With the growing global population, the demand for beef is increasing, making the genetic improvement of beef cattle crucial for sustainable production. This study aimed to estimate genetic parameters using different models and predict body weight in Angus cattle to enhance the accuracy of genetic evaluation and support optimal breeding and selection programs. We used the inclusion or exclusion of maternal genetic effects, maternal permanent environmental effects, and the presence or absence of covariance between maternal and direct genetic effects to distinguish between the six animal models. The variance components and genetic parameters of 13,607 weight records from Angus cattle were estimated using the Average Information Restricted Maximum Likelihood (AI-REML) method. The best estimated model was selected based on the Akaike Information Criterion (AIC) and Likelihood Ratio Test (LRT). The results of this study revealed that, in addition to individual genetic effects, maternal genetic effects had a significant impact on unbiased and accurate genetic parameter estimates of body weight in Angus cattle. The total heritability estimated with the best model for body weight at birth (BW0), 3 months (BW3), 6 months (BW6), 12 months (BW12), and 18 months (BW18) was 0.215 ± 0.007, 0.340 ± 0.021, 0.239 ± 0.035, 0.362 ± 0.044, and 0.225 ± 0.048, respectively. The maternal heritability ranges from 0.017~0.438 and significantly affects Angus cattle throughout their growth and development stages, with the effect decreasing with increasing age. Positive correlations were observed between body weights at different months of age, ranging from 0.061 to 0.828. BW6 has a high positive genetic correlation with later age weight, and BW6 is a good predictor of later age weight. Thus, it is possible to optimize breeding programs and accelerate genetic progress by selecting for higher 6-month-old live weights for early Angus selection. In addition, our results emphasize the importance of considering maternal effects in genetic evaluation to improve the efficiency and accuracy of selection programs and thereby contribute to sustainable genetic improvement in beef cattle.

The genetic improvement of beef cattle breeds is crucial for the advancement of the beef cattle industry. Whole-genome resequencing technology has been widely applied in genetic breeding as well as research on selection signatures in beef cattle. In this study, 20× whole-genome resequencing was performed on 282 Angus cattle from the Ningxia region, and a high-quality dataset encompassing extensive genomic variations across the entire genome was constructed. The iHS test identified 495 selection signal regions, which included pregnancy-associated glycoprotein (PAG) family genes and immune-related genes such as UL16-binding protein 21 (ULBP21), CD1b molecule (CD1B), and tumor necrosis factor ligand superfamily member 11 (TNFSF11). A quantitative trait locus (QTL) enrichment analysis revealed that several economic traits, including longissimus muscle area, marbling score, carcass weight, average daily gain, and milk yield, were significantly enriched in cattle with these selection signatures. Although the enrichment of QTLs for health traits was low, immune-related genes may indirectly contribute to improvements in production performance. These findings show the genetic basis of economic and adaptive traits in Ningxia Angus cattle, providing a theoretical foundation and guidance for further genetic improvement and breeding strategies.

The metabolic status of periparturient dairy cows is highly heterogeneous, making it difficult for traditional assessment methods based on a single biomarker to comprehensively reflect systemic metabolic dysregulation. This study aimed to apply unsupervised clustering to identify distinct metabolic phenotypes using integrated blood physiological indicators and to evaluate the prepartum identifiability of metabolically compromised profiles. Venous blood samples were collected from 316 healthy Holstein cows (from − 7 to + 7 days relative to calving) across seven commercial farms in China for blood gas and electrolyte analysis. After data cleaning and variable selection, six key indicators—pH, PCO₂, HCO₃⁻, glucose, potassium, and sodium—were standardized and subjected to K-means clustering. The optimal number of clusters was determined to be three, yielding the following phenotypes: (1) a “metabolically compromised” group characterized by acidosis (low pH), hypercapnia (elevated PCO₂), hypoglycemia, and electrolyte imbalance; (2) a “normoacidemic–hyperkalemic” group with normal acid–base status but persistently elevated potassium; and (3) a “metabolically homeostatic” group exhibiting alkalotic tendencies, higher sodium and glucose levels, and stable electrolyte balance. A substantial proportion of cows already belonged to the metabolically compromised phenotype before calving, and its prevalence peaked during the mid-postpartum period (+ 4 to + 7 d). In unadjusted analyses, cows in this cluster exhibited modestly elevated postpartum β-hydroxybutyrate concentrations ( P = 0.001) and significantly reduced milk protein percentage ( P < 0.001); however, the BHBA association was not significant after adjusting for farm of origin in a mixed-effects model ( P = 0.622), whereas the milk protein association remained robust. These findings indicate that impaired metabolic adaptation in this cluster is more consistently reflected by reduced milk protein than by elevated BHBA. Based on cluster centroids and validated by exploratory ROC analysis, prepartum thresholds for identifying the metabolically compromised profile were proposed: venous pH < 7.25 (AUC = 0.928), PCO₂ > 58.75 mmHg (AUC = 0.956), and glucose < 27.5 mg/dL (AUC = 0.876). Potassium was not retained as a discriminatory threshold because its AUC was only 0.577. These thresholds are exploratory and require validation in independent cohorts. These findings demonstrate that periparturient dairy cows segregate into three distinct metabolic states, and that a metabolically compromised acidotic profile can be identified antepartum. This provides a foundation for early risk stratification; future intervention studies are needed to determine whether targeting this profile improves clinical outcomes.