• Proceedings of the Korea Society of Poultry Science Conference
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• 2003.11a
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• pp.127-128
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• 2003

This study was conducted to obtain for genetic information with 11, 538 progenies of 5 strains in Korean native chicken. The heritability estimates of the body weights at 150 and 270days of age were 0.25~0.61 and 0.32~0.59 in 5 strains, the heritabilities of the body weight at 270 days were relatively higher than that of the body weight at 150 days. The medium or low heritability estimates were observed in the ages at first egg as 0.15 ~0.31, and heritability estimates of the egg weights at the first egg and 270 days of age were 0.06~0.09 and 0.24~0.41. Medium or low heritability estimates were observed for the number of egg production as 0.09 ~0.26, respectively.

• Journal of Animal Science and Technology
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• v.50 no.1
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• pp.19-26
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• 2008

Heritability and genetic correlation for growth traits in Duroc pig breed were estimated using Bayesian method via Gibbs sampling. The data set consisted of 3,526 performance records at National Institute of Animal Science. For estimating those parameters using Gibbs sampling, 5,000 cycles of ‘burn-in’ period were discarded among a total of 55,000 samples. Out of the remaining 50,000 samples, 5,000 estimates by each parameter were retained and used for analyses to avoid any correlation among adjacent samples. The growth traits considered in this study were average daily gain at 30kg(ADG1), average daily gain at 90kg(ADG2), backfat thickness(BF), days to 90kg(D90) and feed conversion ratio(FC). The estimated heritabilities and their standard deviation using Gibbs sampler were 0.43±0.04, 0.49±0.038, 0.31±0.040, 0.48±0.039 and 0.62±0.086, respectively. Genetic correlations were －0.02, －0.13, －0.55 and －0.15 between ADG1 with ADG2, BF, D90 and FC, respectively, 0.16, －0.73, －0.32 between ADG2 with BF, D90 and FC respectively, 0.01, －0.08 between BF with D90, FC, respectively, and 0.23 between D90 with FC.

• 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.23 no.2
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• pp.217-225
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• 2007

This study was conducted to estimate the general genetic parameters, heritabilities, and genetic and phenotypic correlations on growth-related traits by studying multiple trait animal model in two Korean abalone species, Haliotis discus hannai and H. discus discus. The data was collected from the records of 3,795 individuals produced from 54 sires and 74 dams in Haliotis discus hannai and 399 individuals produced from 7 sires and 7 dams in Haliotis discus discus. The data was evaluated by the Genetics and Breeding Research Center, National Fisheries Research & Development Institute (NFRDI). Genetic parameters were estimated for two abalone species raised in Bukjeju branch, NFRDI, from May 20 to November 1, 2004. The heritability estimates for growth traits of shell length, shell width and body weight obtained from restricted maximum likelihood (REML) were ranging from 0.73 to 0.78 in Haliotis discus hannai, and from 0.87 to 0.89 in H. discus discus. The heritabilities for shell shape and condition factor were ranging from 0.17 to 0.20 in Haliotis discus hannai, and from 0.01 to 0.45 in H. discus discus. Genetic and phenotypic correlations were over than 0.96 between shell parameters and weight in both of abalone subspecies, indicating that breeding for weight gains could successfully be achieved by selecting for shell length.

• Journal of Animal Science and Technology
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• v.55 no.5
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• pp.389-398
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• 2013

This study was conducted to investigate the genetic relationships among four reproductive traits. Data for this study were 7616 records from 1910 Landrace (L) and 10,454 records from 2283 Yorkshire (Y) in a closed nucleus swine herd. Traits considered on this study were gestation length (GL), total number of piglets born (TNB), wean to first service (WFS), and number of stillborn per litter (NSB). Heritabilities and genetic correlations were estimated by using the Bayesian inferences via Gibbs sampling in a four trait linear-threshold repeatability animal mixed model by designating NSB as a categorical trait in the L and Y purebred populations. Effects on the statistical model were considered for parity, contemporary group as fixed and service sire, permanent environmental, animal additive genetic effects as random. Estimates of heritability were 0.21, 0.23, 0.16, and 0.09 for GL, WFS, TNB, and NSB in the L population and 0.35, 0.16, 0.14 and 0.10 for corresponding traits in the Y population, respectively. Genetic correlation for GL was -0.59 and -0.28 with TNB and -0.58 and -0.17 with NSB in the L and Y populations, respectively. The NSB was positively correlated with TNB in the L and Y populations in genetic and environmental aspects. Therefore, the NSB should be taken into account in selecting sows for improving prolificacy of dam line breeding swine stock.

• Journal of Animal Science and Technology
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• v.44 no.5
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• pp.507-518
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• 2002

A multiple-trait stochastic computer simulation model was constructed to predict the breeding schemes and selection methods on Hanwoo(Korean cattle). The model could be used four kinds of selection criteria (random, phenotype and true or estimated breeding values). At the test run in various population size for 20 years, all estimated parameters of the each simulated populations were resulted similar to input parameters. The deviations between input and output values of parameter in the large population were smaller than in the small population. The simulated results obtained from ten small populations consisted with one sire and ten dams in each population for 500 years were as follows; Inbreeding coefficients of population were similar to theoretical estimating function. Mean values of each traits selected were randomly drifted by generation, but they were converged into a value when inbreeding coefficients came close to one. Additive genetic variances within each population were reduced by generation, and they were converged into zero when inbreeding coefficients came close to one. These results indicated that the simulated populations hold to statistical properties of input parameters.

• 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.48 no.6
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• pp.759-766
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• 2006

This study was conducted to investigate the genetic correlations among the traits used to select young bulls and proven bulls in Hanwoo Performance and Progeny Test Program in Korea. For the estimation of heritabilities and correlations among the growth traits of bulls and carcass traits of progeny steers, 2,532 records of performance tested bull calves and 1,819 records of progeny tested steers were collected from Livestock Improvement Main Center (LIMC), National Agricultural Cooperative Federation (NACF). Fixed effects of mixed model for each traits were selected by using stepwise regression analysis and prior values of variance components were estimated by MTDFREML. The prior values of variance components were estimated with pairwise 2 traits model followed by single trait analysis. The estimated heritability of backfat thickness(BF), dressing percentage(DP), loin-eye muscle area(LMA), marbling score(MS) and weight at 12 months(WT12) was 0.51, 0.32, 0.27, 0.33, 0.50 and 0.26, respectively. Genetic correlation of WT12 of bull calves with backfat thickness, carcass weight and loin-eye muscle area of steers was positive correlation as 0.05, 0.35 and 0.21, respectively. However genetic correlation of WT12 with DP and MS showed negative correlation as 󰠏0.09 and 󰠏0.27, respectively and these negative genetic correlations implies that bulls that may be superior in carcass traits can be lost at the first step of selection and current selection method should be modified to solve this problem.

• Journal of Animal Science and Technology
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• v.45 no.3
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• pp.343-354
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• 2003

Simulation studies were carried out to investigate the responses of selection for three carcass traits (longissimus muscle area: EMA, fat thickness: BF, and marbling score: MS) based on either adjusted phenotypes (APH) or estimated breeding values (EBV) in multivariate animal model with different breeding schemes. Selection responses were estimated and compared on six different models with respect to breeding schemes using either carcass measurements or real time ultrasonic (RTU) scans generated by Monte Carlo computer simulation supporting closed breeding population. From the base population with 100 sires and 2000 dams, 20 sires and 1000 dams by each generation were selected by either APH or EBV for 10 generations. Relative economic weights were equal of three traits as EMA(1): BF(－1) : MS(1) for standardized either APH or EBV. For first two models which were similarly designed with current progeny-test program in Korean cattle, three carcass traits with records either only on male progenies (Model 1) or on male and female progenies (Model 2) were used for selecting breeding stocks. Subsequently, generation intervals on males were assumed as 6${\sim}$10 years in these two models. The other two models were designed with tools of selection by RTU rather than carcass measurements with genetic correlations of 0.81${\sim}$0.97 between RTU and corresponding carcass traits in addition to whether with records (Model 4) or without records (Model 3) on female. In these cases, generation intervals on males were assumed as 2${\sim}$4 years. The remaining last two models were designed as similar with Models 3 and 4 except genetic correlations of 0.63${\sim}$0.68 between RTU and corresponding carcass traits with records (Model 6) and without records (Model 5) on females. The results from 10 replicates on each model and selecting methods suggested that responses indirect selection for carcass traits in Model 4 were 1.66${\sim}$2.44 times efficient rather than those in Model 1. Otherwise, in Model 6 with assuming moderate genetic correlations, those efficiencies were 1.18${\sim}$2.08 times with comparing to responses in Model 1. However, selection response for marbling score was the smallest among three carcass traits because of small variation of measurements. From these results, this study suggested that indirect selection using RTU technology for improving high meat qualities in Korean cattle would be valuable with modifying measuring rules of marbling score forward to large variation or modifying relative economic weight for selection.

• Journal of Animal Science and Technology
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• v.53 no.4
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• pp.283-288
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• 2011

This study was conducted to study the environment effects on live weight, carcass weight, dressing percentage, eye muscle area, backfat thickness, and marbling score, which are the carcass traits of Hanwoo, based on the estimates and all the possible regression for the selection of variable and significance test for 231,382 heads that underwent the carcass measurements. The average and standard deviation for the live weight, carcass weight, dressing percentage, eye muscle area, backfat thickness, and marbling score were 654.79${\pm}$91.61 kg, 362.30${\pm}$67.15 kg, 59.52${\pm}$0.03%, 81.79${\pm}$12.21 $cm^2$, 11.39${\pm}$5.40 mm, 4.38${\pm}$2.29, respectively. The live weight, carcass weight, dressing percentage, eye muscle area, backfat thickness, and marbling score for cow were 532.79${\pm}$78.38 kg, 313.40${\pm}$44.90 kg, 56.50${\pm}$0.03%, 75.24${\pm}$10.69 $cm^2$, 11.82${\pm}$5.10 mm, 4.30${\pm}$2.06, respectively, while for bull were 619.74${\pm}$93.27 kg, 376.89${\pm}$48.62 kg, 58.61${\pm}$0.02%, 85.61${\pm}$10.46 $cm^2$, 5.64${\pm}$2.71 mm, 1.41${\pm}$0.83, respectively, and for steer were 681.78${\pm}$70.72 kg, 415.23${\pm}$49.43 kg, 60.19${\pm}$0.02%, 88.29${\pm}$10.27 $cm^2$, 12.71${\pm}$5.23 mm, 5.42${\pm}$1.99, respectively. In the environmental variables selection based on the variables selection method, the examination by carcass traits suggested that the most appropriate model could be determined when five variables were selected for the live weight, carcass weight, dressing percentage, eye muscle area, and four variables for backfat thickness, and marbling score. When they were considered at a time altogether based on multiple traits, it was deemed to be desirable to insert all five variables into the variables for analysis. In addition, high significance was found by carcass traits.