Search Results - EGA European Genome-Phenome Archive

Global Microbiome Conservancy Sequence Data

The human body is a complex ecosystem, hosting tens of billions of bacteria, primarily in the gut. This community, known as the 'gut microbiome', is vital to human health, affecting functions ranging from metabolism, immunity, and development, to behavior. Conversely, reduced microbiome diversity and other imbalances of the gut community (dysbiosis) are associated with many diseases of the industrialized world, such as metabolic syndrome, asthma, and inflammatory bowel diseases among others. Microbiome-based interventions thus hold the potential to revolutionize our understanding of, and therapeutic approaches to, many aspects of human health and disease. Despite many advances, current understanding of the human microbiome is largely limited to majority ethnic groups in industrialized nations, and does not reflect the full diversity of human commensal microbiota. In addition to the scientific ramifications of minority underrepresentation, there are significant translational and ethical implications as well: first, failing to capture the full diversity of healthy human microbiota may limit our ability to understand and generate effective therapeutics for many microbiome-associated diseases. Second, underrepresented groups are less likely to benefit from microbiome-based medical advances tailored to well-studied populations, propagating healthcare inequities.  Human gut microbial diversity is diminishing around the globe, due to the spread of industrialized diets and lifestyles, which are associated with reduced microbiome biodiversity. The Global Microbiome Conservancy (GMbC) is a non-profit collaboration between scientists and communities around the world that aims to 1) preserve the full biodiversity of the human gut microbiome, 2) characterize its evolution across populations and host lifestyles and 3) investigate its interactions with the human host. We are collecting gut microbiome and human DNA samples from diverse populations around the world, from isolated groups to urbanized populations, to culture and study this essential biodiversity and to preserve it in a living "seed bank" of bacterial isolates as a long-term safeguard against future extinctions. Human genetic data for the samples sequenced in this study can be found in phs002205.

Study phs002235

Genetic Associations in Idiopathic Talipes Equinovarus (Clubfoot) - GAIT

Isolated clubfoot or idiopathic talipes equinovarus (ITEV) is one of the five most common birth defects, affecting approximately 4,000 newborns each year in the US. While the orthopedic care of these children has improved, long-term problems persist and the health care costs are significant. Studies suggest that clubfoot is a complex disorder with segregation analyses and family studies indicating that genetic factors play an important etiologic role in the development of clubfoot. Only one environmental factor, maternal smoking during pregnancy, has been implicated. We postulate that a small number of genes account for a substantial fraction of clubfoot and that these genes can be identified in a defined population. The challenge now is to identify the genetic loci and, later, the effect of environmental exposures. To accomplish this task, it is important to have a well-defined population and the methodology to detect linkage with and without association. Towards these goals, we have identified and characterized multiplex clubfoot families, including two large clubfoot families, a resource which is among the largest ITEV populations in existence. We will perform a high-density SNP genome scan (GWAS) on our clubfoot dataset to identify chromosomal regions that may harbor clubfoot genes with subsequent interrogation of these regions and candidate genes. We are in a position with our unique clubfoot population and with the methodology in place to undertake this study. The study phases are: 1) continued ascertainment of multiplex and simplex families and 2) GWAS and characterization of identified chromosomal regions and candidate genes. The results of this study will provide data essential to the identification of the gene(s) contributing to the clubfoot phenotype. Identification of high-risk genotypes can lead to the development of prevention programs in selected populations and may suggest gene-based prevention strategies.

Study phs000314

Identifying novel DNA damage response genes in radiosensitive individuals

Ionizing radiation is an effective therapeutic agent for cancer treatment as well as a potent carcinogen. Sensitivity to the cell-killing effects of radiation can vary across human population with a subset of individuals displaying extreme hypersensitivity. It is usually attributable to inherited defects in DNA damage response pathways. The present study was designed to elucidate the genetic basis of variation in hypersensitivity to radiation exposure through exome sequencing of radiosensitive individuals, with the ultimate goal of identifying genes with the most significant effects on cellular DNA damage responses. The study participants included subjects referred for clinical testing for Ataxia-telangiectasia (A-T), Nijmegen Breakage Syndrome (NBS) or Ligase IV Syndrome. These are rare, recessive genetic disorders and hypersensitivity to radiation exposure is a common phenotype among individuals affected by all the three disorders. The study participants exhibited phenotypic characteristics similar to individuals with A-T, NBS or Ligase IV Syndrome, but lacked the causative mutations in ATM (GeneID:472) or NBN (GeneID:4683) genes. For further validation of the radiation sensitivity phenotype among the enrolled subjects, B-lymphoblastoid cells lines were established for each subject from peripheral blood lymphocytes. Each cell line was evaluated for displaying impaired survival rates relative to normal controls after exposure to ionizing radiation. 53 subjects with validated phenotype were finally included in the study and DNA extracted from their B-lymphoblastoid cell lines was used for exome sequencing. This sequencing data for radiation sensitive subjects is being made available in the dbGaP. It is hoped that this resource will be beneficial for researchers who wish to further investigate components of human cellular DNA damage response pathways and/or genetic architecture underlying radiation hypersensitivity. This data may also aid in the rational design of new radiosensitizing or radioprotective agents.

Study phs001911

Investigating Genetics of Human Natural Short Sleepers (IGHNSS)

Sleep is essential for life. A good night's sleep is pleasurable and sleep deprivation is stressful. Prolonged sleep loss impairs temperature control, metabolism, immunity, and ultimately leads to death. Extensive observational and epidemiological evidence indicates that optimal sleep duration of 8 hours is associated with the maintenance of good health. In our society, however, most people only get 6.5 - 7 hours. Suboptimal sleep duration has a strong association with mortality and morbidity. Lack of sleep has been linked to cardiovascular diseases, obesity, hypertension, type 2 diabetes, and other health/cognition conditions. It is clear that the biological need for sleep varies dramatically among humans. Sleep and circadian disorders can include Familial Advanced Sleep Phase (FASP), Delayed Sleep Phase (DSP), Advanced Sleep Phase (ASP), Natural Short Sleepers (NSS) or Long Sleeping. In example, Natural Short Sleepers (NSS) have a lifelong tendency to sleep only 4 - 6 hours per night and to awaken refreshed and energetic. Natural Long Sleepers biologically require 9 - 10 hours/night to feel well rested. The 'Sleep and Circadian Disorders Study' (SACDS) at the University of California San Francisco, set out to investigate the mechanisms involved in regulating sleep duration, patterns and sleep quality regulation by identifying and characterization of individuals and families with unusual sleep and circadian rhythm behavior patterns. SACDS participants were screened with a "General Sleep Questionnaire" that inquired about multiple aspects of sleep, including habitual work-day versus non-work day sleep-wake schedules, permits calculation of subjective habitual initial sleep onset, final sleep offset, and number of awakenings. There was an additional screening process including demographic data, sleep, mood, behavioral and general medical questionnaires, plus the study consent. After the extensive screening of 117 participants, blood samples were collected from 38 individuals and of those 10 samples were chosen for whole exome sequencing analysis.

Study phs001270

Polymorphisms in the mitochondrial genome are associated with bullous pemphigoid in Germans

Bullous pemphigoid (BP) is the most prevalent autoimmune skin blistering disease and is characterized by the generation of autoantibodies against the hemidesmosomal proteins BP180 (type XVII collagen) and BP230. Most intriguingly, BP is distinct from other autoimmune diseases because it predominantly affects elderly individuals above the age of 75 years, raising the question why autoantibodies and the clinical lesions of BP emerges mostly in this later stage of life, even in individuals harboring known putative BP-associated germline gene variants. The mitochondrial genome (mtDNA) is a potential candidate to provide additional insights into the BP etiology; however, the mtDNA has not been extensively explored to date. Therefore, we sequenced the whole mtDNA of German BP patients (n=180) and age- and sex-matched healthy controls (n=188) using next generation sequencing (NGS) technology, followed by the replication study using Sanger sequencing of an additional independent BP (n=89) and control cohort (n=104). While the BP and control groups showed comparable mitochondrial haplogroup distributions, the haplogroup T exhibited a tendency of higher frequency in BP patients suffering from neurodegenerative diseases (ND) compared to BP patients without ND (p= 0.1448, Fisher’s exact test). A total of four single nucleotide polymorphisms (SNPs) in the mtDNA, namely, m.16263T>C, m.11914G>A, m.16051A>G, and m.16162A>G, were found to be significantly associated with BP based on the meta-analysis of our NGS data and the Sanger sequencing data (p=0.0017, p=0.0132, p=0.0129, and p=0.0076, respectively, Peto’s test). In summary, our study is the first to interrogate the whole mtDNA in BP patients and controls and to implicate multiple novel mtDNA variants in disease susceptibility. Studies using larger cohorts and more diverse populations are warranted to explore the functional consequences of the mtDNA variants identified in this study on immune and skin cells to understand their contributions to BP pathology.

Study EGAS00001003932

Exploration of CNV’s and SNV’s in cancers with well-known genetic rearrangements: Identification of additional genetic changes in rearrangements-driven cancer

Specific cancers are well known to have specific gene rearrangements. Representative cancer is chronic myeloid leukemia (CML) which is famous for BCR/ABL gene rearrangements. By the way, also genetic rearrangements are not infrequently found in acute leukemia, and those include CBFB/MYH11 rearrangements, PML/RARA rearrangements, AML/ETO rearrangements, and BCR/ABL rearrangements. The situation is same is for lung adenocarcinoma of non-smokers and prostate cancers, and ALK and RET gene rearrangements are recently found. However, not all these rearrangements are sufficient to develop cancer, and it is suggested that some genetic aberrations are required for the development and progression of cancer in addition to these genetic rearrangements. In this study, we have a plan to explore the existence of additional genetic changes including single nucleotide variants (SNV’s) and copy number variations (CNV’s) in cancers with specific genetic rearrangements so as to define additional genetic changes that confer full oncogenic potential to cancer cells. So as to look at SNV’s and small indels, we are going to use whole exome sequencing (WES) data. And as for CNV’s, we are going to use array-CGH data. Also, to check the functionality of these genetic changes, we are going to use whole transcriptome data (WTS). In addition, in samples with whole genome sequencing (WGS ) data, we are going to explore genetic changes in intronic regions also. In fact, we sequenced 10 AML samples which are known to have specific genetic rearrangements. Analysis of these WGS, WES and WTS data are under the way. If we have a chance to access WES, array-CGH and WTS data of other cancer samples with specific genetic rearrangements, we would be able to compare additional genetic changes required for oncogenesis in cancer cells with various genetic rearrangements. We hope this result will enlighten our understanding of cancer genetics further.

Study EGAS00001000673

Best Practices for DACs

Best Practices Data access committees (DACs) are institutional safeguards responsible for ensuring a balance between data protection and accessibility. However, there are no procedural standards that apply across DACs, which can lead to inconsistencies in their reviews and compromise their quality and effectiveness. Standardising DAC processes can foster trust and mutual recognition, paving the way for greater coordination, collaboration, and delegation between DACs and other oversight bodies to improve the efficiency of data access without sacrificing protection. To provide guidance, the following are suggestions for actions a DAC might take upon receiving a data access request: Aim to respond to all initial requests in less than 2 weeks. Failure to do so may result in follow-up emails from the EGA, journals, and ultimately dataset withdrawal. First, check that the data/EGAD number is consistent with your data submission. In other words, ensure that the requester has contacted the correct DAC. Verify that the user will be using the data within the terms of consent by asking them to sign up to the terms within the DAA. Ensure that data users who are granted access to the data comply with the terms of a Data Access Agreement (DAA) and to use the data only in approved ways. Look for an institutional email address for the requester. Search for evidence that the requester is "appropriately qualified/bona fide" for using the data, for example on PubMed, Research Gate, LinkedIn, etc. Confirm that the affiliated organisation is real and that the requester still works there. Inquire with the requester about who should have accounts created at the EGA under the terms of the agreement. If a negative decision is made, promptly communicate it to the requester and support it with the terms of the Data Access Policy that the requester has not met or cannot meet. Keep the information in the DAC up to date. If you leave your institution and are unable to manage data access requests on their behalf, you should add your replacement as a new contact in the DAC. Assist with data access requests for any further questions related to your data. The EGA can only check information that has been deposited in the repository. If the user has a specific question that the EGA cannot answer, we will redirect the user to the DAC. All the EGA studies and datasets referenced in a publication under your DAC must be publicly searchable on the EGA website before the paper is released. If possible, describe the permitted purposes for subsequent research projects, including associated limits and conditions, for all resources hosted in a repository using a common ontology, such as the GA4GH Data Use Ontology (DUO). It is considered best practice to try and release data only to those with an institutional email address. This gives reassurance to the EGA, research participants, and the general public that an appropriate individual is accessing and using the data. To prevent potential data breaches and ensure adherence to GDPR regulations, it is essential that the European Genome-Phenome Archive (EGA) is informed via the Helpdesk team of any changes to the Data Access Committee (DAC). This should be done in addition to any changes being made on the DAC portal. Data Controllers (as per the definition in the DPA) are also responsible for notifying the previous DAC of any modifications. Without proper notification, changes might not be automatically updated in our system, leading to the risk of incorrect permissions being applied and potential data access issues. Therefore, it is imperative that all Data Controllers follow this protocol to maintain data integrity and security.

Documentation access/data-access-committee/best-practices

Prognostic and therapeutic significance of leukemia subtypes and minimal residual disease measurements in pediatric acute lymphoblastic leukemia treated with contemporary risk-directed trial: a cohort study

Background Genomic analyses to identify leukemia subtypes and refinement of minimal residual disease (MRD) determination are two recent advances that would have substantial impact on the management of children with acute lymphoblastic leukemia (ALL). However, the combined application of these two approaches to determine the prognostic and therapeutic significance of MRD measurement and full spectrum of leukemia subtypes in the context of contemporary risk-directed therapy has not been studied comprehensively.MethodsBetween October 29, 2007 and March 26, 2017, 598 consecutive children with newly diagnosed ALL were enrolled in a clinical trial at St. Jude Children’s Research Hospital. Patients were provisional classified according to the presenting clinical and laboratory features including immunophenotype and cytogenetics. Patients with ETV6-RUNX1 and high hyperdiploid B-progenitor ALL were provisionally included in the low-risk groups; those with TCF3-PBX1, hypodiploidy and T-cell ALL in the standard (intermediate)-risk group; and patients with BCR-ABL1, KMT2A and early T-cell precursor ALL in the high-risk group. Final risk assignment was based primarily on MRD levels measured by flow cytometry on Day 15 (≥1%) and Day 42 (<0.01% or ≥1%) of remission induction. Using genomic classification based on cytogenetics and genomic analysis (single nucleotide polymorphism array analysis and transcriptome, exome, and genome sequencing), patients were classified into 17 subtypes. The primary aim was to determine the event-free survival, overall survival and cumulative risk of relapse according to specific leukemia subtypes and MRD levels on Days 8, 15 and 42 of remission induction. This clinical trial was registered at ClinicalTrials.gov, number NCT00549848.FindingsPatients with ETV6-RUNX1, high hyperdiploidy and DUX4-rearranged B-ALL all received low- or standard-risk therapy, and had the highest 5-year event-free survival rates: 98.4% (95% CI, 95.9-100), 95.3% (91.2-99.4), and 95.0% (84.2-100), respectively. All 142 patients with Day 8 MRD<0.01%, except two with KMT2A-rearranged ALL and one with TCF3-PBX1, were in continuous complete remission. The TCF3-PBX1, PAX5alt, T-cell, early T-cell precursor, iAMP21 and hypodiploid ALL had intermediate outcome with 5-year event-free survival rates ranging from 80.0% (39.4-100) to 88.2% (71.7-100). Patients with KMT2A-rearranged, BCR-ABL1, BCR-ABL1-like and ETV6-RUNX1-like ALL had the worst outcomes, with 5-year event-free survival rates between 64.1% (43.9-84.3) and 76.2% (51.9-100), despite most patients receiving standard- or high-risk therapy. Treatment intensification based on MRD and genotype improved outcome. Relapse did not occur in any of the patients with ETV6-RUNX1, DUX4-rearranged, TCF3-PBX1, iAMP21, ZNF384-rearranged, hypodiploid, or BCR-ABL1-like B-ALL who had day 15 MRD≥1% and received standard-risk therapy. However, day 42 MRD<0.01% did not preclude relapse in patients with intermediate-risk or unfavorable subtypes including TCF3-PBX1, PAX5alt, T-cell, iAMP21, BCR-ABL1, BCR-ABL1-like, ETV6-RUNX1-like, KMT2A-rearranged, and MEF2D-rearranged ALL, despite intensified treatment in the standard- or high-risk arms. InterpretationContemporary risk-directed treatment based on genotypes and MRD can cure virtually all patients with ETV6-RUNX1, hyperdiploid>50 and DUX4-rearranged ALL. While intensified treatment improve outcome for patients with high MRD during induction, substantial proportions of patients with intermediate-risk or unfavorable subtypes would still relapse even achieving negative MRD status at the end of induction and require novel therapeutics to improve outcome.

Study EGAS00001004739

The British Autozygosity Populations BioResource (2022-04-26)

This data set contains whole exome sequences of individuals from 8086 (mostly British Pakistani/British Bangladeshi, mostly self-reported parentally related) individuals from the following studies: 1. 5236 British Pakistani/British Bangladeshi adults from East London Genes & Health, now known as Genes & Health 2. 2624 British South Asian mothers from Born in Bradford (mostly Pakistani) 3. 1061 British South Asian adults from Birmingham (mostly Pakistani) This dataset contains all the exome sequence data available for this study on 2022-04-26

Dataset EGAD00001008736

Programmatic submission based on XML

Programmatic submissions (XML based) For further information please check our Submission FAQs, submission quickguide as well as submission terms! Introduction Besides the Submitter Portal tool, EGA supports programmatic sequence and clinical data metadata submissions. If you are not sure what this means, you may want to explore our brief metadata introduction. Programmatic submissions are recommended for array-based submission. Moreove, it may be of help if your submission is recurrent or it is difficult to manage manually due to its sheer size. Otherwise, we highly recommend using the Submitter Portal to perform submissions. In this page we will guide you through the required steps to programmatically submit data to the EGA. Programmatic submissions require your metadata to be structured for an easy and straightforward validation and archival. It basically consists in formatting your metadata as Extensible markup language (XML) files and submitting them to the EGA using the WEBIN Before submitting metadata to the EGA, it is important to ensure that the information in your XML files is compliant with our standards. You can see further details on how these standards are maintained at EGA at our EGA Schemas documentation page. Using WEBIN, you can validate your XML files against EGA's schemas to ensure that your metadata is compliant before submission. WEBIN services WEBIN production service WEBIN test service We advise you to submit your metadata to the test service when submitting to the production service for the first time. The test service is identical to the production service except that all submissions will be discarded in the following 24 hours. This allows you to learn about the submission process without having to worry about data being submitted. Authentication Authentication is required each time a submission is made. The submission service uses HTTPS protocol for metadata encryption and identification to provide a secure submission environment. Data file upload Both Runs and Analyses reference files (e.g. FASTQ need to be uploaded to the EGA before these metadata objects are submitted. In other words, if you submit a Run that references a file that we cannot find associated with your account, the metadata submission will fail. See further details on how to upload your files in our File Upload documentation. Metadata model of the EGA Our metadata model is formed by multiple metadata objects. Check further details in our documentation at our EGA Schema documentation page. Working with EGA XMLs files Now that the basic concepts of the EGA metadata have been described, you can start preparing your programmatic submission through XML. Here you will find the guidance on how to prepare the XML files. Programmatic Submission Tutorial Video Take a look at the Programmatic Submission Tutorial Video, which explains the workflow of a programmatic submission and goes over an example metadata submission. Programmatic Submission Tutorial Video. When building your XML files, we recommend using text editors (e.g.Sublime Text or VisualStudio) that allow you to visualise the structure of the XML with ease. Furthermore, these editors constantly check the consistency of the XML structure. Alternatively, and if the submission consists of a big number of objects (specially analyses), you may find the tool star2xml handy. This tool allows for a direct conversion between metadata in a tabular format (e.g. a spreadsheet) into XMLs. Identifying objects: Aliases and center names Every EGA object must be uniquely identified within the submission account using their alias attribute. The aliases can be used in submissions to make references between EGA objects. Let us dig into EGA's use of aliases and center names: alias: every object should have a name that is unique within your submission account. Once submitted successfully, every alias will be assigned a unique and permanent accession (EGA ID). refname: when an object references another by its alias, the alias of the referenced object goes into the "refname" attribute of the referencing object. For example, if a sample has the alias "sample1", and an experiment uses this sample, then the experiment's "EXPERIMENT/SAMPLE/refname" attribute should be "sample1". center_name: The "center_name" attribute is required within the submission XML and, if not provided when the object is submitted, it will be automatically filled using your default EGA account center_name. This element is the "controlled vocabulary acronym or abbreviation that is provided to the account holder when the account is first generated". If the submitter is brokering a submission for another institute, the submitter should use their special broker account name in broker_name while the data centre acronym remains in center_name. Log-in details should have been provided when you requested a submission account. Please contact our Helpdesk team if you have any questions. run_center: Many submitting centers contract out the actual sample sequencing to another center. In these cases, the sequencing center should be acknowledged in the run_center attribute. Again, this is controlled vocabulary and the acronym should be sought from EGA helpdesk before submitting. Please contact our Helpdesk team if you have any questions. Prepare your XMLs The goal of this section is to provide sufficient information to be able to create the metadata XML documents required for programmatic submissions. Please note, the EGA utilises the XML schemas maintained at the European Nucleotide Archive (ENA). It is important due to the fact that by using a similar system, some pieces of documentation from the ENA's programmatic submission can also help you with your programmatic submission to the EGA. For example, you can submit programmatically without using a Submission XML by following the steps at Submission actions without submission XML. A submission does not have to contain all different types of XMLs. For example, it is possible to submit only a few samples; or a study that is later to be referenced. You can submit each object one by one, or submit all in a batch: you choose what method of submission works best for you. We do recommend, nevertheless, that you submit the objects to be referenced (e.g. samples or studies) first, and the objects that reference these (e.g. experiments or datasets) afterwards. You can see a graphical view of these objects and their relationships at our EGA Schemas page. Independently of the submission scenario, you will always require a Dataset XML. The entity of a dataset is what is used to control access to the given data, in the form of runs or analyses. In other words, when a requester is granted access, it is through the dataset and the objects (e.g. runs or analyses) that the dataset contains, granting access to them in one go. Given the nature of the EGA, a dataset XML will always be required for the data access. First, we will differentiate between submissions of "raw" and "processed" data: Runs and Analyses, respectively. Run data submissions Raw data derives from instruments "as is". For example, a plain sequence file (e.g. FASTQ or unaligned BAM files) would be considered raw data. A typical raw (unaligned) sequence read submission consists of 8 XMLs: Submission Study Sample Experiment Run DAC Policy Dataset When technical reads (e.g. barcodes, adaptors or linkers) are included in the submitted raw sequences, a spot descriptor must be submitted to describe the position of the technical reads so that they can be removed. The following data files can be submitted without providing spot descriptor information in the experiment/run XML: BAM files (single reads) SFF files (single reads without barcodes) FastQ files (single reads without any technical reads) Complete Genomics files Analysis data submissions Processed data is, in some way, refined raw data. This includes raw data that has been processed by some form of analysis method (e.g. alignment, noise reduction, etc.). For example, an aligned sequence (e.g. BAM file), that was created using raw FASTQ files, would be a processed file. This category includes most types of data: sequence alignment files (e.g. BAM or CRAM), clinical data (e.g. phenopackets), sequence variation files (e.g. VCF), sequence annotation, etc. A typical EGA analysis data submission consists of 7 EGA XML: Submission Study Sample Analysis DAC Policy Dataset We accept three different types of analysis data submissions: BAM files (for multiple read alignments) VCF files (for sequence variations) Phenotype files (in any format) In anycase, keep in mind that samples must be created in order to be referenced in the analyses. In other words, the provenance of the information within the BAM, VCF and phenotype files Example XMLs Below you can find a non-extensive list of example XMLs with descriptive fields (i.e. explaining what to provide in each field). Furthermore, you can also find real examples (i.e. the true value of the provided fields) in our GitHub repository. Submission XML The submission XML is used to validate, submit or update any number of other objects. The submission XML refers to other XMLs. New submissions use the ADD action to submit new objects. Object updates are done using the MODIFY action and objects can be validated using the VERIFY action. Descriptive submission XML example True values submission XML example Study XML The study XML is used to describe the study containing a title, a study type and abstract as it would appear in a publication. Descriptive study XML example True values study XML example Please use the following notation within the property "STUDY_LINKS" when including PubMed citations in the Study XML: <STUDY_LINKS> <STUDY_LINK> <XREF_LINK> <DB>PUBMED</DB> <ID>18987735</ID> </XREF_LINK> </STUDY_LINK> </STUDY_LINKS> Sample XML The sample XML is used to describe the samples used to obtain the data, whether they were sequenced, measured in any other way, or have an associated phenotype. The mandatory fields include information about the taxonomy of the sample, sex, subject ID and phenotype. For example, the mandatory attribute fields for each sample would look like these, within the array of "SAMPLE_ATTRIBUTES": <SAMPLE_ATTRIBUTES> <SAMPLE_ATTRIBUTE> <TAG>subject_id</TAG> <VALUE>free text!</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>sex</TAG> <VALUE>female/male/unknown</VALUE> </SAMPLE_ATTRIBUTE> <SAMPLE_ATTRIBUTE> <TAG>phenotype</TAG> <VALUE>Free text, EFO terms (e.g. EFO:0000574) are recommended</VALUE> </SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTES> Sample is one of the most important objects to be described biologically, it is highly recommended that “TAG-VALUE” pairs are generated as SAMPLE_ATTRIBUTES to describe the sample in as much detail as possible. For example, were we to give the population ancestry of the sample, we could add a new attribute to the array, in which, for example, we would indicate that the sample derives from an individual of "Mende in Sierra Leone" (MSL), with an african ancestry: <SAMPLE_ATTRIBUTE> <TAG>Population</TAG> <VALUE>MSL</VALUE> </SAMPLE_ATTRIBUTE> Given that VALUE and TAG are free text, the combinations are limitless in order to give you full flexibility on the information you want to provide. We recommend you use the Experimental Factor Ontology (EFO) to describe the phenotypes of your samples. You can provide more than one phenotype by adding more items to the array of SAMPLE_ATTRIBUTES. Phenotypes considered essential for understanding the data submission should be provided. Each phenotype described should be listed as a separate sample attribute <SAMPLE_ATTRIBUTE> </SAMPLE_ATTRIBUTE>. There is no limit to the number of phenotypes that can be submitted. If a suitable EFO accession cannot be found for your phenotype attribute, please consider using another controlled ontology database (e.g. HPO, MONDO, etc.) before using free text. Descriptive sample XML example True values sample XML example Experiment XML The experiment XML is used to describe the experimental setup, including instrument platform and model details, library preparation details, and any additional information required to correctly interpret the submitted data. Where any of these values differ between runs, a new experiment object must exist, since runs are grouped by experiments. Each experiment references a study and a sample by alias, or if previously-submitted, by accession. Pooled data must be demultiplexed by barcode for submission. Descriptive experiment ( Illumina paired read ) XML example True values experiment ( Illumina paired read ) XML example Run XML The run XML is used to associate data files with experiments and typically comprises a single data file (e.g. a FASTQ file). Please note that pooled samples should be de-multiplexed prior submission and submitted as different runs. Descriptive run XML example True values run XML example Analysis XML Given that an analysis can be used to submit any type of processed data to the EGA, we will list below an example of each of the three most common types of analysis XMLs submitted to the EGA: sequence alignments (e.g. BAM files); sequence variation (e.g. VCF files); and clinical metadata or phenotypes (e.g. phenopackets). Regardless of the type of processed data submitted in the analysis, the analysis must be associated with a Study and can reference multiple types of other objects, from samples to experiments, if they are available at the EGA. Just like with Runs, whenever a file is submitted to the EGA through an analysis object, the file MD5 checksums must be present, in order for the EGA to validate file integrity upon transfer. This also includes index files when applicable (e.g. .bai.md5 files). Ideally, any analysis that uses a reference sequence for some kind of alignment (e.g. BAM, CRAM or VCF files), would contain metadata about the alignment, such as INSDC reference assemblies and sequences, by either using accessions (e.g. CM000663.1) or common labels (e.g. GRCh37). Read alignment (BAM) Analysis XML The Analysis can be used to submit BAM alignments to EGA. Only one BAM file can be submitted in each analysis and the samples used within the BAM read groups must be associated with Samples. Descriptive bam alignments XML example True values bam alignments XML example Sequence variation (VCF) Analysis XML The Analysis can be used to submit VCF files to EGA. Only one VCF file can be submitted in each analysis and the samples used within the VCF files must be associated with Samples. Download analysis XML (VCF) Phenotype files The Analysis XML can be used to submit phenotype files to the EGA. Only one phenotype file can be submitted in each analysis and the samples used within the phenotype files must be associated with EGA Samples. Download analysis XML (Phenotype) DAC XML The DAC XML describes the Data Access Committee (DAC) affiliated to the data submission. The DAC may consist of a group or a single individual and is responsible for the data access decisions based on the application procedure described in the POLICY.XML. As with any other object, if it was already submitted to the EGA, there is no need to submit it again: you can reference an existing object within the EGA. Hence, A DAC XML does not need to be provided if your submission is affiliated to an existing EGA DAC.. Further information on DACs can be found here, and you can always contact our Helpdesk team if you have further inquiries. Descriptive dac XML example True values dac XML example Policy XML The Policy XML describes the Data Access Agreement (DAA) to be affiliated to the named Data Access Committee. Descriptive policy XML example True values study XML example Dataset XML The dataset XML describes the data files, defined by the Run.XML and Analysis.XML, that make up the dataset and links the collection of data files to a specified Policy. The dataset xml is commonly the last metadata object to be submitted, since it references multiple other entities. Please consider the number of datasets that your submission consists of. For example, a case-control study is likely to consist of at least two datasets. In addition, we suggest that multiple datasets should be described for studies using the same samples but different sequence technologies. Descriptive dataset XML example True values dataset XML example Validating and submitting your EGA Validating EGA's XMLs through Webin After you have ensured that the XMLs are properly formatted and contain all the required information. You can proceed to validate and submit your data. Use the curl command to validate your XML file: Once you have prepared your XML file and asserted you have access to Webin, you can validate your XML file programmatically against EGA's schemas using the curl command. There are multiple ways in which you can validate your XMLs. This variety has to do with the fact that: (1) there are 2 instances of Webin (test and production); and (2) that validation is a default step during submission. In other words, any time that you submit your data through Webin, it will be validated automatically before being accepted. This allows for 4 possible routes of validation, all having the same validation result: validating or submitting to either the production service or the test service of Webin. For example, directly validating a "study" object XML in the testing service (wwwdev…) would look like the following: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" In this command, you would need to replace <USERNAME> and <PASSWORD> with your EGA account username and password, respectively. You would also replace <INPUT_FILE> with the path to your XML file. A mock example would look like the following: curl -u ega-test-data@ebi.ac.uk:egarocks -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" The validation attempt can have different results depending on the given arguments: If your XML file is valid according to EGA's schemas, you will see a message indicating that your XML file is compliant. For example, see below for our mock example, where the "success" was "true" (i.e. no validation errors found). Nevertheless, notice how the "<STUDY accession=" is empty: it is because we were simply validating, so the study did not get an accession or ID. <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:19:28.850+01:00" submissionFile="submission-EBI-TEST_1681222768850.xml" success="true"> <STUDY accession="" alias="Mock example" status="PRIVATE"/> <SUBMISSION accession="" alias="SUBMISSION-11-04-2023-15:19:28:840"/> <MESSAGES> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If there are any errors or warnings, the tool will display them, allowing you to correct them before submitting your data to EGA. For example, in the following response, it is said that the object we were trying to submit was already existing, and therefore the "success" was "false". <?xml version="1.0" encoding="UTF-8"?> <?xml-stylesheet type="text/xsl" href="receipt.xsl"?> <RECEIPT receiptDate="2023-04-11T15:12:35.609+01:00" submissionFile="submission-EBI-TEST_1681222355609.xml" success="false"> <STUDY alias="Example!_Human Microbiome Project SP56J" status="PRIVATE" holdUntilDate="2023-03-11Z"/> <SUBMISSION alias="SUBMISSION-11-04-2023-15:12:35:576"/> <MESSAGES> <ERROR>In study, alias: "Example!_Human Microbiome Project SP56J". The object being added already exists in the submission account with accession: "ERP127584".</ERROR> <INFO>VALIDATE action has been specified.</INFO> <INFO>Submission has been rolled back.</INFO> <INFO>This submission is a TEST submission and will be discarded within 24 hours</INFO> </MESSAGES> <ACTIONS>VALIDATE</ACTIONS> <ACTIONS>PROTECT</ACTIONS> If the curl command retrieves no response at all, please double check if your username and password are correctly provided. Also notice the "ACTION=..." argument passed to the Curl command. This specifies the action to take during the call to Webin, so we do not need a "Submission" XML just for a validation attempt. See more at submission actions without submission XML. Furthermore, validation of multiple files or objects (e.g. sample, experiment, study…) can be done in a single command by adding more arguments (i.e. '-F'). For example: curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" -F "SAMPLE=@sample.xml" -F "DATASET=@dataset.xml" As mentioned above, beside "validate" action in the test environment, you can also validate your metadata by three other methods: "Validate" in the production server. From our example above, you simply need to take the "dev" away from the URL. curl -u <USERNAME>:<PASSWORD> -F "ACTION=VALIDATE" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the development server. From our example above, you would simply need to replace the action: from "validate" to "add". Whatever is submitted to this service will be discarded in 24h, so whether something gets submitted or not would not matter in the long run. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://wwwdev.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" "Add" in the productionserver. A combination of the previous two methods, which would render this attempt into a submission. This path is just to be taken when you are sure your metadata is compliant and what you want to submit. curl -u <USERNAME>:<PASSWORD> -F "ACTION=ADD" "https://www.ebi.ac.uk/ena/submit/drop-box/submit/" -F "STUDY=@study.xml" What happens after the submission of a dataset XML? Once you have completed the registration of your dataset/s please contact our Helpdesk Team to provide a release date for your study. Please note that all datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitter or DAC contact contact the EGA Helpdesk for the study to be released. We strongly advise you not to delete your data until EGA Helpdesk confirms that your data has been successfully archived.

Documentation submission/metadata/submission/programmatic-submission-xml

IL7-receptor expression is frequent in T-cell acute lymphoblastic leukemia and predicts sensitivity to JAK-inhibition

T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematological malignancy with a dismal prognosis related to refractory/relapsing diseases, raising the need for new targeted-therapies. Activating mutations of the IL7-receptor pathway genes (IL7Rp) play a proven leukemia-supportive role in T-ALL. JAK-inhibitors such as ruxolitinib have recently demonstrated preclinical efficacy. However, prediction markers for sensitivity to JAK-inhibitors are still lacking. Herein, we show that IL7R (CD127) expression is more frequent (~70%) than IL7Rp-mutations in T-ALL (~30%). We compared the so-called non-expressers (no IL7R-expression/IL7Rp-mutation), expressers (IL7R-expression without IL7Rp-mutation) and mutants (IL7Rp-mutations). Integrative multi-omics analysis outlined IL7R-deregulation in virtually all T-ALL subtypes, at the epigenetic-level in non-expressers, genetic-level in mutants, and post-transcriptional level in expressers. Ex-vivo data using primary-derived xenografts support that IL7Rp is functional whenever the IL7R is expressed, regardless of the IL7Rp mutational status. Consequently, ruxolitinib impaired T-ALL survival in both expressers and mutants. Interestingly, we show that expressers displayed ectopic IL7R-expression and IL7Rp-addiction conferring a deeper sensitivity to ruxolitinib. Conversely, mutants were more sensitive to venetoclax than expressers. Overall, combination of ruxolitinib and venetoclax resulted in synergistic effects in both groups. We illustrate the clinical relevance of this association by reporting achievement of complete remission in two patients with refractory/relapsed-T-ALL. This provides proof of concept for translation of this strategy into clinics as bridge to transplant. Altogether, IL7R-expression can be used as a biomarker for sensitivity to JAK-inhibition, thereby expanding the fraction of T-ALL patients eligible to ruxolitinib up to nearly ~70% of T-ALL.

Study EGAS00001007144

Gabriella Miller Kids First Pediatric Research Program in Craniofacial Microsomia

The Gabriella Miller Kids First Pediatric Research Program (Kids First) is a trans-NIH effort initiated in response to the 2014 Gabriella Miller Kids First Research Act and supported by the NIH Common Fund. This program focuses on gene discovery in pediatric cancers and structural birth defects and the development of the Gabriella Miller Kids First Pediatric Data Resource (Kids First Data Resource). All of the WGS and phenotypic data from this study are accessible through dbGaP and kidsfirstdrc.org, where other Kids First datasets can also be accessed. Craniofacial microsomia (CFM), also termed hemifacial microsomia or oculo-auricular-vertebral spectrum, is the third most common congenital craniofacial condition. CFM comprises a variable phenotype and the most common features include malformations of the ear (i.e. microtia) and lower jaw (i.e. mandibular hypoplasia) on one or both sides. Microtia in the absence of other anomalies is believed to represent the mildest form of CFM. The cause of CFM is unknown for most affected individuals. We have established a cohort through previous studies and collected DNA to identify the genetic contributions to CFM, which could facilitate diagnosis, tailored treatments and guide prevention strategies. Successful completion of this proposal will advance knowledge in the genetic architecture of susceptibility to CFM and will provide insight about the biological mechanisms underlying craniofacial development. The results from this study have the potential to further research on the etiology of other craniofacial conditions, and the pathogenesis of typical and atypical craniofacial development.

Study phs002130

Phenotype Risk Scores Identify Patients with Unrecognized Mendelian Disease Patterns

This study was designed to assess the phenotypic effects of rare variants. Rare variants are difficult to study in a high-throughput manner because most cohorts are underpowered to detect associations. In order to gain power to test rare variants, we use Phenotype Risk Scores (PheRS) based on features of Mendelian diseases. PheRS is calculated using claims data from an EHR that is mapped to clinical features from OMIM's clinical descriptions of Mendelian disease. We calculated PheRS for 1,204 Mendelians diseases in a cohort of 21,701 individuals genotyped on the HumanExome BeadChip. We then tested for association between these diseases and rare variants in causal genes. A phenotype risk score is calculated as the weighted sum of features for a given disease. The features are defined as a set of phecodes (consolidated ICD codes) associated with a particular Mendelian disease as described in OMIM. The weights for each phecode are calculated as the log inverse prevalence of the phecode in our cohort. For an individual, their score equals the sum of the weights for each phecode that is present in their medical record. Our study was based on a cohort of individuals with genotype data linked to de-identified electronic health records (EHR) from Vanderbilt's BioVU resource. We hope that this method will help generate phenotype-genotype correlation data on rare variants which in turn may inform rare variant interpretations use for diagnostics.

Study phs001516

Transdisciplinary Studies of Genetic Variation in Colorectal Cancer(CORECT): Meta-analysis

This study is part 2 of 2 for Schumacher et al, Nat Communication. 2015 Jul 7;6:7138. Part 2 is the pooled meta-analysis of 29 different studies of 37,955 European (18,299 Cases/19,656 Controls) participants using eight different genotyping platforms/variations. table, th, td { border: 1px solid black; border-collapse: collapse; } Study Name Participants Platform CFR-1 2976 Illumina 1M, 1M Duo, Omni1 CFR-2 3053 Affymetrix Axiom MECC 1 982 Illumina Omni 2.5 MECC2 1940 Affymetrix Axiom Kentucky 2172 Affymetrix Axiom ACS/CAPII 1086 Affymetrix Axiom Melbourne 1008 Affymetrix Axiom Newfoundland 672 Affymetrix Axiom ASTERKSI 1895 Illumina 300K COLO23 212 Illumina 300K DACHS1 3417 Illumina 300K DACHS2 1173 Illumina OmniExpress DALS1 1416 Illumina 550k/610K DALS2 874 Illumina 300K HPFS1 457 Illumina OmniExpress HPFS2 348 Illumina OmniExpress HPFS AA 658 Illumina OmniExpress MEC 674 Illumina 300K NHS1 1168 Illumina OmniExpress NHS2 340 Illumina OmniExpress NHSAA 1091 Illumina OmniExpress OFCCR 1172 Affymetrix 100k/500K PHS 771 Illumina OmniExpress PMH 402 Illumina 300K PLCO1 2509 Illumina 550k/610K PLCO2 901 Illumina 300K VITAL 573 Illumina 300K WHI1 1999 Illumina 550k/610K WHI2 573 Illumina 300K Since this study is a meta-analysis no corresponding phenotype, pedigree, sample, subjects, or primary molecular data are available. Each contributing study/platform will have fulfilled its own DBGAP submission commitment. This DBGAP entry is for European participants in the the pooled meta-analysis exclusively.

Study phs001499

Single Cell Genotypic and Phenotypic Analysis of Measurable Residual Disease in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a group of blood cancers with high mortality. Even though complete remission is often achieved, majority of the AML patients still relapse. Measurable residual disease (MRD), defined as the population of cancer cells which persists following chemotherapy, is responsible for AML relapse. Understanding the biology enabling MRD clones to resist therapy is necessary to guide the development of curative treatments more effectively. Discriminating residual leukemic clones, preleukemic clones, and normal precursors remains a challenge with current MRD tools. We developed a single cell MRD (scMRD) assay by combining flow cytometric enrichment of the targeted precursor/blast population with integrated single cell DNA sequencing (scDNA-seq) and immunophenotyping (Tapestri, Missionbio). The platform utilizes a custom amplicon panel covering 109 amplicons across 31 genes frequently mutated in AML and a panel of 42 oligo-conjugated antibodies against surface makers. We applied our scMRD assay to sequence 30 MRD samples (multiplexed into 6) from 29 AML patients to study the clonal architecture and protein correlation. In addition, 2 MRD samples were sequenced with scDNA+protein sequencing technology without enrichment or multiplexing. Five patients with AML at diagnosis were also sequenced with scDNA+protein sequencing technology without enrichment or multiplexing. In order to establish the sensitivity of our scMRD assay, we spiked AML cells into normal bone marrow cells. We sequenced 12 such spiked samples after multiplexing.

Study phs003233

Longitudinal Multi-Omic Immune Resource Reveals Dynamic Responses in Healthy Human Aging

The generation and maintenance of protective immunity is a dynamic interplay between host and environment that is impacted by age. Understanding fundamental changes in the healthy immune system that occur over a lifespan is critical in developing interventions for age-related susceptibility to infections and diseases. Here, we use multi-omic profiling (scRNA-seq, proteomics, flow cytometry) to examine human peripheral immunity in over 300 healthy adults, with 96 young and older adults followed over two years with yearly vaccination. The resulting resource includes scRNA-seq datasets of >16 million PBMCs, interrogating 71 immune cell subsets from our new Immune Health Atlas. This study allows unique insights into the composition and transcriptional state of immune cells at homeostasis, with vaccine perturbation, under Cytomegalovirus (CMV) infection and across age. We find that T cells specifically accumulate age-related transcriptional changes more than other immune cells, independent from inflammation and chronic perturbation. Moreover, impaired memory B cell responses to vaccination are linked to a Th2-like state shift in older adults' memory CD4 T cells, revealing possible mechanisms of immune dysregulation during healthy human aging. This extensive resource is provided with a suite of exploration tools at https://apps.allenimmunology.org/aifi/insights/dynamics-imm-health-age/ to enhance data accessibility and further the understanding of immune health across age. Please note that the Sound Life Cohort raw data are spread across three dbGap submissions: phs003841.v1 (this study), phs003944.v1, and phs003400.v1.

Study phs003841

Geriatric Oncology Database of genotypes and methylation, gene expression, clinical data, and survey results on psychosocial and physical conditions in Japanese elderly cancer patients to establish truly effective treatment strategy

Owing to the advent of super-aged society, cancer mortality in Japan is forecasted to become an all-time high. The management of elderly patients with cancer is a serious health issue and the need for specific care guidelines for this population is critical in Japan. The significant individual differences in comorbidity, physical and mental functioning, and social problems among aging individuals are obstacles to the progress. The response to cancer treatment varies significantly among patients, even in the same stage of the same type of cancer. Older aged patients are still less likely to be offered participation in clinical trials, thus the number of clinical evidences is limited. The molecular mechanisms regulating the physiological process of ageing and senescence, and cancer are far from understood. To establish a truly effective treatment algorithm, we attempted to develop a geriatric oncology database for genotypes and methylation, gene expression, clinical data, and survey results on psychosocial and physical conditions in Japanese elderly cancer patients in this retrospective study. The genomic analysis was performed in 62, 106, and 173 cases, respectively for gastric, colon and lung cancers. The clinical data and survey results on psychosocial and physical conditions were collected from 166, 313, and 344 patients respectively for gastric, colon and lung cancers, and also collected from 357, 49, and 300 benign cases respectively for cardiovascular disorder, respiratory dysfunction, diabetes and nephropathy as the analysis control.

Study JGAS000061

Tissue-specific mutation accumulation in human adult stem cells during life

Gradual accumulation of mutations in human adult stem cells during life is associated with various age-related diseases, including cancer. The number of stem cell divisions throughout life is believed to be a major determinant for mutation accumulation and could explain the extreme variation of cancer incidence across different organs. Yet, mutation patterns and rates of healthy adult stem cells remain unknown. Here, we determined genome-wide mutation patterns in primary adult stem cells of the small intestine, colon and liver of human donors with ages ranging from 3 to 87 years. We find that the number of mutations increases linearly with age up to several thousand mutations per cell at 87 years of age, while mutation spectra remain constant throughout life. Small intestine and colon stem cells have a 2-fold higher mutation rate per year compared with liver stem cells. These differences could be exclusively attributed to the mutagenic action of spontaneous deamination of cytosine residues and may reflect the high stem cell division rate in these tissues. The genomic distribution of somatic mutations is non-random and predominantly associated with DNA replication dynamics in the small intestine and colon, and with transcription in the liver. These results indicate that a stable balance between various mutagenic and DNA repair processes is maintained throughout life and that the activity of these processes in adult stem cells varies between tissues.

Study EGAS00001001682

Genomic Alterations in Gingivo-buccal Cancer: ICGC-India Project_YR01

As a part of the ICGC, India has undertaken genomic studies on gingivobuccal cancer of the oral cavity, which is the most prevalent form of cancer among men in India. There are various known environmental (life-style) correlates of this cancer, the most important of which are tobacco chewing and HPV infection. Paired DNA samples – isolated from the tumor tissue and from the blood of fifty patients – have been analyzed to catalog germline and somatic mutations. Association between each observed genomic alteration and exposure to environmental risk factors is being explored.Detailed clinical characterization of the patients, collection of data on demographic and environmental exposures, and isolation of DNA samples from blood and tumor tissues collected from each patient are being done at the Advanced Centre for Research, Treatment and Education on Cancer, Mumbai. Exome capture and deep resequencing are being performed at the National Institute of Biomedical Genomics, Kalyani. We have analyzed the exomes of paired blood and tumor DNA samples using two orthogonal platforms – exome capture by Roche-Nimblegen SeqCap Ez kit followed by sequencing on Roche-454 GS FLX, and exome capture by Illumina TrueSeq Exome Enrichment kit followed by sequencing on Illumina HiSeq-2000. Each exome is being sequenced at a mean depth of 30x. In addition, we have used Illumina Omni Quad SNP-chips to generate genotype data at 1.14 million genomic markers. The average concordance between SNP-chip and sequence data is 99.8%.

Study EGAS00001000249

The Southern African Human Genome Programme

The Southern African Human Genome Programme (SAHGP) is a national and regional initiative that aims to unlock the unique genetic character of southern African populations. Its vision is to improve quality of life through understanding human genetic diversity. It is timely to advance the SAHGP initiative since global genomic initiatives are increasingly pointing to populations in Africa, more specifically the San and Khoe from southern Africa, as harbouring the most ancient genetic signatures among the living populations of the world. Studies on African populations have already generated a wealth of information upon which the SAHGP can build. This information will be used to promote and support genomic research programmes both nationally and regionally to address critical questions that would benefit the people of our region. The SAHGP aims to make a significant contribution to the understanding of DNA variation among southern Africans and how this impacts on the health of the people of this region. Aims of the SAHGP: To develop capacity for genomic research in southern Africa; To establish sustainable resources for genomic research in the region; and To translate knowledge and information into improvements in human health in the region. The objective is to derive the maximum benefit from the SAHGP, and that the data should be widely shared with the scientific community for the benefit of the people of the sub-continent and beyond.

Study EGAS00001002639

Whole transcriptome profiling of liquid biopsies from tumour xenografted mouse models: validation cohort

While cell-free DNA (cfDNA) in liquid biopsies is widely being used and investigated, free circulating RNA (extracellular RNA, exRNA) has the potential to improve therapy response monitoring and cancer detection due to its dynamic nature. A fundamental open question hampering the initiation of large-scale liquid biopsy collections for tumour exRNA analysis is that it remains unclear in which blood subcompartment tumour-derived exRNAs primarily reside. We set out to develop a host-xenograft deconvolution framework, exRNAxeno, with cDNA mapping strategies to either a combined human-mouse reference genome or both species genomes in parallel, that can be applied to exRNA sequencing data from liquid biopsies of human xenograft mouse models, enabling to distinguish (human) tumoural RNA from (murine) host RNA, and as such to specifically analyse tumour-derived exRNA. Subsequently, the preferred exRNAxeno combination pipeline was applied to total exRNA sequencing data from blood platelets and three plasma fractions from a breast cancer patient-derived and neuroblastoma cell line-derived xenograft mouse model. We show that tumoural exRNA concentrations are not determined by plasma platelet levels, while host exRNA concentrations increase with platelet content. Furthermore, a large variability in exRNA levels and gene content across individual mice is observed. In general, the tumoural gene detectability in plasma is correlated with the RNA expression levels in the tumour tissue or cancer cell line. Our results unravel new aspects of tumour-derived exRNA biology in xenograft mouse models.

Study EGAS00001006582

H3Africa - Stroke Investigative Research and Education Networks

Although stroke is a discrete phenotype, it is the clinical culmination of several complex and interacting biological processes, precipitated by various genetic and environmental factors, thereby making ready analyses of its underlying mechanisms a challenge. However, the formulation of successful tailor-made prevention strategies for limiting the immense personal and societal burden of stroke in Sub-Saharan Africa (SSA) is contingent on a better understanding of the predisposing risk factors for stroke in the region, which may not necessarily match the nature of stroke risk factors described among other people on other continents. Indeed, the underlying risk factors for stroke in SSA have hitherto not been comprehensively characterized, and currently there is a paucity of expertise, experience, and infrastructure in genomics and environmental research in SSA, to initiate and sustain such investigation. We seek to establish the Stroke Investigative Research and Education Network (SIREN) as a multidisciplinary collaborative research network of investigators in SSA and the United States collectively focused on exploring ways to promote a better understanding of the genetic and environmental risk factors for stroke among people of African ancestry. SIREN will evaluate unique contributors to stroke in SSA and compare these findings with those in a cohort of African American stroke survivors, all while simultaneously building sustainable capacities in phenomics, biobanking, genomics, biostatistics and bioinformatics for future high-level investigation of stroke and other vascular disease entities in SSA.

Study EGAS00001007331

Pediatric Non-Down Syndrome Acute Megakaryoblastic Leukemia is Characterized by Distinct Genomic Subsets with Varying Outcomes

Acute Megakaryoblastic Leukemia (AMKL) is a subtype of acute myeloid leukemia (AML) in which cells morphologically resemble abnormal megakaryoblasts. While extremely rare in adults, AMKL accounts for 4-15% of newly diagnosed childhood AML1-3. A significant proportion of pediatric AMKL cases occur in children with Down syndrome (DS). These patients have excellent outcomes and are characterized at the genomic level by a founding GATA1 mutation4-6. In contrast, AMKL in patients without DS (non-DS-AMKL) is frequently associated with poor outcomes. Previous efforts have identified chimeric oncogenes in a significant number of cases, including RBM15-MKL1, CBFA2T3-GLIS2, KMT2A gene rearrangements and NUP98-KDM5A7-9. The etiology of 30-40% of cases, however, remains unknown. To better understand the genomic landscape of non-DS-AMKL, we performed RNA and exome sequencing on specimens from 99 patients (75 pediatric and 24 adult). We demonstrate that pediatric non-DS-AMKL is a heterogeneous malignancy that can be divided into seven subgroups with varying outcomes. These subgroups are characterized by chimeric oncogenes with cooperating mutations in epigenetic and kinase signaling genes. Overall, these data shed light on the etiology of AMKL and provide useful information for treatment tailoring.

Study EGAS00001002183

Genome-wide DNA Methylation is Predictive of Outcome in Juvenile Myelomonocytic Leukemia

Juvenile myelomonocytic leukemia (JMML) is a myeloproliferative disorder of childhood caused by mutations in the Ras pathway. Outcomes in this disease vary dramatically from spontaneous resolution with little or no treatment to rapid relapse after hematopoietic stem cell transplantation. Given the high morbidity and late effects of transplant, it is critical to identify patients at diagnosis who can be observed rather than transplanted. We hypothesized that assessing DNA methylation status would help predict disease outcome. Genome-wide DNA methylation profiling using the Illumina 450k platform in a discovery cohort of 39 patients was performed. Unsupervised hierarchical clustering based on the most highly variable CpG sites identified three clusters of patients. Importantly, these clusters differed significantly in terms of 4-year event-free survival, with the lowest methylation cluster having the highest rates of survival. These findings were validated in an independent cohort of 40 patients. Of particular interest is that all but one of fourteen patients experiencing spontaneous resolution of their disease clustered together and closer to 22 healthy controls than the other JMML cases. This study demonstrates that DNA methylation patterns in JMML are predictive of outcome in this heterogeneously behaving disease and can identify patients who are most likely to experience spontaneous resolution.

Study EGAS00001002700

using-ega-account

Using your EGA account The purpose of this page is to help you access and download the data you have been granted permission to view through your EGA account. To obtain an EGA account please register here. Once your account is created, you can find details on how to manage your EGA account here. EGA access policy (New account holders please read) Access to EGA services are only provided to EGA account holders. EGA accounts are created, updated and closed upon direct instruction from the relevant DAC contact, using the EGA account management tools or through EGA Helpdesk. As part of the EGA account creation process, the EGA account holder must reset their password when instructed. Password resets are checked and verified usually within 24 business hours, during which time the EGA account is inaccessible. EGA accounts are assigned on an individual basis for the exclusive use of the registered EGA account holder; log-in details must remain private and only be used by the EGA account holder. Controlled access data files are only provided, in accordance to the permissions set by the DAC, to EGA account holders. EGA controlled access data files are encrypted prior to download and must be decrypted on the EGA account holders side. EGA account holders are provided access to the EGA download streamer to download access approved datasets and files therein. The EGA download streamer represents the primary means for the EGA account holder to download access approved datasets and files therein. EGA account holders may be provided with an EGA download account for accessing approved datasets and files therein, using FTP or Aspera in response to clearly defined technical reasons, which may restrict the use of the EGA download streamer. It is the sole responsibility of the EGA account holder to ensure full compliance to all terms and conditions of the Data Access Agreement are met once data is downloaded from the EGA. Should you move institute you must contact all Data Access Committee's contacts to update your details and, if necessary, re-apply for data access. The Data Access Agreement (DAA) is a contract made between the DAC and the EGA account holder; the EGA is not responsible for enforcing the DAA. EGA reserves the right to place EGA accounts 'onhold', pending DAC approval to re-activate, should EGA account 'misuse' be suspected. Further information Introduction to the EGA Frequently Asked Questions Subscribe to the EGA mailing list

Documentation using-ega-account

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