Search Results - EGA European Genome-Phenome Archive

Identification of improved IL28B SNPs and haplotypes for prediction of drug response in treatment of hepatitis C using massively parallel sequencing in a cross-sectional European cohort

The hepatitis C virus infects nearly 3% of the World’s population, causing severe liver disease in many. Standard of care therapy is currently pegylated interferon alpha and ribavirin (PegIFN/R), which is effective in less than half of those infected with the most common viral genotype. Two IL28B SNPs, rs8099917 and rs12979860, predict response to (PegIFN/R) therapy in treatment of hepatitis C virus infection. These SNPs were identified in genome wide analyses using Illumina genotyping chips. In people of European ancestry, there are 6 common (>1%) haplotypes for IL28B, one tagged by rs8099917 minor allele, four tagged by rs12979860. We used massive parallel sequencing of the IL28B and IL28A gene regions generated by PCR from pooled DNA samples from 100 responders and 99 non-responders to therapy, to identify common variants. Variants that had high odds ratios and were validated were then genotyped in a cohort of 905 responders and non-responders. Their predictive power was assessed, alone and in combination with HLA-C. Only SNPs in the IL28B linkage disequilibrium block predicted drug response. Eighteen SNPs were identified with evidence for association with drug response, and with a high degree of confidence in the sequence call. We found that two SNPs, rs4803221 (homozygote minor allele positive predictive value (PPV) of 77%) and rs7248668 (PPV 78%), predicted failure to respond better than the current best, rs8099917 (PPV 73%) and rs12979860 (PPV 68%) in this cross-sectional cohort. The best SNPs tagged a single common haplotype, haplotype 2. Genotypes predicted lack of response better than alleles. However, combination of IL28B haplotype 2 carrier status with the HLA-C C2C2 genotype, which has previousl y been reported to improve prediction in combination with IL28B, provides the highest PPV (80%). The haplotypes present alternative putative transcription factor binding and methylation sites. Massive parallel sequencing allowed identification and comparison of the best common SNPs for identifying treatment failure in therapy for HCV SNPs tagging a single haplotype have the highest PPV, especially in combination with HLA-C. The functional basis for the association may be due to altered regulation of the gene. These approaches have utility in improving diagnostic testing and identifying causal haplotypes or SNPs.

Study EGAS00001000096

Dental Caries: Whole Genome Association and Gene x Environment Studies

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. Although overall caries prevalence has declined over the last 40 years, dental caries in the primary dentition and mean caries rates in children ages 2-11 has increased markedly over the past 12 years. Childhood caries is a serious public health issue because of associated health problems and because disparities in oral health have led to substantially higher average disease prevalence among children in poverty and in under-served racial and ethnic groups. These issues are of such concern that in 2005, the American Academy of Pediatrics made children's oral health one of their top areas of focus, as it is for the majority of the NIDCR "Disparities Centers". 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%. Despite the strong evidence of a genetic component to risk for dental caries, there have been only a few studies of candidate genes in caries, and no published genome-wide scans. A comprehensive genome wide search is the only approach that will allow us to identify those genetic regions likely to harbor genes increasing the risk for dental caries, and eventually to identify the etiologic genes and to explore the interaction of those genes with microbiological, dietary, fluoride, and behavioral factors that are known to be associated with caries risk and progression. Therefore, the goal of this study is to perform genome-wide association (GWA) studies of dental caries with a large panel of SNP's (610,000) in families and individuals ascertained through multiple US sites (University of Pittsburgh and University of Iowa). The v2 release of this study includes 96 additional individuals who were genotyped with the CCDG: Dental Caries and CL/P in Guatemala project (dbGaP accession number phs000440) to augment the data initially presented here. These subjects were genotyped on the Illumina 610 platform to make their data comparable. 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 through large-scale genome-wide association studies of well-characterized families and individuals at multiple sites in the U.S. Genotyping was performed at the Johns Hopkins University Center for Inherited Disease Research (CIDR). 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. Samples of some participants in this study were selected for further genomic analysis. These results will be available in the dbGaP CIDR study: Center for Oral Health Research in Appalachia (COHRA) Genomic Studies of Oral Health and Disease (phs001591).

Study phs000095

Center for Common Disease Genomics [CCDG] - Inflammatory Bowel Disease (IBD) - Global Microbiome Conservancy Host Exomes

The human body is a complex ecosystem, hosting tens of billions of bacteria, primarily in the gut. This community, known as the 'gut microbiome', is vital to human health, affecting functions ranging from metabolism, immunity, and development, to behavior. Conversely, reduced microbiome diversity and other imbalances of the gut community (dysbiosis) are associated with many diseases of the industrialized world, such as metabolic syndrome, asthma, and inflammatory bowel diseases among others. Microbiome-based interventions thus hold the potential to revolutionize our understanding of, and therapeutic approaches to, many aspects of human health and disease. Despite many advances, current understanding of the human microbiome is largely limited to majority ethnic groups in industrialized nations, and does not reflect the full diversity of human commensal microbiota. In addition to the scientific ramifications of minority underrepresentation, there are significant translational and ethical implications as well: First, failing to capture the full diversity of healthy human microbiota may limit our ability to understand and generate effective therapeutics for many microbiome-associated diseases. Second, underrepresented groups are less likely to benefit from microbiome-based medical advances tailored to well-studied populations, propagating healthcare inequities. The window of opportunity to characterize human microbiome diversity is closing rapidly. Human gut microbial diversity is diminishing around the globe, due to the spread of Westernized diets and lifestyles, which are associated with reduced microbiome biodiversity. The Global Microbiome Conservancy (GMbC) is a non-profit collaboration between scientists and communities around the world that aims to 1) preserve the full biodiversity of the human gut microbiome, 2) characterize its evolution across populations and host lifestyles and 3) investigate its interactions with the human host. We are collecting gut microbiome and human DNA samples from diverse populations around the world, from isolated groups to urbanized populations, to culture and study this essential biodiversity and to preserve it in a living "seed bank" as a long-term safeguard against future extinctions. The National Human Genome Research Institute (NHGRI) has funded a collaborative large-scale genome sequencing effort to comprehensively identify rare risk and protective variants contributing to multiple common disease phenotypes. Called the Centers for Common Disease Genomics (CCDG), this initiative will explore a range of diseases with the ultimate goal of: Undertaking variant discovery for enough different examples of disease architectures and study designs to better understand the general principles of the genomic architecture underlying common, complex inherited diseases. Understanding how best to design rare variant studies for common disease. Developing resources, informatics tools, and innovative approaches and technologies for multiple disease research communities and the wider biomedical research community. The initial focus of the CCDGs will be in cardiovascular disease (early-onset coronary artery disease, atrial fibrillation, hemorrhagic stroke), neuropsychiatric disease (epilepsy, autism), and autoimmune/inflammatory disease (type 1 diabetes, inflammatory bowel disease). The Broad Institute is one of four selected CCDG project centers. Both exome and genotyping information of GMbC participants will help understanding how recruited human populations are structured and genetically related. This human genetic structure will be analyzed in light of gut microbiome compositional and genomic differences to illuminate associations between specific microbiome traits and human groups and the evolutionary processes involved in the co-evolution of human hosts and their gut microbiome. It will also provide variant information for critical genes shaping the composition of microbiomes, such as genes involved in innate and adaptive immunity (e.g. antimicrobial peptides) or in the regulation of the immune response (e.g. Nod2). Association studies between specific variants in these genes and microbiome features will be performed, which will then be validated with in vitro and in vivo experiments. Human data from GMbC cohorts will also increase the genetic diversity of human populations, providing additional variant information for many susceptible loci that were found to be associated with Crohn's disease and ulcerative colitis. The whole genome sequencing data generated is comprised of samples from diverse and indigenous populations. Raw sequencing data, metadata, vcf and phenotype data at the individual level for a subset of the cohort's samples are available at https://anvilproject.org/data. For questions about availability, contact help@lists.anvilproject.org. Microbiome data for the samples sequenced in this study can be found in phs002235 Global Microbiome Conservancy Sequence Data.

Study phs002205

Submitting array based metadata

Submitting array based metadata For further information please check our Submission FAQs, submission quickguide as well as submission terms! The submission metadata required for Array-based submission must be submitted using EGA programmatic submission and by completing the Array-based format (AF) template. The guidelines for this workflow are described on this page. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA Array-based-Format (AF) template. You can request a legacy EGA submission account (ega-box-XXXX) by populating this form. Please, allow two business days for our Helpdesk team to contact you after populating this form. Please notice that all files should be encrypted and uploaded prior to the processing of your EGA-Array-based-Format (AF) template. Metadata Model Registering Metadata Use the EGA programmatic submission to register your Study, Samples, Data Access Committee (DAC) and Policy. This online interface enables you to create new and edit existing submissions. Do not use the Submitter Portal to register the metadata objects for array submissions. Please, go to the EGA programmatic submission. Registering Study Go to the EGA programmatic submission Submit an study XML Take a note of your study accession number (EGASXXXXXXXXXXX) To use the study accession number in a publication, we suggest the following format: "Sequence data has been deposited at the European Genome-phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGASXXXXXXXXXXX. Further information about EGA can be found on https://ega-archive.org "The European Genome-phenome Archive of human data consented for biomedical research"(https://doi.org/10.1093/nar/gkab1059 ). Registering Samples Go to the EGA programmatic submission Submit a sample XML Save the list of accession numbers. These will be needed for the AF template. Registering Data Access Committee Further information on the role of your DAC. Go to the EGA programmatic submission Submit a DAC XML Take a note of the DAC accession number (EGACXXXXXXXXXXX) Registering Policy Your Data Access Policy provides the terms and conditions of data use. This is also referred to as the Data Access Agreement (DAA). Completion of a DAA by the applicant/s should form part of the application process to the Data Access Committee (DAA) Go to the EGA programmatic submission Submit a Policy XML Take a note of the Policy accession number (EGAPXXXXXXXXXXX) Complete the Array-based format (AF) spreadsheet Once you have completed the registration of your Study, DAC and Policy using the programmatix submission, you must then complete and return the AF spreadsheet The AF spreadsheet consists on four components: Do not use EGA IDs registered using the Submitter Portal. You can easily identify objects registered in the Submitter Portal by their EGA ID pattern: EGA[A-Z]5{10 more digits} (e.g. for a sample EGAN50000002506). For populating an AF spreadsheet, please exclusively use EGA IDs following this specific pattern: EGA[A-Z]0{10 more digits} (e.g. for a sample EGAN00001691542). Tab1) Webin accessions : Provide the accession numbers for your study, DAC and policy. Please, also add your ega-box number. Tab2) Sample & phenotypes: Please, leave this tab blank. Tab3) Dataset: Describe the dataset to be created Tab4) Data files: Define how your data is going to be organised into datasets and packets for distribution (linkage between samples and files). Should further assistance be required after going through the guide below; please do not hesitate to contact the EGA helpdesk Once the AF spreadsheet is populated, please send it to our EGA helpdesk for further validation. AF spreadsheet Should your submission require multiple DAC's or policies, use ' ; ' to separate the accession numbers. Accessions AF spreadsheet: Samples & phenotypes Samples and phenotypes AF spreadsheet: Datasets We suggest that each dataset consists of a common set of data. The example below consists of two datasets, grouped according to shared data type, technology and by case/control. We also like to capture the number of unique samples that make up the dataset and the Data Access Committee (DAC) responsible for providing the named dataset and their policy (EGAP). Datasets AF spreadsheet: Data files What follows is an example of how to map your samples to the array based files added to your upload account (4th tab). Data Files Please, find below some practical examples on how to register the linkage between samples-files Case 1) 1 sample or list of samples in different datasets: Data Files In case you have a list of samples that belong to different datasets, please, repeat the samples accession number/s in the first column and link the sample to the corresponding dataset each time (each row). Each row is one linkage between sample-file-dataset. Case 2) 1 sample links to several files: Data Files In order to add multiple files to one sample you MUST use “ ; “ between filenames. Example: file1.gpg;file2.gpg;file3.gpg In case that you want to add an extra file to the sample (phenotype or .Rdata), please use “Additional files” column. Important note: You MUST upload the encrypted and unencrypted md5sum values of all files uploaded to your submission account using the filename nomenclature (file.gpg, file.md5,file.md5.gpg). Your submission will not be processed without md5values supplied for all files in the CORRECT format. What happens after the submission of a dataset? All datasets affiliated to unreleased studies are automatically placed on hold until the authorised submitted or DAC contact instructs our EGA helpdesk for the study to be released. Finally, your data is archived within our databases and prepared for encrypted distribution upon the request of permitted EGA account holders. We strongly advise you NOT to delete your data until we confirm that your data has been successfully archived.

Documentation submission/metadata/submission/array

The Primary Open-Angle Glaucoma Genes and Environment (GLAUGEN) Study

Background Information: Primary open-angle glaucoma (POAG) is an age-related, intraocular pressure (IOP)-dependent progressive optic neuropathy that ultimately leads to irreversible blindness. Vision loss from POAG is a condition of public health significance. Current evidence suggests that POAG is a polygenetic disease modified by environmental influences. Despite the fact that a positive family history of disease is an important risk factor for POAG, conventional linkage and candidate gene approaches have revealed less than 5% of the genetic component of the disease. Furthermore, there is no consensus on environment risk factors for POAG. Elevated IOP is the only modifiable risk factor for POAG; yet, lowering IOP slows, but does not halt the disease process. Study Objectives: The overall goal of our research is to elucidate the pathogenesis of POAG so that cost-effective disease detection and primary prevention strategies can be implemented. The primary aim of the Glaucoma Gene Environment Initiative (GLAUGEN), funded by the Human Genome Research Institute (NHGRI), is to discover genetic loci associated with POAG. The secondary aim of GLAUGEN, funded by NHGRI and the National Eye Institute, is to discover gene environment interactions in POAG. Methods and study populations: For this study, we have assembled cases and controls from three studies: the Nurses' Health Study (NHS), the Health Professionals Follow-up Study (HPFS) and the Genetic Etiologies of POAG (GEP) project based at Massachusetts Eye and Ear Infirmary (MEEI). This case-control group includes 1057 unrelated cases and 1272 controls. Members of the NHS and HPFS also have repeated environmental exposure data collected prior to a diagnosis of POAG. NHS - The Nurses Health Study started in 1976 under the direction of Dr. Frank E. Speizer. With funding from the NIH, registered nurses from 11 US states were invited to complete a detailed questionnaire regarding lifestyle and health biennially. Initially, 121,000 women responded to the baseline questionnaire. Currently, Dr. Susan Hankinson serves as the program director for the NHS. HPFS - The Health Professionals Follow-up Study began in 1986 under the direction of Drs. Walter Willett and Meir Stampfer. Under the auspices of the NIH, they enlisted 51,529 male health professionals from throughout the US to complete similarly designed biennial questionnaires. Beginning in 1990, questions regarding ocular health were added to biennial questionaires completed by health professionals participating in the NHS and HPFS. This allowed us to formulate (PI: S. Hankinson; NEI) and maintain (PI: L. Pasquale; NEI) a cohort at risk for POAG derived from the respective general cohorts who were under ophthalmic care. We then developed a definition of POAG that allowed us to identify cases from a population that was geographically dispersed. The centerpiece of this definition is the presence of reproducible visual field loss consistent with nerve fiber layer (NFL) dropout (the NFL contains the axons that comprise the optic nerve) on reliable tests. Reproducible visual field loss occurred in the context of anterior segment findings that did not suggest a secondary cause of elevated IOP and posterior segment findings that did not suggest a secondary cause of visual field loss. We selected controls from the cohort at risk for POAG on the basis of age, gender and time period when cases were identified. GEP - The Genetic Etiologies of POAG was initiated in 1996 with funding from the National Eye Institute under the direction of Dr. Janey Wiggs. The purpose of this work was to discover novel genetic loci associated with POAG. In the GEP, cases were derived predominantly from the Glaucoma Service at MEEI. The majority of cases had an examination by a glaucoma specialist and met the definition for POAG used in NHS and HPFS. Cases with only one reliable visual field consistent with NFL dropout were included if there was a cup-disc ratio of 0.7 or more. The majority of controls were patients who presented to the MEEI comprehensive ophthalmology service for routine eye examination or from spouses of MEEI patients with secondary forms of glaucoma. Other controls were identified from regional glaucoma screenings held throughout Massachusetts. Members of GEP have detailed ocular phenotype data but limited information on environmental exposures. 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 primary open-angle glaucoma through large-scale genome-wide association studies of three well-characterized cohorts of cases and controls, some in matched pairs. Genotyping was performed at the Broad Institute of MIT and Harvard, a GENEVA genotyping center. Data cleaning and harmonization were performed at the GEI-funded GENEVA Coordinating Center at the University of Washington.

Study phs000308

WGS data of paediatric TCF3::PBX1 acute lymphoblastic leukemia (set2)

The dataset includes FASTQ and BAM files from diagnostic and matched remission (control) samples of one TCF3::PBX1 ALL patient (ALLT-376). Sequencing library was made using PCR free-based WGS library preparation (PCR-), using NEB Next® Ultra™ DNA Library Prep Kit. Both diagnostic (90x) and remission (30x) samples were sequenced using HiSeq X.

Dataset EGAD50000001795

WGS data of pediatric TCF3::PBX1 acute lymphoblastic leukemia (set1)

The dataset includes FASTQ and BAM files from diagnostic and matched remission (control) samples of one TCF3::PBX1 ALL patient (ALLT-335). Sequencing library was made using standard PCR-based WGS library preparation (PCR+), using NEB Next® Ultra™ DNA Library Prep Kit. Both diagnostic (90x) and remission (30x) samples were sequenced using HiSeq X.

Dataset EGAD50000001796

WGS data of paediatric BCR::ABL1 acute lymphoblastic leukemia

The dataset includes FASTQ and CRAM files from diagnostic and matched remission (control) samples of one BCR::ABL1 ALL patient (ALLT-399). Sequencing library was made using PCR free-based WGS library preparation (PCR-), using Novogene NGS DNA Library Prep Set Kit (Cat No. PT004). Both diagnostic (90x) and remission (30x) samples were sequenced using NovaSeqX Plus.

Dataset EGAD50000002185

Single-cell RNA sequencing of 16 NPM1 mutated AML patients

This dataset contains paired-end fastq files for single-cell RNA-seq of 16 NPM1 mutated AML patients. 19 fastq pairs are included, with two samples split into two lanes and a second (technical) replicate for one patient. Libraries were constructed using 10x Genomics Chromium Next GEM Single cell 3’ v3.1 and sequencing was performed using Illumina NovaSeq6000.

Dataset EGAD50000000478

EGAD00000000114

Whole transcriptome sequence data from 18 ovarian clear-cell carcinoma samples and one TOV21G ovarian clear-cell carcinoma cell line

Dataset EGAD00000000114

exome sequence of female patients suffering from Ovarian Meiotic Defects

Exome sequencing was performed on 15 unrelated female patients suffering from primary infertility due to Ovarian Meiotic Defects (OMD). Each reference number corresponds to one of the tested subject. DNA was extracted from Saliva using Oragene saliva DNA collection kit (DNAgenotek Inc., Ottawa, Canada).Exome capture was performed with the Agilent V5 kit and sequencing was performed on Illumina HiSeq 2000.

Dataset EGAD00001004035

Exome sequencing data

Exome sequencing fastq files from 6 mutation carriers and 5 non-carriers from 2 families. One µg DNA was used for library preparation using the TruSeq DNA LT Sample Prep Kit v2 according to the manufacturer’s instructions (Illumina). Hybridization was performed using Nimblegen SeqCap EZ Exome v3 (Roche) and Paired-end Sequencing (2x100 bp) on the Illumina HiSeq 2000 with TruSeq v3 chemistry (Illumina).

Dataset EGAD00001003745

PIK3R4 (VPS15)

WES of human: A mutation in VPS15 (PIK3R4) causes a ciliopathy and affects IFT20 release from the cis-Golgi WES (Agilent SureSelect All Exon XT2 50 Mb kit) has been realized on three affected siblings (II.1, II.3, II.5) and one healthy sister (II.4). Raw data (BAM files) are provided: - II.1.aligned.sorted.dedup.realign.recal.bam - II.3.aligned.sorted.dedup.realign.recal.bam - II.5.aligned.sorted.dedup.realign.recal.bam - II.4.aligned.sorted.dedup.realign.recal.bam

Dataset EGAD00001002736

Whole Exome Sequencing of Mixed Histology Lung Cancer

We conducted whole-exome sequencing (WES) and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histologic patterns including 6 LUAD, 6 LCNEC, 3 SCLC, 3 LUSC, and one poorly differentiated NSCLC-NOS.

Dataset EGAD00001007076

Nuclease deficiencies alter plasma cell-free DNA methylation profiles (Human samples)

The dataset contains 2x75bp paired-end sequencing data in DNASE1L3-deficient human subjects. We performed bisulfite sequencing of plasma samples from three DNASE1L3-deficient subjects and one heterozygous parent to investigate how nuclease deficiencies alter plasma cell-free DNA methylation profiles.

Dataset EGAD00001007750

Long read mRNA sequencing of blood cells exposed to different immune stimuli

Long-read (PacBio) RNA sequencing dataset of in vitro stimulated PBMC cells. 5 samples consisting of 1 RPMI control and 4 stimulus conditions (lipopolysaccharide (LPS), polyI-polyC, S. aureus and C. albicans) all originating from one donor. Files are raw BAM format files generated by Sequel 2 machine.

Dataset EGAD00001009998

From research to data sharing: exploring EGA user's experiences

The Cell Plasticity and Regeneration Group at the Bellvitge Biomedical Research Institute-IDIBELL focuses on the process of recruitment of macrophages that takes place in the small intestine during injury and healing. They recently published a paper titled “Mucosal Macrophages Govern Intestinal Regeneration in Response to Injury" in Gastroenterology Journal. As part of the research, some experiments were conducted using human intestinal organoid lines. These cells were processed for RNA sequencing, and the sequencing data were deposited at the EGA to be made available to the scientific community. When dealing with human genomic information, repositories must ensure the availability of the datasets while ensuring data protection. In this context, the European Genome-phenome Archive stands as a service for secure archiving and sharing of genetic, phenotypic and clinical data resulting from biomedical research. Following the recent publication of their paper, we took the opportunity to talk to Ilias Moraitis, first author, and Jordi Guiu, group leader, to find out about their experience with data sharing. Could you explain the focus of your research? In the lab, we study intestinal regeneration and how immune cells participate in this process. We use several techniques: engineered mouse models, image tracing, as well as mouse and human cells intestinal organoids. What challenges do you face regarding data management? We didn’t have a lot of problems. Always the informatic part, the data analysis, can give problems. But everything was smooth and working. We think it’s very important to deposit the data in repositories. For the mouse data it is very straightforward, but for us it was the first time depositing human data, which is sensitive because it comes from patients, and this is legally regulated. That’s why we thought about the EGA, and it was our first time. Why do you think it is important to submit data to repositories such as EGA? Because we think data is important for the science. Nowadays, we are sequencing a lot everywhere worldwide and having these resources shared with the scientific community it’s not only good for science, but it is also saving money and reducing costs. And there is so much data in there that can be used for other projects and for other questions. And it saves time! How was the process of submitting the data to the EGA? The communication worked very well but it was slower than we would like. But I think this is something we learn on the way. People usually wait until the last minute to do this before publication but if you know it in advance, you can start the process earlier. If you had to repeat the process, would you do anything differently? It’s like everything, isn’t it? For the first time you don’t know, you aren’t sure, but for the second time you know the steps, you know what to do and it’s faster. Of course, we would to do it earlier. Also, there were some things related to the control access that we didn’t know how to manage, but it was something more internal to us. Who is going to receive the communications and when, is our ethical committee going to evaluate this? All these technicalities that we didn’t know before starting this process, so we had to think about all these things. Next time it will be faster on our side. Do you have any suggestions for us to improve the submission process? Maybe we’ll need to ask our bioinformatician who did it! He was in charge of this part. Besides that, we think if the process were faster, it would be better for everyone. Also, in science a lot of times you have to do a lot of things at the very last minute, because of the nature of the experiments, or the need to accomplish for submitting a paper. Being faster in the process would be a plus. On your side, we went through the process and were able to find some information, but there were aspects we weren't aware of and weren't sure how to handle. I'm not sure if other institutes have different protocols for managing data requests or how they handle these internally. We believe the main issue lies in how the forms are filled out. Do you have any recommendations for other submitters? The main advice is to do this with enough time. Submit the data when you have the sequences, and don’t wait until the last minute. And then it can be under embargo, so you don’t need to make it public at that moment. As soon as you sequence, upload it and then it will be there for whenever you need to publish. How do you think we could encourage other researchers to submit their data? There are different layers here. One is that it is mandatory. You have to do this. The other is that we belong to a research community, to the same community and sharing this is making our research community stronger and more efficient. And then, also because this gives you visibility. The data is there, other people can analyse your data, and this also will bring citations to your papers. It has many advantages.

Blog from-research-to-data-sharing

Genetic Diseases of Immune Homeostasis and Autoimmunity Caused by GIMAP Deficiency

We investigated 17 affected individuals in 9 kindreds with lymphopenia, splenomegaly, liver disease, thrombocytopenia, lymphadenopathy, severe recurrent lung infections, skin diseases, and lymphoma. Whole-exome sequencing of the cohort revealed mutations in GIMAP5. We also found another patient from one kindred with a unique mutation in the gene GIMAP6, discovered by whole-exome sequencing. A comprehensive investigation of this gene mutation through in vivo and in vitro experiments revealed that GIMAP6 deficiency results in defects in autophagy in patient T cells and interruption of GIMAP7 and GABARAPL2 interactions. GIMAP6 deficiency leads to affected patients to present with clinical phenotypes such as recurrent infections, lymphoproliferation, autoimmunity, and inflammation.

Study phs002816

Lysosomal Disease Network (LDN6719) Immune Response to Intrathecal Enzyme Therapy in Mucopolysaccharidosis 1 Patients

In this study we characterized the immune response to intrathecal (IT) recombinant human alpha-L-iduronidase (rhIDU) in mucopolysaccharidosis I (MPS I) subjects with spinal cord compression who had been previously treated with intravenous rhIDU. Concentrations of specific antibodies and cytokines were measured in serum and cerebrospinal fluid (CSF) collected before monthly IT rhIDU infusions. These serologic findings were compared with clinical adverse event (AE) reports to establish temporal correlations with clinical symptoms. We found that IT rhIDU was generally well tolerated in the subjects studied although one subject had moderate to severe clinical symptoms and serologic abnormalities consistent with an immune response.

Study phs000862

NHLBI TOPMed: Childhood Asthma Management Program (CAMP)

The Childhood Asthma Management Program (CAMP) was designed to evaluate whether continuous, long-term treatment (over a period of four to six years) with either an inhaled corticosteroid (budesonide) or an inhaled noncorticosteroid drug (nedocromil) safely produces an improvement in lung growth as compared with treatment for symptoms only (with albuterol and, if necessary, prednisone, administered as needed). The primary outcome in the study was lung growth, as assessed by the change in forced expiratory volume in one second (FEV1, expressed as a percentage of the predicted value) after the administration of a bronchodilator. Secondary outcomes included the degree of airway responsiveness, morbidity, physical growth, and psychological development.

Study phs001726

Single-Cell Transcriptomic Characterization of Microscopic Colitis

Patients with microscopic colitis (MC), chronic diarrhea without histological signs of MC, or unaffected controls were profiled using single-cell RNA sequencing (scRNA-seq; inDrops and 10X Genomics were used). One profiled patient without clinical symptoms was found to have histological signs of MC (asymptomatic MC). Some of the subjects from dbGaP study phs003418 are controls in this study. T-Cell Receptor sequencing (TCR-Seq) was also performed on a subset of patients. Individual transcriptomes were analyzed to characterize the cellular profile associated with MC and search for mediators of disease pathology. Raw reads and processed count matrices will be available through dbGaP.

Study phs003876

Transcriptomic Profiling after B-Cell Depletion Reveals Central and Peripheral Immune Cell Changes in Multiple Sclerosis

We investigated the immunological effects of B cell depletion therapy using ocrelizumab in treatment-naïve relapsing-remitting multiple sclerosis (RRMS) patients. Our primary aim was to delineate transcriptomic alterations in immune cell populations in peripheral blood before and after therapy, using single-cell RNA sequencing (scRNAseq). This study included 18 newly diagnosed, treatment-naïve RRMS patients who provided matched pre- and 6-month post-treatment peripheral blood mononuclear cells (PBMCs). A secondary cohort of five patients also contributed paired cerebrospinal fluid (CSF) samples. All RRMS patients were treated with ocrelizumab, except one patient in the CSF cohort who received rituximab.

Study phs003938

Single-cell atlas of penile cancer reveals TP53 mutations as driver for an aggressive phenotype, irrespective of HPV status, and provides clues for treatment personalization

Our aim was to identify molecular differences in the microenvironment of penile squamous cell carcinoma (PSCC) stratified by TP53 loss-of-function (TP53LOF) mutations and HPV-status. Hence, we present a first single-cell transcriptomic atlas of PSCC. Stratification based on TP53 and HPV-status uncovered differences in CSS and underlying molecular pathways. Our data indicate that TP53LOF tumors are characterized by worse biological behavior regardless of HPV-status. These findings and further research are essential for trial design in PSCC, allowing us to move away from the current one-size-fits-all approach.

Study EGAS50000000217

Mutational landscape of renal cell carcinoma with venous tumor thrombus

Renal cell carcinoma with venous tumor thrombus opens the unique opportunity to investigate the clonal evolution of a tumor in two fundamentally different compartments i.e., within the confinement of an organ and inside a large blood vessel. We performed multiregion whole exome sequencing of a total of 37 samples from five consecutive patients (normal tissue, n=5; primary tumors, n=16; tumor thrombi, n=16) to >35-fold target coverage. Four patients had a clear cell RCC, one patient had a poorly differentiated type II papillary RCC (RCC-VTT-04). The latter patient had a friable thrombus, the others were of solid consistency.

Study EGAS00001001950

BAM files of targeted next-generation DNA sequencing data of 13 chordoid gliomas of the third ventricle

BAM files of targeted next-generation DNA sequencing data of 13 chordoid gliomas of the third ventricle (2 paired tumor-normal samples and 11 tumor-only samples). Genomic DNA was extracted from formalin-fixed, paraffin-embedded blocks of tumor tissue from 13 patients with chordoid glioma of the third ventricle using the QIAamp DNA FFPE Tissue Kit (Qiagen). Genomic DNA was also extracted from leukocytes in a peripheral blood sample from one of the patients and a non-neoplastic gastric biopsy specimen from one of the patients. Capture-based next-generation DNA sequencing was performed at the University of California, San Francisco Clinical Cancer Genomics Laboratory, using an assay that targets all coding exons of approximately 500 cancer-related genes, select introns of 47 genes, and TERT promoter with a total sequencing footprint of 2.8 Mb (UCSF500 Cancer Panel). Sequencing libraries were prepared from genomic DNA, and target enrichment was performed by hybrid capture using a custom oligonucleotide library (Nimblegen SeqCap EZ Choice). Captured libraries were sequenced as paired-end 100 bp reads on an Illumina HiSeq 2500 instrument. Duplicate sequencing reads were removed computationally to allow for accurate allele frequency determination and copy number calling.

Dataset EGAD00001003818

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