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Simple Summary Due to the undeniable detrimental impact of bovine viral diarrhea virus (BVDV) on cattle worldwide, various preventive approaches are carried out to control the spread of this disease. Among the established preventive approaches, vaccination remains the most widely used cost-effective method of control. Hence, a deeper study into the host immune response to vaccines will further refine the efficacy of these vaccines; the identification of differentially expressed genes (DEGs) related to immune response might bring a long-lasting solution. Thus far, studies showing the genes related to the immune response of cattle to vaccines are still limited. Therefore, this study identified DEGs in animals with high and low sample to positive (S/P) ratio based on the BVDV antibody level, using RNA sequencing (RNA-seq) transcriptome analysis, and functional enrichment analysis in gene ontology (GO) annotations and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Results revealed that several upregulated and downregulated genes were significantly annotated to antigen processing and presentation (MHC class I), immune response, and interferon-gamma production, indicating the immune response of the animals related to possible shaping of their adaptive immunity against the BVDV type I. Moreover, significant enrichment to various KEGG pathways related to the development of adaptive immunity was observed. Abstract Immune response of 107 vaccinated Holstein cattle was initially obtained prior to the ELISA test. Five cattle with high and low bovine viral diarrhea virus (BVDV) type I antibody were identified as the final experimental animals. Blood samples from these animals were then utilized to determine significant differentially expressed genes (DEGs) using the RNA-seq transcriptome analysis and enrichment analysis. Our analysis identified 261 DEGs in cattle identified as experimental animals. Functional enrichment analysis in gene ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways revealed the DEGs potentially induced by the inactivated BVDV type I vaccine, and might be responsible for the host immune responses. Our findings suggested that inactivated vaccine induced upregulation of genes involved in different GO annotations, including antigen processing and presentation of peptide antigen (via MHC class I), immune response, and positive regulation of interferon-gamma production. The observed downregulation of other genes involved in immune response might be due to inhibition of toll-like receptors (TLRs) by the upregulation of the Bcl-3 gene. Meanwhile, the result of KEGG pathways revealed that the majority of DEGs were upregulated and enriched to different pathways, including cytokine-cytokine receptor interaction, platelet activation, extracellular matrix (ECM) receptor interaction, hematopoietic cell lineage, and ATP-binding cassette (ABC) transporters. These significant pathways supported our initial findings and are known to play a vital role in shaping adaptive immunity against BVDV type 1. In addition, type 1 diabetes mellitus pathways tended to be significantly enriched. Thus, further studies are needed to investigate the prevalence of type 1 diabetes mellitus in cattle vaccinated with inactivated and live BVDV vaccine.

Genetic parameters and trends in the average daily gain (ADG), backfat thickness (BF), loin muscle area (LMA), lean percentage (LP), and age at 90 kg (D90) were estimated for populations of Landrace and Yorkshire pigs. Additionally, the correlations between these production traits and litter traits were estimated. Litter traits included total born (TB) and number born alive (NBA). The data used for this study were obtained from eight farms during 1999 to 2016. Analyses were carried out with a multivariate animal model to estimate genetic parameters for production traits while bivariate analyses were performed to estimate the correlations between production and litter traits. The heritability estimates were 0.52 and 0.43 for ADG; 0.54 and 0.45 for BF; 0.25 and 0.26 for LMA; 0.54 and 0.48 for LP; and 0.56 and 0.46 for D90 in the Landrace and Yorkshire breeds, respectively. The ADG and D90 showed low genetic correlation with BF and LP. The LMA had -0.40, -0.32, 0.49, and 0.39 genetic correlations with ADG, BF, LP, and D90, respectively. Genetic correlations between production and litter traits were generally low, except for the correlations between LMA and TB (-0.23) in Landrace and ADG and TB (-0.16), ADG and NBA (-0.18), D90 and TB (0.19), and D90 and NBA (0.20) in Yorkshire. Genetic trends in production traits were all favorable except for LMA.

The genomic best linear unbiased prediction (GBLUP) method has been widely used in routine genomic evaluation as it assumes a common variance for all single nucleotide polymorphism (SNP). However, this is unlikely in the case of traits influenced by major SNP. Hence, the present study aimed to improve the accuracy of GBLUP by using the weighted GBLUP (WGBLUP), which gives more weight to important markers for various carcass traits of Hanwoo cattle, such as backfat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). Linear and different nonlinearA SNP weighting procedures under WGBLUP were evaluated and compared with unweighted GBLUP and traditional pedigree-based methods (PBLUP). WGBLUP methods were assessed over ten iterations. Phenotypic data from 10,215 animals from different commercial herds that were slaughtered at approximately 30-month-old of age were used. All these animals were genotyped using Illumina Bovine 50k SNP chip and were divided into a training and a validation population by birth date on 1 November 2015. Genomic prediction accuracies obtained in the nonlinearA weighting methods were higher than those of the linear weighting for all traits. Moreover, unlike with linear methods, no sudden drops in the accuracy were noted after the peak was reached in nonlinearA methods. The average accuracies using PBLUP were 0.37, 0.49, 0.40, and 0.37, and 0.62, 0.74, 0.67, and 0.65 using GBLUP for BFT, CWT, EMA, and MS, respectively. Moreover, these accuracies of genomic prediction were further increased to 4.84% and 2.70% for BFT and CWT, respectively by using the nonlinearA method under the WGBLUP model. For EMA and MS, WGBLUP was as accurate as GBLUP. Our results indicate that the WGBLUP using a nonlinearA weighting method provides improved predictions for CWT and BFT, suggesting that the ability of WGBLUP over the other models by weighting selected SNPs appears to be trait-dependent.

Simple Summary Genetic parameters play an important role in designing a breeding program. Many software and methods are used to estimate genetic parameters in livestock population. Multi-trait models are efficiently used these days for productive traits, reproductive traits, milk traits, etc. These models are more useful in the case of low heritability trait and with missing phenotypes. In this study, Hanwoo cattle, an indigenous breed from South Korea, is studied for four carcass traits (back fat thickness, carcass weight, eye muscle area, and marbling score). Single-trait and multi-trait models are constructed using BLUPF90 software to estimate variance components. In addition, the effect of genetic correlations among traits is scrutinized in multi-trait models for these traits. Abstract Hanwoo breed is preferred in South Korea because of the high standards in marbling and the palatability of its meat. Numerous studies have been conducted and are ongoing to increase the meat production and quality in this beef population. The aim of this study was to estimate and compare genetic parameters for carcass traits using BLUPF90 software. Four models were constructed, single trait pedigree model (STPM), single-trait genomic model (STGM), multi-trait pedigree model (MTPM), and multi-trait genomic model (MTGM), using the pedigree, phenotype, and genomic information of 7991 Hanwoo cattle. Four carcass traits were evaluated: Back fat thickness (BFT), carcass weight (CWT), eye muscle area (EMA), and marbling score (MS). Heritability estimates of 0.40 and 0.41 for BFT, 0.33 and 0.34 for CWT, 0.36 and 0.37 for EMA, and 0.35 and 0.38 for MS were obtained for the single-trait pedigree model and the multi-trait pedigree model, respectively, in Hanwoo. Further, the genomic model showed more improved results compared to the pedigree model, with heritability of 0.39 (CWT), 0.39 (EMA), and 0.46 (MS), except for 0.39 (BFT), which may be due to random events. Utilization of genomic information in the form of single nucleotide polymorphisms (SNPs) has allowed more capturing of the variance from the traits improving the variance components.

Comparison of the multi-trait animal model and the traditional repeatability model was carried out using data obtained from 6,424 Landrace and 20,835 Yorkshire sows farrowed from January 2000 to April 2018 in order to estimate genetic parameters for litter traits at different parities. Specifically, records of the total number born (TNB), number born alive (NBA), total number of mortality (MORT), number of stillborn (NSB) and number of mummified pigs (MUM) were used. Although results showed the heterogeneity of heritability for litter traits at different parities, the mean heritability estimates from the multi-trait model were found to be higher than those of the repeatability model for all traits in both pig breeds. In terms of genetic correlation between parities, a slight difference in genetic control in the first parity was noted for TNB and NBA in Landrace and Yorkshire pigs. The correlation between the first parity and later parities ranged from 0.48 to 0.74 for TNB and NBA in both breeds. Moreover, genetic correlation between parities for MORT and NSB was observed to be high for parities higher than 2 in Yorkshire pigs. For MUM, genetic correlation between the first and other parities was generally low in both breeds, indicating that culling pigs on the basis of MUM at the first parity could probably be unreasonable. Overall, the results of this study suggest that the multi-trait approach for litter size traits is useful for the accurate estimation of genetic parameters.

This study aimed to evaluate the accuracy of genomic prediction using single-trait and multi-trait single-step genomic best linear unbiased prediction (ssGBLUP) based on 50 K SNP chip dataset and imputed high-density (HD) genotypes for various carcass traits in commercial Hanwoo population such as backfat thickness (BFT), carcass weight (CWT), rib eye area (REA), and marbling score (MS). Analyses involved phenotypes from 18,269 animals born between April 2006 and June 2017 from different commercial herds in South Korea as well as genomic information from 16,892 animals genotyped using customized Hanwoo 50 K SNP chip and imputed to HD genotypes. Cross-validation was performed on 3,041 animals in the validation population born from November 2016 to June 2017. The results showed that HD genotypes led to a marginal increase in prediction accuracies (0.6 to 2%) than 50 K genotypes for BFT, REA, and MS, while no improvement was noted for CWT. Compared to the single-trait model, the use of multi-trait model based on 50 K or HD genotypes produced only a small improvement in prediction accuracies (1.2 to 2%) for BFT and REA but without improvement for CWT. For MS, the accuracy of genomic prediction using the multi-trait model based on 50 K or HD genotype datasets was slightly lower than the single-trait model. Generally, inflation of predictions increased using the HD genotypes and multi-trait model. Therefore, we recommend the use of 50 K genotypes and single-trait model in the estimation of genomic breeding values for carcass traits in commercial Hanwoo population.

Meat quality and carcass characteristics have gained the attention of breeders due to their increasing economic value. Thus, this study investigated the genomic prediction efficiencies of genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) for traits associated with meat quality, sensory characteristics, and fatty-acid composition. A total of 1237 Duroc finishing pigs with 654 individuals genotyped using the Illumina Porcine SNP 60k marker panel were used in this study. Prediction accuracy and bias for GBLUP and ssGBLUP were evaluated using a five-replicates of five-fold cross-validation. Estimation of genetic parameters for traits associated with meat quality, including lightness, yellowness, redness, pH at 24 h post-mortem, moisture content, fat content, water-holding capacity, cooking loss except for shear force (0.19), as well as fatty-acid composition (palmitic, stearic, oleic, linoleic, and linolenic fatty acids), revealed moderate to high heritability estimates ranging from 0.25 to 0.72 and 0.27 to 0.50, respectively, whereas all traits related to sensory characteristics (color, flavor, tenderness, juiciness, and palatability) showed low heritability estimates ranging from 0.08 to 0.14. Meanwhile, assessment of genomic prediction accuracy revealed that ssGBLUP exhibited higher prediction accuracy than GBLUP for meat quality traits, fatty-acid composition, and sensory characteristics, with percentage improvements ranging from 1.90% to 56.07%, 0.73% to 23.21%, and 0.88% to 11.85%, respectively. In terms of prediction bias, ssGBLUP showed less bias estimates than GBLUP for the majority of traits related to meat quality traits, sensory characteristics, and fatty-acid composition of Duroc meat. In this study, ssGBLUP outperformed GBLUP in terms of prediction accuracy and bias for the majority of traits. Through selection and breeding, our findings could be used to promote meat production with improved nutritional value.

The authors wish to make the following corrections to this paper [...]

The phenotype or genomic enhanced breeding value (GEBV) of ultrasound intramuscular fat (UIMF) was used as the target trait to improve meat quality. The ZPLAN+ software was employed to calculate and compare the genetic gain and accuracy of each selection scenario. The first scenario reflected the current conventional selection program in which the selection index is composed of average daily gain (ADG), feed conversion ratio (FCR) and ultrasound backfat (UBF). In the second scenario, UIMF was added into the basic selection index as an indicator trait for meat quality. In scenario 3, UIMF was also incorporated into index; however, the GEBV was used instead of phenotype. In scenario 4 and 5, selection was based strictly on the GEBV, and UIMF was included in scenario 5. The results showed that the accuracies of scenario 3, 4 and 5, in which GEBV information was used, increased with increasing accuracy of the GEBV. Moreover, the trends of scenario 4 and 5 changed more rapidly relative to scenario 3. The addition of UIMF to the selection index had a positive effect on the genetic gain of ADG and FCR, but a negative effect on UBF. The addition of UIMF to the selection index led to improvement of other traits and to the overall meat quality, especially when genomic selection was applied.