Bagging optimal ROC curve method for predictive genetic tests, with an application for rheumatoid arthritis

J Biopharm Stat. 2010 Mar;20(2):401-14. doi: 10.1080/10543400903572811.


Translation studies have been initiated to assess the combined effect of genetic loci from recently accomplished genome-wide association studies and the existing risk factors for early disease prediction. We propose a bagging optimal receiver operating characteristic (ROC) curve method to facilitate this research. Through simulation and real data application, we compared the new method with the commonly used allele counting method and logistic regression, and found that the new method yields a better performance. The new method was applied on the Wellcome Trust data set to form a predictive genetic test for rheumatoid arthritis. The formed test reached an area under the curve (AUC) value of 0.7.

Publication types

  • Comparative Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Arthritis, Rheumatoid / diagnosis*
  • Arthritis, Rheumatoid / genetics*
  • Computer Simulation
  • Data Interpretation, Statistical
  • Gene Frequency
  • Genetic Markers*
  • Genetic Predisposition to Disease
  • Genetic Testing / statistics & numerical data*
  • Genome-Wide Association Study / statistics & numerical data
  • Humans
  • Logistic Models
  • Models, Statistical*
  • Predictive Value of Tests
  • ROC Curve*
  • Risk Factors
  • Statistics, Nonparametric


  • Genetic Markers