Heritability estimates of predicted blood β-hydroxybutyrate and nonesterified fatty acids and relationships with milk traits in early-lactation Holstein cows

Blood parameters predicted from milk spectra are candidate indicator traits of hyperketonemia - a retrospective study in the Italian Holstein population

At the onset of lactation, high-producing dairy cows commonly face a negative energy balance and consequent metabolic disorders, such as hyperketonemia. Blood concentrations of nonesterified fatty acids (NEFA), BHB, cholesterol, glucose and urea provide valuable information about the metabolic, health, and nutritional status of lactating animals. Milk mid-infrared (MIR) spectroscopy has been successfully used for the prediction of several health traits, including concentration of blood metabolites even though the models' accuracy is moderate. In fact, MIR-predicted blood parameters are useful for population screening and may be used for selective breeding if they are heritable and genetically variable within a population. In the present study we estimated h2 and genetic correlation of MIR-predicted BHB, NEFA, glucose, cholesterol and urea and assessed their genetic correlation with milk yield and composition traits in the Italian Holstein population using phenotypes of 9,943 cows in 460 herds. Two sets were considered: early (8,277 records - 1 per cow - between 5 and 35 d in milk) and whole lactation (105,293 records - at least 5 per cow - between 5 and 305 d in milk). The h2 and genetic variability of blood traits were greater in early than whole lactation, confirming that there is room to manipulate metabolic disease incidence in the transition period through selection. Blood BHB was the most heritable trait, no matter the lactation stage (0.13 and 0.08 in early and whole lactation), while NEFA was the least heritable trait, with h2 not significantly different from zero. Blood BHB was positively genetically correlated with NEFA, whereas glucose was negatively correlated with BHB, NEFA and urea. Blood BHB, NEFA and cholesterol were generally positively correlated with milk fat-to-protein ratio; BHB was negatively correlated with lactose content and positively with SCS. Estimated breeding values of sires with at least 20 daughters with phenotypes available were extrapolated for a-posteriori evaluation of the observed performance. The progeny of the top 5 sires exhibited a lower incidence of hyperketonemia compared with the other cows, with only 2.16% of cows having BHB concentration above the conventional threshold (1.2 mmol/L). Conversely, the prevalence of hyperketonemia was 5 times higher in the offspring of the bottom 5 bulls (10.55% cows with BHB >1.2 mmol/L). These findings suggest that, despite of the low h2 estimates, there is room to identify animals with low or high genetic merit for traits linked to the cow's metabolism. Therefore, the selection toward healthier and metabolically resistant cows is pursuable, with the infrared-predicted blood traits being potential auxiliary traits.

Unravelling the Signature Follicular Fluid Metabolites in Dairy Cattle Follicles Growing Under Negative Energy Balance: An In Vitro Approach

The astringent selection criteria for milk-oriented traits in dairy cattle have rendered these animals prone to various metabolic disorders. Postpartum lactational peak and reduced feed intake lead to negative energy balance in cattle. As a compensatory mechanism, cattle start mobilizing fat reserves to meet the energy demand for vital body functions. Consequently, diminished glucose concentrations and elevated ketone body levels lead to poor ovarian function. The impaired follicular development and subpar oocyte quality diminish the conception rates, which poses significant economic repercussions. Follicular fluid is integral to the processes of follicular growth and oocyte development. Hence, the present study was performed to identify potential alterations in metabolites in the follicular fluid under in vitro culture conditions mimicking negative energy balance. Our results revealed nine distinct metabolites exhibiting differential expression in follicular fluid under negative energy balance. The differentially expressed metabolites were predominantly associated with pathways related to amino acid metabolism, lipid metabolism, signal transduction mechanisms, and membrane transport, alongside other biological processes. The identified signature metabolites may be further validated to determine oocyte fitness subjected to in vitro fertilization or embryo production from slaughterhouse source ovaries.

Metabolic profile of prepartum dairy cows and its influence on the immediate postpartum period, colostrum quality and passive immunity transference

The objective of this study was to evaluate which biochemical markers in the prepartum period of dairy cows influence the immediate postpartum period, the quality of colostrum, and the passive immunity transference in the calves. The experiment was performed on a commercial dairy farm with 52 pregnant multiparous Holstein cows. Animals that gave birth to twins or males were discarded from the experiment. On days -20 of the expected calving date and 24hours after calving, blood collections, body condition score assessments, and animals weighing were performed. Blood samples from calves were performed 24hours after colostrum intake. Calf plasma was used to estimate the passive immunity transfer by % brix and total plasma proteins (TPP). In the principal component analysis, it was shown that postpartum NEFA and BHBA was higher in cows with higher prepartum urinary pH and Ca lower prepartum. The cows with the highest NEFA and BHBA in pre and postpartum were the ones that had the lowest % brix in the colostrum. The % brix of the cow's colostrum directly influenced the % brix and TPP of the calves. The NEFA in the cows prepartum negatively influenced the markers of immunity, the higher the NEFA, the lower the % brix of the cows' colostrum, % brix of the calves, and TPP. In multivariate regression analyses it was shown that pre-calving NEFA was the marker that most influenced post-calving cow markers and calf % brix, along with colostrum time and % brix of the colostrum (P= 0.0092; r2= 0.83). Cows with higher values NEFA in the prepartum had lower calcemic in the immediate postpartum period. Prepartum NEFA was the marker that most influenced the cows' immediate postpartum period, being directly related to Ca serum, and also to the passive immunity transference.

Genomic prediction of blood biomarkers of metabolic disorders in Holstein cattle using parametric and nonparametric models

BACKGROUND

Metabolic disturbances adversely impact productive and reproductive performance of dairy cattle due to changes in endocrine status and immune function, which increase the risk of disease. This may occur in the post-partum phase, but also throughout lactation, with sub-clinical symptoms. Recently, increased attention has been directed towards improved health and resilience in dairy cattle, and genomic selection (GS) could be a helpful tool for selecting animals that are more resilient to metabolic disturbances throughout lactation. Hence, we evaluated the genomic prediction of serum biomarkers levels for metabolic distress in 1353 Holsteins genotyped with the 100K single nucleotide polymorphism (SNP) chip assay. The GS was evaluated using parametric models best linear unbiased prediction (GBLUP), Bayesian B (BayesB), elastic net (ENET), and nonparametric models, gradient boosting machine (GBM) and stacking ensemble (Stack), which combines ENET and GBM approaches.

RESULTS

The results show that the Stack approach outperformed other methods with a relative difference (RD), calculated as an increment in prediction accuracy, of approximately 18.0% compared to GBLUP, 12.6% compared to BayesB, 8.7% compared to ENET, and 4.4% compared to GBM. The highest RD in prediction accuracy between other models with respect to GBLUP was observed for haptoglobin (hapto) from 17.7% for BayesB to 41.2% for Stack; for Zn from 9.8% (BayesB) to 29.3% (Stack); for ceruloplasmin (CuCp) from 9.3% (BayesB) to 27.9% (Stack); for ferric reducing antioxidant power (FRAP) from 8.0% (BayesB) to 40.0% (Stack); and for total protein (PROTt) from 5.7% (BayesB) to 22.9% (Stack). Using a subset of top SNPs (1.5k) selected from the GBM approach improved the accuracy for GBLUP from 1.8 to 76.5%. However, for the other models reductions in prediction accuracy of 4.8% for ENET (average of 10 traits), 5.9% for GBM (average of 21 traits), and 6.6% for Stack (average of 16 traits) were observed.

CONCLUSIONS

Our results indicate that the Stack approach was more accurate in predicting metabolic disturbances than GBLUP, BayesB, ENET, and GBM and seemed to be competitive for predicting complex phenotypes with various degrees of mode of inheritance, i.e. additive and non-additive effects. Selecting markers based on GBM improved accuracy of GBLUP.

The effect of first-lactation calving season, milk production, and morphology on the survival of Simmental cows

Longevity in dairy and dual-purpose cattle is a complex trait which depends on many individual and managerial factors. The purpose of the present study was to investigate the survival (SURV) rate of Italian Simmental dual-purpose cows across different parities. Data of this study referred to 2 173 primiparous cows under official milk recording that calved between 2002 and 2020. Only cows linearly classified for type traits, including muscularity (MU) and body condition score (BCS) were kept. Survival analysis was carried out, through the Cox regression model, for different pairwise combinations of classes of milk productivity MU, BCS, and calving season. Herd-year of first calving was also considered in the model. SURV (0 = culled; 1 = survived) at each lactation up to the 6th were the dependent variables, so that, for example, SURV2 equal to 1 was attributed to cows that entered the 2nd lactation. Survival rates were 98, 71, 63, 56, and 53% for 2nd, 3rd, 4th, 5th, and 6th lactation, respectively. Results revealed that SURV2 was not dependent on milk yield, while in subsequent parities, low-producing cows were characterized by higher SURV compared to high-producing ones. Additionally, cows starting the lactation in autumn survived less (47.38%) than those starting in spring (53.49%), suggesting that facing the late gestation phase in summer could increase the culling risk. The present study indicates that SURV in Italian Simmental cows is influenced by various factors in addition to milk productivity. However, it is important to consider that in this study all first-calving cows culled before the linear evaluation - carried out between mid- and late lactation in this breed - were not accounted for. Finding can be transferred to other dual-purpose breeds, where the cows' body conformation and muscle development - i.e. meat-related features - are often considered as important as milk performance by farmers undertaking culling decisions.

Genetic Background of Blood β-Hydroxybutyrate Acid Concentrations in Early-Lactating Holstein Dairy Cows Based on Genome-Wide Association Analyses

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

Genetic association between fat-to-protein ratio and traits of economic interest in early lactation Holstein cows in Brazil

The aims of this study were to estimate the genetic parameters for fat-to-protein ratio (F:P) within the first 90 days of lactation and to examine their genetic associations with daily milk yield (MY), somatic cell score (SCS), and calving interval between the first and second calving (IFSC) and between the second and third calving (ISTC) during the first three lactations of Holstein cows. We utilized 200,626 production-related data officially recorded from 77,436 cows milked two or three times a day from 2012 to 2022, sourced from the Holstein Cattle Breeders Association of Paraná State, Brazil. The (co)variance components were estimated using animal models, adopting the restricted maximum likelihood (REML) method with single-trait analysis (for heritability and repeatability) and two-trait analysis (for genetic and phenotypic correlations), per lactation. Regardless of lactation number, heritability estimates were relatively low, ranging from 0.08 ± 0.005 to 0.10 ± 0.003 for F:P; 0.08 ± 0.01 to 0.18 ± 0.005 for MY; 0.04 ± 0.01 to 0.07 ± 0.004 for SCS; and 0.03 ± 0.01 for both IFSC and ISTC. Repeatability estimates within the same lactation were low for F:P (ranging from 0.17 ± 0.002 to 0.19 ± 0.03), high for MY (between 0.50 ± 0.003 and 0.53 ± 0.002), and moderate to high for SCS (between 0.39 ± 0.003 and 0.44 ± 0.004). Genetic correlations between F:P and MY ranged from -0.26 ± 0.03 to -0.15 ± 0.02; F:P and SCS, from -0.06 ± 0.03 to -0.03 ± 0.08; F:P and IFSC, 0.31 ± 0.01; F:P and ISTC, 0.20 ± 0.01; MY and IFSC, 0.24 ± 0.05; and MY and ISTC, 0.13 ± 0.08. The fat-to-protein ratio during early lactation showed low genetic variability, regardless of lactation number. Furthermore, it was genetically correlated with MY, IFSC, and ISTC, although there is an antagonistic and unfavorable correlation between traits that can limit genetic progress.

Structural equation models to infer relationships between energy-related blood metabolites and milk daily energy output in Holstein cows

During lactation, high-yielding cows experience metabolic disturbances due to milk production. Metabolic monitoring offers valuable insights into how cows manage these challenges throughout the lactation period, making it a topic of considerable interest to breeders. In this study, we used Bayesian networks to uncover potential dependencies among various energy-related blood metabolites, i.e., glucose, urea, beta-hydroxybutyrate (BHB), non-esterified fatty acids (NEFA), cholesterol (CHOL), and daily milk energy output (dMEO) in 1,254 Holstein cows. The inferred causal structure was then incorporated into structural equation models (SEM) to estimate heritabilities and additive genetic correlations among these phenotypes using both pedigree and genotypes from a 100k chip. Dependencies among traits were determined using the Hill-Climbing algorithm, implemented with the posterior distribution of the residuals obtained from the standard multiple-trait model. These identified relationships were then used to construct the SEM, considering both direct and indirect relationships. The relevant dependencies and path coefficients obtained, expressed in units of measurement variation of 1σ, were as follows: dMEO → CHOL (0.181), dMEO → BHB (-0.149), dMEO → urea (0.038), glucose → BHB (-0.55), glucose → urea (-0.194), CHOL → urea (0.175), BHB → urea (-0.049), and NEFA → urea (-0.097). Heritabilities for traits of concern obtained with SEM ranged from 0.09 to 0.2. Genetic correlations with a minimum 95% probability (P) of the posterior mean being >0 for positive means or <0 for negative means include those between dMEO and glucose (-0.583, P = 100), dMEO and BHB (0.349, P = 99), glucose and CHOL (0.325, P = 100), glucose and NEFA (-0.388, P = 100), and NEFA and BHB (0.759, P = 100). The results of this analysis revealed the existence of recursive relationships among the energy-related blood metabolites and dMEO. Understanding these connections is paramount for establishing effective genetic selection strategies, enhancing production and animal welfare.

Assessing the relationship between somatic cell count and the milk mid-infrared spectrum in Chinese Holstein cows

BACKGROUND

Milk produced by dairy cows is a complex combination of many components, but the effect of mastitis has only been investigated for a few of these components. Milk mid-infrared (MIR) spectra can reflect the global composition of milk, and this study aimed to detect the relationships between milk MIR spectral wavenumbers and milk somatic cell count (SCC)-a sensitive biomarker for mastitis.

METHODS

Pearson correlation analysis was used to calculate the correlation coefficient between somatic count score (SCS) and spectral wavenumbers. A general linear mixed model was applied to investigate the effect of three different classes of SCC (low, middle and high) on spectral wavenumbers.

RESULTS

The mean correlation coefficient between the 'fingerprint region' (wavenumbers 925-1582 cm-1 ) and the SCS was higher than that for other regions of the MIR spectrum, and the specific wavenumber with the strongest correlation with the SCS was within the 'fingerprint region'. SCC class had a significant (p < 0.05) effect on 639 spectral wavenumbers. In particular, some spectral wavenumbers within the 'fingerprint region' were highly affected by the SCC class.

LIMITATION

The data were collected from only one province in China, so the generalisability of the findings may be limited.

CONCLUSION

SCC had close relationships with milk spectral wavenumbers related to important milk components or chemical bonds.

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

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

Udder health-related traits in cow milk: phenotypic variability and effect on milk yield and composition

The milk differential somatic cell count (DSCC) has been proposed in recent years as a mean by which to better monitor the udder health status (UHS) in dairy cows. Milk DSCC is the amount of polymorphonuclear neutrophils and lymphocytes contributing to the total somatic cell count (SCC) and can be determined on a routine basis in individual milk samples subjected to official analysis. In the present study, 522 865 milk test-day records of 77 143 cows were scrutinised to identify factors affecting the variability of both DSCC and SCC in Holstein Friesian, Jersey, Simmental and Rendena cows through linear mixed models. The fixed effects were breed, parity, lactation stage, sampling season, and all the first-order interactions of breed. Cow and herd-test-date were considered as random. Subsequently, four UHS groups were created (1: SCC ≤ 200 000 cells/mL and DSCC ≤ 65%; 2: SCC ≤ 200 000 cells/mL and DSCC > 65%; 3: SCC > 200 000 cells/mL and DSCC > 65%; 4: SCC > 200 000 cells/mL and DSCC ≤ 65%) to compare milk yield and quality. Milk SCS and DSCC differed across lactation, parity, sampling season and breed. In particular, Simmental cows had the lowest SCC and Jersey the lowest DSCC. Depending on the breed, UHS affected daily milk yield and composition to a different extent. The UHS group 4, i.e. the one grouping test-day records with high SCC and low DSCC, presented the lowest estimate of milk yield and lactose content no matter the breeds. Our findings support that udder health-related traits (SCS and DSCC) are useful information to improve udder health at individual cow and herd levels. Moreover, the combination of SCS and DSCC is useful to monitor milk yield and composition.

Total and Differential Somatic Cell Count in Italian Local Cattle Breeds: Phenotypic Variability and Effect on Milk Yield and Composition

Milk differential somatic cell count (DSCC) represents the percentage of polymorphonuclear neutrophils and lymphocytes out of the total somatic cell count (SCC) and has been proposed in recent years as a proxy for udder health in dairy cows. We investigated phenotypic factors affecting SCC and DSCC using 3978 records of 212 Alpine Grey and 426 Burlina cows farmed in Northern Italy. The linear mixed model accounted for the fixed effects of breed, parity, lactation stage, sampling season, and first-order interactions of breed with the other effects. Cow, herd-test-date nested within breed were random. Subsequently, four udder health status groups (UHS) were created by combining SCC and DSCC to assess the UHS impact on milk yield and quality. DSCC was greater in Alpine Grey (66.2 ± 0.8%) than Burlina cows (63.2 ± 0.6%) and, similarly to SCC, it increased with days in milk and parity regardless of breed. Milk yield and composition were affected by UHS in both breeds. These results suggest that also udder health of local breeds can be monitored on a large scale through SCC and DSCC for reduction in biodiversity loss and increased farm profitability. However, in addition to milk data, the introduction of mastitis recording and monitoring plans is advisable.

Heritabilities and genetic and phenotypic correlations for milk production and fertility traits of spring-calved once-daily or twice-daily milking cows in New Zealand

The objectives of this study were to estimate the genetic and phenotypic correlations and heritabilities for milk production and fertility traits in spring-calved once-daily (OAD) milking cows for the whole season in New Zealand and compare those estimates with twice-daily (TAD) milking cows. Data used in the study consisted of 69,252 first parity cows from the calving seasons 2015-2016 to 2017-2018 in 113 OAD and 531 TAD milking herds. Heritability estimates for production and fertility traits were obtained through single-trait animal models, and estimates of genetic and phenotypic correlations were obtained through bivariate animal models. Heritability estimates of production traits varied from 0.26 to 0.61 in OAD and from 0.13 to 0.63 in TAD. Heritability estimates for fertility traits were low in both OAD and TAD milking cow populations, and estimates were consistent (OAD: 0.01 to 0.10 and TAD: 0.01 to 0.08) across milking regimens. Estimates of phenotypic and genetic correlations among production traits were consistent across populations. In both populations, phenotypic correlations between milk production and fertility traits were close to zero, and most of the genetic correlations were antagonistic. In OAD milking cows, genetic correlations of milk and lactose yields with the start of mating to conception, 6-wk in-calf, not-in-calf, and 6-wk calving rate were close to zero. Interval from first service to conception was negatively genetically correlated with milk and lactose yields in OAD milking cows. Protein percentage was positively genetically correlated with 3-wk and 6-wk submission, 3-wk in-calf, 6-wk in-calf, first service to conception, 3-wk calving, and 6-wk calving rate in the TAD milking cow population, but these correlations were low in the OAD milking cow population. Further studies are needed to understand the relationship of protein percentage and fertility traits in the OAD milking system. The phenotypic correlations between fertility traits were similar in OAD and TAD milking populations. Genetic correlations between fertility traits were strong (≥0.70) in cows milked TAD, but genetic correlations varied from weak to strong in cows milked OAD. Further research is required to evaluate the interaction between genotype by milking regimen for fertility traits in terms of sire selection in the OAD milking cow population.

Across countries implementation of handheld near-infrared spectrometer for the on-line prediction of beef marbling in slaughterhouse

Only few studies have used Near-Infrared (NIR) spectroscopy to assess meat quality traits directly in the chiller. The aim of this study was therefore to investigate the ability of a handheld NIR spectrometer to predict marbling scores on intact meat muscles in the chiller. A total of 829 animals from 2 slaughterhouses in France and Italy were involved. Marbling was assessed according to the 3G (Global Grading Guaranteed) protocol using 2 different scores. NIR measurements were collected by performing 5 scans at different points of the Longissimus thoracis. An average MSA marbling score of 330-340 was obtained in the two countries. The prediction models provided a R² in external validation between 0.46 and 0.59 and a standard error of prediction between 83.1 and 105.5. Results did provide a moderate prediction of the marbling scores but can be useful in the European industry context to predict classes of MSA marbling.

A meta-analysis of the genetic contribution estimates to major indicators for ketosis in dairy cows

The present study aimed to perform a meta-analysis using the random-effects model to merge published genetic parameter estimates for major indicators of ketosis [milk concentrations of acetone (ACETm) and β-hydroxybutyrate (BHBAm), and blood concentration of β-hydroxybutyrate (BHBAb)] in dairy cows. Overall, 51 heritability estimates and 130 genetic correlations from 19 papers published between 2012 and 2022 were used in this study. The average heritability estimates for ACETm, BHBAm, and BHBAb were 0.164, 0.123, and 0.141, respectively. The genetic correlation estimates between BHBAm and milk yield (MY), milk protein percentage (PP), and body condition score (BCS) were negative and moderate (-0.252, -0.200, and - 0.314, respectively). Genetic correlation estimates between BHBAm and milk fat percentage (FP), milk fat to protein ratio (FPR), and ketosis (KET) were moderate to high (0.411, 0.512, and 0.614, respectively). The genetic correlation estimates between BHBAb and MY and FP were low and equal to 0.128 and 0.035, respectively. The genetic correlation estimates between ACETm-MY and ACETm-PP were negative and moderate (-0.374 and - 0.398, respectively). Estimates of genetic correlation between ACETm and FP, FPR, and KET were moderate to high (0.455, 0.626, and 0.876, respectively). The results of this meta-analysis indicated the existence of additive genetic variation for ketosis indicator metabolites which could be exploited in genetic selection programs to reduce ketosis in dairy cows. Moreover, the results propose that selection for lower concentrations of indicator traits could be an effective plan for indirect improvement of production and reproduction performance, and health in dairy cows.

Transcriptome Analysis Reveals That NEFA and β-Hydroxybutyrate Induce Oxidative Stress and Inflammatory Response in Bovine Mammary Epithelial Cells

Non-esterified fatty acids (NEFA) and β-hydroxybutyrate (BHBA) are the metabolites of fat mobilization initiated by negative energy balance (NEB) during the perinatal period in dairy cows, which have an adverse effect on cell physiology of various bovine cell types. The aim of this study was to explore the biological roles of NEFA and BHBA on provoking oxidative stress and inflammatory responses in bovine mammary epithelial cells (BMECs). RNA sequencing analysis showed that there are 1343, 48, and 1725 significantly differentially expressed genes (DEGs) in BMECs treated with NEFA, BHBA and their combination. GO functional analysis revealed that the DEGs were significantly enriched in "response to oxidative stress" and "inflammatory response". Further study demonstrated that NEFA and BHBA elevated the malondialdehyde (MDA) and reactive oxygen species (ROS) accumulation and reduced the total superoxide dismutase (T-SOD) and glutathione peroxidase (GSH-Px) activity to cause oxidative stress. In addition, expression of inflammatory markers (NO, TNF-α, IL-6, and IL-1β) were increased after NEFA and BHBA stimulation. Mechanistically, our data showed that NEFA and BHBA activated the MAPK signaling pathway. Collectively, our results indicate that NEFA and BHBA induce oxidative stress and inflammatory response probably via the MAPK signaling pathway in BMECs.

Effect of an Immunomodulatory Feed Additive in Mitigating the Stress Responses in Lactating Dairy Cows to a High Concentrate Diet Challenge

Dairy cows are often exposed to multiple stressors in a lactation-cycle, with sub-acute ruminal acidosis (SARA) a frequent example of nutritional stress. SARA affects ruminal and intestinal equilibrium resulting in dysbiosis with localized and systemic inflammation impacting animal health and productivity. OmniGen-AF (OMN, Phibro Animal Health Corporation, Teaneck, NJ, USA) is a feed product recognized for modulating innate immune function, especially during periods of stress. The objective of this study was to determine the effects of OMN in lactating dairy cows fed a high-starch, low-fiber diet. Twenty-four blocked cows were assigned to control or treatment (55 g/d). After the additive adaptation (49 d) cows were fed the challenge diet (28 d). Milk, rumination and pH were continuously recorded; components, rumen fluid, and blood were taken in multiple time-point and analyzed. Results showed that the challenge decreased the rumination, shifted ruminal fluid composition, decreased milk production and the components, and slightly increased the time below pH 5.5, with no differences between groups. The treatment produced greater rumen butyrate and lower lactate, prompter regeneration of red blood cells, increase of neutrophils, lower paraoxonase, gamma-glutamyl-transferase, and β-hydroxybutyrate, with no differences on other tested inflammatory markers. Results show that OMN helps modulating some of the metabolic and immunological responses to SARA.

Breeding for improved protein fractions and free amino acids concentration in bovine milk

Considerable resources are required to routinely measure detailed milk compositional traits. Hence, an insufficient volume of phenotypic data can hinder genetic progress in these traits within dairy cow breeding programmes. The objective of the present study was to quantify the opportunities for breeding for improved milk protein and free amino acid (FAA) composition by exploiting mid‐infrared spectroscopy (MIRS) predictions routinely recorded from milk samples. Genetic parameters for protein fractions and FAA composition were estimated using 134,546 test‐day records from 16,166 lactations on 9,572 cows using linear mixed models. Heritability of MIRS‐predicted protein fractions ranged from 0.19 (α‐lactalbumin) to 0.55 (β‐lactoglobulin A), while heritability of MIRS‐predicted FAA ranged from 0.08 for glycine to 0.29 for glutamic acid. Genetic correlations among the MIRS‐predicted FAA were moderate to strong ranging from −0.44 (aspartic acid and lysine) to 0.97 (glutamic acid and total FAA). Adjustment of the genetic correlations for the genetic merit of 24‐h milk yield did not greatly affect the correlations. Results from the current study highlight the presence of exploitable genetic variation for both protein fractions and FAA in dairy cow milk. Besides, the direction of genetic correlations reveals that breeding programmes directly selecting for greater milk protein concentration carry with them favourable improvement in casein and whey fractions.

The Genetics of Thermoregulation in Pigs: A Review

Heat stress (HS) affects pig performance, health and welfare, resulting in a financial burden to the pig industry. Pigs have a limited number of functional sweat glands and their thermoregulatory mechanisms used to maintain body temperature, are challenged by HS to maintain body temperature. The genetic selection of genotypes tolerant to HS is a promising long-term (adaptation) option that could be combined with other measures at the production system level. This review summarizes the current knowledge on the genetics of thermoregulation in pigs. It also discusses the different phenotypes that can be used in genetic studies, as well as the variability in thermoregulation between pig breeds and the inheritance of traits related to thermoregulation. This review also considers on-going challenges to face for improving heat tolerance in pigs.

Random Regression Model for Genetic Evaluation and Early Selection in the Iranian Holstein Population

Simple Summary

The objective of this study was to use a model to predict breeding values for sires and cows at an early stage of the first lactation of cows and progeny groups in the Iranian Holstein population to support the early selection of sires. Our results show that we can select sires according to their daughters’ early lactation performance before they finish first lactation. Cross-validation results show that early selection accuracy can be high, and such an early selection can decrease the generation interval and lead to an increased genetic gain in the Iranian Holstein population.

Abstract

The objective of this study was to use a model to predict breeding values for sires and cows at an early stage of the first lactation of cows and progeny groups in the Iranian Holstein population to enable the early selection of sires. An additional objective was to estimate genetic and phenotypic parameters associated with this model. The accuracy of predicted breeding values was investigated using cross-validation based on sequential genetic evaluations emulating yearly evaluation runs. The data consisted of 2,166,925 test-day records from 456,712 cows calving between 1990 and 2015. (Co)-variance components and breeding values were estimated using a random regression test-day model and the average information (AI) restricted maximum likelihood method (REML). Legendre polynomial functions of order three were chosen to fit the additive genetic and permanent environmental effects, and a homogeneous residual variance was assumed throughout lactation. The lowest heritability of daily milk yield was estimated to be just under 0.14 in early lactation, and the highest heritability of daily milk yield was estimated to be 0.18 in mid-lactation. Cross-validation showed a highly positive correlation of predicted breeding values between consecutive yearly evaluations for both cows and sires. Correlation between predicted breeding values based only on records of early lactation (5–90 days) and records including late lactation (181–305 days) were 0.77–0.87 for cows and 0.81–0.94 for sires. These results show that we can select sires according to their daughters’ early lactation information before they finish the first lactation. This can be used to decrease generation interval and to increase genetic gain in the Iranian Holstein population.

Investigation of β-hydroxybutyrate in early lactation of Simmental cows: Genetic parameters and genomic predictions

Genomic information allows for a more accurate calculation of relationships among animals than the pedigree information, leading to an increase in accuracy of breeding values. Here, we used pedigree-based and single-step genomic approaches to estimate variance components and breeding values for β-hydroxybutyrate milk content (BHB). Additionally, we performed a genome-wide association study (GWAS) to depict its genetic architecture. BHB concentrations within the first 90 days of lactation, estimated from milk medium infrared spectra, were available for 30,461 cows (70,984 records). Genotypes at 42,152 loci were available for 9,123 animals. Low heritabilities were found for BHB using pedigree-based (0.09 ± 0.01) and genomic (0.10 ± 0.01) approaches. Genetic correlation between BHB and milk traits ranged from -0.27 ± 0.06 (BHB and protein percentage) to 0.13 ± 0.07 (BHB and fat-to-protein ratio) using pedigree and from -0.26 ± 0.05 (BHB and protein percentage) to 0.13 ± 0.06 (BHB and fat-to-protein ratio) using genomics. Breeding values were validated for 344 genotyped cows using linear regression method. The genomic EBV (GEBV) had greater accuracy (0.51 vs. 0.45) and regression coefficient (0.98 vs. 0.95) compared to EBV. The correlation between two subsequent evaluations, without and with phenotypes for validation cows, was 0.85 for GEBV and 0.82 for EBV. Predictive ability (correlation between (G)EBV and adjusted phenotypes) was greater when genomic information was used (0.38) than in the pedigree-based approach (0.31). Validation statistics in the pairwise two-trait models (milk yield, fat and protein percentage, urea, fat/protein ratio, lactose and logarithmic transformation of somatic cells count) were very similar to the ones highlighted for the single-trait model. The GWAS allowed discovering four significant markers located on BTA20 (57.5-58.2 Mb), where the ANKH gene is mapped. This gene has been associated with lactose, alpha-lactalbumin and BHB. Results of this study confirmed the usefulness of genomic information to provide more accurate variance components and breeding values, and important insights about the genomic determination of BHB milk content.

A Comparison of Rice Husks and Peanut Shells as Bedding Materials on Dairy Cows' Preferences, Behaviour, and Health

Simple Summary

Good bedding materials can increase the comfort potential of the lying surface and enhance the welfare of cows in intensive dairy farms. The preference, behaviour, hygiene, and health of cows are affected by different bedding surfaces. In the current study, we evaluated the preference, behaviour, cleanliness, and health of cows on three bedding materials, peanut shells (PS), rice husks (RH), and a combination of two-thirds peanut shells, one-third rice husk (PRC). The daily behaviour, serum metabolites, and productivity of dairy cows were all within normal values, and no statistical differences were found between all three bedding materials, although cows showed a preference for rice husk when given access to all three bedding materials at the same time. Finally, the results suggest that bedding comprised of peanut shells and peanut–rice combinations are all suitable for maintaining the health and comfort of dairy cows.

Abstract

The provision and quality of bedding materials affect the behaviour, welfare, and health of dairy cows. The objective of this study was to evaluate the preference, behaviour, cleanliness, and physiological status of cows on three bedding materials, peanut shells, rice husks, and a combination of two-thirds peanut shells, one-third rice husk. In an initial experiment, 15 nonlactating, pregnant Holstein cows had free access to all 3 bedding treatments for 39 d. Cows spent more time lying down on rice husk (337 min/d) than on peanut–rice combination (212 min/d) and peanut shell (196 min/d) (p < 0.05), and lay down most often on rice husk (4.35 bouts/d) than on peanut shell (2.55 bouts/d) (p < 0.05) but did not differ between peanut shells and peanut–rice combinations in terms of lying time and lying bouts. In Experiment 2, 12 nonlactating cows were used to assess the effects of the 3 bedding materials on dairy cow behaviour, cleanliness, serum indicators, and productivity. The total duration of lying down (PS: 699.1 min/d, PRC: 645.6 min/d, RH: 852.5 min/d), the frequency of bouts of lying down (PS: 8.7 bouts/d, PRC: 7.6 bouts/d, RH: 11.1 bouts/d), and the mean duration of lying bouts (PS: 83.5 min/bouts, PRC: 91.8 min/bouts, RH: 81.4 min/bouts) did not differ between treatments. Similarly, no differences in eating or drinking behaviour of dairy cows were observed. In terms of hygiene, cleanliness scores did not differ between the three bedding materials, but udder and flank cleanliness decreased and improved, respectively. In addition, treatments did not affect serum metabolites or productivity of the cows. In summary, daily behaviour, serum metabolites, and productivity of dairy cows were all within the normal range, and no statistical differences occurred between the three bedding materials, although cows showed a preference for rice husk when given access to all three bedding materials at the same time. Finally, the results suggest that bedding comprised of peanut shells and peanut–rice combinations are all suitable for maintaining the health and comfort of dairy cows.

Antunes A, Cramer G, Caixeta LS. Association of Body Condition Score and Score Change during the Late Dry Period on Temporal Patterns of Beta-Hydroxybutyrate Concentration and Milk Yield and Composition in Early Lactation of Dairy Cows

Simple Summary

In order to develop strategies to monitor and mitigate high incidences of hyperketonemia in dairy herds, the factors associated with the fluctuations of beta-hydroxybutyrate (BHB) concentration during early lactation need to be assessed. Body condition score (BCS), as well as the change in BCS during the late dry period are elements that are highly related to the mobilization of body reserves, and the proper adaptation during the transition period of dairy cattle. Our objective was to describe the pattern of blood BHB concentration and the development of hyperketonemia during the first 14 days of lactation on the basis of both a single measurement of BCS (−21 d) and the change in BCS during the late dry period. Additionally, we aimed to characterize the association between changes in BCS and milk yield and milk composition in the first monthly test. Our results suggest that changes in BCS are associated with fluctuations in BHB concentration during early lactation. In addition, we observed that cows with a loss in BCS greater than 0.5 units during the late dry period have a higher risk of having elevated BHB concentrations and incidence of hyperketonemia than cows with no change in BCS in the late dry period. Moreover, these cows also experienced lower milk production at the first monthly milk test.

Abstract

Monitoring the body condition score (BCS) of dairy cows is a management strategy that can assist dairy producers in decision-making. The BCS and its variations reflect the level of body fat reserves and fat mobilization throughout the different stages of lactation. Cows that mobilize excessive amounts of fat reserves in response to the increased energy requirements of the transition period are more likely to have higher beta-hydroxybutyrate (BHB) concentration in blood, leading to a higher incidence of hyperketonemia postpartum. In this study, our main objective was to evaluate how both BCS (at 21 d prior to the expected calving date, −21 BCS) and change in BCS during the late dry period (−21 d to calving, ∆BCS) are associated with temporal patterns of blood BHB concentrations during the first two weeks of lactation. Our secondary objective was to characterize the relationship between the change in BCS in the late dry period, and milk yield and milk composition in the first milk test postpartum. In this retrospective cohort study, we assessed BCS at 21 (±3) days before the expected calving date and within three days after calving. Blood BHB concentration was measured at days 3 (±1), 7 (±1), and 14 (±1) postpartum. Hyperketonemia (HYK) was defined as blood BHB ≥ 1.2 mmol/L. To evaluate how −21 BCS and ∆BCS during the late dry period were associated with BHB in early lactation, linear mixed-effects regression models with an unstructured covariate matrix were performed. The association between ∆BCS and incidence of postpartum HYK were determined using a multivariable log-binomial model. A linear regression model was used to evaluate the association between ∆BCS and milk yield and milk composition in the first monthly test-day. Covariates used for model adjustment include parity, season, and baseline BCS. We observed that cows with BCS ≥ 4.0 at 21 d before their expected calving date had the highest BHB concentration postpartum, but no evidence that BCS ≥ 4.0 at 21 d was associated with fluctuations of BHB over time. Cows that experienced a large BCS loss (larger than 0.5 units) during the late dry period had a 61% (95% CI: 1.04, 2.50) higher risk of developing HYK in early lactation and had higher BHB concentrations during early lactation compared with cows with no ∆BCS prepartum. These associations were observed independently of the BCS at −21 d prepartum (baseline). In addition, cows that lost more than 0.5 BCS unit in the late dry period produced 3.3 kg less milk (95% CI: −7.06, 0.45) at the first milk test compared to cows that had no ∆BCS during the late dry period. No evidence of an association between −21 BCS and ∆BCS in the late dry period and milk composition was observed in our study. These results suggest that dynamic measures of BCS during the late dry period, such as ∆BCS, are better at evaluating blood BHB patterns in early lactation than BCS measured at a single time point. Cows with larger BCS loss during the late dry period and with greater parity are more likely to have higher concentrations of blood BHB postpartum, with the highest concentrations reported at 7 d post-calving.

Genetic correlations between fertility traits and milk composition and fatty acids in Holstein-Friesian, Brown Swiss, and Simmental cattle using recursive models

This study aimed to investigate the genetic and putative causal relationships between fertility traits [i.e., days open (DO) and calving rate (CR)] and milk quality, composition, and fatty acid contents (milk composition traits) in Holstein-Friesian, Brown Swiss, and Simmental cattle, using recursive models within a Bayesian framework. Trivariate animal models were run, each including one fertility trait, one milk composition trait, and, in all models, milk yield. The DO and CR data were merged with the test days closest to the insemination date for milk composition traits. After editing, 16,468 to 23,424 records for Holstein-Friesian, 23,424 to 46,660 for Brown Swiss, and 26,105 to 35,574 for Simmental were available for the analyses. Recursive animal models were applied to investigate the possible causal influences of milk composition traits on fertility and the genetic relationships among these traits. The results suggested a potential cause-and-effect relationship between milk composition traits and fertility traits, with the first trait influencing the latter. We also found greater recursive effects of milk composition traits on DO than on CR, the latter with some putative differences among breeds in terms of sensitivity. For instance, the putative causal effects of somatic cell score on CR (on the observed scale, %) varied from -0.96 to -1.39%, depending on the breed. Concerning fatty acids, we found relevant putative effects of C18:0 on CR, with estimates varying from -7.8 to -9.9%. Protein and casein percentages, and short-chain fatty acid showed larger recursive effects on CR, whereas fat, protein, and casein percentages, C16:0, C18:0, and long-chain fatty acid had larger effects on DO. The results obtained suggested that these milk traits could be considered as effective indicators of the effects of animal metabolic and physiological status on fertility.

Effects of Rumen-Protected Niacin on Dry Matter Intake, Milk Production, Apparent Total Tract Digestibility, and Faecal Bacterial Community in Multiparous Holstein Dairy Cow during the Postpartum Period

Extensive studies about rumen-protected niacin (RPN) supplementation on dairy cows in early-lactation have been done, but the effects of RPN on changes in dry matter intake (DMI), milk production, feed digestibility, and fecal bacterial community were conflicting. The aim of this study was to investigate them affected by RPN in postpartum cows. Multiparous Holstein dairy cows (n = 12, parity = 3.5 ± 0.5, body weights = 740 ± 28 kg) were divided into two groups supplemented with either 0 (CON) or 20 g/d RPN (RPN). Our results showed that RPN supplementation increased DMI and milk production of cows during the first three weeks after calving (p < 0.05). The concentrations of neuropeptide Y and orexin A were significantly higher in RPN group than that in the CON group during postpartum period (p < 0.05). The apparent total-tract digestibility of nutrients was similar between the CON and RPN groups at 2 weeks after calving (p > 0.05). The 16S rRNA gene sequencing analysis showed that RPN had no impact on the alpha and beta diversity, although 4 genera were changed in cow feces at 14 days after calving. Overall, 20 g/d RPN added to the diet could improve DMI and milk yield up to two weeks after calving with little influence on feed digestibility.

Short communication: Iodine content in bovine milk is lowly heritable and shows limited genetic variation

Milk and dairy products are considered important sources of iodine in several countries. Despite this, there is a paucity of studies that have investigated sources of variation of milk iodine, especially on a large scale. So far, it is not clear if milk iodine content could be increased through breeding in dairy cattle. Recently, a mid-infrared spectroscopy prediction model has been developed for an indirect quantification of iodine content in cow milk, as it is a faster and less expensive method that allows the prediction at population level. The model has coefficient of determination and ratio of performance to deviation in external validation of 0.57 and 1.44, respectively, and it was used in the present study to predict the iodine content from historical milk spectral data to investigate phenotypic and genetic aspects in the Italian Holstein cattle. Based on the accuracy of the model, the prediction was interpreted as proxy for the real milk iodine concentration (IOD_P). The data set comprised 33,776 test-day records with IOD_P from 4,072 cows. Data of IOD_P were transformed through natural logarithm to achieve a normal distribution. The effect of parity, lactation stage, and month of sampling were investigated, and genetic parameters were estimated using a test-day repeatability animal model. Milk IOD_P decreased with parities and was the lowest in early lactation. Heritability of IOD_P was low (0.025) and it was positively genetically correlated with milk yield and negatively with fat content. Results suggested that it would be challenging to directly improve this trait through breeding strategies in dairy cattle, because IOD_P is mainly affected by temporary environmental factors and thus, cannot be easily improved through genetics. Although preliminary, findings of this study suggest that it would be more convenient to develop feeding and management strategies to drive milk iodine level than to put efforts and resources into breeding strategies. Further studies should validate IOD_P as an indicator trait of milk iodine content by improving reference data and estimating genetic correlation between predicted and measured values.

The use of milk mid-infrared spectroscopy to improve genomic prediction accuracy of serum biomarkers

Breeding objectives in the dairy industry have shifted from being solely focused on production to including fertility, animal health, and environmental impact. Increased serum concentrations of candidate biomarkers of health and fertility, such as β-hydroxybutyric acid (BHB), fatty acids, and urea are difficult and costly to measure, and thus limit the number of records. Accurate genomic prediction requires a large reference population. The inclusion of milk mid-infrared (MIR) spectroscopic predictions of biomarkers may increase genomic prediction accuracy of these traits. Our objectives were to (1) estimate the heritability of, and genetic correlations between, selected serum biomarkers and their respective MIR predictions, and (2) evaluate genomic prediction accuracies of either only measured serum traits, or serum traits plus MIR-predicted traits. The MIR-predicted traits were either fitted in a single trait model, assuming the measured trait and predicted trait were the same trait, or in a multitrait model, where measured and predicted trait were assumed to be correlated traits. We performed all analyses using relationship matrices constructed from pedigree (A matrix), genotypes (G matrix), or both pedigree and genotypes (H matrix). Our data set comprised up to 2,198 and 9,657 Holstein cows with records for serum biomarkers and MIR-predicted traits, respectively. Heritabilities of measured serum traits ranged from 0.04 to 0.07 for BHB, from 0.13 to 0.21 for fatty acids, and from 0.10 to 0.12 for urea. Heritabilities for MIR-predicted traits were not significantly different from those for the measured traits. Genetic correlations between measured traits and MIR-predicted traits were close to 1 for urea. For BHB and fatty acids, genetic correlations were lower and had large standard errors. The inclusion of MIR predicted urea substantially increased prediction accuracy for urea. For BHB, including MIR-predicted BHB reduced the genomic prediction accuracy, whereas for fatty acids, prediction accuracies were similar with either measured fatty acids, MIR-predicted fatty acids, or both. The high genetic correlation between urea and MIR-predicted urea, in combination with the increased prediction accuracy, demonstrated the potential of using MIR-predicted urea for genomic prediction of urea. For BHB and fatty acids, further studies with larger data sets are required to obtain more accurate estimates of genetic correlations.

Genetic and Non-Genetic Variation of Milk Total Antioxidant Activity Predicted from Mid-Infrared Spectra in Holstein Cows

Simple Summary

The total antioxidant activity (TAA) of food is important for human health and results from the contribution of different nutraceutical compounds. Direct determination of TAA in food is time-consuming and expensive. Infrared technologies allow the prediction of difficult-to-measure traits with certain accuracy in several organic matrices, including TAA of bovine milk. In order to understand the background of TAA and identify potential strategies to improve this feature in bovine milk, we explored its non-genetic sources of variation and estimated heritability and correlations with traits of economic interest in a large database of Holstein cows.

Abstract

Food antioxidants enhance products shelf life and stability during technological treatments through the maintenance of their physical and chemical properties. Moreover, they are endowed with several positive effects on human health, including cell membranes preservation, enzyme functionality, and DNA integrity. Milk has been described in relation to a wide array of fat soluble and water-soluble antioxidant compounds, in particular vitamin A, C, and E, lactoferrin and peptides derived from casein and whey proteins. The total antioxidant activity (TAA) of milk is a novel and scarcely explored trait, defined as the sum of antioxidant contributions of the aforementioned compounds. On this background, the aims of the present study were to investigate the variability of milk TAA on a large scale exploiting predictions obtained through mid-infrared (MIR) spectroscopy and to estimate genetic parameters of this trait in Holstein cows. Individual milk samples were collected between January 2011 and December 2018 during the routine milk recording procedure. Samples were analysed for gross composition through MIR spectroscopy and MIR spectra were stored. Milk TAA was then predicted (pTAA) from the stored milk MIR spectra (111,653 test-day records of 9519 cows in 344 herds) using the previously developed prediction model; considering the prediction accuracy, pTAA might be considered a proxy of the TAA determined through the reference method. Overall, pTAA averaged 7.16 mmoL/L of Trolox equivalents, showed a nadir around 40 days after calving and increased thereafter, following a linear trend up to the end of lactation. The lowest pTAA was observed in milk sampled from June to September. Milk pTAA was heritable (0.401 ± 0.015) and genetically associated to fat yield (0.366 ± 0.049), crude protein (CP) yield (0.238 ± 0.052), fat percentage (0.616 ± 0.022) and CP percentage (0.754 ± 0.015). The official selection index of Italian Holstein put the 49% of the emphasis on fat and protein yield and percentage; therefore, it derives that an indirect favourable selection for milk pTAA should be already in progress in Italian Holstein population.