Disease association studies often test large numbers of markers, and various methods have been proposed to correct for multiple testing. In this paper, we propose an admixture maximum likelihood approach that estimates both the proportion of associated single nucleotide polymorphisms (SNPs) and their typical effect size. We assessed this method and compared it with several previously proposed approaches by simulation. The maximum likelihood approach performed similarly to or better than all other tests across a wide range of alternative hypotheses. The rank truncated product method also had good power, though somewhat inferior to the maximum likelihood approach in most cases. A simple Bonferroni correction performed best only when the number of associated SNPs was small.
(c) 2006 Wiley-Liss, Inc.