Originally published as Genetics Published Articles Ahead of Print on November 1, 2004.

Genetics, Vol. 169, 2305-2318, April 2005, Copyright © 2005
doi:10.1534/genetics.104.034181

Mapping Multiple Quantitative Trait Loci by Bayesian Classification

Min Zhang^*, Kristi L. Montooth^,1, Martin T. Wells^*,, Andrew G. Clark and Dabao Zhang^,2

^* Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, New York 14853
Department of Molecular Biology and Genetics, Cornell University, Ithaca, New York 14853
Department of Statistical Science, Cornell University, Ithaca, New York 14853
Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, New York 14642

² Corresponding author: Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 601 Elmwood Ave., Box 630, Rochester, NY 14642.
E-mail: dabao_zhang{at}urmc.rochester.edu

We developed a classification approach to multiple quantitative trait loci (QTL) mapping built upon a Bayesian framework that incorporates the important prior information that most genotypic markers are not cotransmitted with a QTL or their QTL effects are negligible. The genetic effect of each marker is modeled using a three-component mixture prior with a class for markers having negligible effects and separate classes for markers having positive or negative effects on the trait. The posterior probability of a marker's classification provides a natural statistic for evaluating credibility of identified QTL. This approach performs well, especially with a large number of markers but a relatively small sample size. A heat map to visualize the results is proposed so as to allow investigators to be more or less conservative when identifying QTL. We validated the method using a well-characterized data set for barley heading values from the North American Barley Genome Mapping Project. Application of the method to a new data set revealed sex-specific QTL underlying differences in glucose-6-phosphate dehydrogenase enzyme activity between two Drosophila species. A simulation study demonstrated the power of this approach across levels of trait heritability and when marker data were sparse.

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