title: Detecting disease gene in DNA haplotype sequences by nonparametric dissimilarity test.
abstract: Association studies for complex diseases based on haplotype data have received increasing attention in the last few years. A commonly used nonparametric method, which takes haplotype structure into consideration, is to use the U-statistic to compare the similarities between genetic compositions in the case and control populations. Although the method and its variants are convenient to use in practice, there are some areas where the tests cannot detect even large differences between cases and controls. To overcome this problem and enhance the power, we propose a new form of the weighted U-statistic, which directly compares the dissimilarity between the haplotype structures in the case and control populations. We show that this test statistic is asymptotically a linear combination of the absolute values of normal random variables under the null hypothesis, and shifts strictly toward the right under the alternative, and therefore has no blind areas of detection. Simulation studies indicate that our test statistic overcomes the weakness of the existing ones and is robust and powerful as well.
authors.affiliation: National Human Genome Center, Department of Community Health and Family Medicine, Howard University, Washington, DC, USA. ayuan@howard.edu
authors.forename: Ao
authors.initials: A
authors.lastname: Yuan
authors.forename: Qingqi
authors.initials: Q
authors.lastname: Yue
authors.forename: Victor
authors.initials: V
authors.lastname: Apprey
authors.forename: George
authors.initials: G
authors.lastname: Bonney
mesh_terms: D001483:Base Sequence; D016022:Case-Control Studies; D003198:Computer Simulation; D020022:Genetic Predisposition to Disease; D006239:Haplotypes; D006801:Humans; D016013:Likelihood Functions; D018709:Statistics, Nonparametric