Jianbin Li

Background: Genome-wide association studies (GWAS) has identified many genetic variants associated with milk-related traits in dairy cattle. However, the causal variants or genes remain largely unknown. In this study, using a large population (> 10,000 individuals) of Chinese Holstein cattle, we performed GWAS for six milk-related traits (milk yield, fat percentage, protein percentage, fat yield, protein yield, and somatic cell score) and subsequently prioritized putative causal variants by multi-trait Bayesian fine-mapping and examined the causal genes by Mendelian randomization (MR) analysis incorporating GWAS and cis -eQTL summary data from CattleGTEx. We also conducted a colocalization analysis to identify the share putative causal variants behind the milk-related traits and gene expressions. Results: A total of 9,688 genome-wide significant SNPs ( P < 1.2 × 10 ⁻⁷ ) were identified across the GWAS results for six milk-related traits, and these SNPs were distributed in 25 unique QTL regions. Subsequently, the multi-trait Bayesian fine-mapping identified 211 independent credible sets (CS) containing putative causal variants within these QTL regions. Among these CSs, 189 CSs were significantly associated with at least one trait (average lfsr < 0.01). Notably, the lead SNPs within these significant CSs included 3 missense variants and 62 non-coding transcript variants. The MR analysis detected 268 causal associations between gene expression and milk-related traits. The colocalization analysis identified two regions containing common putative causal variants for one or multiple milk-related traits and the expressions of some genes. Conclusions: Our integrative analysis of GWAS, Bayesian fine-mapping, MR, and colocalization further confirmed the well-known causal associations of DGAT1 and GHR and the milk-related traits. In addition, we revealed some novel potential causal genes, including AHNAK , ARHGEF2 , SOX13 , FDPS , SCGB2A2 , and MROH2B . These results enhance our understanding of genetic mechanisms underlying the milk-related traits in dairy cattle.

Heat stress (HS) severely significantly reduces milk yield and causes substantial economic losses of dairy cows. TMT-based proteomes and an untargeted metabolomics approach were used to conduct the proteomics and metabolomics in heat-stressed (HS, n = 6) and heat-resistant (HR, n = 6) Chinese Holstein. The proteomics showed that 29 differentially expressed proteins (DEPs), with SERPINA3-7, ACTN4, and PLOD1 up-regulated, and GSN down-regulated in HR cows. The metabolomics showed that 168 differential positive metabolites and 170 differential negative metabolites were identified, with HR cows exhibiting lower levels of anti-inflammatory compounds, such as N6-Acetyl-L-lysine. In addition, 29 DEPs and 338 metabolites revealed four key pathways, including the lysine degradation (ko00310) and metabolic pathway (ko01100) with underlying protein–metabolite interactions, where up-regulated PLOD1 and ACTN4 and down-regulated EXT1 and GSN were observed to be interacting with the down-regulated N6-Acetyl-L-lysine, citric acid, 4-Pyridoxic acid, uracil, and uric acid, and the up-regulated arachidonic acid was enriched, which could be used for rapid and noninvasive screening of heat-tolerant cows. Functional validation through cell experiments, qPCR, and Western blot analyses showed that the interference of the ACTN4 gene could induce dairy cow mammary epithelial cell apoptosis, which could be regarded as a potential biomarker for HS in Chinese Holstein. Our results facilitate a better understanding of the molecular mechanism underlying the HS issue in dairy cows and provide a crucial insight into the alternative strategies to enhance animal welfare and productivity under high-temperature conditions.

Cattle body size measurements constitute the conformation traits that facilitate their production, fertility, and longevity status. Prioritizing functional variants and causal genes of conformation traits is essential for understanding their genetic basis. In this study, we conducted single-trait and multitrait GWAS for 20 body conformation traits using imputed sequence data in 7,674 Chinese Holstein individuals and identified 27 QTL regions. Leveraging these QTL regions, we performed multitrait Bayesian fine-mapping to identify 30 independent credible sets of putative causal variants. Incorporating GWAS and cis-acting expression QTL data, Mendelian randomization was used to infer 153 putative causal gene-trait relationships. The previously reported genes, such as CCND2, TMTC2, and NRG3, were confirmed in our study. Of note, several novel candidate causal genes were also identified, such as C1R, RIMS1, SERPINB8, NETO2, TTYH3, TTC3, ANAPC4, and PSMD13. Our results provide new insights into the regulatory mechanisms of body conformation traits in cattle.

A2 β-CN milk has gained widespread acceptance due to its nutritional benefits. To verify the authenticity and detect adulteration and contamination in A2 milk, we developed an HPLC-MS/MS method for determining the characteristic peptides of A1 and A2 β-CN in cow milk. The method demonstrated good specificity, sensitivity, and linearity for both A1 and A2 characteristic peptides, with limit of detection of 0.01 mg/L and 0.03 mg/L, limit of quantitation of 0.03 mg/L and 0.1 mg/L, and determination coefficients of 0.9994 and 0.9992, respectively. Whereas accuracy and precision were reasonable, the recoveries varied (69.4%-151%) across concentration levels (0.04, 0.2, 1.0 g/kg), with higher recoveries for both peptides at low concentrations and lower recoveries for A2 peptide at medium and high concentrations, influenced by factors such as adsorption and ionization efficiency. We optimized the tryptic hydrolysis conditions, selecting a trypsin-to-casein ratio of 1:25 and a hydrolysis time of 6 h at 37°C. However, the hydrolysis of A1 and A2 β-CN was incomplete and asynchronous, exhibiting parabolic relationships with their respective concentrations, with hydrolysis degrees of 12.3% for A1 β-CN and 9.6% for A2 β-CN in pure powders. We finally established a regression model to calculate the actual proportion of A1 and A2 β-CN, with the detection limits of 5% for both β-CN. In the quantitation range of this model, A1 β-CN accounting for 10% to 80% or A2 β-CN accounting for 20% to 90%, the measured value of A1/A2 or A2/A1 was a power function relationship with the theoretical value. This method effectively verifies the authenticity of A1 and A2 milk, providing a reliable tool for detecting adulteration and contamination.

The elite bull plays an extremely important role in the genetic progression of the dairy cow population. The previous results indicated the potential positive relationship of large scrotal circumference (SC) with improved semen volume, concentration, and motility. In order to improve bull’s semen quantity and quality by selection, it is necessary to estimate the genetic parameters of semen traits and their correlations with other conformation traits such as SC that could be used for an indirect selection. In this study, the genetic parameters of seven semen traits (n = 66,260) and nine conformation traits (n = 3,642) of Holstein bulls (n = 453) were estimated by using the bivariate repeatability animal model with the average information‐restricted maximum likelihood (AI‐REML) approach. The results showed that the estimated heritabilities of semen traits ranged from 0.06 (total number of motile sperm, TNMS) to 0.37 (percentage of abnormal sperm, PAS) and conformation traits ranged from 0.23 (pin width, PW) to 0.69 (hip height, HH). The highest genetic correlations were found between semen volume per ejaculation (SVPE), semen concentration per ejaculation (SCPE), total number of sperm (TNS), and TNMS traits that were 0.97, 0.98, 1.00, and 0.99, respectively. Phenotypic correlations between SC and SVPE, SCPE, TNS, and TNMS were 0.35, 0.35, 0.48, and 0.42, respectively. In summary, the moderate or high heritability of semen traits indicates that genetic improvement of semen quality by selection is feasible, where SC could be a useful trait for indirect selection or as correlated information to improve semen quantity and production in the practical bull breeding programs.

Milk MIR spectra have been shown to provide valuable information on a wide range of traits to be used in dairy cattle breeding programs. Selecting the most informative variables from complex data can improve prediction accuracy and model robustness and, consequently, the interpretability of MIR spectra. Thus, we aimed to investigate the prediction performance of feature selection methods based on MIR spectra data, using the milk fatty acid (FA) profile as an example to illustrate the evaluated procedure. Data of MIR spectra, milk test-day records, and reference FA concentrations of 155 first-parity Holstein cows were used in the analyses. Four models comprising different explanatory variables and five feature selection methods were evaluated. The results indicated that the Competitive Adaptive Reweighted Sampling (CARS) method can effectively select the most informative variables from the MIR spectra, resulting in higher prediction accuracies than other variable selection approaches. The model including selected MIR spectra and cow information variables [days in milk at the test day, age at the test day, pregnancy stage (in days), number of days open, number of inseminations, and somatic cell count] yielded the best FA profile predictions based on Partial Least Square regression. In particular, ten FAs (C8:0, C10:0, C14:1, C17:0 isomers, C18:1, C18:1 isomer, medium-chain FA, unsaturation FA, monounsaturated FA, and polyunsaturated FA) presented accuracies based on the determination coefficient (R2cv) ranging from 0.66 to 0.85 in internal validation and from 0.65 to 0.84 in external validation. By running CARS 1,000 times in internal validations, we obtained the frequency of selected milk MIR wavenumber for 35 FAs. The most related wavenumbers to FAs were found within 1,003 to 1,145 cm-1, while other discrete areas were between 1,651 to 1,797 and 2,834 to 2,954 cm-1. These biomarkers may give insights into the relationship between MIR spectra and FA phenotypes. In conclusion, using CARS and cow information improved predictions of FAs based on MIR spectra in Chinese Holstein dairy cows. Additional validation studies should be conducted as larger datasets become available.

The current study conducted a preliminary investigation of genetic correlations in Chinese Holstein bulls to improve their semen quantity and quality by indirect selection of conformation traits. The results of seven semen traits and nine conformation traits showed that the heritability estimates of semen traits ranged from 0.24 (post-thaw motility) to 0.63 (volume per ejaculation), while the conformation traits ranged from 0.29 (pin width) to 0.80 (withers height). Phenotypic correlation between scrotal circumference (SC) and semen concentration per ejaculation (SCPE), SC and total number of sperm per ejaculation (TNS), and SC and total number of motile sperm per ejaculation (TNMS) was 0.22, 0.25, and 0.24, respectively. Genetic correlation between SC and SCPE, SC and TNS, and SC and TNMS was 0.41, 0.40, and 0.38, respectively. To summarize, moderate or high heritability of semen traits indicated that genetic improvement of semen quality by selection is feasible, where SC could be a useful trait for indirect selection or as correlated information to improve semen quantity and production.

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.