Pfeiffer Christina, Fuerst-Waltl Birgit, Schwarzenbacher Hermann, Steininger Franz, Fuerst Christian
Department of Sustainable Agricultural Systems, Division of Livestock Sciences, University of Natural Resources and Life Sciences Vienna, Gregor-Mendel-Straße 33, 1180, Vienna, Austria.
ZuchtData EDV-Dienstleistungen GmbH, Dresdner Straße 89/19, 1200, Vienna, Austria.
Genet Sel Evol. 2015 May 2;47(1):36. doi: 10.1186/s12711-015-0118-4.
Modern dairy cattle breeding goals include several production and more and more functional traits. Estimated breeding values (EBV) that are combined in the total merit index usually come from single-trait models or from multivariate models for groups of traits. In most cases, a multivariate animal model based on phenotypic data for all traits is not feasible and approximate methods based on selection index theory are applied to derive the total merit index. Therefore, the objective of this study was to compare a full multitrait animal model with two approximate multitrait models and a selection index approach based on simulated data.
Three production and two functional traits were simulated to mimic the national Austrian Brown Swiss population. The reference method for derivation of the total merit index was a multitrait evaluation based on all phenotypic data. Two of the approximate methods were variations of an approximate multitrait model that used either yield deviations or de-regressed breeding values. The final method was an adaptation of the selection index method that is used in routine evaluations in Austria and Germany. Three scenarios with respect to residual covariances were set up: residual covariances were equal to zero, or half of or equal to the genetic covariances.
Results of both approximate multitrait models were very close to those of the reference method, with rank correlations of 1. Both methods were nearly unbiased. Rank correlations for the selection index method showed good results when residual covariances were zero but correlations with the reference method decreased when residual covariances were large. Furthermore, EBV were biased when residual covariances were high.
We applied an approximate multitrait two-step procedure to yield deviations and de-regressed breeding values, which led to nearly unbiased results. De-regressed breeding values gave even slightly better results. Our results confirmed that ignoring residual covariances when a selection index approach is applied leads to remarkable bias. This could be relevant in terms of selection accuracy. Our findings suggest that the approximate multitrait approach applied to de-regressed breeding values can be used in routine genetic evaluation.
现代奶牛育种目标包括多个生产性状以及越来越多的功能性状。总综合选择指数中所包含的估计育种值通常来自单性状模型或性状组的多变量模型。在大多数情况下，基于所有性状表型数据的多变量动物模型不可行，因此需采用基于选择指数理论的近似方法来推导总综合选择指数。因此，本研究的目的是基于模拟数据，比较一个完整的多性状动物模型、两个近似多性状模型和一种选择指数方法。
模拟了三个生产性状和两个功能性状，以模拟奥地利全国褐牛群体。推导总综合选择指数的参考方法是基于所有表型数据的多性状评估。其中两种近似方法是近似多性状模型的变体，分别使用产量偏差或去回归育种值。最后一种方法是奥地利和德国常规评估中使用的选择指数方法的一种变体。设置了三种关于残差协方差的情景：残差协方差等于零、等于遗传协方差的一半或等于遗传协方差。
两种近似多性状模型的结果与参考方法的结果非常接近，秩相关系数为1。两种方法几乎无偏。当残差协方差为零时，选择指数方法的秩相关系数显示出良好的结果，但当残差协方差较大时，与参考方法的相关性降低。此外，当残差协方差较高时，估计育种值存在偏差。
我们对产量偏差和去回归育种值应用了一种近似多性状两步法，得到了几乎无偏的结果。去回归育种值的结果甚至略好一些。我们的结果证实，应用选择指数方法时忽略残差协方差会导致显著偏差。这在选择准确性方面可能很重要。我们的研究结果表明，应用于去回归育种值的近似多性状方法可用于常规遗传评估。
