Type 2 Diabetes in African Americans, GWAS and Exome Sequencing
Ontology highlight
ABSTRACT: The T2D Seq GWAS AA 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 phs001167 T2D Seq GWAS AA Cohort.
SECONDARY ACCESSION(S): PRJNA328294PRJNA328295
REPOSITORIES: dbGaP
Similar Datasets
Project description:<p><p><b>The T2D Seq GWAS Starr County Cohort is utilized in the following dbGaP sub-studies.</b> 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" section of this top-level study page <a href="study.cgi?study_id=phs001166">phs001166</a> T2D Seq GWAS Starr County Cohort. <ul> <li><a href="study.cgi?study_id=phs000143">phs000143</a> CIDR T2D Hanis</li> <li><a href="study.cgi?study_id=phs001099">phs001099</a> T2D GENES Starr County</li> </ul> </p></p>
| phs001166 | dbGaP
Project description:<p>The Jackson Heart Study (JHS) is a large, community-based, observational study whose participants were recruited from urban and rural areas of the three counties (Hinds, Madison and Rankin) that make up the Jackson, MS metropolitan statistical area (MSA). Participants were enrolled from each of 4 recruitment pools: random, 17%; volunteer, 30%; currently enrolled in the Atherosclerosis Risk in Communities (ARIC) Study, 31% and secondary family members, 22%. The final cohort of 5,306 participants included 6.59% of all African American Jackson MSA residents aged 35-84 during the baseline exam (N-76,426, US Census 2000). Among these, approximately 3,600 gave consent that allows genetic research and deposition of data into dbGaP. Major components of three clinic examinations (Exam 1 - 2000-2004; Exam 2 - 2005-2008; Exam 3 - 2009-2013) include medical history, physical examination, blood/urine analytes and interview questions on areas such as: physical activity; stress, coping and spirituality; racism and discrimination; socioeconomic position; and access to health care. Extensive clinical phenotyping includes anthropometrics, electrocardiography, carotid ultrasound, ankle-brachial blood pressure index, echocardiography, CT chest and abdomen for coronary and aortic calcification, liver fat, and subcutaneous and visceral fat measurement, and cardiac MRI. At 12-month intervals after the baseline clinic visit (Exam 1), participants have been contacted by telephone to: update information; confirm vital statistics; document interim medical events, hospitalizations, and functional status; and obtain additional sociocultural information. Questions about medical events, symptoms of cardiovascular disease and functional status are repeated annually. Ongoing cohort surveillance includes abstraction of medical records and death certificates for relevant International Classification of Diseases (ICD) codes and adjudication of nonfatal events and deaths. CMS data are currently being incorporated into the dataset.</p> <p>A note regarding date variables warehoused in the Jackson Heart Study (JHS) Cohort on dbGaP: The coordinating center has developed an algorithm that will systematically review and de-identify any of the (nearly 100) date-related variable types stored in the data package. To simultaneously minimize (i) de-identifiability of the data and (ii) impact on analyses utilizing sensitive data elements, a participant-level random number was generated to avoid the necessity of sharing any potentially sensitive data. The coordinating center maintains an archived linkage of these data in their raw form and regularly reviews ad hoc requests to utilize the raw data on a project-by-project basis.</p> <p><b>The JHS Cohort is utilized in the following dbGaP sub-studies.</b> 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 <a href="./study.cgi?study_id=phs000286">phs000286</a> JHS Cohort. <ul> <li><a href="./study.cgi?study_id=phs000402">phs000402</a> HeartGO JHS</li> <li><a href="./study.cgi?study_id=phs000498">phs000498</a> JHS Allelic Spectrum Seq</li> <li><a href="./study.cgi?study_id=phs000499">phs000499</a> JHS CARe</li> <li><a href="./study.cgi?study_id=phs001098">phs001098</a> T2D GENES Exome Seq</li> <li><a href="./study.cgi?study_id=phs001069">phs001069</a> MIGen JHS</li> <li><a href="./study.cgi?study_id=phs001356">phs001356</a> Exome Chip</li> </ul> </p>
| phs000286 | dbGaP
Project description:<p>The Cancer Genome Atlas (TCGA) is a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies, including large-scale genome sequencing. TCGA is a joint effort of the National Cancer Institute (NCI) and the National Human Genome Research Institute (NHGRI), which are both part of the National Institutes of Health, U.S. Department of Health and Human Services.</p> <p>TCGA projects are organized by cancer type or subtype. Click <a href="http://cancergenome.nih.gov/cancersselected" target="_blank">here</a> for a current list of cancer types selected for study in TCGA.</p> <p>Data from TCGA (e.g., gene expression, copy number variation and clinical information), are available via the <a href="https://tcga-data.nci.nih.gov/tcga/" target="_blank">TCGA Data Portal</a>, EXCEPT for the genomic sequence data (.bam files), which are hosted at the <a href="https://cghub.ucsc.edu/" target="_blank">Cancer Genomics Hub (CGHub)</a>.</p> <p>Data from TCGA projects are organized into two tiers: <b>Open Access and Controlled Access</b>. <ul> <li>Open Access data tier contains data that cannot be attributed to an individual research participant. The Open Access data tier does not require user certification. Data in Open Access tier are available in the TCGA Data Portal.</li> <li>Controlled Access data tier contains individual-level genotype data that are unique to an individual. Access to data in the Controlled Access data tier requires user certification through <a href="https://dbgap.ncbi.nlm.nih.gov/aa/wga.cgi?login=&page=login" target="_blank">dbGaP Authorized Access</a>.</li> <li>Controlled Access data types consist of the following: <ul> <li>Individual germline variant data (SNP .cel files)</li> <li>Primary sequence data (.bam files), which are available at CGHub</li> <li>Clinical free text fields</li> <li>Exon Array files (for Glioblastoma and Ovarian projects only)</li> </ul> </li> </ul> </p> <p><b>NOTE: TCGA strives to release most data in the open access tier. Individual genotype or sequence files are prominent exceptions. Commonly requested files such as descriptions of somatic mutations or clinical data are open access.</b></p> <p><b>The TCGA study is utilized in the following dbGaP substudies.</b> To view genotypes and other molecular data collected in these substudies, please click on the following substudies below or in the "Substudies" box located on the right hand side of this top-level study page phs000178 TCGA study. <ul> <li><a href="./study.cgi?study_id=phs000441">phs000441</a> Integrated Genomic Analyses of Ovarian Carcinoma (OV)</li> <li><a href="./study.cgi?study_id=phs000489">phs000489</a> Comprehensive Genomic Characterization Defines Human Glioblastoma Genes and Core Pathways</li> </ul> </p>
| phs000178.v6.p6 | EGA
Project description:This SuperSeries is composed of the SubSeries listed below. The raw sequencing data is available through dbGaP (controlled access) due to patient privacy concerns. https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001961.v1.p1
2020-11-23 | GSE161383 | GEO
Project description:<p>The goal of the study is to find susceptibility genes for schizophrenia.</p> <p><b>Dataset versioning</b><br/> <ul> <li>Version 1: European-American (EA) ancestry only</li> <li>Version 2: Version 1 plus African-American (AA) ancestry</li> </ul> </p> <p><b>Consent groups and participant set</b><br/> <ul> <li>General research use (GRU): 4591 cases and controls (1217 EA cases, 1442 EA controls, 953 AA cases, 979 AA controls)<br/> This consent group includes a subset of schizophrenia cases and all controls (which overlap with Bipolar study controls in Bipolar: GRU dataset).</li> <li>Schizophrenia and related disorders (SARC): 475 cases (187 EA cases, 288 AA cases)<br/> This consent group includes a subset of schizophrenia cases.</li> </ul> </p>
| phs000021.v3.p2 | EGA
Project description:<p>Within the framework of the International Lymphoma Epidemiology Consortium (InterLymph) Consortium and NCI-sponsored Cohort Consortium, investigators from 22 studies came together to conduct a genome-wide association study (GWAS) of non-Hodgkin lymphoma (NHL). The goal of the study was to identify common genetic variants associated with the risk of NHL. As described previously (Berndt SI et al., Nature Genetics, 2013, PMID: <a href="http://www.ncbi.nlm.nih.gov/pubmed/?term=23770605">23770605</a> ), cases and controls from 22 studies, including nine prospective cohort studies, eight population-based case-control studies, and five clinic or hospital-based case-control studies were included in this effort. Cases of NHL were ascertained from cancer registries, clinics or hospitals, or through self-report verified by medical and pathology reports. The phenotype information for all NHL cases was reviewed centrally at the InterLymph Data Coordinating Center and harmonized according to the hierarchical classification proposed by the InterLymph Pathology Working Group based on the World Health Organization (WHO) classification (2008). Controls were frequency matched to cases on age and gender by study, and the study was limited to subjects of European ancestry. Participants were genotyped using the Illumina Omni Express.</p> <p><b>The NCI Non-Hodgkin Lymphoma GWAS is utilized in the following dbGaP sub-studies.</b> To view genotypes 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 <a href="study.cgi?study_id=phs000801">phs000801</a> NCI Non-Hodgkin Lymphoma GWAS. <ul> <li><a href="study.cgi?study_id=phs000802">phs000802</a> NCI Non-Hodgkin Lymphoma GWAS: CLL</li> <li><a href="study.cgi?study_id=phs000818">phs000818</a> NCI Non-Hodgkin Lymphoma GWAS: Controls</li> <li><a href="study.cgi?study_id=phs000889">phs000889</a> NCI Non-Hodgkin Lymphoma GWAS: DLBCL</li> <li><a href="study.cgi?study_id=phs000890">phs000890</a> NCI Non-Hodgkin Lymphoma GWAS: FL</li> <li><a href="study.cgi?study_id=phs000891">phs000891</a> NCI Non-Hodgkin Lymphoma GWAS: MZL</li> </ul> </p>
| phs000801 | dbGaP
Project description:<p>Genetics & Epidemiology of Colorectal Cancer Consortium (GECCO) is a collaborative effort which aims to accelerate the discovery of colorectal cancer-related variants by discovering, replicating and fine-mapping Genome Wide Association Study (GWAS) findings, conducting a meta-analysis of GWAS data, and investigating how genetic variants are modified by environmental risk factors. The coordinating center for this consortium is based at the Fred Hutchinson Cancer Research Center (Principal investigator: Ulrike Peters).</p> <p><b>The GECCO Consortium is utilized in the following dbGaP individual studies.</b> To view genotypes, and derived variables collected in these individual studies, please click on the following individual studies below or in the "Sub-studies" box located on the right hand side of this top-level study page <a href="study.cgi?study_id=phs001078">phs001078</a> GECCO Consortium. <ul> <li><a href="study.cgi?study_id=phs000965">phs000965</a> VITAL</li> <li><a href="study.cgi?study_id=phs001056">phs001056</a> DALS</li> <li><a href="study.cgi?study_id=phs001077">phs001077</a> WHI Colorectal Cancer</li> <li><a href="study.cgi?study_id=phs001092">phs001092</a> PMH CCFR</li> <li><a href="study.cgi?study_id=phs001094">phs001094</a> PLCO</li> <li><a href="study.cgi?study_id=phs001104">phs001104</a> ASTERISK</li> <li><a href="study.cgi?study_id=phs001113">phs001113</a> DACHS</li> <li><a href="study.cgi?study_id=phs001114">phs001114</a> HPFS</li> <li><a href="study.cgi?study_id=phs001128">phs001128</a> PHS</li> <li><a href="study.cgi?study_id=phs001129">phs001129</a> NHS</li> <li><a href="study.cgi?study_id=phs001170">phs001170</a> Colo2and3</li> <li><a href="study.cgi?study_id=phs001171">phs001171</a>MEC Colorectal Cancer</li> </ul> </p>
| phs001078 | dbGaP
Project description:<p>The Pediatric Investigation for Genetic Factors Associated with Renal Progression (PediGFR) (RO1-DK082394) is an international collaborative study among three large prospective cohort studies of children with chronic kidney disease. The participating parent cohort studies are the Chronic Kidney Disease in Children (CKiD), the Effect of Strict Blood Pressure Control and ACE Inhibition on CRF Progression in Pediatric Patients (ESCAPE), and the Cardiovascular Comorbidity in Children with Chronic Kidney Disease (4C) study. In these cohorts, pediatric subjects with CKD have been prospectively followed with standardized measurements for renal progression. The dbGaP data upload will utilize the structure of sub-studies to include the genotype and baseline phenotype for the three cohorts, as well as the RBC trait and anemia data for a sub-study under PI Susan Furth: Role of Genetic Variation in the Anemia of Chronic Kidney Disease (K24DK078737). </p> <p>In brief PediGFR is a prospective study of children with chronic kidney disease (CKD) to determine genetic factors associated with kidney function measured by the estimated glomerular filtration rate (eGFR) by Schwartz equation.</p> <p><b>The PediGFR_v2 Cohort is utilized in the following dbGaP sub-studies.</b> 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 <a href="study.cgi?study_id=phs000842">phs000842</a> PediGFR_v2 Cohort. <ul> <li><a href="study.cgi?study_id=phs000843">phs000843</a> 4C</li> <li><a href="study.cgi?study_id=phs000650">phs000650</a> CKiD</li> <li><a href="study.cgi?study_id=phs000844">phs000844</a> ESCAPE</li> </ul> </p>
| phs000842 | dbGaP
Project description:<p>The goal of the project is to identify genes that make individuals more susceptible to bipolar disorder (manic depressive illness) and to better understand the brain pathways involved in the disease.</p> <p><b>Dataset versioning</b><br/> <ul> <li>Version 1: European-American (EA) ancestry only</li> <li>Version 2: Version 1 plus African-American (AA) ancestry</li> <li>Version 3: EA and AA samples plus updated diagnostic criteria</li> </ul> </p> <p><b>Consent groups and participant set</b><br/> <ul> <li>General research use (GRU): 1767 controls (1081 EA controls, 686 AA controls)<br/> This consent group includes all controls for the Bipolar study, which are a subset of controls for the Schizophrenia study (subset of Schizophrenia: GRU).</li> <li>Bipolar and related disorders (BARD): 841 cases (691 EA cases, 150 AA cases)<br/> This consent group includes a subset of the Bipolar cases.</li> <li>Bipolar disorder only (BDO): 653 cases (388 EA cases, 265 AA cases)<br/> This consent group includes a subset of the Bipolar cases.</li> </ul> </p>
| phs000017.v3.p1 | EGA
Project description:<p>For the NIH Roadmap Epigenomics project, we applied ChlP-Seq, HTBS and WGBS pipelines to generate comprehensive high-resolution maps of chromatin state and DNA methylation for 100 diverse cell types. Cell types were selected for their biological and medical importance, and for their potential to maximize the comprehensiveness of acquired epigenomic data. They include human ES cells, ES-derived cells, mesenchymal stem cells, reprogrammed stem cells and primary tissues. Comprehensive characterization of epigenetic marks ("the epigenome") is a critical step towards a global understanding of the human genome in health and disease. In this study we provide unprecedented views of the human epigenetic landscape and its variation across cell states, which offer fundamental insight into the functions and interrelationships of epigenetic marks, and provide a framework for future studies of normal and diseased epigenomes.</p> <p><b>The Roadmap Epigenomics Broad cohort is utilized in the following dbGaP sub-study.</b> To view molecular data and derived variables collected in this sub-study, please click on the following sub-study below or in the "Sub-studies" box located on the right hand side of this top-level study page <a href="study.cgi?study_id=phs000700">phs000700</a> the Roadmap Epigenomics Broad cohort. <ul> <li><a href="study.cgi?study_id=phs000610">phs000610</a> RM_Epigenomics_Broad_Alz</li> </ul> </p>
| phs000700 | dbGaP