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Establishing fresh cow monitoring procedures is considered beneficial for cow health, welfare, and productivity. However, they are time consuming and require the cows to be locked up, which restricts their natural behavior. In this study, different fresh cow monitoring procedures were evaluated. Two experiments were conducted to determine: (1) the duration of various examinations and treatments; (2) the time cows remain locked up in headlocks; and (3) the proportion of examination and treatment times relative to the total headlock time. In advance, standard operating procedures were established. Three veterinarians conducted the examinations and treatments based on changes in milk yield, clinical symptoms, and alarms by an accelerometer system. The headlock time was evaluated for three workflow strategies, which differed in the order of examinations and treatments. To determine the duration, cameras were installed, and the video footage was analyzed. The examinations lasted between 1 and 227 s, and the cows were locked up in headlocks between 0.01 and 1.76 h. The lock-up times differed significantly among the three strategies, as well as the proportion. This study provides information that can be used as a basis for the development of time-efficient strategies, and to minimize the impact on cows’ time budgets.

Bovine respiratory syncytial virus (BRSV) is one of the causative viral agents of the bovine respiratory disease complex. This study was conducted to determine the seropositivity and risk factors associated with BRSV infection and to evaluate the phylogenetic relatedness of the BRSVs in the inner Aegean region of Turkey. In this cross-sectional study, serum samples (n = 557) and nasal swabs (n = 21) were collected from cattle herds (n = 43) between February 2018 and March 2019. A commercial indirect-ELISA kit was used for the detection of antibodies in the sera samples. Reverse-transcriptase PCR was used to detect viral RNA in nasal swabs. Nasal samples were also examined for the detection of bovine parainfluenza-3, bovine viral diarrhoea virus, and bovine herpesvirus 1 by molecular detection methods. Genetic characterization of the local BRSV field isolates was conducted by sequencing attachment glycoprotein (G) gene segment. Epidemiological data on potential risk factors were collected from each sampled herd during blood collection. All herds had at least one seropositive animal. After adjustment for assay sensitivity and specificity, the overall true seropositivity was 58.48% (95% CI: 53.32–63.47). BRSV RNA was detected in 2 of the 21 nasal swabs, whereas other infectious agents were not detected in the investigated samples. Phylogenetic analysis showed that the field isolates of BRSV obtained in this study belonged to subgroup III, but they were located on separate branch from previously characterised Turkish subgroup III isolates. BRSV field strains from this study displayed 3 new amino acid substitutions (P89S, D115G, and S165L) in the G protein chains compared to other main reference BRSV isolates, demonstrating that BRSV is still evolving. Generalised estimating equation model showed that there were positive associations between BRSV infection, age (OR = 2.36, p = 0.001), herd size (OR = 10.32, p

Sickness behaviour has been suggested as an applicable indicator for monitoring disease. Deviating feeding behaviour and activity can provide information about animals' health and welfare status. Recent advances in sensor technology enable monitoring of such behaviours and could potentially be utilized as an indicator of gastrointestinal nematode (GIN) infections. This study investigated activity and rumination patterns in first-season grazing steers exposed to subclinical infection levels of the GIN Ostertagia ostertagi and Cooperia oncophora. At turnout, animals were allocated to one of four experimental groups and were faced with "high" (H1, n = 15; H2, n = 17) or "low" (L1, n = 17; L2, n = 11) levels of parasite exposure by grazing in similar enclosures contaminated with overwintering third stage (13) GIN larvae. Animals in H1 and H2 (HP) received a 1:1 mix of approximately 10,000 0. ostertagi and C. oncophora L3 at turnout; whereas the animals in Ll and L2 (LP) were treated monthly with ivermectin. Activity and rumination patterns were monitored by fitting animals with leg- (IceQube) and neck-mounted (Heatime) sensors. BW was recorded every fortnight, whereas faecal and blood samples were examined every four weeks for nematode faecal egg count and serum pepsinogen concentrations (SPCs). There was an interaction effect of exposure level and period (P < 0.0001) on average lying daily time across the entire grazing time. A higher mean daily lying time (P = 0.0037) was found in HP compared with LP during the first 40 days on pasture. There was also interaction effects of treatment and day since turnout on rumination time (P < 0.0001) and rumination change (P = 0.0008). Also mean daily steps (P < 0.0001) and mean daily motion index (P < 0.0001) were markedly higher in HP during days 62-69, coinciding with peaking SPC in HP. Strongyle eggs were observed both in HP and LP from 31 days after turnout. Eggs per gram (EPG) differed between parasite exposure levels (P < 0.0001), with mean EPG remaining low in LP throughout the experiment. Similarly, an increase in SPC was observed (P < 0.0001), but only in HP where it peaked at day 56. In contrast, no difference in BW gain (BWG) (P = 0.78) between HP and LP was observed. In conclusion, this study shows that behavioural measurements monitored with sensors were affected even at low infection levels not affecting BWG. These combined results demonstrate the potential of automated behavioural recordings as a tool for detection of subclinical parasitism.

peer-reviewed Current bovine pregnancy detection methods are not reliable until at least day 28 post artificial insemination (AI). The bovine estrous cycle is approximately 21 days; consequently, producers miss an opportunity to rebreed at the next estrous event. Therefore, commercial interest exists for the discovery of novel biomarkers of pregnancy which could reliably detect pregnancy status at or before day 21 of pregnancy. The objective of the present study was to use liquid chromatography tandem mass spectrometry (LC-MS/MS) to perform a global, label-free, proteomics study on (i) milk whey and (ii) extracellular vesicle (EV) enriched milk whey samples, from day 21 of pregnancy, compared with day 21 of the estrous cycle, in order to identify potential protein biomarkers of early pregnancy. The estrous cycles of 10 dairy cows were synchronized, they went through one (control) estrous cycle and these cows were artificially inseminated during the following estrus. These cows were confirmed pregnant by ultrasound scanning. Milk whey samples were collected on day 21 of the estrous cycle and on day 21 post AI. Milk whey samples and EV enriched milk whey samples were analyzed by LC-MS/MS and subsequent analyzes of the label-free quantitative data was performed in MaxQuant and Perseus. Four proteins (APOB, SPADH1, PLIN2 and LPO) were differentially expressed between the proteomes of milk whey from day 21 of pregnancy and day 21 of the estrous cycle (P

The aim of this study was to investigate the sources of variation of milk fatty acids (FA) routinely predicted by mid-infrared spectroscopy in Holstein-Friesian (HF), Brown Swiss (BS), Simmental (SI) and Alpine Grey (AG) cattle breeds. A linear mixed model was used to analyse the data which included 153,801 individual milk samples from 14,301 cows. Fixed effects included in the model were breed, month of sampling, year of sampling, stage of lactation, parity and first order interactions between them, and random effects were herd nested within breed, cow nested within breed and the residual error. Milk of AG cows exhibited the lowest content of saturated FA and the greatest content of unsaturated FA, whereas milk of BS had an opposite pattern. Holstein-Friesian and SI were intermediate between AG and BS breeds for all the FA except for total C18:1, which was the greatest in HF. Saturated FA, C14:0 and C16:0 increased from calving until 120 days in milk, whereas unsaturated FA and C18:1 decreased. First parity cows produced milk with lower concentration of de novo FA than multiparous animals. The greatest content of unsaturated FA and C18:1 was observed in summer, whereas saturated FA, C14:0, C16:0 and C18:0 decreased in summer and increased in winter. The differences in milk FA profile among breeds are useful for genetic selection and for feeding strategies.

The killing of cattle for human consumption (slaughtering) can take place in a slaughterhouse or on farm. The processes of slaughtering that were assessed for welfare, from the arrival of cattle until their death (including slaughtering without stunning), were grouped into three main phases: pre-stunning (including arrival, unloading from the truck, lairage, handling and moving of cattle); stunning (including restraint); and bleeding. Stunning methods were grouped into two categories: mechanical and electrical. Twelve welfare consequences that cattle may be exposed to during slaughter were identified: heat stress, cold stress, fatigue, prolonged thirst, prolonged hunger, impeded movement, restriction of movements, resting problems (inability to rest or discomfort during resting), social stress, pain, fear and distress. Welfare consequences and their relevant animal-based measures are described. In total, 40 welfare hazards that could occur during slaughter were identified and characterised, most of them related to stunning and bleeding. Staff were identified as the origin of 39 hazards, which were attributed to the lack of appropriate skill sets needed to perform tasks or to fatigue. Measures to prevent and correct hazards were identified, and structural and managerial measures were identified as those with a crucial role in prevention. Outcome tables linking hazards, welfare consequences, animal-based measures, origin of hazards, and preventive and corrective measures were developed for each process. Mitigation measures to minimise welfare consequences are proposed. (C) 2020 European Food Safety Authority. EFSA Journal published by John Wiley and Sons Ltd on behalf of European Food Safety Authority. Non

The competent authorities of the Member States of the European Union are required to perform animal welfare inspections on livestock farms. The data obtained from these official inspections performed in Finnish cattle and pig farms in 2010&ndash, 2015 were used with the aim of estimating the prevalence of the most common non-compliances and identifying underlying risk factors. The prevalence of non-compliant cattle and pig farms was 24.2% and 27.9%, respectively. In cattle, the most common problem was an inadequate lying area followed by deficient housing conditions for calves, in pigs, it was a lack of enrichment material. The non-compliances concerning cattle were most frequently detected in autumn and in farms with small herd size, with tie-stall housing and outdoor rearing year-round. The pig farms with a farrow-to-finish unit had a higher prevalence of non-compliances than other production types. The prevalence of the non-compliant farms differed notably between the regions. It can be concluded that the cattle welfare inspections should be performed with a focus on the cold and rainy seasons and at small farms, whereas the pig welfare inspections should mainly focus on farrow-to-finish units. The data received from official inspections should be efficiently utilized in the development of animal welfare inspection system, with the aim of risk-based, consistent and uniform inspections. In addition, the data should be utilized in targeting information for farmers.

Document Type:Correction : GENETICS SELECTION EVOLUTION Volume: 51/Issue: 1 /Article Number: 69/DOI: 10.1186/s12711-019-0511-5/Published: NOV 22 2019 WOS:000504108800001 / PubMed ID: 31757207; International audience; AbstractBackgroundCross-validation tools are used increasingly to validate and compare genetic evaluation methods but analytical properties of cross-validation methods are rarely described. There is also a lack of cross-validation tools for complex problems such as prediction of indirect effects (e.g. maternal effects) or for breeding schemes with small progeny group sizes.ResultsWe derive the expected value of several quadratic forms by comparing genetic evaluations including “partial” and “whole” data. We propose statistics that compare genetic evaluations including “partial” and “whole” data based on differences in means, covariance, and correlation, and term the use of these statistics “method LR” (from linear regression). Contrary to common belief, the regression of true on estimated breeding values is (on expectation) lower than 1 for small or related validation sets, due to family structures. For validation sets that are sufficiently large, we show that these statistics yield estimators of bias, slope or dispersion, and population accuracy for estimated breeding values. Similar results hold for prediction of future phenotypes although we show that estimates of bias, slope or dispersion using prediction of future phenotypes are sensitive to incorrect heritabilities or precorrection for fixed effects. We present an example for a set of 2111 Brahman beef cattle for which, in repeated partitioning of the data into training and validation sets, there is very good agreement of statistics of method LR with prediction of future phenotypes.ConclusionsAnalytical properties of cross-validation measures are presented. We present a new method named LR for cross-validation that is automatic, easy to use, and which yields the quantities of interest. The method compares predictions based on partial and whole data, which results in estimates of accuracy and biases. Prediction of observed records may yield biased results due to precorrection or use of incorrect heritabilities.

Unbalanced metabolic status in the weeks after calving predisposes dairy cows to metabolic and infectious diseases. Blood glucose, IGF-I, non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHB) are used as indicators of the metabolic status of cows. This work aims to (1) evaluate the potential of milk mid-IR spectra to predict these blood components individually and (2) to evaluate the possibility of predicting the metabolic status of cows based on the clustering of these blood components. Blood samples were collected from 241 Holstein cows on six experimental farms, at days 14 and 35 after calving. Blood samples were analyzed by reference analysis and metabolic status was defined by k-means clustering (k=3) based on the four blood components. Milk mid-IR analyses were undertaken on different instruments and the spectra were harmonized into a common standardized format. Quantitative models predicting blood components were developed using partial least squares regression and discriminant models aiming to differentiate the metabolic status were developed with partial least squares discriminant analysis. Cross-validations were performed for both quantitative and discriminant models using four subsets randomly constituted. Blood glucose, IGF-I, NEFA and BHB were predicted with respective R2 of calibration of 0.55, 0.69, 0.49 and 0.77, and R2 of cross-validation of 0.44, 0.61, 0.39 and 0.70. Although these models were not able to provide precise quantitative values, they allow for screening of individual milk samples for high or low values. The clustering methodology led to the sharing out of the data set into three groups of cows representing healthy, moderately impacted and imbalanced metabolic status. The discriminant models allow to fairly classify the three groups, with a global percentage of correct classification up to 74%. When discriminating the cows with imbalanced metabolic status from cows with healthy and moderately impacted metabolic status, the models were able to distinguish imbalanced group with a global percentage of correct classification up to 92%. The performances were satisfactory considering the variables are not present in milk, and consequently predicted indirectly. This work showed the potential of milk mid-IR analysis to provide new metabolic status indicators based on individual blood components or a combination of these variables into a global status. Models have been developed within a standardized spectral format, and although robustness should preferably be improved with additional data integrating different geographic regions, diets and breeds, they constitute rapid, cost-effective and large-scale tools for management and breeding of dairy cows.

The current study evaluated the effects of protein provision to calves fed a combination of solid feed (SF) and milk replacer (MR) at equal total N intake on urea recycling and N retention. Nitrogen balance traits and [ 15 N 2 ]urea kinetics were measured in 30 calves (23 wk of age, 180±3.7kg of body weight), after being exposed to the following experimental treatments for 11 wk: a low level of SF with a low N content (SF providing 12% of total N intake), a high level of SF with a low N content (SF providing 22% of total N intake), or a high level of SF with a high N content (SF providing 36% of total N intake). The SF mixture consisted of 50% concentrates, 25% corn silage, and 25% straw on a dry matter basis. Total N intake was equalized to 1.8g of N·kg of BW −0.75 ·d −1 by adjusting N intake via MR. All calves were housed individually on metabolic cages to allow for quantification of a N balance of calves for 5 d, and for the assessment of urea recycling from [ 15 N 2 ]urea kinetics. Increasing low-N SF intake at equal total N intake resulted in a shift from urinary to fecal N excretion but did not affect protein retention (0.71g of N·kg of BW −0.75 ·d −1 ). Increasing low-N SF intake increased urea recycling but urea reused for anabolism remained unaffected. Total-tract neutral detergent fiber digestibility decreased (−9%) with increasing low-N SF intake, indicating reduced rumen fermentation. Increasing the N content of SF at equal total N intake resulted in decreased urea production, excretion, and return to ornithine cycle, and increased protein retention by 17%. This increase was likely related to an effect of energy availability on protein retention due to an increase in total-tract neutral detergent fiber digestion (>10%) and due to an increased energy supply via the MR. In conclusion, increasing low-N SF intake at the expense of N intake from MR, did not affect protein retention efficiency in calves. Increasing the N content of SF at equal total N intake decreased urea production, increased protein retention, and coincided with improved fiber degradation. Therefore, results suggest that low N availability in the rumen limits microbial growth and rumen fermentation in calves fed low-N SF (93g of CP/kg of DM), and this effect cannot be compensated for by recycling of urea originating from MR.

Single nucleotide polymorphism (SNP) data enable the estimation of inbreeding at the genome level. In this study, we estimated inbreeding levels for 19,075 Finnish Ayrshire cows genotyped with a low-density SNP panel (8K). The genotypes were imputed to 50K density, and after quality control, 39,144 SNPs remained for the analysis. Inbreeding coefficients were estimated for each animal based on the percentage of homozygous SNPs (F-PH), runs of homozygosity (F-ROH) and pedigree (F-PED). Phenotypic records were available for 13,712 animals including non-return rate (NRR), number of inseminations (AIS) and interval from first to last insemination (IFL) for heifers and up to three parities for cows, as well as interval from calving to first insemination (ICF) for cows. Average F-PED was 0.02, F-ROH 0.06 and F-PH 0.63. A correlation of 0.71 was found between F-PED and F-ROH, 0.66 between F-PED and F-PH and 0.94 between F-ROH and F-PH. Pedigree-based inbreeding coefficients did not show inbreeding depression in any of the traits. However, when F-ROH or F-PH was used as a covariate, significant inbreeding depression was observed; a 10% increase in F-ROH was associated with 5days longer IFL0 and IFL1, 2weeks longer IFL3 and 3days longer ICF2 compared to non-inbred cows.

Improving feed efficiency in dairy cattle could result in more profitable and environmentally sustainable dairy production through lowering feed costs and emissions from dairy farming. In addition, beef production based on dairy herds generates fewer greenhouse gas emissions per unit of meat output than beef production from suckler cow systems. Different scenarios were used to assess the profitability of adding traits, excluded from the current selection index for Finnish Ayrshire, to the breeding goal for combined dairy and beef production systems. The additional breeding goal traits were growth traits (average daily gain of animals in the fattening and rearing periods), carcass traits (fat covering, fleshiness and dressing percentage), mature live weight (LW) of cows and residual feed intake (RFI) traits. A breeding scheme was modeled for Finnish Ayrshire under the current market situation in Finland using the deterministic simulation software ZPLAN+. With the economic values derived for the current production system, the inclusion of growth and carcass traits, while preventing LW increase generated the highest improvement in the discounted profit of the breeding program (3.7%), followed by the scenario where all additional traits were included simultaneously (5.1%). The use of a selection index that included growth and carcass traits excluding LW, increased the profit (0.8%), but reduced the benefits resulted from breeding for beef traits together with LW. A moderate decrease in the profit of the breeding program was obtained when adding only LW to the breeding goal (-3.1%), whereas, adding only RFI traits to the breeding goal resulted in a minor increase in the profit (1.4%). Including beef traits with LW in the breeding goal showed to be the most potential option to improve the profitability of the combined dairy and beef production systems and would also enable a higher rate of self-sufficiency in beef. When considering feed efficiency related traits, the inclusion of LW traits in the breeding goal that includes growth and carcass traits could be more profitable than the inclusion of RFI, because the marginal costs of measuring LW can be expected to be lower than for RFI and it is readily available for selection. In addition, before RFI can be implemented as a breeding objective, the genetic correlations between RFI and other breeding goal traits estimated for the studied population as well as information on the most suitable indicator traits for RFI are needed to assess more carefully the consequences of selecting for RFI.

Breeding values for dry matter intake (DMI) are important to optimize dairy cattle breeding goals for feed efficiency. However, generally, only small data sets are available for feed intake, due to the cost and difficulty of measuring DMI, which makes understanding the genetic associations between traits across lactation difficult, let alone the possibility for selection of breeding animals. However, estimating national breeding values through cheaper and more easily measured correlated traits, such as milk yield and liveweight (LW), could be a first step to predict DMI. Combining DMI data across historical nutritional experiments might help to expand the data sets. Therefore, the objective was to estimate genetic parameters for DMI, fat- and protein-corrected milk (FPCM) yield, and LW across the entire first lactation using a relatively large data set combining experimental data across the Netherlands. A total of 30,483 weekly records for DMI, 49,977 for FPCM yield, and 31,956 for LW were available from 2,283 Dutch Holstein-Friesian first-parity cows between 1990 and 2011. Heritabilities, covariance components, and genetic correlations were estimated using a multivariate random regression model. The model included an effect for year-season of calving, and polynomials for age of cow at calving and days in milk (DIM). The random effects were experimental treatment, year-month of measurement, and the additive genetic, permanent environmental, and residual term. Additive genetic and permanent environmental effects were modeled using a third-order orthogonal polynomial. Estimated heritabilities ranged from 0.21 to 0.40 for DMI, from 0.20 to 0.43 for FPCM yield, and from 0.25 to 0.48 for LW across DIM. Genetic correlations between DMI at different DIM were relatively low during early and late lactation, compared with mid lactation. The genetic correlations between DMI and FPCM yield varied across DIM. This correlation was negative (up to -0.5) between FPCM yield in early lactation and DMI across the entire lactation, but highly positive (above 0.8) when both traits were in mid lactation. The correlation between DMI and LW was 0.6 during early lactation, but decreased to 0.4 during mid lactation. The highest correlations between FPCM yield and LW (0.3-0.5) were estimated during mid lactation. However, the genetic correlations between DMI and either FPCM yield or LW were not symmetric across DIM, and differed depending on which trait was measured first. The results of our study are useful to understand the genetic relationship of DMI, FPCM yield, and LW on specific days across lactation.

Crossing of lines or strains within and between breeds has been demonstrated to be beneficial for dairy cattle performance. However, even within breed, differences between strains may also give rise to heterosis. A key question is whether an interaction exists between heterosis and environment (H x E) that is independent of genotype by environment (G x E) interactions. In this study, H x E and G x E interactions were estimated in a population of approximately 300,000 Danish Jersey cows. The cows were a mixture of pure Danish Jerseys and crosses of US and Danish Jerseys. The phenotype studied was protein yield. A reaction norm model where the unknown environmental covariates are inferred simultaneously with the other parameters in the model was used to analyze the data. When H x E, but not G x E, was included in the model, heterosis was estimated to be 3.8% for the intermediate environmental level. However, when both H x E and G x E were included in the model, heterosis was estimated to be 4.1% for the intermediate environmental level. Furthermore, when only H x E was included in the model, the regression on the unknown environmental covariate was estimated to be 0.15, interpreted as meaning that an increase of average herd-year protein yield by 1 kg of protein led to an increase in heterosis of 0.15 kg above the average heterosis for a first-cross cow. When both H x E and G x E were included in the model, the regression on the unknown environmental covariate was not significantly different from zero, meaning that heterosis was similar in all environments investigated. The genetic correlation of protein yields for different environmental levels ranged from 0.72 to 0.93, which was significantly different from unity, indicating that G x E exist for protein yield.

The ability to monitor dairy cow feeding behavior and activity could improve dairy herd management. A 3-dimensional accelerometer (SensOor; Agis Automatisering BV, Harmelen, the Netherlands) has been developed that can be attached to ear identification tags. Based on the principle that behavior can be identified by ear movements, a proprietary model classifies sensor data as “ruminating,” “eating,” “resting,” or “active.” The objective of the study was to evaluate this sensor on accuracy and precision. First, a pilot evaluation of agreement between 2 independent observers, recording behavior from 3 cows for a period of approximately 9 h each, was performed. Second, to evaluate the sensor, the behavior of 15 cows was monitored both visually (VIS) and with the sensor (SENS), with approximately 20 h of registration per cow, evenly distributed over a 24-h period, excluding milking. Cows were chosen from groups of animals in different lactation stages and parities. Each minute of SENS and VIS data was classified into 1 of 9 categories (8 behaviors and 1 transition behavior) and summarized into 4 behavioral groups, namely ruminating, eating, resting, or active, which were analyzed by calculating kappa (¿) values. For the pilot evaluation, a high level of agreement between observers was obtained, with ¿ values of =0.96 for all behavioral categories, indicating that visual observation provides a good standard. For the second trial, relationships between SENS and VIS were studied by ¿ values on a minute basis and Pearson correlation and concordance correlation coefficient analysis on behavior expressed as percentage of total registration time. Times spent ruminating, eating, resting, and active were 42.6, 15.9, 31.6, and 9.9% (SENS) respectively, and 42.1, 13.0, 30.0, and 14.9% (VIS), respectively. Overall ¿ for the comparison of SENS and VIS was substantial (0.78), with ¿ values of 0.85, 0.77, 0.86, and 0.47 for “ruminating,” “eating,” “resting,” and “active,” respectively. Pearson correlation and concordance correlation coefficients between SENS and VIS for “ruminating,” “eating,” “resting,” and “active” were 0.93, 0.88, 0.98, and 0.73, and 0.93, 0.75, 0.97, and 0.35, respectively. In conclusion, the results provide strong evidence that the present ear sensor technology can be used to monitor ruminating and resting behavior of freestall-housed dairy cattle. Our results also suggest that this technology shows promise for monitoring eating behavior, whereas more work is needed to determine its suitability to monitor activity of dairy cattle.

A major quantitative trait locus (QTL) for milk fat content and fatty acids in both milk and adipose tissue has been detected on Bos taurus autosome 19 (BTA19) in several cattle breeds. The objective of this study was to refine the location of the QTL on BTA19 for bovine milk fat composition using a denser set of markers. Opportunities for fine mapping were provided by imputation from 50,000 genotyped single nucleotide polymorphisms (SNP) toward a high-density SNP panel with up to 777,000 SNP. The QTL region was narrowed down to a linkage disequilibrium block formed by 22 SNP covering 85,007 bp, from 51,303,322 to 51,388,329 bp on BTA19. This linkage disequilibrium block contained 2 genes: coiled-coil domain containing 57 (CCDC57) and fatty acid synthase (FASN). The gene CCDC57 is minimally characterized and has not been associated with bovine milk fat previously, but is expressed in the mammary gland. The gene FASN has been associated with bovine milk fat and fat in adipose tissue before. This gene is a likely candidate for the QTL on BTA19 because of its involvement in de novo fat synthesis. Future studies using sequence data of both CCDC57 and FASN, and eventually functional studies, will have to be pursued to assign the causal variant(s).

In livestock, many studies have reported the results of imputation to 50k single nucleotide polymorphism (SNP) genotypes for animals that are genotyped with low-density SNP panels. The objective of this paper is to review different measures of correctness of imputation, and to evaluate their utility depending on the purpose of the imputed genotypes. Across studies, imputation accuracy, computed as the correlation between true and imputed genotypes, and imputation error rates, that counts the number of incorrectly imputed alleles, are commonly used measures of imputation correctness. Based on the nature of both measures and results reported in the literature, imputation accuracy appears to be a more useful measure of the correctness of imputation than imputation error rates, because imputation accuracy does not depend on minor allele frequency (MAF), whereas imputation error rate depends on MAF. Therefore imputation accuracy can be better compared across loci with different MAF. Imputation accuracy depends on the ability of identifying the correct haplotype of a SNP, but many other factors have been identified as well, including the number of genotyped immediate ancestors, the number of animals with genotypes at the high-density panel, the SNP density on the low- and high-density panel, the MAF of the imputed SNP and whether imputed SNP are located at the end of a chromosome or not. Some of these factors directly contribute to the linkage disequilibrium between imputed SNP and SNP on the low-density panel. When imputation accuracy is assessed as a predictor for the accuracy of subsequent genomic prediction, we recommend that: (1) individual-specific imputation accuracies should be used that are computed after centring and scaling both true and imputed genotypes; and (2) imputation of gene dosage is preferred over imputation of the most likely genotype, as this increases accuracy and reduces bias of the imputed genotypes and the subsequent genomic predictions.

Heritability of mastitis (and diseases in general) tends to be low. One possible cause is that no clear distinction can be made between resistant and nonresistant animals, because healthy animals include animals that have not been exposed to pathogens and resistant animals. To account for this, we quantified the prevalence of clinical mastitis (CM) and subclinical mastitis (SCM) in 2,069 Swedish Holstein herds as a measure of exposure. Herd prevalence averaged 26.5% for SCM and 6.4% for CM; 61% of the first lactations of 177,309 cows were classified as having at least one case of SCM and 10% as having CM. In a reaction norm approach, heritability of (S)CM was quantified as a function of herd prevalence of (S)CM. The best-fitting model was a second-order polynomial of first-lactation cow SCM as a function of herd prevalence SCM, and a first-order (linear) polynomial of first-lactation cow CM as a function of CM herd prevalence. Heritability for SCM ranged from 0.069 to 0.105 and for CM from 0.016 to 0.032. For both, we found no clear effect of herd prevalence on their heritability. Genetic correlations within traits across herd prevalences were all greater than 0.92. Whether relationships among prevalence, exposure, disease, and genetics were as expected is a matter of discussion, but reaction norm analyses may be a valuable tool for epidemiological genetics.

Background Selection for sound conformation has been widely used as a primary approach to reduce lameness and leg weakness in pigs. Identification of genomic regions that affect conformation traits would help to improve selection accuracy for these lowly to moderately heritable traits. Our objective was to identify genetic factors that underlie leg and back conformation traits in three Danish pig breeds by performing a genome-wide association study followed by meta-analyses. Methods Data on four conformation traits (front leg, back, hind leg and overall conformation) for three Danish pig breeds (23,898 Landrace, 24,130 Yorkshire and 16,524 Duroc pigs) were used for association analyses. Estimated effects of single nucleotide polymorphisms (SNPs) from single-trait association analyses were combined in two meta-analyses: (1) a within-breed meta-analysis for multiple traits to examine if there are pleiotropic genetic variants within a breed; and (2) an across-breed meta-analysis for a single trait to examine if the same quantitative trait loci (QTL) segregate across breeds. SNP annotation was implemented through Sus scrofa Build 10.2 on Ensembl to search for candidate genes. Results Among the 14, 12 and 13 QTL that were detected in the single-trait association analyses for the three breeds, the most significant SNPs explained 2, 2.3 and 11.4% of genetic variance for back quality in Landrace, overall conformation in Yorkshire and back quality in Duroc, respectively. Several candidate genes for these QTL were also identified, i.e. LRPPRC, WRAP73, VRTN and PPARD likely control conformation traits through the regulation of bone and muscle development, and IGF2BP2, GH1, CCND2 and MSH2 can have an influence through growth-related processes. Meta-analyses not only confirmed many significant SNPs from single-trait analyses with higher significance levels, but also detected several additional associated SNPs and suggested QTL with possible pleiotropic effects. Conclusions Our results imply that conformation traits are complex and may be partly controlled by genes that are involved in bone and skeleton development, muscle and fat metabolism, and growth processes. A reliable list of QTL and candidate genes was provided that can be used in fine-mapping and marker assisted selection to improve conformation traits in pigs. Electronic supplementary material The online version of this article (doi:10.1186/s12711-017-0289-2) contains supplementary material, which is available to authorized users.

In recent years, it has been shown that not only is the phenotype under genetic control, but also the environmental variance. Very little, however, is known about the genetic architecture of environmental variance. The main objective of this study was to unravel the genetic architecture of the mean and environmental variance of somatic cell score (SCS) by identifying genome-wide associations for mean and environmental variance of SCS in dairy cows and by quantifying the accuracy of genome-wide breeding values. Somatic cell score was used because previous research has shown that the environmental variance of SCS is partly under genetic control and reduction of the variance of SCS by selection is desirable. In this study, we used 37,590 single nucleotide polymorphism (SNP) genotypes and 46,353 test-day records of 1,642 cows at experimental research farms in 4 countries in Europe. We used a genomic relationship matrix in a double hierarchical generalized linear model to estimate genome-wide breeding values and genetic parameters. The estimated mean and environmental variance per cow was used in a Bayesian multi-locus model to identify SNP associated with either the mean or the environmental variance of SCS. Based on the obtained accuracy of genome-wide breeding values, 985 and 541 independent chromosome segments affecting the mean and environmental variance of SCS, respectively, were identified. Using a genomic relationship matrix increased the accuracy of breeding values relative to using a pedigree relationship matrix. In total, 43 SNP were significantly associated with either the mean (22) or the environmental variance of SCS (21). The SNP with the highest Bayes factor was on chromosome 9 (Hapmap31053-BTA-111664) explaining approximately 3% of the genetic variance of the environmental variance of SCS. Other significant SNP explained less than 1% of the genetic variance. It can be concluded that fewer genomic regions affect the environmental variance of SCS than the mean of SCS, but genes with large effects seem to be absent for both traits.

The genomic breeding value accuracy of scarcely recorded traits is low because of the limited number of phenotypic observations. One solution to increase the breeding value accuracy is to use predictor traits. This study investigated the impact of recording additional phenotypic observations for predictor traits on reference and evaluated animals on the genomic breeding value accuracy for a scarcely recorded trait. The scarcely recorded trait was dry matter intake (DMI, n = 869) and the predictor traits were fat-protein-corrected milk (FPCM, n = 1520) and live weight (LW, n = 1309). All phenotyped animals were genotyped and originated from research farms in Ireland, the United Kingdom and the Netherlands. Multi-trait REML was used to simultaneously estimate variance components and breeding values for DMI using available predictors. In addition, analyses using only pedigree relationships were performed. Breeding value accuracy was assessed through cross-validation (CV) and prediction error variance (PEV). CV groups (n = 7) were defined by splitting animals across genetic lines and management groups within country. With no additional traits recorded for the evaluated animals, both CV- and PEV-based accuracies for DMI were substantially higher for genomic than for pedigree analyses (CV: max. 0.26 for pedigree and 0.33 for genomic analyses; PEV: max. 0.45 and 0.52, respectively). With additional traits available, the differences between pedigree and genomic accuracies diminished. With additional recording for FPCM, pedigree accuracies increased from 0.26 to 0.47 for CV and from 0.45 to 0.48 for PEV. Genomic accuracies increased from 0.33 to 0.50 for CV and from 0.52 to 0.53 for PEV. With additional recording for LW instead of FPCM, pedigree accuracies increased to 0.54 for CV and to 0.61 for PEV. Genomic accuracies increased to 0.57 for CV and to 0.60 for PEV. With both FPCM and LW available for evaluated animals, accuracy was highest (0.62 for CV and 0.61 for PEV in pedigree, and 0.63 for CV and 0.61 for PEV in genomic analyses). Recording predictor traits for only the reference population did not increase DMI breeding value accuracy. Recording predictor traits for both reference and evaluated animals significantly increased DMI breeding value accuracy and removed the bias observed when only reference animals had records. The benefit of using genomic instead of pedigree relationships was reduced when more predictor traits were used. Using predictor traits may be an inexpensive way to significantly increase the accuracy and remove the bias of (genomic) breeding values of scarcely recorded traits such as feed intake.

Background Mortality due to cannibalism causes both economic and welfare problems in laying hens. To limit mortality due to cannibalism, laying hens are often beak-trimmed, which is undesirable for animal welfare reasons. Genetic selection is an alternative strategy to increase survival and is more efficient by taking heritable variation that originates from social interactions into account, which are modelled as the so-called indirect genetic effects (IGE). Despite the considerable heritable variation in survival time due to IGE, genetic improvement of survival time in laying hens is still challenging because the detected heritable variation of the trait with IGE is still limited, ranging from 0.06 to 0.26, and individuals that are still alive at the end of the recording period are censored. Furthermore, survival time records are available late in life and only on females. To cope with these challenges, we tested the hypothesis that genomic prediction increases the accuracy of estimated breeding values (EBV) compared to parental average EBV, and increases response to selection for survival time compared to a traditional breeding scheme. We tested this hypothesis in two lines of brown layers with intact beaks, which show cannibalism, and also the hypothesis that the rate of inbreeding per year is lower for genomic selection than for the traditional breeding scheme. Results and discussion The standard deviation of genomic prediction EBV for survival time was around 22 days for both lines, indicating good prospects for selection against mortality in laying hens with intact beaks. Genomic prediction increased the accuracy of the EBV by 35 and 32 % compared to the parent average EBV for the two lines. At the current reference population size, predicted response to selection was 91 % higher when using genomic selection than with the traditional breeding scheme, as a result of a shorter generation interval in males and greater accuracy of selection in females. The predicted rate of inbreeding per generation with truncation selection was substantially lower for genomic selection than for the traditional breeding scheme for both lines. Conclusions Genomic selection for socially affected traits is a promising tool for the improvement of survival time in laying hens with intact beaks. Electronic supplementary material The online version of this article (doi:10.1186/s12711-016-0247-4) contains supplementary material, which is available to authorized users.

The objectives of this study were to estimate heritabilities of, and genetic correlations among, clinical mastitis (CM), subclinical mastitis (SCM), and alternative somatic cell count (SCC) traits in the first 3 lactations of Swedish Holstein cows, and to estimate genetic correlations for the alternative traits across lactations. Data from cows having their first calving between 2002 and 2009 were used. The alternative SCC traits were based on information on CM and monthly test-day (TD) records of SCC traits of 178,613, 116,079, and 64,474 lactations in first, second, or third parity, respectively. Sires had an average of 230, 165, or 124 daughters in the data (parities 1, 2, or 3, respectively). Subclinical mastitis was defined as the number of periods with an SCC >150,000 cell/mL and without a treatment for CM. Average TD SCC between 5 and 150 d was used as a reference trait. The alternative SCC traits analyzed were 1) presence of at least 1 TD SCC between 41,000 and 80,000 cell/mL (TD41–80), 2) at least 1 TD SCC >500,000 cells/mL, 3) standard deviation of log SCC over the lactation, 4) number of infection peaks, and 5) average days diseased per peak. The same variables in different parities were treated as distinct traits. The statistical model considered the effects of herd-year, year, month, age at calving, animal, and residual. Heritability estimates were 0.07 to 0.08 for CM, 0.12 to 0.17 for SCM, and 0.14 for SCC150. For the alternative traits, heritability estimates were 0.12 to 0.17 for standard deviation of log SCC, TD SCC >500,000 cells/mL, and average days diseased per peak, and 0.06 to 0.10 for TD41–80 and number of infection peaks. Genetic correlations between CM with SCM were 0.62 to 0.74, and correlations for these traits with the alternative SCC traits were positive and very high (0.67 to 0.82 for CM, and 0.94 to 0.99 for SCM). Trait TD41–80 was the only alternative trait that showed negative, favorable, genetic correlations with CM (−0.22 to −0.50) and SCM (−0.48 to −0.85) because it is associated with healthy cows. Genetic correlations among the alternative traits in all 3 parities were high (0.93 to 0.99, 0.92 to 0.98, and 0.78 to 0.99, respectively). The only exception was TD41–80, which showed moderate to strong negative correlations with the rest of the traits. Genetic correlations of the same trait across parities were in general positive and very high (0.83 to 0.99). In conclusion, these alternative SCC traits could be used in practical breeding programs aiming to improve udder health in dairy cattle.

The correlated responses between traits may differ depending on the makeup of genetic covariances, and may differ from the predictions of polygenic covariances Therefore, the objective of the present study was to investigate the makeup of the genetic covariances between the well-studied traits: milk yield, fat yield, protein yield, and their percentages in more detail. Phenotypic records of 1,737 heifers of research farms in 4 different countries were used after homogenizing and adjusting for management effects. All cows had a genotype for 37,590 single nucleotide polymorphisms (SNP). A Bayesian stochastic search variable selection model was used to estimate the SNP effects for each trait. About 0.5 to 1.0% of the SNP had a significant effect on 1 or more traits; however, the SNP without a significant effect explained most of the genetic variances and covariances of the traits. Single nucleotide polymorphism correlations differed from the polygenic correlations, but only 10 regions were found with an effect on multiple traits; in 1 of these regions the DGAT1 gene was previously reported with an effect on multiple traits. This region explained up to 41% of the variances of 4 traits and explained a major part of the correlation between fat yield and fat percentage and contributes to asymmetry in correlated response between fat yield and fat percentage. Overall, for the traits in this study, the infinitesimal model is expected to be sufficient for the estimation of the variances and covariances.

Compared with traditional selection, the use of genomic information tends to increase the accuracy of estimated breeding values (EBV). The cause of this increase is, however, unknown. To explore this phenomenon, this study investigated whether the increase in accuracy when moving from traditional (AA) to genomic selection (GG) was mainly due to genotyping the reference population (GA) or the evaluated animals (AG). In it, a combined relationship matrix for simultaneous use of genotyped and ungenotyped animals was applied. A simulated data set reflected the dairy cattle population. Four differently designed (i.e., different average relationships within the reference population) small reference populations and 3 heritability levels were considered. The animals in the reference populations had high, moderate, low, and random (RND) relationships. The evaluated animals were juveniles. The small reference populations simulated difficult or expensive to measure traits (i.e., methane emission). The accuracy of selection was expressed as the reliability of (genomic) EBV and was predicted based on selection index theory using relationships. Connectedness between the reference populations and evaluated animals was calculated using the prediction error variance. Average (genomic) EBV reliabilities increased with heritability and with a decrease in the average relationship within the reference population. Reliabilities in AA and AG were lower than those in GG and were higher than those in GA (respectively, 0.039, 0.042, 0.052, and 0.048 for RND and a heritability of 0.01). Differences between AA and GA were small. Average connectedness with all animals in the reference population for all scenarios and reference populations ranged from 0.003 to 0.024; it was lowest when the animals were not genotyped (AA; e.g., 0.004 for RND) and highest when all the animals were genotyped (GG; e.g., 0.024 for RND). Differences present across designs of the reference populations were very small. Genomic relationships among animals in the reference population might be less important than those for the evaluated animals with no phenotypic observations. Thus, the main origin of the gain in accuracy when using genomic selection is due to genotyping the evaluated animals. However, genotyping only one group of animals will always yield less accurate estimates.

Energy balance, especially in early lactation, is known to be associated with subsequent health and fertility in dairy cows. However, its inclusion in routine management decisions or breeding programs is hindered by the lack of quick, easy, and inexpensive measures of energy balance. The objective of this study was to evaluate the potential of mid-infrared (MIR) analysis of milk, routinely available from all milk samples taken as part of large-scale milk recording and milk payment operations; to predict body energy status and related traits in lactating dairy cows. The body energy status traits investigated included energy balance and body energy content. The related traits of body condition score and energy intake were also considered. Measurements on these traits along with milk MIR spectral data were available on 17 different test days from 268 cows (418 lactations) and were used to develop the prediction equations using partial least squares regression. Predictions were externally validated on different independent subsets of the data and the results averaged. The average accuracy of predicting body energy status from MIR spectral data was as high as 75% when energy balance was measured across lactation. These predictions of body energy status were considerably more accurate than predictions obtained from the sometimes proposed fat-to-protein ratio in milk. It is not known whether the prediction generated from MIR data are a better reflection of the true (unknown) energy status than the actual energy status measures used in this study. However, results indicate that the approach described may be a viable method of predicting individual cow energy status for a large scale of application.

Matrix metalloproteinases (MMPs) and counteracting tissue inhibitors of metalloproteinases (TIMPs) are balancing extracellular matrix (ECM) formation and degradation. The latter is believed to be an important aspect for the detachment of fetal membranes postpartum when loosening the feto-matemal connection which is a prerequisite to avoid placental retention a common disease in cows leading to considerable economic loss. Membrane-type (MT) MMPs have been suggested as potential activators controlling ECM remodelling. In particular, MT1-MMP (MMP-14) is able to degrade ECM substrates and activate MMP-2 through binding TIMP-2 at the cell surface. Since the connection between the trophoblast and the maternal caruncular epithelium is supported by integrin receptors bound to ECM, we hypothesize that impaired modulation of the ECM by TIMPs/MMPs participates in the aetiology of bovine retained fetal membranes. To analyse this involvement, placentomes were collected from cows after term parturition and timely release of fetal membranes (n = 4) and cows with retained fetal membranes after various treatments for the induction of parturition using progesterone antagonist (aglepristone), PGF(2 alpha) analogue, glucocorticoid, and after elective caesarean sections (each group n = 3). The expression of MMP-14, MMP-2 and of TEMP-2 was examined by real-time-PCR, irnmunohistochemistry, Western blot and zymography. The relative mRNA expression levels of MMP-14 remained unchanged, while the expression levels of TIMP-2 and MMP-2 partly increased in animals with induced parturition and retention of fetal membranes compared to animals without placental retention. MMP-14 protein was expressed in cells of the uninucleated trophoblast, the fetal mesenchyme and maternal stoma. TIMP-2 was present exclusively in trophoblast giant cells, while MMP-2 could be detected in uninucleated trophoblast cells and the fetal mesenchyme. The presence of the activated enzyme was confirmed by zymography. In conclusion, MMP-14, MMP-2 and TIMP-2 are co-localized in the fetal compartment and therefore could influence the timely release of fetal membranes in cattle. (c) 2011 Elsevier Inc. All rights reserved.

Marker-assisted breeding value estimation is expected to increase the accuracy of estimated breeding values, especially for traits with low heritability. Several quantitative trait loci (QTL) have been found for somatic cell score and clinical mastitis. The objective of this study was to demonstrate marker-assisted breeding value estimation, combining data of genotyped and ungenotyped animals in a large pedigree population using either identical-by-descent (IBD) or identical-by-state (IBS) haplotypes for some previously identified QTL regions for somatic cell score and clinical mastitis in Finnish Ayrshire cattle. For both methods, QTL variances were estimated based on daughter yield deviations of genotyped bulls. The QTL explained only a small proportion of genetic variance, especially with IBS haplotypes. Using IBD haplotypes gave more reranking of bulls and cows than using IBS haplotypes. Cross-validation showed no increase in predictive ability when using IBS haplotypes compared with conventional breeding value estimation, whereas a decrease in predictive ability was observed with IBD haplotypes. Furthermore, computing time was lower and convergence was better with IBS haplotypes than with IBD haplotypes. In this study on mastitis resistance in Finnish Ayrshire, conventional breeding value estimation would be advocated because of the lack in improvement of accuracy and predictive ability when using marker-assisted breeding value estimation. However, in situations where IBS haplotypes would explain 10 to 20% or more of the genetic variance, marker-assisted breeding value estimation with IBS haplotypes may yield greater accuracy and predictive ability than conventional breeding value estimation.

Longevity in beef cattle is an important economic trait. Including this trait in a breeding scheme increases profit and has a positive impact on the well-being and welfare of the animals. The aim of the present study was to evaluate the conse- quences of alternative selection strategies to include longevity in different breeding schemes using deterministic simula- tion. Different schemes were compared and economic (EcW) and empirical weights (EmW) were used to evaluate the responses. The empirical weights of average daily gain (ADG) and muscularity (MU) were identical because both traits have an identical importance for the breeders. Economic weights have been derived from profit equations. Traits used in the Basescenarios were: average daily gain pre-performance test (ADG1), average daily gain during the performance test (ADG2) and muscularity (MU); longevity (L) was included in the alternative schemes. When longevity was included both in the breeding index and in the breeding goal (scenario A-2), the total longevity response using EmW and EcW was +2.97 d/yr and +4.92 d/yr, respectively. The total economic response for scenario A-2 using EmW and EcW were 3.020 €/yr and 3.342 €/yr, respectively, and the total response in units of Bull Selection Index were +0.699 and +0.678, respectively. Longevity decreased when it was not included in either the breeding goal or in the breeding index (scenario Base), and economic response was the lowest found. The results of the current study indicate that the highest total response using either economic weights or empirical weights was found when information on longevity was included both in the breeding index and in the breeding goal (scenario A-2).

Animal breeding faces one of the most significant changes of the past decades - the implementation of genomic selection. Genomic selection uses dense marker maps to predict the breeding value of animals with reported accuracies that are up to 0.31 higher than those of pedigree indexes, without the need to phenotype the animals themselves, or close relatives thereof. The basic principle is that because of the high marker density, each quantitative trait loci (QTL) is in linkage disequilibrium (LD) with at least one nearby marker. The process involves putting a reference population together of animals with known phenotypes and genotypes to estimate the marker effects. Marker effects have been estimated with several different methods that generally aim at reducing the dimensions of the marker data. Nearly all reported models only included additive effects. Once the marker effects are estimated, breeding values of young selection candidates can be predicted with reported accuracies up to 0.85. Although results from simulation studies suggest that different models may yield more accurate genomic estimated breeding values (GEBVs) for different traits, depending on the underlying QTL distribution of the trait, there is so far only little evidence from studies based on real data to support this. The accuracy of genomic predictions strongly depends on characteristics of the reference populations, such as number of animals, number of markers, and the heritability of the recorded phenotype. Another important factor is the relationship between animals in the reference population and the evaluated animals. The breakup of LD between markers and QTL across generations advocates frequent re-estimation of marker effects to maintain the accuracy of GEBVs at an acceptable level. Therefore, at low frequencies of re-estimating marker effects, it becomes more important that the model that estimates the marker effects capitalizes on LD information that is persistent across generations.

We present the results of a genome-wide scan to identify quantitative trait loci (QTL) that contribute to genetic variation in long-chain milk fatty acids. Milk-fat composition phenotypes were available on 1,905 Dutch Holstein-Friesian cows. A total of 849 cows and their 7 sires were genotyped for 1,341 single nucleotide polymorphisms across all Bos taurus autosomes (BTA). We detected significant QTL on BTA14, BTA15, and BTA16: for C18:1 cis-9, C18:1 cis-12, C18:2 cis-9,12, CLA cis-9,trans-11, C18:3 cis-9,12,15, the C18 index, the total index, total saturated fatty acids, total unsaturated fatty acids (UFA), and the ratio of saturated fatty acids:unsaturated fatty acids on BTA14; for C18:1 trans fatty acids on BTA15; and for the C18 and CLA indices on BTA16. The QTL explained 3 to 19% of the phenotypic variance. Suggestive QTL were found on 16 other chromosomes. The diacylglycerol acyltransferase 1 (DGAT1) K232A polymorphism on BTA14, which is known to influence fatty acid composition, most likely explains the QTL that was detected on BTA14.

The objective of this study was to examine the impact of polymorphisms in the acyl-CoA:diacylglycerol acyltransferase (DGAT1), leptin and growth hormone receptor genes on body energy (body condition score, total body energy content and cumulative effective energy balance) and blood metabolic traits (levels of beta-hydroxybutyrate, glucose and non-esterified fatty acids), measured once before the first calving and then repeatedly throughout first lactation in 497 Holstein cows. The influence of the same polymorphisms on cow reproductive performance and health during the first and second lactations was also assessed. Several reproductive traits were considered including interval, conception and insemination traits, as well as incidence of metritis and reproductive problems. Genotyping was performed using PCR-RFLP (DGAT1, leptin) or allele-specific PCR (growth hormone receptor). For each locus, the effect of allele substitution on body energy and blood metabolic traits was estimated using random regression models. The same effect on reproductive traits was assessed with single-trait mixed linear models. Significant (P

The objective of this study was to perform a whole genome scan to detect quantitative trait loci (QTL) for milk protein composition in 849 Holstein-Friesian cows originating from seven sires. One morning milk sample was analysed for the major milk proteins using capillary zone electrophoresis. A genetic map was constructed with 1341 single nucleotide polymorphisms, covering 2829 centimorgans (cM) and 95% of the cattle genome. The chromosomal regions most significantly related to milk protein composition (P(genome)0.05) were found on Bos taurus autosomes (BTA) 6, 11 and 14. The QTL on BTA6 was found at about 80 cM, and affected alpha(S1)-casein, alpha(S2)-casein, beta-casein and kappa-casein. The QTL on BTA11 was found at 124 cM, and affected beta-lactoglobulin, and the QTL on BTA14 was found at 0 cM, and affected protein percentage. The proportion of phenotypic variance explained by the QTL was 3.6% for beta-casein and 7.9% for kappa-casein on BTA6, 28.3% for beta-lactoglobulin on BTA11, and 8.6% for protein percentage on BTA14. The QTL affecting alpha(S2)-casein on BTA6 and 17 showed a significant interaction. We investigated the extent to which the detected QTL affecting milk protein composition could be explained by known polymorphisms in beta-casein, kappa-casein, beta-lactoglobulin and DGAT1 genes. Correction for these polymorphisms decreased the proportion of phenotypic variance explained by the QTL previously found on BTA6, 11 and 14. Thus, several significant QTL affecting milk protein composition were found, of which some QTL could partially be explained by polymorphisms in milk protein genes.

Animals inheriting the slick hair gene have a short, sleek, and sometimes glossy coat. The objective of the present study was to determine whether slick-haired Holstein cows regulate body temperature more effectively than wild-type Holstein cows when exposed to an acute increase in heat stress. Lactating slick cows (n = 10) and wild-type cows (n = 10) were placed for 10 h in an indoor environment with a solid roof, fans, and evaporative cooling or in an outdoor environment with shade cloth and no fans or evaporative cooling. Cows were exposed to both environments in a single reversal design. Vaginal temperature, respiration rate, surface temperature, and sweating rate were measured at 1200, 1500, 1800, and 2100 h (replicate 1) or 1200 and 1500 h (replicate 2), and blood samples were collected for plasma cortisol concentration. Cows in the outdoor environment had higher vaginal and surface temperatures, respiration rates, and sweating rates than cows in the indoor environment. In both environments, slick-haired cows had lower vaginal temperatures (indoor: 39.0 vs. 39.4 degrees C; outdoor 39.6 vs. 40.2 degrees C; SEM = 0.07) and respiration rate (indoor: 67 vs. 79 breaths/min; outdoor 97 vs. 107 breaths/min; SEM = 5.5) than wild-type cows and greater sweating rates in unclipped areas of skin (indoor: 57 vs. 43 g.h(-1)/m(2); outdoor 82 vs. 61 g.h(-1)/m(2); SEM = 8). Clipping the hair at the site of sweating measurement eliminated the difference between slick-haired and wild-type cows. Results indicate that slick-haired Holstein cows can regulate body temperature more effectively than wild-type cows during heat stress. One reason slick-haired animals are better able to regulate body temperature is increased sweating rate. Initiative for Future Agricultural and Food Systems Program (2001-52101-11318 )

The economic impact of abortions in ruminant breeders is one of the biggest problems in livestock. Of the infectious agents, viruses, especially herpesviruses and pestiviruses, are the most important causative agents of abortion in ruminants. In the present study, the role of herpesviruses (bovine herpesvirus-1 (BoHV-1), bovine herpesvirus-4 (BoHV-4)) and pestiviruses (bovine viral diarrhea virus (BVDV), border disease virus (BDV)) was investigated in cases of ruminant abortion between 2007 and 2015 in western Turkey. Out of 81 aborted fetal samples (60 calves, 19 lambs, and 2 kids), 42 were positive, which included 31 calves, 9 lambs, and 2 goats; 39 aborted fetal samples were negative for the pestivirus antigen ELISA. BoHV-1 antigen ELISA was positive in 3 cases which included 2 calves and 1 lamb; the remainder 78 cases were negative. Pestivirus and BoHV-1 were positive in 51.85 and 3.70 %, respectively, of the samples. According to PCR analysis, BoHV-4 was not encountered in any of the tested samples. In one of the calf fetus samples, both BVDV and BoHV-1 were positive; in one of the lamb fetus samples, BoHV-1 was positive. There was a much higher level of pestivirus antigen than the other viral agents evaluated in the study (p

Dairy cattle breeding programs and dairy farmers are selecting sires and dams across environments. Genotype x environment interaction (G x E) limits the possibilities for cooperation between breeding programs operating in different environments. The objectives of this study were 2-fold: 1) to investigate the effects of heritability, selection intensity, number of progeny per bull, and size of breeding programs on possibilities for cooperation between dairy cattle breeding programs in the short and long term in the presence of G x E, and 2) to quantify the effect of such cooperation on genetic gain. A dairy cattle situation with 2 breeding programs operating in 2 environments was simulated using a deterministic pseudo-BLUP selection index model. Long-term cooperation between the 2 breeding programs was possible in the presence of G x E, when the genetic correlation was higher than 0.80 to 0.90, resulting in up to 15% extra genetic gain. In addition, in the initial generations of selection, the breeding programs could benefit from mutually selecting sires and dams from each other when the genetic correlation was as low as 0.40 to 0.60. With more intense selection, breeding programs were less likely to benefit from cooperation with breeding programs in other environments. Heritability and number of progeny per bull had little effect on possibilities for cooperation, unless the heritabilities and the number of progeny per bull were extremely different in the 2 environments. Small breeding programs benefited more from cooperation than did large breeding programs, and benefits were possible even at lower values (i.e.

The objective of this paper was to investigate the association of descriptors of herd environment with phenotypic levels and breeding values of fertility and health traits. Analyses were performed for 82,080 first-lactation heifers and 173,787 multiparous cows. Fourteen environmental parameters were defined that described herd environment, such as average protein production, average somatic cell score (SCS), average calving interval, and average body condition score (BCS). Herds with lower average SCS had, in general, more desirable values for almost all analyzed traits (i.e., days to first service was 7 d shorter), as did herds with lower average calving interval (i.e., 2.8% lower incidence of predicted mastitis). Herds with higher average protein production had slightly poorer fertility but more desirable values for all other analyzed traits (i.e., 5.1% less predicted mastitis, 0.4 lower SCS, and 0.6 higher BCS). Variance components and breeding values of sires were estimated by applying a random regression on the environmental parameters. In general, genetic variances varied generally only slightly across environments. However, based on data exclusively for heifers, the genetic variance for number of inseminations was 4.1 times higher in herds with a higher number of inseminations, 1.9 times higher for survival in herds with higher fat to protein ratio, and 1.7 times higher for predicted mastitis in herds with higher number of inseminations. Based on the heifer data, the lowest estimated genetic correlation across environments was 0.76 (SE 0.21) for first-service conception between herds with differing average BCS. The minimum based on the cow data was 0.65 (SE 0.10) for survival between herds with differing average ages at calving. The relative importance of some fertility traits compared with yield traits doubled across environments. Possible reranking of individual animals within a population and the changes in genetic variance across environments suggests that environment-specific breeding values should be estimated for use in customized selection indices.

Economic decisions on animal health strategies address the cost-benefit aspect along with animal welfare and public health concerns. Decision tree analysis at an individual cow level highlighted that there is little economic difference between the use of either dry cow antibiotic or an internal teat sealant in preventing a new intramammary infection in a cow free of infection in all quarters of the mammary gland at drying off. However, a potential net loss of over £20 per cow might occur if the uninfected cow was left untreated. The only economically viable option, for a cow with one or more quarters infected at drying off, is antibiotic treatment, although a loss might still be incurred depending on the pathogen concerned and the cure rates achievable. There was a net loss for cows with quarters infected with Corynebacterium spp. at drying off, for both the teat sealant and untreated groups (£22 and £48, respectively) with only antibiotic-treated cows showing a gain.

The objective of this study was to estimate effects of environmental sensitivity of milk production traits for several environmental parameters and to investigate the impact of combining traits with different environmental sensitivity in an economic index. Variance components and breeding values were estimated for milk, fat, and protein yield, and fat and protein percentage by applying a random regression on values of an environmental parameter for each sire. Fourteen environmental parameters were defined and fitted to data consisting of 151,696 heifers in 6780 herds in The Netherlands with first-lactation records for milk production, somatic cell count, body condition score, and number of inseminations. Milk, fat, and protein yield showed environmental sensitivity in combination with 12 environmental parameters. Herd-year averages of protein, body condition score, age at calving, calving interval, and peak date of calving explained most genotype by environment interaction, mainly resulting from scaling effects. Almost all genetic correlations across environments were 0.99 or higher. Although heterogeneity of genetic variances was considerable, heterogeneity of heritabilities was limited. Scaling had a large effect on the weights of the economic index, but environmental sensitivities of milk, fat, and protein yields were approximately of equal magnitude. Consequently, very little reranking occurred based on the economic index.

As with any measurement procedure, the performance of a subjective classification procedure must be evaluated. Observers have to be trained and their performance has to be assessed, preferably on a regular basis, to guarantee sufficient consistency and accuracy of classification results. The current paper is a study of observer performance where observers were asked to classify the gait of cows from video recordings. Gait was classified in nine ordered categories (ranging from 1=normal gait to 9=severely abnormal gait) and also as a continuous fraction by putting a mark on a paper strip (the left end corresponding to 0=normal gait and the right end to 1=severely abnormal gait). The use of statistical models and methodology for analysis of these visual scores is demonstrated and discussed. Observers were assessed by comparing their classification results with the results of an expert. Models and methodology take proper account of typical features of the data, i.e. the fact that data are discrete scores or continuous scores with an upper and lower bound, the variance heterogeneity and non-linearity of model terms that arises from this, and the dependence between repeated classifications of videos of the same cow. Results of the analyses are summarized in simple tables and plots. These are useful tools to indicate possible flaws in judgement of an observer, that may be corrected by further training. When a high standard is developed, which usually takes the form of the opinion of one or more experts, this methodology can be applied prior to any experiment where responses are ordered subjective scores.

Background Genomic prediction requires estimation of variances of effects of single nucleotide polymorphisms (SNPs), which is computationally demanding, and uses these variances for prediction. We have developed models with separate estimation of SNP variances, which can be applied infrequently, and genomic prediction, which can be applied routinely. Methods SNP variances were estimated with Bayes Stochastic Search Variable Selection (BSSVS) and BayesC. Genome-enhanced breeding values (GEBV) were estimated with RR-BLUP (ridge regression best linear unbiased prediction), using either variances obtained from BSSVS (BLUP-SSVS) or BayesC (BLUP-C), or assuming equal variances for each SNP. Datasets used to estimate SNP variances comprised (1) all animals, (2) 50% random animals (RAN50), (3) 50% best animals (TOP50), or (4) 50% worst animals (BOT50). Traits analysed were protein yield, udder depth, somatic cell score, interval between first and last insemination, direct longevity, and longevity including information from predictors. Results BLUP-SSVS and BLUP-C yielded similar GEBV as the equivalent Bayesian models that simultaneously estimated SNP variances. Reliabilities of these GEBV were consistently higher than from RR-BLUP, although only significantly for direct longevity. Across scenarios that used data subsets to estimate GEBV, observed reliabilities were generally higher for TOP50 than for RAN50, and much higher than for BOT50. Reliabilities of TOP50 were higher because the training data contained more ancestors of selection candidates. Using estimated SNP variances based on random or non-random subsets of the data, while using all data to estimate GEBV, did not affect reliabilities of the BLUP models. A convergence criterion of 10−8 instead of 10−10 for BLUP models yielded similar GEBV, while the required number of iterations decreased by 71 to 90%. Including a separate polygenic effect consistently improved reliabilities of the GEBV, but also substantially increased the required number of iterations to reach convergence with RR-BLUP. SNP variances converged faster for BayesC than for BSSVS. Conclusions Combining Bayesian variable selection models to re-estimate SNP variances and BLUP models that use those SNP variances, yields GEBV that are similar to those from full Bayesian models. Moreover, these combined models yield predictions with higher reliability and less bias than the commonly used RR-BLUP model. Electronic supplementary material The online version of this article (doi:10.1186/s12711-014-0052-x) contains supplementary material, which is available to authorized users.

We argue that animal-breeding schemes need well-designed breeding plans to maximise long-term genetic gains from genomic information. Genomic information has been implemented in livestock breeding schemes with ad hoc breeding plans, suggesting that the potential benefits of genomic information are not being fully exploited. Breeding schemes need well-designed breeding plans to exploit the benefits of genomic information for two reasons. First, there are several components of breeding schemes with genomic information that impact on long-term genetic gains. Second, these components interact, which implies that breeding schemes need to optimise components simultaneously in order to maximise long-term genetic gains. Designing breeding plans that optimise components simultaneously is a complex task. In more cases than not, breeding schemes, their components, and interactions between these components do not allow optimum breeding plans to be designed by mere reasoning. We recommend using decision frameworks to design breeding plans for schemes that use genomic information: testing sound hypotheses by designing and executing controlled experiments using decision tools, such as mathematical-statistical models. These decision frameworks enable us to design optimum breeding plans by providing an objective and theoretical basis to make and validate breeding decisions, enabling us to understand the underlying mechanisms of breeding schemes with genomic information, and allowing us to test the practical implementation of breeding decisions against theoretical models. Genomic information is an exciting prospect for animal breeding, and there is clearly an important role for breeding plans that maximise long-term genetic gains in breeding schemes using genomic information. (C) 2014 Elsevier B.V. All rights reserved.

Casein phosphorylation is a posttranslational modification catalyzed by kinase enzymes that attach phosphate groups to specific AA in the protein sequence. This modification is one of the key factors responsible for the stabilization of calcium phosphate nanoclusters in casein micelles and for the internal structure of the casein micelles. α(S1)-Casein (α(s1)-CN) is of special interest because it constitutes up to 40% of the total casein fraction in milk, and it has 2 common phosphorylation states, with 8 (α(S1)-CN-8P) and 9 (α(S1)-CN-9P) phosphorylated serine residues. Factors affecting this variation in the degree of phosphorylation are not currently known. The objective of this research was to determine the genetic background of α(S1)-CN-8P and α(S1)-CN-9P. The genetic and phenotypic correlation between α(S1)-CN-8P and α(S1)-CN-9P was low (0.18 and 0.19, respectively). This low genetic correlation suggests a different genetic background. These differences were further investigated by means of a genome-wide association study, which showed that both α(S1)-CN-8P and α(S1)-CN-9P were affected by a region on Bos taurus autosome (BTA) 6, but only α(S1)-CN-8P was affected by a region on BTA11 that contains the gene that encodes for β-lactoglobulin (β-LG), and only α(S1)-CN-9P was affected by a region on BTA14 that contains the diacylglycerol acyltransferase 1 (DGAT1) gene. Estimated effects of β-LG protein genotypes showed that only α(S1)-CN-8P was associated with the β-LG A/B polymorphism (g.1772G>A and g.3054C>T); the AA genotype of β-LG was associated with a lower concentration of α(S1)-CN-8P (-0.32% wt/wt) than the BB genotype (+0.41% wt/wt). Estimated effects of DGAT1 K232A genotypes showed that only α(S1)-CN-9P was associated with the DGAT1 gene polymorphism; DGAT1 AA genotype was associated with a higher α(S1)-CN-9P concentration (+0.53% wt/wt) than the DGAT1 KK genotype (-0.44% wt/wt). The results give insight in phosphorylation of α(S1)-CN-8P and α(S1)-CN-9P, which seem to be regulated by a different set of genes.

Background Genomic prediction faces two main statistical problems: multicollinearity and n ≪ p (many fewer observations than predictor variables). Principal component (PC) analysis is a multivariate statistical method that is often used to address these problems. The objective of this study was to compare the performance of PC regression (PCR) for genomic prediction with that of a commonly used REML model with a genomic relationship matrix (GREML) and to investigate the full potential of PCR for genomic prediction. Methods The PCR model used either a common or a semi-supervised approach, where PC were selected based either on their eigenvalues (i.e. proportion of variance explained by SNP (single nucleotide polymorphism) genotypes) or on their association with phenotypic variance in the reference population (i.e. the regression sum of squares contribution). Cross-validation within the reference population was used to select the optimum PCR model that minimizes mean squared error. Pre-corrected average daily milk, fat and protein yields of 1609 first lactation Holstein heifers, from Ireland, UK, the Netherlands and Sweden, which were genotyped with 50 k SNPs, were analysed. Each testing subset included animals from only one country, or from only one selection line for the UK. Results In general, accuracies of GREML and PCR were similar but GREML slightly outperformed PCR. Inclusion of genotyping information of validation animals into model training (semi-supervised PCR), did not result in more accurate genomic predictions. The highest achievable PCR accuracies were obtained across a wide range of numbers of PC fitted in the regression (from one to more than 1000), across test populations and traits. Using cross-validation within the reference population to derive the number of PC, yielded substantially lower accuracies than the highest achievable accuracies obtained across all possible numbers of PC. Conclusions On average, PCR performed only slightly less well than GREML. When the optimal number of PC was determined based on realized accuracy in the testing population, PCR showed a higher potential in terms of achievable accuracy that was not capitalized when PC selection was based on cross-validation. A standard approach for selecting the optimal set of PC in PCR remains a challenge. Electronic supplementary material The online version of this article (doi:10.1186/s12711-014-0060-x) contains supplementary material, which is available to authorized users.

This paper presents follicle development and hormone profiles for sows with normal ovulation or cystic follicles during an intermittent-suckling (IS) regime that started at Day 14 of lactation. Sows were subjected to separation from their piglets during blocks of 6 h or 12 h. In total, 8 out of 52 sows developed cystic follicles; either full cystic ovaries (n = 6) or partial ovulation (n = 2). Increase in follicle size of these sows was similar to that of normal ovulating sows until pre-ovulatory size at Day 5 after the start of separation, but from then on became larger (P

Validating genomic prediction equations in independent populations is an important part of evaluating genomic selection. Published genomic predictions from 2 studies on (1) residual feed intake and (2) dry matter intake (DMI) were validated in a cohort of 78 multiparous Holsteins from Australia. The mean realized accuracy of genomic prediction for residual feed intake was 0.27 when the reference population included phenotypes from 939 New Zealand and 843 Australian growing heifers (aged 5–8 mo) genotyped on high density (770k) single nucleotide polymorphism chips. The 90% bootstrapped confidence interval of this estimate was between 0.16 and 0.36. The mean realized accuracy was slightly lower (0.25) when the reference population comprised only Australian growing heifers. Higher realized accuracies were achieved for DMI in the same validation population and using a multicountry model that included 958 lactating cows from the Netherlands and United Kingdom in addition to 843 growing heifers from Australia. The multicountry analysis for DMI generated 3 sets of genomic predictions for validation animals, one on each country scale. The highest mean accuracy (0.72) was obtained when the genomic breeding values were expressed on the Dutch scale. Although the validation population used in this study was small (n=78), the results illustrate that genomic selection for DMI and residual feed intake is feasible. Multicountry collaboration in the area of dairy cow feed efficiency is the evident pathway to achieving reasonable genomic prediction accuracies for these valuable traits.

A genomic region associated with milk fatty acid (FA) composition has been detected on Bos taurus autosome (BTA)17 based on 50,000 (50K) single nucleotide polymorphism (SNP) genotypes. The aim of our study was to fine-map BTA17 with imputed 777,000 (777 K) SNP genotypes to identify candidate genes associated with milk FA composition. Phenotypes consisted of gas chromatography measurements of 14 FA based on winter and summer milk samples. Phenotypes and genotypes were available on 1,640 animals in winter milk, and on 1,581 animals in summer milk samples. Single-SNP analyses showed that several SNP in a region located between 29.0 and 34.0 Mbp were in strong association with C6:0, C8:0, and C10:0. This region was further characterized based on haplotypes. In summer milk samples, for example, these haplotypes explained almost 10% of the genetic variance in C6:0, 9% in C8:0, 3.5% in C10:0, 1.8% in C12:0, and 0.9% in C14:0. Two groups of haplotypes with distinct predicted effects could be defined, suggesting the presence of one causal variant. Predicted haplotype effects tended to increase from C6:0 to C14:0; however, the proportion of genetic variance explained by the haplotypes tended to decrease from C6:0 to C14:0. This is an indication that the quantitative trait locus (QTL) region is involved either in the elongation process or in early termination of de novo synthesized FA. Although many genes are present in this QTL region, most of these genes on BTA17 have not been characterized yet. The strongest association was found close to the progesterone receptor membrane component 2 (PGRMC2) gene, which has not yet been associated with milk FA composition. Therefore, no clear candidate gene associated with milk FA composition could be identified for this QTL.

Failure of the timely expulsion of the fetal membranes, called retained placenta, leads to reduced fertility, increased veterinary costs and reduced milk yields. The objectives of this study were to concurrently look at the heritable and non-heritable genetic effects on retained placenta and test the hypothesis that a greater coefficient of relationship between dam and calf increases the risk of retained placenta in the dam. The average incidence of retained placenta in 43,661 calvings of Meuse-Rhine-Yssel cattle was 4.5%, ranging from 0% to 29.6% among half-sib groups. The average pedigree based relationship between the sire and the maternal grandsire was 0.05 and ranged from 0 to 1.04. Using a sire-maternal grandsire model the heritability was estimated at 0.22 (SEM=0.07) which is comparable with estimates for other dual purpose breeds. The coefficient of relationship between the sire and the maternal grandsire had an effect on retained placenta. The coefficient of relationship between the sire and the maternal grandsire was used as a proxy for the coefficient of relationship between dam and calf, which is correlated with the probability of major histocompatibility complex (MHC) class I compatibility between dam and calf. MHC class I compatibility is an important risk factor for retained placenta. Although the MHC class I haplotype is genetically determined, MHC class I compatibility is not heritable. This study shows that selection against retained placenta is possible and indicates that preventing the mating of related parents may play a role in the prevention of retained placenta.

The objectives of this study were to 1) identify determinants of poor welfare in commercial broiler chicken flocks by studying the associations between selected resource-based measures (RBM, potential risk factors), such as litter quality and dark period, and animal-based welfare indicators (ABM), such as foot pad dermatitis and lameness, and 2) establish the breadth of effect of a risk factor by determining the range of animal welfare indicators associated with each of the risk factors (i.e., the number of ABM related to a specific RBM). Eighty-nine broiler flocks were inspected in 4 European countries (France, Italy, the United Kingdom, and the Netherlands) in a cross-sectional study. The ABM were contact dermatitis (measured using scores of foot-pad dermatitis and hock burn, respectively), lameness (measured as gait score), fear of humans (measured by the avoidance distance test and the touch test), and negative emotional state (measured using qualitative behavior assessment, QBA). In a first step, risk factors were identified by building a multiple linear regression model for each ABM. Litter quality was identified as a risk factor for contact dermatitis. Length of dark period at 3 wk old (DARK3) was a risk factor for the touch test result. DARK3 and flock age were risk factors for lameness, and the number of different stockmen and DARK3 were risk factors for QBA results. Next, the ABM were grouped according to risk factor and counted. Then, in a second step, associations between the ABM were investigated using common factor analysis. The breadth of a risk factor's effect was judged by combining the number (count) of ABM related to this factor and the strength of association between these ABM. Flock age and DARK3 appeared to affect several weakly correlated ABM, thus indicating a broad range of effects. Our findings suggest that manipulation of the predominant risk factors identified in this study (DARK3, litter quality, and slaughter age) could generate improvements in the related ABM and thereby enhance the birds' overall welfare status.

Streptococcus uberis is a highly prevalent causative agent of bovine mastitis, which leads to large economic losses in the dairy industry. The aim of this study was to examine the host response during acute inflammation after experimental challenge with capsulated Strep. uberis. Gene expression in response to Strep. uberis was compared between infected and control quarters in 3 animals. All quarters (n=16) were sampled at 16 different locations. Microarray data showed that 239 genes were differentially expressed between infected and control quarters. No differences in gene expression were observed between the different locations. Microarray data were confirmed for several genes using quantitative PCR analysis. Genes differentially expressed due to early Strep. uberis mastitis represented several stages of the process of infection: (1) pathogen recognition; (2) chemoattraction of neutrophils; (3) tissue repair mechanisms; and (4) bactericidal activity. Three different pathogen recognition genes were induced: ficolins, lipopolysaccharide binding protein, and toll-like receptor 2. Calgranulins were found to be the most strongly upregulated genes during early inflammation. By histology and immunohistochemistry, we demonstrated that changes in gene expression in response to Strep. uberis were induced both in infiltrating somatic milk cells and in mammary epithelial cells, demonstrating that the latter cell type plays a role in milk production as well as immune responsiveness. Given the rapid development of inflammation or mastitis after infection, early diagnosis of (Strep. uberis) mastitis is required for prevention of disease and spread of the pathogen. Insight into host responses could help to design immunomodulatory therapies to dampen inflammation after (early) diagnosis of Strep. uberis mastitis. Future research should focus on development of these early diagnostics and immunomodulatory components for mastitis treatment.