Abstract
BackgroundTwo Bayesian methods, BayesCπ and BayesDπ, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior probability π that a SNP has zero effect as unknown. The methods were compared in terms of inference of the number of QTL and accuracy of genomic estimated breeding values (GEBVs), using simulated scenarios and real data from North American Holstein bulls.ResultsEstimates of π from BayesCπ, in contrast to BayesDπ, were sensitive to the number of simulated QTL and training data size, and provide information about genetic architecture. Milk yield and fat yield have QTL with larger effects than protein yield and somatic cell score. The drawback of BayesA and BayesB did not impair the accuracy of GEBVs. Accuracies of alternative Bayesian methods were similar. BayesA was a good choice for GEBV with the real data. Computing time was shorter for BayesCπ than for BayesDπ, and longest for our implementation of BayesA.ConclusionsCollectively, accounting for computing effort, uncertainty as to the number of QTL (which affects the GEBV accuracy of alternative methods), and fundamental interest in the number of QTL underlying quantitative traits, we believe that BayesCπ has merit for routine applications.
Highlights
Two Bayesian methods, BayesCπ and BayesDπ, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior probability π that a SNP has zero effect as unknown
The objectives of this study were to evaluate the ability of these methods to make inferences about the number of quantitative trait loci (QTL) (NQTL) of a quantitative trait by simulated and real data, and to compare accuracies of genomic estimated breeding values (GEBVs) from these new methods compared to existing methods
Estimates of π from BayesCπ, in contrast to those from BayesDπ, are sensitive to training data size and SNP density, and provide information about the genetic architecture of a quantitative trait; the traits milk yield and fat yield measured in North American Holsteins have QTL with larger effects than protein yield and somatic cell score
Summary
The methods were compared in terms of inference of the number of QTL and accuracy of genomic estimated breeding values (GEBVs), using simulated scenarios and real data from North American Holstein bulls. The statistical challenge is to estimate SNP effects in a situation where the number of training individuals is much smaller than the vast number of SNPs. The statistical challenge is to estimate SNP effects in a situation where the number of training individuals is much smaller than the vast number of SNPs For this purpose, Meuwissen et al [1] presented two hierarchical Bayesian models, termed BayesA and BayesB, that are discussed extensively in animal and plant breeding research (e.g., [2,3,4,5,6]).
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