Genomic selection (GS) is a technique that integrates genomic data, pedigree information, and individual phenotypes to enhance genetic improvements of economically important traits in livestock. While it has shown significant effects in dairy cattle, its efficacy in beef cattle is lower due to breed diversity and differences in reproductive structures. Therefore, this study evaluated the impact of heritability levels, marker densities, and assessment methods (such as pedigree-based BLUP, genomic BLUP, and weighted genomic BLUP) on genomic prediction accuracy across multiple beef cattle breeds through simulations. Three beef cattle populations were simulated with heritability levels set at 0.3, 0.5, and 0.7 and marker densities set at 50 k and 770 k. The results showed that the predictive accuracy of PBLUP and GBLUP increased with higher heritability and larger reference populations. Increasing the marker density also improved the accuracy of genomic predictions; even a low marker density (50 k SNP) can significantly enhance the accuracy of genetic evaluation, although the size of the reference population needs to be optimized according to population structure, heritability, and the genetic architecture of the trait. Overall, integrating pedigree, genomic, and weighted SNP information can significantly improve the precision of GEBV prediction and reduce bias. In particular, the wGBLUP method demonstrated an improvement in the prediction accuracy of low-heritability traits in small but high-density marker populations.

Although genomic selection can accelerate livestock breeding, its application in many countries is hindered due to the limited size of reference populations. To address this issue, researchers have explored methods of combining multiple breeds to create reference populations, aiming to enhance the accuracy of genomic prediction. The main objective of this study was to evaluate the impact of the construction of mixed reference populations at different genetic distance levels on the accuracy of multi-breed genome prediction in multi-breed beef cattle populations using three evaluation methods: GBLUP, ssGBLUP, and wGBLUP. In order to study the effect of genetic correlation on multiple populations and to resolve the optimal mixing ratio, we considered six scenarios, including (1) population A as the main body, where the nearest 10% of individuals in populations B and C were added; (2) population A was the main body, where the 15% of individuals with the closest genetic distance in groups B and C were added; and (3) population A as the main body, where the 20% of individuals in populations B and C with the closest genetic distance were added. Our results suggest that the wGBLUP model can be enhanced when the mixing ratio is 15%, and the wGBLUP model shows higher accuracy in predicting populations with different LD decay patterns. Among them, whether combined with PopB or PopC, the wGBLUP model shows better prediction ability than the GBLUP and ssGBLUP models. However, when the mixing ratio is 10% or 20%, the accuracy of the three models is less than 15%, and the wGBLUP and ssGBLUP models show high and stable accuracy. Our results highlight the importance of considering the proportion of mixing between different populations when using genetic assessment models to predict accuracy, especially for endemic beef cattle breeds with different genetic structures and LD patterns and limited resources. However, this study also has some limitations. First, the determination of the optimal mixing ratio still needs further exploration, especially for populations with different genetic structures and LD patterns. Second, future studies can introduce more advanced models to further improve prediction accuracy.

The Bactrian camel exemplifies mammalian adaptation to deserts, but the spatial organization of its innate immune system remains uncharacterized. This study integrated transcriptomes from 110 samples across 11 major tissues and organs to resolve tissue-specific gene expression and innate immune modularity. Through differential expression analysis, Tau specificity index (τ > 0.8), and machine learning validation (Random Forest F1-score = 0.86 ± 0.11), we identified 4242 high-confidence tissue-specific genes (e.g., LIPE/PLIN1 in adipose). Weighted gene co-expression network analysis (WGCNA) of 1522 innate immune genes revealed 11 co-expression modules, with six exhibiting significant tissue associations (FDR < 0.01): liver-specific (r = 0.96), spleen-adipose-enriched (r = 0.88), muscle-associated (r = 0.82), and blood-specific (r = 0.80) modules. These networks demonstrated multifunctional coordination of immune pathways—including Pattern Recognition, Cytokine Signaling, and Phagocytosis—rather than isolated functions. Our results establish that camel innate immunity is organized into spatially modular networks tailored to tissue microenvironments, providing the first systems-level framework for understanding immune resilience in desert-adapted mammals and may inform strategies for enhancing livestock resilience in arid regions.

Genomic selection often faces challenges of insufficient prediction accuracy in cross-population applications, primarily due to differences in linkage disequilibrium patterns between populations. This study proposes an Fst-based strategy to enhance prediction performance by constructing a cross-population reference set with high genetic similarity to the target population (PopA). By integrating Fst-mediated SNP screening and Euclidean genetic distance analysis, the top 10%, 15% and 20% of individuals genetically most similar to PopA were screened from PopB and PopC, respectively, leading to the generation of six reference sets characterized by different mixing proportions. The results demonstrate that incorporating the top 10–20% of the most similar individuals significantly improves the accuracy and robustness of genomic estimated breeding value predictions. Among the methods evaluated, ssGBLUP and wGBLUP performed best, with prediction accuracy increasing as the mixing proportion rose up to 20%. This approach effectively mitigates structural bias caused by inter-population genetic differences and significantly enhances prediction efficiency. The multi-level mixing experiment not only validates the practical value of Fst and Euclidean distance but also provides theoretical support and a feasible solution for the efficient integration of cross-population germplasm resources.

Elucidating the molecular mechanisms underlying beef quality differences is crucial for precision breeding of high-quality cattle. In this study, we first characterized the myofibrillar morphology of high-quality (H group) and low-quality (L group) beef samples using hematoxylin–eosin (HE) staining. Transcriptomic and metabolomic analyses were then conducted to reveal the molecular regulatory basis of quality variation. HE staining revealed highly significant differences in muscle fiber area and diameter between H and L groups (p < 0.01), along with significant differences in muscle fiber density (p < 0.05), but no significant differences in muscle fiber perimeter. Furthermore, by focusing on five core metabolic pathways shared across the transcriptome and metabolome datasets, 30 differentially expressed genes (DEGs) and 14 differentially accumulated metabolites (DAMs) were identified. Pearson correlation analysis revealed synergistic regulation between DEGs and DAMs: AMPD2 modulates umami flavor by regulating inosine accumulation via the purine metabolism pathway; ACOX3 promotes unsaturated fatty acid synthesis and intramuscular fat deposition through carbohydrate metabolism; genes in the glycolysis/gluconeogenesis pathway maintain post-slaughter muscle pH homeostasis, thereby influencing beef tenderness. Collectively, this study integrates morphological and molecular evidence to elucidate the multi-level basis of beef quality formation, providing key candidate genes, metabolites, and pathways for molecular breeding. These findings offer comprehensive theoretical and technical support for the sustainable development of the premium beef industry.