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doi:10.1534/genetics.107.071365
A more recent version of this article appeared on July 1, 2007.
REGULAR RESEARCH PAPERS |
An Efficient Bayesian Model Selection Approach for Interacting QTL Models with Many Effects
Nengjun Yi 1*, Daniel Shriner 1, Samprit Banerjee 1, Tapan Mehta 1, Daniel Pomp 2 and Brian Yandell 3
1 University of Alabama, Birmingham
2 University of North Carolina, Chapel Hill
3 University of Wisconsin, Madison
* To whom correspondence should be addressed. E-mail: nyi{at}ms.soph.uab.edu.
Submitted on January 24, 2007
Revised on April 8, 2007
Accepted on 23 April 2007
We extend our Bayesian model selection framework for mapping epistatic QTL in experimental crosses (Yi et al. 2005) to include environmental effects and gene-environment interactions. We propose a new, fast Markov chain Monte Carlo algorithm to explore the posterior distribution of unknowns. In addition, we take advantage of any prior knowledge about genetic architecture to increase posterior probability on more promising models. These enhancements have significant computational advantages in models with many effects. We illustrate the proposed method by detecting new epistatic and gene-sex interactions for obesity-related traits in two real datasets of mice. Our method has been implemented in the freely available package R/qtlbim (www.qtlbim.org) to facilitate the general usage of the Bayesian methodology for genome-wide interacting QTL analysis.
Key Words: Bayesian model averaging, Bayesian model selection, Markov chain Monte Carlo algorithm, gene- environment interaction, gene-gene interaction
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