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• RNA-seq of murine osteosarcoma cell line genetically modified for CYR61

  Transcriptomic analysis of K7M2 murine osteosarcoma cells stably modified to overexpress or repress CYR61

  Dataset EGAD50000000747

• Picuris Pueblo Genomic Project – Modern Dataset

  Please refer to the Supplemental Materials of the paper for more information.

  Dataset EGAD50000001245

• WES PTCL Data

  Whole Exome Sequencing (WES) of 153 FFPE DNA samples of Peripheral T-Cell Lymphoma. Associated with "Molecular biomarkers for stratification periheral T cell lymphoma" study.

  Dataset EGAD50000001250

• DAC policy The Kids Research Institute Australia

  DAC policy related to the study: Visium CytAssist Spatial Gene Expression analysis for FFPE glioblastoma samples. Committee: Emily Fletcher emily.fletcher@thekids.org.au Anya Jones anya.jones@thekids.org.au Emma Stone emma.stone@thekids.org.au

  Dac EGAC50000000734

• Brain Mapping by Integrated Neurotechnologies for Disease Studies: Human Brain Aging Imaging Study

  Compare the MRI data with amyloid PET images and cognitive data.

  Study JGAS000277

• Low coverage whole genome sequencing form CSF-derived cell free DNA

  Low coverage whole genome sequencing form CSF-derived cell free DNA for matching samples with EM-seq cohort

  Dataset EGAD50000002148

• Reference epigenome ADMSC02_smRNA-Seq data generated from KEP study

  A ADMSC02_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003868

• Reference epigenome ADMSC03_smRNA-Seq data generated from KEP study

  A ADMSC03_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003869

• Reference epigenome DB31_N_Alpha_smRNA-Seq data generated from KEP study

  A DB31_N_Alpha_smRNA-Seq single end data for alpha cells(PSA-NCAM(-), pancreas)

  Dataset EGAD00001003494

• RNA-Seq Single End

  RNA analysis of six patients 34, 35, 36, 37, 38 and 39 with WGS done on Illumina HiSeq2500. For research purpose and authorised user only.

  Dataset EGAD00001003430

• Reference epigenome DB31_N_Alpha_mRNA-Seq data generated from KEP study

  A DB31_N_Alpha_mRNA-Seq paired end data for alpha cells(PSA-NCAM(-), pancreas)

  Dataset EGAD00001003485

• Reference epigenome ADMSC01_smRNA-Seq data generated from KEP study

  A ADMSC01_smRNA-Seq single end data for adipose-derived mesenchymal stroaml cells

  Dataset EGAD00001003867

• Whole exome sequencing of inflammatory bowel disease cases

  Whole exome sequencing of around 700 inflammatory bowel disease cases.This data can only be used for the identification of IBD/immune-mediated disease loci.

  Dataset EGAD00001001354

• Exome sequencing of relapsed/refractory DLBCL

  This dataset consists of the exome sequencing data for 30 tumour and germline DNA pairs derived from relapsed/refractory DLBCL.

  Dataset EGAD00001003395

• Myeloma Follow up Pilot

  Targeted capture of exonic and intronic regions of interest for the study of genomic alterations in multiple myeloma.

  Dataset EGAD00001000998

• RNA sequencing study for 8 pairs of primary NSCLCs and distant metastases

  This study is to understand of the molecular mechanisms driving metastasis.

  Study EGAS00001004078

• Reference epigenome KNIH005 WGBS data generated from KEP study

  A KNIH005 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for islet cells

  Dataset EGAD00001002753

• Whole exome sequencing study for 8 pairs of primary NSCLCs and distant metastases

  This study is to understand of the molecular mechanisms driving metastasis.

  Study EGAS00001004077

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0740_SA609X6

  60 samples; filetype=bam

  Dataset EGAD00001004821

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0680_SA501X10A

  61 samples; filetype=bam

  Dataset EGAD00001004816

• Reference epigenome KNIH003 WGBS data generated from KEP study

  A KNIH003 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for islet cells

  Dataset EGAD00001002751

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0837_SA501X2

  40 samples; filetype=bam

  Dataset EGAD00001004812

• JIA family

  Exome sequencing data for two sibs with juvenile idiopathic arthitis and one unaffected sib

  Dataset EGAD00001004806

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0443_SA501X7A

  48 samples; filetype=bam

  Dataset EGAD00001004813

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0650_SA532X6

  71 samples; filetype=bam

  Dataset EGAD00001004818

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0738_SA609X6

  48 samples; filetype=bam

  Dataset EGAD00001004819

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0739_SA609X6

  52 samples; filetype=bam

  Dataset EGAD00001004820

• long RNA data

  long RNA data for 27 CLL samples (8 controls, 19 tumor) of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005968

• Lund RNAseq data

  RNAsequencing data from human pancreatic islets from 191 donors, Lund University. Processed for the Inspire consortium.

  Dataset EGAD00001005512

• Neoantigen study samples for prostate cancer

  Whole exome sequencing and RNA sequencing data from 30 patients with prostate cancer.

  Dataset EGAD00001005713

• Hyperpolarized 13C MRI in breast cancer

  Transcriptomics for samples obtained from six patients (MBR01, MBR03, MBR05, MBR07, MBR10, MBR11)

  Dataset EGAD00001005760

• Whole Genome Sequencing of INTERVAL (2019-06-12)

  Whole genome sequencing of participants from the INTERVAL study. . This dataset contains all the data available for this study on 2019-06-12.

  Dataset EGAD00001005084

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0198_005A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0198_005A for Lymph node patient sample RLN_03_5Prime

  Dataset EGAD00001010238

• DLP+ Single Cell Genomic Library 98211

  DLP+ Single Cell Genomic Library 98211 for Diffuse large B-cell lymphoma patient sample TFRIPAIR11_DLBCL

  Dataset EGAD00001010239

• Reference epigenome KNIH004 WGBS data generated from KEP study

  A KNIH004 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for islet cells

  Dataset EGAD00001002752

• Reference epigenome KNIH002 WGBS data generated from KEP study

  A KNIH002 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for islet cells

  Dataset EGAD00001002750

• Glioblastoma stem cell lines RNA-seq

  RNA-seq data for three Glioblastoma stem cell (GSC) lines exposed to PRMT5 inhibitor and control samples.

  Dataset EGAD00001006095

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0198_004A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0198_004A for Lymph node patient sample RLN_02_5Prime

  Dataset EGAD00001010237

• Reference epigenome KNIH009 WGBS data generated from KEP study

  A KNIH009 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for preadipocytes

  Dataset EGAD00001002757

• FPKM expression values of the CUP/reference/validation cohort (H021)

  FPKM expression values of the CUP/reference/validation cohort used for tissue-of-origin prediction based on transcriptomic data

  Dataset EGAD00001008638

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0741_SA609X6

  55 samples; filetype=bam

  Dataset EGAD00001004822

• Single-nucleus RNA-sequencing of meningiomas

  Single-nucleus RNA-sequencing of meningiomas for integrative molecular classification

  Dataset EGAD00001007677

• Raw count matrix for 418 baseline samples

  Count matrix from 418 pre-treatment (Ven-Obi or Clb-Obi) CD19+ B cells

  Dataset EGAD00001009501

• liCHi-C Samples of B-ALL

  Libraries of liCHi-C for 2 B-ALL (B Acute Lymphocytic Leukaemia) from human patients. Fastq file format.

  Dataset EGAD00001008829

• Reference epigenome KNIH006 WGBS data generated from KEP study

  A KNIH006 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for beta cells

  Dataset EGAD00001002754

• Reference epigenome KNIH007 WGBS data generated from KEP study

  A KNIH007 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for adipocytes

  Dataset EGAD00001002755

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0472_SA501X7A

  50 samples; filetype=bam

  Dataset EGAD00001004814

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0582_SA501X10A

  45 samples; filetype=bam

  Dataset EGAD00001004815

• Hi-C of cohesin-mutated and wildtype adult AMLs

  High-throughput chromosome conformation capture (Hi-C) data generated for cohesin-mutated (STAG2 or RAD21) and cohesin-wildtype AMLs.

  Dataset EGAD00001011198

• Sputum metagenome for COPD patients and healthy controls

  This dataset contains the sputum metagenome from 99 COPD patients and 36 healthy individuals in China.

  Dataset EGAD00001009063

• FMI data

  This dataset contains somatic alteration calls summarized at the gene level for 715 patients profiled by FoundationOne (Foundation Medicine).

  Dataset EGAD00001006616

• DLP+ Single Cell Genomic Library A98167

  DLP+ Single Cell Genomic Library A98167 for Diffuse large B-cell lymphoma patient sample TFRIPAIR4_DLBCL

  Dataset EGAD00001010249

• 7 samples RNA-seq raw data

  Dataset comprising raw paired RNA-seq data in fastq.gz format for 7 samples of rosette forming brain tumors

  Dataset EGAD00001009265

• Epiclomal: probabilistic clustering of sparse single-cell DNA methylation data for library px0544_SA532X6

  68 samples; filetype=bam

  Dataset EGAD00001004817

• Reference epigenome KNIH001 WGBS data generated from KEP study

  A KNIH001 Whole-Genome Bisulfite Sequencing(WGBS) paired end data for islet cells

  Dataset EGAD00001002749

• CYP2C19 long-read sequencing

  We have assessed the added value of long-read sequencing for PGx focusing on the clinically important and highly polymorphic CYP2C19 gene within 48 samples.

  Dataset EGAD00001009883

• DLP+ Single Cell Genomic Library A98203

  DLP+ Single Cell Genomic Library A98203 for Diffuse large B-cell lymphoma patient sample TFRIPAIR8_DLBCL

  Dataset EGAD00001010242

• DLP+ Single Cell Genomic Library A98180

  DLP+ Single Cell Genomic Library A98180 for Diffuse large B-cell lymphoma patient sample TFRI_Pair_13_DLBCL

  Dataset EGAD00001010240

• DLP+ Single Cell Genomic Library A98225

  DLP+ Single Cell Genomic Library A98225 for Diffuse large B-cell lymphoma patient sample TFRIPAIR12_DLBCL_rel

  Dataset EGAD00001010246

• KCL PRECSION lpWGS

  KCL lpWGS samples for copy number analysis

  Dataset EGAD00001008380

• Genome-wide data Iberian Roma

  VCF file with genome-wide data for 62 Iberian Roma samples.

  Dataset EGAD00001006358

• Biomarker data

  Biomarker data for KATHERINE: Biomarker data include RNA-seq time point, Percent of tumor content, PAM50 subtypes, normalized gene expression of ERBB2, CD8 and CD274, normalized immune signature expression.

  Dataset EGAD00001008786

• How to upload Crypt4GH files

  Uploading files Users who hold an ega-box-XXX account can upload files using either INBOX or FTP. Users who have a Submitter role associated with their email will only be able to upload files using INBOX. Before uploading your files, please make sure that any files that will be uploaded to EGA do not use special characters in their naming convention, such as # ? ( ) [ ] / \ = + < > : ; " ' , * ^ | &. This can cause issues with the archiving process, leading to problems for end users. The EGA is a shared, public service with limited storage. To manage the available resources, we enforce a limit of 10TB per submission account at any one time. If you exceed this limit, a “permission denied” message will be displayed. This will prevent you from uploading more files, but connecting to your inbox.For submissions larger than 10TB, please perform uploads in 10TB batches: register all the metadata and then finalise the submission. Upload the next batch of files and repeat the same metadata registration and finalisation process until you have completed the file upload. Further information can be found in the SP documentation. INBOX FTP The INBOX is only compatible with files encrypted using the Crypt4gh tool Before uploading If you are not a registered EGA user, you will first need an EGA user account. Please note that it may take a few days for your account to be activated, as it needs to be vouched for by the EGA Helpdesk. Once your account is validated, you will be able to request a submitter role. [Optional] Meanwhile, you can create and add your public key to your EGA account profile. This option is not available for old submission accounts (e.g., ega-box-NNN). As soon as you have been granted a submitter role, you will be able to connect with your username and password to the EGA inbox using the SFTP protocol. If you have also registered a public key in your profile, you can also connect using this key. To upload files to your account, you can use the graphical user interface (GUI) or the command line. Graphical User Interface (GUI)We recommend using FileZilla, a free, open-source FTP client. However, you can use any other GUI that allows connecting over the SFTP protocol. For FileZilla as your GUI, follow these steps to upload files: Create a new connection in Site Manager (File > Site Manager) and select the following options (Figure 1): Protocol: SFTP - SSH File Transfer ProtocolHost: __EGA_INBOX_DOMAIN__Logon Type: Key fileUser: your EGA usernameKey file: Path/to/your/private_keyFigure 1: Process of establishing a new connection to __EGA_INBOX_DOMAIN__ using a key file as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers.Click Connect, and you will log in remotely to your home directory. You can think of this folder as a storage "in the EGA cloud" in which you will add your files for the EGA. The uploading area has three folders:To-encrypt: Files uploaded in this folder will be encrypted automatically on the fly.Encrypted: Files uploaded in this folder must already be encrypted with Crypt4gh. Upload your files here if your connection is unstable or you have problems completing the upload into-encrypt.Etc: This folder contains two files that allow the server to show you your username and group instead of some internal numbers. Please do not upload files here; otherwise, you will obtain a permission denied error. Find the files you want to upload by browsing your local storage (left side of your screen in FileZilla). Select all the files you want to upload, then right-click on them and select Upload (Figure 2). Figure 2: Step-by-step process of manually uploading files to __EGA_INBOX_DOMAIN__ using FileZilla, with FileZilla version 3.52.2 operating on IOS v11.2.3. The figure demonstrates how users can transfer data from their local storage to the "EGA cloud" by following the depicted steps Please note that regardless of which folder you upload your files in, both folders (to-encrypt, encrypted) will point to the same path (/) (Figure 3). Therefore, you will see your files in both folders. Figure 3: Both folders, to-encrypt and encrypted, point to the same path (/)" If your connection is unstable, please encrypt your files first using Crypt4gh. Then upload them to the ‘encrypted’ folder. The example above shows how to connect to __EGA_INBOX_DOMAIN__ using the private key. However, if you prefer to log in using your credentials, you can do so. Please go to the Frequently Asked Questions (FAQs)  for more information. SFTP command line To upload files securely to your private area of the EGA, you can use SFTP(Secure File Transfer Protocol) with your favorite FTP client. Here's what you need to know to get started: Connect to the target host __EGA_INBOX_DOMAIN__. This is the new hostname for the EGA SFTP service. Log in with your EGA username and key files (or password). Upload files to your private EGA inbox to ensure that only you can access the files. By following these steps, you can securely upload your files to the EGA for safe storage and sharing. Using the SFTP command line client in Linux/Unix Open a terminal and type sftp username@hostnameEnter your EGA passwordTo see a list of available SFTP commands, type helpsftp> put – Upload filesftp> get – Download filesftp> cd path – Change remote directory to ‘path’sftp> pwd – Display remote working directorysftp> lcd path – Change the local directory to ‘path’sftp> lpwd – Display local working directorysftp> ls – Display the contents of the remote working directorysftp> lls – Display the contents of the local working directoryType the "put" command to upload files. For example: put *.bamUse the bye command to close the connection (SFTP session). After uploading- Once you have uploaded files to the inbox, please bear in mind that the checksum needs to be calculated, which can take up to two days. You will only be able to link your files to a run/analysis once the encrypted checksum has been calculated.- When linking your files to the 'Run' or 'Analysis', ensure that the file name matches the file path '/name' in the INBOX folder.- Please delete the files from your SFTP INBOX after all the runs/analyses have been registered and files are ingested (SP > Files > Files ingested). This will clear your inbox space an allow you to upload more files. This will also prevent the files from reappearing in your Submitter Portal inbox. Frequently Asked Questions Specific to the inbox What username should I use to log in to my inbox? The authentication process for logging in to the EGA website, as well as accessing your inbox and outbox, requires the use of your username.  If you have forgotten your registered username, please contact our Helpdesk team for assistance. How are checksums calculated in your inbox? If you encrypt the file beforehand and upload it to the "encrypted" folder, the unencrypted checksum will not be calculated until the file is ingested (i.e., until it is used in a run/analysis). If the file is uploaded to the "to-encrypt" folder, then both checksums are calculated.Please bear in mind that after files have been uploaded to the inbox, the checksum must be calculated, which can take from a few hours to two days.  Specific to using keys to authenticate Can I access one EGA account from different devices? Yes, you can access your account from different devices by linking several public keys to your EGA account. Each device can generate a unique public-private key pair, and the corresponding public keys can be linked to the same account. This way, you can use different public keys on different devices and still have access to the same account and data. I have several keys and I don't remember which one is which When generating SSH keys, it's a good practice to add a comment using the -C flag. This will allow you to add a descriptive tag to your key, making it easier to identify later on. Here's an example command that generates an SSH key with a comment: ssh-keygen -t ed25519 -C work-pass In this example, we're generating an ed25519 SSH key with the comment work-pass. Once you have multiple keys with different comments, you can use the comments to easily identify each key. To view the comments for your existing SSH keys, you can use the following command: ssh-keygen -l -f /path/to/key This will display the key fingerprint and the associated comment. By checking the comments, you should be able to identify which key is which. What if I can't find my SSH keys for uploading files with a key file, and how can I use new keys? If you can't find your SSH keys, don't worry - you can make new ones. To do this, open your terminal or command prompt and type a command to make a new SSH key. You can pick a name for the key, and choose a password to keep it safe. After making the key, you can add the new key to your account or server where you want to upload files using the key file. This usually involves copying and pasting the key's "public" (e.g. file.pub) part to the right place. If you lose track of the key again, just make a new one and add it again. Keep in mind that SSH keys belong to you and your computer, so if you switch computers or accounts, you'll need to make new keys. I don't want to type the passphrase every time I use the key. What can I do? You can use an ssh-agent to avoid typing the passphrase every time you use the key. An ssh-agent is a program that stores your private keys in memory and provides them to ssh when needed. You can add your key to the ssh-agent using the command ssh-add followed by the path to your key file.Here's an example of the steps to follow: Open a terminal window.Start the ssh-agent by typing the command eval $(ssh-agent).Add your key to the ssh-agent by typing the command ssh-add [key filepath]. For instance, if your key file is located in the home directory with the name mykey, the command will look like this: ssh-add ~/mykey After adding your, key to the ssh-agent, you should be able to use ssh without having to enter your passphrase every time. Can I use my password for authentication (without my private key)? If you prefer to use your username and password for authentication instead of your private key, you can still do so. When using a Graphical User Interface (GUI) such as FileZilla, you can select Ask for password as your Logon Type (Figure 3). This option will prompt you to enter your password when you click Connect, instead of using your private key. Figure 3: This option will prompt you to enter your password when you click "Connect", instead of using your private key. Figure 3: Process of establishing a new connection to __EGA_INBOX_DOMAIN__ using your password as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers. It's worth noting that using a password for authentication can be less secure than using an SSH key, as passwords can be more easily compromised through various means. However, if you choose to use your password for authentication, selecting "Ask for password" as your Logon Type is a good way to do so securely via a GUI. Why is it better to use my key and not my password? SSH keys for authentication is generally considered to be more secure and convenient than using passwords. SSH keys are more difficult to crack than passwords, and they can be restricted to specific users and machines, giving you more control over access. Once you set up your SSH keys, you can use them to authenticate quickly and easily, without having to enter a password every time. This makes automation of tasks, such as uploading encrypted files, much simpler. Additionally, SSH keys provide better logging, allowing you to keep track of who is accessing your systems and when. All in all, using SSH keys is a good practice for improving security and convenience in your authentication process.

  Documentation submission/data/uploading-files/inbox

• GeneSTAR (Genetic Study of Atherosclerosis Risk) NextGen Consortium: Functional Genomics of Platelet Aggregation Using iPS and Derived Megakaryocytes

  The causal mechanisms of common diseases and their therapies have been only marginally illuminated by genetic variants identified in genome wide association studies (GWAS) utilizing single nucleotide polymorphism (SNPs). Platelet activation pathways reflecting hemostasis and thrombosis are the underlying substrate for many cardiovascular diseases and related acute events. To overcome GWAS limitations, genomic studies are needed that integrate molecular surrogates for platelet-related phenotypes assayed in cell-based models derived from individuals of known genotypes and phenotypes. In our GWAS study of native platelet aggregation phenotypes and aggregation in response to low dose aspirin in 2200 subjects (GeneSTAR, Genetic Study of Aspirin Responsiveness), important genome wide "signals" (p&lt;5x10-8) associated with native platelet aggregation and important "signals" associated with platelet responsiveness to aspirin were identified and replicated. Although we are currently performing functional genomics studies to elucidate our most promising findings in known genes (PEAR1, MET, PIKC3G), most "signals" occurred in intergenic regions or in introns. Mechanistic interpretation is limited by uncertainty as to which gene(s) are up- or down-regulated in the presence of most SNP modifications. In this 3 phase proposal, we will (1) create pluripotent stem cells (iPS) from peripheral blood mononuclear cells, and then differentiate these stem cells into megakaryocytes (2) develop an efficient strategy to produce iPS and megakaryocytes using a novel pooling method, and (3) produce iPS and megakaryocytes from 250 subjects in GeneSTAR (European Americans and African Americans), selected based on specific hypotheses derived from GWAS signals in native and post aspirin platelet function; characterize genetic mRNA transcripts using a comprehensive Affymetrix array; measure protein expression for transcripts of interest using mass spectrometry; examine mRNA and protein expression patterns for each GWAS signal to determine the functional pathway(s) involved in native platelet phenotypes; and examine the functional genomics of variations in responsiveness to aspirin using our prior genotyped and phenotyped population. Precise information about the exact functional processes in megakaryocytes and platelets may lead to innovative and tailored approaches to risk assessment and novel therapeutic targets to prevent first and recurrent cardiovascular and related thrombotic events.

  Study phs001074

• A New Reference Panel to Boost African American Genotype Imputation

  Modern genetic studies have been conducted predominantly in cohorts of individuals of European ancestry. By 2010, there were approximately ten times as many published genome wide association studies (GWAS) in people of European ancestry than studies in people of all other ancestries combined. This research disparity has led to an uneven understanding of the genetic basis underlying disease in Europeans and non-Europeans. 23andMe's web-based, large scale research model is ideal for scaling genetics research within non-European populations and thereby bringing more parity to genetics research. Our database is composed of genotypes and phenotypes of over 1,000,000 consenting customers, including over 200,000 individuals with non-European ancestry. The data derived from non-European individuals represent a particularly valuable resource for genetic discovery of novel variants that may not be found in the European population. However, research studies in non-European populations are weakened by the lack of availability of large-scale reference datasets and, in particular, genotype imputation panels. Genotype imputation is a statistical methodology that uses observations of genotypes in a large reference panel to infer unobserved genotypes in a target dataset. This methodology is widely used within GWAS, and allows novel genetic associations to be identified and refined. Due to this utility, very large reference panels have been constructed, containing thousands or tens of thousands of whole genome sequences. Unfortunately, the largest imputation panels are composed of predominantly European genomes, reflecting the modern bias towards European studies in GWAS. This proposal aims to address this imbalance by constructing an imputation panel specifically for the African American population. In doing so, we will expand 23andMe's ability to perform genetic discovery in non-European populations, and improve the understanding of global genetic variation underlying diseases and traits. Key commercial outcomes of the research include the identification of novel genetic targets for internal and external therapeutic development. The long-term aim is to improve understanding of disease in minority populations, which we hope may eventually lead to improved treatments of disease in these historically medically understudied groups.

  Study phs001798

• A Single Cell Atlas of Gene Regulatory Elements in the Human Heart

  Background: Cis-regulatory elements such as enhancers and promoters are crucial for directing gene expression in the human heart. Dysregulation of these elements can result in many cardiovascular diseases that are major leading causes of morbidity and mortality worldwide. In addition, genetic variants associated with cardiovascular disease risk are enriched within cis-regulatory elements. However, the location and activity of these cis-regulatory elements in individual cardiac cell types remains to be fully defined. Methods: We performed single nucleus ATAC-seq and single nucleus RNA-seq to define a comprehensive catalogue of candidate cis-regulatory elements (cCREs) and gene expression patterns for the distinct cell types comprising each chamber of four non-failing human hearts. We used this catalogue to computationally deconvolute dynamic enhancers in failing hearts and to assign cardiovascular disease risk variants to cCREs in individual cardiac cell types. Finally, we applied reporter assays, genome editing, and electrophysiogical measurements in in vitro differentiated human cardiomyocytes to validate the molecular mechanisms of cardiovascular disease risk variants. Results: We defined &gt;287,000 candidate cis-regulatory elements (cCREs) in human hearts at single-cell resolution, which notably revealed gene regulatory programs controlling specific cell types in a cardiac region/structure-dependent manner and during heart failure. We further report enrichment of cardiovascular disease risk variants in cCREs of distinct cardiac cell types, including a strong enrichment of atrial fibrillation variants in cardiomyocyte cCREs, and reveal 38 candidate causal atrial fibrillation variants localized to cardiomyocyte cCREs. Two such risk variants residing within a cardiomyocyte-specific cCRE at the KCNH2/HERG locus resulted in reduced enhancer activity compared to the non-risk allele. Finally, we found that deletion of the cCRE containing these variants decreased KCNH2 expression and prolonged action potential repolarization in an enhancer dosage-dependent manner. Conclusion: This comprehensive atlas of human cardiac cCREs provides the foundation for not only illuminating cell type-specific gene regulatory programs controlling human hearts during health and disease, but also interpreting genetic risk loci for a wide spectrum of cardiovascular diseases.

  Study phs002204

• NHLBI and NIDDK Sponsored GWAS in Benign Ethnic Neutropenia/Leukopenia (BEN) in African-Americans (age >45 yrs old) from the REGARDS

  Benign ethnic neutropenia (BEN) is a clinical condition more commonly observed in African-Americans. It is characterized by a relative reduction in neutrophil count by about 1000 cells per microliter, leading to a decrease in total leukocyte count by similar decrement. Previous reports of this condition showed that there was neither higher frequency nor increased severity of infections in affected individuals. Bone marrow examinations showed normal white cell maturation; and ex vivo culture of marrow cells showed low normal or slightly reduced number of myeloid colonies. Under physiologic stress, the increases in neutrophil and leukocyte counts of BEN individuals are slightly lower, compared to normal African-Americans or Caucasians. These clinical observations suggest that BEN results from a lower &#39;set point&#39; for cell number in the marrow. Additionally, case reports of familial BEN, the persistence of BEN over many decades in the US, UK, and Africa, and the recent report of Duffy antigen and chemokine receptor (DARC) being associated with neutropenia, all suggest a strong genetic association to neutropenia/leukopenia. Our initial look into microarray analyses in a pilot trial of subjects showed that there were no significant differences in mRNA signals between BEN and normal subjects. Therefore, we are now proposing a larger study, utilizing Illumina Omni Express chips, to look for genetic associations. We have partnered with the Reasons for Geographic and Racial Differences in Stroke study (REGARDS), where nearly half of the cohort are African-Americans. This will be one of the few GWAS being performed in only African-Americans, and will provide valuable genetic information to link with neutropenia and possibly other conditions/diseases. Genotyping was performed by the Johns Hopkins University Center for Inherited Disease Research (CIDR). Quality control of the genotypic and phenotypic data was performed through a collaboration between CIDR and the Genetics Coordinating Center, Department of Biostatistics at the University of Washington, which is funded by a federal contract supported by 14 NIH Institutes (HHSN268200782096C).

  Study phs000507

• Longitudinal Studies of Patients with Familial Platelet Disorder with Associated Myeloid Malignancy (FPDMM)

  FPDMM (OMIM #601399) is a rare autosomal dominant disease characterized by defective megakaryocytic development, thrombocytopenia, platelet functional defects, and a life-long risk of developing hematological malignancies. FPDMM is caused by germline mutations in the RUNX1 gene, which encodes a master regulator of hematopoiesis. The overall lifetime risk of hematological malignancies in FPDMM patients is 35 - 45%. It is hypothesized that mutations in other genes cooperate with the germline RUNX1 mutations for leukemia development. However, it is not clear which genes need to be mutated for leukemia to develop and which biomarkers or clinical tests can be used to predict which patients will progress to leukemia. Likewise, there are no treatments and/or preventative measures that can reduce or stop disease progression except for stem cell transplantation, which is not available for all patients and may be associated with significant complications. To address these questions, we launched a longitudinal natural history study of patients associated with RUNX1 germline mutations at the NIH Clinical Center in early 2019 (Trial Registration ID: NCT03854318). The goals of this study are to: (1) perform comprehensive longitudinal phenotyping of FPDMM patients to document the full spectrum of both hematopoietic and non-hematopoietic manifestations of the disease; and (2) perform extensive genomic analyses of FPDMM patient samples to identify cooperating genomic variants, which may confer growth advantage that leads to clonal expansion of affected hematopoietic cells that may serve as the precursor to malignancies. The data available through dbGaP include exome sequencing and RNA-seq data from these patients. Diverse germline mutation types were identified in the RUNX1 gene, including splice site mutations and large deletions, in addition to nucleotide substitutions and small indels. Somatic mutations, especially in genes associated with clonal hematopoiesis of indeterminate potential (CHIP), were found in a substantial proportion of FPDMM patients who were leukemia-free. The findings suggest that the genomes in these patients are prone to somatic mutations and monitoring the dynamic changes of these somatic mutations prospectively may provide a way to predict progression to malignancies.

  Study phs003075

• National Institute of Mental Health (NIMH) Duke Cognition Cohort

  The overall goal is to identify genetic variants influencing normal variation between humans, with a focus on cognitive and neurosensory traits. Because of the focus of funding agencies on disease, very few studies of the genetics of non-disease traits have been performed. The genetic variation underlying the vast majority of differences between humans therefore remains not only undiscovered, but also unexplored. We have assessed &#62;1,200 healthy volunteers for &#62;70 diverse, non-disease phenotypes, with a focus on cognitive and neurosensory traits that are understudied and seem to have a strong biological basis. Our study includes measurements of face recognition, night vision, cognitive performance, auditory pitch discrimination, contagious yawn susceptibility, time perception, sense of direction, taste preferences, personality, sexual behaviors, sleep habits, acne, headache locations, synesthesia, and teeth sensitivity. This method is very different from the focus of most studies on collecting cohorts of patients with disease and controls with minimal phenotypic information. Because we phenotype the same healthy volunteers for multiple highly variable yet heritable traits, we can perform genetic analyses on a large number of phenotypes by just investing once in sequencing the DNA of these participants. An additional &#62;2,000 participants in our cohort have been measured just for cognitive performance instead of for the more extensive phenotyping. Most of the genetic studies in this cohort have been about cognitive perfromance. The number of participants with genetic data generated who have undergone the more extensive phenotyping is limited. Our studies of the more novel traits like contagious yawning have therefore focused more on correlations between different phenotypes until we have a sufficient sample size to truly attempt to make genetic discoveries. . The dataset also includes two small challenge studies. In one study, the cognitive performance of 150 healthy volunteers was measured before and after an acute dose of the antiepileptic drug topiramate. In the other study, the cognitive performance of 13 healthy volunteers was measured before and after 24 hours of sleep deprivation.

  Study phs001406

• Exceptional Responders Initiative

  The Exceptional Responders Initiative is a pilot study to investigate the underlying molecular factors driving exceptional treatment responses of cancer patients to drug therapies. Study researchers will examine molecular profiles of tumors from patients either enrolled in a clinical trial for an investigational drug(s) and who achieved an exceptional response relative to other trial participants, or who achieved an exceptional response to a non-investigational chemotherapy. An exceptional response is defined as achievement of either a complete response or a partial response for at least 6 months duration in a trial or treatment where the overall response rate is &#60; 10%. The hope is to discover underlying molecular features that can be further investigated and may eventually predict benefit from a given drug or class of drugs for a particular patient. This pilot project will successfully characterize approximately 100 cases of tumor tissue and, when available, case-matched germline DNA. All samples will undergo whole exome sequencing, and cases with sufficient nucleic acids will undergo additional analyses (e.g. whole genome sequencing, mRNA-sequencing, mi RNA sequencing, promoter methylation analysis, SNP etc). Each case will be annotated with demographic and clinical information, along with follow-up information minimally sufficient to correlate molecular profiles with response. Both retrospective and prospective collections will be considered. The project will also accept sequencing data and clinical data from patients who have had sequencing performed outside of this project. All data will be de-identified and placed in a controlled access database so other investigators may use them for additional insights. Clinically annotated tissue specimens meeting the criteria will be provided by groups participating in the Exceptional Cases Initiative to a Biospecimen Core Resource (BCR), which will perform quality control on the tissues, and will use a standard operating procedure to isolate nucleic acids. The nucleic acids will be shipped to a sequencing center to perform whole exome sequencing and analysis. These findings will be made available to the broader cancer research community in a controlled access database.

  Study phs001145

• Bacterial Vaginosis, Cervical Immune Cells and HIV Susceptibility

  This was a cross-sectional study to characterize cervical immune cell composition and activation status in the presence versus absence of bacterial vaginosis (BV) using bulk messenger RNA sequencing (mRNA-Seq). Women enrolled in a longitudinal, open cohort study and engaged in a sex work in Mombasa, Kenya, were eligible. On the day of enrollment, participants self-collected vaginal swabs for generation of Gram-stained slide. The slides were scored from 0-10 using the method of Nugent and Hillier; in this system, women with scores of 0-3 are considered to have normal vaginal flora, those with scores of 4-6 are considered to have intermediate flora, and those with scores of 7-10 are considered to have BV. For this study, only women with normal flora (Nugent scores 0-3) or BV (Nugent scores 7-10) were eligible. Participants underwent speculum-assisted pelvic examination and collection of cervical biopsy specimens using Tischler forceps. Biopsies were placed immediately in ice cold medium and transferred to the laboratory where they were weighed and transferred to freezing medium (10% DMSO + fetal bovine serum) and cooled slowly overnight. Samples were shipped from Kenya to the United States in liquid nitrogen dry shippers, collagenase digested into single cell suspensions, and sorted on a FACS aria to enrich for a population of live, CD45+ cells. Sorted samples (N=17 with BV and N=13 without BV) were immediately lysed and processed using the Takara SMART-Seq v4 Ultra Low Input RNA Kit for sequencing. Library generation was completed using the manufacturer's instructions and sequencing performed on an Illumina NextSeq500 platform using Illumina NextSeq 500/550 High Output v2.5 (150 Cycles). The FASTQ files generated and BV status (N=9 with BV and N=12 without BV that passed quality control) of the donor sample will be available on dbGaP. We identified over 1000 differentially expressed genes between women with versus without BV at an adjusted p-value of <0.05 (listed under "gene names" below).

  Study phs002329

• Neoadjuvant atezolizumab plus chemotherapy in gastric and gastroesophageal junction adenocarcinoma: the phase 2 PANDA trial

  Gastric and gastroesophageal junction (G/GEJ) cancers carry a poor prognosis, and despite recent advancements most patients die of their disease. While immune checkpoint blockade has become part of the standard of care for patients with metastatic G/GEJ cancers, its efficacy and impact on the tumor microenvironment in the early disease setting remain largely unknown. We hypothesized a higher efficacy of neoadjuvant immunotherapy plus chemotherapy in patients with non-metastatic G/GEJ cancer. In the phase 2 PANDA trial, patients with previously untreated resectable G/GEJ tumors (n=21) received neoadjuvant treatment with one cycle of atezolizumab monotherapy followed by 4 cycles of atezolizumab plus docetaxel, oxaliplatin and capecitabine. Treatment was well-tolerated, and all patients underwent resection without treatment-related delays, meeting the primary endpoint of safety and feasibility. Tissue was obtained at multiple time points, allowing analysis of the effects of single-agent PD-L1 blockade and the subsequent combination with chemotherapy on the tumor microenvironment. A total of 21 patients were included, of whom 20 patients underwent surgery and were evaluable for the secondary pathologic response and survival endpoints, while 19 were evaluable for exploratory translational analyses. A major pathologic response (MPR, ≤10% residual viable tumor) was observed in 14/20 (70%; 95% CI 46-88%) patients, including 9 (45%; 95% CI 23-68%) pathologic complete responses. At a median follow-up of 47 months, 13/14 responders were alive and disease-free, while 5/6 nonresponders had died due to disease recurrence. Notably, baseline PD-1+CD8+ T cell infiltration was significantly higher in responders than in nonresponders, and comparison of the tumor microenvironment alterations following anti-PD-L1 monotherapy versus the subsequent combination with chemotherapy revealed that increased immune activity was achieved upon single-agent PD-1/L1 axis blockade. Based on these data, anti-PD-L1 plus chemotherapy warrants further exploration in patients with non-metastatic G/GEJ cancer, and these results call for validation in a larger cohort.

  Study EGAS50000000168

• Kids First: Whole Genome Sequencing in Recessive Structural Brain Defects

  Almost 5% of all live births in the US (1:20 births) display an inborn defect, including both structural and functional/metabolic abnormalities. These are among the most common causes of Infant mortality in the developed world and underlie nearly half of hospitalizations in the first 3 years of life. Of the 35 major defects observable at birth from the International Clearinghouse for Birth Defects Surveillance Program (www.icbdsr.org, external link), about half involve the nervous system. Many of these result in lifelong neurodevelopmental disorders. Structural Brain Defects (SBDs) result from errors in development of the central nervous system, including defects in the forebrain, midbrain and hindbrain. Many SBDs arise as the consequence of a single gene bi-allelic mutation, and for this reason, occur more commonly in populations or communities with elevated consanguinity.Our lab has identified dozens of novel SBD genes using WES/WGS in consanguineous SBD families. Importantly, the genes that we and others have identified in these unique families are then used to advance diagnosis in pediatric SBDs around the world. We have built an enormous cohort of SBD families, including newly recruited families not yet studied genetically, and previous families that were negative for cause following WES analysis. Here we propose to collaborate with the Gabriella Miller Kids First Pediatric Research Program (X01) to have sequencing performed in individuals from a total of 200 families with genetically undiagnosed SBDs. The Gleeson Lab team of researchers is dedicated to the field of SBDs, with an outstanding track record of high-impact science, and a collaborative approach to discovery. We have 150 newly recruited families that we propose to study by WES by sequencing blood-derived DNA from two affected patients, or the parents and one affected patient. We also have 50 families in which WES was negative, which we propose to study in a multi-omics approach combining WGS from blood-derived DNA. We anticipate that this study will lead to the identification of many new molecular causes of SBDs, as well as uncover new genotype-phenotype correlations and new disease mechanisms, paving the way for future breakthroughs in detection, treatment and prevention.

  Study phs002590

• Gabriella Miller Kids First Pediatric Research Program in Pediatric T-Cell Acute Lymphoblastic Leukemia

  The outcome for patients with relapsed T-ALL is dismal with 3-year event free survival of AALL0434 was a Children's Oncology Group-initiated phase 3 randomized clinical trial comparing Capizzi-style escalating methotrexate plus pegaspargase (CMTX) vs. high dose methotrexate (HDMTX), with/without six 5-day courses of nelarabine. Survival on this study was superior to any prior trial for de novo T-ALL, changing the standard of care. Yet, a substantial minority (~15%) of patients had relapsed or refractory (r/r) disease. Through the TARGET initiative, RNA sequencing (RNA-Seq), DNA copy number analysis, and whole-exome sequencing (WES) were performed on 264 T-ALL patients treated on AALL0434, demonstrating recurrent alterations could be grouped into 10 different potentially targetable functional pathways. This analysis was not powered to examine associations between genetic lesions with outcome, because too few patients with r/r disease were included. This project is dedicated to testing the hypothesis that comprehensive genomic profiling of the entire AALL0434 cohort will identify recurrent genetic alterations that can be segregated into biologically relevant deregulated pathways that can be combined with MRD to identify patients at risk for poor outcomes before they relapse and provide rationale for treatment with alternative therapies. In addition, a number of small recent studies demonstrated that many of the biologically relevant alterations in T-ALL occur in non-coding regions of the genome, but no large studies have performed whole genome sequencing (WGS) in T-ALL. This project also tests the hypothesis that WGS of a large cohort of patients with T-ALL will identify novel lesions in coding and non-coding regions that will be highly impactful in the understanding of T-ALL pathogenesis.These hypotheses are tested by performing comprehensive genomic profiling (WGS, WES, RNA-Seq, and copy number analysis) of the entire AALL0434 cohort with available samples with the following specific aims: (1) identify recurrent genetic alterations that predict poor outcome in T-ALL; (2) identify novel alterations, including non-coding alterations in T-ALL; and (3) identify germline genetic variants that predispose to T-ALL and to increased toxicity to chemotherapy.

  Study phs002276

• Psychophysiological Investigation of Myocardial Ischemia (PIMI-BioLINCC)

  Data Access NOTE: Please refer to the "Authorized Access" section below for information about how access to the data from this accession differs from many other dbGaP accessions. Objectives: To investigate psychophysiological factors related to both symptomatic and asymptomatic cardiac ischemia.Background: An important hypothesis has been generated from current research in this area: manifestations and expressions of cardiac ischemia are influenced by specified psychophysiological mechanisms.Participants: The study population consisted of 196 participants recruited from four clinical units, all of which were clinical units for the Asymptomatic Cardiac Ischemia Pilot (ACIP). Participants eligible for PIMI were identified throughout the screening process established for the enrollment of participants in ACIP.Design: The primary goal of PIMI was to test the hypothesis, "Manifestations and expressions of cardiac ischemia are influenced by specific psychophysiological mechanisms." The specific relationships investigated were:Patients who are susceptible to mental stress ischemia (indexed by new left ventricular wall motion abnormalities) would display more ischemia during daily activities and would have ambulatory ischemia at lower heart rate and activity level thresholds than patients who are not susceptible. Cardiovascular and catecholamine reactivity to mental stress would be a predictive of severity of mental stress ischemia and of ambulatory ischemia. &#914;-edorphin responses to mental stress and to exercise would be predictive of asymptomatic ischemia. Patients with systematic ischemia and those with asymptomatic ischemia would show differences in each of the following at rest and after response to stresses (exercise and/or mental tests). Mental stress would produce different hormonal and perceptual responses than exercise. Asymptomatic ischemia would be associated with a lesser severity of ischemia than symptomatic ischemia. The location of the ischemic myocardium would be inferior in a higher proportion of patients with asymptomatic ischemia than of patients with symptomatic ischemia. The latency internal that would be the onset of ST segment depression and the onset of ischemic pain would be related to somatic sensory perception, autonomic nervous system reflux control of the heart, &#914;-endorphin responses, and psychosocial characteristics.

  Study phs004183

• UK10K NEURO IOP COLLIER

  In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. The Genetics and Psychosis (GAP) set consists of samples from subjects with schizophrenia, ascertained as a new-onset sample. This set is of UK origin, with data on cognition, brain imaging and other endophenotypes. The Maudsley twin series consists of probands ascertained from the Maudsley Twin Register, defined as patients of multiple birth who had suffered psychotic symptoms. This set is of UK origin, with data on cognition, brain imaging and other endophenotypes, with DNA available from an MZ or DZ affected or unaffected co-twin. The Maudsley family study (MFS) consists of over 250 families who have a history of schizophrenia or bipolar disorder Within the Maudsley Family Study, biological markers of psychosis include neuropsychological tests, Evoked Response Potentials Tests (ERPs), MRI scans, dermatoglyphics and eye tracking. Early risk factors for psychosis and clinical symptoms are also investigated. This set is of UK origin, with DNA available from both affected and unaffected relatives in many of the probands. For further information on this study please contact David Collier (david.collier@kcl.ac.uk).

  Study EGAS00001000121

• Programmatic submission based on XML

  Programmatic submissions (XML based) For further information please check our Submission FAQs, submission quickguide as well as submission terms! Introduction Besides the Submitter Portal tool, EGA supports programmatic sequence and clinical data metadata submissions. If you are not sure what this means, you may want to explore our brief metadata introduction. Programmatic submissions are recommended for array-based submission. Moreove, it may be of help if your submission is recurrent or it is difficult to manage manually due to its sheer size. Otherwise, we highly recommend using the Submitter Portal to perform submissions. In this page we will guide you through the required steps to programmatically submit data to the EGA. Programmatic submissions require your metadata to be structured for an easy and straightforward validation and archival. It basically consists in formatting your metadata as Extensible markup language (XML) files and submitting them to the EGA using the WEBIN Before submitting metadata to the EGA, it is important to ensure that the information in your XML files is compliant with our standards. You can see further details on how these standards are maintained at EGA at our EGA Schemas documentation page. Using WEBIN, you can validate your XML files against EGA's schemas to ensure that your metadata is compliant before submission. WEBIN services WEBIN production service WEBIN test service We advise you to submit your metadata to the test service when submitting to the production service for the first time. The test service is identical to the production service except that all submissions will be discarded in the following 24 hours. This allows you to learn about the submission process without having to worry about data being submitted. Authentication Authentication is required each time a submission is made. The submission service uses HTTPS protocol for metadata encryption and identification to provide a secure submission environment. Data file upload Both Runs and Analyses reference files (e.g. FASTQ need to be uploaded to the EGA before these metadata objects are submitted. In other words, if you submit a Run that references a file that we cannot find associated with your account, the metadata submission will fail. See further details on how to upload your files in our File Upload documentation. Metadata model of the EGA Our metadata model is formed by multiple metadata objects. Check further details in our documentation at our EGA Schema documentation page. Working with EGA XMLs files Now that the basic concepts of the EGA metadata have been described, you can start preparing your programmatic submission through XML. Here you will find the guidance on how to prepare the XML files. Programmatic Submission Tutorial Video Take a look at the Programmatic Submission Tutorial Video, which explains the workflow of a programmatic submission and goes over an example metadata submission. Programmatic Submission Tutorial Video. When building your XML files, we recommend using text editors (e.g.Sublime Text or VisualStudio) that allow you to visualise the structure of the XML with ease. Furthermore, these editors constantly check the consistency of the XML structure. Alternatively, and if the submission consists of a big number of objects (specially analyses), you may find the tool star2xml handy. This tool allows for a direct conversion between metadata in a tabular format (e.g. a spreadsheet) into XMLs. Identifying objects: Aliases and center names Every EGA object must be uniquely identified within the submission account using their alias attribute. The aliases can be used in submissions to make references between EGA objects. Let us dig into EGA's use of aliases and center names: alias: every object should have a name that is unique within your submission account. Once submitted successfully, every alias will be assigned a unique and permanent accession (EGA ID). refname: when an object references another by its alias, the alias of the referenced object goes into the "refname" attribute of the referencing object. For example, if a sample has the alias "sample1", and an experiment uses this sample, then the experiment's "EXPERIMENT/SAMPLE/refname" attribute should be "sample1". center_name: The "center_name" attribute is required within the submission XML and, if not provided when the object is submitted, it will be automatically filled using your default EGA account center_name. This element is the "controlled vocabulary acronym or abbreviation that is provided to the account holder when the account is first generated". If the submitter is brokering a submission for another institute, the submitter should use their special broker account name in broker_name while the data centre acronym remains in center_name. Log-in details should have been provided when you requested a submission account. Please contact our Helpdesk team if you have any questions. run_center: Many submitting centers contract out the actual sample sequencing to another center. In these cases, the sequencing center should be acknowledged in the run_center attribute. Again, this is controlled vocabulary and the acronym should be sought from EGA helpdesk before submitting. Please contact our Helpdesk team if you have any questions. Prepare your XMLs The goal of this section is to provide sufficient information to be able to create the metadata XML documents required for programmatic submissions. Please note, the EGA utilises the XML schemas maintained at the European Nucleotide Archive (ENA). It is important due to the fact that by using a similar system, some pieces of documentation from the ENA's programmatic submission can also help you with your programmatic submission to the EGA. For example, you can submit programmatically without using a Submission XML by following the steps at Submission actions without submission XML. A submission does not have to contain all different types of XMLs. For example, it is possible to submit only a few samples; or a study that is later to be referenced. You can submit each object one by one, or submit all in a batch: you choose what method of submission works best for you. We do recommend, nevertheless, that you submit the objects to be referenced (e.g. samples or studies) first, and the objects that reference these (e.g. experiments or datasets) afterwards. You can see a graphical view of these objects and their relationships at our EGA Schemas page. Independently of the submission scenario, you will always require a Dataset XML. The entity of a dataset is what is used to control access to the given data, in the form of runs or analyses. In other words, when a requester is granted access, it is through the dataset and the objects (e.g. runs or analyses) that the dataset contains, granting access to them in one go. Given the nature of the EGA, a dataset XML will always be required for the data access. First, we will differentiate between submissions of "raw" and "processed" data: Runs and Analyses, respectively. Run data submissions Raw data derives from instruments "as is". For example, a plain sequence file (e.g. FASTQ or unaligned BAM files) would be considered raw data. A typical raw (unaligned) sequence read submission consists of 8 XMLs: Submission Study Sample Experiment Run DAC Policy Dataset When technical reads (e.g. barcodes, adaptors or linkers) are included in the submitted raw sequences, a spot descriptor must be submitted to describe the position of the technical reads so that they can be removed. The following data files can be submitted without providing spot descriptor information in the experiment/run XML: BAM files (single reads) SFF files (single reads without barcodes) FastQ files (single reads without any technical reads) Complete Genomics files Analysis data submissions Processed data is, in some way, refined raw data. This includes raw data that has been processed by some form of analysis method (e.g. alignment, noise reduction, etc.). For example, an aligned sequence (e.g. BAM file), that was created using raw FASTQ files, would be a processed file. This category includes most types of data: sequence alignment files (e.g. BAM or CRAM), clinical data (e.g. phenopackets), sequence variation files (e.g. VCF), sequence annotation, etc. A typical EGA analysis data submission consists of 7 EGA XML: Submission Study Sample Analysis DAC Policy Dataset We accept three different types of analysis data submissions: BAM files (for multiple read alignments) VCF files (for sequence variations) Phenotype files (in any format) In anycase, keep in mind that samples must be created in order to be referenced in the analyses. In other words, the provenance of the information within the BAM, VCF and phenotype files Example XMLs Below you can find a non-extensive list of example XMLs with descriptive fields (i.e. explaining what to provide in each field). Furthermore, you can also find real examples (i.e. the true value of the provided fields) in our GitHub repository. Submission XML The submission XML is used to validate, submit or update any number of other objects. The submission XML refers to other XMLs. New submissions use the ADD action to submit new objects. Object updates are done using the MODIFY action and objects can be validated using the VERIFY action. Descriptive submission XML example True values submission XML example Study XML The study XML is used to describe the study containing a title, a study type and abstract as it would appear in a publication. Descriptive study XML example True values study XML example Please use the following notation within the property "STUDY_LINKS" when including PubMed citations in the Study XML: <STUDY_LINKS> <STUDY_LINK> <XREF_LINK> <DB>PUBMED</DB> <ID>18987735</ID> </XREF_LINK> </STUDY_LINK> </STUDY_LINKS> Sample XML The sample XML is used to describe the samples used to obtain the data, whether they were sequenced, measured in any other way, or have an associated phenotype. The mandatory fields include information about the taxonomy of the sample, sex, subject ID and phenotype. For example, the mandatory attribute fields for each sample would look like these, within the array of "SAMPLE_ATTRIBUTES": <SAMPLE_ATTRIBUTES> <SAMPLE_ATTRIBUTE> <TAG>subject_id</TAG> <VALUE>free text!</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>sex</TAG> <VALUE>female/male/unknown</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>phenotype</TAG> <VALUE>Free text, EFO terms (e.g. EFO:0000574) are recommended</VALUE> </SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTES> Sample is one of the most important objects to be described biologically, it is highly recommended that “TAG-VALUE” pairs are generated as SAMPLE_ATTRIBUTES to describe the sample in as much detail as possible. For example, were we to give the population ancestry of the sample, we could add a new attribute to the array, in which, for example, we would indicate that the sample derives from an individual of "Mende in Sierra Leone" (MSL), with an african ancestry: <SAMPLE_ATTRIBUTE> <TAG>Population</TAG> <VALUE>MSL</VALUE> </SAMPLE_ATTRIBUTE> Given that VALUE and TAG are free text, the combinations are limitless in order to give you full flexibility on the information you want to provide. We recommend you use the Experimental Factor Ontology (EFO) to describe the phenotypes of your samples. You can provide more than one phenotype by adding more items to the array of SAMPLE_ATTRIBUTES. Phenotypes considered essential for understanding the data submission should be provided. Each phenotype described should be listed as a separate sample attribute <SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTE>. There is no limit to the number of phenotypes that can be submitted. If a suitable EFO accession cannot be found for your phenotype attribute, please consider using another controlled ontology database (e.g. HPO, MONDO, etc.) before using free text. Descriptive sample XML example True values sample XML example Experiment XML The experiment XML is used to describe the experimental setup, including instrument platform and model details, library preparation details, and any additional information required to correctly interpret the submitted data. Where any of these values differ between runs, a new experiment object must exist, since runs are grouped by experiments. Each experiment references a study and a sample by alias, or if previously-submitted, by accession. Pooled data must be demultiplexed by barcode for submission. Descriptive experiment ( Illumina paired read ) XML example True values experiment ( Illumina paired read ) XML example Run XML The run XML is used to associate data files with experiments and typically comprises a single data file (e.g. a FASTQ file). Please note that pooled samples should be de-multiplexed prior submission and submitted as different runs. Descriptive run XML example True values run XML example Analysis XML Given that an analysis can be used to submit any type of processed data to the EGA, we will list below an example of each of the three most common types of analysis XMLs submitted to the EGA: sequence alignments (e.g. BAM files); sequence variation (e.g. VCF files); and clinical metadata or phenotypes (e.g. phenopackets). Regardless of the type of processed data submitted in the analysis, the analysis must be associated with a Study and can reference multiple types of other objects, from samples to experiments, if they are available at the EGA. Just like with Runs, whenever a file is submitted to the EGA through an analysis object, the file MD5 checksums must be present, in order for the EGA to validate file integrity upon transfer. This also includes index files when applicable (e.g. .bai.md5 files). Ideally, any analysis that uses a reference sequence for some kind of alignment (e.g. BAM, CRAM or VCF files), would contain metadata about the alignment, such as INSDC reference assemblies and sequences, by either using accessions (e.g. CM000663.1) or common labels (e.g. GRCh37). Read alignment (BAM) Analysis XML The Analysis can be used to submit BAM alignments to EGA. Only one BAM file can be submitted in each analysis and the samples used within the BAM read groups must be associated with Samples. Descriptive bam alignments XML example True values bam alignments XML example Sequence variation (VCF) Analysis XML The Analysis can be used to submit VCF files to EGA. Only one VCF file can be submitted in each analysis and the samples used within the VCF files must be associated with Samples. Download analysis XML (VCF) Phenotype files The Analysis XML can be used to submit phenotype files to the EGA. Only one phenotype file can be submitted in each analysis and the samples used within the phenotype files must be associated with EGA Samples. Download analysis XML (Phenotype) DAC XML The DAC XML describes the Data Access Committee (DAC) affiliated to the data submission. The DAC may consist of a group or a single individual and is responsible for the data access decisions based on the application procedure described in the POLICY.XML. As with any other object, if it was already submitted to the EGA, there is no need to submit it again: you can reference an existing object within the EGA. Hence, A DAC XML does not need to be provided if your submission is affiliated to an existing EGA DAC.. Further information on DACs can be found here, and you can always contact our Helpdesk team if you have further inquiries. Descriptive dac XML example True values dac XML example Policy XML The Policy XML describes the Data Access Agreement (DAA) to be affiliated to the named Data Access Committee. Descriptive policy XML example True values study XML example Dataset XML The dataset XML describes the data files, defined by the Run.XML and Analysis.XML, that make up the dataset and links the collection of data files to a specified Policy. The dataset xml is commonly the last metadata object to be submitted, since it references multiple other entities. Please consider the number of datasets that your submission consists of. For example, a case-control study is likely to consist of at least two datasets. In addition, we suggest that multiple datasets should be described for studies using the same samples but different sequence technologies. Descriptive dataset XML example True values dataset XML example Validating and submitting your EGA Validating EGA's XMLs through Webin After you have ensured that the XMLs are properly formatted and contain all the required information. You can proceed to validate and submit your data. Use the curl command to validate your XML file: Once you have prepared your XML file and asserted you have access to Webin, you can validate your XML file programmatically against EGA's schemas using the curl command. There are multiple ways in which you can validate your XMLs. This variety has to do with the fact that: (1) there are 2 instances of Webin (test and production); and (2) that validation is a default step during submission. In other words, any time that you submit your data through Webin, it will be validated automatically before being accepted. This allows for 4 possible routes of validation, all having the same validation result: validating or submitting to either the production service or the test service of Webin. For example, directly validating a "study" object XML in the testing service (wwwdev…) would look like the following: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" In this command, you would need to replace <USERNAME> and <PASSWORD> with your EGA account username and password, respectively. You would also replace <INPUT_FILE> with the path to your XML file. A mock example would look like the following: curl -u ega-test-data@ebi.ac.uk:egarocks -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" The validation attempt can have different results depending on the given arguments: If your XML file is valid according to EGA's schemas, you will see a message indicating that your XML file is compliant. For example, see below for our mock example, where the "success" was "true" (i.e. no validation errors found). Nevertheless, notice how the "<STUDY accession=" is empty: it is because we were simply validating, so the study did not get an accession or ID. <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:19:28.850+01:00" submissionFile="submission-EBI-TEST_1681222768850.xml" success="true"> <STUDY accession="" alias="Mock example" status="PRIVATE"/> <SUBMISSION accession="" alias="SUBMISSION-11-04-2023-15:19:28:840"/> <MESSAGES> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If there are any errors or warnings, the tool will display them, allowing you to correct them before submitting your data to EGA. For example, in the following response, it is said that the object we were trying to submit was already existing, and therefore the "success" was "false". <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:12:35.609+01:00" submissionFile="submission-EBI-TEST_1681222355609.xml" success="false"> <STUDY alias="Example!_Human Microbiome Project SP56J" status="PRIVATE" holdUntilDate="2023-03-11Z"/> <SUBMISSION alias="SUBMISSION-11-04-2023-15:12:35:576"/> <MESSAGES> <ERROR>In study, alias: "Example!_Human Microbiome Project SP56J". The object being added already exists in the submission account with accession: "ERP127584".</ERROR> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If the curl command retrieves no response at all, please double check if your username and password are correctly provided. Also notice the "ACTION=..." argument passed to the Curl command. This specifies the action to take during the call to Webin, so we do not need a "Submission" XML just for a validation attempt. See more at submission actions without submission XML. Furthermore, validation of multiple files or objects (e.g. sample, experiment, study…) can be done in a single command by adding more arguments (i.e. '-F'). For example: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" -F "SAMPLE=@sample.xml" -F "DATASET=@dataset.xml" As mentioned above, beside "validate" action in the test environment, you can also validate your metadata by three other methods: "Validate" in the production server. From our example above, you simply need to take the "dev" away from the URL. curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the development server. From our example above, you would simply need to replace the action: from "validate" to "add". Whatever is submitted to this service will be discarded in 24h, so whether something gets submitted or not would not matter in the long run. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the productionserver. A combination of the previous two methods, which would render this attempt into a submission. This path is just to be taken when you are sure your metadata is compliant and what you want to submit. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" What happens after the submission of a dataset XML? Once you have completed the registration of your dataset/s please contact our Helpdesk Team to provide a release date for your study. Please note that all datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitter or DAC contact contact the EGA Helpdesk for the study to be released. We strongly advise you not to delete your data until EGA Helpdesk confirms that your data has been successfully archived.

  Documentation submission/metadata/submission/programmatic-submission-xml

• A small cell lung cancer genome reports complex tobacco exposure signatures

  Cancer is driven by mutation. Worldwide, tobacco smoking is the major lifestyle exposure that causes cancer, exerting carcinogenicity through 60 chemicals that bind and mutate DNA. Using massively parallel sequencing technology, we sequenced a small cell lung cancer cell line, NCI-H209, to explore the mutational burden associated with tobacco smoking. 22,910 somatic substitutions were identified, including 132 in coding exons. Multiple mutation signatures testify to the cocktail of carcinogens in tobacco smoke and their proclivities for particular bases and surrounding sequence context. Effects of transcription-coupled repair and a second, more general expression-linked repair pathway were evident. We identified a tandem duplication that duplicates exons 3-8 of CHD7 in-frame, and another two lines carrying PVT1-CHD7 fusion genes, suggesting that CHD7 may be recurrently rearranged in this disease. These findings illustrate the potential for next-generation sequencing to provide unprecedented insights into mutational processes, cellular repair pathways and gene networks associated with cancer.

  Study EGAS00000000051

• Colon cancer targeted sequencing study contaning WBCs, primary tumor tissue and plasma samples

  In the setting of localized colon cancer (CC), circulating tumor DNA (ctDNA) monitoring in plasma has shown potential for detecting minimal residual disease (MRD) and predicting higher risk of recurrence. With the tumor-only sequencing approach, however, germline variants may be misidentified as somatic variations, precluding the possibility of tracking in up to 11% of patients due to a lack of known somatic mutations. In this study comprising 148 prospectively recruited localized CC patients, a custom 29-gene panel was utilized to sequence both tumor tissue and matched white blood cells (WBCs) to enhance the accuracy of sequencing results. Performing targeted sequencing of paired tumor tissue and WBCs samples detected additional somatic mutations and increased the number of patients eligible for MRD tracking in plasma, although MRD detection sensitivity was not increased. Furthermore, the germline testing approach revealed the presence of pathogenic germline variants, thereby helping identify patients at elevated risk of hereditary cancer syndromes.

  Study EGAS50000000059

• scRNAseq data of CAP

  Fastq files from scRNAseq data of cancer associated pericytes-like from 3 patients. CAP were isolated from a total of 3 primary BC (surgical residues prior to any treatment) by using BDFACS ARIA III sorter (BD Biosciences). BC were collected directly from the operating room after surgical specimen macroscopic examination and selection of areas of interest by a pathologist. Samples were cut into small pieces (around 1 mm3) and digested in CO2-independent medium (Gibco #18045-054) supplemented with 150 μg/mL liberase (Roche #05401020001) and Dnase I (Roche #11284932001) for 40 min at 37°C with shaking (180 rpm). After digestion, cells were processed and stained as described above (#Flow Cytometry analysis of BC samples). CAP fibroblasts were then gated on the Live/Dead negative fraction and defined as EPCAM- CD45- CD31- CD235a- FAPMed CD29High. CAP scRNA-seq: Upon isolation, CAP cells were directly collected into RNase-free tubes (Thermo Fisher Scientific, #AM12450) precoated with DMEM (GE Life Sciences, #SH30243.01) supplemented with 10% FBS (Biosera, #1003/500). Single-cell capture, lysis, and cDNA library construction were performed using Chromium system from 10X Genomics, with the following kits: Chromium Single Cell 3′ Library & Gel Bead Kit v2 kit (10X Genomics, #120237) and Chromium Single Cell A Chip Kits (10X Genomics, #1000009). Generation of gel beads in Emulsion (GEM), barcoding, post GEM-reverse transcription cleanup and cDNA amplification were performed according to the manufacturer’s instructions. Cells were loaded accordingly on the Chromium Single cell A chips, and 12 cycles were performed for cDNA amplification. cDNA quality and quantity were checked on Agilent 2100 Bioanalyzer using Agilent High Sensitivity DNA Kit (Agilent, #5067-4626) and library construction followed according to 10X Genomics protocol. Libraries were next run on the Illumina HiSeq (for patients P1) and NovaSeq (for patients P2–3) with a depth of sequencing of 50,000 reads per cell.

  Dataset EGAD50000000321

• Somatic mutations of non-malignant T cells

  Somatic mutations in T cells can cause cancer but also have implications for immunological diseases and cell therapies. The mutation spectrum in non-malignant T cells is unclear. Here, we examined somatic mutations in CD4+ and CD8+ T cells from 90 patients with hematological and immunological disorders and used T cell receptor (TCR) and single-cell sequencing to link mutations with T cell expansions and phenotypes. CD8+ cells had higher mutation burden than CD4+ cells. Notably, the biggest variant allele frequency (VAF) of non-synonymous variants was higher than synonymous variants in CD8+ T cells, indicating non-random occurrence. The non-synonymous VAF in CD8+ T cells strongly correlated with the TCR frequency, but not age. We identified mutations in pathways essential for T cell function and often affected in lymphoid neoplasia. Single-cell sequencing revealed cytotoxic Temra phenotypes of mutated T cells. Our findings suggest that somatic mutations contribute to CD8+ T cell expansions without malignant transformation.

  Study EGAS50000000237

• Mechanisms of Extreme Genomic Instability at Large Transcribed Genes

  Inhibiting DNA replication leads to copy number variant (CNV) formation throughout the genome, especially at chromosome fragile sites (CFSs). We previously showed that these hotspots for genome instability reside in late-replicating domains associated with large transcribed genes. In this study, we compared aphidicolin (APH)-induced CNV and CFS frequency between wild-type cells and isogenic cells in which FHIT gene transcription was ablated. We further examined the impact of altering RNase H1 expression on CNV or CFS induction frequency and analyzed R-loop formation genome-wide in a human fibroblast cell line. Data sets include Bru-seq nascent RNA transcription, whole genome sequencing for replication timing, SNP microarray analysis, and DRIP-seq. Results suggest that large gene transcription is a determining factor in replication stress-induced genomic instability and that CFSs mainly result from transcription-dependent passage of unreplicated DNA into mitosis with low R-loop levels observed at these loci.

  Study phs002066

• University of Miami Udall Center of Excellence Identification of Rare Variants in PD through Whole Exome Sequencing

  The focus of this study is to identify a new level of genetic variation, i.e. rare genetic variants with a population frequency less than 5%, usually less than 1%, which are believed to provide a stronger risk per variant than those studied to date in the large genome wide association studies (GWAS). To do this we are generating whole exome sequencing data on the Illumina HiSeq. Each HiSeq produces at least 600 billion base pairs of DNA sequence in one run. Whole exome sequencing sequence data, about 50 million base pairs, or about 1.5% of the total DNA of each person&#39;s genome, is generated. We are using this data to look for new DNA variations that give risk for Parkinson disease, as well as &#34;modifiers&#34;, that may lead to having more severe or milder disease or later or earlier ages of onset.

  Study phs000908

• Genetic Determinants of EGFR-Driven Lung Cancer Growth and Therapeutic Response In Vivo

  Oncogenic alterations in EGFR frequently co-occur with additional genetic alterations in EGFR-driven lung adenocarcinoma (LUAD), but how specific combinations of mutations affect tumor phenotypes and responses to targeted therapy is yet unknown. We leveraged a genetically engineered mouse model of EGFR mutant/Trp53-deficient LUAD to study the consequences of inactivating 10 different tumor suppressor genes on the fitness and tyorosine kinase inhibitor (TKI) sensitivity of these tumors. We found that loss of Keap1 is associated with a reduced response to therapy. In patients, we found that mutations in the KEAP1/NFE2L2/CUL3 pathway are associated with a significantly shorter time to treatment failure for EGFR TKI therapy compared to matched patients with wild-type KEAP1/NFE2L2/CUL3 tumors. We also analyzed whole exome sequencing data from tumor specimens before TKI treatment and at the time of treatment resistance for mutations in the KEAP1 pathway and these data are being submitted here.&nbsp;&nbsp;

  Study phs002334

• GeneScreen, a Population Based, Targeted Genomic Screening Study

  The GeneScreen research study was based at the University of North Carolina (UNC) at Chapel Hill. It brought together researchers from different disciplines, including genetics, clinical medicine, bioethics, sociology, anthropology, psychology, and public health, to study the best ways to offer targeted genetic screening to the general adult population. DNA samples were collected from people who joined GeneScreen to look at a targeted panel of 17 genes. It tested for genetic differences (called mutations) that can cause one of 11 rare, but preventable or treatable conditions, including certain types of cancer and heart disease. Each condition has specific medical advice people can follow with their doctors to prevent or treat a serious health problem. The GeneScreen study was approved by the UNC Institutional Review Board and by the Kaiser Permanente Center for Health Research Institutional Review Board. GeneScreen is funded by the National Human Genome Research Institute of the National Institutes of Health.

  Study phs001817

• Differential Transcription Start Site Usage in Brain-related Samples

  RNA-Seq is an effective method to study the transcriptome, but specialized methods are required to identify 5&#39; ends of transcripts. Several published strategies exist for this specific purpose, but their relative merits have not been systematically analyzed. Here, we directly compare the performance of six such methods - testing five with cellular RNA as well as a novel spike-in RNA assay that helps address interpretation challenges that arise from uncertainties in annotation or RNA processing. Using a single human RNA sample, we constructed and sequenced 18 libraries with these methods and one standard, control RNA-Seq library. We find that the CAGE method performed best for mRNA and that most of its unannotated peaks are supported by evidence from other genomic methods. We then applied CAGE to eight brain-related samples and revealed sample-specific transcription start site (TSS) usage as well as a transcriptome-wide shift in TSS usage between fetal and adult brain.

  Study phs001463

• Integrated Single-Cell Genetic and Transcriptional Analysis Suggests Novel Drivers of Chronic Lymphocytic Leukemia

  Intratumoral genetic heterogeneity has been characterized across cancers by genome sequencing of bulk tumors, including chronic lymphocytic leukemia (CLL). In order to more accurately identify subclones, define phylogenetic relationships, and probe genotype-phenotype relationships, we developed methods for targeted mutation detection in DNA and RNA isolated from thousands of single cells from five CLL samples. By clearly resolving phylogenic relationships, we uncovered mutated LCP1 and WNK1 as novel CLL drivers, supported by functional evidence demonstrating their impact on CLL pathways. Integrative analysis of somatic mutations with transcriptional states prompts the idea that convergent evolution generates phenotypically similar cells in distinct genetic branches, thus creating a cohesive expression profile in each CLL sample despite the presence of genetic heterogeneity. Our study highlights the potential for single-cell RNA-based targeted analysis to sensitively determine transcriptional and mutational profiles of individual cancer cells leading to increased understanding of driving events in malignancy. Reprinted from Genome Research, with permission from Publisher.

  Study phs001372

• Reproductive Health in Men and Women with Vasculitis

  The purpose of this study is to learn about reproductive health, including fertility and pregnancies, in people with vasculitis. All patients enrolled in the Vasculitis Clinical Research Consortium&#39;s Contact Registry will be invited via email to participate in this study. The Contact Registry includes people who self-identify as having one of 11 vasculities: Beh&#231;et&#39;s disease, central nervous system vasculitis, drug-induced vasculitis, eosinophilic granulomatosis with polyangiitis (Churg-Strauss), giant cell arteritis, granulomatosis with polyangiitis (Wegener&#39;s), Henoch-Schoenlein purpura, Kawasaki disease, microscopic polyangiitis, polyarteritis nodosa, or Takayasu arteritis. People voluntarily enroll in this Registry with the understanding that they will receive information about clinical studies for which they might be eligible. The introductory email included basic information about the study and all of the required elements for informed consent in a brief format. Once participants agreed to participate in the study, they were directed to an online questionnaire.

  Study phs001382

• Mitochondrial Abnormalities in Schizophrenia and Bipolar Disorder

  Mitochondrial DNA structural variation is one proposed indicator of mitochondrial dysfunction. The large common deletion of mitochondrial DNA (mtDNA) increases with aging across multiple brain regions. We found other large deletions of mtDNA in brain at levels comparable to the common deletion (PMID: 30869147). Since mtDNA molecules replicate independently of nuclear division, the mtDNA copy number is a measure of mitochondrial health; levels are negatively correlated with age and all-cause morbidity. Complex I activity is another functional measure of mitochondria. We previously published a subset of this data from DLPFC (PMID: 29594135) for three indicators (common deletion, mtDNA copy number, Complex I activity). For this study, four indicators (common deletion, large deletions, mtDNA copy number, Complex I activity) were measured in four brain regions from subjects with schizophrenia (SZ) and bipolar disorder (BD) and compared to controls. The four brain regions were: the dorsolateral prefrontal cortex, superior temporal gyrus, primary visual cortex (V1), and nucleus accumbens.&nbsp; &nbsp;&nbsp;

  Study phs002395

• The Etiological Bases of Giftedness: Epidemiological Study of Cognitive Ability in Children in Saudi Arabia

  Recent studies of genetic foundations of cognitive ability rely on large samples (hundreds of thousands) of individuals from relatively outbred populations of mostly European ancestry. Hypothesizing that the genetic foundation of cognitive ability depends on the broader population-specific genetic context, we performed a genome-wide association study and homozygosity mapping of cognitive ability estimates obtained through latent variable modeling in a sample of 354 children from the consanguineous population of Saudi Arabia. Approximately half of the sample demonstrated significantly elevated homozygosity levels indicative of inbreeding, and among those with elevated homozygosity, it was negatively associated with cognitive ability. Further homozygosity mapping identified a specific run, inclusive of the GRIA4 gene, that survived corrections for multiple testing for association with cognitive ability. The results suggest that in a consanguineous population, a notable proportion of the variance in cognitive ability in the normal range in children might be regulated by population-specific mechanisms such as patterns of elevated autozygosity.

  Study phs001884

• Complete Genomics Deep Whole-Genome Sequencing of Circulating Tumor Cells from a Patient with Metastatic Breast Cancer

  Circulating tumor cells (CTCs) are considered to be informative non-invasive biopsy for the early detection, diagnosis or therapy guidance of cancer. CTCs also contain most of the heterogeneity found across multiple metastatic sites. In this study, we combine recently improved CTC isolation methods, resulting in almost pure CTCs, with advanced whole genome sequencing (WGS) based on Long Fragment Read (LFR) technology to demonstrate, for the first time, the research and clinical utility of an accurate, comprehensive, phased, and quantitative genomic analysis of CTCs. In particular, genomes of 34 CTCs, collected at two different time points from a patient whose metastatic breast cancer ultimately became resistant to standard treatments, were analyzed as 3072 barcoded sub-genomic compartments of long DNA. An average read coverage of 23X per cell enabled the high-resolution detection of somatic variants, cancer copy number variations (CNVs) and structural variants, including 133 CNVs shorter than 1Mb and an early chromothripsis-like event.

  Study phs001028

• Towards a Genomic Understanding of Myeloma

  This project was designed to describe genetic abnormalities in primary samples from patients with multiple myeloma by next generation sequencing. We generated sequence data from multiple myeloma (MM) patients analyzing DNA both from tumor cells (purified from bone marrow using CD138 selection as a marker of plasma cells) and from normal peripheral blood cells (either whole blood or Ficoll-purified mononuclear cells). Using massively parallel sequencing technology (Illumina GA-2 or HiSeq)), we performed whole-genome sequencing (WGS) and/or whole-exome sequencing (WES). The initial set currently deposited contains data from 38 MM patients (23 patients surveyed by WGS and 16 patients by WES, with one patient analyzed by both approaches). Genomes were sampled to high depth, obtaining an average of 33X coverage and 104X coverage for WGS and WES tumors, respectively. The normal samples had similar coverage. Our goal is to help researchers understand the complex genetic landscape of multiple myeloma and provide a resource for the generation of biological hypotheses.

  Study phs000348

• Rare Disease Susceptibility Alleles in Children with Crohn Disease

  The overall goal of this proposed project is to identify rare genetic variants contributing to childhood onset-Crohn disease. Crohn disease is a chronic inflammatory disorder of the gastrointestinal tract of unclear etiology and no known cure. Affected children suffer from diarrhea, abdominal pain, growth disturbances, and an impaired quality of life. The identified Crohn disease susceptibility alleles have improved our understanding of Crohn disease pathogenesis. However, the identified susceptibility alleles do not account for the observed heritability, nor have disease-causing alleles in many genomic regions been identified. For the proposed studies, we will use 1) existing DNA samples collected from high-risk Crohn kindreds identified using the extensive genealogical records available only in Utah, 2) existing DNA samples obtained from very young children with Crohn disease and their parents, and 3) existing DNA samples obtained from healthy controls that are free of a personal or family history of autoimmune disorders.

  Study phs000926

• Novel Strategies to Eliminate Resistance to B-cell Receptor Inhibitor Therapy in Lymphoid Malignancies

  Ibrutinib and acalabrutinib are BTK inhibitors (BTKi) used as frontline therapies for chronic lymphocytic leukemia (CLL). Clonal evolution during ibrutinib treatment has previously been studied, but limited study of clonal evolution in patients receiving acalabrutinib has been done. Our group analyzed 216 blood samples from 38 CLL patients at multiple time points during their BTKi treatment. Samples were prepared by standard protocols and sequenced using 200x Illumina sequencing. Using the longitudinal data, we reconstructed the clonal evolution of each patient using somatic mutations found within the B-cell populations. Three patterns of clonal evolution were identified: 1) new clone emergence, 2) clonal selection, and 3) clonal replacement. As previously seen in ibrutinib treatment, the evolution of subclones containing CLL-driver mutations was associated with worse clinical outcomes for patients treated with acalabrutinib (Log-rank p=0.04). Understanding clonal evolution during treatment with acalabrutinib can help determine and modify the clinical course of patients with CLL.

  Study phs003042

• Blepharospasm in a Multiplex African-American Pedigree

  Blepharospasm (BSP) is a late-onset focal dystonia characterized by involuntary contractions of the orbicularis oculi muscles. Genetics plays an important role on the pathogenesis of BSP, especially in hereditary BSP families. In this project, a large African-American pedigree with BSP was phenotypically characterized and its relationship with known common dystonia genes was investigated with Sanger sequencing on dystonia genes such as THAP1, TOR1A and GNAL. And whole-exome sequencing (EXOME) of the proband was performed to identify all other dystonia-associated genes for potentially pathogenic SVs. A novel THAP1 SV (c.314T&#62;C, p.L105S) was identified in the family, however, this SV did not co-segregate with blepharospasm in the pedigree. No pathogenic or likely pathogenic SVs in other dystonia-associated genes were identified with whole-exome sequencing. The current study makes available phenotype data and summary genotype data of n=12 family members (mutation absent/heterozygous for THAP1); whole exome sequencing data of one (het) subject are available through public SRA.

  Study phs001206

• Genome-Wide Association Study of Parkinson Disease: Genes and Environment

  This is a gene-environment study of Parkinson&#39;s disease. PD is a common, progressive, age-related, movement disorder that affects 1-2% of the people over the age of 65. The NeuroGenetics Research Consortium (NGRC) is the infrastructure of the study. Protocols and methods are standardized across NGRC clinics and labs as much as possible. The 2000 patients and 2000 control subjects selected for this GWAS were recruited at the NGRC-affiliated movement disorder clinics in Oregon, Washington, Georgia and New York. All 4000 subjects were white and all DNA samples were extracted from whole blood and unamplified. Seventy-five percent of subjects have data on cigarette smoking and caffeinated coffee consumption, which are inversely associated with PD risk. This study population represents the norm for clinical genetic studies of PD; i.e., NGRC estimates of risk to relatives, heritability, genotype frequencies, and exposure frequencies (see references) are very close to estimates from meta-analyses.

  Study phs000196

• NHLBI TOPMed: Pediatric Asthma Controller Trial (PACT)

  After a 2-4 week assessment/characterization run-in period, 6-14 year-old children who met NAEPP criteria for mild-moderate persistent asthma specifically based on symptom criteria and methacholine PC20 &#8804; 12.5 mg/ml and FEV1 &#8805; 80% were randomized to one of the three active treatment arms for 12 months. Randomization was stratified according to clinical center, bronchodilator response (&lt; 12% or &#8805; 12%), race (Caucasian or non-Caucasian), and methacholine PC20 (&lt; 2 or &#8805; 2 mg/ml). The primary outcome variable was the proportion of asthma-free days during the 12-month treatment period. Secondary outcomes included other measures of asthma control (percentage of rescue-free days, albuterol-free days, and episode-free days; the number of asthma exacerbations requiring prednisone therapy and the time to the first asthma exacerbation), forced oscillation and spirometry, reversibility (FEV1 pre- and post 2 puffs of albuterol MDI), methacholine PC20, exhaled nitric oxide, and asthma-related quality of life.

  Study phs001730

• Transcriptome study of differential expression in schizophrenia

  Schizophrenia is a common and severe psychotic disorder. While some common SNPs and rare copy number variants have been identified as being significantly associated with disease risk, the biological mechanisms remain undefined. To identify gene expression abnormalities in schizophrenia, we generated whole-genome gene expression profiles using microarrays on lymphoblastoid cell lines from a total of 413 cases and 446 controls. Regression analysis identified 95 transcripts differentially expressed by affection status at a genome-wide false discovery rate of 0.05, while simultaneously controlling for confounding effects. These transcripts represented 89 genes with functions such as neurotransmission, gene regulation, cell cycle progression, differentiation, apoptosis, and immunity. The observed differential expression of extended major histocompatibility complex region genes converges with the genetic evidence from schizophrenia genome-wide association studies, which find the same region to be the most significant schizophrenia susceptibility locus. Our analysis also provides novel candidate genes for further study to assess their potential contribution to schizophrenia.

  Study phs000775

• Human Liver Cohort (HLC)

  The Human Liver Cohort (HLC) study aimed to characterize the genetic architecture of gene expression in human liver using genotyping, gene expression profiling, and enzyme activity measurements of Cytochrom P450. The HLC was assembled from a total of 780 liver samples screened. DNA samples were genotyped on the Affymetrix 500K SNP and Illumina 650Y SNP genotyping arrays. Only data from those samples which were collected postmortem are accessible through this submission. These 228 samples represent a subset of the 427 samples included in the Human Liver Cohort Publication (Schadt, Molony et al. 2008, PMID: 18462017). RNA samples were profiled on a custom Agilent 44,000 feature microarray composed of 39,280 oligonucleotide probes targeting transcripts representing 34,266 known and predicted genes, including high-confidence, noncoding RNA sequences. Gene Expression data for the samples from which it could be obtained is available in GEO under accession number GSE9588. Results and networks will be made available for download from Sage Bionetworks.

  Study phs000253

• Epigenomics of Patient Outcomes after Aneurysmal SAH

  This project was focused on understanding the pathophysiology following aneurysmal subarachnoid hemorrhage (aSAH). DNA methylation is a key mechanism for regulation of gene expression and function in the adult brain and appears to play an important role in neuroprotection during ischemic events in the brain. Delayed cerebral ischemia (DCI), occurring during the acute phase following aSAH, is a major contributor to the complications that later develop; however, the pathophysiology of DCI and the development of complications is not known. We hypothesize that characterizing the methylome representing the CNS environment post aSAH will clarify the pathophysiology associated with DCI and patient outcomes. The aims of this project focused on the daily genome-wide methylomic changes that occur in DNA representing the CNS for the first 14 days after aSAH, as well as in peripheral blood in a subset of partcipants, and determining if this methylomic data impacts DCI and the development of complications after aSAH.

  Study phs001990