An alternative computing strategy for genomic prediction using a Bayesian mixture model

Abstract

Chen, L., Li, C. and Schenkel, F. 2015. An alternative computing strategy for genomic prediction using a Bayesian mixture model. Can. J. Anim. Sci. 95: 1–11. Bayesian methods for genomic prediction are commonly implemented via Markov chain Monte Carlo (MCMC) sampling schemes, which are computationally demanding in large-scale applications. An alternative computing algorithm, called right-hand side updating strategy (RHSU), was proposed by exploiting the sparsity feature of the marker effects in a Bayesian mixture model. The new algorithm was compared with the conventional Gauss–Seidel residual update (GSRU) algorithm by the number of floating point operations (FLOP) required in one round of MCMC sampling. The two algorithms were also compared in a Holstein data example with the training data size varying from 1000 to 10 000 and a marker density of 35 790 single nucleotide polymorphisms (SNP). Results showed that the proposed RHSU algorithm would outperform the traditional GSRU algorithm when the sample size exceeded a fraction of the number of the SNPs, which typically varied from 0.05 to 0.18 when the proportion of SNPs with no effect on the trait varied from 0.90 to 0.95. Results from the Holstein data example agreed very well with theoretical expectations. With adoption of a 50 k SNP panel and an increasing training data size, RHSU would be very useful if Bayesian methods are preferable for genomic prediction.