Animal Science Unit, Gembloux Agro Bio-Tech, University of Liège, B-5030 Gembloux, Belgium.
J Dairy Sci. 2012 Mar;95(3):1513-26. doi: 10.3168/jds.2011-4322.
The aim of this research was to compare different Bayesian procedures to integrate information from outside a given evaluation system, hereafter called external information, and in this context estimated breeding values (EBV), into this genetic evaluation, hereafter called internal evaluation, and to improve the Bayesian procedures to assess their potential to combine information from diverse sources. The 2 improvements were based on approximations of prior mean and variance. The first version of modified Bayesian evaluation considers all animals as animals associated with external information. For animals that have no external information (i.e., internal animals), external information is predicted from available external information. Thereby, propagation of this external information through the whole pedigree is allowed. Furthermore, the prediction of external information for internal animals allows large simplifications of the computational burden during setup and solving of mixed model equations. However, double counting among external animals (i.e., animals associated with available external information) is not avoided. Double counting concerns multiple considerations of contributions due to relationships by integration of external EBV for related external animals and is taken into account by the second version of modified Bayesian evaluation. This version includes the estimation of double counting before integration of external information. To test the improvements, 2 dairy cattle populations were simulated across 5 generations. Milk production for the first lactation for each female was simulated in both populations. Internal females were randomly mated with internal males and 50 external males. Results for 100 replicates showed that rank correlations among Bayesian EBV and EBV based on the joint use of external and internal data were very close to 1 for both external and internal animals if all internal and external animals were associated with external information. The respective correlations for the internal evaluation were equal to 0.54 and 0.95 if no external information was integrated. If double counting was avoided, mean squared error, expressed as a percentage of the internal mean squared error, was close to zero for both external and internal animals. However, computational demands increased when double counting was avoided. Finally, the improved Bayesian procedures have the potential to be applied for integrating external EBV, or even genomic breeding values following some additional assumptions, into routine genetic evaluations to evaluate animals more reliably.
本研究的目的是比较不同的贝叶斯方法，以整合来自给定评估系统之外的信息，以下简称外部信息，并在这种情况下估计遗传评估中的育种值（EBV），以下简称内部评估，并改进贝叶斯方法以评估它们结合来自不同来源信息的潜力。这两个改进都基于对先验均值和方差的近似。改进后的贝叶斯评估的第一个版本将所有动物视为与外部信息相关的动物。对于没有外部信息的动物（即内部动物），从可用的外部信息中预测外部信息。由此，允许通过整个系谱传播这种外部信息。此外，内部动物的外部信息预测允许在设置和解决混合模型方程时大大简化计算负担。然而，并没有避免在外部动物之间（即与可用外部信息相关的动物）的重复计算。重复计算涉及由于与相关外部动物的关系而对外部 EBV 的多次考虑，并且在改进后的贝叶斯评估的第二个版本中得到了考虑。该版本包括在整合外部信息之前对重复计算的估计。为了测试改进，模拟了两个奶牛群体跨越 5 代。在两个群体中模拟了每个雌性的第一个泌乳期的产奶量。内部雌性与内部雄性随机交配，并选择 50 只外部雄性。100 次重复的结果表明，如果所有内部和外部动物都与外部信息相关联，则基于联合使用外部和内部数据的贝叶斯 EBV 与 EBV 的等级相关非常接近 1，对于外部和内部动物都是如此。如果没有整合外部信息，则内部评估的相应相关性等于 0.54 和 0.95。如果避免重复计算，则对于外部和内部动物，以内部均方误差的百分比表示的均方误差接近零。然而，当避免重复计算时，计算需求会增加。最后，改进后的贝叶斯方法有可能应用于将外部 EBV 甚至基因组育种值纳入常规遗传评估中，以更可靠地评估动物。
