Genetic parameters and single-step genomic predictions for feed efficiency, carcass, and meat quality traits in Senepol beef cattle

Improving feed efficiency, carcass, and meat quality is crucial for sustainable beef production and meeting consumer demand. This study aimed to estimate (co)variance components and genetic parameters using pedigree and genomic information and assessed the impact of genomic data on genetic prediction for feed efficiency, carcass, and meat quality traits in Senepol beef cattle. Phenotypic data were available from 4194 animals for residual feed intake (RFI), 4071 for dry matter intake (DMI), 11,411 for rib eye area (REA), 11,165 for subcutaneous fat thickness (SFT), 9563 for marbling (MAR). Additionally, 4419 animals were genotyped. Genetic evaluations were performed using the traditional pedigree-based BLUP and the single-step GBLUP (ssGBLUP) via Bayesian inference, single-trait and multi-trait animal models. Predictive ability was validated through linear regression (LR) analysis, excluding 50 % of the phenotypic data. Heritability estimates were consistent across methods, ranging from 0.094 for RFI to 0.25 for REA. A positive genetic correlation (0.56) was observed between RFI and DMI, indicating that selection for lower RFI reduces DMI. However, an unfavorable positive correlation was observed between RFI and MAR (BLUP: 0.447; ssGBLUP: 0.33), suggesting that the intense selection for RFI alone could reduce marbling in the evaluated population. Genetic correlations were null between RFI-SFT and RFI-REA. Notably, ssGBLUP generally resulted in slightly reduced genetic correlation estimates for some traits. Overrall, ssGBLUP consistently yielded higher prediction accuracy, with an average increase of 16.20 % across traits compared to BLUP. These results underscore that incorporating genomic data significantly enhances genetic evaluation in Senepol beef cattle, enabling more accurate selection decisions for improved residual feed intake and meat quality traits in breeding programs.