Xiaoxue Zhang

Bulk skin transcriptomes cannot distinguish cell-intrinsic gene regulation from shifts in tissue-type composition, and computational deconvolution is widely applied to resolve this limitation. In this study, we performed deconvolution on published Hetian sheep skin RNA-seq data from three high-wool-density and three low-wool-density animals, using the available ovine skin single-cell reference. All hypotheses and analytical workflows were prospectively specified and timestamped prior to estimating cell-type fractions. Our work identifies two major methodological pitfalls for livestock tissue deconvolution. First, gene-length bias distorts deconvolution outputs derived from mixed bulk and 3′ single-cell references. After bias correction, the estimated hair-follicle epithelial fraction changed substantially. This bias cannot be uncovered by standard benchmark workflows, highlighting a hidden failure mode for deconvolution. Second, limited signature separability within the reference prevents reliable discrimination between related epidermal compartments. This non-identifiability strongly disturbs apparent compartment-level effects. When analysed at the granularity the reference reliably supports, we found no compositional difference between high- and low-wool-density groups for the epithelial hypothesis. The dermal hypothesis could not be meaningfully evaluated due to incompatible reference compartment merging requirements. Group-level differential expression remained robust despite this null compositional outcome. We recovered a large set of differentially expressed genes consistent with the original study, including most qPCR-validated candidates. Adjusting for cell-type composition altered part of this gene list, as expected given negligible group-level compositional divergence. Co-expression modules correlated more strongly with inter-individual compositional variation than with wool-density phenotype. This indicates compositional heterogeneity shapes transcriptome co-expression even without measurable mean differences across experimental groups. To avoid misleading biological inference, we propose a set of practical diagnostic checks for deconvolution when developmental or limited single-cell atlases are used for livestock tissues. Collectively, these transcriptomic patterns support cell-state-driven regulation rather than detectable mean shifts in tissue compartment composition.

Genomic selection (GS) is a new landmark method in modern animal breeding programs, and it has become a tool for routine genetic evaluation regarding dual-purpose cattle breeding. In this study, we employed data on milk-production, reproduction, and growth measurements of dual-purpose Simmental cows during the period 1987–2022 from two large-scale farms in Northwest China. For this purpose, we used a single-trait model based on the A-array PBLUP and H-array ssGBLUP to perform genetic evaluation of milk-production, reproduction, and growth traits by applying the restricted maximum likelihood (REML) methods. The results revealed that the heritability based on the additive genetic correlation matrix was approximately 0.09–0.31 for milk-production traits, 0.03–0.43 for reproduction traits, and 0.13–0.43 for growth traits. In addition, the heritability based on the genome–pedigree association matrix was similarly 0.09–0.32 for milk-production traits, 0.04–0.44 for reproductive traits, and 0.14–0.43 for growth traits. In the entire population, the reliability of genomic estimated breeding values (GEBVs) increased by 0.6–3.2%, 0.2–2.4%, and 0.5–1.5% for milk-production, reproductive traits, and growth traits, respectively. In the genotyped population, the reliability of GEBV for milk-production and reproduction traits increased by 1.6–4.0% and 0.4–3.6%, respectively, whereas the reliability of GEBV for growth traits decreased by 12.0–17.0%. These results suggest that the construction of an H-matrix with ssGBLUP could improve the heritability and reliability of breeding values for milk-production and reproduction traits. However, the advantage was not evident for growth traits in smaller populations. The present results thus provide a basis for future application of genomic genetic evaluation of dual-purpose Simmental cattle, providing data support for the selection and marketing of excellent breeding bulls, thereby helping to establish a basis for their independently bred breeding bull.

Xinjiang Brown cattle is an elite dual-purpose breed (raised for dairy and beef) developed in China. To elucidate its genomic architecture, we conducted whole-genome resequencing of 169 Xinjiang Brown cattle, followed by structural variation (SV) detection and a genome-wide association study (GWAS). We identified 71,668 SVs, among which deletions were the most prevalent, followed by translocations, inversions, duplications, and insertions. We further identified 1286 high-frequency SVs involving 2016 protein-coding genes. Through functional enrichment analysis of these genes, we revealed associations of genetic variation at genomic positions near genes implicated in immune response and disease resistance (NFKBIZ and PTPRT), growth and development (HDAC4 and MEF2A), and milk production (TP63, FABP4, and MEF2A). GWAS analysis of 31 body conformation traits revealed 58 SVs significantly associated with five traits (chest width, rear udder width, udder depth, rump width, and heel depth) at the genome-wide level. Additionally, nine candidate genes (CLINT1, EBF1, PAM16, GRIP1, CFAP54, SLC22A16, DOK5, ETAA1, and IPMK) were identified as potentially involved in the genetic regulation of body conformation traits. These findings provide novel insights for genetic improvement strategies and indicate that precision breeding could further enhance the production performance of this breed in the future.

Body conformation traits are linked to the health, longevity, reproductivity, and production performance of cattle. These traits are also crucial for herd selection and developing new breeds. This study utilized pedigree information and phenotypic (1185 records) and genomic (The resequencing of 496 Xinjiang Brown cattle generated approximately 74.9 billion reads.) data of Xinjiang Brown cattle to estimate the genetic parameters, perform factor analysis, and conduct a genome-wide association study (GWAS) for these traits. Our results indicated that most traits exhibit moderate to high heritability. The principal factors, which explained 59.12% of the total variance, effectively represented body frame, muscularity, rump, feet and legs, and mammary system traits. Their heritability estimates range from 0.17 to 0.73, with genetic correlations ranging from −0.53 to 0.33. The GWAS identified 102 significant SNPs associated with 12 body conformation traits. A few of the SNPs were located near previously reported genes and quantitative trait loci (QTLs), while others were novel. The key candidate genes such as LCORL, NCAPG, and FAM184B were annotated within 500 Kb upstream and downstream of the significant SNPs. Therefore, factor analysis can be used to simplify multidimensional conformation traits into new variables, thus reducing the computational burden. The identified candidate genes from GWAS can be incorporated into the genomic selection of Xinjiang Brown cattle, enhancing the reliability of breeding programs.

Xinjiang brown cattle are highly resistant to disease and tolerant of roughage feeding. The identification of genes regulating mastitis resistance in Xinjiang brown cattle is a novel means of genetic improvement. In this study, the blood levels of IL-1β, IL-6, IL-10, TNF-α, and TGF-β in Xinjiang brown cattle with high and low somatic cell counts (SCCs) were investigated, showing that cytokine levels were higher in cattle with high SCCs. The peripheral blood transcriptomic profiles of healthy and mastitis-affected cattle were constructed by RNA-seq. Differential expression analysis identified 1632 differentially expressed mRNAs (DE-mRNAs), 1757 differentially expressed lncRNAs (DE-lncRNAs), and 23 differentially expressed circRNAs (DE-circRNAs), which were found to be enriched in key pathways such as PI3K/Akt, focal adhesion, and ECM-receptor interactions. Finally, ceRNA interaction networks were constructed using the differentially expressed genes and ceRNAs. It was found that keynote genes or mRNAs were also enriched in pathways such as PI3K-Akt, cholinergic synapses, cell adhesion molecules, ion binding, cytokine receptor activity, and peptide receptor activity, suggesting that the key genes and ncRNAs in the network may play an important role in the regulation of bovine mastitis.

Mastitis causes serious economic losses in the dairy industry, but there are no effective treatments or preventive measures. In this study, the ZRANB3, PIAS1, ACTR3, LPCAT2, MGAT5, and SLC37A2 genes in Xinjiang brown cattle, which are associated with mastitis resistance, were identified using a GWAS. Pyrosequencing analysis showed that the promoter methylation levels of the FHIT and PIAS1 genes in the mastitis group were higher and lower, respectively, than those in the healthy group (65.97 ± 19.82% and 58.00 ± 23.52%). However, the methylation level of the PIAS1 gene promoter region in the mastitis group was lower than that in the healthy group (11.48 ± 4.12% and 12.17 ± 4.25%). Meanwhile, the methylation levels of CpG3, CpG5, CpG8, and CpG15 in the promoter region of the FHIT and PIAS1 genes in the mastitis group were significantly higher than those in the healthy group (p < 0.01), respectively. RT-qPCR showed that the expression levels of the FHIT and PIAS1 genes were significantly higher in the healthy group than those in the mastitis group (p < 0.01). Correlation analysis showed that the promoter methylation level of the FHIT gene was negatively correlated with its expression. Hence, increased methylation in the promoter of the FHIT gene reduces the mastitis resistance in Xinjiang brown cattle. Finally, this study provides a reference for the molecular-marker-assisted selection of mastitis resistance in dairy cattle.

In this study, we estimated the genetic parameters for 6 composite traits and 27 body conformation traits of 1016 dual-purpose Simmental cattle reared in northwestern China from 2010 to 2019 using a linear animal mixed model. To integrate these traits, a variety of methods were used as follows: (1) genetic parameters estimates for composite and individual body conformation traits based on the pedigree relationship matrix (A) and combined genomic-pedigree relationship matrix (H); (2) factor analysis to explore the relationships among body conformation traits; and (3) genetic parameters of factor scores estimated using A and H, and the correlations of EBVs of the factor scores and EBVs of the composite traits. Heritability estimates of the composite traits using A and H were low to medium (0.07–0.47). The 24 common latent factors explained 96.13% of the total variance. Among factors with eigenvalues ≥ 1, F1 was mainly related to body frame, muscularity, and rump; F2 was related to feet and legs; F3, F4, F5, and F6 were related to teat placement, teat size, udder size, and udder conformation; and F7 was related to body frame. Single-trait analysis of factor scores yielded heritability estimates that were low to moderate (0.008–0.43 based on A and 0.04–0.43 based on H). Spearman and Pearson correlations, derived from the best linear unbiased prediction analysis of composite traits and factor scores, showed a similar pattern. Thus, incorporating factor analysis into the morphological evaluation to simplify the assessment of body conformation traits may improve the genetics of dual-purpose Simmental cattle.

One-step genomic selection is a method for improving the reliability of the breeding value estimation. This study aimed to compare the reliability of pedigree-based best linear unbiased prediction (PBLUP) and single-step genomic best linear unbiased prediction (ssGBLUP), single-trait and multitrait models, and the restricted maximum likelihood (REML) and Bayesian methods. Data were collected from the production performance records of 2207 Xinjiang Brown cattle in Xinjiang from 1983 to 2018. A cross test was designed to calculate the genetic parameters and reliability of the breeding value of 305 daily milk yield (305 dMY), milk fat yield (MFY), milk protein yield (MPY), and somatic cell score (SCS) of Xinjiang Brown cattle. The heritability of 305 dMY, MFY, MPY, and SCS estimated using the REML and Bayesian multitrait models was approximately 0.39 (0.02), 0.40 (0.03), 0.49 (0.02), and 0.07 (0.02), respectively. The heritability and estimated breeding value (EBV) and the reliability of milk production traits of these cattle calculated based on PBLUP and ssGBLUP using the multitrait model REML and Bayesian methods were higher than those of the single-trait model REML method; the ssGBLUP method was significantly better than the PBLUP method. The reliability of the estimated breeding value can be improved from 0.9% to 3.6%, and the reliability of the genomic estimated breeding value (GEBV) for the genotyped population can reach 83%. Therefore, the genetic evaluation of the multitrait model is better than that of the single-trait model. Thus, genomic selection can be applied to small population varieties such as Xinjiang Brown cattle, in improving the reliability of the genomic estimated breeding value.