Application of various models for the genomic evaluation of bovine tuberculosis in dairy cattle
Keywords:
bovine tuberculosis, SNP-BLUP, BayesCpi, ssGBLUP, polygenic effectAbstract
Using de-regressed breeding values (DRPs) from the routine genetic evaluations for resistance to bovine tuberculosis (bTB), genomic evaluation was undertaken model with a reference and validation populations of 1,700 and 537 bulls, respectively, genotyped with the Illumina 50Kchip. The validation set of 537 bulls (VAL1) resulted from using a cut off birth year of 2007. In an attempt to equate infection rate in the validation data set to that in the reference, two additional validation data sets were created based on sample of the first 30 bulls with reliability >=89 in the reference set plus all bulls in VAL1 with the same level of reliability (VAL2) and a third validation set (VAL3) made up of a random sample of the first 30 bulls with reliability >=93 in the reference set plus all bulls in VAL1 with the same level of reliability. The models used for the analyses included SNP-BLUP and BayesCpi and a single-step (ssGBLUP) which was based on phenotypic observations. Different levels of polygenic effects were investigated and their impact on SNP effects for SNPs with different allele frequency. The accuracy of evaluations from the SNP-BLUP based on the correlation between genomic breeding values in the validation set and individual daughter deviations (IDD) for bulls was 0.20 with no polygenic effects in the model. A similar estimate was from BayesCpi. However, the estimates of accuracies increased with increasing levels of polygenic effects with values of 0.24 at 30% polygenic effects for SNP-BLUP. However, the estimates of accuracy from ssGBLUP were much higher at 0.48 or 0.54 at 0% or 30% polygenic effect. The use of VAL2 and Val3 generally increased the accuracy of genomic prediction for SNP-BLUP and BayesCpi but had very little impact in ssGBLUP. Fitting a polygenic effect in the model does not have a uniform impact on the estimates of SNP effects but its influence is dependent on the allele frequency of the SNP
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