• Journal of the Korean Data and Information Science Society
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• v.28 no.6
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• pp.1349-1360
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• 2017

This study was conducted to estimate the genetic parameters for common ketosis indicators (${\beta}$-hydroxybutyrate acid, BHBA; milk acetone), feed intake efficiency indicator (energy-corrected milk, ECM), and milk yield (MY) in Korean Holstein. A total of 75,072 monthly test-day records from 14,397 first parity cows were collected, between 2012 and 2016, from Korea animal improvement association enrolled farms. Variance components were estimated using a multiple trait random regression model. The heritability of BHBA and acetone levels ranged from 0.06 to 0.15 at different DIMs. The phenotypic and genetic correlations between BHBA and acetone were between 0.73 and 0.90, and between 0.93 and 0.98, respectively. The phenotypic correlation between BHBA and MY, between acetone and MY, between BHBA and ECM, and between acetone and ECM ranged from -0.18 to -0.05, -0.23 to -0.05, 0 to 0.10, and -0.09 to 0.01, respectively. Genetic correlation estimates between BHBA and MY, between acetone and MY, between BHBA and ECM, and between acetone and ECM also ranged from -0.55 to 0.05, -0.62 to -0.04, -0.10 to 0.11, and -0.20 to 0.00, respectively. We hope that these results would greatly assist in the improvement of ketosis disease in the local Holsteins.

• Journal of Animal Science and Technology
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• v.54 no.2
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• pp.89-94
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• 2012

The objective of this study was to estimate the genetic parameters and breeding value of swine using their economic traits. The traits considered were age at 90 kilograms body weight (D90 kg), backfat thickness (BF) and eye muscle area (EMA). Estimation of genetic parameters and breeding value from 18,668 heads considering the economic traits were based on farm performance data from May 2007 to April 2011. Estimation of genetic parameters based on economic traits revealed that the single best model was fitted after finding source of variance on fixed and random effects and estimated by a multiple trait model using DF-REML (Derivative-FREE Restricted Maximum Likelihood). In this study, the estimated heritabilities of Duroc, Berkshire, Landrace and Yorkshire were about 0.22-0.59 for the D90 kg, 0.47-0.62 for the BF and 0.23-0.37 for the EMA. Genetic correlation of D90 kg with BF and EMA of the four breeds were -0.01-0.24 and -0.35--0.23, respectively. Moreover, the genetic correlation of BF with EMA was -0.68--0.17. On the other hand, the phenotypic correlation of D90 kg with BF and EMA of the four breeds were about 0.01-0.11 and -0.37--0.21, respectively, while the phenotypic correlation of BF with EMA was -0.68--0.17. Results showed that the genetic trends of breeding value every year were decreasing for D90 kg, increasing for BF while for EMA inconsistent values were obtained.

• Journal of Animal Science and Technology
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• v.54 no.2
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• pp.83-87
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• 2012

The purpose of this study was to estimate genetic parameters for productive traits in swine. Productive traits were considered on average daily gain (ADG), body height (BH) and body length (BL). Genetic analysis was consisted of 18,668 heads for productive traits which were based on on-farm performance tested from May, 2007 to Apr, 2011. For estimating genetic parameters on productive traits, single best model was fitted after finding source of variance on fixed and random effects and estimated with a multiple trait animal model by using DF-REML (Derivative-Free Restricted Maximum Likelihood). The estimated heritabilities of Duroc, Berkshire, Landrace and Yorkshire 0.22-0.58 for the average daily gain, 0.34-0.41 for the body height and 0.4-0.52 for the body length, respectively. Phenotypic correlations of average daily gain with body height and body length for the four breeds were 0.42-0.48, 0.53-0.58, 0.34-0.46 and 0.47-0.56, respectively. Phenotypic correlations of body height with body length were 0.41, 0.57, 0.52, 0.59, respectively. The estimated genetic correlation coefficients of average daily gain with body height and body length estimated for the four breeds were 0.34-0.47, 0.70-0.75, 0.17-0.38 and 0.50-0.53, respectively. The estimated genetic correlation coefficients of body height with body length were 0.57, 0.69, 0.61 and 0.71, respectively.

• Journal of Animal Science and Technology
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• v.46 no.2
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• pp.145-154
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• 2004

This study was conducted to estimate genetic parameter of Duroc, Landrace and Yorkshire breeds based on the on-farm performance tested records of 57,316 pigs under the supervision of Korean Animal Improvement Association from 1992 to 1999. Genetic parameters were estimated with a multiple trait animal model by using DF - REML. The result obtained in this study was summarized as follow ; The estimated heritabilities of Duroc, Landrace and Yorkshire were 0.46${\sim}$0.65 for the average backfat thickness, 0.28${\sim}$0.31 for loin depth, 0.50～0.60 for percent lean, 0.45${\sim}$0.55 for the average daily gain, 0.38${\sim}$0.50 for age at 90kg, respectively. Phenotypic correlation of average backfat thickness with loin depth, percent lean, average daily gain and age at 90㎏ for the three breeds were -0.12${\sim}$-0.01, -0.81${\sim}$-0.76, 0.34${\sim}$0.46, and -0.41${\sim}$-0.33, respectively. Phenotypic correlation of loin depth with percent lean, average daily gain and age at 90kg were 0.12${\sim}$0.23, 0.03${\sim}$0.21, and -0.17${\sim}$-0.03, respectively. Phenotypic correlation of percent lean with average daily gain and age at 90kg were -0.37${\sim}$-0.26 and 0.26～0.35, respectively. Phenotypic correlation of average daily gain with age at 90kg was -0.97${\sim}$-0.95. The estimated genetic correlation coefficients of average backfat thickness with loin depth, percent lean, average daily gain and age at 90kg estimated for the three breeds were -0.17${\sim}$0.03, -0.79${\sim}$-0.69, 0.24${\sim}$0.45 and -0.41${\sim}$-0.19, respectively. The estimated genetic correlation coefficients of loin depth with percent lean, average daily gain and age at 90kg were 0.11～0.19, 0.23 and -0.30～-0.20, respectively. The estimated correlation coefficients of percent lean with average daily gain and age at 90kg were -0.36${\sim}$-0.13 and 0.10～0.34, respectively. The estimated genetic correlation coefficients of average daily gain with age at 90㎏ was -0.96${\sim}$-0.95.

• Journal of Animal Science and Technology
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• v.44 no.5
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• pp.499-506
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• 2002

This study was conducted to estimate the heritabilities and genetic and phenotypic correlations among average daily gain, age at 90kg and backfat thickness in Yorkshire pigs. The data were obtained from 2,111 heads of Yorkshire tested at National Livestock Research Institute from May, 1994 to April, 2000. Genetic parameters were estimated with a multiple trait animal model by using DF-REML (Deri- vative-Free REstricted Maximum Likelihood). The results obtained are summarized as follows ; The means of traits studied were 0.871${\pm}$0.124 kg for average daily gain, 145.397${\pm}$11.718 days for age at 90kg and 1.476${\pm}$0.241 cm for backfat thickness. The estimated heritabilities were 0.55 for average daily gain, 0.56 for age at 90kg and 0.55 for backfat thickness. The genetic correlation of average daily gain with age at 90kg and backfat thickness were －0.82, 0.10, respectively. The genetic correlation of age at 90kg with backfat thickness was －0.25. The phenotypic correlations of average daily gain(ADG) with age at 90kg and backfat thickness and age at 90kg with backfat thickness were －0.77, 0.02 and －0.05 respectively. Though phenotypic correlation of ADG and age at 90kg was low, breeding project should be carefully considered by high genetic correlation. High heritabilities on all economic traits were obtained. Therefore, it is considered that suitable selection and management is needed successful improvement.

• 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.