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• BLUEPRINT release August 2016, ChIP-Seq for Lymphoma_Follicular, on genome GRCh38

  ChIP-Seq data for 3 Lymphoma_Follicular sample(s). 11 run(s), 11 experiment(s), 11 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 EGAD00001002389

• BLUEPRINT release August 2015, Bisulfite-Seq for hematopoietic multipotent progenitor cell, on genome GRCh38

  Bisulfite-Seq data for 1 hematopoietic multipotent progenitor cell sample(s). 5 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001493

• BLUEPRINT release August 2015, RNA-Seq for Leukemia, on genome GRCh38

  RNA-Seq data for 1 Leukemia sample(s). 1 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_rnaseq_analysis_crg_20150820

  Dataset EGAD00001001551

• BLUEPRINT release August 2015, Bisulfite-Seq for inflammatory macrophage, on genome GRCh38

  Bisulfite-Seq data for 6 inflammatory macrophage sample(s). 83 run(s), 6 experiment(s), 6 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001491

• Distinct genomic profile and specific targeted drug responses in adult cerebellar glioblastoma

  This dataset includes genomic information of 19 adult cerebellar glioblastomas (C-GBMs). Whole-exome sequencing data are available for 9 C-GBMs and their 8 corresponding matched blood samples, and glioma-specific targeted-DNA sequencing (GliomaSCAN) data from additional 10 C-GBMs are also available. Among them, Whole-transcriptome sequencing data were conducted for 6 C-GBM tumors.

  Dataset EGAD00001003417

• Fetal body map

  From 2nd trimester human foetuses we derived liver and intestinal stem cells. These were clonally expanded until enough material was available for whole genome sequencing. For each foetus, reference tissue (skin or bulk liver) was also sequenced to determine all germline variants. These were subtracted from the clones to determine all somatic mutations that had been acquired during embryonic and fetal development.

  Dataset EGAD00001003997

• Genotype calls (vcf files)

  Genotype calls for 83 Aboriginal Australian genomes split by chromosomes. In short, genotypes were called individually with samtools. They were subsequently filtered with thresholds related to sequencing depth, location of variants, sequencing error, and strand bias. Once combined, the genotypes were filtered when not in Hardy-Weinberg equilibrium. The genomes were phased with IMPUTE using the 1000 Genomes reference panel. NB: for the Y chromosomes​, only the 44 Aboriginal Australian males are included.

  Dataset EGAD00001004155

• Repeated clinical malaria episodes are associated with modification of the immune system in children. (2019-01-17)

  Repeated clinical malaria episodes are associated with modification of the immune system in children.This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/. . This dataset contains all the data available for this study on 2019-01-17.

  Dataset EGAD00001004570

• BLUEPRINT release January 2015, Bisulfite-Seq for CD4-positive, alpha-beta T cell

  Bisulfite-Seq data for 3 CD4-positive, alpha-beta T cell sample(s). 61 run(s), 3 experiment(s), 3 alignment(s). Part of BLUEPRINT release January 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20140811/homo_sapiens/README_bisulphite_analysis_CNAG_20140811

  Dataset EGAD00001001157

• BLUEPRINT release August 2015, Bisulfite-Seq for conventional dendritic cell, on genome GRCh38

  Bisulfite-Seq data for 2 conventional dendritic cell sample(s). 30 run(s), 2 experiment(s), 2 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001497

• BLUEPRINT release August 2015, Bisulfite-Seq for mature eosinophil, on genome GRCh38

  Bisulfite-Seq data for 1 mature eosinophil sample(s). 15 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001507

• BLUEPRINT release August 2015, Bisulfite-Seq for regulatory T cell, on genome GRCh38

  Bisulfite-Seq data for 1 regulatory T cell sample(s). 15 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001564

• BLUEPRINT release August 2015, Bisulfite-Seq for monocytes - T=0days, on genome GRCh38

  Bisulfite-Seq data for 1 monocytes - T=0days sample(s). 15 run(s), 1 experiment(s), 1 alignment(s) on human genome GRCh38. Part of BLUEPRINT release August 2015. Analysis documentation available at http://ftp.ebi.ac.uk/pub/databases/blueprint/releases/20150820/homo_sapiens/README_bisulphite_analysis_CNAG_20150820

  Dataset EGAD00001001565

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_LPS_T=24hrs, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_LPS_T=24hrs sample(s). 22 run(s), 2 experiment(s), 2 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 EGAD00001002302

• BLUEPRINT release August 2016, Bisulfite-Seq for monocyte - RPMI_BG_T=1hr, on genome GRCh38

  Bisulfite-Seq data for 1 monocyte - RPMI_BG_T=1hr sample(s). 14 run(s), 1 experiment(s), 2 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 EGAD00001002327

• BLUEPRINT release August 2016, Bisulfite-Seq for naive B cell, on genome GRCh38

  Bisulfite-Seq data for 3 naive B cell sample(s). 39 run(s), 3 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_bisulphite_analysis_CNAG_20160816

  Dataset EGAD00001002361

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

  Bisulfite-Seq data for 6 macrophage sample(s). 88 run(s), 7 experiment(s), 12 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 EGAD00001002501

• Multi-Region WES of Metastatic Colorectal Cancer

  In order to reconstruct the evolutionary history of metastatic colorectal cancer, we performed whole-exome sequencing of 12 metastatic colorectal cancer patients for whom the primary tumor and matched distant metastases to the brain (n=10) and liver (n=2). For 8 of the 12 patients, multiple regions (n=3-7) of the primary tumor and distant metastases were sequenced.

  Dataset EGAD00001004896

• Keratinocyte CRISPR screens (2019-08-14)

  Immortalised HaCaT keratinocytes were transduced with Cas9 and the CRISPR-KO v1.1 genome-wide gRNA library. The gRNA library was prepared from genomic DNA isolated 14 days post library transduction. gRNA representation will be compared to the original CRISPR-KO v1.1 library to reveal genes essential for HaCaT survival and growth. . This dataset contains all the data available for this study on 2019-08-14.

  Dataset EGAD00001005252

• Improved Sezary cell detection and novel insights into immunophenotypic and molecular heterogeneity in Sézary syndrome

  We performed a prospective investigation in Sézary syndrome by the application of a standardized multiparameter flow cytometry, FACS-cell sorting, and RNA-sequencing for an in-depth immunophenotypic and transcriptional profiling of Sézary cells.

  Study EGAS00001005229

• scRNAseq of patients with chronic graft-versus-host-disease

  This dataset contains 10 samples from 9 patients with chronic graft-versus-host disease (GVHD). Each sample is analysed with Chromium V(D)J and 5' Gene Expression Platform v1.1 (10X Genomics). The raw data includes fastq files for Gene expression and fastq files for V(D)J Expression. The processed data have been deposited in the ArrayExpress database at EMBL-EBI (www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-13419.

  Dataset EGAD00001012121

• DETECT-A NGS Data Batch 4

  This batch is a subset of the full DETECT-A dataset, containing 25210 fastq files generated from 3040 subjects. All sequencing was conducted using Illumina HiSeq 4000 and Illumina MiSeq platforms. Note that the division into batches follows no specific criteria and that the sequencing data for each subject has multiple files which may span multiple datasets. Thus, for a comprehensive analysis, it is recommended to request access to all datasets that comprise this study.

  Dataset EGAD00001015360

• DETECT-A NGS Data Batch 6

  This batch is a subset of the full DETECT-A dataset, containing 9 fastq files generated from 8 subjects. All sequencing was conducted using Illumina HiSeq 4000 and Illumina MiSeq platforms. Note that the division into batches follows no specific criteria and that the sequencing data for each subject has multiple files which may span multiple datasets. Thus, for a comprehensive analysis, it is recommended to request access to all datasets that comprise this study.

  Dataset EGAD00001015458

• The Institute for Genomic Medicine at Nationwide Children's Hospital Pediatric Cancer and Blood Disorder Project

  The aim of this study is to enable the unification of the clinical and research arms of comprehensive genomic profiling in the setting of cancer and hematologic disease. Increasingly, studies of the genomic etiology of cancer and hematologic diseases are being utilized for patient management, including prognostication, informing diagnosis, and evaluating eligibility for targeted therapeutics and clinical trials. Clinically available testing can be limited in scope and follow-up, and thus assessment of the impact on patient outcomes is difficult to ascertain. N-of-1 studies have the ability to more broadly impact our understanding of the mechanisms of cancer and hematologic disease, and can have benefit for the individual as well as the larger population of patients with these diseases. Thus, we seek to study and characterize the genomic underpinnings of cancer and hematologic disease through genomic profiling studies bridging both clinical and research components. We will rigorously perform and vet research sequencing and genomic studies protocols for the purpose of transitioning these assays to clinically valid testing for use in clinical patient care. We have been enrolling patients onto this comprehensive genomic profiling study since January 2018. As part of the genomic workup for these patients, we have been performing tumor and normal whole genome sequencing, whole exome sequencing and RNA-Seq. The project is largely supported by the Nationwide Children's Foundation, and we anticipate adding another 50-70 cases per year. This protocol is significant in that it expands our understanding of the etiology of cancer and hematologic disease through a comprehensive analysis aimed at detecting a broad scope of genomic aberrations. Additionally, it improves our understanding of the clinical utility of genomic profiling and has the potential to ultimately improve patient management and surveillance at both level of an individual and general populations of patients affected by these diseases. Primary Objectives: Aim 1: Use diagnostic, relapse, and/or germline samples for augmented whole exome sequencing to characterize the spectrum of germline and somatic genomic alterations in pediatric malignancies and hematologic disease. Aim 2: Examine gene expression and fusion events using whole RNA-seq "transcriptome" analysis or targeted RNA-seq analysis such as the Archer FusionPlex technology. Aim 3: Compare the results of our enhanced molecular profiling vs. that which is detectable by traditional methods as related to cytogenetic, molecular, and minimal residual disease analyses performed as part of standard of care. Aim 4: Examine the utility of comprehensive genomic profiling by collection and evaluation of outcomes data as related to enhancement/alteration in diagnosis, prognosis, treatment management, and clinical benefit. Secondary Objective: Aim 1: Use and clinically validate expanded laboratory methodologies and bioinformatic approaches to more broadly assess genomic complexity (e.g. whole genome sequencing, transcriptome sequencing, in vitro analyses, minimal residual disease testing, clonality assays, proteomics) in the setting of hematologic and oncologic disease. In so doing, we aspire to make novel discoveries and associations, as related to disease phenotypes and subtypes, variant causality, therapeutic targeting, and cancer biology.

  Study phs001820

• Center for Common Disease Genomics [CCDG] - Neuropsychiatric: SPARK Simons Foundation Powering Autism Research for Knowledge

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants, both in coding and noncoding regions, will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of New York Genome Center (NYGC). WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal.An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases, pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002511

• National Heart Lung and Blood Institute Exome sequencing in SCID

  Analysis of the molecular etiologies of severe combined immunodeficiency (SCID) has led to important insights into the control of immune cell development. Most cases of SCID result from either X-linked or autosomal recessive inheritance of mutations in a known causative gene. However, in some cases, the molecular etiology remains unclear. To identify the cause of SCID in a patient known to lack the protein tyrosine phosphatase CD45, we utilized single nucleotide polymorphisms (SNP) arrays and whole exome sequencing. The patient's mother was heterozygous for an inactivating mutation in CD45, while the paternal alleles lacked mutations. The patient exhibited a single CD45 mutation identical to the maternal allele. Patient SNP array analysis revealed no change in copy number but loss of heterozygosity for the entire length of chromosome 1 (Chr1), indicating that disease was caused by uniparental disomy (UPD) with isodisomy of the entire maternal Chr1 bearing the CD45 mutation. Non-lymphoid blood cells and other mesoderm and ectoderm-derived tissues retained UPD of the entire maternal Chr1 in this patient who had undergone successful bone marrow transplantation. Exome sequencing revealed mutations in 7 additional genes bearing nonsynonymous SNPs predicted to have deleterious effects. These findings represent the first reported case of SCID caused by UPD and suggest UPD should be considered in SCID and other recessive disorders, especially when the patient appears homozygous for an abnormal gene found in only one parent. Evaluation for alterations in other genes affected by UPD should also be considered in such cases.

  Study phs000479

• Genetic Markers of Lipids in Indians: A Validation Study of Most Relevant Findings of Genome-Wide Association Studies

  We aimed to validate the GWAS loci (identified in European populations) related to serum lipid levels (total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and very low-density lipoprotein cholesterol (VLDL-C)) in the Indian population originally recruited to examine the 20-year trend of cardiometabolic risk factors in rural and urban areas around National Capital region in India. Isolated DNA was processed for genotyping of 12 established GWAS variants using multiplex Sequenom Mass ARRAY technology. We examined the association of these genetic variants with different lipid levels and found association of eight variants for lipid levels. We also derived weighted genetic risk scores (wGRAS) using the validated variants. The wGRS for triglycerides and VLDL-C was derived based on four associated variants (rs174546 at FADS1, rs17482753 at LPL, rs2293889 at TRPS1, rs4148005 at ABCA8, rs4420638 at APOC1) which was associated with 36.31 mg/dL elevated triglycerides and VLDL-C (β=0.95, SE=0.16, pTRPS1 and rs4147536 at ADH1B) was associated with 40.62 mg/dL higher total cholesterol (β=1.01, SE=0.23, pTRPS1, rs4147536 at ADH1B, rs4420638 at APOC1 and rs660240 at CELSR2). The wGRS derived from five associated variants (rs174546 and FADS1, rs17482753 at LPL, rs4148005 at ABCA8, rs4420638 at APOC1 and rs7832643 at PLEC) was associated with 10.64 mg/dL lower HDL-C (β= -0.87, SE =0.14, pThe phenotypic and genotypic data will be made available on the dbGaP portal for access.

  Study phs003469

• ILyAD (Indolent Lymphoma And vitamin D)

  Indolent non-Hodgkin lymphomas are incurable diseases, and require intermittent and often morbid and expensive therapy during their prolonged natural history. Lower intensity and better-tolerated, cost-effective treatment strategies are needed for these patients. Low vitamin D levels at diagnosis of indolent lymphomas are strongly associated with inferior outcomes to treatment. Standard therapy for low tumor burden, indolent lymphoma includes a single agent monoclonal antibody (rituximab). In our double-blind, placebo-controlled, randomized trial we evaluated if vitamin D supplementation with 2000IU oral cholecalciferol daily for three years improved the progression-free survival of patients with indolent lymphoma treated with single agent rituximab. Stratification factors for randomization included histology and Follicular Lymphoma International Prognostic Index (FL-IPI) prognostic score. The primary endpoint of the trial was progression-free survival over three years; secondary endpoints included response at 13 weeks, and overall survival. Eligible participants were adults with biopsy proven follicular lymphoma (grades 1-3a), small lymphocytic lymphoma, marginal zone lymphoma and mucosal-associated lymphoid tissue with no prior anti-lymphoma systemic therapy. Participants had Ann Arbor stage II or greater, measurable disease defined by Lugano criteria. Participants with osteoporosis requiring prescription treatment, symptomatic primary hyperthyroidism, hypercalcemia, creatinine greater than 1.5 times the upper limit of normal or a history of calcium-related kidney stones were excluded. Germline DNA was isolated from saliva samples obtained from participants at baseline. Whole exome sequencing was performed and SNPs were analyzed using the Agilent SureSelectXT Human All Exon V6 +UTR array. SNP data from 185 participants are available through dbGaP.

  Study phs003503

• Multiple Myeloma Clinical Targeted Sequencing of Patient Samples

  Purpose: Molecular diagnostics for the risk stratification of patients with multiple myeloma (MM) have languished in the area of cytogenetics and fluorescence in situ hybidization (FISH). However, here we present the case for next generation sequencing of MM patient samples to not only identify high-risk markers, but also mutations and deletions relating to immunomodulatory drugs (IMiDs), and more importantly to guide the sequencing of immunotherapy regimens in response to intrinsic antigen escape as a means of treatment resistance and relapse. Experimental design: CD138+ cell selected samples (n=134) from bone marrow aspirates from patients with multiple myeloma were sequenced using a targeted panel in a clinical diagnostics laboratory. Data were analyzed for high-risk markers, treatment-related resistance mechanisms, and precision medicine targets to guide the future treatment of patients in the clinic. Results: High-risk markers including t(4;14), t(14;16), t(14;20), gain or amplification 1q, deletion of CDKN2C, and deletion or mutation of TP53 were identified in 15% of newly diagnosed myeloma patient samples. At relapse, alterations in the cereblon degradation pathway were found in 24.3% of IMiD treated patients. Deletions of 4p (CD38) were also enriched in patients who received anti-CD38 treatment (P=0.03) which were mostly monoallelic. Deletions and mutations were detected in TNFRSF17 encoding BCMA in patients treated with anti-BCMA regimens, and the information was used to change the treatment of the patients. Conclusions: Targeted sequencing of multiple myeloma patient diagnostic samples can be used for risk stratification, and also to monitor and adjust treatments as resistance mechanisms evolve.

  Study phs003908

• Nanopore sequencing of blood, saliva, buccal mucosa samples of patients with inherited retinal disorders

  X-linked genetic disorders typically affect females less severely than males due to the presence of a second X chromosome not carrying the deleterious variant. However, the phenotypic expression in females is highly variable, which may be explained by an allelic skew in X chromosome inactivation. Accurate measurement of X inactivation skew is crucial to understand and predict disease phenotype in carrier females, with prediction especially relevant for degenerative conditions. We propose a novel approach using nanopore sequencing to quantify skewed X inactivation accurately. By phasing sequence variants and methylation patterns, this single assay reveals the disease variant, X inactivation skew, its directionality, and is applicable to all patients and X-linked variants. Enrichment of X-chromosome reads through adaptive sampling enhances cost-efficiency. Our study includes a cohort of 16 X-linked variant carrier females affected by two X-linked inherited retinal diseases: choroideremia and RPGR-associated retinitis pigmentosa. As retinal DNA cannot be readily obtained, we instead determine the skew from peripheral samples (blood, saliva and buccal mucosa), and correlate it to phenotypic outcomes. This revealed a strong correlation between X inactivation skew and disease presentation, confirming the value in performing this assay and its potential as a way to prioritise patients for early intervention, such as gene therapy currently in clinical trials for these conditions. Our method of assessing skewed X inactivation is applicable to all long-read genomic datasets, providing insights into disease risk and severity and aiding in the development of individualised strategies for X-linked variant carrier females.

  Study EGAS50000000440

• Coding and regulatory somatic profiling of triple-negative breast cancer in Sub-Saharan African patients

  The burden of triple-negative breast cancer (TNBC) may be shaped by genetic factors, particularly inherited and somatic mutation profiles. However, data on this topic remain limited, especially for the African continent, where a higher TNBC incidence is observed. In the age of precision medicine, cataloguing TNBC diversity in African patients becomes imperative. We performed whole exome sequencing, including untranslated regions, on 30 samples from Angola and Cape Verde, which allowed to ascertain on potential regulatory mutations in TNBC for the first time. A high somatic burden was observed for the African cohort, with 86% of variants being so far unreported. Recurring to predictive functional algorithms, 17% of the somatic single nucleotide variants were predicted to be deleterious at the protein level, and 20% overlapped with candidate cis-regulatory elements controlling gene expression. Several of these somatic functionally-impactful mutations and copy number variation (mainly in 1q, 8q, 6 and 10p) occur in known BC- and all cancer-driver genes, enriched for several cancer mechanisms, including response to radiation and related DNA repair mechanisms. TP53 is the top of these known BC-driver genes, but our results identified possible novel TNBC driver genes that may play a main role in the African context, as TTN, CEACAM7, DEFB132, COPZ2 and GAS1. These findings emphasize the need to expand cancer omics screenings across the African continent, the region of the globe with highest genomic diversity, accelerating the discovery of new somatic mutations and cancer-related pathways.

  Study EGAS50000001013

• Analysis of mutational and proteomic heterogeneity of gastric cancer to monitor post-treatment tumor burden using circulating tumor DNA

  It remains unclear whether complete spectrum of tumor heterogeneity is reflected in circulating tumor DNA (ctDNA). Here, we performed multi-regional target sequencing of primary tumors from 10 patients who underwent surgical resection for gastric cancer. A total of 126 non-synonymous mutations were identified from 30 samples from 10 tumors. Of these, 16 (12.7%) were likely mutations that occurred early in tumor development (i.e., present in all three regions) and were designated founder mutations. Among those validated with highly sensitive digital PCR (dPCR), 93% of non-founder mutations were found in all three regions. Variant allele frequencies (VAFs) of founder mutations were significantly higher than those of non-founder mutations. Phylogenetic analysis demonstrated that five of eight tumors (62.5%, excluding two hypermutator) had at least one identifiable truncal/founder mutation. These findings led us to prioritize founder mutations for ctDNA detection with individually-designed primer/probe sets for dPCR. Marked proteomic diversity was present across the tumor regions both within and between patients independent of mutational status. In preoperative plasma, the average ctDNA VAF of founder mutations was significantly higher than that of non-founder mutations (p = 0.039). Our present data demonstrated that high VAF mutations in the tumor likely represent founder or truncal mutations. However, mutation patterns alone appear insufficient to predict protein heterogeneity. These results suggest that, in practice, mutations having high VAF identified without multi-regional sequencing may be useful for ctDNA monitoring but are inadequate to fully predict proteomic heterogeneity of the tumor.

  Study JGAS000231

• Sequencing and analysis of a South Asian-Indian personal genome

  India with over 1.3 billion people is estimated to carry three times more genetic diversity compared to Europe. Next-generation sequencing technologies have facilitated the understanding of diversity by enabling whole genome sequencing at greater speed and lower cost. While multiple genomes from people of European and a few of Asian descent have been sequenced, only recently a single male genome from the Indian subcontinent at sufficient depth and coverage was published. We have in this study sequenced and analyzed the genome of a South Asian Indian female (SAIF) from the Indian state of Kerala. We identified over 3.4 million SNPs in this genome including over 89,873 private variations. Comparison of the SAIF genome with several published personal genomes revealed that this individual shared ~50% of the SNPs with each of these genomes. Analysis of the SAIF mitochondrial genome showed that it is closely related to the U1a3 haplogroup which has been previously observed in Kerala. We assessed the SAIF genome for SNPs with health and disease consequences and found that the individual was at a higher risk for multiple sclerosis and a few other diseases. In assessing SNPs that modulate drug response we found a variation that predicts a favorable response to metformin used for treating diabetes. SNPs predictive of adverse reaction to warfarin indicated that the SAIF individual is not at risk for bleeding if treated with typical doses of warfarin. In addition to these, we report the presence of several other SNPs of medical relevance.

  Study EGAS00001000328

• Analysis of recent shared ancestry in a familial cohort identifies coding and noncoding autism spectrum disorder variants

  Autism spectrum disorder (ASD) is a collection of neurodevelopmental disorders characterized by deficits in social communication and restricted, repetitive patterns of behavior or interests. ASD is highly heritable, but genetically and phenotypically heterogeneous, reducing the power to identify causative genes. We performed whole genome sequencing (WGS) in an ASD cohort of 68 individuals from 22 families enriched for recent shared ancestry. We identified an average of 3.07 million variants per genome, of which an average of 112,512 were rare. We mapped runs of homozygosity (ROHs) in affected individuals and found an average genomic homozygosity of 9.65%, consistent with expectations for multiple generations of consanguineous unions. We identified potentially pathogenic rare exonic or splice site variants in 12 known (including KMT2C, SCN1A, SPTBN1, SYNE1, ZNF292) and 12 candidate (including CHD5, GRB10, PPP1R13B) ASD genes. Furthermore, we annotated noncoding variants in ROHs with brain-specific regulatory elements and identified putative disease-causing variants within brain-specific promoters and enhancers for 5 known ASD and neurodevelopmental disease genes (ACTG1, AUTS2, CTNND2, CNTNAP4, SPTBN4). We also identified copy number variants in two known ASD and neurodevelopmental disease loci in two affected individuals. In total we identified potentially etiological variants in known ASD or neurodevelopmental disease genes for ~61% (14/23) of affected individuals. We combined WGS with homozygosity mapping and regulatory element annotations to identify candidate ASD variants. Our analyses add to the growing number of ASD genes and variants and emphasize the importance of leveraging recent shared ancestry to map disease variants in complex neurodevelopmental disorders.

  Study EGAS00001006058

• Microsatellite data show recent demographic expansions in sedentary but not in nomadic human populations in Africa and Eurasia.

  The transition from hunting and gathering to plant and animal domestication was one of the most important cultural and technological revolutions in human history. According to archeologists and paleoanthropologists, this transition triggered major demographic expansions. However, few genetic studies have found traces of Neolithic expansions in the current repartition of genetic polymorphism, pointing rather toward Paleolithic expansions. Here, we used microsatellite autosomal data to investigate the past demographic history of 87 African and Eurasian human populations with contrasted lifestyles (nomadic hunter-gatherers, semi-nomadic herders and sedentary farmers). Likely due to the combination of a higher mutation rate and the possibility to analyze several loci as independent replicates of the coalescent process, the analysis of microsatellite data allowed us to infer more recent expansions than previous genetic studies, potentially resulting from the Neolithic transition. Despite the variability in their location and environment, we found consistent expansions for all sedentary farmers, while we inferred constant population sizes for all hunter-gatherers and most herders, which could result from constraints linked to a nomadic or semi-nomadic lifestyle and/or competition for land between herders and farmers. As an exception, we inferred expansions for Central Asian herders. This might be linked with the arid environment of this area, which may have been more favorable to nomadic herders than to sedentary farmers. Alternatively, current Central Asian herders may descent from populations who have first experienced a transition from hunter-gathering to sedentary agropastoralism, and then a second transition to nomadic herding.

  Study EGAS00001000652

• Genome-Wide Association Study of aspirin-induced PUD in a UK cohort

  Aspirin is a widely prescribed non-steroidal anti-inflammatory drug used as an effective analgesic and at low doses as anti-platelet drug for the prevention of cardiovascular diseases. However, aspirin is associated with a significant incidence of upper gastrointestinal (GI) complications, notably peptic ulceration. This study aims to identify genetic risk factors associated with the pathogenesis of aspirin-induced upper GI ulceration.A cohort of 248 patients with aspirin-induced, endoscopiclly confirmed GI ulceration and 491 controls (ulcer, without aspirin or aspirin without ulcer) were genotyped for 1.524,956 SNPs using the Illumina Omni 2.5 SNP array. A replication cohort (206 NSAID-induced ulcer cases [of which 87 were aspirin] and 309 controls) was subsequently genotyped for identified SNP association signals using Sequenom iPLEX Massarry. Logistic regression analysis of identified two SNP signal with suggestive association with aspirin-induced GI ulceration. A SNP within the EYA1 locus (rs12678747) was found to be associated with aspirin GI ulceration in both the discovery cohort (p=3.79x10-7, OR=2.06 ([1.55-2.73]) and, to a lesser extent, in the replication cohort (p=.0005, OR=1.71[1.17-2.50]. Combined analysis of both cohorts yielded a genome wide significant association (p=1.46x10-8). The rs12678747 genotype was found to be associated with significantly higher EYA1 transcript in ulcer biopsy tissue (p<0.05).Data suggest that EYA1 may represent a risk locus for aspirin-induced GI ulceration. Though it is thought that EYA1 may play a key role in mediating cell death/ survival

  Study EGAS00001002052

• Sequencing-based counting and size profiling of plasma Epstein-Barr virus DNA enhance population screening of nasopharyngeal carcinoma.

  Circulating tumor-derived DNA testing for cancer screening has recently been demonstrated in a prospective study on identification of nasopharyngeal carcinoma (NPC) among 20,174 asymptomatic individuals. Plasma EBV DNA, a marker for NPC, was detected using real-time PCR. While plasma EBV DNA was persistently detectable in 97.1% of the NPCs identified, ∼5% of the general population had transiently detectable plasma EBV DNA. We hypothesized that EBV DNA in plasma of subjects with or without NPC may have different molecular characteristics. We performed target-capture sequencing of plasma EBV DNA and identified differences in the abundance and size profiles of EBV DNA molecules within plasma of NPC and non-NPC subjects. NPC patients had significantly higher amounts of plasma EBV DNA, which showed longer fragment lengths. Cutoff values were established from an exploratory dataset and tested in a validation sample set. Adopting an algorithm that required a sample to concurrently pass cutoffs for EBV DNA counting and size measurements, NPCs were detected at a positive predictive value (PPV) of 19.6%. This represented superior performance compared with the PPV of 11.0% in the prospective screening study, which required participants with an initially detectable plasma EBV DNA result to be retested within 4 weeks. The observed differences in the molecular nature of EBV DNA molecules in plasma of subjects with or without NPC were successfully translated into a sequencing-based test that had a high PPV for NPC screening and achievable through single time-point testing.

  Study EGAS00001002707

• Open_Targets_020_Epigenomes_of_Cell_Lines

  There is currently a drive to establish cell based assay systems of greater human biological and disease relevance through the use of well characterised transformed cell lines, primary cells and complex cellular models (e.g. co-culture, 3D models). However, although the field is gaining valuable experience in running more non-standard & complex cell assays for target validation and compound pharmacology studies, there is the lack of a systematic approach to determine if this expansion in cell assay models is reflected in increased human biological and disease relevance. The increasing wealth of publically available transcriptomic, and epigenome (ENCODE and Epigenome Roadmap) data represents an ideal reference mechanism for determining the relationship between cell types used for target & compound studies to primary human cells and tissues from both healthy volunteers & patients. The CTTV020 epigenomes of cell line project aims to generate epigenetic and transcriptomic profiles of cell lines and compare these with existing and newly generated reference data sets from human tissue and cell types. The aim is to identify assay systems which will provide greater confidence in translating target biology and compound pharmacology to patients. Multiple cell types commonly used within research have been grouped according to biology. Examples include erythroid, lung epithelial, hepatocyte cell types and immortalised models of monocyte / macrophage biology. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001003136

• Quantification of chromosomal copy number aberrations by shallow whole-genome sequencing

  Detection of DNA copy number aberrations by shallow whole-genome sequencing (WGS) faces many challenges including lack of completion and errors in the human reference genome, repetitive sequences, polymorphisms, variable sample quality, and biases in the sequencing procedures.Formalin-fixed paraffin-embedded (FFPE) archival material, the analysis of which is important for studies of cancer, presents particular analytical difficulties due to degradation of the DNA and frequent lack of matched reference samples. We present a robust, cost-effective WGS method for DNA copy number analysis that addresses these challenges more successfully than currently available procedures. In practice very useful profiles can be obtained with 0.1 fold genome coverage. We improve on previous methods by; first, implementing a combined correction for sequence mappability and GC content, and second, applying this procedure to sequence data from the 1000 Genomes Project in order to develop a blacklist of problematic genome regions. A small subset of these blacklisted regions were previously identified by ENCODE, but the vast majority are novel unappreciated problematic regions. Our procedures are implemented in a pipeline called QDNAseq. We have analyzed over 1,000 samples, most of which were obtained from the fixed tissue archives of over 25 institutions.We demonstrate that for most samples our sequencing and analysis procedures yield genome profiles with noise levels near the statistical limit imposed by read counting. The described procedures also provide better correction of artifacts introduced by low DNA quality than prior approaches, and better copy number data than high-resolution microarrays at substantially lower cost.

  Study EGAS00001000642

• Prediction of pigmentation phenotypes by SNP typing in a Northern German population

  Human pigmentation traits are of great interest to many research areas, from ancient DNA analysis to forensic science. We aimed to develop a gene-based predictive model for pigmentation phenotypes in a realistic target population for forensic case work from Northern Germany. Our aim was to determine whether better prediction accuracy can be achieved, or fewer genetic markers may suffice, than in previously studied, genetically more heterogeneous populations. We investigated the association between eye, hair and skin colour, and 12 candidate single nucleotide polymorphisms (SNPs) from six genes. Our study comprised two samples of 300 and 100 individuals from Northern Germany who were carefully characterized with regard to pigmentation phenotypes. The first sample was used to select trait-associated SNPs whereas the second sample served to estimate odds ratios (ORs) and to quantify the predictive capability of the respective SNP genotypes. SNP rs12913832 in HERC2 was found to be strongly associated with blue eye colour (OR=15.6, p<1.2•10-4) and to yield reasonable predictive power (90% sensitivity, 63% specificity). SNP associations with hair and skin colour were weaker and genotypes less predictive. A comparison to two recently published sets of markers to predict eye and hair colour revealed that the consideration of additional SNPs with weak to moderate effect increases the predictive power in Northern Germans for eye colour, but not for hair colour. In addition, fine phenotyping and differentiation of hair colour (light / dark and red tint / no red tint) were found to increase the number of significant genotype-phenotype associations.

  Study EGAS00001001174

• The Dutch Microbiome Project: Defining the (un)healthy gut microbiome

  The Dutch Microbiome Project (DMP) data includes raw data generated by shotgun metagenomic sequencing of faecal samples of 8,208 Dutch individuals, processed microbiome data analysed using Biobakery2 tools (including taxonomy, pathway, virulence factors and antibiotic resistance gene profiles), and basic phenotypes (age and sex, BMI). Data is made available in two randomly assigned batches to facilitate the data access and maintenance. Access to this dataset requires a minimal access procedure (data access form at https://forms.gle/eHeBdXJMXbVvCJRc8 or email request to the data access committee (DAC) of this study, listed at DAC EGAC00001001996, https://ega-archive.org/dacs/EGAC00001001996). This access procedure is to ensure that the data is being requested for research/scientific purposes only and thus complies with the informed consent signed by Lifelines participants, which specifies that the collected data will not be used for commercial purposes. Submitted data access forms will be evaluated by the DAC and Lifelines, and a response to requests will be given within two weeks. For requests from verified academic parties, access will be given without further delay. For requests from commercial parties, Lifelines will perform a pre-DPIA (Data Privacy Impact Assessment) to assess the risks of the proposed processing of personal data (e.g. purpose, storage, access, archiving, etc.) with respect to the GDPR (EU privacy laws) subject rights. Based on the outcome of the pre-DPIA, Lifelines will decide whether sharing data with the commercial entity is allowed and/or whether additional measures have to be taken.

  Study EGAS00001005027

• Open_Targets_020_Epigenomes_of_Cell_Lines

  There is currently a drive to establish cell based assay systems of greater human biological and disease relevance through the use of well characterised transformed cell lines, primary cells and complex cellular models (e.g. co-culture, 3D models). However, although the field is gaining valuable experience in running more non-standard & complex cell assays for target validation and compound pharmacology studies, there is the lack of a systematic approach to determine if this expansion in cell assay models is reflected in increased human biological and disease relevance. The increasing wealth of publically available transcriptomic, and epigenome (ENCODE and Epigenome Roadmap) data represents an ideal reference mechanism for determining the relationship between cell types used for target & compound studies to primary human cells and tissues from both healthy volunteers & patients. The CTTV020 epigenomes of cell line project aims to generate epigenetic and transcriptomic profiles of cell lines and compare these with existing and newly generated reference data sets from human tissue and cell types. The aim is to identify assay systems which will provide greater confidence in translating target biology and compound pharmacology to patients. Multiple cell types commonly used within research have been grouped according to biology. Examples include erythroid, lung epithelial, hepatocyte cell types and immortalised models of monocyte / macrophage biology. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001003137

• Open_Targets_020_Epigenomes_of_Cell_Lines

  There is currently a drive to establish cell based assay systems of greater human biological and disease relevance through the use of well characterised transformed cell lines, primary cells and complex cellular models (e.g. co-culture, 3D models). However, although the field is gaining valuable experience in running more non-standard & complex cell assays for target validation and compound pharmacology studies, there is the lack of a systematic approach to determine if this expansion in cell assay models is reflected in increased human biological and disease relevance. The increasing wealth of publically available transcriptomic, and epigenome (ENCODE and Epigenome Roadmap) data represents an ideal reference mechanism for determining the relationship between cell types used for target & compound studies to primary human cells and tissues from both healthy volunteers & patients. The CTTV020 epigenomes of cell line project aims to generate epigenetic and transcriptomic profiles of cell lines and compare these with existing and newly generated reference data sets from human tissue and cell types. The aim is to identify assay systems which will provide greater confidence in translating target biology and compound pharmacology to patients. Multiple cell types commonly used within research have been grouped according to biology. Examples include erythroid, lung epithelial, hepatocyte cell types and immortalised models of monocyte / macrophage biology. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001003139

• Healthy_ageing_thymus

  Single cell RNA sequencing of immune and non-immune cells from healthy ageing thymus Thymus is a primary lymphoid organ that creates an environment for the T lymphocyte precursors differentiation into the naive T cells, which involves TCR gene rearrangement, negative and positive selection. Despite thymus critical function for the recognition of the pathogens it starts to atrophy very early in life. Thymic Epithelial Space (TES), where thymocyte education occurs, starts to decline rapidly just after birth, while with the onset of puberty thymus is also progressively replaced with adipose tissue. This leads to a reduced naive T cell output,which decreases an organism's ability to recognize pathogens. In our lab, we have already collected and characterized cell type composition and gene regulatory networks of the fetal tand adult human thymus (Park et al, Science 2020). In the current project we plan to expand the number of paediatric and adult samples in the study with the aim to understand how the process of thymic involution happens. We will use single-cell transcriptomics to zoom on changes that occur in immune and non-immune cell types in different phases of thymic involution with the hope to understand which gene expression changes are causal for the thymic involution process and how naïve T cell production can be increased in elderly. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001004311

• Robust methylation-based classification of brain tumours using nanopore sequencing

  Background: DNA methylation-based classification of cancer provides a comprehensive molecular approach to diagnose tumours. In fact, DNA methylation profiling of human brain tumours already profoundly impacts clinical neuro-oncology. However, current implementations using hybridization microarrays are time-consuming and costly. We recently reported on shallow nanopore whole-genome sequencing for rapid and cost-effective generation of genome-wide 5-methylcytosine profiles as input to supervised classification. Here, we demonstrate that this approach allows to discriminate a wide spectrum of primary brain tumours. Results: Using public reference data of 82 distinct tumour entities, we performed nanopore genome sequencing on N=382 tissue samples covering 46 brain tumour (sub)types. Using bootstrap sampling in a cohort of N = 56 cases, we find that a minimum set of 1,000 random CpG features is sufficient for high-confidence classification by ad hoc random forests. We implemented score recalibration as confidence measure for interpretation in a clinical context and empirically determined a platform-specific threshold in a randomly sampled discovery cohort (N = 185). Applying this cut-off to an independent validation series (N = 184) yielded 148 classifiable cases (sensitivity 80.4%) and demonstrated 100 % specificity. Cross-lab validation demonstrated robustness with concordant results across four laboratories in 10/11 (90.9%) cases. In a prospective benchmarking (N = 15), median time to results was 21.1 hours. Conclusions: In conclusion, nanopore sequencing allows robust and rapid methylation-based classification across the full spectrum of brain tumours. Platform-specific confidence scores facilitate clinical implementation for which prospective evaluation is warranted and ongoing.

  Study EGAS00001006540

• Treatment stratification and biomarker validation using patient-derived head and neck cancer organoids

  Background Organoids are three-dimensional structures that can be grown from patient-derived material including carcinomas. These in vitro models can be cryopreserved to generate “living” biobanks that reflect patient heterogeneity. Head and neck cancer (HNC) is a collective term used for tumors arising in the head and neck area, including squamous cell carcinomas (HNSCC) and adenocarcinoma of the salivary gland. Methods to culture patient-derived HNC organoids have previously been described. Methods Organoids were derived from resection or biopsy material of patients included between 2016 and 2022. Organoids were characterised with immunohistochemistry and whole exome sequencing (WES) and exposed to chemo-, radio- and targeted-therapies. CRISPR/Cas9-based gene editing was applied to validate proposed biomarkers. Results We have extended our previously published organoid biobank to a total of 110 organoid models, some of which were characterised by histology and WES. Organoids recapitulated both single nucleotide variants and copy number alterations as found in patient HNSCC samples. Organoid and patient response to radiotherapy (RT; primary (n=6) and adjuvant (n=15)) could be compared in 21 HNC cases. In the adjuvant RT group, organoids derived from patients that relapsed were more resistant to RT in vitro, showing potential for guiding treatment options in this subset of patients. Conclusion Taken together, these results indicate illustrate the potential of organoids as a diagnostic tool in personalised medicine for HNC and indicate the potential of this technology for biomarker discovery and validation within the context of relevant patient heterogeneity.

  Study EGAS00001007076

• Efficacy of nivolumab in pediatric cancers with high mutation burden and mismatch-repair deficiency

  Purpose: Checkpoint-inhibitors have limited efficacy for children with unselected solid and brain tumors. We report the first prospective pediatric trial (NCT02992964) using nivolumab exclusively for refractory non-hematological cancers harboring tumor mutation burden (TMB) ≥5 mutations/megabase (mut/Mb) and/or mismatch-repair deficiency (MMRD). Patients and methods: Twenty patients were screened, and ten ultimately included in the response cohort of whom nine had TMB >10mut/Mb (three initially eligible based on MMRD) and one patient had TMB between 5-10 mut/Mb. Results: Delayed immune responses contributed to best overall response of 50%, improving on initial objective responses (20%) and leading to 2-year overall survival (OS) of 50% (95% CI; 27, 93). Four children, including three with refractory malignant gliomas are in complete remission at a median follow-up of 37-months (range: 32.4-60), culminating in 2-year OS of 43% (95% CI; 18.2, 100). Biomarker analyses confirmed benefit in children with germline MMRD, microsatellite instability, higher activated and lower regulatory circulating T-cells. Stochastic mutation accumulation driven by underlying germline MMRD impacted the tumor microenvironment, contributing to delayed responses. No benefit was observed in the single patient with a MMR-proficient tumour and TMB 7.4 mut/Mb. Conclusions: Nivolumab resulted in durable responses and prolonged survival for the first time in a pediatric trial of refractory hypermutated cancers including malignant gliomas. Novel biomarkers identified here need to be translated rapidly to clinical care to identify children who can benefit from checkpoint-inhibitors, including for upfront management of their cancers.

  Study EGAS00001007393

• shallow WGS and deep targeted panel on ctDNA in rhabdomyosarcomas

  The genomic spectrum of rhabdomyosarcoma (RMS) progression from primary to relapse is not fully understood. In this study we investigate 35 patients with relapsed RMS from two contributing institutions, 18 fusion-positive (FP-RMS) and 17 fusion-negative RMS (FN-RMS). Targeted DNA or whole exome sequencing (WES) was used to detect alterations in paired primary/relapsed samples. In 10 cases, circulating tumor DNA (ctDNA) from multiple timepoints through clinical care and progression was analyzed for feasibility of liquid biopsy in monitoring treatment response/relapse. ctDNA alterations were evaluated using a targeted custom RMS panel (36 genes) at high coverage for single nucleotide variation and fusion detection, and a shallow whole genome sequencing for copy number variation. FP-RMS had a stable genome with relapse, with the most common secondary alterations : CDKN2A/B, MYCN and CDK4 alterations, being already present at diagnosis and impacting overall survival. FP-RMS lacking major secondary events at baseline acquired recurrent MYCN and AKT1 alterations. FN-RMS acquired a higher number of new alterations, most commonly SMARCA2 missense mutations. ctDNA analyses detected pathognomonic variants in all RMS patients at diagnosis, regardless of FP/FN or type of alterations, while at relapse selected alterations were confirmed in 86% of FP-RMS and 100% FN-RMS. Moreover, a higher number of fusion reads was detected with increased disease burden and at relapse in patients following a fatal outcome. These results underscore patterns of tumor progression within a relatively stable genomic landscape and provide rationale for using liquid biopsy to monitor treatment response.

  Study EGAS00001007399

• OMKar

  The whole genome karyotype refers to the sequence of large chromosomal segments that make up an individuals genotype. karyotype analysis, which includes descriptions of aneuploidies and other rearrangements is crucial for understanding genetic risk factors, for diagnosis, treatment decisions, and genetic counseling linked to constitutional disorders. The current karyotyping standard is based on microscopic examination of chromosomes, a complex process that requires high expertise and offers Mb scale resolution. Optical Genome Mapping (OGM) technology can identify large DNA lesions in a cost-effective manner. In this paper, we developed OMKar, a method that uses OGM data to create a virtual karyotype. OMKar processes Structural (SV) and Copy Number (CN) Variants as inputs and encodes them into a compact breakpoint graph. It recomputes copy numbers using Integer Linear Programming to maintain CN balance and then identifies constrained Eulerian paths representing entire donor chromosomes. In tests using 38 whole genome simulations of constitutional disorders, OMKar reconstructed the karyotype with 88% precision and 95% recall on SV concordance and 95% Jaccard score on CN concordance. We applied OMKar to 50 prenatal, 41 postnatal, and 63 parental samples from ten different sites. OMKar reconstructed the correct karyotype in 144 out of 154 samples, covering 25 of 25 aneuploidies, 32 of 32 balanced translocations, and 72 of 82 unbalanced variations. Detected constitutional disorders included Cri-du-chat, Wolf-Hirschhorn, Prader-Willi deletions, Down, and Turner syndromes. Importantly, it identified a plausible genetic mechanism for five cases of constitutional disorder that were not detected by other technologies. Together, these results demonstrate the robustness of OMKar for OGM-based karyotyping.

  Study EGAS00001008245

• CD3 bispecific antibody-induced cytokine release is dispensable for cytotoxic T cell activity

  Study EGAS00001003734

• DAC for Whole Genome Sequencing Expands Diagnostic Utility and Improves Clinical Management in Pediatric Medicine

  Dac EGAC00001000419

• The data access committee for High Grade Serous Ovarian Carcinomas Originate in the Fallopian Tube

  Dac EGAC00001000755

• Data Access Committee for the UCSF Chordoid Glioma of the Third Ventricle Genome Project Dataset

  Dac EGAC00001000776

• Data access committee handling data access requests for biomarker data from the clinical trial IMmotion150.

  Dac EGAC00001000946

• The data access committee for genome-wide cell-free DNA fragmentation in patients with cancer

  Dac EGAC00001001180

• Data access policy for PDTX Breast Cancer RNA-seq data from Ros et al (2020)

  Dac EGAC00001001620

• DAC for "Detection of Gene Fusions using Targeted Next-Generation Sequencing – a Comparative Evaluation"

  Dac EGAC00001001905

• Data Access Committee for study Genetics of Preeclampsia at High Altitudes

  Dac EGAC00001002097

• DAC Committee for the "PIK3CA mutation in a case of CTNNB1 mutant sinonasal glomangiopericytoma" study

  Dac EGAC00001002371

• NPY methylated ctDNA is a promising biomarker for treatment response monitoring in metastatic colorectal cancer

  Dac EGAC00001003001

• EGAD00000000027

  eQTL data for European newborns

  Dataset EGAD00000000027

• EGAD00010000052

  Monozygotic twins that are discordant for schizophrenia (Genotyping)

  Dataset EGAD00010000052

• HC_genotyping

  HC genotyping data for lead SNPs using Illuminia Global Array V2.0

  Dataset EGAD00010002475

• DAC for YCC Sarcoma

  Access can be granted by contacting Hyo Song Kim (hyosong77@yuhs.ac).

  Dac EGAC50000000046

• DAC Endoresist

  Data access committee for the Endoresist project of panel sequencing: Professor Johan Hartman

  Dac EGAC50000000174

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

  Study EGAS00001004288

• A method for multiplexed full-length single-molecule sequencing of the human mitochondrial genome

  Study EGAS00001006280

• Identification of IQCH as a calmodulin-associated protein required for sperm motility in humans

  Study EGAS00001007423

• DAC - A standardised framework for robust fragmentomic feature extraction from cell-free DNA sequencing data

  Dac EGAC00001003530

• CReATe Fertility Centre DAC

  DAC for the management of dataset own by CReATe Fertility Centre.

  Dac EGAC50000000646

• Human glioblastoma single cell DAC (Linnarsson)

  DAC for human glioblastoma single cell sequencing samples

  Dac EGAC50000000575

• Human developing meninges single cell DAC (Linnarsson)

  DAC for human developing meninges single cell sequencing

  Dac EGAC50000000576

• Asan Medical Center Data Access Committee

  Asan Medical Center Data Access Committee for BRISK study.

  Dac EGAC50000000733

• Hepatocellular Carcinoma Spatial transcriptomics Data Access Committee

  This is the DAC for study ST HCC

  Dac EGAC50000000635

• Pan Prostate Cancer Group Data Access Control Committee

  DACC for the PanProstate Cancer Group projects

  Dac EGAC50000000602

• GenomeWideData_for_Present_Day_Peruvian_individuals_living_in_urban_areas

  Genome-wide data for 432 Admixed individuals from Peru

  Dataset EGAD00010002795

• DAC SysMed

  Data access control for datasets submitted by SysMed

  Dac EGAC50000000089

• MOSAIC Window DAC

  Data Access Committee for the MOSAIC Window initiative.

  Dac EGAC50000000398

• DAC for Genetic and Microenvironmental Analysis of PTCL at Department of Hematology, University of Tsukuba

  Dac EGAC50000000600

• Genome sequencing in monozygotic twins discordant for Mayer-Rokitansky-Küster-Hauser syndrome

  Study EGAS00001006371

• Exome reads

  Exome sequencing for 2 infertile brothers

  Dataset EGAD00001003193

• Exome data for PDXs

  Exome data of PDX models.

  Dataset EGAD00001001863

• Whole-Genome Sequencing in Multiplex Epilepsy Families

  Epilepsy is one of the most common neurologic disorders, affecting approximately 4% of individuals at some time in their lives. More than 30% of people with epilepsy continue to have seizures despite treatment, and improved approaches to treatment and prevention are sorely needed. In the search for new strategies to reduce the burden of disease, the discovery of specific genes that influence risk offers a novel opportunity to clarify pathogenic mechanisms, identify susceptible individuals prior to seizure onset, and treat and prevent seizures in people at risk. Despite clear evidence of the importance of genetics in susceptibility to epilepsy, only limited progress has been made in identifying the specific genes that influence risk. One of the greatest challenges for genetic research on this disorder is its extreme clinical and genetic heterogeneity. Although epilepsy is broadly defined by recurrent unprovoked seizures, it is so variable in its clinical manifestations, natural history, and treatment response that most epileptologists view it as a collection of different syndromes ("epilepsies") with distinct etiologies. The genetic effects on susceptibility are also likely to be extremely variable, ranging from rare variants with high penetrance (some of which produce Mendelian patterns of inheritance) to common variants with low penetrance. Recent findings strongly suggest that rare gene variants play a major role in the genetic architecture of the epilepsies. The purpose of this study was to discover new genetic risk factors for epilepsy. The primary approach was to use whole-genome sequencing to interrogate classes of genetic variants, including very rare variants, in multiplex families. Our main hypothesis was that, in at least some proportion of these families, a single variant would explain all instances of epilepsy. Variants identified within the families could then be tested for cosegregation within the family and also validated by seeing enrichment in sporadic epilepsy cases. Furthermore, understanding the impact of rare variation in epilepsy also has the potential to provide insight into the genetic architecture of other complex human diseases. Our analysis focused on families containing multiple individuals with non-acquired (idiopathic or unknown cause) epilepsy under the hypothesis that these would be enriched for genetic control. The families studied had been previously collected and phenotyped in detail, and contain an average of 3.8 individuals per family with a range of different types of epilepsy. We selected one or two affected individuals from each family for sequencing. This work has generated NGS data on 60 samples from 29 multiplex epilepsy families. We have established that, under a monogenic model of inheritance, sequencing pairs of distantly related relatives is an effective method for reducing the number of candidate variants. Critically, we have found that no single variant can explain a large proportion of these epilepsy families. This work emphasizes that identifying causal variants among the many genetic candidates found in this work will require very large sample sizes and gene-based analyses.

  Study phs000690

• Whole transcriptome RNA sequencing as comprehensive diagnostic tool for acute myeloid leukemia.

  BackgroundAcute myeloid leukemia (AML) is characterized by uncontrolled proliferation of malignant hematopoietic cells in the bone marrow that are arrested in differentiation. In AML pathogenesis, hematopoietic progenitor cells acquire multiple genetic aberrations often occurring in the same set of genes that ultimately lead to malignant transformation. In the WHO classification 2016, six AML classes with different prognosis are identified by chromosomal translocations measured by standard cytogenetics. All balanced translocations produce fusion genes, except for t(3;3)/inv(3;3), which lead to overexpression of MECOM/EVI1 associated with poor prognosis. For accurate risk assessment of cytogenetically normal AML, four genes need to be screened to distinguish AML with mutated NPM1 in the absence of FLT3 mutations and AML with bi-allelic CEBPA mutations, which have a favorable prognosis, and AML with RUNX1 mutations which have a poor prognosis. Recently, a full genomic classification system has been proposed by Papaemmanuil et al. (NEJM 2016). In this system, the same six classes with chromosomal translocations are identified by standard cytogenetics as well as five additional classes characterized by genetic mutations in 14 different genes. AimThe aim of the study was to investigate whether whole transcriptome RNA-sequencing (RNAseq) can be used as single technology for classification of AML. MethodA panel of 100 AML were analyzed and a bio-informatics pipeline called HAMLET (Human AML Expedited Transcriptomics) was developed in which 4 modules are integrated to detect (1) fusion genes, (2) small variants in 13 recurrently mutated genes, (3) internal tandem duplications in FLT3 (FLT3-ITD) and partial tandem duplications in KMT2A (KMT2A-PTD) and (4) overexpression of MECOM/EVI1. All mutations that were called by HAMLET were validated by diagnostic data as available for all 100 AML or targeted PCR followed by Sanger or next generation sequencing on all positive AML and at least an equal number of negative cases. Results & discussionThe data showed that HAMLET accurately identified all genetic aberrations with high sensitivity and specificity. In addition, in 7 AML, fusion transcripts were detected that are not measured by standard cytogenetics including three cases that lack any class-defining lesion according to Papaemmanuil et al. Moreover, overexpression of MECOM/EVI1 was detected in two AML with inv(3) as well as in five cases without inv(3)/t(3;3), and a gene signature was measured to distinguish AML with CEBPA mutations with favorable prognosis. In conclusion, HAMLET provides a comprehensive and flexible pipeline for RNAseq analysis to retrieve all relevant information for current classification of AML as well as additional information that may improve classification in the future.

  Study EGAS00001003096

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

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

  Study phs000938

• GWAS for Genetic Determinants of Bone Fragility in European-American Premenopausal Women

  Osteoporotic fractures are largely due to an increased propensity to fall with aging and a reduction in bone strength. Although skeletal architecture contributes to fracture risk, bone mineral density (BMD) is the most important determinant of bone strength and fracture risk. Between 60 and 80% of the variance of BMD of adult Caucasian women is due to heritable factors. Final BMD is a function of peak bone mass attained during young adulthood and the subsequent rate of bone loss, which occurs as a result of both post-menopausal estrogen loss and aging. The evidence for a genetic contribution to rate of loss in BMD is substantially weaker than that for peak BMD. Therefore, we have focused our sample collection on the recruitment of premenopausal women, in whom we have sought to identify the genes influencing peak BMD at the spine and hip, the two major skeletal sites of osteoporotic fracture. The primary goal of this study is to identify genes that affect peak BMD in premenopausal women. Identification of these genes may: 1) lead to molecular tests that predict risk of osteoporosis and allow institution of early preventive measures; 2) provide insight into basic bone cell biology and other factors that affect peak BMD; and 3) provide molecular targets for therapeutic agents to increase BMD.

  Study phs000138

• 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

• NHLBI TOPMed: Novel Risk Factors for the Development of Atrial Fibrillation in Women

  The Women's Genome Health Study (WGHS) is a prospective cohort comprised of over 25,000 initially healthy female health professionals enrolled in the Women's Health Study, which began in 1992-1994. All participants in WGHS provided baseline blood samples and extensive survey data. Women who reported atrial fibrillation during the course of the study were asked to report diagnoses of AF at baseline, 48 months, and then annually thereafter. Participants enrolled in the continued observational follow-up who reported an incident AF event on at least one yearly questionnaire were sent an additional questionnaire to confirm the episode and to collect additional information. They were also asked for permission to review their medical records, particularly available ECGs, rhythm strips, 24-hour ECGs, and information on cardiac structure and function. For all deceased participants who reported AF during the trial and extended follow-up period, family members were contacted to obtain consent and additional relevant information. An end-point committee of physicians reviewed medical records for reported events according to predefined criteria. An incident AF event was confirmed if there was ECG evidence of AF or if a medical report clearly indicated a personal history of AF. The earliest date in the medical records when documentation was believed to have occurred was set as the date of onset of AF.

  Study phs001040

• Investigating Host Genetic Risk Factors for Tuberculosis in Highly Endemic South African Populations

  It is apparent from our recent population genetic and admixture mapping work 1–3 that a substantial part of the ancestral input into the South African Coloured (SAC) population is from the San or Khoe groups, which are not well represented in publicly available genetic databases. It is a reasonable assumption that the SAC population in the Western Cape may have derived more genetic input from the present KhoeSan population in the Northern Cape than the San residing in Namibia. It was concluded by our group that it is indeed the southern African KhoeSan group, the ≠Khomani, that best represent the KhoeSan contribution seen in the SAC 4. More recently, it has been shown that there are ancestry related increases in TB susceptibility, especially with increased Bantu-speaking African and KhoeSan ancestry 5. We aim to elucidate the epidemiological and human-host genetic risk factors for TB and the immunological pathways modulating TB infection. The proposed sub-study will enroll people evaluated for TB at Northern Cape community health clinics and their contacts from their households and/or community. We will conduct a demographic interview, collect saliva, which will be used for genetic analysis, and blood to determine latent TB infection status and capture immunological responses to mycobacterial infections at the cellular level and RNA sequencing.

  Study EGAS00001007850

• A Cancer Cell-Line Titration Series for Evaluating Somatic Classification

  Accurate detection of somatic single nucleotide variants and small insertions and deletions from DNA sequencing experiments of tumour-normal pairs is a challenging task. Tumour samples are often contaminated with normal cells confounding the available evidence for the somatic variants. Furthermore, tumours are heterogeneous so sub-clonal variants are observed at reduced allele frequencies. We present here a cell-line titration series dataset that can be used to evaluate somatic variant calling pipelines with the goal of reliably calling true somatic mutations at low allele frequencies. Cell-line DNA was mixed with matched normal DNA at 8 different ratios to generate samples with known tumour cellularities, and exome sequenced on Illumina HiSeq to depths of >300x. The data was processed with several different variant calling pipelines and verification experiments were performed to assay >1,500 somatic variant candidates using Ion Torrent PGM as an orthogonal technology. By examining the variants called at varying cellularities and depths of coverage, we show that the best performing pipelines are able to maintain a high level of precision at any cellularity. In addition, we estimate the number of true somatic variants undetected as cellularity and coverage decrease. Our cell-line titration series dataset, along with the associated verification results, is effective for this evaluation and will serve as a valuable dataset for future somatic calling algorithm development.

  Study EGAS00001001016

• Single cell transcriptional characterization of human megakaryocyte lineage commitment and maturation

  Single cell RNA-sequencing of sternal bone marrow reciding Hematopoietic Stem Cells (HSCs) and Megakaryocytes (MKs) from individuals undergoing elective open heart valve replacement. HSCs were defined as Lineage-, CD34+, CD38-, CD45RA-, CD90+, CD49f+ cells. MKs where CD41a+, CD42b+ and ploidy was determined with Hoechst. A sternal bone marrow scraping was taken directly following median sternotomy using a Volkmann’s spoon. The sample was collected into an EDTA Vacutainer tube containing 1.8mg/ml EDTA. 4mL of Dulbecco’s phosphate buffered saline (PBS, Sigma) containing 10% human serum albumin (HSA, Gemini Bio Products) was added and the whole volume was resuspended by pipetting 2-3 times. The sample was then put on metallic thermal beads (ThermoFisher Scientific) at a temperature between 0-4°C and transported to the University of Cambridge for further processing. For HSC isolation the cells were stained with the following antibody cocktail: PECy5 conjugated anti-lineage specific antibodies: CD2 (BD), CD3 (BD), CD10 (BD), CD11b (BD), CD11c (BD), CD19 (BD), CD20 (BD), CD56 (BD), biotinylated CD42b (Pab5, NHS Blood and Transplant, International Blood Group Reference Laboratory [IBGRL]), biotinylated GP6 (Pab5, NHS Blood and Transplant, International Blood Group Reference Laboratory [IBGRL]) used in combination with PECy5 conjugated streptavidin (Biolegend). Alexa Fluor 700 conjugated anti-CD34 (BD), PerCP-Cy5.5 conjugated anti-CD38 (BD), Pacific Blue conjugated anti-CD45RA (Invitrogen), PECy7 conjugated anti-CD90 (BD),PE conjugated anti-CD49f (BD). After staining cells were kept at 4°C before sorting using a FACS Aria Fusion flow sorter (BD). Single HSCs defined as Lineage-, CD34+, CD38-, CD45RA-, CD90+, CD49f+ cells were sorted by FACS directly into individual wells of a 96-well plate. Index sort data was collected for each single cell. For MK isolation the cells were stained for surface MK markers with mouse anti-human CD41a APC conjugated antibody (BD) and mouse anti-human CD42b PE conjugated antibody (BD) and for ploidy analysis with 1ug/ml Hoechst 33342 (Invitrogen). After incubation at 37°C for 30 minutes, the cells were kept at 4°C before sorting using a FACS Aria Fusion flow sorter (BD). Single cells and MK pools of 20 cells were sorted by FACS according to ploidy level using a 100uM nozzle directly into individual wells of a 96-well plate. cDNA synthesis and poly(A) enrichment was performed following the G&T-seq protocol (Macaulay et al. 2015), a variation of the Smart-seq2 protocol1. ERCC spike-in RNA (Ambion) was added to the lysis buffer in a dilution of 1:4,000,000.

  Dataset EGAD00001006677

• RNA-sequencing of human skin samples obtained from SSc patients and healthy controls.

  Patient information SSc patients were recruited at the Department of Rheumatology of the Leiden University Medical Center (Leiden, The Netherlands). All patients met the American Rheumatism Association classification criteria for SSc (Subcommittee for scleroderma criteria 1980), and were classified according to LeRoy and Medsger criteria as either limited or diffuse cutaneous disease (LeRoy EC, Black C, Fleischmajer R, Jablonska S, Krieg T, Medsger TA Jr, Rowell N 1988). Institutional review board approval and written informed consent was obtained before patients entered this study. Two 4 mm skin biopsies were taken from a standardized location on the most proximal part of the lower arm, distal from the elbow. In 10 patients the skin biopsy came from a clinically affected area and in 4 patients the skin was locally unaffected. One sample was used for RNA sequencing and one sample was used for immunohistochemistry. Skin biopsies from healthy individuals were commercially sourced (Tissue Solutions, UK) and collected from donors undergoing skin resection surgery and after informed consent. To match the healthy skin with patients as much as possible, skin biopsies from healthy controls were also taken from a similar position (the under-arm (for 4 controls) and leg (for 2 controls)). Healthy skin donors were selected to match the age and sex of the SSc patient cohort. Biopsies from patients and controls were equally treated and were both stored at -80°C until RNA isolation was performed. RNA from frozen skin biopsies was isolated using RNeasy kit from fibrous tissue (Qiagen, the Netherlands). RNA quantity was determined by using SimplyNano 2000 and quality was assessed on Tapestation (Agilent, the Netherlands). All samples included in the study had a RIN score above 7.0. Transcriptome characterisation and analysis RNA sequencing was performed using polyA selection and a stranded protocol using Ion Torrent next generation sequencing technology (Service XS, The Netherlands). The Ion PI Template OT2 200 Kit v3 and Ion PI Sequencing 200 Kit v3 were used according to the manufacturer’s instructions. 20 samples were run on 11 PI chips. PI chip analyses, base calling and quality checks were performed using the Torrent Server Suite. An average of 42 million 100 bp reads was generated per sample. Following quality control, reads were aligned to the human genome (Homo sapiens GRh38.78) using Bowtie2 and STAR (Dobin et al. 2013; Langmead and Salzberg 2012). Reads were first aligned with STAR. For the unmapped reads from STAR, a second alignment step was performed using bowtie2 (local very sensitive options)

  Dataset EGAD00001003832

• Genome-Wide Association Study of Patients with Coccidioidomycosis

  BackgroundValley Fever is typically an infection of the lungs caused by the fungi Coccidioides immitis and Coccidioides posadasii. The incidence of Coccidioidomycosis (CM), or infection with Coccidioides, has dramatically increased over the last 20 years. This is particularly true in the Southwest of the United States, where people often breathe fungal spores that arise from the soil. Reasons for increased infection rates are thought to include population growth and construction in these endemic regions, an increase in the number of people whose immune systems are compromised due to infection or treatment with drugs required for organ transplants, climate change, as well as improved testing practices and greater physician awareness. Mild CM most commonly presents itself with flu-like symptoms and rashes, which can last weeks to months. Individuals with compromised immune systems, specifically-- substantial suppression of the immune cells known as T cells, can develop severe pulmonary and disseminated disease. Infection that remains localized to the lungs is referred to as pulmonary disease, but when the infection spreads out of the lungs into other parts of the body it represents a more serious condition referred to as a disseminated disease, or disseminated CM. In nature, Coccidioides spp. exists as mold and lives in dust and soil. When the contaminated soil or dust is disturbed by human activity, animals, or weather, the Coccidiodies spores are released into the air. Airborne spores are taken up by breathing and settle in the lungs. Once in the moist and warm environment of the lung, spores transform into spherules, which divide and become filled with smaller spores, called endospores. When the spherules get large enough, they rupture and release these endospores, which can spread and disseminate to surrounding tissue. The cycle then repeats itself as these endospores develop into new spherules3. Different ethnic groups have been described to vary in their susceptibility to developing disseminated CM after initial infection with Coccidioides. For example, evidence suggests that African-American and Filipino patients suffer the disseminated disease at a greater rate than other ethnicities. The suggestion that race plays a role in the clinical expression of the disease is still a source of debate amongst the scientific community and any genetic mechanisms responsible for these differences have yet to be fully elucidated. If our genetic makeup influences our ability to limit the spread of infection, finding which DNA differences cause these variances could provide clues to how the body successfully fights infection, and provide opportunities to boost the body’s ability to do this. Further, if we are able to identify the specific genetic risk factors that correlate with the development of disseminated infection, physicians could perform genetic screenings to identify high-risk patients and provide them with preemptive antifungal therapy prior to developing disseminated disease.The genome, made up of DNA, contains all of the information needed for humans to develop and grow. Genome-wide association studies (GWAS) allow us to look for inherited differences that are more common between people who share a particular trait, for example, height or susceptibility to certain diseases, compared to those who do not share the trait. Although some traits and diseases are controlled by a single gene, the majority are influenced by contribution from several, or even many, different genes. To find evidence of genes that contribute to specific traits, GWAS typically compares genome information from large numbers of people who have a particular disease (referred to as “cases”) looking for DNA sequences that are common among these samples, and are different from DNA sequences seen in large numbers of people who lack the trait, but are as much like the cases as possible (referred to as “controls”). The DNA sequence data from each group, cases versus controls, are analyzed to see if there are specific genomic differences that tend to be associated with the disease. MethodsTwo separate GWAS approaches were taken to look for genetic differences that could be responsible for the observed differences between the different patient populations we are studying. The first method, known as genotyping, scans for differences at a set of positions across the genome, which includes both the genes that encode our proteins and the larger amount of DNA that does not. The second method, known as exome sequencing, allows us to compare the entire sequence of the portion of the genome that codes for proteins.  For this study, DNA from patients with either pulmonary or disseminated CM were genotyped and exome sequenced to look for DNA differences that are associated with one condition or the other. All patients were at least 18 years old, had no evidence of immunosuppression, and had proven or probable pulmonary coccidioidomycosis according to established diagnostic criteria. Of these patients, a subset demonstrated disseminated disease, i.e., they showed evidence of coccidioidal infection outside of the thorax by biopsy/aspiration, had radiographic imaging, and show positive coccidioidal serology. Our criteria for including patients with the pulmonary disease were that they must not require ongoing antifungal treatment or show evidence of active CM (in skin test positive patients), show no evidence of extrapulmonary dissemination, and have no evidence of ongoing pulmonary infection (pulmonary nodules are accepted) beyond six months from diagnosis.Patient DNA was purified from blood or from sputum samples by the labs of our collaborators, Drs. George Thompson (UC Davis School of Medicine) and John Galgiani (University of Arizona Health Sciences). Genome-wide association (GWAS) analysis was carried out to look for candidate loci associated with pulmonary versus disseminated disease, taking into account the population structure of the samples. Single nucleotide or insertion/deletion variants were identified from whole-exome sequences (WES) using the Picard/BWA/GATK pipeline. ResultsTable 1. Pulmonary versus Disseminated Cases of Coccidiomycosis for GWAS, Sorted by EthnicityEthnicityPulmonary CasesDisseminated CasesAsian85Black/African American1664Caucasian/White4015Filipino03Hispanic/Latino3414Indian21Mexican American1039Pacific Islander01Samoan03Vietnamese10Unknown16917More than one race02Total373134Table 1 shows the number of samples analyzed from patients with pulmonary versus disseminated disease, and patient ethnicity, where known. In all, we worked with 507 samples, including 134 samples from patients with disseminated disease and 373 samples from patients with pulmonary disease. Of these, 505 samples were genotyped using the Multi-Ethnic Global Array from Illumina Inc. In addition, we were able to generate whole-exome sequence from 498 patient samples. No significant associations were detected that differed between samples from patients with pulmonary versus disseminated disease; that is, no particular DNA sequences were found to be significantly enriched in patients with disseminated disease compared to patients with pulmonary disease. The ability to detect genetic association between specific sequences and genetically determined traits is influenced by several factors, including how many patient samples are available to compare, how many different genes contribute to the trait and how strong their contributions are. When the number of genes is small and the contribution of each gene is great, smaller numbers of patient samples are needed to detect an association. When more genes are involved, or the contribution from each gene is more modest, larger numbers of patient samples must be examined. While we were not able to detect any associated within this study, it does not mean that subsequent studies would not find this connection. Our study suggests significantly more samples should be analyzed in further studies.Whole-exome sequences were generated from 498 samples and were aligned to reference sequences to identify positions where the sequences differed from the reference. These data are being analyzed to determine if any variants are associated with pulmonary, versus disseminated, disease. 

  Study phs002170

• Progress in Diabetes Genetics in Youth (ProDIGY) Exome Sequencing Study: SEARCH for Diabetes in Youth

  The ~52,000 sample Type 2 Diabetes Exome Sequencing project is a collaboration of six consortia with various funding mechanisms that have joined together to investigate genetic variants for type 2 diabetes (T2D) using the largest T2D case/control sample set compiled to date. This includes samples from: Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) Genetics of Type 2 Diabetes (GoT2D) Exome Sequencing Project (ESP) Slim Initiative in Genomic Medicine for the Americas (SIGMA) Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCAMP) Progress in Diabetes Genetics in Youth (ProDIGY) This data generated from the SEARCH for Diabetes in Youth cohort was part of the ProDIGY (Progress in Diabetes Genetics in Youth) consortium, which is a NIDDK-funded research consortium that investigated genetic variants for childhood type 2 diabetes (T2D). This consortium involved sequencing multiethnic samples from childhood T2D cases located across the United States. The individuals were obtained from over 20 cohorts across the 6 consortia that are listed in Table 1. The strategy was to perform deep exome sequencing of individuals, 24,991 with T2D and 24,953 controls, divided among five ancestry groups: Europeans, East Asians, South Asians, American Hispanics, and African Americans. The T2D-GENES, ProDIGY and SIGMA studies, sequencing was performed at the Broad Institute using the Agilent v2 capture reagent or Illumina Rapid Capture on Illumina HiSeq machines. Please note that while we summarize the full sample list in publications and below, two of the cohorts below are not in dbGAP, due to the samples not being consented for deposition. This includes the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) study and Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCamp) study. The Exome Sequencing Project (ESP) was deposited in dbGAP as part of their initial study and the phs numbers for that project can be found here: https://esp.gs.washington.edu/drupal/dbGaP_Releases. Table 1. 52,000 sample T2D Case/Control Whole Exome Sequencing Studies table, th, td { border: 1px solid black; border-collapse: collapse; } Ancestry Consortia Study Countries of Origin # Cases # Controls African American T2D-GENES Project 1 Jackson Heart Study US 500 526 African American T2D-GENES Project 1 Wake Forest School of Medicine Study US 518 530 African American ESP Exome Sequencing Project (ESP) US 467 1374 African American T2D-GENES Follow up study BioMe Biobank Program (BioMe) US 1297 1256 East Asian T2D-GENES Project 1 Korea Association Research Project Korea 526 561 East Asian T2D-GENES Project 1& Follow up Study Singapore Diabetes Cohort Study; Singapore Prospective Study Program Singapore (Chinese) 1486 1568 East Asian T2D-GENES Follow up study Korea SNUH South Korea 450 475 East Asian T2D-GENES Follow up study Research Studies in Hong Kong (Hong Kong) Hong Kong 493 485 European T2D-GENES Project 1 Ashkenazi US, Israel 506 355 European T2D-GENES Project 1 Metabolic Syndrome in Men Study (METSIM) Finland 484 498 European GoT2D Finland-United States Investigation of NIDDM Genetics (FUSION) Study Finland 472 476 European GoT2D Kooperative Gesundheitsforschung in der Region Augsburg (KORA) Germany 97 90 European GoT2D UK Type 2 Diabetes Genetics Consortium (UKT2D) UK 322 320 European GoT2D Malmo-Botnia Study Finland, Sweden 478 443 European LuCamp Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCamp) Denmark 997 997 European ESP Exome Sequencing Project (ESP) US 390 2843 European T2D-GENES Follow up study Genetics of Diabetes and Audit Research Tayside Study (GoDARTS) Scotland, UK 960 966 European T2D-GENES Follow up study Framingham Heart Study (FHS) US 396 596 Hispanic T2D-GENES Project 1 San Antonio Family Heart Study, San Antonio Family Diabetes/ Gallbladder Study, Veterans Administration Genetic Epidemiology Study, and the Investigation of Nephropathy and Diabetes Study Family Component US 272 218 Hispanic T2D-GENES Project 1 & SIGMAv2 Starr County, Texas US 1762 1738 Hispanic SIGMAv1 Mexico City Diabetes Study Mexico 281 549 Hispanic SIGMAv1 & v2 Multiethnic Cohort (MEC) US 1476 1443 Hispanic SIGMAv1 & v2 UNAM/INCMNSZ Diabetes Study (UIDS) Mexico 1998 1977 Hispanic SIGMAv1 & v2 Diabetes in Mexico Study (DMS) Mexico 1522 1546 Multi ethnic ProDIGY Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) US 3097 0 Multi ethnic ProDIGY SEARCH for Diabetes in Youth (SEARCH) US 553 0 South Asian T2D-GENES Project 1 London Life Sciences Population Study (LOLIPOP) UK (Indian Asian) 551 538 South Asian T2D-GENES Project 1 & Follow up study Singapore Indian Eye Study Singapore (Indian Asian) 1640 1478 South Asian T2D-GENES Follow up study Pakistan Risk of Myocardial Infarction Study (PROMIS) Pakistan 914 932 The SEARCH study contributed 553 cases and 0 controls to 52k T2D exome sequencing study.

  Study phs001511

• Slim Initiative in Genomic Medicine for the Americas (SIGMA): Diabetes in Mexico Study (DMS)

  The ~52,000 sample Type 2 Diabetes Exome Sequencing project is a collaboration of six consortia with various funding mechanisms that have joined together to investigate genetic variants for type 2 diabetes (T2D) using the largest T2D case/control sample set compiled to date. This includes samples from: Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) Genetics of Type 2 Diabetes (GoT2D) Exome Sequencing Project (ESP) Slim Initiative in Genomic Medicine for the Americas: Type 2 Diabetes (SIGMA T2D) Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCAMP) Progress in Diabetes Genetics in Youth (ProDIGY) This data generated from the SIGMA Diabetes in Mexico Study (DMS) was part of the Slim Initiative in Genomic Medicine for the Americas: Type 2 Diabetes (SIGMA T2D), which is an international research consortium funded by the Carlos Slim Foundation that seeks to identify the genetic risk factors for type 2 diabetes (T2D) in Mexico and Latin America and translate those findings into improved treatment and prevention of diabetes. The SIGMA T2D project has sequenced and genotyped more than 13,000 samples from Mexican and Mexican Americans. The individuals were obtained from over 20 cohorts across the 6 consortia that are listed in Table 1. The strategy was to perform deep exome sequencing of individuals, 24,991 with T2D and 24,953 controls, divided among five ancestry groups: Europeans, East Asians, South Asians, American Hispanics, and African Americans. The T2D-GENES, ProDIGY and SIGMA studies, sequencing was performed at the Broad Institute using the Agilent v2 capture reagent or Illumina Rapid Capture on Illumina HiSeq machines. Please note that while we summarize the full sample list in publications and below, two of the cohorts below are not in dbGaP, due to the samples not being consented for deposition. This includes the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) study and Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCamp) study. The Exome Sequencing Project (ESP) was deposited in dbGaP as part of their initial study and the phs numbers for that project can be found here: https://esp.gs.washington.edu/drupal/dbGaP_Releases. Table 1. 52,000 sample T2D Case/Control Whole Exome Sequencing Studies Ancestry Consortia Study Countries of Origin # Cases # Controls African American T2D-GENES Project 1 Jackson Heart Study US 500 526 African American T2D-GENES Project 1 Wake Forest School of Medicine Study US 518 530 African American ESP Exome Sequencing Project (ESP) US 467 1374 African American T2D-GENES Follow up study BioMe Biobank Program (BioMe) US 1297 1256 East Asian T2D-GENES Project 1 Korea Association Research Project Korea 526 561 East Asian T2D-GENES Project 1& Follow up Study Singapore Diabetes Cohort Study; Singapore Prospective Study Program Singapore (Chinese) 1486 1568 East Asian T2D-GENES Follow up study Korea SNUH South Korea 450 475 East Asian T2D-GENES Follow up study Research Studies in Hong Kong (Hong Kong) Hong Kong 493 485 European T2D-GENES Project 1 Ashkenazi US, Israel 506 355 European T2D-GENES Project 1 Metabolic Syndrome in Men Study (METSIM) Finland 484 498 European GoT2D Finland-United States Investigation of NIDDM Genetics (FUSION) Study Finland 472 476 European GoT2D Kooperative Gesundheitsforschung in der Region Augsburg (KORA) Germany 97 90 European GoT2D UK Type 2 Diabetes Genetics Consortium (UKT2D) UK 322 320 European GoT2D Malmo-Botnia Study Finland, Sweden 478 443 European LuCamp Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCamp) Denmark 997 997 European ESP Exome Sequencing Project (ESP) US 390 2843 European T2D-GENES Follow up study Genetics of Diabetes and Audit Research Tayside Study (GoDARTS) Scotland, UK 960 966 European T2D-GENES Follow up study Framingham Heart Study (FHS) US 396 596 Hispanic T2D-GENES Project 1 San Antonio Family Heart Study, San Antonio Family Diabetes/ Gallbladder Study, Veterans Administration Genetic Epidemiology Study, and the Investigation of Nephropathy and Diabetes Study Family Component US 272 218 Hispanic T2D-GENES Project 1 & SIGMAv2 Starr County, Texas US 1762 1738 Hispanic SIGMAv1 Mexico City Diabetes Study Mexico 281 549 Hispanic SIGMAv1 & v2 Multiethnic Cohort (MEC) US 1476 1443 Hispanic SIGMAv1 & v2 UNAM/INCMNSZ Diabetes Study (UIDS) Mexico 1998 1977 Hispanic SIGMAv1 & v2 Diabetes in Mexico Study (DMS) Mexico 1522 1546 Multi ethnic ProDIGY Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) US 3097 0 Multi ethnic ProDIGY SEARCH for Diabetes in Youth (SEARCH) US 553 0 South Asian T2D-GENES Project 1 London Life Sciences Population Study (LOLIPOP) UK (Indian Asian) 531 538 South Asian T2D-GENES Project 1 & Follow up study Singapore Indian Eye Study Singapore (Indian Asian) 1640 1478 South Asian T2D-GENES Follow up study Pakistan Genomic Resource (PGR) Pakistan 914 932 The Diabetes in Mexico Study (DMS) study contributed 1,522 cases and 1,546 controls to the 52k T2D exome sequencing study.

  Study phs001388

• Slim Initiative in Genomic Medicine for the Americas (SIGMA): Mexico City Diabetes Study (MCDS)

  The ~52,000 sample Type 2 Diabetes Exome Sequencing project is a collaboration of six consortia with various funding mechanisms that have joined together to investigate genetic variants for type 2 diabetes (T2D) using the largest T2D case/control sample set compiled to date. This includes samples from: Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES) Genetics of Type 2 Diabetes (GoT2D) Exome Sequencing Project (ESP) Slim Initiative in Genomic Medicine for the Americas: Type 2 Diabetes (SIGMA T2D) Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCAMP) Progress in Diabetes Genetics in Youth (ProDIGY) This data generated from the SIGMA Mexico City Diabetes Study was part of the Slim Initiative in Genomic Medicine for the Americas: Type 2 Diabetes (SIGMA T2D), which is an international research consortium funded by the Carlos Slim Foundation that seeks to identify the genetic risk factors for type 2 diabetes (T2D) in Mexico and Latin America and translate those findings into improved treatment and prevention of diabetes. The SIGMA T2D project has sequenced and genotyped more than 13,000 samples from Mexican and Mexican Americans. The individuals were obtained from over 20 cohorts across the 6 consortia that are listed in Table 1. The strategy was to perform deep exome sequencing of individuals, 24,991 with T2D and 24,953 controls, divided among five ancestry groups: Europeans, East Asians, South Asians, American Hispanics, and African Americans. The T2D-GENES, ProDIGY and SIGMA studies, sequencing was performed at the Broad Institute using the Agilent v2 capture reagent or Illumina Rapid Capture on Illumina HiSeq machines. Please note that while we summarize the full sample list in publications and below, two of the cohorts below are not in dbGaP, due to the samples not being consented for deposition. This includes the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) study and Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCamp) study. The Exome Sequencing Project (ESP) was deposited in dbGaP as part of their initial study and the phs numbers for that project can be found here: https://esp.gs.washington.edu/drupal/dbGaP_Releases Table 1. 52,000 sample T2D Case/Control Whole Exome Sequencing Studies Ancestry Consortia Study Countries of Origin # Cases # Controls African American T2D-GENES Project 1 Jackson Heart Study US 500 526 African American T2D-GENES Project 1 Wake Forest School of Medicine Study US 518 530 African American ESP Exome Sequencing Project (ESP) US 467 1374 African American T2D-GENES Follow up study BioMe Biobank Program (BioMe) US 1297 1256 East Asian T2D-GENES Project 1 Korea Association Research Project Korea 526 561 East Asian T2D-GENES Project 1& Follow up Study Singapore Diabetes Cohort Study; Singapore Prospective Study Program Singapore (Chinese) 1486 1568 East Asian T2D-GENES Follow up study Korea SNUH South Korea 450 475 East Asian T2D-GENES Follow up study Research Studies in Hong Kong (Hong Kong) Hong Kong 493 485 European T2D-GENES Project 1 Ashkenazi US, Israel 506 355 European T2D-GENES Project 1 Metabolic Syndrome in Men Study (METSIM) Finland 484 498 European GoT2D Finland-United States Investigation of NIDDM Genetics (FUSION) Study Finland 472 476 European GoT2D Kooperative Gesundheitsforschung in der Region Augsburg (KORA) Germany 97 90 European GoT2D UK Type 2 Diabetes Genetics Consortium (UKT2D) UK 322 320 European GoT2D Malmo-Botnia Study Finland, Sweden 478 443 European LuCamp Lundbeck Foundation Centre for Applied Medical Genomics in Personalised Disease Prediction, Prevention, and Care (LuCamp) Denmark 997 997 European ESP Exome Sequencing Project (ESP) US 390 2843 European T2D-GENES Follow up study Genetics of Diabetes and Audit Research Tayside Study (GoDARTS) Scotland, UK 960 966 European T2D-GENES Follow up study Framingham Heart Study (FHS) US 396 596 Hispanic T2D-GENES Project 1 San Antonio Family Heart Study, San Antonio Family Diabetes/ Gallbladder Study, Veterans Administration Genetic Epidemiology Study, and the Investigation of Nephropathy and Diabetes Study Family Component US 272 218 Hispanic T2D-GENES Project 1 & SIGMAv2 Starr County, Texas US 1762 1738 Hispanic SIGMAv1 Mexico City Diabetes Study Mexico 281 549 Hispanic SIGMAv1 & v2 Multiethnic Cohort (MEC) US 1476 1443 Hispanic SIGMAv1 & v2 UNAM/INCMNSZ Diabetes Study (UIDS) Mexico 1998 1977 Hispanic SIGMAv1 & v2 Diabetes in Mexico Study (DMS) Mexico 1522 1546 Multi ethnic ProDIGY Treatment Options for Type 2 Diabetes in Adolescents and Youth (TODAY) US 3097 0 Multi ethnic ProDIGY SEARCH for Diabetes in Youth (SEARCH) US 553 0 South Asian T2D-GENES Project 1 London Life Sciences Population Study (LOLIPOP) UK (Indian Asian) 531 538 South Asian T2D-GENES Project 1 & Follow up study Singapore Indian Eye Study Singapore (Indian Asian) 1640 1478 South Asian T2D-GENES Follow up study Pakistan Genomic Resource (PGR) Pakistan 914 932

  Study phs001375

• Distribution of data using Live Distribution

  Live Distribution Welcome to the documentation for using the Live Distribution feature for distributing data files securely through the EGA platform. This guide will walk you through the process of downloading encrypted files and decrypting them using Crypt4GH. Please follow the steps below to ensure a smooth experience. Before Downloading Create an EGA user. Make sure that you have the permissions to download the dataset of interest. In case you don’t have access, request access to the dataset. Add your Crypt4GH-compatible public key to your EGA account. Please allow a few hours for your public key to be synced with your profile. Afterwards, you will be able to connect to your EGA outbox using the SFTP protocol. Download Graphical User Interface (GUI) You can use any GUI that supports SFTP connections, such as FileZilla, an open-source FTP client. For Filezilla as your GUI, follow these steps to download files: Open FileZilla and access Site Manager (File > Site Manager). Create a new connection with the following settings (Figure 1): Protocol: SFTP - SSH File Transfer Protocol Host: __EGA_OUTBOX_DOMAIN__ Logon Type: Key file User: your EGA username Key file: path/to/your/private_key Figure 1: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using a key file as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the EGA outbox, ensuring seamless data transfers. Click Connect to access your Outbox. This folder serves as your storage space within the EGA cloud, containing files accessible for download in a secure way. Browse the remote directory on the right side of the FileZilla screen. Select the files you wish to download, right-click, and choose Download (Figure 2). Figure 2: Step-by-step process of manually downñoad files from __EGA_OUTBOX_DOMAIN__ using FileZilla, with FileZilla version 3.52.2 operating on IOS v11.2.3. The figure demonstrates how users can downñoad data from the EGA outbox to their local storage by following the depicted steps SFTP command line You can also use the SFTP command line to securely download files from the EGA Outbox. Using SFTP command line client in Linux/Unix Open a terminal window Enter the following command to connect: sftp username@hostname Enter your EGA password To see a list of available sftp commands type help sftp> put – Upload file sftp> get – Download file sftp> cd path – Change remote directory to ‘path’ sftp> pwd – Display remote working directory sftp> lcd path – Change the local directory to ‘path’ sftp> lpwd – Display local working directory sftp> ls – Display the contents of the remote working directory sftp> lls – Display the contents of the local working directory Type get command to download files. For example: get encrypted_file.c4gh Use the bye command to close the connection (SFTP session). Convenient SSH settings Include the following settings in your SSH config file, located in ~/.ssh/config Host __EGA_OUTBOX_DOMAIN__ EGA-outbox hostname __EGA_OUTBOX_DOMAIN__ User username IdentityFile path/to/the/private/key Replace username and path/to/the/private/key with the appropriate settings, and you will be able to connect to the __EGA_OUTBOX_DOMAIN__ simply using sftp EGA-outbox. How to decrypt Files archived at the EGA are encrypted based on Crypt4GH. Hence, to decrypt the files you need to install Crypt4GH. You can install a python implementation of it, with pip install crypt4gh or directly from the Github repository pip install git+https://github.com/EGA-archive/crypt4gh.git After installing Crypt4GH, decrypt files using the following command: crypt4gh decrypt --sk /path/private/key < encrypted_file.c4gh > decrypted_filename The command reads the encrypted file from stdin (with <) and output the decrypted version to stdout (with >). Replace encrypted_file.c4gh and decrypted_filename with the appropriate filenames but make sure to not use the same filename for both reading and writing because your SHELL would then truncate both files before you even read or write. Frequently Asked Questions What username should I use to log in to my outbox? The authentication process for logging in to the EGA website, as well as accessing your inbox and outbox, requires the use of your username, not your email address. Therefore, if you registered a username different from your email address when creating your EGA account, you must use that username to log in. If you have forgotten your registered username, please, contact our Helpdesk team for assistance. I see that some files in my dataset have 'unavailable' as extension. What should I do? Within your Outbox, you'll find a list of all the datasets available for download. Occasionally, certain files may be marked as "unavailable". These unavailable files can be identified by the "unavailable" extension added to their filenames (e.g. filename.fastq.gz.unavailable.c4gh). If you encounter an unavailable file that you need, please reach out to our Helpdesk. We'll promptly work on making the file accessible for download as soon as possible. Specific to using keys Can I access one EGA account from different devices? Yes, you can access your account from different devices by linking several public keys to your EGA account. Each device can generate a unique public-private key pair, and the corresponding public keys can be linked to the same account. This way, you can use different public keys on different devices and still have access to the same account and data. I have several keys and I don't remember which one is which When generating SSH keys, it's a good practice to add a comment using the -C flag. This will allow you to add a descriptive tag to your key, making it easier to identify later on. Here's an example command that generates an SSH key with a comment: ssh-keygen -t ed25519 -C work-pass In this example, we're generating an ed25519 SSH key with the comment work-pass. Once you have multiple keys with different comments, you can use the comments to easily identify each key. To view the comments for your existing SSH keys, you can use the following command: ssh-keygen -l -f /path/to/key This will display the key fingerprint and the associated comment. By checking the comments, you should be able to identify which key is which. What if I can't find my SSH keys for uploading files with a key file, and how can I use new keys? If you can't find your SSH keys, don't worry - you can make new ones. To do this, open your terminal or command prompt and type a command to make a new SSH key. You can pick a name for the key, and choose a password to keep it safe. After making the key, you can add the new key to your account or server where you want to upload files using the key file. This usually involves copying and pasting the key's "public" (e.g. file.pub) part to the right place. If you lose track of the key again, just make a new one and add it again. Keep in mind that SSH keys belong to you and your computer, so if you switch computers or accounts, you'll need to make new keys. I don't want to type the passphrase every time I use the key. What can I do? You can use an ssh-agent to avoid typing the passphrase every time you use the key. An ssh-agent is a program that stores your private keys in memory and provides them to ssh when needed. You can add your key to the ssh-agent using the command ssh-add followed by the path to your key file.Here's an example of the steps to follow: Open a terminal window.Start the ssh-agent by typing the command eval $(ssh-agent).Add your key to the ssh-agent by typing the command ssh-add [key filepath]. For instance, if your key file is located in the home directory with the name mykey, the command will look like this: ssh-add ~/mykey After adding your, key to the ssh-agent, you should be able to use ssh without having to enter your passphrase every time. Can I use my password for authentication (without my private key)? If you prefer to use your username and password for authentication instead of your private key, you can still do so. When using a Graphical User Interface (GUI) such as FileZilla, you can select Ask for password as your Logon Type (Figure 3). This option will prompt you to enter your password when you click Connect, instead of using your private key. Figure 3: This option will prompt you to enter your password when you click "Connect", instead of using your private key. Figure 3: Process of establishing a new connection to __EGA_OUTBOX_DOMAIN__ using your password as the logon method in FileZilla. The figure showcases the FileZilla version 3.52.2 operating on IOS v11.2.3. By following the depicted steps, users can create a secure and efficient connection to the inbox, ensuring seamless data transfers. It's worth noting that using a password for authentication can be less secure than using an SSH key, as passwords can be more easily compromised through various means. However, if you choose to use your password for authentication, selecting "Ask for password" as your Logon Type is a good way to do so securely via a GUI. Why is it better to use my key and not my password? SSH keys for authentication is generally considered to be more secure and convenient than using passwords. SSH keys are more difficult to crack than passwords, and they can be restricted to specific users and machines, giving you more control over access. Once you set up your SSH keys, you can use them to authenticate quickly and easily, without having to enter a password every time. This makes automation of tasks, such as uploading encrypted files, much simpler. Additionally, SSH keys provide better logging, allowing you to keep track of who is accessing your systems and when. All in all, using SSH keys is a good practice for improving security and convenience in your authentication process.

  Documentation access/download/files/live-outbox

• Successful Clinical Response in Pneumonia Therapy (SCRIPT)

  The goal of this study is to iteratively identify, validate and refine biomarkers to predict clinical failure, and identify novel targets for therapy, beyond traditional antibiotics, for patients with severe pneumonia requiring intubation, including severe community-acquired pneumonia (CAP), hospital-acquired pneumonia, and ventilator-associated pneumonia (HAP/VAP). We hypothesize that factors within the alveolar microenvironment in many patients with pneumonia prevent the active process of pneumonia resolution.The major emphasis will be on HAP. Mechanically ventilated CAP patients are a very high-risk group for subsequent VAP, and therefore CAP samples will be included to determine baseline bacteria, microbiome, and host defense patterns for those that do or do not develop HAP/VAP. Hospital-acquired pneumonia is the leading cause of death from nosocomial infections. Even in patients receiving pathogen-appropriate therapy, reported clinical failure rates are increasing, approaching 50% in recent clinical trials. Current approaches to improve outcomes in patients with pneumonia almost exclusively focus on the development of antibiotics to address the problem of resistance. However, the prevalence of antibiotic-resistant pathogens is insufficient to explain the high rates of clinical failure. Instead, we hypothesize that factors within the alveolar microenvironment in many patients with pneumonia prevent the active process of pneumonia resolution. These factors include pathogen-specific adaptations to the injured lung that modulate the innate and adaptive immune response, as well as changes to the alveolar microbiome, driven by selective pressure within our modern intensive care units. Understanding this complex adaptive system requires the integration of clinical information with unbiased measures of the host, pathogen, and microbiome changes within the alveolar microenvironment. These measures must be repeated over time to identify factors that distinguish successful from failed therapy for pneumonia sufficiently early during the clinical course to allow for effective interventions. To address this challenge, we have assembled a talented group of investigators in the Successful Clinical Response In Pneumonia Therapy (SCRIPT) Systems Biology Center. We will leverage our routine clinical practice of safe alveolar sampling in mechanically ventilated patients with pneumonia with repeated non-bronchoscopic bronchoalveolar lavage (NBBAL) sampling over the course of pneumonia. From this fluid, we will combine flow cytometry with multi-omic technologies (cell population-specific transcriptomics, epigenomics, shotgun microbiome sequencing, and pathogen-specific sequencing). Our systems scientists will integrate these genomic data with robust clinical -omics with the goal of identifying biomarkers that can be prospectively tested in patients and causally evaluated in humanized mouse models.The addition of a pooled sample artificially increases the number of automatically computed subjects by one.

  Study phs002300

• The Genomics and Randomized Trials Network (GARNET) Vitamin Intervention Stroke Prevention (VISP) Trial

  The VISP trial (PI Jim Toole, M.D., Wake Forest University School of Medicine) was a multi-center, double-blind, randomized, controlled clinical trial that enrolled patients aged 35 or older with homocysteine (Hcy) levels above the 25th percentile at screening and a non-disabling cerebral infarction (NDCI) within 120 days of randomization (Toole, 2002, PMID: 12417369). The trial was designed to determine if daily intake of a multivitamin tablet with high dose folic acid, vitamin B6 and vitamin B12 reduced recurrent cerebral infarction (primary endpoint), and nonfatal myocardial infarction (MI) or mortality (secondary endpoints). Subjects were randomly assigned to receive daily doses of the high-dose formulation (n=1,827), containing 25mg pyridoxine (B6), 0.4mg cobalamin (B12), and 2.5mg folic acid; or the low-dose formulation (n=1,853), containing 200mcg pyridoxine, 6mcg cobalamin and 20mcg folic acid. Enrollment in VISP began in August 1997, and was completed in December 2001, with 3,680 participants enrolled. Within the trial, 2,164 participants from 46 clinic sites provided DNA and agreed for it to be shared for use in a genetic subset study of VISP. This study is part of Genomics and Randomized Trials Network (GARNET), funded by the National Human Genome Research Institute (NHGRI). The overarching goal is to identify novel genetic factors that contribute to stroke through large-scale genome-wide association studies of treatment response in randomized clinical trials. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were performed at the GARNET Coordinating Center at the University of Washington. The data of the VISP trial have been released to dbGaP users in several segments: Version 1 (phs000343.v1.p1), consisted of n=4 phenotype datasets, and all raw, cleaned and imputed genotype data. Version 2 (phs000343.v2.p1) included n=14 additional phenotype datasets (plus pedigree, consent, and sample-mapping data), and increased the available data to a total of n=970 phenotype variables. Version 3 (phs000343.v3.p1) included all n=36 phenotype datasets (plus pedigree, consent, and sample-mapping data), and increased the available data to a total of n=1918 phenotype variables.Version 4 (phs000343.v4.p1), the current release, includes all previous phenotypic datasets, includes one new phenotypic dataset for the untargeted metabolomics data of 50 individuals, includes DNA methylation profiles for n=180 individuals, and sequencing data for n=179 individuals.

  Study phs000343

• Multiomic Landscape of Multiple Myeloma Precursor and Relapsed Disease

  Multiple myeloma (MM) is a treatable, though incurable, plasma cell malignancy. The molecular mechanisms driving disease onset and the emergence of drug resistance remain elusive. To better characterize the mutational, transcriptional, and epigenetic alterations that accompany MM disease evolution, we accessed molecular data from MM patients treated at Moffitt Cancer Center, with bone marrow biopsies collected between 2011 and 2023.We performed scMultiome (single-cell, paired RNA/ATAC-Seq, 10x Genomics) on 18 CD138+-selected bone marrow aspirate samples, including: 2 healthy donors, 3 patients with monoclonal gammopathy of undetermined significance (MGUS), 4 with smoldering myeloma (SMM), 3 with newly diagnosed MM (NDMM), 1 with early relapse MM (ERMM, 1 to 3 lines of therapy), and 5 with late relapse MM (LRMM, 4 or more lines of therapy). Our dataset includes a sequential sample: a patient with a premalignant condition ("scMultiome_SMM_4") that progressed to active myeloma ("scMultiome_NDMM_3"). These data allow for the assessment of transcriptional and epigenetic dysregulation at the cellular level. Our findings illustrate the dynamic epigenetic and transcriptional landscapes that accompany MM disease progression.We also conducted CUT&#38;Tag analysis on 4 NDMM and 4 LRMM samples to map histone modifications, specifically H3K27ac, to investigate their role in transcriptional reprogramming observed in advanced stages of MM. This analysis revealed sample heterogeneity in terms of super-enhancer-regulated genes associated with active transcription.This cohort also includes samples from the clinical trial NCT04151667, &#8220;Daratumumab-Based Response-Adaptive Therapy for Older Adults With Newly Diagnosed Multiple Myeloma.&#8221; We performed scRNAseq (single-cell RNA sequencing, 10x Genomics) on 30 CD138+-selected bone marrow aspirate samples and 31 pre-sort samples featuring bone marrow mononuclear cells of various cell types from 5 healthy donors and 11 patients with newly diagnosed MM (NDMM). Our dataset includes sequential samples for all 11 NDMM patients, both prior to induction therapy (denoted by &#8216;a') with Daratumumab and Dexamethasone, and at cycle 2 day 22 (C2D22, i.e., 60 days after induction therapy start, denoted by &#8216;b'). For three NDMM patients, we also have samples at relapse (denoted by &#8216;c'). These data allow for the assessment of transcriptional dysregulation at the single-cell level in both the tumor and immune-tumor microenvironment, associated with Daratumumab treatment in NDMM patients.In addition to the molecular data, demographic, and phenotypic information for all samples is provided.

  Study phs003892