Calus M P L, Vandenplas J, Ten Napel J, Veerkamp R F
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 6700 AH Wageningen, the Netherlands.
Animal Breeding and Genomics Centre, Wageningen UR Livestock Research, 6700 AH Wageningen, the Netherlands.
J Dairy Sci. 2016 Aug;99(8):6403-6419. doi: 10.3168/jds.2016-11028. Epub 2016 May 18.
Training of genomic prediction in dairy cattle may use deregressed proofs (DRP) as phenotypes. In this case, DRP should be estimated breeding values (EBV) corrected for information of relatives included in the data used for genomic prediction, and adjusted for regression to the mean (i.e., their reliability). Deregression is especially important when combining animals with EBV with low reliability, as commonly the case for cows, and high reliability. The objective of this paper, therefore, was to compare the performance of different deregression procedures for data that include both cow and bull EBV, and to develop and test procedures to obtain the appropriate deregressed weights for the DRP. Considered DRP were EBV: without any adjustment, adjusted for information of parents and regression to the mean, or adjusted for information of all relatives and regression to the mean. Considered deregressed weights were weights of initial EBV: without any adjustment, adjusted for information of parents, or adjusted for information of all relatives. The procedures were compared using simulated data based on an existing pedigree with 1,532 bulls and 13,720 cows that were considered to be included in the data used for genomic prediction. For each cow, 1 to 5 records were simulated. For each bull, an additional 50 to 200 daughters with 1 record each were simulated to generate a source of data that was not used for genomic prediction. The simulated trait had either a heritability of 0.05 or 0.3. The validation involved 3 steps: (1) computation of initial EBV and weights, (2) deregression of those EBV and weights, (3) using deregressed EBV and weights to compute final EBV, (4) comparison of the initial and final EBV and weights. The methods developed to compute appropriate weights for the DRP were either very precise and computationally somewhat demanding for larger data sets, or were less precise but computationally trivial due their approximate nature. Adjusting DRP for all relatives, known as matrix deregression, yields by definition final EBV that are identical to the original EBV. Matrix deregression is therefore preferred over other approaches that only correct for information of parents or not performing any deregression at all. It is important to use appropriate weights for the DRP, properly corrected for information of relatives, especially when individual reliabilities of final EBV are computed based on the prediction error variance of the model.
奶牛基因组预测的训练可以使用去回归证明（DRP）作为表型。在这种情况下，DRP应该是根据用于基因组预测的数据中所包含的亲属信息进行校正，并针对均值回归进行调整（即它们的可靠性）后的估计育种值（EBV）。当将可靠性较低的母牛的EBV与可靠性较高的公牛的EBV相结合时，去回归尤为重要，母牛的EBV通常就是这种情况。因此，本文的目的是比较包含母牛和公牛EBV的数据的不同去回归程序的性能，并开发和测试程序以获得DRP的适当去回归权重。考虑的DRP是EBV：不做任何调整、根据父母信息和均值回归进行调整，或根据所有亲属信息和均值回归进行调整。考虑的去回归权重是初始EBV的权重：不做任何调整、根据父母信息进行调整，或根据所有亲属信息进行调整。使用基于一个现有系谱的模拟数据对这些程序进行比较，该系谱中有1532头公牛和13720头母牛，这些被认为包含在用于基因组预测的数据中。对于每头母牛，模拟了1至5条记录。对于每头公牛，额外模拟了50至200头女儿，每头女儿有1条记录，以生成一个不用于基因组预测的数据源。模拟性状的遗传力为0.05或0.3。验证包括3个步骤：（1）计算初始EBV和权重，（2）对这些EBV和权重进行去回归，（3）使用去回归后的EBV和权重计算最终EBV，（4）比较初始和最终的EBV及权重。为DRP计算适当权重所开发的方法要么非常精确，对于较大数据集在计算上有些要求较高，要么由于其近似性质而不太精确但计算简单。对所有亲属进行DRP调整，即所谓的矩阵去回归，根据定义得出的最终EBV与原始EBV相同。因此，矩阵去回归比仅根据父母信息进行校正或根本不进行去回归的其他方法更可取。对于DRP，使用针对亲属信息进行适当校正的权重非常重要，特别是在根据模型的预测误差方差计算最终EBV的个体可靠性时。
