Association mapping, using a mixture model for complex traits
Document Type
Article
Publication Date
8-2002
Department
Department of Mathematical Sciences
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.
Publication Title
Genetic Epidemiology
Recommended Citation
Zhu, X.,
Zhang, S.,
Zhao, H.,
&
Cooper, R.
(2002).
Association mapping, using a mixture model for complex traits.
Genetic Epidemiology,
23(2), 181-196.
http://doi.org/10.1002/gepi.210
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/3642