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Bayesian model selection for genome-wide epistatic quantitative trait loci analysis.

Nengjun Yi1, Brian S Yandell, Gary A Churchill

  • 1Department of Biostatistics, University of Alabama, Birmingham 35294, USA. nyi@ms.soph.uab.edu

Genetics
|May 25, 2005
PubMed
Summary

Identifying complex epistatic quantitative trait loci (QTL) is challenging. This study introduces a Bayesian model selection framework using a composite model space approach to efficiently detect epistatic QTL for complex traits.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Identifying epistatic quantitative trait loci (QTL) for complex traits across the genome is a significant challenge.
  • The large number of potential QTL and epistatic interactions complicates genome-wide analysis.

Purpose of the Study:

  • To develop a Bayesian model selection framework for identifying epistatic QTL in experimental crosses.
  • To simplify calculations by restricting models to a fixed dimension using a composite model space approach.

Main Methods:

  • Utilized a composite model space approach with a Bayesian framework for model selection.
  • Employed indicators to specify main and epistatic effects of putative QTL.
  • Developed a computationally efficient Markov chain Monte Carlo (MCMC) algorithm, incorporating Gibbs sampling and the Metropolis-Hastings algorithm.

Related Experiment Videos

  • Incorporated prior knowledge to bound the number of detectable QTL and specify prior distributions for genetic effects.

    • Main Results:

    • Successfully detected new epistatic QTL associated with obesity in a mouse backcross (CAST/Ei onto M16i).
    • The proposed Bayesian framework and MCMC algorithm efficiently explored the posterior distribution for model selection.

    Conclusions:

    • The developed Bayesian model selection framework provides an efficient method for identifying epistatic QTL in complex traits.
    • This approach simplifies genome-wide epistatic analysis by managing model complexity and utilizing prior knowledge effectively.