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• Characterization of mutational signatures in human cancer cell lines reveals episodic APOBEC mutagenesis

  Multiple signatures of somatic mutations have been identified in human cancer genomes. To investigate whether mutational signatures continue to be generated, and if so their temporal patterns of activity, subsets of cell lines were cultured in vitro for extended periods and subjected to single cell cloning and whole genome or exome sequencing or directly to single cell whole genome sequencing. As expected, signatures of past exogenous exposures, such as tobacco smoke and ultraviolet light, were not generated in vitro. In contrast, signatures of normal and defective DNA repair and replication continued to be generated at essentially constant mutation rates. Signatures of APOBEC cytidine deaminase DNA-editing activity exhibited a distinctive pattern with substantial fluctuations in mutation rate over time and episodic bursts of mutations. The initiating factors for these bursts are unclear although retrotransposon mobilisation may play a role. This cell line set now constitutes a comprehensive resource of live experimental models of mutational processes of both known and unknown aetiologies potentially retaining the patterns of activity and regulatory influences operative in human cells in vivo.

  Dataset EGAD00001004202

• SNV and indel calls from 8086 individuals in the British Autozygosity Populations BioResource dataset

  This includes variant calls (single nucleotide variants and small insertions/deletions) from 8086 (mostly British Pakistani/British Bangladeshi) individuals from the following studies: 1. 3781 British Pakistani/British Bangladeshi adults from East London Genes and Health 2. 2791 British South Asian mothers from Born in Bradford 3. 1428 British South Asian adults from Birmingham 4. 86 individuals (mixed ancestries) from families with rare diseases, from Queen Mary University London All of the Birmingham and most of the Born in Bradford samples were previously sequenced as part of PMID: 26940866. Mapping was done with bwa-mem and variant calling was carried out with GATK HaplotypeCaller. We removed variant sites for which the following was true: SNPs: "QD < 2.0 || FS > 30 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0" Indels: "QD < 2.0 || FS > 30 || ReadPosRankSum < -20.0"

  Dataset EGAD00001004581

• Interval whole-genome sequence (WGS) data

  Whole-genome sequencing (WGS) genotype data generated as part of the Interval project. The data are reported, separately per chromosome, in variant call format (VCF). The genotypes are denoted in diploid format (for chrY the genotype 1 denoted as 1/1 and 0 denoted as 0/0). Note that multi-allelic variants are present in the data, but encoded to appear on separate, consecutive lines. The data are reported in following versions - unphased, phased, phased with imputation, sites only. Note: the unphased version has additional genotype information, while the phased versions only contain the genotypes.

  Dataset EGAD00001008661

• Smoking-dependent expression alterations in nasal epithelium reveal immune impairment linked to germline variation and lung cancer risk.

  Lung cancer is the leading cause of cancer-related death in the world. In contrast to many other cancers, a direct connection to lifestyle risk in the form of cigarette smoke has long been established. More than 50% of all smoking-related lung cancers occur in former smokers, often many years after smoking cessation. Despite extensive research, the molecular processes for persistent lung cancer risk are unclear. CT screening of current and former smokers has been shown to reduce lung cancer mortality by up to 26%. To examine whether clinical risk stratification can be improved upon by the addition of genetic data, and to explore the mechanisms of the persisting risk in former smokers, we have analyzed transcriptomic data from accessible airway tissues of 487 subjects. We developed a model to assess smoking associated gene expression changes and their reversibility after smoking is stopped, in both healthy subjects and clinic patients. We find persistent smoking associated immune alterations to be a hallmark of the clinic patients. Integrating previous GWAS data using a transcriptional network approach, we demonstrate that the same immune and interferon related pathways are strongly enriched for genes linked to known genetic risk factors, demonstrating a causal relationship between immune alteration and lung cancer risk. Finally, we used accessible airway transcriptomic data to derive a non-invasive lung cancer risk classifier. Our results provide initial evidence for germline-mediated personalised smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality.

  Dac EGAC50000000169

• scRNA-seq of total bone marrow and T cells from multiple myeloma long-term survivors

  scRNA This dataset contains 50 scRNA-seq samples from bone marrow aspirates of 11 multiple myeloma patients experiencing long-term survival and 3 healthy donors. For each donor, total bone marrow and CD3+ T cells were sequenced. For multiple myeloma patients, paired samples were collected at initial diagnosis and between 7-17 years after first-line therapy. Bone marrow mononuclear cells were isolated by Ficoll density gradient centrifugation. For sorting of total bone marrow cells singlet, live cells were gated and sorted, for sorting of T cells CD45+, CD3+ cells were gated and sorted on either FACSAria Fusion or FACSAria II. Single-cell RNA sequencing were generated using 10x Genomics single-cell RNAseq technology (Chromium Single Cell 3’ Solution v2) according to the manufacturer’s protocol and sequenced on an Illumina HiSeq4000 (paired end, 26 and 74 bp). Bulk RNA Singlet, live CD3+CD4- CXCR3+CD8+ and CD3+CD4- CXCR3-CD8+ cells were sorted from 7 bone marrow and 3 peripheral blood samples of 7 multiple myeloma patients using a FACSAria Fusion machine. Bulk-RNA sequencing libraries were generated using the SMART Seq Stranded Total RNA-Seq kit (Takara) and sequenced using the Illumina NovaSeq 6000 platform (2 x 100 bp).

  Dataset EGAD00001010025

• Precision Medicine for ABCA4 Disease: Modifier Alleles

  Genetic variation affecting the structure and function of the ABCA4 protein gives rise to a broad collection of genetically and clinically heterogeneous diseases, collectively known as ABCA4 disease, which are characterized by both phenotypic overlap and variable penetrance and expressivity. These include autosomal recessive Stargardt disease, cone-rod dystrophy, occult maculopathy, bull's eye macular dystrophy, retinitis pigmentosa-like retinal dystrophy, etc. Some phenotypic variation can be attributed to variability of highly penetrant ABCA4 mutations; however, much of the heterogeneity cannot be explained by ABCA4 mutations alone. The concept of Precision Medicine requires, by definition, identification of all genetic variation contributing to specific disease phenotypes. The main goal of this study is to identify all genetic variation resulting in phenotypes compatible with ABCA4 disease, which, in addition to biallelic disease-causing variants in the ABCA4 locus, include cis- and trans-acting modifiers and mutations in genes other than ABCA4.The study was carried out by performing whole exome sequencing (WES) of affected individuals to identify causal and modifying genetic variation for patients with macular dystrophies compatible with ABCA4 disease. All samples were sequenced with the IDT's Exome Research Panel Version 1 with SeqCap EZ Exome v2 and SeqCap EZ Exome v3 capture kits. Samples were sequenced on Illumina next-generation sequencing machines (San Diego, CA, USA) using DRAGEN Bio-IT Platform v. 2.5.1 (Illumina) to align reads to the Genome Reference Consortium Human Build 37, calling variants in accordance with the Genome Analysis Tool Kit (GATK, v.4.0.2.1) Best Practices Workflow, using ATAV (v.7.0.16) variant-calling pipeline. Variants for analysis were restricted to the consensus coding sequencing public transcripts (CCDS release 20) plus 2 base pair (bp) intronic extensions. Variants were annotated using ClinEff (DnaMiner) with Ensembl-HGRCh37.73 annotations.This set of WES data from unrelated patients of different racial and ethnic backgrounds can be stored and shared through dbGaP. These data provide information on frequencies of disease-causing variants in genes other than ABCA4 in patients with macular dystrophies. These also provide frequencies of selected pathogenic and none-pathogenic variants in different ethnicities.

  Study phs002393

• Security Overview

  Security Overview The European Genome-phenome Archive (EGA) houses consented human data under controlled access. Access decisions are handled by the relevant Data Access Committee (DAC), which autonomously manages requests through the DAC Portal. This document provides an overview of EGA’s practices in ensuring the security of data stored at EGA. As security is a prime concern of the EGA, the EGA is a member of the Global Alliance for Genomics and Health (GA4GH) Data Security work stream. The EGA contributes and helps develop the recommendations outlined the GA4GH Security Technology Infrastructure document, which defines guidelines, best practices, and standards for building and operating an infrastructure that promotes responsible data sharing in accordance with the GA4GH Privacy and Security Policy. Explore the EGA dataflow to gain deeper insights! Accessing data in the EGA involves several steps. First, users need to create an EGA account. Once logged in, they can request access to data controlled by a DAC through the EGA website (see documentation). The DAC oversees these requests through the DAC Portal, and if approved, grants the necessary permissions to the user's EGA account, allowing them to access and download all relevant data and metadata for the requested dataset(s). The key points of EGA security strategy are: 1 Regular Risk Assessment The EGA regularly identifies and assesses risk related to the following: Breach of confidentiality,Breach of privacy or autonomy,Malicious or accidental corruption or destruction of data archived at EGA,Disruption of services provided by the EGA. 2 Risk mitigation The EGA implements and maintains safeguards to minimise the risks identified above in accordance with the 6 control objectives listed in Appendix 1 and outlined in the GA4GH Security and Infrastructure document.If a breach is discovered, the EGA applies a defined protocol to minimise damage. 3 Identity and authorisation management The EGA authenticates the identity of individuals or software accessing controlled access data held at the EGA.The EGA ensures an appropriate level of assurance (LoA) is applied to the identity consistent with the risk associated with that individual, such as multi-factor authentication for DACs.The EGA provides the minimum access rights and privileges consistent with the user’s identity, allowing access consistent with the GA4GH Privacy and Security Policy, as determined by the appropriate DAC. 4 Audit Logs The EGA maintains a set of logs recording: Changes to user access rights,Data access requests,Resource usage. 5 Cryptography, communication security, and data integrity The EGA ensures data transmission integrity using a hash function.All data transmitted to or from the EGA is end-to-end encrypted.All data at EGA is stored using strong encryption.Encryption keys are not stored in the same system as the encrypted data.All data archived at EGA must be accompanied by a signed submission statement ensuring appropriate consent or ethical approval has been obtained, and is in accordance with all applicable laws and regulations. The EGA has a defined protocol defining the response in the event of a security breach, and is continuing to work with the GA4GH Data Security Work Stream to help define best practice and associated standards for breach responses. Appendix 1 GA4GH Control Objectives Control Objective 1: Implement technology safeguards to minimise the risk of unauthorised access, use, or disclosure of confidential and private data.Control Objective 2: Implement technology safeguards to minimise the risk of discovery, access, and use of individuals’ clinical and genomic data, and individual identities, other than as authorised by applicable jurisdictional law, institutional policy, and individual consents.Control Objective 3: Implement technology safeguards to minimise the risk of accidental or malicious corruption or destruction of data.Control Objective 4: Implement technology safeguards to minimise the risk of disruption, degradation, and interruption of services enabling access to data.Control Objective 5: Implement technology safeguards to minimise the risk of potential security attacks and misuse of authorised accesses and privileges.Control Objective 6: Implement technology safeguards to promptly detect the failure to attain the above control objectives and to respond with proper countermeasures.Appendix 2Refer to the document below to learn more about EGA long-term data preservation policy and procedures at EMBL-EBI.Long-term data preservation

  Documentation about/security

• Paroxysmal Neurological Disorders - rare epilepsies

  We are sequencing the exomes of patients with paroxysmal neurological disorders mainly focusing on migraine and epilepsy. Cases are collected from performance sites of members of EuroEPINOMICS. Most cases have a strong family history. The study sample will include both cases and controls.

  Dataset EGAD00001000647

• UQCCR/QCMG brain metastasis sequence analysis

  The present study, conducted by the UQCCR and QCMG at the University of Queensland, investigated the genomic landscape of brain metastases from 36 patients (with matched normal samples) that originated from a range of primary tumour sites (including breast (11), lung (18), melanoma (6) and oesophageal (1)) utilizing Illumina 2500 HiSeq Exome sequencing. Somatic substitutions were identified using a dual qSNP/GATK calling strategy. The Pindel tool was used to determined short insertions and deletions. Key biological pathways were investigated using pathway analysis tools. It is envisaged that the current work may provide important biological insights into the genomic characterization and evolution of brain metastasis and additionally identify druggable targets of clinical relevance.

  Study EGAS00001000722

• RNA-seq data for study 'Smoking-dependent expression alterations in nasal epithelium reveal immune impairment linked to germline variation and lung cancer risk.'

  RNA-seq data from nasal and bronchial tissues in 649 subjects, many with lung cancer. Lung cancer is the leading cause of cancer-related death in the world. In contrast to many other cancers, a direct connection to lifestyle risk in the form of cigarette smoke has long been established. More than 50% of all smoking-related lung cancers occur in former smokers, often many years after smoking cessation. Despite extensive research, the molecular processes for persistent lung cancer risk are unclear. CT screening of current and former smokers has been shown to reduce lung cancer mortality by up to 26%. To examine whether clinical risk stratification can be improved upon by the addition of genetic data, and to explore the mechanisms of the persisting risk in former smokers, we have analyzed transcriptomic data from accessible airway tissues of 487 subjects. We developed a model to assess smoking associated gene expression changes and their reversibility after smoking is stopped, in both healthy subjects and clinic patients. We find persistent smoking associated immune alterations to be a hallmark of the clinic patients. Integrating previous GWAS data using a transcriptional network approach, we demonstrate that the same immune and interferon related pathways are strongly enriched for genes linked to known genetic risk factors, demonstrating a causal relationship between immune alteration and lung cancer risk. Finally, we used accessible airway transcriptomic data to derive a non-invasive lung cancer risk classifier. Our results provide initial evidence for germline-mediated personalised smoke injury response and risk in the general population, with potential implications for managing long-term lung cancer incidence and mortality.

  Dataset EGAD50000000333