Calpastatin (CAST) is a naturally occurring protein that inhibits the normal tenderization of meat as it ages postmortem. A SNP was identified in the CAST gene (a G to C substitution) and genotyped on crossbred commercially fed heifers (n = 163), steers (n = 226), and bulls (n = 61) from beef feedlots, and steers (n = 178) from a University of Guelph feeding trial. The association of the CAST SNP with carcass and meat quality traits was studied. Carcass traits included fat, lean, and bone yield; grade fat; LM area; and HCW. Meat quality traits included marbling grade; i.m. fat content of LM; tenderness evaluation of LM (Warner-Bratzler shear force) at 2, 7, 14, and 21 d of postmortem aging; and tenderness evaluation of semitendinosus muscle at 7 d of postmortem aging. The mixed model used in the analyses included fixed effects of CAST genotype, sex, slaughter group, and breed composition (linear covariate); sire was a random effect. For the analysis of shear force, i.m. fat content of LM was also included in the model as a linear covariate. Shear force measures were analyzed within days of postmortem aging and by repeated measures analysis. The CAST SNP allele C was more frequent (63%) in the crossbred population than allele G. The CAST SNP was associated with shear force across days of postmortem aging (P = 0.005); genotype CC yielded beef that was more tender than GG (-0.32 kg +/- 0.13), and CG had intermediate tenderness. The corresponding average allele substitution effect (G to C substitution) was also highly significant (-0.15 +/- 0.05 kg, P = 0.002). A lower percentage of unacceptably tough steaks (shear force > 5.7 kg) at 2 and 7 d postmortem was associated with an increasing number of C alleles (P < or = 0.05). At 7 d postmortem, the percentage of unacceptably tough steaks decreased by 24 and 35%, respectively, for animals carrying 1 and 2 copies of the C allele relative to animals with no C alleles. However, genotype CC had a greater fat yield (+1.44 +/- 0.56%; P = 0.037) than genotype GG, with a corresponding allele substitution effect of 0.67 +/- 0.27% (P = 0.015). Therefore, the CAST SNP allele C was associated with increased LM tenderness across days of postmortem aging and, importantly for the beef industry, had a significant reduction in the percentage of steaks rated unacceptably tough by consumers based on an assumed threshold level.

The accuracy of genomic predictions can be used to assess the utility of dense marker genotypes for genetic improvement of beef efficiency traits. This study was designed to test the impact of genomic distance between training and validation populations, training population size, statistical methods, and density of genetic markers on prediction accuracy for feed efficiency traits in multibreed and crossbred beef cattle. A total of 6,794 beef cattle data collated from various projects and research herds across Canada were used. Illumina BovineSNP50 (50K) and imputed Axiom Genome-Wide BOS 1 Array (HD) genotypes were available for all animals. The traits studied were DMI, ADG, and residual feed intake (RFI). Four validation groups of 150 animals each, including Angus (AN), Charolais (CH), Angus-Hereford crosses (ANHH), and a Charolais-based composite (TX) were created by considering the genomic distance between pairs of individuals in the validation groups. Each validation group had 7 corresponding training groups of increasing sizes ( = 1,000, 1,999, 2,999, 3,999, 4,999, 5,998, and 6,644), which also represent increasing average genomic distance between pairs of individuals in the training and validations groups. Prediction of genomic estimated breeding values (GEBV) was performed using genomic best linear unbiased prediction (GBLUP) and Bayesian method C (BayesC). The accuracy of genomic predictions was defined as the Pearson's correlation between adjusted phenotype and GEBV (), unless otherwise stated. Using 50K genotypes, the highest average achieved in purebreds (AN, CH) was 0.41 for DMI, 0.34 for ADG, and 0.35 for RFI, whereas in crossbreds (ANHH, TX) it was 0.38 for DMI, 0.21 for ADG, and 0.25 for RFI. Similarly, when imputed HD genotypes were applied in purebreds (AN, CH), the highest average was 0.14 for DMI, 0.15 for ADG, and 0.14 for RFI, whereas in crossbreds (ANHH, TX) it was 0.38 for DMI, 0.22 for ADG, and 0.24 for RFI. The of GBLUP predictions were greatly reduced with increasing genomic average distance compared to those from BayesC predictions. The results indicate that 50K genotypes, used with BayesC, are more effective for predicting GEBV in purebred cattle. Imputed HD genotypes found utility when dealing with composites and crossbreds. Formulation of a fairly large training set for genomic predictions in beef cattle should consider the genomic distance between the training and target populations.

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