Three Different Gibbs Samplers for BayesB Genomic Prediction

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2014-01-01
Authors
Cheng, Hao
Fernando, Rohan
Garrick, Dorian
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Altmetrics
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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.

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Animal Science
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