Combining genetic markers, on-farm information and infrared data for the in-line prediction of blood biomarkers of metabolic disorders in Holstein cattle

BACKGROUND

Various blood metabolites are known to be useful indicators of health status in dairy cattle, but their routine assessment is time-consuming, expensive, and stressful for the cows at the herd level. Thus, we evaluated the effectiveness of combining in-line near infrared (NIR) milk spectra with on-farm (days in milk [DIM] and parity) and genetic markers for predicting blood metabolites in Holstein cattle. Data were obtained from 388 Holstein cows from a farm with an AfiLab system. NIR spectra, on-farm information, and single nucleotide polymorphisms (SNP) markers were blended to develop calibration equations for blood metabolites using the elastic net (ENet) approach, considering 3 models: (1) Model 1 (M1) including only NIR information, (2) Model 2 (M2) with both NIR and on-farm information, and (3) Model 3 (M3) combining NIR, on-farm and genomic information. Dimension reduction was considered for M3 by preselecting SNP markers from genome-wide association study (GWAS) results.

RESULTS

Results indicate that M2 improved the predictive ability by an average of 19% for energy-related metabolites (glucose, cholesterol, NEFA, BHB, urea, and creatinine), 20% for liver function/hepatic damage, 7% for inflammation/innate immunity, 24% for oxidative stress metabolites, and 23% for minerals compared to M1. Meanwhile, M3 further enhanced the predictive ability by 34% for energy-related metabolites, 32% for liver function/hepatic damage, 22% for inflammation/innate immunity, 42.1% for oxidative stress metabolites, and 41% for minerals, compared to M1. We found improved predictive ability of M3 using selected SNP markers from GWAS results using a threshold of > 2.0 by 5% for energy-related metabolites, 9% for liver function/hepatic damage, 8% for inflammation/innate immunity, 22% for oxidative stress metabolites, and 9% for minerals. Slight reductions were observed for phosphorus (2%), ferric-reducing antioxidant power (1%), and glucose (3%). Furthermore, it was found that prediction accuracies are influenced by using more restrictive thresholds (-log10(P-value) > 2.5 and 3.0), with a lower increase in the predictive ability.

CONCLUSION

Our results highlighted the potential of combining several sources of information, such as genetic markers, on-farm information, and in-line NIR infrared data improves the predictive ability of blood metabolites in dairy cattle, representing an effective strategy for large-scale in-line health monitoring in commercial herds.

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.

RNA-Seq Analysis of Peripheral Whole Blood from Dairy Bulls with High and Low Antibody-Mediated Immune Responses-A Preliminary Study

Enhancing the immune response through breeding is regarded as an effective strategy for improving animal health, as dairy cattle identified as high immune responders are reported to have a decreased prevalence of economically significant diseases. The identification of differentially expressed genes (DEGs) associated with immune responses might be an effective tool for breeding healthy dairy cattle. In this study, antibody-mediated immune responses (AMIRs) were induced by the immunization of hen egg white lysozyme (HEWL) in six Chinese Holstein dairy bulls divided into high- and low-AMIR groups based on their HEWL antibody level. Then, RNA-seq was applied to explore the transcriptome of peripheral whole blood between the two comparison groups. As a result, several major upregulated and downregulated genes were identified and attributed to the regulation of locomotion, tissue development, immune response, and detoxification. In addition, the result of the KEGG pathway analysis revealed that most DEGs were enriched in pathways related to disease, inflammation, and immune response, including antigen processing and presentation, Staphylococcus aureus infection, intestinal immune network for IgA production, cytokine-cytokine receptor interaction, and complement and coagulation cascades. Moreover, six genes (BOLA-DQA5, C5, CXCL2, HBA, LTF, and COL1A1) were validated using RT-qPCR, which may provide information for genomic selection in breeding programs. These results broaden the knowledge of the immune response mechanism in dairy bulls, which has strong implications for breeding cattle with an enhanced AMIR.

The Effects of Rumen-Protected Choline and Rumen-Protected Nicotinamide on Liver Transcriptomics in Periparturient Dairy Cows

To investigate the effects of rumen-protected choline (RPC) and rumen-protected nicotinamide (RPM) on liver metabolic function based on transcriptome in periparturient dairy cows, 10 healthy Holstein dairy cows with similar parity were allocated to RPC and RPM groups (n = 5). The cows were fed experimental diets between 14 days before and 21 days after parturition. The RPC diet contained 60 g RPC per day, and the RPM diet contained 18.7 g RPM per day. Liver biopsies were taken 21 days after calving for the transcriptome analysis. A model of fat deposition hepatocytes was constructed using the LO2 cell line with the addition of NEFA (1.6 mmol/L), and the expression level of genes closely related to liver metabolism was validated and divided into a CHO group (75 μmol/L) and a NAM group (2 mmol/L). The results showed that the expression of a total of 11,023 genes was detected and clustered obviously between the RPC and RPM groups. These genes were assigned to 852 Gene Ontology terms, the majority of which were associated with biological process and molecular function. A total of 1123 differentially expressed genes (DEGs), 640 up-regulated and 483 down-regulated, were identified between the RPC and RPM groups. These DEGs were mainly correlated with fat metabolism, oxidative stress and some inflammatory pathways. In addition, compared with the NAM group, the gene expression level of FGF21, CYP26A1, SLC13A5, SLCO1B3, FBP2, MARS1 and CDH11 in the CHO group increased significantly (p < 0.05). We proposed that that RPC could play a prominent role in the liver metabolism of periparturient dairy cows by regulating metabolic processes such as fatty acid synthesis and metabolism and glucose metabolism; yet, RPM was more involved in biological processes such as the TCA cycle, ATP generation and inflammatory signaling.

[The lipidosis of the liver of dairy cows: Part 1 - Role of insulin and the Growth Hormone-IGF-1 axis]

Lipidosis of the liver of dairy cows is a metabolic disease known since many years and is caused by an uptake of nonesterified fatty acids (NEFA) into the liver cells, limited metabolism of NEFA (oxidation and production of β-hydroxybutyrate), and resynthesis in relation to a low efflux as triglyceride (TG). The pathogenesis of lipidosis includes a) an augmented release of NEFA by mobilisation of adipose tissue, b) uptake of NEFA into the liver cells, c) metabolism of NEFA and d) re-synthesis of triglyceride and e) an efflux of TG as very low density lipoprotein (VLDL). The steps a-e are postpartum modified by hormones as an increase of growth hormone, a pronounced insulin resistance in combination with a decreased insulin and of IGF-1 concentrations. These hormonal changes are related to an uncoupling of the growth hormone-IGF-1-axis with enhanced lipolysis and consequences mentioned above. These alterations are associated with inflammation, oxidative and endoplasmatic stress. The metabolic and hormonal alterations are the result of the selection of dairy cows primarily for milk production without adequate food intake with the consequence of lipidosis, ketosis and further health risks (production diseases).

Genetic analysis of production traits and body size measurements and their relationships with metabolic diseases in German Holstein cattle

This study sheds light on the genetic complexity and interplay of production, body size, and metabolic health in dairy cattle. Phenotypes for body size-related traits from conformation classification (130,166 animals) and production (101,562 animals) of primiparous German Holstein cows were available. Additionally, 21,992, 16,641, and 7,096 animals were from herds with recordings of the metabolic diseases ketosis, displaced abomasum, and milk fever in first, second, and third lactation. Moreover, all animals were genotyped. Heritabilities of traits and genetic correlations between all traits were estimated and GWAS were performed. Heritability was between 0.240 and 0.333 for production and between 0.149 and 0.368 for body size traits. Metabolic diseases were lowly heritable, with estimates ranging from 0.011 to 0.029 in primiparous cows, from 0.008 to 0.031 in second lactation, and from 0.037 to 0.052 in third lactation. Production was found to have negative genetic correlations with body condition score (BCS; -0.279 to -0.343) and udder depth (-0.348 to -0.419). Positive correlations were observed for production and body depth (0.138-0.228), dairy character (DCH) (0.334-0.422), and stature (STAT) (0.084-0.158). In first parity cows, metabolic disease traits were unfavorably correlated with production, with genetic correlations varying from 0.111 to 0.224, implying that higher yielding cows have more metabolic problems. Genetic correlations of disease traits in second and third lactation with production in primiparous cows were low to moderate and in most cases unfavorable. While BCS was negatively correlated with metabolic diseases (-0.255 to -0.470), positive correlations were found between disease traits and DCH (0.269-0.469) as well as STAT (0.172-0.242). Thus, the results indicate that larger and sharper animals with low BCS are more susceptible to metabolic disorders. Genome-wide association studies revealed several significantly associated SNPs for production and conformation traits, confirming previous findings from literature. Moreover, for production and conformation traits, shared significant signals on Bos taurus autosome (BTA) 5 (88.36 Mb) and BTA 6 (86.40 to 87.27 Mb) were found, implying pleiotropy. Additionally, significant SNPs were observed for metabolic diseases on BTA 3, 10, 14, 17, and 26 in first lactation and on BTA 2, 6, 8, 17, and 23 in third lactation. Overall, this study provides important insights into the genetic basis and interrelations of relevant traits in today's Holstein cattle breeding programs, and findings may help to improve selection decisions.

Genetic and nongenetic profiling of milk β-hydroxybutyrate and acetone and their associations with ketosis in Holstein cows

Ketosis is a metabolic disorder of increasing importance in high-yielding dairy cows, but accurate population-wide binary health trait recording is difficult to implement. Against this background, proper Gaussian indicator traits, which can be routinely measured in milk, are needed. Consequently, we focused on the ketone bodies acetone and β-hydroxybutyrate (BHB), measured via Fourier-transform infrared spectroscopy (FTIR) in milk. In the present study, 62,568 Holstein cows from large-scale German co-operator herds were phenotyped for clinical ketosis (KET) according to a veterinarian diagnosis key. A sub-sample of 16,861 cows additionally had first test-day observations for FTIR acetone and BHB. Associations between FTIR acetone and BHB with KET and with test-day traits were studied phenotypically and quantitative genetically. Furthermore, we estimated SNP marker effects for acetone and BHB (application of genome-wide association studies) based on 40,828 SNP markers from 4,384 genotyped cows, and studied potential candidate genes influencing body fat mobilization. Generalized linear mixed models were applied to infer the influence of binary KET on Gaussian-distributed acetone and BHB (definition of an identity link function), and vice versa, such as the influence of acetone and BHB on KET (definition of a logit link function). Additionally, linear models were applied to study associations between BHB, acetone and test-day traits (milk yield, fat percentage, protein percentage, fat-to-protein ratio and somatic cell score) from the first test-day after calving. An increasing KET incidence was statistically significant associated with increasing FTIR acetone and BHB milk concentrations. Acetone and BHB concentrations were positively associated with fat percentage, fat-to-protein ratio and somatic cell score. Bivariate linear animal models were applied to estimate genetic (co)variance components for KET, acetone, BHB and test-day traits within parities 1 to 3, and considering all parities simultaneously in repeatability models. Pedigree-based heritabilities were quite small (i.e., in the range from 0.01 in parity 3 to 0.07 in parity 1 for acetone, and from 0.03-0.04 for BHB). Heritabilites from repeatability models were 0.05 for acetone, and 0.03 for BHB. Genetic correlations between acetone and BHB were moderate to large within parities and considering all parities simultaneously (0.69-0.98). Genetic correlations between acetone and BHB with KET from different parities ranged from 0.71 to 0.99. Genetic correlations between acetone across parities, and between BHB across parities, ranged from 0.55 to 0.66. Genetic correlations between KET, acetone, and BHB with fat-to-protein ratio and with fat percentage were large and positive, but negative with milk yield. In genome-wide association studies, we identified SNP on BTA 4, 10, 11, and 29 significantly influencing acetone, and on BTA 1 and 16 significantly influencing BHB. The identified potential candidate genes NRXN3, ACOXL, BCL2L11, HIBADH, KCNJ1, and PRG4 are involved in lipid and glucose metabolism pathways.

Effects of Propylene Glycol on Negative Energy Balance of Postpartum Dairy Cows

Simple Summary

After calving, the milk production of dairy cows increases rapidly, but the nutrient intake cannot meet the demand for milk production, forming a negative energy balance. Dairy cows in a negative energy balance have an increased risk of developing clinical or subclinical ketosis. The ketosis in dairy cows has a negative impact on milk production, dry matter intake, health, immunity, and reproductive performance. Propylene glycol can be used as an important gluconeogenesis in ruminants and can effectively inhibit the formation of ketones. Supplementary propylene glycol to dairy cows during perinatal is an effective method to alleviate the negative energy balance. This review summarizes the reasons and consequences of negative energy balance as well as the mechanism and effects of propylene glycol in inhibiting a negative energy balance in dairy cows. In addition, the feeding levels and methods of using propylene glycol to alleviate negative energy balance are also discussed.

Abstract

With the improvement in the intense genetic selection of dairy cows, advanced management strategies, and improved feed quality and disease control, milk production level has been greatly improved. However, the negative energy balance (NEB) is increasingly serious at the postpartum stage because the intake of nutrients cannot meet the demand of quickly improved milk production. The NEB leads to a large amount of body fat mobilization and consequently the elevated production of ketones, which causes metabolic diseases such as ketosis and fatty liver. The high milk production of dairy cows in early lactation aggravates NEB. The metabolic diseases lead to metabolic disorders, a decrease in reproductive performance, and lactation performance decline, seriously affecting the health and production of cows. Propylene glycol (PG) can alleviate NEB through gluconeogenesis and inhibit the synthesis of ketone bodies. In addition, PG improves milk yield, reproduction, and immune performance by improving plasma glucose and liver function in ketosis cows, and reduces milk fat percentage. However, a large dose of PG (above 500 g/d) has toxic and side effects in cows. The feeding method used was an oral drench. The combination of PG with some other additives can improve the effects in preventing ketosis. Overall, the present review summarizes the recent research progress in the impacts of NEB in dairy cows and the properties of PG in alleviating NEB and reducing the risk of ketosis.

Estimation of genetic parameters for dairy traits and somatic cell score in the first 3 parities using a random regression test-day model in French Alpine goats

Lactation curve shape can affect an animal's health, feed requirements, and milk production throughout the year. We implemented a random regression model for the genetic evaluation of lactation curve shapes of dairy traits in French Alpine goats for their first 3 parities. Milk, fat, and protein yields, fat and protein contents, somatic cell score, and fat/protein ratio were considered. The data consisted of test-day records from 49,849 first lactation Alpine goats during their first 3 lactations. The reference model used a Legendre polynomial of order 2 for each parity to describe the genetic and permanent environmental effects, and was compared with a model that combined the second and third parities. A rank reduction of the variance-covariance matrix was also performed using an eigenvalue decomposition for each parity from the 2 models. Genetic parameters were consistent between the models tested. With a reduction to rank 2 and combining the second and third parities, the first 2 principal components correctly summarized the genetic variability of milk yield level and persistency, with a near-nil correlation between the 2, and with a much shorter computation time than the reference model. A favorable correlation of +0.43 between milk yield persistency and fat/protein ratio persistency at the beginning of the lactation was found from buck estimated breeding values.

Genetic correlations among selected traits in Canadian Holsteins

In the Canadian dairy industry, there are currently over 80 traits routinely evaluated, and more are considered for potential selection. Particularly, in the last few years, recording has commenced for several new phenotypes required to introduce novel traits with high economic importance into the selection program. However, without a systematic estimation of the genetic correlations that exist among traits, the potential results of indirect selection are unknown. Therefore, 29 traits representative of the trait diversity for first lactation Canadian animals were selected. Their two-by-two genetic correlations were estimated from a dataset of 62 498 first lactation Holstein cows, using a Markov Chain Monte Carlo Gibbs sampling approach. The general tendencies among the groups of traits confirm that production traits are negatively correlated with fertility traits and that functional traits are positively correlated with one another. The association of udder depth with fertility and disease resistance has also been highlighted. This contribution offers a comprehensive overview of current estimates across traits and includes correlations with novel traits that constitute an original addition to the literature. These new estimates can be used for newly developed genomic evaluation models and possibly lead to more accurate estimations of the dairy cows’ overall genetic merit.

Beta-hydroxybutyrate in milk as screening test for subclinical ketosis in dairy cows

Ketosis is a very frequent metabolic disease in dairy cows, resulting in lower milk production, impaired fertility and increased frequency of other diseases. The course of the disease is often subclinical, so early detection is very important. The aim of the study was to investigate the relation between the concentration of beta-hydroxybutyrate in blood and milk and to determine the cut-off value in milk for detection of subclinical ketosis. The study included 94 cows, which were in the first third of lactation. Beta-hydroxybutyrate (BHB) concentrations were measured in blood and milk serum using a biochemical analyser. The average concentration of BHB in the blood serum samples was 1.14 mmol/L while in the milk it was about ten times lower at 0.117 mmol/L. A statistically significant positive correlation between the concentration of BHB in blood and milk (r=0.705, p<0.001) was found. In cows with BHB in blood below 2.0 mmol/L a stronger correlation between blood and milk BHB was established (r=0.658, p<0.001) than in cows with blood BHB above 2.0 mmol/L (r=-0.292, p=0.206). Therefore, BHB in milk is a very suitable indicator in the diagnosis of subclinical ketosis as there is a good correlation between BHB in the blood and milk of cows with subclinical ketosis. The cut-off concentration of BHB in milk set at ≥0.080 mmol/L (AUC=0.91±0.03; p<0.001) is a significant indicator for subclinical ketosis in dairy cows. The sensitivity of the test was 94% and specificity 74%. Beta-hydroxybutyrate in milk is a good indicator of subclinical ketosis in dairy cows and can be measured accurately with a biochemical analyser.

Genome-wide association study identifies energy metabolism genes for resistance to ketosis in Chinese Holstein cattle

Ketosis (KET) is one common metabolic disorder that occurs mainly in early lactation and affects the dairy industry with significant economic losses. Cows with ketosis have lower milk yield and reproductive performance and greater risk of other periparturient diseases. As a metabolic disease, the pathogenesis of KET is multifactorial. To better understand the genetic background of KET, a genome-wide association study was performed using the Illumina BovineSNP50 BeadChip. Single-step genomic BLUP methodology was used to incorporate genomic data into a threshold-liability model. Results of the GWAS are reported as the proportion of variance explained by 20-SNP windows. Six genomic regions on Bos taurus autosomes 10, 13, 14 and 25 showed association with KET. Most interestingly, several candidate genes, including previously reported genes (BMP4, HNF4A and APOBR) and newly identified genes (SOCS4, GCH1, ATG14, RGS6, CYP7A1 and MAPK3), are involved in insulin metabolism or lipid metabolism, implicating the contribution of energy-metabolism-associated genes to the genetic basis of KET. Our results provide new information about the underlying biology and molecular mechanisms associated with KET. Future studies that combine genomic variation analysis and functional gene information may help elucidate the biology of KET.

Phenotypic relationships, genetic parameters, genome-wide associations, and identification of potential candidate genes for ketosis and fat-to-protein ratio in German Holstein cows

Energy demand for milk production in early lactation exceeds energy intake, especially in high-yielding Holstein cows. Energy deficiency causes increasing susceptibility to metabolic disorders. In addition to several blood parameters, the fat-to-protein ratio (FPR) is suggested as an indicator for ketosis, because a FPR >1.5 refers to high lipolysis. The aim of this study was to analyze phenotypic, quantitative genetic, and genomic associations between FPR and ketosis. In this regard, 8,912 first-lactation Holstein cows were phenotyped for ketosis according to a veterinarian diagnosis key. Ketosis was diagnosed if the cow showed an abnormal carbohydrate metabolism with increased content of ketone bodies in the blood or urine. At least one entry for ketosis in the first 6 wk after calving implied a score = 1 (diseased); otherwise, a score = 0 (healthy) was assigned. The FPR from the first test-day was defined as a Gaussian distributed trait (FPRgauss), and also as a binary response trait (FPRbin), considering a threshold of FPR = 1.5. After imputation and quality controls, 45,613 SNP markers from the 8,912 genotyped cows were used for genomic studies. Phenotypically, an increasing ketosis incidence was associated with significantly higher FPR, and vice versa. Hence, from a practical trait recording perspective, first test-day FPR is suggested as an indicator for ketosis. The ketosis heritability was slightly larger when modeling the pedigree-based relationship matrix (pedigree-based: 0.17; SNP-based: 0.11). For FPRbin, heritabilities were larger when modeling the genomic relationship matrix (pedigree-based: 0.09; SNP-based: 0.15). For FPRgauss, heritabilities were almost identical for both pedigree and genomic relationship matrices (pedigree-based: 0.14; SNP-based: 0.15). Genetic correlations between ketosis with FPRbin and FPRgauss using either pedigree- or genomic-based relationship matrices were in a moderate range from 0.39 to 0.71. Applying genome-wide association studies, we identified the specific SNP rs109896020 (BTA 5, position: 115,456,438 bp) significantly contributing to ketosis. The identified potential candidate gene PARVB in close chromosomal distance is associated with nonalcoholic fatty liver disease in humans. The most important SNP contributing to FPRbin was located within the DGAT1 gene. Different SNP significantly contributed to ketosis and FPRbin, indicating different mechanisms for both traits genomically.

Genome-wide association study identifies QTLs for displacement of abomasum in Chinese Holstein cattle1

Displacement of abomasum (DA) is one of the most common and important disorders in dairy cattle. The objective of the present study was to detect the quantitative trait loci (QTL) for DA in Chinese Holstein using single-step genomic BLUP methodology. A total of 60,556 producer-recorded DA event records from 32,190 cows, together with 2,336 genotyped animals with 40,054 SNP markers, were used for the analysis. Genomic data were incorporated into a threshold model for variance component estimation, and the estimated heritability of DA was 0.108 (SE = 0.086). Results of genome-wide association studies were reported as the proportion of genetic variance explained 20-SNP windows. Eight QTLs covering 129 genes on Bos taurus autosomes 2, 4, 7, 10, 14, 17, 20 showed associations with DA. Ten genes, namely BMP4, SOCS4, GCH1, DDHD1, ATG14, ACBP/DBI, SMO, AHCYL2, CYP7A1, and CACNA1A, involved in insulin metabolism and lipid metabolism pathways may be considered as candidate genes of DA in dairy. The identified QTLs, biological pathways, and associated genes underlying DA identified from the present study will contribute to the understanding of the genetic architecture of this complex disease.

Association of rumination time and health status with milk yield and composition in early-lactation dairy cows

The objective of this study was to determine the associations of rumination time (RT) and health status with milk yield and milk composition. This study used 339 dairy cows from 4 commercial dairy farms in Ontario, Canada (first lactation, n = 107; second lactation, n = 112; ≥third lactation, n = 120). Rumination time was monitored (24 h/d) using an automated system from 1 to 28 d in milk (DIM). Cows were milked 3×/d on each farm, and 2 farms recorded milk weights at each milking to determine daily milk yield (n = 170). Cows were also monitored for milk composition (fat and protein content) 1×/wk. Last, subclinical ketosis (SCK) was diagnosed 1×/wk; cows with at least one blood sample with β-hydroxybutyrate ≥1.2 mmol/L postcalving were diagnosed with SCK. Cases of retained placenta, metritis, milk fever, or mastitis during the study period were also recorded. Cows were categorized into 1 of 4 groups: healthy cows that had no SCK or any other health issue (HLT; n = 139); cows that were treated for at least one health issue other than SCK (HLT+; n = 50); SCK cows with no other health problems during transition (HYK; n = 97); or cows that had SCK and one or more other health problems (HYK+; n = 53). All data were summarized by week across cows, and the associations between rumination time and milk yield (n = 170) and milk composition (n = 339) were modeled. Across all lactations, and including all health categories, milk yield increased by week, whereas fat and protein content both decreased by week. A positive association was found between summarized RT and milk yield in first-lactation (+0.006 ± 0.003 kg/min of RT) and second-lactation (+0.015 ± 0.004 kg/min of RT) cows from 4 to 28 DIM, as well as in ≥third-lactation cows; however, the relationship between RT and milk yield differed across weeks in those cows. A negative association between RT and milk fat content was found in ≥third-lactation cows (-0.002 ± 0.00059 percentage points/min of RT). From 4 to 28 DIM, ≥third-lactation HYK and HYK+ cows produced less protein (0.11 ± 0.051 and 0.13 ± 0.056 percentage points, respectively) than HLT cows. Over the 4-wk observation period, first-lactation HYK+ cows tended to deposit 0.11 ± 0.056 percentage points less protein in their milk compared with HLT cows. Second-lactation HYK+ cows produced less milk than HLT cows each week during early lactation. In summary, RT was positively associated with milk yield in early-lactation dairy cows, across all lactations, and negatively associated with milk fat content in ≥third-lactation cows. Further, the results showed that early-lactation cows that experience SCK, particularly with one or more other health problems, might have decreased milk yield and milk protein content.

Prevalence of metabolic disorders and effect on subsequent daily milk quantity and quality in Holstein cows

Abstract. Aiming at the determination of the metabolic disorder prevalence as well as the effect of subclinical disorders on daily milk traits of Holsteins, over 1 million (1 962 831) test day records were analysed. The metabolic disorder (ketosis or acidosis) prevalence was indicated by the fat-to-protein (F ∕ P) ratio, while subclinical disorder was indicated by the F ∕ P ratio and cows' daily production. The effect of subclinical disorders on daily milk traits was tested by Scheffe's method (SAS/STAT). The highest ketosis prevalence occurred in early lactation. In first-parity cows ketosis prevalence was highest in the first 15 days of lactation, while in cows in higher lactations, the prevalence peak occurred on the 25th day. A higher ketosis prevalence during the entire lactation was observed in multiparous cows compared to first-parity cows. Regarding the acidosis prevalence, high values were determined at the beginning of the lactation with a decreasing trend until mid-lactation, when the prevalence increased up to 22 %. The highest decrease in daily milk yield as a consequence of subclinical ketosis was determined as 4.21 and 3.72 kg day−1 in first-parity cows and those with more than four lactations, respectively. A significant negative effect of subclinical acidosis on daily milk yield (2.79 kg day−1) was highest in cows in the third lactation. A production decline in subsequent milk controls due to subclinical ketosis or acidosis in all cows was also determined. Subclinical disorders also significantly alter daily milk quality. This indicates that the test day records could be used as a cost-effective and non-invasive method for monitoring herd health.

Genetic parameters of subclinical macromineral disorders and major clinical diseases in postparturient Holstein cows

The main objective of this study was to assess the genetic parameters of subclinical disorders associated with subclinical hypocalcemia, hypophosphatemia, subclinical hypomagnesemia, hypokalemia, and hyperphosphatemia, as well as major clinical diseases after calving in Holstein cows. The secondary objective was to estimate the associated genetic and phenotypic correlations among these subclinical and clinical conditions after calving in Holstein cows. The study was conducted in 9dairy herds located in Northern Greece. None of the herds used any kind of preventive measures for milk fever (MF). A total of 1,021 Holstein cows with pedigree information were examined from November 2010 until November 2012. The distribution across parities was 466 (parity 1), 242 (parity 2), 165 (parity 3), and 148 (parity 4 and above) cows. All cows were subjected to a detailed clinical examination and blood was sampled on d 1, 2, 4, and 8 after calving. Serum concentrations of Ca, P, Mg, and K were measured in all samples, whereas β-hydroxybutyrate (BHB) was measured only for d 8. The final data set included 4,064 clinical and 16,848 biochemical records (4,020 Ca, 4,019 P, 4,020Mg, 3,792K, and 997 BHB). Data of 1,988 observations of body condition score at d 1 and 8 were also available. All health traits were analyzed with a univariate random regression model. The genetic analysis for macromineral-related disorders included 986 cows with no obvious signs of MF (35 cows with MF were excluded). Analysis for other health traits included all 1,021 cows. A similar single record model was used for the analysis of BHB. Genetic correlations among traits were estimated with a series of bivariate analyses. Statistically significant daily heritabilities of subclinical hypocalcemia (0.13-0.25), hypophosphatemia (0.18-0.33), subclinical hypomagnesemia (0.11-0.38), and hyperphosphatemia (0.14-0.22) were low to moderate, whereas that of hypokalemia was low (0.08-0.10). The heritability of body condition score was 0.20±0.10. Statistically significant daily heritabilities of clinical diseases were those of MF (0.07-0.11), left displaced abomasum (0.19-0.31), and mastitis (0.15-0.41). Results suggest that these health disorders are heritable traits and could be minimized with proper genetic selection. Statistically significant phenotypic correlations were estimated for the first time between macromineral concentrations and almost all transition cow metabolic and infectious health disorders.

Invited review: Opportunities for genetic improvement of metabolic diseases

Metabolic disorders are disturbances to one or more of the metabolic processes in dairy cattle. Dysfunction of any of these processes is associated with the manifestation of metabolic diseases or disorders. In this review, data recording, incidences, genetic parameters, predictors, and status of genetic evaluations were examined for (1) ketosis, (2) displaced abomasum, (3) milk fever, and (4) tetany, as these are the most prevalent metabolic diseases where published genetic parameters are available. The reported incidences of clinical cases of metabolic disorders are generally low (less than 10% of cows are recorded as having a metabolic disease per herd per year or parity/lactation). Heritability estimates are also low and are typically less than 5%. Genetic correlations between metabolic traits are mainly positive, indicating that selection to improve one of these diseases is likely to have a positive effect on the others. Furthermore, there may also be opportunities to select for general disease resistance in terms of metabolic stability. Although there is inconsistency in published genetic correlation estimates between milk yield and metabolic traits, selection for milk yield may be expected to lead to a deterioration in metabolic disorders. Under-recording and difficulty in diagnosing subclinical cases are among the reasons why interest is growing in using easily measurable predictors of metabolic diseases, either recorded on-farm by using sensors and milk tests or off-farm using data collected from routine milk recording. Some countries have already initiated genetic evaluations of metabolic disease traits and currently most of these use clinical observations of disease. However, there are opportunities to use clinical diseases in addition to predictor traits and genomic information to strengthen genetic evaluations for metabolic health in the future.

Metabolic Profile and Inflammatory Responses in Dairy Cows with Left Displaced Abomasum Kept under Small-Scaled Farm Conditions

Simple Summary

This research established an association between lactation number and milk production and metabolic and inflammatory responses in high-producing dairy cows affected by left abomasal displacement in small-scaled dairy farms. The study showed metabolic alterations, liver damage, and inflammation in the sick cows, which were further exacerbated with increasing lactation number and milk yield of the cows.

Abstract

Left displaced abomasum (LDA) is a severe metabolic disease of cattle with a strong negative impact on production efficiency of dairy farms. Metabolic and inflammatory alterations associated with this disease have been reported in earlier studies, conducted mostly in large dairy farms. This research aimed to: (1) evaluate metabolic and inflammatory responses in dairy cows affected by LDA in small-scaled dairy farms; and (2) establish an association between lactation number and milk production with the outcome of metabolic variables. The cows with LDA had lower serum calcium (Ca), but greater concentrations of non-esterified fatty acids (NEFA) and beta-hydroxy-butyrate (BHBA), in particular when lactation number was >2. Cows with LDA showed elevated levels of aspartate aminotransferase, glutamate dehydrogenase, and serum amyloid A (SAA), regardless of lactation number. In addition, this study revealed strong associations between milk yield and the alteration of metabolic profile but not with inflammation in the sick cows. Results indicate metabolic alterations, liver damage, and inflammation in LDA cows kept under small-scale farm conditions. Furthermore, the data suggest exacerbation of metabolic profile and Ca metabolism but not of inflammation and liver health with increasing lactation number and milk yield in cows affected by LDA.

Genetics of bovine abomasal displacement

Displacement of the abomasum (DA) is a common inherited condition in Holstein cows. This article reviews the genetics of DA including risk factors, genetic parameters and molecular genetic results. Breeds other than Holsteins affected by DA include Guernseys, Jerseys, Brown Swiss, Ayrshires and Simmental-Red Holsteins. In most DA cases, left displacements of the abomasum (LDA) are seen. Lactation incidence rates are higher for DA in first lactation Holsteins compared to later lactations. For Holstein cows, heritability estimates for DA are between 0.03 and 0.53. Genetic correlation estimates among DA and milk production traits range from positive to negative. Genome-wide significant genomic regions associated with LDA are located on bovine chromosomes (BTA) 1, 3, 11, 20 and 23. Motilin-associated single nucleotide polymorphisms on BTA23 exhibit a functional relationship with LDA. Pathways for deposition of calcium, insulin-dependent diabetes mellitus and synaptic transmission are significantly related to LDA in Holsteins. Deciphering the DA-associated genomic regions and genes may be an important step in the quest to understand the underlying disease-causing mechanisms and in unravelling mutations with a causal relationship to DA.

Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits

For several decades, breeding goals in dairy cattle focussed on increased milk production. However, many functional traits have negative genetic correlations with milk yield, and reductions in genetic merit for health and fitness have been observed. Herd management has been challenged to compensate for these effects and to balance fertility, udder health and metabolic diseases against increased production to maximize profit without compromising welfare. Functional traits, such as direct information on cow health, have also become more important because of growing concern about animal well-being and consumer demands for healthy and natural products. There are major concerns about the impact of drugs used in veterinary medicine on the spread of antibiotic-resistant strains of bacteria that can negatively impact human health. Sustainability and efficiency are also increasingly important because of the growing competition for high-quality, plant-based sources of energy and protein. Disruptions to global environments because of climate change may encourage yet more emphasis on these traits. To be successful, it is vital that there be a balance between the effort required for data recording and subsequent benefits. The motivation of farmers and other stakeholders involved in documentation and recording is essential to ensure good data quality. To keep labour costs reasonable, existing data sources should be used as much as possible. Examples include the use of milk composition data to provide additional information about the metabolic status or energy balance of the animals. Recent advances in the use of mid-infrared spectroscopy to measure milk have shown considerable promise, and may provide cost-effective alternative phenotypes for difficult or expensive-to-measure traits, such as feed efficiency. There are other valuable data sources in countries that have compulsory documentation of veterinary treatments and drug use. Additional sources of data outside of the farm include, for example, slaughter houses (meat composition and quality) and veterinary labs (specific pathogens, viral loads). At the farm level, many data are available from automated and semi-automated milking and management systems. Electronic devices measuring physiological status or activity parameters can be used to predict events such as oestrus, and also behavioural traits. Challenges concerning the predictive biology of indicator traits or standardization need to be solved. To develop effective selection programmes for new traits, the development of large databases is necessary so that high-reliability breeding values can be estimated. For expensive-to-record traits, extensive phenotyping in combination with genotyping of females is a possibility.