• 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.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.7
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• pp.913-921
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• 2019

Objective: The objectives of this study were to compare identified informative regions through two genome-wide association study (GWAS) approaches and determine the accuracy and bias of the direct genomic value (DGV) for milk production traits in Korean Holstein cattle, using two genomic prediction approaches: single-step genomic best linear unbiased prediction (ss-GBLUP) and Bayesian Bayes-B. Methods: Records on production traits such as adjusted 305-day milk (MY305), fat (FY305), and protein (PY305) yields were collected from 265,271 first parity cows. After quality control, 50,765 single-nucleotide polymorphic genotypes were available for analysis. In GWAS for ss-GBLUP (ssGWAS) and Bayes-B (BayesGWAS), the proportion of genetic variance for each 1-Mb genomic window was calculated and used to identify informative genomic regions. Accuracy of the DGV was estimated by a five-fold cross-validation with random clustering. As a measure of accuracy for DGV, we also assessed the correlation between DGV and deregressed-estimated breeding value (DEBV). The bias of DGV for each method was obtained by determining regression coefficients. Results: A total of nine and five significant windows (1 Mb) were identified for MY305 using ssGWAS and BayesGWAS, respectively. Using ssGWAS and BayesGWAS, we also detected multiple significant regions for FY305 (12 and 7) and PY305 (14 and 2), respectively. Both single-step DGV and Bayes DGV also showed somewhat moderate accuracy ranges for MY305 (0.32 to 0.34), FY305 (0.37 to 0.39), and PY305 (0.35 to 0.36) traits, respectively. The mean biases of DGVs determined using the single-step and Bayesian methods were $1.50{\pm}0.21$ and $1.18{\pm}0.26$ for MY305, $1.75{\pm}0.33$ and $1.14{\pm}0.20$ for FY305, and $1.59{\pm}0.20$ and $1.14{\pm}0.15$ for PY305, respectively. Conclusion: From the bias perspective, we believe that genomic selection based on the application of Bayesian approaches would be more suitable than application of ss-GBLUP in Korean Holstein populations.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.2
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• pp.176-182
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• 2019

Objective: This study was conducted to investigate basic information on genetic structure and characteristics of Limousin population in Hungary. Obtained results will be taken into consideration when adopting the new breeding strategy by the Association of Hungarian Limousin and Blonde d'Aquitaine Breeders (AHLBB). Methods: Genetic diversity and phylogenetic relationship of 3,443 Limousin cattle from 16 different herds were investigated by performing genotyping using 18 microsatellite markers. Amplified DNA was genotyped using an automated genetic analyzer. Results: Mean of effective alleles ($n_e$) of the populations was 3.77. Population C had the lowest number of effective alleles (3.01) and the lowest inbreeding coefficient ($F_{IS}$) value (-0.15). Principal component analysis of estimated genetic distance ($F_{ST}$) values (p<0.000) revealed two herds (C and E) distinct from the majority of other Limousin herds. The pairwise $F_{ST}$ values of population C compared to the others (0.066 to 0.120) fell into the range of moderate genetic distance: 0.050 to 0.150, while population E displayed also moderate genetic distance ($F_{ST}$ values in range 0.052 to 0.064) but only to six populations (G, H, J, L, N, and P). $F_{ST(C-E)}$ was 0.148, all other pairs -excluding C and E herds- displayed low genetic distance ($F_{ST}$<0.049). Population D, F, I, J, K, L, N, O, and P carried private alleles, which alleles belonged to 1.1% of the individuals. Most probable number of clusters (K) were 2 and 7 determined by Structure and BAPS software. Conclusion: This study showed useful genetic diversity and phylogenetic relationship data that can be utilized for the development of a new breeding strategy by AHLBB. The results presented could also contribute to the proper selection of animals for further whole genome scan studies of Limousins.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.3
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• pp.334-340
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• 2019

Objective: The objective of this study was to investigate the genetic components of daily milk yield and to re-rank bulls in South Korea by estimated breeding value (EBV) under heat stress using the temperature-humidity index (THI). Methods: This study was conducted using 125,312 monthly test-day records, collected from January 2000 to February 2017 for 19,889 Holstein cows from 647 farms in South Korea. Milk production data were collected from two agencies, the Dairy Cattle Genetic Improvement Center and the Korea Animal Improvement Association, and meteorological data were obtained from 41 regional weather stations using the Automated Surface Observing System (ASOS) installed throughout South Korea. A random regression model using the THI was applied to estimate genetic parameters of heat tolerance based on the test-day records. The model included herd-year-season, calving age, and days-in-milk as fixed effects, as well as heat tolerance as an additive genetic effect, permanent environmental effect, and direct additive and permanent environmental effect. Results: Below the THI threshold (${\leq}72$; no heat stress), the variance in heat tolerance was zero. However, the heat tolerance variance began to increase as THI exceeded the threshold. The covariance between the genetic additive effect and the heat tolerance effect was -0.33. Heritability estimates of milk yield ranged from 0.111 to 0.176 (average: 0.128). Heritability decreased slightly as THI increased, and began to increase at a THI of 79. The predicted bull EBV ranking varied with THI. Conclusion: We conclude that genetic evaluation using the THI function could be useful for selecting bulls for heat tolerance in South Korea.

• 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.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.235-235
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• 2022

Genome selection is a promising tool for plant and animal breeding, which uses genome-wide molecular marker data to capture large and small effect quantitative trait loci and predict the genetic value of selection candidates. Genomic selection has been shown previously to have higher prediction accuracies than conventional marker-assisted selection (MAS) for quantitative traits. In this study, the prediction accuracy of 10 agricultural traits in the wheat core group with 567 points was compared. We used a cross-validation approach to train and validate prediction accuracy to evaluate the effects of training population size and training model.As for the prediction accuracy according to the model, the prediction accuracy of 0.4 or more was evaluated except for the SVN model among the 6 models (GBLUP, LASSO, BayseA, RKHS, SVN, RF) used in most all traits. For traits such as days to heading and days to maturity, the prediction accuracy was very high, over 0.8. As for the prediction accuracy according to the training group, the prediction accuracy increased as the number of training groups increased in all traits. It was confirmed that the prediction accuracy was different in the training population according to the genetic composition regardless of the number. All training models were verified through 5-fold cross-validation. To verify the prediction ability of the training population of the wheat core collection, we compared the actual phenotype and genomic estimated breeding value using 35 breeding population. In fact, out of 10 individuals with the fastest days to heading, 5 individuals were selected through genomic selection, and 6 individuals were selected through genomic selection out of the 10 individuals with the slowest days to heading. Therefore, we confirmed the possibility of selecting individuals according to traits with only the genotype for a shorter period of time through genomic selection.

• Journal of Animal Science and Technology
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• v.46 no.2
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• pp.137-144
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• 2004

Algorithms for estimating breeding values on several categorical data by using latent variables with threshold conception were developed and showed. Thresholds on each categorical trait were estimated by Newton’s method via gradients and Hessian matrix. This algorithm was developed by way of expansion of bivariate analysis provided by Quaas(2001). Breeding values on latent variables of categorical traits and observations on linear traits were estimated by preconditioned conjugate gradient(PCG) method, which was known having a property of fast convergence. Example was shown by simulated data with two linear traits and a categorical trait with four categories(CE=calving ease) and a dichotomous trait(SB=Still Birth) in threshold animal mixed model(TAMM). Breeding value estimates in TAMM were compared to those in linear animal mixed model (LAMM). As results, correlation estimates of breeding values to parameters were 0.91${\sim}$0.92 on CE and 0.87${\sim}$0.89 on SB in TAMM and 0.72~0.84 on CE and 0.59~0.70 on SB in LAMM. As conclusion, PCG method for estimating breeding values on several categorical traits with linear traits were feasible in TAMM.

• The Korean Journal of Malacology
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• v.29 no.4
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• pp.325-334
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• 2013

The purpose of this paper is to compare and analyze family variations for growth-related traits of Pacific abalone, Haliotis discus hannai. Genetic parameters and breeding values were estimated using all measurement data like shell length, shell width, and total weight as 18-month-old growth traits of 5,334 individuals of selected third generation's Pacific abalone produced in 2011. Family variations of 865 individuals of the upper 10 families with the largest number were inspected. Overall mean in phenotypic traits of 18-month-old Pacific abalone which was investigated in this study showed 54.5 mm of shell length, 36.8 mm of shell width and 21.3 g of total weight respectively. And, variation coefficient of total weight was 51.0%, so variability of data was shown to be higher than 21.1% of shell length and 20.7% of shell width. The family effects showed significant difference by each family (p < 0.05), and heritability of shell length, shell width, and total weight was medium with 0.370, 0.382, and 0.367 respectively. So it is considered that family selection is more advantageous than individual selection. On the basis of breeding values of estimated shell length and total weight, to investigate distribution and ranking by each individual about the upper 10 families with the largest number of individuals, the values were used by being changed into standardized breeding values. Based on shell length, it was investigated that the individual number of the upper 5.4% is 152 and the number of the lower 5.4% is 8. In case of total weight, it was inspected that the individual number of the upper 5.4% is 164 and the number of the lower 5.4% is 1. Like these, phenotypic and genetic diverse variations between families could be checked. By estimating genetic parameters and breeding values of a population for production of the next generation, if they are used properly in selection and mating, it is considered that more breeding effects can be expected.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.63 no.4
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• pp.294-303
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• 2018

To set an optimal harvest time for five main whole-crop silage (WCS) rice cultivars, Nokyang (Ny), Mogwoo (Mw), Mogyang (My), Nokwoo (Nw) and Yeongwoo(Yw), based on feed value and dry matter yield, we analyzed water content (WC), feed values such as percent crude protein (CP), crude fat (CF), crude ash (CA), neutral detergent fiber (NDF), acid detergent fiber (ADF), estimated total digestible nutrients (TDN), and calculated their dry matter yield (DMY). These parameters were estimated at the booting stage and every 10 days from start of heading to 45 days after heading (DAH) to identify the optimal harvest time for these rice varieties. In all varieties, except Ny, the WC was about 65% on the $30^{th}$ DAH. In terms of WC, it is judged that there would be no major problem in preparing high quality silage when harvested between 20~30 DAH for Ny and about 30 days for the other cultivars. The CP and CA decreased significantly as the harvest time was delayed in all varieties (p<0.05). However, the CF in the tested varieties generally declined as DAH increased, and then increased again(p<0.05). The decrease in ADF and NDF was significantly different from the booting stage to 45 DAH in all varieties (p<0.05) and the estimated TDN increased gradually in all varieties as the harvest time was delayed. The TDN of all varieties, except Nw, was over 70% around 30 DAH. As harvest time was delayed, the DMY for all WCS rice increased significantly (p<0.05). The maximum DMY for each cultivar was observed at 45 DAH, however, no significant difference (p<0.05) was seen after 40 DAH for Ny and Yw, 30 DAH for Mw and Nw, and 20 DAH for My. Considering these results and others factors such as lodging, shattering, digestibility of grains, conversion of eating rice, etc., the optimal harvest time for silage use of all WCS rice tested was generally the yellow ripening stage, at about 30 DAH (30~40 DAH for Ny and Mw, and 30 DAH for My, Nw and Yw).

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.3
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• pp.299-303
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• 2012

Inbreeding is the mating of relatives that produce progeny having more homozygous alleles than non-inbred animals. Inbreeding increases numbers of recessive alleles, which is often associated with decreased performance known as inbreeding depression. The magnitude of inbreeding depression depends on the level of inbreeding in the animal. Level of inbreeding is expressed by the inbreeding coefficient. One breeding goal in livestock is uniform productivity while maintaining acceptable inbreeding levels, especially keeping inbreeding less than 20%. However, in closed herds without the introduction of new genetic sources high levels of inbreeding over time are unavoidable. One method that increases selection response and minimizes inbreeding is selection of individuals by weighting estimated breeding values with average relationships among individuals. Optimum genetic contribution theory (OGC) uses relationships among individuals as weighting factors. The algorithm is as follows: i) Identify the individual having the best EBV; ii) Calculate average relationships ($\bar{r_j}$) between selected and candidates; iii) Select the individual having the best EBV adjusted for average relationships using the weighting factor k, $EBV^*=EBV_j(1-k\bar{{r}_j})$ Repeat process until the number of individuals selected equals number required. The objective of this study was to compare simulated results based on OGC selection under different conditions over 30 generations. Individuals (n = 110) were generated for the base population with pseudo random numbers of N~ (0, 3), ten were assumed male, and the remainder female. Each male was mated to ten females, and every female was assumed to have 5 progeny resulting in 500 individuals in the following generation. Results showed the OGC algorithm effectively controlled inbreeding and maintained consistent increases in selection response. Difference in breeding values between selection with OGC algorithm and by EBV only was 8%, however, rate of inbreeding was controlled by 47% after 20 generation. These results indicate that the OGC algorithm can be used effectively in long-term selection programs.