Mapping quantitative trait loci for expression abundance.

@article{citeulike:2803514, abstract = {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 F(2) mice measured for 25 different obesity-related quantitative traits. In the experiment, approximately 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.}, address = {Department of Botany and Plant Sciences, University of California, Riverside, California 92521, USA.}, author = {Jia, Z. and Xu, S. }, citeulike-article-id = {2803514}, doi = {10.1534/genetics.106.065599}, issn = {0016-6731}, journal = {Genetics}, keywords = {eqtl}, month = {May}, number = {1}, pages = {611--623}, posted-at = {2008-05-16 02:34:50}, priority = {2}, title = {Mapping quantitative trait loci for expression abundance.}, url = {http://dx.doi.org/10.1534/genetics.106.065599}, volume = {176}, year = {2007} }

TY - JOUR ID - citeulike:2803514 L3 - citeulike-article-id:2803514 TI - Mapping quantitative trait loci for expression abundance. JF - Genetics VL - 176 IS - 1 SP - 611 EP - 623 AD - Department of Botany and Plant Sciences, University of California, Riverside, California 92521, USA. SN - 0016-6731 N2 - 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 F(2) mice measured for 25 different obesity-related quantitative traits. In the experiment, approximately 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. KW - eqtl AU - Jia, Z AU - Xu, S PY - 2007/05// UR - http://dx.doi.org/10.1534/genetics.106.065599 ER -