Multiple-trait random regression modeling of feed efficiency in US Holsteins

Authors: KHANAL, PIUSH - Michigan State University; PARKER GADDIS, KRISTEN - Council On Dairy Cattle Breeding; VANDEHAAR, MICHAEL - Michigan State University; WEIGEL, KENT - University Of Wisconsin; WHITE, HEATHER - University Of Wisconsin; PENAGARICANO, FRANCISCO - University Of Wisconsin; KOLTES, JAMES - Iowa State University; SANTOS, JOSE - University Of Florida; Baldwin, Ransom; BUCHARD, JAVIER - Council On Dairy Cattle Breeding; DURR, JOAO - Council On Dairy Cattle Breeding; TEMPLETON, ROBERT - Michigan State University

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/22/2022
Publication Date: 7/1/2022
Citation: Khanal, P., Parker Gaddis, K.L., Vandehaar, M., Weigel, K., White, H., Penagaricano, F., Koltes, J., Santos, J., Baldwin, R.L., Buchard, J., Durr, J., Templeton, R. 2022. Multiple-trait random regression modeling of feed efficiency in US Holsteins. Journal of Dairy Science. 105(7):5954-5971. https://doi.org/10.3168/jds.2021-21739.
DOI: https://doi.org/10.3168/jds.2021-21739

Interpretive Summary: We investigated the use of a statistical model that could accommodate the use of more sparsely recorded data on dry matter intakes in the future while accounting for the influence of lactation stage on the genetic parameters involving residual feed intake (RFI) and feed saved (FS). Heritabilities of these traits indeed varied across stage of lactation such that the genetic correlation between RFI at early and later stages of lactation was close to 0 in primiparous cows. Hence, future efforts should be directed to collecting data on DMI across a broad range of lactation stages.

Technical Abstract: Residual feed intake (RFI) and feed saved (FS) are important feed efficiency traits that have been increasingly considered in genetic improvement programs. Future sustainability of these genetic evaluations will depend upon greater flexibility to accommodate sparsely recorded dry matter intake (DMI) records on many more cows, especially from commercial environments. Recent multiple trait random regression modeling (MTRR) developments have facilitated days in milk (DIM)-specific inferences on RFI and FS, particularly in modeling the effect of change (dMBW) in metabolic body weight (MBW). The MTRR analyses using daily data on the core traits of DMI, MBW, and milk energy (MilkE) were conducted separately for 2,532 primiparous and 2,379 multiparous US Holstein cows from 50 to 200 DIM. Estimated MTRR variance components were used to derive genetic RFI and FS and DIM-specific genetic partial regressions of DMI on MBW, MilkE, and dMBW. Estimated daily heritabilities of RFI and FS varied across lactation for both primiparous (0.05 to 0.07; 0.11 to 0.17, respectively) and multiparous (0.03 to 0.13; 0.10 to 0.17, respectively) cows. Genetic correlations of RFI across DIM varied(>0.05) widely compared to FS (>0.54) within either parity class. Heritability estimates based on average lactation-wise measures were substantially larger than daily heritabilities, ranging from 0.17 to 0.25 for RFI and from 0.35 to 0.41 for FS. The partial genetic regression coefficients of DMI on MBW (0.11 to 0.16 kg/kg0.75 for primiparous and 0.12 to 0.14 kg/kg0.75 for multiparous cows) and of DMI on MilkE (0.45 to 0.68 kg/Mcal for primiparous and 0.36 to 0.61 kg/Mcal for multiparous cows) also varied across lactation. In spite of the computational challenges encountered with MTRR, the model potentially facilitates an efficient strategy for harnessing more data involving a wide variety of data recording scenarios for genetic evaluations on feed efficiency.