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Originally published as Genetics Published Articles Ahead of Print on May 23, 2005.
Genetics, Vol. 170, 1333-1344, July 2005, Copyright © 2005
doi:10.1534/genetics.104.040386
Bayesian Model Selection for Genome-Wide Epistatic Quantitative Trait Loci Analysis
Nengjun Yi*,
,1,
Brian S. Yandell
,
Gary A. Churchill
,
David B. Allison*,,
Eugene J. Eisen** and
Daniel Pomp
* Department of Biostatistics, Section on Statistical Genetics
Clinical Nutrition Research Center, University of Alabama, Birmingham, Alabama 35294
Departments of Statistics and Horticulture, University of Wisconsin, Madison, Wisconsin 53706
The Jackson Laboratory, Bar Harbor, Maine 04609
** Department of Animal Science, North Carolina State University, Raleigh, North Carolina 27695
Department of Animal Science, University of Nebraska, Lincoln, Nebraska 68583
1 Corresponding author: Department of Biostatistics, University of Alabama, Ryals Public Health Bldg., 1665 University Blvd., Birmingham, AL 35294-0022.
E-mail: nyi{at}ms.soph.uab.edu
The problem of identifying complex epistatic quantitative trait loci (QTL) across the entire genome continues to be a formidable challenge for geneticists. The complexity of genome-wide epistatic analysis results mainly from the number of QTL being unknown and the number of possible epistatic effects being huge. In this article, we use a composite model space approach to develop a Bayesian model selection framework for identifying epistatic QTL for complex traits in experimental crosses from two inbred lines. By placing a liberal constraint on the upper bound of the number of detectable QTL we restrict attention to models of fixed dimension, greatly simplifying calculations. Indicators specify which main and epistatic effects of putative QTL are included. We detail how to use prior knowledge to bound the number of detectable QTL and to specify prior distributions for indicators of genetic effects. We develop a computationally efficient Markov chain Monte Carlo (MCMC) algorithm using the Gibbs sampler and Metropolis-Hastings algorithm to explore the posterior distribution. We illustrate the proposed method by detecting new epistatic QTL for obesity in a backcross of CAST/Ei mice onto M16i.
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