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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #382750
Research Project: Improving Dairy Animals by Increasing Accuracy of Genomic Prediction, Evaluating New Traits, and Redefining Selection Goals

Location: Animal Genomics and Improvement Laboratory

Title: Multiple trait random regression modelling of feed efficiency in dairy cattle

Author
item KHANAL, PIUSH - Michigan State University
item PARKER GADDIS, KRISTEN - Council On Dairy Cattle Breeding
item Vanraden, Paul
item WEIGEL, KENT - University Of Wisconsin
item WHITE, H - University Of Wisconsin
item PENAGARICANO, FRANCISCO - University Of Wisconsin
item KOLTES, JAMES - Iowa State University
item SANTOS, JOSE - University Of Florida
item Baldwin, Ransom - Randy
item BURCHARD, JAVIER - Council On Dairy Cattle Breeding
item DURR, JOAO - Council On Dairy Cattle Breeding
item VANDEHAAR, MIKE - Michigan State University
item TEMPELMAN, ROBERT J - Michigan State University

Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: 4/8/2021
Publication Date: 6/28/2021
Citation: Khanal, P., Parker Gaddis, K.L., Van Raden, P.M., Weigel, K.A., White, H.M., Penagaricano, F., Koltes, J.E., Santos, J.E., Baldwin, R.L., Burchard, J.F., Durr, J.W., Vandehaar, M.J., Tempelman, R. 2021. Multiple trait random regression modelling of feed efficiency in dairy cattle [abstract]. Journal of Dairy Science. 104(Suppl. 1):120(abstr. 303).

Interpretive Summary:

Technical Abstract: Genetic improvement of feed efficiency (FE) is an increasing priority in dairy cattle breeding programs. Currently popular traits used to characterize FE include residual feed intake (RFI) and feed saved (FS) which are often defined to be based on fixed intervals (e.g., 4 or 6 weeks) of time. Furthermore, these intervals may occur throughout various portions of lactation, thereby potentially affecting genetic inferences. Random regression (RR) models allow flexibility in allowing data recording intervals of variable lengths at various stages of lactation. Furthermore, multiple trait extensions to RR models to jointly model dry matter intake (DMI), milk energy (MILKE) and metabolic body weight (MBW) facilitate estimation of genetic parameters that are specific to days in milk (DIM) not only for these three traits but also indirectly for body weight change (BWC), RFI and FS. We adapted a Bayesian multiple trait random regression approach using 17,633 weekly records from 50 to 150 DIM on 1,756 cows from across five different research herds. For computational tractability, only pedigree information, rather than genomic data, was used to specify genetic relationships. The heritability estimates of MBW, DMI, MILKE, genetic RFI and genetic FS ranged within the respective intervals of [0.59,0.72], [0.22, 0.30], [0.17, 0.38], [0.16, 0.20], and [0.25,0.31] across DIM whereas the estimated heritability of BWC never exceeded 0.0005. Across DIM, the estimated genetic regressions of DMI on MBW and MILKE ranged within [0.09, 0.14 kg/kg^0.75] and [0.21, 0.39 kg/Mcal], respectively, whereas the phenotypic regressions of DMI on MBW and MILKE ranged within [0.10, 0.13 kg/kg^0.75] and [0.30, 0.40 kg/Mcal], respectively. We resolve to continue to address the computational challenges of this model to estimate genetic parameters using genomic information on our much larger reference population of over 5,000 cows.