• Journal of Embryo Transfer
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• v.22 no.3
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• pp.143-147
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• 2007

Records on 546 Korea native pigs for average daily gain (ADG), age at 70 kg (D70 kg) and backfat thickness (BF) made between 2001 and 2006 in herds on National Institutes of Animal Science in Korea were used to estimate genetic parameters. The data was analyzed by the DF-REML (Derivative-Free Restricted Maximum Likelihood) program of Boldman using a single-trait animal model. Heritabilities were 0.26, 0.09, and 0.29 for ADG, D70 kg and BF, respectively. The phenotypic correlations of ADG with D70 kg and BF were -0.71 and 0.30. The phenotypic correlation of D70 kg with BF was -0.15. The genetic correlations of ADG with D70 kg and BF were -0.11, 0.41, respectively. The genetic correlation of D70 kg with BF was -0.16. The data of weights and measurements on body length, body height and chest width after age at 11 months (days to 330) were shown scarcely less differences compare to data of age at 11 months.

• Korean Journal of Agricultural Science
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• v.40 no.4
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• pp.339-345
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• 2013

The objective of this study was to establish genetic evaluation systems with carcass data collected by 68 individual farms from 2007 to 2011 in Pyeongchang area of Kangwon province. All the possible of environment effects were corrected by analysis of variance (ANOVA) to estimate more accurate genetic parameters. Heritabilities and genetic correlations were estimated from carcass data collected from Hanwoo steers(n=10,441) born in Pyeongchang region from 2005 to 2008. Traits evaluated included carcass weight (CWT), eye muscle area (EMA), back fat thickness (BF) and marbling score (MS). As for the mean value and standard deviation for carcass traits, CWT, EMA, BF and MS were 424.5, 92, 13.7 and 5.7. Parameters were estimated using a multiple trait animal model and derivative-free restricted maximum likelihood procedures. Estimated heritabilities for CWT, EMA, BF and MS were 0.30, 0.21, 0.42 and 0.42, respectively. Genetic correlation of CWT with EMA, BF and MS were estimated to 0.24, 0.36 and 0.07, respectively. Genetic correlation of EMA with BF and MS was -0.27 and 0.61, respectively.

• Asian-Australasian Journal of Animal Sciences
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• v.29 no.9
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• pp.1222-1228
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• 2016

The objective of this research was to estimate genetic parameters and trends for length of productive life (LPL), lifetime number of piglets born alive (LBA), lifetime number of piglets weaned (LPW), lifetime litter birth weight (LBW), and lifetime litter weaning weight (LWW) in a commercial swine farm in Northern Thailand. Data were gathered during a 24-year period from July 1989 to August 2013. A total of 3,109 phenotypic records from 2,271 Landrace (L) and 838 Yorkshire sows (Y) were analyzed. Variance and covariance components, heritabilities and correlations were estimated using an Average Information Restricted Maximum Likelihood (AIREML) procedure. The 5-trait animal model contained the fixed effects of first farrowing year-season, breed group, and age at first farrowing. Random effects were sow and residual. Estimates of heritabilities were medium for all five traits ($0.17{\pm}0.04$ for LPL and LBA to $0.20{\pm}0.04$ for LPW). Genetic correlations among these traits were high, positive, and favorable (p<0.05), ranging from $0.93{\pm}0.02$ (LPL-LWW) to $0.99{\pm}0.02$ (LPL-LPW). Sow genetic trends were non-significant for LPL and all lifetime production traits. Sire genetic trends were negative and significant for LPL ($-2.54{\pm}0.65d/yr$; p = 0.0007), LBA ($-0.12{\pm}0.04piglets/yr$; p = 0.0073), LPW ($-0.14{\pm}0.04piglets/yr$; p = 0.0037), LBW ($-0.13{\pm}0.06kg/yr$; p = 0.0487), and LWW ($-0.69{\pm}0.31kg/yr$; p = 0.0365). Dam genetic trends were positive, small and significant for all traits ($1.04{\pm}0.42d/yr$ for LPL, p = 0.0217; $0.16{\pm}0.03piglets/yr$ for LBA, p<0.0001; $0.12{\pm}0.03piglets/yr$ for LPW, p = 0.0002; $0.29{\pm}0.04kg/yr$ for LBW, p<0.0001 and $1.23{\pm}0.19kg/yr$ for LWW, p<0.0001). Thus, the selection program in this commercial herd managed to improve both LPL and lifetime productive traits in sires and dams. It was ineffective to improve LPL and lifetime productive traits in sows.

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.11
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• pp.1550-1556
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• 2017

Objective: This study estimated the genetic parameters for productive and reproductive traits. Methods: The data included production and reproduction records of animals that have calved between 1979 and 2013. The genetic parameters were estimated using multivariate mixed models (DMU) package, fitting univariate and multivariate mixed models with average information restricted maximum likelihood algorithm. Results: The estimates of heritability for milk production traits from the first three lactation records were $0.03{\pm}0.03$ for lactation length (LL), $0.17{\pm}0.04$ for lactation milk yield (LMY), and $0.15{\pm}0.04$ for 305 days milk yield (305-d MY). For reproductive traits the heritability estimates were, $0.09{\pm}0.03$ for days open (DO), $0.11{\pm}0.04$ for calving interval (CI), and $0.47{\pm}0.06$ for age at first calving (AFC). The repeatability estimates for production traits were $0.12{\pm}0.02$, for LL, $0.39{\pm}0.02$ for LMY, and $0.25{\pm}0.02$ for 305-d MY. For reproductive traits the estimates of repeatability were $0.19{\pm}0.02$ for DO, and to $0.23{\pm}0.02$ for CI. The phenotypic correlations between production and reproduction traits ranged from $0.08{\pm}0.04$ for LL and AFC to $0.42{\pm}0.02$ for LL and DO. The genetic correlation among production traits were generally high (>0.7) and between reproductive traits the estimates ranged from $0.06{\pm}0.13$ for AFC and DO to $0.99{\pm}0.01$ between CI and DO. Genetic correlations of productive traits with reproductive traits were ranged from -0.02 to 0.99. Conclusion: The high heritability estimates observed for AFC indicated that reasonable genetic improvement for this trait might be possible through selection. The $h^2$ and r estimates for reproductive traits were slightly different from single versus multi-trait analyses of reproductive traits with production traits. As single-trait method is biased due to selection on milk yield, a multi-trait evaluation of fertility with milk yield is recommended.

• Asian-Australasian Journal of Animal Sciences
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• v.31 no.1
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• pp.26-31
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• 2018

Objective: This study estimated the genetic parameters for body weight gain and growth curve parameter traits in Korean native chicken (KNC). Methods: A total of 585 $F_1$ chickens were used along with 88 of their $F_0$ birds. Body weights were measured every 2 weeks from hatching to 20 weeks of age to measure weight gain at 2-week intervals. For each individual, a logistic growth curve model was fitted to the longitudinal growth dataset to obtain three growth curve parameters (${\alpha}$, asymptotic final body weight; ${\beta}$, inflection point; and ${\gamma}$, constant scale that was proportional to the overall growth rate). Genetic parameters were estimated based on the linear-mixed model using a restricted maximum likelihood method. Results: Heritability estimates of body weight gain traits were low to high (0.057 to 0.458). Heritability estimates for ${\alpha}$, ${\beta}$, and ${\gamma}$ were $0.211{\pm}0.08$, $0.249{\pm}0.09$, and $0.095{\pm}0.06$, respectively. Both genetic and phenotypic correlations between weight gain traits ranged from -0.527 to 0.993. Genetic and phenotypic correlation between the growth curve parameters and weight gain traits ranged from -0.968 to 0.987. Conclusion: Based on the results of this study population, we suggest that the KNC could be used for selective breeding between 6 and 8 weeks of age to enhance the overall genetic improvement of growth traits. After validation of these results in independent studies, these findings will be useful for further optimization of breeding programs for KNC.

• 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.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.

• Journal of Animal Science and Technology
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• v.54 no.5
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• pp.331-336
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• 2012

This study was conducted to estimate genetic parameters for ultrasound and carcass traits in Hanwoo. Heritabilities and genetic and phenotypic correlations were estimated for carcass and ultrasound measurements collected from Hanwoo cows (n=312) born at Hanwoo experiment station. Traits evaluated were eye muscle area (EMA), backfat thickness (BF), marbling score (MS) from carcass, and ultrasound eye muscle area (UEMA), ultrasound backfat (UBF), and ultrasound marbling score (UMS). Parameters were estimated using multi-trait animal models byderivative-free restricted maximum likelihood procedures. Estimated heritabilities for UBF, UEMA and UMS were 0.43, 0.23 and 0.32, while heritabilities for BF, EMA and MS were 0.33, 0.13 and 0.33 in fattened cows, respectively. Genetic correlations between ultrasound and carcass measurements were estimated to -0.19, -0.61, and -0.36 for UBF: UEMA, UBF: UMS, and UEMA: UMS in fattened cows, respectively. Phenotypic correlations between ultrasound and carcass measurements were 0.03, 0.13 and 0.26 for UBF: UEMA, UBF: UMS, and UEMA: UMS in fattened cows, respectively. As for the steer, genetic correlations between ultrasound and carcass measurements were 0.36, -0.80 and 0.27 for UBF: UEMA, UBF: UMS, and UEMA: UMS in steers, respectively. Phenotypic correlations between ultrasound and carcass measurements were 0.13, 0.07 and 0.41 for UBF: UEMA, UBF: UMS, and UEMA: UMS in steers, respectively. In conclusion, this finding would be very useful to implement into Hanwoo breeding program.

• Journal of Animal Science and Technology
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• v.49 no.6
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• pp.689-698
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• 2007

This study aims to estimate the genetic parameters for carcass traits on Hanwoo of breeding farmhouses using Animal Products Grading Service’s data of 428,812 cattle from 101 slaughterhouses nationwide from 2000 to 2005. Using carcass traits of carcass weight, eye muscle area, backfat thickness, marbling score, meat color and fat color that greatly influence Hanwoo's grade, the effects of carcass year, carcass season, sex and carcass region were estimated. Based upon carcass traits of carcass weight, eye muscle area, backfat thickness, marbling score and meat color that greatly influence Hanwoo’s grade, the heritabilities and genetic parameters were estimated of 17,578 Hanwoo slaughtered in 2005 with existing herdbook, where EM-REML algorithm was used in estimating genetic parameters. The mean and standard deviation of each carcass trait are 321.42±53.62kg, 76.25±10.43cm2, 9.96± 4.14mm, 3.75±2.00, 4.83±0.48 and 2.99±0.40, for carcass weight, eye muscle area, backfat thickness, marbling score, meat color and fat color, respectively. As a result of analysis on the effects of carcass year, the carcass weight, backfat thickness and meat color came out highest as 359.40±0.181, 9.82±0.017 and 4.90±0.002, respectively in 2004. As a result of analysis on the effects of carcass season, the carcass weight and eye muscle area came out highest as 345.88±0.144 and 79.57±0.033 respectively in spring, and the backfat thickness was highest as 8.78±0.013 in winter, and the meat color and fat color slightly came out higher as 4.88±0.002 and 2.96±0.001 in fall, while the marbling score was highest as 3.29±0.006 in summer. The results of the analysis on the effects of sex indicated that the backfat thickness and fat color were highest as 10.53±0.010 and 3.07±0.001 in cow, the carcass weight came out highest in Hanwoo steer as 368.03±0.068kg, the eye muscle area were highest as 82.96±0.042 in bull, and the marbling score was highest as 4.19±0.007 in steer, and the meat color was highest as 4.89±0.001 in cow. Regarding the results of analysis on the effects of carcass region, the carcass weight, eye muscle area,