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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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Identifying Predictive Biomarkers of Response in Patients With Rheumatoid Arthritis Treated With Adalimumab Using

Chuan Fu Yap¹, Nisha Nair², Ann W Morgan³

¹Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, United Kingdom.

Arthritis & Rheumatology (Hoboken, N.J.)

|May 26, 2025

Summary

Biomarkers predict response to tumor necrosis factor inhibitors (TNFi) in rheumatoid arthritis (RA). Whole-blood transcriptomics and machine learning identified gene signatures, including MZB1, for personalized RA treatment strategies.

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Area of Science:

Immunology
Genomics
Pharmacogenomics

Background:

Tumor necrosis factor inhibitors (TNFi) have improved rheumatoid arthritis (RA) management.
Patient response to TNFi varies significantly, with ~40% discontinuing treatment due to nonresponse or adverse effects.
Identifying predictive biomarkers is crucial for optimizing RA treatment strategies.

Purpose of the Study:

To identify biomarkers predicting adalimumab treatment response in RA patients.
To leverage whole-blood transcriptomics and machine learning for biomarker discovery.
To elucidate the mechanisms underlying treatment response and nonresponse.

Main Methods:

RNA sequencing of baseline and 3-month follow-up blood samples from 100 RA patients starting TNFi therapy.
Machine learning classifiers (e.g., random forest) to identify predictive gene signatures.
Network analysis and survival analysis to identify key biomarkers and their interactions.

Main Results:

Differential gene expression analysis identified 84 genes associated with treatment response.
Machine learning models achieved high predictive accuracy (AUC up to 0.86).
Marginal zone B And B1 cell-specific protein 1 (MZB1) emerged as a novel biomarker linked to B cell function and anti-drug antibody formation.

Conclusions:

Transcriptomic alterations provide insights into RA treatment response mechanisms.
Identified gene signatures, including MZB1, offer potential for personalized therapeutic strategies in RA.
Further independent replication is needed to validate these findings for clinical application.