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• Joslin Study on the Genetics of Type 2 Diabetes

  Between 1993 and 2003, families were recruited in the Joslin Study on the Genetics of Type 2 Diabetes for the presence of an autosomal dominant mode of inheritance of diabetes. Recruiting and screening of families occurred through probands who were receiving medical care at the Joslin Clinic (Boston, MA). Screening of families included 1) a proband and at least one sibling with type 2 diabetes, 2) diabetes occurring in at least three generations, 3) diabetes inherited on one side of the prospective family. Probands had to have a diabetes diagnosis between the age range from 10 to 60 years. Demographic data, clinical data, and family history were collected from participating family members, along with blood and urine samples. This study includes genetic and phenotypic data from one family examined in Simeone, Wilkerson, et. al. (NPJ Genomic Medicine 2022, PMID: 35869090). Genotype data for 14 family members and whole genome sequencing data for 6 individuals were generated with the goal of identifying a potential genetic cause of disease in this family. To de-identify this family and protect confidentiality, information on sex in phenotype data and variants identified in X, Y, and MT chromosomes have been removed in compliance with IRB guidelines.

  Study phs002959

• Alzheimer's Disease Sequencing Project (ADSP)

  NOTICE OF CHANGE IN LOCATION FOR ALZHEIMER'S DISEASE SEQUENCING PROJECT (ADSP) GENETIC AND PHENOTYPIC DATA: ADSP whole exome and whole genome sequence data that are shared through dbGaP were mapped to the Genome Reference Consortium human genome GRCh37 (build 37). These data are from the Discovery Phase of the project (described below) and will continue to be available at this site. Please see the ADSP Design page for the complete study description. All data that are mapped to GRCh38 (hg38) are being shared through the NIA Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) Data Sharing Service (DSS). For instructions on how access the ADSP Build 38 data that are shared through NIAGADS DSS, visit the Application Instructions page. STUDY DESCRIPTION FOR dbGaP BUILD 37 ADSP DATA: The overarching goals of the Alzheimer's Disease Sequencing Project (ADSP) are to: (1) identify new genomic variants contributing to increased risk of developing Alzheimer's Disease (AD), (2) identify new genomic variants contributing to protection against developing AD, and (3) provide insight as to why individuals with known risk factor variants escape from developing AD. These factors will be studied in multi-ethnic populations in order to identify new pathways for disease prevention. Such a study of human genomic variation and its relationship to health and disease requires examination of a large number of study participants and needs to capture information about common and rare variants (both single nucleotide and copy number) in well phenotyped individuals. Using existing samples from NIH funded and other studies, three NHGRI funded Large Scale Sequencing and Analysis Centers (LSAC) - Broad, Baylor, and Washington University - produced the DNA sequence data. Variant call data are being made available to the scientific community through NIH-approved data repositories. Statistical analysis of the sequence data is anticipated to identify new genetic risk and protective factors. The ADSP will conduct and facilitate analysis of sequence data to extend previous discoveries that may ultimately result in new directions for AD therapeutics. Analysis of ADSP data will be done in two phases. The Discovery Phase analysis (2014-2018) is funded under PAR-12-183. The entire Discovery dataset contains whole-genome sequencing data on 584 subjects from 113 families, and pedigree data for > 4000 subjects; whole exome sequencing data on 5096 cases 4965 controls; and whole exome sequence data on an additional 853 (682 Cases [510 Non-Hispanic, 172 Hispanic]), and 171 Hispanic Control subjects from families that are multiply affected with AD. The Replication Phase (2016-2021) analysis will be funded under RFA-AG-16-001 and RFA-AG-16-002 and is expected to include a combination of genotyping and sequencing approaches on at least 30,000 subjects. Targeted sequencing will be done by the LSACs. GRCh37 Data Releases The first ADSP data release occurred on November 25, 2013. It included the whole-genome sequencing data in BAM file format on 410 individuals. The second ADSP data release occurred on March 31, 2014, and included the whole-genome sequencing data in BAM file format for an additional 168 individuals. The third ADSP data release occurred on November 03, 2014 and included whole-exome sequencing data in BAM file format for 10,939 individuals. The fourth ADSP data release occurred on February 13, 2015 and included revised ethnic data for subjects with whole-exome sequencing data. The fifth ADSP data release occurred on July 13, 2015 and included whole-genome genotypes and updated phenotypes as well as changes to pedigree structures and sample IDs. The sixth ADSP data release occurred on December 8, 2015, and included whole-exome genotypes and updated phenotypes as well as changes to subject IDs. This seventh ADSP data release on April 12, 2016 includes: (1) WES and WGS SNV VCF files (2) WES and WGS Indel PLINK files ADSP Data Available through dbGaP: ADSP - Whole Genome Sequencing ADSP - Whole Exome Sequencing Comments DNA-Seq (BAM) n=578 n=10913 Sequence data available (plus n=38 replications w/out genotype data) Concordant SNV Genotypes (PLINK format) N/A n=10913 QC'ed genotypes that are concordant between the Atlas (Baylor's) and GATK (Broad's) calling pipelines (a subset of the consensus genotype set) Consensus Genotypes (PLINK and VCF format) n=578 n=10913 QC'ed genotypes that are concordant between Atlas and GATK pipelines as well as those that that were called uniquely by Atlas or GATK Concordant Indel Genotypes (PLINK format) n=578 n=10913 QC'ed genotypes that are concordant between the Atlas and GATK calling pipelines Phenotype Data n=4735 n=10913 Data of n=53 phenotype variables available (plus administrative data), including APOE genotype. WGS phenotypes include data of connecting family members. Please use the release notes provided by dbGaP to obtain detailed information about study release updates. The ADSP data portal provides a customized interface for users to quickly identify and retrieve files by covariates, phenotypes, and data properties such as sequencing facility or coverage. For more information about the ADSP study and the data portal, please visit https://www.niagads.org/adsp/.

  Study phs000572

• EGA synthetic data

  Synthetic Data One of the limitations in genomics research is that human genomics data is not openly available; access must be controlled according to participant consent agreements and data protection regulations such as GDPR. Obtaining authorization to access such data can sometimes take a long time, resulting in delays to important research work. In this context, synthetic genomic and phenotype data can be useful resources for researchers to avoid these delays. Synthetic data are artificially generated datasets, often created with algorithms, which can be used without the need for authorization to test new products and tools, build technical demonstrators, validate data models, and train AI models. The EGA provides access to synthetic cohort datasets augmented with rich synthetic metadata that overcomes these real data usage restrictions. Whilst synthetic datasets are not included in the general EGA mandate and services, we can consider such submissions and evaluate their acceptance on the basis of their unique use cases not already covered by existing synthetic datasets. Access to synthetic data studies is managed by the EGA Helpdesk Data Access Committee. Study ID Title Located in EGAS00001002472 CINECA synthetic cohort EUROPE UK1 referencing fake samples Central EGA EGAS00001005591 Synthetic data - Genome in a Bottle Central EGA EGAS00001005042 Test Study for EGA using data from 1000 Genomes Project - Phase 3 Central EGA EGAS00001005702 Human genomic and phenotypic synthetic data for the study of rare diseases Central EGA EGAS50000000190 EOSC4Cancer Synthetic Colorectal Cancer Genomic data Central EGA EGAS50000000086 Synthetic - FEGA Sweden Heilsa synthetic dataset December 2023 Federated EGA Sweden EGAS50000000678 Synthetic - GDI synthetic data Federated EGA Spain

  Documentation synthetic-data

• ACTIV-6: COVID-19 Outpatient Randomized Trial to Evaluate Efficacy of Repurposed Medications

  This study is a platform protocol designed to be flexible so that it is suitable for a wide range of settings within healthcare systems and in community settings where it can be integrated into routine COVID-19 testing programs and subsequent treatment plans. This platform protocol will enroll participants in an outpatient setting with a confirmed polymerase chain reaction (PCR) or antigen test for SARS-CoV-2. Each appendix will describe a repurposed medication (study drug) to meet the protocol objectives. When only one study drug/appendix is under study, allocation between study drug and placebo will be 1:1. If multiple study drugs/appendices are under study, participants will also be randomized among the study drugs for which eligibility is confirmed. Since the route of administration of each study drug may differ, the placebos may also differ. To achieve blinding and an equitable randomization probability, a two-step randomization process will be used.In the first step, the participant will be randomized m:1 active study drug to placebo, where m is the number of active study drugs for which the participant is eligible. Then, participants will be randomized among the m study drugs for which they are eligible. Participants will carry their ‘study drug' versus ‘placebo' randomization with them into the study drug appendix. In this way, a participant allocated to placebo who is randomized to study drug A will be given the placebo that matches study drug A. This achieves equal probability of exposure to a placebo or an active study drug, and equitable distribution among all study arms for which a participant is eligible. Sites will be informed to which study drug appendix the participant is randomized, but not whether they are allocated to the study drug arm or placebo arm within that appendix. For analysis, concurrent placebo participants who were eligible for the study drug appendix will be pooled. This will result in approximately a 1:1 allocation ratio for any study drug to placebo. If a study drug appendix is stopped for efficacy and becomes standard of care, the active study drug arm may serve as a concurrent placebo for other study drugs.Participants will receive complete supply of repurposed medication (study drug) or placebo with length of treatment and amount of study drug/placebo depending on the study drug appendix and arm to which they are randomized.This study is designed so that it can be done completely remotely. However, screening and enrollment may occur in person at sites and unplanned study visits may occur in person or remotely, as deemed appropriate by an investigator for safety purposes. Participants will be on-study for up to 180 days, during which they will complete various questionnaires.

  Study phs003941

• MutationalPatterns2: The one stop shop for the analysis of mutational processes: DNA repair deletions

  Study EGAS00001004789

• Center for Craniofacial and Dental Genetics: Study of Dental Caries and Cleft Lip/Palate in Guatemala

  This study provides an opportunity to investigate the genetics of both dental caries and orofacial clefts (OFCs) in one set of families ascertained in Guatemala. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org), which was developed through the trans-NIH Genes, Environment, and Health Initiative (GEI). Furthermore this study brings together multiple research priorities of the University of Pittsburgh Center for Craniofacial and Dental Genetics (www.ccdg.pitt.edu). A genome-wide panel of 610,000 SNPs was genotyped at the Broad Institute to be comparable to our other pertinent GENEVA studies that are also part of dbGaP (dbGaP accession number phs000095, "Dental Caries: Whole Genome Association and Gene x Environment Studies" and dbGaP accession number phs000094, "International Consortium to Identify Genes and Interactions Controlling Oral Clefts"). The goal of this study is to investigate genetic determinants to dental caries and to OFCs in a novel study population. To date, most genetic studies of dental caries have been conducted in Caucasians, and of OFCs in Caucasians and Asians. The Guatemalan population under study is rural and ethnically mixed with a high proportion of Native-South-Americans. Thirty-six Guatemalans from this dataset were also part of the recent GWAS studies of cleft lip and cleft palate (Beaty et al., 2010 and 2011, dbGaP Study Accession: phs000094, "International Consortium to Identify Genes and Interactions Controlling Oral Clefts"). In addition to the families ascertained in Guatemala, some subjects were genotyped with this cohort to augment the data in the parent GENEVA study (Dental Caries: Whole Genome Association and Gene x Environment Studies, dbGaP accession number phs000095), in particular individuals from the IOWA and PITT GENEVA study sites. Their data are available with the parent study. Dental caries (also known as tooth decay) remains the most common chronic disease of childhood, five times more common than asthma and seven times more common than environmental allergies, with more than 40% of children exhibiting caries when they enter kindergarten. In 2005, it was estimated that dental health care costs were approximately $84 billion, of which 60% or about $50 billion were related to treatment of dental caries. The etiology of dental caries has been studied for many years. Multiple factors contribute to a person's risk for caries, including: 1) environmental factors such as diet, oral hygiene, fluoride exposure and the level of colonization of cariogenic bacteria and 2) host factors such as salivary flow, salivary buffering capacity, position of teeth relative to each other, surface characteristics of tooth enamel and depth of occlusal fissures on posterior teeth. In spite of all that is known about this disease, there are still individuals who appear to be more susceptible to caries and those who are extremely resistant, regardless of the environmental risk factors to which they are exposed, implying that genetic factors also play an important role in caries etiology. This conclusion is supported by studies in both humans and animals, with the most compelling evidence coming from studies of twins reared apart in which investigators found significant resemblance within monozygotic (MZ) but not dizygotic (DZ) twin pairs for percentage of teeth and surfaces restored or carious and estimated the genetic contribution to caries as 40%. Other recent studies of twins reared together estimated the heritability for caries, adjusted for age and gender, as ranging from 45-64%. In our study populations of families, we also estimated caries heritability as approximately 54%-70% of variation in primary dentition caries scores and 35%-55% in the permanent dentition (Wang et al., 2010). Orofacial clefts (OFCs), particularly cleft lip with or without cleft palate (CL/P) and isolated cleft palate (CP) are a major public health problem, affecting one in every 500-1000 births worldwide thus representing the most common facial birth defect and one of the most common of all congenital anomalies. CL/P is a major structural birth defect that is notable for significant lifelong morbidity and complex etiology. The extensive psychological, surgical, speech and dental involvement emphasize the importance of understanding the underlying causes of CL/P. Therefore, many research groups have attempted to elucidate the etiology of CL/P, with some recent success by our research group and others (see Beaty et al., 2010, 2011; Dixon et al., 2011). It is clear that CL/P can occur as part of Mendelian syndromes, that certain chromosomal abnormalities include CL/P in the phenotype, and that certain teratogens can increase the risk of having an offspring with CL/P. However, phenotypes of known etiology comprise only a small portion of all individuals with a CL/P or CP, and the major focus of research into OFCs is to develop an understanding of the etiology of nonsyndromic (NS) forms of clefting. A major focus of the University of Pittsburgh CCDG has been to study additional phenotypes within nonsyndromic OFC families in order to identify sub-clinical expressions of OFC risk genes or risk variants, e.g. SNPs (see Weinberg et al., 2006). A detailed oral exam is conducted as part of these extended phenotypic studies, including a dental caries exam. Note that although there are some reports in the literature of higher caries experience in individuals with clefts, the most recent meta-analysis of those literature reports concluded that individuals born with these defects do not have a higher frequency of caries (Hasslöf and Twetman, 2007). Notably, we investigated the association of CL/P and caries in three of our study populations (including part of the Guatemalan population in this GWAS study) and also found no increase in caries rates of CL/P cases versus controls (Jindal et al., 2011). This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org), which was developed through the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to dental caries and oral clefts through large-scale genome-wide association studies of well-characterized Guatemalan families and individuals. Genotyping was performed at the Broad Institute of MIT and Harvard. The study was supported by the National Institute of Dental and Craniofacial Research (NIDCR, U01-DE018903). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000440

• Angelman, Rett, Prader-Willi Syndromes Consortium (ARP) Angelman Syndrome Natural History Protocol

  This study is a longitudinal multidisciplinary investigation on the natural history, morbidity and mortality of Angelman Syndrome (AS). We will collect detailed longitudinal data on a cohort of AS individuals to gain a better understanding of the disease progression, and to follow the natural history of the clinical features of this cohort including assessment of quality of life and longevity. The participants to be recruited for the study will include 1) patients who have a documented molecular diagnosis of AS and 2) patients with a clear clinical diagnosis of Angelman Syndrome as determined by the Principal Investigator (PI) and the Co-investigators in this study but who do not have a known molecular defect. One of the goals of the natural history study will be to characterize the phenotypic differences between patients with Class I deletions and those with Class II deletions, particularly with respect to the issue of autism. A blood sample may be collected on the participants in order to create a DNA repository, and in some cases, to establish cell lines if further material is required for molecular studies. Alternatively, DNA may be obtained by buccal mucosa swabs/brushing. In those AS patients with known deletions involving the 15q11-q13 regions, a blood sample will be collected to perform comparative genome hybridization (CGH) microarray studies to characterize the extent of the deletion. No drugs or treatments will be administered through this protocol. In rare instances, a skin biopsy to establish a fibroblast cell line may be requested (using separate consent).

  Study phs000576

• Cancer Moonshot Biobank

  The National Cancer Institute initiated the Cancer Moonshot Biobank to accelerate research through the collection and distribution of biospecimens for key research efforts. The Biobank will engage with over one thousand cancer patients, being treated with standard-of-care therapies, for longitudinal biospecimen and data collection. Cancer patients will be enrolled into the study from a diverse participant population engaged at medical institutions throughout the U.S. The biospecimens will be distributed to qualified scientists to accelerate research progress. The Biobank program, working closely with Leidos Biomedical, Inc. (LBR), will partner with community hospitals and other medical institutions, designated as Biospecimen Source Sites (BSS), to engage and consent eligible participants and collect biospecimens and associated clinical data; work with a central Biospecimen Core Resource (BCR) to support biospecimen collection, processing and storage activities and perform pathology quality control; work with LBR's Molecular Characterization (MoCha) lab to perform clinical tumor molecular characterization assays and return results to patients and their physicians; and make the biospecimens and associated data available to researchers to accelerate cancer research progress. Return of clinical results, a dedicated Patient and Provider Engagement (PPE) website, electronic informed consent, local participant engagement projects, and an embedded ethical, legal and social issues sub-study will together support patient and provider engagement in the program. Cancer Moonshot Biobank imaging data is being made available in The Cancer Imaging Archive (TCIA) on a release schedule that is coordinated with the program releases of data to dbGaP. The imaging data can be found at the following link: Cancer Moonshot Biobank Imaging

  Study phs002192

• Lactate metabolism in cancer stem cell fate regulation

  Tumors arise as a result of uncontrolled cell proliferation driven by mutations in the signaling pathways that regulate the balance between cell division and differentiation. Despite of that, in the tumor, cancer stem cells seem to retain their capability to differentiate, which can lead to differential chemotherapy response. We previously showed that intestinal stem cells and differentiated cells display specific metabolic features and that they interact through metabolites. Moreover, we found that altering mitochondrial metabolism is sufficient to lead and define stem cell differentiation. In cancer, cellular metabolism is highly derailed. However, how metabolic changes in the cancer cell contribute to cell fate decisions remains unknown. Recently, the crosstalk between metabolism and epigenetics has emerged as one of the players in tumor development. Here we aim to address the impact of the altered metabolism of cancer cells on their epigenome as a regulatory axis of cell identity and fate. We use human-derived tumor organoids where we introduce genetic encoded fluorescent-based reporters to visualize cell identity and metabolism at single cell level. We combine 4D live imaging with a recently developed machine learning method to re-construct cell-lineages, trace stem cell state and metabolic changes during tumor organoid development. We find that, mimicking the tumor metabolic microenvironment (Warburg effect-driven low-glucose high-lactate) leads to changes cellular metabolism and histone acetylation. Strikingly, these alterations provoke profound changes in tumor development by increasing the population of cancer stem cells and their proliferation and differentiation dynamics.

  Study EGAS50000000063

• RB Loss in Resistant EGFR Mutant Lung Adenocarcinomas that Transform to Small Cell Lung Cancer

  Tyrosine kinase inhibitors (TKIs) are effective treatments for non-small cell lung cancers (NSCLCs) with epidermal growth factor receptor (EGFR) mutations. However, they do not lead to cures, and, on average, relapse occurs typically occurs afterafter an average of one year of continuous treatment. In a subset of patients, aA fundamental histological transformation from NSCLC to small cell lung cancer (SCLC) is observed in a subset of the resistant cancers, but the molecular changes associated with this transformation remain unknown. Analysis of a cohort of tumor samples and cell lines derived from resistant EGFR mutant patients with SCLC transformation revealed that RB is lost in 100% of these SCLC transformed cases, but rarely in those that remain NSCLC. Global changes in gene expression, including increased neuroendocrine marker expression and absence of EGFR expression, are observed in resistant EGFR mutant cancers that transformed to SCLC. Consistent Further, with their similarities to classical SCLC at the genetic and gene expression levelincreased neuroendocrine marker and decreased EGFR expression as well as greater sensitivity to BCL2 family inhibition are observed in resistant SCLC transformed cancers compared to resistant NSCLCs. , cell lines derived from resistant EGFR mutant SCLC biopsies are substantially more sensitive to Bcl-2 family inhibition with ABT-263 compared to those derived from resistant EGFR mutant NSCLCs. Together, these findings suggest that despite developing initially as EGFR mutant adenocarcinomas, this subset of resistant cancers ultimately adopt many of the molecular and phenotypic characteristics of classical SCLC.

  Study EGAS00001001102

• Immunodeficiency syndrome caused by LCP1 mutations

  Severe congenital neutropenia (SCN) is characterized by neutropenia, recurrent infections and an increased leukemia risk. Multiple genetic defects underlying SCN have been identified, but a genetic diagnosis is still lacking in a significant proportion of patients. Here, we report three independent pedigrees with autosomal dominant variants in LCP1. Variants c.740-1G>T and c.740-20_744del resulted in the same alternatively spliced RNA product, causing an in-frame deletion (p.A247_E254del). Variant c.509C>T in the third pedigree resulted in p.S170L. Variant carriers suffer from neutropenia, poor or complete lack of response to G-CSF treatment, and variable degrees of lymphopenia. Patients with A247_E254del presented with tetraploid cells in the bone marrow, indicative of disturbed cytokinesis. In one of these kindreds, two individuals developed acute leukemia. G-CSF non-responsiveness and defective cell cycling were repaired upon correction of the LCP1 A247_E254del variant in patient-derived induced pluripotent stem cells, supporting the monogenic origin of the disease. Indicative of their gain-of-function, both A247_E254del and S170L variants increased F-actin bundling and formation of abnormal protrusions. Single-cell transcriptome analysis of A247_E254del bone marrow-derived hematopoietic stem and progenitor cells (HSPCs) showed deregulation of signaling pathways controlling mitosis in multi-lineage and lymphoid-primed HSPC subsets. We conclude that activating LCP1 variants cause a new autosomal dominant hematopoietic disorder. Depending on the consequences of the LCP1 variants for protein structure, patients may suffer from G-CSF refractory severe neutropenia, lymphopenia, defective cytokinesis and predisposition to leukemia.

  Study EGAS00001008293

• RNA-Seq in Patients with Primordial Dwarfism

  Transcriptome studies in patients with rare genetic diseases can potentially aid in the interpretation of likely causal genetic variation through identification of altered transcript abundance and/or structure. RNA-Seq is the most sensitive assay for both investigating transcript structure and abundance The primary aim of this pilot project is to investigate to what degree integrating exome-Seq and RNA-Seq data on the same individual can accelerate the identification of causal alleles for rare genetic diseases. There are two main strands to this: (i) identifying which variants discovered in exome-seq appear to be having a functional impact on transcripts, and (ii) identifying transcript outliers, especially among known causal genes, that may not necessarily have a causal variant identified from exome sequencing. The latter may identify the presence of causal variants that lie far from coding regions (e.g. the formation of cryptic splice sites deep within introns, or loss of long range regulatory elements), which can be confirmed with further targeted genetic assays. Just over 50% of all disease-causing variants recorded in the Human Gene Mutation Database (HGMD) affect transcript structure and abundance (e.g. nonsense SNVs, essential splice site SNVs, frameshifting indels, CNVs). This pilot project will study RNA from lymphoblastoid cell-lines from 12 patients with primordial dwarfism syndromes, for 10 of these samples we have previously generate exome data as part of our collaboration with the group of Prof Andrew Jackson. The two remaining samples are positive controls where the causal mutation is known, and is known to affect transcript structure and/or abundance. Primordial dwarfism is a prime candidate for these RNA-seq studies because all known causal mutations to date have key roles in DNA replication and thus, unsurprisingly, the products of the causal genes are typically ubiquitously expressed. Each RNA will be sequenced, with two technical replicates (independent RT-PCR and libraries) per sample, and each replicate run in 1/2 of a HiSeq lane using 100bp paired reads. Samples preparation was as follows :The cells were grown to confluency, then pellets frozen at -80. RNA samples were prepared using the Qiagen RNeasy kit, then nanodropped and analyzed using the bioanalyzer to determine concentration and purity. 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 EGAD00001000640

• High-coverage whole exome sequence in non-TRU-type lung adenocarcinomas

  In order to elucidate the key genetic events in non-TRU-type lung cancer, we selected 43 non-TRU-type lung adenocarcinomas and performed a whole exome sequence and RNA-seq analysis using a next-generation sequencer. The results of the analysis identified mutations in TP53, KRAS and NKX2-1 as the top three significantly mutated genes, while the EGFR mutation was rare in this cohort. Eight NKX2-1 mutations (5 frameshift, 2 nonsense, and one missense) were identified, with one case harboring two distinct NKX2-1 mutations (missense and frameshift). All NKX2-1 mutants were transcriptionally inactive in reporter assays. Histologically, invasive mucinous adenocarcinomas accounted for most of the NKX2-1 mutations (5 cases), while 1 enteric and 1 acinar adenocarcinoma harbored the NKX2-1 mutation.

  Study JGAS000105

• Gliomas, glioneuronal and neuronal tumors

  The dataset represents a total of 85 DNA samples from 22 male and 20 female pediatric patients affected with gliomas, glioneuronal, and neuronal tumors. The samples were subject to whole genome sequencing, WGS, [71 samples, (representing 18 male and 17 female individuals)] and whole exome sequencing, WES, [14 samples, (representing 4 males and 3 female individuals)]. One tumor tissue sample and one peripheral blood sample were analyzed from each of 84 patients, whereas two tumor tissue samples and one peripheral blood sample were analyzed from one patient. The WGS samples were sequenced 2x150 bp paired-end on an Illumina HiSeqX v2.5 instrument, and the WES samples were sequenced 2x100 bp paired-end on an Illumina HiSeq 2500 instrument. The FASTQ files generated were aligned to the human reference genome sequence GRCh38/hg38 using bwa-mem, with the ALT-aware option turned on. Sorting of reads and marking of PCR duplicates was performed with GATK. Base quality score recalibration and joint realignment of reads around insertions and deletions (indels) were conducted using GATK tools. The dataset consists of 85 files in the CRAM format (lossless compression) with a total file size of ~13,3 TB. Additional genomic and molecular data (FASTQ, BAM, IDAT, and VCF files) and limited clinical data can be requested by ethically approved projects conducting research in the field of pediatric cancer.

  Dataset EGAD50000000300

• Synthetic data - Genome in a Bottle

  In May, the National Institute of Standards and Technology (NIST) released its first genome in a bottle, a reference sample of DNA for validating human genome sequences. This so-called truth sequence comes from a decades-old sample donated by a Utah woman for (other) research purposes (NA12878 cell line), which, over the years, has been one of the most studied, and hence best-characterized, human samples. Seeing genomic medicine moving toward mainstream healthcare, researchers at NIST recognized the need for a reference human genome and assembled a private-public consortium in 2012 to create one. As detailed in a 2014 Nature Biotechnology paper (Nat. Biotechnol.32, 246–251, 2014), the group integrated and arbitrated among sequences from 14 data sets, five sequencing technologies, seven read mappers and three variant callers.

  Study EGAS00001005591

• A first-in-human clinical study of an allogenic iPSC-derived corneal endothelial cell substitute transplantation for bullous keratopathy

  iPSC-derived corneal endothelial cell substitutes recover pathological corneal edema. No adverse reactions are observed after one year follow-up. Gene mutations may appear despite whole genome sequencing of the master cell bank. This study contains the RNA-seq data.

  Study EGAS50000000673

• RNA sequencing data of pediatric T-cell acute lymphoblastic leukemia (set 3)

  The dataset includes RNA sequencing FASTQ files from diagnostic tumor samples of two T-ALL patients (ALLK-126, ALLT-503). Two diagnostic samples of ALLT-503 were sequenced: one extracted from the bone marrow (ALLT-503D-RNA) and one from the lymph node (ALLT-503lympD-RNA). Novogene's internal method was used in directional library preparation including rRNA removal. The diagnostic tumor samples were sequenced using Illumina Novaseq X Plus, and the target sequencing depth was 21 Gb per sample.

  Dataset EGAD50000002586

• the Yemeni-Somali 5 million SNP array dataset

  The three new samples (two from Yemen and one from Somalia) that carry a maternal lineage affiliated with the Malagasy motif were assayed for genome-wide SNP genotypes using the Illumina Human Omni5 Bead Chip (Illumina), which surveys 4,284,426 single nucleotide markers regularly spaced across the genome.

  Study EGAS00001003425

• MCO colorectal cancer genomics at UNSW

  This dataset contains DNA sequencing data from 95 colorectal cancer and matched-normal samples. The dataset contains targeted deep sequencing of selected regulatory elements in 95 cancer and matched-normal samples, and data for one sample that was additionally whole-genome sequenced (cancer and matched-normal).

  Study EGAS00001003450

• Two monogenic disorders masquerading as one: Severe congenital neutropenia with monocytosis and non-syndromic sensorineural hearing loss

  Study EGAS00001004176

• LCCC1122: Defining the Triple Negative Breast Cancer Kinome Response to GSK1120212

  In this study the investigators looked at adaptive reprogramming impact on the kinome when a MEK inhibitor called GSK1120212 (trametinib) was administered in a "window of opportunity" trial. GSK1120212 is not yet approved by the FDA for use in breast cancer patients. The investigators gave GSK1120212 for a short period of time (one week) to examine MEK and the other kinase expression in cancer cells both before and after the study drug is given. The investigators gave this drug for research purposes only. The length of time it was given is not intended to treat cancer. Recently researchers at UNC developed a process that can comprehensively profile the majority of the individual kinases in the kinome and examine the impact on kinase expression of kinase inhibitors (Duncan et al, Cell 2012, PMID: 22500798). This can tell us which kinases need to be concurrently blocked to augment responsiveness and prevent acquired resistance so that the investigators can design the best combinations of kinase blocking drugs for triple negative breast cancer. This is especially important for individuals with triple negative breast cancer (TNBC) because there are no targeted drugs available that can block molecules that affect tumor growth. The investigators believe that kinase-blocking drugs have the potential to be a more effective treatment for people with TNBC. In this recently published study (Zawistowski et al, Cancer Discovery 2017, PMID: 28108460), TNBC patients treated with trametinib for 7 days resulted in a transcriptional response characterized by significant reprogramming of the tyrosine kinome, and this adaptive bypass response in human tumors was found to be similar to that seen in preclinical models including TNBC cell lines and mouse xenografts. In this study we also examined whether reprogramming differed between TNBC molecular subtypes, finding that basal-like and claudin-low human TNBC cells and mouse tumor subtypes had different adaptive transcriptional responses to MEK-ERK inhibition. Mechanistically we found that genome-wide enhancer remodeling drove the adaptive transcriptional response, suggesting that epigenetic approaches to reprogramming may be more durable than kinase inhibitor polypharmacology.

  Study phs001405

• HCA_Thymus_Disease_DiGeorge_Transplant_RNA

  Single cell transcriptomics study of thymic transplant biopsies Allogeneic thymus transplantation is the only curative therapy for complete DiGeorge Syndrome (cDGS), a rare severe primary immunodeficiency characterised by athymia. GOSH is one of only two centres worldwide to offer this treatment. Despite a lack of major histocompatibility complex (MHC)-matching between donor and host, transplanted thymus becomes repopulated by recipient bone marrow derived precursor cells and supports development of functional T-cells. The mechanisms underlying thymopoiesis in this context are poorly understood, but over time we observe reconstitution of T-cell immunity, with the ability to produce host naïve T-cells showing a broad T-cell receptor (TCR) repertoire and to generate MHC-restricted T-cell proliferative responses. Although lifesaving, the achieved immunological reconstitution is typically not complete with circulating T-cell numbers usually remaining below the age related normal ranges. Additionally, we observe persistence of donor-derived T-cells of unknown clinical significance. To gain more insight into the mechanisms by which MHC-mismatched transplanted thymus supports T-cell development with self-tolerance, as well as into the basis of suboptimal T-cell immunity, we now aim to investigate immune reconstitution after thymus transplantation in further detail by using single-cell transcriptomics, applied to thymic transplant biopsies and peripheral blood samples collected during standard post-transplant patient care. By identifying which lineages of host- derived cells repopulate the thymic tissue after transplantation, we will be able to address the role of MHC in positive and negative T-cell selection during T-cell differentiation. We will also be able to clarify the exact ontogeny of the persistent donor T-cells, as well as their possible role. Understanding the mechanisms of action of HLA-mismatched transplanted thymus will contribute to treatment optimisation. Additionally, our research provides a unique opportunity to further investigate key immunological concepts, such as tolerance and autoimmunity, challenging existing paradigms in thymus immunology. 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/

  Study EGAS00001004025

• Effects of Metformin on Transcriptomic and Metabolomic Profiles in Breast Cancer Survivors Enrolled in the Randomized Placebo-Controlled MetBreCS Trial

  RNA from snap-frozen breast tissue biopsies were purified after lysing by tissuelyser (Qiagen) and using RNA Purification Plus Kit (Norgen biotek CORP, 47700) with additional on-column DNase-I treatment (Qiagen, 79254) at 27 °C. RNA purity and integrity (RIN) were quantified using RNA 6000 Nano kit (Agilent Technologies, 5067-1511) on the 4200 TapeStation (Agilent, Santa Clara, USA). Library preparation and 2x75bp paired-end of 160 ng total RNA input was performed using Illumina Stranded Total RNA Prep Ligation kit and Illumina HiSeq4000 system (Illumina, Sand Diego, CA, USA). RNA sequencing data from HiSeq4000 were quality checked and aligned to GRCh38 (GCA_000001405.15) reference genome using HISAT2 2.0.5 and submitted to subread v.1.5.2 for feature counts calculation. Finally, 36 paired biopsies samples (metformin n=26 and placebo n=12) were sequenced and included in the final analyses.

  Dataset EGAD50000001274

• Whole Exome Sequencing of Calcitonin Producing Pancreatic Neuroendocrine Neoplasms (CT-pNENs) Indicates a Unique Molecular Signature

  Calcitonin-producing pancreatic neuroendocrine neoplasms (CT-pNENs), which can mimic clinical and laboratory features of medullary thyroid carcinoma, are an extremely rare clinical entity with approximately 60 cases reported worldwide. The molecular profile of CT-pNENs is not yet known. Whole-exome sequencing (WES) was performed on tumor and corresponding serum samples of five patients with increased calcitonin serum levels and histologically proven calcitonin-positive CT-pNENs. cBioPortal analysis and gene enrichment analysis with DAVID were performed to validate candidate genes. Immunohistochemistry was used to detect protein expression of MUC4 and MUC16 in CT-pNEN specimens. Common somatic mutations of pNENs like MEN1, ATRX and PIK3CA were identified in cases of CT-pNENs. New somatic SNVs in ATP4A, HES4, and CAV3 have not yet been described in CT-pNENs. Pathogenic germline mutations in FGFR4 and DPYD were found in three of five cases. Mutations of CALCA (calcitonin) and the corresponding receptor CALCAR were found in all five tumor samples, but none of them resulted in clinical or protein sequelae. All five tumor cases showed single nucleotide variations (SNVs) in MUC4, and four cases showed SNVs in MUC16, both of which were membrane-bound mucins. Immunohistochemistry showed protein expression of MUC4 and MUC16 in one case each, and the liver metastasis of a third case was double positive for MUC4 and MUC16. The homologous recombination deficiency (HRD) score of all tumors was low.CT-pNENs have a unique molecular signature compared to other pNEN subtypes, specifically involving the FGFR4, DPYD, MUC4, MUC16 and the KRT family genes. However, these WES data from only five cases must be interpreted with caution because causal interpretation of the mutational landscape in CT-pNENs is difficult. Further research is needed to explain differences in pathogenesis compared with other pNENs. In particular, multi-omics data such as RNASeq, methylation, and whole genome sequencing could be informative.

  Study phs003060

• Metagenomic Analysis to Identify Novel Infectious Agents in Systemic Anaplastic Large Cell Lymphoma

  Anaplastic large cell lymphoma (ALCL) is a rare T-cell non-Hodgkin lymphoma (NHL). Risk of systemic ALCL is markedly increased among immunosuppressed people, such as those with human immunodeficiency virus (HIV) infection or solid organ transplant recipients. This increased risk in immunosuppressed populations suggests a viral etiology, given similar elevations for diffuse large B-cell lymphoma (DLBCL), Burkitt lymphoma, and Hodgkin lymphoma (caused by Epstein-Barr virus [EBV]) and other virus-related cancers in immunosuppressed individuals. EBV might be a plausible candidate, but most reported ALCL tumors are EBV-negative. Therefore, we conducted a metagenomic analysis of microbial associations with ALCL to determine whether a known or novel pathogen is associated with the tumor. We obtained archived formal fixed paraffin embedded (FFPE) ALCL tumor tissue from the Iowa cancer registry and an additional case from an immunosuppressed individual infected with human immunodeficiency virus. Controls included 3 diffuse large B-cell lymphomas (DLBCLs) in transplant recipients (shown to be related to Epstein-Barr virus [EBV] by in situ hybridization) and 3 breast cancers (no known viral etiology). RNA was extracted from FFPE specimens, and total RNA libraries were prepared using the KAPA RNA HyperPrep Kit (Roche). Five samples (IA01, IA53, IA57, IA07, and IA17) were sequenced on a NextSeq 500 (Illumina) and the remainder on a NovSeq 6000 (Illumina). Using novel computational approaches, we eliminated human reads and aligned residual non-human reads to known microbial references using GATK-PathSeq or conducted de novo assembly of unassigned reads using virID pipeline. Although EBV was detected in the 3 DLBCL controls, we did not detect EBV or any novel pathogen in ALCL tumors or in the breast cancers. Our study had a modest sample size and only one case arising in HIV-infected individual. Therefore, further studies are required to characterize the metagenome of systemic ALCLs, especially cases arising in immunosuppressed people.

  Study phs002064

• National Institute on Aging - Late Onset Alzheimer's Disease Family Study: Genome-Wide Association Study for Susceptibility Loci

  Alzheimer disease is the most common neurodegenerative disorder of the elderly affecting an estimated five million Americans. Genetic factors contribute to the risk for disease with heritability estimates ranging from 57% to 79%. More than a decade ago, the ε4 variant of APOE was identified and remains the most consistently replicated genetic variant influencing the risk of late onset Alzheimer disease. A segregation analysis suggests there may be four additional genes influencing the age-at-onset of Alzheimer disease. In 2007 there were 968 association studies in 398 candidate genes reported, but none replicated consistently. There are many reasons for the lack of consistency, but one important reason for the lack of progress is the paucity of a sufficient number of well characterized families and patients available to the entire scientific community. The extensive effort and expense required to ascertain such a population has been addressed by the NIA-LOAD Family Study. Its goal is to identify and recruit families with two or more siblings with the late-onset form of Alzheimer's disease and a cohort of unrelated, non-demented controls similar in age and ethnic background, and to make the samples, the clinical and genotyping data and preliminary analyses available to qualified investigators world-wide. Genotyping by the Center for Inherited Disease Research (CIDR) was performed using the Illumina Infinium II assay protocol with hybridization to Illumina Human 610Quadv1_B Beadchips. This genotyping represents the largest collection of families ever assembled with Alzheimer's disease combining the NIA-LOAD Genetics Initiative Multiplex Family Study, the National Cell Repository for Alzheimer's Disease (NCRAD) with additional controls from the University of Kentucky. These genotyping results will serve as a focal point for future research that will identify all of the remaining genetic variants in Alzheimer's disease.

  Study phs000168

• Gene Environment Association Studies (GENEVA): Genetics of Early Onset Stroke (GEOS) Study

  The Genetics of Early Onset Stroke (GEOS) Study is a population-based case-control study designed to identify genes associated with early-onset ischemic stroke and to characterize interactions of identified stroke genes and/or SNPs with environmental risk factors such as smoking and oral contraceptive use. The GEOS study consists of 921 ischemic stroke cases with age of first stroke 16-50 years and a similar number of controls, identified from the Baltimore-Washington area. Cases and controls were recruited in 3 different time periods: Stroke Prevention in Young Women-1 (SPYW-1) conducted from 1992-1996, Stroke Prevention in Young Women-2 (SPYW-2) conducted from 2001-2003, and Stroke Prevention in Young Men (SPYM) conducted from 2003-2007. The overall GEOS sample includes 477 cases who self-reported their race as "white" and 396 cases who self-reported their race as "African American." Traditional stroke risk factors and other study variables, including age, ethnicity, and history of hypertension, diabetes, myocardial infarction (MI), current smoking status, and current oral contraceptive use (both defined as use within one month prior to event for cases and at a comparable reference time for controls), were also collected during standardized interview and were included as covariates in our analyses. This study is part of the Gene Environment Association Studies initiative (GENEVA, http://www.genevastudy.org) funded by the trans-NIH Genes, Environment, and Health Initiative (GEI). The overarching goal is to identify novel genetic factors that contribute to early-onset ischemic stroke through large-scale genome-wide association studies of cases and controls of European and African descent from the Baltimore-Washington area. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). Data cleaning and harmonization were done at the GEI-funded GENEVA Coordinating Center at the University of Washington.

  Study phs000292

• Heart Failure Network - Effectiveness of Ultrafiltration in Treating People with Acute Decompensated Heart Failure and Cardiorenal Syndrome (HFN CARRESS - BioLINCC)

  ObjectiveThe CARRESS trial examined the effectiveness of ultrafiltration compared with a strategy of diuretic-based stepped pharmacologic therapy on renal function and weight loss in patients with heart failure who have worsening renal function and persistent congestion.BackgroundAcute cardiorenal syndrome is defined as worsening renal function in patients with acute decompensated heart failure. It occurs in 25 to 33% of patients and is associated with poor outcomes. Current heart failure treatments focus on removing excess fluid buildup, however administration of intravenous diuretics may contribute to worsening renal function. Venovenous ultrafiltration is one alternative therapy that may be effective in patients with acute decompensated heart failure complicated by acute cardiorenal syndrome and persistent congestionSubjectsA total of 188 patients were randomized.DesignHospitalized subjects were randomly assigned to receive either ultrafiltration therapy or pharmacologic therapy. Ultrafiltration was performed at a fluid-removal rate of 200 ml per hour and loop diuretics were discontinued for the duration of the intervention. The addition of intravenous vasodilators or positive inotropic agents was prohibited, unless they were deemed to be necessary as rescue therapy. Patients assigned to stepped pharmacologic therapy received intravenous diuretics to manage congestion and maintain a urine output of 3 to 5 liters per day. In both groups, the assigned treatment strategy was to be continued until the signs and symptoms of congestion in the patient were reduced as much as possible. The primary end points were baseline changes in serum creatinine and weight as assessed at 96 hours after treatment assignment.ConclusionsThe use of a stepped pharmacologic-therapy algorithm was superior to a strategy of ultrafiltration for the preservation of renal function, and there was a similar amount of weight loss between the two groups. Ultrafiltration was associated with a higher rate of adverse events (Bart, et al., 2012, PMID: 23131078).

  Study phs003510

• Copy number profiling of primary samples and cell lines of retinoblastoma

  While RB1 loss initiates retinoblastoma development, additional somatic copy number alterations (SCNAs) can drive tumor progression. Although SCNAs have been identified with good concordance between studies at a cytoband resolution, accurate identification of single genes for all recurrent SCNAs is still challenging. This study presents a comprehensive meta-analysis of genome-wide SCNAs integrated with gene expression profiling data, narrowing down the list of plausible retinoblastoma driver genes. We performed SCNA profiling of 45 primary retinoblastoma samples and 8 retinoblastoma cell lines by high-resolution microarrays. We combined our data with genomic, clinical and histopathological data of ten published genome-wide SCNA studies, which strongly enhanced the power of our analyses (N=310). Comprehensive recurrence analysis of SCNAs in all studies integrated with gene expression data allowed us to reduce candidate gene lists for 1q, 2p, 6p, 7q and 13q to a limited gene set. Besides the well-established driver genes RB1 (13q-loss) and MYCN (2p-gain) we identified CRB1 and NEK7 (1q-gain), SOX4 (6p-gain) and NUP205 (7q-gain) as novel retinoblastoma driver candidates. Depending on the sample subset and algorithms used, alternative candidates were identified including MIR181 (1q-gain) and DEK (6p gain). Remarkably, our study showed that copy number gains rarely exceeded change of one copy, even in pure tumor samples with 100% homozygosity at the RB1 locus (N=34), which is indicative for intra-tumor heterogeneity. In addition, profound between-tumor variability was observed that was associated with age at diagnosis and differentiation grades. Since focal alterations at commonly altered chromosome regions were rare except for 2p24.3 (MYCN), further functional validation of the oncogenic potential of the described candidate genes is now required. For further investigations, our study provides a refined and revised set of candidate retinoblastoma driver genes.

  Study EGAS00001001715

• ctDNA quantification in Ewing sarcoma patients

  Background. Treatment stratification and response assessment in pediatric sarcomas has relied on imaging studies and surgical/histopathological evidence of vital tumor cells. Such studies and evidence collection processes often involve radiation and/or general anesthesia in children. Cell-free circulating tumor DNA (ctDNA) detection in blood plasma is one available method of so-called liquid biopsies that has been shown to correlate qualitatively and quantitatively to some extent with the existence of vital tumor cells in the body. Our clinical observational study focused on the utility and feasibility of ctDNA detection in pediatric Ewing sarcoma (EWS) as a marker of minimal residual disease (MRD). Patients and Methods. We performed whole genome sequencing to identify the exact breakpoints in tumors known to carry the EWS-FLI1 fusion gene. Patient-specific fusion breakpoints were tracked in peripheral blood plasma using digital droplet PCR (ddPCR) before, during, and after therapy in six consecutive children and young adults with EWS. Presence and levels of fusion breakpoints were correlated with clinical disease courses. The genetic changes in the tumors were defined, and inter- and intra-individual changes in the tumor DNA copy loads were observed over time. Results. We show that the detection of ctDNA in the peripheral blood of EWS patients i) is feasible in the clinical routine and ii) allows for the longitudinal real-time monitoring of MRD activity in children and young adults. Although changing ctDNA levels correlated well with clinical outcome within patients, between patients, a high variability was observed (inter-individually) for the six patients described here. Conclusions. ctDNA detection by ddPCR is a highly sensitive, specific, feasible, and highly accurate method that can be applied in EWS for follow-up assessments as an additional surrogate parameter for clinical MRD monitoring and, potentially, also for treatment stratification in the near future.

  Study EGAS00001006433

• Genome-wide DNA methylation sequencing identifies epigenetic perturbations in the upper airways under long-term exposure to moderate levels of ambient air pollution

  While the link between exposure to high levels of ambient particulate matter (PM) and increased incidences of respiratory and cardiovascular diseases is widely recognized, recent epidemiological studies have shown that low PM concentrations are equally associated with adverse health effects. As DNA methylation is one of the main mechanisms by which cells regulate and stabilize gene expression, changes in the methylome could constitute early indicators of dysregulated signaling pathways. So far, little is known about PM-associated DNA methylation changes in the upper airways, the first point of contact between airborne pollutants and the human body. Here, we focused on cells of the upper respiratory tract and assessed their genome-wide DNA methylation pattern to explore exposure-associated early regulatory changes. Using a mobile epidemiological laboratory, nasal lavage samples were collected from a cohort of 60 adults that lived in districts with records of low (Simmerath) or moderate (Stuttgart) PM10 levels in Germany. PM10 concentrations were verified by particle measurements on the days of the sample collection and genome-wide DNA methylation was determined by enzymatic methyl sequencing at single-base resolution. We identified 231 differentially methylated regions (DMRs) between moderately and lowly PM10 exposed individuals. A high proportion of DMRs overlapped with regulatory elements, and DMR target genes were involved in pathways regulating cellular redox homeostasis and immune response. In addition, we found distinct changes in DNA methylation of the HOXA gene cluster whose methylation levels have previously been linked to air pollution exposure but also to carcinogenesis in several instances. The findings of this study suggest that regulatory changes in upper airway cells occur at PM10 levels below current European thresholds, some of which may be involved in the development of air pollution-related diseases.

  Study EGAS00001007374

• 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

• CSER: Clinical Implementation of Carrier Testing Using Next Generation Sequencing (NextGen)

  We will investigate the clinical implementation of genome sequencing (GS) for carrier screening to aid reproductive decision-making in adults. The study is organized into three inter-related Projects. The study population will include women and their partners who receive pre-conception testing. In Project 1, we will conduct a Randomized Clinical Trial to test clinical implementation of WGS compared with usual care. We will integrate WGS results with the electronic medical record (EMR), and measure outcomes from patient and physician perspectives. In Project 2, we will perform whole genome sequencing, including validation and interpretation of the identified variants to identify &quot;actionable variants&quot;, meaning variants deemed worthy of reporting. This will include a robust approach using a Return of Results Committee (RORC) for determination of reportable results. In Project 3, we will evaluate the ethical and psychosocial implications of expanded carrier screening using WGS for the return of carrier status and secondary findings, and measure the impact on downstream healthcare utilization and cost. Two groups of women are identified through the electronic medical record. The first group has a clinical carrier screening test, usually Cystic Fibrosis (CF), ordered and completed as part of a preconception planning visit. The second group has a carrier screening test, usually CF, performed during pregnancy, they have delivered their baby and are planning a future pregnancy. Eligible women are randomized to either usual care or genome sequencing (GS). Women randomized into the usual care arm have already received their clinical genetic test. These women will not receive genome sequencing. Women in the GS arm will receive their carrier status results for about 700 gene/condition pairs approximately three months after randomization. If there are results to return, women come in for a genetic counseling session. If there are no results, a letter is sent in the mail. Incidental findings (IF) are returned about one month later. As with the carrier status results, all IF are returned in person, and if there are no results a letter is sent in the mail. If a participant is found to be a carrier of an autosomal recessive condition, her male partner is then eligible and invited to participate in the study and receive GS.

  Study phs000927

• EOSC4Cancer Longitudinal Synthetic Colorectal Cancer Genomic data developed at BSC

  The synthetic genomes have been created trying to mimic real cancer data of 4 patients (Named 185,186,187 and 188). Mutations are based on real CRC patients from the PCAWG dataset. For each patient, two tumor samples at different time points and one healthy sample have been simulated. The cancer intra-tumor heterogeneity and evolution in the patients is depicted by simulating reads from tumor subclones separately and then mixing them according to their clonal proportions in each sample. For rapid use and transfer only selected chromosomes have been generated for each patient. Chromosomes per patient: -185: chr4, chr5, chr7, chr17 -186: chr1, chr7, chr12, chr17 -187: chr1, chr2, chr5, chr12, chr17 -188: chr2, chr5, chr12, chr13, chr17 Worflows used to create BAM/BAI, VCF and MAF files from FASTQ (Alignment with GRCh38): - https://usegalaxy.eu/published/workflow?id=2c3d05023c02113e - https://usegalaxy.eu/published/workflow?id=1da86d74f8535f4e

  Dataset EGAD50000000276

• SNF_OLINK_20

  Matrix of normalized values from Olink assay performed on baseline BM Plasma. The Olink Immuno-Oncology multiplex proteomic Panel included 92 proteins associated with human inflammatory conditions. Data is analyzed using real-time PCR analysis software via the Ct method and Normalized Protein Expression (NPX) manager. Data were normalized using internal controls in every single sample, inter-plate controls, negative controls and a correction factor and expressed as Log2 scale, which was proportional to the protein concentration. One NPX difference equals the doubling of the protein concentration.

  Dataset EGAD00001011147

• WES data for Early evolutionary branching across spatial domains predisposes to clonal replacement under chemotherapy in neuroblastoma

  Neuroblastoma (NB) is one of the most lethal childhood cancers due its propensity to treatment resistance. By spatial mapping of subclone geographies before and after chemotherapy across 89 tumor regions from 12 NBs, we find that densely packed territories of closely related subclones present at diagnosis are replaced under effective treatment by islands of distantly related survivor subclones, originating from a different most recent ancestor compared to lineages dominating before treatment. Conversely, in tumors that progressed under treatment, ancestors of subclones dominating later in disease are present already at diagnosis. Chemotherapy treated xenografts and cell culture models replicates these two contrasting scenarios and shows branching evolution to be a constant feature of proliferating NB cells. Phylogenies based on whole genome sequencing of 505 individual NB cells indicate that a rich repertoire of parallel subclones, emerges already with the first oncogenic mutations and lays the foundation for clonal replacement under treatment.

  Dataset EGAD00001015413

• scDNA-seq for 'Early evolutionary branching across spatial domains predisposes to clonal replacement under chemotherapy in neuroblastoma'

  Neuroblastoma (NB) is one of the most lethal childhood cancers due its propensity to treatment resistance. By spatial mapping of subclone geographies before and after chemotherapy across 89 tumor regions from 12 NBs, we find that densely packed territories of closely related subclones present at diagnosis are replaced under effective treatment by islands of distantly related survivor subclones, originating from a different most recent ancestor compared to lineages dominating before treatment. Conversely, in tumors that progressed under treatment, ancestors of subclones dominating later in disease are present already at diagnosis. Chemotherapy treated xenografts and cell culture models replicates these two contrasting scenarios and shows branching evolution to be a constant feature of proliferating NB cells. Phylogenies based on whole genome sequencing of 505 individual NB cells indicate that a rich repertoire of parallel subclones, emerges already with the first oncogenic mutations and lays the foundation for clonal replacement under treatment.

  Dataset EGAD00001015414

• NHLBI GO-ESP: Heart Cohorts Exome Sequencing Project (Siblings with Ischemic Stroke Study, SWISS)

  The NHLBI "Grand Opportunity" Exome Sequencing Project (GO-ESP), a signature project of the NHLBI Recovery Act investment, was designed to identify genetic variants in coding regions (exons) of the human genome (the "exome") that are associated with heart, lung and blood diseases. These and related diseases that are of high impact to public health and individuals from diverse racial and ethnic groups will be studied. These data may help researchers understand the causes of disease, contributing to better ways to prevent, diagnose, and treat diseases, as well as determine whether to tailor prevention and treatments to specific populations. This could lead to more effective treatments and reduce the likelihood of side effects. GO-ESP is comprised of five collaborative components: 3 cohort consortia - HeartGO, LungGO, and WHISP - and 2 sequencing centers - BroadGO and SeattleGO. The Siblings with Ischemic Stroke Study (SWISS) is an affected sibpair (ASP) study of patients and their brothers/sisters who have had similar strokes. The study was conducted to determine the genes that contribute to an individual&#39;s risk of developing an ischemic stroke (a stroke due to sudden interruption of blood flow to a part of the brain). Over 600 people have participated in SWISS, with the coordination of the recruitment and flow of the samples occurring at the Mayo Clinic, Jacksonville, FL, under the direction of James F. Meschia, MD. The University of Virginia (Stephen S. Rich, PhD) served as the analytic site for the genetic data. Participants were excluded from SWISS if they had stroke following an invasive cerebrovascular or cardiovascular procedure, if their stroke occurred shortly following an aneurismal subarachnoid hemorrhage, if the stroke occurred in the setting of a mechanical aortic or mitral valve, if the patient had definite CNS vasculitis or if the patient had a Mendelian mitochondrial stroke disorder, including CADASIL, MELAS, Fabry disease, sickle cell anemia or homocysteinemia. For the NHLBI ESP, the youngest 51 ASPs (102 individuals) from SWISS were selected to undergo exome sequencing. These ASPs were selected from among all SWISS participants, excluding those pedigrees with individuals who had TOAST subtypes of stroke of other etiology or of stroke with undetermined etiology. In addition, the affected siblings were required to have the same TOAST subtype (both small vessel (lacunar) or both large vessel (atherosclerotic)). Of those selected, 98 samples passed initial quality control metrics, and 94 completed exome sequencing and were deposited in dbGaP. An additional set of 590 samples from the SWISS collection has been genotyped on the ExomeChip that contained a subset of rare/infrequent variants identified by exome sequencing. These samples include 495 affected individuals (from multiple affected family members) as well as 95 unaffected family members. These genetic and phenotypic data also will reside in dbGaP.

  Study phs000327

• NIDDK IBD Genetics Consortium Crohn's Disease Genome-Wide Association Study

  This dataset contains data from a genome-wide association study performed with 968 Inflammatory Bowel Disease (IBD) affected cases and 995 unrelated controls using the Illumina HumanHap300 Genotyping BeadChip. Cases were selected to have Crohn&#39;s disease with ileal involvement, and controls were matched to cases based on sex and year of birth. Subjects were drawn from two cohorts: (1) persons with non-Jewish, European ancestry (561 cases and 563 controls), and (2) persons with Jewish ancestry (407 cases and 432 controls). Genotyping was performed at the Feinstein Institute for Medical Research. Seven-hundred fifty-four of the samples (468 cases and 286 controls) were taken from the NIDDK IBD Genetics Consortium cell line repository. These samples are identified in the IBD_Sample file. The subject IDs for these individuals may be used to request corresponding samples for follow-up research through the repository. In addition, complete phenotype data for these individuals are available, together with the Consortium&#39;s phenotyping manual and the forms used to collect the data. The remaining 1,209 samples were obtained from pre-existing collections ascertained through Cedars-Sinai Medical Center, Johns Hopkins University, University of Chicago, University of Montreal, University of Pittsburgh, University of Toronto, and the New York Health project (controls only). For these samples, only sex, cohort (Jewish vs. non-Jewish), and age at diagnosis (cases only) are available. Two-hundred three individuals from among the pre-existing samples did not provide consent to release their genotype data (designated as consent group 2 in the file IBD_Subject). Thus, individual genotype data are only provided for 1,760 samples. To compensate for this, we have provided summary results for each SNP. These are based on a stratified analysis testing case/control association. Fifty-one samples had a call rate less than 93% and were therefore excluded from this analysis, leaving an overall sample size of 1,963 - 51 = 1,912. X Chromosome Heterozygosity Nine samples have X chromosome heterozygosity that is neither consistent nor inconsistent with their phenotypic sex. One of these samples was found to have Turner Syndrome. The remaining 8 samples have heterozygosity ranging from 35-76%.

  Study phs000130

• Population Architecture Using Genomics and Epidemiology (PAGE): Epidemiologic Architecture for Genes Linked to Environment (EAGLE) - BioVU Cancer Project

  As part of Population Architecture using Genomics and Epidemiology PAGE study (Phase I), the Epidemiologic Architecture using Genomics and Epidemiology (EAGLE I) project accessed both epidemiologic- and clinic-based collections. The epidemiologic-based collection of EAGLE I included the National Health and Nutritional Examination Surveys (NHANES), ascertained between 1991-1994 (NHANES III), 1999-2002, and 2007-2008. NHANES is a population-based cross-sectional survey now conducted every year in the United States to assess the health status of Americans at the time of ascertainment and to assess trends over the years of survey. Genetic NHANES consists of 19,613 DNA samples linked to thousands of variables including demographics, health and lifestyle variables, physical examination variables, laboratory variables, and exposures. NHANES is diverse with almost one-half of the samples (46.4%) coming from self-reported Mexican Americans and non-Hispanic blacks. In contrast to NHANES, BioVU is a clinic-based collection of &#62;150,000 DNA samples from Vanderbilt University Medical Center linked to de-identified electronic medical records (EMRs). Approximately 12% of BioVU&#39;s overall DNA sample collection is from African American, Hispanic, and Asian patients. The overall goals of PAGE I and EAGLE I were broad and several-fold: Replicate genome-wide association study (GWAS)- identified variants in European Americans; Identify population-specific and trans-population genotype-phenotype associations; Identify genetic and environmental modifiers of these associations. NHANES is an excellent resource for the study of quantitative traits associated with common human diseases. However, given that the age range of NHANES spans childhood to late adulthood and not all diseases are surveyed, NHANES is less useful for the study of adult-onset diseases such as major cancers. Therefore, under American Recovery and Reinvestment Act (ARRA) funding, EAGLE as part of PAGE I defined eight major cancers sites for genetic analysis in BioVU, Vanderbilt&#39;s biorepository linked to de-identified EMRs. The eight major cancers defined for this study included melanoma, breast, ovarian, prostate, colorectal, lung, endometrial, and Non-Hodgkin&#39;s lymphoma (NHL). Cancer cases were defined using a combination of ICD-9 codes and tumor registry entries. Controls include BioVU participants without cancer and encompassing the age and gender distributions of cancer cases. Targeted genotyping of GWAS-identified variants for these diseases (124 SNPs) and ancestry informative markers (128 AIMs) was performed by the Center for Human Genetics Research Vanderbilt DNA Resources Core. After quality control, a total of 116 cancer-associated SNPs and 122 AIMs were available for downstream analyses.

  Study phs000559

• IYDP Indonesian Y chromosome Diversity Project

  Island Southeast Asia and Oceania host one of the world’s richest assemblages of human phenotypic, linguistic and cultural diversity. We compiled ~9.7 Mb of Y chromosome sequence from a diverse sample of over 380 men from this region, including 126 from Indonesian islands, first reported here. The granularity of this dataset allows us to fully resolve and date the regional Y chromosome phylogeny. This improved Y chromosome topology highlights localized events with important historical implications, including pre-Holocene contact between Mainland and Island Southeast Asia, ongoing interactions between Australia and the Papuan world, and a sustained period of diversification following the flooding of the ancient Sunda and Sahul continents as the insular landscape of today formed. The high-resolution phylogeny of the Y chromosome presented here thus enables a detailed exploration of past isolation, interaction and change in one of the world’s least understood regions.

  Study EGAS00001006028

• Heterogeneity and evolution of DNA mutation rates in microsatellite-stable colorectal cancer

  A mutation accumulation experiment in colorectal cancer (CRC) derived tumoroids, aimed at measuring the spontaneous rates of accumulation of new single-nucleotide sequence variations in seven microsatellite stable CRCs and one microsatellite unstable CRC. Mutations accumulated over six+six months were identified via whole genome sequencing of clonal tumoroids.

  Study EGAS50000000284

• mRNA-seq data from ALL patients with NUP214::ABL1 disease

  Dataset contains mRNA-sequencing data (fastq files) from 2 patients with NUP214::ABL1 disease (one T-ALL and one B-ALL patient). Patient samples (peripheral blood or bone marrow) were taken at diagnosis (B-ALL) or relapse (T-ALL). mRNA was extracted from lymphoblasts and underwent paired-end sequencing (75bp) using the Illumina NextSeq platform. Transcriptomic data was used for identification of gene fusions, SNVs and InDels. mRNA-seq data for an additional B-ALL patient with NUP214::ABL1 disease utilised in this study has already been published (EGAS00001006460).

  Dataset EGAD50000001395

• RNA-seq of granulosa cells from 8 IVF patients in two age groups

  This dataset contains raw sequencing data of mRNA from granulosa cell samples from 8 human patients undergoing in vitro fertilization. All patients were normal responders referred for male infertility and were grouped into two age groups (younger than 31 or older than 38 years old). Each sample contains all granulosa cells collected from one patient on one collection date. Raw sequencing fastq files are available in this dataset, count tables are available in ArrayExpress (E-MTAB-13496).

  Dataset EGAD50000001210

• Pediatric B-cell precursor acute lymphoblastic leukemia samples with very early relapse

  In this study we performed a genomic analysis of diagnosis-relapse pairs of 12 children who relapsed very early, followed by a deep-sequencing validation of all identified mutations. In addition, we included one case with a good initial treatment response and on-treatment relapse at the end of upfront therapy.

  Study EGAS00001005615

• Evaluation of capture and amplicon-based targeted sequencing methods on formalin-fixed tumours

  Evaluated performance of three capture (Agilent’s SureSelectXT HS, Illumina’s Nextera Rapid Capture Custom, and New England Biolabs’ Next Direct Custom) and one amplicon-based (Qiagen’s Human Breast Cancer Panel) targeted sequencing methods on paired blood and FFPE, using whole exome sequencing data from matched fresh-frozen tissue as a comparison.

  Study EGAS00001004965

• TK-EPN862 - Patient-derived xenograft model of Posterior Fossa A Ependymoma - RNAseq

  In Vivo Loss of Tumorigenicity in a Patient-Derived Orthotopic Xenograft Mouse Model of Ependymoma. Whitehouse et al. 2023 Frontiers in Oncology. We describe the establishment of a patient-derived orthotopic xenograft (PDOX) model of posterior fossa A (PFA) EPN, derived from a metastatic cranial lesion. Patient and PDOX tumors were analyzed using RNA sequencing. RNAseq data (paired end) provided here correspond to Primary tumour, two metastatic lesions (one spinal, one cranial), and a patient-derived xenograft derived from the patient.

  Dataset EGAD00001009870

• Concatenated long-read single-cell RNA sequencing of momentum biopsies of ovarian cancer patients

  Concatenated long-read single-cell RNA sequencing samples prepared using 10X and the HIT-scIsoSeq protocol. The sequencing was performed on Sequel II Pacbio machine. 3 ovarian cancer patients, 5 omentum biopsies samples: 3 metastasis samples (one per patient), 2 healthy samples (one per patient except Patient2). 4 bam files per metastasis sample, 2 bam files per healthy sample.

  Dataset EGAD00001009814

• MPNST study - Spatial data

  10X Visium spatial transcriptomic data of one MPNST patient

  Dataset EGAD50000002564

• Genetic mechanisms of resistance to chemotherapy in breast cancer

  Resistance towards chemotherapy is one of the main causes of treatment failure and deathamong breast cancer patients.The main objective of this project is toidentify genetic mechanisms causing some breast cancer patients not torespond to a particluar type of chemotherapy (epirubicin) while otherpatients respond very well to the same treatment. In the project wewill perform genome / exome sequencing of a selection of breast cancerpatients (n=30). These patients are drawn from a cohort where allpatients have recieved treatment with epirubicin monotherapy before surgical removal of alocally advanced breast tumour, and where all patients have beensubjected to objective evaluation of the response to thetherapy. Subsequent to sequencing, we will analyse the data andcompare with the clinical data for each patient (object response totherapy). The main aim being to identify mutations that are associatedwith resistance to epirubicin. Identification of mutations with strongpredictive value, may have a direct impact on cancer treatment sinceit opens the possibility for genetic testing of a tumour, and desicionon which drug is likely to work best, prior to treatment start.

  Dataset EGAD00001000264