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• 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

• NHLBI Cleveland Family Study (CFS) Candidate Gene Association Resource (CARe)

  The Cleveland Family Study is the largest family-based study of sleep apnea world-wide, consisting of 2284 individuals (46% African American) from 361 families studied on up to 4 occasions over a period of 16 years. The study was begun in 1990 with the initial aims of quantifying the familial aggregation of sleep apnea. NIH renewals provided expansion of the original cohort (including increased minority recruitment) and longitudinal follow-up, with the last exam occurring in February 2006. Index probands (n=275) were recruited from 3 area hospital sleep labs if they had a confirmed diagnosis of sleep apnea and at least 2 first-degree relatives available to be studied. In the first 5 years of the study, neighborhood control probands (n=87) with at least 2 living relatives available for study were selected at random from a list provided by the index family and also studied. All available first degree relatives and spouses of the case and control probands also were recruited. Second-degree relatives, including half-sibs, aunts, uncles and grandparents, were also included if they lived near the first degree relatives (cases or controls), or if the family had been found to have two or more relatives with sleep apnea. Blood was sampled and DNA isolated for participants seen in the last two exam cycles (n=1447). The sample, which is enriched with individuals with sleep apnea, also contains a high prevalence of individuals with sleep apnea-related traits, including: obesity, impaired glucose tolerance, and HTN. Phenotyping data have been collected over 4 exam cycles, each occurring ~every 4 years. The last three exams targeted all subjects who had been studied at earlier exams, as well as new minority families and family members of previously studied probands who had been unavailable at prior exams. Data from one, two, three and four visits are available for 412, 630, 329 and 67, participants, respectively. In the first 3 exams, participants underwent overnight in-home sleep studies, allowing determination of the number and duration of hypopneas and apneas, sleep period, heart rate, and oxygen saturation levels; anthropometry (weight, height, and waist, hip, and neck circumferences); resting blood pressure; spirometry; standardized questionnaire evaluation of symptoms, medications, sleep patterns, quality of life, daytime sleepiness measures and health history; venipuncture and measurement of total and HDL cholesterol. The 4th exam (2001-2006) was designed to collect more detailed measurements of sleep, metabolic and CVD phenotypes and included measurement of state-of-the-art polysomnography, with both collection of blood and measurement of blood pressure before and after sleep, and anthropometry, upper airway assessments, spirometry, exhaled nitric oxide, and ECG performed the morning after the sleep study. Data have been collected by trained research assistants or GCRC nurses following written Manuals of Procedures who were certified following standard approaches for each study procedure. Ongoing data quality, with assessment of within or between individual drift, has been monitored on an ongoing basis, using statistical techniques as well as regular re-certification procedures. Between and within scorer reliabilities for key sleep apnea indices have been excellent, with intra-class correlation coefficients (ICCs) exceeding 0.92 for the apnea-hypopnea index (AHI). Sleep staging, assessed with epoch specific comparisons, also demonstrate excellent reliability for stage identification (kappas>0.82). There has been no evidence of significant time trends-between or within scorers- for the AHI variables. We also have evaluated the night-to-night variability of the AHI and other sleep variables in 91 subjects, with each measurement made 1-3 months apart. There is high night to night consistency for the AHI (ICC: 0.80), the arousal index (0.76), and the % sleep time in slow-wave sleep (0.73). We have demonstrated the comparability of the apnea estimates (AHI) determined from limited channel studies obtained at in-home settings with in full in-laboratory polysomnography. In addition to our published validation study, we more recently compared the AHI in 169 Cleveland Family Study participants undergoing both assessments (in-home and in-laboratory) within one week apart. These showed excellent levels of agreement (ICC=0.83), demonstrating the feasibility of examining data from either in-home or in-laboratory studies for apnea phenotyping. Data collected in the GCRC were obtained, when possible, with comparable, if not identical techniques, as were the same measures collected at prior exams performed in the participants' homes. To address the comparability of data collected over different exams, we calculated the crude age-adjusted correlations ~3 year within individual correlations between measures made in the most recent GCRC exam with measures made in a prior exam and demonstrated excellent levels of agreement for BMI (r=.91); waist circumference (0.91); FVC (0.88); and FEV1 (0.86). As expected due to higher biological and measurement variability, 149 somewhat lower 3-year correlations were demonstrated for SBP (0.56); Diastolic BP (0.48); AHI (0.62); and nocturnal oxygen desaturation (0.60). NHLBI Candidate-gene Association Resource. The NHLBI initiated the Candidate gene Association Resource (CARe) to create a shared genotype/phenotype resource for analyses of the association of genotypes with phenotypes relevant to the mission of the NHLBI. The resource comprises nine cohort studies funded by the NHLBI: Atherosclerosis Risk in Communities (ARIC), Cardiovascular Health Study (CHS), Cleveland Family Study (CFS), Coronary Artery Risk Development in Young Adults (CARDIA), Cooperative Study of Sickle Cell Disease (CSSCD), Framingham Heart Study (FHS), Jackson Heart Study (JHS), Multi-Ethnic Study of Atherosclerosis (MESA), and the Sleep Heart Health Study (SHHS). A database of genotype and phenotype data will be created that includes records for approximately 50,000 study participants with approximately 50,000 SNPs from more than 1,200 selected candidate genes. In addition, a genome wide association study using a 1,000K SNP Chip will be conducted on approximately 9,500 African American participants drawn from the 50,000 participants in the nine cohorts. Some relevant CARe publications CARe Study: PMID 20400780 CVD Chip Design: PMID 18974833

  Study phs000284

• National Cancer Institute (NCI) Genome Wide Association Study (GWAS) of Lung Cancer in Never Smokers

  A genomewide study of lung cancer in never smokers Abstract and specific aims In the United States, lung cancer incidence and mortality rates have been steadily declining over the past decade, following decline in the prevalence of tobacco smoking. However, lung cancer remains the leading cause of cancer death, killing more patients than breast, colon, and prostate cancers combined. Although tobacco smoke is the predominant risk factor for development of lung cancer, some patients develop the disease without a history of tobacco smoking. About 10 - 15% of all lung cancers occur in lilfetime never smokers. This figure will increase as the proportion of never smokers increases in the population. Even at present rates, lung cancer in never smokers, if considered a separate disease, is 6th to 8th top cause of cancer death. The growing number of never smokers in the USA and other countries emphasizes the importance of understanding the epidemiology and biology underlying lung cancer in this group. Genetic polymorphisms associated with the risk of lung cancer in never smokers are expected to overlap with those associated with the risk of lung cancer in ever smokers only partially. Epidemiological, molecular and clinical data suggest that molecular mechanisms of LC may differ in smokers and non-smokers, implying that lung cancer in never smokers is a different disease compared to the lung cancer in smokers. One can expect that there should be stronger genetic component in the control lung cancer in never smokers because effects of the genetic factors in never smokers are unmasked by the lack of tobacco smoke exposure. The genetic epidemiology of lung cancer in never smokers has not been well explored, largely because of difficulties in accruing the needed sample size for association studies. We propose a multicenter (total 14 sites from the US and Europe) genomewide association study of lung cancer in never smokers with the following specific aims: Aim 1: To identify candidate SNPs influencing risk for lung cancer in never smokers using Discovery sample. In the Discovery phase we will genotype 1256 Caucasian cases and 1365 age- and gender-matched never smoker controls using the Illumina Human660W-Quad platform. In addition, we will include in the analysis 284 cases and 175 matched controls already genotyped on the 610Quad platform. In this phase we will only include the study sites that have collected blood specimens (MDACC, Mayo Clinic, Karmanos Cancer Institute, The University of Liverpool Cancer research Centre, Institute of Cancer Research in Sutton, and Lunenfeld Research Institute in Toronto, Canada). All the samples will be sent to the independent lab for genotyping, to reduce site-specific technical artifacts. The final sample will consist of 1540 cases and 1540 controls matched by study site. Aim 2: To perform the second phase (validation) analysis of significant SNPs identified in aim 1 using an independent set of cases and controls. SNPs associated with risk at the significance level of 0.01 or below in the discovery set will be included in the replication phase. The proposed threshold guarantees an adequate power to retain SNPs with the typical effect size of 1.3. We plan to carry 6000-7000 SNPs for validation. The independent replication set will include 800 cases and 800 controls, mostly from sites that collected tissue (Mayo Clinic, Karmanos Cancer Institute, UT Southwestern) or buccal specimens (UCLA), but also blood samples (Imperial College London, University of Pennsylvana, German Cancer Research Center, Heidelberg, National Research Center for Environment and Health, Neuherberg, Carmel Medical Center, Haifa). We will then perform a joint analysis to test the significance of the SNPs identified in the first stage using a stringent critical p-value of 10-7. There will be 2340 cases and 2340 controls in the joint set. Based on our experience with GWAS in smokers and assuming that genetic component in lung cancer risk in never smokers can be higher than genetic component in smokers, we expect to identify about 5-10 candidate regions associated with lung cancer risk in never smokers. Aim 3: To identify and explore pathways associated with the risk of lung cancer in never smokers. Results of the number of studies on the molecular mechanisms and drug response suggest that lung cancer in never smokers is a different disease and different pathways will be associated with lung cancer risk in non-smokers and smokers. To identify pathways and molecular functions associated with lung cancer risk in never smokers we will apply Ingenuity and DAVID bioinformatics tools. We will use at least 300 top candidate genes identified in joint and discovery analysis. The reason why we select rather large number of candidate genes for functional annotation is two-fold: 1. Both algorithms are looking for enrichment of pathways and function by most significant genes and they produce statistically robust results only when number of genes is relatively high. 2. Despite the fact that this study will be largest possible for never smokers we still are underpowered to detect SNPs with relatively small effect size. But though those SNPs will not reach genome wide level of significance they will tend to be on the top of the list. In other words genes from the gray zone (significant on individual level and non-significant for genome wide level) are expected to be enriched by true discoveries. True discoveries are likely to be associated with limited number of pathways / functions while false positives are expected to be uniformly distributed across functions and pathways. Therefore significant clustering of the gene to a given function will suggest that that those genes are true discoveries. This is the first GWAS aiming at identifying the genetic control of susceptibility to lung cancer in Caucasian never smokers. We will combine the available resources from the multiple sites to achieve the sample size sufficient for this study. The study will identify genetic architecture of the predisposition to the lung cancer in never smokers.

  Study phs000634

• Whole-genome sequencing of primary mediastinal large B-cell lymphoma

  To understand the molecular pathogenesis of Primary mediastinal large B-cell lymphoma (PMBCL), we performed whole-genome sequencing of 10 PMBCLs from 8 patients including 2 relapsed tumors.

  Study EGAS50000000340

• Long-read and short-read RNA sequencing of human normal liver organoid with or without SF3B4 overexpression

  There are 2 matched short and long read RNA sequencing samples. The project aims to find alternative splicing events and resulting transcript isoforms modulated by SF3B4

  Study EGAS50000001131

• BLUEPRINT release August 2016, ChIP-Seq for macrophage - T=6days untreated, on genome GRCh38

  ChIP-Seq data for 2 macrophage - T=6days untreated sample(s). 6 run(s), 6 experiment(s), 6 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002458

• BLUEPRINT release August 2016, ChIP-Seq for macrophage - T=6days LPS, on genome GRCh38

  ChIP-Seq data for 2 macrophage - T=6days LPS sample(s). 6 run(s), 6 experiment(s), 6 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002498

• Paired RNA-Seq of Tumor Organoids from glioblastoma, 2 patients, treated with different small molecule inhibitors

  Tumor Organoids from glioblastoma, 2 patients, treated with different small molecule inhibitors. Paired RNA-Seq was done on NovaSeq 6000 with the Illumina TruSeq stranded mRNA Kit. The small inhibitors GW2580, BLZ945 and PLX3397 were used. DMSO was used as control.

  Dataset EGAD00001011274

• RNA sequencing of tumor samples from patients with Waldenstrom macroglobulinemia

  RNA was extracted from flow-sorted CD19+. RNA-Seq was performed on 32 samples of 30 patients (2 replicates per samples). RNA-Seq libraries were subjected to non-stranded paired-end (2 x 75 bp) sequencing on HiSeq 2500 (Illumina). The files are in FASTQ format.

  Dataset EGAD00001005324

• CMML NGS data collection from Gustave Roussy

  In this dataset are the data from :- 17 patients studied by WGS- 49 patients studied by WES- 9 (/49) patients studied by RNASeq at 2 time points- the same 9 patients studied by ERRBS at 2 time points

  Dataset EGAD00001001853

• BLUEPRINT release August 2016, ChIP-Seq for macrophage - T=6days B-glucan, on genome GRCh38

  ChIP-Seq data for 2 macrophage - T=6days B-glucan sample(s). 6 run(s), 6 experiment(s), 6 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_chipseq_analysis_ebi_20160816

  Dataset EGAD00001002314

• DNA methylation and Metabolic data from type 2 diabetes adolescents

  This data contains DNA methylation data obtained from the PBMCs obtained from type 2 diabetes adolescents and controls. There are 21 diabetic samples and 10 controls. This dataset also contains metabolic data obtained from the serum of 155 samples. There are 113 diabetic and 42 control samples.

  Dataset EGAD00001005271

• Endometrial RNASeq of Patients with MRKH Syndrome and Healthy Controls

  Raw FastQ Files of 69 samples of endometrial tissue from uterus (rudiments) of patients diagnosed with MRKH Type 1/2 or healthy controls. Each sample consists of 2 lanes paired-end RNA sequencing data.

  Dataset EGAD00001006345

• Patient-tailored design for selective co-inhibition of leukemic cell subpopulations

  Single-cell RNA sequencing was performed on bone marrow mononuclear cells of 2 acute myeloid leukemia patients at refractory stage. The profiling was performed using 10x Genomics Chromium Single Cell 3ʹ Gene Expression platform. The raw data are available as fastq files.

  Dataset EGAD00001006360

• MPRA-SCZ

  RNA-Seq and ATAC-Seq of iPSC derived neurons under baseline and KCl stimulation conditions from 10 distinct donors, including 5 healthy controls and 5 schizophrenic individuals. scATAC of human post mortem prefrontal cortex from 4 adult individuals including 2 neurotypical individuals and 2 schizophrenic individuals.

  Dataset EGAD00001011335

• EGAD00000000033

  NcOEDG Helsinki 2 samples

  Dataset EGAD00000000033

• EGAD00000000037

  NcOEDG Stockholm 2 samples

  Dataset EGAD00000000037

• Drug development for pediatric cancers: the importance of patient' s genomic data re-use

  Cancer in children is uncommon and the overall prognosis for most pediatric cancers is good. However, while the combined survival rates have improved over the last decades, certain childhood malignancies, such as high-grade gliomas or metastatic sarcomas, still remain incurable in most patients. New strategies for targeting those devastating diseases are imperative, and patient genomic data can become a key asset in the process [1]. The Pediatric Cancer Genome Project’s datasets At EGA, we store over 500 datasets with sequencing data from pediatric cancer patients. A remarkable case is the datasets belonging to the Pediatric Cancer Genome Project (PCGP) from St. Jude Children’s Research Hospital–Washington University (Table 1 - EGAS list). PCGP is an ambitious effort to identify the mutations that drive childhood cancer and find new cures. Those datasets include 600 patients with complete tumor and normal genomes from 15 different tumor types. PCGP datasets have been an important resource for various studies that pooled genomic data from different public and in-house datasets to perform extensive genomic characterizations and publish comprehensive pan-cancer pediatric studies [2],[3]. Other interesting pediatric cancer datasets are from the Pediatric Brain Tumor Consortium, Sickkids or ICGC PedBrain project. How data reuse impacts drug development for pediatric cancers Recently, in October 2024, Nature Communications published a work led by the University of Michigan where authors used data from EGA (PCGP) check the table below as part of their strategy to pursue the identification of new tumor vulnerabilities susceptible to becoming novel therapeutic targets in diffuse midline glioma. Diffuse midline gliomas (DMG) are treatment-resistant and uniformly fatal pediatric brain tumors. The prognosis of this brainstem tumor is dismal with a median overall survival of 9–12 months from diagnosis. In this study, the authors first developed a lab model of the disease and reanalyzed data from the EGA to identify genes potentially linked to tumor severity. This approach revealed involvement of specific metabolic pathways, suggesting new possibilities for therapeutic intervention. In mice, the study shows that the use of statins can improve survival. For a brief overview of the underlying science, diffuse midline gliomas present intratumor heterogeneity with subpopulations of less-differentiated oligodendrocyte precursors and more differentiated astrocytes. Authors established in vitro models to recapitulate both phenotypes and identify metabolic programs in both subpopulations. To determine the clinical relevance, the authors re-used gene expression data from 76 DMG patients identifying a gene signature predicting decreased overall survival. After extensive metabolic characterization of subpopulations, authors defined strategies to target specific metabolic vulnerabilities. Pre-clinical experiments in mice using OXPHOS inhibitors and statins showed a reduction in tumor burden and increased overall survival [4]. In this scenario, the re-use of high-quality patient’s sequencing data appeared as an advantageous strategy to support the clinical relevance of in-vitro and pre-clinical findings. On the other hand, in rare conditions such as childhood cancer, the availability of public datasets and the possibility of pooling data from different sources can be the only way to obtain impactful results that lead to improvements for patients. References Davidoff, A. M. Pediatric Oncology. Seminars in pediatric surgery 19, 225–233 (2010). Gröbner, S. N. et al. The landscape of genomic alterations across childhood cancers. Nature 555, 321–327 (2018). Venu Thatikonda et al. Comprehensive analysis of mutational signatures reveals distinct patterns and molecular processes across 27 pediatric cancers. Nature Cancer 4, 276–289 (2023). Mbah, N. E. et al. Therapeutic targeting of differentiation-state dependent metabolic vulnerabilities in diffuse midline glioma. Nature Communications 15, (2024). Datasets and Studies ID Title Access Policy Year EGAD00001000134 Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma McGill-DKFZ Pediatric Brain Tumour Consortium 2016 EGAS0000100192 Somatic Histone H3 Mutations in Diffuse Intrinsic Pontine Gliomas and Non-Brainstem Paediatric Glioblastomas St. Jude Children's Research Hospital - Washington University Pediatric Cancer Genome Project 2017 EGAS00001000575 Whole genome sequencing and whole exome sequencing of DIPG tumors and matched normal tissue The hospital for sick children ("SickKids") 2016 EGAD00001000792 Exome sequencing reads of paediatric glioblastoma McGill-DKFZ Pediatric Brain Tumour Consortium 2016 EGAD00001002006 Whole genome sequencing of paediatric glioblastoma in the ICGC PedBrain project ICGC PedBrain project 2016

  Blog drug-development-for-pediatric-cancers

• Genetic Determinants of Viral Clearance in HCV-Infected Populations

  The World Health Organization estimates there are 170 million persons with HCV infection worldwide, and an estimated 4 million persons are infected in the United States. Persistent HCV infection can cause cirrhosis and hepatocellular cancer. Long term disease consequences do not occur in persons who spontaneously recover. It is not known why 10-40% recover from hepatitis C (and are not at increased risk of cirrhosis and cancer), but that knowledge would contribute to efforts to treat and/or prevent HCV infection. The overall purpose of this study is to investigate the host genetic basis for recovery from hepatitis C virus (HCV) infection. There is strong evidence that host genetic differences determine HCV recovery. After accidental exposure to the same HCV inoculum, some persons recover while others develop persistent infection. Recovery from HCV infection is an outstanding phenotype for genetic studies since both viral exposure and viral recovery can be ascertained with high sensitivity and specificity and since there are no known viral or environmental determinants. The study group is comprised of an ethnically and geographically diverse international population. All cohorts comprising this study group of 3350 individuals are well characterized. This case-control study is comprised of approximately 40% clearance individuals who are matched by age and HIV status within their respective cohorts at a ratio of 1:1 or 1:2 with HCV chronically infected individuals.

  Study phs000248

• BrainCloud: Data from human postmortem brain procurement for the neuropathology section

  This study explores the temporal dynamics and genetic control of transcription and DNA methylation in the human dorsolateral prefrontal cortex in postmortem tissue. This study examines 269 subjects for gene expression (version 1) and 108 subjects for DNA methylation (version 2). The subjects are normal controls without neuropathological and neuropsychiatric diagnosis and range in age from fetal weeks 14-20 through old age (>80). We discover fast changes in gene expression occurring during early brain development. Later in life, the changes are considerably slower. Many genes reverse pattern of expression between fetal and early postnatal development. We identify thousands of strong associations of SNPs with gene expression. We examine DNA methylation in ~14,500 genes at ~27,000 CpG loci focused on 5' promoter regions. The fastest changes in DNA methylation also occur during the prenatal period, slow down markedly after birth and continue to slow further with aging. DNA methylation is strongly associated with genotypic variants and correlates with expression of a subset of genes. DNA for genotyping was obtained from the cerebella and applied to either Illumina 650K or 1 million BeadArrays - only genotypes common to both platforms are analyzed here. Genotypes were called using BeadExpress software. Doi: 10.1038/nature10524 Nature, 478:519-524, 2011; doi:10.1016/j.ajhg.2011.12.020, AJHG 90, 1-13, Feb 10, 2012. The methylation data can be downloaded at: BrainCloud.

  Study phs000417

• Genetic insights into the biological mechanisms governing human ovarian ageing

  Reproductive longevity is critical for fertility and impacts healthy ageing in women, yet insights into the underlying biological mechanisms and treatments to preserve it are limited. Here, we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in ~200,000 women of European ancestry. These common alleles influence clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations. Identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increase fertility and extend reproductive life in mice. Causal inference analyses using the identified genetic variants indicates that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases risks of hormone-sensitive cancers. These findings provide insight into the mechanisms governing ovarian ageing, when they act across the life-course, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease.

  Study EGAS00001004947

• The malaria-protective human glycophorin structural variant DUP4 shows somatic mosaicism and association with hemoglobin levels

  Glycophorin A and glycophorin B are red blood cell surface proteins that are both receptors for the parasite Plasmodium falciparum, which is the principal cause of malaria in sub-Saharan Africa. DUP4 is a complex structural genomic variant that carries extra copies of a glycophorin A - glycophorin B fusion gene, and has a dramatic effect on malaria risk by reducing the risk of severe malaria by up to 40% Using fiber-FISH and Illumina sequencing, we validate the structural arrangement of the glycophorin locus in the DUP4 variant, and reveal somatic variation in copy number of the glycophorin A- glycophorin B fusion gene. By developing a simple, specific, PCR-based assay for DUP4 we show the DUP4 variant reaches a frequency of 13% in a malaria-endemic village in south-eastern Tanzania. We genotype a substantial proportion of that village and demonstrate an association of DUP4 genotype with hemoglobin levels, a phenotype related to malaria, using a family-based association test. Taken together, we show that DUP4 is a complex structural variant that may be susceptible to somatic variation, and show that it is associated with a malarial-related phenotype in a non-hospitalizedlongitudinally-followed population.

  Study EGAS00001003239

• Fecal microbiome of T2D patients undergoing semaglutide or empagliflozin treatment

  The gut microbiome has been shown to be affected by the use of many human-targeted medications, and the interaction can be bidirectional. This has been clearly demonstrated for type 2 diabetes medications that have been in clinical use for several decades. However, the bidirectional effects of novel type 2 diabetes drugs semaglutide, empagliflozin, and the gut microbiome have yet to be clearly described. We investigated the effect of semaglutide and empagliflozin initiation on the gut microbiome of type 2 diabetes patients. In addition, we analyzed whether the pre-treatment gut microbiome can predict the treatment efficacy. Twenty subjects (10 women and 10 men) were enrolled in the study between November 2019 and January 2023 from the University of Tartu Hospital, Tartu, Estonia. All participants had been diagnosed with type 2 diabetes. The study group taking semaglutide included 6 women and 4 men, and the group prescribed empagliflozin included 4 women and 6 men. Gut microbiome fecal samples donated at four timepoints (Baseline, Month 1, Month 3; Month 12) were studied using 16S ribosomal RNA gene sequencing and analysis.

  Dataset EGAD50000000756

• Type 2 Diabetes Data access committee (DAC)

  Dac EGAC00001001293

• MET amplification in gastric cancer

  WES sequencing of two cases of MET amplified gastric cancer. Six unfixed fresh frozen samples were obtained from the primary tumors (three samples from each) and three samples from corresponding non-neoplastic mucosa. In addition, formalin-fixed and paraffin-embedded (FFPE) samples were obtained from the primary tumors (n=2), lymph node metastases (n=7) and non-neoplastic mucosa (n=2).

  Dataset EGAD50000001099

• Chlamydia trachomatis exploits sphingolipid metabolic pathways during infection of phagocytes

  This study looks at the response of M2 macrophages stimulated with GM-CSF and IL4 for 7 days and 2 days respectively to C. trachomatis infection on an Illumina platform.

  Study EGAS50000000960

• RNA sequencing of intestinal metaplasia

  We perform bulk RNA-seq of 10 and single cell RNAseq of 2 intestinal metaplasia samples. We also perform stereoseq spatial transcriptomic analysis of 4 gastric cancer samples. The dataset contain cram files from 10 bulk RNAseq samples, and raw fastq files from 2 single cell RNAseq and 4 stereo-seq samples.

  Dataset EGAD50000002010

• WholeGenomeSeq-EGAS00001001306

  Whole Genome sequencing. 2 μg of genomic DNA from each sample was used for the construction of two short-insert paired-end sequencing libraries. Both types of libraries were sequenced in paired-end mode on Illumina GAIIx (2 × 151 bp) using Sequencing kit v4 or Illumina HiSeq2000 (2x101 bp) using TruSeq SBS Kit v3.

  Dataset EGAD00001001466

• Transcriptome

  Transcriptome array dataset

  Dataset EGAD00010001280

• Hi-C on the OCIAML-2 Cell Line

  Study EGAS00001004743

• GTestimate: Improving relative gene expression estimation in scRNA-seq using the Good-Turing estimator

  This study introduces a novel scRNA-seq normalization method, which takes unobserved genes into account. To validate GTestimate we designed a cell targeted amplification strategy, enabling us to sequence a small set of cells twice, once at a "typical" sequencing depth and once at a "ultra-deep" sequencing depth.

  Study EGAS50000000915

• Proteomic predictors of individualized nutrient-specific insulin secretion in health and disease

  Population level variation and molecular mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized despite ramifications for personalized nutrition. We now define prototypical insulin secretion dynamics in response to the three macronutrients in islets from 140 cadaveric donors, including those diagnosed with type 2 diabetes. We leverage the insulin response heterogeneity and use transcriptomics and proteomics to identify molecular pathways of specific nutrient responsiveness. Surprisingly, we find robust insulin secretion to fatty acid stimulus in ~8% of donors, challenging the idea that fat has negligible effects on insulin release. Distinct islet proteomes with differences in metabolic signalling networks convey this hyper-responsiveness to fat relative to carbohydrate. By comparing human islets to human embryonic stem cell-derived islet clusters, we show that, unlike glucose-responsiveness, fat hyper-responsiveness is equivalent and may be a hallmark of functionally immature cells. Our study represents the first comparison of dynamic responses to nutrients and multi-omics analysis in human insulin secreting cells. Responses of different people’s islets to carbohydrate, protein, and fat lay the groundwork for personalized nutrition.

  Dataset EGAD50000000519

• PD-associated regulatory variants in human dopaminergic neurons reveals modulators of SCARB2 and BAG3 expression

  SNPs have been associated with the risk of Parkinson’s disease but not in regulatory regions. While those potentially disrupt TFBS altering gene expression and contributing to cis-regulatory variation. To study changes in A and B compartment interactions and in topologically associated domains (TADs), we sequenced: - 3D chromatin contact using LowC, 6 samples. 3 replicates for each condition: smNPC (controls) cells and differentiated neurons after 30 days. Resulting paired-end are provided as BAM files. In order to confirm the predicted impact of the PD-associated allele of rs144814361 on BAG3 promoter, we proceeded with genome editing of the TH-REP1 cell line by using prime editing to insert the “T” allele at the position chr10:119651405 in the BAG3 promoter. - Chromatin accessibility using ATAC-seq, 9 samples of derived cell line TH-REP1. 3 replicates for each condition: 1) iPSC Wild Type 2) SNP-BAG3 variant in iPSC 3) SNP-BAG3 variant in smNPC. BAG3 variant: heterozygous for the SNP variant: rs144814361 (chr10 119651405) Resulting sequencing are provided as paired-end FASTQ files.

  Dataset EGAD50000002258

• Whole blood RNA-sequencing of covid-19 patients and healthy controls

  This dataset contains whole blood transcriptome data generated from 93 patients with COVID-19 across a range of severities and 23 healthy controls. All patients were PCR positive for SARS-CoV-2 and disease severity ranged from asymptomatic to severe disease requiring ventilation. Individuals without symptoms, or with mild symptoms, were recruited from routine screening of healthcare workers, while COVID-19 patients were recruited at or soon after admission to Addenbrooke’s or Royal Papworth hospitals. Blood samples were taken at recruitment and then again four weeks later. Further details of the cohort and the generation of the RNA-Sequencing data can be obtained from Bergamaschi, L. et al. Longitudinal analysis reveals that delayed bystander CD8+ T cell activation and early immune pathology distinguish severe COVID-19 from mild disease. Immunity 54, 1257-1275 e8 (2021).

  Dataset EGAD00001007776

• Somatic mutations in facial skin from countries of contrasting skin cancer risk

  The incidence of non-melanoma skin cancer is 17-fold lower in Singapore compared to the UK1, despite Singapore receiving 2-3 times more year-round ultraviolet radiation (UV)2,3. The ageing epidermis of the skin comprises competing somatic mutant clones4,5, from which such cancers develop. We question if differences in keratinocyte skin cancer incidence are reflected in the mutational landscape by comparing ageing facial epidermis from donors of Singapore and the UK. We find UK skin to be a highly competitive, densely mutated landscape with 4-fold greater mutation burden compared to Singaporean skin and differences in clonal selection by country. We disproportionately observe multiple features common to keratinocyte skin cancers6,7,8 in UK skin, such as UV mutagenesis, copy number aberration and hotspot mutations (in particular TP53 R248W). We conclude that keratinocyte skin cancer incidence is reflected in the somatic clones of non-cancerous epidermis. Finally, we re-analyse squamous cell carcinoma exomes from Korea9 to show, even in low incidence populations, carcinogenesis is driven by UV damage.

  Dataset EGAD00001009666

• Tobacco exposure and somatic mutations in normal human bronchial epithelium

  That tobacco smoking causes lung cancer is well-established, but we lack quantitative understanding of its effects on genomes of normal bronchial epithelium. We sequenced whole genomes of 632 colonies derived from single bronchial epithelial cells across 16 subjects. Tobacco smoking is the major influence on mutation burden, adding 1000-10,000+ mutations/cell, massively increasing both between-subject and within-subject variance, and generating several distinct signatures of substitutions and indels. A population of cells in subjects with smoking history had mutation burdens equivalent to that expected for never-smokers: these cells lacked tobacco-specific mutational signatures, were four-fold more frequent in ex-smokers than current smokers, and had significantly longer telomeres than their more mutated counterparts. Driver mutations increased in frequency with age, affecting 4-14% of cells in middle-aged never-smokers. In current smokers, ≥25% of cells carried driver mutations and 0-6% cells had 2 or even 3 drivers. Thus, tobacco smoking increases mutation burden, cell-to-cell heterogeneity and driver mutations, but quitting promotes replenishment of bronchial epithelium from mitotically quiescent cells that have avoided tobacco mutagenesis.

  Dataset EGAD00001005193

• Targeted Validation Samples

  The dataset consists of targeted sequencing data obtained from an independent cohort of 11 patients to validate discriminative DMRs (differentially methylated regions) for Acute coronary syndrome (ACS) subtypes. The cohort includes 2 healthy subjects, 4 STEMI (ST-segment elevation myocardial infarction), 3 NSTEMI (non-ST-segment elevation myocardial infarction), and 2 UA (unstable angina) patients. The sequencing panel targeted 18,831 CpGs for analysis, reaching at least 5 reads coverage for 75% of the targeted CpGs in each sample. The sequencing was performed using the NEBNext Enzymatic Methyl-seq Module and the Nonacus Cell3TMTarget: Library Preparation kit, followed by probe hybridization, capture enrichment, and sequencing on the Novaseq 6000 platform, generating 400 million reads. The dataset is in raw fastq format.

  Dataset EGAD00001010934

• The Finland-United States Investigation of NIDDM Genetics (FUSION) Study - Islet Expression and Regulation by RNAseq and ATACseq

  This study evaluates gene expression and its regulation in human pancreatic islets, a tissue relevant in the study of genetic risk factors contributing to diabetes. We obtained pancreatic tissue sections and purified pancreatic islets from deceased donors and processed these biological samples to generate various data types. We generated spatial transcriptomic data from pancreatic tissue samples using the 10X Genomics VISIUM technology with sequencing on the Illumina NovaSeq platform. We also generated multiple data types from the purified pancreatic islets using genome-wide SNP chips, bulk RNA-Seq, microRNA (miRNA)-Seq, whole genome sequence, DNA methylation (methyl)-Seq, cap analysis of gene expression (CAGE)-Seq, single cell RNA-seq, and single nuclei ATAC-seq approaches. These data include ATAC-seq of two islet subjects, RNA-seq of 31 additional subjects, genome-wide chip genotypes, and imputed genotypes of the 33 subjects released with phs001188.v1. For genotyping, 500-1000 islet equivalents (IEQ) were cultured as in Gershengorn (Science, 2004, PMID: 15564314); genomic DNA isolated from islet cultures. For RNA analyses, 2500-5000 IEQ from each islet source were used for bulk or single-cell RNA isolation. Messenger RNA was isolated with trizol extraction and 12-plex libraries were generated using the Illumina TruSeq directional mRNA-seq library protocol. Bulk RNA sequencing was performed on HiSeq2000/HiSeq2500/Hiseq4000/NovaSeq6000 sequencers using paired-end reads at the NIH Intramural Sequencing Center (NISC). miRNA libraries were prepared from total RNA from 68 samples, pooled and sequenced 50bp single-end reads on Illumina HiSeq2500. CAGE libraries were prepared from total RNA samples using the nAnT-iCAGE protocol at DNAFORM, Japan. CAGE libraries were sequenced at the NIH Intramural Sequencing Center (NISC) on the HiSeq2000 sequencer. Genotyping on the Illumina Omni2.5M array was performed at the NHGRI Genomics Core facility. Genotypes were imputed using the HRC.r1.1.2016 reference panel. To assess regions of open chromatin in islets, we performed bulk ATAC-seq on HiSeq2000 sequencers using paired-end reads at NISC. Single-nuclei ATAC-seq libraries were prepared using single-cell-combinatorial-indexing (sci-) ATAC-seq protocol and sequenced on Illumina NextSeq using paired-end reads. scRNA-seq libraries were generated using the 10X Genomics platform and sequenced on Illumina HiSeq3000 at the Genomics Technology Core of the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS). Greater than 90% of the loci associated with T2D through genome-wide association studies occur in non-coding regions, suggesting a strong regulatory component to disease susceptibility. Therefore, there is a critical need to understand the full spectrum of genetic variation and regulatory element usage in T2D-relevant tissues. To that end, this study contains multiple genomic and transcriptomic data sets from pancreas tissue sections and pancreatic islets, a resource that will contribute to our efforts to investigate the genetic risk factors for type 2 diabetes.

  Study phs001188

• CAYA glioma sequencing data

  Low pass WGS and targeted glioma panel sequencing of low and high-grade gliomas in children, adolescent, and young adult patients.. File type is paired-read fastq files (2 per sample). Sequencing was performed on Illumina NextSex instruments. Genes covered on panel: TP53, H3F3A, HIST1H3B, HIST1H3C, IDH1, IDH2, KRAS, PIK3CA, TERT promoter, PTPN11, FGFR1/2/3, MYB, BRAF, MYBL1, EGFR, PDGFRA, MYCN, MYC, CDKN2A.

  Dataset EGAD50000000560

• Dopaminergic neuron differentiation of human embryonic stem cells

  2 replicates of dopaminergic neuron differentiation of human embryonic stem cells from timepoints day 17 and day 28, performed with 10X Genomics single cell RNA-seq. Part of the Ásgrimsdottir et al. (Arenas Lab).

  Study EGAS50000001099

• RNU2-2 splicing signature RNA-Seq

  This dataset is composed of RNA-Seq samples: 49 controls which are probands without snRNA rare variants and 19 affected probands with either monoallelic or biallelic variants in RNU2-2. RNA was extracted from short-term lymphocytes cultures. RNA-Seq libraries were obtained by hybrid capture of fragmented cDNA with RNA probes from exome v8 (Agilent)

  Dataset EGAD50000002045

• Single cell sequences in patients with malignant tumors

  We perfomed single cell RNA and TCR sequencing of 2 lung adenocarcinoma patients and 3 head and neck squamous cell carcinoma to assess the detailed biological mechanisms of resistance to immune checkpoint inhibitors.

  Study JGAS000480

• Using Exome-sequencing to characterize the resistance to lirafugratinib

  We characterized resistance to lirafugratinib with whole exome sequencing in patients with FGFR2-driven cancers, treated in the phase 1/2 ReFocus trial (NCT04526106) and enrolled in the UNLOCK program at Gustave Roussy

  Study EGAS50000001371

• BLUEPRINT WP10 Quantitative Trait Loci (QTLs) Phase 2 Full Summary Statistics

  BLUEPRINT WP10 Quantitative Trait Loci (QTLs) Phase 2 full summary statistics data include five molecular traits (eQTL, hQTL(H3K27ac), hQTL(H3K4me1), mQTL, and psiQTL) for three primary blood cells (Monocytes, Neutrophils, and T-cells). Each full summary statistics file contains the associations for all tested variants for each phenotype ID.

  Dataset EGAD00001005199

• Single-cell long-read transcriptomes from CH, MDS and AML patients with CH mutations

  Single-cell long-read (ONT) transcriptome sequencing from CH (n = 3), MDS (n = 5, MDS02 in 2 replicates) and AML (n =1, in 2 replicates) samples with mutations in splicing factors (SF3B1 - n = 8, U2AF1 n = 1) or transcription regulators (DNMT3A - CH04). Full length cDNA from 10x Genomics 3' V3.

  Dataset EGAD00001011282

• Genetic Basis of Hepatosplenic T Cell Lymphoma (HSTL)

  Whole exome sequencing of hepatosplenic T cell lymphoma (HSTL) tumors, paired normals, and cell lines, including (1) 68 exome capture, paired-end Illumina Hiseq sequencing, BAM files from HSTL tumor samples, (2) 20 exome capture, paired-end Illumina Hiseq sequencing, BAM files from HSTL paired normal samples, and (3) 2 exome capture, paired-end Illumina Hiseq sequencing, BAM files from HSTL cell lines.

  Dataset EGAD00001003246

• Bulk RNA sequencing data in "Three-dimensional human alveolar stem cell culture models reveal infection response to SARS-CoV-2"

  This dataset includes 14 bulk RNA sequencing data (28 fastq files) in the study entitled "Three-dimensional human alveolar stem cell culture models reveal infection response to SARS-CoV-2". RNA sequencing library was generated with Truseq stranded total RNA Gold kit.

  Dataset EGAD00001006241

• PLCG1 R707Q mutation is counter selected under targeted therapy in a patient with a hepatic angiosarcoma

  PLCG1 R707Q mutation is counter selected under targeted therapy in a patient with a hepatic angiosarcoma

  Study EGAS00001001281

• Sequencing of two human samples

  Dataset EGAD00001001001

• HUMANOMNI1QUAD_Genotyping

  samples using Illumina HUMANOMNI1QUAD

  Dataset EGAD00010000919

• RAGE engagement by SARS-CoV-2 Data Access Commitee

  Dac EGAC00001003405

• bed_files

  Bed files as whitelist for Tapestri Insights analysis

  Dataset EGAD00010002560

• The Extracellular RNA Quality Control (exRNAQC) study (phase 2)

  Study EGAS00001006499

• Single_Cell_Targeted_Sequence_Capture

  This study is to ascertain whether it is feasible to extract single cell from a tumour, perform amplification, generate a library and sequence a targeted pulldown.

  Study EGAS00001000435

• Results of scRNA-seq analysis of a PBMC collected from a male with a mosaic 45,X/48,XYYY karyotype

  Results of scRNA-seq analysis of a PBMC collected from a male with a mosaic 45,X/48,XYYY karyotype

  Dataset EGAD00001008645

• Single-cell analysis reveals transcriptomic and epigenomic impacts on the maternal-fetal interface upon SARS-CoV-2 infection

  During pregnancy, the maternal-fetal interface plays vital roles in fetal development. Its disruption is frequently found in pregnancy complications. Recent works show increased incidences of adverse pregnancy outcomes in COVID-19 patients; however, the mechanism remains unclear. Here, we analyzed the molecular impacts of SARS-CoV-2 infection on the maternal-fetal interface. Generating bulk and single-nucleus transcriptomic and epigenomic profiles from COVID-19 patients and control samples, we discovered aberrant immune activation and angiogenesis patterns in distinct cells from patients. Surprisingly, retrotransposons were also dysregulated in specific cell types. Notably, reduced enhancer activities of LTR8B elements were functionally linked to the downregulation of Pregnancy-Specific Glycoprotein genes in syncytiotrophoblasts. Our findings revealed that SARS-CoV-2 infection induced significant changes to the epigenome and transcriptome at the maternal-fetal interface, which may be associated with pregnancy complications.

  Study EGAS00001006263

• Genome-Wide Association Study of Leprosy and Dapsone Hypersensitivity Syndrome (DHS) in Chinese population

  Leprosy GWAS (1) Leprosy is a chronic infectious disease caused by Mycobacterium leprae (M. leprae). Due to M. leprae's narrow host range and an inability to be cultured in vitro, the biological investigation of this disease has been difficult. Host genetic factors have been suggested to play an important role in disease development, but few have been identified. In this study, we attempted to identify the host genetic factors by performing a two-stage genome-wide association study (GWAS) in Chinese population. The initial genome-wide scan was done by genotyping 706 patients and 1225 controls using the Illumina HumanHap610 BeadChip, and the follow-up study was performed by genotyping 93 SNPs in three independent samples consisting of 3254 cases and 5955 controls. We identified significant association (P < 10-10) within six genes CCDC122 (13q14), C13orf31 (13q14), NOD2(16q12), TNFSF15 (9q32), HLA-DR (6p21) and RIPK2 (8q21), and suggestive association (P = 5.68x10-6) within LRRK2 (12q12). We also revealed suggestive evidence for C13orf31, LRRK2, NOD2 and RIPK2 to show stronger association in the multibacillary form than the paucibacillary form of leprosy. Our findings highlight the importance of the innate immune response, particularly NOD2-mediated signaling, in leprosy and suggests a new therapeutic target for leprosy. DHS GWAS Dapsone (DDS), as both an antibiotic and an anti-inflammatory agent, has been widely used for preventing and treating pathogen-caused infectious diseases and chronic inflammatory diseases. Currently, about 0.5-3.6% of individuals treated with DDS develop severe dapsone hypersensitivity syndrome (DHS) and the mortality rate is up to about 11-13%. However, until now, no tests are available to predict the risk of DHS. To identify the genetic risk factors of DHS, we performed a two-stage GWAS in Chinese population. In this study, the initial genome-wide scan was done by genotyping 39 DHS cases and 833 controls using Illumina Human 660W-Quad Beadchips and imputed human leukocyte antigen (HLA) molecules. Validation was performed by genotyping 24 SNPs using the Sequenom MassARRAY platform in additional 31 DHS cases and 1,089 controls through genotyping and 32 four-digit HLA-B alleles in an independent series of 37 DHS cases and 201 controls through Roche 454 sequencing. We identified significant association (OR =6.18, P = 3.84x10-13) with SNP rs2844573, which located between the HLA-B and MICA loci. HLA-B*13:01 was confirmed to be a strong risk factor for DHS (OR = 20.53, P = 6.84x10-25) and responsible for the association at rs2844573. The presence of HLA-B*13:01 had a sensitivity of 85.5% and specificity of 85.7% as a predictor for DHS, and its absence can reduce the risk by 7 fold (from 1.4% to 0.2%). HLA-B*13:01 is strongly associated with the development of DHS and can be used as a risk predictor of DHS in the individuals of Chinese and other Asian populations. Leprosy GWAS (2) This is a three-stage GWAS of leprosy in the Chinese population. The genome-wide discovery analysis involved two independent data sets: the new unpublished data set by genotyping 842 leprosy patients and 925 controls from northern and southern China using the Human660w-quad beadchips and a previously published GWAS data set of 706 leprosy cases, 1,225 healthy controls and 4,362 individuals with immune-related diseases as population controls from northern China of Chinese Han descent. Further validation studies were performed in two stages in a total of 6,765 cases and 9,505 controls from Chinese Han and minority descent in multiple areas of China, similar to the discovery stage. From this study, we discovered six new susceptibility loci with a combined P-value from discovery and validation stage surpassing genome-wide significance, including BATF3 at 1q32.3, CDH18 at 5p14.3, DEC1 at 9q32, EGR2 at 10q21.3, CCDC88B at 11q13.1 and CIITA at 16p13.13. Our current study has advanced the genetic understanding of leprosy by substantially increasing the number of confirmed genetic susceptibility loci. Leprosy GWAS (3) To discover additional leprosy susceptibility loci, we carried out a large-scale three-stage GWAS analysis of leprosy in Chinese population. The genome-wide discovery analysis (Stage 1) involved a new GWAS data set of 1,197 leprosy cases and 1,426 controls from northern and southern China conducted by using Illumina Omni Zhonghua chips with 900,015 single-nucleotide polymorphisms (SNPs), two published GWAS dataset (GWAS2 data set including 706 leprosy cases, 1,225 healthy controls genotyped by Illumina HumanHap610 beadchips, GWAS3 data set including 842 leprosy patients and 925 controls from northern and southern China genotyped by the Human660w-quad beadchips). Further validation studies were performed in two stages in a total of 5,413 cases and 9,771 controls from Chinese Han descent in multiple areas of China. In this study, we identified four novel associations at genome-wide significance (P < 5 x 10-8), all of which can indicate candidate genes within the susceptibility loci, SYN2 (3p25.2), BBS9 (7p14.3), CTSB (8p23.1) and MED30 (8q24.11), through a differential gene expression and eQTL analysis. Altogether, these findings have provided new insight and significantly expanded our understanding of the genetic basis of leprosy. Leprosy GWAS (4) In this study, we attempted to systematically investigate the contribution of protein-coding variants to leprosy susceptibility by performing a three-stage genome-wide association study (GWAS) of protein-coding variants in Chinese population. The initial genome-wide scan was done by genotyping 1,670 persons affected by leprosy and 2,321 controls using the Illumina Infinium Human Exome Beadchips (v1.0).The validation study was performed by genotyping 39 SNPs in an additional 3,169 leprosy patients and 9,814 healthy controls from the northern region of China, and the replication study was performed by genotyping eight SNPs in three independent samples from the southern regions of China consisting of 2,231 cases and 2,266 controls. We identified significant association (P < 1.23 x 10-6) within seven genes FLG (Gene ID: 2312), IL23R (Gene ID: 149233), CARD9 (Gene ID: 64170), NCKIPSD (Gene ID: 51517), TYK2 (Gene ID: 7297), SLC29A3 (Gene ID: 55315), IL27 (Gene ID: 246778). Our findings discover novel involvement of skin barrier and endocytosis/phagocytosis/autophagy, besides known innate and adaptive immunity, in the pathogenesis of leprosy, highlight the merits of protein coding variant studies for complex diseases. Here, the summary statistics from the five genome-wide association analyses were published.

  Study phs000217

• Genome-Wide Analysis of Diffuse Large B-Cell Lymphoma (De Novo and Derived from the High Grade Transformation of Follicular Lymphoma)

  Version 1. Diffuse Large B-cell Lymphoma (DLBCL) represents the most common form of B-cell non-Hodgkin Lymphoma (B-NHL), accounting for ~30% of the de novo diagnoses and also arising as a frequent clinical evolution of Follicular Lymphoma (FL). The molecular pathogenesis of DLBCL is associated with multiple genetic lesions that in part distinctly segregate with individual phenotypic subtypes, suggesting the involvement of distinct oncogenic pathways. However, the lesions identified so far likely represent only a fraction of those necessary for malignant transformation. In order to characterize the entire set of structural alterations present in the DLBCL genome, we have integrated next generation whole exome sequencing analysis of 6 DLBCL cases and genome-wide high-density SNP array analysis of 72 DLBCL cases. We report here that FL and DLBCL harbor frequent structural alterations inactivating CREBBP and, more rarely, EP300, two highly related histone and non-histone acetyltransferases (HATs) that act as transcriptional co-activators in multiple signaling pathways. Overall, ~37% of DLBCL and 36% of FL cases display genomic deletions and/or somatic point mutations that remove or inactivate the HAT coding domain of these two genes. These lesions commonly affect a single allele, suggesting that reduction in HAT dosage is important for lymphomagenesis. We demonstrate specific defects in the acetylation-mediated inactivation of the BCL6 oncoprotein and activation of the p53 tumor suppressor. These results identify CREBBP/EP300 mutations as a major pathogenic mechanism shared by common forms of B-NHL, and have direct implications for the use of drugs targeting acetylation/deacetylation mechanisms. Version 2. Follicular lymphoma (FL) is an indolent, but incurable disease that, in 30-40% of cases, undergoes transformation to an aggressive diffuse large B cell lymphoma (DLBCL). The history of clonal evolution and the mechanisms that underlie transformation to DLBCL (tFL) remain largely unknown. Using whole exome sequencing and copy number analysis of 39 tFL patients, including 12 with paired sequential FL/tFL biopsies, we show that, in most cases, FL and tFL arise by divergent evolution from a common mutated precursor cell through the acquisition of distinct genetic lesions. Mutations in epigenetic modifiers (e.g., MLL2, CREBBP, EZH2, ARID1A) and anti-apoptotic genes (BCL2, FAS) were observed in 93% (36/39) and 78% (30/39) of cases, respectively, and were invariably shared between the two disease phases, suggesting an early acquisition in the common mutated precursor. Conversely, the development of tFL is associated with deregulation of genes involved in the control of cell proliferation, cell cycle progression and DNA damage responses (CDKN2A/2B, MYC, TP53), as well as with an aberrant activity of the somatic hypermutation mechanism. Finally, we show that the genomic profile of tFL shares significant similarities with that of germinal center B-cell type de novo DLBCL, but also displays unique combinations of altered genes that may explain the dismal clinical course of tFL.Version 3. This version includes thirty-five additional de novo DLBCL samples, newly diagnosed, and their paired normal DNA from previously untreated patients, which were analyzed by whole exome sequencing (n=27T and 25N), whole genome sequencing (8T and 10N), and/or targeted HLA-next generation sequencing (n=26T/N pairs) in order to identify genetic alterations associated with loss of surface MHC-II expression.

  Study phs000328

• Gene Expression Study of Individuals with Sex Chromosome Aneuploidies

  Males and females show dramatic differences in their vulnerability to the same diseases. For example, compared to men, lupus is six times more prevalent, thyroid cancer is three times more prevalent, and unipolar depression is twice as prevalent in women. Diseases with a strong male bias include autism (5:1), dilated cardiomyopathy (3:1), and ankylosing spondylitis (5:1). Historically, such differences have been attributed solely to extrinsic factors such as circulating sex hormones or environmental influences. We hypothesized that intrinsic factors - genetic differences between XX and XY cells - have unappreciated biological consequences throughout the body and contribute to sex differences in disease incidence and severity. This hypothesis stems from our long-term effort to sequence the sex chromosomes of diverse mammalian species, which has identified a set of homologous genes on the X and Y chromosomes that are dosage-sensitive, expressed throughout the body, and encode regulators of chromatin modification, transcription, translation, and protein stability. These X- and Y-encoded genes differ in sequence and expression pattern, which likely manifests in genome-wide differences in gene regulation between XX and XY cells and influences all aspects of human biology, including sex differences in disease susceptibility. These hard-wired molecular sex differences have been largely overlooked and understudied, representing a significant gap in our knowledge of human biology.The gene expression study of individuals with sex chromosome aneuploidies takes advantage of natural human variation in sex chromosome number, i.e. sex chromosome aneuploidy, to investigate alterations in genome-wide gene expression that correlate with changes in X- and Y-chromosome dosage. We analyzed samples from 114 individuals with a variety of sex chromosome aneuploidies, including 45,X; 47,XXY; 47,XYY; 47,XXX; 48,XXYY; and 49,XXXXY. We generated lymphoblastoid cell lines (LCLs) from blood samples and, in some cases, fibroblast cultures from skin biopsies. We supplemented our collection with previously-derived cell lines. To evaluate gene expression, we performed deep profiling of the transcriptome (RNA-seq) from these LCLs and fibroblasts. We performed parallel analyses on samples collected from 62 control 46,XX and 46,XY individuals, 6 individuals with trisomy 21, and 14 individuals with structural variations of the X and Y chromosomes. In addition, we performed CRISPRi knockdowns on 3 of the 46,XX and 3 of the 46,XY fibroblast samples for the homologous transcription factors ZFX and ZFY, encoded on the X and Y chromosomes, respectively.In the April 2024 update, we added RNA-seq datasets derived from isolated CD4+ T cells and monocytes from 76 and 72 adults, respectively, with the following sex chromosome constitutions: 45,X; 46,XX; 46,XY; 47,XXX; 47,XXY; 47,XYY. These individuals are largely a subset of the same cohort described above. In addition, we performed RNA-seq on in-vitro stimulated CD4+ T cells with the following sex chromosome constitutions: 45,X; 46,XX; 46,XY; 47,XXY.In the August 2024 update, we added RNA-seq datasets generated from the following: 1) LCLs derived from individuals with AZFa deletions of the Y chromosome, 2) DDX3X and DDX3Y knockdown (via CRISPRi) in XY fibroblasts, and 3) 5-ethyl uridine (5-EU) treatment in XY and XYYYY LCLs.

  Study phs002481

• ARDSNet 07-08: Randomized, Blinded, Placebo-Controlled, Multi-Center Trial of Omega-3 Fatty Acid, Gamma-Linolenic Acid, and Antioxidants in Acute Lung Injury or ARDS (OMEGA) (ARDSNet-Omega-BioLINCC)

  Data Access NOTE: Please refer to the “Authorized Access” section below for information about how access to the data from this accession differs from many other dbGaP accessions. Biospecimens: Access to Biospecimens is through the NHLBI Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC). Biospecimens from ARDSNet-Omega include bronchial lavage, plasma, urine, and DNA. Please note that use of biospecimens in genetic research is subject to a tiered consent.Objectives: To determine if dietary supplementation of omega-3 (n-3) fatty acids, γ-linolenic acid and antioxidants to participants with acute lung injury would increase ventilator-free days to study day 28.Background: Early acute lung injury (ALI) is characterized by neutrophilic lung inflammation, permeability, and intravascular and alveolar fibrin deposition. The type and inflammatory activity of eicosanoids liberated during inflammation depends on the membrane phospholipid composition: omega 6 (n-6) fatty acid arachidonate yields highly reactive and inflammatory dienoic prostaglandins and series 4 leukotrienes, whereas omega-3 (n-3) fatty acids favor production of less active and potentially anti-inflammatory trienoic prostaglandins and series 5 leukotrienes. Participants at risk of developing ALI have n-3 levels approximately 25% of normal and those with established ALI have n-3 levels as low as 6% of normal, suggesting a potential role for n-3 dietary supplementation in participants with ALI.Three randomized controlled studies, conducted in participants with ALI or sepsis-induced respiratory failure, demonstrated an association between the administration of an enteral formula enriched in n-3 fatty acids, GLA, and antioxidants and improved oxygenation and respiratory physiology compared with an unenriched, high-fat formula. However, interpretation of these results is limited by the small sample sizes and as-treated analyses of only those participants who tolerated full enteral nutrition. Participants: The total number of participants in the study was 272.Design: Participants were stratified by hospital and the presence of shock at baseline and then randomized to receive either twice-daily enteral supplementation of n-3 fatty acids, GLA, and antioxidants (n-3 supplement) or an isocaloric-isovolemic carbohydrate-rich control. Participants were also simultaneously randomized to a separate ongoing trial (the EDEN study) comparing low- vs full-calorie enteral nutrition in a 2×2 factorial design.The n-3 or control supplement was administered enterally as twice-daily boluses of 120 mL beginning within 6 hours of randomization. Dosing continued until the earliest of 21 days, 48 hours of unassisted breathing, or extubation. The energy provided by the boluses supplemented that provided by each primary physician's choice of standard continuous non–n-3-enriched enteral formula. The rate of continuous enteral feeding was managed by a protocol with an algorithm for gastrointestinal intolerances. The supplement was administered even if enteral nutrition was interrupted, as long as the patient was tolerating enteral medications. Conclusions: The study was stopped by the DSMB for futility at the first interim analysis after 143 participants had been randomized to receive the n-3 supplement and 129 to receive the isocaloric control. Despite an 8-fold increase in plasma eicosapentaenoic acid levels, participants receiving the n-3 supplement had fewer ventilator-free days, intensive care unit–free days, and nonpulmonary organ failure-free days.

  Study phs003744

• DAC Portal documentation

  DAC Portal Welcome to the DAC Portal documentation! If you are involved in governance or legal aspects, technical or operational aspects, or serving as a data steward, this page will be helpful for you. By exploring these materials, you can define your own Data Access Committee (DAC) and policies, understand the minimal requirements for a DAC and a policy object, and comprehend how the EGA data access requests are managed. DAC Portal Index Setting up your account DACs and Policies Pending Request table Manage Data Requests History page Audit your DACs, policies, and datasets Deprecation User Preferences DAC API - A programmatic approach Setting up your account Register yourself as an EGA user. The Helpdesk team will validate your account (this could take up to 48 hours). After validation, you will receive an email with a link to verify your email - Make sure you click on the link to verify your account! If you don’t receive the email, please check your spam folder. Once your account is active you can login to the DAC Portal. We recommend you to check out the Take The Tour! DACs and Policies Create a DAC Click on Create a DAC Add the title and description to your DAC. Once ready, click on the ‘Create’ button. You will have to wait until the Helpdesk Team validates the creation of your DAC. Once approved by Helpdesk, your DAC will be assigned with a persistent identifier (EGAC). Edit a DAC Once your DAC is registered and approved, you can edit it and add contacts. Remember, you can add as many contacts as needed. And, you can also remove/add contacts anytime. To add a contact, you must write the username or email of a registered EGA user in the Members field. Once you have typed the whole username or email, a drop-down menu will appear where you can select the contact. Select a contact from the drop-down menu. Make sure you write the full username or email of the contact so it appears as an option. Make sure that you select one main contact. Set up the role you want to grant to the new contact of the DAC. We recommend you to check out the FAQ to learn more about the different roles! After adding all the necessary contacts and assigning roles, click on Update. This will send an email notification to the new DAC member, letting them know about the invitation to managing the DAC. Once they log into the DAC Portal, they will be able to either accept or turn down the invitation. Create a policy First, please note that all information registered in a policy metadata object will be publicly available on the EGA website. For each released dataset, all users will be able to read the policy under the “Data Access” tab. Select Create a Policy from the Policies tab. Select the DAC that you want your policy to depend on. Add the title and terms and conditions for accessing and using your data. If you already have an external website for data access requests, you can add the URL directly here (see example here). You can find a template of a Data Access Agreement (DAA) document in our Policy documentation page. The DAA template is provided for guidance only and should be adapted to suit your own purpose depending on the security policies, terms for publication or embargoes, and restrictions on data use or sharing. You can also add Data Use Ontologies (DUO) to your policy. These DUO codes are used to semantically tag the terms and conditions of using the data linked to the policy (example). For more information, you can refer to the Data Uso Ontology documentation. Once completed, make sure to click the Create button in order to register your policy. A persistent identifier (EGAP) will be assigned automatically. Edit a policy Please, note that you will not be able to edit the policy if it shows in orange. Meaning that you are a member of the DAC linked to that policy. Members don’t have edit rights. Only admins can edit objects. You can check your registered policies on My policies, available at the menu in the top-right corner. Select your policy. It will then display the information of that policy, allowing you to edit the information. Once you are done with the modifications, click Update. Pending Requests Table Table Settings Upon selecting a specific DAC, you will be presented with a customisable table showcasing all pending data access requests. This table allows you to tailor the displayed information according to your preferences by selecting desired columns. Available Columns (Displayed with the Eye Icon): Date: The date when the data access request was submitted. Full Name: Name and surname of the user submitting the request. Email: Email address associated with the user's request. Username: The username linked to the EGA account of the user submitting the request. Organisation: Affiliated organisation of the user submitting the request. Dataset: EGA accession ID for the requested EGA dataset. Dataset Title: Title of the requested dataset. DAC Comment: Space provided for internal comments related to the request. Expiration: Option to specify an expiration date for granted permissions. Check more information on the pending request table here! Table Filters By default, all data access requests are displayed. However, you have the option to apply filters to refine your view. Follow these steps to apply filters: Click on the More button. This action will reveal the different columns available for applying filters. As you select columns, the checked fields will be displayed at the top of the request table. Choose the specific values within the selected columns that you want to filter for. Click the Search button to apply the selected filters. Do you find yourself frequently applying the same filters? You can save multiple combinations of filters for easy access at any time! Here's how: Click the Save button. Provide a descriptive name for the filter combination. Click Save to confirm. To load saved filters, simply select the saved filter from the dropdown menu to apply it automatically. Check more information on filters here! Advanced Search For users requiring more granular control over their data access requests, the DAC Portal offers an Advanced Search feature using JIRA Query Language (JQL). With JQL, you can craft precise and complex queries to filter and retrieve specific data access requests based on various criteria such as date, user details, dataset attributes, and more. Using JQL's flexibility, you can create custom queries to meet your specific needs, allowing for advanced filtering options beyond the standard filters provided. You can find the Advanced Search feature following these steps: Click the … button Select Advanced To view the allowed fields for filtering and explore all available options, simply click on the info icon. Once you have written the filtering values, click the Search button to apply. Check more information on filters here! Manage Data Requests Upon logging into the DAC Portal, you'll notice a sand clock icon next to the DAC ID, indicating the number of pending requests. Click on the DAC to review these requests. Accept requests Click on the right side of the toggle button to grant access to the user. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with granting permissions. Deny requests Click on the left side of the toggle button to deny access to the user. Provide a reason for the denial. Note that the user will receive an email with this reason. Click on the Done button. You can manage other requests before proceeding to the next step. After managing the requests, click on the Apply button. A confirmation box will appear summarising the options to be applied. Click on Yes, Confirm to proceed with denying permissions. Tips! Apply a filter to view all requests to be managed at once. Use the toggle in the row with column names to grant or deny permissions for multiple requests simultaneously. You can grant and deny permissions in the same action, simplifying the process. The confirmation box will provide a summary of all actions, including grants, denials, DAC comments, and expiration dates. History Page The History page serves as a dedicated space to view information regarding all requests managed by all DAC members. Here, you can review active permissions and revoke them as needed. Go to the History page by clicking on the "HISTORY" button from a DAC page in the DAC Portal. Here are the different row types you may find: Current permissions: row in green with a toggle button to revoke permissions on the right. Approved requests: row in green, with no toggle button. Request denied: row in red with “request denied” on the right. Permission revoked: row in red with “permission revoked” on the right. Distinctions to Note! Between Request Denied and Permission Revoked: Request Denied: Refers to requests that were rejected from the outset, indicating that access to the dataset was never granted. Permission Revoked: Indicates that permissions were previously granted but have since been revoked. Users with permissions revoked have previously accessed the dataset. Between Approved Request and Current Permissions: Approved Request: Represents an entry when a data access request has been approved in the past. Current Permissions: Denotes ongoing permissions where a user has present access to the dataset. An approved request may now appear as Permission Revoked in the present. By observing these different rows, users can gain insight into the complete history of a user's interactions and permissions regarding a dataset over time. To revoke access to a specific user for a dataset, follow these steps: Go to History page Look for the row with the specific permissions. You can use the filters! Click on the toggle button to revoke access. Add a denial reason. Bear in mind that the requester will receive the denial reason! Click on the Apply button. Check more information on the Hisotry page here! Audit your metadata objects In the DAC Portal, you can efficiently manage and audit various metadata objects pertinent to your role as a Data Controller. Upon accessing the DAC Portal, you will encounter three primary tabs on the homepage: DAC: Contains information about Data Access Committees (DACs). Policies: Provides insights into linked policies. Datasets: Displays datasets and relevant details. Within each tab, you'll find a comprehensive list of the objects you manage, these being grouped by type (DAC, Policies, Datasets), as well as by your role (member/admin). Whilst the lists give you a quick look, to check how things are connected, we've added a table at the bottom DACs and policies. DACs Let’s check which policies are linked to a specific DAC: Go to "My DACs." Select a DAC. Click on "EDIT" to see more details. You are now on this page: Scroll down to see the linked policies. Click on “List of linked policies of this DAC” and you will see a list of all policies linked to your selected DAC. Policies Do you want to view a list of datasets connected to a particular policy? Follow the same steps mentioned earlier, but head to the policy tab this time. In the policy tab, you'll find a list of all your policies. Here, you may notice two different icons next to the policy ID (EGAP): DAC Icon (): This represents the DAC. Hover your mouse over the icon to see the DAC ID. Dataset icon (): This indicates datasets falling under that policy. The number next to the icon tells you how many datasets are linked to the policy. For example, if you see "2" next to the icon, it means there are two datasets linked to that policy. If you want to check the linked datasets and their relevant information, simply click on a specific policy. You'll then find the "List of linked datasets of this policy" at the bottom of the page. Datasets Finally, in the dataset tab, by default you will see a list of all the datasets you can manage with all your DACs and policies. Yet, we've included two handy ways to organise them: DAC vs. policy: You can group by datasets, either by DAC or policy. Released vs. unreleased: You can sort out datasets based on their release status. Feel free to experiment with both options! For instance, if you want to see which DACs have unreleased datasets, simply select DAC and unreleased, and you'll get the details you need! Deprecation Do you have a bunch of metadata objects like DACs and policies that you don’t need anymore? This section shows you how to get rid of them! But what does "deprecation" mean for EGA? It's basically changing the status of a metadata object to "deprecated," which means we won't be using it in the future. In simple terms, it's like saying these objects are no longer useful. However, because we believe in making metadata FAIR, once an object has a persistent identifier, we can't just delete it. So, instead of deleting, we deprecate it. Here's a helpful tip! If you want to make a metadata object disappear from the DAC Portal, deprecate it. You won’t see it in the portal anymore! Let’s say you want to deprecate a DAC. Let’s do it! Go to "My DACs." Select a DAC. Click "EDIT" to see more details. You are now in this page: Click on the Deprecate button It will then appear a message. There are two options here: Your DAC is not linked to any policy, hence it’s ready to be deprecated. Click on “Yes. Confirm” to deprecate your DAC object! Your DAC is linked to at least one policy, and you need to either: Deprecate the linked policy first, or Link the policy to a different DAC Now, let’s say you don’t want to get rid of the policy altogether, but you want to change the DAC it’s linked to. Here's how: Go to the policy tab. Find the policy you want to change (for example, EGAP50000000019). Choose a new DAC to link it to. Click on Update. See the Edit Policy section for more details. After ensuring that the DAC we want to deprecate isn’t linked to any policy, return to the "My DACs" section and follow the steps outlined previously. This will lead you to a confirmation message. You can only deprecate DACs and Policies. To deprecate a dataset, please contact our Helpdesk team. Check more information on how deprecation works here! User Preferences We have implemented email notifications in the DAC Portal. Here’s the complete list: DACs: Pending requests Approved by Helpdesk Rejected by Helpdesk DAC Invitation Requesters: Data access request approved Data access request denied Permissions revoked Upcoming expiration date As a DAC member, you will be able to decide whether you want to receive the DAC notifications or not. For that, go to the top-right corner menu, select User Preference. You will be able to decide whether you want to receive notifications for: Approved by Helpdesk Rejected by Helpdesk DAC Invitation For pending requests, you will be able to select how often do you want to receive the notification: Daily Weekly Fortnightly As a DAC member, you are responsible for managing data access requests. Consequently, you will receive notifications for pending requests. If you prefer not to manage these requests, please arrange to be removed as a contact for your DAC. Here's a tip! Do you have a pending request that you don’t want to receive a notification? Add a DAC comment! The EGA understands that a request resolution can take some time, for this reason, if you add a comment (make sure you save it by clicking the APPLY button!) we will filter those requests at the time of sending the notification! DAC API - A programmatic approach In addition to the new DAC Portal, we are excited to announce the release of the DAC API. This enables users to programmatically manage permissions. If you are interested in learning more about the technical specifications, you can click the button below. Check out the DAC API specification!

  Documentation access/data-access-committee/dac-portal

• Deep intronic homozygous variation in PSMC3 causes a syndromic neurosensory disorder combining deafness and cataract

  The association of congenital deafness and early-onset cataracts inherited as a recessive trait is a rare combination described in only a few syndromes with very few genes identified to date. We propose that the PSMC3 proteasome subunit dysfunction leads to a novel human syndrome that includes early-onset cataracts and deafness and suggest that Rpt5 plays a major role in inner ear and lens development.

  Study EGAS00001003942

• Investigating immunopathological signatures associated with COVID-19 severity post Omicron

  The aim of this study was to investigate the type and duration of protective immunity alongside immunopathological signatures associated with COVID-19 severity. RNA sequencing was performed using the Illumina Stranded mRNA Prep Ligation kit to investigate the protein coding transcriptome of PBMC cells. T cell receptor (TCR) repertoire profiling was performed with the TakaraBio SMART-Seq Human TCR (with UMIs) on PBMC cells to investigate the depth and persistence of SARS-CoV-2 targeting TCRs. The cohort (n=20) consisted of 10 non-hospitalised and 10 Hospitalised individuals with samples analysed 3 months post positive SARS-CoV-2 PCR.

  Study EGAS50000000926

• Long-read single-cell RNA sequencing uncovers cell-type specific transcript regulation in COVID-19

  SARS-CoV-2 infection leads to extensive host transcriptomic changes, but the role of alternative splicing in shaping the immune response remains underexplored. Here, we present the first application of long-read single-cell RNA sequencing on nasopharyngeal swabs from COVID-19 patients and healthy controls to resolve transcript-level changes across cell types. Leveraging the strengths of long-read sequencing, we characterize alterations in both gene and isoform expression within cell populations of the upper respiratory tract—the primary site of SARS-CoV-2 entry. This approach provides new insights into how transcriptional regulation and splicing shape host immune responses to viral infection.

  Study EGAS50000001290

• Type 2 Diabetes GoT2D/T2D-GENES DAC

  Dac EGAC00001000391

• EGAD00010000220

  Ovarian & matched normal (Genotypes)

  Dataset EGAD00010000220

• S3-EGA-s234-sample

  S3 genotype data wave 2-4 (phenotypes)

  Dataset EGAD00010002621

• Human Autism Genetics

  The goal of this study is to bring together the power of 1) whole exome sequencing, 2) homozygosity mapping in consanguineous families, and 3) transcriptional analysis in brain tissue to identify genetic causes of autism spectrum disorders (ASD).

  Study phs000639

• WGS of single CTCs from 4 patients with metastatic cancer

  This dataset contains 352 paired fastq files sequenced by illumina Novoseq6000. Single-cell low-pass WGS analysis (illumina platform, PE150, 1-2x coverage) was performed on CTCs enriched using high-throughout microfluidic device from entire leukopak from patients with metastatic prostate cancer (GU-1 and GU-2) and metastatic hepatocellular carcinoma (HCC-1 and HCC-2). Patient matched white blood cells (WBC) served as internal controls.

  Dataset EGAD50000001006

• Single-cell CITE-seq and TCR-seq data from AIM⁺ HIV-1-specific T cells

  Single-cell sequencing data from 2 HIV-1 post-intervention controllers and 2 non-controllers reported in Fisher, Garcia, Frattari, Naasz, et al. Longitudinal samples were collected before antiretroviral therapy inititation, during suppressive therapy, and after analytical treatment interruption.

  Dataset EGAD50000002247

• 10X single-cell Multiome (RNA+ATAC) of cord blood-derived HSPC

  10X genomics chromium single-cell ATAC+RNA (Multiome) was use to prepare single-nucleus RNA- and ATAC-seq libraries, sequenced on an Illumina NovaSeq 6000 platform. BAM files are provided. Samples from 4 donors were sequenced after sorting for primitive HSPCs in 2 libraries, each with cells from 1 male and 1 female CB.

  Dataset EGAD50000002327

• INDIA

  We collected PBMCs from 10 Indian and 10 Dutch individuals before BCG vaccination and 10-12 post-vaccination. We assessed the open chromatin profiles under baseline conditions, and the transcriptomic profiles upon stimulation with Influenza and SARS-CoV-2.

  Study EGAS00001006417

• BLUEPRINT WP10 Quantitative Trait Loci (QTLs) Phase 2 Summary Statistics of Most Significant Associations

  BLUEPRINT WP10 Quantitative Trait Loci (QTLs) Phase 2 summary statistics of most significant association data include five molecular traits (eQTL, hQTL(H3K27ac), hQTL(H3K4me1), mQTL, and psiQTL) for three primary blood cells (Monocytes, Neutrophils, and T-cells). Each summary statistics file contains the most significant association for each phenotype ID.

  Dataset EGAD00001005200

• Exome sequencing data from two small cell prostate cancer patients

  Exome sequencing data from two small cell prostate cancer patients - 4 cancer samples (FFPE) from Patient 1 collected at 3 different time points and 2 cancer samples (FFPE) from Patient 2 collected at 1 time point. Exonic DNA was enriched using the TruSeq Exome Kit (Illumina) and sequenced on the Illumina NextSeq 500 as 75bp paired end reads (total read length 150bp).

  Dataset EGAD00001011149

• BLUEPRINT release August 2016, Bisulfite-Seq for osteoclast, on genome GRCh38

  Bisulfite-Seq data for 2 osteoclast sample(s). 88 run(s), 4 experiment(s), 4 analysis(s) on human genome GRCh38. Part of BLUEPRINT release August 2016. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20160816/homo_sapiens/README_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002427

• AWI-GEN 2 Microbiome Dataset

  AWI-Gen Phase 2 Microbiome Project for 1820 samples.

  Dataset EGAD00001015449

• AWI-GEN 2 Phenotype Dataset

  Core phentoype data for AWI-Gen Phase 2 Microbiome Project for 1824 samples.

  Dataset EGAD00001015440

• RNA-seq data of BRCA1/2 carriers from non-tumorous breast tissue

  Dac EGAC00001001859

• CytoSNP850K

  HumanCytoSNP 850K on tissue DNA

  Dataset EGAD00010002090

• ATAC-Seq and CTCF ChIP-Seq on the OCIAML-2 cell line

  Study EGAS00001004741

• LP2100082-DNA_G04

  LP2100082-DNA_G04

  Dataset EGAD00001009891

• Employing_single_cell_sequencing_for_detection_of_mutational_signatures_reflecting_on_going_mutagenesis_

  12 single cells will be sequenced from a previously single-cell derived clone of a JSC-1 cell line. Additional 12 single cells are likely to be sequenced from a previously single-cell derived clone of a SW756 line. Both belong to CGP’s panel of approximately 1000 human cancer cell lines.

  Study EGAS00001002679

• Genomewide detection of cytosine methylation by single molecule real-time sequencing

  5-methylcytosine (5mC) is an important type of epigenetic modification. In this study, we enhanced 5mC detection using SMRT sequencing by holistically analyzing kinetic signals of a DNA polymerase and sequence context for every base within a measurement window. We employed a convolutional neural network to train a methylation classification model. This methodology has provided a system for simultaneous genome-wide genetic and epigenetic analyses. 

  Study EGAS00001004642

• A GWAS meta-analysis on severe acne on a European population of 26,722 individuals

  Acne vulgaris is a highly heritable common, chronic inflammatory disease of the skin. We performed a genome-wide association study of 3,823 cases and 16,144 controls followed by meta-analysis with summary statistics from a previous study, with a total sample size of 26,722. We identified 20 independent association signals at 15 risk loci, 12 of which have not been previously implicated in the disease.

  Study EGAS00001003278

• Multi-regional tumour biopsies of a RET fusion patient

  Identifying modes of resistance to a new experimental drug for a patient resistant to other therapies

  Study EGAS00001004023

• CIDR: Discovery, Biology, and Risk of Inherited Variants in Glioma

  This is a gliogene brain tumor family study. This study includes glioma cases with a family history of glioma in a first, second, or third degree relative. 

  Study phs002250

• Cabozantinib Response in ETV6-NTRK3 G623R Positive Carcinoma HIPO-021

  We report a case of marked response to cabozantinib in a secondary entrectinib-resistant tumor harboring the ETV6-NTRK3 fusion with a G623R solvent-front mutation.

  Study EGAS00001004494

• Whole-genome analysis of a healthy man with common trichromatic vision

  Humans have a diversity of color vision that cannot be divided into normal and abnormal. As a first step to elucidate the genetic basis that contributes to the diversity of human color vision, we conducted a whole-genome analysis using DNA extracted from the saliva of a healthy man with common trichromatic vision. Further comparison with the genomes of people with various types of color vision may provide a model study of human nature that cannot be divided into normal and abnormal.

  Study JGAS000348

• CRU-Ukrainian National Research Center for Radiation Medicine Trio Study

  Data on transgenerational effects following nuclear accidents are important for understanding fully the consequences of parental exposure to ionizing radiation. Few studies to date have had adequate statistical power to detect effects of the magnitude expected based on animal data, and most have not been of low-dose, protracted exposures associated with nuclear accidents and their aftermath. Although, to date, scant use has been made of the new genomic technologies, in Chernobyl-exposed areas of Ukraine and Belarus, excess minisatellite mutations have been seen in children born after the accident. We propose a study of parent-child trios in which at least one parent was exposed to Chernobyl radiation as a clean-up worker (mean dose>=100 mGy) and/or evacuee from a contaminated area (mean >=50 mGy). The specific aims are to investigate the transgenerational and de novo mutation rates of the spectrum of genetic variants in trios, in particular looking at effects in children and mapping them to possible parental origin of the chromsoome. Together with long-term collaborators at the Research Center for Radiation Medicine (RCRM) in Kiev, epidemiologic data will be collected for up to 450 trios of parents with preconceptional doses and their unexposed offspring. We will use state-of-the-art genomic technologies to characterize the landscape of the genomes of the trios to determine whether parental radiation exposure is associated with genetic mutations transmitted to the offspring, by examining de novo mutation rates, minisatellite mutations, copy number alterations, and variations in telomere length. The analysis will be conducted in peripheral blood and/or buccal samples (when blood is not available) from complete father-mother-child trios. Doses to the gonads from the time of the accident to the time of conception will be reconstructed for all parents using existing records supplemented by interview data. Trio subjects will be selected from representative populations exposed to radiation from Chernobyl who are under active follow-up in the Clinico-Epidemiologic Registry at RCRM. To help identify specific effects of paternal and maternal radiation exposure, we will initially select sets of trio subjects in five categories: (1) exposed father, unexposed mother; (2) unexposed father, exposed mother; (3) both parents exposed; (4) both parents unexposed; and (5) a group of high dose "emergency workers" with acute radiation syndrome. All trio members will be invited to the RCRM outpatient clinic for collection of a 20 ml blood sample (or buccal cells for those who refuse phlebotomy). Both parents will be asked to complete a general questionnaire to obtain demographic and lifestyle data. Then one or both will complete detailed dosimetry questionnaires, based on forms used in previous collaborations with RCRM and administered by specially trained interviewers. Once 50 trios have been recruited (10 from each of the 5 exposure categories), we will conduct an interim evaluation of participation rates, sample collection and quality, and dose reconstruction in order to modify the protocol as needed. The analytical approach will be to correlate the extent, especially for de novo events of genetic alterations in the offspring with parental pre-conceptional radiation dose overall and by parental origin. The statistical power in relation to de novo mutations is very high, in excess of 90%, but somewhat lower for trends in minisatellite mutations. Study findings will contribute importantly to knowledge of the heritable effects of moderate- and low-dose radiation exposure in humans and to radiation risk projection. Eventually data from the Trio Study may be shared with the international community through dbGap.

  Study phs001163

• The evolutionary trajectory of a highly recurrent paediatric high grade neuroepithelial tumour with MN1:BEND2 fusion

  Astroblastomas are rare brain tumours which predominate in children and young adults, and have a controversial claim as a distinct entity, with no established WHO grade. Reports suggest a better outcome than high grade gliomas, though they frequently recur. Recently, they have been described to overlap with a newly-discovered group of tumours described as ’high grade neuroepithelial tumour with MN1 alteration’ (CNS HGNET-MN1), defined by global methylation patterns and strongly associated with gene fusions targeting MN1. We have studied a unique case of astroblastoma arising in a 6 year old girl, with multiple recurrences over a period of 10 years, with the pathognomonic MN1:BEND2 fusion. Exome sequencing allowed for a phylogenetic reconstruction of tumour evolution, which when integrated with clinical, pathological and radiological data provide for a thorough understanding of disease progression, with initial treatment driving tumour dissemination along four distinct trajectories. Infiltration of distant sites was associated with a later genome doubling, whilst there was evidence of convergent evolution of different lesions acquiring distinct alterations targeting NF-κB. These data represent an usual opportunity to understand the evolutionary history of a highly recurrent childhood brain tumour, and provide novel therapeutic targets for astroblastoma / CNS HGNET-MN1.

  Study EGAS00001002432

• monozygotic twin discordant for schizophrenia

  monozygotic twin discordant for schizophrenia

  Dataset EGAD00001000048

• Enrichment of CRC

  Enrichment of CRC

  Dataset EGAD00001000224

• Cell-line Mixing Experiment Verification Data

  Dataset EGAD00001001044

• Whole-exome sequenicing of Fanconi anemia

  Dataset EGAD00001001302

• Agilent_miRNA

  Agilent miRNA dataset

  Dataset EGAD00010000934

• scpMS_human_bone_marrow_panel2

  Single-cell proteomics of human bone marrow, FACS panel 2

  Dataset EGAD00010002686

• Comprehensive molecular profiling of ETMR

  ETMR RIPSeq

  Dataset EGAD00001004802

• Exome

  Exome from EGAS00001002441

  Dataset EGAD00001003325

• Exome

  Exome Seq for EGAS00001001845

  Dataset EGAD00001002160

• Exome

  Exome from EGA00001001848

  Dataset EGAD00001002164