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An alternative approach to modeling genetic merit of feed efficiency in dairy cattle.
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.
Afiliação
  • Lu Y; Department of Animal Science, Michigan State University, East Lansing 48824.
  • Vandehaar MJ; Department of Animal Science, Michigan State University, East Lansing 48824.
  • Spurlock DM; Department of Animal Science, Iowa State University, Ames 50011.
  • Weigel KA; Department of Dairy Science, University of Wisconsin, Madison 53706.
  • Armentano LE; Department of Dairy Science, University of Wisconsin, Madison 53706.
  • Staples CR; Department of Animal Sciences, University of Florida, Gainesville 32611.
  • Connor EE; Animal Genomics and Improvement Laboratory, USDA Agricultural Research Service, Beltsville, MD 20705.
  • Wang Z; Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, T6G 2P5 Canada.
  • Bello NM; Department of Statistics, Kansas State University, Manhattan 66506.
  • Tempelman RJ; Department of Animal Science, Michigan State University, East Lansing 48824. Electronic address: tempelma@msu.edu.
J Dairy Sci ; 98(9): 6535-51, 2015 Sep.
Article em En | MEDLINE | ID: mdl-26210274
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Dieta / Ração Animal / Modelos Genéticos Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Revista: J Dairy Sci Ano de publicação: 2015 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Dieta / Ração Animal / Modelos Genéticos Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Revista: J Dairy Sci Ano de publicação: 2015 Tipo de documento: Article