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Mastitis (MAS), endometritis (MET), and ketosis (KET) are prevalent diseases in dairy cows that result in substantial economic losses for the dairy farming industry. This study gathered 26,014 records of the health and sickness of dairy cows and 99,102 data of reproduction from 13 Holstein dairy farms in Central China; the milk protein and milk fat content from 56,640 milk samples, as well as the pedigree data of 37,836 dairy cows were obtained. The logistic regression method was used to analyze the variations in the prevalence rates of MAS, MET, and KET among various parities; the mixed linear model was used to examine the effects of the three diseases on milk production, milk quality, and reproductive traits. DMU software (version 5.2) utilized the DMUAI module in conjunction with the single-trait and two-trait animal model, as well as best linear unbiased prediction (BLUP), to estimate the genetic parameters for the three diseases, milk production, milk quality, and reproductive traits in dairy cows. The primary findings of the investigation comprised the following: (1) The prevalence rates of MAS, MET, and KET in dairy farms were 20.04%, 10.68%, and 7.33%, respectively. (2) MAS and MET had a substantial impact (p < 0.01) on milk production, resulting in significant decreases of 112 kg and 372 kg in 305-d Milk Yield (305-d MY), 4 kg and 12 kg in 305-d Protein Yield (305-d PY), and 6 kg and 16 kg in 305-d Fat Yield (305-d FY). As a result of their excessive 305-d MY, some cows were diagnosed with KET due to glucose metabolism disorder. The 305-d MY of cows with KET was significantly higher than that of healthy cows (205 kg, p < 0.01). (3) All three diseases resulted in an increase in the Interval from Calving to First Service (CTFS, 0.60-1.50 d), Interval from First Service to Conception (FSTC, 0.20-16.20 d), Calving Interval (CI, 4.00-7.00 d), and Number of Services (NUMS, 0.07-0.35). (4) The heritabilities of cows with MAS, MET, and KET were found to be low, with values of 0.09, 0.01, and 0.02, respectively. The genetic correlation between these traits ranged from 0.14 to 0.44. This study offers valuable insights on the prevention and control of the three diseases, as well as feeding management and genetic breeding.

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Ketosis is a common metabolic disorder in the early lactation of dairy cows. It is typically diagnosed by measuring the concentration of ß-hydroxybutyrate (BHB) in the blood. This study aimed to estimate the genetic parameters of blood BHB and conducted a genome-wide association study (GWAS) based on the estimated breeding value. Phenotypic data were collected from December 2019 to August 2023, comprising blood BHB concentrations in 45,617 Holstein cows during the three weeks post-calving across seven dairy farms. Genotypic data were obtained using the Neogen Geneseek Genomic Profiler (GGP) Bovine 100 K SNP Chip and GGP Bovine SNP50 v3 (Illumina Inc., San Diego, CA, USA) for genotyping. The estimated heritability and repeatability values for blood BHB levels were 0.167 and 0.175, respectively. The GWAS result detected a total of ten genome-wide significant associations with blood BHB. Significant SNPs were distributed in Bos taurus autosomes (BTA) 2, 6, 9, 11, 13, and 23, with 48 annotated candidate genes. These potential genes included those associated with insulin regulation, such as INSIG2, and those linked to fatty acid metabolism, such as HADHB, HADHA, and PANK2. Enrichment analysis of the candidate genes for blood BHB revealed the molecular functions and biological processes involved in fatty acid and lipid metabolism in dairy cattle. The identification of novel genomic regions in this study contributes to the characterization of key genes and pathways that elucidate susceptibility to ketosis in dairy cattle.

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Hematological parameters refer to the assessment of changes in the number and distribution of blood cells, including leukocytes (LES), erythrocytes (ERS), and platelets (PLS), which are essential for the early diagnosis of hematological system disorders and other systemic diseases in livestock. In this context, the primary objectives of this study were to investigate the genomic background of 19 hematological parameters in Holstein cattle, focusing on LES, ERS, and PLS blood components. Genetic and phenotypic (co)variances of hematological parameters were calculated based on the average information restricted maximum likelihood method and 1,610 genotyped individuals and 5,499 hematological parameter records from 4,543 cows. Furthermore, we assessed the genetic relationship between these hematological parameters and other economically important traits in dairy cattle breeding programs. We also carried out genome-wide association studies and candidate gene analyses. Blood samples from 21 primiparous cows were used to identify candidate genes further through RNA sequencing (RNA-seq) analyses. Hematological parameters generally exhibited low-to-moderate heritabilities ranging from 0.01 to 0.29, with genetic correlations between them ranging from -0.88 ± 0.09 (between mononuclear cell ratio and lymphocyte cell ratio) to 0.99 ± 0.01 (between white blood cell count and granulocyte cell count). Furthermore, low-to-moderate approximate genetic correlations between hematological parameters with one longevity, 4 fertility, and 5 health traits were observed. One hundred ninety-nine significant SNP located primarily on the Bos taurus autosomes (BTA) BTA4, BTA6, and BTA8 were associated with 16 hematological parameters. Based on the RNA-seq analyses, 6,687 genes were significantly downregulated and 4,119 genes were upregulated when comparing 2 groups of cows with high and low phenotypic values. By integrating genome-wide association studies (GWAS), RNA-seq, and previously published results, the main candidate genes associated with hematological parameters in Holstein cattle were ACRBP, ADAMTS3, CANT1, CCM2L, CNN3, CPLANE1, GPAT3, GRIP2, PLAGL2, RTL6, SOX4, WDFY3, and ZNF614. Hematological parameters are heritable and moderately to highly genetically correlated among themselves. The large number of candidate genes identified based on GWAS and RNA-seq indicate the polygenic nature and complex genetic determinism of hematological parameters in Holstein cattle.

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BACKGROUND: Our previous research identified the Kruppel like factor 6 (KLF6) gene as a prospective candidate for milk production traits in dairy cattle. The expression of KLF6 in the livers of Holstein cows during the peak of lactation was significantly higher than that during the dry and early lactation periods. Notably, it plays an essential role in activating peroxisome proliferator-activated receptor α (PPARα) signaling pathways. The primary aim of this study was to further substantiate whether the KLF6 gene has significant genetic effects on milk traits in dairy cattle. RESULTS: Through direct sequencing of PCR products with pooled DNA, we totally identified 12 single nucleotide polymorphisms (SNPs) within the KLF6 gene. The set of SNPs encompasses 7 located in 5' flanking region, 2 located in exon 2 and 3 located in 3' untranslated region (UTR). Of these, the g.44601035G > A is a missense mutation that resulting in the replacement of arginine (CGG) with glutamine (CAG), consequently leading to alterations in the secondary structure of the KLF6 protein, as predicted by SOPMA. The remaining 7 regulatory SNPs significantly impacted the transcriptional activity of KLF6 following mutation (P < 0.005), manifesting as changes in transcription factor binding sites. Additionally, 4 SNPs located in both the UTR and exons were predicted to influence the secondary structure of KLF6 mRNA using the RNAfold web server. Furthermore, we performed the genotype-phenotype association analysis using SAS 9.2 which found all the 12 SNPs were significantly correlated to milk yield, fat yield, fat percentage, protein yield and protein percentage within both the first and second lactations (P < 0.0001 ~ 0.0441). Also, with Haploview 4.2 software, we found the 12 SNPs linked closely and formed a haplotype block, which was strongly associated with five milk traits (P < 0.0001 ~ 0.0203). CONCLUSIONS: In summary, our study represented the KLF6 gene has significant impacts on milk yield and composition traits in dairy cattle. Among the identified SNPs, 7 were implicated in modulating milk traits by impacting transcriptional activity, 4 by altering mRNA secondary structure, and 1 by affecting the protein secondary structure of KLF6. These findings provided valuable molecular insights for genomic selection program of dairy cattle.

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The mammary glands, responsible for milk secretion, are regulated at a local level by various hormones, growth factors, non-coding RNAs, and other elements. Recent research has discovered the presence of lncRNAs in these glands, with suggestions that they may be essential for the maintenance and function of mammary glands. Besides directly controlling the gene and protein expression, lncRNAs are believed to play a significant part in numerous physiological and pathological processes. This study focused on examining the mammary gland tissues of Chinese Holstein cows, to identify and categorize long non-coding RNAs (lncRNAs). The research intended to distinguish lncRNAs in the mammary tissues of Holstein cows and contrast them between lactation and non-lactation periods. In this study, mammary gland tissues were sampled from three Holstein cows in early lactation (n = 3, 30 days postpartum) and non-lactation (n = 3, 315 days postpartum) on a large dairy farm in Jiangsu province. Mammary tissue samples were collected during early lactation and again during non-lactation. In total, we detected 1905 lncRNAs, with 57.3% being 500 bp and 612 intronic lncRNAs. The exon count for lncRNAs varied from 2 to 10. It was observed that 96 lncRNA expressions markedly differed between the two stages, with 83 genes being upregulated and 53 downregulated. Enrichment analysis results revealed that Gene Ontology (GO) analysis was primarily abundant in cellular processes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that target genes were predominantly abundant in metabolic pathways, fatty acid biosynthesis, the immune system, and glycosphingolipid biosynthesis. This study analyzed the expression profile and characteristics of lncRNAs in the mammary gland tissues of Holstein cows during both lactation and non-lactation stages, forming a foundation for further investigation into the functional roles of lncRNAs in Holstein cows throughout lactation.

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Lactation traits are economically important for dairy cows. Southern China has a high-temperature and high-humidity climate, and environmental and genetic interactions greatly impact dairy cattle performance. The aim of this study was to identify novel single-nucleotide polymorphism sites and novel candidate genes associated with lactation traits in Chinese Holstein cows under high-temperature and humidity conditions in southern China. A genome-wide association study was performed for the lactation traits of 392 Chinese Holstein cows, using GGP Bovine 100 K SNP gene chips. Some 23 single nucleotide polymorphic loci significantly associated with lactation traits were screened. Among them, 16 were associated with milk fat rate, 7 with milk protein rate, and 3 with heat stress. A quantitative trait locus that significantly affects milk fat percentage in Chinese Holstein cows was identified within a window of approximately 0.5 Mb in the region of 0.4-0.9 Mb on Bos taurus autosome 14. According to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, ten genes (DGAT1, IDH2, CYP11B1, GFUS, CYC1, GPT, PYCR3, OPLAH, ALDH1A3, and NAPRT) associated with lactation fat percentage, milk yield, antioxidant activity, stress resistance, and inflammation and immune response were identified as key candidates for lactation traits. The results of this study will help in the development of an effective selection and breeding program for Chinese Holstein cows in high-temperature and humidity regions.

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BACKGROUND: Folic acid is a water-soluble B vitamin (B9), which is closely related to the body's immune and other metabolic pathways. The folic acid synthesized by rumen microbes has been unable to meet the needs of high-yielding dairy cows. The incidence rate of subclinical mastitis in dairy herds worldwide ranged between 25%~65% with no obvious symptoms, but it significantly causes a decrease in lactation and milk quality. Therefore, this study aims at exploring the effects of folic acid supplementation on the expression profile of lncRNAs, exploring the molecular mechanism by which lncRNAs regulate immunity in subclinical mastitic dairy cows. RESULTS: The analysis identified a total of 4384 lncRNA transcripts. Subsequently, differentially expressed lncRNAs in the comparison of two groups (SF vs. SC, HF vs. HC) were identified to be 84 and 55 respectively. Furthermore, the weighted gene co-expression network analysis (WGCNA) and the KEGG enrichment analysis result showed that folic acid supplementation affects inflammation and immune response-related pathways. The two groups have few pathways in common. One important lncRNA MSTRG.11108.1 and its target genes (ICAM1, CCL3, CCL4, etc.) were involved in immune-related pathways. Finally, through integrated analysis of lncRNAs with GWAS data and animal QTL database, we found that differential lncRNA and its target genes could be significantly enriched in SNPs and QTLs related to somatic cell count (SCC) and mastitis, such as MSTRG.11108.1 and its target gene ICAM1, CXCL3, GRO1. CONCLUSIONS: For subclinical mastitic cows, folic acid supplementation can significantly affect the expression of immune-related pathway genes such as ICAM1 by regulating lncRNAs MSTRG.11108.1, thereby affecting related immune phenotypes. Our findings laid a ground foundation for theoretical and practical application for feeding folic acid supplementation in subclinical mastitic cows.

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OBJECTIVES: This study was performed in the frame of a more extensive study dedicated to the integrated analysis of the single-cell transcriptome and chromatin accessibility datasets of peripheral blood mononuclear cells (PBMCs) with a large-scale GWAS of 45 complex traits in Chinese Holstein cattle. Lipopolysaccharide (LPS) is a crucial mediator of chronic inflammation to modulate immune responses. PBMCs include primary T and B cells, natural killer (NK) cells, monocytes (Mono), and dendritic cells (DC). How LPS stimulates PBMCs at the single-cell level in dairy cattle remains largely unknown. DATA DESCRIPTION: We sequenced 30,756 estimated single cells and mapped 26,141 of them (96.05%) with approximately 60,075 mapped reads per cell after quality control for four whole-blood treatments (no, 2 h, 4 h, and 8 h LPS) by single-cell RNA sequencing (scRNA-seq) and single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq). Finally, 7,107 (no), 9,174 (2 h), 6,741 (4 h), and 3,119 (8 h) cells were generated with ~ 15,000 total genes in the whole population. Therefore, the single-cell transcriptome and chromatin accessibility datasets in this study enable a further understanding of the cell types and functions of PBMCs and their responses to LPS stimulation in vitro.

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Considering that artificial insemination is the most widely used assisted reproductive technique in the dairy industry, the semen quality of bulls is very important for selecting excellent stud bulls. Sperm motility is one of the important traits of semen quality, and related genes may be regulated by environmental factors. Seminal plasma can affect sperm cell transcriptome and further affect sperm motility through exosome or other processes. However, the molecular regulation mechanism of bull sperm motility has not been studied by combining the sperm cell transcriptome with seminal plasma metabolome. The number of motile sperm per ejaculate (NMSPE) is an integrated indicator for assessing sperm motility in stud bulls. In the present study, we selected 7 bulls with higher NMSPE (5,698.55 million +/- 945.40 million) as group H and 7 bulls with lower NMSPE (2,279.76 million +/- 1,305.69 million) as group L from 53 Holstein stud bulls. The differentially expressed genes (DEGs) in sperm cells were evaluated between the two groups (H vs. L). We conducted gene co-expression network analysis (WGCNA) on H and L groups of bulls, as well as two monozygotic twin Holstein bulls with different NMSPE values, to screen candidate genes for NMSPE. The regulatory effect of seminal plasma metabolome on the candidate genes of NMSPE was also investigated. A total of 1,099 DEGs were identified in the sperm cells of H and L groups. These DEGs were primarily concentrated in energy metabolism and sperm cell transcription. The significantly enriched Kyoto encyclopedia of genes and genomes (KEGG) pathways of the 57 differential metabolites were the aminoacyl-tRNA biosynthesis pathway and vitamin B6 metabolism pathway. Our study discovered 14 genes as the potential candidate markers for sperm motility, including FBXO39. We observed a broad correlation between transcriptome of sperm cells and seminal plasma metabolome, such as three metabolites, namely, mesaconic acid, 2-coumaric acid, and 4-formylaminoantipyrine, might regulate FBXO39 expression through potential pathways. The genes related to seminal plasma metabolites expressed in sperm cells are not only located near the quantitative trait loci of reproductive traits, but also enriched in the genome-wide association study signal of sire conception rate. Collectively, this study was the first to investigate the interplays among transcriptome of sperm cells and seminal plasma metabolome from Holstein stud bulls with different sperm motility.

A Holstein stud bull can produce thousands of doses of frozen semen, which are used to distribute its selected genetics to dairy herds all over the world. The semen quality of stud bulls has an impact on the economics of the breeding centers. Our previous study found that monozygotic twin stud bulls showed different semen quality traits and different transcriptomic profiles in sperm cells. The number of motile sperm per ejaculate (NMSPE) is an integrated trait for assessing sperm motility in stud bulls, which is one of the most important semen quality traits. In the present study, we selected 7 stud bulls that had a high NMSPE (named as H group) and 7 stud bulls with low NMSPE (named as L group) from a Chinese Holstein bull population based on 9 yr of semen quality records. In this study, we investigated the sperm cells transcriptomic differences between the two groups and observed the influences of seminal plasma metabolites on the transcriptomic profiles of the sperm cells. The results showed that the expression level of the differentially expressed genes in the sperm cells is closely related to NMSPE. Our study discovered 14 genes as the potential candidate markers for sperm motility, including FBXO39. Our data provide new insights into the improvement of bovine semen quality traits.

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Selecting suitable feed types and understanding the gastrointestinal digestive mechanism are helpful for the growth and health of calves in intensive dairy farming. However, the effects on rumen development of changing the molecular genetic basis and the regulatory mechanism by using different feed types are still unclear. Nine 7-day-old Holstein bull calves were randomly divided into GF (concentrate), GFF (alfalfa: oat grass = 3:2) and TMR (concentrate: alfalfa grass: oat grass: water = 0.30:0.12:0.08:0.50) diet experiment groups. Rumen tissue and serum samples were collected for physiological and transcriptomic analysis after 80 days. The results showed that serum α-amylase content and ceruloplasmin activity were significantly higher in the TMR group, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis ncRNAs and mRNAs were significantly enriched in the pathways of rumen epithelial development and stimulated rumen cell growth, including the Hippo signaling pathway, Wnt signaling pathway, thyroid hormone signaling pathway, ECM-receptor interaction and the absorption of protein and fat. The circRNAs/lncRNA-miRNAs-mRNA networks constructed, including novel_circ_0002471, novel_circ_0012104, TCONS_00946152, TCONS_00960915, bta-miR-11975, bta-miR-2890, PADI3 and CLEC6A, participated in metabolic pathways of lipid, immune system, oxidative stress and muscle development. In conclusion, the TMR diet could improve rumen digestive enzyme activities, stimulate rumen nutrient absorption and stimulate the DEGs related to energy homeostasis and microenvironment balance, and is thus better than the GF and GFF diets for promoting rumen growth and development.

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Body size is one of the most economically important traits of dairy cattle, as it is significantly associated with cow longevity, production, health, fertility, and environmental adaptation. The identification and application of genetic variants using a novel genetic approach, such as genome-wide association studies (GWASs), may give more insights into the genetic architecture of complex traits. The identification of genes, single nucleotide polymorphisms (SNPs), and pathways associated with the body size traits may offer a contribution to genomic selection and long-term planning for selection in dairy cows. In this study, we performed GWAS analysis to identify the genetic markers and genes associated with four body size traits (body height, body depth, chest width, and angularity) in 1000 Chinese Holstein cows. We performed SNPs genotyping in 1000 individuals, based on the GeneSeek Genomic Profiler Bovine 100 K. In total, we identified 11 significant SNPs in association with body size traits at the threshold of Bonferroni correction (5.90 × 10-7) using the fixed and random model circulating probability unification (FarmCPU) model. Several genes within 200 kb distances (upstream or downstream) of the significant SNPs were identified as candidate genes, including MYH15, KHDRBS3, AIP, DCC, SQOR, and UBAP1L. Moreover, genes within 200 kb of the identified SNPs were significantly enriched (p ≤ 0.05) in 25 Gene Ontology terms and five Kyoto Encyclopedia of Genes and Genomes pathways. We anticipate that these results provide a foundation for understanding the genetic architecture of body size traits. They will also contribute to breeding programs and genomic selection work on Chinese Holstein cattle.

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Genetic improvement of milk fatty acid content traits in dairy cattle is of great significance. However, chromatography-based methods to measure milk fatty acid content have several disadvantages. Thus, quick and accurate predictions of various milk fatty acid contents based on the mid-infrared spectrum (MIRS) from dairy herd improvement (DHI) data are essential and meaningful to expand the amount of phenotypic data available. In this study, 24 kinds of milk fatty acid concentrations were measured from the milk samples of 336 Holstein cows in Shandong Province, China, using the gas chromatography (GC) technique, which simultaneously produced MIRS values for the prediction of fatty acids. After quantification by the GC technique, milk fatty acid contents expressed as g/100 g of milk (milk-basis) and g/100 g of fat (fat-basis) were processed by five spectral pre-processing algorithms: first-order derivative (DER1), second-order derivative (DER2), multiple scattering correction (MSC), standard normal transform (SNV), and Savitzky-Golsy convolution smoothing (SG), and four regression models: random forest regression (RFR), partial least square regression (PLSR), least absolute shrinkage and selection operator regression (LassoR), and ridge regression (RidgeR). Two ranges of wavebands (4000~400 cm-1 and 3017~2823 cm-1/1805~1734 cm-1) were also used in the above analysis. The prediction accuracy was evaluated using a 10-fold cross validation procedure, with the ratio of the training set and the test set as 3:1, where the determination coefficient (R2) and residual predictive deviation (RPD) were used for evaluations. The results showed that 17 out of 31 milk fatty acids were accurately predicted using MIRS, with RPD values higher than 2 and R2 values higher than 0.75. In addition, 16 out of 31 fatty acids were accurately predicted by RFR, indicating that the ensemble learning model potentially resulted in a higher prediction accuracy. Meanwhile, DER1, DER2 and SG pre-processing algorithms led to high prediction accuracy for most fatty acids. In summary, these results imply that the application of MIRS to predict the fatty acid contents of milk is feasible.

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The main objectives of this study were to estimate genetic parameters for milk urea nitrogen (MUN) in Holstein cattle and to conduct a single-step (ss)GWAS to identify candidate genes associated with MUN. Phenotypic measurements from 24,435 Holstein cows were collected from March 2013 to July 2019 in 9 dairy farms located in the Beijing area, China. A total of 2,029 cows were genotyped using the Illumina 150K Bovine Bead Chip, containing 121,188 SNP. A single-trait repeatability model was used to evaluate the genetic background of MUN. We found that MUN is a trait with low heritability (0.06 ± 0.004) and repeatability (0.12). Considering similar milk production levels, a lower MUN concentration indicates higher nitrogen digestibility. The genetic correlations between MUN and milk yield, net energy concentration, fat percentage, protein percentage, and lactose percentage were positive and ranged from 0.02 to 0.26. The genetic correlation between MUN and somatic cell score (SCS) was negative (-0.18), indicating that animals with higher MUN levels tend to have lower SCS. Both ssGWAS and pathway enrichment analyses were used to explore the genetic mechanisms underlying MUN. A total of 18 SNP (located on BTA11, BTA12, BTA14, BTA17, and BTA18) were found to be significantly associated with MUN. The genes CFAP77, CAMSAP1, CACNA1B, ADGRB1, FARP1, and INTU are considered to be candidate genes for MUN. These candidate genes are associated with important biological processes such as protein and lipid metabolism and binding to specific proteins. This set of candidate genes, metabolic pathways, and their functions provide a better understanding of the genomic architecture and physiological mechanisms underlying MUN in Holstein cattle.

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This study was to explore potential SNP loci for reproductive traits in Chinese Holstein cattle and identify candidate genes. Genome-wide Association Study based on mixed linear model was performed on 643 Holstein cattle using GeneSeek Bovine 50 K SNP chip. Our results detected forty significant SNP loci after Bonferroni correction. We identified five genes (VWC2L, STAT1, PPP3CA, LDB3, and CTNNA3) as being associated with pregnancy ratio of young cows, five genes (PAEP, ACOXL, EPAS1, GLRB, and MARVELD1) as being associated with pregnancy ratio of adult cows, and nine genes (PDE1B, SLCO1A2, ARHGAP26, ADAM10, APBB1, MON1B, COQ9, CDC42BPB, MARVELD1, and HPSE2) as being associated with daughter pregnancy rate. Our study may provide valuable insights into identifying genes related to reproductive traits and help promote the application of molecular breeding in dairy cows.

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Introduction: Chinese Holstein in South China suffer heat stress for a long period, which leads to evolutionary differences from Chinese Holstein in North China. The aim of this study was to estimate the genetic parameters of fertility traits for Chinese Holstein in South China. Methods: A total of 167,840 Chinese Holstein heifers and cows from Guangming Animal Husbandry Co., LTD farms were used in this study. The fertility traits analyzed were calving interval (CI), days open (DO), age of first service (AFS), age of first calving (AFC), calving to first insemination (CTFS), first insemination to conception (FSTC), gestation length (GL), non-return rate to 56 days (NRR), and number of services (NS). Results: The descriptive statistics revealed that the same trait in heifers performed better than in cows, which was consistent with the other studies. The heritabilities of fertility traits in this study ranged from close to 0 (for NS of cows) to 0.2474 (for AFC of heifers). The genetic correlation of NRR between heifers and cows was 0.9993, which indicates that the NRR for heifers and cows could be treated as one trait in this population. Conclusion: The heritabilities of fertility traits in Chinese Holstein in south China were quite different from the heritabilities of fertility traits in North China. NRR56, NS, AFC, and CI were suggested to be included into the selection index to improve fertility performance of Chinsese Holstein of south China. The results of this study could provide genetic parameters for the animal breeding program of Chinese Holstein in the south of China.

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The present study was designed to evaluate the association of polymorphisms in bovine trafficking protein particle complex subunit 9 (TRAPPC9) and cluster of differentiation 4 (CD4) genes with milk production and mastitis resistance phenotypic traits in a different cattle population. Three single nucleotide polymorphisms (SNPs) (SNP1 Position: Chr14:2484891, SNP2 (rs110017379), SNP3 Position: Chr14:2525852) in bovine TRAPPC9 and one SNP (Position: Chr5:104010752) in CD4 were screened through Chinese Cow's SNPs Chip-I (CCSC-I) and genotyped in a population of 312 Chinese Holsteins (156: Mastitis, 156: Healthy). The results were analyzed using the general linear model in SAS 9.4. Our analysis revealed that milk protein percentage, somatic cell count (SCC), somatic cell score (SCS), serum cytokines interleukin 6 (IL-6) and interferon-gamma (IFN-γ) were significantly (P < 0.05) associated with at least one or more identified SNPs of TRAPPC9 and CD4 genes. Furthermore, the expression status of SNPs in CD4 and TRAPPC9 genes were verified through RT-qPCR. The expression analysis showed that genotypes GG in SNP3 of TRAPPC9 and TT genotype in SNP4 of CD4 showed higher expression level compared to other genotypes. The GG genotype in SNP2 and TT genotype in SNP3 of TRAPPC9 were associated with higher bovine milk SCC and lower IL6. Altogether, our findings suggested that the SNPs of TRAPPC9 and CD4 genes could be useful genetic markers in selection for milk protein improvement and mastitis resistance phenotypic traits in dairy cattle. The CCSC-I used in current study is proposed to be validate in different and large population of dairy cattle not only in China but also in other countries. Moreover, our analyses recommended that besides SCC and SCS, the association of genetic markers could also be considered with the serum cytokines (IL-6, IFN-γ) while selecting genetically mastitis resistance dairy cattle.

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In dairy cows, supernumerary teats (SNT) are not desired as they are considered a repository for bacteria; thus, SNT are a risk factor for mastitis. Supernumerary teats are a heritable oligo- or polygenic trait. The incidence of SNT in offspring must be reduced by genomic selection. However, in modern dairy farming, farmers often ignore the effects of SNT on cows. The study aimed to elucidate the effects of SNT on dairy cows from the blood transcriptome level and identify genes associated with SNT in Chinese Holstein cows. We selected 6 SNT cows (Yes) and 6 non-SNT cows (No). In the 6 SNT cows, 3 cows had 1 SNT (One) and 3 cows had 2 SNT (Two). They were divided into 3 comparison groups (One vs. No; Two vs. No; and Yes vs. No). RNA was extracted from blood white membrane cells of 12 cows, and RNA sequencing was performed. Differential gene expression analysis based on the negative binomial distribution was used to detect differentially expressed genes in the One versus No and Two versus No comparison groups. Genes that were significantly upregulated or downregulated both in the One versus No and Two versus No groups (shared genes, SG) were obtained for further analysis. We also performed gene set enrichment analysis for all genes expressed in the Yes versus No group, correlation analysis between SG and the hematological parameters, protein-protein interaction network analysis of SG to select hub genes, and alternative splicing analysis for Yes versus No group to explore the functions of differentially spliced genes. We detected 289 SG. Gene set enrichment analysis, gene ontology, and the Kyoto Encyclopedia of Genes and Genomes enrichment analysis results showed that SNT affect immunity, inflammation, and lactation-related pathways in dairy cows. Correlation analysis showed that LOC104968484, SLC25A6, GADD45G, BAX, APAF1, ATM, XIAP, MDM4, BDP1, CEP350, MED13, TAOK1, SMG1, and RIF1 are associated with white blood cell count and absolute value of lymphocytes in SNT cows only, so they might be genes associated with SNT in Chinese Holstein cows. We found 2 genes (BAX and MDM4) were also differentially spliced genes. However, the causal relationship between these genes and the SNT phenotype needs to be further studied. This study is the first to reveal the adverse effects of SNT on dairy cows at a transcriptional level, and the genes we found can be used as a reference for further searching for candidate genes for the SNT phenotype.

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Genomic selection (GS) is an efficient method to improve genetically economic traits. Feature selection is an important method for GS based on whole-genome sequencing (WGS) data. We investigated the prediction performance of GS of milk production traits using imputed WGS data on 7957 Chinese Holsteins. We used two regularized regression models, least absolute shrinkage and selection operator (LASSO) and elastic net (EN) for feature selection. For comparison, we performed genome-wide association studies based on a linear mixed model (LMM), and the N single nucleotide polymorphisms (SNPs) with the lowest p-values were selected (LMMLASSO and LMMEN), where N was the number of non-zero effect SNPs selected by LASSO or EN. GS was conducted using a genomic best linear unbiased prediction (GBLUP) model and several sets of SNPs: (1) selected WGS SNPs; (2) 50K SNP chip data; (3) WGS data; and (4) a combined set of selected WGS SNPs and 50K SNP chip data. The results showed that the prediction accuracies of GS with features selected using LASSO or EN were comparable to those using features selected with LMMLASSO or LMMEN. For milk and protein yields, GS using a combination of SNPs selected with LASSO and 50K SNP chip data achieved the best prediction performance, and GS using SNPs selected with LMMLASSO combined with 50K SNP chip data performed best for fat yield. The proposed method, feature selection using regularization regression models, provides a valuable novel strategy for WGS-based GS.

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The cow's milk production characteristics are a significant economic indicator in the livestock industry. Serum cytokines such as interleukin-17 (IL-17) may be potential indicators for bovine mastitis concerning the milk somatic cell count (SCC) and somatic cell score (SCS). The current study aims to find previously undiscovered single nucleotide polymorphisms in the bovine (IL-17A) gene and further investigates their associations with milk production traits in Chinese Holstein cows. Twenty Chinese Holstein cows were randomly chosen from six farms in Jiangsu Province, China. The DNA was extracted from selected samples of bloods for PCR amplification Sequence analyses were used to find SNPs in the bovine (IL-17A) gene. The discovered five SNPs are g-1578A>G, g-1835G>A, and g-398T>A in the 5'UTR; g3164T>C and g3409G>C in the exon region. The genotyping of Holstein cows (n = 992) was performed based on Sequenom Mass ARRAY and SNP data. The connection between SNPs, milk production variables, and the somatic cell score was investigated using the least-squares method. Based on the results, SNP g-398T>A had a significant linkage disequilibrium with g3164T>C. SNPs were found to have significant (p < 0.05) correlations with the test-day milk yield. In conclusion, IL-17A affects cow's milk production traits significantly.

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Ketosis is one of the most prevalent and complex metabolic disorders in high-producing dairy cows and usually detected through analyses of ß-hydroxybutyrate (BHB) concentration in blood. Our main objectives were to evaluate genetic parameters for blood BHB predicted based on Fourier-transform mid-infrared spectra from 5 to 305 d in milk, and estimate the genetic relationships of blood BHB with 7 reproduction traits and 6 longevity traits in Holstein cattle. Predicted blood BHB records of 11,609 Holstein cows (after quality control) were collected from 2016 to 2019 and used to derive 4 traits based on parity number, including predicted blood BHB in all parities (BHBp), parity 1 (BHB1), parity 2 (BHB2), and parity 3+ (BHB3). Single- and multitrait repeatability models were used for estimating genetic parameters for the 4 BHB traits. Random regression test-day models implemented via Bayesian inference were used to evaluate the daily genetic feature of BHB variability. In addition, genetic correlations were calculated for the 4 BHB traits with reproduction and longevity traits. The heritability estimates of BHBp, BHB1, BHB2, and BHB3 ranged from 0.100 ± 0.026 (± standard error) to 0.131 ± 0.023. The BHB in parities 1 to 3+ were highly genetically correlated and ranged from 0.788 (BHB1 and BHB2) to 0.911 (BHB1 and BHB3). The daily heritability of BHBp ranged from 0.069 to 0.195, higher for the early and lower for the later lactation periods. A similar trend was observed for BHB1, BHB2, and BHB3. There are low direct genetic correlations between BHBp and selected reproductive performance and longevity traits, which ranged from -0.168 ± 0.019 (BHBp and production life) to 0.157 ± 0.019 (BHBp and age at first calving) for the early lactation stage (5 to 65 d). These direct genetic correlations indicate that cows with higher BHBp (greater likelihood of having ketosis) in blood usually have shorter production life (-0.168 ± 0.019). Cows with higher fertility and postpartum recovery, such as younger age at first calving (0.157 ± 0.019) and shorter interval from calving to first insemination in heifer (0.111 ± 0.006), usually have lower BHB concentration in the blood. Furthermore, the direct genetic correlations change across parity and lactation stage. In general, our results suggest that selection for lower predicted BHB in early lactation could be an efficient strategy for reducing the incidence of ketosis as well as indirectly improving reproductive and longevity performance in Holstein cattle.

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