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This study was conducted to estimate the genetic parameters of different feeding pattern traits, including average daily feed intake (ADFI), average occupation time per day (AOTD), average occupation time per visit (AOTV), average daily feeding rate (ADFR), average feeding rate per feeding visit (AFRV), average feed intake per feeding visit (AFIV), and average number of visits per day (ANVD), and their genetic relationship to production traits, such as on-test average daily gain (ADG), backfat thickness (BFT), loin muscle area (LMA), lean percentage (LP), and feed efficiency traits, such as feed conversion ratio (FCR) and five measures of residual feed intake (RFI1 to RFI5), in Duroc pigs (DD). The non-heritable common spatial pen effect was also estimated in all studied traits. The feeding pattern traits used in this study were derived from filtered feeding visits of 602 DD pigs. Using three animal models and the REML method, the genetic parameters revealed low to moderate heritability for ADFI (0.19 to 0.32) and AFIV (0.18 to 0.33), moderate heritability for ANVD (0.28 to 0.35) and AOTV (0.21 to 0.31), and high heritability for AOTD (0.73), ADFR (0.62 to 0.64), and AFRV (0.59 to 0.63). The addition of a common spatial pen effect in models 2 and 3 had a substantial impact, ranging from 8% to 23%, on the total variability of most feeding pattern traits, with the exception of AOTD, which only had a percentage variance of 0.30% due to the pen effect. The genetic and phenotypic correlation revealed that ADFI had consistent moderate to high genetic and phenotypic correlation with production and feed efficiency (FE) traits. However, selection against ADFI would negatively affect on-test ADG. Interestingly, the AOTD had no genetic correlation with ADG (0.04), low to moderate positive genetic correlation with FCR (0.27) and all RFI measures (0.24 to 0.33), and moderate negative correlation with LP (−0.39), indicating that selection for DD pigs with lower AOTD would not influence on-test ADG but may increase LP and improve feed efficiency by lowering FCR and all RFI measures. However, the corresponding phenotypic correlation of AOTD with production and feed efficiency traits was mostly weak, which can be attributed to the low residual or environmental correlation between these correlated traits. At the genetic level, the feeding pattern traits showed potential in improving feed efficiency and production traits. However, further studies are needed to evaluate their impact at phenotypic level.

Whole-genome sequence (WGS) data are increasingly being applied into genomic predictions, offering a higher predictive ability by including causal mutations or single-nucleotide polymorphisms (SNPs) putatively in strong linkage disequilibrium with causal mutations affecting the trait. This study aimed to improve the predictive performance of the customized Hanwoo 50 k SNP panel for four carcass traits in commercial Hanwoo population by adding highly predictive variants from sequence data. A total of 16,892 Hanwoo cattle with phenotypes (i.e., backfat thickness, carcass weight, longissimus muscle area, and marbling score), 50 k genotypes, and WGS imputed genotypes were used. We partitioned imputed WGS data according to functional annotation [intergenic (IGR), intron (ITR), regulatory (REG), synonymous (SYN), and non-synonymous (NSY)] to characterize the genomic regions that will deliver higher predictive power for the traits investigated. Animals were assigned into two groups, the discovery set (7324 animals) used for predictive variant detection and the cross-validation set for genomic prediction. Genome-wide association studies were performed by trait to every genomic region and entire WGS data for the pre-selection of variants. Each set of pre-selected SNPs with different density (1000, 3000, 5000, or 10,000) were added to the 50 k genotypes separately and the predictive performance of each set of genotypes was assessed using the genomic best linear unbiased prediction (GBLUP). Results showed that the predictive performance of the customized Hanwoo 50 k SNP panel can be improved by the addition of pre-selected variants from the WGS data, particularly 3000 variants from each trait, which is then sufficient to improve the prediction accuracy for all traits. When 12,000 pre-selected variants (3000 variants from each trait) were added to the 50 k genotypes, the prediction accuracies increased by 9.9, 9.2, 6.4, and 4.7% for backfat thickness, carcass weight, longissimus muscle area, and marbling score compared to the regular 50 k SNP panel, respectively. In terms of prediction bias, regression coefficients for all sets of genotypes in all traits were close to 1, indicating an unbiased prediction. The strategy used to select variants based on functional annotation did not show a clear advantage compared to using whole-genome. Nonetheless, such pre-selected SNPs from the IGR region gave the highest improvement in prediction accuracy among genomic regions and the values were close to those obtained using the WGS data for all traits. We concluded that additional gain in prediction accuracy when using pre-selected variants appears to be trait-dependent, and using WGS data remained more accurate compared to using a specific genomic region.

Background In this study, we assessed the accuracy of genomic prediction for carcass weight (CWT), marbling score (MS), eye muscle area (EMA) and back fat thickness (BFT) in Hanwoo cattle when using genomic best linear unbiased prediction (GBLUP), weighted GBLUP (wGBLUP), and a BayesR model. For these models, we investigated the potential gain from using pre-selected single nucleotide polymorphisms (SNPs) from a genome-wide association study (GWAS) on imputed sequence data and from gene expression information. We used data on 13,717 animals with carcass phenotypes and imputed sequence genotypes that were split in an independent GWAS discovery set of varying size and a remaining set for validation of prediction. Expression data were used from a Hanwoo gene expression experiment based on 45 animals. Results Using a larger number of animals in the reference set increased the accuracy of genomic prediction whereas a larger independent GWAS discovery dataset improved identification of predictive SNPs. Using pre-selected SNPs from GWAS in GBLUP improved accuracy of prediction by 0.02 for EMA and up to 0.05 for BFT, CWT, and MS, compared to a 50 k standard SNP array that gave accuracies of 0.50, 0.47, 0.58, and 0.47, respectively. Accuracy of prediction of BFT and CWT increased when BayesR was applied with the 50 k SNP array (0.02 and 0.03, respectively) and was further improved by combining the 50 k array with the top-SNPs (0.06 and 0.04, respectively). By contrast, using BayesR resulted in limited improvement for EMA and MS. wGBLUP did not improve accuracy but increased prediction bias. Based on the RNA-seq experiment, we identified informative expression quantitative trait loci, which, when used in GBLUP, improved the accuracy of prediction slightly, i.e. between 0.01 and 0.02. SNPs that were located in genes, the expression of which was associated with differences in trait phenotype, did not contribute to a higher prediction accuracy. Conclusions Our results show that, in Hanwoo beef cattle, when SNPs are pre-selected from GWAS on imputed sequence data, the accuracy of prediction improves only slightly whereas the contribution of SNPs that are selected based on gene expression is not significant. The benefit of statistical models to prioritize selected SNPs for estimating genomic breeding values is trait-specific and depends on the genetic architecture of each trait.

Hanwoo is one of the most economically important animal species in Korea due to its significant contribution to nutrition. However, the current selection index only focuses to improve carcass traits of Hanwoo. Thus, this study aimed to estimate the genetic parameters of birth weight (BW) and weaning weight (WW) and their genetic and phenotypic relationship to the age at first calving (AFC) and gestation length (GL) of Hanwoo. The genetic parameters for birth weight (BW) and weaning weight (WW) were estimated using the data obtained from 52,173 and 35,800 Hanwoo calves born from February 1998 to March 2017, respectively. Further, these data were used to determine their genetic and phenotypic correlation to age at first calving (AFC) and gestation length (GL). The heritability estimates of BW and WW and correlation coefficients were obtained using the average information restricted maximum likelihood (AIREML) procedure, fit in single and two-trait linear animal models. The estimated direct heritability for BW and WW was moderate (0.22 &plusmn, 0.02) and high (0.51 &plusmn, 0.03), respectively, while the maternal heritability for both traits was 0.12 &plusmn, 0.01 and 0.17 &plusmn, 0.01, respectively. The genetic correlation of BW and reproductive traits (AFC and GL) showed a moderate and high positive correlation coefficient of 0.33 &plusmn, 0.06 and 0.53 &plusmn, 0.02, respectively, while close to zero and low positive phenotypic correlations of 0.06 &plusmn, 0.01 and 0.21 &plusmn, 0.06 were also observed between the correlated traits, respectively. For the correlation analysis between WW and AFC, both the genetic and phenotypic correlation showed close to zero values of 0.04 &plusmn, 0.06 and &minus, 0.01 &plusmn, 0.01, respectively. Meanwhile, the genetic and phenotypic correlation between WW and GL showed low and negative correlations of &minus, 0.09 &plusmn, 0.01, respectively. These obtained estimated variances for BW and WW and their corresponding genetic and phenotypic correlation to AFC and GL can be used as information for genetic improvement and subsequent economic improvement of Hanwoo farming.

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.

Objective This study was conducted to estimate breed-specific variance components for total number born (TNB), number born alive (NBA) and mortality rate from birth through weaning including stillbirths (MORT) of three main swine breeds in Korea. In addition, the importance of including maternal genetic and service sire effects in estimation models was evaluated. Methods Records of farrowing traits from 6,412 Duroc, 18,020 Landrace, and 54,254 Yorkshire sows collected from January 2001 to September 2016 from different farms in Korea were used in the analysis. Animal models and the restricted maximum likelihood method were used to estimate variances in animal genetic, permanent environmental, maternal genetic, service sire and residuals. Results The heritability estimates ranged from 0.072 to 0.102, 0.090 to 0.099, and 0.109 to 0.121 for TNB; 0.087 to 0.110, 0.088 to 0.100, and 0.099 to 0.107 for NBA; and 0.027 to 0.031, 0.050 to 0.053, and 0.073 to 0.081 for MORT in the Duroc, Landrace and Yorkshire breeds, respectively. The proportion of the total variation due to permanent environmental effects, maternal genetic effects, and service sire effects ranged from 0.042 to 0.088, 0.001 to 0.031, and 0.001 to 0.021, respectively. Spearman rank correlations among models ranged from 0.98 to 0.99, demonstrating that the maternal genetic and service sire effects have small effects on the precision of the breeding value. Conclusion Models that include additive genetic and permanent environmental effects are suitable for farrowing traits in Duroc, Landrace, and Yorkshire populations in Korea. This breed-specific variance components estimates for litter traits can be utilized for pig improvement programs in Korea.

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.

Genetic parameters for the reproductive traits of Hanwoo cattle were estimated using data obtained from 15,355 cows in 92 herds across South Korea, which were inseminated from May 1997 to July 2016. An &ldquo, average information&rdquo, restricted maximum likelihood (REML) procedure that fit in single-trait and multi-trait animal models was used to estimate the variance components of age at first calving (AFC), calving interval (CI), days open (DO), and gestation length (GL). Results showed the low estimates of heritability for all reproductive traits from both single-trait and multi-trait models. Estimates of heritability for AFC were 0.08 and 0.10 with single-trait and multi-trait models, respectively, while the estimates of heritability using the same animal models ranged from 0.01 to 0.07, 0.01 to 0.09, and 0.10 to 0.16 for CI, DO, and GL, accordingly. While AFC showed positive genetic correlations of 0.52 and 0.46 with CI and DO, respectively, the estimates of genetic and phenotypic correlations of GL with AFC were close to zero. Moreover, phenotypic correlations of GL with CI and DO were also close to zero, however, the corresponding genetic correlations were 0.13 and &ndash, 0.38 for CI and DO, respectively. These estimated variance components and genetic correlations for reproductive traits can be utilized for genetic improvement programs of Hanwoo cattle.

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.