Abstract
The main objectives of this study were to compare the prediction accuracy of different Bayesian methods for traits with a wide range of genetic architecture using simulation and real data and to assess the sensitivity of these methods to the violation of their assumptions. For the simulation study, different scenarios were implemented based on two traits with low or high heritability and different numbers of QTL and the distribution of their effects. For real data analysis, a German Holstein dataset for milk fat percentage, milk yield, and somatic cell score was used. The simulation results showed that, with the exception of the Bayes R, the other methods were sensitive to changes in the number of QTLs and distribution of QTL effects. Having a distribution of QTL effects, similar to what different Bayesian methods assume for estimating marker effects, did not improve their prediction accuracy. The Bayes B method gave higher or equal accuracy rather than the rest. The real data analysis showed that similar to scenarios with a large number of QTLs in the simulation, there was no difference between the accuracies of the different methods for any of the traits.
Funding source: Razi University
Award Identifier / Grant number: 1113
Acknowledgments
The author acknowledges the Razi University for funding this research project (grant number: 1113). The author gratefully thanks Dr. Noorbakhsh Hooti for improving the use of English in the manuscript.
Author contribution: The author has accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: This research project was funded by Razi University (grant number: 1113).
Conflict of interest statement: The author declares no conflicts of interest regarding this article.
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