Originally published as Genetics Published Articles Ahead of Print on May 4, 2007.

Genetics, Vol. 176, 1865-1877, July 2007, Copyright © 2007
doi:10.1534/genetics.107.071365

An Efficient Bayesian Model Selection Approach for Interacting Quantitative Trait Loci Models With Many Effects

Nengjun Yi*,1, Daniel Shriner*, Samprit Banerjee*, Tapan Mehta*, Daniel Pomp{dagger} and Brian S. Yandell{ddagger}

* Department of Biostatistics, Section on Statistical Genetics, University of Alabama, Birmingham, Alabama 35294, {dagger} Departments of Nutrition, Cell and Molecular Physiology, University of North Carolina, Chapel Hill, North Carolina 27599 and {ddagger} Departments of Statistics, Horticulture and Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706

1 Corresponding author: Department of Biostatistics, University of Alabama, Birmingham, AL 35294-0022.
E-mail: nyi{at}ms.soph.uab.eduUT

We extend our Bayesian model selection framework for mapping epistatic QTL in experimental crosses 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 probable 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 data sets of mice. Our method has been implemented in the freely available package R/qtlbim (http://www.qtlbim.org) to facilitate the general usage of the Bayesian methodology for genomewide interacting QTL analysis.




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