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A computationally efficient algorithm for genomic prediction using a Bayesian model

Overview of attention for article published in Genetics Selection Evolution, April 2015
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Title
A computationally efficient algorithm for genomic prediction using a Bayesian model
Published in
Genetics Selection Evolution, April 2015
DOI 10.1186/s12711-014-0082-4
Pubmed ID
Authors

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

Abstract

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. 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. 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. 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.

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Mendeley readers

The data shown below were compiled from readership statistics for 39 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 10%
Brazil 1 3%
Poland 1 3%
Luxembourg 1 3%
Australia 1 3%
Unknown 31 79%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 33%
Student > Ph. D. Student 11 28%
Student > Doctoral Student 4 10%
Unspecified 3 8%
Other 2 5%
Other 6 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 29 74%
Unspecified 3 8%
Biochemistry, Genetics and Molecular Biology 1 3%
Mathematics 1 3%
Pharmacology, Toxicology and Pharmaceutical Science 1 3%
Other 4 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 22 March 2016.
All research outputs
#3,817,708
of 7,424,901 outputs
Outputs from Genetics Selection Evolution
#137
of 280 outputs
Outputs of similar age
#116,540
of 213,911 outputs
Outputs of similar age from Genetics Selection Evolution
#10
of 20 outputs
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So far Altmetric has tracked 280 research outputs from this source. They receive a mean Attention Score of 2.6. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
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