Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Identifying rheumatoid arthritis susceptibility genes using high-dimensional methods.

Xueying Liang1, Ying Gao, Tram K Lam

  • 1Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, 6120 Executive Boulevard, Bethesda, Maryland 20892, USA. liangx2@mail.nih.gov.

BMC Proceedings
|December 19, 2009
PubMed
Summary

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Incidence patterns and trends of chordoma in the United States: a population-based analysis of over 6,000 cases.

Journal of bone oncology·2026
Same author

Disruption of CTCF binding by germline non-coding variants in <i>CDKN2B</i> suppress <i>CDKN2A</i> expression and predispose to melanoma.

medRxiv : the preprint server for health sciences·2026
Same author

Prevalence of Familial Melanoma Genes and Cancer Risk Among Genomically Ascertained Individuals.

JAMA dermatology·2026
Same author

Ensemble test for microbiome data.

Microbiome·2026
Same author

Epstein-Barr-virus-specific functional antibody signatures in the context of nasopharyngeal carcinoma development.

Med (New York, N.Y.)·2025
Same author

Immunotherapy Resistance in Lethal Prepubescent Spitz Melanoma With <i>TERT</i> Promoter Variant.

JCO precision oncology·2025

This study compared three statistical methods for identifying rheumatoid arthritis (RA) genetic factors. Multifactor dimensionality reduction, random forests, and an omnibus approach successfully identified known RA susceptibility genes, aiding future research.

Area of Science:

  • Genetics
  • Immunology
  • Computational Biology

Background:

  • Rheumatoid arthritis (RA) etiology involves genetic and environmental factors, with the human leukocyte antigen (HLA) region being significant.
  • Understanding complex disease genetics requires efficient genome-wide screening methods, especially for high-dimensional data.

Purpose of the Study:

  • To compare the effectiveness of three statistical approaches: multifactor dimensionality reduction (MDR), random forests (RF), and an omnibus approach.
  • To evaluate these methods in identifying gene effects and gene-gene interactions associated with rheumatoid arthritis susceptibility.

Main Methods:

  • Utilized a test set of genes based on prior linkage and association findings for rheumatoid arthritis.
  • Applied multifactor dimensionality reduction (MDR), random forests (RF), and an omnibus approach to analyze high-dimensional genetic data.

Related Experiment Videos

  • Assessed the performance of each method in detecting single nucleotide polymorphisms (SNPs) associated with RA.

    • Main Results:

    • All three methods successfully identified single nucleotide polymorphisms (SNPs) in PTPN22 and TRAF1-C5 as important for rheumatoid arthritis.
    • The presence of the HLA shared-epitope factor influenced the detection of weaker gene effects.
    • No novel RA-associated genes were discovered in this specific test set.

    Conclusions:

    • Multifactor dimensionality reduction (MDR), random forests (RF), and omnibus approaches are valuable for initial screening of genetic associations in complex diseases like RA.
    • These high-dimensional methods can identify promising candidate genes for further investigation and replication studies.
    • The findings support the utility of advanced statistical techniques in rheumatoid arthritis genetic research.