Somatic Cell Score Research Articles

Genetic parameters for novel mastitis traits defined by combining test-day somatic cell score and differential somatic cell count in the first lactation of Japanese Holsteins

In this study, we aimed to improve current udder health genetic evaluations by addressing the limitations of monthly sampled somatic cell score (SCS) for distinguishing cows with robust innate immunity from those susceptible to chronic infections. The objectives were to (1) establish novel somatic cell traits by integrating SCS and the differential somatic cell count (DSCC), which represents the combined proportion of polymorphonuclear leukocytes and lymphocytes in somatic cells and (2) estimate genetic parameters for the new traits, including their daily heritability and genetic correlations with milk production traits and SCS, using a random regression test-day model (RRTDM). We derived 3 traits, namely ML_SCS_DSCC, SCS_4_DSCC_65_binary, and ML_SCS_DSCC_binary, by using milk loss estimates at corresponding SCS and DSCC levels, thresholds established in previous studies, and a threshold established from milk loss estimates, respectively. Data consisted of test-day records collected during January 2021 through March 2022 from 265 herds in Hokkaido, Japan. From these records, we extracted records between 7 to 305 d in milk (DIM) in the first lactation to fit the RRTDM. The model included the random effect of herd-test-day, the fixed effect of year-month, fixed lactation curves nested with calving age groups, and random regressions with Legendre polynomials of order 3 for additive genetic and permanent environmental effects. The analysis was performed using Gibbs sampling with Gibbsf90+ software. The averages (ranges) of daily heritability estimates over lactation were 0.086 (0.075 to 0.095) for SCS, 0.104 (0.073 to 0.127) for ML_SCS_DSCC, 0.137 (0.014 to 0.297) for SCS_4_DSCC_65_binary, and 0.138 (0.115 to 0.185) for ML_SCS_DSCC_binary; the heritability curve for SCS_4_DSCC_65_binary was erratic. Genetic correlations within the trait decreased as the DIM interval widened, especially for those integrating DSCC, indicating that these traits should be analyzed using RRTDM rather than repeatability models. The averages (ranges) of genetic correlations with milk yield over lactation were 0.01 (-0.22 to 0.28) for SCS, -0.05 (-0.40 to 0.13) for ML_SCS_DSCC, -0.08 (-0.17 to 0.09) for SCS_4_DSCC_65_binary, and -0.08 (-0.22 to 0.27) for ML_SCS_DSCC_binary. Compared with SCS, the newly defined traits exhibited slightly stronger negative genetic correlations with milk yield. Especially in late lactation stages, the genetic correlation between ML_SCS_DSCC and milk yield was significantly below zero, with a posterior median of -0.40. Furthermore, the new traits showed positive correlations with SCS, having estimates varying from 0.68 to 0.85 for ML_SCS_DSCC, 0.14 to 0.47 for SCS_4_DSCC_65_binary, and 0.61 to 0.66 for ML_SCS_DSCC_binary, depending on DIM. Considering that ML_SCS_DSCC and ML_SCS_DSCC_binary have relatively high heritability (compared with SCS) and favorable genetic correlations with milk production traits and SCS, their incorporation into breeding programs appears promising. Nevertheless, their genetic relationships with (sub)clinical mastitis require further investigation.

Adding behavior traits to select for heat tolerance in dairy cattle

The increase in periods of heat waves leads to an increase in heat stress events in dairy cattle leading to welfare issues, production losses, and health issues. However, the low frequency of milk recording data makes genetic evaluation for heat tolerance still a challenge. A possible solution could be to add behavior data captured through sensors which are recorded permanently, mostly reported on a daily basis. The objective of this study was to evaluate the potential gain of adding behavior traits as proxies for genetic evaluation of heat tolerance. Behavior traits including activity time (ACT), rumination time (RUM), and eating time (EAT) were recorded for 453 Holstein cows equipped with SenseHub (Allflex Livestock Intelligence) collars from October 2019 to July 2022 in 6 herd located in the Walloon Region of Belgium. A multitrait reaction norm model based on separate temperature and humidity index (THI) thresholds was used. Results showed that behavior traits present not only interesting characteristics for genetic evaluation of heat tolerance but also for heat stress detection in farms. Indeed, sensors allow recording of behavior for all events of heat stress in lactating and nonlactating animals. Moderate heritability values were also found for the behavior traits (0.14 for ACT, 0.19 for RUM, and 0.12 for EAT), and a high ratio between the general and thermotolerance additive genetic variances was obtained. In addition, positive correlations of thermotolerance for ACT and EAT with thermotolerance for milk production (fat- and protein-corrected milk: 0.45 and 0.28, respectively) and negative genetic correlations of thermotolerance for ACT with somatic cells (somatic cell score: −0.39) were estimated. The genetic correlation matrix allows us to explain a high part of the variation for the reaction to heat stress of 2 economic traits (fat- and protein-corrected milk: 59% and somatic cell score: 31%) based on behavior data. Based on these results, behavior traits could be used to assess heat stress in nonlactating cattle for which the number of genetic evaluations for heat tolerance is still limited.

Alternative Traits for Genetic Evaluation of Mastitis Based on Lifetime Merit.

Genetic selection has achieved little progress in reducing mastitis incidence. Mastitis traits are problematic due to the lack of sensitivity of the data and reliance on clinical diagnosis, often missing subclinical cases, and/or on monthly somatic cell count (SCC) measurements. The current measure for mastitis is the lactation average of the somatic cells score (LSCS). We studied two datasets: (1) 148 heifers divided into non-intramammary infected, sub-clinically infected and clinical mastitis groups; (2) data from 89,601 heifers from Israeli Holsteins through the same period divided into "udder healthy" (UH) and "non-healthy" (UNH) by a threshold of SCC 120,000 cells/mL in all nine monthly milk recordings. In study 1, non-infected heifers had significantly (p < 0.05) more partum, production days and overall lifetime milk production compared to clinical and sub-clinically infected. In study 2, UH heifers (20.3%) had significantly higher (p < 0.01) lifetime milk, production days, and lactations. Subdividing datasets by sires, the same analyses detected differences in percentages of UH daughters between the sire groups. Lifetime milk production correlated (r = +0.83, p < 0.001) with udder health status. SCC threshold of less than 120,000 cells/mL during all first lactation measurements indicated healthy udder, providing a valuable insight that this dichotomous trait is advantageous for calculating lifetime net-merit index (NM$) over LSCS.

Exploring the presence of genotype-by-environment interactions between dairy cow herds milking once-a-day or twice-a-day for the entire lactation

The objective was to explore if the regression of phenotypic performance for six milk production traits on the respective estimate of genetic merit for that trait differed by herd milking frequency; variance components for each trait in the two milking frequency environments were also estimated as well as the genetic correlation between the same trait in both environments. The data used included 12,581 lactations from 5,456 cows in 32 spring-calving once-a-day (OAD) milking herds. Each OAD herd was matched with three contemporary twice-a-day (TAD)-milking herds; 35,823 lactations from 15,188 cows in 96 TAD herds were used. Mean yield was 20% (fat yield) to 31% (milk yield) lower in OAD herds. Milk protein concentration was 11% higher in OAD herds, while milk fat concentration was 16% higher in OAD herds. The mean back-transformed somatic cell score (SCS) was 100,390 cells/mL in OAD herds and 72,493 cells/mL in TAD herds. The association between each milk production trait and its respective estimate of genetic merit differed by herd milking frequency; the estimated regression coefficients were larger in TAD for just milk yield and SCS. The genetic correlation between the same trait in OAD versus TAD was all ≥0.73 with the exception of SCS (genetic correlation of 0.48) which suggests some re-ranking of sires between environments. In conclusion, differences in the scale of the genetic variance were evident for both milking frequencies and possible re-ranking was evident for SCS.

Variations in Casein Genes Are Associated with Milk Protein and Fat Contents in Sarda Goats (Capra hircus), with an Important Role of CSN1S2 for Milk Yield.

This work aimed to assess the variability of casein genes in a population of 153 bucks and 825 lactating does of the Sarda breed, and to perform association analysis between polymorphic sites and milk yield and composition traits. To genotype the casein genes, we chose an SNP panel including 44 SNPs mapping to the four casein genes CSN1S1, CSN2, CSN1S2, and CSN3. Genotyping (made by KASP™ genotyping assay, based on competitive allele-specific PCR) revealed the high variability of the Sarda goat, and haplotype analysis revealed linkage disequilibrium (LD) between CSN1S1 and CSN2 genes, in addition to two LD blocks within the CSN1S2 and two LD blocks within the CSN3 gene, in bucks and does. Association analysis revealed that variability at all four casein genes was associated with milk protein content, total solids, and milk energy. The three Ca-sensitive casein genes were associated with lipid content, and CSN1S2 showed a unique pattern, with intron variants associated with milk yield, in addition to milk pH, NaCl, and SCS (Somatic Cell Score). This information might prove useful in selection schemes and in future investigations aiming to better understand the biology of lactation, and the direct link between genotype and phenotype.

Relationship between Somatic Cell Score and Fat Plus Protein Yield in the First Three Lactations in Spanish Florida Goats

The aim of this study was to estimate genetic parameters of somatic cell score (SCS) and fat plus protein yield (FPY) using repeatability (RM) and random regression (RRM) models in Florida goats. The data consisted of 340,654 test-day controls of the first three lactations, and the pedigree contained 36,144 animals. Covariance components were estimated with a bivariate RM and RRM using the REML approach. Both models included as fixed effects the combination of herd and control date, litter size, kidding number and lactation length, and as random effects, the additive genetic and permanent environmental effects. A variation in the shape of the genetic parameters along the lactation curve was observed for both traits, and h2 oscillated between 0.272 and 0.279 for SCS and 0.099 and 0.138 for FPY. The genetic correlation between SCS and FPY was negative and medium (−0.304 to −0.477), indicating that a low-SCS EBV is associated with a genetic predisposition to high FPY production. Our results showed that given the magnitude of h2 for SCS and its rg with FPY, the SCS could be used as a selection criterion to increase resistance to mastitis, thus obtaining an improved dairy and cheese aptitude in this breed.

Study of polymorphism of toll like receptor-4 gene in crossbred cattleand its association with somatic cell count

The present study was undertaken to study the TLR4 gene polymorphism in exon 3 and its association with somatic cell count in HF×Gir cows using PCR-RFLP. Sixty crossbred cows (40 affected and 20 normal) between 3rd to 5th lactation were selected from Bombay Gowrakshak Mandali, Betegaon, Maharashtra. The confirmation of mastitis was done with the help of California MastitisTest (CMT) and the blood samples of 40 affected animals were collected aseptically along with 20 normal animals. The mean of somatic cell score in normal cows ranged between 2.86 to 2.99 × 105 cells/ml, whereas for affected animal the range of somatic cell score was 3.45 to 5.72 × 105 cells/ml. The genomic DNA was isolated and exon 3 of TLR4 gene was successfully amplified by PCR using a specific primer. The PCR product was digested with HaeIII restriction enzyme. The PCR-RFLP of exon 3 with HaeIII enzyme revealed two genotypes ‘BB’ and ‘AB’ with band pattern 49 bp, 98 bp &amp; 196 bp (BB) and 49 bp, 98 bp, 196 bp &amp; 245 bp (AB), respectively. The mean of somatic cell score estimated for animals with genotypes ‘BB’ and ‘AB’ was 3.99±0.13 and 3.66±0.03, respectively. The twomean ‘t’ test analysis showed non-significant difference between somatic cell count with genotypes ‘BB’ and ‘AB’. It indicated that there was no association between the genotypes of exon 3 of TLR4 gene with somatic cell count in crossbred cows under present study.

Exploring the heights: Impact of altitude on dairy milk composition

The aim of the present study was to assess the impact of altitude on the quality of milk from dairy cows housed in small-scale farms in alpine areas. Therefore, a dataset comprising 5,680 bulk milk samples from 32 farms located at different geographical altitudes was considered. Milk traits such as fat, protein, lactose, free fatty acids, casein, milk urea nitrogen content, pH value, and somatic cell score were examined in the laboratories of the South Tyrolean Dairy Association using mid-infrared spectroscopy. The data were analyzed using a statistical model that considered altitude category, grazing practice, housing system, and season of milk analysis as fixed effects. The findings revealed a positive association between altitude and milk fat, free fatty acids, and somatic cell count. Conversely, lactose content, milk urea nitrogen, and pH value in milk from cows kept on farms at higher altitude (>1,200 m above sea level) showed a negative relationship with altitude. Farms located at altitudes above 1,200 m exhibited higher fat, protein, urea, and somatic cell content than farms located at lower levels. The results provide new insights into a production effect that has so far received little attention and should be considered in farm management (e.g., feeding management, breed decision) to ensure animal health and the associated animal welfare as well as the productivity of dairy cows reared in traditional small-scale mountain dairy farms.

The effects of dietary iodine content, milking system, and farming practices on milk iodine concentration and quality traits

Various management practices can influence milk quality traits in dairy cattle. As an example, an increasing investment in automatic milking system to substitute milking parlors has been observed in the last 2 decades in dairy farms which could have affected certain bulk milk quality traits. What is more, milking practices can also affect certain milk parameters; as an example, teat disinfectants containing I are used in commercial farms where pre- or postdipping is performed, leading to presence of some I in the bulk milk. However, this trace mineral is also supplied in cows' diet to fulfill their nutritional requirements, partly contributing to the milk I final concentration. Therefore, the aim of this study was to evaluate the sources of variation of milk I along with other traditional milk quality traits. A total of 91 dairy farms in northeastern Italy were enrolled in the study. In each farm, diet and bulk milk samples were collected on the same day for chemical analysis. Concentration of I, in particular, was determined in both milk and feed with gold standard. Pearson correlations were calculated among the traits available for milk and diet, and a general linear model was used to test significance of fixed effects (feeding system, milking system, farming system, herd size, herd stage of lactation, and sampling month) on milk quality traits including the I concentration. In the case of milk I, diet I and presence of I-based predipping and postdipping teat disinfect application were also tested as fixed effects. Results showed a positive linear correlation between milk and diet I content (correlation coefficient [r] = 0.78). Although milk I was also positively correlated with lactose content (r = 0.25), dietary I was not correlated with other milk traits. Milk I content was significantly affected by dietary I, I-based predipping teat disinfectant application, and herd composition. Compared with conventional farms, organic farms showed lower protein content and greater somatic cell score (SCS) but similar milk I. Milking system significantly affected only lactose content and SCS of milk. Sampling month was only significant for milk urea nitrogen and herd composition, feeding system, herd size, and herd average days in milk did not modify milk gross composition and SCS. In conclusion, dietary supply of I is the main factor affecting milk I concentration and findings suggest that I level in milk can be naturally improved in dairy cows by modulating the I content in the diet administered. However, further research is needed to evaluate the effect of I-based sanitizers on milk I.

Designing Selection Indices for the Florida Dairy Goat Breeding Program

The aim of this study was to compare selection indices for important traits in intensive Spanish goat breeds in four economic scenarios, using the Florida as most representative breed of this production system in Spain. For this analysis, we considered the following traits: milk yield (MY), fat plus protein yields (FPY), casein yield (CY), somatic cell score (SCS), reproductive efficiency (RE), litter size (LS), mammary system (MS), final score (FS), body capacity index (BCI), and length of productive life (LPL). We estimated the genetic parameters and EBVs of most of these traits with REML methodology, while LPL was modeled through survival analysis. Four scenarios were proposed, depending on the overall objective for improvement: (1) milk production, (2) milk production and cheese extract, (3) cheese extract, and (4) milk production, cheese extract and sale of animals. Then, within each scenario, three different types of indices were designed using the different primary and secondary objectives/criteria considered suitable to improve the overall objective. The results indicated that selecting only for primary traits yielded the highest genetic response for all the scenarios. Including secondary traits led to positive correlated responses in those traits, but a decrease in the responses in the primary criteria.

142 Genotype-By-Environment Interactions in Dairy Cattle Raised in California and New England

Abstract Genotype-by-environmental interactions (G x E) have been reported to cause sire reranking in dairy populations. However, the effects of such interactions are not included in the current US national dairy genetic evaluation. Phenotypes collected from animals across the country are considered the same trait, disregarding interactions between genetic effects and region or other environmental factors. Variations in production systems may interact with the genotypes of the animals, leading to distinct phenotypes. Separating data into regions could change decision-making on bull selection, which is the most important step of a breeding program. The goal of this study is to evaluate the extent of genotype-by-environment interaction between the state of California and the region of New England in the US Holstein Population. Data from Holstein cows in the aforementioned regions collected from 2010 to 2020 were provided by the Council on Dairy Cattle Breeding. The genetic evaluation models used for the national evaluation were modified to account for New England and California phenotypes as separated yet correlated traits. The mixed model accounted for additive genetic, sire-by-herd interaction, and permanent environmental effects as random, and for the management group, age of parity, birth year, and parity as fixed effects. In some scenarios, sire-by-herd interaction and permanent environment were removed due to convergence problems. Variance components were estimated using AIREML under the Method R procedure. Such a method allows computing variance components for large databases by splitting the data into several random groups. Ten random samples of data containing 100,000 records were used for each analysis. The scenarios included single trait for each state, single trait with both states, and two-trait with separated states. Breeding values from the top 100 bulls in each region were compared with evaluate potential sire reranking. Heritabilities varied across regions for all three traits (Table 1). California presented lower heritability than New England across methods. The genetic correlations on the two-trait analysis averaged at 0.96, 0.92, and 0.95 for yield, fat, and protein, respectively. The Spearman correlation coefficient between breeding values of the bulls with more than 200 daughters was 0.87 for milk yield, suggesting that the reranking is unimportant. The genetic correlations suggested that GxE between the two regions is small and that the traits should not be split in the current genetic evaluation. In conclusion, no critical effect of genotype-by-environment interaction was observed, even though top bulls may differ across regions. The next step of this project is to evaluate GxE for somatic cell score and to include genomic information in the evaluations. Finally, a genome-wide association study across the two regions will be performed to investigate changes in genomic regions associated with the phenotypes.

Polymorphism of ADAM12, DPP6 and PRKN genes and their associations with milk production traits in Holstein.

Milk production traits as the most important economic traits of dairy cows, they directly reflect the benefits of breeding and the economic benefits of pasture. In this study, A disintegrin and metalloproteinase-12 (ADAM12), Parkinson's disease gene 2 (PRKN) and dipeptidyl peptidase-like protein subtype 6 (DPP6) polymorphism in 384 Chinese Holstein cows were detected by time-of-flight mass spectrometry and through statistical analysis using software such as Popgene 32, SAS 9.4 and Origin 2022, the relationship between single nucleotide polymorphisms (SNPs) of three genes with four milk production traits such as daily milk yield (DMY), milk fat percentage (MFP), milk protein percentage (MPP) and somatic cell score (SCS) was verified at molecular level. The results showed that four polymorphic loci (116,467,133, 116,604,487, 116,618,268 and 116,835,111) of DPP6 gene, two polymorphic loci (97,665,052 and 97,159,837) of PRKN gene and two polymorphic loci (45,542,714 and 45,553,888) of ADAM12 gene were detected. PRKN-97665052, DPP6-116467133, ADAM12-45553888, DPP6-116604487 and DPP6-116835111 were all in Hardy-Weinberg equilibrium state (p > .05). ADAM12-45542714, PRKN-97159837 and PRKN-97665052 were moderately polymorphic (0.25 ≤ PIC <0.50) in Holstein. It is evident that the selection potential and genetic variation of these five loci are relatively large, and the genetic richness is relatively high. The correlation analysis of different genotypes between these eight loci and milk production traits of Holstein showed that ADAM12-45542714 and DPP6-116835111 (p < .01) had an extremely significant effects on the DMY of Chinese Holstein in Ningxia, while PRKN-97665052 had an extremely significant effect on MFP (p < .01). The effect of PRKN-97665052 and DPP6-116467133 on MPP of Holstein were extremely significant (p < .01). DPP6-116618268 had an extremely significant effect on the SCS of Holstein in Ningxia (p < .01), and AA genotype individuals showed a higher SCS than GG genotype individuals; the other two loci (ADAM12-45553888 and DPP6-116604487) had no significant effects on milk production traits of Holstein (p > .05). In addition, through the joint analysis of DPP6, PRKN and ADAM12 gene loci, it was found that the interaction effect between the three gene loci could significantly affect the DMY, SCS (p < .01) and MPP (p < .05). In conclusion, several different loci of DPP6, PRKN and ADAM12 genes can affect the milk production traits of Holstein to different degrees. PRKN, DPP6 and ADAM12 genes can be used as potential candidate genes for milk production traits of Holstein for marker-assisted selection, providing theoretical basis for breeding of Holstein.

Combined single-step evaluation of functional longevity of dairy cows including correlated traits

BackgroundFor years, multiple trait genetic evaluations have been used to increase the accuracy of estimated breeding values (EBV) using information from correlated traits. In France, accurate approximations of multiple trait evaluations were implemented for traits that are described by different models by combining the results of univariate best linear unbiased prediction (BLUP) evaluations. Functional longevity (FL) is the trait that has most benefited from this approach. Currently, with many single-step (SS) evaluations, only univariate FL evaluations can be run. The aim of this study was to implement a “combined” SS (CSS) evaluation that extends the “combined” BLUP evaluation to obtain more accurate genomic (G) EBV for FL when information from five correlated traits (somatic cell score, clinical mastitis, conception rate for heifers and cows, and udder depth) is added.ResultsGEBV obtained from univariate SS (USS) evaluations and from a CSS evaluation were compared. The correlations between these GEBV showed the benefits of including information from correlated traits. Indeed, a CSS evaluation run without any performances on FL showed that the indirect information from correlated traits to evaluate FL was substantial. USS and CSS evaluations that mimic SS evaluations with data available in 2016 were compared. For each evaluation separately, the GEBV were sorted and then split into 10 consecutive groups (deciles). Survival curves were calculated for each group, based on the observed productive life of these cows as known in 2021. Regardless of their genotyping status, the worst group of heifers based on their GEBV in 2016 was well identified in the CSS evaluation and they had a substantially shorter herd life, while those in the best heifer group had a longer herd life. The gaps between groups were more important for the genotyped than the ungenotyped heifers, which indicates better prediction of future survival.ConclusionsA CSS evaluation is an efficient tool to improve FL. It allows a proper combination of information on functional traits that influence culling. In contrast, because of the strong selection intensity on young bulls for functional traits, the benefit of such a “combined” evaluation of functional traits is more modest for these males.

Genetic aspects of immunoglobulins and cyclophilin A in milk as potential indicators of mastitis resistance in Holstein cows

Mastitis is one of the most frequent and costly diseases affecting dairy cattle. Natural antibodies (immunoglobulins) and cyclophilin A (CyPA), the most abundant member of the family of peptidyl prolyl cis/trans isomerases, in milk may serve as indicators of mastitis resistance in dairy cattle. However, genetic information for CyPA is not available, and knowledge on the genetic and nongenetic relationships between these immune-related traits and somatic cell score (SCS) and milk yield in dairy cattle is sparse. Therefore, we aimed to comprehensively evaluate whether immune-related traits consisting of 5 Ig classes (IgG, IgG1, IgG2, IgA, and IgM) and CyPA in the test-day milk of Holstein cows can be used as genetic indicators of mastitis resistance by evaluating the genetic and nongenetic relationships with SCS in milk. The nongenetic factors affecting immune-related traits and the effects of these traits on SCS were evaluated. Furthermore, the genetic parameters of immune-related traits according to health status and genetic relationships under different SCS environments were estimated. All immune-related traits were significantly associated with SCS and directly proportional. Additionally, evaluation using a classification tree revealed that IgA, IgG2, and IgG were associated with SCS levels. Genetic factor analyses indicated that heritability estimates were low for CyPA (0.08) but moderate for IgG (0.37), IgA (0.44), and IgM (0.44), with positive genetic correlations among Ig (0.25-0.96). We also evaluated the differences in milk yield and SCS of cows between the low and high groups according to their sires' estimated breeding value for immune-related traits. In the high group, IgA had a significantly lower SCS in milk at 7 to 30 d compared with that in the low group. Furthermore, the Ig in milk had high positive genetic correlations between healthy and infected conditions (0.82-0.99), suggesting that Ig in milk under healthy conditions could interact with those under infected conditions, owing to the genetic ability based on the level of Ig in milk. Thus, Ig in milk are potential indicators for the genetic selection of mastitis resistance. However, because only the relationship between immune-related traits and SCS was investigated in this study, further study on the relationship between clinical mastitis and Ig in milk is needed before Ig can be used as an indicator of mastitis resistance.

Effects of oyster glycogen intramammary challenge on primiparous cow milk somatic cell counts, milk yields, and milk composition

Mastitis is a common and costly disease in the dairy industry that reduces milk production in affected mammary glands. The local mechanisms that result in reduced milk production of affected mammary glands are incompletely understood; elucidation of these mechanisms is dependent on the use of hypothesis testing studies, but few experimental models exist. The objective of this study was to develop a mastitis challenge model, using a split udder design, to reduce milk yields by approximately 15% in udder halves challenged with oyster glycogen, a known inducer of leukocyte recruitment, relative to udder halves treated with saline. Four primiparous Holstein cows in mid lactation were used. One udder half of each cow was randomly selected and challenged with oyster glycogen (OYGLN), and the opposite udder half was treated with saline (SAL). Milk yields and components were measured at each milking (3×/d) for 3 d postchallenge. No signs of clinical mastitis were observed. Milk somatic cell scores, yields, and components were similar between OYGLN and SAL udder halves at time of challenge. Milk somatic cell scores markedly increased in OYGLN halves postchallenge and were greater than SAL halves for the duration of the trial. Lactose concentrations of OYGLN udder halves were transiently lower than in SAL udder halves, but protein concentrations were greater at 2 milkings postchallenge in OYGLN halves. Milk yields and energy-corrected milk yields did not differ between OYGLN and SAL udder halves overall, nor at any postchallenge milking. A single intramammary challenge of oyster glycogen was unsuccessful in eliciting a disparity in milk yields between challenged and saline control udder halves despite the marked leukocyte infiltration observed in the former. These results indicate an incomplete understanding of how milk yields are reduced in mammary glands affected by subclinical mastitis and that transient somatic cell recruitment and infiltration alone do not directly reduce milk yields during subclinical mastitis.

Identification of hub genes associated with somatic cell score in dairy cow.

Somatic cell count (SCC) is used as an indicator of udder health. The log transformation of SCC is called somatic cell score (SCS). Several QTL and genes have been identified that are associated with SCS. This study aimed to identify the most important genes associated with SCS. This study compiled 168 genes that were reported to be significantly linked to SCS. Pathway analysis and network analysis were used to identify hub genes. Pathway analysis of these genes identified 73 gene ontology (GO) terms associated with SCS. These GO terms are associated with molecular function, biological processes, and cellular components, and the identified pathways are directly or indirectly linked with the immune system. In this study, a gene network was constructed, and from this network, the 17 hub genes (CD4, CXCL8, TLR4, STAT1, TLR2, CXCL9, CCR2, IGF1, LEP, SPP1, GH1, GHR, VWF, TNFSF11, IL10RA, NOD2, and PDGFRB) associated to SCS were identified. The subnetwork analysis yielded 10 clusters, with cluster 1 containing all identified hub genes (except for the VWF gene). Most hub genes and pathways identified in our study were mainly involved in inflammatory and cytokine responses. Result obtained in current study provides knowledge of the genetic basis and biological mechanisms controlling SCS. Therefore, the identified hub genes may be regarded as the main gene for the genomic selection of mastitis resistance.

Relationships of beta-casein genetics with production, fertility, and survival of purebred organic Holstein dairy cows

The objective of this study was to compare β-casein genotype of purebred certified-organic Holstein cows, and their effect on production, fertility, and survival. Holstein cows (n = 1,982) from 13 certified-organic dairy herds from the western, midwestern, and northeastern United States were genomically tested with CLARIFIDE Plus (Zoetis) for β-casein genotype. Two hundred fourteen cows were A1A1 (11%), 848 cows were A1A2 (43%), and 920 cows were A2A2 (46%). In total, 2,249 lactation records, 1,025 from the first parity and 1,224 records during second and greater parities were used. Test-day milk, fat, and protein production (305-d) and somatic cell score were obtained from the Dairy Herd Improvement Association. A lower limit of 50 d for days open was applied, and cows with more than 250 d open had days open set to 250 d. Independent variables for statistical analysis were the fixed effects of herd, parity, β-casein genotype (A1A1, A1A2, A2A2), and β-casein genotype by parity interaction. Cow nested within parity was the random effect in the statistical models for fertility and production traits. Herd had a significant effect on all fertility, production, and survival variables. Parity affected the number of times bred per pregnancy and days open, milk, fat, and protein production, and somatic cell score. Beta-casein genotype and herd influenced the percentage of cows surviving to first and second lactation. Results indicate no difference in production and fertility regarding β-casein genotype for organic dairy herds. Survival was biased against the A1 allele, which is indicated by lower survival rates during first lactation. These results may offer organic producers more flexibility in breeding and culling decisions to produce A2A2 milk.

Genetic selection for reduced health treatment costs in Holstein cows: implications from a long-term study.

The objective of this study was to estimate genetic parameters of health treatment cost of Holstein cows from producer-recorded health treatments in 8 herds over an 8-yr period of time. Genetic parameters of health treatment cost were estimated in first (n = 2,214), second (n = 1,487) and third (n = 800) parities of US Holstein cows. The health treatments were uniformly defined and consistently recorded by 8 high-performance dairy herds in Minnesota from 2008 to 2015. A fixed treatment cost was assigned to 14 types of health treatments, and the cost included the mean veterinary expense obtained from the veterinary clinics that serviced the 8 herds, pharmaceuticals, and labor cost. The labor cost was $18/h, and the time incurred for each type of health treatment was determined from interviews with the herd owners. The 14 types of health treatment costs were partitioned into 5 categories: mastitis (including mastitis diagnostic test), reproduction (cystic ovary, retained placenta, and metritis), lameness (hoof treatments), metabolic (milk fever, displaced abomasum, ketosis, and digestive), and miscellaneous (respiratory, injury, and other). Health treatment cost for each cow was summed by category within lactation and also across categories within lactation. The estimates of heritability for health treatment cost were 0.13, 0.04, 0.10, 0.12, and 0.04 for the mastitis, reproduction, lameness, metabolic, and miscellaneous categories, respectively, in first parity. Genetic correlations between categories of health treatment cost in first parity were greatest for mastitis and reproduction (r = 0.85); however, phenotypic correlations between all categories were small (r < 0.16). Total health treatment cost had a large genetic correlation with somatic cell score (0.93) and 305-d milk production (0.44) in first parity; however, the genetic correlation (-0.60) between total health treatment cost and udder depth in first parity indicated a genetic relationship exists between shallow udders and less total health treatment cost. Total health treatment cost across categories had a heritability estimate of 0.25 in first parity, 0.16 in second parity, and 0.17 in third parity. Consequently, genetic selection for reduced health treatment cost should be possible by using producer-recorded health treatment records supplemented with treatment costs.

Genetic parameters and genome-wide association studies for mozzarella and milk production traits, lactation length, and lactation persistency in Murrah buffaloes

Genetic and genomic analyses of longitudinal traits related to milk production efficiency are paramount for optimizing water buffaloes breeding schemes. Therefore, this study aimed to: 1) compare single-trait random regression models under a Single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) setting based on alternative covariance functions (i.e., Wood - WD, Wilmink - WL, and Ali-and-Schaeffer - AS) to describe milk (MY), fat (FY), protein (PY), and mozzarella (MZY) yields, fat-to-protein ratio (FPR), somatic cell score (SCS), lactation length (LL), and lactation persistency (LP) in Murrah dairy buffaloes (Bubalus bubalis); 2) combine the best functions for each trait under a multiple-trait framework; 3) estimate time-dependent SNP effects for all the studied longitudinal traits; and, 5) identify the most likely candidate genes associated with the traits. A total of 323,140 test-day records from the first lactation of 4,588 Murrah buffaloes were made available for the study. The model included the average curve of the population nested within herd-year-season of calving, systematic effects of number of milkings per day, and age at first calving as linear and quadratic covariates, and additive genetic, permanent environment, and residual as random effects. The WD model had the best goodness of fit based on the deviance information criterion and posterior model probabilities for all traits. Moderate heritabilities were estimated over time for most traits (0.30 ± 0.02 for MY; 0.26 ± 0.03 for FY; 0.45 ± 0.04 for PY; 0.28 ± 0.05 for MZY; 0.13 ± 0.02 for FPR; and 0.15 ± 0.03 for SCS). The heritability estimates for LP ranged from 0.38 ± 0.02 to 0.65 ± 0.03 depending on the trait definition used. Similarly, heritabilities estimated for LL ranged from 0.10 ± 0.01 to 0.14 ± 0.03. The genetic correlation estimates across DIM for all traits ranged from -0.06 (186 to 215 DIM for MY-SCS) to 0.78 (66 to 95 DIM for PY-MZY). The SNP effects calculated for the random regression model coefficients were used to estimate the SNP effects throughout the lactation curve (from 5 to 305 d). Numerous relevant genomic regions and candidate genes were identified for all traits, confirming their polygenic nature. The candidate genes identified contribute to a better understanding of the genetic background of milk-related traits in Murrah buffaloes and reinforce the value of incorporating genomic information in their breeding programs.

Exploring the sources of variation of electrical conductivity and total and differential somatic cell count in Italian Mediterranean buffaloes

In the buffalo dairy sector, a huge effort is still needed to improve mastitis prevention, detection, and management. Electrical conductivity (EC) and total somatic cell count (SCC) are well-known indirect indicators of mastitis. Differential somatic cell count (DSCC), which represents the proportion of neutrophils and lymphocytes on the total SCC, is instead a novel phenotype collected in the dairy cattle sector in the last lustrum. As little is known about this novel trait in dairy buffalo, in the present study we explored the nongenetic factors affecting DSCC, as well as EC and total somatic cell score (SCS), in the Italian Mediterranean buffalo. The data set used for the analysis included 14,571 test-day (TD) records of 1,501 animals from 6 herds, and climatic information of the sampling locations. The original data were filtered to exclude animals with less than 3 TD per lactation and, for the investigated traits, outliers beyond 4 standard deviations. In the statistical model we included the fixed effects of herd (6 classes), days in milk (DIM; 10 classes of 30 d, with the last being an open class until 360 d), parity (6 classes, from 1 to 6+), year-season of calving (11 classes, from summer 2019 to winter 2021/2022), year-season of sampling (9 classes, from spring 2020 to spring 2022), production level (4 classes based on quartiles of average milk yield by herd), and temperature-humidity index (THI; 4 classes based on quartiles, calculated using the average temperature and relative humidity of the 5 d before sampling). Average EC, SCS, and DSCC vary across herds. Considering DIM, greater EC values were observed at the beginning and the end of lactation; SCS was slightly lower, but DSCC was greater around the lactation peak. Increased EC, SCS, and DSCC levels with increasing parity were reported. Year-season calving and year-season sampling only slightly affected the variation of the investigated traits. Milk of high-producing buffaloes was characterized by lower EC and SCS mean values, nevertheless it had slightly greater DSCC percentages. Buffaloes grouped in the highest THI classes (classes 3 and 4) showed, on average, greater EC, SCS, and DSCC in comparison to the lower classes, especially to class 2. Results of the present study represent a preliminary as well as necessary step for the possible future inclusion of EC, SCS, or DSCC in breeding programs aimed to improve mastitis resistance in dairy buffaloes.