Review on the genetic improvement and application of genomic selection in Korean Hanwoo cattle

Chiemela Peter Nwogwugwu,Yeong Kuk Kim,Ejike Henry Ugbo,이준헌,이승환 한국동물유전육종학회 2020 한국동물유전육종학회지 Vol.4 No.2

Hanwoo cattle (HC) being an indigenous breed are greatly adapted to Korean hot-humid climate. They can survive and thrive in harsh environmental conditions. This makes the HC a valuable genetic resource, given the challenges of climate adjustment and varying demands of the livestock sector. Respects to these genetic attributes of HC, breeding initiatives were designed for genetic improvements, such as the Hanwoo-Gaeryang-Danji (HGD) and Hanwoo-Gaeryang-Nongga (HGN), respectively. These initiatives have resulted in tremendous success in the meat industry. The genetic improvement of HC is somehow fulfilling the breeding objectives of increasing the growth performance traits, enhancing meat quality, improving fertility and maintaining adaptability. The breeding and production systems have also contributed towards achieving the breeding goal. The HC production system comprised of 3-tier, the seed stock, multiplier and feedlot sector. The production system provides a link that enable genetic material from the nucleus herd down to various sectors. The results from various studies on the evaluation of genetic improvement and parameters in Korean HC have revealed the degree of genetic progress. Furthermore, the implementation and the used of pedigree and performance records have been helpful using best linear unbiased prediction (BLUP) to estimate breeding values. In addition, the EBV and accuracy of estimated breeding values (EBVs) are important tool for selecting superior animals to replace the next generation. However, several factors can influence the accuracy of EBVs, such as selection accuracy, selection intensity, pedigree errors and the generation interval (GI). Applying genomic selection (GS) is a potential method to improve prediction accuracy and genetic gains in economically important traits in dairy and beef cattle. Therefore, this study reviews the genetic improvement and application of genomic selection in Korean Hanwoo cattle.

Effect of errors in pedigree on the accuracy of estimated breeding value for carcass traits in Korean Hanwoo cattle

Nwogwugwu Chiemela Peter,김영국,Chung Yun Ji,Jang Sung Bong,Roh Seung Hee,김시동,이준헌,최태정,이승환 아세아·태평양축산학회 2020 Animal Bioscience Vol.33 No.7

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 cm2, 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.

Assessment of genomic prediction accuracy using different selection and evaluation approaches in a simulated Korean beef cattle population

Nwogwugwu Chiemela Peter,김영국,Choi Hyunji,이준헌,이승환 아세아·태평양축산학회 2020 Animal Bioscience Vol.33 No.12

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.

Optimal population size to detect quantitative trait loci in Korean native chicken: a simulation study

Nwogwugwu Chiemela Peter,김영국,Cho Sunghyun,노희종,Cha Jihye,이승환,이준헌 아세아·태평양축산학회 2022 Animal Bioscience Vol.35 No.4

Objective: A genomic region associated with a particular phenotype is called quantitative trait loci (QTL). To detect the optimal F2 population size associated with QTLs in native chicken, we performed a simulation study on F2 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 F1 population which was composed of different breed to create two different F2 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 compared 100, 500, 1,000 reference 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 RP size of 100, no QTL was detected to satisfy Bonferroni value at three different heritability levels. In a RP size of 500, two QTLs were detected when the heritability was 0.5. With a RP size of 1,000, 0.1 heritability was detected only one QTL, and 0.5 heritability detected five QTLs. To sum up, RP size and heritability play a key role in detecting QTLs in a QTL study. The larger RP size and greater heritability value, the higher the probability of detection of QTLs.Conclusion: Our study suggests that the use of a large RP and heritability can improve QTL detection in an F2 chicken population. Objective: A genomic region associated with a particular phenotype is called quantitative trait loci (QTL). To detect the optimal F2 population size associated with QTLs in native chicken, we performed a simulation study on F2 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 F1 population which was composed of different breed to create two different F2 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 compared 100, 500, 1,000 reference 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 RP size of 100, no QTL was detected to satisfy Bonferroni value at three different heritability levels. In a RP size of 500, two QTLs were detected when the heritability was 0.5. With a RP size of 1,000, 0.1 heritability was detected only one QTL, and 0.5 heritability detected five QTLs. To sum up, RP size and heritability play a key role in detecting QTLs in a QTL study. The larger RP size and greater heritability value, the higher the probability of detection of QTLs. Conclusion: Our study suggests that the use of a large RP and heritability can improve QTL detection in an F2 chicken population.