A powerful strategy to account for multiple testing in the context of haplotype analysis

Am J Hum Genet. 2004 Oct;75(4):561-70. doi: 10.1086/424390. Epub 2004 Jul 30.


Haplotypes--that is, linear arrangements of alleles on the same chromosome that were inherited as a unit--are expected to carry important information in the context of association fine mapping of complex diseases. In consideration of a set of tightly linked markers, there is an enormous number of different marker combinations that can be analyzed. Therefore, a severe multiple-testing problem is introduced. One method to deal with this problem is Bonferroni correction by the number of combinations that are considered. Bonferroni correction is appropriate for independent tests but will result in a loss of power in the presence of linkage disequilibrium in the region. A second method is to perform simulations. It is unfortunate that most methods of haplotype analysis already require simulations to obtain an uncorrected P value for a specific marker combination. Thus, it seems that nested simulations are necessary to obtain P values that are corrected for multiple testing, which, apparently, limits the applicability of this approach because of computer running-time restrictions. Here, an algorithm is described that avoids such nested simulations. We check the validity of our approach under two disease models for haplotype analysis of family data. The true type I error rate of our algorithm corresponds to the nominal significance level. Furthermore, we observe a strong gain in power with our method to obtain the global P value, compared with the Bonferroni procedure to calculate the global P value. The method described here has been implemented in the latest update of our program FAMHAP.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Chromosome Mapping / methods*
  • Computer Simulation
  • Genetic Markers / genetics
  • Genetic Predisposition to Disease*
  • Genetic Testing / methods*
  • Haplotypes / genetics*
  • Humans
  • Models, Genetic*
  • Research Design


  • Genetic Markers