Ma P, Lund M S, Nielsen U S, Aamand G P, Su G
Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark.
Center for Quantitative Genetics and Genomics, Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark.
J Dairy Sci. 2015 Dec;98(12):9026-34. doi: 10.3168/jds.2015-9703. Epub 2015 Nov 11.
A bias in the trend of genomic estimated breeding values (GEBV) was observed in the Danish Jersey population where the trend of GEBV was smaller than the deregressed proofs for individuals in the validation population. This study attempted to improve the prediction reliability and reduce the bias of predicted genetic trend in Danish Jersey. The data consisted of 1,238 Danish Jersey bulls and 611,695 cows. All bulls were genotyped with the 54K chip, and 1,744 cows were genotyped with either 7K chips (1,157 individuals) or 54K chips (587 individuals). The trait used in the analysis was protein yield. All cows with EBV were used in a single-step approach. Deregressed proofs were used as the response variable. Four alternative approaches were compared with genomic best linear unbiased prediction (GBLUP) model with bulls in the reference data (GBLUPBull): (1) GBLUP with both bulls and genotyped cows in the reference data; (2) GBLUP including a year of birth effect; (3) GEBV from a GBLUP model that accounted for the difference of EBV between dams and maternal grandsires; and (4) using a single-step approach. The results indicated all 4 alternatives could reduce the bias of predicted genetic trend and that the single-step approach performed best. However, not all these approaches improved reliability or reduced inflation of GEBV. The reliability was 0.30 and regression coefficients of deregressed proofs on GEBV were 0.69 in the scenario GBLUPBull. When genotyped cows were included in the reference population, the regression coefficients decreased to 0.59 but the reliability increased to 0.35. If a year effect was included in the model, the prediction reliability decreased to 0.29 and the regression coefficient improved to 0.75. The method in which GEBV were adjusted for the difference between dam EBV and maternal grandsire EBV led to much lower regression coefficients though the reliability increased to 0.4. The single-step approach improved both the reliability, to 0.38 and regression coefficient to 0.78. Therefore, the bias in genetic trend was reduced. The results suggest that implementing the single-step approach is an effective way to improve genomic prediction in Danish Jersey cattle.
在丹麦泽西牛群体中，观察到基因组估计育种值（GEBV）趋势存在偏差，即验证群体中GEBV的趋势小于去回归证明。本研究试图提高丹麦泽西牛预测的可靠性，并减少预测遗传趋势的偏差。数据包括1238头丹麦泽西公牛和611695头母牛。所有公牛都用54K芯片进行了基因分型，1744头母牛用7K芯片（1157头个体）或54K芯片（587头个体）进行了基因分型。分析中使用的性状是蛋白质产量。所有具有估计育种值（EBV）的母牛都采用单步方法。去回归证明用作响应变量。将四种替代方法与参考数据中包含公牛的基因组最佳线性无偏预测（GBLUP）模型（GBLUPBull）进行比较：（1）参考数据中同时包含公牛和基因分型母牛的GBLUP；（2）包含出生年份效应的GBLUP；（3）来自考虑了母牛和外祖父EBV差异的GBLUP模型的GEBV；（4）采用单步方法。结果表明，所有4种替代方法都能减少预测遗传趋势的偏差，且单步方法表现最佳。然而，并非所有这些方法都能提高可靠性或减少GEBV的膨胀。在GBLUPBull情景中，可靠性为0.30，去回归证明对GEBV的回归系数为0.69。当基因分型母牛包含在参考群体中时，回归系数降至0.59，但可靠性提高到0.35。如果模型中包含年份效应，预测可靠性降至0.29，回归系数提高到0.75。对GEBV根据母牛EBV和外祖父EBV之间的差异进行调整的方法，尽管可靠性提高到0.4，但导致回归系数低得多。单步方法将可靠性提高到0.38，回归系数提高到0.78。因此，遗传趋势的偏差得以减少。结果表明，实施单步方法是提高丹麦泽西牛基因组预测的有效途径。
