Integrative Genomic and Metabolomic Analysis Identifies mQTLs Associated with Genetic Selection for Tenderness in...

Beef tenderness is a key quality attribute that significantly influences consumer satisfaction; however, it exhibits considerable variability due to both genetic and environmental factors. While genomic selection based on Expected Progeny Differences (EPDs) has improved the accuracy of predictions, a substantial portion of tenderness variability remains unexplained. Metabolomics has emerged as a valuable approach to address this gap, as metabolites reflect gene-environment interactions and may serve as biomarkers for complex traits such as meat tenderness.

This study aimed to integrate genomic and metabolomic data to identify genetic loci associated with serum metabolites in Nellore calves, offspring of sires with contrasting EPDs for meat tenderness.

Ninety-five male calves were evaluated and divided into two groups according to the sires' genetic merit: FA-T (favorable EPD for tenderness, n = 45) and UN-T (unfavorable EPD for tenderness, n = 46). Blood serum samples were analyzed by ¹H NMR spectroscopy to quantify 40 metabolites, and genotyping was performed using a medium-density SNP panel. Metabolite quantitative trait loci (mQTL) were identified using the MatrixEQTL package, and metabolic enrichment analysis was performed in MetaboAnalyst 6.0.

In the FA-T group, SNPs were associated with metabolites such as phenylalanine, tyrosine, and succinate, suggesting enhanced oxidative metabolism and preservation of proteolysis. In the UN-T group, associations of pyruvate, creatinine, and glutamine with distinct SNPs indicated greater reliance on anaerobic glycolysis and early ATP consumption, potentially impairing phosphorylation and postmortem proteolytic activity.

These findings suggest that genetic selection for tenderness may induce early divergent metabolic profiles, likely leading to persistent differences in postmortem biochemical pathways, with important implications for meat tenderness.