Nwogwugwu Chiemela Peter, Kim Yeongkuk, Choi Hyunji, Lee Jun Heon, Lee Seung-Hwan
Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, Korea.
Asian-Australas J Anim Sci. 2020 Dec;33(12):1912-1921. doi: 10.5713/ajas.20.0217. Epub 2020 Jun 24.
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.
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.
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.
Our study suggests that the use of ssGBLUP_0.95, EBV selection, and low marker density could help improve genetic gains in beef cattle.
本研究在一个模拟的韩国肉牛群体中，基于不同的选择方法、评估程序、训练群体（TP）大小、遗传力（h2）水平、标记密度和系谱错误（PE）率，评估基因组预测准确性。
使用两种不同的选择方法（表型选择和估计育种值（EBV）选择）进行模拟，h2分别为0.1、0.3或0.5，标记密度为10K、50K或777K。从最后一代中随机选择275头雄性和2475头雌性，以模拟最近的十代。仅使用标记密度为50K、遗传力为0.3的EBV选择方法对PE数据集进行模拟修改。替换错误的比例分别为10%、20%、30%和40%。使用具有不同加权值的基因组最佳线性无偏预测（GBLUP）和单步GBLUP（ssGBLUP）进行遗传评估。确定预测的准确性。
与表型选择相比，结果表明在EBV选择过程中，使用GBLUP和ssGBLUP获得的预测准确性随着遗传力水平和TP大小的增加而提高。然而，除了在EBV选择方法下h2为0.3时，标记密度的增加在两种方法中均未产生更高的准确性。基于遗传力为0.1、标记密度为10K的EBV选择，GBLUP和ssGBLUP_0.95的预测准确性高于表型选择。在所有情况下，ssGBLUP_0.95的预测准确性均优于GBLUP方法。当在系谱数据集中引入错误时，在所有情况下预测准确性仅受到最小影响。
我们的研究表明，使用ssGBLUP_0.95、EBV选择和低标记密度有助于提高肉牛的遗传增益。
