title: Genetic analysis of carcass traits in beef cattle using random regression models.
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.
authors.forename: T M
authors.initials: TM
authors.lastname: Englishby
authors.forename: G
authors.initials: G
authors.lastname: Banos
authors.forename: K L
authors.initials: KL
authors.lastname: Moore
authors.forename: M P
authors.initials: MP
authors.lastname: Coffey
authors.forename: R D
authors.initials: RD
authors.lastname: Evans
authors.forename: D P
authors.initials: DP
authors.lastname: Berry
mesh_terms: D000818:Animals; D001823:Body Composition / Q000235:genetics*; D001835:Body Weight / Q000235:genetics; D002417:Cattle / Q000235:genetics* / Q000502:physiology; D005260:Female; D005820:Genetic Testing; D006824:Hybridization, Genetic; D008297:Male; D008957:Models, Genetic; D010641:Phenotype