Zhiying Wang

Objective: Genome-wide association study (GWAS) and haplotype analysis were employed to identify molecular markers and candidate genes associated with fleece traits in Inner Mongolia cashmere goats (IMCGs).Methods: GWASs using whole-genome resequencing data together with phenotypic data from 2,299 IMCGs, applying four models: mixed linear model, multiple locus mixed linear model, fixed and random model circulating probability unification, and Bayesian-information and linkage-disequilibrium iteratively nested keyway. We focused on the GWAS signals to conduct gene annotation and performed functional enrichment analyses to explore the biological processes underlying these signals. Additionally, haplotypes were constructed for the significant loci, and haplotype-phenotype association analyses were performed to identify molecular markers and candidate genes associated with these fleece traits in IMCGs.Results: We identified 542 SNPs and 179 candidate genes linked to fleece traits through GWAS and gene annotation. Genes such as LAMA3, KCTD1, PTK7, FGFR3, LEF1, TAPT1, PTCH1, ELOVL6, and EVC have emerged as important candidates that may influence fleece traits. Furthermore, 11 haplotype blocks related to fleece traits were constructed, among which A1A1, C1C1, E2E2, F1F1, G1G1, H1H1 and K1K1 were identified as the superior haplotype combinations for fleece traits. These could serve as important molecular markers to improve the accuracy of early selection and the economic efficiency of breeding programs for fleece traits in IMCGs.Conclusion: This study successfully employed GWAS to identify key genetic loci significantly associated with the fleece traits of IMCGs. The genetic basis of these traits was revealed through additional gene annotation and haplotype analysis. The findings provide important theoretical and practical foundations for molecular breeding in IMCGs.

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Objective: Inner Mongolia cashmere goats are superior indigenous breeds developed through long-term natural selection and systematic artificial selection, which have experienced a certain intensity of selection pressure during the breeding process, leading to bipolar differentiation trends in cashmere traits. Therefore, identifying genomic selection signatures associated with cashmere traits in Inner Mongolia cashmere goats is crucial for breeding high-quality cashmere-producing goats.Methods: To unravel the genetic basis of cashmere traits, this study stratified 375 Inner Mongolia cashmere goats into eight subgroups based on breeding values for cashmere traits: high-yield vs low-yield cashmere types (HYCG vs LYCG), fine vs coarse cashmere types (FCG vs CCG), long vs short cashmere types (LCG vs SCG), and long vs short fleece types (LFCG vs SFCG). Whole-genome resequencing was performed for genotyping, followed by detection of selection signatures.Results: Results revealed 144, 158, 147, and 147 high-frequency run of homozygosity (ROH) regions in HYCG, FCG, LCG, and LFCG subgroups, respectively, annotating to 515, 565, 510, and 521 genes. Additionally, genomic regions under positive selection were identified using Fst, θπ ratios, and XP-EHH methods, with overlapping regions detected by ≥2 methods defined as candidate regions. Gene annotation identified 777, 660, 712, and 726 candidate genes in HYCG vs LYCG, FCG vs CCG, LCG vs SCG, and LFCG vs SFCG comparisons, respectively. These genes were enriched in 3,051 GO terms and 318 KEGG pathways, including Hippo, MAPK, Wnt, PI3K-Akt, and mTOR signaling pathways associated with cashmere growth and development, involving genes such as LGR6, RUNX2, IGF1R, FGF9, and TCF7L1.Conclusion: In this study, we employed four complementary approaches, including ROHs, Fst, θπ ratios, and XP-EHH, to identify genomic signatures of selection for cashmere traits in Inner Mongolia cashmere goats. These findings provide valuable insights for improving cashmere production performance and developing novel strains with highquality cashmere in Inner Mongolia cashmere goats.

Objective: The growth and development of secondary hair follicles primarily determine the economic value of cashmere traits, significantly influencing the quality of cashmere fineness. Previous studies have concentrated on the periodic growth regulation of hair follicles in Inner Mongolia cashmere goats (IMCGs), identifying numerous candidate genes that influence cashmere traits. Research on the factors and regulatory mechanisms affecting cashmere fineness in different body parts is currently limited.Methods: The differences of cashmere fineness traits among different body parts or ages were determined by multiple comparison analysis testing in analysis of variance. RNA-seq and Gene Ontology (GO) & Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to assess the differentially expressed genes (DEGs) across different body parts of IMCGs. The candidate genes were validated using quantitative realtime polymerase chain reaction techniques.Results: Ages and different body parts had significant effects on cashmere diameter of IMCGs (p<0.05). Cashmere diameter was coarsest in the abdomen, but finest in the neck and back. A total of 2,178 DEGs were specifically screened among four body parts based on cashmere diameter. GO and KEGG analyses showed that these DEGs were mainly enriched in signaling pathways related to hair growth, such as MAPK signaling pathway and extracellular matrix-receptor interaction. The expression of MATN2 and CA12 were consistent with the phenotype of cashmere fineness in different body parts.Conclusion: The differences of cashmere fineness among different body parts of IMCGs were investigated through transcriptome and phenotype analysis, which provide a basis for understanding molecular regulation of cashmere growth in cashmere goats. ATN2 and CA12 have been validated as regulatory genes influencing the heterogeneity of cashmere fineness in various parts of IMCGs.

The Inner Mongolia Cashmere goat (IMCG) industry is a major contributor to global cashmere production, with fleece traits serving as key economic indicators that directly impact both income and the long-term sustainability of the industry. When genome-wide SNPs are used to estimate kinship matrices, the traditional animal model implicitly assumes that all SNPs have the same effect-size distribution. However, in practice, there are differences in the genetic mechanisms and complexity of different traits. We conducted a genome-wide association study (GWAS) on 2299 IMCGs genotyped with 67,021 SNPs, which were obtained after imputation. The traits measured included cashmere yield (CY), wool length (WL), cashmere length (CL), and cashmere diameter (CD), with a total of 33,564 records collected. The top 5% to 20% of the significant SNPs from the GWAS were used as biological prior information. We then assigned proportional weights based on their contribution to the overall genetic variance and further integrated them with the remaining loci to construct a kinship relationship matrix for estimating genetic parameters and genomic breeding value. By incorporating prior marker information from the GWAS, it was found that the heritability estimates for CY, WL, CL, and CD were 0.26, 0.37, 0.09, and 0.35, respectively. For CY and CL, integrating the top 5% of prior SNP markers yielded the highest genomic prediction accuracies of 0.742 and 0.673, representing improvements of 16.67% and 19.75% over models that did not utilize prior information. In contrast, for WL and CD, the highest accuracies of 0.851 and 0.780 were achieved by integrating the top 10% of prior SNP markers, reflecting improvements of 9.81% and 10.14%, respectively. Compared with the conventional GBLUP method, this method of integrating GWAS-derived prior markers for genomic genetic evaluation can significantly improve the accuracy of genomic prediction for fleece traits in IMCGs. This approach facilitates accurate selection for fleece traits in IMCGs, enabling accelerated genetic progress through long-term breeding programs.

Introduction: The early growth traits including birth weight (BW), weaning weight (WW), pre-weaning average daily gain (ADG) and yearling weight (YW) are crucial productivity indicators that directly influence growth rates of cashmere goats and economic income of herdsmen in the cashmere goat breeding programs. However, the genetic mechanism of these traits in Inner Mongolia Cashmere Goats (IMCGs) has not been elucidated.Copy number variation (CNV), as a prevalent form of genomic structural variation and a significant contributor to the genetic diversity, has emerged as a valuable molecular marker for analysis of complex traits.Methods: In this study, Whole Genome Sequencing (WGS) data of 461 IMCGs were used to detect CNVs on autosomes and the Genome-Wide Association Study (GWAS) analysis based on CNVs was performed for early growth traits (BW, WW, ADG and YW) of IMCGs.The identified CNVs were further validated through PCR verification. In addition, t-test was performed on the phenotypes of individuals of IMCGs with significant CNVs.Results: The 26,003 non-redundant CNVs and 5,014 non-redundant CNVRs were detected, covering a total of 1,015.4 Mb (38.97 %) of the autosomal goat genome. The 11 CNVs were significantly associated with early growth traits through GWAS analysis, including two pleiotropic CNVs simultaneously influencing ADG and WW. Through integrated bioinformatics analysis, seven key candidate genes (ZN845, SOX15, FGF11, GPS2, DVL2, SPRY4 and STAT2) were identified as being associated with early growth traits.Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses demonstrated that these genes were primarily involved in biological pathways related to cell proliferation, differentiation and protein phosphorylation.Among the 11 significant CNVs, 9 CNVs were demonstrated to show significant associations with individual phenotypes.Discussion: This study significantly expands the genomic CNV map of IMCGs through large-scale genotyping.The findings demonstrate the utility of CNV-based GWAS analysis in elucidating the genetic mechanisms underlying complex traits, providing valuable insights for molecular marker-assisted breeding and molecular genetic research of economically important traits in cashmere goats.

The accuracy of genomic selection has a significant impact on the selection of superior individuals in livestock. Studies have reported that integrating GWAS information can improve the accuracy of genomic prediction. In this study, phenotypic data, systematic environmental data, and genotypic data of important economic traits (cashmere yield, cashmere diameter, body weight, and cashmere length) of Inner Mongolia cashmere goats were utilized. Based on the results of a previous genome-wide association study that considered additive and dominance effects, the top 5%, top 10%, top 15%, and top 20% of loci were extracted as prior marker information. The genomic breeding values for each trait were estimated using the GBLUP–GA method based on GWAS prior information, and the accuracy of genomic prediction was further evaluated using a five-fold cross-validation method. The results showed that the contribution of significant loci to the genetic variance of each trait gradually increased with an increase of the number of integrated loci. The genetic variance contribution rates of significant loci to cashmere yield, cashmere diameter, body weight, and cashmere length were 64–71%, 47–57%, 76–82%, and 66–80%, respectively. The additive heritability estimates for cashmere yield, cashmere diameter, body weight, and cashmere length using GWAS prior information were 0.252–0.266, 0.297–0.580, 0.305–0.330, and 0.107–0.117, respectively. These values were higher than those obtained using the traditional G matrix constructed from all loci, with increases of 0.052–0.066, 0.007–0.29, 0.134–0.159, and 0.015–0.025, respectively. The results of genomic prediction accuracy showed that when 5% of the GWAS prior information was integrated, the highest genomic prediction accuracy was achieved for cashmere yield (0.8156), body weight (0.8361), and cashmere length (0.7571). When 20% of the GWAS prior information was integrated, the genomic prediction accuracy for cashmere diameter was 0.8074, which was significantly higher than that at other levels. Additionally, it was found that the dominance heritability for cashmere diameter, body weight, and cashmere length was very small and could be ignored when integrating GWAS prior information. Therefore, when integrating prior information for genomic selection of these traits, the influence of dominance effects can be disregarded.

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.