MTT Agrifood Research Finland, Biotechnology and Food Research, Biometrical Genetics FI-31600 Jokioinen, Finland.
J Dairy Sci. 2012 Jul;95(7):4065-73. doi: 10.3168/jds.2011-4874.
Several strategies to use genomic data in predictions have been proposed. The aim of this study was to compare different genomic prediction methods. The response variables used in the genomic predictions were deregressed proofs, which were derived from 2 estimated breeding value (EBV) data sets. The full EBV data set from March 2010 included the EBV for production and mastitis traits for all Nordic red bulls. The reduced data set included the same animals as the full data set, but the EBV were predicted from a data set that excluded the last 5 yr of observations. Genomic predictions were obtained using different BLUP models: BLUP at the single nucleotide polymorphism level (SNP-BLUP), BLUP at the individual level (G-BLUP), and the one-step approach (H-BLUP). For the selection candidate bulls, the SNP-BLUP and G-BLUP models gave the same direct genomic breeding values (e.g., correlation of direct genomic breeding values between SNP-BLUP and G-BLUP for protein was 0.99), but slightly different from genomic EBV obtained from H-BLUP (correlations of SNP-BLUP or G-BLUP with H-BLUP were about 0.96). For all traits, SNP-BLUP and G-BLUP gave the same validation reliability, whereas H-BLUP led to slightly higher reliability. Therefore, the results support a slight advantage of using H-BLUP for genomic evaluation.
已经提出了几种利用基因组数据进行预测的策略。本研究的目的是比较不同的基因组预测方法。基因组预测中使用的响应变量是去回归证明,这些证明是从 2 个估计育种值(EBV)数据集推导出来的。2010 年 3 月的完整 EBV 数据集包括所有北欧红牛的生产和乳腺炎性状的 EBV。简化数据集包括与完整数据集相同的动物,但 EBV 是根据一个排除了最后 5 年观测值的数据集进行预测的。基因组预测是使用不同的 BLUP 模型获得的:单核苷酸多态性水平的 BLUP(SNP-BLUP)、个体水平的 BLUP(G-BLUP)和一步法(H-BLUP)。对于选择的候选公牛,SNP-BLUP 和 G-BLUP 模型给出了相同的直接基因组育种值(例如,SNP-BLUP 和 G-BLUP 之间的蛋白质直接基因组育种值的相关性为 0.99),但与 H-BLUP 获得的基因组 EBV 略有不同(SNP-BLUP 或 G-BLUP 与 H-BLUP 的相关性约为 0.96)。对于所有性状,SNP-BLUP 和 G-BLUP 给出了相同的验证可靠性,而 H-BLUP 导致可靠性略高。因此,结果支持使用 H-BLUP 进行基因组评估的略微优势。