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For swine breeding programs, testing and selection programs are usually within purebred (PB) populations located in nucleus units that are generally managed differently and tend to have a higher health level than the commercial herds in which the crossbred (CB) descendants of these nucleus animals are expected to perform. This approach assumes that PB animals selected in the nucleus herd will have CB progeny that have superior performance at the commercial level. There is clear evidence that this may not be the case for all traits of economic importance and, thus, including data collected at the commercial herd level may increase the accuracy of selection for commercial CB performance at the nucleus level. The goal for this study was to estimate genetic parameters for five maternal reproductive traits between two PB maternal nucleus populations (Landrace and Yorkshire) and their CB offspring: Total Number Born (TNB), Number Born Alive (NBA), Number Born Alive > 1 kg (NBA > 1 kg), Total Number Weaned (TNW), and Litter Weight at Weaning (LWW). Estimates were based on single-step GBLUP by analyzing any two combinations of a PB and the CB population, and by analyzing all three populations jointly. The genomic relationship matrix between the three populations was generated by using within-population allele frequencies for relationships within a population, and across-population allele frequencies for relationships of the CB with the PB animals. Utilization of metafounders for the two PB populations had no effect on parameter estimates, so the two PB populations were assumed to be genetically unrelated. Joint analysis of two (one PB plus CB) vs. three (both PB and CB) populations did not impact estimates of heritability, additive genetic variance, and genetic correlations. Heritabilities were generally similar between the PB and CB populations, except for LWW and TNW, for which PB populations had about four times larger estimates than CB. Purebred-crossbred genetic correlations (rpc) were larger for Landrace than for Yorkshire, except for NBA > 1 kg. These estimates of rpc indicate that there is potential to improve selection of PB animals for CB performance by including CB information for all traits in the Yorkshire population, but that noticeable additional gains may only occur for NBA > 1 kg and TNW in the Landrace population.

Previous studies proposed the use of antibody response to Porcine Reproductive and Respiratory Syndrome virus (PRRSV), measured as sample-to-positive (S/P) ratio, as a potential indicator trait to improve the reproductive performance of PRRSV-infected Landrace sows. However, this indicator trait has not yet been validated in Landrace sows or evaluated in a terminal sire line, such as Duroc. The main objective of this work was to perform host-genetic analyses of S/P ratio and reproductive traits during a PRRSV outbreak in maternal and terminal breeds. The data consisted of 690 Duroc and 541 Landrace multiparous sows (1.9±1.2 and 2.3±1.5, respectively) with S/P ratio collected at approximately 54 days after the predicted beginning of the outbreak. Of these, 644 Duroc and 528 Landrace sows also had reproductive data recorded during the PRRSV outbreak comprising number of piglets born alive (NBA), stillborn piglets (NSB), mummified piglets (NBM), number born dead (NBD; sum of NSB and NBM), total number born (TNB; sum of NBA and NBD), and number weaned (NW). All animals had genotype data on ~30K SNPs common across both breeds. Heritability estimates (± standard error) of S/P ratio during the PRRSV outbreak were moderate, with 0.33±0.06 for Duroc and 0.28±0.07 for Landrace. Reproductive traits during the PRRSV outbreak had overall low heritability estimates (≤0.18). Favorable genetic correlations of S/P ratio with NBA (0.65±0.33), in accordance with previous studies, and NBD (-0.33±0.28) were observed for Landrace sows only. Estimates of genetic correlation with other traits were -0.21±0.30 (NBM), -0.12±0.29 (NSB), 0.10±0.38 (NW), and 0.54±1.29 (TNB) for Landrace. For Duroc, these estimates were weaker: -0.33±0.40 (NBA), 0.26±0.27 (NBA), and 0.28±0.30 (NW), with convergence issues for mortality traits. These results further support the use of S/P ratio as an indicator trait for improved reproductive performance during a PRRSV outbreak in Landrace sows.

Studies on differences in resilience to porcine reproductive and respiratory syndrome (PRRS) virus (PRRSV) between breeds are scarce in the literature. Thus, the objective of this work was to assess PRRSV resilience in PRRSV wild-type infected sows from two breeds. Farrowing data included 2546 and 2522 litters from 894 Duroc and 813 Landrace sows, respectively, which were housed together and experienced the same PRRSV outbreak. Traits used for this study were number of piglets born alive (NBA), number born dead (NBD), total number born (TNB), and number weaned (NW). The impact of PRRSV infection was evaluated by comparing the reproductive performance of breeds between PRRS phases (pre-PRRS, PRRS, and post-PRRS). PRRS phases were defined based on the reproductive performance data. NBA, NBD, and NW were analyzed as a proportion of TNB using a Poisson mixed model. Pre-defined contrasts were used to evaluate the effect of breed on PRRSV resilience and on return to PRRSV-free performance, representing the differences between breeds for the difference between pre-PRRS and PRRS phases, and pre-PRRS and post-PRRS phases, respectively. There was a significant (P ≤ 0.003) interaction between PRRS phase and breed for all traits, as shown in Table 1. In general, reproductive performance reduced from pre-PRRS to PRRS, and then increased from PRRS to post-PRRS, as expected. The resilience contrast was significant for all traits (P ≤ 0.003). In all cases, the drop in percent reproductive performance from pre-PRRS to PRRS was lower for Duroc than for Landrace, indicating that Duroc sows have greater PRRSV resilience than Landrace sows. The return to PRRSV-free performance contrast had a trending effect for NBD (P = 0.055), and it was not significant for the other traits (P ≥ 0.515). These results indicate that Duroc sows have overall greater phenotypic PRRSV resilience for reproductive performance than Landrace sows.

Although vaccination is an effective measure in reducing the risk of bovine respiratory disease complex (BRDC) in cattle, BRDC losses remain significant. Increasing the efficacy of vaccination depends on elucidating the protective immune response to different antigens included in vaccines, determining the best timing for vaccination, and understanding the impact of the age of the calf on vaccination. This study measured the serum antibodies present in calves following vaccination against 4 viruses commonly associated with BRDC: bovine viral diarrhea virus type 1 and 2 (BVDV1 and BVDV2), bovine respiratory syncytial virus (BRSV), and bovine herpesvirus 1 (BHV1). Serum antibody titers were measured in more than 1,600 calves at 3-wk intervals starting at the time of the first vaccination. This first vaccination occurred at weaning for approximately half of the individuals and 3 wk before weaning for the other half. Dam age (years), time of weaning (initial vaccination or booster vaccination), and age of calf within year-season (days within year-season) classification all were found to have a significant effect on measured traits such as the initial titer and overall response. An increased initial titer was negatively correlated with each response trait (initial, booster, and overall response). Calves that were weaned at initial vaccination had greater overall antibody response to BVDV1 and BVDV2 compared with calves weaned 3 wk before initial vaccination. In contrast, calves given their initial vaccination 3 wk before weaning had greater overall antibody response to BRSV and BHV1 compared with calves that were vaccinated at weaning. Furthermore, the circulating antibody titer at which each virus needed to be below for an individual calf to positively respond to vaccination was determined (log titer of 0.38 for BVDV1, 1.5 for BVDV2, 3.88 for BRSV, and 1.5 for BHV1). This information can be used to improve vaccination protocols to allow for a greater response rate of individuals to vaccination and, hopefully, improved protection.

Prediction of feed intake from indicators would benefit the dairy industry since on-farm feed intake data are rare. The objective of this study was to examine the ability of sensor data to improve predictions of feed intake. Dry matter intake (DMI), milk yield (MY) and components, metabolic body weight (MBW; body weight0.75), and veterinary health records were collected from two cow groups (n1=47, n2=60). Automated sensors (ear tags, rumen bolus, environmental) captured measurements of cow activity, temperature, rumination and rumen pH, and barn temperature and humidity which were used to calculate THI. Random forest (RF) models were trained in R (Caret package) by 10-fold cross validation, with DMI as the response variable. Training data originated from the full study with the exception of the final day, for which DMI was then predicted. Predictive ability was evaluated against a base model excluding automated sensor data to determine changes in accuracy and the percent of variance explained (VAR). The base model included MY and components, MBW, THI, health status and parity. Base model mean square error (MSE) was 9.86, 13.25 and 12.50 kg of DMI and VAR 44.71, 42.9 and 44.85% (n = 92, 56 and 41, respectively). The correlation between actual and predicted final day DMI (CORR) was 0.05, 0.03 and 0.02 (n = 92, 56 and 41, respectively). Adding activity and temperature (first ear tag; n = 92) reduced MSE to 9.70 kg and VAR increased to 45.62% (CORR=0.20). Independently adding bolus activity, rumen temperature and pH (n = 56) to the base model also decreased MSE to 12.53 kg (VAR=46.24% and CORR=0.26). Lastly, adding activity and rumination from the second ear tag (n = 41) to the base model decreased MSE to 12.32 kg (VAR=45.63%, CORR=0.18). Automated sensors appear to explain additional variation in DMI that is not captured in the typical energy sink variables utilized when predicting intake.

For swine breeding programs, testing and selection programs are located in nucleus units that are generally managed differently and with higher health levels than commercial herds where descendants of nucleus animals are expected to perform. This approach assumes that superior animals selected in nucleus herds will have progeny with superior performance at the commercial level. There is clear evidence that this may not be true for all traits of economic importance and thus methods including data collected at the commercial level may increase accuracy of selection at the nucleus level. This study’s goal was to estimate genetic parameters for five reproductive traits between two purebred maternal nucleus populations and their commercial F1 offspring: Total Number Born, Number Born Alive, Number Born Alive > 1kg, Number Weaned, and Litter Weight. Estimates were based on single-step GBLUP in the BLUPF90 programs by utilizing any two combinations of a purebred and the F1 population, and by using all three populations jointly. The genomic relationship matrix between the three populations was generated by using within-population allele frequencies for relationships within a population, and across-population allele frequencies for relationships of the F1 with the purebred animals. The two purebred populations were assumed to be genetically unrelated. The use of two versus three populations did not impact estimates of heritability, additive variance, or genetic correlations. Heritabilities ranged from 0.02 to 0.09 for the F1, from 0.07 to 0.18 for Landrace, and from 0.07 to 0.21 for Yorkshire. Genetic correlations between the same traits in F1 and Landrace ranged from 0.22 to 0.93, and from 0.31 to 0.84 for F1 and Yorkshire. This range of genetic correlations indicates that the use of crossbred information can aid in the selection of purebreds for commercial crossbred performance for some traits. Funded by Genome Canada Genomic Applications Partnerships Program.

Recent studies on host-genetics of response to porcine reproductive and respiratory syndrome virus (PRRSV) infection have shown that genetic markers can be used to predict performance in growing pigs. On the other hand, no work has been done on the genomic prediction of reproductive performance in PRRSV-infected sows. Thus, the objective of this work was to assess the accuracies of genomic prediction for reproductive traits in PRRSV-infected sows. A total of 475 Duroc and 405 Landrace sows with ~30K SNP genotypes had farrowing performance data during the outbreak on: number of piglets born alive (NBA), stillborn piglets (NSB), mummified piglets (NM), piglets born dead (NBD; NSB+NM), total number of piglets born (TNB; NBA+NBD), and piglets weaned (NW). Genomic prediction was performed by Bayes-B with a model fitting parity as fixed-effect and SNP effects as random. Analyses were performed for two scenarios: within-breed and between-breed. For the within-breed prediction, 4-fold cross-validation was used. For the between-breed prediction, all data for each breed were used. Accuracy of genomic prediction (AGP) was calculated as the correlation between genomic estimated breeding values and pre-adjusted phenotype, divided by square-root of heritability. For the within-breed prediction, AGP (± standard deviation) for NBA, NBD, NM, NSB, NW, and TNB were 0.24±0.05, 0.07±0.10, -0.27±0.06, -0.24±0.09, -0.05±0.05, 0.19±0.03, respectively, for Duroc, and 0.37±0.02, 0.38±0.11, 0.30±0.07, 0.60±0.10, 0.09±0.11, 0.15±0.10, respectively, for Landrace. For the between-breed prediction, AGP for NBA, NBD, NM, NSB, NW, and TB were 0.28, -0.17, -0.01, 0.37, 0.27, and 0.29, respectively, when training on Landrace and validating on Duroc, and 0.28, -0.14, 0.01, 0.15, 0.23, and -0.01, respectively, when training on Duroc and validating on Landrace. Results show that the accuracies of genomic prediction for reproductive traits during a PRRSV outbreak are low to moderate.

Disease incidence and feed costs are two main drivers in cattle production operations. Frequently environmental stressors such as fescue toxicosis have negative effects on livestock performance and health. Low-cost methods to measure these types of health and stress response phenotypes are needed to capture their impact on production traits. Previous research has correlated white blood cell parameters to disease resistance in beef cattle. Based on this, blood parameters at weaning may be candidates that could be a proxy for selection and identification of high-performance animals in commercial settings. To identify candidate blood parameters, blood samples were collected at weaning on approximately 500 crossbred animals (Angus background crossed with Hereford, Charolais, Sim-Angus, Brangus) born between 2015 and 2016 and raised on toxic fescue. The animals were also genotyped at an approximate density of 50,000 SNPs. Complete blood counts (CBC) were obtained the blood samples and heritabilities for 15 peripheral blood parameters were estimated. For the CBC traits that were measured, heritabilities ranged from low to moderate (0.02 to 0.35). Based on current findings, a substantial genetic component for some CBC parameters exists and selection could be effective at improving these traits. Further research will estimate genetic correlations between peripheral blood parameters, weaning weight and average daily gain (ADG) with the intention of identifying correlated traits to be used in commercial selection programs.

The triacylglyceride (TAG) fatty acid content in meat from Angus-sired cattle was analyzed for non-additive genetic effects. A total of 11,482 significant DNA marker interactions (false discovery rate [FDR] < 0.05) were detected across thirty-seven different TAG fatty acids. Interactions were not evenly distributed amongst all fatty acids analyzed, and types of interactions (additive-by-additive, additive-by-dominance, and dominance-by-dominance) varied within each individual fatty acid. These results indicate that it may be possible to account for additional genetic variance amongst TAG fatty acids over and above individual markers.