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in 25.28 milliseconds.

• Genome-Wide Association Study of Relapse of Childhood Acute Lymphoblastic Leukemia

  Using risk-directed therapy for childhood acute lymphoblastic leukemia (ALL), outcome has improved dramatically in the last 40 years. However, a substantial portion of patients experience relapse, many of whom have no known risk factors. Taking a genome-wide approach, we sought to evaluate the relationships between germline SNP genotypes and the risk of relapse in 2,535 children with newly diagnosed ALL after adjusting for genetic ancestry and treatment regimen. We examine prognostic value of selected SNPs in the context of known relapse risk factors (molecular subtypes, minimal residual disease, age and leukocyte count at diagnosis). Associations of relapse-related SNPs with pharmacokinetic and pharmacodynamics of antileukemic drugs offer plausible mechanism by which they are linked to treatment outcome. Finally, we aim to identify SNPs that are related to both genetic ancestry and relapse which are likely to contribute to racial disparities in ALL survival.

  Study phs000638

• The evolutionary trajectory of a highly recurrent paediatric high grade neuroepithelial tumour with MN1:BEND2 fusion

  Astroblastomas are rare brain tumours which predominate in children and young adults, and have a controversial claim as a distinct entity, with no established WHO grade. Reports suggest a better outcome than high grade gliomas, though they frequently recur. Recently, they have been described to overlap with a newly-discovered group of tumours described as ’high grade neuroepithelial tumour with MN1 alteration’ (CNS HGNET-MN1), defined by global methylation patterns and strongly associated with gene fusions targeting MN1. We have studied a unique case of astroblastoma arising in a 6 year old girl, with multiple recurrences over a period of 10 years, with the pathognomonic MN1:BEND2 fusion. Exome sequencing allowed for a phylogenetic reconstruction of tumour evolution, which when integrated with clinical, pathological and radiological data provide for a thorough understanding of disease progression, with initial treatment driving tumour dissemination along four distinct trajectories. Infiltration of distant sites was associated with a later genome doubling, whilst there was evidence of convergent evolution of different lesions acquiring distinct alterations targeting NF-κB. These data represent an usual opportunity to understand the evolutionary history of a highly recurrent childhood brain tumour, and provide novel therapeutic targets for astroblastoma / CNS HGNET-MN1.

  Study EGAS00001002432

• Longitudinal Studies of Brain Structure and Function in MPS Disorders

  Mucopolysaccharidosis (MPS) types I, II, IV, VI and VII, are inherited diseases that result from the bodies inability to break down large sugar molecules that are by-products of metabolism. MPS affects most organs of the body and causes abnormalities in the liver, spleen, bones, and brain. We are studying the central nervous system (primarily the brain) so we can better understand the nature of the problems individuals with MPS I, II, IV, VI and VII have so we can find ways of better treating these problems. We would like to find out about the changes in the brain of individuals with MPS I, II, IV, VI and VII using data from MRI and neuropsychological tests. This is a longitudinal study so patients who enroll will need to be seen 3 times over 5 years. The longitudinal nature of the study allows us to make conclusions about how changes in some structures of the brain and changes in cognitive ability are related. The research objectives are: To identify abnormalities of brain (and central nervous system) structure and function in patients with MPS I, II, IV, VI and VII, regardless of how they are being treated or have been treated in the past; and to track disease progression over time. To examine the degree to which independent variables (e.g., age at first treatment, severity of disease, types of medical abnormalities, mutation, medical events, and sensory abnormalities) have an impact on both functional and structural outcomes as well as on quality-of-life. To identify, through longitudinal study, those measures that most accurately reflect the current disease state. This is a longitudinal study of 75-100 individuals with either MPS I, II, IV, VI and VII. Those participating in the study will be evaluated 3 times over 5 years. The primary site for this study is the University of Minnesota but there are an additional 6 centers in the United States and Canada that are also participating and will provide data for analysis. You will need to be able to travel to Minnesota or one of the participating centers in order to be a part of this study.

  Study phs001328

• National Cancer Institute (NCI) Biospecimen Pre-analytical Variables (BPV) Analysis

  The Biospecimen Pre-analytical Variables (BPV) Program is a National Cancer Institute-sponsored study to systematically assess the effects of pre-analytical factors on the molecular profile of biospecimens. A robust biospecimen collection infrastructure was established to prospectively collect biospecimens using rigorous standard operating procedures to control for most variables while introducing experimental conditions to study specific biospecimen handling issues, including the cold ischemic time (delay to formalin fixation), time in formalin, freezing methods, and storage temperatures and durations. RNA and DNA from biospecimens collected under these conditions was analyzed on multiple molecular platforms. The potential effects of these pre-analytical conditions on protein integrity and detection of metabolites were also examined. Data from this study will be used to develop evidence-based biospecimen standard operating procedures and best practices for fit-for-purpose collection, processing, and storage of biospecimens. The BPV 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 sub-studies below or in the "Substudies" box located on the right hand side of this top-level study page phs001304 BPV Cohort. The substudy links will be active once they are released by dbGaP. Preanalytical Impacts on Global Metabolite Profiling - plasma (MassSpec by Metabolon) This study was to evaluate the impact of the storage temperature (s) (-80°C and LN2 vapor) and the length of storage on human plasma quality using LC-MS/MS (liquid-chromatography-mass spectrometry/mass spectrometry) based global metabolite profiling. The study includes 240 plasma samples collected from 40 donors. Investigate the effect of the delay to fixation on the proteome and phosphoproteome -FFPE (MassSpec by Caprion). The study is to do proteome and phosphoproteome analysis on Delay to fixation was carried out using FFPE tumor samples from colon and ovarian cancer patients comparing 2, 3, and 12hr delay to fixation to the 1hr time point. The study includes 100 samples 20 donors. Investigate the effect of storage conditions of tumor specimens on the proteome and phosphoproteome profiling- Frozen tissue and plasma (MassSpec by Caprion). The study was to evaluate the effects of storage conditions on tumor specimens. Plasma samples from 40 cancer patients stored at two different temperatures (-80°C and LN2) for a given period (0-2, 6-8, and 12-14 months) were evaluated. Frozen kidney tumor samples from 20 patients were compared for effects of different snap frozen (dry ice vs. LN2) and storage temperatures (-80°C and LN2). The study includes 100 tissue and 240 plasma samples from 60 donors. Preanalytical Impacts on Genomic Sequencing by Next Generation Sequencing (NGS) technology (mRNA/miRNA and WES by Expression Analysis). The goal of the study is to determine the effects of cold ischemic delay-to-fixation (4 time points) and formalin preservation (FFPE) on the nature and quality of genomic profiles using the matched freshly frozen sample as the gold standard, which including WES, RNAseq. The study includes 395 samples from 37 donors. Preanalytical Impacts on Copy Number Variation (CNV) Detection by aCGH technology (aCGH by Georgetown University). This study was to use aCGH to evaluate the effect of variation in cold ischemia time and time in formalin fixation on CNV in DNA extracted from kidney cancer specimens. The study includes 235 samples from 40 donors. Evaluation of frozen conditions on mRNA profiling by TaqMan assay (mRNA expression by Georgetown University). This study was to utilize gene expression profiling, using custom TaqMan arrays, to compare the molecular profiles of RNA from frozen tumor samples collected using two freezing methods (dry ice or LN2), two storage temperatures (-80°C or LN2 vapor), as well as Optimal Cutting Temperature (OCT) compound and non-OCT embedded. The study includes 100 samples from 20 donors. mRNA signature for stratification by cold ischemia time (mRNA expression by IBBL). The study was to determine the effects of cold ischemic time (delay-to-fixation) and formalin preservation (FFPE) on mRNA detection by Taqman assay using tumor tissue specimens from kidney, colon and ovarian cancer patients. There are160 samples from 40 donors. The Biospecimen PV cohort is utilized in the following dbGaP individual studies. To view molecular data, and derived variables collected in these individual studies, please click on the following individual studies below or in the "Sub-studies" box located on the right hand side of this top-level study page phs001304 Biospecimen PV cohort. phs001634 CIT mRNA phs001635 CNV aCGH phs001636 Fixation Delay phs001637 Global Metabolite Profiling phs001638 mRNA TaqMan phs001639 NGS phs001640 Tumor Storage

  Study phs001304

• Chromatin immunoprecipitation linked to next-generation whole genome sequencing (ChIP-Seq) for H3K36me3 in paediatric high grade glioma cell lines KKNS4 and SF188 with and without a G34V mutation in H3F3A

  Glioblastomas of children and young adults have a median survival of only 12-15months and are clinically and biologically distinct from histologically similar cancers in older adults1. They are defined by highly specific mutations in the gene encoding the histone H3.3 variant H3F3A2, occurring either at or close to key residues marked by methylation for regulation of transcription – K27 and G34. We performed chromatin immunoprecipitation linked to next-generation whole genome sequencing (ChIP-Seq) for H3K36me3 in order to test the hypothesis that, rather than total H3K36me3, the G34V mutation may instead result in differential binding of the trimethyl mark throughout the genome. Compared with H3F3A wild-type SF188 paediatric GBM cells, H3K36me3 was found to be significantly differentially bound in KNS42 cells at 5130 distinct regions of the genome corresponding to 156 genes. Concurrent whole genome DNA sequencing demonstrated that this was not confounded by copy number alterations. The cerebral hemispheric-specific G34 mutation drives a distinct expression signature through differential genomic binding of the K36 trimethylation mark (H3K36me3). The transcriptional program induced recapitulates that of the developing forebrain, and involves numerous markers of stem cell maintenance, cell fate decisions and self-renewal. Critically, H3F3A G34 mutations cause profound upregulation of MYCN, a potent oncogene which is causative of glioblastomas when expressed in the correct developmental context. This driving aberration is selectively targetable in this patient population by inhibiting kinases responsible for stabilisation of the protein.

  Study EGAS00001001437

• RB Loss in Resistant EGFR Mutant Lung Adenocarcinomas that Transform to Small Cell Lung Cancer

  Tyrosine kinase inhibitors (TKIs) are effective treatments for non-small cell lung cancers (NSCLCs) with epidermal growth factor receptor (EGFR) mutations. However, they do not lead to cures, and, on average, relapse occurs typically occurs afterafter an average of one year of continuous treatment. In a subset of patients, aA fundamental histological transformation from NSCLC to small cell lung cancer (SCLC) is observed in a subset of the resistant cancers, but the molecular changes associated with this transformation remain unknown. Analysis of a cohort of tumor samples and cell lines derived from resistant EGFR mutant patients with SCLC transformation revealed that RB is lost in 100% of these SCLC transformed cases, but rarely in those that remain NSCLC. Global changes in gene expression, including increased neuroendocrine marker expression and absence of EGFR expression, are observed in resistant EGFR mutant cancers that transformed to SCLC. Consistent Further, with their similarities to classical SCLC at the genetic and gene expression levelincreased neuroendocrine marker and decreased EGFR expression as well as greater sensitivity to BCL2 family inhibition are observed in resistant SCLC transformed cancers compared to resistant NSCLCs. , cell lines derived from resistant EGFR mutant SCLC biopsies are substantially more sensitive to Bcl-2 family inhibition with ABT-263 compared to those derived from resistant EGFR mutant NSCLCs. Together, these findings suggest that despite developing initially as EGFR mutant adenocarcinomas, this subset of resistant cancers ultimately adopt many of the molecular and phenotypic characteristics of classical SCLC.

  Study EGAS00001001102

• Whole_genome_sequencing_of_100_DDD_trios_with_suspected_noncoding_causal_mutations

  We would like to whole-genome sequence 100 trios (300 individuals) from the DDD project in which we suspect the proband has a pathogenic noncoding mutation, to 30X coverage. These have been carefully selected to maximise the chances of finding pathogenic noncoding mutations. Specifically, we have selected probands who have no likely causal mutation within the coding region but who have a heterozygous loss-of-function mutation inherited from one parent in a known recessive developmental disorder gene, and whose phenotype is a good match for the typical phenotype caused by recessive variants in that gene. We suspect that these probands may have a heterozygous noncoding mutation on the other allele which reduces expression of the gene so that, together, these two mutations are effectively equivalent to a homozygous loss-of-function mutation. About a quarter of the probands have affected siblings and, if those siblings have been exome sequenced, we have checked that they are identical-by-descent in the region of the gene. We propose to search in the promoter region, intronic and annotated regulatory regions around these genes of interest for rare variants inherited from the other parent (i.e. not the one who transmitted the loss-of-function variant), or de novo variants. If we find such variants, and demonstrate an enrichment of this compound heterozygous LoF noncoding mechanism above that expected by chance, we intend to follow up specific candidates by RNAseq and potentially CRISPR/Cas9 experiments. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002452

• Mult-omics Palbociclib Resistance Study in HR+/HER2– Metastatic Breast Cancer

  This study is multi-omics study of a Asian longitudinal metastatic breast cancer (MBC) cohort treated with palbociclib plus endocrine therapy. It contains NGS of baseline (BL) and progressive disease (PD) from 70 patients, consisting of 79 tumor/normal matched whole exome sequencing (WES) from 62 patients and 90 tumor whole transcriptome sequecing samples (WTS) from 70 patients. There were 56 BL biopsies profiled by WES and 64 by WTS; 23 PD biopsies were profiled by WES and 26 by WTS. Twenty and 23 patients had paired BL and PD biopsies profiled by WES and WTS, respectively.

  Dataset EGAD00001008314

• NHLBI TOPMed: Best ADd-on Therapy Giving Effective Response (BADGER)

  BADGER is a 56-week randomized, double-blind, three-treatment, three-period cross-over trial that will evaluate the differential improvement in control that is achieved following three separate treatment interventions in children whose asthma is not acceptably controlled on a low dose of ICS (per NAEPP guidelines). All participants will enter an 8-week run-in period during which time they will receive a dose of 1x ICS (fluticasone 200 μg/day). During this 8-week time period, running 2-week averages to establish the lack of acceptable asthma control will be calculated. Thus, a child could qualify for randomization at any time during this 8-week run-in period. This approach should maximize both patient safety and successful enrollment. Children will continue to receive 1x ICS during the entire treatment phase. During each period of the treatment phase, they also will receive one add-on therapy in the form of LABA, LTRA or additional 1x ICS. The order of the add-on therapy assignment will be determined by randomization into one of six treatment sequences (order determined randomly). Each treatment period will be 16 weeks in length; the initial 4 weeks of each period will be considered to be the washout period for the previous treatment. The primary outcome measures will be frequency of asthma exacerbations, asthma control days, and FEV1.

  Study phs001728

• Rapid Acceleration of Diagnostics - Digital Health Technologies (RADx-DHT): Large Scale Flu Surveillance Study (LSFS)

  The purpose of this study was to better understand behavioral and physiological functioning in relation to recent self-reported influenza and influenza-like-illness (ILI), including coronavirus disease (COVID-19). Over 65,000 Achievement members responded to a weekly one-click survey asking if they had experienced ILI within the previous 7-day period. If they responded no, they were given the option to complete a survey about their risk factors and behavior. If they responded yes, they were given the option to complete a survey asking about the specifics of the incident. Participants were also asked to sync their wearable activity trackers and health apps in order for researchers to better understand changes in behavioral and physiological outcomes related to self-reported ILI experiences.DOI: https://rapids.ll.mit.edu/10.57895/fkth-d352

  Study phs002539

• Lothian Birth Cohort 1921 whole genome sequencing study

  297 members of the Lothian Birth Cohort 1921 (LBC1921) were sequenced using the Illumina HiSeq X platform. The LBC1921 are a relatively healthy cohort of 550 individuals who underwent cognitive and medical testing at a mean age of 79.1 (SD=0.6) years. They have since undergone four further waves of testing, approximately three years apart.

  Study EGAS00001003818

• Lothian Birth Cohort 1936 whole genome sequencing study

  1075 members of the Lothian Birth Cohort 1936 (LBC1936) were sequenced using the Illumina HiSeq X platform. The LBC1936 are a relatively healthy cohort of 1091 individuals who underwent cognitive and medical testing at a mean age of 69.5 (SD=0.8) years. They have since undergone four further waves of testing, approximately three years apart.

  Study EGAS00001003819

• Atherosclerosis Risk in Communities (ARIC) Cohort

  The Atherosclerosis Risk in Communities (ARIC) Study, sponsored by the National Heart, Lung and Blood Institute (NHLBI), is a prospective epidemiologic study conducted in four U.S. communities. The four communities are Forsyth County, NC; Jackson, MS; the northwest suburbs of Minneapolis, MN; and Washington County, MD. ARIC is designed to investigate the etiology and natural history of atherosclerosis, the etiology of clinical atherosclerotic diseases, and variation in cardiovascular risk factors, medical care and disease by race, gender, location, and date. ARIC includes two parts: the Cohort Component and the Community Surveillance Component. The Cohort Component began in 1987, and each ARIC field center randomly selected and recruited a cohort sample of approximately 4,000 individuals aged 45-64 from a defined population in their community. A total of 15,792 participants received an extensive examination, including medical, social, and demographic data. These participants were examined with the baseline visit occurring in 1987-89, the second visit in 1990-92, the third visit in 1993-95, the fourth visit in 1996-98, the fifth visit in 2011-13, the sixth visit 2016-17 and the seventh visit 2018-19. Follow-up occurs yearly by telephone to maintain contact with participants and to assess health status of the cohort. In the Community Surveillance Component, these four communities were investigated to determine the community-wide occurrence of hospitalized myocardial infarction and coronary heart disease deaths in men and women aged 35-84 years. Hospitalized stroke is investigated in cohort participants only. Starting in 2006, the study conducted community surveillance of inpatient (ages 55 years and older) and outpatient heart failure (ages 65 years and older) for heart failure events beginning in 2005. Community Surveillance for non-cohorts ended in event year 2014. ARIC is currently funded through 2028. The ARIC Cohort is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in these sub-studies, please click on the following sub-studies below or in the "Sub-studies" section of this top-level study page phs000280 ARIC Cohort. phs000557 ARIC_CARe phs000090 GENEVA_ARIC phs000223 PAGE_CALiCo_ARIC phs000398 GO-ESP: HeartGo_ARIC phs000668 CHARGE_ARIC phs000860 MICORTEX phs001536 CCDG_ARIC

  Study phs000280

• Multiple Myeloma Genomic Study (MMGS)

  Multiple Myeloma (MM) is a plasma cell dyscrasia characterized by bone marrow (BM) infiltration and lytic bone lesions. Recent studies of massive parallel sequencing of tumor cells obtained from the BM of patients with MM have demonstrated significant clonal heterogeneity in MM. Despite this remarkable clonal heterogeneity, it could be envisioned that such clonal diversity may be even higher since single BM samples only represent a small fraction of the whole BM compartment, and the pattern of BM infiltration in MM is typically patchy. In addition, BM biopsies are painful and cannot be repeated multiple times during the course of therapy, indicating a need for less invasive methods to molecularly characterize MM patients and monitor disease progression during the therapy. Thus, optimal characterization of circulating tumor cells (CTCs) may represent a non-invasive method to capture relevant mutations present in PC clones. In addition, MM almost always progresses from precursor states of monoclonal gammopathy of undetermined significance (MGUS)/smoldering multiple myeloma (SMM) to overt MM. However, some patients rapidly progress from MGUS/SMM to overt MM (progressors) with a rate of progression of up to 70% over 5 years, while others remain indolent with minimal progression over the same time period (non-progressors). Although many patients are diagnosed with earlier phases of disease, most patients do not receive treatment until their disease progresses, at which time they have overt end-organ damage. This concept of initiating therapy at the time of symptomatic disease is analogous to initiating therapy in patients with solid tumors only after the development of measurable metastatic disease. It is therefore not surprising that cure is not achieved for most patients with MM. Interestingly, studies have demonstrated that MGUS/SMM clones may already harbor chromosomal alterations (Ig loci or hyperdiploidy) and that progression to MM is mainly due to expansion of clones that were already present in the early stages of MGUS/SMM. However, the biological factors that discriminate progressors from non-progressors in MGUS/SMM are not well known. Therefore, our overarching hypothesis is that an effective therapeutic intervention will result from defining genomic and transcriptomic markers that are associated with disease progression. We believe, therefore, that focused research studies that define molecular mechanisms of clonal evolution in MGUS/SMM/MM will identify novel biomarkers of disease progression and help develop therapeutic agents that prevent or delay progression from MGUS to overt MM. Indeed, by eradicating the disease at the precursor stages, MM may become a preventable disease. Recently, a new term called Clonal Hematopoiesis of Indeterminate Potential (CHIP) has been proposed to describe asymptomatic individuals with hematologic malignancy-associated somatic mutations. Those individuals do not fulfill any diagnostic criteria for any hematological malignancy yet they have a tendency to progress into myelodysplastic syndrome (MDS) or myeloid or lymphoid neoplasia at a rate of around 0.5-1% per year, similar to MGUS. The frequency of CHIP and role of HSCs mutations in enhancing acquisition of somatic mutations in MM plasma cells, allowing progression following treatment, has not been studied. Investigating the dysregulated pathways in early progenitor cells would allow us to understand the reasons of progression and establish novel therapeutic and potentially preventive strategies. This study dissects genomic and transcriptomic characteristics of clonal evolution from MGUS/SMM to MM as well as the characteristics of the tumor microenvironment/immune cells/peripheral blood. Our hypothesis is that molecular biomarkers will be strong predictors of progression from MGUS/SMM to MM and will allow for the development of novel therapeutic agents that prevent or delay this progression. We aim to define genomic and transcriptomic markers that lead to progression from MGUS/SMM to MM in tumor cells, blood biopsies (cell free DNA and circulating tumor cells), and the tumor microenvironment.

  Study phs001323

• 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

• Rare Cancer Tumors Project

  A large proportion of common cancers affecting patients around the world have been selected for comprehensive cancer genome studies. Further efforts will be needed to tackle the remaining tumor types, including the rare forms of cancers. Although rare, these cancers tend to be more aggressive and fast growing with an early recurrence following initial chemotherapy and poor prognosis. Besides, patients diagnosed with rare cancers may have difficulty finding a physician knowledgeable in treating their type of cancer. While sample collection is a major challenge, the integrated genomic analyses would identify novel causative genes in these rare cancers, shed new light on the biology of the rare cancers, as well as guide novel targeted cancer therapies. Through efficient collaboration, the Human Genome Sequencing Center (HGSC) at Baylor College of Medicine (BCM) has collected/is expected to collect 20 different types of rare cancers, 15-30 cases each. Whole-exome sequencing and high-resolution SNP array analysis were/will be performed for all cases and whole-genome sequencing was designed for a selected subset of the cases. The Rare Cancer Tumors Cohort is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in this sub-study, please click on the following sub-study below or in the "Sub-studies" section of this top-level study page phs000725 Rare Cancer Tumors Cohort. phs000754 Intracranial Germ Cell Tumors phs000861 Craniopharyngioma Tumors phs000859 Sezary Syndrome Genomic Analysis

  Study phs000725

• TRanscriptomic ANalySis of left ventriCulaR gene Expression (TRANSCRibE)

  Myocardial ischemia occurs when there is a mismatch between coronary oxygen delivery and metabolic requirements of the myocardium, which may be clinically manifested during angina, coronary angioplasty or cardiopulmonary bypass (CPB). Myocardial ischemia may lead to a spectrum of myocardial stunning, hibernating myocardium, and ultimately cell death if the ischemic insult is severe. In the human heart, irreversible damage begins after approximately 20 to 40 minutes of oxygen deprivation. Observed molecular and cellular changes of myocardial ischemia are characteristic of an inflammatory response, but the exact mechanisms that underlie this pathological process are unclear and may not be full emulated by animal models of ischemia or infarction. Thus, we felt it valuable to investigate a human ischemia model. During cardiac surgery, CPB with aortic cross-clamping (AoXC) and cardioplegic arrest is associated with excellent clinical outcomes and suitable operative conditions. However, despite the use of cardioprotective strategies, AoXc during CPB is accompanied by a variable, yet obligate ischemic period lasting 1 to 3 hours, resulting in hypoxia, metabolic substrate depletion, reperfusion injury, apoptosis, and necrosis. Cardiac specific biomarkers of ischemia and infarction, including troponin, are elevated even after routine coronary artery bypass graft surgery and correlate with the duration of ischemia from AoXc.This process of CPB provides us with the ability to examine the transcriptional profile before and after an expected, consistent, and reproducible human ischemic event, albeit induced by cold cardioplegic arrest and not coronary occlusion. In addition, the absence of reperfusion in this time period allows us to examine the transcriptomic response to intermittent ischemia, without having to account for the perturbations of reperfusion injury. Although various animal models have been used to examine the effects of ischemia on cardiac function, no human data exist which examine the early transcriptomic response to a left ventricular (LV) ischemic insult. We therefore characterized the effect of cold cardioplegia induced acute ischemia on the transcriptional profile of the LV by performing whole transcriptome next-generation RNA-sequencing (RNA-seq) in patients undergoing cardiac surgery by sampling human LV tissue prior to, and after, the obligate ischemia during AoXC. We hypothesized that the cold cardioplegia induced ischemic injury will dramatically alter transcription in the human myocardium, and that we would identify genes and pathways, which will identify interventional targets for pharmacological therapy. Methods:We have collected left ventricle tissue samples and blood sample from patients undergoing heart surgery. We obtained punch biopsies (~3-5μg total RNA content) from the site of a routinely placed surgical vent in the anterolateral apical left ventricular wall of patients undergoing elective aortic valve replacement surgery with cardiopulmonary bypass. After an average of 79 minutes of aortic cross-clamping with intermittent cold blood cardioplegia for myocardial protection every 20 minutes, a second biopsy was obtained in the same manner. Tissue samples were immediately placed in RNAlater® (Ambion, ThermoFisher Scientific, Waltham, MA), and after 48 hours at +4°C were stored at -80°C until RNA extraction. Total RNA was isolated with Trizol and RNA quality was assessed using the Agilent Bioanalyzer 2100 (Agilent, Santa Clara, CA). Libraries were prepared by poly(A) mRNA isolation and reverse transcription Polymerase Chain Reaction (RT-PCR), then sequenced on the Illumina HiSeq2000 or HiSeq2500 (Illumina, San Diego, CA). As samples were analyzed at different times, different read lengths were employed, initially using single-end reads (n=20) and then transitioning to paired end reads (n=216), ranging from 36 - 100 base pairs. Raw sequencing files were processed using Sickle, Skewer, and STAR software, and aligned to GrCh37 or UCSC Hg19. DNA was isolated from whole blood using standard methods. SNP genotyping was performed using the Illumina Omni2.5Exome-8 BeadChip array with additional exome content (Illumina, San Diego, CA) chip, version 1.1. We first phased and imputed 93 subjects using a phasing tool called SHAPEIT and an imputation tool called MINIMAC, with 1000 Genomes phase 1 version 3 for the reference panel. We then phased and imputed 26 more subjects using SHAPEIT, an imputation tool called IMPUTE2, and 1000 Genomes phase 3 version 5.

  Study phs001679

• Exome sequencing of blastic plasmacytoid dendritic cell neoplasms

  Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare and aggressive haematological malignancy derived from precursors of plasmacytoid dendritic cells. Due to the rarity of BPDCNs our knowledge of their molecular pathogenesis was until recently confined to observations describing reccurent chromosomal deletions involving chromosomes 5q, 12p, 13q, 6q, 15q and 9. A recent publication went on to delineate the common deleted regions using aCGH and demonstrated that these centred around known tumour suppressor genes including CDKN2A/B (9p21.3), RB1 (12p13.2-14.3), CDKN1B (13q11-q12) and IKZF1 (7p12.2). These mutations are found recurrently in several different cancers and in most cases are thought to be involved in tumour progression rather than initiation. However, the well-defined nature and cellular ontogeny of these neoplasms suggests strongly that they share one or a few characteristic mutations as has been demonstrated for other uncommon but well-defined neoplasms such as Hairy Cell Leukemia (BRAF) and ovarian Granulosa Cell tumours (FOXL2).

  Dataset EGAD00001000406

• NextGen Consortium: The iPSCORE (iPSC Collection for Omic Research) Resource for Studying the Impact of Genetic Variation on Molecular Phenotypes

  The iPSC Collection for Omic Research (iPSCORE) Resource was created as part of the Next-Gen Consortium funded by NHLBI. The overarching purpose of the resource is to provide a large collection of human induced pluripotent stem cells (iPSCs) for use in studying the impact of genetic variation on molecular and physiological phenotypes. The resource has been used in a number of studies in the Dr. Kelly A. Frazer's Lab examining both the characteristics of human iPSCs and a variety of iPSC-derived cell types including cardiovascular progenitor cells (iPSC-CVPCs), pancreatic precursor cells (iPSC-PPCs), and retina pigment epithelium cells (iPSC-RPEs). We have shown that the iPSCs, CVPCs, and PPCs are suitable surrogate models to identify genetic factors active in early developmental processes because they exhibit fetal-like molecular properties. A total of 273 individuals have participated in the study, of which 222 have had iPSCs generated from fibroblasts. Of the 273 individuals, 181 are part of 55 families that include 24 monozygotic twin pairs and 5 dizygotic twin pairs, allowing for the incorporation of familial relationships into genetic analyses. Germline DNA has been sequenced from blood or fibroblast samples for all 273 individuals (available through dbGaP phs001325) and other genomic data (RNA-seq, DNA methylation, genotype arrays, ATAC-seq, H3K27ac ChIP-seq, HiC-seq) have been generated from the 222 iPSCs as well as derived cell types (available through dbGaP phs000924). QTL analyses were conducted for multiple omics data types and summary statistics are available through dbGaP phs0001325. Important note: Of the 273 individuals, 268 are consented for general research use and 5 are consented for cardiac research only. For detailed information about iPSCORE Collection including samples, methods used to generate the data, and how to access the datasets visit the Frazer lab website.The 222 well characterized iPSC lines that constitute the iPSCORE resource are available, please contact Dr. Kelly A. Frazer (kafrazer@health.ucsd.edu) if you are interested in obtaining the collection.

  Study phs000924

• Clonal haematopoiesis in patients with AAA (2019-04-03)

  Recent advances in genomics have demonstrated that clonal haemopoiesis driven by leukaemia associated somatic mutations is a relatively common phenomenon that increases in frequency with advancing age. Whilst individuals with clonal haemopoiesis have an increased risk of developing haematological malignancies, they also have an increased mortality from other causes. Additionally, certain mutations are almost exclusively seen in individuals aged 70 years or older, whilst others are seen in individuals with non-haematological cancers including breast and ovarian. Recently, clonal haemopoiesis was found to be associated with a significantly increased risk of atherosclerotic cardiovascular disease. This association is thought to be causative with clonally-derived macrophages showing elevated expression of several chemokine and cytokine genes that contribute to atherosclerosis. Another vascular pathology, abdominal aortic aneurysm (AAA), increases with age and shares risk factors with atherosclerosis (including smoking, male sex, high cholesterol). However, the impact of these risk factors and the overlap between AAA and atherosclerosis is poorly understood. To investigate a possible link between clonal haemopoiesis and AAA, we will study DNA samples from 300 patients with AAA and up to 200 controls for evidence of clonal haemopoiesis. This will be done using target DNA enrichment with biotinylated RNA baits followed by high throughput sequencing. . This dataset contains all the data available for this study on 2019-04-03.

  Dataset EGAD00001004895

• How to request data

  Request Data Access If you're looking to access data from the EGA for your research, you are in the right documentation page! In this page you will find a step by step guide on how to request data archived at the EGA, as well as some frequently asked questions you might find useful. When requesting data, you'll need to provide information about your research project and intended use of the data. Keep in mind that your data access request may require additional approvals or agreements depending on the datasets and data providers involved. Step by step guide Register yourself as an EGA user. Validate your account. A validation link will be sent to your email to activate your EGA user account. Log into the EGA page. Search the dataset of interest from the dataset catalogue or search bar. Click on “Request access” Add a comment requesting these files and send your request Your request is successfully registered, and the DAC will receive a notification about your request In addition to submitting your request, you should also contact the DAC directly via e-mail. You can access the DAC's contact information from the relevant dataset page. Frequently Asked Questions (FAQ) Which is the role of the EGA in the data access process? The European Genome-phenome Archive (EGA) facilitates the secure distribution under controlled access of personally identifiable genetic and phenotypic data. Data Access Committee (DAC) is a body of one or more individuals who are responsible for data release to external requestors, based on participant consent and/or National Research Ethics terms. How do I request access to multiple datasets? To request access for multiple datasets managed by the same Data Access Committee (DAC), please start by visiting the DAC catalogue to locate the DAC of your choice. Once you've found the relevant DAC, proceed to its dedicated page where you'll find a "Request access" button. After logging in to the EGA website, you'll be presented with a comprehensive list of datasets under the chosen DAC. Select the datasets you're interested in, and then compose a message indicating your access request. Click the "Send" button to submit your request, which will include the chosen datasets. Please be sure to review the data usage policies for the requested datasets, as policies can vary among datasets managed by the same DAC. I am a co-applicant. Will I be given a joint access account? Each applicant needs to create an EGA account themselves, with unique login details. User log-in details are for individual use only and should never be shared, under any circumstances. EGA services are only provided to EGA account holders. Therefore, the co-applicant will send a formal request to the DAC. What happens if a co-applicant leaves or a new member needs to be added to an existing DAC application? The appropriate Data Access Committee (DAC) should be contacted and informed about the required changes so that the application can be updated. I have changed my place of work since my EGA account was created. Can I still have access to data? You must contact the relevant Data Access Committee (DAC). The DAC did not reply to my data access request. How should I proceed? There is no estimated time for DAC response. Please allow up to four weeks for a response. In the meantime, please send them a weekly reminder of the status of your request by emailing them directly.If the DAC becomes unresponsive, meaning that they fail to communicate with a requestor within the specified time frame, please contact the EGA Helpdesk team. I have an account with the EGA and would like access to further datasets. How should I proceed? You need to reapply to the relevant Data Access Committee (DAC). Once the DAC has approved your application your account will be updated and an email will be sent to notify you of a change made to your account. How do I download files of the datasets I have been granted access to? The preferred method for data download and decryption is the EGA Secure Download Client. Please carefully review the provided documentation. How do I download metadata? Metadata is retrieved from the EGA webpage; you will not need to use any download client. Be informed that you will only be able to download metadata of your approved datasets. For such download, you should first log in the EGA webpage with your credentials. Then browse the metadata's dataset you have gained access and you will find a metadata download button. You may also want to have a look our metadata schema. I have forgotten or misplaced my user account password; how do I get a new one? Click on “Log in”, located in the top right corner of the EGA homepage. Then click “Forgot password”, type in your registered institutional email address, and instructions for resetting your password will be sent to you. Please check both regular and junk/spam folders. I was sent a link to set a password for my download account, but it seems like the link has expired. What should I do? The procedure is the same as the one for a forgotten password, explained in the previous question.

  Documentation access/request-data/how-to-request-data

• METABRIC miRNA landscape

  MicroRNAs (miRNAs) are key players in cancer pathogenesis1. They display differential expression across breast cancer subtypes, and play oncogenic and tumor-suppressive roles2-6. A systematic study of the global miRNA landscape in breast cancer and the factors shaping it has not been undertaken. Here we report the results of profiling miRNA expression in 1,302 breast tumors with detailed clinical annotation and long-term follow-up, and for which matching genomic and mRNA expression data were available7. This provides a comprehensive view of the quantity, distribution and variation of the miRNA population and dissects how much genomic, transcriptional and post-transcriptional events contribute to miRNA expression architecture. The key clinical parameters and cellular pathways related to the miRNA landscape are identified, exemplified by context-dependent interaction with cell adhesion and Wnt signaling. Strikingly, in breast cancer miRNAs appear to act as modulators of mRNA-mRNA interactions rather than molecular switches. We demonstrate an important modulatory role for miRNAs in the biology of breast tumors devoid of somatic copy number aberrations, a common subtype where immune response is prominent. Remarkably, prognostic miRNA signatures derived in this subtype are consistently prognostic across several other subtypes and validate in external cohorts. These findings represent a new framework to study the biology of miRNA in human cancer.

  Study EGAS00000000122

• Biopsy-Derived Organoids in Personalised Early Breast Cancer Care: Challenges of Tumour Purity and Normal Cell Overgrowth Cap Their Practical Utility

  The ability to establish organoids composed exclusively of tumour rather than healthy cells is essential for their implementation into clinical practice. Organoids have in recent years emerged as a powerful tool to expand patient material in culture and generate modifiable 3D models derived from humans or animal models. For translational research, they enable the creation of model systems for an ever-increasing number of cell types and diseases. And in personalised medicine, they potentially allow for functional drug testing with high predictive power in certain settings. We found that using biopsy material from untreated, early-stage primary breast cancer patients poses significant challenges for consistently culturing tumour cells as organoids. Specifically, we observed frequent outgrowth of genetically normal, non-cancerous epithelial cells. We analysed >100 biopsy samples from early-stage breast cancer and present our large collection of >70 organoid lines. We also show methods of assessing successful tumour cell culture in a time and cost-efficient manner, proving a high rate (>85%) of normal cell overgrowth in early-stage breast cancer organoids. Finally, we show a number of successful attempts to culture cancer organoids from mastectomy-derived tissue of advanced, metastatic breast cancer. We conclude that the usefulness of organoids from early breast cancer for translational research and personalised medicine, especially guidance of adjuvant or post-surgical maintenance therapy, is strongly limited by the low success rate of culturing cancerous cells under organoid conditions.

  Study EGAS50000000605

• Genetic Aberrations and Subclonal Structure Impact Chronic Lymphocytic Leukemia

  Large-scale whole-exome sequencing (WES) of primary tumor samples enables the unbiased discovery of recurrent putative driver events and patterns of clonal evolution. We report the identification of 44 recurrently mutated genes and 11 recurrent CNVs through the WES of 538 chronic lymphocytic leukemia (CLL) and matched germline DNAs. These include previously unrecognized cancer drivers (e.g., RPS15, IKZF3), and collectively identify nuclear export, MYC activity and MAPK signaling as central pathways affected by somatic mutation in CLL. A clonality analysis of this large dataset further enabled the reconstruction of temporal relationships between these driver events. Several drivers were associated with shorter progression-free survival (PFS) in 280 samples that were collected prior to uniform treatment with front line chemo-immunotherapy, with mature follow up of greater than 10 years. Direct comparison between matched pretreatment and relapse CLL from 59 samples demonstrated marked clonal evolution occurring in more than 95% of these patients. Distinct patterns of clonal evolution in relationship to specific gene alteration were observed, suggesting a hierarchy of fitness amongst mutations. Thus, large WES datasets of clinically informative samples enable the discovery of novel driver genes as well as the network of relationships between the drivers and their impact on disease relapse and clinical outcome.Additionally, we performed RNA-seq for 268 CLL samples (including 26 follow-up samples) and used them to identify expression subtypes of CLL. RRBS for 30 of these samples was also generated. In an integrative analysis of genetic, transcriptomic, and epigenetic data, incorporating known and newly identified subtypes of CLL, we built new models to improve patient prognostication.

  Study phs000922

• 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's Hospital Medical Center (CCHMC): Cincinnati Children'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 >261,000 consents that allow return of results to >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's Hospital of Philadelphia (CHOP): The Center for Applied Genomics (CAG) is a specialized Center of Emphasis at the Children's Hospital of Philadelphia (CHOP), and one of the world'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>450,000), specifically from >100,000 CHOP pediatric patients and family members, which is enriched for rare-diseases (n>12,000). Center for Applied Genomics, The Children'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's Hospital of Philadelphia. Sequencing was supported by the National Institutes of Health through an award from the National Human Genome Research Institute'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 (<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® 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's health is affected by their genes, lifestyle, and environment. This large research data and sample repository provides access to high-quality, consented blood samples to help foster research, advance our understanding of the causes of common diseases, and advance the practice of medicine. For the Partners research community (Massachusetts General Hospital and Brigham and Women's Hospital), the Biobank provides: Banked samples (plasma, serum, and DNA) collected from consented patients Blood samples that were discarded after clinical testing in the Crimson Cores maintained in the Brigham and Women's Hospital and Massachusetts General Hospital Pathology Departments Sample handling and preparation services Link to the biobank data to the Partners Research Patient Data Registry (RPDR) a research instance of our electronic clinical chart Data access through our research portal. To date, over 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'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 >155 or >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' 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