Objective: This study evaluated the effect of pedigree errors (PEs) on the accuracy of estimated breeding value (EBV) and genetic gain for carcass traits in Korean Hanwoo cattle.Methods: The raw data set was based on the pedigree records of Korean Hanwoo cattle. The animals’ information was obtained using Hanwoo registration records from Korean animal improvement association database. The record comprised of 46,704 animals, where the number of the sires used was 1,298 and the dams were 38,366 animals. The traits considered were carcass weight (CWT), eye muscle area (EMA), back fat thickness (BFT), and marbling score (MS). Errors were introduced in the pedigree dataset through randomly assigning sires to all progenies. The error rates substituted were 5%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, and 80%, respectively. A simulation was performed to produce a population of 1,650 animals from the pedigree data. A restricted maximum likelihood based animal model was applied to estimate the EBV, accuracy of the EBV, expected genetic gain, variance components, and heritability (h2) estimates for carcass traits. Correlation of the simulated data under PEs was also estimated using Pearson’s method.Results: The results showed that the carcass traits per slaughter year were not consistent. The average CWT, EMA, BFT, and MS were 342.60 kg, 78.76 cm<sup>2, 8.63 mm, and 3.31, respectively. When errors were introduced in the pedigree, the accuracy of EBV, genetic gain and h2 of carcass traits was reduced in this study. In addition, the correlation of the simulation was slightly affected under PEs.Conclusion: This study reveals the effect of PEs on the accuracy of EBV and genetic parameters for carcass traits, which provides valuable information for further study in Korean Hanwoo cattle.

Objective: This study assessed genomic prediction accuracies based on different selection methods, evaluation procedures, training population (TP) sizes, heritability (h²) levels, marker densities and pedigree error (PE) rates in a simulated Korean beef cattle population.Methods: A simulation was performed using two different selection methods, phenotypic and estimated breeding value (EBV), with an h² of 0.1, 0.3, or 0.5 and marker densities of 10, 50, or 777K. A total of 275 males and 2,475 females were randomly selected from the last generation to simulate ten recent generations. The simulation of the PE dataset was modified using only the EBV method of selection with a marker density of 50K and a heritability of 0.3. The proportions of errors substituted were 10%, 20%, 30%, and 40%, respectively. Genetic evaluations were performed using genomic best linear unbiased prediction (GBLUP) and single-step GBLUP (ssGBLUP) with different weighted values. The accuracies of the predictions were determined.Results: Compared with phenotypic selection, the results revealed that the prediction accuracies obtained using GBLUP and ssGBLUP increased across heritability levels and TP sizes during EBV selection. However, an increase in the marker density did not yield higher accuracy in either method except when the h² was 0.3 under the EBV selection method. Based on EBV selection with a heritability of 0.1 and a marker density of 10K, GBLUP and ssGBLUP_0.95 prediction accuracy was higher than that obtained by phenotypic selection. The prediction accuracies from ssGBLUP_0.95 outperformed those from the GBLUP method across all scenarios. When errors were introduced into the pedigree dataset, the prediction accuracies were only minimally influenced across all scenarios.Conclusion: Our study suggests that the use of ssGBLUP_0.95, EBV selection, and low marker density could help improve genetic gains in beef cattle.

This study assessed the breeding practice and selection criteria of dairy cows in two districts. A total number of 288 structured questionnaires were utilized to gather information from households in the study areas. Logit model, indices, and descriptive statistics were employed for data analysis. Education, marital status, and family size of respondents from Chora district were confirmed as predictors for practicing the controlled mating system and significantly influenced at p < 0.05 . The odds of practicing the controlled mating system by educated and married farmers in Chora district were 10.01 and 4.82 times higher compared to uneducated and unmarried farmers, respectively, and also, for every additional increase in family size, they increased by the factor of 1.21. Educational and marital status of farmers in Gechi district also influenced the use of controlled mating. The odds of performing controlled mating based on the educational level and marital status of the farmers were higher among educated and married individuals. Based on indigenous knowledge, teat size, udder size, and pelvic width were the 1st three ranked traits used as major selection criteria of dairy cows in Gechi district, whereas body length was the 1st among others in Chora district. This finding indicated that the combination of indigenous knowledge with modern science is important to improve cow’s genetics. The study suggests that mating systems and selection criteria should be considered as baseline information for designing the genetic improvement programs.

Objective: A genomic region associated with a particular phenotype is called QTL. To detect the optimal F 2 population size associated with QTLs in native chicken, we performed a simulation study on F 2 population derived from crosses between two different breeds.Methods: A total of 15 males and 150 females were randomly selected from the last generation of each F 1 population which was composed of different breed to create two different F 2 populations.The progenies produced from these selected individuals were simulated for six more generations.Their marker genotypes were simulated with a density of 50K at three different heritability levels for the traits such as 0.1, 0.3 and 0.5. Our study is that to compare 100, 500, 1000 TPreference population (RP) groups to each other with three different heritability levels. And a total of 35 QTLs were used, and their locations were randomly created.Results: With a TPRP size of 100, no QTL was detected to satisfy Bonferroni value at three different heritability levels. In a TPRP size of 500, two QTLs were detected when the heritability was 0.5. With a TPRP size of 1,000, 0.1 heritability was detected only one QTL, and 0.5 heritability shows that five QTLs were detected. To sum up, TPRP size and heritability are playing a key role to detect QTLs in QTL study. The larger TPRP size and greater heritability value, the higher the probability of detection of QTLs.With a TPRP size of 100, some QTLs were found, even though the number of QTLs were somewhat similar for h 2 = 0.1, 0.3, and 0.5, respectively. This result indicates an increased in h 2 did not improve number of QTLs at TPRP size of 100. With a TPRP size of 1000, many QTLs were detected at different h 2 levels of traits, even at the h 2 value of 0.1 Conclusion:Our study suggests that the use of a large TPRP and heritability can improve QTL detection in an F 2 chicken population.