Kim Eun Ho, Kim Hyeon Kwon, Sun Du Won, Kang Ho Chan, Lee Doo Ho, Lee Seung Hwan, Lee Jae Bong, Lim Hyun Tae
Department of Animal Science, Gyeongsang National University, Jinju 52828, Korea.
Institute of Agriculture and Life Science, Gyeongsang National University, Jinju 52828, Korea.
J Anim Sci Technol. 2020 Jul;62(4):429-437. doi: 10.5187/jast.2020.62.4.429. Epub 2020 Jul 31.
This study was conducted to construct basic data for the selection of elite cows by analyzing the estimated breeding value (EBV) and accuracy using the pedigree of Hanwoo cows in Gyeongnam. The phenotype trait used in the analysis are the carcass weight (CWT), eye muscle area (EMA), backfat thickness (BFT) and marbling score (MS). The pedigree of the test group and reference group was collected to build a pedigree structure and a numeric relationship matrix (NRM). The EBV, genetic parameters and accuracy were estimated by applying NRM to the best linear unbiased prediction (BLUP) multiple-trait animal model of the BLUPF90 program. Looking at the pedigree structure of the test group, there were a total of 2,371 cows born between 2003 to 2009, of these 603 cows had basic registration (25%), 562 cows had pedigree registration (24%) and 1,206 cows had advanced registration (51%). The proportion of pedigree registered cows was relatively low but it gradually increased and reached a point of 20,847 cows (68%) between 2010 to 2017. Looking at the change in the EBV, the CWT improved from 4.992 kg to 9.885 kg, the EMA from 0.970 cm to 2.466 cm, the BFT from -0.186 mm to -0.357 mm, and the MS from 0.328 to 0.559 points. As a result of genetic parameter estimation, the heritability of CWT, EMA, BFT, and MS were 0.587, 0.416, 0.476, and 0.571, respectively, and the accuracy of those were estimated to be 0.559, 0.551, 0.554, and 0.558, respectively. Selection of superior genetic breed and efficient improvement could be possible if cow ability verification is implemented by using the accurate pedigree of each individual in the farms.
本研究旨在通过分析庆南韩牛的系谱，利用估计育种值（EBV）和准确性来构建优秀母牛选择的基础数据。分析中使用的表型性状为胴体重（CWT）、眼肌面积（EMA）、背膘厚度（BFT）和大理石花纹评分（MS）。收集测试组和参考组的系谱以构建系谱结构和数字关系矩阵（NRM）。通过将NRM应用于BLUPF90程序的最佳线性无偏预测（BLUP）多性状动物模型来估计EBV、遗传参数和准确性。查看测试组的系谱结构，2003年至2009年间共出生2371头母牛，其中603头母牛有基本登记（25%），562头母牛有系谱登记（24%），1206头母牛有高级登记（51%）。系谱登记母牛的比例相对较低，但逐渐增加，在2010年至2017年间达到20847头母牛（68%）。查看EBV的变化，CWT从4.992千克提高到9.885千克，EMA从0.970厘米提高到2.466厘米，BFT从-0.186毫米提高到-0.357毫米，MS从0.328分提高到0.559分。遗传参数估计结果显示，CWT、EMA、BFT和MS的遗传力分别为0.587、0.416、0.476和0.571，其准确性估计分别为0.559、0.551、0.554和0.558。如果通过使用农场中每头个体的准确系谱来进行母牛能力验证，就有可能选择优良遗传品种并实现高效改良。
