Originally published as Genetics Published Articles Ahead of Print on March 4, 2007.

Genetics, Vol. 176, 611-623, May 2007, Copyright © 2007
doi:10.1534/genetics.106.065599

Mapping Quantitative Trait Loci for Expression Abundance

Zhenyu Jia and Shizhong Xu¹

Department of Botany and Plant Sciences, University of California, Riverside, California 92521

¹ Corresponding author: Department of Botany and Plant Sciences, University of California, 900 University Ave., Riverside, CA 92521-0124.
E-mail: xu{at}genetics.ucr.edu

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 and (2) individual marker analysis where differentially expressed transcripts are detected on the basis of 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 a 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, 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.

This article has been cited by other articles:

				W. Pan Network-based multiple locus linkage analysis of expression traits Bioinformatics, June 1, 2009; 25(11): 1390 - 1396. [Abstract] [Full Text] [PDF]

				H. Chun and S. Keles Expression Quantitative Trait Loci Mapping With Multivariate Sparse Partial Least Squares Regression Genetics, May 1, 2009; 182(1): 79 - 90. [Abstract] [Full Text] [PDF]

				L. Zhang, H. Li, Z. Li, and J. Wang Interactions Between Markers Can Be Caused by the Dominance Effect of Quantitative Trait Loci Genetics, October 1, 2008; 180(2): 1177 - 1190. [Abstract] [Full Text] [PDF]

International Access Link

Online ISSN: 1943-2361  Print ISSN: 0016-6731
Copyright 2009 by the Genetics Society of America
phone: 412-268-1812   fax: 412-268-1813   email: genetics-gsa{at}andrew.cmu.edu