title: Association mapping, using a mixture model for complex traits.
abstract: Association mapping for complex diseases using unrelated individuals can be more powerful than family-based analysis in many settings. In addition, this approach has major practical advantages, including greater efficiency in sample recruitment. Association mapping may lead to false-positive findings, however, if population stratification is not properly considered. In this paper, we propose a method that makes it possible to infer the number of subpopulations by a mixture model, using a set of independent genetic markers and then testing the association between a genetic marker and a trait. The proposed method can be effectively applied in the analysis of both qualitative and quantitative traits. Extensive simulations demonstrate that the method is valid in the presence of a population structure.
authors.affiliation: Department of Preventive Medicine and Epidemiology, Loyola University Medical Center, Maywood, Illinois 60153, USA. xzhul@lumc.edu
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Zhu
authors.forename: ShuangLin
authors.initials: S
authors.lastname: Zhang
authors.forename: Hongyu
authors.initials: H
authors.lastname: Zhao
authors.forename: Richard S
authors.initials: RS
authors.lastname: Cooper
mesh_terms: D000483:Alleles; D016022:Case-Control Studies; D002874:Chromosome Mapping / Q000706:statistics & numerical data*; D003198:Computer Simulation; D005787:Gene Frequency; D005828:Genetics, Population; D005838:Genotype; D006801:Humans; D008957:Models, Genetic