Using tree-based recursive partitioning methods to group haplotypes for increased power in association studies.
Motivated by the increasing availability of high-density single nucleotide polymorphism (SNP) markers across the genome, various haplotype-based methods have been developed for candidate gene association studies, and even for genome-wide association studies. Although haplotype approaches dramatically reduce the multiple comparisons problem (as compared to single SNP analysis), even the number of existing haplotypes is relatively large, which increases the degrees of freedom and decreases the power for the corresponding test statistic. Grouping haplotypes is a way to reduce the degrees of freedom. We propose a procedure that uses a tree-based recursive partitioning algorithm to group haplotypes into a small number of clusters, and conducts the association test based on groups of haplotypes instead of individual haplotypes. The method can be used for both population-based and family-based association studies, with known or ambiguous phase information. Simulation studies suggest that the proposed method has the right type I error rate, and is more powerful than some existing haplotype-based tests.
Yu
Kai
K
Division of Biostatistics, School of Medicine, Washington University, St. Louis, MO 63110, USA. kai@wubios.wustl.edu
Xu
Jun
J
Rao
D C
DC
Province
Michael
M
D000465:Algorithms; D003198:Computer Simulation; D020022:Genetic Predisposition to Disease; D005828:Genetics, Population; D005838:Genotype; D006239:Haplotypes; D006801:Humans; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D015233:Models, Statistical; D005810:Multigene Family; D009154:Mutation; D020641:Polymorphism, Single Nucleotide; D012306:Risk