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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 #314956

Research Project: Understanding Genetic and Physiological Factors Affecting Nutrient Use Efficiency of Dairy Cattle

Location: Animal Genomics and Improvement Laboratory

Title: Hierarchical Bayesian inference on genetic and non-genetic components of partial efficiencies determining feed efficiency in dairy cattle

item Lu, Yongfang - Michigan State University
item Vandehaar, Mike - Michigan State University
item Spurlock, Diane - Iowa State University
item Weigel, Kent - University Of Wisconsin
item Armentano, Lou - University Of Wisconsin
item Staples, Charlie - University Of Florida
item Connor, Erin
item Wang, Zhiquan - University Of Alberta
item Coffey, Mike - Scottish Agricultural College
item Veerkamp, Roel - Collaborator
item Dehaas, Yvette - Collaborator
item Tempelman, Rob - Michigan State University

Submitted to: Journal of Dairy Science
Publication Type: Abstract Only
Publication Acceptance Date: 4/20/2015
Publication Date: 7/12/2015
Citation: Lu, Yongfang, Vandehaar, M.J., Spurlock, D.M., Weigel, K.A., Armentano, L.E., Staples, C.R., Connor, E.E., Wang, Z., Coffey, M., Veerkamp, R.F., Dehaas, Y., Tempelman, R.J. 2015. Hierarchical Bayesian inference on genetic and non-genetic components of partial efficiencies determining feed efficiency in dairy cattle[abstract]. Journal of Dairy Science. 98(Suppl. 2):573.

Interpretive Summary:

Technical Abstract: Dairy cattle feed efficiency (FE) can be defined as the ability to convert DMI into milk energy (MILKE) and maintenance or metabolic body weight (MBW). In other words, DMI is conditional on MILKE and MBW (DMI|MILKE,MBW). These partial regressions or partial efficiencies (PE) of DMI on MILKE and MBW can be separately partitioned into genetic or residual PE; furthermore, either PE category might be heterogeneous across various environmental or management factors. We developed a hierarchical Bayesian multivariate mixed model to infer upon such heterogeneity in PE as well as that of variance components (VC) of DMI|MILKE,MBW by modeling genetic and residual components of PE and of VC as mixed model functions of various factors, such as station (fixed), parity (fixed), days in milk (fixed), and ration (random). After validating our proposed model with a simulation study, we applied it to analysis of a dairy consortium dataset involving 5,088 Holstein cows from 13 research stations in 4 countries. Although no significant differences were detected across stations for the genetic PE of DMI/MILKE (0.38 kg/Mcal ) and of DMI/MBW (0.10 kg/kg0.75) and the residual PE of DMI/MILKE (0.33 kg/Mcal), the residual PE of DMI/MBW ranged across stations from 0.05 kg/kg0.75 to 0.18 kg/kg0.75 (P < 0.05). Substantial heterogeneity in genetic and residual VC in FE across stations, rations, and parities also was inferred. Estimated heritabilities of FE ranged from 0.16 to 0.46 across stations, whereas the overall estimated heritability of FE was 0.23. These results suggest that FE is more complex than what is currently considered in most quantitative genetic analyses.