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Three Different Gibbs Samplers for BayesB Genomic Prediction

Authors
  • Hao Cheng (Iowa State University)
  • Rohan L Fernando (Iowa State University)
  • Dorian J. Garrick (Iowa State University)

Abstract

Typical implementations of genomic prediction utilize Markov chain Monte Carlo (MCMC) sampling to estimate effects. Metropolis-Hastings (MH) is a commonly-used algorithm. We considered three different Gibbs samplers to speed up BayesB, a commonly-used model for genomic prediction. These differ in the manner they sample the marker effect, the locus-specific variance and the indicator variable. They are a single-site Gibbs Sampler, a blocking Gibbs Sampler and a Gibbs Sampler with pseudo prior. These three versions of BayesB are about twice as fast as the one using a MH algorithm.

Keywords: Animal Science

How to Cite:

Cheng, H., Fernando, R. L. & Garrick, D. J., (2014) “Three Different Gibbs Samplers for BayesB Genomic Prediction”, Iowa State University Animal Industry Report 11(1). doi: https://doi.org/10.31274/ans_air-180814-1152

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Published on
01 Jan 2014
Peer Reviewed