Increasing feed efficiency in beef cattle is critical for meeting the growing global demand for beef while managing rising feed costs and environmental impacts. Challenges in recording feed intake and combining genomic and nutritional models hinder improvements in feed efficiency for sustainable beef production. This review examines the progression from traditional data collection methods to modern genetic and nutritional approaches that enhance feed efficiency. We first discuss the technological advancements that allow precise measurement of individual feed intake and efficiency, providing valuable insights for research and industry. The role of genomic selection in identifying and breeding feed-efficient animals is then explored, emphasizing the benefits of combining data from multiple populations to enhance genomic prediction accuracy. Additionally, the paper highlights the importance of nutritional models that could be used synergistically with genomic selection. Together, these tools allow for optimized feed management in diverse production systems. Combining these approaches also provides a roadmap for reducing input costs and promoting a more sustainable beef industry.

Increasing feed efficiency in beef cattle is critical for meeting the growing global demand for beef while managing rising feed costs and environmental impacts. This review examines the progression from traditional data collection methods to modern genetic and nutritional approaches that enhance feed efficiency. We first discuss the technological advancements that allow precise measurement of individual feed intake and efficiency, providing valuable insights for research and industry. The role of genomic selection in identifying and breeding feed-efficient animals is then explored, emphasizing the benefits of integrating multi-population data to improve prediction accuracy. Additionally, the paper highlights the importance of nutritional models that could be used synergistically with genomic selection, allowing for optimized feed management in diverse production systems. Combining these approaches provides a roadmap for reducing input costs and promoting a more sustainable beef industry.

Mature cow size can be effectively controlled through genetic selection. Understanding the genetic relationships between mature cow weight (MWT), mature cow height (MHT), and body condition score (BCS) is essential for implementing effective selection programs to enhance cow efficiency. The objectives of this study were to 1) estimate variance components and genetic parameters for MWT, MHT, and BCS measured in the United States and Australia; 2) estimate the genetic correlation between these traits and early growth and carcass traits; and 3) estimate the genetic correlations among these traits across the two countries. The dataset provided by the American Angus Association comprised of 434,746; 213,875; and 382,156 records for MWT, MHT, and BCS, respectively. The dataset provided by Angus Australia comprised of 206,003; 15,379; and 36,184 records for MWT, MHT, and BCS, respectively. Single-trait repeatability models were used to estimate heritabilities and multiple-trait models were used to estimate genetic correlations between traits and across countries. Heritabilities (SE) for MWT were 0.45 (0.005) and 0.40 (0.006); for MHT 0.57 (0.006) and 0.63 (0.02); and for BCS 0.18 (0.004) and 0.18 (0.01), in the United States and Australia, respectively, highlighting their moderate-to-high genetic control. Genetic correlations within the United States and Australian datasets between MWT and MHT, and MWT and BCS were >0.50, and <0.20 between MHT and BCS. Genetic correlations between MWT, MHT and early growth traits were generally positive and moderate-to-high, ranging from 0.51(0.01) to 0.92(0.003) in the United States and 0.41(0.03) to 0.79(0.05) in Australia. Genetic correlations between BCS and early growth traits were in general positive (<0.30), while genetic correlations between mature cow traits and ultrasound traits ranged from strong positive to strong negative. Genetic correlations between the traits in the two countries were high for MWT = 0.91 (0.02) and MHT = 0.97 (0.02); and moderate for BCS = 0.65 (0.08). Our results suggest that optimizing selection for mature cow traits is feasible, and that a joint evaluation between the United States and Australia could be beneficial.

Mature cow size, often defined by mature cow weight (MWT), height (MHT) and body condition score (BCS), is crucial to cow‐calf profitability, maintenance efficiency and reproductive performance. Although MWT and MHT are often included in national cattle evaluations by many breed organisations, BCS adjustments are applied less consistently. This study investigated the impact of different modelling approaches on the estimation of breeding values for MWT, with a focus on how BCS is accounted for across models. The dataset provided by American Angus Association comprised 382,156 MWT and BCS records from 209,491 cows. Four modelling approaches were evaluated: Model 1 does not consider BCS; Model 2 treated BCS as a categorical fixed effect; Model 3 used pre‐adjusted records standardised for BCS and age; and Model 4 used a recursive model to assess MWT as a genetically independent trait from BCS. Spearman correlations between breeding values predicted across models ranged from 0.79 (between Models 1 and 4) to 0.95 (between Models 1 and 2), indicating that 5%–21% of bulls could have different rankings depending on the model used. Concordance in sire selection was assessed between the top 10% of sires in each model, and model‐pair comparison revealed differences ranging from 19% (between Models 1 and 2) to 40% (between Models 3 and 4). These differences highlight the potential for model choice to influence the selection outcomes. Model selection can significantly affect the sire rankings, highlighting the importance of carefully selecting the model that best aligns with the selection objectives and the underlying biology of the traits being evaluated. Although Model 4 offers theoretical advantages, this study does not allow for a definitive conclusion on its overall effectiveness, as no simulations were performed. Additional research is needed to confirm its advantages.

Mature cow weight (MWT), height (MHT), and body condition score (BCS) are economically important traits that significantly influence cowherd profitability by affecting maintenance feed requirements, which is the largest component of production costs. This study aimed to identify genomic regions, candidate genes, and pleiotropic variants associated with mature cow size traits in American Angus cattle, and to characterize their related gene ontology terms and metabolic pathways. The dataset provided by the American Angus Association comprised 434,746 MWT records from 222,907 animals; 213,875 MHT records from 112,987 animals; and 382,156 BCS records from 209,696 animals. Of these, 45,606 cows were genotyped and imputed to a common marker density of 54,609 markers. A final dataset of 51,410 SNPs from 45,452 animals remained after quality control for further analyses. The single-step genome-wide association study (ssGWAS) method was used in the analyses. Significant associations for mature cow size were detected on BTA7, BTA14, and BTA20, overlapping with previously reported QTLs for growth, feed efficiency, and carcass traits. Candidate genes such as FBXO32 , STC2 , DUSP1 , CREBRF , LAP3 , and PENK were found to be related to muscle development, skeletal growth, lipid metabolism, and energy regulation. The genes FBXO32 , NTAQ1 , ATAD2 , DUSP1 , ERGIC1 , RPL26L1 , ATP6V0E1 , CREBRF , BNIP1 , NKX2-5 , and STC2 emerged as candidate genes exhibiting pleiotropic effects on mature cow size traits. Pathway enrichment highlighted the roles of insulin/IGF1R signaling, fibroblast growth factor receptors cascades, and mitogen-activated protein kinase pathways in MWT and MHT, while for BCS, only RHOD GTPase cycle was enriched. Pleiotropy-based analysis of regions affecting mature cow size traits identified shared genomic loci, and subsequent pathway analysis revealed G protein signaling as a common regulatory mechanism linking energy balance, adiposity, and growth. These results contribute to a better understanding of the genomic regions associated with mature cow size traits in Angus cattle.

Mature cow weight (MWT) is a trait genetically correlated with body condition score (BCS). Previous research has shown that sire rankings can shift depending on how BCS is accounted for, indicating that different modeling strategies can influence selection outcomes. The recursive modeling approach has been established as a method for obtaining MWT that is genetically independent of BCS, providing an alternative to phenotypic pre-adjustment. The objective of this study was to determine whether different modeling approaches capture different genetic architectures or merely produce statistical artifacts. Genome-wide association studies (GWAS) and functional genomic analyses were performed to compare the genomic architecture of phenotypically pre-adjusted MWT (MWTadj) with MWT that is genetically independent of BCS, obtained using the recursive approach (MWTRM). A total of 42 significant SNP across 8 chromosomes were identified for MWTadj and 44 SNP across nine chromosomes for MWTRM, with 28 SNP shared between models. These variants corresponded to 107 annotated genes in MWTadj and 137 in MWTRM, including 62 shared genes. Major association signals were concentrated on BTA20, BTA7, and BTA14 for both models, with all significant SNP jointly explaining 3.93% of the total additive genetic variance for MWTadj and 4.29% for MWTRM. The Pearson correlation coefficient of estimated SNP effects between the models was 0.76, while the correlation of genomic estimated breeding values was 0.87. Compared to MWTadj, which shared 19 genes with unadjusted MWT, the MWTRM shared 33 genes. In addition, 28 of the 31 pathways identified for MWTRM were also identified for unadjusted MWT, whereas no Gene Ontology pathways were shared between MWTadj and unadjusted MWT. The MWTRM was associated with genes annotated to growth, skeletal development, feed efficiency, and carcass-related traits, whereas MWTadj identified a distinct set of genes and pathways. Despite these differences, MWTadj and MWTRM converged on similar core biological signals, highlighting that they effectively capture the primary genetic drivers of MWT independent of BCS.