Wu Xiao-Lin, Parker Gaddis Kristen L, Burchard Javier, Norman H Duane, Nicolazzi Ezequiel, Connor Erin E, Cole John B, Durr Joao
Council on Dairy Cattle Breeding, Bowie, MD 20716.
Department of Animal and Dairy Sciences, University of Wisconsin, Madison 53706.
JDS Commun. 2021 Sep 23;2(6):371-375. doi: 10.3168/jdsc.2021-0080. eCollection 2021 Nov.
There has been increasing interest in residual feed intake (RFI) as a measure of net feed efficiency in dairy cattle. Residual feed intake phenotypes are obtained as residuals from linear regression encompassing relevant factors (i.e., energy sinks) to account for body tissue mobilization. By rearranging 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 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 was described. The recursive model is asymptotically equivalent to one-step linear regression for RFI, yet extends the analytical capacity to multiple-trait analysis.
作为衡量奶牛净饲料效率的指标，剩余采食量（RFI）越来越受到关注。剩余采食量表型是通过包含相关因素（即能量消耗）的线性回归残差获得的，以解释身体组织的动员情况。通过重新排列单性状线性回归，我们展示了RFI线性回归背后的因果RFI解释。它假定能量消耗对干物质采食量的能量分配存在递归效应，但不存在反馈或同时效应。提出了一种贝叶斯递归结构方程模型，用于直接预测RFI和能量消耗，并同时估计相关遗传参数。描述了一种简化的马尔可夫链蒙特卡罗算法。递归模型与RFI的一步线性回归渐近等效，但将分析能力扩展到多性状分析。
