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doi:10.1534/genetics.106.065599
A more recent version of this article appeared on May 1, 2007.
REGULAR RESEARCH PAPERS |
Mapping Quantitative Trait Loci for Expression Abundance
Zhenyu Jia 1 and Shizhong Xu 1*
1 University of California, Riverside
* To whom correspondence should be addressed. E-mail: xu{at}genetics.ucr.edu.
Submitted on September 5, 2006
Revised on January 10, 2007
Accepted on 11 February 2007
Mendelian loci that control the expression levels of transcripts are called expression quantitative trait loci (eQTL). When mapping eQTL, we often deal with thousands of expression traits simultaneously, which complicates the statistical model and data analysis. Two simple approaches may be taken in eQTL analysis: (1) individual transcript analysis in which a single expression trait is mapped at a time and the entire eQTL mapping involves separate analysis of thousands of traits; (2) individual marker analysis where differentially expressed transcripts are detected based on their association with the segregation pattern of an individual marker and the entire analysis requires scanning markers of the entire genome. Neither approach is optimal because data are not analyzed jointly. We develop a Bayesian clustering method that analyzes all expressed transcripts and markers jointly in a single model. A transcript may be simultaneously associated with multiple markers. Additionally, a marker may simultaneously alter the expression of multiple transcripts. This is a model-based method that combines Gaussian mixture of expression data with segregation of multiple linked marker loci. Parameter estimation for each variable is obtained via the posterior mean drawn from a Markov chain Monte Carlo sample. The method allows a regular quantitative trait to be included as an expression trait and subject to the same clustering assignment. If an expression trait links to a locus where a quantitative trait also links, the expressed transcript is considered to be associated with the quantitative trait. The method is applied to a microarray experiment with 60 F2 mice measured for 25 different obesity related quantitative traits. In the experiment, about 40,000 transcripts and 145 codominant markers are investigated for their associations. A program written in SAS/IML is available from the authors on request.
Key Words: Cluster analysis, Gibbs sampler, Markov chain Monte Carlo, Microarray experiment, eQTL
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