BackgroundThe marker density, the heritability level of trait and the statistical models adopted are critical to the accuracy of genomic prediction (GP) or genomic selection (GS). The studies on the impact of the above factors on accuracy of GP are usually focused on the comparison and discussion of simulated datasets. If the potential of GS is to be fully utilized to optimize the effect of breeding and selection, it is essential to incorporate these factors into real data for understanding their impact on GP accuracy, more clearly and intuitively. Herein, we studied the genomic prediction of six wool traits of sheep by two different models, including genomic best linear unbiased prediction (GBLUP), and Bayes-Alphabet. We adopted 5-fold cross-validation to perform the accuracy evaluation based on the genotyping data of Alpine Merino sheep (n=821). ResultsThe GP accuracy of the six traits was found to be between 0.28 and 0.60, as demonstrated by the cross-validation results. We showed that the accuracy of GP could be improved by increasing the marker density, which is closely related to the model adopted and the heritability level of the trait. Moreover, based on two different marker densities, it was derived that the prediction effect of GBLUP model for traits with low heritability was better (GBLUP has the highest accuracy of 28.57% higher than Bayes-Alphabet); while with the increase of heritability level, the advantage of Bayes-Alphabet would be more obvious, therefore, different models of GP are appropriate in different traits. ConclusionThis is the first study of optimization of GP has been applied to the domesticated Alpine Merino sheep populations. The main aim was to study the influence and interaction of different models and marker densities on GP accuracy. These findings indicated the significance of applying appropriate models for GP which would assist in further exploring the optimization of GP.
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