An alternative approach to modeling genetic merit of feed efficiency in dairy cattle

Abstract

Genetic improvement of feed efficiency (FE) in dairy cattle requires greater attention given increasingly important resource constraint issues. A widely accepted yet occasionally contested measure of FE in dairy cattle is residual feed intake (RFI). The use of RFI is limiting for several reasons, including interpretation, differences in recording frequencies between the various component traits that define RFI, and potential differences in genetic versus nongenetic relationships between dry matter intake (DMI) and FE component traits. Hence, analyses focusing on DMI as the response are often preferred. We propose an alternative multiple-trait (MT) modeling strategy that exploits the Cholesky decomposition to provide a potentially more robust measure of FE. We demonstrate that our proposed FE measure is identical to RFI provided that genetic and nongenetic relationships between DMI and component traits of FE are identical. We assessed both approaches (MT and RFI) by simulation as well as by application to 26,383 weekly records from 50 to 200 d in milk on 2,470 cows from a dairy FE consortium study involving 7 institutions. Although the proposed MT model fared better than the RFI model when simulated genetic and nongenetic associations between DMI and FE component traits were substantially different from each other, no meaningful differences were found in predictive performance between the 2 models when applied to the consortium data.

Harnessing the genetics of the modern dairy cow to continue improvements in feed efficiency

“…Accurate estimates of genetic correlations with these traits, as well as with health, fertility, and longevity, would be needed. In practice, these 2 approaches are not that different, and in both cases, the idea is to select against unnecessary feed intake (Lu et al, 2015). The ideal cow for the future is shown in Table 1.…”

Genome-wide association analyses based on a multiple-trait approach for modeling feed efficiency

“…We propose extending our previously proposed MT model (Lu et al, 2015) to conduct GWA analyses on FE as well as on its key component traits: DMI, MILKE, and MBW. We reintroduce this MT model in Equation [1] for the 3 component traits arranged in order as MILKE (i = 1), MBW (i = 2), and DMI (i = 3), assuming one phenotypic record for each trait on each of n cows:…”

“…Nevertheless, it is equally important to identify potential causal variants or QTL affecting FE to potentially develop physiological management strategies (e.g., drug targets) for further improving FE (Herd and Arthur, 2009). Lu et al (2015) proposed a model that elegantly derived a measure of FE based on a simple reparameterization of a standard multiple-trait (MT) analysis of the key component traits of FE; namely, DMI, milk energy (MILKE), and metabolic BW (MBW). This…”

“…Furthermore, a MT model is potentially useful for identifying potential pleiotropic SNP markers associated with multiple traits (Banerjee et al, 2008). Thus, MT extensions to GWA based on the model of Lu et al (2015) could help further elucidate how candidate regions for FE differ, if at all, from those of its component traits.…”

“…In this study, we compared GWA analysis on FE based on using the classical RFI model and the MT model proposed by Lu et al (2015) on an international dairy consortium data set involving phenotypes on 6,937 cows, of which 4,916 cows are genotyped. Our specific objectives were to (1) infer upon the genetic architecture for FE and its components traits; (2) compare GWA inferences for FE between using the MT model and using the RFI model; and (3) demonstrate the utility of windows-based inference for GWA.…”