Alpine Merino Sheep is a novel breed reared from Australian Merino Sheep as the father and Gansu Alpine Fine-Wool Sheep as the mother, living all year in cold and arid alpine areas with exceptional wool quality and meat performance. Body weight is an important economic trait of the Alpine Merino Sheep, but there is limited research on identifying the genes associated with live weight in the 14th month for improving the accuracy of the genomic prediction of this trait. Therefore, this study’s sample comprised 1310 Alpine Merino Sheep ewes, and the Fine Wool Sheep 50K Panel was used for genome-wide association study (GWAS) analysis to identify candidate genes. Moreover, the trial population (1310 ewes) in this study was randomly divided into two groups. One group was used as the population for GWAS analysis and screened for the most significant top 5%, top 10%, top 15%, and top 20% SNPs to obtain prior marker information. The other group was used to estimate the genetic parameters based on the weight assigned by heritability combined with different prior marker information. The aim of this study was to compare the accuracy of genomic breeding value estimation when combined with prior marker information from GWAS analysis with the optimal linear unbiased prediction method for genome selection (GBLUP) for the breeding value of target traits. Finally, the accuracy was evaluated using the five-fold cross-validation method. This research provides theoretical and technical support to improve the accuracy of sheep genome selection and better guide breeding. The results demonstrated that eight candidate genes were associated with GWAS analysis, and the gene function query and literature search results suggested that FAM184B, NCAPG, MACF1, ANKRD44, DCAF16, FUK, LCORL, and SYN3 were candidate genes affecting live weight in the 14th month (WT), which regulated the growth of muscle and bone in sheep. In genome selection analysis, the heritability of GBLUP to calculate the WT was 0.335–0.374, the accuracy after five-fold cross-verification was 0.154–0.190, and after assigning different weights to the top 5%, top 10%, top 15%, and top 20% of the GWAS results in accordance with previous information to construct the G matrix, the accuracy of the WT in the GBLUP model was improved by 2.59–7.79%.

Crossbreeding significantly improves meat production performance in sheep; however, whether hybridization changes the meat quality characteristics of lambs is uncertain. We analyzed the effects of three different hybrid sires on muscle fiber characteristics (MFCs), fatty acid composition (FAC), and volatile flavor compounds (VFCs) in lambs under identical feeding conditions. Compared with those of purebred lambs, the muscle fiber diameter and cross-sectional areas of the crossbred lambs were significantly decreased (p < 0.05), and the collagen fiber content was significantly increased (p < 0.05). The numbers and area ratios of the fast and slow muscle fibers did not significantly differ between the purebred and crossbred lambs, but the expressions of four MyHC gene types differed significantly (p < 0.05). Twenty-three fatty acids were identified in both the purebred and crossbred lambs, of which thirteen were differentially expressed (p < 0.05). Saturated fatty acid (SFA) contents in the crossbred lambs were significantly increased (p < 0.05), whereas the monounsaturated fatty acid content was significantly decreased (p < 0.05). Polyunsaturated fatty acid/SFA and n-6/n-3 ratios were significantly lower in the crossbred lambs than in the purebred lambs (p < 0.05). Twenty-five VFCs were identified among the three hybrids, and aldehydes were the main VFCs. Eleven VFCs were differentially expressed in the crossbred lambs (p < 0.05). Hybrid sires affected the MFCs, FAC, and VFCs of the F₁ lambs, thus providing a reference for high-quality mutton production.

The marker density, the heritability level of trait and the statistical models adopted are critical to the accuracy of genomic prediction (GP) or selection (GS). If the potential of GP is to be fully utilized to optimize the effect of breeding and selection, in addition to incorporating the above factors into simulated data for analysis, it is essential to incorporate these factors into real data for understanding their impact on GP accuracy, more clearly and intuitively. Herein, we studied the GP of six wool traits of sheep by two different models, including Bayesian Alphabet (BayesA, BayesB, BayesC $π$⁠, and Bayesian LASSO) and genomic best linear unbiased prediction (GBLUP). We adopted fivefold cross-validation to perform the accuracy evaluation based on the genotyping data of Alpine Merino sheep (n = 821). The main aim was to study the influence and interaction of different models and marker densities on GP accuracy. The GP accuracy of the six traits was found to be between 0.28 and 0.60, as demonstrated by the cross-validation results. We showed that the accuracy of GP could be improved by increasing the marker density, which is closely related to the model adopted and the heritability level of the trait. Moreover, based on two different marker densities, it was derived that the prediction effect of GBLUP model for traits with low heritability was better; while with the increase of heritability level, the advantage of Bayesian Alphabet would be more obvious, therefore, different models of GP are appropriate in different traits. These findings indicated the significance of applying appropriate models for GP which would assist in further exploring the optimization of GP.

The Ordos fine-wool sheep is a high-quality breed in China that produces superior natural textiles and raw materials such as wool and lamb meat. However, compared to the Australian Merino sheep, there is still a gap in terms of the wool fiber fineness and wool yield. The hair follicle is the main organ that controls the type of wool fiber, and the morphological changes in the secondary hair follicle are crucial in determining wool quality. However, the process and molecular mechanisms of hair follicle morphogenesis in Ordos fine-wool sheep are not yet clear. Therefore, analyzing the molecular mechanisms underlying the process of follicle formation is of great significance for improving the fiber diameter and wool production of Ordos fine-wool sheep. The differential expressed genes, APOD, POSTN, KRT5, and KRT15, which related to primary hair follicles and secondary hair follicles, were extracted from the dermal papillae. Based on pseudo-time analysis, the differentiation trajectories of dermal lineage cells and epidermal lineage cells in the Ordos fine-wool sheep were successfully constructed, providing a theoretical basis for breeding research in Ordos fine-wool sheep.

Numerous genes govern male reproduction, modulating testicular development and spermatogenesis. Our study leveraged RNA-Seq to explore candidate genes and pivotal pathways influencing fecundity in an F1 hybrid of Southdown × Hu sheep testes across four developmental milestones: M0 (0 months old, newborn), M3 (3 months old, sexually immature), M6 (6 months old, sexually mature), and Y1 (1 years old, adult). Histological examination using hematoxylins and eosin staining revealed that the cross-sectional area of the spermatid tubules and the number of supportive cells increased in the other groups, as compared to the M0 group. The cross-sectional area of the vasculature and the number of supporting cells were found to be significantly increased in all other groups in comparison to the M0 group. We conducted GO and KEGG analyses of the differentially expressed genes (DEGs) in the three comparison groups and identified key pathways, including cAMP, MAPK, ECM–receptor interactions, PI3K-Akt, and FOXO signaling, which are closely related to testicular development and spermatogenesis. Notably, alternative splicing (AS) events were markedly elevated in M6 and Y1 stages. Key genes like GATA4, GATA6, SMAD4, SOX9, YAP1, ITGB1 and MAPK1 emerged as significantly enriched in these pathways, potentially orchestrating the transition from immature to mature testes in sheep. These findings offer valuable insights into male reproductive potential and can inform strategies for optimizing animal breeding.