Literature DB >> 19717782

Phenotypic and genetic relationships of residual feed intake with performance and ultrasound carcass traits in Brangus heifers.

P A Lancaster1, G E Carstens, D H Crews, T H Welsh, T D A Forbes, D W Forrest, L O Tedeschi, R D Randel, F M Rouquette.   

Abstract

The objective of this study was to characterize residual feed intake (RFI) and to estimate phenotypic and genetic correlations with performance and ultrasound carcass traits in growing heifers. Four postweaning feed efficiency trials were conducted using 468 Brangus heifers. The complete Brangus pedigree file from Camp Cooley Ranch (Franklin, TX), which included 31,215 animals, was used to generate genetic parameter estimates. The heifer progeny from 223 dams were sired by 36 bulls, whereas the complete pedigree file contained 1,710 sires and 8,191 dams. Heifers were individually fed a roughage-based diet (ME = 1.98 Mcal/kg of DM) using Calan gate feeders for 70 d. Heifer BW was recorded weekly and ultrasound measures of 12th- to 13th-rib fat thickness (BF) and LM area (LMA) obtained at d 0 and 70. Residual feed intake (RFIp) was computed as actual minus predicted DMI, with predicted DMI determined by linear regression of DMI on mid-test BW(0.75) (MBW) and ADG with trial, trial x MBW, and trial x ADG as random effects. Overall means for ADG, DMI, and RFI were 1.01 (SD = 0.15), 9.51 (SD = 1.02), and 0.00 (SD = 0.71) kg/d, respectively. Stepwise regression analysis revealed that inclusion of gain in BF and final LMA into the base model increased the R(2) (0.578 vs. 0.534) and accounted for 9% of the variation in DMI not explained by MBW and ADG (RFIp). Residual feed intake and carcass-adjusted RFI (RFIc) were strongly correlated phenotypically and genetically with DMI and FCR, but not with ADG or MBW. Gain in BF was phenotypically correlated (P < 0.05) with RFIp (0.22), but not with FCR or RFIc; however, final BF was genetically correlated (P < 0.05) with RFIp (0.36) and RFIc (0.39). Gain in LMA was weakly phenotypically correlated with FCR, but not with RFIp or RFIc; however, gain in LMA was strongly genetically correlated with RFIp (0.55) and RFIc (0.77). The Spearman rank correlation between RFIp and RFIc was high (0.96). These results suggest that adjusting RFI for ultrasound carcass composition traits will facilitate selection phenotypically independent of growth, body size, and carcass composition; however, genetic relationships may still exist between RFI and carcass composition.

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Year:  2009        PMID: 19717782     DOI: 10.2527/jas.2009-2041

Source DB:  PubMed          Journal:  J Anim Sci        ISSN: 0021-8812            Impact factor:   3.159


  14 in total

Review 1.  Residual feed intake: a nutritional tool for genetic improvement.

Authors:  Leilson Rocha Bezerra; José Lindenberg Rocha Sarmento; Severino Gonzaga Neto; Ney Rômulo Oliveira de Paula; Ronaldo Lopes Oliveira; Wagner Martins Fontes do Rêgo
Journal:  Trop Anim Health Prod       Date:  2013-10-31       Impact factor: 1.559

2.  The impact of selection using residual average daily gain and marbling EPDs on growth, performance, and carcass traits in Angus steers1.

Authors:  Rachael A Detweiler; T Dean Pringle; Romdhane Rekaya; Jonathan B Wells; Jacob R Segers
Journal:  J Anim Sci       Date:  2019-05-30       Impact factor: 3.159

3.  Characterization of feeding behavior traits in steers with divergent residual feed intake consuming a high-concentrate diet.

Authors:  Ira L Parsons; Jocelyn R Johnson; William C Kayser; Luis O Tedeschi; Gordon E Carstens
Journal:  J Anim Sci       Date:  2020-07-01       Impact factor: 3.159

4.  The impact of feed efficiency selection on the ruminal, cecal, and fecal microbiomes of Angus steers from a commercial feedlot.

Authors:  Christina B Welch; Jeferson M Lourenco; Dylan B Davis; Taylor R Krause; Mia N Carmichael; Michael J Rothrock; T Dean Pringle; Todd R Callaway
Journal:  J Anim Sci       Date:  2020-07-01       Impact factor: 3.159

5.  Feed efficiency of tropically adapted cattle when fed in winter or spring in a temperate location.

Authors:  Sam W Coleman; Chad C Chase; William A Phillips; David Greg Riley
Journal:  J Anim Sci       Date:  2018-06-04       Impact factor: 3.159

6.  Analysis of residual feed intake in Nellore bulls of different ages, rib eye area, and backfat thickness.

Authors:  Matheus Henrique Vargas de Oliveira; Jessica Moraes Malheiros; Alejandra Maria Toro Ospina; Pablo Dominguez-Castaño; Lorena Ferreira Benfica; Luiz Eduardo Cruz Dos Santos Correia; Leila de Genova Gaya; Maria Eugênia Zerlotti Mercadante; André Michel de Castilhos; Joslaine Noely Dos Santos Gonçalves Cyrillo; Jéssica Biasotto Sartori; Lúcia Galvão de Albuquerque; Josineudson Augusto Ii de Vasconcelos Silva
Journal:  Trop Anim Health Prod       Date:  2022-09-14       Impact factor: 1.893

7.  Life cycle efficiency of beef production: IX. Relationship between residual feed intake of heifers and cow efficiency ratios based on harvest, carcass, and wholesale cut weight outputs.

Authors:  M E Davis; P A Lancaster; J J Rutledge; L V Cundiff
Journal:  J Anim Sci       Date:  2018-03-06       Impact factor: 3.159

8.  Liver transcriptome profiling of beef steers with divergent growth rate, feed intake, or metabolic body weight phenotypes1.

Authors:  Robert Mukiibi; Michael Vinsky; Kate Keogh; Carolyn Fitzsimmons; Paul Stothard; Sinéad M Waters; Changxi Li
Journal:  J Anim Sci       Date:  2019-11-04       Impact factor: 3.159

9.  Bivariate genome-wide association analysis of the growth and intake components of feed efficiency.

Authors:  Nick V L Serão; Dianelys González-Peña; Jonathan E Beever; Germán A Bollero; Bruce R Southey; Daniel B Faulkner; Sandra L Rodriguez-Zas
Journal:  PLoS One       Date:  2013-10-29       Impact factor: 3.240

10.  Integrating Genomics with Nutrition Models to Improve the Prediction of Cattle Performance and Carcass Composition under Feedlot Conditions.

Authors:  Luis O Tedeschi
Journal:  PLoS One       Date:  2015-11-24       Impact factor: 3.240

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