Abstract
ObjectiveIntramuscular fat is one of the meat quality traits that is considered in the selection strategies for Hanwoo (Korean cattle). Different methods are used to estimate the breeding value of selection candidates. In the present work we focused on accuracy of different genotype relationship matrices as described by forni and pedigree based relationship matrix.MethodsThe data set included a total of 778 animals that were genotyped for BovineSNP50 BeadChip. Among these 778 animals, 72 animals were sires for 706 reference animals and were used as a validation dataset. Single trait animal model (best linear unbiased prediction and genomic best linear unbiased prediction) was used to estimate the breeding values from genomic and pedigree information.ResultsThe diagonal elements for the pedigree based coefficients were slightly higher for the genomic relationship matrices (GRM) based coefficients while off diagonal elements were considerably low for GRM based coefficients. The accuracy of breeding value for the pedigree based relationship matrix (A) was 13% while for GRM (GOF, G05, and Yang) it was 0.37, 0.45, and 0.38, respectively.ConclusionAccuracy of GRM was 1.5 times higher than A in this study. Therefore, genomic information will be more beneficial than pedigree information in the Hanwoo breeding program.
Highlights
Owing to reduction of genotyping cost and availability of high density single nucleotide polymorphism (SNP) panels, genome assisted selection method has become a popular selection method in animal breeding
Genomic selection refers to incorporation of DNA marker information, often the whole genome SNP data, to predict the genomic breeding values used to make selection decisions
In the present study we report genomic prediction for intramuscular fat and compare the accuracy of genomic breeding values using best linear unbiased prediction (BLUP) and GBLUP from different genomic relationship matrices
Summary
Owing to reduction of genotyping cost and availability of high density single nucleotide polymorphism (SNP) panels, genome assisted selection method has become a popular selection method in animal breeding. One of the most important components of GEBV is the size and structure of reference population and the linkage disequilibrium between SNP and quantitative trait loci (QTL). Both of these factors affect the prediction accuracy considerably. Since economic traits are likely to be influenced by many loci with small effects, whole genome SNP data will be more accurate detecting QTL than statistical models that analyse few SNP markers of candidate gene. In animal breeding, estimated breeding value (EBV) has been calculated using phenotype and pedigree data using a statistical model called BLUP and it has been one of the important criteria that animal breeders use to select genetically superior animals. The accuracies obtained were compared to the accuracies obtained by using deterministic prediction equations given by Goddard [5] and Daetwyler [6]
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