Association mapping, using a mixture model for complex traits

Genetic Epidemiology
Xiaofeng ZhuRichard Cooper

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.

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Citations

Jan 18, 2003·Current Hypertension Reports·Xiaofeng Zhu, Richard S Cooper
Jan 12, 2005·The American Psychologist·Richard S Cooper
Sep 2, 2008·Nature·John NovembreCarlos D Bustamante
Apr 22, 2008·Nature Genetics·John Novembre, Matthew Stephens
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Mar 16, 2011·American Journal of Epidemiology·Andrew S Allen, Glen A Satten
Jun 10, 2010·Bioinformatics·Kriti PuniyaniEric P Xing
Oct 15, 2008·Human Molecular Genetics·Richard S CooperXiaofeng Zhu
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Sep 4, 2009·Annals of Human Genetics·Hui Zhou, Wei Pan
Oct 7, 2011·Annals of Human Genetics·Adan NiuQiuying Sha
Nov 14, 2012·Genetics, Selection, Evolution : GSE·Simon TeyssèdreAnne Ricard
Feb 28, 2009·BMC Bioinformatics·Chih LeeChun-Hsi Huang
Dec 4, 2009·PloS One·Jun ZhangMary Sara McPeek
Dec 4, 2009·PloS One·Lei ZhangHong-Wen Deng
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