Residual feed intake (RFI) is one measure of feed efficiency, which is usually obtained by multiple regression of feed intake (FI) on measures of production, body weight gain and tissue composition. If phenotypic regression is used, the resulting RFI is generally not genetically independent of production traits, whereas if RFI is computed using genetic regression coefficients, RFI and production traits are independent at the genetic level. The corresponding regression coefficients can be easily derived from the result of a multiple trait model that includes FI and production traits. However, this approach is difficult to apply in the case of multiple repeated measurements of FI and production traits. To overcome this difficulty, we used a structured antedependence approach to account for the longitudinality of the data with a phenotypic regression model or with different genetic and environmental regression coefficients [multi- structured antedependence model (SAD) regression model]. After demonstrating the properties of RFI obtained by the multi-SAD regression model, we applied the two models to FI and production traits that were recorded for 2435 French Large White pigs over a 10-week period. Heritability estimates were moderate with both models. With the multi-SAD regression model, heritability estimates were quite stable over time, ranging from 0.14 ± 0.04 to 0.16 ± 0.05, while heritability estimates showed a U-shaped profile with the phenotypic regression model (ranging from 0.19 ± 0.06 to 0.28 ± 0.06). Estimates of genetic correlations between RFI at different time points followed the same pattern for the two models but higher estimates were obtained with the phenotypic regression model. Estimates of breeding values that can be used for selection were obtained by eigen-decomposition of the genetic covariance matrix. Correlations between these estimated breeding values obtained with the two models ranged from 0.66 to 0.83. The multi-SAD model is preferred for the genetic analysis of longitudinal RFI because, compared to the phenotypic regression model, it provides RFI that are genetically independent of production traits at all time points. Furthermore, it can be applied even when production records are missing at certain time points.

中文翻译：

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纵向数据的新残留采食量标准

剩余采食量 (RFI) 是饲料效率的一种衡量标准，通常通过饲料摄入量 (FI) 对生产、体重增加和组织组成的测量进行多元回归获得。如果使用表型回归，产生的 RFI 通常不与生产性状遗传无关，而如果使用遗传回归系数计算 RFI，则 RFI 和生产性状在遗传水平上是独立的。相应的回归系数可以很容易地从包含 FI 和生产性状的多性状模型的结果中推导出来。然而，这种方法很难应用于多次重复测量 FI 和生产性状的情况。为了克服这个困难，我们使用结构化前依存方法来解释具有表型回归模型或不同遗传和环境回归系数的数据的纵向性 [多结构化前依存模型 (SAD) 回归模型]。在证明了通过多 SAD 回归模型获得的 RFI 的特性后，我们将这两个模型应用于 2435 头法国大白猪在 10 周内记录的 FI 和生产性状。两种模型的遗传力估计都是中等的。使用多 SAD 回归模型，遗传力估计随时间非常稳定，范围从 0.14 ± 0.04 到 0.16 ± 0.05，而遗传力估计显示出具有表型回归模型的 U 形曲线（范围从 0.19 ± 0.06 到 0.28 ± 0.06 ）。两个模型在不同时间点对 RFI 之间的遗传相关性的估计遵循相同的模式，但使用表型回归模型获得了更高的估计。可用于选择的育种值估计值通过遗传协方差矩阵的特征分解获得。用两个模型获得的这些估计育种值之间的相关性在 0.66 到 0.83 之间。多 SAD 模型首选用于纵向 RFI 的遗传分析，因为与表型回归模型相比，它提供的 RFI 在所有时间点都与生产性状遗传无关。此外，即使在某些时间点缺少生产记录，它也可以应用。