Milk Traits Research Articles

Milk traits characterization and association studies with DGAT1 polymorphisms in Bagnolese sheep.

The Bagnolese sheep is an authochtonous dual-purpose breed (milk and meat) reared in the Campania region, whose milk is used to produce Pecorino Bagnolese cheese. Genetic information on this sheep is extremely limited, especially regarding genes affecting productions. The aim of this study was to investigate milk production traits in Bagnolese sheep and the variability of diacylglycerol acyltransferase 1 (DGAT1) gene and its effects on milk production. Milk quantity was recorded during the morning milking, while Kjeldahl and Gerber methods were used to assess protein and fat percentage (w/v) of collected milk samples. Two PCR-RFLP protocols using BamHI and MspI endonucleases for genotyping of g.5553C>T and g.8539C>T at DGAT1 locus, respectively, were set up. Bagnolese sheep milk shows high fat and protein concentrations. Genotyping revealed a high frequency of the g.5553C and g.8539C alleles (0.56 and 0.95, respectively). The association study between the SNP g.5553C>T and milk traits showed that animals with the CT genotype had a higher percentage of fat produced per milking than those with the CC and TT genotypes (p<0.01). Similar results were found for protein yield percentage, with CT individuals being more productive than CC individuals (p<0.01). Bagnolese sheep milk parameters found are associated with high yields in the resulting dairy products. CT genotype at the SNP g.5553 of DGAT1 has shown a positive association with fat and protein milk yield percentage suggesting it could be considered a marker to improve productions of this breed. Finally, the new genotyping techniques used for this study enable a cheap and reliable characterization of two DGAT1 SNPs in sheep.

An Investigation of Polymorphism on the FASN, SCD1, and SLC27A3 Genes in Sheep

Milk traits of sheep are affected by many environmental and genetic factors. These traits are quantitative traits and are determined by many genes. This study aimed to investigate polymorphisms of milk-related Fatty-acid synthase (FASN), Stearoyl-CoA desaturase (SCD1), and solute carrier 27A3 (SLC27A3) genes in cross-bred Hamdani sheep. Blood samples were collected from 100 healthy cross-bred Hamdani ewes from the jugular vein into K3-EDTA-containing tubes. Genomic DNA was extracted using a commercial DNA isolation kit. PCR products of FASN (275 bp), SCD1 (225 bp), SLC27A3-P1 (341 bp), and SLC27A3-P2 (319 bp) were subjected to restriction fragment length polymorphism (RFLP) analysis using SSil (AciI), Cfr13I, AluI, and Hpy188III the restriction enzyme, respectively. However, all gene regions were found to be monomorphic. In the study, only TT, AA, AA, and GG genotypes were detected for FASN, SCD1, SLC27A3-P1, and SLC27A3-P2, respectively. Allele and genotype frequencies were 1.00 for all genotypes and alleles. Although this study did not reveal favorable genotypes in FASN, SCD1, and SLC27A3 genes that can be used for milk traits, more comprehensive studies with larger sample sizes should be conducted to investigate polymorphisms in cross-bred Hamdani sheep.

Quantifying the effects of the mitochondrial genome on milk production traits in dairy cows: empirical results and modelling challenges

Significant advances in livestock traits have been achieved primarily through selection strategies targeting variation in the nuclear genome, with little attention given to mitogenome variation. We analyzed the influence of the mitogenome on milk production traits of Holstein cattle in Croatia based on strategically generated next-generation sequencing data for 109 cows pedigree-linked to 7115 milk production records (milk, fat and protein yield) from 3006 cows (first 5 lactations). Since little is known about the biology of the relationship between mitogenome variation and production traits, our quantitative genetic modeling was complex. Thus, the proportion of total variance explained by mitogenome inheritance was estimated using 5 different models: (1) cytoplasmic model with maternal lineages (CYTO), (2) haplotypic model with mitogenome sequences (HAPLO), (3) amino acid model with unique amino acid sequences (AMINO), (4) evolutionary model based on a phylogenetic analysis using Bayesian Evolutionary Analysis Sampling Trees phylogenetic analysis (EVOL), and (5) mitogenome SNP model (SNPmt). The polygenic autosomal and X chromosome additive genetic effects based on pedigree were modeled, together with the effects of herd-year-season interaction, permanent environment, location, and age at first calving. The estimated proportions of phenotypic variance explained by mitogenome in 4 different models (CYTO, HAPLO, AMINO, and SNPmt) were found to be substantial given the size of mitogenome, ranging from 5% to 7% for all 3 milk traits. At the same time, a negligible proportion of the phenotypic variance was explained by mitogenome with the EVOL model. Similarly, in all models, no proportion of phenotypic variance was explained by the X chromosome. Although our results should be confirmed in other dairy cattle populations, including a large number of sequenced mitogenomes and nuclear genomes, the potential of utilizing mitogenome information in animal breeding is promising, especially as the acquisition of complete genome sequences becomes cost-effective.

Evaluation of performance and estimation of genetic parameters for milk yield and reproductive traits in Awassi and Asaf sheep breeds and crosses in the Palestinian territory.

This study aimed to estimate genetic parameters, including heritability and repeatability, for milk yield and prolificacy in sheep. It included 3682 records from 1837 ewes across various breeds: Awassi, Assaf, and Awassi x Assaf crosses, two Awassi lines: Improved Awassi, AFEC Awassi. The study measured total milk yield (TMY), yield up to 120 days (TMY120), and yield up to 150 days (TMY150), alongside reproductive traits: litter size (LS), number of lambs born alive (NLBA), and lambing interval (LI). The analysis utilized a mixed model and the REML procedure to estimate genetic parameters. Results indicated that litter size (LS) had no significant impact on milk traits, whereas breed, location, ewe parity, and lambing season showed significant effects. Lactation length also significantly influenced TMY. For reproductive traits, treatment was significant for NLBA, with location associated with breed, parity, and season affecting all traits. Heritability estimates for TMY ranged from 0.00 in Awassi x Assaf crosses to 0.11 in Awassi, and for TMY120 and TMY150, from 0.00 in Awassi x Assaf crosses to 0.16 in Awassi. Estimates for LS and NLBA varied similarly. The heritability for LI was 0.03 in Awassi and zero in other breeds and crosses. The findings suggest that in the northern West Bank regions of Nablus and Jenin, Assaf or Awassi x Assaf breeds are preferable. The study underscores the importance of comprehensive performance and pedigree recording for effective sheep farm management.

The investigation of SNP in SOCS2 gene and its effect on milk yield, fat, protein, and somatic cell count in Awassi ewes.

Livestock farmers face financial losses every year because milk yield and components are severely affected by udder diseases. These udder infections attract the immune response from the host and lead to the influx of neutrophils into milk to fight infection and thus the number of somatic cell count (SCC) is increased. The SCC value of milk could be used as an important indicator in detecting clinical mastitis in dairy animals. Also, the milk yield and milk quality (e.g. fat) are negatively affected by the increased SCC. The SCC is used to estimate the somatic cell score (SCS)of the milk, which is used as an indirect measure to detect subclinical mastitis. Therefore, the purpose of this study was to investigate the presence of a significant SNP rs868996547, on the suppressor of cytokine signaling 2 gene (SOCS2) which is related to milk yield and milk quality in Awassi sheep. In this study, milk production data was obtained from 210 healthy Awassi ewes with different parties and ages. The general linear model (GLM) process analysis of variance (ANOVA) was used to determine fixed effects on milk traits. The DNA extraction was done using a blood DNA extraction kit from Qiagen. To validate the presence of SNP a customized SNP detection developed by Thermofisher Scientific was used. The presence of the SNP in the SOCS2 gene was detected with genotypes (C/T, T/T, and C/C) and T being the mutated allele and it had a significant (p < 0.015) effect on the milk yield (p < 0,015;0.091), fat (p < 0,001;0,003), fat/protein ratio (p < 0.001;0,037) and log10SCC value (p < 0,006;0,015) of Awassi ewes. However, the protein, total solid, and lactose percentages in the wild type and the mutated ewes found having no significant difference (P > 0.05). Our result showed the increase in SCC or SCS of the milk significantly affected the milk yield and composition. Parity and age had significant effects on ewes' milk yield (p < 0.001). In conclusion, we investigated the presence of SOCS2 gene of Awassi ewes in the study flock and its effect on milk yield, fat, and somatic cell count, and the change in milk composition and milk yield because of SCC.

Different Types of Milk Flow Curves and Factors Affecting Milkability in Buffalo Species

Buffaloes are characterized by longer teats and teat canals and stronger muscular resistance of the teat wall than cattle; it is necessary to have a high vacuum level to open the teat canal and begin milk ejection. In buffalo milking management, milk yield, and flow profiles are essential parameters to record and evaluate. The milking machine is a critical point, and the characteristics of the milking vacuum and the pulsation rate are closely related to milk flow observations. In Italy, the most used vacuum levels are 44-46 kPa (range 40-53 kPa). The data on the milkability traits of the Mediterranean Italian breed made it possible to classify eight different types of milk flow curves due to anatomical, physiological, and management differences. This study aims to evaluate the main factors influencing milkability in dairy buffaloes. The results suggest the detachment of the milking cluster to reduce the decreasing and blind phases with the following advantages: reduction of the total milking time and consequently of the worker's time, improvement of the farmer's profitability and milk quality through decreasing the incidence of mastitis. Milk ability is influenced by physiological, sanitary, management, and genetic factors. In Mediterranean Italian buffaloes milked with the Automatic Milking System (AMS), a considerable variation in milk ejection and, consequently, in the milk flow curve was found compared to the conventional one, with better pre-stimulation, independent milk ejection for each teat, optimal milking of all quarters. In conclusion, continuous milkability monitoring will help optimize milking practices by reducing labor time and increasing farmers’ income through better milk quality. In addition, the identification of buffaloes with desirable types of milk flow curves could be helpful for buffalo breeders’ associations to address farmer management and to define potential new breeding objectives.

Variation in bovine milk stability according to lactational stage and genetic group.

We address the hypothesis that at early and late lactation milk presents low ethanol stability due to high acidity and ionic calcium values. Our aim was to evaluate the functional traits of milk (milk ethanol stability: MES, acidity and ionic calcium: iCa) according to lactation stage in different genetic groups. Raw milk samples were collected from Jersey (n = 271), Holstein (n = 248) and Jersey × Holstein crossbred cows (n = 82), raised on five commercial farms located in the state of Paraná, Brazil. Milk composition, somatic cell count (SCC), milk urea nitrogen (MUN), MES, pH, acidity and iCa were determined. Days in milk (DIM) were categorized into four classes: 1-60, 61-150, 151-305 and over 305 DIM. Data were submitted to analysis of variance. Fixed and random effects were incorporated into the model, in a repeated measures in time arrangement using the mixed models methodology. Significant interactions between DIM class and genetic groups were detected. The comparison between each combination of genetic group and DIM class showed that at the beginning of lactation, Holsteins produced milk with higher MES than Jersey and crossbreds. At 105-305 DIM Holstein milk presented higher MES than Jersey, while beyond 305 DIM Holstein milk showed higher MES than crossbred cows. At the beginning of lactation acidity was higher in Holstein milk and crossbreds compared with Jersey, while acidity was lower in Holstein milk compared with Jersey and crossbreds in the other lactation stages. Ionic calcium was highest after lactation peak for Holstein, but did not vary between lactation stages for Jersey and crossbreds. Functional characteristics of bovine raw milk such as MES, iCa and acidity varied between lactation stages in a distinct manner according to genetic groups. Early and end lactation stages are challenging in terms of low stability, especially for Jersey and crossbreds.

Mitochondrial sequence variants: testing imputation accuracy and their association with dairy cattle milk traits

BackgroundMitochondrial genomes differ from the nuclear genome and in humans it is known that mitochondrial variants contribute to genetic disorders. Prior to genomics, some livestock studies assessed the role of the mitochondrial genome but these were limited and inconclusive. Modern genome sequencing provides an opportunity to re-evaluate the potential impact of mitochondrial variation on livestock traits. This study first evaluated the empirical accuracy of mitochondrial sequence imputation and then used real and imputed mitochondrial sequence genotypes to study the role of mitochondrial variants on milk production traits of dairy cattle.ResultsThe empirical accuracy of imputation from Single Nucleotide Polymorphism (SNP) panels to mitochondrial sequence genotypes was assessed in 516 test animals of Holstein, Jersey and Red breeds using Beagle software and a sequence reference of 1883 animals. The overall accuracy estimated as the Pearson’s correlation squared (R2) between all imputed and real genotypes across all animals was 0.454. The low accuracy was attributed partly to the majority of variants having low minor allele frequency (MAF < 0.005) but also due to variants in the hypervariable D-loop region showing poor imputation accuracy. Beagle software provides an internal estimate of imputation accuracy (DR2), and 10 percent of the total 1927 imputed positions showed DR2 greater than 0.9 (N = 201). There were 151 sites with empirical R2 > 0.9 (of 954 variants segregating in the test animals) and 138 of these overlapped the sites with DR2 > 0.9. This suggests that the DR2 statistic is a reasonable proxy to select sites that are imputed with higher accuracy for downstream analyses. Accordingly, in the second part of the study mitochondrial sequence variants were imputed from real mitochondrial SNP panel genotypes of 9515 Australian Holstein, Jersey and Red dairy cattle. Then, using only sites with DR2 > 0.900 and real genotypes, we undertook a genome-wide association study (GWAS) for milk, fat and protein yields. The GWAS mitochondrial SNP effects were not significant.ConclusionThe accuracy of imputation of mitochondrial genotypes from the SNP panel to sequence was generally low. The Beagle DR2 statistic enabled selection of sites imputed with higher empirical accuracy. We recommend building larger reference populations with mitochondrial sequence to improve the accuracy of imputing less common variants and ensuring that SNP panels include common variants in the D-loop region.

Genome-wide selection reveals candidate genes associated with multiple teats in Hu sheep

Increasing the number of teats in sheep helps to improve the survival rate of sheep lambs after birth. In order to analyze the candidate genes related to the formation of multiple teats in Hu sheep, the present study was conducted to investigate the genetic pattern of multiple teats in Hu sheep. In this study, based on genome-wide data from 157 Hu sheep, Fst, xp-EHH, Pi and iHS signaling were performed, and the top 5% signal regions of each analyzed result were annotated based on the Oar_v4.0 for sheep. The results show that a total of 142 SNP loci were selected. We found that PTPRG, TMEM117 and LRP1B genes were closely associated with polypodium formation in Hu sheep, in addition, among the candidate genes related to polypodium we found genes such as TMEM117, SLC25A21 and NCKAP5 related to milk traits. The present study screened out candidate genes for the formation of multiple teats at the genomic level in Hu sheep.

Short communication: Bifidobacterium abundance in the faecal microbiota is strongly associated with milk traits in dairy cattle

The performance of dairy cows is influenced by the microbial communities hosted within their digestive tract. While the rumen microbiota has long been associated with host phenotypes, the impact of the faecal microbiota remains elusive. In this study, we collected 697 faecal samples from commercial Holstein cows and analysed them with 16S rRNA gene analyses. For each animal, routinely recorded data, i.e., milk yield, fat yield, protein yield, fat content, protein content, and an aggregate production trait (pINEL) based on the French economic dairy index, were available to assess the links between the faecal microbiota and host production. Our findings revealed a strong and significant association between the structure of the bacterial and prokaryote community (β-diversity) and dairy production. In addition, differential abundance analyses identified 48 genera whose abundances were significantly associated with pINEL, milk, fat and protein yield. Among these genera, the increased abundance of Bifidobacterium, and particularly an amplicon sequence variant with a 16S rRNA V3-V4 gene region identical to B. globosum and B. pseudolongum, was found to be the most important for high-yielding animals. Bifidobacterium seemed to be a potential key member of the bovine faecal microbiota that should be further investigated. Conversely, the p-1088-a5 gut group genus was found more abundant in low-productive cows. In conclusion, this study demonstrates significant associations between the faecal microbiota and the performance of dairy cows at the whole lactation scale. A better understanding of the physiology of the gut microbiota could help to improve dairy cow production.

Exploring a Bayesian sparse factor model-based strategy for the genetic analysis of thousands of MIR-spectra traits for animal breeding

With the rapid development of animal phenomics and deep phenotyping, we can get thousands of traditional but also molecular phenotypes per individual. However, there is still a lack of exploration regarding how to handle this huge amount of data in the context of animal breeding, presenting a challenge that we are likely to encounter more and more in the future. This study aimed to (1) explore the use of the Mega-scale linear mixed model (MegaLMM), a factor model-based approach, able to simultaneously estimate (co)variance components and genetic parameters in the context of thousands of milk traits, hereafter called thousand-trait (TT) models; (2) compare the phenotype values and genomic breeding values (u) predictions for focal traits (i.e., traits that are targeted for prediction, compared with secondary traits that are helping to evaluate), from single-trait (ST) and TT models, respectively; (3) propose a new approximate method of estimated genomic breeding values (U) prediction with TT models and MegaLMM. 3,421 milk mid-infrared (MIR) spectra wavepoints (called secondary traits) and 3 focal traits [average fat percent (Fat), average methane (CH4), and average somatic cell score (SCS)] collected on 3,302 first-parity Holstein cows were used. The 3,421 milk MIR wavepoints traits were composed of 311 wavepoints in 11 classes (months in lactation). Genotyping information of 564,439 SNP was available for all animals and was used to calculate the genomic relationship matrix. The MegaLMM was implemented in the framework of the Bayesian sparse factor model and solved through Gibbs sampling (Markov chain Monte Carlo). The heritabilities of the studied 3,421 milk MIR wavepoints gradually increased and then decreased in units of 311 wavepoints throughout the lactation. The genetic and phenotypic correlations between the first 311 wavepoints and the other 3,110 wavepoints were low. The accuracies of phenotype predictions from the ST model were lower than those from the TT model for Fat (0.51 vs. 0.93), CH4 (0.30 vs. 0.86), and SCS (0.14 vs. 0.33). The same trend was observed for the accuracies of u predictions: Fat (0.59 vs. 0.86), CH4 (0.47 vs. 0.78), and SCS (0.39 vs. 0.59). The average correlation between U predicted from the TT model and the new approximate method was 0.90. The new approximate method used for estimating U in MegaLMM will enhance the suitability of MegaLMM for applications in animal breeding. This study conducted an initial investigation into the application of thousands of traits in animal breeding and showed that the TT model is beneficial for the prediction of focal traits (phenotype and breeding values), especially for difficult-to-measure traits (e.g., CH4).

Investigation of Some Environmental Factors on Reproductive Characteristics, Milk Traits and Kleiber Ratios of Kilis Goats Reared in the Fertile Crescent of Türkiye

This study was carried out to determine the effect of environmental factors on some yield traits in Kilis goats. For this purpose, 6000 heads of Kilis goats each year, and a total of 30000 goats and kids born from them for 5 years, were the subject of the research. Milk control in goats was calculated by the ICAR-AT method. At the end of the study, Average birth weight was 3.56 kg, weaning weight was 15.06 kg and daily live weight gain was 191.64 g in kids. KR value found to be 23.6±0.03 in kids. Lactation milk yield in mothers was determined as 367.64 kg. As a result, it was determined that environmental factors were effective on the reproductive parameters and milk yield characteristics of Kilis goats and the developmental characteristics of kids. The crucial determination of these factors in the breeding of this breed will increase the accuracy of selection. In addition, the success will also be increased by starting the transition from the traditional breeding approach to the genomic breeding method and by combining these two strategies.

A Comparative Assessment Study on the Prolactin Gene as a Genetic Marker for Milk Production in High Milk-Producing Mammals through Phylogenetic Analysis

The prolactin (PRL) gene is a focus in milk-related studies for high-yielding ruminants like dairy cows, water buffaloes, dairy goats, and sheep. Despite its recognition as a genetic marker for milk traits, little is known about the specific genetic features enabling heightened milk production. The PRL nucleotide sequence of the ruminants and the human outgroup were obtained from the GenBank database. Using the MEGA 7 software, amino acid translation was conducted and multisequence analysis was done, allowing for the construction of phylogenetic trees. In both trees, ruminants were consistently clustered together forming pairings of the dairy cow: water buffalo and the dairy goat: sheep. In the nucleotide sequences of all the involved taxa, cytosine and guanine were the unanimous most and least comprised nucleotide. In the amino acid composition, the unanimous most and least amino acid percentages were leucine and tryptophan, respectively. Leucine, being the highest comprised, was substantiated by related studies stating that such amino acid is highly involved in milk production and yield. Glycine percentages were found to be higher in ruminants than in humans, providing a possible reasoning for such milk-yield disparity in the involved taxa.

CRISPR-mediated editing of β-lactoglobulin (BLG) gene in buffalo

Milk is a good source of nutrition but is also a source of allergenic proteins such as α-lactalbumin, β-lactoglobulin (BLG), casein, and immunoglobulins. The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas technology has the potential to edit any gene, including milk allergens. Previously, CRISPR/Cas has been successfully employed in dairy cows and goats, but buffaloes remain unexplored for any milk trait. In this study, we utilized the CRISPR/Cas9 system to edit the major milk allergen BLG gene in buffaloes. First, the editing efficiency of designed sgRNAs was tested in fibroblast cells using the T7E assay and Sanger sequencing. The most effective sgRNA was selected to generate clonal lines of BLG-edited cells. Analysis of 15 single-cell clones, through TA cloning and Sanger sequencing, revealed that 7 clones exhibited bi-allelic (−/−) heterozygous, bi-allelic (−/−) homozygous, and mono-allelic (−/+) disruptions in BLG. Bioinformatics prediction analysis confirmed that non-multiple-of-3 edited nucleotide cell clones have frame shifts and early truncation of BLG protein, while multiple-of-3 edited nucleotides resulted in slightly disoriented protein structures. Somatic cell nuclear transfer (SCNT) method was used to produce blastocyst-stage embryos that have similar developmental rates and quality with wild-type embryos. This study demonstrated the successful bi-allelic editing (−/−) of BLG in buffalo cells through CRISPR/Cas, followed by the production of BLG-edited blastocyst stage embryos using SCNT. With CRISPR and SCNT methods described herein, our long-term goal is to generate gene-edited buffaloes with BLG-free milk.

Dissecting loci that underpin the genetic correlations between production, fertility, and urea traits in Australian Holstein cattle.

Unfavorable genetic correlations between milk production, fertility, and urea traits have been reported. However, knowledge of the genomic regions associated with these unfavorable correlations is limited. Here, we used the correlation scan method to identify and investigate the regions driving or antagonizing the genetic correlations between production vs. fertility, urea vs. fertility, and urea vs. production traits. Driving regions produce an estimate of correlation that is in the same direction as the global correlation. Antagonizing regions produce an estimate in the opposite direction of the global estimates. Our dataset comprised 6567, 4700, and 12,658 Holstein cattle with records of production traits (milk yield, fat yield, and protein yield), fertility (calving interval) and urea traits (milk urea nitrogen and blood urea nitrogen predicted using milk-mid-infrared spectroscopy), respectively. Several regions across the genome drive the correlations between production, fertility, and urea traits. Antagonizing regions were confined to certain parts of the genome and the genes within these regions were mostly involved in preventing metabolic dysregulation, liver reprogramming, metabolism remodeling, and lipid homeostasis. The driving regions were enriched for QTL related to puberty, milk, and health-related traits. Antagonizing regions were mostly related to muscle development, metabolic body weight, and milk traits. In conclusion, we have identified genomic regions of potential importance for dairy cattle breeding. Future studies could investigate the antagonizing regions as potential genomic regions to break the unfavorable correlations and improve milk production as well as fertility and urea traits.

Study of association of type traits with lifetime milk traits in Frieswal cattle

Study of association of type traits with lifetime milk traits in Frieswal cattle

Presence of pathogen DNA in milk harvested from quarters is associated to changes in cows’ milk yield and composition

BackgroundIntramammary infection is the result of invasion and multiplication of microorganisms in the mammary gland and commonly leads to mastitis in dairy animals. Although much has been done to improve cows’ udder health, mastitis remains a significant and costly health issue for dairy farmers, especially if subclinical. In this study, quarter milk samples from clinically healthy cows were harvested to detect pathogens via quantitative PCR (qPCR) and evaluate changes in individual milk traits according to the number of quarters infected and the type of microorganism(s). A commercial qPCR kit was used for detection of Mycoplasma bovis, Mycoplasma spp., Staphylococcus aureus, coagulase-negative staphylococci (CNS), Streptococcus agalactiae, Streptococcus dysgalactiae, Streptococcus uberis, Prototheca spp., Escherichia coli, Klebsiella spp., Enterococcus spp. and Lactococcus lactis ssp. lactis. Quarter and pooled milk information of 383 Holstein, 132 Simmental, 129 Rendena, and 112 Jersey cows in 9 Italian single-breed herds was available.ResultsAmong the cows with pathogen(s) present in at least 1 quarter, CNS was the most commonly detected DNA, followed by Streptococcus uberis, Mycoplasma bovis, and Streptococcus agalactiae. Cows negative to qPCR were 206 and had the lowest milk somatic cell count. Viceversa, cows with DNA isolated in ≥ 3 quarters were those with the highest somatic cell count. Moreover, when major pathogens were isolated in ≥ 3 quarters, milk had the lowest casein index and lactose content. In animals with pathogen(s) DNA isolated, the extent with whom milk yield and major solids were impaired did not significantly differ between major and minor pathogens.ConclusionsThe effect of the number of affected quarters on the pool milk quality traits was investigated in clinically healthy cows using a commercial kit. Results remark the important negative effect of subclinical udder inflammations on milk yield and quality, but more efforts should be made to investigate the presence of untargeted microorganisms, as they may be potentially dangerous for cows. For a smarter use of antimicrobials, analysis of milk via qPCR is advisable – especially in cows at dry off - to identify quarters at high risk of inflammation and thus apply a targeted/tailored treatment.

Review of equations to predict methane emissions in dairy cows from milk fatty acid profiles and their application to commercial dairy farms

Greenhouse gas emission from the activities of all productive sectors is currently a topic of foremost importance. The major contributors in the livestock sector are ruminants, especially dairy cows. This study aimed to evaluate and compare 21 equations for predicting enteric methane emissions (EME) developed on the basis of milk traits and fatty acid profiles, which were selected from 46 retrieved through a literature review. We compiled a reference database of the detailed fatty acid profiles, determined by GC, of 992 lactating cows from 85 herds under 4 different dairy management systems. The cows were classified according to DIM, parity order, and dairy system. This database was the basis on which we estimated EME using the selected equations. The EME traits estimated were methane yield (20.63 ± 2.26 g/kg DMI, 7 equations), methane intensity (16.05 ± 2.76 g/kg of corrected milk, 4 equations), and daily methane production (385.4 ± 68.2 g/d, 10 equations). Methane production was also indirectly calculated by multiplying the daily corrected milk yield by the methane intensity (416.6 ± 134.7 g/d, 4 equations). We also tested for the effects of DIM, parity, and dairy system (as a correction factor) on the estimates. In general, we observed little consistency among the EME estimates obtained from the different equations, with exception of those obtained from meta-analyses of a range of data from different research centers. We found all the EME predictions to be highly affected by the sources of variation included in the statistical model: DIM significantly affected the results of 19 of the 21 equations, and parity order influenced the results of 13. Different patterns were observed for different equations with only some of them in accordance with expectations based on the cow's physiology. Finally, the best predictions of daily methane production were obtained when a measure of milk yield was included in the equation or when the estimate was indirectly calculated from daily milk yield and methane intensity.

Identification of genetic markers associated with hyperketonemia patterns in early lactation Holstein cows.

Ketosis, evidenced by hyperketonemia with elevated blood β-hydroxybutyrate (BHB) levels, is a significant metabolic disorder of dairy cattle, typically diagnosed within the first 6 weeks post-calving when high energy levels are essential to milk production. Our study aimed to identify genetic markers linked to hyperketonemia (HYK) patterns in Holstein cows during early lactation and compare these to HYK-negative cows. We screened 964 cows for HYK using a threshold of BHB ≥1.2 mmol/L during the first 2 weeks postpartum (screening period, SP). Cows that tested negative initially were retested the following week. Cows were deemed HYK-negative (CON group) if BHB levels were below 1.2 mmol/L in both tests, while those with BHB levels exceeding this threshold at any test were treated and classified as HYK-positive (HYK+). Post-treatment, HYK+ cows were monitored for two-week follow-up period (FP) and classified based on their recovery: cured (CUR; consistently low BHB), recurrent (REC; fluctuating BHB levels), severe (SEV; high initial BHB that decreased), or chronic (CHR; persistently high BHB). Using 489 cows that were genotyped, a GWAS was conducted using GCTA software, revealing significant associations of several SNPs across different HYK patterns when compared to the CON group. These SNPs were primarily linked to genes affecting milk traits and were enriched in biological pathways relevant to protein glycosylation, inflammatory response, glucose homeostasis, and fatty acid synthesis. Our findings highlight genomic regions, potential candidate genes, and biological pathways related to ketosis, underscoring potential targets for improving health management in dairy cattle. These insights could lead to better strategies for managing ketosis through genetic selection, ultimately enhancing dairy cattle welfare and productivity. Further research with a larger number of cows is recommended to validate these findings and help confirm the implicated SNPs and genes.

CSN1S1, CSN3 and LPL: Three Validated Gene Polymorphisms Useful for More Sustainable Dairy Production in the Mediterranean River Buffalo.

The search for DNA polymorphisms useful for the genetic improvement of dairy farm animals has spanned more than 40 years, yielding relevant findings in cattle for milk traits, where the best combination of alleles for dairy processing has been found in casein genes and in DGAT1. Nowadays, similar results have not yet been reached in river buffaloes, despite the availability of advanced genomic technologies and accurate phenotype records. The aim of the present study was to investigate and validate the effect of four single nucleotide polymorphisms (SNP) in the CSN1S1, CSN3, SCD and LPL genes on seven milk traits in a larger buffalo population. These SNPs have previously been reported to be associated with, or affect, dairy traits in smaller populations often belonging to one farm. A total of 800 buffaloes were genotyped. The following traits were individually recorded, monthly, throughout each whole lactation period from 2010 to 2021: daily milk yield (dMY, kg), protein yield (dPY, kg) and fat yield (dFY, kg), fat and protein contents (dFP, % and dPP, %), somatic cell count (SCC, 103 cell/mL) and urea (mg/dL). A total of 15,742 individual milk test day records (2496 lactations) were available for 680 buffalo cows, with 3.6 ± 1.7 parities (from 1 to 13) and an average of 6.1 ± 1.2 test day records per lactation. Three out four SNPs in the CSN1S1, CSN3 and LPL genes were associated with at least one of analyzed traits. In particular, the CSN1S1 (AJ005430:c.578C>T) gave favorable associations with all yield traits (dMY, p = 0.022; dPY, p = 0.014; dFY, p = 0.029) and somatic cell score (SCS, p = 0.032). The CSN3 (HQ677596: c.536C>T) was positively associated with SCS (p = 0.005) and milk urea (p = 0.04). Favorable effects on daily milk yield (dMY, p = 0.028), fat (dFP, p = 0.027) and protein (dPP, p = 0.050) percentages were observed for the LPL. Conversely, the SCD did not show any association with milk traits. This is the first example of a confirmation study carried out in the Mediterranean river buffalo for genes of economic interest in the dairy field, and it represents a very important indication for the preselection of young bulls destined for breeding programs aimed at more sustainable dairy production.