Department of Animal Science, Michigan State University, East Lansing 48824.
Axio Research, a Cytel Company, Seattle, WA 98121.
J Dairy Sci. 2020 Jun;103(6):5327-5345. doi: 10.3168/jds.2019-17781. Epub 2020 Apr 22.
A greater number of dairy economic selection indexes are incorporating a measure of feed efficiency (FE) as a key trait. Definitions of FE traits have ranged from dry matter intake (DMI) to residual feed intake (RFI), noting that RFI is effectively DMI adjusted for various energy sink traits such as body weight (BW) and milk energy (MilkE). Other definitions of FE fall between these 2 extremes such as feed saved (FS), which combines RFI and the portion of DMI required to maintain BW. The choice between different FE traits can create confusion as to how to meaningfully compare their heritabilities, estimated breeding values (EBV) and their corresponding reliabilities, and how to differentially incorporate these EBV into selection indexes. If RFI and FS are merely linear functions of DMI, BW, and MilkE with known genetic variances and covariances between these 3 traits, there may be no need to directly compute RFI or FS phenotypes to determine their heritabilities, genetic correlations, EBV, and respective reliabilities for individual animals. We demonstrate how the estimated total genetic merit is invariant to the specification of a FE trait within a selection index. That is, economic weights for a selection index involving one particular FE trait readily convert into the economic weights for a selection index involving a different linear function of that FE trait. We use these different specifications of FE to provide insight as to the effect of the degree of missingness (e.g., paucity of DMI relative to milk yield records) on the EBV accuracies of the various derivative FE traits. We particularly highlight that the generally observed higher EBV accuracies for DMI, then for FS, and lastly for RFI are partly driven by the greater genetic correlations of DMI with BW and MilkE and of FS with BW. Finally, we advocate a genetic regression approach to deriving FS and RFI recognizing that genetic versus residual relationships between FE component traits may differ substantially from each other.
越来越多的奶制品经济选择指数将饲料效率（FE）作为一个关键特征纳入其中。FE 特征的定义从干物质摄入量（DMI）到剩余饲料摄入量（RFI）不等，值得注意的是，RFI 实际上是 DMI 调整了各种能量消耗特征，如体重（BW）和牛奶能量（MilkE）。FE 的其他定义则介于这两个极端之间，例如饲料节约（FS），它结合了 RFI 和维持 BW 所需的 DMI 部分。不同 FE 特征之间的选择可能会导致混淆，因为如何有意义地比较它们的遗传率、估计育种值（EBV）及其相应的可靠性，以及如何将这些 EBV 差异纳入选择指数。如果 RFI 和 FS 仅仅是 DMI、BW 和 MilkE 的线性函数，具有这三个特征之间已知的遗传方差和协方差，那么可能没有必要直接计算 RFI 或 FS 表型来确定它们的遗传率、遗传相关、EBV 以及个体动物的相应可靠性。我们展示了如何在选择指数中，FE 特征的指定不会影响估计的总遗传优势。也就是说，涉及特定 FE 特征的选择指数的经济权重很容易转换为涉及该 FE 特征的不同线性函数的选择指数的经济权重。我们使用这些不同的 FE 规范来深入了解各种缺失程度（例如，相对于产奶量记录，DMI 稀少）对各种衍生 FE 特征的 EBV 准确性的影响。我们特别强调，DMI 的 EBV 准确性通常较高，其次是 FS，最后是 RFI，这部分是由于 DMI 与 BW 和 MilkE 以及 FS 与 BW 的遗传相关性更高所致。最后，我们提倡使用遗传回归方法来推导 FS 和 RFI，认识到 FE 组成特征之间的遗传关系与残差关系可能有很大的不同。
