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• Genomic Psychiatry Cohort (GPC) Whole Genome Sequencing and Genotyping Study

  The Center for Genomic Psychiatry at the University of Southern California (USC), SUNY Downstate and an extensive network of academic medical centers have created the Genomic Psychiatry Cohort (GPC). The GPC consists of a large clinical cohort of patients with schizophrenia, bipolar disorder, and healthy controls. The data generation of genotyping and whole genome sequencing of the GPC has been done in collaboration with the USC and the Broad Institute of MIT and Harvard. Genome-wide association studies are being performed with the samples to identify genetic risks for psychiatric disease. Genotyping is also being used to determine ancestry informative markers and replication of candidate genes. Whole genome sequencing identifies functional variants associated with disease risk.

  Study phs001020

• Submitting array based metadata

  Submitting array based metadata For further information please check our Submission FAQs, submission quickguide as well as submission terms! The submission metadata required for Array-based submission must be submitted using EGA programmatic submission and by completing the Array-based format (AF) template. The guidelines for this workflow are described on this page. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA Array-based-Format (AF) template. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA-Array-based-Format (AF) template. Metadata Model Registering Metadata Use the EGA programmatic submission to register your Study, Samples, Data Access Committee (DAC) and Policy. This online interface enables you to create new and edit existing submissions. Do not use the Submitter Portal to register the metadata objects for array submissions. Please, go to the EGA programmatic submission. Registering Study Go to the EGA programmatic submission Submit an study XML Take a note of your study accession number (EGASXXXXXXXXXXX) To use the study accession number in a publication, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"(https://doi.org/10.1093/nar/gkab1059 ). Registering Samples Go to the EGA programmatic submission Submit a sample XML Save the list of accession numbers. These will be needed for the AF template. Registering Data Access Committee Further information on the role of your DAC. Go to the EGA programmatic submission Submit a DAC XML Take a note of the DAC accession number (EGACXXXXXXXXXXX) Registering Policy Your Data Access Policy provides the terms and conditions of data use. This is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAA) Go to the EGA programmatic submission Submit a Policy XML Take a note of the Policy accession number (EGAPXXXXXXXXXXX) Complete the Array-based format (AF) spreadsheet Once you have completed the registration of your Study, DAC and Policy using the programmatix submission, you must then complete and return the AF spreadsheet The AF spreadsheet consists on four components: Do not use EGA IDs registered using the Submitter Portal. You can easily identify objects registered in the Submitter Portal by their EGA ID pattern: EGA[A-Z]5{10 more digits} (e.g. for a sample EGAN50000002506). For populating an AF spreadsheet, please exclusively use EGA IDs following this specific pattern: EGA[A-Z]0{10 more digits} (e.g. for a sample EGAN00001691542). Tab1) Webin accessions : Provide the accession numbers for your study, DAC and policy. Please, also add your ega-box number. Tab2) Sample & phenotypes: Please, leave this tab blank. Tab3) Dataset: Describe the dataset to be created Tab4) Data files: Define how your data is going to be organised into datasets and packets for distribution (linkage between samples and files). Should further assistance be required after going through the guide below; please do not hesitate to contact the EGA helpdesk Once the AF spreadsheet is populated, please send it to our EGA helpdesk for further validation. AF spreadsheet Should your submission require multiple DAC's or policies, use ' ; ' to separate the accession numbers. Accessions AF spreadsheet: Samples & phenotypes Samples and phenotypes AF spreadsheet: Datasets We suggest that each dataset consists of a common set of data. The example below consists of two datasets, grouped according to shared data type, technology and by case/control. We also like to capture the number of unique samples that make up the dataset and the Data Access Committee (DAC) responsible for providing the named dataset and their policy (EGAP). Datasets AF spreadsheet: Data files What follows is an example of how to map your samples to the array based files added to your upload account (4th tab). Data Files Please, find below some practical examples on how to register the linkage between samples-files Case 1) 1 sample or list of samples in different datasets: Data Files In case you have a list of samples that belong to different datasets, please, repeat the samples accession number/s in the first column and link the sample to the corresponding dataset each time (each row). Each row is one linkage between sample-file-dataset. Case 2) 1 sample links to several files: Data Files In order to add multiple files to one sample you MUST use “ ; “ between filenames. Example: file1.gpg;file2.gpg;file3.gpg In case that you want to add an extra file to the sample (phenotype or .Rdata), please use “Additional files” column. Important note: You MUST upload the encrypted and unencrypted md5sum values of all files uploaded to your submission account using the filename nomenclature (file.gpg, file.md5,file.md5.gpg). Your submission will not be processed without md5values supplied for all files in the CORRECT format. What happens after the submission of a dataset? All datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitted or DAC contact instructs our EGA helpdesk for the study to be released. Finally, your data is archived within our databases and prepared for encrypted distribution upon the request of permitted EGA account holders. We strongly advise you NOT to delete your data until we confirm that your data has been successfully archived.

  Documentation submission/metadata/submission/array

• Whole Genome Sequencing Analysis in a Family of Discordant Twins With Non-Syndromic Microtia and Hemifacial Microsomia: Identification of Novel Candidate Genes and Variants

  This research is a non-NIH effort to discover the pathogenesis of non-syndromic microtia and to detect potential candidate genes. Microtia (OMIM 600674, 612290, 251800) is a spectrum of congenital anomalies and facial abnormalities, 90% of the cases followed by hearing loss. Two types of the disease are described in terms of connection to the affected syndromes: anomalies (syndromic) and only clinical manifestation (non-syndromic) associated with other symptoms. Though the exact pathogenesis of microtia remains unknown, pathogenic genes of majority of the microtia syndromic types have been identified. However, the genetic mutation of the non-syndromic microtia has not been confirmed. In this study, whole genome sequencing (WGS) was utilized to screen mutations in monozygotic discordant twins of congenital microtia-atresia and their family members. One of the twins, along with her grandfather, was discordant for microtia-atresia. By bioinformatic analysis of the WGS data from monozygotic twins with microtia and their non-microtia relatives, we proposed that among 28 identified potential genes, 4 of them (CNOT1, ODAD4, TBX15, and GIPC3) could be more responsible for non-syndromic microtia development and phenotypic features. Interested investigators may request to access the DNA genomic sequencing data of the study participants via dbGaP authorized access. In addition to raw genomic sequencing data, datasets of germline mutations and candidate gene mutations may also be available for investigators to request.

  Study phs003216

• The clinicopathologic spectrum and genomic landscape of de-/trans-differentiated melanoma

  De- and trans-differentiation is a rare and only poorly understood phenomenon in cutaneous melanoma. To study this disease more comprehensively we have retrieved 11 primary cutaneous melanomas from our pathology archives showing biphasic features characterized by a conventional melanoma and additional areas of de-/trans-differentiation as defined by a lack of immunohistochemical expression of all conventional melanocytic markers (S-100 protein, SOX10, Melan-A and HMB-45). The clinical, histologic and immunohistochemical findings were recorded and follow-up was obtained. The patients were mostly elderly (median: 81 years; range: 42-86 years) without significant gender predilection, and the sun-exposed skin of the head and neck area was most commonly affected. The tumors were deeply invasive with a mean tumor thickness of 7 mm (range: 4-80 mm). The dedifferentiated component showed atypical fibroxanthoma-like features in the majority (7), while additional rhabdomyosarcomatous and epithelial transdifferentiation was noted histologically and/or immunohistochemically in two tumors each. The background conventional melanoma component was of desmoplastic (4), superficial spreading (3), nodular (2), lentigo maligna (1) or spindle cell (1) types. For the 7 patients with available follow-up data (median follow-up period of 25 months; range: 8-36 months), 2 died from their disease and 3 developed metastases. Next-generation sequencing of the cohort revealed somatic mutation of established melanoma drivers including mainly NF1 mutations in the conventional component (5 cases), which were also detected in the corresponding de-/trans-differentiated components. In summary, the diagnosis of de-/trans-differentiated melanoma is challenging and depends on the morphologic identification of the conventional melanoma component. Molecular analysis is diagnostically helpful as the mutated gene profile is shared between the conventional and de-/trans-differentiated components. Importantly, de-/trans-differentiation does not appear to confer a more aggressive behavior.

  Dataset EGAD00001007033

• The clinicopathologic spectrum and genomic landscape of de-/trans-differentiated melanoma

  De- and trans-differentiation is a rare and only poorly understood phenomenon in cutaneous melanoma. To study this disease more comprehensively we have retrieved 11 primary cutaneous melanomas from our pathology archives showing biphasic features characterized by a conventional melanoma and additional areas of de-/trans-differentiation as defined by a lack of immunohistochemical expression of all conventional melanocytic markers (S-100 protein, SOX10, Melan-A and HMB-45). The clinical, histologic and immunohistochemical findings were recorded and follow-up was obtained. The patients were mostly elderly (median: 81 years; range: 42-86 years) without significant gender predilection, and the sun-exposed skin of the head and neck area was most commonly affected. The tumors were deeply invasive with a mean tumor thickness of 7 mm (range: 4-80 mm). The dedifferentiated component showed atypical fibroxanthoma-like features in the majority (7), while additional rhabdomyosarcomatous and epithelial transdifferentiation was noted histologically and/or immunohistochemically in two tumors each. The background conventional melanoma component was of desmoplastic (4), superficial spreading (3), nodular (2), lentigo maligna (1) or spindle cell (1) types. For the 7 patients with available follow-up data (median follow-up period of 25 months; range: 8-36 months), 2 died from their disease and 3 developed metastases. Next-generation sequencing of the cohort revealed somatic mutation of established melanoma drivers including mainly NF1 mutations in the conventional component (5 cases), which were also detected in the corresponding de-/trans-differentiated components. In summary, the diagnosis of de-/trans-differentiated melanoma is challenging and depends on the morphologic identification of the conventional melanoma component. Molecular analysis is diagnostically helpful as the mutated gene profile is shared between the conventional and de-/trans-differentiated components. Importantly, de-/trans-differentiation does not appear to confer a more aggressive behavior.

  Dataset EGAD00001007034

• NHLBI TOPMed: The Jackson Heart Study (JHS)

  Since there is a greater prevalence of cardiovascular disease among African Americans, the purpose of the Jackson Heart Study (JHS) is to explore the reasons for this disparity and to uncover new approaches to reduce it. The JHS is a large, community-based, observational study whose 5306 participants were recruited from among the non-institutionalized African-American adults from urban and rural areas of the three counties (Hinds, Madison, and Rankin) that make up the Jackson, MS, metropolitan statistical area (MSA). Jackson is the capital of Mississippi, the state with the largest percentage (36.3%) of African Americans in the United States. The JHS design included participants from the Jackson ARIC study who had originally been recruited through random selection from a drivers' license registry. Approximately six months before the JHS was to begin, an amendment to the federal Driver's Privacy Protection Act was passed that changed the level of consent for public release of personal information from driver's license lists from an "opt out" to an "opt in" basis. The Mississippi Highway Patrol was no longer able to release a complete listing of all persons with driver's licenses or state identification cards, which prevented its use in the JHS. New JHS participants were chosen randomly from the Accudata America commercial listing, which provides householder name, address, zip code, phone number (if available), age group in decades, and family components. The Accudata list was deemed to provide the most complete count of households for individuals aged 55 years and older in the Jackson MSA. A structured volunteer sample was also included in which demographic cells for recruitment were designed to mirror the eligible population. Enrollment was opened to volunteers who met census-derived age, sex, and socioeconomic status (SES) eligibility criteria for the Jackson MSA. In addition, a family component was included in the JHS. The sampling frame for the family study was a participant in any one of the ARlC, random, or volunteer samples whose family size met eligibility requirements. Eligibility included having at least two full siblings and four first degree relatives (parents, siblings, children over the age of 21) who lived in the Jackson MSA and who were willing to participate in the study. No upper age limit was placed on the family sample. Known contact information was obtained during the baseline clinic examination from the index family member with a verbal pedigree format to identify name(s), age(s), address (es), and telephone number(s). Recruitment was limited to persons 35-84 years old except in the family cohort, where those 21 years old and above were eligible. Only persons who otherwise met study criteria but were deemed to be physically or mentally incompetent by trained recruiters were excluded from study eligibility.1 1 Wyatt SB, Diekelmann N, Henderson F, Andrew ME, Billingsley G, Felder SH et al. A community-driven model of research participation: the Jackson Heart Study Participant Recruitment and Retention Study. Ethn Dis 2003; 13(4):438-455 (PMID: 14632263).

  Study phs000964

• National Cancer Institute (NCI) Head and Neck Cancer Study

  Head and neck cancers have well-documented associations with tobacco and alcohol exposure, but the disease develops in only a small fraction of users, which implies an important role for genetic susceptibility. Therefore, head and neck cancers are an excellent model for studying genetic susceptibility to environmental carcinogens. The primary goal of this study is to perform a comprehensive two-stage, high-density, genome-wide single-nucleotide polymorphism (SNP) analysis of head and neck cancer cases and corresponding frequency matched controls to identify novel genetic risk factors for head and neck cancer. In the study, we genotyped 2242 head and neck cancer cases and 1198 controls using Illumina HumanOmniExpress-12v1 BeadChip.

  Study phs001173

• Improving CLL Vγ9Vδ2-T cell fitness for cellular therapy by ex vivo activation and ibrutinib

  We studied whether Vγ9Vδ2-T cells can exploited as autologous effector lymphocytes in chronic lymphocytic leukemia (CLL). Healthy controls (HC) Vγ9Vδ2-T cells were activated by and had potent cytolytic activity against CLL cells, however CLL-derived Vγ9Vδ2-T cells proved dysfunctional with respect to effector cytokine production and cytotxicity. Dysfunction of Vγ9Vδ2-T cells was reversible upon activation with autologous monocyte-derived dendritic cells (mODCs). RNA sequencing confirmed that Vγ9Vδ2-T cells from CLL patients have a transcriptional profile that is distinct from HC Vγ9Vδ2-T cells prior to ex vivo expansion, although the profile after ex vivo expansion is similar between CLL and HC Vγ9Vδ2-T cells. Gene expression profiling implicated alterations in both synapse formation and exhaustion as conceivable contributors to compromised Vγ9Vδ2-T cell function in CLL patients. Taken together, CLL-mediated dysfunction of autologous Vγ9Vδ2-T cells is fully reversible, resulting in potent cytotoxicity towards CLL cells.

  Study EGAS00001003193

• Genomic Sequencing of Pediatric Rhaboid Cancers

  In this study, we sequenced the exomes of 35 rhabdoid tumors, highly aggressive cancers of early childhood. This study is part of a larger effort to characterize pediatric cancers as part of the Slim Initiative for Genomic Medicine (SIGMA) project.

  Study phs000508

• Isotype_resolved_sequencing_of_B_cell_receptor_in_measles_virus_infection

  Isotype-resolved sequencing of B cell receptor in measles virus infection 1) This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002635