Display options
Cite
Sun YV, Cai Z, Desai K, et al. Classification of rheumatoid arthritis status with candidate gene and genome-wide single-nucleotide polymorphisms using random forests. BMC Proc. 2007;1:S62doi: 10.1186/1753-6561-1-s1-s62.
Sun, Y. V., Cai, Z., Desai, K., Lawrance, R., Leff, R., Jawaid, A., Kardia, S. L., & Yang, H. (2007). Classification of rheumatoid arthritis status with candidate gene and genome-wide single-nucleotide polymorphisms using random forests. BMC proceedings, 1S62. https://doi.org/10.1186/1753-6561-1-s1-s62
Sun, Yan V, et al. "Classification of rheumatoid arthritis status with candidate gene and genome-wide single-nucleotide polymorphisms using random forests." BMC proceedings vol. 1 (2007): S62. doi: https://doi.org/10.1186/1753-6561-1-s1-s62
Sun YV, Cai Z, Desai K, Lawrance R, Leff R, Jawaid A, Kardia SL, Yang H. Classification of rheumatoid arthritis status with candidate gene and genome-wide single-nucleotide polymorphisms using random forests. BMC Proc. 2007;1:S62. doi: 10.1186/1753-6561-1-s1-s62. Epub 2007 Dec 18. PMID: 18466563; PMCID: PMC2367463.
Copy Download .nbib Format: NLM
Share it on

BMC Proc. 2007;1:S62. doi: 10.1186/1753-6561-1-s1-s62. Epub 2007 Dec 18.

Classification of rheumatoid arthritis status with candidate gene and genome-wide single-nucleotide polymorphisms using random forests.

BMC proceedings

Yan V Sun, Zhaohui Cai, Kaushal Desai, Rachael Lawrance, Richard Leff, Ansar Jawaid, Sharon Lr Kardia, Huiying Yang

Affiliations

  1. Department of Epidemiology, School of Public Health, University of Michigan, 611 Church Street #244, Ann Arbor, Michigan 48104, USA.
  2. AstraZeneca Pharmaceuticals, 1800 Concord Place, FOC W1-462, Wilmington, Delaware 19850, USA.
  3. AstraZeneca Pharmaceuticals, Mereside, Alderley Park, Macclesfield, Cheshire, SK10 4TG, UK.

PMID: 18466563 PMCID: PMC2367463 DOI: 10.1186/1753-6561-1-s1-s62

Abstract

Using the North American Rheumatoid Arthritis Consortium (NARAC) candidate gene and genome-wide single-nucleotide polymorphism (SNP) data sets, we applied regression methods and tree-based random forests to identify genetic associations with rheumatoid arthritis (RA) and to predict RA disease status. Several genes were consistently identified as weakly associated with RA without a significant interaction or combinatorial effect with other candidate genes. Using random forests, the tested candidate gene SNPs were not sufficient to predict RA patients and normal subjects with high accuracy. However, using the top 500 SNPs, ranked by the importance score, from the genome-wide linkage panel of 5742 SNPs, we were able to accurately predict RA patients and normal subjects with sensitivity of approximately 90% and specificity of approximately 80%, which was confirmed by five-fold cross-validation. However, in a complete training-testing framework, replication of genetic predictors was less satisfactory; thus, further evaluation of existing methodology and development of new methods are warranted.

References

  1. Rheumatology (Oxford). 2007 Apr;46(4):617-21 - PubMed
  2. Arthritis Res. 1999;1(1):37-44 - PubMed
  3. N Engl J Med. 2004 Jun 17;350(25):2591-602 - PubMed
  4. Arthritis Rheum. 1988 Mar;31(3):315-24 - PubMed
  5. Pac Symp Biocomput. 2002;:6-17 - PubMed
  6. Genome Res. 1998 Dec;8(12):1273-88 - PubMed
  7. Am J Hum Genet. 2005 Dec;77(6):1044-60 - PubMed

Publication Types