Evaluation of genome based estimated breeding values for meat quality in a Berkshire population using high density single nucleotide polymorphism chips

Abstract

The accuracy of genomic estimated breeding values (GEBV) was evaluated for sixteen meat quality traits in a Berkshire population (n = 1,191) that was collected from Dasan breeding farm, Namwon, Korea. The animals were genotyped with the Illumina porcine 62 K single nucleotide polymorphism (SNP) bead chips, in which a set of 36,605 SNPs were available after quality control tests. Two methods were applied to evaluate GEBV accuracies, i.e. genome based linear unbiased prediction method (GBLUP) and Bayes B, using ASREML 3.0 and Gensel 4.0 software, respectively. The traits composed different sets of training (both genotypes and phenotypes) and testing (genotypes only) data. Under the GBLUP model, the GEBV accuracies for the training data ranged from 0.42±0.08 for collagen to 0.75±0.02 for water holding capacity with an average of 0.65±0.04 across all the traits. Under the Bayes B model, the GEBV accuracy ranged from 0.10±0.14 for National Pork Producers Council (NPCC) marbling score to 0.76±0.04 for drip loss, with an average of 0.49±0.10. For the testing samples, the GEBV accuracy had an average of 0.46±0.10 under the GBLUP model, ranging from 0.20±0.18 for protein to 0.65±0.06 for drip loss. Under the Bayes B model, the GEBV accuracy ranged from 0.04±0.09 for NPCC marbling score to 0.72±0.05 for drip loss with an average of 0.38±0.13. The GEBV accuracy increased with the size of the training data and heritability. In general, the GEBV accuracies under the Bayes B model were lower than under the GBLUP model, especially when the training sample size was small. Our results suggest that a much greater training sample size is needed to get better GEBV accuracies for the testing samples.