DNA methylation patterns in the peripheral blood of Xinjiang brown cattle with variable somatic cell counts.

The use of wide-ranging dairy herd improvement (DHI) measurements has resulted in the investigation of somatic cell count (SCC) and the identification of many genes associated with mastitis resistance. In this study, blood samples of Xinjiang brown cattle with different SCCs were collected, and genome-wide DNA methylation was analyzed by MeDIP-seq. The results showed that peaks were mostly in intergenic regions, followed by introns, exons, and promoters. A total of 1,934 differentially expressed genes (DEGs) associated with mastitis resistance in Xinjiang brown cattle were identified. The enrichment of differentially methylated CpG islands of the TRAPPC9 and CD4 genes was analyzed by bisulfate genome sequencing. The methylation rate of differentially methylated CpGs was higher in the TRAPPC9 gene of cattle with clinical mastitis (mastitis group) compared with healthy cattle (control group), while methylation of differentially methylated CpGs was significantly lower in CD4 of the mastitis group compared with the control group. RT-qRCR analysis showed that the mastitis group had significantly reduced expression of CD4 and TRAPPC9 genes compared to the control group (p < 0.05). Furthermore, Mac-T cells treated with lipopolysaccharide and lipoteichoic acid showed significant downregulation of the TRAPPC9 gene in the mastitis group compared with the control group. The identified epigenetic biomarkers provide theoretical reference for treating cow mastitis, breeding management, and the genetic improvement of mastitis resistance in Xinjiang brown cattle.

Comparative Study of Bacterial Microbiota Differences in the Rumen and Feces of Xinjiang Brown and Holstein Cattle.

Xinjiang Brown cattle are a unique and widely distributed breed of dual-purpose cattle in the Xinjiang area of China, whose milk production performance differs from Holstein cattle. It has been known that variations in bacterial species of the gastrointestinal tract influence milk protein, fat, and lactose synthesis. However, the microbiota differences between Xinjiang Brown and Holstein cattle are less known. This study aims to compare the bacterial community composition of the rumen and feces of these two cattle breeds under the same dietary and management conditions. The 16s rRNA sequencing data and milk production of 18 Xinjiang Brown cows and 20 Holstein cows on the same farm were obtained for analysis. The results confirmed differences in milk production between Xinjiang Brown and Holstein cattle. Microbiota with different relative abundance between these two cattle breeds were identified, and their biological functions might be related to milk synthesis. This study increases the understanding of the differences in microbiota between Xinjiang Brown and Holstein cattle and might provide helpful information for microbiota composition optimization of these dairy cattle.

Estimation of Genetic Parameters for Early Growth Traits in Luzhong Mutton Sheep.

In this study, six different animal models were fitted, and the constrained maximum likelihood method was used to assess the genetic parameters and genetic trends of early growth traits in Luzhong mutton sheep. The experimental data of this study included the newborn weight (BWT, N = 2464), weaning weight (WWT, N = 2923), weight at 6 months of age (6WT, N = 2428), average daily weight gain from birth to weaning (ADG1, N = 2424), and average daily weight gain from weaning to 6 months of age (ADG2, N = 1836) in Luzhong mutton sheep (2015~2019). The best model for the genetic parameters of the five traits in Luzhong mutton sheep was identified as Model 4 using the Akaike information criterion (AIC) and likelihood ratio test (LRT) methods, in which the estimated values of direct heritability for the BWT, WWT, 6WT, ADG1, and ADG2 were 0.156 ± 0.057, 0.547 ± 0.031, 0.653 ± 0.031, 0.531 ± 0.035, and 0.052 ± 0.046, respectively, and the values for maternal heritability were 0.201 ± 0.100, 0.280 ± 0.047, 0.197 ± 0.053, 0.275 ± 0.052, and 0.081 ± 0.092, respectively. The genetic correlation between the ADG2 and WWT was negative, and the genetic and phenotypic correlations among the remaining traits were positive. In this study, maternal effects had a more significant influence on early growth traits in Luzhong mutton sheep. In conclusion, to effectively improve the accuracy of genetic parameter estimation, maternal effects must be fully considered to ensure more accurate and better breeding planning.

Genomic prediction based on a joint reference population for the Xinjiang Brown cattle.

Introduction: Xinjiang Brown cattle constitute the largest breed of cattle in Xinjiang. Therefore, it is crucial to establish a genomic evaluation system, especially for those with low levels of breed improvement. Methods: This study aimed to establish a cross breed joint reference population by analyzing the genetic structure of 485 Xinjiang Brown cattle and 2,633 Chinese Holstein cattle (Illumina GeneSeek GGP bovine 150 K chip). The Bayes method single-step genome-wide best linear unbiased prediction was used to conduct a genomic evaluation of the joint reference population for the milk traits of Xinjiang Brown cattle. The reference population of Chinese Holstein cattle was randomly divided into groups to construct the joint reference population. By comparing the prediction accuracy, estimation bias, and inflation coefficient of the validation population, the optimal number of joint reference populations was determined. Results and Discussion: The results indicated a distinct genetic structure difference between the two breeds of adult cows, and both breeds should be considered when constructing multi-breed joint reference and validation populations. The reliability range of genome prediction of milk traits in the joint reference population was 0.142-0.465. Initially, it was determined that the inclusion of 600 and 900 Chinese Holstein cattle in the joint reference population positively impacted the genomic prediction of Xinjiang Brown cattle to certain extent. It was feasible to incorporate the Chinese Holstein into Xinjiang Brown cattle population to form a joint reference population for multi-breed genomic evaluation. However, for different Xinjiang Brown cattle populations, a fixed number of Chinese Holstein cattle cannot be directly added during multi-breed genomic selection. Pre-evaluation analysis based on the genetic structure, kinship, and other factors of the current population is required to ensure the authenticity and reliability of genomic predictions and improve estimation accuracy.

Transcriptomic Changes and Regulatory Networks Associated with Resistance to Mastitis in Xinjiang Brown Cattle.

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-1ß, IL-6, IL-10, TNF-α, and TGF-ß 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.

Whole-genome resequencing uncovers diversity and selective sweep in Kazakh cattle.

The Kazakh cattle in the Xinjiang Uygur Autonomous Region of China are highly adaptable and have multiple uses, including milk and meat production, and use as draft animals. They are an excellent original breed that could be enhanced by breeding and hybrid improvement. However, the genomic diversity and signature of selection underlying the germplasm characteristics require further elucidation. Herein, we evaluated 26 Kazakh cattle genomes in comparison with 103 genomes of seven other cattle breeds from regions around the world to assess the Kazakh cattle genetic variability. We revealed that the relatively low linkage disequilibrium at large SNP distances was strongly correlated with the largest effective population size among Kazakh cattle. Using population structural analysis, we next demonstrated a taurine lineage with restricted Bos indicus introgression among Kazakh cattle. Notably, we identified putative selected genes associated with resistance to disease and body size within Kazakh cattle. Together, our findings shed light on the evolutionary history and breeding profile of Kazakh cattle, as well as offering indispensable resources for germplasm resource conservation and crossbreeding program implementation.

MiR-23b and miR-133 Cotarget TGFß2/NOTCH1 in Sheep Dermal Fibroblasts, Affecting Hair Follicle Development.

Wool is produced and controlled by hair follicles (HFs). However, little is known about the mechanisms involved in HF development and regulation. Sheep dermal fibroblasts (SDFs) play a key role in the initial stage of HF development. Analyzing the molecular mechanism that regulates early HF development in superfine wool sheep is of great importance for better understanding the HF morphogenesis process and for the breeding of fine wool sheep. Here, we show that two microRNAs (miRNAs) affect the development of HFs by targeting two genes that are expressed by SDFs. Meanwhile, the overexpression and inhibition of oar-miR-23b and oar-miR-133 in SDFs cells and cell proliferation, apoptosis, and migration were further detected using a CCK-8 assay, an Annexin V-FITC assay, a Transwell assay, and flow cytometry. We found that oar-miR-23b, oar-miR-133, and their cotarget genes TGFß2 and NOTCH1 were differentially expressed during the six stages of HF development in superfine wool sheep. Oar-miR-23b and oar-miR-133 inhibited the proliferation and migration of SDFs and promoted the apoptosis of SDFs through TGFß2 and NOTCH1. oar-miR-23b and oar-miR-133 inhibited the proliferation and migration of SDFs by jointly targeting TGFß2 and NOTCH1, thereby inhibiting the development of superfine wool HFs. Our research provides a molecular marker that can be used to guide the breeding of ultrafine wool sheep.

Genome-Wide DNA Methylation Analysis and Functional Validation of Litter Size Traits in Jining Grey Goats.

DNA methylation (DNAm) is associated with the reproductive system. However, the genetic mechanism through which DNAm regulates gene expression and thus affects litter size in goats is unclear. Therefore, in the present work, genome-wide DNAm profiles of HP and LP Jining Grey goat ovary tissues were comprehensively analyzed via WGBS, and RNA-Seq data were combined to identify candidate genes associated with litter size traits in the Jining Grey goat. Finally, BSP and RT-qPCR were used to verify the sequencing results of the key genes. Notably, the DNMT genes were downregulated at the expression level in the HP group. Both groups exhibited comparable levels of methylation. A total of 976 differentially methylated regions (DMRs) (973 DMRs for CG and 3 DMRs for CHG) and 310 differentially methylated genes (DMGs) were identified in this study. Through integration of WGBS and RNA-Seq data, we identified 59 differentially methylated and differentially expressed genes (DEGs) and ultimately screened 8 key DMGs (9 DMRS) associated with litter size traits in Jining Grey goats (SERPINB2: chr24_62258801_62259000, NDRG4: chr18_27599201_27599400, CFAP43: chr26_27046601_27046800, LRP1B. chr2_79720201_79720400, EPHA6: chr1_40088601_40088800, TTC29: chr17_59385801_59386000, PDE11A: chr2_117418601_117418800 and PGF: chr10_ 16913801_16914000 and chr10_16916401_16916600). In summary, our research comprehensively analyzed the genome-wide DNAm profiles of HP and LP Jining Grey goat ovary tissues. The data findings suggest that DNAm in goat ovaries may play an important role in determining litter size.

Whole-genome resequencing provides insights into the diversity and adaptation to desert environment in Xinjiang Mongolian cattle.

BACKGROUND: Xinjiang Mongolian cattle is an indigenous breed that inhabits the Taklimakan Desert and is characterized by its small body size. However, the genomic diversity, origin, and genetic basis underlying the adaptation to the desert environment have been poorly studied. RESULTS: We analyzed patterns of Xinjiang Mongolian cattle genetic variation by sequencing 20 genomes together with seven previously sequenced genomes and comparing them to the 134 genomes of nine representative breeds worldwide. Among the breeds of Bos taurus, we found the highest nucleotide diversity (0.0024) associated with the lower inbreeding coefficient (2.0110-6), the lowest linkage disequilibrium (r2 = 0.3889 at distance of 10 kb), and the highest effective population size (181 at 20 generations ago) in Xinjiang Mongolian cattle. The genomic diversity pattern could be explained by a limited introgression of Bos indicus genes. More importantly, similarly to desert-adapted camel and same-habitat sheep, we also identified signatures of selection including genes, GO terms, and/or KEGG pathways controlling water reabsorption and osmoregulation, metabolic regulation and energy balance, as well as small body size in Xinjiang Mongolian cattle. CONCLUSIONS: Our results imply that Xinjiang Mongolian cattle might have acquired distinct genomic diversity by virtue of the introgression of Bos indicus, which helps understand the demographic history. The identification of selection signatures can provide novel insights into the genomic basis underlying the adaptation of Xinjiang Mongolian cattle to the desert environment.

Underwater object detection method based on learnable query recall mechanism and lightweight adapter.

With the rapid development of ocean observation technology, underwater object detection has begun to occupy an essential position in the fields of aquaculture, environmental monitoring, marine science, etc. However, due to the problems unique to underwater images such as severe noise, blurred objects, and multi-scale, deep learning-based target detection algorithms lack sufficient capabilities to cope with these challenges. To address these issues, we improve DETR to make it well suited for underwater scenarios. First, a simple and effective learnable query recall mechanism is proposed to mitigate the effect of noise and can significantly improve the detection performance of the object. Second, for underwater small and irregular object detection, a lightweight adapter is designed to provide multi-scale features for the encoding and decoding stages. Third, the regression mechanism of the bounding box is optimized using the combination loss of smooth L1 and CIoU. Finally, we validate the designed network against other state-of-the-art methods on the RUOD dataset. The experimental results show that the proposed method is effective.

Methylation Levels in the Promoter Region of FHIT and PIAS1 Genes Associated with Mastitis Resistance in Xinjiang Brown Cattle.

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.

Combined therapy with pioglitazone and FGF21 mRNA synergistically ameliorates metabolic disorders in NAFLD rats.

Background: Current study aims to investigate the ameliorative effect of pioglitazone (PIO) combined with mRNA encoding FGF21 (termed mFGF21) on the metabolic disorders in rats with nonalcoholic fatty liver disease (NAFLD) and its potential mechanism. Methods: In vitro functional activity of FGF21 protein expressed by mFGF21 was evaluated in human adipose-derived stem cells (hASCs). The pharmacokinetic profiles of FGF21 protein expressed by mFGF21 were investigated in normal SD rats and NAFLD rats, respectively. Results: As the results, it showed that the PIO could enhanced in vitro functional activity of FGF21 protein expressed from mFGF21 in hASCs. Not only that, mFGF21 turns the body into a processing plant for endogenous protein expression, which enhanced the pharmacokinetic profiles of FGF21 proteins. Combined treatment with PIO and mFGF21 significantly reduced body weight, fasting blood glucose levels, insulin levels and lipid metabolism in NAFLD rats compared with control or both two monotherapy groups. The results of H&E staining and Western blot revealed that combined treatment with PIO and mFGF21 significantly decreased hepatic fat accumulation in NAFLD rats by activating the SHP1/AMPK signaling pathway. Conclusions: Our finding collectively demonstrated that PIO and mFGF21 combination therapy could synergistically ameliorate metabolic disorders in NAFLD rats.

Transcriptome analysis reveals genes associated with wool fineness in merinos.

Hair/wool usually plays an important role in maintaining animal physiological activities, and the economic value of wool cannot be ignored. At present, people set higher demands on wool fineness. Hence, improving wool fineness is the concern of fine wool sheep breeding. Using RNA-Seq to screen the potential candidate genes that associate with wool fineness can provide theoretical references for fine-wool sheep breeding, and also provide us new ideas for further understand the molecular regulation mechanism of hair growth. In this study, we compared the expression pattern difference of genome-wide genes between the skin transcriptomes of Subo and Chinese Merinos. The results showed that, 16 candidate differentially expressed genes (DEGs) (Included: CACNA1S, GP5, LOC101102392, HSF5, SLITRK2, LOC101104661, CREB3L4, COL1A1, PTPRR, SFRP4, LOC443220, COL6A6, COL6A5, LAMA1, LOC114115342 and LOC101116863 genes) that may associate with wool fineness were screened, and these genes were located in signaling pathways that regulate hair follicle development, cycle or hair growth. It is worth noting that, among the 16 DEGs, COL1A1 gene has the highest expression level in Merino skins, and the fold change of LOC101116863 gene is the highest, and the structures of these two genes are both highly conserved in different species. In conclusion, we speculate that these two genes may play a key role in regulating wool fineness and respectively have similar and conserved functions in different species.

Surface defect detection method for electronic panels based on double branching and decoupling head structure.

During the production of electronic panels, surface defects will inevitably appear. How to quickly and accurately detect these defects is very important to improve product quality. However, some problems such as high cost and low accuracy are still prominent when existing manual detection and traditional techniques are used to solve such problems. Therefore, more and more computer vision techniques are proposed to solve such problems, but the current application of deep learning-based object detection networks for surface defect detection of electronic panels is in a gap. The analysis found that there are two main reasons for this phenomenon. On the one hand, the surface defects of electronic panels have their unique characteristics such as multi-scale and irregular shape, and the current object detection networks cannot effectively solve these problems. On the other hand, the regression and classification tasks coupled in the current computational mechanism of each network are commonly found to cause the problem of conflict between them, which makes it more difficult to adapt these network models to the detection tasks in this scenario. Based on this, we design a supervised object detection network for electronic panel surface defect detection scenario for the first time. The computational mechanism of this network includes a prediction box generation strategy based on the double branch structure and a detection head design strategy that decouples the regression task from the classification task. In addition, we validated the designed network and the proposed method on our own collected dataset of surface defects in electronic panels. The final results of the comparative and ablation experiments show that our proposed method achieves an average accuracy of 78.897% for 64 surface defect categories, proving that its application to electronic panel surface defect detection scenarios can achieve better results.

Underwater low-light enhancement network based on bright channel prior and attention mechanism.

At present, there are some problems in underwater low light image, such as low contrast, blurred details, color distortion. In the process of low illumination image enhancement, there are often problems such as artifacts, loss of edge details and noise amplification in the enhanced image. In this paper, we propose an underwater low-light enhancement algorithm based on U-shaped generative adversarial network, combined with bright channel prior and attention mechanism, to address the problems. For the problems of uneven edges and loss of details that occurred in traditional enhanced images, we propose a two-channel fusion technique for the input channel. Aiming at the problems of brightness, texture and color distortion in enhanced images, we propose a feature extraction technique based on the attention mechanism. For the problems of noise in enhanced output images, we propose a multi-loss function to constrain the network. The method has a wide range of applications in underwater scenes with large depth. This method can be used for target detection or biological species identification in underwater low light environment. Through the enhancement experiment of underwater low light image, the proposed method effectively solves the problems of low contrast, blurred details, color distortion, etc. of underwater low light image. Finally, we performed extensive comparison experiments and completed ablation experiments on the proposed method. The experimental results show that the proposed method is optimal in human visual experience and underwater image quality evaluation index.

Surface defect detection method for electronic panels based on attention mechanism and dual detection heads.

Automatic detection of surface defects in electronic panels is receiving increasing attention in the quality control of products. The surface defect detection of electronic panels is different from other target detection scenarios and is a meaningful and challenging problem. Its main manifestation is that surface defects of electronic panels usually exhibit extreme irregularity and small target characteristics, which bring great difficulties to the task of surface defect target detection including feature extraction and so on. The traditional methods can only detect a very small number of defect classes under specific detection conditions. And due to the weak robustness of these methods, they cannot be applied in real production scenarios on a large scale. Based on this, this paper applies the target detection technique under deep learning to the surface defect detection scenario of electronic panels for the first time. At the same time, in order to make the designed target detection network applicable to the electronic panel surface defect detection scenario and to enhance the interpretability of the designed target detection network in terms of computer mechanism. We design a deformable convolution module with a convolutional self-attentive module to learn the offset and a dual detection head incorporating the SE (Squeeze-and-Excitation) mechanism for the irregular characteristics of electronic panel surface defects and the small target characteristics, respectively. Finally, we carried out a series of experiments on our own electronic panel defect data set, including comparison with the most advanced target detection algorithms and a series of ablation experiments against our proposed method. The final experimental results prove that our method not only has better interpretability, but also has better metric performance, in which the map_0.5 metric reaches 78.257%, which is an increase of 13.506 percentage points over YOLOV5 and 33.457 percentage points higher than Retinanet. The results prove the proposed method is effective.

Integrated analysis of miRNAs and mRNA profiling reveals the potential roles of miRNAs in sheep hair follicle development.

BACKGROUND: Merino sheep exhibit high wool production and excellent wool quality. The fleece of Merino sheep is predominantly composed of wool fibers grown from hair follicles (HFs). The HF is a complex biological system involved in a dynamic process governed by gene regulation, and gene expression is regulated by microRNAs (miRNAs). miRNA inhibits posttranscriptional gene expression by specifically binding to target messenger RNA (mRNA) and plays an important role in regulating gene expression, the cell cycle and biological development sequences. The purpose of this study was to examine mRNA and miRNA binding to identify key miRNAs and target genes related to HF development. This will provide new and important insights into fundamental mechanisms that regulate cellular activity and cell fate decisions within and outside of the skin. RESULTS: We analyzed miRNA data in skin tissues collected from 18 Merino sheep on four embryonic days (E65, E85, E105 and E135) and two postnatal days (D7 and D30) and identified 87 differentially expressed miRNAs (DE-miRNAs). These six stages were further divided into two longer developmental stages based on heatmap cluster analysis, and the results showed that DE-mRNAs in Stage A were closely related to HF morphogenesis. A coanalysis of Stage A DE-mRNAs and DE-miRNAs revealed that 9 DE-miRNAs and 17 DE-mRNAs presented targeting relationships in Stage A. We found that miR-23b and miR-133 could target and regulate ACVR1B and WNT10A. In dermal fibroblasts, the overexpression of miR-133 significantly reduced the mRNA and protein expression levels of ACVR1B. The overexpression of miR-23b significantly reduced the mRNA and protein expression levels of WNT10A. CONCLUSION: This study provides a new reference for understanding the molecular basis of HF development and lays a foundation for further improving sheep HF breeding. miRNAs and target genes related to hair follicular development were found, which provided a theoretical basis for molecular breeding for the culture of fine-wool sheep.

Development and application of arachnomelia syndrome genetic detection in four Chinese dual-purpose cattle populations.

Arachnomelia syndrome (AS) is an autosomal recessive hereditary disorder in cattle, and affected calves are usually stillborn and characterized by complex anomalies. Therefore, identification of the carrier animals based on genetic tests is important for the control and elimination of this defect. The aim of this study was to build an effective workflow to routinely screen the AS mutations in bovine MOSC1 and SUOX genes and determine individuals carrying the AS mutations in four Chinese cattle populations. By combining the fluorescence-labeled PCR and capillary electrophoresis, we established a convenient and cost-effective workflow to detect two AS casual mutations simultaneously. Sanger sequencing was further used as a validation criterion and showed that 100% of the tests (37/37) had consistent results with genotype calls determined by our established workflow. Then, 582 bulls and 1-926 cows from Chinese dual-purpose cattle populations of Simmental, Sanhe, Shuxuan, and Xinjiang Brown were subjected to AS detection. The results showed that four bulls and 11 cows in the Simmental population, and six bulls and six cows in the Sanhe population were identified as AS carriers with the MOCS1 mutation c.1224_1225delCA. However, no animal was found to carry the c.363_364insG mutation in the SUOX gene. The frequencies of AS carriers were 1.08% and 1.65% in the Simmental and Sanhe populations, respectively, with a frequency of 1.076% in four populations. The pedigree analysis found that all carriers could be traced back to a common ancestor, the German Simmental sire ROMEL. Those findings suggested that this genetic defect spread into China mainly through the wide use of ROMEL. In conclusion, the occurrence of AS has not had a wide impact on the Chinese cattle industry; however, a screening system and mating strategy should be employed to gradually eliminate this recessive gene from the Chinese dual-purpose cattle population.

Factor Analysis of Genetic Parameters for Body Conformation Traits in Dual-Purpose Simmental 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.

Whole genome resequencing reveals the genetic contribution of Kazakh and Swiss Brown cattle to a population of Xinjiang Brown cattle.

Xinjiang Brown cattle is characterized by wide environmental adaptation from its female parent (Kazakh cattle) and good production performance from its male parent (Swiss Brown cattle). However, the genetic basis underlying these characteristics hasnotbeenexplored. Here we compared 50 genomes of Xinjiang Brown cattle to the genomes of other eight breeds worldwide to analyze patterns of genetic variation in the Xinjiang Brown cattle. We found canonical genomic characteristics of cross breed with the lowest linkage disequilibrium and the highest effective population size. At the global level, Xinjiang Brown cattle had 9.88% Kazakh cattle and 90.12% Swiss Brown cattle inheritance. Our local ancestry inference revealed the segments with the excess of Kazakh cattle blood enriched in genes or pathways involved in digestion,absorption,metabolism and disease. More importantly, we also observed the completely fixed haplotypes inherited from Swiss Brown cattle harboring genes (LCORL, GHR, MEF2D, PCSK1 and MSRB3), KEGG pathways, cattle QTLs or human NHGRIGWAS catalog related to body measurement and growth traits. Our findings will not only help understand the process of cross breeding but can provide basic materials for further QTL mapping and improvement of important traits in Xinjiang Brown cattle.