Abstract
BACKGROUND: Efficiency is characterized by maximum productivity with lower inputs and minimal waste, resulting in greater output with the same or even fewer resources. In livestock, more efficient animals in converting food into protein may improve the economic efficiency of production systems, as feed costs represent a significant expense in beef production. Thus, the present study aimed to use imputed whole-genome sequencing (WGS) data to perform a genome-wide association study (GWAS) in order to identify genomic regions and potential candidate genes involved in the biological processes and metabolic pathways associated with feed efficiency-related traits (RFI: residual feed intake, DMI: dry matter intake, FE: feed efficiency, FC: feed conversion, and RWG: residual weight gain) in Nellore cattle. RESULTS: The GWAS identified significant SNPs associated with feed efficiency traits in Nellore cattle. A total of 42 SNPs were detected for RFI, 10 for DMI, 99 for FC, 15 for FE, and 3 for RWG, distributed in different autosomes. Annotation analysis identified several candidate genes, and the prioritization highlighted 21, 9, 68, 23, and 8 key genes for RFI, DMI, FC, FE, and RWG, respectively. The prioritized candidate genes are involved in muscle development, lipid metabolism, response to oxidative stress, nutrient metabolism, neurotransmission, and oxidative phosphorylation. Additionally, enrichment analysis indicated that these genes act in several signaling pathways related to signal transduction, the nervous system, the endocrine system, energy metabolism, the digestive system, and nutrient metabolism. CONCLUSION: The use of imputed WGS data in GWAS analyses enabled the broad identification of regions and candidate genes throughout the genome that regulate expression of feed efficiency-related traits in Nellore cattle. Our results provide new perspectives into the molecular mechanisms underlying feed efficiency in Nellore cattle, offering a genetic basis to guide the breeding of efficient animals, thereby optimizing resource utilization and the profitability of production systems.