The Inner Mongolia cashmere goat is an excellent local breed in China. According to the characteristics of wool quilts, the Inner Mongolia cashmere goat can be divided into three types: a long-hair type (hair length of >22 cm), a short-hair type (hair length of ≤13 cm), and an intermediate type (hair length of >13 cm and ≤22 cm). It is found that hair length has a certain reference value for the indirect selection of other important economic traits of cashmere. In order to explore the molecular mechanisms and related regulatory genes of the different hair types, a weighted gene coexpression network analysis (WGCNA) was carried out on the gene expression data and phenotypic data of 12-month-old Inner Mongolia cashmere goats with a long-hair type (LHG) and a short-hair type (SHG) to explore the coexpression modules related to different coat types and nine candidate genes, and detect the relative expression of key candidate genes. The results showed that the WGCNA divided these genes into 19 coexpression modules and found that there was a strong correlation between one module and different hair types. The expression trends of this module’s genes were different in the two hair types, with high expression in the LHG and low expression in the SHG. GO functions are mainly concentrated in cellular components, including intermediate filaments (GO:0005882), intermediate filament cytoskeletons (GO:0045111), and cytoskeletal parts (GO:0044430). The KEGG pathway is mainly enriched in arginine as well as proline metabolism (chx00330) and the MAPK signaling pathway (chx04010). The candidate genes of the different hair types, including the KRT39, KRT74, LOC100861184, LOC102177231, LOC102178767, LOC102179881, LOC106503203, LOC108638293, and LOC108638298 genes, were screened. Through qRT-PCR, it was found that there were significant differences in these candidate genes between the two hair types, and most of them had a significant positive correlation with hair length. It was preliminarily inferred that these candidate genes could regulate the different hair types of cashmere goats and provide molecular markers for hair growth.

In recent years, Machine Learning (ML) has garnered increasing attention for its applications in genomic prediction. ML effectively processes high-dimensional genomic data and establishes nonlinear models. Compared to traditional Genomic Selection (GS) methods, ML algorithms enhance computational efficiency and offer higher prediction accuracy. Therefore, this study strives to achieve the optimal machine learning algorithm for genome-wide selection of cashmere traits in Inner Mongolian cashmere goats. This study compared the genomic prediction accuracy of cashmere traits using four machine learning algorithms—Random Forest (RF), Extreme Gradient Boosting Tree (XGBoost), Gradient Boosting Decision Tree (GBDT), and LightGBM—based on genotype data and cashmere trait phenotypic data from 2299 Inner Mongolian cashmere goats. The results showed that after parameter optimization, LightGBM achieved the highest selection accuracy for fiber length (56.4%), RF achieved the highest selection accuracy for cashmere production (35.2%), and GBDT achieved the highest selection accuracy for cashmere diameter (40.4%), compared with GBLUP, the accuracy improved by 0.8–2.7%. Among the three traits, XGBoost exhibited the lowest prediction accuracy, at 0.541, 0.309, and 0.387. Additionally, following parameter optimization, the prediction accuracy of the four machine learning methods for cashmere fineness, cashmere yield, and fiber length improved by an average of 2.9%, 2.7%, and 3.8%, respectively. The mean squared error (MSE) and mean absolute error (MAE) for all machine learning methods also decreased, indicating that hyperparameter tuning can enhance prediction accuracy in ML algorithms.

Background: Copy number variation (CNV) is an important class of structural variations (SVs) that contribute to phenotypic diversity and environmental adaptation in animals. However, large-scale population-level analyses of CNVs in goats remain limited. This study aimed to comprehensively characterize CNVs and explore their potential roles in economically important traits in Chinese goat populations. Methods: Whole-genome resequencing data from 151 individuals representing 17 Chinese goat breeds were analyzed. CNV regions (CNVRs) were identified across the genome, followed by gene annotation, functional enrichment analysis, and population differentiation analysis based on V_ST. Results: A total of 5636 CNVRs were identified from 151 individuals of 17 goat breeds, including 1365 duplication CNVRs, 4241 deletion CNVRs, and 30 both CNVRs. These CNVRs collectively spanned 2.38% of the goat genome. A total of 912 protein-coding genes overlapped with these CNVRs. After Bonferroni correction, GO enrichment analysis showed that these genes were significantly enriched in terms related to transmembrane transport, cell projection, ion binding, and ATP binding. Population differentiation analysis identified several CNVR-associated candidate genes with potential relevance to production or adaptive traits, including ABCC4, APOL3, EXOC3L4, ERG, B4GALT1, and FTH. Conclusions: This study provides a comprehensive CNVR map of Chinese goat populations and offers insights into the genetic basis of economically important traits, contributing to future genetic improvement and breeding strategies in goats.

Economic traits in livestock and poultry arise from the intricate interplay between genetic inheritance and environmental factors, mediated largely by epigenetic regulation. Histone modifications, particularly methylation and acetylation, serve as fundamental epigenetic mechanisms that dynamically remodel chromatin architecture and regulate gene expression in response to developmental and environmental cues. By bridging the gap between static DNA sequences and complex phenotypes, these dynamic marks offer a novel perspective for elucidating trait formation. This review examines the regulatory roles of histone modifications in shaping key economic traits, focusing on skeletal muscle development, fat deposition, and reproductive performance. Furthermore, we highlight two prospective strategies for integrating histone modification data into modern breeding programs: utilizing comprehensive epigenomic maps as novel biomarkers for precision selection, and implementing targeted nutritional regimens to program early phenotypic development. Despite substantial mechanistic advances, critical challenges persist, including high detection costs, inherent tissue specificity, and the necessity to validate transgenerational stability. Looking forward, the integration of multi-omics approaches is anticipated to propel animal breeding beyond traditional genomic selection toward an era of precise epigenomic design.

High-altitude environments, characterized by hypoxia, intense ultraviolet radiation, and low temperatures, pose major challenges to livestock survival. In recent years, researchers have gradually uncovered adaptive mechanisms in livestock across different altitudes using whole-genome resequencing. Previous studies of goat altitude adaptation have been limited by small breed numbers and low sequencing depth, hindering comprehensive exploration of adaptive mechanisms across different altitudes. This study analyzed whole-genome resequencing data from 151 individuals across 17 goat breeds representing three distinct altitude gradients (high, middle, and low). Using both SNPs and structural variations (SVs), we characterized population relationships, gene flow, and the SV landscape, including QTL–SV associations and transposable element interactions. Selective sweep analyses using F_ST, θπ ratio, XP-CLR, XP-EHH, and LFMM identified several candidate genes associated with altitude adaptation, including ABCC4, RPS6, DSG4, and LY9, which were significantly enriched in pathways related to hypoxia response, oxidative stress, energy metabolism, angiogenesis, and nervous system regulation. Notably, ABCC4 showed ABCC4 showed recurrent candidate selection signals in both SNP and SV analyses, suggesting its potential involvement in altitude adaptation. These findings provide multi-level genomic evidence for goat adaptation to high-altitude stress and provide important insights into the adaptive evolution of goats.