Literature DB >> 26210274

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

Y Lu1, M J Vandehaar1, D M Spurlock2, K A Weigel3, L E Armentano3, C R Staples4, E E Connor5, Z Wang6, N M Bello7, R J Tempelman8.   

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.
Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Cholesky decomposition; feed efficiency; multiple trait model; residual feed intake

Mesh:

Year:  2015        PMID: 26210274     DOI: 10.3168/jds.2015-9414

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  4 in total

Review 1.  Application of Genetic, Genomic and Biological Pathways in Improvement of Swine Feed Efficiency.

Authors:  Pourya Davoudi; Duy Ngoc Do; Stefanie M Colombo; Bruce Rathgeber; Younes Miar
Journal:  Front Genet       Date:  2022-06-09       Impact factor: 4.772

2.  Comprehensive analyses of 723 transcriptomes enhance genetic and biological interpretations for complex traits in cattle.

Authors:  Lingzhao Fang; Wentao Cai; Shuli Liu; Oriol Canela-Xandri; Yahui Gao; Jicai Jiang; Konrad Rawlik; Bingjie Li; Steven G Schroeder; Benjamin D Rosen; Cong-Jun Li; Tad S Sonstegard; Leeson J Alexander; Curtis P Van Tassell; Paul M VanRaden; John B Cole; Ying Yu; Shengli Zhang; Albert Tenesa; Li Ma; George E Liu
Journal:  Genome Res       Date:  2020-05-18       Impact factor: 9.043

3.  Effects of Incorporating Dry Matter Intake and Residual Feed Intake into a Selection Index for Dairy Cattle Using Deterministic Modeling.

Authors:  Kerry Houlahan; Flavio S Schenkel; Dagnachew Hailemariam; Jan Lassen; Morten Kargo; John B Cole; Erin E Connor; Silvia Wegmann; Oliveira Junior; Filippo Miglior; Allison Fleming; Tatiane C S Chud; Christine F Baes
Journal:  Animals (Basel)       Date:  2021-04-17       Impact factor: 2.752

4.  Genome-wide copy number variant analysis reveals variants associated with 10 diverse production traits in Holstein cattle.

Authors:  Yang Zhou; Erin E Connor; George R Wiggans; Yongfang Lu; Robert J Tempelman; Steven G Schroeder; Hong Chen; George E Liu
Journal:  BMC Genomics       Date:  2018-05-02       Impact factor: 3.969

  4 in total

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