3955 results for "*geneti*"

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• CINECA_synthetic_cohort_EUROPE_UK1 referencing fake samples

  Please note: This synthetic data set (with cohort “participants” / ”subjects” marked with FAKE) has no identifiable data and cannot be used to make any inference about cohort data or results. The purpose of this dataset is to aid development of technical implementations for cohort data discovery, harmonization, access, and federated analysis. In support of FAIRness in data sharing, this dataset is made freely available under the Creative Commons Licence (CC-BY). Please ensure this preamble is included with this dataset and that the CINECA project (funding: EC H2020 grant 825775) is acknowledged. For any questions please contact isuru@ebi.ac.uk or cthomas@ebi.ac.uk This dataset (CINECA_synthetic_cohort_EUROPE_UK1) consists of 2521 samples which have genetic data based on 1000 Genomes data (https://www.nature.com/articles/nature15393), and synthetic subject attributes and phenotypic data derived from UKBiobank (https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1001779). These data were initially derived using the TOFU tool (https://github.com/spiros/tofu), which generates randomly generated values based on the UKBiobank data dictionary. Categorical values were randomly generated based on the data dictionary, continuous variables generated based on the distribution of values reported by the UK Biobank showcase, and date / time values were random. Additionally we split the phenotypes and attributes into 4 main classes - general, cancer, diabetes mellitus, and cardiac. We assigned the general attributes to all the samples, and the cardiac / diabetes mellitus / cancer attributes to a proportion of the total samples. Once the initial set of phenotypes and attributes were generated, the data data was checked for consistency and where possible dependent attributes were calculated from the independent variables generated by TOFU. For example, BMI was calculated from height and weight data, and age at death generated by date of death and date of birth. These data were then loaded to the development instance of Biosamples (https://www.ebi.ac.uk/biosamples/) which accessioned each of the samples. The genetic data are derived from the 1000 Genomes Phase 3 release (https://www.internationalgenome.org/category/phase-3/). The genotype data consists of a single joint call vcf files with call genotypes for all 2504 samples, plus bed, bim, fam, and nosex files generated via plink for these samples and genotypes. The genotype data has had a variety of errors introduced to mimic real data and as a test for quality control pipelines. These include gender mismatches, ethnic background mislabelling and low call rates for a randomly chosen subset of sample data as well as deviations from Hardy Weinberg equilibrium and low call rates for a random selection of variants. Additionally 40 samples have raw genetic data available in the form of both bam and cram files, including unmapped data. The gender of the samples in the 1000 genomes data has been matched to the synthetic phenotypic data generated for these samples. The genetic data was then linked to the synthetic data in BioSamples, and submitted to EGA.

  Dataset EGAD00001006673

• An Integrated Approach to Patient Stratification and Therapy Selection in Acute Myeloid Leukemia

  As more clinically-relevant genomic features of myeloid malignancies are revealed, it has become clear that targeted clinical genetic testing is inadequate for risk stratification. Here, we developed and validated a clinical transcriptome-based assay for stratification of acute myeloid leukemia (AML). Comparison of RNA-Seq to whole genome and exome sequencing revealed that as a standalone assay, RNA-Seq offered the greatest diagnostic return, enabling identification of expressed gene fusions, single nucleotide and short insertion/deletion variants, and whole-transcriptome expression information. Expression data were used to develop a novel risk score which, when combined with molecular risk guidelines, allowed for the re-stratification of 22.1 to 25.3% of AML patients from three independent cohorts into correct risk groups. Within the adverse-risk subgroup, we identified a subset of patients characterized by dysregulated integrin signaling and RUNX1 or TP53 mutation. We show that these patients may benefit from therapy with inhibitors of focal adhesion kinase (PTK2), demonstrating additional utility of transcriptome-based testing for therapy selection in myeloid malignancy.

  Dataset EGAD00001006701

• Cell of Origin and Early Evolution of Leukemia in Down Syndrome

  To investigate the mechanism by which GATA1s and STAG2 deficiency contribute to Down Syndrome leukemogenesis, specifically within the propagating CD34/CD117 cell fractions from primary xenografts, we carried out transcriptional and epigenetic profiling by RNAseq and ATACseq. The chromatin accessibility landscape was compared to bulk ATACseq of individually sorted N-FL HSPC subpopulations. To investigate the mechanism underlying the synergy between T21 and GATA1s in driving preleukemia development, we analyzed the binding occupancy of GATA1. We performed Cut&Run assays to profile genome-wide GATA1 binding sites and also to quantify binding changes upon GATA1s editing in N-FL and T21-FL CD34+ enriched HSPCs. Lastly, we profiled miRNAs from N-FL and T21-FL CD34+ enriched HSPCs by miRNA-Seq.

  Dataset EGAD00001006555

• Paired-WGS-two-families-with-GBA-variants-and-Parkinsons-disease

  We performed whole- genome sequencing, rare variant filtering, segregation analysis and functional validation of PD cosegregating rare genetic variation in two families (6 samples) segregating PD associated GBA variants c.115+1G>A (ClinVar ID: 93445, ) and p.L444P (ClinVar ID: 4288) respectively. The paired WGS sequencing was run on HiSeq X Ten and the library preparation kit was Illumina TruSeq DNA nano.

  Dataset EGAD00001006561

• Measurable residual disease in elderly acute myeloid leukemia: results from the PETHEMA-FLUGAZA phase III clinical trial

  In our study, we hypothetyzed that CD34progenitors from cases with undetectable Minimal residual Disease (MRD) by flow cytometry would contain cells with leukemic-initiating-potential that could be identified on genetic (rather than phenotypic) grounds by Whole Exome Sequencing.

  Dataset EGAD00001006585

• Fine-mapping clustered GWAS hits enhances the identification of disease risk and protective genetic variants

  This dataset contains: 1) Raw FASTQ and BAM files for short reads. Here, DNA libraries were prepared using Nextera Rapid Capture Custom Enrichment kit (Illumina) and paired-end sequenced on a HiSeq2500 (Illumina). 2) Raw FASTQ and BAM files for long reads. Here, DNA libraries were prepared using 1D DNA ligation Sequencing Kit (SQK-LSK109, Oxford Nanopore) and single-end sequenced on a MinION device (Oxford Nanopore).

  Dataset EGAD00001006916

• MutWP4: CRUK Grand Challenge Mutographs of Cancer: Pancreatic Organoids (2021-02-02)

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006933

• Dedifferentiated Melanoma (2021-02-02)

  De- and transdifferentiation of melanoma is a rare histopathological phenomenon that has not be characterised genetically. In this project we plan to sequence the genomes of de and transdifferentiated cases so as to define their genetic make-up. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006931

• STAT1 AM RNAseq

  The transcriptome of peripheral blood cells (PBMCs) from control or patients with an activation mutation on the STAT1 gene was analyzed. This analysis aimed to identify the major changes in the circulating immune cells of patients with STAT1 mutation and compare this with the result of the perturbation prediction tool huva (human variation, R).

  Dataset EGAD00001006962

• GDAP - Genome Diversity in Africa Project (2021-02-12)

  The Genomic Diversity in Africa Project (GDAP) started with the plan to develop a genomic resource from African populations, characterise genomic diversity and population history, and facilitate clinical studies in Africa. Currently, 25 individuals from 24 ethnolinguistic groups have been whole-genome sequenced at high depth totalling 585 individuals. An additional 41 individuals have been sequenced with 10X Genomics libraries. At this stage, the initial curation of this dataset has been finished and we are performing the analysis in coordination with our collaborators. The current state of the GDAP represents a very diverse panel of African populations that maximizes geographical and ethnic variation and represents a great starting point to achieve the aforementioned goals. However, southern sub-Saharan countries, Bantu speakers and hunter gatherer groups are currently underrepresented, despite being crucial to understand the evolutionary history of the continent. After extensive effort to collate studies documentation, we finally have the opportunity to sequence 600 new individuals from these groups, including countries as Gabon, Rwanda and Zambia, and address these deficiencies. We aim to proceed with the same strategy: to sequence at high depth 25 individuals with standard PCR free libraries, with 2 additional individuals with 10X Genomics Chromium libraries per ethnolinguistic group. The former allows a good representation of variants down to low frequency in any given population, and the latter allows accurate phasing and the analysis of structural variation. By including these new populations, we want to investigate three crucial questions in African history in addition to the initial objectives: the Bantu expansion, the evolutionary history of hunter gatherers and the transatlantic slave trade. Additionally, the expanded dataset will help us better discover the genetic variation present in Africa and characterize the African pangenome. 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 2021-02-12.

  Dataset EGAD00001006965

• Whole Exome sequencing data from Shwachman-Diamond syndrome bone marrow samples

  This dataset includes paired WES from bone marrow samples of patients with SDS and paired bone marrow-derived fibroblasts as a germline reference. Some patients have multiple samples collected serially over time. There are 74 BAM files included in this dataset.

  Dataset EGAD00001006815

• August 2020 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  August 2020 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001006383

• Pre-clinical evolution of haematological malignancies_WGS

  Leukaemia and related blood cancers occur due to genetic changes that typically accumulate over many years. This study will employ targeted next-generation sequencing to retrace the preclinical evolution of several types of haematological malignancy. Investigating the progression of the earliest pre-malignant ancestral clones promises to offer valuable insights into early leukaemia evolution and therapeutic vulnerabilities of leukaemia stem cells.

  Dataset EGAD00001006423

• Pre-clinical evolution of haematological malignancies_TGS

  Leukaemia and related blood cancers occur due to genetic changes that typically accumulate over many years. This study will employ targeted next-generation sequencing to retrace the preclinical evolution of several types of haematological malignancy. Investigating the progression of the earliest pre-malignant ancestral clones promises to offer valuable insights into early leukaemia evolution and therapeutic vulnerabilities of leukaemia stem cells.

  Dataset EGAD00001006424

• Genetic variability in exon 1 of the glucocorticoid receptor gene NR3C1 is associated with postoperative complications

  Bisulfite sequencing of a 3kb region within the CpG island of the NR3C1 exone 1 was performed with Illumina Miseq. 24 samples from major hepatic or pancreatic surgery with complications (cases) and without complications (controls). The files are in FASTQ format.

  Dataset EGAD00001008317

• Genetic drivers of epigenetic and transcriptional variation of human immune responses to infection (RNA-seq, ATAC-seq and ChIPmentation)

  RNA-seq, ATAC-seq and ChIPmentation data from monocyte-derived macrophages that were infected with Influenza A virus strain PR8WT, or a matching non-infected control.

  Dataset EGAD00001008422

• Epigenetic, transcriptome and TF analysis of human NK cell and T cells

  RNA-seq 10 subsets; 5 donors. ATAC-seq 9 subsets, 4 donors; Histone modification profiling 10 subsets, 2 donors all using human NK cell and T cell subsets. TF ChIP-seq Bcl11b, Bach2, Runx2, Gata3, PLZF. Illuminia sequencing platform, ATAC-seq is Paired-end, RNA-seq/ChIP-seq is Single-end

  Dataset EGAD00001008449

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

  We recruited 98 hospitalised patients displaying severe COVID-19 symptoms from the first wave of infection. A stringent exclusion criteria based on non-genetic factors such as age, blood oxygen, radiologic findings and other typical COVID-19 signs was performed. Gingival or peripheral blood samples were taken for 98 individuals and whole exome sequencing performed using ExomeCapture-Seq capture KAPA HyperExome on Illumina machines.

  Dataset EGAD00001008464

• Mutational landscape of normal epithelial cells in Lynch Syndrome patients

  Lynch Syndrome (LS) is an autosomal dominant disease conferring a high risk of colorectal cancer due to germline heterozygous mutations in a DNA mismatch repair (MMR) gene. Although cancers in LS patients show elevated somatic mutation burdens, information on mutation rates in normal tissues and understanding of the trajectory from normal to cancer cell is limited. Here we whole-genome sequenced 152 crypts from normal and neoplastic epithelial tissues from LS patients. In normal tissues the repertoire of mutational processes and mutation rates were similar to those found in wild type individuals. A morphologically normal colonic crypt with an increased mutation burden and mutational signatures consistent with MMR deficiency was identified, which may represent a very early stage of LS pathogenesis. Phylogenetic tress of tumour crypts indicated that the most recent ancestor cell of each tumour was already MMR deficient and had experienced multiple clonal evolution cycles. This study demonstrates the genomic stability of epithelial cells with heterozygous germline MMR gene mutations and highlights important differences in the pathogenesis of LS from other colorectal cancer predisposition syndromes.

  Dataset EGAD00001008092

• May 2021 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  May 2021 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001008100

• August 2021 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  August 2021 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001008101

• Genetic and immune landscape evolution defines subtypes of MMR deficient colorectal cancer

  Genomic data from a cohort of 19 MMR deficient colorectal cancers and 1 MMR proficient colorectal cancer. All cases were target gene DNA sequenced using multiple primary and where available metastatic tumour regions from surgical resection samples.

  Dataset EGAD00001008334

• Genetic drivers of epigenetic and transcriptional variation of human immune responses to infection (WGS and WGBS)

  WGS and WGBS data from monocyte-derived macrophages that were infected with Influenza A virus strain PR8WT, or a matching non-infected control.

  Dataset EGAD00001008359

• Salivary Gland Cancer TSO500 dataset

  DNA was extracted from archival tissue of 119 patients with various SGC subtypes and sequenced using a targeted NGS panel encompassing 523 cancer related genes (TruSight Oncology 500, TSO500). This dataset belongs to the publication entitled: 'Identification of fusion genes and targets for genetically matched therapies in a large cohort of salivary gland cancer patients'.

  Dataset EGAD00001008759

• MBL2 genetic variation in critical Covid-19

  The data set includes MBL2 genotypes and clinical phenotypes of a cohort of patients with critical Covid-19. The files included in the data set include a vcf file with single nucleotide variants, and a file with clinical phenotypes.

  Dataset EGAD00001008771

• DDD_1 hypermutated individual

  CRAM files and VCF for DDD_1 and their parents. Also de novo mutations file for hypermutated DDD_1 child as described in the manuscript ‘Genetic and chemotherapeutic influences of germline hypermutation’ by Kaplanis et al. which will be published in Nature shortly.

  Dataset EGAD00001008497

• Single-Cell Atlas of Common Variable Immunodeficiency shows germinal center-associated epigenetic dysregulation in B cell responses

  Common variable immunodeficiency (CVID) is the most prevalent primary immunodeficiency. Here the authors perform single cell omics analyses in CVID discordant monozygotic twins and show epigenetic and transcriptional alterations associated with activation in memory B cells.

  Dataset EGAD00001008575

• Genetic architecture of autism spectrum disorder in India

  The dataset consists of whole exome sequencing data (fastq format) of 100 non-syndromic autism spectrum disorder patients from India. Whole exome sequencing data is generated using Agilent SureSelect v6 capture kit and Illumina HiSeq sequencing platform. Paired end fastq files are available.

  Dataset EGAD00001008621

• Enhancer profiling to identify identifies epigenetic markers of endocrine resistance in metastatic castration resistant prostate cancer patients

  ChIP-seq and matched input data of AR, FOXA1 and H3K27ac for mCRPC patient samples taken prior to and after treatment with AR targeted therapy

  Dataset EGAD00001008688

• Mixture of 4

  Mixture of 4 unrelated individuals sequenced by 10x as a scRNA-seq. The dataset was then processed by Cell Ranger and deconvoluted to yield each individuals genetic profile. The clustering of SNPs is submitted as the processed file. The Sequencing fastqs are submitted as unprocessed files.

  Dataset EGAD00001008724

• Mixture of 2

  Mixture of 2 unrelated individuals sequenced by 10x as a scRNA-seq. The dataset was then processed by Cell Ranger and deconvoluted to yield each individuals genetic profile. The clustering of SNPs is submitted as the processed file. The Sequencing fastqs are submitted as unprocessed files.

  Dataset EGAD00001008726

• Mixture of 2 (closer mtDNA)

  Mixture of 2 unrelated individuals (of close mtDNA haplogroup) sequenced by 10x as a scRNA-seq. The dataset was then processed by Cell Ranger and deconvoluted to yield each individuals genetic profile. The clustering of SNPs is submitted as the processed file. The Sequencing fastqs are submitted as unprocessed files.

  Dataset EGAD00001008727

• Mixture of 3

  Mixture of 3 unrelated individuals sequenced by 10x as a scRNA-seq. The dataset was then processed by Cell Ranger and deconvoluted to yield each individuals genetic profile. The clustering of SNPs is submitted as the processed file. The Sequencing fastqs are submitted as unprocessed files.

  Dataset EGAD00001008729

• Genotype data for The admixture histories of Cabo Verde - genotype data

  Datasets used in the article "The genetic and linguistic admixture histories of the islands of Cabo Verde" by Laurent R et al. eLife 2023 (DOI: https://doi.org/10.7554/eLife.79827 - URL: https://elifesciences.org/articles/79827) As per Materials and Methods herein, the genotype data corresponds to 2,118,722 autosomal SNPs genotyped from the IlluminaOmni 2.5 Million BeadChip for 233 Cabo Verdean volunteer participants, family unrelated at the 2nd degree based on population genetics analyses (see Material and Methods). SNP rsID, Chromosome position and genetic position in (bp) are in Build GRCh38. Cabo Verdean individuals are designated with an alphanumeric unique code

  Dataset EGAD00001008979

• Anthropological dataset 1 for The admixture histories of Cabo Verde

  Datasets used in the article "The genetic and linguistic admixture histories of the islands of Cabo Verde" by Laurent R et al. eLife 2023 (DOI: https://doi.org/10.7554/eLife.79827 - URL: https://elifesciences.org/articles/79827) File name "eGAdeposit_233CaboVerde_SampleInfo_FINAL_01062022.txt" Column 1 corresponds to individual alphanumeric codes as in the "eGAdeposit_233CaboVerde_GenotypeFile_FINAL_01062022.vcf" genotype file Column 2 corresponds to individual's biological sex as per genetic inference Column 3 corresponds to individual's self-reported age in years Column 4 corresponds to individual's self-reported cumulated number of years spent in academic or professional education

  Dataset EGAD00001008976

• Anthropological dataset 2 for The admixture histories of Cabo Verde

  Datasets used in the article "The genetic and linguistic admixture histories of the islands of Cabo Verde" by Laurent R et al. eLife 2023 (DOI: https://doi.org/10.7554/eLife.79827 - URL: https://elifesciences.org/articles/79827) File name "eGAdeposit_233CaboVerde_GEOcoordFULL_FINAL_01062022.txt" Column 1 corresponds to individual alphanumeric codes as in the "eGAdeposit_233CaboVerde_GenotypeFile_FINAL_01062022.vcf" genotype file Column 2-3 corresponds to X-Y GPS coordinates of individual's interview location in Cabo Verde Column 4-5 corresponds to X-Y GPS coordinates of individual's self-reported residence location at the time of the interview Column 6-7 corresponds to X-Y GPS coordinates of individual's self-reported birth-place location Column 8-9 corresponds to X-Y GPS coordinates of individual's self-reported paternal birth-place location Column 10-11 corresponds to X-Y GPS coordinates of individual's self-reported maternal birth-place location

  Dataset EGAD00001008977

• Linguistic utterance counts for The admixture histories of Cabo Verde

  Datasets used in the article "The genetic and linguistic admixture histories of the islands of Cabo Verde" by Laurent R et al. eLife 2023 (DOI: https://doi.org/10.7554/eLife.79827 - URL: https://elifesciences.org/articles/79827) File name "eGAdeposit_225CaboVerde_FreeSpeech_Utterance_counts_FINAL_01062022.txt" Column 1 corresponds to individual alphanumeric codes as in the "eGAdeposit_233CaboVerde_GenotypeFile_FINAL_01062022.vcf" genotype file. Note that only 225 unrelated Cabo Verdean-born individuals are considered here, out of the 233 individuals in the genotype file. See Material and Methods in Romain Laurent et al. 2022 - doi pending Each 4831 other column correspond to the respective individual's utterance count in the free speech transcribed in ALUPEC and provided as column header. See See Material and Methods in Romain Laurent et al. 2022 - doi pendingColumn 1 corresponds to individual alphanumeric codes as in the "eGAdeposit_233CaboVerde_GenotypeFile_FINAL_01062022.vcf" genotype file. Note that only 225 unrelated Cabo Verdean-born individuals are considered here, out of the 233 individuals in the genotype file. See Material and Methods in Romain Laurent et al. 2022 - doi pending Each 4831 other column correspond to the respective individual's utterance count in the free speech transcribed in ALUPEC and provided as column header. See Material and Methods in Laurent R et al. eLife 2023

  Dataset EGAD00001008978

• Exome Sequencing of 44 subjects with very severe or fatal COVID-19

  In this study, the DNA of 44 subjects with severe COVID-19 have been sequenced in order to explore rare genetic variants.

  Dataset EGAD00001008993

• Diagnostic utility of whole genome sequencing in adults with B-other acute lymphoblastic leukemia - RNA

  Genomic profiling at diagnosis of B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL) in adults is used to guide disease classification, risk stratification and treatment decisions. Patients for which diagnostic screening fails to identify disease defining or risk stratifying lesions are classified as B-other ALL. We screened a cohort of 652 BCP-ALL cases enrolled in UKALL14 to identify and perform whole genome sequencing (WGS) on paired tumor-normal samples. For 52 B-other patients we compared WGS findings to data from clinical and research cytogenetics. WGS identifies a cancer associated event in 51/52 cases, this includes an established subtype defining genetic alteration in 5/52 that were previously missed by standard-of-care genetics. Of the 47 true B-other ALL we identified a recurrent driver in 87% (41).  Complex karyotype by cytogenetics emerges as a heterogeneous group, underlied by distinct genetic alterations associated with either favorable (DUX4-r) or poor outcomes (MEF2D-r, IGK::BCL2). For a subset of 31 cases, we integrate findings from RNA-sequencing (RNA-seq) analysis to include fusion gene detection, and classification by gene expression. Compared to RNA-seq, WGS was sufficient to detect and resolve recurrent genetic subtypes, however RNA-seq can provide orthogonal validation of findings. In conclusion, we demonstrate that WGS can identify clinically relevant genetic abnormalities missed by standard-of-care testing and identify leukemia driver events in virtually all cases of B-other ALL.

  Dataset EGAD00001009305

• Single-cell omics data for COVID-19 patients

  Single-cell RNA-seq, single-cell ATAC-seq, and genotypes used in the analysis for the study "Altered and allele-specific open chromatin landscape reveal epigenetic and genetic regulators of innate immunity in COVID-19". The RNA-seq and ATAC-seq are raw data in FASTQ format while the genotypes are in the VCF format which was filtered and imputed (more details are available in the main text of the study).

  Dataset EGAD00001009331

• HSP90 inhibitor resistant K562 cells

  K562 cell line has been treated with two different HSP90 inhibtors. After resistance clones emerged, they have been genetically characterized using WES in comparison to the parental K562 cell line.

  Dataset EGAD00001009051

• Single-cell study of 14 childhood medulloblastoma patients

  Medulloblastoma intra-tumoural genetic heterogeneity and clonal evolution, and their role in disease pathogenesis and clinical behaviour, are poorly understood. We used single-cell whole-genome sequencing (sc-WGS) to reconstruct the natural history and temporal evolution of 14 medulloblastomas, representing its major clinico-molecular sub-classes. We identified wholly-clonal tumours which displayed single-clone expansion (i.e. linear evolution); all were observed in favourable-outcome sub-classes (i.e. MBWNT and infant MBSHH). In contrast, remaining tumours harboured sub-clonal structures which displayed punctuated or gradual trajectories; highest-risk sub-classes, typically characterised by MYC-amplification (MBGroup3) or TP53-mutation (MBSHH), and linked to genomic instability and LCA pathology, were most clonally-diverse. Clinically-adopted biomarkers were typically early-clonal/initiating events, representing exploitable targets for early-disease detection; in analyses of spatially-distinct tumour regions, a single biopsy was sufficient to assess their status. sc-WGS revealed events not previously appreciated in bulk tumour analysis, which arose later and/or sub-clonally and more commonly displayed spatial diversity; their clinical significance and role in disease evolution post-diagnosis now require establishment. In summary, our findings reveal diverse modes of tumour initiation and clonal evolution in the major medulloblastoma sub-classes, highlighting their pathogenic relevance and clinical potential.

  Dataset EGAD00001009057

• Convergent somatic evolution from early life in a germline ribosomopathy

  Clonal tracking of stem cells and their progeny by whole genome sequencing permits exploration of evolutionary genetics in human disease. In this study, we performed phylogenetic reconstruction of haematopoiesis using somatically acquired mutations in 323 single haematopoietic stem and progenitor cell-derived colonies from 10 individuals with an inherited disorder of ribosome assembly, Shwachman-Diamond syndrome. We observed numerous clonal expansions, with recurrent acquisition of mutually exclusive mutations (EIF6, TP53, RPL5, RPL22, PRPF8, chromosomes 7 and 15) in multiple different clones in utero or early childhood converging on the p53-dependent nucleolar surveillance pathway that monitors ribosome integrity. In contrast to clones carrying biallelic TP53 mutations, genomes derived from colonies carrying mono-allelic TP53 mutations displayed no increase in mutation burden or specific mutational signatures. Our study highlights striking loss of clonal diversity with convergent somatic evolution on the p53-dependent nucleolar surveillance pathway from early life to offset the deleterious effects of a germline mutation in a Mendelian haematopoietic disorder.

  Dataset EGAD00001009061

• Genetic regulation of RNA splicing in human pancreatic islets

  Here we provide access to newly generated RNA-seq data for 101 human islet samples used to map genetic effects on gene expression and alternative splicing (eQTLs and sQTLs) in a total of 399 human islets. We also make publicly available genotyping array data for 128 human islets, including the fraction of 101 human islet samples with existing RNA-seq data.

  Dataset EGAD00001009102

• Clonal origin of lineage switch leukemia following CAR-T cell and blinatumomab therapy

  Children with ALL treated with anti-CD19 therapy occasionally develop a phenotypically distinct AML. However, the precise clonal origin of such class switch leukemias remains unresolved. Here, we reconstructed the evolution of leukemia in a child with primary ALL, two ALL relapses and AML after treatment with anti-CD19 CAR-T and blinatumomab through whole-genome sequencing. The phylogeny revealed that the AML was a monoclonal outgrowth descending from the initial ALL and harbored biallelic loss of CDKN2A, PAX5 and TP53. However, none of the ALL or AML relapses directly descended from one another, suggesting the presence of a reservoir of persistent clones. Our findings suggest anti-CD19 treatment selects pre-existing clones, with many key genetic alterations underpinning the lineage switch detectable prior to treatment.

  Dataset EGAD00001009161

• Germline TP53 mutations undergo copy number gain years prior to tumor diagnosis

  Whole-genome sequence (WGS) analysis of tumors from 22 TP53 mutation carriers. We observed somatic mutations affecting Wnt, PI3K/AKT signaling, epigenetic modifiers and homologous recombination genes as well as mutational signatures associated with prior chemotherapy. We identified near-ubiquitous early loss of heterozygosity of TP53, with gain of the mutant allele. This occurred earlier in these tumors compared to tumors with somatic TP53 mutations, suggesting the timing of this mark may distinguish germline from somatic TP53 mutations. Phylogenetic trees of tumor evolution, reconstructed from bulk and multi-region WGS, revealed that LFS tumors exhibit comparatively limited heterogeneity. Overall, our study delineates early copy number gains of mutant TP53 as a characteristic mutational process in LFS tumorigenesis, likely arising very early in life or in utero.years prior to tumor diagnosis.

  Dataset EGAD00001009280

• Epigenomic analysis of human dopaminergic neuron differentiation reveals LBX1, NHLH1 and NR2F1/2 as necessary for lineage specification

  Sample information: The 56 samples produced in this project come from the human iPSC line GM17602 (Coriell) where a tyrosine hydroxylase-T2A-mCherry reporter was inserted. A combination of epigenetic analysis (ATAC/ChIP) along transcriptomics.

  Dataset EGAD00001009288

• Diagnostic utility of whole genome sequencing in adults with B-other acute lymphoblastic leukemia

  Genomic profiling at diagnosis of B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL) in adults is used to guide disease classification, risk stratification and treatment decisions. Patients for which diagnostic screening fails to identify disease defining or risk stratifying lesions are classified as B-other ALL. We screened a cohort of 652 BCP-ALL cases enrolled in UKALL14 to identify and perform whole genome sequencing (WGS) on paired tumor-normal samples. For 52 B-other patients we compared WGS findings to data from clinical and research cytogenetics. WGS identifies a cancer associated event in 51/52 cases, this includes an established subtype defining genetic alteration in 5/52 that were previously missed by standard-of-care genetics. Of the 47 true B-other ALL we identified a recurrent driver in 87% (41).  Complex karyotype by cytogenetics emerges as a heterogeneous group, underlied by distinct genetic alterations associated with either favorable (DUX4-r) or poor outcomes (MEF2D-r, IGK::BCL2). For a subset of 31 cases, we integrate findings from RNA-sequencing (RNA-seq) analysis to include fusion gene detection, and classification by gene expression. Compared to RNA-seq, WGS was sufficient to detect and resolve recurrent genetic subtypes, however RNA-seq can provide orthogonal validation of findings. In conclusion, we demonstrate that WGS can identify clinically relevant genetic abnormalities missed by standard-of-care testing and identify leukemia driver events in virtually all cases of B-other ALL.

  Dataset EGAD00001009304

• Oropharyngeal Squamous Cell Carcinoma Mutanome

  This dataset comprises genetic variation data (as somatic indels and snvs VCFs) of 38 OPSCC tumors. WES was done using NextSeq 500 System running in 150 cycles (2x 75bp paired-end) mode. Sequence information was converted to FASTQ format using bcl2fastq v2.20.0.422. VCFs were generated using the Strelka package.

  Dataset EGAD00001009167

• HELIUS cohort gut microbiome batch2

  The gut microbiota composition is unique to every individual but is shaped by common factors including diet, lifestyle, medication use, early-life determinants, living environment or genetics. Most of these factors may be influenced by ethnicity. This study explored variations in fecal microbiota composition in 6048 individuals with different ethnic backgrounds living in the same geographical area (Amsterdam, the Netherlands). The HELIUS data are owned by the Amsterdam University Medical Centers, location AMC in Amsterdam, The Netherlands. To allow sharing of microbiome data collected in HELIUS with (inter)national researchers, 16s rRNA sequence analysis has been stored at the European genome-phenome archive (EGA; accession code EGAD00001004106). This requires that access needs to be granted, also because the HELIUS data are stored with relevant phenotypical variables. Access is granted to all researchers affiliated with an internationally recognized research institution who request to use the HELIUS data within the EGA context, after having signed the data transfer agreement. Any researcher can request the data by submitting a proposal to the HELIUS Executive Board as outlined at http://www.heliusstudy.nl/en/researchers/collaboration, by email: heliuscoordinator at amsterdamumc dot nl. The HELIUS Executive Board will check proposals if they do not conflict with ethical approvals and informed consent forms of the HELIUS study.

  Dataset EGAD00001009732