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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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Related Experiment Video

Updated: Jan 14, 2026

Preliminary Study on Acupuncture Combined with Grain-sized Moxibustion for Treating Rheumatoid Arthritis with Finger Joint Pain
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A DNA methylation-based algorithm for diagnosing rheumatoid arthritis.

Espen Riskedal1, Astanand Jugessur2,3, Silje Watterdal Syversen4

  • 1Age Labs AS, Gaustadalléen 23A, Oslo, Norway.

Arthritis Research & Therapy
|October 18, 2025
PubMed
Summary
This summary is machine-generated.

A new DNA methylation (DNAm) algorithm aids in early rheumatoid arthritis (RA) diagnosis. This tool shows promise for identifying seronegative RA, improving patient outcomes.

Keywords:
Classification algorithmDNA methylationEpigeneticsRheumatoid arthritisSeronegative

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Area of Science:

  • Epigenetics
  • Immunology
  • Computational Biology

Background:

  • Early diagnosis of rheumatoid arthritis (RA), especially seronegative RA, is challenging and can delay treatment.
  • Developing novel diagnostic tools is crucial for timely intervention in RA management.

Purpose of the Study:

  • To develop a binary DNA methylation (DNAm)-based algorithm for diagnosing RA.
  • To assess the algorithm's diagnostic performance, particularly in seronegative RA cases.

Main Methods:

  • Utilized three datasets comprising DNAm profiles from 1366 individuals (RA, other inflammatory diseases, healthy controls).
  • Employed machine learning to create a classification algorithm based on DNAm features differentiating RA.
  • Evaluated the algorithm's diagnostic accuracy using a holdout set, with and without serological status.

Main Results:

  • The DNAm algorithm incorporated 391 features.
  • In the holdout set, the algorithm achieved high diagnostic performance for RA versus controls (AUC 0.96) when combined with serological status.
  • The algorithm demonstrated good performance in distinguishing seronegative RA from other arthritides (AUC 0.81).

Conclusions:

  • The developed DNAm-based algorithm shows potential clinical utility for early RA diagnosis.
  • This tool may be particularly beneficial for diagnosing challenging seronegative RA cases.
  • The algorithm represents a significant advancement in RA diagnostic capabilities.