Impact of Model Parameterisation and Variance Component Estimates on Genomic Predictions of Carcass Traits in Montana Composite Cattle

This study evaluated the influence of variance component (VC) estimates, obtained from different models and two relationship matrices, pedigree-based (BLUP) and genomic information-based (ssGBLUP), on genomic predictions of carcass traits in Montana composite cattle. Phenotypic records from 14,422 animals were analysed for ribeye area, rump fat thickness, backfat thickness and marbling, along with pedigree information from 193,129 animals and genomic data from 3911 animals genotyped with 49,457 SNPs. Variance components and heritability estimates were calculated using restricted maximum likelihood under single-trait linear models. Across five models (M1-M5), fixed effects included contemporary group, embryo transfer, age at ultrasound and cow age at calving, while random effects included direct genetic effect and residual. From model M2 onwards, biological type, heterosis and both combined and specific recombination effects were also considered. The Akaike information criterion (AIC) was used to identify the best-fitting model. Different VC estimates were applied in ssGBLUP predictions to evaluate predictive ability based on accuracy, bias and dispersion. Variance component and heritability estimates were similar between methods, although ssGBLUP yielded higher direct additive genetic variances and heritabilities. More parameterised models using ssGBLUP provided a better fit according to AIC. However, less parameterised models showed superior predictive ability, regardless of whether VCs were estimated by BLUP or ssGBLUP. When comparing predictive ability across sources, pedigree-based VC estimates resulted in more accurate predictions. Thus, the choice of model complexity should be guided by the analysis objective and the available data structure.