• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.1
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• pp.19-25
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• 2018

Objective: In practical breeding, selection is often performed by ignoring the accuracy of evaluations and applying economic weights directly to the selection index coefficients of genetically standardized traits. The denominator of the standardized component trait of estimated genetic evaluations in practical selection varies with its reliability. Whereas theoretical methods for calculating the selection index coefficients of genetically standardized traits account for this variation, practical selection ignores reliability and assumes that it is equal to unity for each trait. The purpose of this study was to clarify the effects of ignoring the accuracy of the standardized component trait in selection criteria on selection responses and economic weights in retrospect. Methods: Theoretical methods were presented accounting for reliability of estimated genetic evaluations for the selection index composed of genetically standardized traits. Results: Selection responses and economic weights in retrospect resulting from practical selection were greater than those resulting from theoretical selection accounting for reliability when the accuracy of the estimated breeding value (EBV) or genomically enhanced breeding value (GEBV) was lower than those of the other traits in the index, but the opposite occurred when the accuracy of the EBV or GEBV was greater than those of the other traits. This trend was more conspicuous for traits with low economic weights than for those with high weights. Conclusion: Failure of the practical index to account for reliability yielded economic weights in retrospect that differed from those obtained with the theoretical index. Our results indicated that practical indices that ignore reliability delay genetic improvement. Therefore, selection practices need to account for reliability, especially when the reliabilities of the traits included in the index vary widely.

• Journal of agriculture & life science
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• v.50 no.3
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• pp.99-117
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• 2016

Data on primal cuts were collected from 1,829 steers of Hanwoo progeny testing programs, between 2010 and 2015 for the ssGWAS. SNP data were analyzed by using Illumina Bovine 50K Beadchip. The SNP data that matches with phenotype data was 674 animals. As a first step, the genomic estimated breeding value(GEBV) of the loin and rib cuts were estimated, which was used in the estimation of SNP marker effects and their variances related to the traits. Then, the estimated variance explained by each marker was expressed as a proportion to the total genetic variance. Finally, the SNP loci and their significance to any possible QTL were examined. Among the 20 best SNP loci explaining a larger proportion of SNP variance to the total genetic variance for tender loin yield, the region between 12,812,193 ~ 12,922,313bp on BTA 10 harbored a cluster of SNPs that explained about 7.32 to 7.34% of the total genetic variance. For strip loin yield, a peak for higher effects for multiple SNPs was found in BTA24, between 38,158,543 and 38,347,278bp distances, which explained about 8.36 to 8.56% of the observed variance for this trait. For loin yield had relatively smaller effects in terms of the total genetic variance. Therefore, loin yield might be affected by a few loci with moderate effects and many other loci with smaller effects across the genome.

• Journal of Animal Science and Technology
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• v.62 no.4
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• pp.438-448
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• 2020

This study was performed to increase the accuracy of genomic estimated breeding value (GEBV) predictions for domestic pigs using single-breed and admixed reference populations (single-breed of Berkshire pigs [BS] with cross breed of Korean native pigs and Landrace pigs [CB]). The principal component analysis (PCA), linkage disequilibrium (LD), and genome-wide association study (GWAS) were performed to analyze the population structure prior to genomic prediction. Reference and test population data sets were randomly sampled 10 times each and precision accuracy was analyzed according to the size of the reference population (100, 200, 300, or 400 animals). For the BS population, prediction accuracy was higher for all economically important traits with larger reference population size. Prediction accuracy was ranged from -0.05 to 0.003, for all traits except carcass weight (CWT), when CB was used as the reference population and BS as the test. The accuracy of CB for backfat thickness (BF) and shear force (SF) using admixed population as reference increased with reference population size, while the results for CWT and muscle pH at 24 hours after slaughter (pH) were equivocal with respect to the relationship between accuracy and reference population size, although overall accuracy was similar to that using the BS as the reference.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.11
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• pp.1551-1557
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• 2015

The missing heritability has been a major problem in the analysis of best linear unbiased prediction (BLUP). We introduced the traditional genome-wide association study (GWAS) into the BLUP to improve the heritability estimation. We analyzed eight pork quality traits of the Berkshire breeds using GWAS and BLUP. GWAS detects the putative quantitative trait loci regions given traits. The single nucleotide polymorphisms (SNPs) were obtained using GWAS results with p value <0.01. BLUP analyzed with significant SNPs was much more accurate than that with total genotyped SNPs in terms of narrow-sense heritability. It implies that genomic estimated breeding values (GEBVs) of pork quality traits can be calculated by BLUP via GWAS. The GWAS model was the linear regression using PLINK and BLUP model was the G-BLUP and SNP-GBLUP. The SNP-GBLUP uses SNP-SNP relationship matrix. The BLUP analysis using preprocessing of GWAS can be one of the possible alternatives of solving the missing heritability problem and it can provide alternative BLUP method which can find more accurate GEBVs.

• Animal Bioscience
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• v.37 no.4
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• pp.555-566
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• 2024

Objective: This study aimed to assess the genetic parameters and accuracy of genomic predictions for twenty-four linear body conformation traits and overall conformation scores in Korean Holstein dairy cows. Methods: A dataset of 2,206 Korean Holsteins was collected, and genotyping was performed using the Illumina Bovine 50K single nucleotide polymorphism (SNP) chip. The traits investigated included body traits (stature, height at front end, chest width, body depth, angularity, body condition score, and locomotion), rump traits (rump angle, rump width, and loin strength), feet and leg traits (rear leg set, rear leg rear view, foot angle, heel depth, and bone quality), udder traits (udder depth, udder texture, udder support, fore udder attachment, front teat placement, front teat length, rear udder height, rear udder width, and rear teat placement), and overall conformation score. Accuracy of genomic predictions was assessed using the single-trait animal model genomic best linear unbiased prediction method implemented in the ASReml-SA v4.2 software. Results: Heritability estimates ranged from 0.10 to 0.50 for body traits, 0.21 to 0.35 for rump traits, 0.13 to 0.29 for feet and leg traits, and 0.05 to 0.46 for udder traits. Rump traits exhibited the highest average heritability (0.29), while feet and leg traits had the lowest estimates (0.21). Accuracy of genomic predictions varied among the twenty-four linear body conformation traits, ranging from 0.26 to 0.49. The heritability and prediction accuracy of genomic estimated breeding value (GEBV) for the overall conformation score were 0.45 and 0.46, respectively. The GEBVs for body conformation traits in Korean Holstein cows had low accuracy, falling below the 50% threshold. Conclusion: The limited response to selection for body conformation traits in Korean Holsteins may be attributed to both the low heritability of these traits and the lower accuracy estimates for GEBVs. Further research is needed to enhance the accuracy of GEBVs and improve the selection response for these traits.

• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.10
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• pp.1544-1557
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• 2020

Objective: Genomic selection (GS) is becoming popular in animals' genetic development. We, therefore, investigated the single-step genomic best linear unbiased prediction (ssGBLUP) as tool for GS, and compared its efficacy with the traditional pedigree BLUP (pedBLUP) method. Methods: A total of 9,952 males born between 1997 and 2018 under Hanwoo proven-bull selection program was studied. We analyzed body weight at 12 months and carcass weight (kg), backfat thickness, eye muscle area, and marbling score traits. About 7,387 bulls were genotyped using Illumina 50K BeadChip Arrays. Multiple-trait animal model analyses were performed using BLUPF90 software programs. Breeding value accuracy was calculated using two methods: i) Pearson's correlation of genomic estimated breeding value (GEBV) with EBV of all animals (r_M1) and ii) correlation using inverse of coefficient matrix from the mixed-model equations (r_M2). Then, we compared these accuracies by overall population, info-type (PHEN, phenotyped-only; GEN, genotyped-only; and PH+GEN, phenotyped and genotyped), and bull-types (YBULL, young male calves; CBULL, young candidate bulls; and PBULL, proven bulls). Results: The r_M1 estimates in the study were between 0.90 and 0.96 among five traits. The r_M1 estimates varied slightly by population and info-type, but noticeably by bull-type for traits. Generally average r_M2 estimates were much smaller than r_M1 (pedBLUP, 0.40 to0.44; ssGBLUP, 0.41 to 0.45) at population level. However, r_M2 from both BLUP models varied noticeably across info-types and bull-types. The ssGBLUP estimates of r_M2 in PHEN, GEN, and PH+ GEN ranged between 0.51 and 0.63, 0.66 and 0.70, and 0.68 and 0.73, respectively. In YBULL, CBULL, and PBULL, the r_M2 estimates ranged between 0.54 and 0.57, 0.55 and 0.62, and 0.70 and 0.74, respectively. The pedBLUP based r_M2 estimates were also relatively lower than ssGBLUP estimates. At the population level, we found an increase in accuracy by 2.0% to 4.5% among traits. Traits in PHEN were least influenced by ssGBLUP (0% to 2.0%), whereas the highest positive changes were in GEN (8.1% to 10.7%). PH+GEN also showed 6.5% to 8.5% increase in accuracy by ssGBLUP. However, the highest improvements were found in bull-types (YBULL, 21% to 35.7%; CBULL, 3.3% to 9.3%; PBULL, 2.8% to 6.1%). Conclusion: A noticeable improvement by ssGBLUP was observed in this study. Findings of differential responses to ssGBLUP by various bulls could assist in better selection decision making as well. We, therefore, suggest that ssGBLUP could be used for GS in Hanwoo proven-bull evaluation program.

• Journal of Animal Science and Technology
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• v.53 no.1
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• pp.1-6
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• 2011

This simulation study was performed to investigate the accuracy of the estimated breeding value by using genomic information (GEBV) by way of Bayesian framework. Genomic information by way of single nucleotide polymorphism (SNP) from a chromosome with length of 100cM were simulated with different marker distance (0.1cM, 0.5cM), heritabilities (0.1, 0.5) and half sibs families (20 heads, 4 heads). For generating the simulated population in which animals were inferred to genomic polymorphism, we assumed that the number of quantitative trait loci (QTL) were equal with the number of no effect markers. The positions of markers and QTLs were located with even and scatter distances, respectively. The accuracies of estimated breeding values by way of indicating correlations between true and estimated breeding values were compared on several cases of marker distances, heritabilities and family sizes. The accuracies of breeding values on animals only having genomic information were 0.87 and 0.81 in marker distances of 0.1cM and 0.5cM, respectively. These accuracies were shown to be influenced by heritabilities (0.87 at $h^2$ =0.10, 0.94 at $h^2$ =0.50). According to half sibs' family size, these accuracies were 0.87 and 0.84 in family size of 20 and 4, respectively. As half sibs family size is high, accuracy of breeding appeared high. Based on the results of this study it is concluded that the amount of marker information, heritability and family size would influence the accuracy of the estimated breeding values in genomic selection methodology for animal breeding.