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

Genetics Selection Evolution | BMC | Published : 2015

Abstract

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..

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University of Melbourne Researchers