Abstract
The aim of this study was to evaluate the impact of genotype imputation on the performance of the GBLUP and Bayesian methods for genomic prediction. A total of 10,309 Holstein bulls were genotyped on the BovineSNP50 BeadChip (50 k). Five low density single nucleotide polymorphism (SNP) panels, containing 6,177, 2,480, 1,536, 768 and 384 SNPs, were simulated from the 50 k panel. A fraction of 0%, 33% and 66% of the animals were randomly selected from the training sets to have low density genotypes which were then imputed into 50 k genotypes. A GBLUP and a Bayesian method were used to predict direct genomic values (DGV) for validation animals using imputed or their actual 50 k genotypes. Traits studied included milk yield, fat percentage, protein percentage and somatic cell score (SCS). Results showed that performance of both GBLUP and Bayesian methods was influenced by imputation errors. For traits affected by a few large QTL, the Bayesian method resulted in greater reductions of accuracy due to imputation errors than GBLUP. Including SNPs with largest effects in the low density panel substantially improved the accuracy of genomic prediction for the Bayesian method. Including genotypes imputed from the 6 k panel achieved almost the same accuracy of genomic prediction as that of using the 50 k panel even when 66% of the training population was genotyped on the 6 k panel. These results justified the application of the 6 k panel for genomic prediction. Imputations from lower density panels were more prone to errors and resulted in lower accuracy of genomic prediction. But for animals that have close relationship to the reference set, genotype imputation may still achieve a relatively high accuracy.
Highlights
(0.9841) when all bulls in the training set were genotyped with 50 k panel and bulls in the validation set were genotyped on the 6 k panel
Our study revealed that the 6 k single nucleotide polymorphisms (SNP) panel performed better than the 3 k panel and resulted in the least reduction of genomic prediction accuracy among all the low density panels
For the traits considered in this study, the Bayesian method performed similar or better than the genomic best linear unbiased prediction (GBLUP) method for genomic prediction under all scenarios and with different densities of the SNP panel or proportions of the training set being imputed
Summary
Genomic selection has become a new tool for genetic improvement in livestock species and plants thanks to the discovery of many thousands of single nucleotide polymorphisms (SNP) and cost-effective high-throughput genotyping technology.Since the first publication of Meuwissen et al [1], numerous statistical methods have been proposed for genomic prediction.Two main categories are genomic best linear unbiased prediction (GBLUP) methods [2,3,4,5,6,7], and Bayesian methods [1,8,9,10].Assumptions of SNP marker effects on the trait vary across different statistical methods. Genomic selection has become a new tool for genetic improvement in livestock species and plants thanks to the discovery of many thousands of single nucleotide polymorphisms (SNP) and cost-effective high-throughput genotyping technology. Since the first publication of Meuwissen et al [1], numerous statistical methods have been proposed for genomic prediction. Two main categories are genomic best linear unbiased prediction (GBLUP) methods [2,3,4,5,6,7], and Bayesian methods [1,8,9,10]. Assumptions of SNP marker effects on the trait vary across different statistical methods. GBLUP methods assume that all markers effects are from a normal distribution, while
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