Comparison of accuracy of breeding value for cow from three methods in Hanwoo (Korean cattle) population.

In Korea, Korea Proven Bulls (KPN) program has been well-developed. Breeding and evaluation of cows are also an essential factor to increase earnings and genetic gain. This study aimed to evaluate the accuracy of cow breeding value by using three methods (pedigree index [PI], pedigree-based best linear unbiased prediction [PBLUP], and genomic-BLUP [GBLUP]). The reference population (n = 16,971) was used to estimate breeding values for 481 females as a test population. The accuracy of GBLUP was 0.63, 0.66, 0.62 and 0.63 for carcass weight (CWT), eye muscle area (EMA), back-fat thickness (BFT), and marbling score (MS), respectively. As for the PBLUP method, accuracy of prediction was 0.43 for CWT, 0.45 for EMA, 0.43 for MS, and 0.44 for BFT. Accuracy of PI method was the lowest (0.28 to 0.29 for carcass traits). The increase by approximate 20% in accuracy of GBLUP method than other methods could be because genomic information may explain Mendelian sampling error that pedigree information cannot detect. Bias can cause reducing accuracy of estimated breeding value (EBV) for selected animals. Regression coefficient between true breeding value (TBV) and GBLUP EBV, PBLUP EBV, and PI EBV were 0.78, 0.625, and 0.35, respectively for CWT. This showed that genomic EBV (GEBV) is less biased than PBLUP and PI EBV in this study. In addition, number of effective chromosome segments (Me) statistic that indicates the independent loci is one of the important factors affecting the accuracy of BLUP. The correlation between Me and the accuracy of GBLUP is related to the genetic relationship between reference and test population. The correlations between Me and accuracy were -0.74 in CWT, -0.75 in EMA, -0.73 in MS, and -0.75 in BF, which were strongly negative. These results proved that the estimation of genetic ability using genomic data is the most effective, and the smaller the Me, the higher the accuracy of EBV.

Identification of Missense Variants Affecting Carcass Traits for Hanwoo Precision Breeding.

This study aimed to identify causal variants associated with important carcass traits such as weight and meat quality in Hanwoo cattle. We analyzed missense mutations extracted from imputed sequence data (ARS-UCD1.2) and performed an exon-specific association test on the carcass traits of 16,970 commercial Hanwoo. We found 33, 2, 1, and 3 significant SNPs associated with carcass weight (CW), backfat thickness (BFT), eye muscle area (EMA), and marbling score (MS), respectively. In CW and EMA, the most significant missense SNP was identified at 19,524,263 on BTA14 and involved the PRKDC. A missense SNP in the ZFAND2B, located at 107,160,304 on BTA2 was identified as being involved in BFT. For MS, missense SNP in the ACVR2B gene, located at 11,849,704 in BTA22 was identified as the most significant marker. The contribution of the most significant missense SNPs to genetic variance was confirmed to be 8.47%, 2.08%, 1.73%, and 1.19% in CW, BFT, EMA, and MS, respectively. We generated favorable and unfavorable haplotype combinations based on the significant SNPs for CW. Significant differences in GEBV (Genomic Estimated Breeding Values) were observed between groups with each favorable and unfavorable haplotype combination. In particular, the missense SNPs in PRKDC, MRPL9, and ANKFN1 appear to significantly affect the protein's function and structure, making them strong candidates as causal mutations. These missense SNPs have the potential to serve as valuable markers for improving carcass traits in Hanwoo commercial farms.

Development of nutrigenomic based precision management model for Hanwoo steers.

Focusing high marble deposition, Hanwoo feedlot system uses high-energy diet over the prolonged fattening period. However, due to the individual genetic variation, around 40% of them are graded into inferior quality grades (QG), despite they utilized the same resources. Therefore, focusing on development of a nutrigenomic based precision management model, this study was to evaluated the response to the divergent selection on genetic merit for marbling score (MS), under different dietary total digestible nutrient (TDN) levels. Total of 111 calves were genotyped and initially grouped according to estimated breeding value (high and low) for marbling score (MS-EBV). Subsequently, managed under two levels of feed TDN%, over the calf period, early, middle, and final fattening periods following 2 × 2 factorial arrangement. Carcasses were evaluated for MS, Back fat thickness (BFT) and Korean beef quality grading standard. As the direct response to the selection was significant, the results confirmed the importance of initial genetic grouping of Hanwoo steers for MS-EBV. However, dietary TDN level did not show an effect (p > 0.05) on the MS. Furthermore, no genetic-by-nutrition interaction for MS (p > 0.05) was also observed. The present results showed no correlation response on BFT (p > 0.05), which indicates that the selection based on MS-EBV can be used to enhance the MS without undesirable effect on BFT. Ultimate turnover of the Hanwoo feedlot operation is primarily determined by the QGs. The present model shows that the initial grouping for MS-EBV increased the proportion of carcasses graded for higher QGs (QG1++ and QG1+) by approximately 20%. Moreover, there appear to be a potential to increase the proportion of QG 1++ animals among the high-genetic group by further increasing the dietary energy content. Overall, this precision management strategy suggests the importance of adopting an MS based initial genetic grouping system for Hanwoo steers with a subsequent divergent management based on dietary energy level.

Optimal population size to detect quantitative trait loci in Korean native chicken: a simulation study.

OBJECTIVE: A genomic region associated with a particular phenotype is called quantitative trait loci (QTL). To detect the optimal F2 population size associated with QTLs in native chicken, we performed a simulation study on F2 population derived from crosses between two different breeds. METHODS: A total of 15 males and 150 females were randomly selected from the last generation of each F1 population which was composed of different breed to create two different F2 populations. The progenies produced from these selected individuals were simulated for six more generations. Their marker genotypes were simulated with a density of 50K at three different heritability levels for the traits such as 0.1, 0.3, and 0.5. Our study compared 100, 500, 1,000 reference population (RP) groups to each other with three different heritability levels. And a total of 35 QTLs were used, and their locations were randomly created. RESULTS: With a RP size of 100, no QTL was detected to satisfy Bonferroni value at three different heritability levels. In a RP size of 500, two QTLs were detected when the heritability was 0.5. With a RP size of 1,000, 0.1 heritability was detected only one QTL, and 0.5 heritability detected five QTLs. To sum up, RP size and heritability play a key role in detecting QTLs in a QTL study. The larger RP size and greater heritability value, the higher the probability of detection of QTLs. CONCLUSION: Our study suggests that the use of a large RP and heritability can improve QTL detection in an F2 chicken population.

Accuracy of genotype imputation based on reference population size and marker density in Hanwoo cattle.

Recently, the cattle genome sequence has been completed, followed by developing a commercial single nucleotide polymorphism (SNP) chip panel in the animal genome industry. In order to increase statistical power for detecting quantitative trait locus (QTL), a number of animals should be genotyped. However, a high-density chip for many animals would be increasing the genotyping cost. Therefore, statistical inference of genotype imputation (low-density chip to high-density) will be useful in the animal industry. The purpose of this study is to investigate the effect of the reference population size and marker density on the imputation accuracy and to suggest the appropriate number of reference population sets for the imputation in Hanwoo cattle. A total of 3,821 Hanwoo cattle were divided into reference and validation populations. The reference sets consisted of 50k (38,916) marker data and different population sizes (500, 1,000, 1,500, 2,000, and 3,600). The validation sets consisted of four validation sets (Total 889) and the different marker density (5k [5,000], 10k [10,000], and 15k [15,000]). The accuracy of imputation was calculated by direct comparison of the true genotype and the imputed genotype. In conclusion, when the lowest marker density (5k) was used in the validation set, according to the reference population size, the imputation accuracy was 0.793 to 0.929. On the other hand, when the highest marker density (15k), according to the reference population size, the imputation accuracy was 0.904 to 0.967. Moreover, the reference population size should be more than 1,000 to obtain at least 88% imputation accuracy in Hanwoo cattle.

Assessment of genomic prediction accuracy using different selection and evaluation approaches in a simulated Korean beef cattle population.

OBJECTIVE: This study assessed genomic prediction accuracies based on different selection methods, evaluation procedures, training population (TP) sizes, heritability (h2) levels, marker densities and pedigree error (PE) rates in a simulated Korean beef cattle population. METHODS: A simulation was performed using two different selection methods, phenotypic and estimated breeding value (EBV), with an h2 of 0.1, 0.3, or 0.5 and marker densities of 10, 50, or 777K. A total of 275 males and 2,475 females were randomly selected from the last generation to simulate ten recent generations. The simulation of the PE dataset was modified using only the EBV method of selection with a marker density of 50K and a heritability of 0.3. The proportions of errors substituted were 10%, 20%, 30%, and 40%, respectively. Genetic evaluations were performed using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with different weighted values. The accuracies of the predictions were determined. RESULTS: Compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection. However, an increase in the marker density did not yield higher accuracy in either method except when the h2 was 0.3 under the EBV selection method. Based on EBV selection with a heritability of 0.1 and a marker density of 10K, GBLUP and ssGBLUP_0.95 prediction accuracy was higher than that obtained by phenotypic selection. The prediction accuracies from ssGBLUP_0.95 outperformed those from the GBLUP method across all scenarios. When errors were introduced into the pedigree dataset, the prediction accuracies were only minimally influenced across all scenarios. CONCLUSION: Our study suggests that the use of ssGBLUP_0.95, EBV selection, and low marker density could help improve genetic gains in beef cattle.

Effect of errors in pedigree on the accuracy of estimated breeding value for carcass traits in Korean Hanwoo cattle.

OBJECTIVE: This study evaluated the effect of pedigree errors (PEs) on the accuracy of estimated breeding value (EBV) and genetic gain for carcass traits in Korean Hanwoo cattle. METHODS: The raw data set was based on the pedigree records of Korean Hanwoo cattle. The animals' information was obtained using Hanwoo registration records from Korean animal improvement association database. The record comprised of 46,704 animals, where the number of the sires used was 1,298 and the dams were 38,366 animals. The traits considered were carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), and marbling score (MS). Errors were introduced in the pedigree dataset through randomly assigning sires to all progenies. The error rates substituted were 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80%, respectively. A simulation was performed to produce a population of 1,650 animals from the pedigree data. A restricted maximum likelihood based animal model was applied to estimate the EBV, accuracy of the EBV, expected genetic gain, variance components, and heritability (h2) estimates for carcass traits. Correlation of the simulated data under PEs was also estimated using Pearson's method. RESULTS: The results showed that the carcass traits per slaughter year were not consistent. The average CWT, EMA, BFT, and MS were 342.60 kg, 78.76 cm2, 8.63 mm, and 3.31, respectively. When errors were introduced in the pedigree, the accuracy of EBV, genetic gain and h2 of carcass traits was reduced in this study. In addition, the correlation of the simulation was slightly affected under PEs. CONCLUSION: This study reveals the effect of PEs on the accuracy of EBV and genetic parameters for carcass traits, which provides valuable information for further study in Korean Hanwoo cattle.

Functional Partitioning of Genomic Variance and Genome-Wide Association Study for Carcass Traits in Korean Hanwoo Cattle Using Imputed Sequence Level SNP Data.

Quantitative traits are usually controlled by numerous genomic variants with small individual effects, and variances associated with those traits are explained in a continuous manner. However, the relative contributions of genomic regions to observed genetic variations have not been well explored using sequence level single nucleotide polymorphism (SNP) information. Here, imputed sequence level SNP data (11,278,153 SNPs) of 2109 Hanwoo steers (Korean native cattle) were partitioned according to functional annotation, chromosome, and minor allele frequency (MAF). Genomic relationship matrices (GRMs) were constructed for each classified region and fitted in the model both separately and together for carcass weight (CWT), eye muscle area (EMA), backfat thickness (BFT), and marbling score (MS) traits. A genome-wide association study (GWAS) was performed to identify significantly associated variants in genic and exon regions using a linear mixed model, and the genetic contribution of each exonic SNP was determined using a Bayesian mixture model. Considering all SNPs together, the heritability estimates for CWT, EMA, BFT, and MS were 0.57 ± 0.05, 0.46 ± 0.05, 0.45 ± 0.05, and 0.49 ± 0.05, respectively, which reflected substantial genomic contributions. Joint analysis revealed that the variance explained by each chromosome was proportional to its physical length with weak linear relationships for all traits. Moreover, genomic variances explained by functional category and MAF class differed greatly among the traits studied in joint analysis. For example, exon regions had larger contributions for BFT (0.13 ± 0.08) and MS (0.22 ± 0.08), whereas intron and intergenic regions explained most of the total genomic variances for CWT and EMA (0.22 ± 0.09-0.32 ± 0.11). Considering different functional classes of exon regions and the per SNP contribution revealed the largest proportion of genetic variance was attributable to synonymous variants. GWAS detected 206 and 27 SNPs in genic and exon regions, respectively, on BTA4, BTA6, and BTA14 that were significantly associated with CWT and EMA. These SNPs were harbored by 31 candidate genes, among which TOX, FAM184B, PPARGC1A, PRKDC, LCORL, and COL1A2 were noteworthy. BayesR analysis found that most SNPs (>93%) had very small effects and the 4.02-6.92% that had larger effects (10-4 × σA2 , 10-3 × σA2 , and 10-2 × σA2 ) explained most of the total genetic variance, confirming polygenic components of the traits studied.