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• Exploring_the_heterogeneity_of_sarcoma_using_single_cell_sequencing_

  Multi region samples are collected from patients, with consent, immediately after resection of the tumour. Samples are digested and sorted using FACS as single cells into lysis buffer. Cells are then stored until further processing for G&T-seq. After sequencing, we will explore intra-tumour heterogeneity using computational approaches to integrate RNA and DNA data onto the tumour phylogeny 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 (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001002866

• NHLBI TOPMed: African American Sarcoidosis Genetics Resource

  This study aims to comprehensively interrogate the genomes of African American sarcoidosis families. Sarcoidosis is characterized by a hyperimmune response resulting in granuloma formation in multiple organs. It affects African Americans (AAs) more frequently and more severely than whites. While previous linkage, admixture, candidate gene and genome-wide association (GWA) studies show statistically compelling effects, causal variants are still unknown and much of sarcoidosis heritability is yet to be explained. This "missing" heritability likely includes effects of both common (minor allele frequency (MAF)>5%) and rare variants (MAF

  Study phs001207

• Capture Hi-C identifies the chromatin interactome of colorectal cancer risk loci

  Multiple regulatory elements distant to their targets on the linear genome can influence the expression of a single gene through chromatin looping. Chromosome conformation capture implemented in Hi-C allows for genome-wide agnostic characterization of chromatin contacts. However, detection of functional enhancer-promoter interactions is precluded by its effective resolution that is determined by both restriction fragmentation and sensitivity of the experiment. Here we have developed a capture Hi-C (cHi-C) approach to allow an agnostic characterisation of these physical interactions on a genome-wide scale. Single nucleotide polymorphisms associated with complex diseases often reside within regulatory elements and exert effects through long-range regulation of gene expression. Applying this cHi-C approach to 14 colorectal cancer risk loci has allowed us to identify key long-range chromatin interactions in cis and trans involving these loci.

  Study EGAS00001001085

• 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

• Longitudinal Studies of Brain Structure and Function in MPS Disorders

  Mucopolysaccharidosis (MPS) types I, II, IV, VI and VII, are inherited diseases that result from the bodies inability to break down large sugar molecules that are by-products of metabolism. MPS affects most organs of the body and causes abnormalities in the liver, spleen, bones, and brain. We are studying the central nervous system (primarily the brain) so we can better understand the nature of the problems individuals with MPS I, II, IV, VI and VII have so we can find ways of better treating these problems. We would like to find out about the changes in the brain of individuals with MPS I, II, IV, VI and VII using data from MRI and neuropsychological tests. This is a longitudinal study so patients who enroll will need to be seen 3 times over 5 years. The longitudinal nature of the study allows us to make conclusions about how changes in some structures of the brain and changes in cognitive ability are related. The research objectives are: To identify abnormalities of brain (and central nervous system) structure and function in patients with MPS I, II, IV, VI and VII, regardless of how they are being treated or have been treated in the past; and to track disease progression over time. To examine the degree to which independent variables (e.g., age at first treatment, severity of disease, types of medical abnormalities, mutation, medical events, and sensory abnormalities) have an impact on both functional and structural outcomes as well as on quality-of-life. To identify, through longitudinal study, those measures that most accurately reflect the current disease state. This is a longitudinal study of 75-100 individuals with either MPS I, II, IV, VI and VII. Those participating in the study will be evaluated 3 times over 5 years. The primary site for this study is the University of Minnesota but there are an additional 6 centers in the United States and Canada that are also participating and will provide data for analysis. You will need to be able to travel to Minnesota or one of the participating centers in order to be a part of this study.

  Study phs001328

• Immune Profiling in Patients with High-Risk Smoldering Myeloma

  Single-cell RNA-sequencing identifies immune biomarkers of response to immunotherapy in patients with high-risk Smoldering Multiple Myeloma (SMM). We conducted a Phase II trial of the immunotherapeutic anti-SLAMF7 antibody, Elotuzumab, in combination with Lenalidomide and Dexamethasone (E-PRISM study), to determine the utility and safety of early immunotherapy in patients with high-risk SMM and develop biomarkers for optimal patient selection and monitoring of response to treatment.There is an overlap in subjects between this study and dbGaP accession phs001323.

  Study phs002476

• Childhood Cancer Data Initiative (CCDI): CCDI Pediatric In Vivo Testing Program - Leukemia

  The goal of this study is to molecularly characterize a large panel of pediatric acute lymphoblastic leukemia (ALL) patient-derived xenografts (PDXs) previously established in immune-deficient mice. These PDXs are utilized as part of the NCI-funded Pediatric Preclinical In vivo Testing (PIVOT) program to identify novel agents and combinations. Biospecimen data include next-generation sequencing (RNAseq, whole exome sequencing, DNA copy number variation), whole-genome analysis of cytogenetic abnormalities, and DNA fingerprint for quality control.

  Study phs003164

• RNAseq of MCL cell lines

  This study provides bulk RNA sequencing data from Mino and Jeko-1 mantle cell lymphoma (MCL) cell lines treated with CDK9 inhibitors, including the novel compound YX0798. CDK9 is a key regulator of RNA polymerase II–mediated transcription, and its inhibition disrupts transcriptional programs critical for tumor survival. The dataset captures transcriptomic changes associated with CDK9 inhibition, including downregulation of oncogenic drivers such as MYC and MCL-1. These data support mechanistic studies of transcriptional reprogramming and therapeutic vulnerability in aggressive MCL.

  Study EGAS50000001089

• Center for Common Disease Genomics [CCDG] - Cardiovascular: Pharmacokinetic Polymorphisms in Japanese General Population

  This study is a part of NHGRI's Center for Common Disease Genomics, which is a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes. Current estimates anticipate that the CCDG program will sequence approximately 140K whole genomes and 225K whole exomes during the life of the project. The Cardiovascular Disease working group of the CCDG considered five diseases: early-onset coronary artery disease (EOCAD), stroke, atrial fibrillation, congestive heart failure and type 2 diabetes. Atrial fibrillation will affect between 6-12 million individuals in the US by 2050. AF also is associated with increased risks of stroke, dementia, heart failure, death, and high health care costs. Many risk factors for AF have been identified, including advancing age, cardiovascular disease (CVD), and CVD risk factors. However, there is little knowledge how to prevent AF. Furthermore, therapies for AF are only partially effective, and are themselves associated with substantial morbidity. Previously, heritable forms of AF have been considered rare; yet in the last decade, it has been established that AF, and in particular early-onset forms of AF, are heritable. Genome-wide association studies (GWAS) provide a powerful tool to identify common variants underlying disease risk. The AFGen Consortium currently consists of investigators from more than 25 studies with >20,000 individuals with AF and >100,000 without AF. In the latest analyses, 14 loci have been identified for AF1 . Broadly, the loci implicate genes related to cardiopulmonary development, cardiac-expressed ion channels, and cell signaling molecules.Source: https://ccdg.rutgers.edu/sites/default/files/CCDG_CVD_EOAF_FINAL_w_link.pdfAnalysis of 165 pharmacokinetic-related gene polymorphisms (the number of polymorphisms may increase) using DNA derived from the blood of approximately 1,000 Japanese general populations. Tokai University School of Medicine Molecular Life Sciences 2 Isogo Lab has been trying to find new genes which contribute to the development of multiple diseases and to develop new methods for human genome diversity analysis that will lead to the development of technology for disease screening by finding genes related to diseases and incorporating gene analysis technology. Using this method, we are trying to clarify the relationship between the characteristics of rheumatoid arthritis and psoriasis vulgaris and genes to seek effective treatments and prevention methods. Raw sequencing data, metadata, vcf and phenotype data at individual level are available at https://anvilproject.org/data. For questions about availability contact help@lists.anvilproject.org.

  Study phs002985

• Genetic analysis of patients with Inherited Retinal Dystrophies (IRDs) using next generation sequencing to identify the causative variants

  Genetic analysis of patients with Inherited Retinal Dystrophies (IRDs) was carried out by performing Whole Genome Sequencing (WGS). The main purpose of this study is to identify simple and complex mutations responsible for IRD in patients. WGS was performed on selected affected and unaffected individuals using the Illumina HiSeqX10. The reads were aligned to human genome 19 (hg19) and variant calling was performed using Genome Analysis Toolkit (GATK). The genotyping quality of single nucleotide variants (SNVs) and indels was assessed using the variant quality score recalibration approach implemented in GATK. Copy number variations (CNVs) were called using Genome STRiP and SpeedSeq. This large set of whole genome sequencing data from different ethnicity can be stored and shared through dbGaP. This data could serve as a source for checking frequencies of variants or the pathogenicity of selected variants in different ethnicities.

  Study phs001619

• GENEVA Genes and Environment Initiatives in Type 2 Diabetes (Nurses' Health Study/Health Professionals Follow-up Study)

  Type 2 diabetes mellitus (T2D) affects approximately 21 million individuals in the U.S., or almost 10% of the U.S. adult population. Because diabetes is determined by both genetic and environmental factors, a better understanding of the etiology of diabetes requires a careful investigation of gene-environment interactions. The Nurses' Health Study (NHS) and Health Professionals' Follow-up Study (HPFS) are well-characterized cohort studies of women and men for whom stored blood and DNA samples are available as well as detailed information on dietary and lifestyle variables. The major goals of the project include: 1. To conduct a GWA analysis among 3,000 cases of T2D and 3,000 healthy controls in NHS/HPFS cohorts. 2. To use information on the joint effects of genes and a list of carefully selected environmental exposures at the initial screening stage to test gene-environment interactions. This approach optimizes our power to detect variants that have a sizeable marginal effect and those with a small marginal effect but a sizeable effect in a stratum defined by an environmental exposure. For this analysis, we have developed a joint test of genetic marginal effect and gene-environment interaction. This flexible two-degree-of-freedom test generally provides greater power than standard methods and has the potential to uncover both marginal genetic effects and stratum-specific effects. The Version 1 (v1) dbGaP release of data from the GENEVA Diabetes Study (NHS/HPFS) includes data from the NHS only. The Version 2 (v2) dbGaP release includes data from both the NHS and HPFS. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to type 2 diabetes mellitus through large-scale genome-wide association studies of well-characterized cohorts of nurses and health professionals. Genotyping was performed at the Broad Institute of MIT and Harvard, a GENEVA genotyping center. Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000091

• Washington University PDX Development and Trial Center

  This data set is a collection of patient specimens from common and rare cancers that have been developed into patient-derived xenograft (PDX) models at the Washington University PDX Development and Trial Center (WU-PDTC). The goal of the PDTC is to develop and characterize PDX models, gaining insight into tumor biology, and validating biomarkers across all major tumor types. Pre-clinical experiments in these PDX models will be used to advance our ability to predict clinical responses to new molecularly targeted agents under development.

  Study phs002305

• Multiomic profiling of pleomorphic rhabdomyosarcoma

  Rhabdomyosarcoma (RMS) describes rare soft-tissue tumors that exhibit features of skeletal muscle differentiation. The most common subtypes in children are alveolar and embryonal rhabdomyosarcoma, with the alveolar subtype characterized by PAX3/7 fusions. A lesser known and rarer subtype, pleomorphic rhabdomyosarcoma (PRMS), occurs most frequently in adults vetween the ages of 40 and 50. This pleomorphic subtype is often misdiagnosed and little is known about its molecular characterization. Here, we conducted comprehensive genomic, transcriptomic, and methylation profiling of these tumors.

  Study EGAS00001007230

• H3Africa AWI-Gen Phase 1 Pilot Microbiome Phenotype

  This is human phenotype data for participants in a gut microbiome study. This data was collected at the same time as the stool samples used for the microbiome component. Participants were also part of the AWI-Gen Phase 1 main study. https://www.ebi.ac.uk/ena/data/view/PRJEB40733

  Dataset EGAD00001006581

• 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

• CRISPR iPSC methods paper

  This submission is of the sequencing data used in the CRISPR iPSC methods paper. Specifically it is 3 fastq files that each represent a replicate of an experiment to transduce the Toronto KnockOut CRISPR Library - Version 3 (TKOv3) into inferred pluripotent stem cell (iPSC) derived macrophages. The sequencing is of the guide RNAs from the TKOv3 having been extracted from the transduced iPSC derived macrophages.

  Dataset EGAD00001007020

• Genetic architecture of disease in Greenland is shaped by demographic history, fine-structure, and selection - MEGA chip data

  This file set has 1478 Greenlandic individuals scored on the Illumina MEGA array (1,748,250 sites). The data is in PLINK bed/bim/fam format. The individuals originate from the B2018 population survey.

  Dataset EGAD50000000934

• TCR sequencing of head and neck cancers

  This data contains the TCR-beta sequences of 10 head and neck squamous carcinomas and 19 nasopharyngeal carcinomas. The library preparation method is a customised targeted amplification of the VDJ regions and is sequenced on the Illumina Miseq.

  Dataset EGAD00001007917

• Genetic Epidemiology of Ovarian Cancer Histotypes

  Epithelial ovarian cancer is a highly fatal malignancy with known genetic etiology; however, it is estimated that a substantial portion of narrow sense heritability remains to be discovered. The goal of this research was to use genetic information from distantly related ovarian cancer cases to identify regions of the genome that may harbor rare risk variants for epithelial ovarian cancer. The study was conducted in the setting of the Utah Population Database, a statewide data resource in which extensive ancestry data are linked to Utah Cancer Registry records. Focusing on families with a statistically significant excess risk of epithelial ovarian cancer, we used the Infinium Global Screening Array v.3.0 to generate germline genotyping data for distantly related cases. We then applied Shared Genomic Segment Analysis to each set of related cases to identify genomic regions that may harbor ovarian cancer risk variants. Eleven regions were identified. Genotype data are available through dbGaP for one case from each pedigree. Genotype data from other ovarian cancer cases within the high-risk pedigrees as well as the pedigrees' corresponding structures can be requested and made available with approval from the Utah Resource for Genetic and Epidemiologic Research (RGE).

  Study phs003140

• Moroccan Genome Project: Sequencing Data from 109 Whole Genomes of Moroccan Individuals

  This study is the first phase of the Moroccan Genome Project, which included the complete sequencing of 109 genomes from the Kingdom of Morocco. The sequencing was performing using the Illumina NovaSeq6000 platform, with a mean coverage of 30X.

  Study EGAS50000000529

• Native American Ancient DNA sequencing

  The morphology of the first humans in the Americas (Paleoamericans) differs from that of Native Americans, and has raised the question of whether or not there are also differences in origin or genetics. A few populations who survived until relatively recently have been suggested to retain Paleoamerican morphology. One of these populations is from La Jolla. Here, we have generated genome sequence data from four La Jolla individuals in order to investigate these questions 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 (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001002144

• Foundation Medicine Adult Cancer Clinical Dataset (FM-AD)

  The Foundation Medicine adult cancer clinical dataset consists of 18,004 unique solid tumor samples that underwent genomic profiling on a single uniform platform as part of standard clinical care. The dataset is derived from the FoundationOne® genomic profiling assay version 2 that interrogates exonic regions of 287 cancer-related genes and selected introns from 19 genes known to undergo rearrangements in human cancer. Genomic DNA samples were sequenced to over 500x median coverage, and custom computational analyses identified all classes of genomic alterations (base substitutions, insertions and deletions, copy number alterations, and rearrangements). Since matched normal tissue was unavailable for analysis, these data underwent additional filtering to enrich for cancer-related events. The reported data includes genomic alterations that are known and suspected tumor drivers, as well as variants of unknown significance. To preserve patient anonymity, all known or suspected germline variants were removed from the data unless known to be associated with cancer development. The dataset contains genomic alteration profiles generated by FoundationOne version 2 testing for adult cancer patients (over 18 y.o.), and represents a vast diversity of tumor subtypes, including many rare diseases not profiled as part of large-scale profiling efforts. Cases are grouped into 16 broad disease categories containing tumors from 162 unique disease subtypes. Since specimens were profiled as part of clinical care, limited clinical parameters were available, including age, gender, tissue of origin, % of tumor nuclei, and diagnosis. Publication of this dataset is intended to allow the broad scientific community access to this unique cohort for use in scientific research projects of common and rare types of cancer, both for generating leads regarding causal mechanisms as well as cross-testing and confirming existing hypotheses. A pediatric cancer clinical dataset consisting of data from 1,215 patients under 18 y.o. is available separately at: FOUNDATION MEDICINE

  Study phs001179

• Flexible and rapid validation of structural variants using adaptive sampling

  This is the DAC that oversees the application and approval process for access to the Oxford Nanopore fastq files associated with the study, "Flexible and rapid validation of structural variants using adaptive sampling". The DAC members include Dr. Stephen W. Scherer (swscherer@sickkids.ca) and Dr. Lars Feuk (lars.feuk@igp.uu.se).

  Dac EGAC50000000748

• The Precision Medicine in Liver Cancer across an Asia-pacific NETwork (PLANET) - Intratumoural immune heterogeneity

  The clinical relevance of immune landscape intratumoural heterogeneity (immune-ITH) and its role in tumour evolution remain largely unexplored. Here, we uncover significant spatial and phenotypic immune–ITH from multiple tumour sectors and decipher its relationship with tumour evolution and disease progression in hepatocellular carcinomas (HCC). Immune–ITH is associated with tumour transcriptomic-ITH, mutational burden, and distinct immune microenvironments. Tumours with low immune–ITH experience higher immunoselective pressure and escape via loss of heterozygosity in human leukocyte antigens and immunoediting. Instead, the tumours with high immune-ITH evolve to a more immunosuppressive/exhausted microenvironment. This gradient of immune pressure along with immune-ITH represents a hallmark of tumour evolution, which is closely linked to the transcriptome-immune networks that contributes to disease progression and immune inactivation. Remarkably, high immune-ITH and its transcriptomic signature are predictive for worse clinical outcome in HCC patients. This in-depth investigation of ITH provides evidence on tumour-immune co-evolution along HCC progression.

  Study EGAS00001003814

• Exome sequencing of samples for Corvin_Dublin_BipEx

  This research project was a collaboration between Trinity College Dublin, Ireland and the Stanley Center at the Broad Institute. In this project we sequenced and analyzed the whole exomes of 191 Bipolar case/control samples from collaborators in Ireland. Genomic DNA from each sample was sequenced to a mean depth of 20x. The project used Illumina WXS sequencing of DNA and the file type is cram.

  Dataset EGAD50000000392