Tosh J J, Wilton J W
Department of Animal and Poultry Science, University of Guelph, Ontario, Canada.
J Anim Sci. 1994 Oct;72(10):2568-77. doi: 10.2527/1994.72102568x.
Several features of data structure were studied to determine their effects on variance of prediction error and accuracy of evaluation. Assigning 50 sires with progeny to a portion of 10, 25, or 50 contemporary groups according to a sire model with and without additive genetic relationships, or assigning 50 individuals with their own record to one of 2, 5, or 10 contemporary groups according to an animal model, established the designs. Additive genetic relationships were based on stimulated pedigree files. Low, medium, and high heritabilities (.10, .25, and .40, respectively) were considered. The inverse of coefficient matrices gave variances of prediction error. Populations derived from the sire model (n = 8,100) consisted solely of progeny-tested individuals. For them, number of progeny had a quadratic (P < .001) association with variance of prediction error (R2 = 56 to 82%), which selection index theory underestimated when there were < 100 progeny. Number of direct connections (sires of contemporaries of progeny) together with progeny numbers explained variance of prediction error (R2 = 76 to 90%) better than either variable alone. With no direct connections, variance of prediction error was maximum unless a relative with at least one direct connection itself existed. Populations derived from the animal model (n = 900) consisted of animals with designs representing a progeny test, performance test, or a combination of both (34, 41, and 25% of the total, respectively). For performance-tested animals (without progeny), number of genetic connections was not highly correlated with variance of prediction error (r = -.10, across h2), but relatives prevented zero accuracies when contemporary groups consisted of one animal. Even when animals had no relatives, more than five members per contemporary group gave little additional increase in accuracy. For other than a progeny test, designs were complex, being described by many variables that were confounded.
研究了数据结构的几个特征,以确定它们对预测误差方差和评估准确性的影响。根据具有或不具有加性遗传关系的 sire 模型,将 50 头有后代的种公牛分配到 10、25 或 50 个当代组中的一部分,或者根据动物模型将 50 个有自身记录的个体分配到 2、5 或 10 个当代组中的一个,从而建立了设计方案。加性遗传关系基于模拟的系谱文件。考虑了低、中、高遗传力(分别为 0.10、0.25 和 0.40)。系数矩阵的逆给出了预测误差的方差。源自 sire 模型的群体(n = 8100)仅由经过后代测试的个体组成。对于它们,后代数量与预测误差方差呈二次关系(P < 0.001)(R2 = 56%至 82%),当后代数量少于 100 时,选择指数理论对其估计不足。直接连接数(后代同代的父亲)与后代数量一起解释预测误差方差(R2 = 76%至 90%)的效果比单独使用任何一个变量都要好。没有直接连接时,预测误差方差最大,除非存在至少有一个直接连接的亲属。源自动物模型的群体(n = 900)由具有代表后代测试、性能测试或两者结合设计的动物组成(分别占总数的 34%、41%和 25%)。对于经过性能测试的动物(没有后代),遗传连接数与预测误差方差的相关性不高(r = -0.10,跨 h2),但当当代组由一只动物组成时,亲属可防止准确性为零。即使动物没有亲属,每个当代组超过五名成员时,准确性的额外增加也很少。对于除后代测试之外的情况,设计很复杂,由许多相互混淆的变量来描述。