Search Results - EGA European Genome-Phenome Archive

Gene Expression and Regulatory Networks in Human Leukocytes - Immunological Variation Consortium

Profiling of gene expression with microarrays holds great potential for human health, for illuminating disease pathways or providing biomarkers to monitor disease or its resolution. Using high-throughput approaches for genotyping, immunophenotyping and gene expression analysis, the project will examine the basis for the control of gene expression in human immune cells, and how it is influenced by natural genetic variation or aging. In practice, the project will combine several cross-informative approaches: 1) Building on the established sample/data pipelines and robust protocols of the Immunological Genome (ImmGen) project, and on established cohorts of ethnically diverse healthy volunteers at hand, microarray techniques will be used to generate whole-genome expression profiles from purified naive CD4+ lymphocytes and monocytes from 600 healthy volunteers. A dense genetic map will be established for all donors. The results will elucidate how variation in the human genome affects the expression of immune genes, of key importance in understanding gene variants that bring susceptibility to immune or inflammatory disease. Computational analysis of these rich data will allow the reconstruction of regulatory connections between genes, helping to establish general modules and those specific of a given immune cell type. These data will be complemented by an orthogonal data group, generated from a restricted subset of 10 individuals, in which we will profile a larger set of 28 carefully delineated cell populations that exist in human blood. This work will also benefit from powerful interspecies comparison with similar experiments being performed in mice by ImmGen. 2) In addition, RNA from the same set of 28 defined cell populations will be probed with microarrays that explore other aspects of the transcriptome: i) microRNAs and other non-coding RNAs; ii) exon or splice junction arrays that will establish a map of differential splicing in human blood leukocyte. 3) The composition and reactivity of blood cells from the same donors will be established at the time of sample collection using high-throughput flow cytometry, correlating immune phenotypes with gene expression and genetic variation. 4) Genetic variability conditions the baseline levels of gene expression, but also the responsiveness to activating challenges. With samples from the same donors, NanoString technology for transcript counting will be used to analyze the transcriptional response of defined gene sets, representing response signatures of T or dendritic cells, for a fine-grained dissection of responses to different triggers (different bacterial ligands for dendritic cells, different cytokine environment for T cells). In keeping with the resource aspect of this project, all data and interpretations will be made publicly available rapidly upon curation, allowing public querying and browsing of the data, by using and evolving the existing web architectures of the ImmGen project, of the Broad Institute, and of international repositories. RELEVANCE: Exploration with DNA chips of the genome's expression microarrays holds great potential for human health, to better understand disease and to serve as diagnostic tools. Using a combination of high-throughput genomic techniques and computational biology, we will perform a broad exploration of gene expression in human blood cells across groups of African-American, Asian and European ancestry, asking how these profiles are affected by genetic variation or by age. These results will provide an invaluable reference benchmark for the interpretation of genetic and immunological studies. Additionally, over 90% of genetic variants associated with complex human traits map to non-coding regions, but little is understood about how they modulate gene regulation in health and disease. One possible mechanism is that genetic variants affect the activity of one or more cis-regulatory elements leading to gene expression variation in specific cell types. To identify such cases, in a second study we analyzed ATAC-seq and RNA-seq profiles from stimulated primary CD4+ T cells in up to 105 healthy donors. We found regions of accessible chromatin (ATAC-peaks) are co-accessible at kilobase and megabase resolution. 15% of genetic variants located within ATAC-peaks affected the accessibility of the corresponding peak (ATAC-QTLs). ATAC-QTLs have the largest effects on co-accessible peaks, are associated with gene expression, and are enriched for autoimmune disease variants. Our results provide insights into how natural genetic variants modulate cis-regulatory elements, in isolation or in concert, to influence gene expression. In another study we analyzed RNA-seq profiles from primary CD4+ T cells of two healthy donors at six time points from 0 to 72 hours after stimulation with bead-bound antibodies against CD3 and CD28.

Study phs000815

Proteomic predictors of individualized nutrient-specific insulin secretion in health and disease

Population level variation and molecular mechanisms behind insulin secretion in response to carbohydrate, protein, and fat remain uncharacterized despite ramifications for personalized nutrition. We now define prototypical insulin secretion dynamics in response to the three macronutrients in islets from 140 cadaveric donors, including those diagnosed with type 2 diabetes. We leverage the insulin response heterogeneity and use transcriptomics and proteomics to identify molecular pathways of specific nutrient responsiveness. Surprisingly, we find robust insulin secretion to fatty acid stimulus in ~8% of donors, challenging the idea that fat has negligible effects on insulin release. Distinct islet proteomes with differences in metabolic signalling networks convey this hyper-responsiveness to fat relative to carbohydrate. By comparing human islets to human embryonic stem cell-derived islet clusters, we show that, unlike glucose-responsiveness, fat hyper-responsiveness is equivalent and may be a hallmark of functionally immature cells. Our study represents the first comparison of dynamic responses to nutrients and multi-omics analysis in human insulin secreting cells. Responses of different people’s islets to carbohydrate, protein, and fat lay the groundwork for personalized nutrition.

Dataset EGAD50000000519

Leucocyte eQTLs in autoimmune disease and health

Peripheral blood was taken from healthy controls and patients with active autoimmune disease. CD4 T cells, CD8 T cells, monocytes, neutrophils, and B cells were separated using magnetic-activated cell sorting. RNA and genomic DNA was extracted from cell lysates using Qiagen Allprep kits. Genotyping of healthy controls and patients with inflammatory bowel disease was performed using the Illumina Human OmniExpress12v1.0 BeadChip at the Wellcome Trust Sanger Institute. 200 ng RNA was processed for hybridization onto Affymetrix Human Gene ST 1.1 microarrays. Gene expression and genotyping data was used to perform eQTL mapping.

Study EGAS00001001251

RNA-seq study of a Princess Margaret Cancer Centre human acute myeloid leukemia patient cohort

The PMCC AML RNAseq dataset consists of 81 AML patient samples (clinical data in Supplemental Table 11 of manuscript), processed in two batches. These patient samples are able to engraft in the NSG (NOD.Cg PrkdcscidIl2rgtm1Wjl /SzJ) mouse model. Five patients (90543, 598, 90240, 110484, 100500) were included in both batches. Viaably frozen material from the Leukemia Tissue Bank at Princess Margaret Cancer Centre/ University Health Network were thawed by dropwise addition of X-VIVO + 50% fetal calf serum supplemented with DNase (100μg/mL final concentration, Roche). RNA was extracted from bulk peripheral blood mononuclear cells (PBMC) using the RNeasy Micro Kit (Qiagen Inc.). A paired-end 76 base-pair flow-cell lane Illumina High seq 2000 yielded an average of 240 million sequence reads aligning to genome per sample at the Genome Sciences Centre, BC Cancer Agency for cohort 1. Cohort 2 was subjected to 125 bp, paired-end RNA-sequencing on the Illumina HiSeq 2500 with an average of 50 million reads/sample at the Centre for Applied Genomics, Sick Kids Hospital.

Dataset EGAD00001006576

Sequencing component for the whole genome methylation analysis in PBMCs and cell subsets (pilot study)

DNA methylation has been shown to play a major role in determining cellular phenotype by regulating gene expression. Moreover, dysregulation of differentially methylated genes has been implicated in disease pathogenesis of various conditions including cancer development as well as autoimmune diseases such as systemic Lupus erythematosus and rheumatoid arthritis. Evidence is rapidly accumulating for a role of DNA methylation in regulating immune responses in health and disease. However, the exact mechanisms remain unknown. The overall aim of the project is to investigate the role of epigenetic mechanisms in regulating immunity and their impact on autoimmune disease pathogenesis.The aim of this pilot study is to perform whole genome methylation analysis in peripheral blood mononuclear cells (PBMCs) and cell subsets (CD4, CD8, CD14, CD19, CD16 and whole PBMCs) obtained from 6 healthy volunteers. Whole genome methylation analysis will be performed using two methodological approaches, the Infinium Methylation Bead Array K450 (Illumina) and MeDIP-seq. mRNA expression arrays will also be performed in order to correlate DNA methylation with gene expression as well as genotyping on the Illumina OmniExpress chip

Dataset EGAD00001000394

Autozygosity pilot - Born in Bradford (2014-11-20)

The offspring of first cousin marriages have ~6% of their genome autozygous, i.e. homozygous identical by descent, or even more if there was further consanguinity in their ancestry. In the UK there are large populations with very high first cousin marriage rates of 20-50%. Sequencing the exomes of a sample of these individuals has the potential both to support genetic health programmes in these populations, and to provide genetic research information about rare loss of function mutations. This pilot study based on existing cohort samples from the Born In Bradford study will identify homozygous individuals for almost all variants down to an allele frequency around 1%, plus individuals carrying hundreds of new homozygous rare loss-of-function variants, and will support development of community relations and ethics for a wider study currently being designed. The data deposited in the EGA consist of low coverage whole exome sequencing on these samples.Data Access is controlled by the Wellcome Trust Sanger Institute DAC and the Born In Bradford Executive Group. This dataset contains all the data available for this study on 2014-11-20.

Dataset EGAD00001001079

Exome sequencing reveals pathogenic variants in known and novel candidate genes for severe sperm motility disorders

STUDY QUESTION: What are the causative genetic variants in patients with male infertility due to severe sperm motility disorders? SUMMARY ANSWER: We identified high confidence disease-causing variants in multiple genes previously associated with severe sperm motility disorders in 9 out of 21 patients (43%). We also identified variants in novel candidate genes in 8 additional patients (38%). WHAT IS KNOWN ALREADY: Severe sperm motility disorders are a form of male infertility characterised by vital, but immotile sperm often in combination with a spectrum of structural abnormalities of the sperm flagellum. Examples of this spectrum of disorders include dysplasia of the fibrous sheath / multiple morphological abnormalities of the sperm flagella (DFS-MMAF) and severe forms of asthenozoospermia associated to non-specific structural defects. These disorders frequently recur in families and may include chronic respiratory disease. As such, genetic causes are suspected and many genes have recently been reported. Currently, depending on the clinical sub-categorisation, up to 50% of causality in patients with severe sperm motility disorders can be explained by pathogenic variants in at least 21 known genes. STUDY DESIGN, SIZE, DURATION: We performed exome sequencing in patients with severe sperm motility disorders from two different clinics, aiming to identify the underlying genetic defects and establishing a diagnostic yield in both. PARTICIPANTS/MATERIALS, SETTING, METHOD: Two groups of infertile men, one from Argentina (n= 9) and one from Australia (n=12), with clinically defined sperm motility disorders were included. All patients in the Argentine cohort were diagnosed with DFS-MMAF, based on transmission electron microscopy. Exome sequencing was performed in all 21 patients and variants with an allele frequency of <1% in the gnomAD population were prioritised and interpreted. MAIN RESULTS AND ROLE OF CHANCE: In 9/21 patients (43%), we identified pathogenic variants in known sperm motility disorder genes: CFAP43 (3 patients); CFAP44 (2 patients), QRICH2 (2 patients), DNAH1 (1 patient) and DNAH6 (1 patient). The diagnostic rate did not differ markedly between the Argentinian and the Australian cohort (44% and 42%, respectively). Furthermore, we identified patients with variants in the novel candidate sperm motility genes CFAP58, DNAH12, DRC1, MDC1, PACRG, SSPL2C and TPTE2. One patient presented with two possibly damaging variants in four candidate genes and it remains unclear which variants were responsible for the severe sperm motility defect in this patient. LIMITATIONS, REASONS FOR CAUTION: In this study, we described patients with potential bi-allelic variants in known and novel candidate genes for severe sperm motility disorders. Due to unavailability of parental DNA, we have not assessed de novo or maternally inherited dominant variants and could not phase the mutations to establish in all cases that the mutations occur on both alleles. WIDER IMPLICATIONS OF THE FINDINGS: Our results confirm an important role for five known genes for sperm motility and we demonstrate that exome sequencing is an effective method to diagnose patients with severe sperm motility defects (9/21 diagnosed; 43%). Furthermore, our analysis revealed seven novel candidate genes for severe sperm motility disorders. Genome-wide sequencing of additional patient cohorts and re-analysis of exome data of currently unsolved cases may reveal additional variants in these novel candidate genes.

Study EGAS00001005018

National Human Genome Research Institute (NHGRI) GENEVA Genome-Wide Association Study of Venous Thrombosis (GWAS of VTE)

Overview: Our overall long-term goal is to determine risk factors for the complex (multifactorial) disease, venous thromboembolism (VTE), that will allow physicians to stratify individual patient risk and target VTE prophylaxis to those who would benefit most. In this genome-wide association case-control study (1300 cases and 1300 controls) we hope to identify susceptibility variants for VTE. Mutations within genes encoding for important components of the anticoagulant, procoagulant, fibrinolytic, and innate immunity pathways are risk factors for VTE. We hypothesize that other genes within these four pathways or within other pathways also are VTE disease-susceptibility genes. Therefore, we performed a genome wide association (GWA) screen and analysis using the Illumina 660W platform to identify SNPs within 1,300 clinic-based, non-cancer VTE cases primarily from Minnesota and the upper Midwest USA, and 1300 clinic-based, unrelated controls frequency-matched on patient age, gender, myocardial infarction/stroke status and state of residence. This is a subset of a slightly larger candidate gene study using 1500 case-control pairs to identify haplotype-tagging SNPs (ht-SNPs) in a large set of candidate genes (n~750) within the anticoagulant, procoagulant, fibrinolytic, and innate immunity pathways. Study Populations. Cases. VTE cases were consecutive Mayo Clinic outpatients with objectively-diagnosed deep vein thrombosis (DVT) and/or pulmonary embolism (PE) residing in the upper Midwest and referred by Mayo Clinic physician to the Mayo Clinic Special Coagulation Laboratory for clinical diagnostic testing to evaluate for an acquired or inherited thrombophilia, or to the Mayo Clinic Thrombophilia Center. Any person contacted to be a control but discovered to have had a VTE was evaluated for inclusion as a case. Cases were primarily residents from Minnesota, Wisconsin, Iowa, Michigan, Illinois, North or South Dakota, Nebraska, Kansas, Missouri and Indiana. A DVT or PE was categorized as objectively diagnosed when (a) confirmed by venography or pulmonary angiography, or pathology examination of thrombus removed at surgery, or (b) if at least one non-invasive test (compression duplex ultrasonography, lung scan, computed tomography scan, magnetic resonance imaging) was positive. A VTE was defined as: Proximal leg deep vein thrombosis (DVT), which includes the common iliac, internal iliac, external iliac, common femoral, superficial [now termed "femoral"] femoral, deep femoral [sometimes referred to as "profunda" femoral] and/or popliteal veins. (Note: greater and lesser saphenous veins, or other superficial or perforator veins, were not included as proximal or distal leg DVT). Distal leg DVT (or "isolated calf DVT"), which includes the anterior tibial, posterior tibial and/or peroneal veins. (Note: gastrocnemius, soleal and/or sural [e.g., "deep muscular veins" of the calf] vein thrombosis was not included as distal leg DVT). Arm DVT, which includes the axillary, subclavian and/or innominate (brachiocephalic) veins. (Note: jugular [internal or external], cephalic and brachial vein thrombosis was not included in "arm DVT"). Hepatic, portal, splenic, superior or inferior mesenteric, and/or renal vein thrombosis. (Note: ovarian, testicular, peri-prostatic and/or pelvic vein thrombosis was not included). Cerebral vein thrombosis (includes cerebral or dural sinus or vein, saggital sinus or vein, and/or transverse sinus or vein thrombosis). Inferior vena cava (IVC) thrombosis Superior vena cava (SVC) thrombosis Pulmonary embolism Patients with VTE related to active cancer, antiphospholipid syndrome, inflammatory bowel disease, vasculitis, a rheumatoid or other autoimmune disorder, a vascular anomaly (e.g., Klippel-Trénaunay syndrome, etc.), heparin-induced thrombocytopenia, or a mechanical cause for DVT (e.g., arm DVT or SVC thrombosis related to a central venous catheter or transvenous pacemaker, portal and/or splenic vein thrombosis related to liver cirrhosis, IVC thrombosis related to retroperitoneal fibrosis, etc.), with hemodialysis arteriovenous fistula thrombosis, or with prior liver or bone marrow transplantation were excluded. Controls. A Mayo Clinic outpatient control group was prospectively recruited for this study. Controls were frequency-matched on the age group (18-29, 30-39, 40-49, 50-59, 60-69, 70-79, and 80+ years), sex, myocardial infarction/stroke status, and state of residence distribution of the cases. We selected clinic-based controls using a controls' database of persons undergoing general medical examinations in the Mayo Clinic Departments of General Internal Medicine or Primary Care Internal Medicine. Additionally persons undergoing evaluation at the Mayo Clinic Sports Medicine Center, and the Department of Family Medicine were screened for inclusion as controls. 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 venous thrombosis through large-scale genome-wide association studies of 1,300 clinic-based, VTE cases and 1300 clinic-based, unrelated controls. 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 phs000289

QUANTitative Chest Computed Tomography UnMasking Emphysema Progression in Alpha-1 Antitrypsin Deficiency

AAT deficiency is a genetic disorder associated with emphysema. Spirometry, the lung function test that measures how well the lungs exhale air, is used to diagnose and track the progression of emphysema. Some studies have suggested that forced expiratory volume in 1 second (FEV1) measurements, a type of spirometry test, may lack accuracy in detecting disease progression in cases of severe AAT deficiency. Another method, high resolution chest CT scans, may be more accurate at measuring the progression of emphysema. The purpose of this study is to determine if high resolution CT scans are better at detecting the progression of emphysema than lung function tests. Results from this study may lead to the development of a more accurate way to assess lung tissue loss and may improve the understanding of lung destruction in AAT deficiency. This study will last 4 years and will enroll people with AAT deficiency who have nearly normal lung function test results. Study visits, each lasting about 4 hours, will occur at baseline and months 6, 12, 18, 24, and 36. At each visit, participants will undergo lung function tests, a CT scan, blood collection, and a physical exam. Female participants will have urine collected for a pregnancy test. All participants will also complete questionnaires to assess health status and lung function. Study researchers will call participants every 2 months to collect information on lung disease symptoms and medication changes.

Study phs000698

Phenotype Risk Scores Identify Patients with Unrecognized Mendelian Disease Patterns

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

Study phs001516

NIAID Centralized Sequencing Program

Investigators at the National Institute of Allergy and Infectious Diseases (NIAID) use next-generation sequencing technologies to help determine genetic contributions to immune diseases. These efforts have increased rates of molecular diagnosis for a subset of NIAID participants as well as uncovered fundamental insights into the cellular and signaling pathways in host defense and immune regulation. Despite these successes, analysis and interpretation of genomic data remain a substantial challenge. Simply, researchers do not understand the functional and clinical consequences of most human genetic variation. Making progress in this area requires a coordinated, systematic, and transparent approach to clinical genomics research. This protocol is specific to genetic testing and explicitly aims to both strengthen clinical care and enhance research. Probands provide biological specimens for genetic testing and primary clinical and research evaluations are completed via a research protocol at NIAID. This protocol serves as a vehicle for a programmatic effort that includes standardized phenotyping, test ordering through the Clinical Research Information System (CRIS) at the National Institutes of Health Clinical Center, sample collection and isolation, nucleic acid analysis, bioinformatics, clinical interpretation, reporting in CRIS, genetic counseling, and supporting effective use of genomics as a research tool throughout the institute. Overall, increased process standardization will support data integrity and efficiency while still accommodating the need for investigator flexibility. This study aims to generate and analyze evidence regarding the genetic contribution to diverse immune diseases studied by the NIAID intramural research program.

Study phs001899

Genome-Wide Association Study in Systemic Sclerosis

The Scleroderma Family Registry and DNA Repository (Registry) was initially developed as a registry and bio-specimen repository of patients with systemic sclerosis (scleroderma), family members and unaffected healthy controls. A case-control design was later adopted due to the lack of availability of many parents in this adult-onset disease, which precluded a linkage approach. In addition to collecting demographic data, the registry included the collection of disease-pertinent, cross-sectional, clinical information from medical records of affected participants. Registry participants were >/= 18 years of age at enrollment. On the basis of medical record review, all cases were verified by the principal investigator to meet the eligibility criteria. 800+ blood samples from verified affected cases of European ancestry were selected for a Genome Wide Association Study (GWAS). These genetic data, as well as phenotypic data, are available in dbGaP. DNA, serum and plasma from Registry participants, which are currently being stored in the University of Texas Health Science Center - Houston, Scleroderma Registry Genetic Repository, are available for research purposes. However, not all of the 800+ registry participants who submitted a blood sample currently have DNA available due to depletion of some samples. In addition to including SNP data from the genome-wide (first study release) and immunochip (second study release) scan on the 800+ samples, the dbGaP database provides a set of data tables with phenotypic information collected from the participants in the registry.

Study phs000357

LCLF1.0 Data

In this study, we investigated ad libitum food intake of 20 healthy adult volunteers who each completed a 4-week stay at the National Institutes of Health (NIH). During their stay, they consumed a low-carbohydrate, high-fat (LCHF) diet and a low-fat, high-carbohydrate (LFHC) diet for 2 weeks each, in random order. We collected information about energy intake and metabolic changes in response to the two test diets. The test diets were presented to subjects in amounts exceeding their daily energy requirements, and subjects were instructed to eat as much or as little of each diet as desired. This study implemented 7-day rotating menus for each test diet. The diets were matched for presented calories and protein, but the LCHF diet was low in carbohydrate (~10% of calories) and high in fat (~75% of calories) whereas the LFHC diet was high in carbohydrates (~75% of calories) and low in fat (~10% of calories). The two test diets had a common foundation of vegetables with low amounts of digestible carbohydrates such as lettuce, spinach, kale, cabbage, cauliflower, zucchini, tomato, asparagus, broccoli, peppers, brussels sprouts, and green beans. The LCHF diet included meat, poultry, fish, eggs, dairy, and nuts. The LFHC diet included legumes, rice, root vegetables, soy products, corn, lentils, peas, whole grains, bread, and fruit. The test diets were designed using a standard computerized nutrition database, and blood samples were collected from the subjects.

Study phs003187

Genomic and Phenotypic Profile of Sickle Cell Disease in Human Population in Cameroon

Sickle cell disease (SCD) is caused by a biallelic single nucleotide substitution in the beta-globin gene (HBB), resulting in an amino acid substitution, Glu7Val, formerly known as Glu6Val. An estimated 300,000 babies with SCD are born worldwide each year, with nearly 75% of these births being in sub-Saharan Africa. In Africa, at least 30-50% of children with untreated SCD die before the age of 5 years. Therefore, accelerating the path for novel therapies for SCD through genomics research on fetal hemoglobin (HbF) is critical. Variants in the currently known HbF‐modulating genes/loci, i.e., BCL11A, HBS1L-MYB, and XmnI-HBG2, explain only 10-20% of the variation of HbF levels in African individuals with SCD, compared with nearly 50% of the variation in HbF levels among Europeans. Expanding genomic research in populations of African ancestry could uncover the missing heritability of HbF-promoting loci. This study used the Human Heredity and Health (H3Africa) consortium SNP genotyping array developed from whole genome enriched for common variants in sub-Saharan Africans to investigate genomic variations associated with HbF levels in individuals with sickle cell anemia of the HbSS genotype from Cameroon. Two of the previously described loci, BCL11A and HBS1L-MYB were replicated, while a novel locus on chromosome 13 mapping to FLT1 was uncovered. The study therefore opens up a novel avenue for exploring SCD gene therapy.

Study phs003748

DNA methylation at HBV integrants and flanking host genomes

Integration of DNA viruses into the human genome plays an important role in various types of tumors, including hepatitis B virus (HBV)-related hepatocellular carcinoma. However, the molecular details and clinical impact of HBV integration on either the human or HBV epigenomes are unknown. Here, we show that methylation of the integrated HBV DNA is related to the methylation status of the flanking human genome. We developed a next-generation sequencing-based method for structural methylation analysis of integrated viral genomes (denoted G-NaVI). This method is a novel approach that enables enrichment of viral fragments for sequencing using unique baits based on the sequence of the HBV genome. We detected integrated HBV sequences in the genome of the PLC/PRF/5 cell line and found variable levels of methylation within the integrated HBV genomes. Allele-specific methylation analysis revealed that the HBV genome often became significantly methylated when integrated into highly methylated host sites. After integration into unmethylated human genome regions such as promoters, however, the HBV DNA remains unmethylated and may eventually play an important role in tumorigenesis. The observed dynamic changes in DNA methylation of the host and viral genomes may functionally affect the biological behavior of HBV. These findings may impact public health given that millions of people worldwide are carriers of HBV. We also believe our assay will be a powerful tool to increase our understanding of the various types of DNA virus-associated tumorigenesis.

Study JGAS000015

GCAT | Genomes for life: cohort study of the genomes of Catalonia

The GCAT Study have recruited 20 000 participants aged 40–65 years. Participants who agreed to take part in the study completed a self-administered computer-driven questionnaire, and underwent blood pressure, cardiac frequency and anthropometry measurements. For each participant, blood plasma, blood serum and white blood cells are collected at baseline. A total of 5459 genomic profiles have been characterised by comprehensive genotyping. Genome-wide genotypes have been generated using Illumina Infinium SNP-bead array technology. We chose the Multi-Ethnic Global (MEGAEX, V.2) consortium array, a multipurpose, multiethnic genotyping array with two million selected markers (including previously described germline mutations, insertions-deletions (InDels) and SNPs).We have strictly followed the standard manufacturer recommended automated protocol for the Infinium HTS Assay scanned with a HiScan confocal scanner (Illumina, San Diego, California, USA). Genome Studio V.2011.1 has been used for raw data analysis. Genotyping was performed at the Genomics and Bioinformatics Unit of the PMPPC Institute for Health Science Research Germans Trias i Pujol, in Badalona, Spain. Future plans The first follow-up study started in December 2017 and will end by March 2018. Residences of all subjects will be geocoded during the following year. Several genomic analyses are ongoing, and metabolomic and genomic integrations will be performed to identify underlying genetic variants, as well as environmental factors that influence metabolites. http://dx.doi.org/10.1136/bmjopen-2017-018324

Study EGAS00001003018

Whole-genome sequencing of normal Singaporean volunteers

Sleep is associated with various health outcomes. Despite their growing adoption, the potential for consumer wearables to contribute sleep metrics to sleep-related biomedical research remains largely uncharacterized. Here we analyzed sleep tracking data, along with questionnaire responses and multi-modal phenotypic data generated from 482 normal volunteers. First, we compared wearable-derived and self-reported sleep metrics, particularly total sleep time (TST) and sleep efficiency (SE). We then identified demographic, socioeconomic and lifestyle factors associated with wearable-derived TST. Among others, male gender (β = -15.539, 95% confidence interval [CI] = -26.245 - -4.832, p = 0.005), older age (β = -0.493, CI = -0.941 - -0.044, p = 0.032) and manual labor (β = -26.856, CI = -49.715 - -3.997, p = 0.022) were associated with reduced TST, whereas alcohol consumption (β = 19.247, CI = 8.008 - 30.486, p = 8.54x10-04) was associated with increased TST. Multi-modal phenotypic data analysis showed that wearable-derived TST and SE were associated with various cardiovascular disease risk markers such as body mass index (β = -0.006, CI = -0.00 - -0.000, p = 0.040) and waist circumference (β = 0.001, CI = -0.015-0.017, p = 0.893), whereas self-reported measures were not. We then showed that insufficient sleep was associated with premature telomere attrition, using telomere length estimated using whole-genome sequencing (β = 1.275, CI = 0.187 - 2.363, p = 0.023) and quantitative PCR (β = 0.001, CI = 8.318x10-05 - 0.001, p = 0.028). Our study highlights the potential for sleep metrics from consumer wearables to provide novel insights into data generated from population cohort studies.

Study EGAS00001004007

First datasets available in the Federated EGA Network

In February 2024, a great milestone was reached for the human omics data community: the first datasets are now available in the Federated European Genome-phenome Archive (FEGA). These datasets - hosted by national nodes in Poland, Norway, and Sweden - are discoverable in the EGA portal and accessible according to national laws. These datasets include: From FEGA Poland - The Genomic Map Of Poland From FEGA Norway - CRCbiome: Gut metagenome of Norwegian screening participants using FIT sampling Also from FEGA Norway - Single cell transcriptomics in expanded Tregs of APS-1 patients From FEGA Sweden - A Combined Omics and Tissue Biobank for Paediatric Cancers The establishment of these datasets represents a significant step towards achieving the broader objectives of the FEGA; to facilitate the sharing of high-quality human genomic and phenotypic data across borders. “The value for users is twofold: it becomes easier to make data available securely compared to storing data locally, and data becomes easier to find for other researchers. The possibility of reuse improves the chances of both verifying scientific discoveries and reaching groundbreaking conclusions, of benefit to the research community and later also patients,” said Anna Hagwall, project manager and head of human data at FEGA Sweden, NBIS. About Federated EGA FEGA was launched in 2022 as a network to store and lawfully share sensitive human omics data safely. It relies on the technology originally developed by the EGA and support from ongoing collaborations with ELIXIR - a European life sciences infrastructure. FEGA has contributed to and benefitted from the implementation of community standards for sensitive data management including those developed by the Global Alliance for Genomics and Health (GA4GH). “The FEGA concept and the development of the Nordic FEGA nodes have been made possible thanks to several projects coordinated by NeIC,” said Kjell Petersen, FEGA Norway technical lead. Managing human omics data is challenging due to its sheer size and privacy concerns, often resulting in restrictions on how and where sensitive data can be shared. The FEGA network follows a federated data access model whereby sensitive data is stored securely within a local or national jurisdiction, while discovery across the network for data of interest is enabled via a central portal. As of February 2024, the FEGA network comprises seven European national nodes with tens of additional national partners working towards establishing a FEGA node. As the FEGA network continues to onboard nodes from around the world, populated with more and more data, we move closer to a vision of a truly global resource for discovery of and access to sensitive human omics data to accelerate disease research and improve human health. "The Federated EGA network is entering a key phase where the first research datasets are now available across the network. In coming years, we look forward to expanding these datasets to cover key areas such as cancer and rare diseases for the benefit of patients" said Thomas Keane, Group Leader at EMBL-EBI.

Blog first-datasets-available-in-Federated-EGA

Low-coverage whole genome sequencing for a highly selective cohort of severe COVID-19 patients

Background Despite advances in identification of genetic markers associated to severe COVID-19, the full genetic characterisation of the disease remains elusive. Imputation of low-coverage whole genome sequencing (lcWGS) has emerged as a competitive method to study such disease-related genetic markers as they enable genotyping of most common genetic variants used for genome wide association studies. This study aims at exploring the potential use of imputation in lcWGS for a highly selected severe COVID-19 patient cohort. Findings We generated an imputed dataset of 79 variant call format (VCF) patient files using the GLIMPSE1 tool, each containing, on average, 9.5 million single nucleotide variants. The validation assessment of imputation accuracy yielded a squared Pearson correlation of approximately 0.97 across sequencing platforms, showing that GLIMPSE1 can be used to confidently impute variants with minor allele frequency up to approximately 2% in Spanish ancestry individuals. We conducted a comprehensive analysis on the patient cohort, examining hospitalisation and intensive care utilisation, sex and age-based differences, and clinical phenotypes using a standardised set of medical terms specifically developed to characterise severe COVID-19 symptoms for this cohort. Conclusion This dataset highlights the utility and accuracy of lcWGS imputation in the study of COVID-19 severity, setting a precedent for other applications in resource-constrained environments. The methods and findings presented here may be leveraged in future genomic projects, providing vital insights for health challenges like COVID-19.

Study EGAS00001007573

Genomics of Opioid Pharmacogenomics and Acute/Chronic Postsurgical Pain after Major Surgery in Children

Pain management after surgery is not optimal, partly due to great interindividual variability in pain perception and coping. This can lead to persistent pain beyond the healing period and disability. Up to two-thirds of the inter-individual variability results from genetic variations in pain perception as well as response to the pain medicine. The investigators aim to identify genomic (genetic/epigenetic), psychological and drug profiles contributing to this variability. Opioids are the mainstay for treatment of postoperative pain in children. Experience dictates that opioids have narrow therapeutic indices and large inter-patient variability in response. This leads to serious side effects like respiratory depression in up to 50% of children undergoing invasive surgery, which can be fatal. It is evident that there are particular children who are more susceptible to suffering side effects and having inadequate pain relief from opioids.It is hypothesized that much of the genetic variability can be explained by gene function which is modulated by a) Single Nucleotide Polymorphisms (SNPs) in genes that encode proteins involved in pain perception, opioid transport/metabolism (pharmacokinetics), and opioid receptor signaling (pharmacodynamics); b) epigenetics which modify gene expression without structural changes to the DNA, and c) genes that influence psychological factors.The purpose of this research study is to identify factors and genes that may be associated with acute and chronic post-surgical pain, and to develop pharmacometric models for response to opioids, like morphine and hydromorphone. While children undergoing different surgeries will be recruited for acute outcomes, children undergoing spine fusion will be followed for 10-12 months for evaluation of psychological and genomic factors affecting chronic post-surgical pain, with a goal of identifying genetic and epigenetic risk models for prediction of acute and chronic post-surgical pain. Although opioids are used every day, some children have bad reactions from their use, like breathing problems, sedation, etc. The investigators want to study factors that may be associated with pain sensitivity, opioid requirements after surgery, their metabolism, efficacy and their side-effects. The investigators expect that the information obtained in this research study will help to develop effective, safer, and tailored treatment options in the future.

Study phs002105

NHLBI TOPMed: Chicago Initiative to Raise Asthma Health Equity (CHIRAH)

The CHIRAH project was a community-based study of the factors associated with asthma morbidity in the African American population. CHIRAH evaluated the role of various variables (biologic/environmental, psychologic/behavioral, and socioeconomic) on asthma morbidity and the function of changes in these variables on asthma morbidity in a longitudinal fashion. This involved collection of a cohort-based on school screening which was sampled to include similar numbers of underprivileged and non-underprivileged subjects which roughly equally represented self-reported African Americans and self-reported non-African Americans. Subjects were followed up every 3 months of this cohort over the course of 2 years.

Study phs001605

How are we funded?

How are we funded? The EGA is provided infrastructure, administrative support, advisory input, and other relevant and necessary aspects by: CRG The EGA at the CRG is funded and supported by “La Caixa Foundation”, The Generalitat de Catalunya (Catalan Government), the Spanish Ministerio de Asuntos Económicos y Transformación Digital (Spanish Government) and the Instituto de Salud Carlos III. The Generalitat de Catalunya contributes with funding to the CRG and to the Barcelona Supercomputing Centre (BSC). The Barcelona Supercomputing Center is decisively contributing to EGA by providing the required compute, storage and networking resources, as well as key personnel for shared operations of EGA infrastructure. EMBL-EBI As part of the European Molecular Biology Laboratory (EMBL), the EGA at EMBL’s European Bioinformatics Institute receives funding from the governments of EMBL’s member states. The UK government, via UK Research and Innovation, also continues to support EMBL-EBI technical infrastructure, with EMBL supporting operational costs. A major source of funding for the EGA both at CRG and EMBL-EBI are competitive projects.The EGA resource continues to be funded through strategic engagement in collaborative partnerships and projects. Please see our projects page for more information. Other funders include the European Commission, the US National Institutes of Health, the Wellcome Trust, UK Research Councils, and our Industry Programme partners. Last updated on October, 2024.

Documentation about/projects-and-funders/funders

SNV and indel calls from 8921 individuals in the British Autozygosity Populations BioResource dataset

This includes variant calls (single nucleotide variants and small insertions/deletions) from 8086 (mostly British Pakistani/British Bangladeshi) individuals from the following studies: 1. 5236 British Pakistani/British Bangladeshi adults from East London Genes and Health (ELGH) 2. 2624 British South Asian mothers from Born in Bradford (mostly Pakistani) (BiB) 3. 1061 British South Asian adults from Birmingham (mostly Pakistani) (Birm) All of the Birmingham and most of the Born in Bradford samples were previously sequenced as part of PMID: 26940866. In the sample list file, the columns of interest to most people will be: vcf.id - sample ID from the vcf cohort - which cohort they're in sex.assigned - sex inferred from coverage on the X and Y chromosomes. Individuals for whom this did not match their reported sex have been discarded total, chrX and chrY - coverage within bait regions across all chromosomes, chrX and chrY respectively Mapping was done with bwa-mem and variant calling was carried out with GATK HaplotypeCaller. We removed variant sites for which the following was true: SNPs: "QD < 2.0 || FS > 30 || MQ < 40.0 || MQRankSum < -12.5 || ReadPosRankSum < -8.0" Indels: "QD < 2.0 || FS > 30 || ReadPosRankSum < -20.0"

Dataset EGAD00001005469

August 2021 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

August 2021 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

Dataset EGAD00001008101

BLUEPRINT EpiVar ChIP-seq in lineage specifying transcription factors

The BLUEPRINT project is a large-scale project investigating epigenetic mechanisms involved in blood formation, in health and disease. The human variation workpackage (WP10) of the project seeks to characterize the effect of common sequence variation on the epigenome status of a cell. To do this, the project will use highly purified blood cells to minimise "experimental noise" and therefore enhance the power to discover modest effects. Two peripheral blood cell types, the CD14+CD16- monocyte (an important central orchestrator of adaptive immunity and a bridge between innate and adaptive immunity) and the CD65+CD9- neutrophilic granulocyte (the frontline cell for innate immunity) have been selected for this purpose. The two types of cells will be obtained at high purity from adult blood (AB) of 200 healthy males and females, respectively. Cells will be purified by using already validated and fully operational protocols that are based on density gradient centrifugation of the buffy coat obtained from whole blood, followed by magnetic bead-based purification using monoclonal antibodies against Cluster of Differentiation (CD) lineage-specific cell surface markers. This data set contains functional genomics data for gene expression and chromatin state.

Dataset EGAD00001004571

1169 results for "NHMRC+youth+mental+health+evaluation+2010..2025+Australia"

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