Ibáñez-Escriche N, López de Maturana E, Noguera J L, Varona L
Genètica i Millora Animal, IRTA-Lleida, Lleida, Spain.
J Anim Sci. 2010 Nov;88(11):3493-503. doi: 10.2527/jas.2009-2557. Epub 2010 Jul 30.
We developed and implemented change-point recursive models and compared them with a linear recursive model and a standard mixed model (SMM), in the scope of the relationship between litter size (LS) and number of stillborns (NSB) in pigs. The proposed approach allows us to estimate the point of change in multiple-segment modeling of a nonlinear relationship between phenotypes. We applied the procedure to a data set provided by a commercial Large White selection nucleus. The data file consisted of LS and NSB records of 4,462 parities. The results of the analysis clearly identified the location of the change points between different structural regression coefficients. The magnitude of these coefficients increased with LS, indicating an increasing incidence of LS on the NSB ratio. However, posterior distributions of correlations were similar across subpopulations (defined by the change points on LS), except for those between residuals. The heritability estimates of NSB did not present differences between recursive models. Nevertheless, these heritabilities were greater than those obtained for SMM (0.05) with a posterior probability of 85%. These results suggest a nonlinear relationship between LS and NSB, which supports the adequacy of a change-point recursive model for its analysis. Furthermore, the results from model comparisons support the use of recursive models. However, the adequacy of the different recursive models depended on the criteria used: the linear recursive model was preferred on account of its smallest deviance value, whereas nonlinear recursive models provided a better fit and predictive ability based on the cross-validation approach.
我们开发并实施了变点递归模型，并将其与线性递归模型和标准混合模型（SMM）进行比较，研究对象是猪的窝产仔数（LS）与死胎数（NSB）之间的关系。所提出的方法使我们能够估计表型之间非线性关系的多段建模中的变化点。我们将该程序应用于一个商业大白猪选育核心群提供的数据集。数据文件包含4462胎的LS和NSB记录。分析结果清楚地确定了不同结构回归系数之间变化点的位置。这些系数的大小随LS增加，表明LS对NSB比率的影响在增加。然而，除残差之间的相关性外，各亚群（由LS上的变化点定义）的相关性后验分布相似。NSB的遗传力估计在递归模型之间没有差异。尽管如此，这些遗传力大于SMM（0.05）的遗传力，后验概率为85%。这些结果表明LS和NSB之间存在非线性关系，这支持了使用变点递归模型进行分析的合理性。此外，模型比较的结果支持使用递归模型。然而，不同递归模型的适用性取决于所使用的标准：线性递归模型因其最小的偏差值而更受青睐，而非线性递归模型基于交叉验证方法提供了更好的拟合度和预测能力。
