Estimation of genetic parameters for novel functional traits in Brown Swiss cattle

Phenotypic and genetic analysis of milking temperament and its correlation with milk production traits in South African Holstein cattle

Milking temperament (MT) is a crucial trait in dairy production; it affects farm profitability as well as animal and human welfare. Furthermore, poor temperament may increase herd costs by compromising the state and durability of the milking system. There is, however, limited knowledge and recording of MT in South African dairy cattle. The purpose of this study was to evaluate factors influencing MT scores and to estimate genetic parameters among MT and milk production traits (milk yield, fat and protein) in South African Holsteins. Data comprised of MT assessments on 2,844 cows from 16 herds collected between September 2020 and November 2021. Non-genetic effects were analysed by general linear models (GLM) procedure, and repeatability of MT scores was estimated using the variance components procedure of the Statistical Analysis Software. Linear animal models were fitted to estimate genetic parameters, using the ASReml software. Herd-test-day and age of cow at calving (p < 0.0001), and lactation stage (p < 0.05), significantly influenced MT. Repeatability estimate was moderate (0.47 ± 0.03), and the heritability estimate was low (0.05 ± 0.04). Heritability estimates were low to moderate for milk yield and composition, varying from 0.11 ± 0.05 for milk yield to 0.24 ± 0.06 for protein percent (%). Genetic correlation for MT with milk yield was moderate (0.60 ± 0.35). Low correlations were observed for MT with fat % (-0.12 ± 0.24) and protein % (-0.30 ± 0.32). There was no discernible genetic trend for MT in animals born from 2009 to 2019, although there was a minimal overall decline over the period. These findings suggest that there was reasonable consistency in the assessment of MT, and that MT may be improved through selection, using multi-trait models including milk yield.

Invited review: Phenotyping strategies and genetic background of dairy cattle behavior in intensive production systems-From trait definition to genomic selection

Understanding and assessing dairy cattle behavior is critical for developing sustainable breeding programs and management practices. The behavior of individual animals can provide valuable information on their health and welfare status, improve reproductive management, and predict efficiency traits such as feed efficiency and milking efficiency. Routine genetic evaluations of animal behavior traits can contribute to optimizing breeding and management strategies for dairy cattle but require the identification of traits that capture the most important biological processes involved in behavioral responses. These traits should be heritable, repeatable, and measured in noninvasive and cost-effective ways in many individuals from the breeding populations or related reference populations. Although behavior traits are heritable in dairy cattle populations, they are highly polygenic, with no known major genes influencing their phenotypic expression. Genetically selecting dairy cattle based on their behavior can be advantageous because of their relationship with other key traits such as animal health, welfare, and productive efficiency, as well as animal and handler safety. Trait definition and longitudinal data collection are still key challenges for breeding for behavioral responses in dairy cattle. However, the more recent developments and adoption of precision technologies in dairy farms provide avenues for more objective phenotyping and genetic selection of behavior traits. Furthermore, there is still a need to standardize phenotyping protocols for existing traits and develop guidelines for recording novel behavioral traits and integrating multiple data sources. This review gives an overview of the most common indicators of dairy cattle behavior, summarizes the main methods used for analyzing animal behavior in commercial settings, describes the genetic and genomic background of previously defined behavioral traits, and discusses strategies for breeding and improving behavior traits coupled with future opportunities for genetic selection for improved behavioral responses.

Evaluation of accuracies of genomic predictions for body conformation traits in Korean Holstein

OBJECTIVE

This study aimed to assess the genetic parameters and accuracy of genomic predictions for twenty-four linear body conformation traits and overall conformation scores in Korean Holstein dairy cows.

METHODS

A dataset of 2,206 Korean Holsteins was collected, and genotyping was performed using the Illumina Bovine 50K single nucleotide polymorphism (SNP) chip. The traits investigated included body traits (stature, height at front end, chest width, body depth, angularity, body condition score, and locomotion), rump traits (rump angle, rump width, and loin strength), feet and leg traits (rear leg set, rear leg rear view, foot angle, heel depth, and bone quality), udder traits (udder depth, udder texture, udder support, fore udder attachment, front teat placement, front teat length, rear udder height, rear udder width, and rear teat placement), and overall conformation score. Accuracy of genomic predictions was assessed using the single-trait animal model genomic best linear unbiased prediction method implemented in the ASReml-SA v4.2 software.

RESULTS

Heritability estimates ranged from 0.10 to 0.50 for body traits, 0.21 to 0.35 for rump traits, 0.13 to 0.29 for feet and leg traits, and 0.05 to 0.46 for udder traits. Rump traits exhibited the highest average heritability (0.29), while feet and leg traits had the lowest estimates (0.21). Accuracy of genomic predictions varied among the twenty-four linear body conformation traits, ranging from 0.26 to 0.49. The heritability and prediction accuracy of genomic estimated breeding value (GEBV) for the overall conformation score were 0.45 and 0.46, respectively. The GEBVs for body conformation traits in Korean Holstein cows had low accuracy, falling below the 50% threshold.

CONCLUSION

The limited response to selection for body conformation traits in Korean Holsteins may be attributed to both the low heritability of these traits and the lower accuracy estimates for GEBVs. Further research is needed to enhance the accuracy of GEBVs and improve the selection response for these traits.

Genomic Selection for Dairy Cattle Behaviour Considering Novel Traits in a Changing Technical Production Environment

Cow behaviour is a major factor influencing dairy herd profitability and is an indicator of animal welfare and disease. Behaviour is a complex network of behavioural patterns in response to environmental and social stimuli and human handling. Advances in agricultural technology have led to changes in dairy cow husbandry systems worldwide. Increasing herd sizes, less time availability to take care of the animals and modern technology such as automatic milking systems (AMSs) imply limited human-cow interactions. On the other hand, cow behaviour responses to the technical environment (cow-AMS interactions) simultaneously improve production efficiency and welfare and contribute to simplified "cow handling" and reduced labour time. Automatic milking systems generate objective behaviour traits linked to workability, milkability and health, which can be implemented into genomic selection tools. However, there is insufficient understanding of the genetic mechanisms influencing cow learning and social behaviour, in turn affecting herd management, productivity and welfare. Moreover, physiological and molecular biomarkers such as heart rate, neurotransmitters and hormones might be useful indicators and predictors of cow behaviour. This review gives an overview of published behaviour studies in dairy cows in the context of genetics and genomics and discusses possibilities for breeding approaches to achieve desired behaviour in a technical production environment.

Behavioural Traits in Bos taurus Cattle, Their Heritability, Potential Genetic Markers, and Associations with Production Traits

People who work with cattle are at severe risk of serious injury due to the size and strength of the cattle. This risk can be minimised by breeding less dangerous cattle, which have a more favourable reaction to humans. This study provides a systematic review of literature pertaining to cattle genetics relating to behaviour. The review protocol was developed using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework, with Population, Exposure and Outcome components identified as Bovine, Genetics and Behaviour respectively. Forty-nine studies were identified in the sifting and assigned non-exclusively to groups of heritability (22), genomic associations (13) and production traits related to behaviour (24). Behavioural traits were clustered into the following groups: "temperament, disposition and/ or docility", "aggression", "chute score", "flight speed", "milking temperament", "non-restrained methods" and "restrained methods". Fourteen papers reported high accuracy (Standard Error ≤ 0.05) estimates of heritability, the majority (n = 12) of these studies measured over 1000 animals. The heritability estimates were found to vary between studies. Gene associations with behavioural traits were found on all chromosomes except for chromosome 13, with associated SNPs reported on all chromosomes except 5, 13, 17, 18 and 23. Generally, it was found that correlations between behaviour and production traits were low or negligible. These studies suggest that additive improvement of behavioural traits in cattle is possible and would not negatively impact performance. However, the variation between studies demonstrates that the genetic relationships are population specific. Thus, to assess the heritability, genetic associations with production and genomic areas of interest for behavioural traits, a large-scale study of the population of interest would be required.

Relationship between Temperament and Stage of Lactation, Productivity and Milk Composition of Dairy Cows

Simple Summary

Cattle temperament can be described as a response to changes in the environment and is crucial for successful herd management using innovative technologies. Despite the economic aspects of animal productivity and welfare, there is still a lack of objective evidence for a wider use of temperament in dairy cattle breeding programmes. The aim of this study was to evaluate the relationship between cow temperament and milk indices describing cow productivity, metabolic status and mastitis resistance. The coefficient of heritability of temperament was determined. Only a small part of the phenotypic changes in this indicator in the analysed population was associated with genetic factors; however, the correlation of cow temperament with milk lactose and somatic cells suggests that temperament could be used in sustainable breeding programmes, giving priority to animal welfare and health. A statistically significant decrease in temperament scores with increasing lactation periods was only found in primiparous cows. It is also argued that changes in milk production, milk composition and quality associated with mastitis and a cow’s metabolic status should be taken into account when assessing the cow’s temperament, as these factors can affect the welfare and behaviour of an animal, and therefore the expression and intensity of their reaction to their environment.

Abstract

The aim of this study was to assess the relationship between temperament and milk performance in cows at different stages of lactation, describing their productivity, metabolic status and resistance to mastitis. This study showed that with increasing lactation, cows’ temperament indicators decreased (p < 0.001) and they became calmer. The highest temperament score on a five-point scale was found in cows between 45 and 100 days of lactation. In the group of pregnant cows, we found more cows (p = 0.005) with a temperament score of 1–2 compared with non-pregnant cows A normal temperament was usually detected in cows with lactose levels in milk of 4.60% or more and when the somatic cell count (SCC) values in cow milk were <100,000/mL and 100,000–200,000/mL, with a milk fat-to-protein ratio of 1.2. A larger number of more sensitive and highly aggressive cows was detected at a low milk urea level. In contrast to a positive phenotypic correlation (p < 0.05), this study showed a negative genetic correlation between the temperament of cows and milk yield (p < 0.001). Positive genetic correlations between temperament scores and milk somatic cells (p < 0.001) and milk fat-to-protein ratio (p < 0.05) were found to indicate a lower genetic predisposition in cows with a calmer temperament to subclinical mastitis and ketosis. On the other hand, the heritability of temperament (h² = 0.044–0.100) showed that only a small part of the phenotypic changes in this indicator is associated with genetic factors.

Estimated genetic parameters for all genetically evaluated traits in Canadian Holsteins

Genetic improvement is a crucial tool to deal with the increasing demand for high quality, sustainably produced dairy. Breeding programs are based on genetic parameters, such as heritability and genetic correlations, for economically important traits in a population. In this study, we estimated population genetic parameters and genetic trends for 67 traits evaluated on heifers and first-lactation Canadian Holstein cows. The data consisted of approximately 500,000 records with pedigree information collected from 1980 to 2019. Genetic parameters were estimated using bivariate linear animal models under a Bayesian approach. Analyses for the 67 traits resulted in 2,211 bivariate combinations, from which the estimated genetic parameters are reported here. The most highly heritable traits were fat percent (0.66) and protein percent (0.69), followed by stature (0.47). Lowest heritabilities (0.01) were observed for disease-related traits, such as lameness and toe ulcer, and calf survival. The genetic correlations between gestation length, calf size, and calving ease measured on both heifer and cows were close to unity. On the other hand, traits such as body condition score and pin width, cystic ovaries and sole ulcer, rear teat placement, and toe ulcer were genetically unrelated. This study reports genetic parameters that have not been previously published for Canadian Holstein cows, and provides updates of those previously estimated. These estimates are useful for building new indexes, updating existing selection indexes, and for predicting correlated responses due to inclusion of novel traits in the breeding programs.

Genetic relationship among somatic cell score and some milking traits in Holstein-Friesian primiparous cows milked by an automated milking system

The automated milking system provides breeders with a large amount of automatically collected information about each cow in herd that cannot be easily obtained in non-robotised systems. This knowledge can be used in breeding programs improving somatic cell count (SCC) level. The objective of this study was to estimate heritabilities and genetic correlations among test-day (TD) somatic cell score (SCS) and selected milking traits, such as daily milk yield (MY), milking frequency (MF), milking time (MT) and milking speed (MS), attachment time (AT) to single teat cups, electrical conductivity (EC) and milk temperature (MTEMP). Data were collected for 1899 Polish Holstein-Friesian primiparous cows milked in an automatic milking system. Genetic parameters of the studied traits were estimated using Bayesian method via Gibbs sampling and two-trait random regression animal model with fixed effect of herd x TD, fixed regressions on days in milk (DIM) nested within age at calving by season of calving and RR for additive genetic and permanent environmental effects. Both fixed and RR were fitted with fourth-order Legendre polynomials on DIM. The estimated daily heritabilities were in the following ranges: MY - 0.162-0.338, MF - 0.156-0.444, MT - 0.090-0.320, MS - 0.252-0.665, AT - 0.105-0.394, EC - 0.269-0.466, MTEMP - 0.135-0.304 and SCS - 0.155-0.321. The heritabilities for traits expressed on a 305-d basis were moderate to high: 0.460 for MY, 0.514 for MF, 0.315 for MT, 0.431 for MS, 0.256 for AT, 0.386 for EC, 0.407 for MTEMP and 0.359 for SCS. Genetic correlations between traits on a 305-d basis showed that SCS was most strongly genetically correlated with MTEMP (0.572) and MS (0.480), whereas genetic relationships of SCS with MT (0.221) and EC (-0.216) were moderate. Phenotypic correlations between traits on a 305-d basis were moderate or low. Somatic cell score was negatively phenotypically correlated with MY, MF and MT, with the highest relationship with MT (-0.302). The largest positive phenotypic correlations were observed between SCS and MS (0.311) and with MTEMP (0.286). In summary, it is concluded that there is a chance to carry out effective selection for lower SCS and for some other traits, in particular MS and MTEMP. The obtained results are promising enough to conduct further research to evaluate how these traits can be used both to increase the accuracy of genetic evaluations of SCC and to improve udder health.

Effect of Animal Welfare on the Reproductive Performance of Extensive Pasture-Based Beef Cows in New Zealand

One key area where animal welfare may relate to productivity is through reproductive performance. This study assesses welfare on 25 extensively managed pastoral New Zealand beef farms, and explores the relationship between welfare and reproductive performance. Relationships between welfare measures and key reproductive performance indicators (pregnancy rate, weaning rate, mating period and bull: cow ratio) are investigated using an exploratory principal components analysis and linear regression model. Seven welfare measures (thinness, poor rumen fill, dirtiness, blindness, mortality, health checks of pregnant cows and yarding frequency/year) showed a potential influence on reproductive performance, and lameness was retained individually as a potential measure. Mean pregnancy rates, in both 2018 (PD18) and 2017 (PD17), were ~91% and mean weaning rate was 84%. Of the welfare measures, only lameness had a direct association with pregnancy rate, as well as a confounding effect on the association between mating period and pregnancy rate. The bull: cow ration (mean 1:31) and reproductive conditions (dystocia, abortion, vaginal prolapse) did not influence pregnancy and weaning rates. In the study population, there was no clear association between welfare and reproductive performance, except for the confounding effects of lameness.

The complex relationship between welfare and reproduction in cattle

Using reproduction parameters as indicators for cattle welfare has limitations and, at best, these parameters should only be viewed as indirect indicators of welfare. On a farm level, measures such as fertility rates emphasize biological performance of the herd but fail to consider the welfare of individual animals. Even on an individual level, the relationship between reproductive effectiveness and animal welfare is complex. Good reproductive performance does not automatically signify good welfare, as domestication and targeted breeding programmes have led to prioritization of high productive and reproductive performance in most modern farm animal species. In this review, we synthesize literature regarding cattle husbandry, reproduction, welfare and their multidimensional relationships. We argue that practices such as artificial insemination or the use of sexed semen may provide potential welfare advantages as these practices reduce the risk of disease transmission and injury or enable selection of specific beneficial traits. Furthermore, they may offer a solution to current practices jeopardizing welfare, such as the management of surplus bull calves in the dairy industry. Conversely, the animals' ability to perform natural behaviours such as oestrous expression, an aspect arguably contributing to welfare, is often limited on commercial farms; this limitation is particularly evident in housing systems such as tie stalls where movement is restricted. Moreover, common management practices such as oestrus manipulation may lead to negative attitudes in citizens who often regard naturalness as important element of animal welfare.

Symposium review: Challenges and opportunities for evaluating and using the genetic potential of dairy cattle in the new era of sensor data from automation

Sensor data from automation are becoming available on an increasingly large scale, and associated research is slowly starting to appear. This new era of sensor data from automation leads to many challenges but also new opportunities for assessing and maximizing the genetic potential of dairy cattle. The first challenge is data quality, because all uses of sensor data require careful data quality validation, potentially using external references. The second issue is data accessibility. Indeed, sensor data generated from automation are often designed to be available on-farm in a given system. However, to make these data useful-for genetic improvement for example-the data must also be made available off-farm. By nature, sensor data often are very complex and diverse; therefore, a data consolidation and integration layer is required. Moreover, the traits we want to select have to be defined precisely when generated from these raw data. This approach is obviously also beneficial to limit the challenge of extremely high data volumes generated by sensors. An additional challenge is that sensors will always be deployed in a context of herd management; therefore, any efforts to make them useful should focus on both breeding and management. However, this challenge also leads to opportunities to use genomic predictions based on these novel data for breeding and management. Access to relevant phenotypes is crucial for every genomic evaluation system. The automatic generation of training data, on both the phenotypic and genomic levels, is a major opportunity to access novel, precise, continuously updated, and relevant data. If the challenges of bidirectional data transfer between farms and external databases can be solved, new opportunities for continuous genomic evaluations integrating genotypes and the most current local phenotypes can be expected to appear. Novel concepts such as federated learning may help to limit exchange of raw data and, therefore, data ownership issues, which is another important element limiting access to sensor data. Accurate genome-guided decision-making and genome-guided management of dairy cattle should be the ultimate way to add value to sensor data from automation. This could also be the major driving force to improve the cost-benefit relationship for sensor-based technologies, which is currently one of the major obstacles for large-scale use of available technologies.

A 100-Year Review: Identification and genetic selection of economically important traits in dairy cattle

Over the past 100 yr, the range of traits considered for genetic selection in dairy cattle populations has progressed to meet the demands of both industry and society. At the turn of the 20th century, dairy farmers were interested in increasing milk production; however, a systematic strategy for selection was not available. Organized milk performance recording took shape, followed quickly by conformation scoring. Methodological advances in both genetic theory and statistics around the middle of the century, together with technological innovations in computing, paved the way for powerful multitrait analyses. As more sophisticated analytical techniques for traits were developed and incorporated into selection programs, production began to increase rapidly, and the wheels of genetic progress began to turn. By the end of the century, the focus of selection had moved away from being purely production oriented toward a more balanced breeding goal. This shift occurred partly due to increasing health and fertility issues and partly due to societal pressure and welfare concerns. Traits encompassing longevity, fertility, calving, health, and workability have now been integrated into selection indices. Current research focuses on fitness, health, welfare, milk quality, and environmental sustainability, underlying the concentrated emphasis on a more comprehensive breeding goal. In the future, on-farm sensors, data loggers, precision measurement techniques, and other technological aids will provide even more data for use in selection, and the difficulty will lie not in measuring phenotypes but rather in choosing which traits to select for.

A multi-trait meta-analysis with imputed sequence variants reveals twelve QTL for mammary gland morphology in Fleckvieh cattle

Background

The availability of whole-genome sequence data from key ancestors in bovine populations provides an exhaustive catalogue of polymorphic sites that segregate within and across cattle breeds. Sequence variants identified from the sequenced genome of key ancestors can be imputed into animals that have been genotyped using medium- and high-density genotyping arrays. Association analysis with imputed sequences, particularly when applied to multiple traits simultaneously, is a very powerful approach to detect candidate causal variants that underlie complex phenotypes.

Results

We used whole-genome sequence data from 157 key ancestors of the German Fleckvieh cattle population to impute 20,561,798 sequence variants into 10,363 animals that had (partly imputed) genotypes based on 634,109 single nucleotide polymorphisms (SNPs). Rare variants were more frequent among the sequence-derived than the array-derived genotypes. Association studies with imputed sequence variants were performed using seven correlated udder conformation traits as response variables. The calculation of an approximate multi-trait test statistic enabled us to detect 12 quantitative trait loci (QTL) (P < 2.97 × 10⁻⁹) that affect different morphological features of the mammary gland. Among the tested variants, the most significant associations were found for imputed sequence variants at 11 QTL, whereas the top association signal was observed for an array-derived variant at a QTL on bovine chromosome 14. Seven QTL were associated with multiple phenotypes. Most QTL were located in non-coding regions of the genome but in close proximity of candidate genes that could be involved in mammary gland morphology (SP5, GC, NPFFR2, CRIM1, RXFP2, TBX5, RBM19 and ADAM12).

Conclusions

Using imputed sequence variants in association analyses allows the detection of QTL at maximum resolution. Multi-trait approaches can reveal QTL that are not detected in single-trait association studies. Most QTL for udder conformation traits were located in non-coding regions of the genome, which suggests that mutations in regulatory sequences are the major determinants of variation in mammary gland morphology in cattle.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-016-0190-4) contains supplementary material, which is available to authorized users.

Alternative strategies for genetic analyses of milk flow in dairy cattle

Measurements for average milk flow (AMF) in kilograms of milk per minute of milking time from 629,161 Holstein cows from calving years 1990 to 2008 were used to estimate genetic covariance components using a variety of statistical models. For bivariate linear-threshold model applications, Gaussian-distributed AMF (linear sire model) was categorized into 2 distinct classes (threshold sire model) by setting arbitrary thresholds for extremely slow or extremely fast milking cows. In different bivariate runs with the 2 traits, Gaussian AMF and binary AMF, within a Bayesian framework, thresholds for the binary trait were 1.2, 1.6, 2.6, and 2.8 kg/min. Posterior heritabilities for AMF from the linear and the threshold models in all runs were in a narrow range and close to 0.26, and the posterior genetic correlation between AMF, defined as either a Gaussian or binary trait, was 0.99. A data subset was used to infer genetic and phenotypic relationships between AMF with test-day traits milk yield, fat percentage, protein percentage, somatic cell score (SCS), fat-to-protein ratio, and energy-corrected milk using recursive linear sire models, standard multiple trait linear sire models, and multiple trait linear sire models accounting for the effect of a trait 1 on a trait 2, and of trait 2 on trait 3, via linear regressions. The time-lagged 3-trait system focused on the first test-day trait after calving (trait 1), on AMF (trait 2), and on the test-day trait (trait 3) after the AMF measurement. Posterior means for heritabilities for AMF from linear and recursive linear models used for the reduced data set ranged between 0.29 and 0.38, and were slightly higher than heritabilities from the threshold models applied to the full data set. Genetic correlations from the recursive linear model and the linear model were similar for identical trait combinations including AMF and test-day traits 1 and 3. The largest difference was found for the genetic correlation between AMF and fat percentage from the first test day (i.e., -0.31 from the recursive linear model vs. -0.26 from the linear model). Genetic correlations from the linear model, including an additional regression coefficient, partly differed, especially when comparing correlations between AMF and SCS and between AMF and fat-to-protein ratio recorded after the AMF measurement data. Structural equation coefficients from the recursive linear model and corresponding regression coefficients from the linear model with additional regression, both depicting associations on the phenotypic scale, were quite similar. From a physiological perspective, all models confirmed the antagonistic relationship between SCS with AMF on genetic and phenotypic scales. A pronounced recursive relationship was also noted between productivity (milk yield and energy-corrected milk) and AMF, suggesting further research using physiological parameters as indicators for cow stress response (e.g., level of hormones) should be conducted.

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.

Lateralization of behavior in dairy cows in response to conspecifics and novel persons

The right brain hemisphere, connected to the left eye, coordinates fight and flight behaviors in a wide variety of vertebrate species. We investigated whether left eye vision predominates in dairy cows' interactions with other cows and humans, and whether dominance status affects the extent of visual lateralization. Although we found no overall lateralization of eye use to view other cows during interactions, cows that were submissive in an interaction were more likely to use their left eye to view a dominant animal. Both subordinate and older cows were more likely to use their left eye to view other cattle during interactions. Cows that predominantly used their left eye during aggressive interactions were more likely to use their left eye to view a person in unfamiliar clothing in the middle of a track by passing them on the right side. However, a person in familiar clothing was viewed predominantly with the right eye when they passed mainly on the left side. Cows predominantly using their left eyes in cow-to-cow interactions showed more overt responses to restraint in a crush compared with cows who predominantly used their right eyes during interactions (crush scores: left eye users 7.9, right eye users 6.4, standard error of the difference=0.72). Thus, interactions between 2 cows and between cows and people were visually lateralized, with losing and subordinate cows being more likely to use their left eyes to view winning and dominant cattle and unfamiliar humans.

Genetic evaluation of aspects of temperament in Nellore-Angus calves

The objective of this work was to estimate heritability of each of 5 subjectively measured aspects of temperament of cattle and the genetic correlations of pairs of those traits. From 2003 to 2013, Nellore-Angus F2 and F3 calves (n = 1,816) were evaluated for aspects of temperament at an average 259 d of age, which was approximately 2 mo after weaning. Calves were separated from a group and subjectively scored from 1 (calm, good temperament) to 9 (wild, poor temperament) for aggressiveness (willingness to hit an evaluator), nervousness, flightiness, gregariousness (willingness to separate from the group), and a distinct overall score by 4 evaluators. Data were analyzed using threshold and linear models with additive genetic random effects. Two-trait animal models (nonthreshold) included the additive genetic covariance for pairs of traits and were used to estimate additive genetic correlations. Contemporary groups (n = 104) represented calves penned together for evaluation on given evaluation days. Heifers had greater (worse) means for all traits than steers (P < 0.05). The regression of score on age in days was included in final models for flightiness (P = 0.05; -0.006 ± 0.003) and gregariousness (P = 0.025; -0.007 ± 0.003). Estimates of heritability were large (0.51, 0.4, 0.45, 0.49, and 0.47 for aggressiveness, nervousness, flightiness, gregariousness, and overall temperament, respectively; SE = 0.07 for each). The ability to use this methodology to distinctly separate different aspects of calf temperament appeared to be limited, as estimates of additive genetic correlations were near unity for all pairs of traits; estimates of phenotypic correlation ranged from 0.88 ± 0.01 to 0.99 ± 0.002 for pairs of traits. Distinct subsequent analyses indicated a significant negative relationship of 4 of the various temperament scores with weight at weaning (regression coefficients ranged from -0.008 ± 0.002 for nervousness, flightiness, and gregariousness to -0.003 ± 0.002 for aggressiveness). In subsequent analyses, the regression of temperament trait on sequence of evaluation within a pen was highly significant and solutions ranged from 0.05 ± 0.007 for aggressiveness to 0.08 ± 0.007 for all other traits. The apparent large additive genetic variance for any one of these traits may be useful in identification of genes responsible for differences in cattle temperament.

Accuracy of direct genomic values for functional traits in Brown Swiss cattle

In this study, direct genomic values for the functional traits general temperament, milking temperament, aggressiveness, rank order in herd, milking speed, udder depth, position of labia, and days to first heat in Brown Swiss dairy cattle were estimated based on ~777,000 (777 K) single nucleotide polymorphism (SNP) information from 1,126 animals. Accuracy of direct genomic values was assessed by a 5-fold cross-validation with 10 replicates. Correlations between deregressed proofs and direct genomic values were 0.63 for general temperament, 0.73 for milking temperament, 0.69 for aggressiveness, 0.65 for rank order in herd, 0.69 for milking speed, 0.71 for udder depth, 0.66 for position of labia, and 0.74 for days to first heat. Using the information of ~54,000 (54K) SNP led to only marginal deviations in the observed accuracy. Trying to predict the 20% youngest bulls led to correlations of 0.55, 0.77, 0.73, 0.55, 0.64, 0.59, 0.67, and 0.77, respectively, for the traits listed above. Using a novel method to estimate the accuracy of a direct genomic value (defined as correlation between direct genomic value and true breeding value and accounting for the correlation between direct genomic values and conventional breeding values) revealed accuracies of 0.37, 0.20, 0.19, 0.27, 0.48, 0.45, 0.36, and 0.12, respectively, for the traits listed above. These values are much smaller but probably also more realistic than accuracies based on correlations, given the heritabilities and samples sizes in this study. Annotation of the largest estimated SNP effects revealed 2 candidate genes affecting the traits general temperament and days to first heat.