Lopes M S, Bovenhuis H, van Son M, Nordbø Ø, Grindflek E H, Knol E F, Bastiaansen J W M
J Anim Sci. 2017 Jan;95(1):59-71. doi: 10.2527/jas.2016.0754.
The first attempts of applying marker-assisted selection (MAS) in animal breeding were not very successful because the identification of markers closely linked to QTL using low-density microsatellite panels was difficult. More recently, the use of high-density SNP panels in genome-wide association studies (GWAS) have increased the power and precision of identifying markers linked to QTL, which offer new possibilities for MAS. However, when GWAS started to be performed, the focus of many breeders had already shifted from the use of MAS to the application of genomic selection (using all available markers without any preselection of markers linked to QTL). In this study, we aimed to evaluate the prediction accuracy of a MAS approach that accounts for GWAS findings in the prediction models by including the most significant SNP from GWAS as a fixed effect in the marker-assisted BLUP (MA-BLUP) and marker-assisted genomic BLUP (MA-GBLUP) prediction models. A second aim was to compare the prediction accuracies from the marker-assisted models with those obtained from a Bayesian variable selection (BVS) model. To compare the prediction accuracies of traditional BLUP, MA-BLUP, genomic BLUP (GBLUP), MA-GBLUP, and BVS, we applied these models to the trait "number of teats" in 4 distinct pig populations, for validation of the results. The most significant SNP in each population was located at approximately 103.50 Mb on chromosome 7. Applying MAS by accounting for the most significant SNP in the prediction models resulted in improved prediction accuracy for number of teats in all evaluated populations compared with BLUP and GBLUP. Using MA-BLUP instead of BLUP, the increase in prediction accuracy ranged from 0.021 to 0.124, whereas using MA-GBLUP instead of GBLUP, the increase in prediction accuracy ranged from 0.003 to 0.043. The BVS model resulted in similar or higher prediction accuracies than MA-GBLUP. For the trait number of teats, BLUP resulted in the lowest prediction accuracies whereas the highest were observed when applying MA-GBLUP or BVS. In the same data set, MA-BLUP can yield similar or superior accuracies compared with GBLUP. The superiority of MA-GBLUP over traditional GBLUP is more pronounced when training populations are smaller and when relationships between training and validation populations are smaller. Marker-assisted GBLUP did not outperform BVS but does have implementation advantages in large-scale evaluations.
在动物育种中首次尝试应用标记辅助选择（MAS）时并不十分成功，因为使用低密度微卫星面板来鉴定与数量性状基因座（QTL）紧密连锁的标记很困难。最近，在全基因组关联研究（GWAS）中使用高密度单核苷酸多态性（SNP）面板提高了鉴定与QTL连锁标记的能力和精度，这为MAS提供了新的可能性。然而，当开始进行GWAS时，许多育种者的关注点已经从使用MAS转移到了基因组选择的应用（使用所有可用标记，而不对与QTL连锁的标记进行任何预选）。在本研究中，我们旨在评估一种MAS方法的预测准确性，该方法通过在标记辅助最佳线性无偏预测（MA-BLUP）和标记辅助基因组最佳线性无偏预测（MA-GBLUP）预测模型中纳入来自GWAS的最显著SNP作为固定效应，从而在预测模型中考虑GWAS的结果。第二个目标是将标记辅助模型的预测准确性与贝叶斯变量选择（BVS）模型的预测准确性进行比较。为了比较传统最佳线性无偏预测（BLUP）、MA-BLUP、基因组最佳线性无偏预测（GBLUP）、MA-GBLUP和BVS的预测准确性，我们将这些模型应用于4个不同猪群的“乳头数”性状，以验证结果。每个群体中最显著的SNP位于7号染色体上约103.50 Mb处。与BLUP和GBLUP相比，在预测模型中考虑最显著的SNP来应用MAS可提高所有评估群体中乳头数的预测准确性。使用MA-BLUP而非BLUP时，预测准确性的提高范围为0.021至0.124，而使用MA-GBLUP而非GBLUP时，预测准确性的提高范围为0.003至0.043。BVS模型的预测准确性与MA-GBLUP相似或更高。对于乳头数性状，BLUP的预测准确性最低，而应用MA-GBLUP或BVS时观察到的预测准确性最高。在同一数据集中，与GBLUP相比，MA-BLUP可产生相似或更高的准确性。当训练群体较小时以及训练群体与验证群体之间的关系较小时，MA-GBLUP相对于传统GBLUP的优势更为明显。标记辅助GBLUP并不优于BVS，但在大规模评估中具有实施优势。
