13384 results for ""60+mice"+"6+weeks"+diet+study+2014+2017+DOI"

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• WXS Normal Samples Javelin head and neck 100

  Study EGAS00001007526

• Test GWAS Data for Training and Computational Benchmarking

  Study EGAS00001007914

• WXS Tumor Samples Javelin head and neck 100

  Study EGAS00001007583

• Gene expression profiles in paediatric ETV6-RUNX1 leukemia

  Study EGAS00001007097

• Interferon-related inflammaging links epigenetic age acceleration to multimorbidity

  Study EGAS00001008029

• Interferon-related inflammaging links epigenetic age acceleration to multimorbidity

  Study EGAS00001008030

• Interferon-related inflammaging links epigenetic age acceleration to multimorbidity

  Study EGAS00001008031

• Common and rare germline variants in Japanese prostate cancer patients

  Study JGAS000487

• Plasma RNA sequencing

  Plasma RNA sequencing

  Study EGAS00001000731

• Deconvolution

  Deconvolution

  Study EGAS00001001219

• Multi-region sequencing of PDAC patients

  Study EGAS00001007379

• CIDR: Collaborative Study on the Genetics of Alcoholism Case Control Study

  This is a case-control study of alcoholism, in which the subjects have been drawn from the Collaborative Study on the Genetics of Alcoholism (COGA), a large, ongoing family-based study that includes subjects from seven sites around the US. COGA has gathered detailed, standardized data on study participants, including diagnostic and neurophysiological assessments. This sample has already proved successful in identifying several genes that influence the risk for alcoholism and neurophysiological endophenotypes, which have been independently replicated. COGA data were included as part of two Genetic Analysis Workshops, and the phenotypes are familiar to the genetics community. Alcoholic probands were recruited from treatment facilities, assessed by personal interview, and after securing permission, other family members were also assessed. A set of comparison families was drawn from the same communities as the families recruited through an alcoholic proband. Assessment involved a detailed personal interview developed for this project, the Semi-Structured Assessment for the Genetics of Alcoholism (SSAGA), which gathers detailed information on alcoholism related symptoms along with other drugs and psychiatric symptoms. Many participants also came to the laboratories for electroencephalographic studies. Neurophysiological features that have been shown to be useful endophenotypes for which we have linkage and in some cases association results are included on a subset of the case-control sample: the beta power of the resting electroencephalogram (EEG), the P3(00) amplitude of the visual event-related potential (ERP), and the theta and delta event-related oscillations (EROs) underlying the P3 (See Porjesz et al., 2005; Porjesz and Rangaswamy, 2007 for reviews). A brief description of COGA is in Edenberg, H. J. (2002) The Collaborative Study on the Genetics of Alcoholism: an update. Alcohol Res Health 26, 214-218., Bierut, LJ, NL Saccone, JP Rice, A Goate, T Foroud, HJ Edenberg, L Almasy, PM Conneally, R Crowe, V Hesselbrock, T-K Li, JI Nurnberger, Jr, B Porjesz, MA Schuckit, J Tischfield, H Begleiter, and T Reich (2002) Defining alcohol-related phenotypes in humans: The Collaborative Study on the Genetics of Alcoholism. Alcohol Res Health 26, 208-213. Edenberg HJ and Foroud T (2006) The genetics of alcoholism: identifying specific genes through family studies. Addiction Biology 11, 386-396. This case-control sample of biologically unrelated individuals was drawn from COGA subjects. All cases meet DSM-IV criteria for alcohol dependence. Controls are individuals who have consumed alcohol, but did not meet any definition of alcohol dependence or alcohol abuse, nor did they meet any DSM-IIIR or DSM-IV definition of abuse or dependence for other drugs (except nicotine). All cases and controls have undergone identical clinical assessments. Many individuals in this case-control sample have not previously been genotyped. The Collaborative Study on the Genetics of Alcoholism (COGA) has four Co-Principal Investigators: B. Porjesz, V. Hesselbrock, H. Edenberg, L. Bierut. COGA includes nine different centers where data collection, analysis, and storage take place. The nine sites and Principal Investigators and Co-Investigators are: University of Connecticut (V. Hesselbrock); Indiana University (H.J. Edenberg, J. Nurnberger Jr., T. Foroud); University of Iowa (S. Kuperman); SUNY Downstate (B. Porjesz); Washington University in St. Louis (L. Bierut, A. Goate, J. Rice); University of California at San Diego (M. Schuckit); Howard University (R. Taylor); Rutgers University (J. Tischfield); Southwest Foundation (L. Almasy). Q. Max Guo serves as the NIAAA Staff Collaborator. This national collaborative study is supported by the NIH Grant U10AA008401 from the National Institute on Alcohol Abuse and Alcoholism (NIAAA) and the National Institute on Drug Abuse (NIDA). Funding support for genotyping, which was performed at the Johns Hopkins University Center for Inherited Disease Research, was provided by the National Institute on Alcohol Abuse and Alcoholism, the NIH GEI (U01HG004438),and the NIH contract "High throughput genotyping for studying the genetic contributions to human disease" (HHSN268200782096C). COGA has over 250 publications listed at www.niaaagenetics.org

  Study phs000125

• dbGaP Collection: NHLBI Heart Failure Related dbGaP Data (No IRB requirement)

  The National Heart, Lung, and Blood Institute (NHLBI) Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research has concluded. The NHLBI will continue to make this collection of data available to the research community.Past information regarding the Data Collection for the NHLBI Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research is below.The National Heart, Lung, and Blood Institute (NHLBI), part of the National Institutes of Health (NIH), is inviting novel Solutions for the NHLBI Big Data Analysis Challenge: Creating New Paradigms for Heart Failure Research. The goal of the challenge is to foster innovation in computational analysis and machine learning approaches utilizing large-scale NHLBI-funded datasets to identify new paradigms in heart failure research. The challenge aims to address the need for new open source disease models that can define sub-categorizations of adult heart failure to serve as a springboard for new research hypotheses and tool development in areas of heart failure research from basic to clinical settings. NHLBI has a history of making considerable investments in the creation of deep data resources including: long-standing, deeply-phenotyped epidemiological cohorts, innovative clinical trials, and large-scale precision medicine efforts that have generated whole genome sequencing and "other omics" data for more than one-hundred thousand individuals. To provide challenge participants with streamlined access to data across NHLBI's numerous studies containing heart failure data, the NHLBI has created this dbGaP study collection. Data access for this collection is controlled by NHLBI's Data Access Committee. Challenge participants are required to follow the use restrictions and acknowledgement instructions from the original dataset(s). Please note that the data in this collection are not harmonized across studies or otherwise altered from the original study. The NHLBI Big Data Analysis Challenge: Heart Failure Data Collection contains all NHLBI studies currently in dbGaP that contain data that may be relevant to research on heart failure. The studies in this collection are approved for General Research Use (GRU), Health/Medical/Biomedical Use (HMB), or Disease-Specific Use (DS) that permits research on heart failure. These studies span a variety of study designs, inclusion and exclusion criteria, sample sizes, and provide a wide breadth and depth of phenotype data on study participants. The available genomic data in these studies also varies, including genotyping arrays, sequencing (targeted, exome, whole genome), and additional -omic data (e.g., RNA or metabolite profiles). Please refer to each study's individual accession page to learn more about how study data were collected. Challenge participants are reminded that in addition to dbGaP, NHLBI's Biologic Specimen and Data Repository Information Coordinating Center (BioLINCC) contains other studies that have collected data relevant to heart failure and may wish to utilize BioLINCC data in the development of their Solution. Please refer to the individual accession page for each study in this collection to learn more about the history of each study.

  Study phs001991

• FHS-Net Social Networks

  Note: Data Use Restrictions: The data must only be used for genetic research projects solely in the area of health-related social-network research. This study compiles detailed information on family and social ties linked to participants in the offspring cohort of the Framingham Heart Study. The investigators took computerized information from self-report data used by the FHS for over 30 years to facilitate health exam scheduling. These tracking sheets asked participants to identify people close to them, which were assumed by the investigators to indicate a social tie. Individuals are listed as "egos" (if the ties are from their perspective) and/or "alters" (if they are linked as a friend or family members to one of the other participants). All egos were FHS offspring participants while alters could be FHS participants in any cohort or non-participants. This information was combined with internal FHS pedigree data on family ties to list family member alters (as well as the nature of their relationship). The resultant dataset therefore includes each ego-alter tie, the nature of their relationship and the start and end dates for their ties for each of seven waves for the offspring study. Due to privacy concerns, all exam dates for individuals are listed relative to the initial FHS exam date. The study investigators chose a random, confidential date, to which all initial exam dates are linked (by number of months from random date to initial exam date). With this information, researchers can have access to the temporal relationships among participants' exam dates while remaining unaware of the actual exam dates for individual participants. During each clinic exam cycle, the participants undergo a detailed examination including physical examination, medical history, laboratory testing, and electrocardiogram. Over the years, other tests (that may not be performed at every exam cycle) have included pulmonary function, lifestyle, physical function, cognitive function questionnaires, and various noninvasive cardiovascular tests including echocardiograms. This study involves phenotypic data from these exams including (when available) basic body measurements (height, weight, blood pressure), laboratory values (blood sugar levels, LDL levels), information on smoking and alcohol use, and tests of depression and cognitive functioning. Important links to apply for individual-level data Data Use Certification Requirements (DUC) Instructions to Request Authorized Access Apply here for controlled access to individual level data Participant Protection Policy FAQ In 1948, researchers recruited men and women from the town of Framingham, Massachusetts, beginning the first round of extensive physical examinations and lifestyle interviews that would later be analyzed for common patterns related to CVD development. Initially, the Framingham Heart Study enrolled 5,209 men and women from the Framingham area who were between the ages of 28 and 62 years. Beginning in 1971, the Framingham Heart Study enrolled 5,124 men and women, who were either offspring of the original cohort or spouses of those offspring. In 2002, 4,095 third generation participants (men and women) were enrolled.

  Study phs000153

• RNA-Seq data from tumor samples collected from 12 UTSW translocation renal cell carcinoma (tRCC) patients.

  This study investigates differentially expressed genes associated with the progression of translocation renal cell carcinoma (tRCC) by analyzing RNA-Seq data from 23 tumor samples obtained from 12 patients. The samples were collected from affiliated hospitals of the University of Texas Southwestern Medical Center (UTSW), including Parkland Hospital and Children’s Medical Center. RNA isolated from tumor samples was sequenced using the Illumina platform.

  Study EGAS50000000127

• Type 2 Diabetes Starr County GWAS and Exome Sequencing

  The T2D Seq GWAS Starr County 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" section of this top-level study page phs001166 T2D Seq GWAS Starr County Cohort. phs000143 CIDR T2D Hanis phs001099 T2D GENES Starr County

  Study phs001166

• Identification of ALS Associated Genes Using Whole Genome Sequencing

  The overall aim of this study is to discover of novel genetic elements associated with amyotrophic lateral sclerosis (ALS). Towards this goal, we have performed exome and whole genome sequencing for over 2000 samples. This data will be process and harmonized to yield high quality variant calling. These variants will be combined with control cohorts to identify novel genetic elements associated with ALS through case: control analyses.

  Study phs001585

• NHLBI's Collection of Datasets for General Research Use (Public Posting of Genomic Summary Results: Allowed)

  This collection contains all of NHLBI's authorized individual-level genomic datasets currently in dbGaP that are approved for General Research Use (GRU) and have no further limitations beyond those outlined in the model Data Use Certification Agreement. Genomic Summary Results (GSR) sharing is allowed (unrestricted) or not applicable. To request access to this study collection, select phs003132 in the dbGaP Authorized Access System.

  Study phs003132

• Interruption of BTK Inhibitor Improves Response to SARS-CoV-2 Booster Vaccination in Patients with Chronic Lymphocytic Leukemia

  This study investigates the effect of booster SARS-CoV-2 vaccination and Bruton tyrosine kinase inhibitor (BTKi) interruption on antibody response to vaccination. Patients with chronic lymphocytic leukemia (CLL) are immunocompromised, at increased risk of infection, and have historically poor response to immunization. BTKi therapy is a standard treatment for CLL in frontline and salvage settings and further suppresses antibody vaccine responses.

  Study phs003319

• Genomic-Enabled Medicine for Recurrent Glioblastoma

  In this prospective study, we performed genome-wide tumor/normal exome sequencing and tumor RNA-sequencing for recurrent glioblastoma patients. A subset of patients had both enhancing tumor and non-enhancing tissue sequenced to investigate spatial heterogeneity of this disease. Together, this information builds our understanding of the genomic underpinnings of glioblastoma, and contributes toward the knowledge base for identifying and developing more effective treatments for patients with glioblastoma.

  Study phs001460

• Maintenance of Genome Sequence Integrity in Long- and Short-lived Rodent Species

  In this study, we compared somatic mutation frequencies and spectra of bleomycin-treated and control fibroblasts of 5 species, i.e., mouse, guinea pig, blind mole-rat, naked mole-rat and humans, by using single-cell whole-genome sequencing. Sequencing data of the human cells are deposited here in the dbGaP. Sequencing data of the rodent cells are in the SRA database, under accession ID: PRJNA753541.

  Study phs002610

• Advanced Genetic and Molecular Analysis of Solid Tumors

  This study is designed to understand the role of immunosuppressive regulatory B cells (Breg) in pancreatic cancer. Breg and conventional B cells (Bcon) from patients are compared by RNA sequencing analysis. Bregs and Bcon were isolated from freshly isolated PBMCs in peripheral blood. These data represent the examination of differentially expressed genes in these two blood-derived B cell subsets from both healthy and pancreatic cancer patients.

  Study phs001999

• Type 2 Diabetes in African Americans, GWAS and Exome Sequencing

  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. phs000140 CIDR T2D Bowdens phs001102 T2D GENES AA

  Study phs001167

• Epigenetic Mechanisms of Inflammation and Fatigue in Head and Neck Cancer

  This was a longitudinal observational study to examine the association between fatigue and DNA methylation as well as gene expression among patients with head and neck cancer. Demographic and clinical variables were collected before chemoradiotherapy and/or at follow-up visits as appropriate. Fatigue was assessed and biosamples (for DNA methylation and gene expression) were collected before, immediately after, and at six- and twelve-months post-chemoradiotherapy.

  Study phs002106

• Cellular Origins and Genetic Landscape of Cutaneous GD T Cell Lymphoma

  Cutaneous gamma-delta T cell lymphomas are a rare and aggressive subtype of cutaneous lymphoma. In the present study, we analyzed these cancers by DNA and RNA sequencing in order to illuminate the genetic landscape for this rare disease. We obtained tumor samples from multiple institutions, including Northwestern University (Chicago, IL), University of Chicago (Chicago, IL), University of Virginia (Charlottesville, VA), and Massachusetts General Hospital (Boston, MA).

  Study phs001969