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Genome-wide Association Studies-GWAS

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...

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Updated: Jun 8, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Omics studies in Behçet's disease.

Yesim Ozguler^1,2, Johannes Nowatzky^3,4,5,6

¹Istanbul University-Cerrahpasa, Cerrahpasa Faculty of Medicine, Department of Internal Medicine, Division of Rheumatology.

Current Opinion in Rheumatology

|November 8, 2024

Summary

This summary is machine-generated.

Recent "-omics" studies in Behçet's disease reveal genetic risk factors and inflammatory pathways. Further research is needed to overcome challenges in data management and experimental validation for mechanistic understanding.

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Area of Science:

Genomics
Transcriptomics
Proteomics
Metabolomics
Immunology

Background:

Behçet's disease is a complex multisystem inflammatory disorder.
Understanding its pathogenesis requires advanced molecular insights.

Purpose of the Study:

To review recent findings from omics studies in Behçet's disease.
To highlight the contribution of omics data to understanding disease mechanisms.

Main Methods:

Genomic analysis to identify risk loci.
Transcriptomic analysis of immune cells.
Proteomic analysis including extracellular vesicles and immunopeptidomes.
Metabolomic profiling of patients.

Main Results:

Genomic studies identified risk loci for uveitis, neurologic, and gastrointestinal involvement, and sex-specific effects.
Transcriptomics revealed the role of NF-κB and MAPK pathways in pathogenesis.
Proteomics highlighted extracellular vesicles and potential biomarkers for vascular involvement.
Metabolomics indicated alterations in fatty acid metabolism and lipid profiles.

Conclusions:

Omics studies are increasingly important for generating large datasets in Behçet's disease research.
These studies offer opportunities for generating testable hypotheses about disease pathogenesis.
Challenges include patient/control group selection, data sharing, cost, and translating observational data into mechanistic understanding.