14430 results for ""Pennsylvania+German"+ACS+study+'Socius'+open+access"

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• Reference epigenome Islet-derived_MSC06 miRNA-Seq data generated from KEP study

  Islet-derived_MSC06 miRNA-Seq single end data

  Dataset EGAD00001007881

• Reference epigenome KNIH011 miRNA-seq data generated from KEP study

  A KNIH011 miRNA-seq single end data for podocytes

  Dataset EGAD00001002770

• Reference epigenome KNIH010 mRNA-seq data generated from KEP study

  A KNIH010 mRNA-seq paired end data for podocytes

  Dataset EGAD00001002176

• Reference epigenome KNIH011 mRNA-seq data generated from KEP study

  A KNIH011 mRNA-seq paired end data for podocytes

  Dataset EGAD00001002177

• Reference epigenome SMC01_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC01_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003845

• Reference epigenome SMC02_WGBS data generated from KEP study

  A SMC02_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003872

• Reference epigenome SMC06_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC06_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003850

• Reference epigenome SMC03_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC03_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003847

• Reference epigenome SMC05_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC05_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003849

• Reference epigenome SMC07_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC07_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003851

• Reference epigenome SMC05_WGBS data generated from KEP study

  A SMC05_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003873

• Reference epigenome CKD23_C_Mesan_smRNA-Seq data generated from KEP study

  A CKD23_C_Mesan_smRNA-Seq single end data for Mesangial cells(kidney)

  Dataset EGAD00001003497

• Reference epigenome SMC03_WGBS data generated from KEP study

  A SMC03_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003888

• Reference epigenome SMC04_WGBS data generated from KEP study

  A SMC04_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003889

• Reference epigenome SMC04_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC04_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003848

• Reference epigenome SMC08_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC08_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003852

• Reference epigenome CKD27_C_Mesan_smRNA-Seq data generated from KEP study

  A CKD27_C_Mesan_smRNA-Seq single end data for Mesangial cells(kidney)

  Dataset EGAD00001003500

• Reference epigenome SMC02_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC02_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003846

• Reference epigenome SMC09_ChIP-Seq(H3K27me3) data generated from KEP study

  A SMC09_ChIP-Seq(H3K27me3) paired end data for skeletal muscle cells

  Dataset EGAD00001003853

• SCLC study Peifer et al. - WES dataset

  SCLC - Whole genome sequencing data Publication Peifer et al., 2012, Nature Genetics

  Dataset EGAD00001000703

• Reference epigenome SMC06_WGBS data generated from KEP study

  A SMC06_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003874

• Reference epigenome SMC07_WGBS data generated from KEP study

  A SMC07_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003875

• Reference epigenome CKD23_C_Mesan_mRNA-Seq data generated from KEP study

  A CKD23_C_Mesan_mRNA-Seq paired end data for Mesangial cells(kidney)

  Dataset EGAD00001003481

• Reference epigenome SMC01_WGBS data generated from KEP study

  A SMC01_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003871

• Reference epigenome SMC08_WGBS data generated from KEP study

  A SMC08_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003876

• Reference epigenome SMC09_WGBS data generated from KEP study

  A SMC09_WGBS paired end data for skeletal muscle cells

  Dataset EGAD00001003877

• Reference epigenome CKD27_C_Mesan_mRNA-Seq data generated from KEP study

  A CKD27_C_Mesan_mRNA-Seq paired end data for Mesangial cells(kidney)

  Dataset EGAD00001003484

• Genetic Epidemiology of COPD (COPDGene) Funded by the National Heart, Lung, and Blood Institute

  Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in the United States and the only leading cause of death that is steadily increasing in frequency. This project will establish a racially diverse cohort that is sufficiently large and appropriately designed for genome-wide association analysis of COPD. A total of 10,000 subjects will be recruited, including control smokers, definite COPD cases (GOLD Stage 2 to 4), and subjects not included in either group (GOLD 1 or GOLD-Unclassified). This cohort will be used for cross-sectional analysis, although long-term longitudinal follow-up will be a future goal. The primary focus of the study will be genome-wide association analysis to identify the genetic risk factors that determine susceptibility for COPD and COPD-related phenotypes. Detailed phenotyping of both cases and controls, including chest CT scan assessment of emphysema and airway disease, will allow identification of genetic determinants for the heterogeneous components of the COPD syndrome. The hypotheses to be studied are: 1) Precise phenotypic characterization of COPD subjects using computed tomography, as well as clinical and physiological measures, will provide data that will enable the broad COPD syndrome to be decomposed into clinically significant subtypes. 2) Genome-wide association studies will identify genetic determinants for COPD susceptibility that will provide insight into clinically relevant COPD subtypes. 3) Distinct genetic determinants influence the development of emphysema and airway disease. The initial phase of genome-wide association analysis included 500 COPD cases and 500 control subjects (all non-Hispanic White) genotyped with the Illumina Omni-1 chip. The second phase genotyped the entire study cohort using the Illumina Omni-Express chip. Unique aspects of the study include: 1) Inclusion of large numbers of African American subjects (approximately 1/3 of the cohort); 2) Obtaining chest CT scans (including inspiratory and expiratory images); and 3) Inclusion of the full range of disease severity. The COPDGene_v6 Cohort is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in these sub-studies, please click on the following sub-studies below or in the "Sub-studies" box located on the right hand side of this top-level study page phs000179 COPDGene_v6 Cohort. phs000296 ESP LungGO COPDGene phs000765 COPDGene_Geno

  Study phs000179

• UK10K_OBESITY_SCOOP

  In the UK10K project we propose a series of complementary genetic approaches to find new low frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will analyse directly quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches.The SCOOP samples are part of the Obesity group and will undergo exome sequencing. Severe Childhood Onset Obesity Project (SCOOP) is a sub-cohort of the Genetics Of Obesity Study (GOOS) cohort established by Sadaf Farooqi and Steve O’Rahilly at the University of Cambridge over the last 12 years. The GOOS cohort contains >4,000 patients of diverse geographic origin, many of whom have monogenic and syndromic forms of obesity, and includes patients that are offspring of consanguineous union. SCOOP is a subset of >1500 UK Caucasian patients with severe, early onset obesity (all patients have a BMI Standard Deviation Score (SDS) > 3 and obesity onset before the age of 10 years), in whom all known monogenic causes of obesity have been excluded. GWAS data on the SCOOP cohort will be available (WTCCC2 independent study) at the time of the start of this study. Data from this cohort has demonstrated that the prevalence of the common obesity risk alleles in FTO, MC4R and NEGR1 are amongst the highest within SCOOP, demonstrating its value also in the study of genetic variants with an impact on more common obesity. For further information with regard to this cohort please contact Sadaf Farooqi (isf20@medschl.cam.ac.uk).

  Study EGAS00001000124

• National Institute on Alcohol Abuse and Alcoholism (NIAAA) Postmortem Prefrontal Cortex eQTL and mQTL Study

  Mapping expression and methylation quantitative trait loci (eQTLs and mQTLs) using genome-wide genotype, transcriptome, and DNA methylome data can provide an unbiased approach to identify novel functional variants. One critical issue of the eQTL/mQTL analysis is that the role of genetic variants in regulating gene expression (or DNA methylation) can be tissue- or cell-specific. To unravel causal variants from disease-associated loci, it would be necessary to map eQTLs and mQTLs in both patients and controls. Those eQTLs and mQTLs in patients are more likely to be causal variants for the disease. Identification of disease- and tissue-specific eQTLs and mQTLs will help disentangle the role of the specific tissue in disease development as well. In the present study, we mapped eQTLs and mQTLs in postmortem prefrontal cortex of patients with alcoholism or alcohol use disorder (AUD) and matched control subjects by integrative analyses of genome-wide SNP genotype, mRNA transcriptome, and DNA methylome data. As AUD is comorbid with schizophrenia (SCZ), bipolar disorder (BIP), major depressive disorder (MDD), and attention deficit/hyperactivity disorder (ADHD), we further examined the co-localization of PFC eQTLs/mQTLs and GWAS-identified SNPs (mainly noncoding SNPs) that were associated with AUD as well as comorbid SCZ, BIP, MDD, and ADHD. Our study demonstrated the enrichment of PFC eQTLs/mQTLs in the above psychiatric disorder-associated genetic variants identified by genome-wide association studies (GWAS).

  Study phs001981

• Center for Common Disease Genomics (CCDG) Neuropsychiatric: Study of Autism Genetics Exploration (SAGE)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs001740

• Center for Common Disease Genomics (CCDG)-Neuropsychiatric: A Study of the Genetic Causes of Complex Pediatric Disorders (CAG)

  In this study, we address the enormous challenges common complex diseases pose for genomic analysis and the enormous opportunities surmounting them offers for advancing healthcare. The common genetic disorders proposed for study here are believed to have extreme locus heterogeneity, requiring the analysis of large numbers of samples to comprehensively identify the genomic variants underlying them. We propose that a combination of deep population studies and joint analysis of SNPs, indels, and structural variants both in coding and noncoding regions will provide the next level of understanding of common genetic disorders. Whole genome sequencing (WGS) will be critical to this next-generation approach to the genomics of complex disease. WGS will need to be accompanied by the technical ability to generate and handle very large data sets, a particular focus and strength of NYGC. WGS will also need to be accompanied by new statistical tools and algorithms, which will be developed by the strong core group committed to this proposal. An overarching goal of this proposal, one that capitalizes on the power of WGS, is to identify disease-associated variants at the individual nucleotide level. In many cases pathogenic mutations fall in noncoding regions of the genome, which can only be fruitfully explored with WGS. A major effort was put into building new computational strategies to functionally annotate noncoding transcribed sequences, and to build new datasets to enable such strategies, opening new frontiers of understanding of disease-related regulatory variants.

  Study phs002004

• National Cancer Institute Multi-Ancestry Genome-Wide Association Study of Kidney Cancer (NCI-3)

  The National Cancer Institute (NCI) multi-ancestral genome-wide association study (GWAS) of kidney cancer ("NCI-3") was performed by NCI to further investigate common genetic variants associated with risk of kidney cancer and its two major subtypes across individuals of different ancestries (European, African, East Asian, Latin American). The NCI-3 GWAS includes 12,315 cases and 73,091 controls from 25 centers in North America, South America and Europe, genotyped using the Illumina Global Screening Array v2.0. SNP-level results from this GWAS were combined by meta-analysis with results from two earlier NCI European-ancestry GWAS (RCC; phs000351: 1,311 cases and 3,424 controls and phs001736: 2,417 cases and 4,391 controls), a NCI GWAS of African Americans (phs000863; 255 cases and 375 controls), GWAS conducted at other institutions [International Agency for Research on Cancer (scan 1: 2,438 cases and 5,071 controls; scan 2: 2,781 cases and 2,940 controls), The University of Texas MD Anderson Cancer Center (scan 1: 893 cases and 556 controls, scan 2: 1,241 cases and 1,712 controls) and the Institute for Cancer Research, UK (944 cases and 4,024 controls)] and GWAS analyses of data from three large biobanks (Biobank Japan: 523 cases and 86,306 controls; FinnGen: 2,095 cases and 304,956 controls; UK Biobank: 1,807 cases and 348,824 controls). Only individual-level data from NCI-3 and the two MD Anderson Cancer Center scans are included in this dbGaP submission. All study-specific and meta-analysis summary statistics are included.

  Study phs003505

• Field Studies of Human Immunity to Amebiasis in Bangladesh (NIH Birth Cohort) and Exploration of the Biologic Basis for Underperformance of Oral Polio and Rotavirus Vaccines in Bangladesh (PROVIDE)

  NIH birth cohort study Amebiasis is a common cause of diarrhea and is associated with malnutrition in grade-school aged children in an urban slum of Dhaka, Bangladesh. Field Studies of Human Immunity to Amebiasis in Bangladesh was designed to determine the contribution of amebiasis to illness in the first 2 years of life when most deaths due to diarrhea occur, and understand the immunologic and genetic factors that protect children from amebiasis. The hypothesis underlying the study is that susceptibility to amebiasis is determined by host innate and acquired immune responses that vary between individuals in part due to: human genetic polymorphisms; environmental influences including malnutrition and concurrent geohelminth infection; and virulence differences among Entamoeba histolytica genotypes. Specific aims proposed in the design of the study were to: a) Measure the incidence of amebiasis and correlate it with human and parasite genetic polymorphisms, immune responses, and environmental factors such as geohelminth infection and malnutrition; b) Test the hypothesis that protective immunity is mediated both by innate immune responses initiated via TLR stimulation as well as by mucosal IgA against the Gal/GalNAc lectin and systemic IFN-γ; c) Test for the association of common genetic polymorphisms in host innate and acquired immune genes with incidence of amebiasis. 629 newborn babies were enrolled and followed regularly through bi-weekly surveillance for diarrheal episodes, anthropometry at 3-month interval until 60 months of age. The infants that were consented for GWAS analysis were genotyped in 3 separate batches at different times, on 3 different arrays. Quality control was performed on the 3 separate data sets and then jointly after merging. Genetic data available on 447 infants together with their phenotype data is made available in this submission PROVIDE (Performance of Rotavirus and Oral Polio Vaccines in Developing Countries) Study Oral polio and rotavirus vaccines are significantly less effective in children living in the developing world. Environmental enteropathy, which is associated with intestinal inflammation, decreased absorption and increased permeability, may contribute substantially to oral vaccine failure in developing country settings. The primary objective of the study was to determine the association of environmental enteropathy with the efficacy of oral polio and rotavirus vaccines in children in Bangladesh. Secondary and exploratory objectives were designed to address other possible causes of oral vaccine underperformance including malnutrition, interference with maternal or breastmilk antibodies, changes in gut microbiota, and genetic susceptibility. The PROVIDE study was structured as a 2x2 clinical trial with a prospective birth cohort enrollment of 700 infants and their mothers in the urban slum of Mirpur, Dhaka, Bangladesh. All children received the full series of Bangladesh's Expanded Program on Immunization vaccines, including oral polio vaccine (OPV) and followed for their first 2 years of life. Half the children were randomized to receive an inactivated polio vaccine (IPV) for their 4th dose at 39 weeks instead of the standard OPV dose. In the other trial, half the infants received the 2-dose rotavirus vaccine (Rotarix) with delayed dosing at 10 and 17 weeks of age. A detailed description of the study design and procedures can be obtained from PROVIDE methods publication: Kirkpatrick B.D., Colgate R.E., Mychaleckyj J.C., Haque R., Dorothy D.M., Carmolli M.P., et al. (2015) Am J Trop Med Hyg 92, 744-751 The Poliovirus trial and results are described in Mychaleckyj et al, Vaccine 2016 The Rotavirus trial and results are described in Colgate et al, Clin Infect Dis 2016 The PROVIDE infants were genotyped on a custom Affymetrix Axiom array (30K SNPs) at the University of Virginia Center for Public Health Genomics; and on a pre-production version of the Illumina MEGA v2 chip at the Wellcome Trust Sanger Institute, UK. The latter genotyping was performed under the auspices of the VaccGene Consortium. Analyses are ongoing to identify variants associated with the included phenotypes. A manuscript describing the genotyping methods and analysis is in preparation for the initial phenotypes. The Infection Malnutrition Bangladesh Cohort is utilized in the following dbGaP sub-studies. To view genotypes, other molecular data, and derived variables collected in these sub-studies, please click on the following sub-studies below or in the "Sub-studies" box located on the right hand side of this top-level study page phs001475 Infection Malnutrition Bangladesh Cohort. phs001476 Amebiasis Immunity phs001478 Polio Rotavirus Vaccines phs001665 Cryptosporidiosis Enteropathogens

  Study phs001475

• Kenya_GWAS-2.5M_b37

  Genome-wide study of resistance to severe malaria in eleven worldwide populations:Kenya

  Dataset EGAD00010000904

• Malawi_GWAS-2.5M_b37

  Genome-wide study of resistance to severe malaria in eleven worldwide populations:Malawi

  Dataset EGAD00010000903

• Gambia_GWAS-2.5M_b37

  Genome-wide study of resistance to severe malaria in eleven worldwide populations:Gambia

  Dataset EGAD00010000902

• cfDNA methylation dataset for colon cancer

  Enzymatic conversion-based methylation sequencing data (EM-seq) for colon cancer used in the MESA study

  Dataset EGAD50000000075

• Transcriptome sequencing of FFPE and patient-derived cultures

  Transcriptome sequencing of FFPE and patient-derived cultures from clinical study NCT03860376

  Dataset EGAD50000000246

• resistance mechanims to targeted therapies from RNA sequencing

  WTS of tumor tissue biopsies collected at resistance to molecular targeted agents

  Study EGAS50000000487

• Colorectal cancer functional annotation - Micro-C

  Ultra high resolution Micro-C data from multiple colorectal cancer cell lines

  Study EGAS50000000206

• Rucaparib in patients presenting a metastatic breast cancer with Homologous Recombination Deficiency, without germline BRCA1/2 mutation, pronostic value of high LOH genomic score and predictive value of HRDetect (WGS)

  Study EGAS00001004756

• Point-of-care monitoring of head and neck cancer treatment response and recurrence development using nanopore-based ctDNA consensus sequencing

  Study EGAS00001007090

• HeteroFiber (Single muscle fiber heterogeneity)

  Individual fibers of human skeletal muscle biopsies - collected at rest. m. vastus lateralis

  Study EGAS50000000418

• Pancreatic Cancer NGS WGS data

  Whole genome sequencing data for Pancreatic Cancer Samples exploring the tandem duplicator phenotype

  Study EGAS50000001078

• Whole gut virome analysis of 476 Japanese revealed a link between phage and autoimmune disease

  Elucidation of disease state by multi-layered omics analysis

  Study JGAS000415

• Multiple_myeloma_precursor_genomics

  Whole genome sequencing of up to 45 multiple myeloma precursor samples and matched normals pairs.

  Study EGAS00001006312

• Juvenile Idiopathic Arthritis exome sequencing in a consanguineous family

  Identify the causative gene defect in three affected patients with JIA.

  Study EGAS00001003510

• OAC RNASeq

  Please see our related publication, 'Molecular profiling to predict immunochemotherapy outcomes in esophageal adenocarcinoma'

  Study EGAS00001006468

• BLUEPRINT Bisulfite-seq and Whole Genome Sequencing of mantle cell lymphoma

  Bisulfite-seq and Whole Genome Sequencing of mantle cell lymphoma

  Study EGAS00001001638