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• eMERGE Network Phase III Clinical Sequencing: eMERGEseq Panel

  The Electronic Medical Records and Genomics (eMERGE) Network is a National Institutes of Health (NIH)-organized and funded consortium of U.S. medical research institutions. The primary goal of the eMERGE Network is to develop, disseminate, and apply approaches to research that combine biorepositories with electronic medical record (EMR) systems for genomic discovery and genomic medicine implementation research. eMERGE was announced in September 2007 and began its third phase in September 2015. eMERGE III consists of nine study sites, two central sequencing and genotyping facilities, and a coordinating center. eMERGE Phase III aims to: 1) sequence and assess the phenotypic implication of rare variants in a custom designed eMERGEseq panel consisting of 109 genes (including 56 ACMG actionable finding list genes and the top 6 genes from each site relevant to their specific aims), as well as approximately 1400 SNPs; 2) assess the phenotypic implications of these variants by developing, validating and implementing new phenotype algorithms, 3) integrate genetic variants into EMRs to inform clinical care; and 4) create community resources. Included in this study are: ~24,000 eMERGE participants from 10 eMERGE III study sites. Corresponding demographics, body mass index measurements. Top PheWAS codes generated from a collated list of ICD codes from all study sites. Study sites and participants include: Cincinnati Children's Hospital Medical Center (CCHMC): Cincinnati Children's Hospital Medical Center (CCHMC) is a not-for-profit hospital and research center pioneering breakthrough treatments, providing outstanding family-centered patient care and training healthcare professionals for the future, and dedicated to improving health and welfare of children and to the shared purpose of discovery and practical application of new genomic information to the ordinary care of children. We bring a comprehensive electronic health record (EPIC), a deidentified i2b2 data warehouse of 680K patient records, a biobank with >261,000 consents that allow return of results to >84,000 patients and guardians who have provided DNA samples, and hundreds of faculty and senior staff who make genomics or informatics an active focus of their research. CCHMC will help the eMERGE III Steering Committee identify genes for the eMERGE III targeted sequencing panel, provide 3,000 DNA samples from CCHMC patients to be sequenced, review targeted gene panels from clinical care at CCHMC for somatic mosaicism and reinterpretation, and further develop and disseminate a software workflow suite for sequence analysis. We will also extend our work generating phenotype algorithms using heuristic and machine learning methods to many new childhood diseases. We will develop tools to evaluate adolescent return of results preferences, examine the ethical and legal obligations and potential to reanalyze results, and develop clinical decision support for phenotyping, test ordering, and returning sequencing results. Children's Hospital of Philadelphia (CHOP): The Center for Applied Genomics (CAG) is a specialized Center of Emphasis at the Children's Hospital of Philadelphia (CHOP), and one of the world's largest genetics research programs, with to state-of-the-art high-throughput sequencing and genotyping technology. Our primary goal is to translate basic research findings to medical innovations. We aim to develop new and better ways to diagnose and treat children affected by rare and complex medical disorders, including asthma, autism, epilepsy, pediatric cancer, learning disabilities, and a range of rare diseases. Ultimately, our objective is to generate new diagnostic tests and to guide physicians to the most appropriate therapies. Participants were recruited from the CAG biorepository (n>450,000), specifically from >100,000 CHOP pediatric patients and family members, which is enriched for rare-diseases (n>12,000). Center for Applied Genomics, The Children's Hospital of Philadelphia We gratefully thank all the children and their families who enrolled in this study, and all individuals who donated blood samples for research purposes. Genotyping for this project was performed at the Center for Applied Genomics and supported by an Institutional Development Award from The Children's Hospital of Philadelphia. Sequencing was supported by the National Institutes of Health through an award from the National Human Genome Research Institute's Electronic Medical Records and Genomics (eMERGE) program (U01HG008684). Columbia University: The goal of the Columbia eMERGE III project is to develop methods for integrating genomic data in EHRs and to study the impact of such genomic informatics interventions on the health of a diverse, underserved urban adult English- and Spanish-speaking patient population in Northern Manhattan served by Columbia University Medical Center/New York-Presbyterian Hospital system. The study group is 2500 patients recruited from diverse clinics and community outreach centers of self-reported White (~61%), Asian (~11%), African-American (~11%), American Indian/Alaska Native (<1%) racial and Hispanic (~33%) ethnic backgrounds. There are two subgroups in the study cohort - a retrospective group (N=1052) that includes patients from oncology and nephrology clinics, and a prospective one (N=1448) that includes healthy individuals as well as participants with diverse medical conditions. Confirmed pathogenic variants in 70 selected genes will be returned to participants and their healthcare providers through the EHR integration. Participants are able to choose the results they receive and will have the freedom to meet with a genetic counselor and a geneticist to review results. The impact of genetic testing on clinical care is determined by periodic monitoring of EHRs. Geisinger: Samples and phenotype data in this study were provided by the Geisinger MyCode® Community Health Initiative. Participants are recruited across the Geisinger System via online consents or in-person consents at a hospital or clinic visit. Enrollment is ongoing with over 100,000 individuals currently consented. Partners Healthcare (Harvard University): The Partners HealthCare Biobank is a large research program designed to help researchers understand how people's health is affected by their genes, lifestyle, and environment. This large research data and sample repository provides access to high-quality, consented blood samples to help foster research, advance our understanding of the causes of common diseases, and advance the practice of medicine. For the Partners research community (Massachusetts General Hospital and Brigham and Women's Hospital), the Biobank provides: Banked samples (plasma, serum, and DNA) collected from consented patients Blood samples that were discarded after clinical testing in the Crimson Cores maintained in the Brigham and Women's Hospital and Massachusetts General Hospital Pathology Departments Sample handling and preparation services Link to the biobank data to the Partners Research Patient Data Registry (RPDR) a research instance of our electronic clinical chart Data access through our research portal. To date, over 70,000 Partners patients have given their consent to enroll, give a blood sample, receive research results and agreed to be re-contacted for additional research studies. The Biobank has enabled Partners investigators to compete for nationally recognized grants in personalized medicine such as a clinical electronic Medical Records and Genomics network (eMERGE) site and the national All of US program. The Biobank currently supports over 120 Partners investigators and over 130 million dollars in NIH research. Kaiser Permanente Washington/ (KPWA) / University of Washington (UW): KPWA participants were enrolled in the eMERGE Network through the Northwest Institute of Genetic Medicine (NWIGM) biorepository, and provided the appropriate consent to receive clinically relevant genetic results (N=2,500.) NWIGM is based at the University of Washington and co-managed by the University of Washington and KPWA. The purpose of the NWIGM biorepository is to build infrastructure and resources to carry out a broad range of future genetic research. KPWA members enrolled in the biorepository are asked to provide informed consent to providing a DNA sample for storage in the NWIGM biorepository. The consent is purposefully broad to serve the dual purpose of reducing the burden on researchers who wish to use this biorepository and the IRB committees who will be responsible for reviewing these requests in the future. Participants were eligible if aged 50 - 65 years old at the time of their enrollment into the NWIGM repository, living, enrolled in KPWA's integrated group practice, and had completed an online Health Risk Appraisal. The selection algorithm was based on several data sources from the EHR at KPWA. 1) Demographics - participants with self-reported race as Asian ancestry were prioritized and selected to enrich for non-European ancestry. The KPWA eMERGE cohort includes N=1,245 members of Asian ancestry. 2) Participants were also selected for a history of colorectal cancer (N=1,255), in order to allow us to enrich germline pathogenic variants. Mayo Clinic: The Return of Actionable Variants Empirical (RAVE) Study was approved by the Mayo Clinic IRB. We recruited 2537 participants from Mayo Clinic biobanks in Rochester, MN, who had hypercholesterolemia or colon polyps, thereby enriching for Familial hypercholesterolemia (FH) and monogenic causes of colorectal cancer (CRC). Additional eligibility criteria were: 1) residents of Southeast MN who were alive and aged 18-70 years; 2) LDL-C level >155 or >120 mg/dl while on lipid-lowering therapy; 3) no known cause of secondary hyperlipidemia; and 4) no cognitive impairment or dementia that would compromise their ability to give written informed consent. Based on these criteria, we identified 5270 eligible patients and obtained informed consent from 3030 participants. Recruitment was conducted in waves and utilized mailed recruitment packets consisting of a study brochure, a written informed consent form, a baseline psychosocial questionnaire, and a return postage-paid envelope. DNA of 2537 participants was sent for CLIA-certified targeted sequencing of 109 genes including genes associated with FH and CRC. Targeted sequencing and genotyping was performed in a Central Laboratory Improvement Amendment (CLIA)-certified laboratory. Northwestern University: Samples and data used in this study were obtained from patients from Northwestern Medicine, an integrated healthcare system, formed through a partnership of Northwestern Memorial HealthCare and Northwestern University Feinberg School of Medicine. Participants include a retrospective cohort from the Northwestern Pharmacogenomics Study, funded through the eMERGE II project, NHGRI (3U01HG006388-02S1) and a prospective cohort from the Genetic Testing and Your Health Study, funded through the eMERGE III project, NHGRI (U01HG008673). Patients were eligible to participate if they were18 years or older and see a physician at Northwestern Medicine. Patients consented to genetic testing and to allow their results to be placed in their electronic medical record. Vanderbilt University Medical Center: Vanderbilt University Medical Center (VUMC) participants were enrolled in the eMERGE Network through the Vanderbilt Genome-Electronic Records (VGER) project. Patients were provided the appropriate consent to receive clinically relevant genetic results (N=2,700). Participants were eligible if aged 21 or over, had a healthcare provider at VUMC, and visited the provider at least 3 times in the past 3 years. Meharry Medical College: Inclusion of ethnic groups in genomic research is critical to identify possible reasons for health disparities. African-Americans are being enrolled in various outpatient clinics of Nashville General Hospital at Meharry, an inner city hospital primary serving a poorer patient group. A total of 500 African Americans with four cancer types demonstrating health disparities in this population - prostate, colon, breast, lung are identified and approached by clinical research coordinators. The purpose of the study is to determine if any genetic information can be identified from these patients who have or are at high risk of one of these disparate cancers. All participants provide written informed consent and HIPAA authorization to provide blood samples for broad research use and permission to access data in their hospital electronic medical record for research now and in the future. An extensive demographic profile is obtained and entered into a REDCap database. Blood samples are obtained for a panel of alleles from extracted DNA at Baylor. In addition, de-identified coded samples are processed and stored in a central biorepository for further DNA, RNA and proteomic analyses. The survey and phlebotomy are performed at the time of the initial contact and agreement to participate. Nearly all patients approached willingly agree to participate for potential benefit to themselves, family members, or humankind. Little concern is voiced of providing samples for genetic analysis. Study investigators will share results with the participants and providers if testing does not indicate high risk. Results indicating increased risk or actionable alleles for the patient and/or family will be returned by a genetic counselor. Monitoring of the patients' health in this cohort will continue to be followed in the EMR to identify any future associations that might explain health disparities in African Americans. Proposals will be reviewed from investigators to study the genetic or proteomic samples as well as the clinical and demographic information in the repository. Please note that this version of the dataset has a handful of mismatches between genotyped and provided sex. Data with the following IDs should be removed prior to analysis: 420252874213744142412243424569384245694642672223

  Study phs001616

• Poland Warmian-Mazurian Voivodeship WGS

  This project used NGS (next generation sequencing), using Illumina NOVASEQ 6000 and Illumina DRAGEN aligner. The dataset includes BAM and BAM.BAI files from Whole Genome Sequencing of 15 samples (8 female, 7 male of Warmian-Mazurian Voivodeship, Poland from POPULOUS collection). Library Construction Protocol: Illumina DNA PCR-Free Prep, Tagmentation. Reference Genome: GRCh37.

  Dataset EGAD50000000128

• Poland West Pomeranian Voivodeship - WGS

  This project used NGS (next generation sequencing), using Illumina NOVASEQ 6000 and Illumina DRAGEN aligner. The dataset includes BAM and BAM.BAI files from Whole Genome Sequencing of 17 samples (7 female, 10 male of West Pomeranian Voivodeship, Poland from POPULOUS collection). Library Construction Protocol: Illumina DNA PCR-Free Prep, Tagmentation. Reference Genome: GRCh37.

  Dataset EGAD50000000129

• Genomic signatures define three subtypes of EGFR-mutant stage II-III non-small-cell lung cancer with distinct adjuvant therapy outcomes

  This dataset contains targeted sequencing data of 204 surgical samples from resected NSCLC. Genomic profiling identifies five predictive biomarkers, which is then integrated into the Multiple-gene INdex to Evaluate the Relative benefit of Various Adjuvant therapies (MINERVA) score. The MINERVA score categorizes patients into three subgroups with relative disease-free survival and overall survival benefits from either adjuvant gefitinib or chemotherapy. This study demonstrates that predictive genomic signatures could potentially stratify resected EGFR-mutant NSCLC patients and provide precise guidance towards future personalized adjuvant therapy.

  Dataset EGAD00001008157

• Papua New Guinean Genome Diversity Project (PGDP)

  The PGDP dataset includes 58 whole genome sequences for Papua New Guinean individuals from different locations. DNA was extrated from saliva samples (Oragen kit). Sequencing libraries were prepared using the TruSeq DNA PCR-Free HT kit. 150 bp paired-end sequencing was performed on the Illumina HiSeq X5 sequencer. The PGDP dataset provides Fastq and BAM files.

  Dataset EGAD00001007783

• Demographically Diverse Substance Use Disorder Cohorts of Dr. Stanley H. Weiss

  The Demographically Diverse Substance Use Disorder Cohorts of Dr. Stanley H. Weiss, which constitute the Epidemiology of the Weiss Cohort Projects, consist of a series of inter-connected projects, building upon a set of cohort projects of various groups, mainly drug users from medication-assisted treatment programs, that Dr. Stanley H. Weiss first developed in the 1980’s plus several newer initiatives, each with an array of collaborators. Beginning in the 1980’s, Dr. Stanley H. Weiss started several long-term studies of persons who inject drugs (PWID) across the United States, ultimately enrolling over 10,000 participants through the early 1990’s with an average age then in their 30’s. About a quarter were enrolled from sites in New Jersey (NJ). These studies included the first testing of PWID for the human immunodeficiency virus (HIV) and the human T-cell lymphotropic viruses (HTLV I and HTLV II). Cumulative past support (initiation thru ~ 1999) for these cohort studies included ~ $20 million from intramural resources from the National Cancer Institute (NCI) and the National Institute on Drug Abuse (NIDA), plus multiple grants and in-kind support from the New Jersey Department of Health (NJDOH) totaling ~ $1 million. The Weiss Cohort Projects include the first large AIDS-era cohorts to include women at high risk for HIV. A high percentage of subjects in these studies are black or Latino. Thus, this is an ethnically diverse US cohort, with a high proportion of women included. These subjects are at high risk of parenteral and sexual infection from both drug use and sexual practices. Samples from other studies conducted by Dr. Weiss, in which detailed interviews were conducted, are included as controls (persons documented by us not to have a history of opioid drug use). As one of our groups of subjects have many persons of Haitian ancestry, we specifically included some Haitians who had never used opioids as controls. Our documentation includes such ancestry. These cohorts demonstrated high rates of HIV and HTLV-II infection in PWIDs, including one study initiated in 1981 with confirmation in the later cohorts. In the first two decades of these studies, among numerous publications was the first study showing a very high rate of hepatitis C infection among PWIDs. An example of how the studies’ long-time horizon proved essential was that it first became possible to test whether a person had ever been infected with hepatitis C virus (HCV), as well as how much HCV was in each person’s blood, many years after the specimens were collected. This allowed HCV amounts in blood to be compared for subjects who had died of liver disease early in the study versus those who survived. Then a sequence of published papers culminated in demonstrating, using a nested case-control design, that a high baseline HCV titer was predictive of early progression to death from end-stage liver failure. Outcomes related to HCV (end stage liver disease and hepatocellular carcinoma) remain under study. In the original cohort studies, the mean age at enrollment was ~ 33 years old, so that those still alive in 2022 are mainly now ~ 60 - 75 years old. Many participants have already died. The tincture of time has led to subjects reaching ages when many more are dying from a wide array of outcomes, including from many chronic diseases (including cancer) as well as from infectious agents (especially HIV, HCV) or drug overdose. Renewed collaboration with local drug treatment programs has led to new field-based studies, including examination of some currently evolving problems among drug users. Dr. Weiss joined the National Institute on Drug Abuse (NIDA) Genetics Consortium (NGC) in 2017, and through the NIDA project officer has had access to NGC contract resources (see below). NIH Certificate of Confidentiality, CC-DA-16-214 (attached) protects these studies. Past arrangements related to data on our subjects leads to restrictions on the use of data emanating from our study, such as potential commercialization and restrictions on whom may access and use these data. NIDA Genetics Consortium (NGC) resources further support these endeavors and will be used as part of the NGC analyses studying the genetics of substance use. Study participants signed informed consent for the information collected from them to be used with no time limit and for biologic specimens collected from them to be used without restriction in future research. Serum samples were collected from participants, and from many also plasma, white blood cells and/or urine samples. About 100,000 vials were stored. All specimens have been continuously preserved at sufficiently cold temperatures to prevent deterioration, and many subjects separated white blood cells were processed and frozen in such a way as to maintain viability. Detailed data from the participants has been accumulated over time, and in general, linkage has been retained in each sub-study in accordance with the consent forms and protocols. For some participants, specimens were collected at multiple times (that is, sequential specimens). Multiple specimens from a single person exist in this database, and efforts at de-duplication remain ongoing. Dr. Weiss should be contacted if an investigator requires unique individuals since: • Multiple phases of enrollment occurred, and as our prospective follow-up continues; Dr. Weiss may identify new instances of multiple enrollment. • Some persons are related to each other. • In general, in this dataset for dbGaP, only a single specimen/record form a given person is included. Advances in laboratory testing techniques now permit innovative new uses for our linked research biospecimen repository. The ongoing focus of an interdisciplinary research program based on these cohorts relates subjects’ diseases, behaviors, medical history, and outcomes with biological and exposure markers. Participants’ use of various substances was ascertained on study enrollments, many serially over time. Quantitative frequency of use data, also sometimes sequential over time, were ascertained. Active ascertainment of outcomes is being conducted, including matching to mortality and cancer databases. Investigators interested in collaborations on specific outcomes (which is not part of this dbGaP dataset) or in the use of our stored specimens are encouraged to contact the principal investigator, Dr. Weiss. The processing of the genomic data was done in conjunction with NIDA, and in accordance with some longstanding data cleaning steps used by NIDA in the NIDA Genetics Consortium (NGC), a group to which we shall be contributing these data for collaborative analyses. Since there is the potential for these steps to introduce certain types of potential biases, we summarize these here. Under contract from NIDA, cryopreserved sera or plasma (-80 C) or cells (in liquid nitrogen) were used, with most stored having been stored for 30 to 40 years in our biorepository. In the case of serum or plasma, in which only (largely) cell-free DNA fragments were available, DNA was extracted and restored prior to amplification. Industry standard DNA amplification techniques were done on all samples prior to genotyping in accord with established protocols of the NIDA Genetics Consortium. Our genotype data were run and processed on the Illumina Infinium OmniExpress_v_1.3 array. This array has 714,238 SNPs, and was designed many years ago. There were 628 SNPs on the array that do not correspond to any chromosome position, and these were removed. Genotype data were submitted by NIDA’s contracted genotyping laboratory in six batches over time to NIDA’s contracted dbGaP data management group, which conducted quality control (QC) analyses. QC analysis included an assessment of batch effects on for five of the six batches. (One of the batches, with only 12 samples, was too small for QC analysis of batch effects.) Standard NIDA Genetic Consortium cleaning was performed. Samples with a call rate <.85 were removed. Only one sample per person was retained. When more than one specimen was genotyped from one subject, only the sample with the higher call rate was retained (provided, of course, that that call rate was ≥ 0.85). We have retained some people we know are related, including some found to have been related through genotyping; the pedigree file describes those relationships. In summary, key cleaning steps include: 1. Using PLINK to check gender discrepancy. 2. Using PREST-PLUS and KING (Kinship-based Inference for GWAS) to check relatedness. 3. Using PEDCHECK and PLINK to check/zero-out Mendelian error. 4. Using PLINK to perform sample QC, SNP QC, along with KING to perform chromosome X and chromosome Y QC. 5. SNP-QC: Batch-effect: 5 Batches were compared (one batch, with few samples, was not). These five batches were compared to each other in all ten possible pairs, one batch vs. another batch, examining SNP allele frequency discrepancies by population (from GRAF), Fisher Exact Allelic test, with the criterion of p<5e-8 for removal. 6. SNP-QC: discordant SNPs in QC duplicates. Compared 25 QC duplicated samples with call rate > 0.95, removed SNPs with 3+ discordance. 7. There were 1,056 SNPs that were monomorphic; these have been retained so they can be included in analyses in which our dbGaP data are combined with those from other cohorts (in the latter of which those SNPs may not be monomorphic). The final cleaned dataset submitted has 8,898 samples and 606,793 SNPs.

  Study phs002140

• How to use EGA Webin?

  EGA Webin EGA Webin serves as a platform for registering metadata for array based submissions, large scale sequence submission as well as for legacy EGA submission accounts (ega-box-XXXX). For large scale submitters of sequence data you have also the option to submit metadata via XMLS programmatic submission, while new submitters are advised to utilise the Submitter Portal for their submissions. 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. WEBIN actions: Register metadata for a sequence submission Register study, samples, experiments, runs, DAC, policy and dataset/s after file upload. Register components for your array-based metadata submission Register study, samples, DAC or policy before uploading files. Edit existing submission metadata Change or update previously submitted metadata. Register metadata for a sequence submission Ensure that all sequence files have been encrypted before uploading them to your submission account using the EgaCryptor. Go to the EGA Webin and log in using your submission account name (ega-box-XXX) and password. Register components of your metadata submission Study Samples Data Access Committee (DAC) Data access policy Dataset For array-base submissions: Study, Samples, Data Access Committee (DAC) and Data access policy may all be registered BEFORE file upload and dataset registration through the array-base template. Register your Study Go to the “Studies (Projects)” box Click on “Register Study” and fill in the information related to your study. Click on “Submit”, this will save the information and generate an EGA study ID. To use the study accession number in a publication, the study has to be previously released on the EGA website, 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 and “The European Genome-phenome Archive in 202 "(10.1093/nar/gkab1059)" Register your Samples Go to the “Samples” box Click on “Register Samples” Select “Download spreadsheet to register samples” and customise your template, there is a default EGA template (EGA default checklist) but more attributes can be added if required. For the EGA default checklist, there are mandatory,recommended and optional attributes. As well custom fields which can be added if required. Mandatory attributes Field Name Description tax_id Taxonomy ID of the organism as in the NCBI Taxonomy database. Entries in the NCBI Taxonomy database have integer taxon IDs. See our tips for sample taxonomy here scientific_name Scientific name of the organism as in the NCBI Taxonomy database. Scientific names typically follow the binomial nomenclature. For example, the scientific name for humans is Homo sapiens. sample_alias Unique name of the sample. If not selected system will auto generate an unique alias sample_title Title of the sample sample_description Description of the sample phenotype *** Where possible, please use the Experimental Factor Ontology (EFO) to describe your phenotypes. Recommended attributes Field Name Description subject_id Identifier for the subject where the sample has been derived from gender * Sex Optional attributes Field Name Description sex sex of the organism from which the sample was obtained disease_site Affected organ sample type Affected organ donor_id ** Identifier of the donor where the sample has been derived from *Gender should be described as 'male', 'female' or 'unknown'. If 'unknown' due to a known sex chromosome aneuploidy, please create a user defined attribute called 'Sex chromosome karyotype' and add the appropriate value, for example, 'XXY'. **Donor id (Subject id) should be a de-identified subject handle. If unknown, please add 'unknown' to the field. ***Phenotypes should, where possible, be an Experimental Factor Ontology accession. If a term cannot be found to describe your phenotype please use free text. All sample phenotypes considered important for further analysis of the data should be provided (for example, tumour type), additional phenotype attributes can be created by defining your own attributes; use the notion 'phenotype2', 'phenotype3', etc. After you have customised the fields for the sample submission, download the template and fill in the information. Example of the sample template: Finally upload the sample template to get the EGA accession IDs for the samples. Register your Data Access Committee (DAC) Further information on the role of your DAC Go to the “Data Access” box Click on “Register Dacs” Input the information about the DAC and register at least one main DAC contact. Register your Data Access 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 (DAC). Go to the “Data Access” box Click on “Register Policies” Select the DAC to which this policy will be linked to and fill in the policy information. Submitting your Runs and Analyses This section is only for sequence data submission, for array-based submission it can be skipped. Please refer to our Submitting array based metadata Runs Registration Go to the “Raw Reads (Experiments and Runs)” box Click on “Submit Reads” Select “Download spreadsheet template for Read submission” Select the template corresponding to your submission type For the templates you have the option to customise the optional fields. To check their description click on “Show Description “ Download the template and fill in the required information. Example of the runs template: We recommend that Fastq, BAM, and CRAM read files are submitted using Webin-CLI When using this interface instead of Webin-CLI, raw sequences must be uploaded in one of the supported data formats before they can be submitted. The files can be uploaded using FTP or Aspera. The study and the sequenced samples must be pre-registered before the raw reads are submitted. Please note that each individual study and sample should be registered only once. You will be asked to provide information about the sequencing libraries and instruments. Submitting your Dataset This section is only for sequence data submissions, for array based submissions it can be skipped. Please refer to our Submitting array based metadata The dataset describes the data files, defined by the run (EGARXXXXXXXXXXX) and analysis (EGAZXXXXXXXXXXX) accessions that make up the dataset and links the collection of data files to a specified Data Access Committee and Data Access Policy. As a result, you must have registered your Reads and experiments, Data Access Committee (DAC) and Data access policy before submitting your Dataset. 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. Please contact EGA Helpdesk for further assistance. Go to the “Data Access” box Click on “Register Dataset” Select the Data Access Committee (DAC) and Data access policy Register your dataset After submitting your dataset you should contact the EGA Helpdesk to provide a release date for your dataset. Datasets are automatically held (i.e. not released) unless they are affiliated to a study that has already been released. Edit/update existing submission metadata Go to the “Report” section of the object you would like to edit. Locate the object and click on the arrow under action. An option menu will be displayed. Objects can be edited through their XML or with the WEBIN menu. After an object has been edited, changes would be available on the website until the submission is released again. Please contact the EGA Helpdesk if you require further assistance.

  Documentation submission/metadata/submission/EGA_webin

• Whole genome sequencing of single cells identifies stochastic aneuploidies, genome replication, states, and clonal repertoires forlibrary A96176A

  Transposase-based amplification-free single-cell genome direct library preparation in nanowell chips; 531 samples; filetype=bam

  Dataset EGAD00001004769

• WGBS Discovery Samples

  The dataset contains valuable genomic data from a discovery cohort consisting of 29 individuals. This cohort is comprised of 8 healthy individuals (control), 8 patients with ST-segment elevation myocardial infarction (STEMI), 7 patients with non-ST-segment elevation myocardial infarction (NSTEMI), and 6 patients with unstable angina (UA). The genomic data was obtained by isolating cell-free circulating DNA (ccfDNA) and subjecting it to bisulfite conversion using a low-input BS-seq (PBAT) protocol. Sequencing was done on the Novaseq 6000 platform.

  Dataset EGAD00001010935

• TNBC ctDNA Targeted Panel

  Circulating tumour DNA (ctDNA) detection in liquid biopsy is an emerging alternative to tissue biopsy, but its utility in treatment response monitoring and prognosis in triple negative breast cancer (TNBC) remains to be well understood. In this study, we determined the presence of early stage TNBC ctDNA detectable actionable mutations with a clinically validated hotspot treatment indication panel. Sequencing of plasma DNA from 130 TNBC patients collected within 7 months of primary treatment completion revealed that 7.7% had detectable residual disease with a hotspot panel. Among neoadjuvant treated patients, we observed a trend where patients with incomplete pathologic response and positive ctDNA within 7 months of treatment completion were at risk of reduced progression free survival. We propose that a high risk subset of early TNBC patients treated in NAT protocols may be identifiable by combining tissue response and sensitive ctDNA detection.

  Study EGAS00001006937

• Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET)

  STARNET is a genetics of RNA expression study of multiple disease-relevant tissues obtained from living patients with cardiovascular disease. Tissue samples are obtained from blood, atherosclerotic-lesion-free internal mammary artery (MAM) and atherosclerotic aortic root (AOR), subcutaneous fat (SF), visceral abdominal fat (VAF), skeletal muscle (SKLM), and liver (LIV) during open thorax surgery of 600 coronary artery disease (CAD) patients. All patients gave written informed consent. The inclusion criterion was eligibility for coronary artery by-pass graft (CABG) surgery. Patients with other severe systemic diseases, such as active systemic inflammatory disease or cancer, were excluded. The primary clinical end points were the SYNTAX score based on the extent of coronary atherosclerosis assessed from preoperative angiograms. The STARNET patients are Caucasians (31% females); 32% had diabetes, 75% had hypertension, and 67% had hyperlipidemia; and 33% had an MI before age 60. By New York Heart Association criteria, 45% were class I, 42% class II, 9% class III, and 1% class IV. TYPES AND RNA SEQUENCING: 566 DNA genotype and 3577 RNA-seq profiles from seven tissues from 600 STARNET CABG patients passed quality control (on average 511 RNA-seq profiles/tissue). DNA was genotyped with the OmniExpress Exome array (Illumina, ~900k SNPs) and imputed to a total of 14,098,063 DNA variant calls (6,245,505 with minor allele frequency >5%). The STARNET subjects mainly overlap with Caucasian of Northern European (Finnish) descent. RNA sequencing was performed using the HighSeq2000 platform, poly-A (LIV, SKLM, VAF, SF and blood) and ribo-zero (AOR, MAM) protocols with 50-100 bp read lengths, single end to 15-30 million read depth.

  Study phs001203

• IYDP Indonesian Y chromosome Diversity Project

  The IYDP dataset includes BAM files of 126 Y chromosomes extracted from whole genome sequences. These are from individuals from a broad range of Indonesian islands - communities close to mainland Asia through to New Guinea. The original whole genome sequencing libraries were prepared using TruSeq DNA PCR-Free and TruSeq Nano DNA HT kits depending on DNA quantity. 150 bp paired-end sequencing was performed on the Illumina HiSeq X sequencer. Individuals were sequenced to expected mean depth of 30x, with an achieved median depth of raw reads across samples of 43x.

  Dataset EGAD00001008573

• WGS of cell-free DNA derived from plasma of CRC patients and healthy controls, and WGS of matched tumor tissue and saliva

  This dataset contains: 1.) Whole-genome sequencing (WGS) data (~6x) of 259 cfDNA samples obtained from 50 colorectal cancer (CRC) patients and 61 healthy controls. Paired-end sequencing was performed with 2x101 bp reads on the NovaSeq 6000 system. Data is provided as mapped .bam files (aligned to GRCh38/hg38). 2.) WGS data (~1x) of 50 tumor biopsy and 45 saliva samples from CRC patients. Paired-end sequencing was performed with 2x101 bp reads on the NovaSeq 6000 system. Data is provided as mapped .bam files (aligned to GRCh38/hg38).

  Dataset EGAD00001009309

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Mediators of Atherosclerosis in South Asians Living in America Study (MASALA)

  Cohort Description The Mediators of Atherosclerosis in South Asians Living in America (MASALA) study is a prospective cohort of South Asians that aims to identify risk factors for heart disease in a large, growing Asian American subgroup. MASALA enrolled 906 South Asians in 2010-2013 and then added a new wave of 258 South Asian participants from 2017-2018, for a full cohort size of 1,164. Data Being Submitted Wave 1 questionnaire data includes 3967 variables for up to 431 MASALA participants in C4R. Wave 2 questionnaire data includes 448 variables for up to 313 MASALA participants in C4R. Dried Blood Spot/Serosurvey data includes 7 variables for up to 248 MASALA participants in C4R. Derived data includes 43 variables for up to 528 MASALA participants in C4R. Phenotype data includes 113 variables for up to 429 MASALA participants in C4R.

  Study phs002980

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Atherosclerosis Risk in Communities Study (ARIC)

  Cohort Description The Atherosclerosis Risk in Communities (ARIC) study began in the mid-1980s with the initial aims being to describe the presence of subclinical atherosclerosis, the progression of atherosclerosis to clinical cardiovascular disease (CVD), and the association of novel risk factors with CVD. ARIC recruited its cohort of men and women aged 45-64 in 1987-89 from four communities (Forsyth County, NC; Jackson, MS; suburban Minneapolis, MN; and Washington County, MD). As of 2025, ARIC counts over 4,000 participants.Data Submitted Wave 1 questionnaire data includes 356 variables for up to 5763 ARIC participants in C4R. Wave 2 questionnaire data includes 367 variables for up to 5000 ARIC participants in C4R. Wave 3 questionnaire data includes 81 variables for up to 3450 participants in C4R.Dried Blood Spot/Serosurvey data includes 8 variables for up to 2437 ARIC participants in C4R. Derived data includes 62 variables for up to 18765 observations. Phenotype data includes 163 variables for up to 5937 ARIC participants in C4R. Brain MRI data includes 13 variables for up to 2550 participants in C4R.HCU data includes 21 variable for up to 631 ARIC participants in C4R.Lung CT data includes 15 variables for 1329 ARIC participants in C4R.Neurocognitive data includes 16 variables and 49826 observations.Serosurvey data includes 20 variables for 1707 ARIC participants in C4R.

  Study phs002988

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): REasons for Geographic and Racial Differences in Stroke (REGARDS)

  Cohort DescriptionThe REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort is one of the nation's largest, most comprehensive population-based cohorts, and it uses innovative home- and telephone-based data collection. REGARDS centrally recruited and initially examined 30,239 Black and White men and women aged ≥45 years in 2003-7 to understand why Southerners and Black Americans have a higher incidence of stroke and related diseases that affect brain health.Data Being Submitted Wave 1 questionnaire data includes 397 variables for 8109 REGARDS participants in C4R. Wave 2 questionnaire data includes 448 variables for 6421 REGARDS participants in C4R. Dried Blood Spot/Serosurvey data includes 7 variables for 4058 REGARDS participants in C4R. Derived data includes 43 variables for 8606 REGARDS participants in C4R. Phenotype data includes 113 variables for 7880 REGARDS participants in C4R.

  Study phs002919

• Ribosome profiling shows variable sensitivity to detect open reading frames for conventional and different types of cryptic T cell antigens

  The aim of the study was to explore the sensitivity of ribosomal profiling to detect ORFs for cryptic antigens targeted by T cells in immune responses. Ribosomal profiling was performed for six EBV-LCLs. Detection of conventional and cryptic MiHA-encoding ORFs was compared. The data showed that open reading frames (ORFs) for conventional MiHAs as well as cryptic MiHAs in upstream ORFs were frequently detected with similar sensitivity. However, only a limited number of MiHAs in in reading frames alternative to protein-coding sequences and no ORFs derived from long non-coding RNAs were detected. In conclusion, Ribo-seq for identification of antigen-encoding ORFs is useful to screen for cryptic antigens in upstream ORFs as potential targets for immunotherapy.

  Study EGAS50000000322

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Severe Asthma Research Program (SARP)

  Cohort Description The Severe Asthma Research Program (SARP) has been investigating the clinical, physiologic and molecular phenotypes of asthma since 2000. It is currently following ~400 deeply phenotyped asthma patients. Data Being Submitted Wave 1 questionnaire data includes 397 variables for up to 375 SARP participants in C4R.Wave 2 questionnaire data includes 448 variables for up to 289 SARP participants in C4R.Dried Blood Spot/Serosurvey data includes 7 variables for up to 290 SARP participants in C4R.Derived data includes 43 variables for up to 463 SARP participants in C4R.Phenotype data includes 113 variables for up to 463 SARP participants in C4R.

  Study phs002913

• Poland Lesser Voivodeship - WGS

  This project used NGS (next generation sequencing), using Illumina NOVASEQ 6000 and Illumina DRAGEN aligner. The dataset includes BAM and BAM.BAI files from Whole Genome Sequencing of 32 samples (14 female, 18 male of Lesser Voivodeship, Poland from POPULOUS collection). Library Construction Protocol: Illumina DNA PCR-Free Prep, Tagmentation. Reference Genome: GRCh37.

  Dataset EGAD50000000166

• Poland Lodz Voivodeship - WGS

  This project used NGS (next generation sequencing), using Illumina NOVASEQ 6000 and Illumina DRAGEN aligner. The dataset includes BAM and BAM.BAI files from Whole Genome Sequencing of 41 samples (20 female, 21 male of Lodz Voivodeship, Poland from POPULOUS collection). Library Construction Protocol: Illumina DNA PCR-Free Prep, Tagmentation. Reference Genome: GRCh37.

  Dataset EGAD50000000165

• Poland Mazovia Voivodeship - WGS

  This project used NGS (next generation sequencing), using Illumina NOVASEQ 6000 and Illumina DRAGEN aligner. The dataset includes BAM and BAM.BAI files from Whole Genome Sequencing of 43 samples (16 female, 27 male of Mazovia Voivodeship, Poland from POPULOUS collection). Library Construction Protocol: Illumina DNA PCR-Free Prep, Tagmentation. Reference Genome: GRCh37.

  Dataset EGAD50000000164

• Poland Silesian Voivodeship - WGS

  This project used NGS (next generation sequencing), using Illumina NOVASEQ 6000 and Illumina DRAGEN aligner. The dataset includes BAM and BAM.BAI files from Whole Genome Sequencing of 47 samples (23 female, 24 male of Silesian Voivodeship, Poland from POPULOUS collection). Library Construction Protocol: Illumina DNA PCR-Free Prep, Tagmentation. Reference Genome: GRCh37.

  Dataset EGAD50000000163

• Poland Holy Cross Voivodeship - WGS

  This project used NGS (next generation sequencing), using Illumina NOVASEQ 6000 and Illumina DRAGEN aligner. The dataset includes BAM and BAM.BAI files from Whole Genome Sequencing of 6 samples (6 female of Holy Cross Voivodeship, Poland from POPULOUS collection). Library Construction Protocol: Illumina DNA PCR-Free Prep, Tagmentation. Reference Genome: GRCh37.

  Dataset EGAD50000000162

• Poland Greater Poland Voivodeship - WGS

  This project used NGS (next generation sequencing), using Illumina NOVASEQ 6000 and Illumina DRAGEN aligner. The dataset includes BAM and BAM.BAI files from Whole Genome Sequencing of 37 samples (19 female, 18 male of Greater Voivodeship, Poland from POPULOUS collection). Library Construction Protocol: Illumina DNA PCR-Free Prep, Tagmentation. Reference Genome: GRCh37.

  Dataset EGAD50000000161

• Poland Pomeranian Voivodeship - WGS

  This project used NGS (next generation sequencing), using Illumina NOVASEQ 6000 and Illumina DRAGEN aligner. The dataset includes BAM and BAM.BAI files from Whole Genome Sequencing of 25 samples (14 female, 11 male of Pomeranian Voivodeship, Poland from POPULOUS collection). Library Construction Protocol: Illumina DNA PCR-Free Prep, Tagmentation. Reference Genome: GRCh37.

  Dataset EGAD50000000160

• Poland Kuyavian-Pomeranian Voivodeship - WGS

  This project used NGS (next generation sequencing), using Illumina NOVASEQ 6000 and Illumina DRAGEN aligner. The dataset includes BAM and BAM.BAI files from Whole Genome Sequencing of 22 samples (11 female, 11 male of Kuyavian-Pomeranian Voivodeship, Poland from POPULOUS collection). Library Construction Protocol: Illumina DNA PCR-Free Prep, Tagmentation. Reference Genome: GRCh37.

  Dataset EGAD50000000158

• Poland Lublin Voivodeship - WGS

  This project used NGS (next generation sequencing), using Illumina NOVASEQ 6000 and Illumina DRAGEN aligner. The dataset includes BAM and BAM.BAI files from Whole Genome Sequencing of 24 samples (14 female, 10 male of Lublin Voivodeship, Poland from POPULOUS collection). Library Construction Protocol: Illumina DNA PCR-Free Prep, Tagmentation. Reference Genome: GRCh37.

  Dataset EGAD50000000155

• Poland Subcarpathian Voivodeship - WGS

  This project used NGS (next generation sequencing), using Illumina NOVASEQ 6000 and Illumina DRAGEN aligner. The dataset includes BAM and BAM.BAI files from Whole Genome Sequencing of 22 samples (11 female, 11 male of Subcarpathian Voivodeship, Poland from POPULOUS collection). Library Construction Protocol: Illumina DNA PCR-Free Prep, Tagmentation. Reference Genome: GRCh37.

  Dataset EGAD50000000157

• Poland Lower Silesian Voivodeship - WGS

  This project used NGS (next generation sequencing), using Illumina NOVASEQ 6000 and Illumina DRAGEN aligner. The dataset includes BAM and BAM.BAI files from Whole Genome Sequencing of 30 samples (15 female, 15 male of Lower Silesian Voivodeship, Poland from POPULOUS collection). Library Construction Protocol: Illumina DNA PCR-Free Prep, Tagmentation. Reference Genome: GRCh37.

  Dataset EGAD50000000156

• Research Study into The Molecular Genetics of Hereditary Neuropathies

  Identification of genes implicated in inherited neuropathy through collecting and sequencing multiple generation families with inherited neuropathy. Patients in this study have been evaluated for demyelinating peripheral neuropathy as determined by decreased motor nerve conduction velocities in the sural nerve. Patients have also been screened for mutations in all genes implicated in demyelinating peripheral neuropathy and are expected to have previously unidentified mutations. Exome (WES) sequencing for two patients affected with inherited neuropathy. VCF files with called variants for each de-identified patient have been submitted.

  Study phs001351

• SweGen: a whole-genome data resource of genetic variability in a cross-section of the Swedish population

  The SweGen project represents a comprehensive map of genetic variation in the Swedish population. This Study contains four datasets where the first one contains whole-genome sequencing data in CRAM-format for 942 individuals from the Swedish Twin Registry, the second one contains genetic variant frequencies in VCF-format for the same individuals, the third one contains whole-genome sequencing data in CRAM-format for 58 individuals from the Northern Sweden Population Health Study and the fourth one contains genetic variant frequencies in VCF-format for the same individuals. The data, serving as a reference dataset, is a valuable and national resource for genetics research and clinical diagnostics and can be used, for instance, to increase understanding of both rare and complex disorders. It also serves as an international resource for larger studies of human population genetics.

  Study EGAS50000000906

• Resequencing (MIPS) of candidate genes for Keratoconus (2020)

  In this study, we selected 34 candidate genes for KC based on previous Whole Exome Sequencing (WES) and the literature, and resequenced them in 745 KC patients and 810 ethnically matched controls. Data analysis was performed using the single variant association test as well as gene-based mutation burden and variance components tests. In our study, we detected enrichment of genetic variation across multiple gene-based tests for the genes COL2A1, COL5A1, TNXB, ZEB1 and ZNF469. The top hit in the Fisher’s exact test was obtained for a common variant in the COL12A1 gene. Interestingly, COL5A1, TNXB, ZNF469 and COL12A1 are all known Ehlers-Danlos Syndrome (EDS) genes. Though the co-occurrence of KC and EDS has been reported previously, this study is the first to demonstrate a consistent role of genetic variants in EDS genes in the etiology of KC. In conclusion, our data show a shared genetic etiology between KC and EDS, and clearly confirm the currently disputed role of ZNF469 in disease susceptibility for KC. Besides the role for EDS genes, we also confirm the previously reported involvement of genetic variation in ZEB1 in KC.

  Study EGAS00001004267

• ATAC-seq data in normal colon mucosa

  This dataset includes ATAC-seq data generated in normal colon mucosa obtained in four healthy volunteers during colonoscopy indicated for colorectal cancer screening. Subjects had no lesions in the colon. Also includes one frozen sw480 colon adenocarcinoma cell line sample for piloting the protocol

  Study EGAS00001005281

• Circulating tumor cells for comprehensive and multiregional non-invasive genetic characterization of multiple myeloma (arrays set)

  Multiple myeloma (MM) patients undergo repetitive bone marrow (BM) aspirates for genetic characterization. Circulating tumor cells (CTCs) are detectable in peripheral blood of virtually all MM cases and are prognostic, but their applicability for non-invasive screening has been poorly investigated. Here, we used next-generation flow cytometry to isolate matched CTCs and BM tumor cells from 42 patients and compare their genetic profile. In 8 cases, tumor cells from extramedullary plasmacytomas were also sorted and whole-exome sequencing performed in the three spatially-distributed clones. Noteworthy, ≥84% mutations present in BM and extramedullary clones were detectable in CTCs. Concordance between BM tumor cells and CTCs was high for chromosome arm-level copy-number alterations (≥94%) though not for translocations (43%). All high-risk genetic abnormalities except one t(4;14) were detected in CTCs whenever present in BM tumor cells. Altogether, these results support CTCs for non-invasive risk-stratification of MM patients based on their numbers and genetic profile.

  Study EGAS00001004314

• COVID-19 Severity of First Wave of Infection for Severe Patients in Madrid

  Through thorough exclusion criteria and deep phenotypic characterisation for a Madrid-based population we intend to find enrichment of gene mutations associated to severity. In addition we will look for statistically overrepresented annotation of particular phenotypes (a controlled vocabulary specifically developed for this project) that associate them to affected genes in our patient sample.

  Study EGAS00001005931

• NIDDK IBD Genetics Consortium Ulcerative Colitis Genome-Wide Association Study

  This dataset contains data for 1,028 white, non-Hispanic, European ancestry individuals with ulcerative colitis who were included in a genome-wide association study published by Silverberg et al. (2009). These individuals were ascertained in North America and selected to have either left-sided or extensive disease (i.e., individuals with proctitis only were excluded). Genotyping was performed using the Illumina HumanHap300v2 (n = 540) and HumanHap550v3 (n = 488) Genotyping BeadChips at the Feinstein Institute for Medical Research. Control data (not included) were obtained from the NIDDK IBD Genetics Consortium's Crohn's Disease GWAS (available from dbGaP) and from studies 64 and 65 deposited in the Illumina iControlDB. Seven hundred eighty individuals in this dataset were taken from the NIDDK IBD Genetics Consortium cell line repository (http://www.niddkrepository.org). These individuals are identified in the file dbGaP_SubjectDS.txt. The subject IDs for these individuals may be used to request corresponding samples for follow-up research through the repository. In addition, complete phenotype data for these individuals are included, collected using the Consortium's forms and phenotyping manual (both included). The remaining 248 individuals were identified from pre-existing collections ascertained by members of the Consortium or their collaborators. For these samples, several of the items in the phenotype file are incomplete. Those who wish to replicate the results in Silverberg et al. should note that 6 individuals with missing genotype rates > 0.07 were excluded from that analysis (leaving 1,022 affected samples total). In addition, the minor allele frequencies (MAFs) reported in the publication were calculated using only those individuals who were included in the allelic association tests (n = 977 for SNPs included in the HumanHap300 and n = 476 for SNPs included only in the HumanHap550). These tests were performed using conditional logistic regression on gender-ancestry strata; individuals who were not placed in a stratum (using the procedure described in the supplementary information for Silverberg et al.) were excluded. The indicator variables hh300 and hh550 in the file dbGaP_PhenotypeDS.txt identify the samples included in the allelic association tests, and may be used to replicate the published MAFs among affected individuals.

  Study phs000345

• BAP1 study

  Background: Mutations in BAP1 have been identified in many aggressive cancers. Current therapeutic strategies for BAP1-deficient tumors are not effective. Goal: Discover novel strategies for BAP1-deficient tumors. Details: In our study, we identified a potential therapeutic strategy for BAP1-deficient cancers. We used multi-omics approaches to understand the underlying mechanisms behind why the discovered potential therapeutic strategy is effective.

  Study EGAS50000000235

• Role of Genetic Factors in the Pathogenesis of Lung Disease

  Three individuals with lymphangioleiomyomatosis (LAM) underwent evaluation for Tuberous Sclerosis Complex (TSC)-associated mucocutaneous and internal findings. Next-generation sequencing (NGS) of cutaneous findings was used to confirm clinical suspicion for mosaic TSC in individuals with LAM. Mosaic mutations in TSC2 were found in these three individuals.

  Study phs003108

• hereditary BrEAst Case CONtrol study (BEACCON)

  The BEACCON study aimed to address the lack of power of previous studies to identify novel BC predisposition genes by performing extensive sequencing in 12,000 women (11,511 analysed following exclusions) and further enhancing power by using an ‘extreme phenotype’ design with enrichment of familial non-BRCA1 and BRCA2 cases, compared with a control population of older women with ongoing confirmation of cancer-free status at June 2019. Three-quarters of the 1303 candidate genes screened were selected based on empiric evidence from local (69 multi-case BC families) or international whole exome sequencing studies, and the remainder were included to provide detailed coverage of functional pathways with established associations with BC.

  Dataset EGAD00001007025

• DIS3 licenses B cells for physiological plasma cell differentiation in humans

  DIS3 licenses B cells for physiological plasma cell differentiation in humans

  Study EGAS50000000853

• Exome Sequencing of Schizophrenia Cases and Controls in the South African Xhosa Population

  The goal of this international collaborative project is to identify genes responsible for Schizophrenia in the Xhosa population of South Africa. The vast majority of the genetic basis for Schizophrenia has yet to be explained. We hypothesize that genes and pathways important to Schizophrenia will harbor different, severe disease-causing mutations in different affected individuals. Given the genetic diversity of African populations, we expect to find genes for Schizophrenia that have not yet emerged from studies of other populations. This project will also foster the development of gene discovery research for neuropsychiatric disorders in Africa. Our approach will identify genes important for the disorder in populations worldwide. These genes will stimulate future efforts to develop more effective treatment and prevention strategies.

  Study phs000959

• Collaborative Cohort of Cohorts for COVID-19 Research (C4R): Genetic Epidemiology of COPD Study (COPDGene)

  Cohort Description The Genetic Epidemiology of COPD (COPDGene) study is a non-interventional, multicenter, longitudinal, case-control study at 21 US sites of smokers with a ≥10 pack-year history of smoking, with and without COPD, and healthy never smokers. The initial goal was to characterize disease-related phenotypes and explore associations with susceptibility genes. Data Being Submitted Wave 1 questionnaire data includes 397 variables for up to 3683 COPDGene participants in C4R. Wave 2 questionnaire data includes 447 variables for up to 2191 COPDGene participants in C4R. Dried Blood Spot/Serosurvey data includes 7 variables for up to 1692 COPDGene participants in C4R. Derived data includes 43 variables for up to 4082 COPDGene participants in C4R. Phenotype data includes 113 variables for up to 4082 COPDGene participants in C4R.

  Study phs002910

• Effective screening strategy for deafness using a new-genetation sequencing platform: a consecutive analysis

  The diagnosis of the genetic etiology of deafness contributes to the clinical management of patients. We performed the following four genetic tests in three stages for 52 consecutive deafness subjects in one facility. We used the Invade assay and Sanger sequencing for the GJB2 gene or SLC26A4 gene in the first stage test, TaqMan genotyping assay in second stage test, and targeted exon sequencing using the massively parallel DNA sequencing in third stage test. Overall, we identified the genetic cause in 40% (21/52) of patients. The diagnostic rates of the first-, second- and third-stage genetic testing were 17%(9/52), 9%(4/43) and 21% (8/39), respectively. The combination approach using these genetic tests appears to be useful as a diagnostic tool for deafness patients. We recommended that genetic testing for the screening of common mutations in deafness genes using the Invader assay or TaqMan genotyping assay be performed as the initial evaluation. For the remaining undiagnosed cases, targeted exon sequencing using MPS is clinically and economically beneficial. This data set was MPS reads data (fastq) for 39 sensorineural hearing loss patients.

  Study JGAS000032

• Glioblastoma_CRISPR_Screen

  This is an in vitro genome-wide CRISPR/cas9 screen in human glioblastoma stem cells, screening for genes essential for survival of these cells. These cells express cas9 and have been transfected with a guide RNA library causing gene knockouts. We will analyse the sequencing data for depletion of guide RNAs.

  Study EGAS00001001519

• Methylation differences in trisomy 21 using monozygotic twins

  We have hypothesized that methylation differences induced by trisomy 21 (T21) contribute to the phenotypic characteristics and heterogeneity in T21. In order to determine the methylation differences in T21 without the interference of the interindividual genomic variation, we have used fetal skin fibroblasts from monozygotic (MZ) twins discordant for T21. We also used skin fibroblasts from MZ twins concordant for T21, normal MZ twins without T21, and unrelated normal and T21 individuals. We applied Reduced Representation Bisulfite Sequencing (RRBS) to generate genome wide nucleotide resolution of DNA methylation based on high throughput sequencing between each pair of samples. RRBS revealed differentially methylated promoter regions (DMRs) in MZ twins discordant for T21 that have also been observed in unrelated normal and T21 individuals. The identified DMRs are enriched for genes involved in embryonic organ morphogenesis. These DMRs are maintained in iPS cells generated from this twin pair and are correlated with the gene expression changes. We have also observed an increase in DNA methylation level in the T21 methylome compared to the normal euploid methylome. This observation is concordant with the up regulation of DNA methyltransferase enzymes (DNMT3B and DNMT3L) and down regulation of DNA demethylation enzymes (TET2 and TET3) in the iPSC of the T21 versus normal twin. Additionally, two sets of T21 MZ twins discordant for heart defect highlighted DMRs in genes that are associated with heart development. In conclusion the study of DNA methylation differences in MZ twins discordant for genomic abnormalities is a promising approach to understand the molecular pathophysiology of aneuploidies.

  Study EGAS00001001051

• Metagenomic Deep Sequencing in Meningitis and Encephalitis

  This is a prospective observational study seeking to identify pathogens in cases of unexplained neurological disease, particularly those in which there is a presumed infectious cause. This next generation analytical approach will hopefully allow for rapid identification of foreign genomic material, and through a sophisticated bioinformatics pipeline developed in the DeRisi lab, these foreign genomic sequences can be aligned to all known sequences in publicly available databases (i.e. GenBank) which may allow for identification of novel pathogens or known pathogens that are not frequently tested for as part of standard diagnostic work-ups.

  Study phs001067

• Genetic analysis in monozygotic twins discordant for bipolar disorder

  To identify genes relevant to bipolar disorder, an exome sequence analysis was conducted in 3 pairs of healthy monozygotic twins as control twins in this study. In addition to this data set, an exome sequence analysis was also conducted in several monozygotic twins discordant for bipolar disorder.

  Study JGAS000014

• The landscape of somatic mutations in epigenetic regulators across 1000 pediatric cancer genomes

  To define the frequency and pattern of somatic mutations in epigenetic regulators in pediatric cancer, we sequenced 633 genes, encoding the majority of proteins involved in patterning the epigenome, in 1000 pediatric tumors spanning 21 different cancer subtypes. This analysis establishes an epigenetic landscape of pediatric cancer, which expands the knowledge for potential therapeutic targets and for developing personalized therapy.

  Study EGAS00001000449

• shallow Whole-Genome sequencing of 14 TNBC tumor in the MATADOR trial - for in silico spike-in experiment

  14 TNBC tumors with tumor percentage ~90% were selected for in silico spike-in experiment

  Dataset EGAD50000001863

• A Comprehensive Platform for Analyzing Longitudinal Multi-Omics Data

  PALMO (Platform for Analyzing Longitudinal Multi-omics data) is a platform for analyzing longitudinal data from bulk as well as single cell. It allows to identify inter-, intra-donor variations in genes over longitudinal time points. The analysis can be done on bulk expression dataset without known cell type information or single cell with cell type or user defined groups. It allows to infer stable and variable features in a given donor and each cell type or a user defined group. The outlier analysis can be performed to identify technical/biological perturbed samples in a donor or a participant. Further, differential analysis can be performed to decipher time-wise changes in gene expression in a cell type. The data that is available in the dbGaP is the demo longitudinal samples used in the study, which includes hashed raw fastq files for single-cell RNA-sequencing (scRNA-seq) and non-hashed fastq files for single-cell ATAC-sequencing (scATAC) experiment.

  Study phs003203