Genetic analysis of carcass traits in beef cattle using random regression models1

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.

Carcass characteristics of cattle differing in Jersey proportion

“…In Ireland, carcass conformation and fat scores are graded using video image analysis (Pabiou et al, 2011); carcasses are appraised under the EU beef carcass classification system (EUROP). This 15-point conformation classification system attempts to describe the conformation of the animal mainly based on the round, back, and shoulder; on the 15-point scale (Englishby et al, 2016), a score of 1 reflects poor conformation while that of 15 reflects excellent conformation. Carcass fat score attempts to describe the fat cover on the outside of the carcass and in the thoracic cavity and is graded 1 (low fat score) to 15 (high fat cover).…”

Herd-specific random regression carcass profiles for beef cattle after adjustment for animal genetic merit

“…Different orders of Legendre polynomials were tested. Model building and the goodness of fit was determined as described by Englishby et al (2016).…”

“…3 beef carcass traits (Englishby et al, 2016), random regression models may also be used to model herd-specific trait profiles after accounting for differences in animal genetic merit (de Roos, Harbers, & de Jong, 2004). These herd-specific trait profiles provide information on how animals within a herd perform compared to their respective performance under average management conditions (Caccamo et al, 2008).…”

“…). For the purposes of the present study, the resulting EUROP classification grades were transformed into a 15-point numeric scale in line with national genetic evaluations for conformation and fat scores as described in Englishby et al (2016).…”

“…Measurements need not necessarily originate from the same animal but an underlying covariance structure, such as pedigree linkages, could connect individual animals measured over time. It is therefore possible to longitudinally model a trait for different sires exploiting information on the genetic relatedness among progeny slaughtered at different ages, thereby providing several effective measurements per sire across a time trajectory (Englishby et al, 2016;Jones, White & Brotherstone, 1999). In addition to producing genetic trait profiles for…”