The objective of this study was to develop and validate a customized cost-effective single nucleotide polymorphism (SNP) panel for genetic improvement of feed efficiency in beef cattle. The SNPs identified in previous association studies and through extensive analysis of candidate genomic regions and genes, were screened for their functional impact and allele frequency in Angus and Hereford breeds used as validation candidates for the panel. Association analyses were performed on genotypes of 159 SNPs from new samples of Angus (n = 160), Hereford (n = 329), and Angus-Hereford crossbred (n = 382) cattle using allele substitution and genotypic models in ASReml. Genomic heritabilities were estimated for feed efficiency traits using the full set of SNPs, SNPs associated with at least one of the traits (at P ≤ 0.05 and P < 0.10), as well as the Illumina bovine 50K representing a widely used commercial genotyping panel. A total of 63 SNPs within 43 genes showed association (P ≤ 0.05) with at least one trait. The minor alleles of SNPs located in the GHR and CAST genes were associated with decreasing effects on residual feed intake (RFI) and/or RFI adjusted for backfat (RFIf), whereas minor alleles of SNPs within MKI67 gene were associated with increasing effects on RFI and RFIf. Additionally, the minor allele of rs137400016 SNP within CNTFR was associated with increasing average daily gain (ADG). The SNPs genotypes within UMPS, SMARCAL, CCSER1, and LMCD1 genes showed significant over-dominance effects whereas other SNPs located in SMARCAL1, ANXA2, CACNA1G, and PHYHIPL genes showed additive effects on RFI and RFIf. Gene enrichment analysis indicated that gland development, as well as ion and cation transport are important physiological mechanisms contributing to variation in feed efficiency traits. The study revealed the effect of the Jak-STAT signaling pathway on feed efficiency through the CNTFR, OSMR, and GHR genes. Genomic heritability using the 63 significant (P ≤ 0.05) SNPs was 0.09, 0.09, 0.13, 0.05, 0.05, and 0.07 for ADG, dry matter intake, midpoint metabolic weight, RFI, RFIf, and backfat, respectively. These SNPs contributed to genetic variation in the studied traits and thus can potentially be used or tested to generate cost-effective molecular breeding values for feed efficiency in beef cattle.

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Technology that facilitates estimation of individual animal intake rates in group-housed settings will result in improvements in animal production and management efficiency. Estimating intake in pasture settings may benefit from models that use other variables as proxies. Relationships among dry matter intake (DMI), animal performance variables, and environmental variables to model DMI were investigated. 202 animals were studied in a drylot setting (153 bulls for 85 days and 55 steers for 55 days) using VYTELLE SENSETM In-Pen-Weighing and Feed-Intake nodes. A machine learning model was calibrated using: DMI, sex, age, full body weight, ADG, water intake, water visit frequency and duration. DMI was positively related to full body weight (r = 0.39, P &lt; 0.001), water intake (r=0.23, P &lt; 0.001), and ADG (r=0.18, P &lt; 0.001). In addition, DMI had significant but weak correlations with water visit frequency (r=0.031, P &lt; 0.001). DMI exhibited weak negative relationships with maximum air temperature (r=-0.094, P &lt; 0.001) maximum relative humidity (r=-0.056, P &lt; 0.001), net radiation (r=-0.040, P &lt; 0.001), and precipitation (r=-0.022, P &lt; 0.001). Weak positive relationships were observed between DMI and maximum wind speed (r=0.031, P &lt; 0.001) and direction (r=-0.022, P &lt; 0.001). The model was validated with resultant average RMSE of 1.06 kg for daily predicted DMI compared to measured daily DMI. In addition, when daily predicted DMI was averaged for each animal, the accuracy of model results improved with RMSE of 0.11 kg. Study results demonstrate that inclusion of water intake and animal performance variables improves predictive accuracy of DMI. Validating and refining the model used to predict DMI in drylots will facilitate future extrapolation to larger group field settings. Vytelle and its logo are trademarks of Vytelle, LLC.