Leclerc H, Minéry S, Delaunay I, Druet T, Fikse W F, Ducrocq V
Interbull Centre, Department of Animal Breeding & Genetics, SLU, Box 7023, Uppsala 75007, Sweden.
J Dairy Sci. 2006 May;89(5):1792-803. doi: 10.3168/jds.S0022-0302(06)72248-2.
The increase in the number of participating countries and the lack of genetic ties between some countries has lead to statistical and computational difficulties in estimating the genetic (co)variance matrix needed for international sire evaluation of milk yield and other traits. Structural models have been proposed to reduce the number of parameters to estimate by exploiting patterns in the genetic correlation matrix. Genetic correlations between countries are described as a simple function of unspecified country characteristics that can be mapped in a space of limited dimensions. Two link functions equal to the exponential of minus the Euclidian distance between the coordinates of two countries and the exponential of minus the square of this Euclidian distance were used for the study on international simulated and field data. On simulated data, it was shown that structural models might allow an easier estimation of genetic correlations close to the border of the parameter space. This is not always possible with an unstructured model. On milk yield data, genetic correlations obtained from 22 countries for structural models based on 2 and 7 dimensions, respectively, were analyzed. Only a structural model with a large number of axes gave reasonable estimates of genetic correlations compared with correlations obtained for an unstructured model: 76.7% of correlations deviated by less than 0.030. Such a model reduces the number of parameters from 231 genetic correlations to 126 coordinates. On foot angle data, large deviations were observed between genetic correlations estimated with an unstructured model and correlations estimated with a structural model, regardless of the number of axes taken into account.
参与国家数量的增加以及一些国家之间缺乏遗传联系，导致在估计国际公牛产奶量和其他性状评估所需的遗传（协）方差矩阵时出现统计和计算困难。已提出结构模型，通过利用遗传相关矩阵中的模式来减少需要估计的参数数量。国家之间的遗传相关性被描述为未指定国家特征的简单函数，这些特征可以映射到有限维度的空间中。在对国际模拟数据和实地数据的研究中，使用了两个链接函数，分别等于两个国家坐标之间欧几里得距离的负指数以及该欧几里得距离平方的负指数。在模拟数据上，结果表明结构模型可能使接近参数空间边界的遗传相关性估计更容易。对于非结构化模型，这并不总是可行的。在产奶量数据方面，分析了分别基于2维和7维结构模型从22个国家获得的遗传相关性。与非结构化模型获得的相关性相比，只有具有大量轴的结构模型给出了合理的遗传相关性估计：76.7%的相关性偏差小于0.030。这样一个模型将参数数量从231个遗传相关性减少到126个坐标。在蹄角度数据方面，无论考虑的轴数量如何，在非结构化模型估计的遗传相关性和结构模型估计的相关性之间都观察到了较大偏差。
