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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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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.
GWAS does not require the identification of the target gene involved in...
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Related Experiment Video

Updated: Dec 25, 2025

Comprehensive Autopsy Program for Individuals with Multiple Sclerosis
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Dense module searching for gene networks associated with multiple sclerosis.

Astrid M Manuel1, Yulin Dai1, Leorah A Freeman2

  • 1Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 600, Houston, TX, 77030, USA.

BMC Medical Genomics
|April 4, 2020
PubMed
Summary

This study used network analysis to identify key genes in multiple sclerosis (MS) etiology, revealing links to immune response and potential drug targets for this complex neurological disease.

Keywords:
Drug targetGWASGene set enrichment analysisMultiple sclerosisNetwork moduledmGWAS

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Area of Science:

  • Genetics and Molecular Biology
  • Neuroimmunology
  • Bioinformatics

Background:

  • Multiple sclerosis (MS) is an autoimmune disease affecting the central nervous system, with poorly understood molecular mechanisms.
  • Genome-wide association studies (GWAS) have identified genetic loci for MS, but primarily non-coding variants.
  • Network-assisted analysis offers a way to interpret functional roles of genetic variants and explore translational medicine applications.

Purpose of the Study:

  • To apply a network-assisted approach to interpret genetic associations in multiple sclerosis.
  • To identify key genes and pathways involved in MS etiology using Dense Module Searching of GWAS (dmGWAS).
  • To explore potential links between identified genetic signals and existing multiple sclerosis drug targets.

Main Methods:

  • Utilized the dmGWAS tool (version 2.4) on two multiple sclerosis GWAS datasets (GeneMSA and IMSGC GWAS).
  • Employed the human protein interactome as a reference network for analysis.
  • Implemented a dual evaluation strategy to ensure result reproducibility.

Main Results:

  • Identified approximately 7500 significant network modules per dataset, with 20 significant modules from dual evaluation.
  • Top modules centered around genes including GRB2, HDAC1, JAK2, MAPK1, and STAT3.
  • Enrichment analysis revealed significant functional terms: 'regulation of glial cell differentiation,' 'T-cell costimulation,' and 'virus receptor activity.'

Conclusions:

  • dmGWAS network analysis highlighted genes (GRB2, HDAC1, IL2RA, JAK2, KEAP1, MAPK1, RELA, STAT3) relevant for interpreting MS GWAS signals and linking to drug targets.
  • Genes involved in glial cell differentiation are crucial for understanding neurodegeneration and investigating remyelination therapies in MS.
  • Genetic signals related to T-cell costimulation and viral receptor activity support the hypothesis of viral infection onset in MS etiology.