title: Haplotype-sharing analysis using Mantel statistics for combined genetic effects.
abstract: We applied a new approach based on Mantel statistics to analyze the Genetic Analysis Workshop 14 simulated data with prior knowledge of the answers. The method was developed in order to improve the power of a haplotype sharing analysis for gene mapping in complex disease. The new statistic correlates genetic similarity and phenotypic similarity across pairs of haplotypes from case-control studies. The genetic similarity is measured as the shared length between haplotype pairs around a genetic marker. The phenotypic similarity is measured as the mean corrected cross-product based on the respective phenotypes. Cases with phenotype P1 and unrelated controls were drawn from the population of Danacaa. Power to detect main effects was compared to the X2-test for association based on 3-marker haplotypes and a global permutation test for haplotype association to test for main effects. Power to detect gene x gene interaction was compared to unconditional logistic regression. The results suggest that the Mantel statistics might be more powerful than alternative tests.
authors.affiliation: German Cancer Research Center DKFZ, Heidelberg, Germany. l.beckmann@dkfz.de
authors.forename: Lars
authors.initials: L
authors.lastname: Beckmann
authors.forename: Christine
authors.initials: C
authors.lastname: Fischer
authors.forename: Markus
authors.initials: M
authors.lastname: Obreiter
authors.forename: Michael
authors.initials: M
authors.lastname: Rabes
authors.forename: Jenny
authors.initials: J
authors.lastname: Chang-Claude
mesh_terms: D006239:Haplotypes / Q000235:genetics*; D006801:Humans; D015233:Models, Statistical