A computationally efficient algorithm for genomic prediction using a Bayesian model

Wang, Tingting, Chen, Yi-Ping Phoebe, Goddard, Michael E., Meuwissen, Theo H. E., Kemper, Kathryn E. and Hayes, Ben J. (2015) A computationally efficient algorithm for genomic prediction using a Bayesian model. Genetics Selection Evolution, 47 . doi:10.1186/s12711-014-0082-4

Author Wang, Tingting
Chen, Yi-Ping Phoebe
Goddard, Michael E.
Meuwissen, Theo H. E.
Kemper, Kathryn E.
Hayes, Ben J.
Title A computationally efficient algorithm for genomic prediction using a Bayesian model
Journal name Genetics Selection Evolution   Check publisher's open access policy
ISSN 1297-9686
Publication date 2015-04-30
Sub-type Article (original research)
DOI 10.1186/s12711-014-0082-4
Open Access Status DOI
Volume 47
Total pages 16
Place of publication London, United Kingdom
Publisher BioMed Central
Language eng
Formatted 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 similar estimates of SNP effects and accuracies of genomic prediction than the MCMC implementation of BayesR (a Bayesian method for genomic prediction), but with greatly reduced computation time.
Methods: emBayesR is an approximate EM algorithm that retains the BayesR model assumption with SNP effects sampled from a mixture of normal distributions with increasing variance. emBayesR differs from other proposed non-MCMC implementations of Bayesian methods for genomic prediction in that it estimates the effect of each SNP while allowing for the error associated with estimation of all other SNP effects. emBayesR was compared to BayesR using simulated data, and real dairy cattle data with 632 003 SNPs genotyped, to determine if the MCMC and the expectation-maximisation approaches give similar accuracies of genomic prediction.
Results: We were able to demonstrate that allowing for the error associated with estimation of other SNP effects when estimating the effect of each SNP in emBayesR improved the accuracy of genomic prediction over emBayesR without including this error correction, with both simulated and real data. When averaged over nine dairy traits, the accuracy of genomic prediction with emBayesR was only 0.5% lower than that from BayesR. However, emBayesR reduced computing time up to 8-fold compared to BayesR.
Conclusions: The emBayesR algorithm described here achieved similar accuracies of genomic prediction to BayesR for a range of simulated and real 630 K dairy SNP data. emBayesR needs less computing time than BayesR, which will allow it to be applied to larger datasets.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Article number 34

Document type: Journal Article
Sub-type: Article (original research)
Collection: Queensland Alliance for Agriculture and Food Innovation
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Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
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Created: Thu, 07 Jul 2016, 12:18:23 EST by Anthony Yeates on behalf of Learning and Research Services (UQ Library)