPOP-24-2015). MC and MPS are research fellows from Liga Portuguesa Contra o Cancro, N&#250;cleo Regional do Norte. SM is a research fellow from FCT (SFRH/BD/71397/2010). IPO-Porto would like to express our gratitude to all patients and families who have participated in this study. Karuprostate: The Karuprostate study was supported by the the Frech National Health Directorate and by the Association pour la Recherche sur les Tumeurs de la ProstateKarusprostate thanks S&#233;verine Ferdinand. KULEUVEN: F.C. and S.J. are holders of grants from FWO Vlaanderen (G.0684.12N and G.0830.13N), the Belgian federal government (National Cancer Plan KPC_29_023), and a Concerted Research Action of the KU Leuven (GOA/15/017). TVDB is holder of a doctoral fellowship of the FWO. LAAPC: This study was funded by grant R01CA84979 (to S.A. Ingles) from the National Cancer Institute, National Institutes of Health. Malaysia: The study was funded by the University Malaya High Impact Research Grant (HIR/MOHE/MED/35). Malaysia thanks all associates in the Urology Unit, University of Malaya, Cancer Research Initiatives Foundation (CARIF) and the Malaysian Men&#39;s Health Initiative (MMHI). MCCS: MCCS cohort recruitment was funded by VicHealth and Cancer Council Victoria. The MCCS was further supported by Australian NHMRC grants 209057, 251553, and 504711, and by infrastructure provided by Cancer Council Victoria. Cases and their vital status were ascertained through the Victorian Cancer Registry (VCR) and the Australian Institute of Health and Welfare (AIHW), including the National Death Index and the Australian Cancer Database. MCC-Spain: The study was partially funded by the Accion Transversal del Cancer, approved on the Spanish Ministry Council on the 11th October 2007, by the Instituto de Salud Carlos III-FEDER (PI08/1770, PI09/00773-Cantabria, PI11/01889-FEDER, PI12/00265, PI12/01270, and PI12/00715), by the Fundaci&#243;n Marqu&#233;s de Valdecilla (API 10/09), by the Spanish Association Against Cancer (AECC) Scientific Foundation and by the Catalan Government DURSI grant 2009SGR1489. Samples: Biological samples were stored at the Parc de Salut MAR Biobank (MARBiobanc; Barcelona) which is supported by Instituto de Salud Carlos III FEDER (RD09/0076/00036). Also sample collection was supported by the Xarxa de Bancs de Tumors de Catalunya sponsored by Pla Director d&#39;Oncologia de Catalunya (XBTC). MCC-Spain acknowledges the contribution from Esther Gracia-Lavedan in preparing the data. We thank all the subjects who participated in the study and all MCC-Spain collaborators. MD Anderson: Prostate Cancer Case-Control Studies at MD Anderson (MDA) supported by grants CA68578, ES007784, DAMD W81XWH-07-1-0645, and CA140388. MDACC_AS: None reported MEC: Funding provided by NIH grant U19CA148537 and grant U01CA164973. MIAMI (WFPCS): ACS MOFFITT: The Moffitt group was supported by the US National Cancer Institute (R01CA128813, PI: J.Y. Park). NMHS: Funding for the Nashville Men&#39;s Health Study (NMHS) was provided by the National Institutes of Health Grant numbers: RO1CA121060. PCaP only data: 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. For HCaP-NC follow-up data: The Health Care Access and Prostate Cancer Treatment in North Carolina (HCaP-NC) study is carried out as a collaborative study supported by the American Cancer Society award RSGT-08-008-01-CPHPS. For studies using both PCaP and HCaP-NC follow-up data please use: The North Carolina - Louisiana Prostate Cancer Project (PCaP) and the Health Care Access and Prostate Cancer Treatment in North Carolina (HCaP-NC) study are carried out as collaborative studies supported by the Department of Defense contract DAMD 17-03-2-0052 and the American Cancer Society award RSGT-08-008-01-CPHPS, respectively. For any PCaP data, please include: The authors thank the staff, advisory committees and research subjects participating in the PCaP study for their important contributions. For studies using PCaP DNA/genotyping data, please include: We would like to acknowledge the UNC BioSpecimen Facility and LSUHSC Pathology Lab for our DNA extractions, blood processing, storage and sample disbursement (https://genome.unc.edu/bsp). For studies using PCaP tissue, please include: We would like to acknowledge the RPCI Department of Urology Tissue Microarray and Immunoanalysis Core for our tissue processing, storage and sample disbursement. For studies using HCaP-NC follow-up data, please use: The Health Care Access and Prostate Cancer Treatment in North Carolina (HCaP-NC) study is carried out as a collaborative study supported by the American Cancer Society award RSGT-08-008-01-CPHPS. The authors thank the staff, advisory committees and research subjects participating in the HCaP-NC study for their important contributions. For studies that use both PCaP and HCaP-NC, please use: The authors thank the staff, advisory committees and research subjects participating in the PCaP and HCaP-NC studies for their important contributions. PCMUS: The PCMUS study was supported by the Bulgarian National Science Fund, Ministry of Education and Science (contract DOO-119/2009; DUNK01/2-2009; DFNI-B01/28/2012) with additional support from the Science Fund of Medical University - Sofia (contract 51/2009; 8I/2009; 28/2010). PHS: The Physicians&#39; Health Study was supported by grants CA34944, CA40360, CA097193, HL26490, and HL34595. PHS members are grateful to the participants and staff of the Physicians&#39; Health Study and Health Professionals Follow-Up Study for their valuable contributions, as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, and WY. PLCO: This PLCO study was supported by the Intramural Research Program of the Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIHPLCO thanks Drs. Christine Berg and Philip Prorok, Division of Cancer Prevention at the National Cancer Institute, the screening center investigators and staff of the PLCO Cancer Screening Trial for their contributions to the PLCO Cancer Screening Trial. We thank Mr. Thomas Riley, Mr. Craig Williams, Mr. Matthew Moore, and Ms. Shannon Merkle at Information Management Services, Inc., for their management of the data and Ms. Barbara O&#39;Brien and staff at Westat, Inc. for their contributions to the PLCO Cancer Screening Trial. We also thank the PLCO study participants for their contributions to making this study possible. Poland: None reported PROCAP: PROCAP was supported by the Swedish Cancer Foundation (08-708, 09-0677). PROCAP thanks and acknowledges all of the participants in the PROCAP study. We thank Carin Cavalli-Bj&#246;rkman and Ami R&#246;nnberg Karlsson for their dedicated work in the collection of data. Michael Broms is acknowledged for his skilful work with the databases. KI Biobank is acknowledged for handling the samples and for DNA extraction. We acknowledge The NPCR steering group: P&#228;r Stattin (chair), Anders Widmark, Stefan Karlsson, Magnus T&#246;rnblom, Jan Adolfsson, Anna Bill-Axelson, Ove Andr&#233;n, David Robinson, Bill Pettersson, Jonas Hugosson, Jan-Erik Damber, Ola Bratt, G&#246;ran Ahlgren, Lars Egevad, and Roy Ehrnstr&#246;m. PROGReSS: The PROGReSS study is founded by grants from the Spanish Ministry of Health (INT15/00070; INT16/00154; FIS PI10/00164, FIS PI13/02030; FIS PI16/00046); the Spanish Ministry of Economy and Competitiveness (PTA2014-10228-I), and Fondo Europeo de Desarrollo Regional (FEDER 2007-2013). ProMPT: Founded by CRUK, NIHR, MRC, Cambride Biomedical Research Centre ProtecT: Founded by NIHR. ProtecT and ProMPT would like to acknowledge the support of The University of Cambridge, Cancer Research UK. Cancer Research UK grants (C8197/A10123) and (C8197/A10865) supported the genotyping team. We would also like to acknowledge the support of the National Institute for Health Research which funds the Cambridge Bio-medical Research Centre, Cambridge, UK. We would also like to acknowledge the support of the National Cancer Research Prostate Cancer: Mechanisms of Progression and Treatment (PROMPT) collaborative (grant code G0500966/75466) which has funded tissue and urine collections in Cambridge. We are grateful to staff at the Welcome Trust Clinical Research Facility, Addenbrooke&#39;s Clinical Research Centre, Cambridge, UK for their help in conducting the ProtecT study. We also acknowledge the support of the NIHR Cambridge Biomedical Research Centre, the DOH HTA (ProtecT grant), and the NCRI/MRC (ProMPT grant) for help with the bio-repository. The UK Department of Health funded the ProtecT study through the NIHR Health Technology Assessment Programme (projects 96/20/06, 96/20/99). The ProtecT trial and its linked ProMPT and CAP (Comparison Arm for ProtecT) studies are supported by Department of Health, England; Cancer Research UK grant number C522/A8649, Medical Research Council of England grant number G0500966, ID 75466, and The NCRI, UK. The epidemiological data for ProtecT were generated though funding from the Southwest National Health Service Research and Development. DNA extraction in ProtecT was supported by USA Dept of Defense award W81XWH-04-1-0280, Yorkshire Cancer Research and Cancer Research UK. The authors would like to acknowledge the contribution of all members of the ProtecT study research group. The views and opinions expressed therein are those of the authors and do not necessarily reflect those of the Department of Health of England. The bio-repository from ProtecT is supported by the NCRI (ProMPT) Prostate Cancer Collaborative and the Cambridge BMRC grant from NIHR. We thank the National Institute for Health Research, Hutchison Whampoa Limited, the Human Research Tissue Bank (Addenbrooke&#39;s Hospital), and Cancer Research UK. PROtEuS: PROtEuS was supported financially through grants from the Canadian Cancer Society (13149, 19500, 19864, 19865) and the Cancer Research Society, in partnership with the Minist&#232;re de l&#39;enseignement sup&#233;rieur, de la recherche, de la science et de la technologie du Qu&#233;bec, and the Fonds de la recherche du Qu&#233;bec - Sant&#233;.PROtEuS would like to thank its collaborators and research personnel, and the urologists involved in subjects recruitment. We also wish to acknowledge the special contribution made by Ann Hsing and Anand Chokkalingam to the conception of the genetic component of PROtEuS. QLD: The QLD research is supported by The National Health and Medical Research Council (NHMRC) Australia Project Grants (390130, 1009458) and NHMRC Career Development Fellowship and Cancer Australia PdCCRS funding to J Batra. The QLD team would like to acknowledge and sincerely thank the urologists, pathologists, data managers and patient participants who have generously and altruistically supported the QLD cohort. RAPPER: RAPPER is funded by Cancer Research UK (C1094/A11728; C1094/A18504) and Experimental Cancer Medicine Centre funding (C1467/A7286). The RAPPER group thank Rebecca Elliott for project management. SABOR: The SABOR research is supported by NIH/NCI Early Detection Research Network, grant U01 CA0866402-12. Also supported by the Cancer Center Support Grant to the Cancer Therapy and Research Center from the National Cancer Institute (US) P30 CA054174. SCCS: SCCS is funded by NIH grant R01 CA092447, and SCCS sample preparation was conducted at the Epidemiology Biospecimen Core Lab that is supported in part by the Vanderbilt-Ingram Cancer Center (P30 CA68485). Data on SCCS cancer cases used in this publication were provided by the Alabama Statewide Cancer Registry; Kentucky Cancer Registry, Lexington, KY; Tennessee Department of Health, Office of Cancer Surveillance; Florida Cancer Data System; North Carolina Central Cancer Registry, North Carolina Division of Public Health; Georgia Comprehensive Cancer Registry; Louisiana Tumor Registry; Mississippi Cancer Registry; South Carolina Central Cancer Registry; Virginia Department of Health, Virginia Cancer Registry; Arkansas Department of Health, Cancer Registry, 4815 W. Markham, Little Rock, AR 72205. The Arkansas Central Cancer Registry is fully funded by a grant from National Program of Cancer Registries, 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 National Program of Cancer Registries (NPCR) of the Centers for Disease Control and Prevention (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. SCPCS: SCPCS is funded by CDC grant S1135-19/19, and SCPCS sample preparation was conducted at the Epidemiology Biospecimen Core Lab that is supported in part by the Vanderbilt-Ingram Cancer Center (P30 CA68485). SEARCH: SEARCH is funded by a program grant from Cancer Research UK (C490/A10124) and supported by the UK National Institute for Health Research Biomedical Research Centre at the University of Cambridge. SNP_Prostate_Ghent: The study was supported by the National Cancer Plan, financed by the Federal Office of Health and Social Affairs, Belgium. SPAG: Wessex Medical ResearchHope for Guernsey, MUG, HSSD, MSG, Roger Allsopp STHM2: STHM2 was supported by grants from The Strategic Research Programme on Cancer (StratCan), Karolinska Institutet; the Linn&#233; Centre for Breast and Prostate Cancer (CRISP, number 70867901), Karolinska Institutet; The Swedish Research Council (number K2010-70X-20430-04-3) and The Swedish Cancer Society (numbers 11-0287 and 11-0624); Stiftelsen Johanna Hagstrand och Sigfrid Linn&#233;rs minne; Swedish Council for Working Life and Social Research (FAS), number 2012-0073STHM2 acknowledges the Karolinska University Laboratory, Aleris Medilab, Unilabs and the Regional Prostate Cancer Registry for performing analyses and help to retrieve data. Carin Cavalli-Bj&#246;rkman and Britt-Marie Hune for their enthusiastic work as research nurses. Astrid Bj&#246;rklund for skilful data management. We wish to thank the BBMRI.se biobank facility at Karolinska Institutet for biobank services. PCPT &#38; SELECT are funded by Public Health Service grants U10CA37429 and 5UM1CA182883 from the National Cancer Institute. SWOG and SELECT thank the site investigators and staff and, most importantly, the participants who donated their time to this trial. TAMPERE: The Tampere (Finland) study was supported by the Academy of Finland (251074), The Finnish Cancer Organisations, Sigrid Juselius Foundation, and the Competitive Research Funding of the Tampere University Hospital (X51003). The PSA screening samples were collected by the Finnish part of ERSPC (European Study of Screening for Prostate Cancer). TAMPERE would like to thank Riina Liikanen, Liisa Maeaettaenen and Kirsi Talala for their work on samples and databases. UGANDA: None reported UKGPCS: UKGPCS would also like to thank the following for funding support: The Institute of Cancer Research and The Everyman Campaign, The Prostate Cancer Research Foundation, Prostate Research Campaign UK (now Prostate Action), The Orchid Cancer Appeal, The National Cancer Research Network UK, The National Cancer Research Institute (NCRI) UK. We are grateful for support of NIHR funding to the NIHR Biomedical Research Centre at The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust. UKGPCS should also like to acknowledge the NCRN nurses, data managers, and consultants for their work in the UKGPCS study. UKGPCS would like to thank all urologists and other persons involved in the planning, coordination, and data collection of the study. ULM: The Ulm group received funds from the German Cancer Aid (Deutsche Krebshilfe). WUGS/WUPCS: WUGS would like to thank the following for funding support: The Anthony DeNovi Fund, the Donald C. McGraw Foundation, and the St. Louis Men&#39;s Group Against Cancer.

  Study phs001391

• Emirati T2T Assembly

  This study generates a telomere-to-telomere (T2T) trio-based genome assembly for a female Emirati individual, creating a high-quality reference representative of an understudied population. We produced multi-platform sequencing for a single family trio: PacBio HiFi (>60× per parent, >120× offspring), ONT ultra-long reads (>110× offspring), and Illumina short-read WGS (>100× for all three). The offspring genome was assembled in a trio framework to leverage parental information for accurate phasing and error resolution. Scaffolding and finishing utilized NTLink for initial scaffolding, RagTag for reference-guided refinement, and Quartett for gap filling, resulting in a contiguous, population-relevant assembly suitable for downstream variant discovery and pangenome integration.

  Study EGAS50000001235