Genetic Association with Multiple Traits in the Presence of Population Stratification

• Activity Number: 410
• Type: Topic Contributed
• Date/Time: Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): Qizhai Li*+ and Ting Yan and Yuanzhang Li and Zhaohai Li and Gang Zheng
• Companies: Academy of Mathematics and Systems Science, CAS and George Washington University and Walter Reed Army Institute of Research and George Washington University and National Heart, Lung and Blood Institute
• Keywords: Inverted regression ; genomic control ; MultiPhen ; oportional odds model ; principal component analysis ; variance inflation factor

Abstract

Testing association between a genetic marker and multiple dependent traits is a challenging task when both binary and quantitative traits are involved. The inverted regression model is a convenient method, in which the traits are treated as predictors while the genetic marker is an ordinal response. It is known that population stratification (PS) often affects population-based association studies. However, how it would affect the inverted regression for pleiotropic association is not examined and whether existing methods to correct for PS are still effective for the inverted regression analysis is unknown. In this paper, we focus on the methods based on genomic control and principal component analysis, and investigate type I error of pleiotropic association using the inverted regression model in the presence of PS with allele frequencies and the distributions (or disease prevalences) of multiple traits varying across the subpopulations. An application to the HapMap data is used for illustration.