, Volume 138, Issue 3, pp 395–407 | Cite as

Bayesian model averaging for evaluation of candidate gene effects

  • Xiao-Lin Wu
  • Daniel Gianola
  • Guilherme J. M. Rosa
  • Kent A. Weigel


Statistical assessment of candidate gene effects can be viewed as a problem of variable selection and model comparison. Given a certain number of genes to be considered, many possible models may fit to the data well, each including a specific set of gene effects and possibly their interactions. The question arises as to which of these models is most plausible. Inference about candidate gene effects based on a specific model ignores uncertainty about model choice. Here, a Bayesian model averaging approach is proposed for evaluation of candidate gene effects. The method is implemented through simultaneous sampling of multiple models. By averaging over a set of competing models, the Bayesian model averaging approach incorporates model uncertainty into inferences about candidate gene effects. Features of the method are demonstrated using a simulated data set with ten candidate genes under consideration.


Bayes factor Bayesian model averaging Candidate genes Linear models Markov chain Monte Carlo Quantitative traits 



This research was supported by the Wisconsin Agriculture Experiment Station, and was partially supported by National Research Initiative Grant no. 2009-35205-05099 from the USDA Cooperative State Research, Education, and Extension Service, NSF DEB-0089742, and NDF DMS-044371. KAW acknowledges financial support from the National Association of Animal Breeders (Columbia, MO). Comments from the anonymous reviewers and the editor are acknowledged.


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  • Xiao-Lin Wu
    • 1
    • 2
  • Daniel Gianola
    • 1
    • 2
    • 3
    • 4
  • Guilherme J. M. Rosa
    • 1
  • Kent A. Weigel
    • 1
  1. 1.Department of Dairy ScienceUniversity of WisconsinMadisonUSA
  2. 2.Department of Animal SciencesUniversity of WisconsinMadisonUSA
  3. 3.Department of Biostatistics and Medical InformaticsUniversity of WisconsinMadisonUSA
  4. 4.Department of Animal and Aquacultural SciencesNorweigian University of Life SciencesÅsNorway

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