Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Genetic Components of Knee Osteoarthritis (GeCKO) Study: OAI

ABSTRACT:

The Osteoarthritis Initiative (OAI) is a publicly and privately funded prospective longitudinal cohort with a primary objective of identifying risk factors for incidence and progression of tibiofemoral knee OA. The OAI utilized a focused population-based recruitment to enroll 4,674 men and women between the ages of 45-79 years who either had radiographic symptomatic knee OA or who were without radiographic symptomatic OA in both knees but were considered high risk for OA because they had two or more known risk factors for knee OA. Subjects were recruited into the baseline phase of the OAI at multiple sites throughout the US between 2004 and 2006. All subjects were invited back for follow-up examinations to assess incidence or progression of OA annually, for up to 5 years.

Phenotype data from the baseline and follow-up examinations are available for public access from the Osteoarthritis Initiative (OAI) database at http://www.oai.ucsf.edu/.

The Genetic Components of Knee Osteoarthritis (GeCKO) Study was initiated in 2009 as a genetic ancillary study to perform a genome-wide association study to identify genetic variants associated with radiographic osteoarthritis. This study included 4,482 individuals participating in the parent OAI study. Following sample cleaning (e.g., removal of duplicates, sex mismatches, poor sample quality, etc.), the final sample set included 4,129 individuals.

PROVIDER: phs000955 | dbGaP |

SECONDARY ACCESSION(S): PRJNA289575PRJNA289576

REPOSITORIES: dbGaP

ACCESS DATA

Json Xml

Similar Datasets

Blood methylomic signatures of pre-symptomatic dementia in elderly subjects with Type 2 Diabetes Mellitus

Project description:Due to an increasingly aging population, the incidence of dementias such as Alzheimer’s disease are steadily rising, with recent estimates predicting >115million dementia sufferers by 2050. The ability to identify early markers in blood, which appear before the onset of clinical symptoms is of considerable interest to allow early intervention, particularly in “high risk” groups such as those with Type 2 Diabetes (T2D). Here we present longitudinal genome-wide DNA methylation data comparing 18 elderly individuals with T2D who developed pre-symptomatic dementia within an 18 month period following baseline assessment to 18 age, sex and education matched controls who maintained normal cognitive function. We identified a highly significant overlap in the effect size of the top-ranked methylation sites at baseline and follow-up, and identified 8 robust loci, some of which have been previously related to neurodegenerative processes, which were consistently differentially methylated prior to symptoms at baseline, and at 18 month follow up, when a diagnosis of pre-symptomatic dementia had been provided. Finally we show a significant overlap in the effect size of the top-ranked methylation sites in converters, only after they develop symptoms of pre-symptomatic dementia, with changes at the same loci in blood samples from patients with clinically-diagnosed Alzheimer’s disease.

2015-02-28 | GSE62003 | GEO

DNA copy number alterations in endobronchial squamous metaplastic lesions predict lung cancer

Project description:BACKGROUND Autofluorescence bronchoscopy (AFB) is a valid strategy for detecting premalignant endobronchial lesions. However, no biomarker can reliably predict lung cancer risk of subjects with AFB-visualized premalignant lesions. Our present study was set out to identify AFB-visualized squamous metaplastic lesions with malignant potential by DNA copy number profiling. METHODS Regular AFB-examinations in 474 subjects at risk of lung cancer identified 6 subjects with SqM lesions at baseline and carcinoma (in situ) at the initial SqM site at follow-up bronchoscopy. These progressive SqM lesions were compared for immunostaining pattern and arrayCGH-based chromosomal profiles to 23 SqM of subjects who remained cancer-free. Specific copy number alterations (CNAs) linked to cancer risk were identified and accuracy of CNAs to predict endobronchial cancer in this series was determined. RESULTS At baseline, p53, p63 and Ki-67 immunostaining were not predictive for a differential clinical outcome of SqM lesions. The mean number of CNAs in baseline SqM of cases was significantly higher compared to controls (p<0.01). Chromosomal regions significantly more frequently altered in SqM of cases were 3p26.3-p11.1, 3q26.2-q29, 9p13.3-p13.2, and 17p13.3-p11.2 (FWER<0.10). CNAs were specifically detected at the site of future cancer. In cases, baseline-detected CNAs persisted in subsequent biopsies taken from the initial site, and levels increased towards cancer progression. CNAs at 3p26.3-p11.1, 3q26.2-29, and 6p25.3-24.3, predicted cancer risk for AFB-visualized SqM with 97% accuracy. CONCLUSION Our data strongly suggest that the presence of specific DNA copy number alterations in endobronchial SqM lesions predict endobronchial cancer.

2011-08-02 | GSE23644 | GEO

Longitudinal global transcriptomic profiling of preclinical systemic sclerosis reveals molecular changes associated with disease progression

Project description:Objective: To investigate peripheral blood cell (PBCs) global gene expression profile of SSc at its preclinical stage (PreSSc) and to characterize the molecular changes associated with progression to a definite disease over time. Material and methods: Clinical data and PBCs of 33 participants with PreSSc and 16 healthy controls (HCs) were collected at baseline and follow-up (mean 4.2 years). Global gene expression profiling was conducted by RNA sequencing and a modular analysis was performed. Results: Comparison of baseline PreSSc to HCs revealed 2889 differentially expressed genes. Interferon signalling was the only activated pathway among top over-represented pathways. Moreover, 10 modules were significantly decreased in PreSSc samples (related to lymphoid lineage, cytotoxic/NK cell, and erythropoiesis) in comparison to HCs. At follow-up, 14 subjects (42.4%) presented signs of progression (evolving PreSSc) and 19 remained in stable preclinical stage (stable PreSSc). Progression was not associated with baseline clinical features or baseline PBC transcript modules. At follow-up stable PreSSc normalized their down-regulated cytotoxic/NK cell and protein synthesis modules while evolving PreSSc kept a down-regulation of cytotoxic/NK cell and protein synthesis modules. Transcript level changes of follow-up vs baseline in stable PreSSc vs evolving PreSSc showed 549 differentially expressed transcripts (336 up and 213 down) with upregulation of the EIF2 Signalling pathway. Conclusions: Participants with PreSSc had a distinct gene expression profile indicating that molecular differences at a transcriptomic level are al_x0002_ready present in the preclinical stages of SSc. Furthermore, a reduced NK signature in PBCs was related to SSc progression over time.

2023-09-13 | GSE224849 | GEO

Extracellular Matrix Proteomics Identifies Molecular Signature of Atherosclerotic Plaques from Symptomatic Patients

Project description:Recent findings have challenged the prevailing histology- or imaging-based definition of the “vulnerable plaque”. To investigate molecular characteristics associated with “clinical instability” of atherosclerosis, we performed a proteomics comparison of the vascular extracellular matrix and associated molecules in human carotid endarterectomy specimens from symptomatic versus asymptomatic patients. The proteomics data were integrated with gene expression profiling and an analysis of protein secretion by lipid-loaded human vascular smooth muscle cells. The molecular signature of plaques from symptomatic patients identified by proteomics and at least one of the other two approaches comprised matrix metalloproteinase-9, chitinase-3-like protein 1, S100A8/S100A9, cathepsin B, fibronectin and galectin-3-binding protein. These biomarker candidates were measured in 685 subjects of the Bruneck Study and found to significantly predict the progression to advanced atherosclerosis (as assessed by repeated carotid ultrasound) and the incidence of cardiovascular disease over a 10-year follow-up period, highlighting the strength of tissue-based proteomics for biomarker discovery.

2017-04-20 | PXD005130 | Pride

Changes in DNA methylation pattern following intervention in PTSD patients

Project description:Objective: The purpose of this study was to observe changes in DNA methylation in service members who experienced PTSD. Methods: We compared DNA Methylation profiles in the blood of those with or without PTSD and also compared those with PTSD at both baseline and follow-up with those service members that only developed PTSD at follow-up (3-months after baseline). Blood was collected and analyzed at baseline and follow up. Results: In participants with PTSD, 10 genes were significantly hyper-methylated and 18 were significantly hypo-methylated. In the second analysis, participants that had PTSD at both time points did not present any differentially methylated gene regions. Interestingly, for individuals’ whose PTSD scores increased from baseline to follow-up, there were 8 hypo-methylated genes detected at the baseline.

2018-06-14 | GSE93673 | GEO

Homo sapiens

Project description:Sarcoidosis + Follow-up study

| PRJNA90973 | ENA

Porcine epidemic diarrhea in Europe

Project description:COMPARE H5N8 Follow-up

| PRJEB19037 | ENA

DNA methylation data for older persons with vs. without mild cognitive impairment

Project description:Genome wide DNA methylation assays was conducted using the Illumina Infinium MethylationEPIC BeadArray technology (Methyl850K chip) that allows genome-wide DNA methylation analysis of 866,836 CpG sites. We included baseline and 2-year follow-up samples from 25 persons with mild cognitive impairment (cases) and 20 persons with cognitively normal (controls). Sample was balanced by age and sex.

2021-12-10 | GSE190540 | GEO

STAMPEED: Cardiovascular Health Study (CHS) GWAS to identify genetic variants associated with aging and CVD risk factors and events

Project description:<p>The primary aim of the study is to conduct a genome-wide association study to identify genetic variants associated with the incidence of myocardial infarction (MI), stroke, and heart failure (HF) among participants enrolled in the Cardiovascular Health Study (CHS) who were free of clinical cardiovascular disease at baseline. The secondary aim is to conduct genome-wide association study of other phenotypes in CHS. The study is an ancillary study to CHS. CHS is a population-based cohort study of risk factors for heart disease and stroke among older adults recruited at 4 US sites in 1989-1990. Subjects underwent an extensive baseline examination, and annual follow-up examinations through 1988-1999.</p>

| phs000226.v2.p1 | EGA

HAVCR/KIM-1 activates the IL-6/STAT-3 Pathway in ccRCC and Determines Tumor Progression and Patient Outcome

Project description:Renal cell carcinoma (RCC), the third most prevalent urological cancer, claims more than 100,000 lives/year worldwide. The clear cell (ccRCC) is the most common and aggressive subtype of renal cancer. Commonly asymptomatic, more than 30% of ccRCCs are diagnosed when they are already metastatic resulting in a 95% mortality rate and one-third of organ-confined cancers treated by nephrectomy develop metastasis during the follow-up. Diagnosis and monitoring requires expensive and frequent imaging examinations; thereby, finding of diagnostic and prognostic biomarkers to screen, diagnose and monitor renal cancers are clearly needed. Hepatitis A virus receptor/kidney injury molecule 1 (HAVCR1/KIM-1) gene has been claimed to be a susceptibility gene for ccRCC and HAVCR1/KIM-1 ectodomain shedding a predictive biomarker of tumor progression.

2020-09-27 | E-MTAB-2071 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data