Using Omics to Predict Residual Feed Intake in Beef Cattle

In beef production, feed efficiency (FE) is essential for profitability, with residual feed intake (RFI) serving as a key indicator. We first evaluated RFI in 302 bulls from three ranches over a 70-day feeding trial and performed target metabolomic analysis on plasma samples. Bulls were ranked as low (LRFI) or high (HRFI) RFI based on feed intake data. Although the model’s predictive power was limited, several individual metabolites were identified as potential RFI biomarkers.

To improve RFI prediction accuracy, we integrated metabolomics and genomics data, including plasma metabolites, hormone levels, isotope variation (¹³C and ¹⁵N), and whole-genome sequencing. GWAS identified SNPs associated with RFI on chromosomes 7 and 13. Metabolome-GWAS (mGWAS) revealed over 500,000 SNP–metabolite associations, reflecting the complex genetic and metabolic interactions influencing RFI. Ten metabolic pathways were significantly associated with RFI. However, gene enrichment analysis from GWAS results was not significant after multiple testing correction. This integrated omics approach enhances our understanding of FE and supports future efforts to refine genomic prediction strategies in cattle.

What You Will Learn:

Residual feed intake in beef cattle
Target metabolomics analysis in SIMCA^®
Real case example of phenomics, metabolomics, and genomics integration database

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Presenter

My name is Angela Gonella. I am a veterinarian from UDCA, Colombia, with a master’s in animal health from the National University of Colombia, and a Ph.D. and postdoctoral training in Animal Reproduction from the University of São Paulo. Since 2019, I have held a position as an Assistant Professor at the North Florida Research and Education Center, University of Florida. My research focuses on understanding reproductive and nutritional performance, identifying why some animals are more efficient than others, and exploring novel predictive tools using omics technologies.