Statistical methods for mapping multiple complex traits
Abstract
Usually, multiple traits are measured on individuals in a quantitative trait locus (QTL) mapping experiment. These traits are typically analyzed separately. Currently, there is great interest in analyzing multiple traits simultaneously (multiple-trait QTL mapping) since more statistical power of QTL detection can be gained by taking advantage of the structure of the residual covariance of the traits. This work studies various multiple-trait QTL mapping methods, including the multiple-trait single-marker approach, the multiple-trait multiple-marker regression approach, the seemingly unrelated regression equations (SURE) approach, and the multiple-trait composite multiple-interval mapping (MTCMIM) approach. While the SURE model has been widely applied in Econometrics and other fields, it has received little attention in QTL mapping applications. By allowing different sets of QTL for different traits, the SURE model provides flexibility for the analysis of multiple traits and multiple QTL. Specific to the MTCMIM model, multiple traits and multiple QTL can be analyzed simultaneously while carefully selected markers are used to control background genetic variation. As an application, these methods are applied to gene expression traits (etraits) in Arabidopsis thaliana gained from Affymetrix microarray technology.
Degree
Ph.D.
Advisors
Xie, Purdue University.
Subject Area
Biostatistics|Statistics
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