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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Related Experiment Video

Updated: Aug 18, 2025

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint
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Osteoarthritis Data Integration Portal (OsteoDIP): A web-based gene and non-coding RNA expression database.

Chiara Pastrello1,2, Mark Abovsky1,2, Richard Lu1,2

  • 1Osteoarthritis Research Program, Division of Orthopedic Surgery, Schroeder Arthritis Institute, University Health Network, Toronto, Canada.

Osteoarthritis and Cartilage Open
|December 7, 2022
PubMed
Summary

OsteoDIP consolidates osteoarthritis gene expression data from literature and GEO, offering a searchable resource for researchers. This platform facilitates cross-study comparisons and enhances translational research pipelines in osteoarthritis.

Keywords:
Data integrationGEO, Gene Expression OmnibusGWAS, Genome Wide Assocaition StudiesGene expressionHGNC, HUGO Gene Nomenclature CommitteeIID, Integrated Interactions DatabaseLong non-coding RNANAViGaTOR, NetworkAnalysis, Visualization, & Graphing TORontoOA, osteoarthritisOsteoDIP, Osteoarthritis Data Integration PortalTCGA, The Cancer Genome Atlasintegrative computational biologymicroRNAmirDIP, MicroRNA Data Integration PortalpathDIP, Pathway Data Integration Portal

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Area of Science:

  • Biomedical Informatics
  • Genomics
  • Molecular Biology

Background:

  • Osteoarthritis (OA) research generates vast amounts of gene expression data.
  • Existing data is often fragmented across publications and databases, hindering comprehensive analysis.
  • A centralized, searchable resource is needed to facilitate OA research.

Purpose of the Study:

  • To create OsteoDIP, a curated, searchable database of high-throughput RNA expression data for osteoarthritis.
  • To integrate data from scientific literature and public repositories like GEO.
  • To support translational research by enabling data comparison and integration.

Main Methods:

  • Systematic literature searches in PubMed for OA gene expression profiles, including patient data and differential gene lists.
  • Expansion of search criteria to include non-coding RNAs and utilization of GEO datasets with specific filters.
  • Annotation of genes using external databases relevant to osteoarthritis.
  • Data curation and integration from 1204 papers (63 included) and 28 GEO datasets.

Main Results:

  • OsteoDIP incorporates data from 1924 samples via literature and 1012 samples via GEO.
  • The majority of data focuses on knee OA and cartilage tissue.
  • GEO datasets are integrated with clinical data, enhancing their utility.
  • The database provides a comprehensive snapshot of OA gene expression research.

Conclusions:

  • OsteoDIP provides a publicly accessible, user-friendly platform for OA expression data.
  • The consolidated data allows for cross-study comparisons and data integration.
  • OsteoDIP is designed to improve and streamline osteoarthritis research workflows.
  • The resource supports translational research by facilitating data accessibility and analysis.