Through natural and artificial selection, goats develop distinct hair phenotypes driven by genomic variations, such as structural variations (SVs). The fatty acid desaturase (FADS) family plays an important role in hair follicle (HF) growth, yet its molecular mechanisms remain unclear. In this study, we construct a goat graph-based pangenome containing 99,792 non-redundant presence-absence variations (PAVs) from 16 goat breeds. Using this pangenome, we identify 15,866 allelic variants of PAVs with distinct dominant frequencies (dPAVs) from the resequencing data of 300 goats. Among them, 1290 dPAVs regulate the expression of 772 corresponding genes in cashmere goats (CGs) with different hair types over 12 months. We identify an expanded FADS2P1 gene family with two intact copies and one truncated copy within segmental duplications. An intron deletion in the truncated FADS2P1 copy shows population-specific distribution patterns among goats with cashmere traits. All FADS2P1 copies are significantly upregulated in short-hair CGs, and their expression levels are negatively correlated with oleic acid (OA) levels. Functional validation in FADS2P1 knock-in mice indicates a slower hair growth rate and reduced HF numbers. These findings demonstrate that SV-driven FADS2P1 expression regulates HF development and growth through OA metabolism, providing insights into how PAVs influence complex phenotypes.

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 VST. 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.

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 FST, 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.

IntroductionInner Mongolia Cashmere Goats (IMCGs) are famous for its cashmere quality and it’s a unique genetic resource in China. Therefore, it is necessary to use genomic selection to improve the accuracy of selection for fleece traits in Inner Mongolia cashmere goats. The aim of this study was to determine the effect of methods (GBLUP, BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) and the reference population size on accuracy of genomic selection in IMCGs.MethodsThis study fully utilizes the pedigree and phenotype records of fleece traits in 2255 individuals, genotype of 50794 SNPs after quality control, and environmental data to perform genomic selection of fleece traits. Then GBLUP and Bayes series methods (BayesA, BayesB, Bayesian LASSO, Bayesian Ridge Region) were used to perform estimates of genetic parameter and genomic breeding value. And the accuracy of genomic estimated breeding value (GEBV) is evaluated using the five-fold cross validation method. And the analysis of variance and multiple comparison methods were used to determine the best method for genomic selection in fleece traits of IMCGs. Further the different reference population sizes (500, 1000, 1500, and 2000) was set. Then the best method was applied to estimate genome breeding values, and evaluate the impact of reference population sizes on the accuracy of genome selection for fleece traits in IMCGs.ResultsIt was found that the genomic prediction accuracy for each fleece trait in IMCGs by GBLUP method is highest, and it is significantly higher than that obtained by Bayesian method. The accuracy of breeding value estimation is 58.52% -68.49%. Also, it was found that the size of the reference population has a significant impact on the accuracy of genome prediction of fleece traits. When the reference population size is 2000, the accuracy of genomic prediction for each fleece trait is significantly higher than other levels, with accuracy of 55.47% -67.87%. This provides a theoretical basis for design a reasonable genome selection plan for Inner Mongolia cashmere goats in the later stag.

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.

Sperm membrane stability is a key factor in determining sperm viability and fertilization capability, with broad implications ranging from basic reproductive biology to livestock breeding practices. This comprehensive review examines the structural and functional mechanisms underlying sperm membrane integrity, including defensive barrier functions, potentiometric ion channel regulation, and motility modulation that collectively optimize sperm survival, motility, and fertilization potential. Environmental factors such as temperature fluctuations, abnormal pH levels (outside the optimal 7.2–8.2 range), pathological conditions, and hormonal imbalances can compromise membrane stability by inducing oxidative stress and protein denaturation. Key regulatory proteins, notably NPC2 for cholesterol homeostasis, Flotillin proteins for lipid raft organization, and Annexin V for membrane repair mechanisms, demonstrate essential roles in maintaining structural integrity. In livestock reproduction, membrane stability research facilitates the optimization of cryoprotectant formulations and freezing protocols, resulting in 15–25% improvements in post-thaw sperm survival rates and enhanced artificial insemination success. These findings provide valuable insights for advancing assisted reproductive technologies and improving reproductive efficiency in animal husbandry.

The goat, an early domesticated ruminant, is a reliable source of cashmere, meat and milk in global agricultural production. Despite this, the genome of cashmere-rich goats has yet to be characterized. Here, we assembled the nearly complete genome of a cashmere goat from a highly economically valuable Inner Mongolian Cashmere buck, utilizing a combination of PacBio HiFi, ONT ultra-long reads, and Hi-C technologies. The size of this genome is 2.76 Gb, with a contig N50 of 95.22 Mb. All assembled sequences were anchored onto 29 autosomes and both sex chromosomes, with only two gaps present on the X chromosome. We identified 1,333.29 Mb (48.26%) of repetitive sequences and predicted 22,480 protein-coding genes. Assembly quality assessment of the genome demonstrated that our assembled cashmere goat genome surpasses the continuity, completeness, and accuracy of other published goat genomes. Taken together, we provided the first cashmere goat assembly, bridging the gap in the genome of important economic breeds of domestic goats, and providing a valuable reference resource for goat genetics and genome research.

Northwest Xizang White Cashmere Goat (NXWCG) is the first new breed of cashmere goat in the Xizang Autonomous Region. It has significant characteristics of extremely high fineness, gloss, and softness. Genome-wide association analysis is an effective biological method used to measure the consistency and correlation of genotype changes between two molecular markers in the genome. In addition, it can screen out the key genes affecting the complex traits of biological individuals. The aim of this study was to analyze the genetic mechanism of cashmere trait variation in NXWCG and to discover SNP locus and key genes closely related to traits such as superfine cashmere. Additionally, the key genes near the obtained significant SNPs were analyzed by gene function annotation and biological function mining. In this study, the phenotype data of the four traits (cashmere length, fiber length, cashmere diameter, and cashmere production) were collected. GGP_Goat_70K SNP chip was used for genotyping the ear tissue DNA of the experimental group. Subsequently, the association of phenotype data and genotype data was performed using Gemma-0.98.1 software. A linear mixed model was used for the association study. The results showed that four fleece traits were associated with 18 significant SNPs at the genome level and 232 SNPs at the chromosome level, through gene annotated from Capra hircus genome using assembly ARS1. A total of 107 candidate genes related to fleece traits were obtained. Combined with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, we can find that CLNS1A, CCSER1, RPS6KC1, PRLR, KCNRG, KCNK9, and CLYBL can be used as important candidate genes for fleece traits of NXWCG. We used Sanger sequencing and suitability chi-square test to further verify the significant loci and candidate genes screened by GWAS, and the results show that the base mutations loci on the five candidate genes, CCSER1 (snp12579, 34,449,796, A → G), RPS6KC1 (snp41503, 69,173,527, A → G), KCNRG (snp41082, 67,134,820, G → A), KCNK9 (14:78472665, 78,472,665, G → A), and CLYBL (12: 9705753, 9,705,753, C → T), significantly affect the fleece traits of NXWCG. The results provide a valuable basis for future research and contribute to a better understanding of the genetic structure variation of the goat.

为了解加权基因共表达网络分析(Weighted gene co-expression network analysis,WGCNA)方法在畜禽中的应用研究,本文对近年来国内外通过WGCNA方法对牛、羊、猪、禽等畜禽进行研究的相关文献进行了整理与分析,总结了 WGCNA方法应用的一般分析流程、策略及畜禽WGCNA的研究现状.结果表明:WGCNA是一种系统生物学的方法,可分析转录组、蛋白组、代谢组、DNA甲基化和单细胞转录组等高通量数据,解析分子间调控的表达模式,确定关键调控因子;在畜禽的研究中,WGCNA方法已经广泛应用在生长性状、繁殖性状、抗病性状和品质性状等复杂性状的优势功能因子的挖掘.综上,WGCNA方法对分析组学数据具有较强的优势,可为畜禽重要经济性状的挖掘,阐明性状生物学机制提供助力.

Genomic selection in plants and animals has become a standard tool for breeding because of the advantages of high accuracy and short generation intervals. Implementation of this technology is hindered by the high cost of genotyping and other factors. The aim of this study was to determine an optional marker density panel and reference population size for using genomic selection of goats, with speculation on the number of QTLs that affect the important economic traits of goats. In addition, the effect of buck population size in the reference population on the accuracy of genomic estimated breeding value (GEBV) was discussed. Based on the previous genetic evaluation results of Inner Mongolia White Cashmere Goats, live body weight (LBW, h2 = 0.11) and fiber diameter (FD, h2 = 0.34) were chosen to perform genomic selection in this study. Reasonable genome parameters and generation transmission processes were set, and phenotypic and genotype data of the two traits were simulated. Then, different sizes of the reference population and validation population were selected from progeny. The GEBVs were obtained by six methods, including GBLUP (Genomic Best Linear Unbiased Prediction), ssGBLUP (Single Step Genomic Best Linear Unbiased Prediction), BayesA, BayesB, Bayesian ridge regression, and Bayesian LASSO. The correlation coefficient between the predicted and realized phenotypes from simulation was calculated and used as a measure of the accuracy of GEBV in each trait. The results showed that the medium marker density Panel (45 K) could be used for genomic selection in goats, which can ensure the accuracy of the GEBV. The reference population size of 1,500 can achieve greater genetic progress in genomic selection for fiber diameter and live body weight in goats by comparing with the population size below this level. The accuracy of the GEBV for live body weight and fiber diameter was better when the number of QTLs was 100 and 50, respectively. Additionally, the accuracy of GEBV was discovered to be good when the buck population size was up to 200. Meanwhile, the accuracy of the GEBV for medium heritability traits (FDs) was found to be higher than the accuracy of the GEBV for low heritability traits (LBWs). These findings will provide theoretical guidance for genomic selection in goats by using real data.

措勤紫绒山羊是中国优良的绒用地方山羊品种,从线粒体水平分析其与其他中国地方山羊品种之间的系统发育关系,对措勤紫绒山羊的种群分类和遗传资源保护具有重要意义.本研究利用全基因组随机测序技术和生物信息学软件对措勤紫绒山羊线粒体基因组进行获取、拼接与注释.结果发现措勤紫绒山羊线粒体基因组全长16 641 bp,主要由22个tRNA基因、2个rRNA基因、13个蛋白编码基因和1个非编码控制区(D-loop区)4部分构成;基于线粒体基因组D-loop区序列构建8个地方山羊品种的系统进化树,发现措勤紫绒山羊与柴达木绒山羊的遗传距离最近,推测是由地理位置相近所导致的.本研究为措勤紫绒山羊的品种鉴定和系统发育关系提供了一定的数据支撑.

Cashmere goat from Inner Mongolia is an excellent local breed in China, and the related cashmere product is a kind of precious textile raw material with high price. Cashmere is generated from secondary hair follicles, which has obvious annual periodicity and includes three different stages: anagen, catagen, and telogen. Therefore, we investigated skin transcriptome data for 12 months using weighted gene co-expression network analysis (WGCNA) to explore essential modules, pathways, and genes responsible for the periodic growth and development of secondary hair follicles. A total of 17 co-expression modules were discovered by WGCNA, and there is a strong correlation between steelblue module and month (0.65, p = 3E-09), anagen (0.52, p = 1E-05), telogen (-0.6, p = 8E-08). Gene expression was generally high during late anagen to catagen (June to December), while expression was downregulated from telogen to early anagen (January-May), which is similar to the growth rule of hair follicle cycle. KEGG pathway enrichment analyses of the genes of steelblue module indicated that genes are mainly enriched in Cell cycle, Wnt signaling pathway, p53 signaling pathway and other important signal pathways. These genes were also significantly enriched in GO functional annotation of the cell cycle, microtubule movement, microtubule binding, tubulin binding, and so on. Ten genes (WIF1, WNT11, BAMBI, FZD10, NKD1, LEF1, CCND3, E2F3, CDC6, and CDC25A) were selected from these modules, and further identified as candidate biomarkers to regulate periodic development of hair follicles using qRT-PCR. The Wnt signaling pathway and Cell cycle play an important role in the periodic development of hair follicles. Ten genes were identified as essential functional molecules related to periodic development of hair follicle. These findings laid a foundation for understanding molecular mechanisms in biological functions such as hair follicle development and hair growth in cashmere goats.

Abstract There is genetic diversity of hair types in the Inner Mongolia cashmere goat population. Previous studies have found that fibroblast growth factor 21 (FGF21) and PI3K-AKT signal pathways may be related to different hair types in Inner Mongolia cashmere goats. Therefore, the purpose of this study was to explore the effects of the PI3K-AKT signal pathway on different hair types, the expression of mRNA and protein expression sites of FGF21 in the hair follicles of cashmere goats with different hair types, so as to lay a foundation for understanding the molecular mechanism of different hair types and the role of skin hair follicle development. In this experiment, the skin tissues of long hair type (LHG) and short hair type (SHG) of Inner Mongolia cashmere goat were collected in three key periods of secondary hair follicle growth, namely, anagen (September), catagen (December), and telogen (March). The relative expression of FGF21 and PI3K-AKT signal pathway candidate gene mRNA in different periods and different hair types was detected by real-time fluorescence quantitative technique (qRT-PCR), and the expression site of FGF21 protein was located by immunohistochemical technique. Through qRT-PCR, it was found that the relative expression of FGF21, FGFR1, AKT3, BRCA1, PKN3, SPP1, and GNG4 was significantly different between LHG and SHG. The expression of FGF21 in the skin of LHG was significantly higher than that of SHG in the three periods. Through immunohistochemical test, it was found that FGF21 protein was mainly expressed in primary hair follicle connective tissue sheath, primary hair follicle outer root sheath, secondary hair follicle outer root sheath, and sebaceous glands. It was also found that the expression of LHG skin tissue in the outer root sheath of primary hair follicles was higher than that of SHG in three periods. In summary, it is suggested that the PI3K-AKT signal pathway may play an important role in the formation of different hair types in Inner Mongolia cashmere goats.

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.

Objective: Body weight is an important economic trait for a goat, which greatly affects animal growth and survival. The purpose of this study was to identify genes associated with birth weight (BW), weaning weight (WW), and yearling weight (YW). Materials and Methods: In this study, a genome-wide association study (GWAS) of BW, WW, and YW was determined using the GGP_Goat_70K single-nucleotide polymorphism (SNP) chip in 1,920 Inner Mongolia cashmere goats. Results: We discovered that 21 SNPs were significantly associated with BW on the genome-wide levels. These SNPs were located in 10 genes, e.g., Mitogen-Activated Protein Kinase 3 (MAPK3), LIM domain binding 2 (LDB2), and low-density lipoprotein receptor-related protein 1B (LRP1B), which may be related to muscle growth and development in Inner Mongolia Cashmere goats. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that these genes were significantly enriched in the regulation of actin cytoskeleton and phospholipase D signaling pathway etc. Conclusion: In summary, this study will improve the marker-assisted breeding of Inner Mongolia cashmere goats and the molecular mechanisms of important economic traits.

角是头部表皮和真皮特化的衍生物,为哺乳动物所特有,在对抗天敌、自卫和争夺交配权中有重要作用.随着动物养殖集约化的发展,动物角对饲养人员及同类的伤害造成严重的经济损失,制约了养殖业的发展,所以越来越多的研究关注角性状.该文从洞角的分类入手,分别阐述了牛、绵羊和山羊与角性状相关的研究进展,旨在为牛、绵羊和山羊培育无角性状的相关研究和角药用性能的开发奠定理论基础.

Objective: The purpose of this study was to discover the population structure and genetic diversity of Inner Mongolia White Cashmere goats (IMCGs) and demonstrate the effect of inbreeding on the live body weight (LBW), cashmere yield (CY), fiber length (FL), and fiber diameter (FD) of IMCGs.Materials and Methods: All data were collected from pedigree information and production performance records of IMCGs from 1983 to 2019. The population structure and genetic diversity were analyzed by Endog 4.8 software. Inbreeding coefficients were obtained by the pedigree package in R. Then, a linear regression model was used to analyze how inbreeding influences economic traits in IMCGs. Four levels of inbreeding coefficients (Fi) were classified in this study, including Fi = 0, 0< Fi ≤ 6.25, 6.25< Fi ≤ 12.5 and Fi≥12.5. Variance analysis was performed to determine whether inbreeding levels had a significant effect on economic traits in IMCGs.Results: The proportions of rams and dams in IMCGs for breeding were relatively small, with values of 0.8 and 20.5%, respectively. The proportion of inbred animals in the entire population was high, with values up to 68.6%; however, the average inbreeding coefficient and relatedness coefficient were 4.50 and 8.48%, respectively. To date, the population has experienced 12 generations. The average generation interval obtained in the present study was 4.11 ± 0.01 years. The ram-to-son pathway was lowest (3.97 years), and the ewe-to-daughter pathway was highest (4.24 years). It was discovered that the LBW, CY, and FL increased by 3.88 kg, 208.7 g, and 1.151 cm, respectively, with every 1% increase in the inbreeding coefficient, and the FD decreased by 0.819 μm with every 1% increase in the inbreeding coefficient. Additionally, multiple comparison analysis indicated that when the inbreeding coefficient was higher than 6.25%, the LBW showed an obvious decreasing trend. The threshold value of inbreeding depression in the CY is 12.5%. However, inbreeding depression has not been observed in the FL and FD.Conclusion: Pedigree completeness needs to be further strengthened. The degree of inbreeding in this flock should be properly controlled when designing breeding programs.

Inner Mongolian Cashmere goat is an excellent local breed selected for the dual-purpose of cashmere and meat. There are three lines of Inner Mongolian Cashmere goat: Erlangshan, Alashan and Aerbasi. Cashmere is a kind of precious textile raw material with a high price. Cashmere is derived from secondary hair follicle (SHF), while hair is derived from primary hair follicle (PHF). The growth cycle of SHF of cashmere goat is 1 year, and it can be divided into three different stages: anagen, catagen and telogen. In this study, we tried to find some important influence factors of SHF growth cycle in skin tissue from Inner Mongolian Cashmere goats by RNA sequencing (RNA-Seq). Three female Aerbasi Inner Mongolian Cashmere goats (2 years old) were used as experimental samples in this study. Skin samples were collected in September (anagen), December (catagen) and March (telogen) at dorsal side from cashmere goats. Results showed that over 511 396 044 raw reads and 487 729 890 clean reads were obtained from sequence data. In total, 51 different expression genes (DEGs) including 29 downregulated genes and 22 upregulated genes were enriched in anagen–catagen comparing group. The 443 DEGs contained 117 downregulated genes and 326 upregulated genes that were enriched in catagen–telogen comparing group. In telogen–anagen comparing group, 779 DEGs were enriched including 582 downregulated genes and 197 upregulated genes. The result of gene ontology (GO) annotation showed that DEGs are in different growth cycle periods, and enriched GO items are mostly related to the transformation of cell and protein. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment result indicated that metabolic process has a great impact on SHF growth cycle. Based on the results of a comprehensive analysis of differentially expressed genes, GO enrichment and KEGG enrichment, we found that FGF5, FGFR1 and RRAS had an effect on the hair follicle growth cycle. The results of this study may provide a theoretical basis for further research on the growth and development of SHF in Inner Mongolian Cashmere goats.

角蛋白是维持毛发形态特征的重要结构蛋白,包括角蛋白和角蛋白关联蛋白.角蛋白74 (Keratins 74,KRT74)属于Ⅱ型内根鞘角蛋白,与毛发的生长和形态结构多样性相关.通过探究KRT74基因的结构特征、蛋白性质及其在内蒙古绒山羊不同毛被类型的表达差异,为挖掘内蒙古绒山羊毛被类型遗传标记和绒毛生长发育研究提供理论依据.利用NCBI公布的山羊KRT74序列信息进行生物信息学分析,通过实时荧光定量PCR技术(qRT-PCR)检测该基因在内蒙古绒山羊不同毛被类型皮肤组织中的表达水平.生物信息学分析发现KRT74蛋白为亲水性稳定蛋白,没有跨膜结构和信号肽;主要分布在细胞核和细胞质中,蛋白质存在4个保守结构域,是细胞骨架和核膜的原始成分,并为上皮细胞提供机械支持.qRT-PCR结果显示,长毛型绒山羊KRT74基因的表达水平显著高于短毛型绒山羊,差异倍数为1.6;相关性分析发现KRT74基因表达量与毛长性状呈显著正相关.推测KRT74基因可能是影响内蒙古绒山羊不同毛被类型的候选基因.

The goat genome is the research basis for the protection and utilization of goat resources, which is important for breeding and improving goat breeds. At present, with the continuous improvement of goat reference genome, various important research progress in goat origin, evolution and adaptability has been achieved. In this review, we summarize the research progress in the goat genome in detail, encompassing goat genome structure, genome map (genetic, physical and comparative maps), goat high throughput sequencing and SNP chip development. We aim to provide a theoretical foundation for the development of goat genome selection.