• Asian-Australasian Journal of Animal Sciences
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• v.32 no.11
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• pp.1657-1663
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• 2019

Objective: A genome-based best linear unbiased prediction (GBLUP) method was applied to evaluate accuracies of genomic estimated breeding value (GEBV) of carcass and reproductive traits in Berkshire, Duroc and Yorkshire populations in Korean swine breeding farms. Methods: The data comprised a total of 1,870, 696, and 1,723 genotyped pigs belonging to Berkshire, Duroc and Yorkshire breeds, respectively. Reference populations for carcass traits consisted of 888 Berkshire, 466 Duroc, and 1,208 Yorkshire pigs, and those for reproductive traits comprised 210, 154, and 890 dams for the respective breeds. The carcass traits analyzed were backfat thickness (BFT) and carcass weight (CWT), and the reproductive traits were total number born (TNB) and number born alive (NBA). For each trait, GEBV accuracies were evaluated with a GEBV BLUP model and realized GEBVs. Results: The accuracies under the GBLUP model for BFT and CWT ranged from 0.33-0.72 and 0.33-0.63, respectively. For NBA and TNB, the model accuracies ranged 0.32 to 0.54 and 0.39 to 0.56, respectively. The realized accuracy estimates for BFT and CWT ranged 0.30 to 0.46 and 0.09 to 0.27, respectively, and 0.50 to 0.70 and 0.70 to 0.87 for NBA and TNB, respectively. For the carcass traits, the GEBV accuracies under the GBLUP model were higher than the realized GEBV accuracies across the breed populations, while for reproductive traits the realized accuracies were higher than the model based GEBV accuracies. Conclusion: The genomic prediction accuracy increased with reference population size and heritability of the trait. The GEBV accuracies were also influenced by GEBV estimation method, such that careful selection of animals based on the estimated GEBVs is needed. GEBV accuracy will increase with a larger sized reference population, which would be more beneficial for traits with low heritability such as reproductive traits.

• Animal Bioscience
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• v.36 no.9
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• pp.1327-1335
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• 2023

Objective: When evaluating individuals with the same parent and no phenotype by pedigree best linear unbiased prediction (BLUP), it is difficult to explain carcass grade difference and select individuals because they have the same value in pedigree BLUP (PBLUP). However, single step GBLUP (ssGBLUP), which can estimate the breeding value suitable for the individual by adding genotype, is more accurate than the existing method. Methods: The breeding value and accuracy were estimated with pedigree BLUP and ssGBLUP using pedigree and genotype of 408 Hanwoo cattle from 16 families with the same parent among siblings produced by fertilized egg transplantation. A total of 14,225 Hanwoo cattle with pedigree, genotype and phenotype were used as the reference population. PBLUP obtained estimated breeding value (EBV) using the pedigree of the test and reference populations, and ssGBLUP obtained genomic EBV (GEBV) after constructing and H-matrix by integrating the pedigree and genotype of the test and reference populations. Results: For all traits, the accuracy of GEBV using ssGBLUP is 0.18 to 0.20 higher than the accuracy of EBV obtained with PBLUP. Comparison of EBV and GEBV of individuals without phenotype, since the value of EBV is estimated based on expected values of alleles passed down from common ancestors. It does not take Mendelian sampling into consideration, so the EBV of all individuals within the same family is estimated to be the same value. However, GEBV makes estimating true kinship coefficient based on different genotypes of individuals possible, so GEBV that corresponds to each individual is estimated rather than a uniform GEBV for each individual. Conclusion: Since Hanwoo cows bred through embryo transfer have a high possibility of having the same parent, if ssGBLUP after adding genotype is used, estimating true kinship coefficient corresponding to each individual becomes possible, allowing for more accurate estimation of breeding value.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.681-691
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• 2021

The estimated breeding value (EBV) and accuracy of Hanwoo steer (Korean cattle) is an indicator that can predict the slaughter time in the future and carcass performance outcomes. Recently, studies using pedigrees and genotypes are being actively conducted to improve the accuracy of the EBV. In this study, the pedigree and genotype of 46 steers obtained from livestock farm A in Gyeongnam were used for a pedigree best linear unbiased prediction (PBLUP) and a genomic best linear unbiased prediction (GBLUP) to estimate and analyze the breeding value and accuracy of the carcass weight (CWT), eye muscle area (EMA), back-fat thickness (BFT), and marbling score (MS). PBLUP estimated the EBV and accuracy by constructing a numeric relationship matrix (NRM) from the 46 steers and reference population I (545,483 heads) with the pedigree and phenotype. GBLUP estimated genomic EBV (GEBV) and accuracy by constructing a genomic relationship matrix (GRM) from the 46 steers and reference population II (16,972 heads) with the genotype and phenotype. As a result, in the order of CWT, EMA, BFT, and MS, the accuracy levels of PBLUP were 0.531, 0.519, 0.524 and 0.530, while the accuracy outcomes of GBLUP were 0.799, 0.779, 0.768, and 0.810. The accuracy estimated by GBLUP was 50.1 - 53.1% higher than that estimated by PBLUP. GEBV estimated with the genotype is expected to show higher accuracy than the EBV calculated using only the pedigree and is thus expected to be used as basic data for genomic selection in the future.

• Journal of Animal Reproduction and Biotechnology
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• v.38 no.1
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• pp.32-37
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• 2023

This study has evaluated the genomic estimated breeding value (GEBV) of the commercial Hanwoo population using the genomic best linear unbiased prediction (GBLUP) method and genomic information. Furthermore, it analyzed the accuracy and realized accuracy of the GEBV. 1,740 heads of the Hanwoo population which were analyzed using a single nucleotide polymorphism (SNP) Chip has selected as the test population. For carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), and marbling score (MS), the mean GEBVs estimated using the GBLUP method were 3.819, 0.740, -0.248, and 0.041, respectively and the accuracy of each trait was 0.743, 0.728, 0.737, and 0.765, respectively. The accuracy of the breeding value was affected by heritability. The accuracy was estimated to be low in EMA with low heritability and high in MS with high heritability. Realized accuracy values of 0.522, 0.404, 0.444, and 0.539 for CWT, EMA, BFT, and MS, respectively, showing the same pattern as the accuracy value. The results of this study suggest that the breeding value of each individual can be estimated with higher accuracy by estimating the GEBV using the genomic information of 18,499 reference populations. If this method is used and applied to individual selection in a commercial Hanwoo population, more precise and economical individual selection is possible. In addition, continuous verification of the GBLUP model and establishment of a reference population suitable for commercial Hanwoo populations in Korea will enable a more accurate evaluation of individuals.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.3
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• pp.382-389
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• 2020

Objective: This study was conducted to test the efficiency of genomic selection for milk production traits in a Korean Holstein cattle population. Methods: A total of 506,481 milk production records from 293,855 animals (2,090 heads with single nucleotide polymorphism information) were used to estimate breeding value by single step best linear unbiased prediction. Results: The heritability estimates for milk, fat, and protein yields in the first parity were 0.28, 0.26, and 0.23, respectively. As the parity increased, the heritability decreased for all milk production traits. The estimated generation intervals of sire for the production of bulls (L_SB) and that for the production of cows (L_SC) were 7.9 and 8.1 years, respectively, and the estimated generation intervals of dams for the production of bulls (L_DB) and cows (L_DC) were 4.9 and 4.2 years, respectively. In the overall data set, the reliability of genomic estimated breeding value (GEBV) increased by 9% on average over that of estimated breeding value (EBV), and increased by 7% in cows with test records, about 4% in bulls with progeny records, and 13% in heifers without test records. The difference in the reliability between GEBV and EBV was especially significant for the data from young bulls, i.e. 17% on average for milk (39% vs 22%), fat (39% vs 22%), and protein (37% vs 22%) yields, respectively. When selected for the milk yield using GEBV, the genetic gain increased about 7.1% over the gain with the EBV in the cows with test records, and by 2.9% in bulls with progeny records, while the genetic gain increased by about 24.2% in heifers without test records and by 35% in young bulls without progeny records. Conclusion: More genetic gains can be expected through the use of GEBV than EBV, and genomic selection was more effective in the selection of young bulls and heifers without test records.

• Journal of Animal Reproduction and Biotechnology
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• v.38 no.3
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• pp.177-187
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• 2023

Background: The grading of Hanwoo (Korean native cattle) is based on four economic traits, and efforts have been continuously made to improve the genetic traits associated with these traits. There is a technology to predict the expected grade based on the 4 economic genetic SNP characteristics of Korean cattle calves using single nucleotide polymorphism (SNP) technology. Selection of highly proliferative, self-renewing, and differentiating satellite cells from cattle is a key technology in the cultured meat industry. Methods: We selected the Hanwoo with high and low-scored of genomic estimated breeding value (GEBV) by using the Hanwoo 50K SNP bead chip. We then isolated the bovine satellite cells from the chuck mass. We then conducted comparative analyses of cell proliferation, immunocytochemistry, qRT-PCR at short- and long-term culture. We also analyzed the differentiation capability at short term culture. Results: Our result showed that the proliferation was significantly high at High scored GEBV (Hs-GEBV) compared to Low scored GEBV (Ls-GEBV) at short- and long-term culture. The expression levels of Pax3 were significantly higher in Hs-GEBV bovine satellite cells at long-term culture. However, there were no significant differences in the expression levels of Pax7 between Hs- and Ls-GEBV bovine satellite cells at short- and long- term culture. The expression levels of MyoG and MyHC were significantly high at Ls-GEBV bovine satellite cells. Conclusions: Our results indicated that selection of bovine satellite cells by Hanwoo 50K SNP bead chip could be effective selection methods for massive producing of satellite cells.

• Korean Journal of Agricultural Science
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• v.39 no.3
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• pp.357-364
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• 2012

Genomic breeding value (GEBV) has recently become available in the beef cattle industry. Genomic selection methods are exceptionally valuable for selecting traits, such as marbling, that are difficult to measure until later in life. One method to utilize information from sparse marker panels is the Bayesian model selection method with RJMCMC. The accuracy of prediction varies between a multiple SNP model with RJMCMC (0.47 to 0.73) and a least squares method (0.11 to 0.41) when using SNP information, while the accuracy of prediction increases in the multiple SNP (0.56 to 0.90) and least square methods (0.21 to 0.63) when including a polygenic effect. In the multiple SNP model with RJMCMC model selection method, the accuracy ($r^2$) of GEBV for marbling predicted based only on SNP effects was 0.47, while the $r^2$ of GEBV predicted by SNP plus polygenic effect was 0.56. The accuracies of GEBV predicted using only SNP information were 0.62, 0.68 and 0.73 for CWT, EMA and BF, respectively. However, when polygenic effects were included, the accuracies of GEBV were increased to 0.89, 0.90 and 0.89 for CWT, EMA and BF, respectively. Our data demonstrate that SNP information alone is missing genetic variation information that contributes to phenotypes for carcass traits, and that polygenic effects compensate genetic variation that whole genome SNP data do not explain. Overall, the multiple SNP model with the RJMCMC model selection method provides a better prediction of GEBV than does the least squares method (single marker regression).

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.5
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• pp.640-645
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• 2016

The objective of this study was to evaluate the present conventional selection program of a swine nucleus farm and compare it with a new selection strategy employing genomic enhanced breeding value (GEBV) as the selection criteria. The ZPLAN+ software was employed to calculate and compare the genetic gain, total cost, return and profit of each selection strategy. The first strategy reflected the current conventional breeding program, which was a progeny test system (CS). The second strategy was a selection scheme based strictly on genomic information (GS1). The third scenario was the same as GS1, but the selection by GEBV was further supplemented by the performance test (GS2). The last scenario was a mixture of genomic information and progeny tests (GS3). The results showed that the accuracy of the selection index of young boars of GS1 was 26% higher than that of CS. On the other hand, both GS2 and GS3 gave 31% higher accuracy than CS for young boars. The annual monetary genetic gain of GS1, GS2 and GS3 was 10%, 12%, and 11% higher, respectively, than that of CS. As expected, the discounted costs of genomic selection strategies were higher than those of CS. The costs of GS1, GS2 and GS3 were 35%, 73%, and 89% higher than those of CS, respectively, assuming a genotyping cost of $120. As a result, the discounted profit per animal of GS1 and GS2 was 8% and 2% higher, respectively, than that of CS while GS3 was 6% lower. Comparison among genomic breeding scenarios revealed that GS1 was more profitable than GS2 and GS3. The genomic selection schemes, especially GS1 and GS2, were clearly superior to the conventional scheme in terms of monetary genetic gain and profit.

• Journal of Animal Science and Technology
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• v.55 no.1
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• pp.13-18
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• 2013

This study was conducted to estimate the Genomic Estimated Breeding Value (GEBV) using Genomic Best Linear Unbiased Prediction (GBLUP) method in Hanwoo (Korean native cattle) population. The result is expected to adapt genomic selection onto the national Hanwoo evaluation system. Carcass weight (CW), eye muscle area (EMA), backfat thickness (BT), and marbling score (MS) were investigated in 552 Hanwoo progeny-tested steers at Livestock Improvement Main Center. Animals were genotyped with Illumina BovineHD BeadChip (777K SNPs). For statistical analysis, Genetic Relationship Matrix (GRM) was formulated on the basis of genotypes and the accuracy of GEBV was estimated with 10-fold Cross-validation method. The accuracies estimated with cross-validation method were between 0.915~0.957. In 534 progeny-tested steers, the maximum difference of GEBV accuracy compared to conventional EBV for CW, EMA, BT, and MS traits were 9.56%, 5.78%, 5.78%, and 4.18% respectively. In 3,674 pedigree traced bulls, maximum increased difference of GEBV for CW, EMA, BT, and MS traits were increased as 13.54%, 6.50%, 6.50%, and 4.31% respectively. This showed that the implementation of genomic pre-selection for candidate calves to test on meat production traits could improve the genetic gain by increasing accuracy and reducing generation interval in Hanwoo genetic evaluation system to select proven bulls.

• Journal of Animal Science and Technology
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• v.65 no.4
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• pp.720-734
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• 2023

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 (M_e) statistic that indicates the independent loci is one of the important factors affecting the accuracy of BLUP. The correlation between M_e and the accuracy of GBLUP is related to the genetic relationship between reference and test population. The correlations between M_e 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 M_e, the higher the accuracy of EBV.