Evaluation of a genomic-enhanced sorting system for feeder cattle1

Area of Science:

• Animal Science
• Genetics
• Quantitative Genetics

Background:

• Molecular breeding values (MBVs) offer potential for predicting carcass traits in livestock.
• Genomic selection is increasingly utilized in beef cattle breeding for trait improvement.

Purpose of the Study:

• To evaluate the efficacy of MBVs for sorting steers into distinct carcass quality and lean meat yield (LMY) categories.
• To assess the accuracy of MBV predictions for LMY and marbling score (MBS).

Main Methods:

• Genomic best linear unbiased prediction (GBLUP) was used to calculate MBVs for LMY and MBS in a discovery set.
• Steers were computationally sorted into four MBV groups: Quality, Lean, Marbling, and Other.
• Carcass phenotypes were collected and analyzed to validate MBV group assignments and assess prediction accuracy.

Main Results:

• MBV groups generally aligned with expected carcass characteristics (e.g., Lean group had leaner carcasses).
• MBV-based sorting reduced variability within groups, enhancing carcass uniformity.
• Prediction accuracy for MBVs ranged from 0.43 to 0.59, indicating significant predictive power for carcass traits.

Conclusions:

• MBVs are a valuable tool for sorting feeder cattle into uniform carcass endpoint groups.
• The application of MBVs can improve predictability and uniformity in beef production.
• Further research is needed to evaluate the economic implications and performance of MBV-sorted cattle in feedlot operations.