Journal article

A computationally efficient algorithm for genomic prediction using a Bayesian model

Tingting Wang, Yi-Ping Phoebe Chen, Michael E Goddard, Theo HE Meuwissen, Kathryn E Kemper, Ben J Hayes



BACKGROUND: Genomic prediction of breeding values from dense single nucleotide polymorphisms (SNP) genotypes is used for livestock and crop breeding, and can also be used to predict disease risk in humans. For some traits, the most accurate genomic predictions are achieved with non-linear estimates of SNP effects from Bayesian methods that treat SNP effects as random effects from a heavy tailed prior distribution. These Bayesian methods are usually implemented via Markov chain Monte Carlo (MCMC) schemes to sample from the posterior distribution of SNP effects, which is computationally expensive. Our aim was to develop an efficient expectation-maximisation algorithm (emBayesR) that gives simi..

View full abstract

University of Melbourne Researchers


Funding Acknowledgements

The authors acknowledge the support and fund from Dairy Future CRC. We would like to thank Iona Macleod (Department of Environment & Primary Industries (DEPI), 5 Ring Road, Bundoora, VIC 3083, Australia) for her work on 10 K simulation.