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Milk fatty acids (FA) have been suggested as biomarkers for early-lactation metabolic diseases and for female fertility status. The aim of the present study was to infer associations between FA, the metabolic disorder ketosis (KET), and the interval from calving to first insemination (ICF) genetically and genomically. In this regard, we focused on a single-step genomic BLUP approach, allowing consideration of genotyped and ungenotyped cows simultaneously. The phenotypic data set considered 38,375 first-lactation Holstein cows, kept in 45 large-scale co-operator herds from 2 federal states in Germany. The calving years for these cows were from 2014 to 2017. Concentrations in milk from the first official milk recording test-day for saturated, unsaturated (UFA), monounsaturated (MUFA), polyunsaturated, palmitic, and stearic (C18:0) FA were determined via Fourier-transform infrared spectroscopy. Ketosis was defined as a binary trait according to a veterinarian diagnosis key, considering diagnoses within a 6-wk interval after calving. A subset of 9,786 cows was genotyped for 40,989 SNP markers. Variance components and heritabilities for all Gaussian distributed FA and for ICF, and for binary KET were estimated by applying single-step genomic BLUP single-trait linear and threshold models, respectively. Genetic correlations were estimated in series of bivariate runs. Genomic breeding values for the single-step genomic BLUP estimations were dependent traits in single-step GWAS. Heritabilities for FA were moderate in the range from 0.09 to 0.20 (standard error = 0.02-0.03), but quite small for ICF (0.08, standard error = 0.01) and for KET (0.05 on the underlying liability scale, posterior standard deviation = 0.02). Genetic correlations between KET and UFA, MUFA, and C18:0 were large (0.74 to 0.85, posterior standard deviation = 0.14-0.19), and low positive between KET and ICF (0.17, posterior standard deviation = 0.22). Genetic correlations between UFA, MUFA, and C18:0 with ICF ranged from 0.34 to 0.46 (standard error = 0.12). In single-step GWAS, we identified a large proportion of overlapping genomic regions for the different FA, especially for UFA and MUFA, and for saturated and palmitic FA. One identical significantly associated SNP was identified for C18:0 and KET on BTA 15. However, there was no genomic segment simultaneously significantly affecting all trait categories ICF, FA, and KET. Nevertheless, some of the annotated potential candidate genes DGKA, IGFBP4, and CXCL8 play a role in lipid metabolism and fertility mechanisms, and influence production diseases in early lactation. Genetic and genomic associations indicate that Fourier-transform infrared spectroscopy FA concentrations in milk from the first official test-day are valuable predictors for KET and for ICF.

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.

To measure methane (CH4) emissions from cattle on-farm, a number of methods have been developed. Combining measurements made with different methods in one data set could lead to an increased power of further analyses. Before combining the measurements, their agreement must be evaluated. We analysed data obtained with a handheld laser methane detector (LMD) and the GreenFeed system (GF), as well as data obtained with LMD and Fourier Transformed Infrared (FTIR) and Non-dispersive Infrared (NDIR) breath analysers (sniffers) installed in the feed bin of automatic milking systems. These devices record short-term breath CH4 concentrations from cows and make it possible to estimate daily CH4 production in g/d which is used for national CH4 emission inventories and genetic studies. The CH4 is released by cows during eructation and breathing events, resulting in peaks of CH4 concentrations during a measurement which represent the respiratory cycle. For LMD, the average CH4 concentration of all peaks during the measurement (P_MEAN in ppm × meter) was compared with the average daily CH4 production (g/d) measured by GF on 11 cows. The comparison showed a low concordance correlation coefficient (CCC; 0.02) and coefficient of individual agreement (CIA; 0.06) between the methods. The repeated measures correlation (rp) of LMD and GF, which can be seen as a proxy for the genetic correlation, was, however, relatively strong (0.66). Next, based on GF, a prediction equation for estimating CH4 in g/d (LMD_cal) using LMD measurements was developed. LMD_cal showed an improved agreement with GF (CCC = 0.22, CIA = 0.99, rp = 0.74). This prediction equation was used to compare repeated LMD measurements (LMD_val in g/d) with CH4 (g/d) measured with FTIR (n = 34 cows; Data Set A) or NDIR (n = 39 cows; Data Set B) sniffer. A low CCC (A: 0.28; B: 0.17), high CIA (A: 0.91; B: 0.87) and strong rp (A: 0.57; B: 0.60) indicated that there was some agreement and a minimal re-ranking of the cows between sniffer and LMD. Possible sources of disagreement were cow activity (LMD: standing idle; sniffer: eating and being milked) and the larger influence of wind speed on LMD measurement. The LMD measurement was less repeatable (0.14–0.27) than the other techniques studied (0.47–0.77). Nevertheless, GF, LMD and the sniffers ranked the cows similarly. The LMD, due to its portability and flexibility, could be used to study CH4 emissions on herd or group level, as a validation tool, or to strengthen estimates of genetic relationships between small-scale research populations.

In this study, the handheld laser methane detector (LMD) was discussed as a tool for estimating the methane emissions of individual dairy cows by measuring the profiles of the exhaled air. Data obtained with the most recent generation of the device were compared with those of indirect open-circuit respiration chambers, which are commonly used to quantify methane emissions from ruminants. Data from two LaserMethane Mini-Green LMD units (Tokyo Gas Engineering Solutions) exhibited high agreement with those from four respiration chambers, two at the AgroVet-Strickhof, Eschikon, Lindau (Switzerland) and two at the Leibniz Institute for Farm Animal Biology (FBN) Dummerstorf (Germany). The results were determined using Pearson and concordance correlations and the Bland–Altman method. An inverse regression analysis was used to predict the amount of methane in the chambers from the LMD data. The two LMD units also agreed well with each other in the same respiration chamber and under farm conditions. Both the LMDs and chambers were suitable for detecting differences in mean methane concentrations in the spent air produced by dairy cows during different cow activities in the chamber (p

In this review, the history of breeding for polled cattle is compiled from literature as well as graphic representations of cattle in art and science. Domesticated taurine cattle originated around 8500 years BCE in the Near East and spread from there to Africa as well as to Europe. Earliest findings of polled domesticated cattle are from today’s Slovakia and Germany from 6000 years BCE. Textual findings and especially graphic displays can be found of polled cattle in Ancient Egypt. Although archaeological finds of skulls that can be evaluated for natural polledness are rare, archaeological texts and graphical displays indicate that genetically polled cattle in Ancient Egypt were present and quite numerous. Polled cattle apparently were also kept by Germanic tribes and can be followed through medieval times into the 17th and 18th century where textual accounts are more frequent. In Scandinavian countries and Great Britain polled cattle have survived in greater proportions up to today while in the middle of Europe polled cattle were not well regarded by farmers and almost became extinct. Among the British breeds, Angus and Galloway were established as beef cattle breeds in the 17th/18th century, and still flourish. However, the British polled dairy breed Suffolk became extinct. It was one of the ancestral breeds of the Red Polled beef breed and was used in attempts to promote polled dairy cattle in France in the 19th century. Reports of single individuals in polled cattle found in predominantly horned populations possibly indicate several origins of the polled mutations. Recently, at least two different types of mutations, located on BTA01, have been found to be responsible for the mutation commonly referred to as the polled allele. One is the so-called ‘Celtic’ type, found in Scandinavia and Britain while the’‘Friesian’ type is found in cattle of the Holstein family. Today, aspects of animal welfare more and more influence farmers’ attitudes towards naturally polled cattle and also governmental policies play an important role. For the important horned dairy breeds of central Europe, e.g. Holstein, Brown Swiss and Fleckvieh, as well as for the beef breed Charolais, breeding programs for the introgression of the polled allele are starting and will be aided by new tools such as genomic selection.

The objectives of this study were (1) to analyze the agreement of a standard laboratory ELISA for progesterone (P4) with an automated on-farm ELISA kit operated under commercial conditions in 1,297 milk samples from 50 dairy cows; (2) to study the influence of the method of detection of luteal activity on genetic parameters of fertility traits based on P4 measured with an automated on-farm ELISA once weekly from wk 3 to 9 postpartum in the milk of 1,304 cows; and (3) to study the influence of sampling frequency (once or twice weekly from wk 3 to 9) on the same traits from 296 cows. Luteal activity can be detected when there is an active corpus luteum in the ovary producing P4 and indicating the onset of reproductive cyclicity after calving. The on-farm ELISA overestimated P4 contents by a mean square error of prediction of 2.76 ng/mL and had an intermediate Spearman correlation with the laboratory kit (0.54). For the second objective, the postpartum interval to the commencement of luteal activity (C-LA), proportion of luteal activity between d 15 and 63 postpartum (P-LA), calculated as the number of samples above the threshold for high P4 values divided by the number of all samples, and delay of first ovulation (DOV1), defined as C-LA occurring later than d 45 postpartum, were derived from the P4 profiles. Both C-LA and DOV1 were determined by (a) thorough qualitative visual inspection of the profile, (b) the profile's mean as threshold for the first increase in P4 postpartum, indicating commencement of luteal activity, and (c) 3 ng/mL as threshold for the first increase in P4, a value that has been used by many other studies. Similarly, P-LA was determined by using methods (b) and (c). Estimates of heritability were 0.04 to 0.13 for C-LA, 0.12 to 0.23 for P-LA, and 0.03 to 0.07 for DOV1. Genetic correlation of P-LA with C-LA and with the profile's mean P4 was −1.00. The profile's mean had a higher estimate of heritability (0.11–0.12) than C-LA or DOV1. It can be calculated as the arithmetic mean of all P4 values of a profile, whereas C-LA, P-LA, and DOV1 need a definition of a threshold for high P4 values. We therefore suggest the profile's mean as a promising candidate for further research. For the third objective, once-weekly sampling was mimicked by neglecting every second sample, and C-LA and DOV1 shifted toward a later onset of cyclicity. Thus, a common standard for sampling regimen and detection algorithm is essential to avoid incompatibility between studies.

In modern dairy cattle breeding, genomic breeding programs have the potential to increase efficiency and genetic gain. At the same time, the requirements and the availability of genotypes and phenotypes present a challenge. The set-up of a large enough reference population for genomic prediction is problematic for numerically small breeds but also for hard to measure traits. The first part of this study is a review of the current literature on strategies to overcome the lack of reference data. One solution is the use of combined reference populations from different breeds, different countries, or different research populations. Results reveal that the level of relationship between the merged populations is the most important factor. Compiling closely related populations facilitates the accurate estimation of marker effects and thus results in high accuracies of genomic prediction. Consequently, mixed reference populations of the same breed, but from different countries are more promising than combining different breeds, especially if those are more distantly related. The use of female reference information has the potential to enlarge the reference population size. Including females is advisable for small populations and difficult traits, and maybe combined with genotyping females and imputing those that are un-genotyped. The efficient use of imputation for un-genotyped individuals requires a set of genotyped related animals and well-considered selection strategies which animals to choose for genotyping and phenotyping. Small populations have to find ways to derive additional advantages from the cost-intensive establishment of genomic breeding schemes. Possible solutions may be the use of genomic information for inbreeding control, parentage verification, within-herd selection, adjusted mating plans or conservation strategies. The second part of the paper deals with the issue of high-quality phenotypes against the background of new, difficult and hard to measure traits. The use of contracted herds for phenotyping is recommended, as additional traits, when compared to standard traits used in dairy cattle breeding can be measured at set moments in time. This can be undertaken even for the recording of health traits, thus resulting in complete contemporary groups for health traits. Future traits to be recorded and used in genomic breeding programs, at least partly will be traits for which traditional selection based on widespread phenotyping is not possible. Enabling phenotyping of sufficient numbers to enable genomic selection will rely on cooperation between scientists from different disciplines and may require multidisciplinary approaches.

Bovine digital dermatitis (DD) is an increasing claw health problem in all cattle production systems worldwide. The objective of this study was to evaluate the use of an improved scoring of the clinical status for DD via M-scores accounting for the dynamics of the disease; that is, the transitions from one stage to another. The newly defined traits were then subjected to a genetic analysis to determine the genetic background for susceptibility to DD. Data consisted of 6,444 clinical observations from 729 Holstein heifers in a commercial dairy herd, collected applying the M-score system. The M-score system is a classification scheme for stages of DD that allows a macroscopic scoring based on clinical inspections of the bovine foot, thus it describes the stages of lesion development. The M-scores were used to define new DD trait definitions with different complexities. Linear mixed models and logistic models were used to identify fixed environmental effects and to estimate variance components. In total, 68% of all observations showed no DD status, whereas 11% were scored as infectious for and affected by DD, and 21% of all observations exhibited an affected but noninfectious status. For all traits, the probability of occurrence and clinical status were associated with age at observation and period of observation. Risk of becoming infected increased with age, and month of observation significantly affected all traits. Identification of the optimal month concerning DD herd status was consistent for all trait definitions; the last month of the trial was identified. In contrast, months exhibiting the highest least squares means of transformed scores differed depending on trait definition. In this respect, traits that can distinguish between healthy, infectious, and noninfectious stages of DD can account for the infectious potential of the herd and can serve as an alert tool. Estimates of heritabilities of traits studied ranged between 0.19 (±0.11) and 0.52 (±0.17), revealing a tendency for higher values for more complex trait definitions. In terms of genetic selection, all trait definitions identified the best (i.e., most resistant) animals, but only the new trait definitions were able to distinguish between animals with average and high predispositions for DD. Considering repeated measurements resulted in heritability estimates ranging between 0.13 (±0.05) and 0.29 (±0.10).