GenPhySE, Université de Toulouse, INRAE, ENVT, Castanet Tolosan, France.
Genet Sel Evol. 2021 Jun 25;53(1):53. doi: 10.1186/s12711-021-00641-2.
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.
残留采食量 (RFI) 是饲料效率的一种衡量标准，通常通过对饲料采食量 (FI) 与生产性能、体重增加和组织组成的度量进行多元回归来获得。如果使用表型回归，得到的 RFI 通常与生产性状在遗传上不独立，而如果使用 RFI 的遗传回归系数进行计算，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，这种方法在 FI 和生产性状多次重复测量的情况下很难应用。为了克服这一困难，我们使用了一种结构依存性方法，通过表型回归模型或不同的遗传和环境回归系数[多结构依存性模型（SAD）回归模型]来解释数据的纵向性。
在证明了多 SAD 回归模型得到的 RFI 的性质后，我们将这两个模型应用于在 10 周内记录的 2435 头法国大白猪的 FI 和生产性状。两个模型的遗传力估计值都适中。在多 SAD 回归模型中，遗传力估计值随时间相当稳定，范围在 0.14±0.04 到 0.16±0.05 之间，而在表型回归模型中，遗传力估计值呈 U 形分布（范围在 0.19±0.06 到 0.28±0.06）。两个模型的不同时间点之间 RFI 的遗传相关估计值遵循相同的模式，但表型回归模型得到的估计值更高。通过遗传协方差矩阵的特征分解获得了可用于选择的育种值估计值。这两个模型得到的这些估计育种值之间的相关性在 0.66 到 0.83 之间。
多 SAD 模型更适合 RFI 的遗传分析，因为与表型回归模型相比，它在所有时间点提供的 RFI 与生产性状在遗传上是独立的。此外，即使在某些时间点生产记录缺失的情况下，它也可以应用。
