title: A new association test using haplotype similarity.
abstract: Association tests based on multi-marker haplotypes may be more powerful than those based on single markers. The existing association tests based on multi-marker haplotypes include Pearson's chi2 test which tests for the difference of haplotype distributions in cases and controls, and haplotype-similarity based methods which compare the average similarity among cases with that of the controls. In this article, we propose new association tests based on haplotype similarities. These new tests compare the average similarities within cases and controls with the average similarity between cases and controls. These methods can be applied to either phase-known or phase-unknown data. We compare the performance of the proposed methods with Pearson's chi2 test and the existing similarity-based tests by simulation studies under a variety of scenarios and by analyzing a real data set. The simulation results show that, in most cases, the new proposed methods are more powerful than both Pearson's chi2 test and the existing similarity-based tests. In one extreme case where the disease mutant induced at a very rare haplotype (<or=3%), Pearson's chi2 is slightly more powerful than the new proposed methods, and in this case, the existing similarity-based tests have almost no power. In another extreme case where the disease mutant was introduced at the most common haplotype, the existing similarity-based methods are slightly more powerful than the new proposed methods, and in this case Pearson's chi2 test is least powerful. The results of real data analysis are consistent with that of our simulation studies.
authors: Department of Mathematical Sciences, Michigan Technological University, Houghton, Michigan 49931, USA. qsha@mtu.edu
authors: Qiuying
authors: Q
authors: Sha
authors: Huann-Sheng
authors: HS
authors: Chen
authors: Shuanglin
authors: S
authors: Zhang
mesh_terms: D000465:Algorithms; D000483:Alleles; D003198:Computer Simulation; D003550:Cystic Fibrosis / Q000235:genetics; D003627:Data Interpretation, Statistical; D005819:Genetic Markers; D005838:Genotype; D006239:Haplotypes; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D012306:Risk
chemical_list: D005819:Genetic Markers