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Genetics, Vol. 167, 967-975, June 2004, Copyright © 2004
doi:10.1534/genetics.104.026286
A Unified Markov Chain Monte Carlo Framework for Mapping Multiple Quantitative Trait Loci
Nengjun Yi1
Section on Statistical Genetics, Department of Biostatistics, University of Alabama, Birmingham, Alabama 35294-0022
1 Address for correspondence: Department of Biostatistics, Ryals Public Health Bldg., 1665 University Blvd., University of Alabama, Birmingham, Alabama 35294-0022.
E-mail: nyi{at}ms.soph.uab.edu
In this article, a unified Markov chain Monte Carlo (MCMC) framework is proposed to identify multiple quantitative trait loci (QTL) for complex traits in experimental designs, based on a composite space representation of the problem that has fixed dimension. The proposed unified approach includes the existing Bayesian QTL mapping methods using reversible jump MCMC algorithm as special cases. We also show that a variety of Bayesian variable selection methods using Gibbs sampling can be applied to the composite model space for mapping multiple QTL. The unified framework not only results in some new algorithms, but also gives useful insight into some of the important factors governing the performance of Gibbs sampling and reversible jump for mapping multiple QTL. Finally, we develop strategies to improve the performance of MCMC algorithms.
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