Exploring unknown parent groups and metafounders in single-step genomic BLUP: Insights from a simulated cattle population.

This study explores how the metafounder (MF) concept enhances genetic evaluations in dairy cattle populations using single-step genomic best linear unbiased prediction (ssGBLUP). By improving the consideration of relationships among founder populations, MF ensures accurate alignment of pedigree and genomic relationships. The research aims to propose a method for grouping MF based on genotypic information, assess different approaches for estimating the gamma matrix, and compare unknown parent groups (UPG) and MF methodologies across various scenarios, including those with low and high pedigree completeness based on a simulated dairy cattle population. In the scenario where unknown ancestors are rare, the impact of UPG or MF on breeding values is minimal but MF still performs slightly better compared with UPG. The scenario with lower genotyping rates and more unknown parents shows significant differences in evaluations with and without UPG and also compared with MF. The study shows that ssGBLUP evaluations where UPG are considered via Quaas-Pollak-transformation in the pedigree-based and genomic relationship matrix (UPG_fullQP) results in double counting and subsequently in a pronounced bias and overdispersion. Another focus is on the estimation of the gamma matrix, emphasizing the importance of crossbred genotypes for accuracy. Challenges emerge in classifying animals into subpopulations and further into MF or UPG, but the method used in this study, which is based on genotypes, results in predictions which are comparable to those obtained using the true subpopulations for the assignment. Estimated validation results using the linear regression method confirm the superior performance of MF evaluations, although differences compared with true validations are smaller. Notably, UPG_fullQP's extreme bias is less evident in routine validation statistics.

Genetic parameters for mid-infrared-spectroscopy-predicted mastitis phenotypes and related traits.

Genetic improvement of udder health in dairy cows is of high relevance as mastitis is one of the most prevalent diseases. Since it is known that the heritability of mastitis is low and direct data on mastitis cases are often not available in large numbers, auxiliary traits, such as somatic cell count (SCC) are used for the genetic evaluation of udder health. In previous studies, models to predict clinical mastitis based on mid-infrared (MIR) spectral data and a somatic cell count-derived score (SCS) were developed. Those models can provide a probability of mastitis for each cow at every test-day, which is potentially useful as an additional auxiliary trait for the genetic evaluation of udder health. Furthermore, MIR spectral data were used to estimate contents of lactoferrin, a glycoprotein positively associated with immune response. The present study aimed to estimate heritabilities (h2) and genetic correlations (ra) for clinical mastitis diagnosis (CM), SCS, MIR-predicted mastitis probability (MIRprob), MIR + SCS-predicted mastitis probability (MIRSCSprob) and lactoferrin estimates (LF). Data for this study were collected within the routine milk recording and health monitoring system of Austria from 2014 to 2021 and included records of approximately 54,000 Fleckvieh cows. Analyses were performed in two datasets, including test-day records from 5 to 150 or 5 to 305 days in milk. Prediction models were applied to obtain MIR- and SCS-based phenotypes (MIRprob, MIRSCSprob, LF). To estimate heritabilities and genetic correlations bivariate linear animal models were applied for all traits. A lactation model was used for CM, defined as a binary trait, and a test-day model for all other continuous traits. In addition to the random animal genetic effect, the fixed effects year-season of calving and parity-age at calving and the random permanent environmental effect were considered in all models. For CM the random herd-year effect, for continuous traits the random herd-test day effect and the covariate days in milk (linear and quadratic) were additionally fitted. The obtained genetic parameters were similar in both datasets. The heritability found for CM was expectedly low (h2 = 0.02). For SCS and MIRSCSprob, heritability estimates ranged from 0.23 to 0.25, and for MIRprob and LF from 0.15 to 0.17. CM was highly correlated with SCS and MIRSCSprob (ra = 0.85 to 0.88). Genetic correlations of CM were moderate with MIRprob (ra = 0.26 and 0.37) during 150 and 305 days in milk, respectively and low with LF (h2 = 0.10 and 0.11). However, basic selection index calculations indicate that the added value of the new MIR-predicted phenotypes is limited for genetic evaluation of udder health.

Effects of particle size reduction of meadow hay on feed intake, performance, and apparent total tract nutrient digestibility in dairy cows.

Forage-based diets are encouraged in organic dairy cattle production as this can increase the net human food supply, but their voluminous nature can limit dry matter intake (DMI) and performance. This study investigates the effects of a substantial particle size reduction of hay on dairy cows' feed intake, performance, and body characteristics, as well as on apparent total tract digestibility (ATTD). Eighteen lactating Holstein cows were allocated to two balanced feeding groups. The control group received long stem hay with a conventional particle size (CON), the experimental group received chopped hay (RED). Both groups were supplemented with concentrates (3.6 kg/d, DM basis). After 14 adaptation days, data were collected for 20 consecutive days. A covariate period of 21 days preceded the experimental feeding period. Particles retained on the 19-, 8- and 4-mm screens and on the pan of the Penn State Particle Separator accounted for 21%, 20%, 20% and 39% of the RED hay. CON hay consisted of 72% large particles, followed by 8%, 7% and 13% retained on the other screens. Average DMI levels of cows in the CON group reached 20.8 kg/d, with a nonsignificant increase (+1.05 kg/d) in the RED group (p = 0.28). Intakes of both NFC (+0.65 kg/d, p = 0.01) and CP (+0.28 kg/d, p = 0.05) were significantly greater in the RED group, resulting in a slightly increased milk yield (+0.8 kg energy corrected milk/d) (p = 0.45), likely because the ATTD decreased significantly when feeding RED hay. No impact was observed on energy balance (103.7 vs 103.9%, p = 0.95), feed conversion efficiency (kg ECM/kg DMI), or N use efficiency. Overall, the results indicate increases in intake of NFC and CP in the RED group when feeding a hay-based (>83%, DM basis) diet, but also a decrease in nutrient digestibility, likely due to increased passage rate, potentially because of the high fraction of hay particles < 4 mm. In conclusion, hay-based rations with a lower proportion of fine particles should be tested to exploit the potential of particle size reduction in terms of improving hay use efficiency.

Comparison of different validation methods for single-step genomic evaluations based on a simulated cattle population.

The validation of estimated breeding values from single-step genomic BLUP (ssGBLUP) is an important topic, as more and more countries and animal populations are currently changing their genomic prediction to single-step. The objective of this work was to compare different methods to validate single-step genomic breeding values (GEBV). The investigations were carried out using a simulation study based on the German-Austrian-Czech Fleckvieh population. To test the validation methods under different conditions, several biased and unbiased scenarios were simulated. The application of the widely used Interbull GEBV test to the single-step method is only possible to a limited extent, partly because of genomic preselection, which biases conventional estimated breeding values. Alternative validation methods considered in the study are the linear regression method proposed by Legarra and Reverter, the improved genomic validation including additional regressions as suggested by VanRaden and an adaptation of the Interbull GEBV test using daughter yield deviations (DYD) from ssGBLUP instead of pedigree BLUP. The comparison of the different methods for the different scenarios showed that for males the methods based on GEBV estimate the dispersion more accurate and less biased compared with the GEBV test using DYD from ssGBLUP, whereas the standard Interbull GEBV test is highly affected by genomic preselection for males. For females, the GEBV test using yield deviations from ssGBLUP results in better estimations for the true dispersion.

Inbreeding depression and its effect on sperm quality traits in Pietrain pigs.

In most cases, inbreeding is expected to have unfavourable effects on traits in livestock. The consequences of inbreeding depression could be substantial, primarily in reproductive and sperm quality traits, and thus lead to decreased fertility. Therefore, the objectives of this study were (i) to compute inbreeding coefficients using pedigree (FPED ) and genomic data based on runs of homozygosity (ROH) in the genome (FROH ) of Austrian Pietrain pigs, and (ii) to assess inbreeding depression on four sperm quality traits. In total, 74,734 ejaculate records from 1034 Pietrain boars were used for inbreeding depression analyses. Traits were regressed on inbreeding coefficients using repeatability animal models. Pedigree-based inbreeding coefficients were lower than ROH-based inbreeding values. The correlations between pedigree and ROH-based inbreeding coefficients ranged from 0.186 to 0.357. Pedigree-based inbreeding affected only sperm motility while ROH-based inbreeding affected semen volume, number of spermatozoa, and motility. For example, a 1% increase in pedigree inbreeding considering 10 ancestor generations (FPED10 ) was significantly (p < 0.05) associated with a 0.231% decrease in sperm motility. Almost all estimated effects of inbreeding on the traits studied were unfavourable. It is advisable to properly manage the level of inbreeding to avoid high inbreeding depression in the future. Further, analysis of effects of inbreeding depression for other traits, including growth and litter size for the Austrian Pietrain population is strongly advised.

The Combined Use of Automated Milking System and Sensor Data to Improve Detection of Mild Lameness in Dairy Cattle.

This study aimed to develop a tool to detect mildly lame cows by combining already existing data from sensors, AMSs, and routinely recorded animal and farm data. For this purpose, ten dairy farms were visited every 30-42 days from January 2020 to May 2021. Locomotion scores (LCS, from one for nonlame to five for severely lame) and body condition scores (BCS) were assessed at each visit, resulting in a total of 594 recorded animals. A questionnaire about farm management and husbandry was completed for the inclusion of potential risk factors. A lameness incidence risk (LCS ≥ 2) was calculated and varied widely between farms with a range from 27.07 to 65.52%. Moreover, the impact of lameness on the derived sensor parameters was inspected and showed no significant impact of lameness on total rumination time. Behavioral patterns for eating, low activity, and medium activity differed significantly in lame cows compared to nonlame cows. Finally, random forest models for lameness detection were fit by including different combinations of influencing variables. The results of these models were compared according to accuracy, sensitivity, and specificity. The best performing model achieved an accuracy of 0.75 with a sensitivity of 0.72 and specificity of 0.78. These approaches with routinely available data and sensor data can deliver promising results for early lameness detection in dairy cattle. While experimental automated lameness detection systems have achieved improved predictive results, the benefit of this presented approach is that it uses results from existing, routinely recorded, and therefore widely available data.

Integrating diverse data sources to predict disease risk in dairy cattle-a machine learning approach.

Livestock farming is currently undergoing a digital revolution and becoming increasingly data-driven. Yet, such data often reside in disconnected silos making them impossible to leverage their full potential to improve animal well-being. Here, we introduce a precision livestock farming approach, bringing together information streams from a variety of life domains of dairy cattle to study whether including more and diverse data sources improves the quality of predictions for eight diseases and whether using more complex prediction algorithms can, to some extent, compensate for less diverse data. Using three machine learning approaches of varying complexity (from logistic regression to gradient boosted trees) trained on data from 5,828 animals in 165 herds in Austria, we show that the prediction of lameness, acute and chronic mastitis, anestrus, ovarian cysts, metritis, ketosis (hyperketonemia), and periparturient hypocalcemia (milk fever) from routinely available data gives encouraging results. For example, we can predict lameness with high sensitivity and specificity (F1 = 0.74). An analysis of the importance of individual variables to prediction performance shows that disease in dairy cattle is a product of the complex interplay between a multitude of life domains, such as housing, nutrition, or climate, that including more and diverse data sources increases prediction performance, and that the reuse of existing data can create actionable information for preventive interventions. Our findings pave the way toward data-driven point-of-care interventions and demonstrate the added value of integrating all available data in the dairy industry to improve animal well-being and reduce disease risk.

A systematic approach to analyse the impact of farm-profiles on bovine health.

In this study we present systematic framework to analyse the impact of farm profiles as combinations of environmental conditions and management practices on common diseases in dairy cattle. The data used for this secondary data analysis includes observational data from 166 farms with a total of 5828 dairy cows. Each farm is characterised by features from five categories: husbandry, feeding, environmental conditions, housing, and milking systems. We combine dimension reduction with clustering techniques to identify groups of similar farm attributes, which we refer to as farm profiles. A statistical analysis of the farm profiles and their related disease risks is carried out to study the associations between disease risk, farm membership to a specific cluster as well as variables that characterise a given cluster by means of a multivariate regression model. The disease risks of five different farm profiles arise as the result of complex interactions between environmental conditions and farm management practices. We confirm previously documented relationships between diseases, feeding and husbandry. Furthermore, novel associations between housing and milking systems and specific disorders like lameness and ketosis have been discovered. Our approach contributes to paving a way towards a more holistic and data-driven understanding of bovine health and its risk factors.

Analysis of lactating cows on commercial Austrian dairy farms: the influence of genotype and body weight on efficiency parameters.

The aim of this study was twofold: first, to evaluate the influence of body weight on the efficiency of dairy cows, and second, to analyze the current state of dairy cattle populations as part of the Austrian Cattle Breeding Association's Efficient Cow project. Data of Fleckvieh (FV, dual-purpose Simmental), Fleckvieh × Red Holstein (FV × RH), Holstein (HF) and Brown Swiss (BS) dairy cows (161 farms, 6098 cows) were collected at each performance recording during the year 2014. In addition to routinely recorded data (e.g., milk yield, fertility), body weight, body measurements, body condition score (BCS) and individual feed information were also collected. The following efficiency traits were considered: body weight efficiency as the ratio of energy-corrected milk (ECM) to metabolic body weight, feed efficiency (kilogram ECM per kilogram dry-matter intake) and energy efficiency expressed as the ratio of energy in milk to energy intake. The relationship of milk yield to body weight was shown to be nonlinear. Milk yield decreased in cows above the 750 kg body weight class for HF, BS and FV × RH with 68 % RH genes, but less dramatically and later for FV at 800 kg. This resulted in an optimum body weight for feed and energy efficiency. BS and HF had the highest efficiency in a narrower and lighter body weight range (550-700 kg) due to a stronger curvature of the parabolic curve. Contrary to this, the efficiency of FV did not change as much as it did in the dairy breeds with increasing body weight, meaning that FV had a similar feed and energy efficiency in a range of 500-750 kg. The breed differences disappeared when body weight ranged between 750 and 800 kg. The average body weight of the breeds studied (FV 722 kg, BS 649 and HF 662 kg) was in the optimum range. FV was located at the upper end of the decreasing segment. In conclusion, an optimum body weight range for efficiency does exist, due to the nonlinear relationship of milk yield and body weight. Specialized dairy breeds seem to respond more intensively to body weight range than dual-purpose breeds, due to the stronger curvature. Cows with medium weights within a population are the most efficient. Heavy cows ( > 750  kg) produce even less milk. A further increase in dairy cows' body weights should therefore be avoided.

On the genomic regions associated with milk lactose in Fleckvieh cattle.

Lactose is a sugar uniquely found in mammals' milk and it is the major milk solid in bovines. Lactose yield (LY, kg/d) is responsible for milk volume, whereas lactose percentage (LP) is thought to be more related to epithelial integrity and thus to udder health. There is a paucity of studies that have investigated lactose at the genomic level in dairy cows. This paper aimed to improve our knowledge on LP and LY, providing new insights into the significant genomic regions affecting these traits. A genome-wide association study for LP and LY was carried out in Fleckvieh cattle by using bulls' deregressed estimated breeding values of first lactation as pseudo-phenotypes. Heritabilities of first-lactation test-day LP and LY estimated using linear animal models were 0.38 and 0.25, respectively. A total of 2,854 bulls genotyped with a 54K SNP chip were available for the genome-wide association study; a linear mixed model approach was adopted for the analysis. The significant SNP of LP were scattered across the whole genome, with signals on chromosomes 1, 2, 3, 7, 12, 16, 18, 19, 20, 28, and 29; the top 4 significant SNP explained 4.90% of the LP genetic variance. The signals were mostly in regions or genes with involvement in molecular intra- or extracellular transport; for example, CDH5, RASGEF1C, ABCA6, and SLC35F3. A significant region within chromosome 20 was previously shown to affect mastitis or somatic cell score in cattle. As regards LY, the significant SNP were concentrated in fewer regions (chromosomes 6 and 14), related to mastitis/somatic cell score, immune response, and transport mechanisms. The 5 most significant SNP for LY explained 8.45% of genetic variance and more than one-quarter of this value has to be attributed to the variant within ADGRB1. Significant peaks in target regions remained even after adjustment for the 2 most significant variants previously detected on BTA6 and BTA14. The present study is a prelude for deeper investigations into the biological role of lactose for milk secretion and volume determination, stressing the connection with genes regulating intra- or extracellular trafficking and immune and inflammatory responses in dairy cows. Also, these results improve the knowledge on the relationship between lactose and udder health; they support the idea that LP and its derived traits are potential candidates as indicators of udder health in breeding programs aimed to enhance cows' resistance to mastitis.

Feeding forages with reduced particle size in a total mixed ration improves feed intake, total-tract digestibility, and performance of organic dairy cows.

The optimal utilization of forages is crucial in cattle production, especially in organic dairy systems that encourage forage-based feeding with limited concentrate amounts. Reduction of the particle size of forages is known to improve feed intake and thus might be a viable option to help cows cope with less nutrient-dense feeds. The main aim of this study was to evaluate the effects of reducing forage particle size with a geometric mean of 52 mm (conventional particle size; CON) to 7 mm (reduced particle size; RED) in a high-forage diet (80% of dry matter) on dairy cows' sorting behavior, feed intake, chewing activity, and performance as well as on total-tract nutrient digestibility. Both diets (CON and RED) consisted of 43% grass hay, 37% clover-grass silage, and 20% concentrate and contained roughly 44% NDF, 15% CP, and 0.5% starch (dry matter basis). For CON, particle size was set by mixing all components for 20 min in a vertical feed mixer. The RED diet was treated the same, but before the mixer was filled, forages were chopped (theoretical length of cut = 0.5 cm) and the hay was hammer-milled (sieve size = 2 cm). Four primiparous and 16 multiparous mid-lactating dairy cows were assigned according to milk yield, body weight (BW), days in milk, and parity into 2 groups and fed 1 of the 2 diets for 34 d. The first 13 d were used for diet adaption, followed by data collection of nutrient intake, chewing activity, sorting behavior, milk production, and nutrient digestibility for the last 21 d of the experiment. Seven days before the start of the experiment, data on BW, dry matter intake (DMI), chewing activity, sorting behavior, and milk production were collected for use as covariates. Results showed that the RED diet improved DMI (+1.8 kg/d) and NDF intake (+0.46 kg/d) but decreased intake of physically effective NDF >8 (-3.25 kg/d). The RED-fed cows increased their intake of smaller particles (<19 mm), whereas CON-fed cows sorted for long particles (>19 mm). The RED cows reduced eating and ruminating time per kilogram of DMI by 4.8 and 1.9 min, respectively, suggesting lower mastication efforts. In addition, the RED diet significantly increased apparent total-tract digestibility of nutrients. As a consequence, RED cows' energy-corrected milk yield was higher (27.0 vs. 29.3 kg/d) without affecting milk solids, cow BW, or feed efficiency. In conclusion, the data support a reduction of forage particle size in high-forage diets as a measure to improve energy intake, performance, and hence forage utilization under these feeding conditions.

Assessment of Piglet Vitality by Farmers-Validation of A Scoring Scheme and Estimation of Associated Genetic Parameters.

Maternal breeds for sows have been bred for high prolificacy during recent decades. Although large litters may be beneficial for economic efficiency, pre-weaning mortality is increased. Thus, focus should instead be put on new traits such as piglet vitality (PV). Until now, no validated scoring scheme for piglet vitality exists, which is feasible to be applied for routine on-farm trait recording. The objective of this study was to validate a four-point vitality scoring scheme (1 = low vitality to 4 = high vitality) applied by farmers based on pre-weaning mortality and to estimate genetic parameters. A linear mixed model was fitted for piglet vitality for 3172 litters from Large White and Landrace sows on 23 farms and correlations were calculated for vitality score and piglet mortality. A subsample of 2900 records was used for genetic analysis. Pre-weaning mortality differed significantly between all vitality score categories except for 1 and 2, ranging between 7.98% (category 4) and 29.1% (category 1). PV was genetically negatively correlated to litter size (-0.68) and mortality rate (-0.65), whereas litter size was positively correlated with mortality rate (0.59). Including PV into breeding programs may, thus, improve animal welfare.

Production level, fertility, health traits, and longevity in local and commercial dairy breeds under organic production conditions in Austria, Switzerland, Poland, and Sweden.

Our aim was to map the performance of local (native) dairy cattle breeds in Austria, Switzerland, Poland, and Sweden with regard to production, fertility, longevity, and health-associated traits and to compare them with commercial (modern) breeds. For this purpose, we analyzed test-day records (July 1, 2011, to June 30, 2014) and treatment records (Austria, Sweden) of cows managed on organic farms. We performed country-wise comparisons of 123,415 lactations from Original Braunvieh (OB) and Grey Cattle (AL) with Braunvieh (BV; Brown Swiss blood >60%) in Switzerland; AL with BV (Brown Swiss blood >50%) in Austria; Polish Black and White (ZB), Polish Red and White (ZR), and Polish Red (RP) with Polish Holstein Friesian (PHF) in Poland; and Swedish Red (SRB) with Swedish Holstein (SH) in Sweden. Average milk yields were substantially lower for local compared with commercial breeds in all countries; differences ranged from 750 kg (Sweden) to 1,822 kg (Austria), albeit on very different average levels. Local breeds showed a longer productive lifetime by 0.64, 0.83, 1.42, and 0.20 lactations in Switzerland, Austria, Poland, and Sweden, respectively, again on very different levels in each country. Regarding fertility traits, calving interval was shorter in local than in commercial breeds by 13 (Sweden), 14 (Switzerland), and 20 d (Austria, Poland). Insemination index was lower in certain local breeds by 0.15 (Switzerland), 0.14 (Austria), 0.21 (Poland), and 0.13 (Sweden). Several local breeds showed a lower proportion of cows with >100,000 somatic cells/mL. This was the case in Switzerland (OB 24.2%; BV 35.8%), Austria (AL 25.3%; BV 36.9%), and Sweden (SRB 42.4%; SH 43.4%). In contrast, the respective proportion in Poland exceeded 82% in all breeds except the commercial PHF (76.1%). In Sweden, lactations with veterinary treatments were considerably less prevalent in SRB (15.6%) than in SH (21.7%). In Austria, breeds differed only in treatments for udder disorders, which favored AL. In conclusion, the markedly lower milk yields of local breeds are partly counterbalanced by (somewhat inconsistent) advantages in longevity, fertility, and health traits across 4 European countries. This indicates that the robustness of local breeds can contribute to improved sustainability of organic dairy systems.

Body weight prediction using body size measurements in Fleckvieh, Holstein, and Brown Swiss dairy cows in lactation and dry periods.

The objective of this study was to predict cows' body weight from body size measurements and other animal data in the lactation and dry periods. During the whole year 2014, 6306 cows (on 167 commercial Austrian dairy farms) were weighed at each routine performance recording and body size measurements like heart girth (HG), belly girth (BG), and body condition score (BCS) were recorded. Data on linear traits like hip width (HW), stature, and body depth were collected three times a year. Cows belonged to the genotypes Fleckvieh (and Red Holstein crosses), Holstein, and Brown Swiss. Body measurements were tested as single predictors and in multiple regressions according to their prediction accuracy and their correlations with body weight. For validation, data sets were split randomly into independent subsets for estimation and validation. Within the prediction models with a single body measurement, heart girth influenced relationship with body weight most, with a lowest root mean square error (RMSE) of 39.0 kg, followed by belly girth (39.3 kg) and hip width (49.9 kg). All other body measurements and BCS resulted in a RMSE of higher than 50.0 kg. The model with heart and belly girth (Model HG BG ) reduced RMSE to 32.5 kg, and adding HW reduced it further to 30.4 kg (Model HG BG HW ). As RMSE and the coefficient of determination improved, genotype-specific regression coefficients for body measurements were introduced in addition to the pooled ones. The most accurate equations, Model HG BG and Model HG BG HW , were validated separately for the lactation and dry periods. Root mean square prediction error (RMSPE) ranged between 36.5 and 37.0 kg (Model HG BG HW , Model HG BG , lactation) and 39.9 and 41.3 kg (Model HG BG HW , Model HG BG , dry period). Accuracy of the predictions was evaluated by decomposing the mean square prediction error (MSPE) into error due to central tendency, error due to regression, and error due to disturbance. On average, 99.6 % of the variance between estimated and observed values was caused by disturbance, meaning that predictions were valid and without systematic estimation error. On the one hand, this indicates that the chosen traits sufficiently depicted factors influencing body weight. On the other hand, the data set was very heterogeneous and large. To ensure high prediction accuracy, it was necessary to include body girth traits for body weight estimation.

Sustainable breeding objectives and possible selection response: Finding the balance between economics and breeders' preferences.

To optimize breeding objectives of Fleckvieh and Brown Swiss cattle, economic values were re-estimated using updated prices, costs, and population parameters. Subsequently, the expected selection responses for the total merit index (TMI) were calculated using previous and newly derived economic values. The responses were compared for alternative scenarios that consider breeders' preferences. A dairy herd with milk production, bull fattening, and rearing of replacement stock was modeled. The economic value of a trait was derived by calculating the difference in herd profit before and after genetic improvement. Economic values for each trait were derived while keeping all other traits constant. The traits considered were dairy, beef, and fitness traits, the latter including direct health traits. The calculation of the TMI and the expected selection responses was done using selection index methodology with estimated breeding values instead of phenotypic deviations. For the scenario representing the situation up to 2016, all traits included in the TMI were considered with their respective economic values before the update. Selection response was also calculated for newly derived economic values and some alternative scenarios, including the new trait vitality index (subindex comprising stillbirth and rearing losses). For Fleckvieh, the relative economic value for the trait groups milk, beef, and fitness were 38, 16, and 46%, respectively, up to 2016, and 39, 13, and 48%, respectively, for the newly derived economic values. Approximately the same selection response may be expected for the milk trait group, whereas the new weightings resulted in a substantially decreased response in beef traits. Within the fitness block, all traits, with the exception of fertility, showed a positive selection response. For Brown Swiss, the relative economic values for the main trait groups milk, beef, and fitness were 48, 5, and 47% before 2016, respectively, whereas for the newly derived scenario they were 40, 14, and 39%. For both Brown Swiss and Fleckvieh, the fertility complex was expected to further deteriorate, whereas all other expected selection responses for fitness traits were positive. Several additional and alternative scenarios were calculated as a basis for discussion with breeders. A decision was made to implement TMI with relative economic values for milk, beef, and fitness with 38, 18, and 44% for Fleckvieh and 50, 5, and 45% for Brown Swiss, respectively. In both breeds, no positive expected selection response was predicted for fertility, although this trait complex received a markedly higher weight than that derived economically. An even higher weight for fertility could not be agreed on due to the effect on selection response of other traits. Hence, breeders decided to direct more attention toward the preselection of bulls with regard to fertility.

A missense mutation in TUBD1 is associated with high juvenile mortality in Braunvieh and Fleckvieh cattle.

BACKGROUND: Haplotypes with reduced or missing homozygosity may harbor deleterious alleles that compromise juvenile survival. A scan for homozygous haplotype deficiency revealed a short segment on bovine chromosome 19 (Braunvieh haplotype 2, BH2) that was associated with high juvenile mortality in Braunvieh cattle. However, the molecular genetic underpinnings and the pathophysiology of BH2 remain to be elucidated. RESULTS: The frequency of BH2 was 6.5 % in 8,446 Braunvieh animals from the national bovine genome databases. Both perinatal and juvenile mortality of BH2 homozygous calves were higher than the average in Braunvieh cattle resulting in a depletion of BH2 homozygous adult animals (P = 9.3x10(-12)). The analysis of whole-genome sequence data from 54 Braunvieh animals uncovered a missense mutation in TUBD1 (rs383232842, p.H210R) that was compatible with recessive inheritance of BH2. The availability of sequence data of 236 animals from diverse bovine populations revealed that the missense mutation also segregated at a low frequency (1.7 %) in the Fleckvieh breed. A validation study in 37,314 Fleckvieh animals confirmed high juvenile mortality of homozygous calves (P = 2.2x10(-15)). Our findings show that the putative disease allele is located on an ancestral haplotype that segregates in Braunvieh and Fleckvieh cattle. To unravel the pathophysiology of BH2, six homozygous animals were examined at the animal clinic. Clinical and pathological findings revealed that homozygous calves suffered from chronic airway disease possibly resulting from defective cilia in the respiratory tract. CONCLUSIONS: A missense mutation in TUBD1 is associated with high perinatal and juvenile mortality in Braunvieh and Fleckvieh cattle. The mutation is located on a common haplotype likely originating from an ancient ancestor of Braunvieh and Fleckvieh cattle. Our findings demonstrate for the first time that deleterious alleles may segregate across closed cattle breeds without recent admixture. Homozygous calves suffer from chronic airway disease resulting in poor growth performance and high juvenile mortality. The respiratory manifestations resemble key features of diseases resulting from impaired function of airway cilia.

A comparison of methods to calculate a total merit index using stochastic simulation.

BACKGROUND: Modern dairy cattle breeding goals include several production and more and more functional traits. Estimated breeding values (EBV) that are combined in the total merit index usually come from single-trait models or from multivariate models for groups of traits. In most cases, a multivariate animal model based on phenotypic data for all traits is not feasible and approximate methods based on selection index theory are applied to derive the total merit index. Therefore, the objective of this study was to compare a full multitrait animal model with two approximate multitrait models and a selection index approach based on simulated data. METHODS: Three production and two functional traits were simulated to mimic the national Austrian Brown Swiss population. The reference method for derivation of the total merit index was a multitrait evaluation based on all phenotypic data. Two of the approximate methods were variations of an approximate multitrait model that used either yield deviations or de-regressed breeding values. The final method was an adaptation of the selection index method that is used in routine evaluations in Austria and Germany. Three scenarios with respect to residual covariances were set up: residual covariances were equal to zero, or half of or equal to the genetic covariances. RESULTS: Results of both approximate multitrait models were very close to those of the reference method, with rank correlations of 1. Both methods were nearly unbiased. Rank correlations for the selection index method showed good results when residual covariances were zero but correlations with the reference method decreased when residual covariances were large. Furthermore, EBV were biased when residual covariances were high. CONCLUSIONS: We applied an approximate multitrait two-step procedure to yield deviations and de-regressed breeding values, which led to nearly unbiased results. De-regressed breeding values gave even slightly better results. Our results confirmed that ignoring residual covariances when a selection index approach is applied leads to remarkable bias. This could be relevant in terms of selection accuracy. Our findings suggest that the approximate multitrait approach applied to de-regressed breeding values can be used in routine genetic evaluation.

Effects of age and environmental factors on semen production and semen quality of Austrian Simmental bulls.

More than 90% of the breeding stock of Austrian dual purpose Simmental cows is artificially inseminated. Knowledge of factors affecting sperm production and semen quality is of importance with regard to reproductive efficiency and thus genetic improvement as well as for the productivity and profitability of AI centers. Hence, semen data from two Austrian AI centres collected in the years 2000 and 2001 were evaluated. In total, 3625 and 3654 ejaculates from 147 and 127 AI bulls, respectively, were analysed regarding ejaculate volume, sperm concentration, percentage of viable spermatozoa in the ejaculate, total spermatozoa per ejaculate and motility. Effects accounted for were the bull (random), age of bull, collection interval, number of collection on collection day, bull handler, semen collector, temperature on day of semen collection, in the course of epididymal maturation (average temperature of days 1-11 before collection) and during spermatogenesis (average temperature of days 12-65 before collection). Age of bull significantly affected all traits (P<0.01 to P<0.001) except motility score in center 2. Ejaculate volume and total number of spermatozoa increased with age of bull while sperm concentration was lower in higher age classes (center 1). The collection team was also found to significantly influence semen quality traits. With increasing collection interval ejaculate volume and total number of spermatozoa increased significantly (P<0.05 to P<0.001) while collection intervals between 4-9 days and 1-6 days were superior with regard to sperm concentration and percentage of viable spermatozoa, respectively (P<0.10 to P<0.001). First ejaculates were superior with respect to ejaculate volumes, sperm concentrations and total number of spermatozoa per ejaculate (P<0.001). Temperature, either on day of semen collection or during epididymal maturation or spermatogenesis, had important but inconsistent effects on semen production and sperm quality. Overall, however, ambient temperatures in the range of 5-15 degrees C were found to be optimal for semen production.