Literature DB >> 27135995

Genetic analysis of carcass traits in beef cattle using random regression models.

T M Englishby, G Banos, K L Moore, M P Coffey, R D Evans, D P Berry.   

Abstract

Livestock mature at different rates depending, in part, on their genetic merit; therefore, the optimal age at slaughter for progeny of certain sires may differ. The objective of the present study was to examine sire-level genetic profiles for carcass weight, carcass conformation, and carcass fat in cattle of multiple beef and dairy breeds, including crossbreeds. Slaughter records from 126,214 heifers and 124,641 steers aged between 360 and 1,200 d and from 86,089 young bulls aged between 360 and 720 d were used in the analysis; animals were from 15,127 sires. Variance components for each trait across age at slaughter were generated using sire random regression models that included quadratic polynomials for fixed and random effects; heterogeneous residual variances were assumed across ages. Heritability estimates across genders ranged from 0.08 (±0.02) to 0.34 (±0.02) for carcass weight, from 0.24 (±0.02) to 0.42 (±0.01) for conformation, and from 0.16 (±0.03) to 0.40 (±0.02) for fat score. Genetic correlations within each trait across ages weakened as the interval between ages compared lengthened but were all >0.64, suggesting a similar genetic background for each trait across different ages. Eigenvalues and eigenfunctions of the additive genetic covariance matrix revealed genetic variability among animals in their growth profiles for carcass traits, although most of the genetic variability was associated with the height of the growth profile. At the same age, a positive genetic correlation (0.60 to 0.78; SE ranged from 0.01 to 0.04) existed between carcass weight and conformation, whereas negative genetic correlations existed between fatness and both conformation (-0.46 to 0.08; SE ranged from 0.02 to 0.09) and carcass weight (-0.48 to -0.16; SE ranged from 0.02 to 0.14) at the same age. The estimated genetic parameters in the present study indicate genetic variability in the growth trajectory in cattle, which can be exploited through breeding programs and used in decision support tools.

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Year:  2016        PMID: 27135995     DOI: 10.2527/jas.2015-0246

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


  15 in total

1.  Genetic structured antedependence and random regression models applied to the longitudinal feed conversion ratio in growing Large White pigs.

Authors:  V H Huynh-Tran; H Gilbert; I David
Journal:  J Anim Sci       Date:  2017-11       Impact factor: 3.159

2.  Feed efficiency and carcass metrics in growing cattle1.

Authors:  David N Kelly; Craig Murphy; Roy D Sleator; Michelle M Judge; Stephen B Conroy; Donagh P Berry
Journal:  J Anim Sci       Date:  2019-11-04       Impact factor: 3.159

3.  The achievement of a given carcass specification is under moderate genetic control in cattle.

Authors:  David Kenny; Michelle M Judge; Roy D Sleator; Craig P Murphy; Ross D Evans; Donagh P Berry
Journal:  J Anim Sci       Date:  2020-06-01       Impact factor: 3.159

4.  Dressing percentage and the differential between live weight and carcass weight in cattle are influenced by both genetic and non-genetic factors1.

Authors:  Jessica M Coyne; Ross D Evans; Donagh P Berry
Journal:  J Anim Sci       Date:  2019-04-03       Impact factor: 3.159

5.  Feed and production efficiency of young crossbred beef cattle stratified on a terminal total merit index.

Authors:  David N Kelly; Stephen B Conroy; Craig P Murphy; Roy D Sleator; Donagh P Berry
Journal:  Transl Anim Sci       Date:  2020-07-01

6.  An index framework founded on the future profit potential of female beef cattle to aid the identification of candidates for culling.

Authors:  Fíona L Dunne; Donagh P Berry; Margaret M Kelleher; Ross D Evans; Siobhan W Walsh; Peter R Amer
Journal:  J Anim Sci       Date:  2020-11-01       Impact factor: 3.159

7.  Genetic correlations between endo-parasite phenotypes and economically important traits in dairy and beef cattle.

Authors:  Alan J Twomey; Rebecca I Carroll; Michael L Doherty; Noel Byrne; David A Graham; Riona G Sayers; Astrid Blom; Donagh P Berry
Journal:  J Anim Sci       Date:  2018-03-06       Impact factor: 3.159

8.  Little genetic variability in resilience among cattle exists for a range of performance traits across herds in Ireland differing in Fasciola hepatica prevalence.

Authors:  Alan J Twomey; David A Graham; Michael L Doherty; Astrid Blom; Donagh P Berry
Journal:  J Anim Sci       Date:  2018-06-04       Impact factor: 3.159

9.  Carcass and efficiency metrics of beef cattle differ by whether the calf was born in a dairy or a beef herd.

Authors:  Alan J Twomey; Siobhán C Ring; Noirin McHugh; Donagh P Berry
Journal:  J Anim Sci       Date:  2020-11-01       Impact factor: 3.159

10.  Validation of a beef cattle maternal breeding objective based on a cross-sectional analysis of a large national cattle database.

Authors:  Alan J Twomey; Andrew R Cromie; Noirin McHugh; Donagh P Berry
Journal:  J Anim Sci       Date:  2020-11-01       Impact factor: 3.159

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