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• Somatic mutation and selection at epidemiological scale - TwinsUK_TargetedEMSeq_Buccal

  Development of a targeted methylation assay to determine the cell-type composition of a sample. As we age, many tissues become colonised by microscopic clones carrying somatic driver mutations. Some of these clones represent a first step towards cancer whereas others may contribute to ageing and other diseases. However, our understanding of the clonal landscapes of human tissues, and their impact on cancer risk, ageing and disease, remains limited due to the challenge of detecting somatic mutations present in small numbers of cells. Here, we introduce a new version of nanorate sequencing (NanoSeq), a duplex sequencing method with error rates of less than 5 per billion base pairs, which is compatible with whole-exome and targeted gene sequencing. Deep sequencing of polyclonal samples with single-molecule sensitivity enables the simultaneous detection of mutations in large numbers of clones, yielding accurate somatic mutation rates, mutational signatures and driver mutation frequencies in any tissue. Applying targeted NanoSeq to 1,042 non-invasive samples of oral epithelium and 371 samples of blood from a twin cohort, we found an unprecedentedly rich landscape of selection, with 46 genes under positive selection driving clonal expansions in the oral epithelium, over 62,000 driver mutations, and evidence of negative selection in some genes. The high number of positively selected mutations in multiple genes provides high-resolution maps of selection across coding and non-coding sites, a form of in vivo saturation mutagenesis. Multivariate regression models enable mutational epidemiology studies on how carcinogenic exposures and cancer risk factors, such as age, tobacco or alcohol, alter the acquisition and selection of somatic mutations. Accurate single-molecule sequencing has the potential to unveil the polyclonal landscape of any tissue, providing a powerful tool to study early carcinogenesis, cancer prevention and the role of somatic mutations in ageing and disease.

  Dataset EGAD00001015622

• Somatic mutation and selection at epidemiological scale - TwinsUK_TargetedEMSeq_Blood

  Development of a targeted methylation assay to determine the cell-type composition of a sample. As we age, many tissues become colonised by microscopic clones carrying somatic driver mutations. Some of these clones represent a first step towards cancer whereas others may contribute to ageing and other diseases. However, our understanding of the clonal landscapes of human tissues, and their impact on cancer risk, ageing and disease, remains limited due to the challenge of detecting somatic mutations present in small numbers of cells. Here, we introduce a new version of nanorate sequencing (NanoSeq), a duplex sequencing method with error rates of less than 5 per billion base pairs, which is compatible with whole-exome and targeted gene sequencing. Deep sequencing of polyclonal samples with single-molecule sensitivity enables the simultaneous detection of mutations in large numbers of clones, yielding accurate somatic mutation rates, mutational signatures and driver mutation frequencies in any tissue. Applying targeted NanoSeq to 1,042 non-invasive samples of oral epithelium and 371 samples of blood from a twin cohort, we found an unprecedentedly rich landscape of selection, with 46 genes under positive selection driving clonal expansions in the oral epithelium, over 62,000 driver mutations, and evidence of negative selection in some genes. The high number of positively selected mutations in multiple genes provides high-resolution maps of selection across coding and non-coding sites, a form of in vivo saturation mutagenesis. Multivariate regression models enable mutational epidemiology studies on how carcinogenic exposures and cancer risk factors, such as age, tobacco or alcohol, alter the acquisition and selection of somatic mutations. Accurate single-molecule sequencing has the potential to unveil the polyclonal landscape of any tissue, providing a powerful tool to study early carcinogenesis, cancer prevention and the role of somatic mutations in ageing and disease.

  Dataset EGAD00001015623

• Family Genomics of Bipolar Disorder

  This study examined the segregation of variants with phenotype in pedigrees harboring bipolar disorder.

  Study phs000866

• Chromatin accessibility in OCI-AML22 cells

  ATAC-seq performed in OCI-AML22 subpopulations sorted based on CD34 and CD38 status

  Dataset EGAD50000001631

• SCC tumor sequencing

  In this study we have sequenced the tumor of 28 Squamous Cell Carcinoma patients.

  Study EGAS00001003988

• Liquid Biopsy by NGS show differences in CRC stage

  Exome sequencing from cfDNA blood samples. 30 sets of 2x76 Illumina reads in Fastq format.

  Dataset EGAD00001004307

• OAC RNASeq

  Please see our related publication, 'Molecular profiling to predict immunochemotherapy outcomes in esophageal adenocarcinoma'

  Study EGAS00001006468

• McGill EMC Release 4 in tissue "Brodmann (1909) area 11"

  McGill EMC Release 4 in tissue "Brodmann (1909) area 11"

  Dataset EGAD00001001284

• McGill EMC Release 4 in tissue "Brodmann (1909) area 44"

  McGill EMC Release 4 in tissue "Brodmann (1909) area 44"

  Dataset EGAD00001001285

• Impact of genetic variants in clinical outcome of a cohort of patients with oropharyngeal squamous cell carcinoma

  Caracterization of somatic variants in patients with OpSCC

  Dataset EGAD00001006151

• ASD WGS DAC

  This data access committee will oversee the request for applications to whole genome sequencing data (WGS) generated at The Centre for Applied Genomics (TCAG), as part of our Autism Genome Project.

  Dac EGAC50000000279

• Japanese population-specific reference panels (BioBank Japan genotype data)

  Develop Japanese population-specific reference panels to improve imputation quality for GWAS

  Study JGAS000738

• COMPASS Next Generation Sequencing WGS data

  Whole genome sequencing data for Pancreatic Cancer Samples from the COMPASS clinical trial

  Study EGAS50000001091

• V2_Colorectal_panel_test

  Targeted gene screen of cell line tumour samples for testing the new V2 Colorectal gene panel.

  Study EGAS00001001806

• DATA FILES FOR PCGP SJINF WES

  DATA FILES FOR PCGP SJINF WES

  Dataset EGAD00001001245

• McGill EMC Release 4 for cell type "induced pluripotent stem cell"

  McGill EMC Release 4 for cell type "induced pluripotent stem cell"

  Dataset EGAD00001001276

• McGill EMC Release 4 for assay "Bisulfite-seq"

  McGill EMC Release 4 for assay "Bisulfite-seq": Methylation profiling by high-throughput sequencing

  Dataset EGAD00001001289

• McGill EMC Release 4 for assay "RNA-seq"

  McGill EMC Release 4 for assay "RNA-seq": Transcriptome profiling by high-throughput sequencing

  Dataset EGAD00001001290

• McGill EMC Release 4 for assay "mRNA-seq"

  McGill EMC Release 4 for assay "mRNA-seq": Transcriptome profiling by high-throughput sequencing

  Dataset EGAD00001001291

• McGill EMC Release 4 for assay "smRNA-seq"

  McGill EMC Release 4 for assay "smRNA-seq": Transcriptome profiling by high-throughput sequencing

  Dataset EGAD00001001292

• Xenograph files for PCGP SJERG

  Additional Xenograph files for PCGP SJERG

  Dataset EGAD00001002741

• Additional data for 01_HepG2_LiHG_Ct1, H3K122ac

  Additional histone modification data, not yet released as part of IHEC, for cell line 01_HepG2_LiHG_Ct1, H3K122ac.

  Dataset EGAD00001005954

• SNP data for Ovarian cancer PRS (controls)

  SNP data of 28 sites required for the Ovarian cancer PRS (controls)

  Dataset EGAD00001008145

• Genome-wide characterization of Kuwaiti Arab Population

  All the 620 individuals were genotyped using Illumina HumanOmniExpress arrays for 730,525 SNPs.

  Study EGAS00001005034

• Integrative Gene Regulatory Network Analysis Discloses Key Driver Genes of Fibromuscular Dysplasia

  Fibromuscular dysplasia (FMD) is a poorly understood vascular disease that can lead to hypertension, stroke, myocardial infarction and even death. In 2013 we initiated the DEFINE study (ClinicalTrials.gov Identifier: NCT01967511), which has now recruited > 440 subjects and which aims to unravel the pathobiological mechanisms of this disease. Here, by integrating DNA genotype and RNA sequence data from primary fibroblasts of 83 FMD patients and 71 matched healthy controls from the DEFINE study, and its precursor the CAUSE study, we inferred 18 gene regulatory co-expression networks, four of which were found to act together as an FMD-associated super network in the arterial wall. Molecular studies indicated that this super network, termed SN-A and with the top key driver gene UBR4, governs multiple aspects of vascular cell physiology and functionality, including collagen/matrix production. The effects of UBR4 knockdown were also investigated in commercially available aortic smooth muscle and fibroblast cell lines by RNA sequencing, and these results are included in these datasets.Note, that of the 154 subjects in this analysis, 62 did not provide consent to have their genomic data made publicly available. Therefore the human subject data available here is from the 92 study participants who provided written informed consent to share their deidentified genomic data publicly.

  Study phs003674