Importance sampling method of correction for multiple testing in affected sib-pair linkage analysis.

Alison P. Klein, Ilija Kovac, Alexa J.M. Sorant, Agnes Baffoe-Bonnie, Betty Q. Doan, Grace Ibay, Erica Lockwood, Diptasri Mandal, Lekshmi Santhosh, Karen Weissbecker, Jessica Woo, April Zambelli-Weiner, Jie Zhang, Daniel Q. Naiman, James Malley, Joan E. Bailey-Wilson

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Using the Genetic Analysis Workshop 13 simulated data set, we compared the technique of importance sampling to several other methods designed to adjust p-values for multiple testing: the Bonferroni correction, the method proposed by Feingold et al., and naïve Monte Carlo simulation. We performed affected sib-pair linkage analysis for each of the 100 replicates for each of five binary traits and adjusted the derived p-values using each of the correction methods. The type I error rates for each correction method and the ability of each of the methods to detect loci known to influence trait values were compared. All of the methods considered were conservative with respect to type I error, especially the Bonferroni method. The ability of these methods to detect trait loci was also low. However, this may be partially due to a limitation inherent in our binary trait definitions.

Original languageEnglish (US)
JournalBMC genetics
Volume4 Suppl 1
StatePublished - 2003
Externally publishedYes

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)


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