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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 #313648

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

Location: Animal Genomics and Improvement Laboratory

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

Author
item Lu, Y - Michigan State University
item Vandehaar, M - Michigan State University
item Spurlock, D - Iowa State University
item Weigel, K - University Of Wisconsin
item Armentano, L - University Of Wisconsin
item Staples, C - University Of Florida
item Connor, Erin
item Wang, Z - University Of Alberta
item Bello, N - Kansas State University
item Tempelman, R - Michigan State University

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/2/2015
Publication Date: 7/22/2015
Citation: Lu, Y., Vandehaar, M.J., Spurlock, D.M., Weigel, K.A., Armentano, L.E., Staples, C.R., Connor, E.E., Wang, Z., Bello, N.M., Tempelman, R.J. 2015. An alternative approach to modeling genetic merit of feed efficiency in dairy cattle. Journal of Dairy Science. 98:6535-6551.

Interpretive Summary: Although feed efficiency (FE) is of increasing interest to dairy scientists, there is considerable controversy regarding how to best characterize FE, particularly from the perspective of genetic evaluation. Two competing strategies are a strategy based on the analysis of an estimate of an animal’s metabolic efficiency called residual feed intake (RFI), and a second based on the analysis of animal dry matter intake. We demonstrate the equivalencies and differences between these two approaches, and propose an alternative measure of FE based on a multiple-trait modeling strategy based on Cholesky decomposition. By simulation, we demonstrated that our proposed approach provided greater accuracy in estimated breeding values compared to RFI analysis when simulated genetic and non-genetic associations between dry matter intake and component traits were substantially different from each other. By applying our approach to 2,470 cows from a dairy FE consortium study involving 7 institutions, we showed no significant differences in cross validation prediction accuracy or appreciable differences in estimated breeding values between RFI analysis and our multiple-trait approach. There may be advantages to our approach when some FE component traits are missing from datasets. This study will benefit dairy scientists working to make genetic improvement in FE among dairy cattle populations.

Technical 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 a number of reasons, including interpretation, differences in recording frequencies between the various component traits that define RFI and potential differences in genetic versus non-genetic relationships between the component traits. Hence, analyses focusing on dry matter intake 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 assessed both approaches by simulation as well as by application to 26,383 weekly records from 50 to 200 days 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 non-genetic associations between dry matter intake and component traits were substantially different from each other, there were no meaningful differences in predictive performance between the two models when applied to the consortium data.