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in 15.37 milliseconds.

• How to encrypt files with EGACryptor

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

  Documentation submission/data/file-preparation/egacryptor

• CIDR: NCI Genome Wide Predictors of Survival in Colorectal Cancer

  CIDR/Molecular Correlates: This study genotyped archived blood samples from colorectal cancer cases participating in clinical trials. The goals of this project were to identify genetic variants associated with survival outcomes treatment and treatment-related severe adverse events among patients with colorectal cancer. Another goal was to examine the impact of adding information on germline genetic loci to existing prognostic models. MOSAIC: Multicenter International Study of Oxaliplatin/ 5FU-LV in the Adjuvant Treatment of Colon Cancer (MOSAIC). A randomized, open label efficacy trial to evaluate the FOLFOX regimen versus LV5FU2 in the adjuvant treatment of stage II and III colon cancer. The primary end point was disease-free survival. CPT.ES1.604: Randomized phase 3 study of weekly irinotecan plus high-dose 5-fluorouracil (FUIRI) versus biweekly irinotecan plus 5-fluorouracil/leucovorin (FOLFIRI) as first-line chemotherapy for patients with metastatic CRC. The aim of this study was to demonstrate that a regimen without leucovorin (LV) (FUIRI) is not inferior to the standard FOLFIRI (response rate). 03-TTD-01: The purpose of this phase III, multicenter, randomized, open-label study is to evaluate the safety and efficacy of combination therapy with capecitabine and oxaliplatin (XELOX) vs. oxaliplatin and 5-fluorouracil in continous infusion (5-FU CI) as first line treatment in advanced or metastatic colorectal cancer. The primary outcome measure is time to disease progression. Secondary outcome measures are to determine safety of combination, objective response rate, time to onset of response, duration of response, time to treatment failure, one year survival time, and overall survival time. 03-TTD-01: The purpose of this phase III, multicenter, randomized, open-label study is to evaluate the safety and efficacy of combination therapy with capecitabine and oxaliplatin (XELOX) vs. oxaliplatin and 5-fluorouracil in continous infusion (5-FU CI) as first line treatment in advanced or metastatic colorectal cancer. The primary outcome measure is time to disease progression. Secondary outcome measures are to determine safety of combination, objective response rate, time to onset of response, duration of response, time to treatment failure, one year survival time, and overall survival time. N0147: Randomized phase III clinical trial for adjuvant therapy in stage III colon cancer patients enrolled at institutions across North America. Originally designed to compare three different chemotherapy regimens, and then later expanded to evaluate adding cetuximab to each original arm (leading to six total arms). Further modification later added pre-screening for KRAS mutation status, with the resulting goal to assess the potential benefit of cetuximab added to the modified sixth version of the FOLFOX regimen (mFOLFOX6) in patients with resected stage III wild-type KRAS colon cancer. Main outcome of interest is disease-free survival in patients with wild-type KRAS, while secondary end points included overall survival and toxicity. N9741: Randomized phase III trial to compare the effectiveness of various combination chemotherapy regimens in treating patients who have advanced, recurrent, or metastatic colorectal cancer that cannot be treated with surgery or radiation therapy. C-08: A Phase III Clinical Trial Comparing Infusional 5-Fluorouracil (5-FU), Leucovorin, and Oxaliplatin (mFOLFOX6) Every Two Weeks With Bevacizumab to the Same Regimen Without Bevacizumab For the Treatment of Patients With Resected Stages II and III Carcinoma of the Colon. The primary aim of the trial was to compare the relative efficacy of mFOLFOX6 + bevacizumab with that of mFOLFOX6 alone in prolonging disease-free survival and the secondary aim was to compare the relative efficacy of mFOLFOX6 + bevacizumab with that of mFOLFOX6 alone in prolonging survival.

  Study phs001290

• Heart Failure: A Controlled Trial Investigating Outcomes of Exercise Training (HF-ACTION-BioLINCC)

  Accessing Data Please refer to &#8220;Authorized Access&#8221; below regarding accessing data through the BioData Catalyst ecosystem. The data from this accession is not available for download through dbGaP.ObjectivesThe HF-ACTION study examined whether exercise training reduces a composite endpoint of all-cause mortality or all-cause hospitalization for patients with left ventricular systolic dysfunction and heart failure symptoms. Background Heart failure (HF) is a major and growing cardiovascular syndrome that is the end result of many cardiovascular disorders. Although evidence-based drug and device therapies decrease mortality, hospitalizations, and HF symptoms and improve quality of life, many patients treated with these regimens often remain burdened by dyspnea and fatigue, diminished exercise tolerance, reduced quality of life, recurrent hospitalizations, and early mortality. Whereas rest was traditionally recommended for HF patients, there has been evidence that physical deconditioning may play a key role in the progression of symptoms and poor outcomes. Previous studies have shown positive effects of exercise training on exercise capacity, quality of life, and biomarkers and suggest that exercise training might improve survival and decrease HF hospitalizations. Nonetheless, there remains a safety concern regarding exercise training in HF and a large scale, multicenter, controlled clinical trial was needed to provide definitive clinical outcome data. Participants A total of 2,331 participants were randomized. 2,130 consented to share data with non-commercial entities, and 1,753 consented to share for commercial purposes. Design All participants underwent baseline cardiopulmonary exercise testing. Test results were reviewed by investigators to identify significant arrhythmias or ischemia that would prevent safe exercise training, to determine appropriate levels of exercise training, and to establish training heart rate ranges. Demographics, socioeconomic status, past medical history, current medications, physical exam, and the most recent laboratory tests were obtained prior to randomization. All participants received detailed self-management educational materials which included information on medications, fluid management, symptom exacerbation, sodium intake, and physical activity. Participants randomized to the exercise training arm first participated in a structured, group-based, supervised exercise program, with a goal of 3 sessions per week for a total of 36 sessions in 3 months. The primary exercises were walking, treadmill, or stationary cycling. Exercise was initiated at 15 to 30 minutes per session at a heart rate corresponding to 60% of heart rate reserve, and was increased after 6 sessions to 30 to 35 minutes duration and 70% of heart rate reserve. Participants began home-based exercise after completing 18 supervised sessions and were to fully transition to home exercise after 36 supervised sessions. The target training regimen for home exercise was 5 times per week for 40 minutes at a heart rate of 60% to 70% of heart rate reserve. Participants randomized to the usual care group were not provided with a formal exercise prescription. To provide comparable levels of attention from study personnel, all participants were called every 2 weeks for the first 9 months, monthly until 24 months of follow-up, and quarterly thereafter. Participants returned for clinic visits every 3 months for the first 2 years of participation and yearly thereafter for up to 4 years. Follow-up assessments included cardiopulmonary exercise testing, a 6-minute walk test, and a physical activity questionnaire. The primary end point was a composite of all-cause mortality or all-cause hospitalization. Secondary end points included all-cause mortality, the composite of cardiovascular mortality or cardiovascular hospitalization, and the composite of cardiovascular mortality or HF hospitalization. Conclusions After adjustment for highly prognostic predictors of the primary end point, exercise training was associated with modestly significant reductions for both all-cause mortality or hospitalization and cardiovascular mortality or heart failure hospitalization.

  Study phs003599

• Metadata Public API

  Metadata Rest Endpoints Common Aspects Functionality Identifiers Requests and Responses Request Response Querying Querying by Object Cross-Querying by Object Common Aspects Functionality The Metadata REST API allows EGA users to request metadata from the EGA. Using this api you will be able to obtain the publicly available information from EGA study, sample, experiment, run, analysis, policy, dac and dataset. The API also allows for objects to be cross-referenced in order to obtain for example all the datasets linked to a dac. Identifiers EGA objects can be identified by their unique accession. These are ID's displayed everywhere, shared among all EGA locations and specific for each data type (More information on the list below) EGA Accession ID EGA Object description EGAS EGA Study Accession ID EGAC EGA DAC Accession ID EGAP EGA Policy Accession ID EGAN EGA Sample Accession ID EGAR EGA Run Accession ID EGAX EGA Experiment ID EGAZ EGA Analysis Accession ID EGAD EGA Dataset Accession ID EGAB EGA Submission ID EGAF EGA File Unique Accession ID Server Request and Response Request All the endpoints implement these two HTTP methods, GET and HEAD. Moreover, there is an authentication layer that allows to disclose the private information about each object if the user runs the endpoint with their token and has permissions to the related dataset. HTTP Method Description GET Returns the objects and their public information (including private information if the user has permissions) HEAD Returns the count of objects to be returned Response API responses are diverse but they always return a status call. Below you have a summary on what these codes mean and some tips that can help you troubleshooting them: HTTP Status Code Description Resolution 200 OK No error handling necessary 206 Partial Content Expected status code when paginating the results. No error handling necessary 400 BAD REQUEST Request incorrectly formulated 401 UNAUTHORISED/FORBIDDEN You do not have permissions over the object you are trying to access. / The authorised user is not permitted to make the given request. 404 NOT FOUND The object/resource you are trying to access does not exist. 500 INTERNAL SERVER ERROR This is a server-side error, try later. If the issue persists, contact the Heldpesk. Querying Querying by Object In the below documentation, worked examples can be found on how to use the API, to query about each unique EGA object. https://metadata.ega-archive.org/analyses/{id} https://metadata.ega-archive.org/dacs/{id} https://metadata.ega-archive.org/datasets/{id} https://metadata.ega-archive.org/runs/{id} https://metadata.ega-archive.org/samples/{id} https://metadata.ega-archive.org/studies/{id} Worked example for the EGA data access committee(DAC) EGAC00001000514 https://metadata.ega-archive.org/dacs/EGAC00001000514 Points to notice Please, be advised that all EGA objects can be queried by using the same endpoint. For example: https://metadata.ega-archive.org/datasets Returns all the datasets registered at the EGA. https://metadata.ega-archive.org/datasets?limit=5&offset=15 Returns 5 results skipping the initial 15 results. Cross-Querying by Object The EGA REST API Metadata also allows for the crossquerying of public metadata. For example, should I want to query the datasets included in the ICGC DAC: https://metadata.ega-archive.org/dacs/EGAC00001000010/datasets Below, you can find worked examples on how to perform the call to the API. https://metadata.ega-archive.org/datasets/EGAD00001000645/analyses https://metadata.ega-archive.org/datasetsEGAD00001000645/files https://metadata.ega-archive.org/datasets/EGAD00001000620/runs https://metadata.ega-archive.org/datasets/EGAD00001000620/samples https://metadata.ega-archive.org/samples/EGAN00001092114/datasets https://metadata.ega-archive.org/files/EGAF00000056146/dacs https://metadata.ega-archive.org/files/EGAF00000056146/datasets https://metadata.ega-archive.org/files/EGAF00000056146/studies There are more endpoints included in this category. Please, find all the possibilities (green tick) in the table below. Table The table displays in a ordered fashion the objects that can be crossreferenced using the metadata API. BY Analysis Dac Dataset Experiment Policy Run Sample Study File Analysis ✔ ✔ Dac ✔ ✔ Dataset ✔ ✔ ✔ ✔ Experiment ✔ ✔ ✔ ✔ Policy ✔ Run ✔ ✔ ✔ Sample ✔ ✔* ✔ Study ✔ ✔ ✔ ✔ ✔ ✔* File ✔ *: These cross queries do not include data from Array-File submissions. Check out the SPEC

  Documentation discovery/metadata/public-metadata-api

• National Cancer Institute Cancer Genome Characterization Initiative (CGCI)

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

  Study phs000235

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

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

  Study phs001048

• Security Overview

  Security Overview The European Genome-phenome Archive (EGA) houses consented human data under controlled access. Access decisions are handled by the relevant Data Access Committee (DAC), which autonomously manages requests through the DAC Portal. This document provides an overview of EGA’s practices in ensuring the security of data stored at EGA. As security is a prime concern of the EGA, the EGA is a member of the Global Alliance for Genomics and Health (GA4GH) Data Security work stream. The EGA contributes and helps develop the recommendations outlined the GA4GH Security Technology Infrastructure document, which defines guidelines, best practices, and standards for building and operating an infrastructure that promotes responsible data sharing in accordance with the GA4GH Privacy and Security Policy. Explore the EGA dataflow to gain deeper insights! Accessing data in the EGA involves several steps. First, users need to create an EGA account. Once logged in, they can request access to data controlled by a DAC through the EGA website (see documentation). The DAC oversees these requests through the DAC Portal, and if approved, grants the necessary permissions to the user's EGA account, allowing them to access and download all relevant data and metadata for the requested dataset(s). The key points of EGA security strategy are: 1 Regular Risk Assessment The EGA regularly identifies and assesses risk related to the following: Breach of confidentiality,Breach of privacy or autonomy,Malicious or accidental corruption or destruction of data archived at EGA,Disruption of services provided by the EGA. 2 Risk mitigation The EGA implements and maintains safeguards to minimise the risks identified above in accordance with the 6 control objectives listed in Appendix 1 and outlined in the GA4GH Security and Infrastructure document.If a breach is discovered, the EGA applies a defined protocol to minimise damage. 3 Identity and authorisation management The EGA authenticates the identity of individuals or software accessing controlled access data held at the EGA.The EGA ensures an appropriate level of assurance (LoA) is applied to the identity consistent with the risk associated with that individual, such as multi-factor authentication for DACs.The EGA provides the minimum access rights and privileges consistent with the user’s identity, allowing access consistent with the GA4GH Privacy and Security Policy, as determined by the appropriate DAC. 4 Audit Logs The EGA maintains a set of logs recording: Changes to user access rights,Data access requests,Resource usage. 5 Cryptography, communication security, and data integrity The EGA ensures data transmission integrity using a hash function.All data transmitted to or from the EGA is end-to-end encrypted.All data at EGA is stored using strong encryption.Encryption keys are not stored in the same system as the encrypted data.All data archived at EGA must be accompanied by a signed submission statement ensuring appropriate consent or ethical approval has been obtained, and is in accordance with all applicable laws and regulations. The EGA has a defined protocol defining the response in the event of a security breach, and is continuing to work with the GA4GH Data Security Work Stream to help define best practice and associated standards for breach responses. Appendix 1 GA4GH Control Objectives Control Objective 1: Implement technology safeguards to minimise the risk of unauthorised access, use, or disclosure of confidential and private data.Control Objective 2: Implement technology safeguards to minimise the risk of discovery, access, and use of individuals’ clinical and genomic data, and individual identities, other than as authorised by applicable jurisdictional law, institutional policy, and individual consents.Control Objective 3: Implement technology safeguards to minimise the risk of accidental or malicious corruption or destruction of data.Control Objective 4: Implement technology safeguards to minimise the risk of disruption, degradation, and interruption of services enabling access to data.Control Objective 5: Implement technology safeguards to minimise the risk of potential security attacks and misuse of authorised accesses and privileges.Control Objective 6: Implement technology safeguards to promptly detect the failure to attain the above control objectives and to respond with proper countermeasures.Appendix 2Refer to the document below to learn more about EGA long-term data preservation policy and procedures at EMBL-EBI.Long-term data preservation

  Documentation about/security

• eMERGE Network Imputed GWAS for 41 Phenotypes

  The electronic Medical Records and Genomics (eMERGE) Network is a consortium of ten participating sites (Cincinnati Children&#39;s Hospital Medical Center/Boston Children&#39;s Hospital, Children&#39;s Hospital of Philadelphia, Essentia Institute of Rural Health, Marshfield Clinic Research Foundation and Pennsylvania State University, Geisinger Clinic, Group Health Cooperative/University of Washington, Mayo Clinic, Icahn School of Medicine at Mount Sinai, Northwestern University, Vanderbilt University Medical Center) funded by the NHGRI to investigate the use of electronic medical record (EMR) systems for genomic research. The goal of eMERGE is to conduct genome-wide association studies in approximately 55,000 individuals using EMR-derived phenotypes and DNA from linked Biorepositories. Using electronic phenotyping methods, the consortium used DNA samples from all participating sites to explore the genetic determinants of over forty phenotypes, including Abdominal aortic aneurysm; Ace-Inhibitor/Cough; Attention Deficit Hyperactivity Disorder; Age-related macular disease; Appendicitis; Asthma; Atopic Dermatitis; Autism; Benign Prostatic Hyperplasia; Carotid artery disease as a Quantitative Measure; caMRSA; Cataract; Clostridium difficile colitis; Extreme Obesity; Chronic Kidney Disease; Chronic Kidney Disease and Type 2 Diabetes; Chronic Kidney Disease, Type 2 Diabetes and Hypertension; Colon Polyps; Cardiorespiratory Fitness; Dementia; Diverticulosis; Diabetic retinopathy; Gastroesophageal Reflux Disease; Glaucoma; Height; Heart failure; Hypothyroidism; Lipids; Ocular hypertension; Peripheral Arterial Disease; QRS duration; Red blood cell indices; Remission of Diabetes after ROUX-EN-Y gastric bypass surgery; Resistant hypertension; MACE while on Statins; Type 2 Diabetes; Venous Thromboembolism; White blood cell indices; and Zoster virus infection, as well as using the phenome-wide association study (PheWAS) paradigm to replicate and discover relationships between targeted genotypes with multiple phenotypes. Sites and participants include: Children&#39;s Hospital of Pennsylvania (CHOP): The Center for Applied Genomics (CAG) at the Children&#39;s Hospital of Philadelphia (CHOP) is a high-throughput, highly automated genotyping and sequencing facility equipped with state-of-the-art genotyping and sequencing platforms. Children who are treated at the Children&#39;s Hospital Healthcare Network and their parents may be eligible to take part in a major initiative to collect more than 100,000 blood samples, covering a wide range of pediatric diseases. A large majority of participants consenting to prospective genomic analyses also consent to analysis of their de-identified electronic medical records (EMRs). EMRs are longitudinal, with a mean duration of 6.5 years. Cincinnati Children&#39;s Hospital Medical Center/Boston&#39;s Children&#39;s Hospital (CCHMC/BCH): Cincinnati Children&#39;s Hospital Medical Center (CCHMC) and Boston Children&#39;s Hospital (BCH) are pediatric institutions dedicated to improving health and welfare of children and to the shared purpose of discovery and practical application of new genomic information to the ordinary care of children. The CCHMC/BCH site has been built on a five-year history of collaboration, particularly in patient electronic record (ERM)-related informatics, the basis of much of eMERGE II. CCHMC and BCH together bring an extraordinary faculty to eMERGE II who are committed to diseases that afflict children, specifically phenotypes that focus upon diseases of children in ways that will leverage the available eMERGE adult GWAS and EMRs to discover meaningful use results. CCHMC/BCH plans to demonstrate real-time execution of phenotypic selection across their two distinct pediatric institutions as a model for ensuring phenotypic standardization and for national scalability. They will also look carefully at parents&#39; responses to results and use of their children&#39;s research results and better understand the factors that influence their decisions about learning incidental findings. In addition to patient and parent perceptions CCHMC/BCH will also explore clinician perceptions of pharmacogenetic research results after EMR integration. Geisinger Health System: A research cohort of adult Geisinger Clinic patients was enrolled from community-based primary care clinics of the Geisinger Health System. Patients were eligible for enrollment if they were a primary care patient of a Geisinger Clinic physician and were scheduled for a non-emergent clinic visit. All participants provided written informed consent and HIPAA authorization. Consenting patients agreed to provide blood samples for broad biomedical research use, and permission to access data in their Geisinger electronic medical record for research. The enrollment rate was 90% of patients approached. The demographics of the cohort approximate those of the Geisinger Clinic outpatient population. Research blood samples were collected during an outpatient clinical phlebotomy encounter. Research blood samples are coded and stored in a central biorepository. Samples are linkable to clinical data in a de-identified manner for research via an IRB-approved data broker process. For genomic analysis, DNA is extracted from EDTA-anticoagulated whole blood. Group Health(GH)/University of Washington (UW): GH participants for the PGx project were enrolled in the eMERGE Network through the Northwest Institute of Genetic Medicine (NWIGM) biorepository, and provided the appropriate consent to receive clinically relevant genetic results (N~6300.) Participants were eligible if aged 50 - 65 years old at the time of their enrollment into the NWIGM repository, living, enrolled in GH&#39;s integrated group practice, and had completed an online Health Risk Appraisal. The selection algorithm was based on several data sources from the EHR at Group Health: 1. Demographics - participants with self-reported race as Asian or African ancestry were prioritized and selected to enrich for non-European ancestry; 2. Diagnosis and procedure codes - participants were selected if found to have a history of hypertension, atrial fibrillation (AF), or congestive heart failure (CHF). Participants with a history of arrhythmia were added if the entire selection algorithm did not generate 900 individuals. We also enriched for participants with EHR evidence of actionable indications related to PGRNSeq genes. Participants were selected if found to have an ICD9 code for malignant hyperthermia, hypertension, atrial fibrillation, congestive heart failure or long QT syndrome (LQTS); 3. Laboratory values - if participants had any laboratory event of creatine kinase (CK) &#62;1000, and were dispensed statins within 6 months of the event, then they were selected; and 4. Medications - participants were excluded if ever on carbamazepine or had a current regimen of warfarin. Essentia Institute of Rural Health, Marshfield Clinic, Pennsylvania State University (Marshfield): The Marshfield Clinic Personalized Medicine Research Project is a population-based biobank in central Wisconsin with more than 20,000 adult subjects who provided written, informed consent to access their medical records and provided a blood sample from which DNA was extracted and plasma and serum stored. In addition to an average of 30 years of medical history data, a questionnaire about environmental exposures, including a detailed food frequency questionnaire, is available to facilitate gene/environment studies. Mayo Clinic: The Mayo biobank is a disease-specific biobank for vascular diseases including peripheral arterial disease (PAD). PAD patients were identified from individuals referred to the non-invasive vascular laboratory for lower extremity arterial evaluation. Since 1997, laboratory findings have been recorded into an electronic database employing an in-house software package for data archiving and retrieval; this data becomes part of the Mayo EMR. Patients referred to the center with suspected PAD undergo a comprehensive non-invasive evaluation including the ankle-brachial index (ABI) - the ratio of blood pressure measured in the upper arms divided by blood pressure measured at the ankles. Controls subjects are identified from patients referred to the Cardiovascular Health Clinic for stress ECG. The prevalence of PAD in patients with normal exercise capacity who do not have inducible ischemia on the stress ECG , was &#60;1%. Data regarding risk factors for atherosclerosis such as diabetes, dyslipidemia, hypertension, and smoking are ascertained from the EMR. Icahn School of Medicine at Mount Sinai School (Mt. Sinai): The Institute for Personalized Medicine (IPM) Biobank Project is a consented, EMR-linked medical care setting biorepository of the Mount Sinai Medical Center (MSMC) drawing from a population of over 70,000 inpatients and 800,000 outpatient visits annually. MSMC serves diverse local communities of upper Manhattan, including Central Harlem (86% African American), East Harlem (88% Hispanic Latino), and Upper East Side (88% Caucasian/white) with broad health disparities. IPM Biobank populations include 28% African American (AA), 38% Hispanic Latino (HL) predominantly of Caribbean origin, 23% Caucasian/White (CW). IPM Biobank disease burden is reflective of health disparities with broad public health impact: average body mass index of 28.9 and frequencies of hypertension (55%), hypercholesterolemia (32%), diabetes (30%), coronary artery disease (25%), chronic kidney disease (23%), among others. Biobank operations are fully integrated in clinical care processes, including direct recruitment from clinical sites, waiting areas and phlebotomy stations by dedicated Biobank recruiters independent of clinical care providers, prior to or following a clinician standard of care visit. Recruitment currently occurs at a broad spectrum of over 30 clinical care sites. Northwestern University: The NUgene Project is a repository with longitudinal medical information from participating patients at affiliated hospitals and outpatient clinics from the Northwestern University Medical Center. Participants&#39; DNA samples are coupled with data from a self-reported questionnaire and continuously updated data from our Electronic Medical Record (EMR) representing actual clinical care events. Northwestern has a state-of-the art, comprehensive inpatient and outpatient EMR system of over 2 million patients. NUgene has broad access to participant data for all outpatient visits as well as inpatient data via a consolidated data warehouse. NUgene participants consent to distribution and use of their coded DNA samples and data for a broad range of genetic research by third-party investigators. Vanderbilt University: BioVU, Vanderbilt&#39;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&#39;s EMR, which forms the basis for phenotype definitions used in genotype-phenotype correlations.

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• 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 &#34;Substudies&#34; 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&#176;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&#176;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&#176;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&#176;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

• Women's Health Initiative Clinical Trial and Observational Study

  The Women&#39;s Health Initiative (WHI) is a long-term national health study that has focused on strategies for preventing heart disease, breast and colorectal cancer, and osteoporotic fractures in postmenopausal women. The original WHI study included 161,808 postmenopausal women enrolled between 1993 and 1998. The Fred Hutchinson Cancer Research Center in Seattle, WA serves as the WHI Clinical Coordinating Center for data collection, management, and analysis of the WHI. The WHI has two major parts: a partial factorial randomized Clinical Trial (CT) and an Observational Study (OS); both were conducted at 40 Clinical Centers nationwide. The CT enrolled 68,132 postmenopausal women between the ages of 50-79 into trials testing three prevention strategies. If eligible, women could choose to enroll in one, two, or all three of the trial components. The components are: Hormone Therapy Trials (HT): This double-blind component examined the effects of combined hormones or estrogen alone on the prevention of coronary heart disease and osteoporotic fractures, and associated risk for breast cancer. Women participating in this component with an intact uterus were randomized to estrogen plus progestin (conjugated equine estrogens [CEE], 0.625 mg/d plus medroxyprogesterone acetate [MPA] 2.5 mg/d] or a matching placebo. Women with prior hysterectomy were randomized to CEE or placebo. Both trials were stopped early, in July 2002 and March 2004, respectively, based on adverse effects. All HT participants continued to be followed without intervention until close-out. Dietary Modification Trial (DM): The Dietary Modification component evaluated the effect of a low-fat and high fruit, vegetable and grain diet on the prevention of breast and colorectal cancers and coronary heart disease. Study participants were randomized to either their usual eating pattern or a low-fat dietary pattern. Calcium/Vitamin D Trial (CaD): This double-blind component began 1 to 2 years after a woman joined one or both of the other clinical trial components. It evaluated the effect of calcium and vitamin D supplementation on the prevention of osteoporotic fractures and colorectal cancer. Women in this component were randomized to calcium (1000 mg/d) and vitamin D (400 IU/d) supplements or a matching placebo. The Observational Study (OS) examines the relationship between lifestyle, environmental, medical and molecular risk factors and specific measures of health or disease outcomes. This component involves tracking the medical history and health habits of 93,676 women not participating in the CT. Recruitment for the observational study was completed in 1998 and participants were followed annually for 8 to 12 years. Extension Studies: The original protocol allowed for follow-up until March 2005, after which participants were invited to enroll in the first WHI Extension Study for follow-up through 2010. Participants were invited again to participate in the second WHI Extension Study with continued follow up from 2010 to at least 2015. As of March 31, 2011 there were 93,122 women enrolled in the second extension. In Extension Study 2, the overall WHI study population was divided into two new subsamples, the Medical Records Cohort (MRC) and the Self-Report Cohort (SRC). The MRC consists of all former hormone trial participants and all African American and Hispanic participants from all study components. The SRC consists of the remaining participants. The extent of outcome information collected differs between the two cohorts, with more extensive outcomes information collection on the MRC. As part of Extension Study 2, selected older WHI participants were invited to participate in an In Person Visit (a.k.a., Long Life Study) at their homes during which additional blood samples were collected and various measurements were taken (such as blood pressure, height, weight, waist circumference, grip strength, etc.). In October 2015, Extension Study 2 was renewed with continued follow-up planned through October 2020, pending annual contract review and renewal. Additional Information: The WHI website, https://www.whi.org/about/SitePages/About%20WHI.aspx has much more information about the study. For WHI data collection forms used over the years, please see https://www.whi.org/researchers/studydoc/SitePages/Forms.aspx. For additional dataset documentation, see https://www.whi.org/researchers/data/Pages/Available%20Data.aspx. For data preparation and use, please refer to &#39;WHI dbGaP Cohort Data Release Data Preparation Guide May 2018&#39; for additional details about the WHI data. The WHI Cohort is utilized in the following dbGaP substudies. To view genotypes, analysis, expression data, other molecular data, and derived variables collected in these substudies, please click on the following substudies below or in the "Substudies" box located on the right hand side of this top-level study page phs000200 WHI Cohort. phs000386 WHI SHARe phs000281 GO-ESP WHISP phs000315 WHI GARNET phs000503 WHISE phs000227 PAGE WHI phs000675 WHIMS+ phs000746 WHI Harmonized and Imputed GWAS phs001334 WHI Metabolomics of CHD phs001335 WHI BA23 phs001614 WHI LLS Phase III GWAS

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