To investigate the regulatory role of lncRNAs in milk fat metabolism in dairy goats, mammary gland tissues from six Saanen dairy goats, with consistently high (above 4.19%) and low (below 2.90%) milk fat percentages, were used as the subjects of this study. Through whole-transcriptome sequencing of mammary tissue from lactating dairy goats, we observed that linc8058 is highly expressed throughout lactation and positively regulates milk fat anabolism. Functional assays confirmed that linc8058 competitively binds with chi-miR-342-5p, modulating the expression of the target scavenger receptor class A member 5 (SCARA5). Interference with linc8058 or the use of chi-miR-342-5p mimics led to reduced SCARA5 mRNA and protein levels and inhibited the activity of the PI3K/AKT signaling pathway, resulting in decreased milk fat secretion by mammary epithelial cells in dairy goats. These findings indicate that linc8058 is a positive regulator of milk fat anabolism in dairy goats.

Genetic background can shape metabolic phenotypes and may contribute to differences in health robustness among livestock breeds. This study investigated Awassi-associated serum metabolic characteristics in dairy sheep by integrating untargeted metabolomic profiling with farm-level morbidity information. Serum samples were collected from 36 adult sheep representing six genetic groups raised under common feeding and management conditions, Awassi, East Friesian, Hu, and three Awassi–East Friesian–Hu crossbred groups, with six animals per group and an equal number of males and females. Following data preprocessing and normalization, 2284 metabolic features were retained for comparative analysis. Principal component analysis revealed marked differences in serum metabolic profiles among the genetic groups, with Awassi sheep displaying a distinct metabolic pattern relative to the purebred and crossbred populations. Across five comparisons between Awassi and the other genetic groups, 17 metabolic features were consistently more abundant in Awassi sheep. These features were mainly associated with lipid mediator metabolism, glycerophospholipid metabolism, aromatic and indole-related metabolism, and host–microbial co-metabolism. A metabolic signature derived from the Awassi–East Friesian comparison differed significantly among the six groups and was positively associated with Awassi ancestry at the individual level. However, the two crossbred groups with 50% Awassi ancestry did not show a simple intermediate metabolic pattern, suggesting that crossbred metabolic phenotypes may involve non-additive genetic effects. Farm records showed lower total morbidity in Awassi and the three crossbred populations than in the East Friesian and Hu populations. Because the morbidity data were summarized at the population level and were not individually matched to the metabolomic samples, they were interpreted as supporting production context rather than direct metabolite–disease associations. Collectively, these results reveal a distinctive Awassi-associated serum metabolic signature and provide new insights into breed- and ancestry-related metabolic variation in dairy sheep.

High-quality reference panels are important resources for genotype imputation and genomic selection in dairy goats; however, dairy-goat reference panels remain limited in sample size, population representation, and standardized workflow evaluation. In this study, 1092 dairy-goat samples from multiple populations, and public resequencing datasets, were integrated to construct and evaluate a dairy-goat reference panel for low-coverage whole-genome sequencing (lcWGS) and SNP-array data. Phasing and imputation strategies were compared using Beagle 5.4, SHAPEIT5, GLIMPSE2, and a BaseVar + Beagle pipeline, and the effects of reference-panel diversity, panel size, sequencing depth, and genotyping platform were evaluated using concordance, imputation quality score (IQS), and squared dosage correlation (r2). Beagle 5.4 phasing combined with GLIMPSE2 imputation reduced computational time by approximately 40% while maintaining high imputation accuracy. The largest evaluated panel (n = 1000) achieved concordance = 0.98, IQS = 0.94, and r2 = 0.91, while a practical population-size threshold of 600-800 individuals balanced accuracy gains and resource costs. Reference panels containing two to three genetically similar dairy-goat populations achieved stable performance, with concordance values above 0.90 and chromosome-level r2 values above 0.91. Low-coverage sequencing at 0.5× or above effectively reduced the loss of accuracy for low-frequency variants, whereas imputed SNP-array data increased marker density from 18 to 180 to 1850-18,500 SNPs per 1 Mb window in representative regions, corresponding to an approximately 103-fold increase, and produced more concentrated GWAS signal peaks on Chr5, Chr8, Chr17, and Chr19. These findings establish a technical framework and reference resource for genotype imputation, association analysis, and genomic selection in dairy goats.

Emerging evidence suggests that goat milk exerts beneficial effects on insulin levels, insulin sensitivity, and glucose homeostasis in type 2 diabetes mellitus (T2DM). However, the underlying mechanisms need further investigation. Here, the metabolic profiles of goat milk, goat milk powder, and cow milk were compared, revealing distinct compositional signatures. In a high-fat diet/streptozotocin-induced T2DM mouse model, goat milk intervention significantly improved glucose homeostasis (fasting blood glucose decreased from 13.87 mmol/L to 8.08 mmol/L, P < 0.05), alleviated hepatic injury, and remodeled serum bile acid metabolism. Further investigation revealed that goat milk intervention altered the gut microbiota composition, characterized by an increased relative abundance of Akkermansia and Lachnoclostridium. Additionally, transcriptomic analysis suggested that goat milk intervention up-regulated Bcl-2 and PI3K and down-regulated G6pc in the hepatic PI3K/AKT pathway. Collectively, these findings suggest that goat milk has the potential to alleviate T2DM and may serve as a dietary intervention for T2DM patients.

Milk composition in dairy goats, an economically important trait, is coordinately governed by complex metabolic networks and genetic factors. This study employed extreme phenotype grouping and multi-omics analysis of Xinong Saanen dairy goats to systematically elucidate the metabolic and genetic regulatory mechanisms underlying milk fat, SNF, protein, and lactose. Using widely targeted metabolomics, we identified 795 milk metabolites. Differential metabolite analysis revealed 57, 94, 50, and 58 significantly altered metabolites in milk fat, SNF, protein, and lactose, respectively. Subsequent metabolomic GWAS of these metabolites demonstrated significant genetic signals for 17 milk fat-associated metabolites, annotating 330 candidate genes (e.g., JAK2, LIPC, LRP1B). Similarly, 33 SNF-associated metabolites exhibited heritable signals linked to 177 candidate genes (including DGAT2, TGFB1, and NPAS3). For the protein-associated metabolites, 9 showed significant signals corresponding to 18 candidate genes (e.g., SRP54, CABYR, PRRX1), and 6 lactose-associated metabolites carried heritable signals that were mapped to 47 key candidate genes (such as HMGCS1, RXRA, and ADCK1). Collectively, this work identifies critical metabolites and candidate genes governing distinct milk components, deciphers the genetic-metabolic regulatory network influencing milk composition traits from a multidimensional perspective, and provides novel targets for the precise molecular breeding of high-quality goat milk.

Goat and sheep meat are highly regarded in the market for their unique flavor qualities. In this study, lipidomics technology was employed to compare the lipid differences in the longissimus dorsi muscle among 8-month-old Saanen dairy goats (DYG, n = 6), Shaanbei white cashmere goats (CAG, n = 6), and Tan sheep (TSH, n = 6). Electronic nose analysis was performed solely on the DYG samples, while the E-nose data for the CAG and TSH groups were referenced against previously published literature. The results indicated that aldehydes, ketones, methyl derivatives, and inorganic sulfides constituted the predominant stable volatile flavor compounds in the DYG muscle. Principal component analysis (PCA) and partial least square discriminant analysis (PLS-DA) revealed clear breed-dependent clustering and separation. Cluster analysis revealed that CAG and TSH groups exhibited highly similar lipid expression patterns, whereas the DYG group showed a distinct profile. Specifically, in the DYG group, SM (d18:1/20:3) and S1P (t17:0) were significantly increased, while most triglycerides (TG) were decreased and diglyceride DG (18:0/16:0) was increased. Additionally, multiple phosphatidylcholines (PC) and phosphatidylethanolamines (PE) were significantly reduced in the DYG group. This study revealed unique lipid signatures in the DYG group muscle compared with CAG and TSH groups, providing a theoretical basis for deciphering the lipid regulatory mechanisms underlying meat quality and flavor formation in goats and sheep.

Goat milk is highly nutritious and represents an ideal alternative for individuals allergic to cow's milk proteins. This study investigated changes in milk composition and metabolic profiles of Xinong Saanen dairy goats across four lactation stages: colostrum, early, peak, and midlactation. Conventional milk composition analysis combined with widely targeted metabolomics was applied to characterize stage-dependent metabolic variation. Milk fat, protein, lactose, solids-not-fat, and inorganic salt contents declined progressively with lactation, with significantly higher levels observed during the colostrum stage (P < 0.05). Metabolomic analysis identified 738 metabolites exhibiting distinct stage-specific patterns, among which d-glucose-1,6-bisphosphate, N1-acetylspermine, and Ser-Ile were consistently differentiated across lactation stages. Correlation analysis further revealed several metabolites significantly associated with milk composition traits. These findings provide insights into metabolic regulation underlying dynamic changes in goat milk composition during lactation and offer a basis for quality evaluation and stage-specific nutritional management.

N6-methyladenosine (m6A), a predominant and reversible modification of mammalian RNA, plays a critical role in regulating growth, development, and metabolism. While methyltransferase-like 14 (METTL14) is an essential component of the m6A methyltransferase complex, its specific function in regulating milk fat metabolism in dairy goats remains unexplored. This study therefore aimed to elucidate the role of METTL14 in lipid metabolism within dairy goat mammary epithelial cells (GMECs). METTL14 overexpression significantly promoted the synthesis of TAG (Triacylglycerol) and TC (Total cholesterol), as well as lipid droplet accumulation in GMECs. Furthermore, METTL14 upregulated CCAAT enhancer binding protein beta (CEBPB) expression at both the mRNA and protein levels by directly inducing m6A modification on its transcripts. Finally, we confirmed that m6A modification occurs specifically at site 1662 of CEBPB mRNA, and the "Readers" YTH N6-methyladenosine RNA binding protein F1 and F3 (YTHDF1/3) were found responsible for the m6A site recognition and interpretation. This study demonstrated that METTL14 facilitates lipid synthesis and deposition in GMECs. Mechanistically, METTL14 installs the m6A modification at site 1662 of CEBPB transcripts. This m6A mark is specifically recognized by the readers YTHDF1 and YTHDF3, which promote the translation of CEBPB mRNA, thereby upregulating its expression.

Genomic selection (GS) provides an effective approach to accelerating genetic gain in dairy goats, but the prediction performance is strongly influenced by the statistical model, marker density, phenotype adjustment strategy, and biological architecture of the target trait. In this study, dairy goat populations comprising Xinong Saanen and Saanen dairy goats from major production regions in China were used to evaluate genomic prediction for milk yield (MY), milk fat percentage (MFP), and milk protein percentage (MPP). Genotypes from 1034 dairy goats were generated using low-coverage whole-genome sequencing (lcWGS), imputed to improve genotype completeness and accuracy; a high-quality chip-based dataset was also constructed from previously developed 25K single-nucleotide polymorphism (SNP) chip loci. Conventional genomic best linear unbiased prediction (GBLUP) models, Bayesian regression models, and machine learning algorithms were compared using 10-fold cross-validation. Bayesian models showed clear trait-specific advantages, with BayesB improving MFP prediction by approximately 12.9% relative to GBLUP under the 25K chip-based strategy. Among machine learning methods, gradient boosting models performed strongly; extreme gradient boosting (XGBoost) improved the prediction accuracy for MY, MFP, and MPP by 14.3%, 17.9%, and 18.5%, respectively, relative to GBLUP under the chip-based strategy. Incorporating genome-wide association study (GWAS)-derived prior information and selection signature priors further improved the prediction accuracy, particularly for milk composition traits. Overall, the results indicate that genomic prediction in dairy goats can be optimized by matching models, genotyping platforms, and prior biological information to the genetic characteristics of the target trait.

Abstract Background The 3-hydroxybutyrate dehydrogenase 1 (BDH1) mainly participates in the regulation of milk fat synthesis and ketone body synthesis in mammary epithelial cells. In our previous study, BDH1 was identified as a key candidate gene regulating lipid metabolism in mammary glands of dairy goats by RNA-seq. This study aimed to investigate the effect of BDH1 on lipid metabolism in mammary epithelial cells of dairy goats (GMECs). Results The results suggest that BDH1 plays a significant role in reducing triacylglycerol content and lipid droplet accumulation in GMECs (p < 0.05). Overexpression of BDH1 significantly decreased the expression of lipid metabolism-related genes (SREBF1 and GPAM) and reduced the levels of C14:0 and C17:1, while increasing FABP3 expression and C10:0 concentration (p < 0.05). Interference with BDH1 significantly increased the expression of SREBF1 and GPAM and the concentration of C14:0, C15:1, and C20:1, but significantly decreased FABP3 and C18:0 (p < 0.05). Treatment of GMECs with β-hydroxybutyric acid (R-BHBA) significantly decreased the expression of FASN, ACACA, LPL, SREBF1, FABP3, ACSL1, GPAM, DGAT1, and triacylglycerol content, while significantly increasing the expression of BDH1 (p < 0.05). Interference with BDH1 rescued the reduction of cellular TAG content and the expression of FASN, LPL, SREBF1, ACSL1, and GPAM in BHBA-treated GMECs. Conclusion In conclusion, BDH1 negatively regulates lipid metabolism in mammary glands of dairy goats. Furthermore, it may mitigate the inhibitory effect of R-BHBA on lipid metabolism in GMECs. Graphical Abstract BDH1 serves as a negative regulator of milk lipid synthesis in GMECs, and BDH1 counteracts the inhibitory effect of R-BHBA on lipid synthesis in mammary epithelial cells of dairy goats.

Understanding the genetic mechanism of cold adaptation in cashmere goats and dairy goats is very important to improve their production performance. The purpose of this study was to comprehensively analyze the genetic basis of goat adaptation to cold environments, clarify the impact of environmental factors on genome diversity, and lay the foundation for breeding goat breeds to adapt to climate change. A total of 240 dairy goats were subjected to genome resequencing, and the whole genome sequencing data of 57 individuals from 6 published breeds were incorporated. By integrating multiple approaches such as phylogenetic analysis, population structure analysis, gene flow and population history exploration, selection signal analysis, and genome-environment association analysis, an in-depth investigation was carried out. Phylogenetic analysis unraveled the genetic relationships and differentiation patterns among dairy goats and other goat breeds. Through signal analysis (θπ, FST, XP-CLR), we identified numerous candidate genes associated with cold adaptation in dairy goats (STRIP1, ALX3, HTR4, NTRK2, MRPL11, PELI3, DPP3, BBS1) and cashmere goats (MED12L, MARC2, MARC1, DSG3, C6H4orf22, CHD7, MYPN, KIAA0825, MITF). Genome-environment association (GEA) analysis confirmed the link between these genes and environmental factors. Moreover, a detailed analysis of the critical genes C6H4orf22 and STRIP1 demonstrated their significant roles in the geographical variations of cold adaptation and allele frequency differences among different breeds. This study contributes to understanding the genetic basis of cold adaptation, providing crucial theoretical support for precision breeding programs aimed at improving production performance in cold regions by leveraging adaptive alleles, thereby ensuring sustainable animal husbandry.

The remodeling of mammary glands during pregnancy is essential for initiating lactation. In dairy animals, the overlap of pregnancy and mammary involution triggers a unique process, regenerative remodeling, which is critical for extending lactation duration and enhancing milk production. Unlike the complete regression of lobuloalveolar structures during involution, the regenerative remodeling preserves alveolar structures and promotes rapid mammary gland renewal. However, the cellular and molecular mechanisms underlying such process remain elusive. Here, taking dairy goats (Capra hircus) as a ruminant model, we identified four luminal cell populations through single-cell RNA-sequencing and found a significant reduction in luminal hormone-responsive (LumHR) cells and an increase in luminal secretory precursors (LumSecP) during regenerative remodeling. A reduction of LumHR cells during regenerative remodeling is essential for promoting the accumulation of LumSecP. Goat mammary organoids and in vivo genetic ablation assays suggested that LumHR cells function as a crucial switch for the differentiation of LumSecP to LumSec cells through the prolactin receptor pathway. Furthermore, high levels of IRF1 inhibited while downregulation of IRF1 stimulated the proliferation of LumHR cells. We showed that IRF1 regulated the dynamics of LumHR cells through hormonal signaling targets, including ESRRB. Our findings identified a key cell type responsible for the dynamics of luminal lineages during regenerative remodeling in large mammals and highlighted the potential for accelerating tissue regeneration through targeted modulation of lineage stage-specific regulators.

Milk products have emerged as a promising strategy for addressing metabolic syndromes. Nonetheless, the ameliorative effects of goat milk products on lipid disorders and their associated molecular mechanism remain uncertain. We constructed a high-fat murine model and detected the hepatic metabolic homeostasis in liver tissue via H&E staining, serum biochemical assay, RNA sequencing analysis, and CUT TAG-seq. Results showed that goat milk can regulate lipid accumulation in the liver by reducing fat deposition and increasing TBIL, IBIL, and CAT level. The CUT TAG-seq results revealed that reduced CTCF activity suppressed expression of the downstream genes Egr1 and Fabp5 by direct binding to their promoter regions. Furthermore, in vitro tests revealed that anti-digestion peptides, such as peptide B, peptide T, and peptide G, in goat milk were capable of binding to CTCF and activating its expression. Our results provide new insights into the role of goat milk-derived functional peptides in lipid metabolism regulation and demonstrate their therapeutic potential in alleviating metabolic syndromes.

The composition of goat milk is important for the dairy industry. However, the metabolic regulatory network underlying milk components in dairy goats has not been systematically characterized. This study aimed to define the metabolite profile of goat milk and uncover the molecular mechanisms and metabolic pathways involved in the synthesis of milk components. Epinephrine and phosphorylcholine can be used as molecular phenotype that distinguish between protein percentage. His-Leu was identified as a molecular phenotype related to lactose percentage. Bicine and cytidine were identified as molecular phenotype related to fat percentage. Piperidine and mesaconic acid can be used as molecular phenotype related to solids-not-fat percentage. Moreover, these metabolites contribute to the variation in milk composition by modulating the tricarboxylic acid cycle in the mammary gland. The key metabolites reveal the metabolic basis of milk component differentiation and provide targets for molecular breeding, precision nutrition and gene mining to improve milk quality.

Milk production is the most important economic trait of dairy goats and a key indicator for genetic improvement and breeding. However, milk yield is a complex phenotypic trait, and its genetic mechanisms are still not fully understood. This study focuses on dairy goats and non-dairy goats. By analyzing the population structure of these two groups, we found that there is a significant genetic distance between the populations of dairy goats and non-dairy goats. Using SNP and Indel analyses to identify selection signals, we identified several genes associated with milk production traits, including MPP7, PRPF6, DNAJC5, TPD52L2, HNF4G, LAMA3, FAM13A, and EPHA5. Through longitudinal GWAS of the milk production traits of 298 dairy goats, we discovered additional genes such as TRNAS-GGA-102, TTC39C, LAMA3, ANKRD29, NPC1, C24H18orf8, LOC108633789, RIOK3, TMEM241, CABLES1, LOC108633781, and RBBP8. Transcriptome sequencing of breast tissues at different lactation stages reveals dynamic LAMA3 expression changes. Three non-synonymous mutations in LAMA3 are identified, with the TT genotype at one site correlating significantly with average milk production in dairy goats. Our study discovered new genetic markers for improving dairy goat genetics and provided valuable insights into the genetic mechanisms underlying complex traits.

The shapes of lactation curves are affected by genetic and environmental factors, and flexible models are required to fit such curves. This study aimed to compare the effects of the Gaussian process regression model (Gaussian model) for fitting lactation curves of Saanen dairy goats versus the parametric Wood’s model. In addition, we investigated the effects of environmental factors on the shape of lactation curves. Principal component analysis (PCA) detected 3 (541 lactations fitted using the Wood’s model [WDS]), 5 (raw data from the WDS fitted using the Gaussian model [GWDS]), and 6 (1,032 lactation datasets fitted using the Gaussian model [GDS]) principal components (PCs). The interpretation of PC1 and PC2 in the 3 datasets was consistent, with PC1 accounting for total milk production, PC2 accounting for persistency (reflecting the difference between early and late lactation), PC3 accounting for milk yield in early lactation (WDS) and relative milk yield in mid-lactation (GWDS and GDS), and PC4 to 6 being associated with fluctuations throughout lactation. The lactation curves of 3 datasets were clustered into 2 (WDS), 2 (GWDS), and 3 (GDS) clusters based on their PC scores, and mainly differed in total milk production and persistency. The total milk production increased from the first to the third parities, but the mid-term relative milk production was highest in first-parity goats. Compared with kidding in spring, kidding in winter led to higher total milk production and persistency, and lower mid-term relative milk production. Low persistency was detected when the number of kids was ≥2. The Gaussian model is suitable for fitting daily milk yield records, with the main sources of variance in the lactation curves of Saanen goats in China being total milk yield and persistency.

Background:Goat milk is increasing valued for its superior nutritional profile, digestibility, and unique compositional properties. Protein acetylation, a pivotal post-translational modification, plays a critical role in the regulation of biosynthesis and metabolic processes. This study aims to identify key acetylated proteins and their modification sites governing milk production and the synthesis of milk components in dairy goats. Our findings establish a mechanistic foundation for elucidating molecular regulation of lactation and enhancing milk quality through targeted breeding strategies. Results:The acetylome profile of mammary gland tissues in dairy goats was successfully established. A total of 862 significantly acetylated proteins were identified across two lactation phases, and a total of 2,028 acetylation modified sites were identified in mammary gland tissues in dairy goats. Differentially acetylated proteins were predominantly localized in the cytoplasm (39.98%). From these, 54 key acetylated proteins, including MTOR, BCAT2, QARS1, GOT1, GOT2, BDH1, ACSS1, STAT5B, FABP5, and GPAM were identified as candidates potentially involved in milk protein synthesis, milk lipid synthesis, lactose synthesis, and other lactation-related biological processes in dairy goats.Among them, the acetylation modification of the β-hydroxybutyrate dehydrogenase 1 (BDH1) protein was characterized in dairy goats. The HDACs family was identified as primary regulators mediating the deacetylation of BDH1. Acetylation of BDH1 promoted the expression of LXRα, ACSL1 and SCD1genes, while its deacetylation induced the expression of SCD1, FASN and ACSL1 genes. BDH1 acetylation/deacetylation significantly reduced the expression of the SREBP1 gene. Furthermore, BDH1 acetylation promoted the formation of lipid droplets and the synthesis of triglycerides in mammary epithelial cells of dairy goats (GMECs). Conclusions:This study established, for the first time, the comprehensive acetylome of mammary gland tissue in dairy goats, revealing a substantial number of differentially acetylated proteins and modification sites. We demonstrate that acetylation of BDH1-regulated by HDACs-promotes lipid droplet biogenesis and triglyceride synthesis in GMECs through transcriptional modulation of key lipogenic genes (LXRα, ACSL1, SCD1, FASN) and suppression of SREBP1. These findings provide novel mechanistic insights into the post-translational regulation of mammary lipid metabolism during lactation.

Pasteurized goat milk is mainly spoiled through the activity of various microorganisms. Microorganisms with protein-hydrolyzing and lipid-degrading activities can act on proteins and lipids, ultimately resulting in the release of undesired metabolites. Characterizing the corresponding microbiota and metabolite profiles and clarifying their relationships will enhance our understanding of the mechanisms underlying the spoilage of pasteurized goat milk during chilled storage. In this study, 16S rRNA sequencing was employed for microbiome profiling, and ultra-performance liquid chromatography and tandem mass spectrometry was used for metabolome profiling. Weighted gene correlation network analysis and Spearman's correlation analysis were both used to investigate correlations between the microbiomes and metabolomes of pasteurized goat milk samples. The results showed significant changes in microbial diversity throughout the spoilage process. The Proteobacteria phylum and Pseudomonas genus were identified as dominant microorganisms involved in the spoilage process. Bacteria may contribute to spoilage through metabolic pathways such as glycerophospholipid metabolism and purine metabolism. In addition, significant correlations were observed between key bacteria taxa and important metabolites. Stenotrophomonas was identified as the primary spoilage bacterium, the relative abundance of which increased significantly (by 3.84%) during chilled storage, and it may serve as a biomarker for goat milk spoilage. Taking all this information into consideration, the present study proposes an integrated microbiome and metabolomics approach to investigate the spoilage mechanisms of pasteurized goat milk caused by microbial activity. These findings provide comprehensive insights into the microbial and metabolic profiles associated with spoilage during chilled storage. Our study can assist the dairy industry in understanding issues that may arise during the preservation of goat milk, thereby enhancing product quality and shelf life.

In the genetic breeding research of dairy goats, traditional genotyping methods have limitations, and existing goat chips have shortcomings in functional loci and other aspects, which cannot meet the precise genetic analysis needs of dairy goats. Genotyping by Target Sequencing (GBTS) in the new generation of sequencing technology provides the possibility to solve these problems. A large number of candidate SNP sites related to important economic traits in dairy goats were identified through various analysis and screening methods. The chip ultimately retained 27,396 SNP sites for probe design, which can detect 46,459 SNPs. The site distribution is uniform, and the sequencing data efficiency, base quality, alignment rate, and other indicators are good. The chip SNP detection rate is high and the heterozygosity of gene typing is reasonable. GWAS was performed on 200 dairy goats for litter size and birth weight traits, and multiple genome-wide significantly related SNPs and related genes (litter size trait: SCAP, PTPN23, KIF9, ANTXRL, and GRID1. birth weight trait: NALCN, LRRN2, TMEM132D, COL5A2, and HS3ST1) were detected. The 25 K multiplex SNP liquid phase capture chip designed in this study has excellent performance and is of great value for genetic research and breeding of dairy goats, providing strong support for the development of the dairy goat industry.

Follicle development in dairy goats is lower after induced estrus during the non-breeding season, reducing conception rates and challenging year-round milk supply. This study investigated follicle development during the breeding and non-breeding seasons and explored molecular mechanisms for variations in the proportions of follicles of different sizes using ovarian RNA-seq and in vitro experiments. Induced estrus during the non-breeding season used a simulated breeding season short photoperiod and male effect methods, while the male effect method was used during the breeding season. This study identified an increase in follicle size during the breeding season and performed RNA-seq on ovaries to explore the underlying causes. The RNA-seq analysis elucidated pathways associated with cellular and hormonal metabolism and identified adenylyl cyclase 5 (ADCY5) as a key differentially expressed gene. In vitro experiments demonstrated that interfering with ADCY5 in ovarian granulosa cells (GCs) reduced steroid synthesis. Conversely, the overexpression of ADCY5 increased steroid synthesis. ADCY5 affects the biological function of GCs and consequently influences follicle development through the cAMP-response element binding protein (CREB) and p38 mitogen-activated protein kinase phosphorylation (MAPK) pathways. Overall, our findings demonstrate that follicle development in dairy goats differs between the breeding and non-breeding seasons and that the differential expression levels of the ADCY5 gene contribute to this discrepancy.