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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...
The JAK-STAT Signaling Pathway01:20

The JAK-STAT Signaling Pathway

Several cytokine receptors have tightly bound Janus kinase or JAK proteins attached at their cytosolic tail. Small signaling molecules such as cytokines, growth hormones, or prolactins bind to the cytokine receptors and initiate their dimerization. The dimerization brings the cytosolic JAKs together that trans-phosphorylate and activates each other. The activated JAKs now phosphorylate cytosolic tails of the cytokine receptors, which serve as binding sites for adaptor proteins such as  SH2...
Pharmacogenomics: Identification of New Drug Targets01:29

Pharmacogenomics: Identification of New Drug Targets

Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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Related Experiment Video

Updated: Jul 2, 2026

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint
06:06

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint

Published on: July 22, 2021

Cross-Tissue Transcriptome-Wide Association Study Prioritizes Candidate Genes and Compound-Associated Signatures for

Ning Zhang1,2, Xinyue Hu1, Guanglei Chen1

  • 1Guizhou University of Traditional Chinese Medicine, Guiyang, China.

Current Pharmaceutical Design
|July 1, 2026
PubMed
Summary
This summary is machine-generated.

This study identifies TACC3, LTBP1, and TMEM129 as key genes linked to osteoarthritis (OA) susceptibility. These findings advance our understanding of OA genetics and offer new targets for future research.

Keywords:
Cross-tissue TWASGeneMANIAenrichrhip osteoarthritisknee Osteoarthritisosteoarthritis

Related Experiment Videos

Last Updated: Jul 2, 2026

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint
06:06

Tissue Collection and RNA Extraction from the Human Osteoarthritic Knee Joint

Published on: July 22, 2021

Area of Science:

  • Genetics
  • Genomics
  • Molecular Biology

Background:

  • Osteoarthritis (OA) is a prevalent degenerative joint disease causing cartilage degradation and pain, significantly impacting quality of life.
  • Current OA therapies manage symptoms but do not halt disease progression.
  • Understanding the genetic basis of OA, particularly the function of noncoding variants, is crucial for developing effective treatments.

Purpose of the Study:

  • To integrate Genome-Wide Association Study (GWAS) data with expression quantitative trait locus (eQTL) data to identify genes associated with OA susceptibility.
  • To prioritize candidate genes by evaluating their regulatory mechanisms and functional relevance.
  • To improve the molecular interpretation of OA genetic loci.

Main Methods:

  • Integrated publicly available OA GWAS summary statistics with Genotype-Tissue Expression (GTEx) eQTL data.
  • Performed cross-tissue and single-tissue transcriptome-wide association analyses (TWAS) using UTMOST and FUSION.
  • Utilized COJO, MAGMA, Summary-based Mendelian Randomization (SMR), Bayesian colocalization, and Mendelian Randomization (MR) for gene prioritization.
  • Employed GeneMANIA for network analysis and Enrichr with DSigDB for compound-signature enrichment.

Main Results:

  • Identified 20 significant genes for knee OA and 12 for hip OA via cross-tissue TWAS.
  • Prioritized TACC3 and LTBP1 for knee OA, and TMEM129 for hip OA, using integrated analytical frameworks.
  • SMR, colocalization, and MR analyses provided varying levels of statistical support.
  • GeneMANIA revealed relevant functional networks for LTBP1 and TACC3, while TMEM129 showed limited enrichment.
  • Enrichr/DSigDB analysis identified significant compound-signature enrichment terms related to prioritized genes.

Conclusions:

  • Provided integrative statistical evidence linking OA-associated loci to gene regulation.
  • Prioritized TACC3, LTBP1, and TMEM129 as candidate genes for OA susceptibility.
  • Highlighted the need for further validation of tissue-specific signals in relevant functional models and datasets.