• Animal Bioscience
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• v.37 no.5
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• pp.795-806
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• 2024

Objective: Karan Fries (KF), a high-producing composite cattle was developed through crossing indicine Tharparkar cows with taurine bulls (Holstein Friesian, Brown Swiss, and Jersey), to increase the milk yield across India. This composite cattle population must maintain sufficient genetic diversity for long-term development and breed improvement in the coming years. The level of linkage disequilibrium (LD) measures the influence of population genetic forces on the genomic structure and provides insights into the evolutionary history of populations, while the decay of LD is important in understanding the limits of genome-wide association studies for a population. Effective population size (N_e) which is genomically based on LD accumulated over the course of previous generations, is a valuable tool for e valuation of the genetic diversity and level of inbreeding. The present study was undertaken to understand KF population dynamics through the estimation of N_e and LD for the long-term sustainability of these breeds. Methods: The present study included 96 KF samples genotyped using Illumina HDBovine array to estimate the effective population and examine the LD pattern. The genotype data were also obtained for other crossbreds (Santa Gertrudis, Brangus, and Beefmaster) and Holstein Friesian cattle for comparison purposes. Results: The average LD between single nucleotide polymorphisms (SNPs) was r² = 0.13 in the present study. LD decay (r² = 0.2) was observed at 40 kb inter-marker distance, indicating a panel with 62,765 SNPs was sufficient for genomic breeding value estimation in KF cattle. The pedigree-based N_e of KF was determined to be 78, while the N_e estimates obtained using LD-based methods were 52 (SNeP) and 219 (genetic optimization for N_e estimation), respectively. Conclusion: KF cattle have an N_e exceeding the FAO's minimum recommended level of 50, which was desirable. The study also revealed significant population dynamics of KF cattle and increased our understanding of devising suitable breeding strategies for long-term sustainable development.

• 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 Life Science
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• v.32 no.4
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• pp.279-284
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• 2022

This study was conducted to establish a genetic evaluation system applicable to general farms for improving cows raised on farms. The analysis used Best Linear Unbiased Prediction (BLUP) and Genomic Best Linear Unbiased Prediction (GBLUP) for 619 cows raised in Gyeonggi-do Province and compared and analyzed the accuracy of the estimated breeding value according to four traits (carcass weight, loineye muscle area, back fat thickness, and marbling). In the case of the GBLUP method, the size of the reference population was divided into different four groups and analyzed. The analysis results confirmed that the accuracy of the breeding value of each trait increased as the size of the GBLUP reference population increased. Comparing the accuracy of the breeding values estimated using the BLUP and GBLUP methods, it was confirmed that when the breeding values were estimated using the GBLUP method, they increased by 0.10, 0.09, 0.09, and 0.11 for carcass weight, eye muscle area, back fat thickness, and marbling scores, respectively. Applying the GBLUP method to the evaluation and selection of cows can enable precise and accurate individual selection, while increasing the size of the reference population can make even more accurate individual selection possible, thus increasing selection efficiency.

• 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.

• Journal of Animal Science and Technology
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• v.55 no.3
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• pp.165-171
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• 2013

Genomic relationship matrix (GRM) was constructed using whole genome SNP markers of swine and genomic breeding value was estimated by substitution of the numerator relationship matrix (NRM) based on pedigree information to GRM. Genotypes of 40,706 SNP markers from 448 pure Landrace pigs were used in this study and five kinds of GRM construction methods, G05, GMF, GOF, $GOF^*$ and GN, were compared with each other and with NRM. Coefficients of GOF considering each of observed allele frequencies showed the lowest deviation with coefficients of NRM and as coefficients of GMF considering the average minor allele frequency showed huge deviation from coefficients of NRM, movement of mean was expected by methods of allele frequency consideration. All GRM construction methods, except for $GOF^*$, showed normally distributed Mendelian sampling. As the result of breeding value (BV) estimation for days to 90 kg (D90KG) and average back-fat thickness (ABF) using NRM and GRM, correlation between BV of NRM and GRM was the highest by GOF and as genetic variance was overestimated by $GOF^*$, it was confirmed that scale of GRM is closely related with estimation of genetic variance. With the same amount of phenotype information, accuracy of BV based on genomic information was higher than BV based on pedigree information and these symptoms were more obvious for ABF then D90KG. Genetic evaluation of animal using relationship matrix by genomic information could be useful when there is lack of phenotype or relationship and prediction of BV for young animals without phenotype.

• 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.

• 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.32 no.12
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• pp.1836-1843
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• 2019

Objective: Social genetic effects (SGE) are an important genetic component for growth, group productivity, and welfare in pigs. The present study was conducted to evaluate i) the feasibility of the single-step genomic best linear unbiased prediction (ssGBLUP) approach with the inclusion of SGE in the model in pigs, and ii) the changes in the contribution of heritable SGE to the phenotypic variance with different scaling ${\omega}$ constants for genomic relationships. Methods: The dataset included performance tested growth rate records (average daily gain) from 13,166 and 21,762 pigs Landrace (LR) and Yorkshire (YS), respectively. A total of 1,041 (LR) and 964 (YS) pigs were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel. With the BLUPF90 software package, genetic parameters were estimated using a modified animal model for competitive traits. Giving a fixed weight to pedigree relationships (${\tau}:1$), several weights (${\omega}_{xx}$, 0.1 to 1.0; with a 0.1 interval) were scaled with the genomic relationship for best model fit with Akaike information criterion (AIC). Results: The genetic variances and total heritability estimates ($T^2$) were mostly higher with ssGBLUP than in the pedigree-based analysis. The model AIC value increased with any level of ${\omega}$ other than 0.6 and 0.5 in LR and YS, respectively, indicating the worse fit of those models. The theoretical accuracies of direct and social breeding value were increased by decreasing ${\omega}$ in both breeds, indicating the better accuracy of ${\omega}_{0.1}$ models. Therefore, the optimal values of ${\omega}$ to minimize AIC and to increase theoretical accuracy were 0.6 in LR and 0.5 in YS. Conclusion: In conclusion, single-step ssGBLUP model fitting SGE showed significant improvement in accuracy compared with the pedigree-based analysis method; therefore, it could be implemented in a pig population for genomic selection based on SGE, especially in South Korean populations, with appropriate further adjustment of tuning parameters for relationship matrices.

• 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.58 no.11
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• pp.40.1-40.5
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• 2016

Background: Currently about 26,000 horses are breeding in Korea and 57.2% (14,776 horses) of them are breeding in Jeju island. According to the statistics published in 2010, the horses breeding in Jeju island are subdivided into Jeju horse (6.1%), Thoroughbred (18.8%) and Halla horse (75.1%). Halla horses are defined as a crossbreed between Jeju and Thoroughbred horses and are used for horse racing, horse riding and horse meat production. However, little research has been conducted on Halla horses because of the perception of crossbreed and people's weighted interest toward Jeju horses. Method: Using 17 Microsatellite (MS) Markers recommended by International Society for Animal Genetics (ISAG), genomic DNAs were extracted from the hair roots of 3,880 Halla horses breeding in Korea and genetic diversity was identified by genotyping after PCR was performed. Results and conclusion: In average, 10.41 alleles (from 6 alleles in HTG7 to 17 alleles in ASB17) were identified after the analysis using 17 MS Markers. The mean value of $H_{obs}$ was 0.749 with a range from 0.612(HMS1) to 0. 857(ASB2). Also, it was found that $H_{\exp}$ and PIC values were lowest in HMS1 (0.607 and 0.548, respectively), and highest in LEX3(0.859 and 0.843, respectively), and the mean value of $H_{\exp}$ was 0.760 and that of PIC was 0.728. 17 MS markers used in this studies were considered as appropriate markers for the polymorphism analysis of Halla horses. The frequency for the appearance of identical individuals was $5.90{\times}10^{-20}$ when assumed as random mating population and when assumed as half-sib and full-sib population, frequencies were $4.08{\times}10^{-15}$ and $3.56{\times}10^{-8}$, respectively. Based on these results, the 17 MS markers can be used adequately for the Individual Identification and Parentage Verification of Halla horses. Remarkably, allele M and Q of ASB23 marker, G of HMS2 marker, H and L of HTG6 marker, L of HTG7 marker, E of LEX3 marker were the specific alleles unique to Halla horses.