Bias correction to secondary trait analysis with case-control design

Hua Yun Chen*, Rick Kittles, Wei Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


In genetic association studies with densely typed genetic markers, it is often of substantial interest to examine not only the primary phenotype but also the secondary traits for their association with the genetic markers. For more efficient sample ascertainment of the primary phenotype, a case-control design or its variants,suchas the extreme-value sampling design for a quantitative trait, are often adopted. The secondarytrait analysis without correcting for the sample ascertainment may yield a biased association estimator. We propose a new method aiming at correcting the potential bias due to the inadequate adjustment of the sample ascertainment. The method yields explicit correction formulas that can be used to both screen the genetic markers and rapidly evaluate the sensitivity of the results to the assumed baseline case-prevalence rate in the population. Simulation studies demonstrate good performance of the proposed approach in comparison with the more computationally intensive approaches, such as the compensator approaches and the maximum prospective likelihood approach. We illustrate the application of the approach by analysis of the genetic association of prostate specific antigen in a case-control study of prostate cancer in the African American population.

Original languageEnglish (US)
Pages (from-to)1494-1508
Number of pages15
JournalStatistics in Medicine
Issue number9
StatePublished - Apr 30 2013


  • Extreme-value sampling design
  • Odds ratio model
  • Semi-parametric likelihood
  • Sensitivity analysis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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