Development of genetic and genomic evaluation for wellness traits in US Holstein cows.

In March 2016, Zoetis Genetics offered the first commercially available evaluation for wellness traits of Holstein dairy cattle. Phenotypic data on health events, pedigree, and genotypes were collected directly from producers upon obtaining their permission. Among all recorded health events, 6 traits were chosen to be included in the evaluation: mastitis, metritis, retained placenta, displaced abomasum, ketosis, and lameness. Each trait was defined as a binary event, having a value of 1 if a cow has been recorded with a disorder at any point during the lactation and zero otherwise. The number of phenotypic records ranged from 1.8 million for ketosis to 4.1 million for mastitis. Over 14 million pedigree records and 114,216 genotypes were included in the evaluation. All traits were analyzed using univariate threshold animal model with repeated observations, including fixed effect of parity and random effects of herd by year by season of calving, animal, and permanent environment. A total of 45,425 single nucleotide polymorphisms were used in the genomic analyses. Animals genotyped with low-density chips were imputed to the required number of single nucleotide polymorphisms. All analyses were based on the single-step genomic BLUP, a method that combines phenotype, pedigree, and genotype information. Predicted transmitting abilities were expressed in percentage points as a difference from the average estimated probability of a disorder in the base population. Reliabilities of breeding values were obtained by approximation based on partitioning of a function of reliability into contributions from records, pedigree, and genotypes. Reliabilities of genomic predicted transmitting abilities for young genotyped and pedigreed females without recorded health events had average values between 50.2% (displaced abomasum) and 51.9% (mastitis). Genomic predictions for wellness traits can provide new information about an animal's genetic potential for health and new selection tools for dairy wellness improvement.

Genetic parameters estimated at receiving for circulating cortisol, immunoglobulin G, interleukin 8, and incidence of bovine respiratory disease in feedlot beef steers.

Bovine respiratory disease complex (i.e., shipping fever and bacterial bronchopneumonia) is a multifaceted respiratory illness influenced by numerous environmental factors and microorganisms. Bovine respiratory disease (BRD) is just one component of BRD complex. Because BRD is moderately heritable, it may be possible to reduce the incidence of BRD through genetic selection. The objectives of this study were to determine the heritability and associative genetic relationships among immune system traits (i.e., cortisol, total IgG, IgG isotypes, and IL-8) in cattle monitored for BRD incidence. At an average of 83 d after weaning (219 d age and mean = 221.7 kg [SD 4.34]), crossbred steer calves ( = 2,869) were received at a commercial feedlot in southeastern Colorado over a 2-yr period. At receiving, jugular blood samples were collected at 212 (yr 1) and 226 d (yr 2) of age for immune trait analyses. The BRD phenotype was defined as a binomial variable (0 = no and 1 = yes) and compared with immune system traits measured at receiving (prior to illness onset). An animal identified as BRD positive exhibited ≥ 2 clinical signs (i.e., eye or nasal discharge, cough, lethargy, rapid breathing, acute interstitial pneumonia, or acute upper respiratory syndrome and/or a rectal temperature > 39.7°C). Heritability and genetic correlation estimates for categorical variable BRD, cortisol, IgG, IgG1, IgG2, and IL-8 were estimated from a sire model using ASREML. Heritability estimates were low to moderate for BRD (0.17 ± 0.08), cortisol (0.13 ± 0.05), IgG (0.15 ± 0.05), IgG1 (0.11 ± 0.05), IgG2 (0.24 ± 0.06), and IL-8 (0.30 ± 0.06). A moderate negative genetic correlation was determined between BRD and cortisol ( = -0.19 ± 0.32). Moderate positive correlations were found between BRD with IgG (0.42 ± 0.28), IgG1 (0.36 ± 0.32), and IL-8 ( = 0.26 ± 0.26). Variation in the BRD phenotype and immune system traits suggested herd health improvement may be achieved through genetic selection.

Genomic predictions in Angus cattle: comparisons of sample size, response variables, and clustering methods for cross-validation.

Advances in genomics, molecular biology, and statistical genetics have created a paradigm shift in the way livestock producers pursue genetic improvement in their herds. The nexus of these technologies has resulted in combining genotypic and phenotypic information to compute genomically enhanced measures of genetic merit of individual animals. However, large numbers of genotyped and phenotyped animals are required to produce robust estimates of the effects of SNP that are summed together to generate direct genomic breeding values (DGV). Data on 11,756 Angus animals genotyped with the Illumina BovineSNP50 Beadchip were used to develop genomic predictions for 17 traits reported by the American Angus Association through Angus Genetics Inc. in their National Cattle Evaluation program. Marker effects were computed using a 5-fold cross-validation approach and a Bayesian model averaging algorithm. The accuracies were examined with EBV and deregressed EBV (DEBV) response variables and with K-means and identical by state (IBS)-based cross-validation methodologies. The cross-validation accuracies obtained using EBV response variables were consistently greater than those obtained using DEBV (average correlations were 0.64 vs. 0.57). The accuracies obtained using K-means cross-validation were consistently smaller than accuracies obtained with the IBS-based cross-validation approach (average correlations were 0.58 vs. 0.64 with EBV used as a response variable). Comparing the results from the current study with the results from a similar study consisting of only 2,253 records indicated that larger training population size resulted in higher accuracies in validation animals and explained on average 18% (69% improvement) additional genetic variance across all traits.