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• The Iberian Roma genetic variant server

  The Roma are the most numerous ethnic minority in Europe. The Iberian Roma arrived in the Iberian Peninsula five centuries ago and still today, they keep a strong group identity. Demographic and cultural reasons lie behind a high rate of Mendelian disease often related to founder variants. We have analysed exome data from 119 Iberian Roma individuals collected from 2018 to 2020. A database of variant frequency has been implemented (IRPVS) and made available online. We have analysed the carrier rate of founder private alleles as well as pathogenic variants present in the general population. Significant enrichment in structural variants involving gene clusters related to keratinization and epidermal growth suggest that evolutive mechanisms have developed towards climate and environmental adaptation. IRPVS can be accessed at http://irpvs.clinbioinfosspa.es/

  Study EGAS00001006758

• GCparagon: Evaluation and correction of GC biases in cell-free DNA at the fragment level

  We present GCparagon, a two-stage algorithm for computing and correcting GC biases in cell-free DNA (cfDNA) samples. The length of the highly fragmented cfDNAs and the number of GC bases are essential parameters in the calculations. Regions of low mappability, known reference genome assembly errors and regions surrounding assembly gaps are excluded from the bias computation. GCparagon outputs a bias matrix and an optional tagged BAM file with GC bias balance weights as alignment tags. Parallelization allows calculation of a GC bias estimate in less than 2 minutes per sample with between 99.0% and 99.9% of fragments already corrected. We propose that GCparagon can help standardize cfDNA applications and evaluate the impact of GC bias on algorithms used in the analysis of liquid biopsy data.

  Study EGAS00001006963

• Evolutionary landscape of clonal hematopoiesis in 3359 individuals from the general population

  This project contains longitudinal data for processed somatic variant calls and data on core phenotypic variables for 3359 community-based individuals ≥60 years, a sub-cohort of the population-based Lifelines cohort (167,729 participants). Cases with peripheral blood count abnormalities and population-based controls were included. Next-generation sequencing data were generated with a median time period of 43 months between baseline and follow-up visit. For 327 individuals a third visit sample was included. Error-corrected sequencing was performed using single-molecule tagged molecular inversion probes targeting 27 myeloid and lymphoid driver genes. The threshold for variant calling was set at ≥1% VAF and ≥10 consensus variant reads. Details on sample selection, sequencing technique, variant calling procedures and data curation are described (van Zeventer et al. 2023).

  Study EGAS00001007087

• Transcriptome human nasal epithelium

  1. Odors are detected, firstly, by olfactory sensory neurons (OSNs) in the olfactory epithelium of the nose. This neurons then project directly to the olfactory bulb in the brain. Olfaction depends on cellular regeneration of the OE, olfactory bulb and hippocampus, and on their continual re-wiring. The olfactory neural pathway includes regions of the frontal, temporal and limbic brain, which in turn overlap with brain areas involved in brain disorders. OSNs are the only aspect of the human brain exposed to the external environment. This not only makes them vulnerable to environmental changes, but also accessible for biomedical studies. We have already sequenced and developed a protocol for analyzing the transcriptome of mouse main olfactory epithelium and single OSNs. We propose here to perform a similar study for samples from the human olfactory epithelium. We have developed a minimally invasive method for obtaining human OSNs, among other cells from the nasal epithelium. In this experiment, we have obtained cell samples from the olfactory epithelium, including OSN, from healthy volunteers. We would like to further characterize them by RNA sequencing. This will give us valuable insight into human olfaction. It will also provide a first step into a new avenue to study, and find biomarkers for, brain diseases though the analysis of these easily available neurons. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001002226

• Direct Comparative Analysis of 10X Genomics Chromium and Smart-seq2

  Single cell RNA sequencing (scRNA-seq) is widely used for profiling transcriptomes of individual cells. The droplet-based 10X Genomics Chromium (10X) approach and the plate-based Smart-seq2 full-length method are two frequently-used scRNA-seq platforms, yet there are only a few thorough and systematic comparisons of their advantages and limitations. Here, by directly comparing the scRNA-seq data by the two platforms from the same samples of CD45- cells, we systematically evaluated their features using a wide spectrum of analysis. Smart-seq2 detected more genes in a cell, especially low abundance transcripts as well as alternatively spliced transcripts, but captured higher proportion of mitochondrial genes. The composite of Smart-seq2 data also resembled bulk RNA-seq data better. For 10X-based data, we observed higher noise for mRNA in the low expression level. Despite the poly(A) enrichment, approximately 10-30% of all detected transcripts by both platforms were from non-coding genes, with lncRNA accounting for a higher proportion in 10X. 10X-based data displayed more severe dropout problem, especially for genes with lower expression levels. However, 10X-data can better detect rare cell types given its ability to cover a large number of cells. In addition, each platform detected different sets of differentially expressed genes between cell clusters, indicating the complementary nature of these technologies. Our comprehensive benchmark analysis offers the basis for selecting the optimal scRNA-seq strategy based on the objectives of each study.

  Dataset EGAD00001005448

• 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

• 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

• African Demographic History Study Using Illumina 1M Array Data

  These data were used as part of a study to characterize genomic diversity in Africa, population substructure, and evolutionary history.

  Study phs001780

• African Demographic History Study Based on WGS Data

  These data were used as part of a study to characterize genomic diversity in Africa, population substructure, and evolutionary history.

  Study phs003096

• MDACC Lymphoma & Myeloma The function of LAMP5 in multiple myeloma

  This dataset includes Fastq files from bulk RNA seq from myeloma cell lines (MM.1S and NCI-H929) expressing a DOX-inducible LAMP5 shRNA, S1 (CACTTCAAAGACGCAGTCAGT) or S5 (GCACACAGAATACAACCTCAT), or a scramble control treated with DOX during 48h.

  Dac EGAC50000000495

• Whole exome sequence analysis in multiple system atrophy

  To identify a susceptible gene for multiple system atrophy, exome sequence analysis was conducted in 14 patients and 7 controls.

  Study JGAS000009

• Analysis of Multiparametric MR imaging and tumor tissue sampling to identify response and acquired resistance to HIF-2 inhibition in patients with advanced clear cell renal cell carcinoma enrolled in a phase 1, multiple-dose, dose-escalation trial of PT2385, a HIF-2alpha inhibitor

  Study EGAS00001003506

• Neoadjuvant_Breast_Cancer_Validations

  This study aims to re-sequence findings from whole genome studies using a bespoke pulldown method to validate mutations in those genomes sequenced.

  Study EGAS00001000428

• Human non-malignant plasma cfRNA study - raw data

  mRNA capture sequencing on plasma sample from 88 patients with a non-malignant disease and from 37 healthy donors.

  Study EGAS50000001264

• Genomic profiling of esthesioneuroblastoma

  We performed whole exome and whole genome sequencing on a cohort of esthesioneurblastoma to understand the genetic underpinnings of this neoplasm.

  Study EGAS00001003225

• WTCCC2 Pre-eclampsia Study

  A WTCCC2 project genome-wide association study for pre-eclampsia (PA) in 4375 individuals from Colombia, genotyped on the Affymetrix 6.0 array.

  Study EGAS00001003349

• Discordant_Monozygotic_Twins_ALS (Epigenetics)

  We conducted a multi-omics study on a monozygotic twin pair discordant for amyotrophic lateral sclerosis. Data refers to 7 samples that are technical replicates from biological samples collected from the twins at two different time points (T1 and T2).

  Dataset EGAD00010002716

• Whole exome sequencing of trio with primary immunodeficiency (IL2RB)

  Whole exome paired-end sequencing data was performed on a trio (patient + parents) who has primary immunodeficiency to identify the genetic cause of the immunodeficiency. Analysis revealed a novel homozygous mutation in IL2RB.

  Dataset EGAD00001004963

• mtDNA in Huntington’s disease samples

  This dataset contains sequencing data from a large-scale study of mtDNA variations measured, using a sensitive mtDNA-targeted sequencing method called STAMP, in lymphoblast and blood samples of Huntington’s Disease patients.

  Dataset EGAD00001005723

• RNA-seq dataset for Mutation-specific non-canonical pathway of PTEN as a distinct therapeutic target for glioblastoma

  RNA-seq dataset for Mutation-specific non-canonical pathway of PTEN as a distinct therapeutic target for glioblastoma

  Dataset EGAD00001006877

• Whole-exome sequencing data from a head and neck cancer patient

  This dataset contains paired-end whole-exome sequencing data (2x50 bp) from the normal sample, three synchronous primary tumors and the recurrence of a head and neck cancer patient.

  Dataset EGAD00001006653

• GEDI: A Developmental Model of Gene-Environment Interplay in SUDs: Combined Genotype Dataset from the "Great Smoky Mountains Study" and the "Caring for Children in the Community Study".

  1. Great Smoky Mountains Study (GSMS; Costello et al. 1996, 1997) Three cohorts of boys and girls, aged 9, 11, and 13 years at intake in 1993, were selected from a rural population of some 20,000 children using a household equal probability design. A two-phase procedure was used for White and African-American youth to increase power by oversampling children at risk for psychiatric and SUDs. Parents (usually mothers) of the first stage random population sample completed a questionnaire about their child&#39;s behavioral problems. Of 4,195 subjects selected, 95% (N=3,896) of parents completed the screen. All children scoring above a predetermined threshold (the top 25% of the total scores), plus a 10% random sample of the remaining 75%, were recruited for detailed interviews. Results can be back-weighted to population levels for analyses. Half of the sample consists of females, and 6% are African Americans, reflecting the population of the study area. The interviewed sample of white and African-American subjects was 1,070 (80% of those recruited). American Indian youth were oversampled (100%) because they are an understudied group known to be at high risk for stressful events, substance disorders, and mood disorders. Of 431 age-eligible children, 350 (81% boys, 49% girls) participated. Thus, the size of total GSMS sample is 1,070 + 350 = 1,420. Data collection is complete for ages 9-26, and age 30 interviews are in progress. By age 26 a total of 9,858 interviews had been completed; the average number of interviews per subject was seven, and by age 26, 97.3% completed two or more interviews. 2. The Caring for Children in the Community Study (CCC; Angold et al., 2002) This representative study of psychiatric illness and service use in African-American and White youth took place in four rural counties in the southeastern USA. The two-stage sampling design and methods are similar to those used in the GSMS. Of 4,500 youth randomly selected from the 17,117 9- to 17-year-olds in the public school&#39;s database, 3,613 (80.0%) were successfully contacted and agreed to complete the behavioral screen. Of the 1,302 selected to participate in the study, 920 (70.7%) interviews were completed. Because CCC was also the only study in GEDI to contain more than a very few African-American participants, these were omitted from the multi-site analyses. Reprinted with permission from Cambridge University Press from Costello et al., 2013: PMID: 23461817 References: Costello et al., 1996: PMID: 8956679 Costello et al., 1997: PMID: 9184514 Angold et al., 2002: PMID: 12365876

  Study phs000852

• Bladder Cancer Organoids as a Functional System to Model Different Disease Stages and Therapy Response

  The study 'Bladder Cancer Organoids as a Functional System to Model Different Disease Stages and Therapy Response' reports the genomic characterization at the single allele level upon Whole Exome Sequencing of Bladder Cancer (BLCa) Patient Tumor (PT) samples obtained from 18 patients and corresponding Patient Derived Organoids (PDOs), implementing the SPICE pipeline (Ciani Y et al). Our cohort included 21 PTs and 22 PDOs. In this study, we generated a unique biobank resource of BLCa organoids that encompasses a broad spectrum of disease stages. We demonstrated that PDOs retain cancer heterogeneity and mutational burden. Furthermore, we observed that BLCa PDO can have different morphologies which may reflect tumor-specific features. Indeed, a solid morphology may be associated with more advanced tumors, as supported by the high PDO genomic burden and high primary tumor stage. Moreover, with our drug screening pipeline, we demonstrated the clinical relevance and feasibility of using a BLCa PDO-based drug assay for comparing the standard of care (SOC) drugs with compounds not currently in use for BLCa. Response to SOC was highly heterogeneous with approximately 50% of non-muscle invasive bladder cancer and 33% of muscle invasive bladder cancer PDOs showing no or minimal response. The most effective targeted therapy was lapatinib. We used an integrative analysis of drug response profiles with matched PDO genomic analysis to determine enrichment thresholds for candidate markers of therapy resistance and sensitivity. Importantly, by assessing the clinical history of longitudinally sampled cases, the clonal evolution of the disease could be determined and matched with PDO drug response profiles.

  Study phs003149

• Genomic Studies of Bipolar Disorder in a Large Cohort from The Netherlands

  The transcriptomes of whole blood samples of a large cohort of individuals from the Netherlands were assayed using RNA-seq. These whole blood samples included patients diagnosed with schizophrenia or bipolar disorder, family members of patients, and healthy controls. Within the whole blood dataset, about 600 of the samples were sequenced to an average of 13.9 million mapped reads per sample, while about 2,000 were sequenced to an average of 5.9 million mapped reads per sample, in order to assess changes in power to detect eQTLs based on sample size. We find that expression, defined as the log transcripts per million reads (TPMs), is highly correlated when comparing the moderate (13.9M) and lower (5.9M) coverage datasets.&nbsp;Additionally, a subset of about 150 individuals provided fibroblast cell lines which were assessed for the levels of gene expression using RNA-seq at an average of 50 million mapped reads per sample. We use downsampling techniques to generate synthetic datasets derived from this real fibroblast RNA-Seq data to explore the relationship between coverage and number individuals in an eQTL analysis.&nbsp;Corresponding individual-level genotypes for the samples included in the expression matrix are also made available. The genotypes were derived from multiple other project cohorts, each using different genotyping platforms including OmniExpress Exome, Global Screening Array, Illumina550, and PsychChip. A Plink file set of these cohorts is provided here after merging the separate project file sets and extracting only the individuals who have expression data also provided here; quality control for SNP-missingness is recommended.&nbsp;We demonstrate in this study that given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample to allow for an a greater number of individuals sequenced.&nbsp;

  Study phs002856

• The Human Phenotype Project (HPP) is a large-scale deep-phenotype prospective longitudinal and ethnically diverse cohort

  The Human Phenotype Project (HPP) is a large-scale deep-phenotype prospective longitudinal and ethnically diverse cohort and biobank that we established. To date, approximately 28,000 participants have enrolled in the study, with over 13,000 having completed their initial visit. The project is aimed at identifying novel molecular signatures with diagnostic, prognostic and therapeutic value, and at developing AI-based predictive models for disease onset and progression. A unique feature of the HPP is its deep and longitudinal profiling that includes medical history, lifestyle and nutritional habits, vital signs, anthropometrics, blood tests, continuous glucose and sleep monitoring, imaging modalities and molecular profiling of the transcriptome, genetics, gut, vaginal and oral microbiome, metabolome and immune system. For several of these modalities, the HPP is the first or largest cohort of its kind. Our analyses of this data provide novel insights into the variation of phenotypes with age and ethnicity, highlighting the importance of having ethnically diverse cohorts and the need for establishing age-dependent norms. We demonstrate how the HPP can be used to unravel personalized molecular signatures of disease by comparison to patient-specific and disease-specific matched healthy controls. Leveraging the extensive dietary and lifestyle HPP data, we systematically identify associations between lifestyle factors and health outcomes. Finally, we present a multi-modal foundation AI model that is trained using self-supervised learning on diet and CGM data and outperforms existing methods in predicting future onset of disease. We extend this AI framework to integrate all data modalities of each subject as a continuous sequence of diverse medical events, creating a digital twin that can simulate interventions and predict health trajectories and outcomes.

  Study EGAS00001008040

• Whole genome sequencing of a breast cancer cohort with known functional homologous recombination status

  Optimal patient selection for PARP inhibitors and double strand break (DSB) inducing chemotherapy is of great clinical importance. In principle, the use of these therapies can be extended beyond germline BRCA1/2 mutated cancers. Although several methods for detection of BRCA-like tumors have been developed, a golden standard is lacking. We have developed the RECAP test, a functional HR assay exploiting the formation of RAD51 foci in proliferating cells after ex vivo irradiation of fresh primary breast cancer (BrC) tissue. The RECAP test seems robust and has an advantage over genetic tests, due to its functional character for exploring the HR phenotype. Another type of HRD test is the BRCAness classifier, which is based on specific genomic patterns derived from copy number data of BRCA1/2 mutated BrCs that also occur in sporadic cancers. The BRCAness classifier is a robust test that is easily applicable in the clinic and has also been validated to predict in vivo response to high dose chemotherapy in clinical trials. More recently, a new HRD test based on genomic signatures which can be extracted from whole genome sequencing has been published, HRDetect. Studies comparing these different HRD tests within the same cohort of patients do not yet exist. Here, we have the unique opportunity to compare three HRD tests within the same cohort. We aim to compare three different HRD tests (RECAP, BRCAness classifier and HRDetect) within the same cohort of primary BrCs (n=41). As a secondary aim, we will analyze additional genetic alterations in HRD tumors (both BRCA-deficient and BRCA-proficient HRD tumors).

  Study EGAS00001005572

• A transcriptomic approach to understand patient susceptibility to pneumonia after abdominal surgery

  Rationale Nosocomial infections are a major healthcare challenge, developing in over 20% of patients aged 45 or over undergoing major-abdominal surgery, with postoperative pneumonia associated with an almost five-fold increase in 30-day mortality. Objectives To describe immune-pathways and gene-networks altered following major-abdominal surgery and identify transcriptomic patterns associated with postoperative pneumonia. Methods and Measurements From a prospective consecutive cohort (n=150) undergoing major-abdominal surgery whole-blood RNA was collected preoperatively and at three time-points postoperatively (2-6, 24 and 48hrs). Twelve patients diagnosed with postoperative pneumonia and 27 matched patients remaining infection-free were identified for analysis with RNA-sequencing. Main Results Compared to preoperative sampling, 3,639 genes were upregulated and 5,043 downregulated at 2-6hrs. Pathway-analysis demonstrated innate-immune activation with neutrophil-degranulation and Toll-like-receptor signalling upregulation alongside adaptive-immune suppression. Cell-type deconvolution of preoperative RNA-sequencing revealed elevated S100A8/9-high neutrophils alongside reduced naïve CD4 T-cells in those later developing pneumonia. Preoperatively, a gene-signature characteristic of neutrophil-degranulation was associated with postoperative pneumonia acquisition (P=0.00092). A previously reported Sepsis Response Signature (SRSq) score, reflecting neutrophil-dysfunction and a more dysregulated host response, at 48hrs postoperatively, differed between patients subsequently developing pneumonia and those remaining infection-free (P=0.045). Analysis of the novel neutrophil gene-signature and SRSq scores in independent major-abdominal surgery and polytrauma cohorts indicated good predictive performance in identifying patients suffering later infection. Conclusions Major-abdominal surgery acutely upregulates innate-immune pathways while simultaneously suppressing adaptive-immune pathways. This is more prominent in patients developing postoperative pneumonia. Preoperative transcriptomic signatures characteristic of neutrophil-degranulation and postoperative SRSq scores may be useful predictors of subsequent pneumonia risk.

  Study EGAS00001007229

• A catalog of the genetic causes of Hereditary Angioedema in the Canary Islands (Spain)

  Hereditary angioedema (HAE) is a rare disease where known causes involve C1 inhibitor dysfunction or dysregulation of the kinin cascade. The updated HAE management guidelines recommend performing genetic tests to reach a precise diagnosis. Unfortunately, genetic tests are still uncommon in the diagnosis routine. Here, we characterized for the first time the genetic causes of HAE in affected families from the Canary Islands (Spain). Whole-exome sequencing data was obtained from 41 affected patients and unaffected relatives from 29 unrelated families identified in the archipelago. The Hereditary Angioedema Database Annotation (HADA) tool was used for pathogenicity classification and causal variant prioritization among the genes known to cause HAE. Manual reclassification of prioritized variants was used in those families lacking known causal variants. We detected a total of eight different variants causing HAE in this patient series, affecting essentially SERPING1 and F12 genes, one of them being a novel SERPING1 variant (c.686-12A&amp;gt;G) with a predicted splicing effect which was reclassified as likely pathogenic in one family. Altogether, the diagnostic yield by assessing previously reported causal genes and considering variant reclassifications according to the American College of Medical Genetics guidelines reached 66.7% (95% Confidence Interval [CI]: 30.1-91.0) in families with more than one affected member and 10.0% (95% CI: 1.8-33.1) among cases without family information for the disease. Despite the genetic causes of many patients remain to be identified, our results reinforce the need of genetic tests as first-tier diagnostic tool in this disease, as recommended by the international WAO/EAACI guidelines for the management of HAE.

  Study EGAS00001006547

• Long-Term Oxygen Treatment Trial (LOTT-BioLINCC)

  Data Access NOTE: Please refer to the &#8220;Authorized Access&#8221; section below for information about how access to the data from this accession differs from many other dbGaP accessions. Objectives: To evaluate the efficacy of long-term treatment with supplemental oxygen in people with stable chronic obstructive pulmonary disease (COPD) and resting or exercise-induced moderate desaturation. Background: COPD is the fourth leading cause of death in the United States, with more than twelve million people currently diagnosed with the disease. In 2011, Medicare reimbursements for oxygen-related costs for people with COPD exceeded two billion dollars. The quality of life of a person with COPD decreases as the disease progresses, making treating and managing COPD in the moderate stages important. The benefits of oxygen supplementation were studied in the 1970s, but these benefits were specific to people with COPD who have severe resting hypoxemia. The LOTT trial was designed to address the effectiveness of supplemental oxygen therapy in treating people with COPD who have moderately low blood oxygen levels at rest or who have normal blood oxygen levels at rest, but have low or very low blood oxygen levels during exercise. Participants: There were a total of 1759 people screened for eligibility for the LOTT trial. Of the 1759 screened, 738 people with COPD were selected for randomization with 368 randomly assigned to the supplemental-oxygen group and 370 to the no-supplemental-oxygen group. Of the 738 people with COPD who underwent randomization, 133 (18%) had resting desaturation only, 319 (43%) had exercise-induced desaturation only, and 286 (39%) had both types of desaturation. Furthermore, in the supplemental-oxygen group, 220 people were prescribed 24-hour oxygen and 148 were prescribed oxygen during exercise and sleep only. Design: The LOTT trial was originally designed to test whether the use of supplemental oxygen would result in a longer time to death for people with COPD and moderate resting desaturation. After seven months, the trial design was judged to be infeasible; therefore, the trial was redesigned to include people with exercise-induced desaturation with a primary composite outcome of death or first hospitalization for any cause, whichever occurs first. Other outcomes measured included: death, health care utilization, COPD exacerbation, quality of life measurements, anxiety, depression, and measures of functional status. Potential participants were screened using questionnaires, a breathing test, a brief physical exam, a blood draw, and measurements of resting and walking blood oxygen levels. Based on those results, eligible participants returned for a second screening visit for further evaluations. At the end of the second visit, eligible participants were assigned randomly to supplemental oxygen therapy or no oxygen therapy. Participants assigned to supplemental oxygen therapy received stationary and/or portable oxygen systems. Participants were required to return for a one hour visit to determine how much oxygen to use while walking and to learn how to use the equipment. Participants who had low blood oxygen levels during rest were instructed to use supplemental oxygen 24 hours per day. Participants with normal resting blood oxygen levels, but low or very low blood oxygen levels during exercise were instructed to use it during physical activity and sleep. Throughout the treatment period, participants were asked to keep records of the number of oxygen tanks emptied or pounds of oxygen delivered, meter readings, and changes in equipment. Study officials contacted participants weekly for the first month, monthly for the next five months, and then every two months until the Year 1 study visit. Participants assigned to receive no oxygen treatment were contacted one week after assignment for a check-up. All participants returned for study visits once a year for up to seven years. At each of these visits, participants completed some of the same tests and questionnaires from the screening visit. Participants underwent a blood draw during the one year study visit. Participants in both treatment groups received two phone calls each year to check on status and use of oxygen. In addition, participants in both groups were asked to complete a quality of life questionnaire by mail at four months and sixteen months. Medicare claims were collected for the duration of each participant's enrollment in the study. Conclusions: In participants with stable COPD and resting or exercise-induced moderate desaturation, the prescription of long-term supplemental oxygen did not result in a longer time to death or first hospitalization than no long-term supplemental oxygen, nor did it provide sustained benefit with regard to any of the other measured outcomes. Long-Term Oxygen Treatment Trial Research Group, Albert RK, Au DH, Blackford AL, Casaburi R, Cooper JA Jr, Criner GJ, Diaz P, Fuhlbrigge AL, Gay SE, Kanner RE, MacIntyre N, Martinez FJ, Panos RJ, Piantadosi S, Sciurba F, Shade D, Stibolt T, Stoller JK, Wise R, Yusen RD, Tonascia J, Sternberg AL, Bailey W. A Randomized Trial of Long-Term Oxygen for COPD with Moderate Desaturation. N Engl J Med. 2016 Oct 27;375(17):1617-1627.

  Study phs003933

• Transcriptome of Chronic Pain and Disease

  The Functional Genomics Section (FGS) is a part of the National Institute of Dental and Craniofacial Research (NIDCR) within the National Institutes of Health (NIH). Research at the FGS focuses on understanding the mechanics involved in nociception through both clinical samples from patients with chronic pain and mouse models of pain. Pain is relayed from the periphery to the central nervous system through specialized sensory neurons known as nociceptors. The cell body of nociceptors resides in neuronal structures that branch off of the spinal cord known as dorsal root ganglia (DRG). The DRG is a heterogenous tissue containing not only sensory neurons like nociceptors, but also glial cells, capillaries, fibroblasts, and resident macrophages. The interaction between the nociceptors and these DRG cell types can have a significant role in affecting neuronal hyperexcitability and downstream pain sensitivity. To better understand the cellular factors involved in producing conditions of chronic pain, DRGs from consenting organ donors were collected from individuals experiencing pain disorders such as painful diabetic neuropathy and rheumatoid arthritis. A multi-omics approach is being used to identify molecular mechanisms that lead to pain hypersensitivity in patients with chronic pain.&nbsp;&nbsp;

  Study phs002548

• Whole Exome Sequencing in Alopecia Areata Identifies Rare Mutations in KRT82

  Alopecia areata (AA) is an autoimmune disorder, in which the body's own immune cells attack hair follicels resulting in hair loss. It is a complex genetic disease with 14 associated loci previously identified in our genome-wide association study (GWAS). In the study, we performed the whole-exome sequencing (WES) on 849 AA patients with the aim to identify the novel rare variants in genes contributing to AA susceptibility. We performed gene-collapsing analyses and identified keratin 82 (KRT82 Gene ID: 3888) as the highest associated AA gene in the three rare damaging collapsing models with p&lt;6.7E-07 in one of the models. The KRT82 gene is a hair-specific type II keratin expressed in the hair shaft cuticle during the anagen phase of the hair cycle. We reported the lower expression of KRT82 in AA hair follicles, compared to controls, and the KRT82 expression is absent from the bulb region, the site of AA immune attack. This study emphasizes the significance of using WES and gene collapsing analyses to identify rare variation in a functionally relevant risk gene using a moderate sample size.&nbsp;&nbsp;

  Study phs002632

• Whole Genome Sequencing in the Inner City Asthma Consortium (ICAC) Cohorts

  We performed whole-genome sequencing on individuals from the Asthma Phenotypes in the Inner City (APIC) and Urban Environment and Childhood Asthma (URECA) study cohorts, which consist of children with asthma who live in low-income neighborhoods across 10 U.S. cities. APIC was a one-year, prospective, observational study of children ages 6-17 with asthma. URECA was a prospective, observational study featuring a birth cohort of children with a high risk of developing asthma based on family history. We performed whole-genome sequencing on 1,035 individuals (ages 5-17 yrs; 67% Black, 25% Hispanic; 66% with MD-diagnosed asthma; APIC=508, URECA=527). Sequence read processing, quality control, and variant calling were performed using the Genome Analysis Toolkit suite (GATK v4). Variant call sites were annotated according to the filters applied in our common variant (minor allele frequency &#8805; 1%) association studies, based on predicted false positive rates, genotype quality and depth, and population-adjusted Hardy-Weinberg equilibrium. Principal components of ancestry were calculated using high quality, linkage-disequilibrium-pruned single-nucleotide variants, accounting for subject relatedness, using PC-Air and PC-Relate.

  Study phs002921

• Whole Genome Association Study of Systemic Lupus Erythematosus

  The goal of this collaborative study was to identify new genetic risk factors for Systemic Lupus Erythematosus (SLE). To do this we conducted a genome-wide scan by combining the resources and expertise from a number of SLE researchers to establish a large sample set comprising 1311 SLE cases and 3340 controls. The SLE case samples were genotyped from the following collections: 338 subjects from the Autoimmune Biomarkers Collaborative Network (ABCoN), an NIH/NIAMS funded repository, 141 subjects from the Multiple Autoimmune Disease Genetics Consortium (MADGC), 613 subjects from the University of California San Francisco (UCSF) Lupus Genetics, and 335 subjects from the University of Pittsburgh Medical Center (UPMC), plus 8 samples collected at The Feinstein Institute for Medical Research. A total of 3583 control samples were examined in the association analyses. As part of this project, 1861 control samples were selected and then genotyped from the New York Cancer Project (NYCP), based on self-described ethnicity, gender and age. In addition, genotype data from 1722 control samples (all self-described North Americans of European descent) were obtained from the publicly available iControlDB database (http://www.illumina.com/pages.ilmn?ID=231).

  Study phs000122

• Genetic Epidemiology of Ovarian Cancer Histotypes

  Epithelial ovarian cancer is a highly fatal malignancy with known genetic etiology; however, it is estimated that a substantial portion of narrow sense heritability remains to be discovered. The goal of this research was to use genetic information from distantly related ovarian cancer cases to identify regions of the genome that may harbor rare risk variants for epithelial ovarian cancer. The study was conducted in the setting of the Utah Population Database, a statewide data resource in which extensive ancestry data are linked to Utah Cancer Registry records. Focusing on families with a statistically significant excess risk of epithelial ovarian cancer, we used the Infinium Global Screening Array v.3.0 to generate germline genotyping data for distantly related cases. We then applied Shared Genomic Segment Analysis to each set of related cases to identify genomic regions that may harbor ovarian cancer risk variants. Eleven regions were identified. Genotype data are available through dbGaP for one case from each pedigree. Genotype data from other ovarian cancer cases within the high-risk pedigrees as well as the pedigrees' corresponding structures can be requested and made available with approval from the Utah Resource for Genetic and Epidemiologic Research (RGE).

  Study phs003140

• HTAN Pilot Project: Single-Cell Transcriptomics Toolbox for Fresh and Frozen Human Tumors (Lung, Breast, Ovarian, Melanoma, Neuroblastoma, Sarcoma, Glioblastoma, Glioma, and Leukemia)

  Single cell transcriptomics is a powerful tool to map the complex tumor microenvironment, providing unprecedented resolution into cell types, cell states, cell interactions, and tumor heterogeneity. Depending on their tissue site and whether processing begins from a fresh or frozen sample, different tumors require different optimizations in order to obtain high quality single cell transcriptomic data. In this study, we profiled a range of tumor types, varying in cell-of-origin, solid and non-solid forms, patient ages, and transitions. The tumors also further varied in their tissue site and in their sample collection method. To enable profiling of such diverse samples, we developed a systematic toolbox for single cell RNA-Seq and single nucleus RNA-Seq of fresh and frozen clinical tumor samples. We validated this toolbox for 216,490 cells and nuclei extracted from 39 samples across 23 tumors (some tumors were split into multiple samples). The 23 tumors represented eight unique tumor types. This toolbox comprises both experimental and computational protocols as well as guidelines for comparing and selecting protocols for the biological problem of interest.

  Study phs001983

• Molecular Determinants of Esophageal Cancer in Tanzania

  We describe the genomic profiles of 61 esophageal squamous cell carcinoma (ESCC) cases from Tanzania and compare them to profiles from an existing cohort of ESCC cases from Malawi. We also provide a comparison to ESCC tumors in The Cancer Genome Atlas Program (TCGA). Despite a high burden of ESCC in Eastern Africa, investigations into the genomics in this region are nascent. This represents the largest comprehensive genomic analysis of ESCC from sub-Saharan Africa to date. We observed substantial transcriptional overlap with other squamous histologies via comparison with TCGA PanCan dataset. DNA analysis revealed known mutational patterns, both genome-wide as well as in genes known to be commonly mutated in ESCC. TP53 mutations were the most common somatic mutation in tumors from both Tanzania and Malawi but were detected at lower frequencies than previously reported in ESCC cases from other settings. In a combined analysis, two unique transcriptional clusters were identified: a proliferative/epithelial cluster and an invasive/migrative/mesenchymal cluster. Mutational signature analysis of the Tanzanian cohort revealed common signatures associated with aging and cytidine deaminase activity and an absence of signature 29, which was previously reported in the Malawi cohort.

  Study phs003217

• Whole-Exome Sequencing Study of Diabetic Nephropathy

  To identify rare variants of diabetic kidney disease (DKD), we conducted a whole-exome sequencing (WES) study. This study employed an extreme case&#8211;control design, which included a selection of the 593 most rapidly progressing DKD cases from the Chronic Renal Insufficiency Cohort (CRIC) Study (phs000524). In this two-stage WES study of DKD, participants with DKD from the CRIC were compared with control groups of participants without DKD from another cohort in the first stage. Suggestive signals identified in the first stage were confirmed in the second stage analysis among a diverse population with existing whole-genome sequencing (WGS) data. Generalized linear mixed models, which accounted for genetic relatedness and adjusted for age, sex and ancestry, were used to test associations between single variants and DKD. Gene-based aggregate rare variant analyses were conducted using an optimized sequence kernel association test implemented within our mixed model framework. As a result, we identified four novel gene loci, DIS3L2, KRT6B, ERAP2 and NPEPPS, that achieved exome-wide significance and may play an important role in the DKD disease course. This large WES study provides novel insights into DKD's molecular mechanisms.

  Study phs003429

• Immunogenetics of BCG Vaccination and Pediatric Tuberculosis

  We examined genetic variation associated with BCG-induced innate immune responses and susceptibility to pediatric tuberculosis (TB) disease. South African study participants were enrolled at the South African Tuberculosis Vaccine Initiative (SATVI) as part of a larger BCG vaccination study. This area has one of the highest rates of TB incidence in the world. Infants were vaccinated with BCG at birth, and a nested case-control study was performed over a 2-year prospective observation period, which resulted in 135 infant TB cases and 521 controls. Whole blood was collected from BCG vaccinated infants at 10 weeks of age. Genotyping was performed with the llumina MegaEx Beadchip, and GWAS was performed against TB disease to assess for associations between genetic variants and susceptibility to TB. In addition, a subset of infants were assessed for BCG-induced innate immune responses (N=198). For these, whole blood was stimulated with BCG or media and flow cytometry was used to measure BCG-induced PDL1, CD40, and cytokine expression in myeloid (mDC) and plasmacytoid (pDC) dendritic cells, monocytes, and neutrophils. Cellular GWAS was then performed to associate genetic variants with BCG-induced immune responses.

  Study phs003406

• Development of cell lines and mouse models of bone and soft tissue sarcoma to establish novel treatment

  Malignant giant-cell tumors are extremely rare bone sarcomas that transform from conventional giant-cell tumors during long periods of treatment. We have applied the culture methods and succeeded in establishing patient-derived organoids (PDO) of rare sarcomas. This study aimed to investigate the genomic characteristics of our established novel organoids from human malignant giant-cell tumors. The developed tumors with xenograft were genomically analyzed to compare their characteristics with those of the original tumors. Genetic changes over time throughout treatment were analyzed, and the genomic status of the established organoid was confirmed. Next generation sequencing revealed that the established organoids lacked the H3-3A G34W mutation. The xenografted organoids of the malignant giant-cell tumor had phenotypes genetically similar to those of the original tumor. The established organoids were confirmed to be derived from human malignant giant-cell tumors. In summary, the present study demonstrated a novel organoid model of a malignant giant-cell tumor that was genetically confirmed to be a malignant transformed tumor. Our organoid model could be used to elucidate the molecular pathogenesis of a malignant giant-cell tumor and develop novel treatment modalities.

  Study JGAS000618

• High-coverage whole-genome sequencing reveals structural variations in triple-negative breast cancer

  Triple-negative breast cancer (TNBC) cells do not express estrogen receptors, progesterone receptors or human epidermal growth factor receptor 2 (HER2). Currently, apart from poly ADP-ribose polymerase (PARP) inhibitors, there are few effective therapeutic options for this cancer type. Here, we present a comprehensive characterization of genetic alterations in TNBC via high-coverage whole-genome sequencing along with transcriptomic and whole-exome sequencing. Silencing of BRCA1 gene impaired the homologous recombination (HR) pathway in a subset of TNBC, which exhibited similar phenotypes with tumors with BRCA1 mutations; they harbored many structural variations (SVs) with relative enrichment for tandem duplication. Clonal analysis suggested that TP53 mutations and methylation of CpG dinucleotides in the BRCA1 promoter were the early events of carcinogenesis. SVs were associated with driver oncogenic events such as amplification of MYC, NOTCH2 or NOTCH3 and impacted tumor suppressor genes including RB1, PTEN and KMT2C. We also identified a variety of oncogenes that could transform 3T3 mouse fibroblast cells, suggesting that individual TNBC tumors may undergo a unique driver event that can be targetable. Thus, we revealed several features of TNBC that had important clinical implications.

  Study JGAS000095

• Single cell atlas of large B-cell lymphoma

  Large B-cell lymphomas (LBCL) are clinically and biologically heterogeneous lymphoid malignancies, shaped by complex tumor and microenvironments that play a critical role in disease progression. There has been no direct and comprehensive single-cell characterization of tumor microenvironment in LBCL that simultaneously captures lymphoid, myeloid, and stromal populations across a sufficiently large cohort to reveal reproducible and biologically meaningful patterns. Here, we performed single-nucleus multiome profiling, coupled with bulk DNA and RNA sequencing, on 232 tumor and control biopsies to comprehensively map cell subpopulations of LBCL. Through this integrative approach, we identified Lymphoma Microenvironment Archetype Profiles (LymphoMAPs)—distinct, reproducible patterns defined by the co-occurrence of functionally specialized cell subsets. We further characterized the cell-cell communication pathways that shape these transcriptional phenotypes within archetypes. Importantly, we discovered associations between specific LymphoMAPs and clinical and molecular features of LBCL, including response to CD19 CAR T-cell therapy. Our study provides a valuable resource of lymphoid, myeloid, and non-hematopoietic cell states in LBCL and offers a framework for understanding how the abundance and functional state of cells within the tumor microenvironment may drive inter-patient variability in therapeutic outcome.

  Study EGAS50000001022

• Novel regional age-associated DNA methylation changes within human common disease-associated loci

  Background: Advancing age progressively impacts on risk and severity of chronic disease. It also modifies the epigenome, with changes in DNA methylation, due to both random drift and variation within specific functional loci. Results: In a discovery set of 2,238 peripheral-blood genome-wide DNA methylomes (MeDIP-seq) aged 19-82 years, we identified 71 age-associated Differentially Methylated Regions (a-DMRs, p &lt; 1.85 x 10-8) within the Linkage Disequilibrium (LD) blocks of the NIH Catalogue of published GWAS SNPs. This included 52 novel regions, 29 within loci not covered by 450k or 27k Illumina array, and with marked enrichment for Poised Promoters and Enhancers across multiple cell types. In a replication set of 2,084 DNA methylomes, 95.7% of the a-DMRs showed the same direction of ageing effect, with 80.3% and 53.3% replicated to p &lt; 0.05 and p &lt; 1.85 x 10-8, respectively. Conclusion: By analysing the functionally enriched disease and trait-associated regions of the human genome, we identified novel epigenetic ageing changes, which could be useful biomarkers or provide mechanistic insights into age-related common diseases.

  Study EGAS00001001910

• Whole-genome methylation profiling of menstrual stem cells identifies novel biomarkers for endometriosis

  Endometriosis is a highly prevalent condition that remains underdiagnosed and poorly managed due to the lack of clinically validated biomarkers and limited understanding of its underlying pathophysiology. Although menstrual blood–derived stem cells (MenSCs) have been implicated in disease pathogenesis, their potential as a diagnostic source has not been systematically explored. We conducted a clinical study including 42 women (19 with endometriosis and 23 controls) to evaluate whether DNA methylation profiles from freshly isolated MenSCs could identify disease-specific biomarkers. Whole-genome DNA methylation sequencing identified differentially methylated regions (DMRs) enriched in genes associated with key features of endometriosis, including inflammation, tissue remodelling, and developmental processes. These DMRs robustly discriminated cases from controls, independently of technical and clinical confounders. Machine learning models trained and validated on these regions demonstrated strong diagnostic performance (specificity 83%, sensitivity 79%). Integration with an independent single-cell RNA sequencing dataset suggested that the identified DMRs may influence gene expression, supporting their biological relevance. Collectively, these findings highlight MenSC DNA methylation profiling as a promising, non-invasive strategy for early diagnosis and personalised management of endometriosis. (PubMed IDs will be provided once the manuscript is published.)

  Study EGAS50000001640

• Pediatric Whole Genome Sequencing Diagnostic Utility

  The standard of care for first-tier clinical investigation of the etiology of congenital malformations and neurodevelopmental disorders is chromosome microarray analysis (CMA) for copy number variations (CNVs), often followed by gene(s)-specific sequencing searching for smaller insertion-deletions (indels) and single nucleotide variant (SNV) mutations. We compared diagnostic rate of WGS to CMA and targeted gene testing of 100 patients referred to The Hospital for Sick Children Genetics clinic in 2014. WGS identified genetic variants meeting clinical diagnostic criteria in 34% of cases, representing a 4-fold increase in diagnostic rate over CMA (8%) (p-value = 1.42e-05) alone and &gt;2-fold increase in CMA plus targeted gene sequencing (13%) (p-value = 0.0009). WGS identified all rare clinically significant CNVs that were detected by CMA. In an additional 26 patients, WGS revealed indel and missense mutations presenting in a dominant (63%) or a recessive (37%) manner. We found four subjects with mutations in at least two genes associated with distinct genetic disorders, including two cases harboring a pathogenic CNV and SNV. When considering medically actionable secondary findings in addition to primary WGS findings, 38% of patients would benefit from genetic counselling.

  Study EGAS00001001623

• Distinct portrayal of lesions in synchronous multifocal lung adenocarcinoma revealed by genome sequencing

  Distinguishing multiple primary lung cancers in the synchronous multifocal intrapulmonary lesions has important significance on clinical staging and therapeutic decision. To investigate genomic aberration profiles, we applied whole genome and whole exome sequencing, and microarray-based comparative genomic hybridization on 15 intrapulmonary tumors derived from six patients with synchronous multifocal lung cancers having similar histological diagnosis. Any pair of intrapulmonary tumors in a single patient, which shared the identical genetic background and environment, showed an extinctive heterogeneity between each other. Phylogenetic relationship analysis indicated an independently branched evolution among all the tumors, suggesting they were multiple primary lung cancers. EGFR or KRAS mutations were found in 7 or 3 out of the 15 tumors, from 3 or 2 patients, respectively. Somatic mutational heterogeneity of these two genes in a single patient was also observed. Our analysis indicates genomic aberration profiling is valuable for identification of multiple primary lung cancer, especially when high histopathological concordance was observed between lesions. We also suggest a thoroughly molecular diagnosis against therapeutic target genes should be taken for each accessible nodule before making a plan for adjuvant therapy.

  Study EGAS00001001572

• Genome-wide cell-free DNA fragmentation in patients with cancer

  Cell-free DNA (cfDNA) in the blood provides a noninvasive diagnostic avenue for patients with cancer. However, characteristics of the origins and molecular features of cfDNA are poorly understood. We developed an approach to evaluate fragmentation patterns of cfDNA across the genome and found that cfDNA profiles of healthy individuals reflected nucleosomal patterns of white blood cells, while patients with cancer had altered fragmentation profiles. We applied this method to analyze fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancers and 245 healthy individuals. A machine learning model incorporating genome-wide fragmentation features had sensitivities of detection ranging from 57% to &amp;gt;99% among the seven cancer types at 98% specificity, with an overall AUC of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation based cfDNA analyses detected 91% of cancer patients. The results of these analyses highlight important properties of cfDNA and provide a proof of principle approach for screening, early detection, and monitoring of human cancer.

  Study EGAS00001003611

• Germline mutations in the transcription factor IKZF5 cause thrombocytopenia.

  To identify novel causes of hereditary thrombocytopenia, we performed a genetic association analysis of whole-genome sequencing data from 13,037 individuals enrolled in the NIHR BioResource, including 233 cases with isolated thrombocytopenia. We found an association between rare variants in the transcription factor encoding gene IKZF5 and thrombocytopenia. We report five causal missense variants in or near IKZF5 zinc fingers (Znfs), of which two occurred de novo and three co-segregated in three pedigrees. A canonical DNA-Znf binding model predicts that three of the variants alter DNA recognition. Expression studies showed that chromatin binding was disrupted in mutant compared to wild-type (WT) IKZF5 and electron microscopy revealed a reduced quantity of alpha granules in normally sized platelets. Proplatelet formation (PPF) was reduced in megakaryocytes (MKs) from seven cases relative to six controls. Comparison of RNA-seq data from platelets, monocytes, neutrophils and CD4 T-cells from three cases and 14 healthy controls showed 1,194 differentially expressed genes in platelets but only four DEGs in each of the other blood cell types. In conclusion, IKZF5 is a novel transcriptional regulator of megakaryopoiesis and the eighth transcription factor associated with dominant thrombocytopenia in humans.

  Study EGAS00001003736

• RNA sequencing and Illumina 2.5M SNP array data collected from 675 commonly used human cancer cell lines.

  Tumor-derived cell lines have served as vital models to advance our understanding of oncogene function and therapeutic response1. Although substantial effort has been directed to defining the genomic constitution of cancer cell line panels2–4, the transcriptome – which represents the active program of a cell – remains understudied. Here, we describe RNA sequencing and SNP array analysis of 675 commonly used human cancer cell lines. We explore numerous transcriptome features including coding and non-coding gene expression, transcribed mutations, gene fusion and expression of non-human sequences. Aside from many known aberrations we find new surprising characteristics, including more than 2200 unique fusion gene pairs representing a vast, testable repertoire of oncogenic fusions, many of which have analogs found in primary human tumors. We show that a combination of multiple genome and transcriptome features in a novel pathway-based approach enhances prediction of response to various targeted therapeutics. Our results provide valuable new insights into these critical pre-clinical models and provide added context for interpreting the numerous studies that employ these widely used cell lines.

  Study EGAS00001000610

• Single-cell multidimensional profiling of tumor cell heterogeneity in supratentorial ependymomas

  Supratentorial ependymomas (ST-EPNs) are aggressive and treatment-resistant pediatric brain tumors that form along the human cortex, with suspected origin from radial glia cells. Although histologically similar, ST-EPNs are molecularly classified into six distinct subgroups, many of them remaining understudied. Here, we integrated molecular cell states and spatial architecture to resolve the cellular origins and aberrant differentiation trajectories of ST-EPNs at single-cell resolution. We show that, while all ST-EPNs display a unified origin from neuroepithelial cells, different subgroups harbor highly diverse cell states and differentiation potential associated with neural or ependymal differentiation. Spatial mapping uncovered a higher-order structural tumor organization driven by presence of the mesenchymal state, and refines classical histological features based on detailed molecular resolution. By benchmarking a morpho-molecular cell-state classification system in different disease models and human tumors, we identified four cellular subtypes defined by the architecture of neurite-like extensions that mediate either self-renewal or fast, saltatory invasive phenotypes reminiscent of neurons during neurodevelopment. Collectively, our study provides a comprehensive framework for the study of ST-EPN cellular states, and reveals biologically relevant tumor compartmentalization with potential clinical implication.

  Study EGAS50000001513

• Whole genome and transcriptome analysis of medullary thyroid cancer

  Medullary thyroid cancer is the neoplasm of the calcitonin-producing parafollicular cells of the thyroid gland. The hormone calcitonin acts to lower the blood calcium level, counteracting the effects of the parathyroid hormone; it can also serve as a biomarker for the presence or recurrence of medullary thyroid cancer. Surgery is the only curative treatment for this cancer. Targeted therapeutics vandetanib and cabozantinib are approved for the treatment of aggressive and metastatic tumors that are not amenable to surgery. The use of these multikinase inhibitors are supported by the observed overactivation of the RET oncoprotein in a large sub-population of medullary thyroid cancers; however, not all patients carry oncogenic alterations of this kinase. Hence, there is a need for molecular characterization of medullary tumors on the genomic scale with the aim of identifying targetable mutations. Here, we describe the genomic profiles of two medullary thyroid cancers and report the presence of a putative oncogenic BRAF fusion in one. Such alterations previously observed in other malignancies and known targets of available drugs, can benefit patients who currently have no therapy options.

  Study EGAS00001001473