奶牛剩余采食量表型递归关系的另一种解释
An alternative Interpretation of residual feed intake by phenotypic recursive relationships in dairy cattle
摘要：
残余采食量（RFI）作为衡量奶牛净饲料效率的指标越来越受到关注。RFI表型作为包含相关因素的线性回归的残差获得（即，能量汇）以解释身体组织的移动。然而，在标准线性回归中将能汇表型拟合为回归变量受到批评，因为表型易受测量误差的影响。多性状模型已被提出，推导RFI后续偏回归。通过重新安排单性状的线性回归，我们显示了因果RFI解释的线性回归RFI。它假定递归的能量分配的影响，从能量汇干物质摄入量，但反馈或同步效应被认为是不存在的。提出了一种贝叶斯递归结构方程模型，可直接预测RFI和能汇，同时估计相关的遗传参数。描述了一种实现贝叶斯递归模型的简化马尔可夫链蒙特卡罗算法。递归模型是渐近等价于一步线性回归的RFI，但扩展的分析能力，多性状分析。它相当于贝叶斯实现的，多性状模型重新参数化的基础上Cholesky分解的表型（协）方差矩阵评估RFI，但不同的能量汇之间的关系的假设。
英文摘要：
There has been an increasing interest in residual feed intake (RFI) as a measure of net feed efficiency in dairy cattle. RFI phenotypes are obtained as residuals from linear regression encompassing relevant factors (i.e., energy sinks) to account for body tissue mobilization. However, fitting energy sink phenotypes as regression variables in standard linear regression was criticized because phenotypes are subject to measurement errors. Multiple-trait models have been proposed which derive RFI by follow-up partial regression. By re-arranging the single-trait linear regression, we showed a causal RFI interpretation underlying the linear regression for RFI. It postulates recursive effects in energy allocation from energy sinks on dry matter intake, but the feedback or simultaneous effects are assumed to be nonexistent. A Bayesian recursive structural equation model was proposed for directly predicting RFI and energy sinks and estimating relevant genetic parameters simultaneously. A simplified Markov chain Monte Carlo algorithm that implemented the Bayesian recursive model was described. The recursive model is asymptotically equivalent to one-step linear regression for RFI, yet extends the analytical capacity to multiple-trait analysis. It is equivalent to Bayesian-implemented, multiple-trait model reparameterized based on Cholesky decomposition of phenotypic (co)variance matrix for evaluating RFI, but varied in assumptions about relationships between energy sinks.
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