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Genomic prediction of beef quality using GWAS-prioritized markers
Gabriel A Zayas1, Raluca G Mateescu1
1Department of Animal Sciences, University of Florida, Gainesville, Florida, United States.
Translational Animal Science
|February 4, 2026
Summary
Genomic selection using GWAS-informed SNP preselection effectively predicts beef tenderness (Warner-Bratzler shear force) and marbling. Trait-specific genetic architecture dictates marker density needs for accurate genomic prediction in cattle.
Area of Science:
- Animal Genetics
- Quantitative Genetics
- Genomic Prediction
Background:
- Beef tenderness and marbling are crucial for consumer satisfaction but are measured postmortem, hindering traditional selection.
- Genomic selection offers a practical approach to improve these economically important carcass traits.
Purpose of the Study:
- To assess the efficacy of Genome-Wide Association Study (GWAS)-informed single nucleotide polymorphism (SNP) preselection for predicting Warner-Bratzler shear force (WBSF) and marbling breeding values.
- To evaluate the impact of reduced marker densities on prediction accuracy in Brangus cattle.
Main Methods:
- Utilized a structured Brangus population (N=1066) for 10-fold cross-validation.
- Compared GWAS-ranked SNP subsets against random SNP subsets and the full SNP panel.
- Performed external validation with an independent population (N=338).
Main Results:
- Small SNP panels (e.g., top 50) accurately predicted WBSF, driven by a major QTL (CAPN1).
- Marbling prediction required broader marker coverage due to its polygenic nature.
- GWAS-ranked SNPs consistently outperformed random subsets, achieving accuracies comparable to the full panel.
Conclusions:
- Trait-specific genetic architecture significantly influences the marker density required for reliable genomic prediction.
- GWAS-informed SNP prioritization is valuable for optimizing genomic prediction strategies in crossbred cattle.
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