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• 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

• Cancer Moonshot Biobank

  The National Cancer Institute initiated the Cancer Moonshot Biobank to accelerate research through the collection and distribution of biospecimens for key research efforts. The Biobank will engage with over one thousand cancer patients, being treated with standard-of-care therapies, for longitudinal biospecimen and data collection. Cancer patients will be enrolled into the study from a diverse participant population engaged at medical institutions throughout the U.S. The biospecimens will be distributed to qualified scientists to accelerate research progress. The Biobank program, working closely with Leidos Biomedical, Inc. (LBR), will partner with community hospitals and other medical institutions, designated as Biospecimen Source Sites (BSS), to engage and consent eligible participants and collect biospecimens and associated clinical data; work with a central Biospecimen Core Resource (BCR) to support biospecimen collection, processing and storage activities and perform pathology quality control; work with LBR's Molecular Characterization (MoCha) lab to perform clinical tumor molecular characterization assays and return results to patients and their physicians; and make the biospecimens and associated data available to researchers to accelerate cancer research progress. Return of clinical results, a dedicated Patient and Provider Engagement (PPE) website, electronic informed consent, local participant engagement projects, and an embedded ethical, legal and social issues sub-study will together support patient and provider engagement in the program. Cancer Moonshot Biobank imaging data is being made available in The Cancer Imaging Archive (TCIA) on a release schedule that is coordinated with the program releases of data to dbGaP. The imaging data can be found at the following link: Cancer Moonshot Biobank Imaging

  Study phs002192

• Colon Cancer Family Registry (Colon CFR)

  The Colon Cancer Family Registry Cohort (CCFRC www.coloncfr.org) is a cohort of families recruited through six study sites located in the USA, Canada, Australia, and New Zealand. The CCFRC was formed as a resource to support studies on the etiology, prevention, and clinical management of colorectal cancer. Recruitment protocols fall broadly into two main categories: population-based and clinic-based ascertainment. Between 1998 and 2013, the CCFR recruited and interviewed probands who were either recently diagnosed with colorectal cancer that was reported to state or regional population cancer registries in the United States (Washington, California, Arizona, Minnesota, Colorado, New Hampshire, North Carolina, and Hawaii), Australia (Victoria), and Canada (Ontario) or were from multiple-case families referred to family-cancer clinics in the United States (Mayo Clinic in Rochester, Minnesota, and Cleveland Clinic in Cleveland, Ohio), Australia (Melbourne, Adelaide, Perth, Brisbane, and Sydney), New Zealand (Auckland), and Canada. Cases with known familial adenomatous polyposis were excluded. Once recruited, probands were asked for permission to contact their relatives for recruitment. Collectively, we have enrolled 42,49874 participants in 15,048 families who participated in standardized protocols used to collect information regarding family cancer history and colorectal cancer risk factors and biospecimens. All participants of population-based case families (excluding control-families) and clinic-based families are asked to provide updates on their personal and family history of cancer as well as their history of surgery, cancer screening, and some risk factors every 4-5 years either by telephone interview or by self-completed questionnaire via mail or online (PMID: 17982118).

  Study phs002733

• A unidirectional histone code in bidirectional promoters across cell types

  Recent studies indicate that thousands of genes are expressed from bidirectional promoters (BDPs). Gene regulation at BDPs is poorly understood, in particular how the cell is able to regulate them differently. Here we investigate the effect of histone Modifications in BDP regulation. In this study, we model the gene activity using different gene expression assays, such as RNA-Seq, GRO-cap, and CAGE around the BDP transcription start sites (TSSs) using different histone modifications in various cell types. We develop a new statistical approach that links histone modifications to gene expression at BDPs. It improves over previous methods, because it is able to capture spatial dependencies of histone modifications along a promoter and leads to more interpretable results. We predict a general histone code that is independent of transcript orientation, cell type, and promoter configuration. The histone code at BDPs reveals that promoters are regulated unidirectionally, such that the majority of histone marks associated with gene expression occur downstream of the gene's TSS. By contrasting associations of histone marks with steady-state levels of capped RNAs in CAGE and nascent capped RNAs in GRO-cap, we show which histone marks have a preference for initiating polymerases and actively elongating polymerases. Using single-cell data we show that the bidirectional histone signal of activating marks is often due averaging of a heterogeneous cell population. Our results have implications for other experiments where the relationship between histone modification and gene initiation or gene elongation is investigated on a genome-wide scale.

  Study EGAS00001001656

• 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

• eMERGE Network Genome-Wide Association Study of Red Cell Indices, White Blood Count (WBC) Differential, Diabetic Retinopathy, Height, Serum Lipid Levels, Specifically Total Cholesterol, HDL (High Density Lipoprotein), LDL (Low Density Lipoprotein), and Triglycerides, and Autoimmune Hypothyroidism.

  The electronic Medical Records and Genomics (eMERGE) Network is a consortium of five participating sites (Group Health Seattle, Marshfield Clinic, Mayo Clinic, Northwestern University, and Vanderbilt University) funded by the NHGRI to investigate the use of electronic medical record systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 19,000 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 red cell indices, white blood count (WBC) differential, diabetic retinopathy, height, serum lipid levels, specifically total cholesterol, HDL (high density lipoprotein), LDL (low density lipoprotein), and triglycerides, and autoimmune hypothyroidism as well as using the phenome-wide association study (PheWAS) paradigm to replicate and discover relationships between targeted genotypes with multiple phenotypes. eMERGE led studies for which original genotyping was performed and are included in this merged set: Genome-Wide Association Study on Cataract and HDL in the Personalized Medicine Research Project Cohort, phs000170 Development and Use of Network Infrastructure for High-Throughput GWA Studies, phs000234 Vanderbilt Genome-Electronic Records (VGER) Project: QRS Duration, phs000188 Northwestern NUgene Project: Type 2 Diabetes, phs000237 A Genome-Wide Association Study of Peripheral Arterial Disease, phs000203 Sites and participants include: 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. Marshfield Clinic: 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. 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. Group Health(GH)/University of Washington (UW): Aging and Dementia eMERGE study biorepository leverages rich population-based longitudinal data from both electronic medical records and in-depth research data to explore genome wide associations. Participants include Seattle-area members of GH (a large integrated health care system in Washington State) consented and enrolled in 1) the UW Alzheimer's Disease Patient Registry (ADPR) and 2) the Adult Changes in Thought (ACT) study. 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 Alzheimer's Disease cases within GH from 1987 to 1996. Medical history, physical, laboratory testing, and neuropsychological testing were performed on all consenting potential cases for determination of dementia status by a consensus conference. 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 of aging and dementia. The original cohort of 2,581 randomly selected dementia-free members age 65 and older was enrolled in 1994-1996 and expanded by 811 in 2000-2002. Continuous enrollment to maintain a cohort of 2,000 dementia free persons began in 2005. Participants receive biennial assessment including cognitive status determination. 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 260/280 ratio. For long-term storage, samples are aliquoted and stored at -70°C. 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.

  Study phs000360

• One-step generation of tumor models by base editor multiplexing in adult stem cell-derived organoids

  Recent optimization of CRISPR/Cas9-mediated genome engineering has resulted in the development of base editors that can efficiently mediate C>T and A>G transitions. Combining these genome engineering tools with human adult stem cell (ASC)-derived organoid technology holds promise for disease modeling. Here, we demonstrate the application of base editors for the generation of complex tumor models in human ASC-derived hepatocyte, endometrial and intestinal organoids. First, using conventional and evolved Cas9-variants, we show efficacy of both cytosine and adenine base editors and use them to model four hot-spot point mutations in CTNNB1 in hepatocyte organoids. Next, we apply C>T base editors in endometrial organoids to insert nonsense mutations in PTEN and demonstrate tumorigenicity even in the heterozygous state. Furthermore, we use cytosine base editors for simultaneous oncogene activation (PIK3CA) and tumor-suppressor inactivation (APC and TP53). To increase the flexibility of base editor multiplexing, we then combine SpCas9 and SaCas9 base editors for simultaneous C>T and A>G editing at individual target sites. Finally, we show the power of base editor multiplexing by modeling colorectal tumorigenesis in a single step by simultaneously transfecting sgRNA’s targeting four cancer genes. Seven clonal organoid lines and one bulk wild-type control sample were paired-end whole-genome sequenced using the Illumina Novaseq 6000 system. We sequenced four clonal intestinal organoid lines harbouring engineered TP53 and FBXW7 mutations as well as three lines targeted for oncogenic APC/TP53/PIK3CA/SMAD4 mutations. This WGS showed, as previously reported, a genome-wide increase in C>T mutations due to C>T base editor off-target activity, which is not enriched in predicted off-target regions based on the sgRNA sequences. Furthermore, we confirmed the absence of editing-induced driver mutations and lack of off-target mutational hotspots created by the genomic engineering.

  Study EGAS00001006886

• Cryptococcosis in Previously Healthy Adults

  Cryptococcus is a fungus that causes infections most commonly in immunocompromised patients, such as those with AIDS and solid organ transplant recipients and is currently responsible for an estimated 15% of all AIDS-related deaths globally. However, in developed countries, approximately one-third of cases fall outside these groups such that cryptococcal meningoencephalitis (CM) has become the most common cause of non-viral meningitis in the U.S. Within the U.S., approximately 15-20% have no readily identifiable immune defect and cryptococcal infection in these hosts has a mortality rate of 30-50% despite optimal antifungal therapy. NIH Clinical Study Protocol 93-I-0106 (NCT00001352). The objectives of this protocol can be broadly categorized as: 1) Characterize the immunologic and genetic mechanisms predisposing to disease acquisition; 2) Understand the post-infectious inflammatory response and distinguish its consequences from those directly due to fungal growth; and 3) Management of post-infectious neuro-inflammatory syndromes associated with cryptococcal meningitis (CM). This protocol recruits patients who have microbiological evidence of cryptococcal neurologic or non-CNS disease (typically pulmonary or bone). Cerebrospinal fluid (CSF) and blood samples collected during clinical care are used to measure serum and CNS cellular and soluble cytokines as well as to perform in-situ immunohistochemistry. CSF and blood samples are also collected to measure transcriptional (gene expression), proteomic (protein), and lipidomic (lipids) changes that occur throughout disease and treatment. Observational data detailing audiological, ophthalmological and neurocognitive deficits in these patients is also recorded. We have recently described a post-infectious inflammatory syndrome (PIIRS) associated with cryptococcal meningoencephalitis (CM) which can be best described as a neuro-inflammatory state during which CM patients present with altered mental status (Montreal Cognitive Assessment Score As part of the protocol, we treat patients for either microbiological failure or neuroinflammatory sequelae by conventional therapies utilizing FDA-approved pharmaceuticals at FDA-approved doses. To understand and identify the predominant biological pathways responsible for PIIRS neuroinflammation, we examined patient CSF at baseline (at time of PIIRS diagnosis) and post-treatment with different FDA-approved pharmaceuticals at FDA-approved doses compared to CSF from non-PIIRS donors. Upstream pathway analysis of differentially expressed genes in the CSF of PIIRS patients revealed a prominent role for the JAK/STAT pathway in the pathogenesis of PIIRS and support the use of the JAK inhibitor, ruxolinib, as a pathway-instructed therapy to treat PIIRS patients. Data available through dbGaP: single cell RNA sequencing (scRNA-seq) data from CSF cells of a patient with PIIRS at the time of diagnosis and following treatment with steroids (1g solumedrol/day) and NanoString transcriptional data from CSF cells of patients with PIIRS at the time of diagnosis compared to CSF cells from non-PIIRS donors.

  Study phs003871

• 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

• ICGC Prostate Cancer Whole Genome Sequencing

  To characterize the subclonal genomic architecture of androgen-deprived metastatic prostate cancer, we performed whole-genome sequencing (WGS) of 51 tumours from 10 patients to an average sequencing depth of 55x, including multiple metastases from different anatomic sites in each patient and, in five cases, the prostate tumour. Noncancerous DNA from blood or other tissue is used as reference comparison for each patient. The patients are part of PELICAN (Project to ELIminate Lethal Cancer) rapid autopsy study led by G. Steven Bova at Johns Hopkins University (USA) and Tampere University (Finland). As of September 2020, some of the studies using these data include: Gundem et al, Nature 2015 (PMID: 25830880). Additional EGAD00001000891 sample metadata is contained in Supplementary Information in this report.Tubio et al, Science 2014 (PMID: 25082706) Behjati et al, Nature Comm 2015 (PMID: 27615322) Wedge et al, Nature Genetics 2018 (PMID: 29662167)Pan-Cancer Analysis of Whole Genomes, Nature 2020 (PMID: 32025007)Rodriguez-Martin et al, Nature Genetics 2020 (PMID: 32024998)Woodcock et al, Nature Comm 2020 (In Press)

  Dataset EGAD00001000891