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The objective of the present study was to evaluate the accuracy and bias of direct and blended genomic predictions using different methods and cross-validation techniques for growth traits (weight and weight gains) and visual scores (conformation, precocity, muscling, and size) obtained at weaning and at yearling in Hereford and Braford breeds. Phenotypic data contained 126,290 animals belonging to the Delta G Connection genetic improvement program, and a set of 3,545 animals genotyped with the 50K chip and 131 sires with the 777K. After quality control, 41,045 markers remained for all animals. An animal model was used to estimate (co)variance components and to predict breeding values, which were later used to calculate the deregressed estimated breeding values (DEBV). Animals with genotype and phenotype for the traits studied were divided into 4 or 5 groups by random and k-means clustering cross-validation strategies. The values of accuracy of the direct genomic values (DGV) were moderate to high magnitude for at weaning and at yearling traits, ranging from 0.19 to 0.45 for the k-means and 0.23 to 0.78 for random clustering among all traits. The greatest gain in relation to the pedigree BLUP (PBLUP) was 9.5% with the BayesB method with both the k-means and the random clustering. Blended genomic value accuracies ranged from 0.19 to 0.56 for k-means and from 0.21 to 0.82 for random clustering. The analyses using the historical pedigree and phenotypes contributed additional information to calculate the GEBV, and in general, the largest gains were for the single-step (ssGBLUP) method in bivariate analyses with a mean increase of 43.00% among all traits measured at weaning and of 46.27% for those evaluated at yearling. The accuracy values for the marker effects estimation methods were lower for k-means clustering, indicating that the training set relationship to the selection candidates is a major factor affecting accuracy of genomic predictions. The gains in accuracy obtained with genomic blending methods, mainly ssGBLUP in bivariate analyses, indicate that genomic predictions should be used as a tool to improve genetic gains in relation to the traditional PBLUP selection. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare two random regression models (RRM) fitted by fourth (RRM4) and fifth-order Legendre polynomials (RRM5) with a lactation model (LM) for evaluating Holstein cattle in Brazil. Two datasets with the same animals were prepared for this study. To apply test-day RRM and LMs, 262,426 test day records and 30,228 lactation records covering 305 days were prepared, respectively. The lowest values of Akaike's information criterion, Bayesian information criterion, and estimates of the maximum of the likelihood function (-2LogL) were for RRM4. Heritability for 305-day milk yield (305MY) was 0.23 (RRM4), 0.24 (RRM5), and 0.21 (LM). Heritability, additive genetic and permanent environmental variances of test days on days in milk was from 0.16 to 0.27, from 3.76 to 6.88 and from 11.12 to 20.21, respectively. Additive genetic correlations between test days ranged from 0.20 to 0.99. Permanent environmental correlations between test days were between 0.07 and 0.99. Standard deviations of average estimated breeding values (EBVs) for 305MY from RRM4 and RRM5 were from 11% to 30% higher for bulls and around 28% higher for cows than that in LM. Rank correlations between RRM EBVs and LM EBVs were between 0.86 to 0.96 for bulls and 0.80 to 0.87 for cows. Average percentage of gain in reliability of EBVs for 305-day yield increased from 4% to 17% for bulls and from 23% to 24% for cows when reliability of EBVs from RRM models was compared to those from LM model. Random regression model fitted by fourth order Legendre polynomials is recommended for genetic evaluations of Brazilian Holstein cattle because of the higher reliability in the estimation of breeding values. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Allocation of treatments to experimental units (EUs) is done at random. In the presence of a concomitant variable (e.g., initial body weight; iBW), one strategy is to block WUs into iBW groups. However, in some scientific manuscripts, EUs are sorted by iBW and then allocated to treatments (e.g., treatments "A" and "B") based on iBW, such that the lightest EU receives "A," the second and third lightest receive "B," etc. Although this strategy guarantees similar iBW between treatments, this ignores the random process required for statistical analysis of the data. We aimed to quantify the impact of lack of randomization on the statistical power and type I error of completely randomized designs (CRD). Data were simulated for ADG using two treatments ("A" having 50 g/d more than "B," and MSE=1250 g2/d2). Data were simulated for different replicates per treatment (RepsPerTreat; from 3 to 18, every 3). We used two scenarios for the correlations between iBW and ADG (ρ ADG,iBW): 0 and 0.5. Treatments were allocated to EUs at random (CRD) or according to the order of EUs based on iBW (completely non-randomized design; CNRD). The model included the fixed-effects of intercept and treatment. For ρ ADG,iBW=0, results showed that CRD had greater statistical power (POW) than CNRD for RepsPerTreat from 3 to 9, whereas CNRD had greater from 12 to 18. For ρ ADG,iBW=0.5, CNRD had an even greater POW than CRD starting at 9 RepsPerTreat. Although the type I error (ERROR) of CRD were close to 5% across all scenarios with different RepsPerTreat, CNRD had consistently greater and lower ERROR than CRD with =0 and 0.5, respectively. Having ERROR deviating from 5% is not expected. Visual inspection of the F-values of these models when the null hypothesis was true showed that a distribution other than the theoretical F-distribution, indicating that the statistical test is not valid. Sorting EUs by iBW does not guarantee greater statistical power but results in invalid F-tests. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Previous studies have shown that reproductive traits in sows during porcine reproductive and respiratory syndrome (PRRS) outbreaks are lowly heritable. However, little is known about the genomic basis of reproductive performance in PRRS virus (PRRSV)-infected sows. Thus, the objective of this work was to evaluate the genomic basis of reproductive performance in PRRSV-infected sows. Farrowing data from 475 Duroc and 405 Landrace sows that experienced a PRRS outbreak were used. Heritability and genomewide association studies (GWAS) based on ~30K SNP genotypes were performed for each breed separately, for 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). Heritabilities were estimated with ASReml using a model with the fixed-effect of parity and an animal random-effect, using a genomic-relationship matrix. GWAS was performed using Bayes-B in GenSel with a model fitting parity as fixed-effect and SNP effects as random. Heritability estimates (± standard error) for NBA, NSB, NM, NBD, TNB, and NW were 0.07±0.07, 0.02±0.11, 0.04±0.10, 0.08±0.08, 0.08±0.07, 0.06±0.04, respectively, for Duroc, and 0.09±0.07, 0.10±0.07, 0.10±0.08, 0.11±0.07, 0.07±0.08, 0.07±0.08, respectively, for Landrace. For Duroc, GWAS identified a quantitative trait locus (QTL) on chromosome (chr) 3 [114-120 megabases (Mb)] explaining 3.9% of the genetic variance (GV) for NBA, on chr 13 (189-193 Mb) explaining 2.7% GV for NBD, and on chr 5 (36-41 Mb) explaining 4.3% GV for TNB. For Landrace, GWAS identified a QTL on chr 13 (154-162 Mb) explaining 12.8% GV for NBA, on chr 14 (59-69 Mb) explaining 2.5% GV for NBD, and on chr 10 (7-9 Mb) explaining 4.1% GV for NW. These results indicate that reproductive traits are lowly heritable during a PRRS outbreak with few QTL identified in Duroc and Landrace sows. [ABSTRACT FROM AUTHOR]

Heat stress is considered a limiting factor for sheep production. We used information from physiological characteristics linked to heat tolerance to determine whether infrared thermography temperatures were able to separate groups of animals and determine the most important variables in this differentiation. Forty-eight four-month-old male lambs from eight genetic groups were used. Physiological (rectal temperature-RT, heart rate-HR, respiratory rate-RR) and blood traits, infrared thermography temperatures, heat tolerance indices, body measurements, weight and carcass traits were measured. Statistical analyses included variance, correlations, factor, discrimination and regression. Observing the correlations between physiological characteristics (RT, RR and HR) with temperatures measured by infrared thermography, regions for further studies should include the mean temperature of flank, nose and rump. Results show that there are strong relationships between thermograph measurements and RR, RT and HR in lambs, which are suggested to be directly correlated with heat tolerance capacity of the different genetic groups evaluated in this study. The assessment of body surface temperature measured by the thermograph could be used as a noninvasive tool to assess heat tolerance of the animals.

Background: The success of genomic selection depends mainly on the extent of linkage disequilibrium (LD) between markers and quantitative trait loci (QTL), the number of animals in the training set (TS) and the heritability (h2) of the trait. The extent of LD depends on the genetic structure of the population and the density of markers. The aim of this study was to calculate accuracy of direct genomic estimated breeding values (DGEBV) using best linear unbiased genomic prediction (GBLUP) for different marker densities, heritabilities and sizes of the TS in simulated populations that mimicked previously reported extent and pattern of LD in beef cattle., Results: The accuracy of DGEBV increased significantly (p < 0.05) with the increase in the number of bulls in the TS (480, 960 or 1920), trait h2 (0.10, 0.25 or 0.40) and marker densities (40 k or 800 k). Increasing the number of animals in the TS by 4-fold and using their phenotypes to estimate marker effects was not sufficient to maintain or increase the accuracy of DGEBV obtained using estimated breeding values (EBVs) when the trait h2 was lower than 0.40 for both marker densities. Comparing to expected accuracies of parent average (PA), the gains by using DGEBV would be of 27%, 13% and 10% for trait h2 equal to 0.10, 0.25 and 0.40, respectively, considering the scenario with 40 k markers and 1920 bulls in TS., Conclusions: As reported in dairy cattle, the size of the TS and the extent of LD have major impact on the accuracy of DGEBV. Based on the findings of this simulation study, large TS, as well as dense marker panels, aiming to increase the level of LD between markers and QTL, will likely be needed in beef cattle for successful implementation of genomic selection.