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• Microinjection of hIPSC-derived intestinal organoids with Salmonella Typhimurium

  To generate an RNA-Seq dataset for organoids apically stimulated with Salmonella Typhimurium.These data are part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001001363

• Whole Exome (WE) sequencing data files for H_NO-JB001

  DNA was derived from the primary tumour, lung metastasis, and peri-aortic lymph node metastasis. DNA from the spleen was used as a normal control.For WE sequencing we user Hybrid capture (Nimblegen version 3.0) of the lymph node and lung metastases, primary tumour and spleen normal; we generated ~100-fold coverage.

  Dataset EGAD00001001252

• Low coverage WGS from plasma DNA

  Low coverage whole genome sequencing plasma DNA from 50 male, 54 female non-cancer donors. For the analysis of nucleosomal positioning all data from the non-cancer controls were merged. Furthermore, two patients with metastasized breast cancer were sequenced on a NextSeq with higher depth.

  Dataset EGAD00001002215

• SCLC CTC genomic analysis data set

  The data set consists of low-pass whole genome sequence data of single CTCs, pools of CTCs and germline controls for a cohort of 31 SCLC patients at both baseline, and for 5 patients at relapse. In addition 9 CDX models and associated germline controls (where available) are included.

  Dataset EGAD00001002678

• A comprehensive characterisation and analysis of human breast cancers through whole-genome sequencing

  We propose to definitively characterise the somatic genetics of breast cancer through generation of comprehensive catalogues of somatic mutations in breast cancer cases by high coverage genome sequencing.

  Dataset EGAD00001002740

• Exome sequencing of synchronous colorectal cancers

  The data consists of 47 exome-sequenced synchronous colorectal cancers from 23 patients. The exomes of corresponding normal samples were used to remove germline variants. All patients are Finnish (white Caucasian). All except one patient (sync_11 who belongs to a LS family) were assumed sporadic. The sequence data was produced with Illumina HiSeq 4000.

  Dataset EGAD00001004884

• Predictor_RIO_TNBC (2019-04-03)

  In this study, samples fom a window study of the PARP inhibitor rucaparib in patients with primary triple negative or BRCA1/2 related breast cancer (RIO trial) will be investigated. Samples will undergo whole genome sequence and analysis, including use of HR Predict. . This dataset contains all the data available for this study on 2019-04-03.

  Dataset EGAD00001004894

• DigiPico sequencing data for the study of active mutational processes in HGSOC

  The dataset contains Bam files from DigiPico runs as well as bulk sequencing data used in the "A highly accurate platform for clone-specific mutation discovery enables the study of active mutational processes" publication.

  Dataset EGAD00001005118

• Deep multi-region WGS of lung cancer tumours

  Deep WGS (germline and 2-5 tumour regions) was performed on 20 patients: 13 lung adenocarcinoma, 5 squamous cell carcinoma, 2 small-cell lung cancers. A total of 68 BAM files are provided, where tumours were sequenced to 60x or 150x depth.

  Dataset EGAD00001005287

• Exome Sequencing of Spanish Patients with rare genetic diseases.

  Whole Exome sequencing of a set of Spanish patients suffering rare genetic diseases. The set consists of 3 patients, two were diagnosed with Aniridia (ANI-0006 and ANI-0023) and another one was diagnosed with Retinitis Pigmentosa (RP-0247).

  Dataset EGAD00001005498

• Pseudodiastrophic dysplasia exome sequencing dataset

  This dataset contains 7 paired end fastq files obtained with Illumina Hiseq and Nextseq sequencing of whole exomes relevant to a study of pseudodiastrophic dysplasia (PDD). It includes 3 patients from 2 unrelated families diagnosed with PDD, together with the four parents.

  Dataset EGAD00001005775

• WGS paired B-Cell lymphoma cells sorted according to CD48

  paired WGS data of one tumor of one patient with nodal B-cell lymphoma. Tumor cells were sorted according to CD48 expression in a high and low fraction. Library preparation with TruSeq Nano and sequencing on Hiseq XTen.

  Dataset EGAD00001006058

• Panel sequencing data for Molecular Characterization of ETMRs

  This dataset contains panel sequencing data from 33 samples. Targeted sequencing was performed by creating libraries using the Agilent SureSelect XT technology. Libraries were sequenced using molecular barcode-indexed ligation-based sequencing using a NextSeq500 (Illumina) instrument. Between three and six lanes per sample have been sequenced resulting in 262 Fastq files (paired end).

  Dataset EGAD00001006208

• HV31 - Illumina PCR-free sequencing

  Illumina PCR-free sequencing data for individual HV31 generated using DNA from peripheral blood mononuclear cells, to a sequencing depth of ~44×. Sequencing was performed at the Wellcome Centre for Human Genetics on the Illumina Novaseq platform.

  Dataset EGAD00001007042

• HV31 - 10x linked-read sequencing

  10x linked-read sequencing data for individual HV31 generated using DNA from CD14+ monocytes, to a sequencing depth of ~40×. Sequencing was performed at Bart’s and the London Genome Centre on the Illumina HiSeq platform.

  Dataset EGAD00001007046

• HV31 - PacBio continuous long read (CLR) sequencing

  PacBio continuous long read (CLR) sequencing data for individual HV31 generated on PacBio Sequel II instrument, using DNA from CD14+ monocytes, to a sequencing depth of ~35×. Sequencing was performed at the Wellcome Sanger Institute.

  Dataset EGAD00001007047

• Cohort of NGS lymphoma samples used as control

  Control cohort of lymphoma samples sequenced with a hybrid capture panel designed to be able to detect translocations and mutations in lymphoma samples. used in the paper "Robust detection of translocations in lymphoma FFPE samples using Targeted Locus Capture-based sequencing"

  Dataset EGAD00001007711

• Epichaperome - Englander Institute for Precision Medicine - Weill Cornell Medicine DAC

  This dataset accompanies the publication of Sugita M et al. "Targeting the Epichaperome As an Effective Precision Medicine Approach in a Novel PML-SYK Fusion Acute Myeloid Leukemia" Npj Precision Oncology 2021

  Dataset EGAD00001006904

• Whole Exome sequencing data from Shwachman-Diamond syndrome bone marrow samples

  This dataset includes paired WES from bone marrow samples of patients with SDS and paired bone marrow-derived fibroblasts as a germline reference. Some patients have multiple samples collected serially over time. There are 74 BAM files included in this dataset.

  Dataset EGAD00001006815

• WES data generated in multifocal ileal NETs study

  This dataset includes whole-exome sequencing data for multifocal ileal tumor samples from two patients. Exonic sequences were enriched using the Agilent V2 capture probe set and sequenced by 76-bp paired-end reads using the Illumina Genome Analyzer IIx system with a mean coverage of 80x for each base

  Dataset EGAD00001006408

• Mixture of 2

  Mixture of 2 unrelated individuals sequenced by 10x as a scRNA-seq. The dataset was then processed by Cell Ranger and deconvoluted to yield each individuals genetic profile. The clustering of SNPs is submitted as the processed file. The Sequencing fastqs are submitted as unprocessed files.

  Dataset EGAD00001008726

• Platelet RNAseq data for SLFN14 K219N patients

  RNAseq dataset containing 20 control and 2 SLFN14 K219N patient samples, derived from platelets. Sequencing libraries were constructed using an Illumina TruSeq stranded Ribo Zero Gold kit and paired end sequenced at a read depth of 30 million reads on an Illumina NovaSeq 6000 platform.

  Dataset EGAD00001008965

• Pacbio HiFI Whole-Genome Sequecing of Trios with Intellectual Disability

  6 trios and 1 proband were whole genome sequenced with PacBio Sequel II to a depth of 30X, using the HiFi chemistry. For each trio the proband was affected with severe ID, and the parents were unaffected. Samples are grouped by trio.

  Dataset EGAD00001009109

• AML-PMP miRNA-Seq

  miRNA libraries of the AML-PMP project were sequenced on the Illumina HiSeq 2000 instrument, approximately 16 samples per HiSeq lane, to a median depth of 5.7 million reads per library.

  Dataset EGAD00001009772

• A molecular atlas of the human postmenopausal fallopian tube and ovary from single-cell RNA

  This datasets contains raw sequencing fastq data of 17 samples from 7 donors of single cell RNA using10x genome technology of human postmenopausal fallopian tube and ovary tissues.

  Dataset EGAD00001010076

• Exome sequencing data

  Libraries were constructed using SureSelect HS XT Target Enrichment System v6 (Agilent). For each patient a library of CD3- cells (myeloid cells, considered tumor cells) and CD3+ cells (T cells, considered healthy) were generated.

  Dataset EGAD00001010190

• Whole exome sequencing (WES) of CIMP leukemias

  Whole exome sequencing of tumor material derived from 14 mixed myeloid/lymphoid leukemias with a CpG Island Methylator Phenotype (CIMP). For 4 of these patients, normal material was also sequenced and used as control (files with the same identifiers prepended by an “h”).

  Dataset EGAD00001011053

• Whole-genome sequencing of Himalayan populations

  Whole-genome sequencing of 60 individuals from 15 Himalayan populations using the Illumina-B HiSeq X platform. These data are part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/.

  Dataset EGAD00001011358

• Whole-Genome Sequences from five human populations of Sudan

  This dataset provides the first high-coverage (~30X) whole-genome sequencing (WGS) data from Sudan, encompassing 125 individuals from five ethnolinguistic groups: Copts, Beja, Fulani, Fur, and Mahas. These populations represent three major linguistic families: Afro-Asiatic, Nilo-Saharan, and Niger-Congo, offering a detailed view of the country’s genetic diversity.

  Dataset EGAD00001015636

• PhIP-seq of Japanese subjects with SLE

  Bacteriophage Immunoprecipitation Sequencing (PhIP-Seq) datasets of three eHHV-6B-positive SLE patients (three technical replicates), six eHHV-6B-negative SLE patients (three technical replicates), and a negative control lacking sera or plasma (12 technical replicates; Illumina NextSeq) and sequencing data of HHV-6 peptide phage library (two technical replicates; Illumina NextSeq).

  Dataset EGAD00001015429

• scRNA-seq dataset of A cycling, progenitor-like cell population at the root of atypical teratoid rhabdoid tumor subtype differentiation trajectories

  Study describing the differentiation trajectory...

  Dataset EGAD00001015545

• scATAC-seq dataset of A cycling, progenitor-like cell population at the root of atypical teratoid rhabdoid tumor subtype differentiation trajectories

  Study describing the differentiation trajectory...

  Dataset EGAD00001015546

• PyEGA3 download client

  PyEGA3 Checks prior to download - Is the dataset available for download? Most datasets are available using the PyEGA3 Download client, except for specific legacy datasets. For these ones, refer to Live Outbox distribution, an alternative way to download these datasets.If the dataset you've been granted access to begins with the ID EGAD5XXXXXXX, please refer to the Live Outbox distribution. If the dataset you have been granted access to contains encrypted data (filename ending in .gpg), contact EGA Helpdesk for detailed information on how to download these files.General Information The PyEGA3 download client is available at its GitHub repository. In its README you will find a step by step guide on how to use it to download files you have been granted access to. PyEGA3 implements the GA4GH-compliant HTSGET protocol supporting requests over genomic ranges. This enables the download of specific regions of interest rather than the entire file. HTSGET is not possible for all the files held by EGA: if the dataset does not contain index files (e.g. filename ending in *.crai), then genomic range requests cannot be performed. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Check out our video tutorial! Frequently Asked Questions General questions How do I download the datasets to which I have been granted access? After setting up your EGA download account, you can proceed to download using the EGA download client – PyEGA3. The pyEGA3 download client is a python-based tool for viewing and downloading files from authorised EGA datasets. This download client is continuously being developed for more user-friendly download experiences. Can I download the dataset’s metadata via the download client? No, the download client cannot be used to download metadata. Registered EGA users can download metadata of an authorised dataset by logging into the EGA webpage and navigating to the dataset of choice. Approximately two thirds down the page you will find the option to download the metadata as a zip file. How do I download large datasets as quickly as possible? The main EGA download client is pyEGA3 download client, a python-based tool for viewing and downloading files from authorised EGA datasets.We could exceptionally consider the option of an Aspera download for users experiencing very slow download rates or facing particular issues.Contact the EGA Helpdesk for further information. Pre-requirement and Installation What are the major requirements for the PyEGA3 download client installation and usage? The PyEGA3 client is compatible with any OS with Python 3.6+ installed. The client requires a connection to the internet, sufficient space on the destination drive, and the EGA download account credentials. Where can I find the PyEGA3 download client installation and download instructions? We strongly recommend checking our video tutorial demonstrating the usage of PyEGA3 from installation to file download. More detailed information is available at PyEGA3 GitHub. What ports are required for the PyEGA3 download client to function? PyEGA3 makes HTTPS calls to the EGA Data API (https://ega.ebi.ac.uk:8443). This port 8443 must be reachable from the location where PyEGA3 is executed to avoid timeouts. PyEGA3 download client How to update the PyEGA3 download client to be on the latest version? Updating the client to the latest version can be achieved by running the following command: pip3 install pyega3 --upgrade I do not have an active EGA account. Can I test the download with the PyEGA3 download client? An EGA download test account (ega-test-data@ebi.ac.uk) has been created for troubleshooting and training purposes. The test account does not require an EGA username and password because it contains publicly accessible files from the 1000 Genomes Project. More information about the use of this account and log in details are available at pyEGA3. I lost my connection while the download was in progress. Does this mean that I have to download the file again from the start? The PyEGA3 download client supports automatic resumption of downloads. To enable quicker download speeds, the API breaks files into up to four segments and downloads them in parallel. With the resume feature, downloads will automatically resume if you encounter any errors or if the connection is interrupted. How can I improve the overall download speed of the PyEGA3 download client? Download speeds can be optimised using the --connections parameter which will parallelise download at the file level. If the --connections parameter is provided, all files >100Mb will be downloaded using the specified number of parallel connections. It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Why is my file taking a long time to be saved? Please note that when a file is being saved, it goes through two processes. First, the downloaded file "chunks" are pieced back together to reconstruct the original file. Secondly, PyEGA3 calculates the checksum of the file to confirm that the file was downloaded successfully. Larger files will take more time to reconstruct and validate the checksum. Why is the HTSGET protocol not working with my dataset of interest? PyEGA3 implements the GA4GH-compliant htsget protocol for supporting requests over genomic ranges. This exciting new feature means that for data files with accompanying index files (e.g. .crai for CRAM files) users can download specific regions of interest rather than the entire file, saving both time and storage space. Please note that in order for the genomic range requests to work for a BAM, CRAM or VCF file there must be an associated BAI, CRAI or TBI file, respectively. If the dataset does not contain these index files then genomic range requests cannot be performed. After download Do I need to decrypt data files downloaded through the pyEGA3 download client? Files are transferred over secure HTTPS connections and received unencrypted, thus there is no need for decryption after data download. What is the purpose of an MD5 file? After the download completes, file integrity is verified using checksums. The PyEGA3 download client automatically verifies each file’s unencrypted md5 after download to ensure that the file was downloaded correctly from the EGA. Errors Why do I get a “400 Client Error” while trying to access the download client? Ensure that your credentials are formatted correctly. Please contact the EGA Helpdesk if you are unsure about your account’s username (i.e. your email address). Why do I get a “500 Server Error”? It is important to note that files are still downloaded sequentially, so using multiple connections does not mean downloading multiple files in parallel. The maximum number of connections allowed is 30. If more than 30 connections are used, the download client will encounter 500 errors. The recommended number of connections for maximum output is one. Otherwise, 500 server errors are internal server errors from EGA’s end. Retry after some time and, if the 500 error persists: check our homepage or Twitter for any outage information or report it to EGA Helpdesk. Why do I get a “slice error”? This type of error occurs when one slice download was prematurely interrupted. Please re-start the same download. The download client will pick up any partial download and download any missing slices. Why do I get the error “Dataset' EGADXXXXXXXXXXX' is not in the list of your authorised datasets”? Please note that the data access was given to the email (username) provided to the Data Access Committee (DAC) in your official request. Therefore, you shall check that the account that you are using is the same one that you sent to the DAC. Why do I get “ERROR:root:Failed to obtain IP address”? This might mean that the connection ports are blocked. Check that the port 8443 is reachable.In case the port is reachable and the error persists, please try to modify the file pyega3.py on line 44 in the python module installation: Replace endpoint = 'https://ipinfo.io/json' with endpoint = 'https://api.myip.com' Why do I get the error “Invalid username, password or secret key - please check and retry”? Make sure that you are using the correct credentials (username and password) and there is no typo. Contact EGA Helpdesk if the problem persists.

  Documentation access/download/files/pyega3

• Detection of rare mutations, copy number variation, and DNA methylation in the same template DNA molecules

  The analysis of cell-free DNA (cfDNA) from plasma offers great promise for the earlier detection of cancer. At present, changes in DNA sequence, methylation, or copy number are the most sensitive ways to detect the presence of cancer. To further increase the sensitivity of such assays with limited amounts of sample, it would be useful to be able to evaluate the same template molecules for all these changes. Here we report an approach, called MethylSaferSeqS, that achieves this goal, and can be applied to any standard library preparation method suitable for massively parallel sequencing. The innovative step was to copy both strands of each DNA-barcoded molecule with a primer that allows the subsequent separation of the original strands (retaining their 5-methylcytosine residues) from the copied strands (in which the 5-methylcytosine residues are replaced with unmodified cytosine residues). The epigenetic and genetic alterations present in the DNA molecules can then be obtained from the original and copied strands, respectively. We applied this approach to plasma from 265 individuals, including 198 with cancers of the pancreas, ovary, lung and colon, and found the expected patterns of mutations, copy number alterations, and methylation. Furthermore, we could determine which original template DNA molecules were methylated and/or mutated. MethylSaferSeqS should be useful for addressing a variety of questions relating genetics and epigenetics in the future.

  Study EGAS00001006839

• Genetic and Phenotypic Analysis of Multiple Sclerosis in Hispanics

  We sought to identify MS susceptibility variants specific to the Major Histocompatibility Complex and to assess the effect of ancestral risk modification within 2652 Latinx and Hispanic individuals, as well as 2435 Black and African American individuals ascertained in collaboration with the Alliance for Research in Hispanic MS (ARHMS). All DNA samples were obtained through whole blood extraction and were genotyped on the MS Chip, an Illumina Infinium custom genotyping array that contains targeted and dense coverage of the extended MHC, specifically designed for imputation. Classical HLA alleles, SNPs, and amino acid residues were imputed simultaneously from genotyped SNPs using HLA-TAPAS and a multi-ethnic reference panel of 2504 samples from the 1000 Genomes Project. We have identified several novel susceptibility alleles which are rare in European populations including HLA-B*53:01, and we have identified an independent role for HLA-DRB1*15:01 and HLA-DQB1*06:02 on MS risk. We found a decrease in Native American ancestry in MS cases vs controls across the MHC, peaking near the previously identified MICB locus; and we have identified several susceptibility variants for which MS risk is modified by global or local ancestry. We are making data on all genotyped and imputed SNPs, classical HLA alleles, and amino acid residues available through dbGaP for the Hispanic and African American samples utilized in these analyses.

  Study phs003105

• Evaluation of Hybridization Capture versus Amplicon-based Methods for Whole Exome Sequencing

  Next generation sequencing has aided characterization of genomic variation. While whole genome sequencing may capture all possible mutations, whole exome sequencing is more cost-effective and captures most phenotype-altering mutations. Initial strategies for exome enrichment utilized a hybridization-based capture approach. Recently, amplicon-based methods were designed to simplify preparation and utilize smaller DNA inputs. We appraised two hybridization capture-based and two amplicon-based whole exome sequencing methods, utilizing both Illumina and Ion Torrent sequencers, comparing on-target alignment, uniformity, and variant calling. While the amplicon methods had higher on-target rates, the hybridization capture-based approaches showed better uniformity. All methods identified many of the same single nucleotide variants, but each amplicon-based method missed variants detected by the other three methods and reported additional variants discordant with all three other technologies. Many of these potential false positives or negatives appear to result from limited coverage, low variant frequency, vicinity to read starts/ends, or the need for platform-specific variant calling algorithms. All methods demonstrated effective copy number variant calling when compared against a single nucleotide polymorphism array. This study illustrates some differences between various whole exome sequencing approaches, highlights the need for selecting appropriate variant calling based on capture method, and will aid laboratories in selecting their preferred approach.

  Study phs000938

• NHLBI TOPMed: Pharmacogenomics of Hydroxyurea in Sickle Cell Disease (PharmHU)

  Sickle cell disease (SCD) is characterized by the presence of sickle hemoglobin (HbS) within circulating erythrocytes resulting in hemolytic anemia, vascular occlusion, and end organ damage due to alterations in the shape and deformability of the cell membrane. The disease is inherited in an autosomal recessive pattern, and is most commonly caused by a single nucleotide substitution in the hemoglobin subunit beta (HBB) gene located on chromosome 11. Participants in this study include children with SCD treated with hydroxyurea to pharmacologically increase fetal hemoglobin (HbF) levels and reduce disease severity. Therefore, the primary phenotype of interest in this study is the change in HbF levels in response to hydroxyurea treatment. Genetic factors have been shown to influence inter-individual variation in drug response, and identification of novel genes and variants associated with clinical outcomes in SCD will be achieved through collaboration between Baylor College of Medicine, Augusta University, Columbia University Medical Center, Emory University School of Medicine and Children's Healthcare of Atlanta, and St. Jude Children's Research Hospital. The NHLBI TOPMed Program is designed to generate scientific resources to enhance understanding of fundamental biological processes that underlie heart, lung, blood and sleep disorders (HLBS). It is part of a broader Precision Medicine Initiative, which aims to provide disease treatments that are tailored to an individual's unique genes and environment.

  Study phs001466

• Fixed single-cell transcriptomic characterization of human radial glial diversity

  The human neocortex is created from diverse intermixed progenitors in the prenatal germinal zones. These progenitors have been difficult to characterize since progenitors - particularly radial glia (RG) - are rare, and are defined by a combination of intracellular markers, position and morphology. To circumvent these problems we developed a method called FRISCR (Fixed and Recovered Intact Single Cell RNA) for transcriptome profiling of individual fixed, stained and sorted cells. We developed and validated FRISCR on human embryonic stem cells. We then profiled primary human RG (96 - 132 days post conception) that constitute only 1% of the mid-gestation cortex. These RG could be classified into ventricular zone-enriched RG (vRG) that express ANXA1 and CRYAB, and outer subventricular zone-localized RG (oRG) that express HOPX. Our study identifies the first markers and molecular profiles of vRG and oRG cells, and provides an essential step for understanding molecular networks driving the lineage of human neocortical progenitors. Reprinted from Thomsen et. al. Nature Methods (2015), with permission from Nature Publishing. Human embryonic stem cell data may be obtained through NCBI's GEO database, using accession number GSE71858. Raw data from one human sample that was not consented to be released to dbGaP may be obtained directly from the authors of Thomsen et. al., 2015.

  Study phs001016

• 3/7 Psychiatric Genomics Consortium: Finding Actionable Variation

  The goal of this study was to generate new therapeutic hypotheses by targeted sequencing of 161 genes most likely to harbor schizophrenia (SCZ) causal variants in multi-ancestry schizophrenia case-control cohorts. The gene panel was intended to contain putative SCZ risk genes, from within which, the majority of new discoveries from additional WES/WGS would come. To this end, we integrated current knowledge of the genetic architecture of SCZ (results from The Schizophrenia Exome Sequencing Meta-Analysis Consortium, or SCHEMA) and gene membership in gene sets that have been implicated in SCZ using a Bayesian framework. A total of 23,352 individuals selected to be non-overlapping with SCHEMA, as well as other previous and ongoing sequencing efforts in the field, were identified from over 20 individual study sites. Sequencing of the exonic regions of 161 prioritized genes was performed on the Ion Torrent platform in between June 2018 and April 2019. Sequencing plates were matched with respect to ancestry and case/control composition whenever possible. The average sequencing depth across all samples was 224x. Single sample calling was performed using Torrent Variant Caller v5.8.0, which is specially optimized to exploit the underlying flow signal information generated by the Ion Torrent sequencing. The schizophrenia case-control status was obtained from each original study site.

  Study phs003138

• Memorial Sloan Kettering (MSKCC) Single Cell Mutational Profiling in Myeloid Malignancies

  Myeloid malignancies have previously been characterized by large scale bulk molecular profiling studies on patient samples. These studies, while clinically informative, are unable to delineate mutation order as well as clonal architecture and complexity. To investigate the clonal framework of myeloid malignancies, the Single Cell Mutational Profiling in Myeloid Malignancies study was designed. This study performed sequencing of 148 samples from 117 adult patients diagnosed with clonal hematopoiesis (n = 21), myeloproliferative neoplasms (MPN; n = 14), and acute myeloid leukemia (AML; n = 82) between 20 and 87 years of age. Sequencing of all samples was performed using the Tapestri single cell DNA sequencing platform from Mission Bio, Inc. The platform utilizes a custom amplicon panel covering 109 amplicons across 31 genes frequently mutated in myeloid malignancies (CH, MPN, AML). Using the single cell DNA sequencing data, we were capable of elucidating clonal architecture and clonal complexity in each sample. In a subset of samples, we also performed simultaneous single cell DNA sequencing and cell surface protein expression analysis using the Tapestri DNA + Protein platform. This study allowed us to determine the expression of 7 cell surface proteins (CD3, CD11b, CD19, CD34, CD38, CD45RA, and CD90) at single cell resolution. We performed DNA+ Protein sequencing on samples from CH patients (n = 7) as well as AML patients (n = 17).

  Study phs002049

• OMRF SLEGEN GWAS Data from European-American Women with Lupus

  "Systemic lupus erythematosus (SLE) is a common systemic autoimmune disease with complex etiology but strong clustering in families (lambda(S) = approximately 30). We performed a genome-wide association scan using 317,501 SNPs in 720 women of European ancestry with SLE and in 2,337 controls, and we genotyped consistently associated SNPs in two additional independent sample sets totaling 1,846 affected women and 1,825 controls. Aside from the expected strong association between SLE and the HLA region on chromosome 6p21 and the previously confirmed non-HLA locus IRF5 on chromosome 7q32, we found evidence of association with replication (1.1 x 10-7 < P(overall) < 1.6 x 10-23; odds ratio = 0.82-1.62) in four regions: 16p11.2 (ITGAM), 11p15.5 (KIAA1542), 3p14.3 (PXK) and 1q25.1 (rs10798269). We also found evidence for association (P < 1 x 10-5) at FCGR2A, PTPN22 and STAT4, regions previously associated with SLE and other autoimmune diseases, as well as at > or =9 other loci (P < 2 x 10-7). Our results show that numerous genes, some with known immune-related functions, predispose to SLE." Reprinted from Nature Genetics, 2008 Feb; 40(2):204-10, PMID:18204446, with permission from Nature Publishing Group. Data submitted to dbGaP includes 297 samples from Oklahoma. (Please see also Study Accession: phs000216.v1.p1)

  Study phs000202

• GWAS in African Americans, Latinos and Japanese

  The Multiethnic Cohort Study is a population-based prospective cohort study (n=215,251) that was initiated between 1993 and 1996 and includes subjects from various ethnic groups - African Americans and Latinos primarily from Californian (great Los Angeles area) and Native Hawaiians, Japanese-Americans, and European Americans primarily from Hawaii. State drivers' license files were the primary sources used to identify study subjects in Hawaii and California. Additionally, in Hawaii, state voter's registration files were used, and, in California, Health Care Financing Administration (HCFA) files were used to identify additional African American study subjects. In the cohort, incident cancer cases are identified annually through cohort linkage to population-based cancer Surveillance, Epidemiology, and End Results (SEER) registries in Hawaii and Los Angeles County as well as to the California State cancer registry. Information on estrogen receptor status is also obtained through these registries. Blood sample collection in the MEC began in 1994 and targeted incident breast cancer cases and a random sample of study participants to serve as controls for genetic analyses. Subjects are frequency matched on age at blood draw and ethnicity. The following number of breast cancer cases and controls were genome-wide scanned as part of this study: African Americans, 473 cases and 464 controls; Japanese: 885 cases and 822 controls; Latinos, 520 cases and 544 controls.

  Study phs000517

• Foregut Microbiome and Risk of Gastric Intestinal Metaplasia, and Gastric Cancer Risk

  The study is composed of two sub-studies. The first study, conducted by NYU Grossman School of Medicine, is a case-control study of 89 cases with gastric intestinal metaplasia (IM) and 89 matched controls who underwent upper gastrointestinal endoscopy at three sites affiliated with NYU Langone Health. Shotgun metagenomic sequencing using oral wash samples from the 89 case-control pairs and antral mucosal brushing samples from 55 case-control pairs were conducted. The other study, conducted by Vanderbilt University Medical Center, is a prospective case-control study on the oral microbiome and gastric cancer risk, utilizing shotgun metagenomic sequencing data of pre-diagnostic buccal samples from 319 Asians, 118 African Americans and 51 European Americans. This study includes data from three population-based studies: the Southern Community Cohort Study (SCCS), the Shanghai Women's Health Study (SWHS), and the Shanghai Men's Health Study (SMHS). The SWHS and SMHS include 74,941 women aged 40-70 years and 61,480 men aged 40-74 years, respectively, who were permanent residents of Shanghai and recruited to the cohorts between 1997 and 2006. The SCCS is an ongoing prospective cohort study, including ~86,000 participants recruited during 2002 and 2009 from 12 Southeastern states. Nearly 67% of study participants are African Americans (AAs) and the remaining are European American (EA) subjects.

  Study phs002566

• NHLBI GO-ESP: Family Studies (JHMI-Molecular Genetics of Atypical Cystic Fibrosis (CF) Study)

  The NHLBI "Grand Opportunity" Exome Sequencing Project (GO-ESP), a signature project of the NHLBI Recovery Act investment, was designed to identify genetic variants in coding regions (exons) of the human genome (the "exome") that are associated with heart, lung and blood diseases. These and related diseases that are of high impact to public health and individuals from diverse racial and ethnic groups will be studied. These data may help researchers understand the causes of disease, contributing to better ways to prevent, diagnose, and treat diseases, as well as determine whether to tailor prevention and treatments to specific populations. This could lead to more effective treatments and reduce the likelihood of side effects. GO-ESP is comprised of five collaborative components: 3 cohort consortia - HeartGO, LungGO, and WHISP - and 2 sequencing centers - BroadGO and SeattleGO. Cystic fibrosis (CF) patients without detectable disease-causing mutations in CFTR present a clinical phenotype very similar to, but possibly discrete from, classic CF. We hypothesize that the phenotype of these atypical CF patients is not due only to mutations in the carbonic anhydrase gene CA12 or in the genes encoding the epithelium sodium ion channel ENaC, but is due to mutations in novel genes that have yet to be associated with CF. The goal of this study is to determine the molecular etiology of this atypical CF.

  Study phs000556

• Genomic Analyses in Neoadjuvant Immunotherapy-Treated Head and Neck Cancers

  This is a phase 2 multicenter interventional trial involving the preoperative (neoadjuvant) administration of pembrolizumab, an anti-programmed cell death protein-1 (anti-PD-1) agent, to patients with pathologically confirmed, surgically resectable advanced head and neck squamous cell carcinoma (HNSCC). Participating centers were Dana-Farber/Harvard Cancer Center, Alvin J. Siteman Cancer Center, Washington University and Memorial Sloan Kettering Cancer Center. The trial (NCT02296684) consisted of two cohorts: Cohort 1 enrolled and treated patients received one dose of pembrolizumab prior to surgical resection with curative intent, and adjuvant pembrolizumab was planned for patients with high risk pathological features (positive margins and/or extranodal extension) in the surgical specimen. Cohort 2 enrolled and treated patients received 2 doses of pembrolizumab prior to surgical resection with curative intent. Adjuvant treatment was not dictated by the trial protocol for Cohort 2. The primary endpoint was rate of at least 50% pathological tumor response (pTR), defined as the presence of tumor necrosis/keratinous debris in the tumor bed following neoadjuvant pembrolizumab. pTR was graded as follows: pTR0 (<10%), pTR1 (10-49%) and pTR2 (≥50%). Baseline, pre-treatment tumor biopsy and post-neoadjuvant pembrolizumab surgically resected tumor specimens were dissociated, and CD3+CD45+ cells were analyzed for T cell receptor (TCR) expression and 5' gene expression analysis using single cell sequencing technologies for a subset of patients.

  Study phs002864

• The Human Gut Microbiome and Recurrent Abdominal Pain in Children

  This project explores the nature of the human intestinal microbiome in healthy children and children with recurrent abdominal pain. The overall goal is to obtain a robust knowledge-base of the intestinal microbiome in children without evidence of pain or gastrointestinal disease, children with functional abdominal pain, and children with abdominal pain and changes in bowel habits (irritable bowel syndrome). Multiple strategies have been deployed to navigate and understand the nature of the intestinal microbiome in childhood. These strategies include 454 pyrosequencing-based strategies to sequence 16S rRNA genes and understand the detailed composition of microbes in healthy and disease groups. Microarray-based hybridization with the PhyloChip and quantitative real-time PCR (qPCR) probes are being applied as complementary strategies to gain an understanding of the intestinal microbiome from various perspectives. Data collected and analyzed during the HMP UH2 and UH3 Demo project, from a set of healthy and IBS children may enable the identification of core microbiomes in children in addition to variable components that may distinguish healthy from diseased pediatric states. We are currently analyzing the dataset for the presence of disease-specific signatures in the human microbiome, and correlating these microbial signatures with pediatric health or IBS disease status. This study explores the nature of core and variable human microbiomes in pre-adolescent healthy children and children with recurrent abdominal pain.

  Study phs000265

• Genetic Epidemiology of Refractive Error in the KORA (Kooperative Gesundheitsforschung in der Region Augsburg) Study

  KORA ("Kooperative Gesundheitsforschung in der Region Augsburg" which translates as "Cooperative Health Research in the Region of Augsburg") is a population based study of adults randomly selected from 430,000 inhabitants living in Augsburg and 16 surrounding counties in Germany. The collection was done in 4 separate groups from 1984-2001 (S1-S4). One of the KORA groups, S3/F3, will be utilized for our GWAS because it is the only group with refractive error (RE) measurements. Consequent to informed consent, each of the surveys sampled subjects from ten strata according to age (range 25-74 years) and sex (equal ratio) with a minimum stratum size of > 400 subjects. In the KORA S3 study 4,856 subjects were studied between 1994 and 1995, and 3,006 individuals from S3 returned for follow up between 2003 and 2005 (S3/F3). For this refractive error study, we are including 1,981 subjects from S3/F3 (mean age 55.7, range 35-84). For each subject, eyeglass prescriptions were measured in addition to an evaluation by the Nikon Retinomax. Subjects with predisposing medical conditions, i.e., connective tissue disorders, and ocular conditions i.e., cataract and corneal opacities, that might predispose them to refractive error will not be included for genotyping. Whole genome association genotyping will be performed to determine common alleles that contribute to the variation of the quantitative trait of refractive error.

  Study phs000303

• Whole exome and transcriptome analysis of UV-exposed epidermis and carcinoma in situ reveals early drivers of carcinogenesis

  Squamous cell carcinoma in situ (SCCIS) is a prevalent precancerous lesion that can progress to cutaneous squamous cell carcinoma (cSCC). Although SCCIS is common, its pathogenesis remains poorly understood. To better understand SCCIS development, we performed laser-captured microdissection of human SCCIS and adjacent epidermis to isolate genomic DNA and RNA for next generation sequencing. Whole exome sequencing (WES) identified UV-signature mutations in multiple genes including Notch 1-3 (GeneID:4851, GeneID:4853, GeneID:4854) in the epidermis and SCCIS and oncogenic TP53 (GeneID:7157) mutations in SCCIS. Novel gene families, including SCHLAFEN genes, contained UV/oxidative-signature disruptive (UVD) epidermal mutations that manifested positive selection in SCCIS. The frequency and distribution of NOTCH and TP53 mutations indicate that NOTCH mutations may precede TP53 mutations. RNA sequencing identified 1166 differentially expressed genes; the top 5 enriched GO biological processes included: 1) immune response, 2) epidermal development, 3) protein phosphorylation, 4) regulation of catalytic activity, and 5) cytoskeletal regulation. The NEURL1 ubiquitin ligase, which targets Notch ligands for degradation, was upregulated in SCCIS. Neuralized 1 protein was found to be elevated in SCCIS suggesting that increased levels could represent a novel mechanism for downregulating Notch during UV-induced carcinogenesis. The data from DNA and RNA sequencing of epidermis and SCCIS provide insights regarding SCCIS formation.

  Study phs002019

• Large Scale Genotyping of Psychiatric Disorders

  Psychiatric disorders cause enormous human suffering and cost to society. The goal of this project is to elucidate the genetics of psychiatric disorders through a large consortium effort and genome-wide genotyping. The Psychiatric Genomics Consortium (PGC) (https://www.med.unc.edu/pgc) is an international consortium to pursue genome-wide analyses of common psychiatric disease. The PGC was founded in 2007 to unite investigators from around the world to conduct mega-analyses of individual genome-wide genotype data for schizophrenia (SCZ), bipolar disorder (BIP), autism (AUT), attention-deficit hyperactivity disorder (ADHD), and major depressive disorder (MDD). Subsequently, the PGC has expanded to include Tourette syndrome (TS), obsessive-compulsive disorder (OCD), eating disorders (ED), post-traumatic stress disorder (PTSD), and substance abuse (SUD). The PGC partnered with Illumina to develop the PsychChip, a custom GWAS array aimed at capturing common variation genome-wide for imputation, rare coding variation drawn from the exome chip and dense genotypes for loci with suggestive evidence for association across the initial PGC analyses of SCZ, MDD, BPD, ASD and ADHD. Samples (cases, controls, and families) were collected from Institutional Review Board-approved protocols that aim to study the genetics of psychiatric disorders. Genome-wide genotyping was performed at the Broad Institute (Cambridge, MA, USA) and the Mount Sinai School of Medicine Friedman Brain Institute (New York, NY, USA) using the PsychChip or Exome Chip.

  Study phs001413

• Center for Common Disease Genomics [CCDG] - Autoimmune: Inflammatory Bowel Disease (IBD) Exomes and Genomes

  The National Human Genome Research Institute (NHGRI) has funded a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes. Called the Centers for Common Disease Genomics (CCDG), this initiative will explore a range of diseases with the ultimate goal of: undertaking variant discovery for enough different examples of disease architectures and study designs to better understand the general principles of the genomic architecture underlying common, complex inherited diseases; understanding how best to design rare variant studies for common disease; developing resources, informatics tools, and innovative approaches and technologies for multiple disease research communities and the wider biomedical research community. The initial focus of the CCDGs will be in cardiovascular disease (early-onset coronary artery disease, atrial fibrillation, hemorrhagic stroke), neuropsychiatric disease (epilepsy, autism), and autoimmune/inflammatory disease (type 1 diabetes, inflammatory bowel disease). The Broad Institute is one of four selected CCDG project centers. The overarching aim of the Inflammatory Bowel Disease (IBD) program is to define the full allelic spectrum of protein-altering variation in genes associated to IBD, and assess their role in both Crohn's Disease (CD) and Ulcerative Colitis (UC) risk. The whole genome sequencing data generated here is comprised of samples from US-based diverse populations including African American, Puerto-Rican, Caribean and Cuban origins.

  Study phs001642