Invited review: Phenotyping strategies and quantitative-genetic background of resistance, tolerance and resilience associated traits in dairy cattle

Key determinants of adaptive strategies of goats to a 2-day nutritional challenge during early lactation

Little is known about the key determinants of the physiological adaptations to environmental challenges and how these determinants interact. We evaluated how the response/recovery profiles to a short-term nutritional challenge during early lactation are affected by early-life nutritional strategies in dairy goats divergently selected for functional longevity. We used 72 females, split into two cohorts, daughters of Alpine bucks divergently selected for functional longevity. The females from the two lines were fed with two divergent diets, normal vs low-energy, from weaning until the middle of first gestation, and then fed with the same standard diet. Individual BW, body condition score, morphology, and plasma samples were collected from birth to first kidding. The adaptative physiological strategy to a nutritional challenge was assessed via a 2-day feed restriction challenge, during early lactation, which consisted of a five-day control period on a standard lactation diet followed by a 2-day challenge with straw-only feeding and then a 10-day recovery period on a standard lactation diet. During the challenge, DM intake, BW, milk yield (MY), and plasma and milk metabolite composition were recorded daily. Linear mixed-effects models were used to analyze all traits, considering the individual nested in the cohort as a random effect and the 2 × 2 treatments (i.e., line and rearing diet) and litter size as fixed effects. Linear mixed-effects models using a piecewise arrangement were used to analyze the response/recovery profiles to nutritional challenge. Random parameters estimated for each individual, using the mixed-effects models without the fixed effects of rearing diet and genetic line, were used in a stepwise model selection based on R2 to identify key determinants of an individual's physiological adaptations to environmental challenges. Differences in stature and body reserves created by the two rearing diets diminished during late gestation and the 5-day control period. Genetic line did not affect body reserves during the rearing phase. Rearing diet and genetic line slightly affected the recovery profiles of evaluated traits and had no effects on prechallenge and response to challenge profiles. The prekidding energy status measures and MY before challenge were selected as strong predictors of variability in response-recovery profiles of milk metabolites that have strong links with body energy dynamics (i.e., isoCitrate, ß-hydroxybutyrate, choline, cholesterol, and triacylglycerols; R2 = 35%). Our results suggested that prekidding energy status and MY are key determinants of adult resilience and that rearing diet and genetic line may affect adult resilience insofar as they affect the animals' energy status.

The genetic architecture of complete blood counts in lactating Holstein dairy cows

Complete blood counts (CBCs) measure the abundance of individual immune cells, red blood cells, and related measures such as platelets in circulating blood. These measures can indicate the health status of an animal; thus, baseline circulating levels in a healthy animal may be related to the productive life, resilience, and production efficiency of cattle. The objective of this study is to determine the heritability of CBC traits and identify genomic regions that are associated with CBC measurements in lactating Holstein dairy cattle. The heritability of CBCs was estimated using a Bayes C0 model. The study population consisted of 388 cows with genotypes at roughly 75,000 markers and 16 different CBC phenotypes taken at one to three time points (n = 33, 131, and 224 for 1, 2, and 3 time points, respectively). Heritabilities ranged from 0.00 ± 0.00 (red cell distribution width) to 0.68 ± 0.06 (lymphocytes). A total of 96 different 1-Mb windows were identified that explained more than 1% of the genetic variance for at least one CBC trait, with 10 windows explaining more than 1% of the genetic variance for two or more traits. Multiple genes in the identified regions have functions related to immune response, cell differentiation, anemia, and disease. Positional candidate genes include RAD52 motif-containing protein 1 (RDM1), which is correlated with the degree of immune infiltration of immune cells, and C-X-C motif chemokine ligand 12 (CXCL12), which is critically involved in neutrophil bone marrow storage and release regulation and enhances neutrophil migration. Since animal health directly impacts feed intake, understanding the genetics of CBCs may be useful in identifying more disease-resilient and feed-efficient dairy cattle. Identification of genes responsible for variation in CBCs will also help identify the variability in how dairy cattle defend against illness and injury.

The Resilient Dairy Genome Project-A general overview of methods and objectives related to feed efficiency and methane emissions

The Resilient Dairy Genome Project (RDGP) is an international large-scale applied research project that aims to generate genomic tools to breed more resilient dairy cows. In this context, improving feed efficiency and reducing greenhouse gases from dairy is a high priority. The inclusion of traits related to feed efficiency (e.g., dry matter intake [DMI]) or greenhouse gases (e.g., methane emissions [CH4]) relies on available genotypes as well as high quality phenotypes. Currently, 7 countries (i.e., Australia, Canada, Denmark, Germany, Spain, Switzerland, and United States) contribute with genotypes and phenotypes including DMI and CH4. However, combining data are challenging due to differences in recording protocols, measurement technology, genotyping, and animal management across sources. In this study, we provide an overview of how the RDGP partners address these issues to advance international collaboration to generate genomic tools for resilient dairy. Specifically, we describe the current state of the RDGP database, data collection protocols in each country, and the strategies used for managing the shared data. As of February 2022, the database contains 1,289,593 DMI records from 12,687 cows and 17,403 CH4 records from 3,093 cows and continues to grow as countries upload new data over the coming years. No strong genomic differentiation between the populations was identified in this study, which may be beneficial for eventual across-country genomic predictions. Moreover, our results reinforce the need to account for the heterogeneity in the DMI and CH4 phenotypes in genomic analysis.

Heterogeneity of variance and genetic parameters for milk production in cattle, using Bayesian inference

The goal of this study was to verify the effect of heterogeneity of variance (HV) on milk production in up to 305 days of lactation (L305) of daughters of Girolando, Gir and Holstein sires, as well as in the genetic evaluation of these sires and their progenies. in Brazil. The model included contemporary groups (consisting of herd, year and calving season) as a fixed effect, cow age at calving (linear and quadratic effects) and heterozygosity (linear effect) as covariates, in addition to the random effects of direct additive genetic and environmental, permanent and residual. The first analysis consisted of the single-trait animal model, with L305 records (disregarding HV). The second considered classes of standard deviations (SD): two-trait model including low and high classes (considering HV), according to the standardized means of L305 for herd-year of calving. The low SD class was composed of herds with SD equal to or less than zero and the high class with positive SD values. Estimates of (co)variance components and breeding values were obtained separately for each scenario using Bayesian inference via Gibbs sampling. Different heritability was estimated. Higher for the high DP class in the Gir (0.20) and Holstein (0.15) breeds, not occurring the same in the Girolando breed, with a lower value among the classes for the high DP (0.10). High values of genetic correlations were also found between low and high SD classes (0.88; 0.85 and 0.79) for the Girolando, Gir and Holstein breeds, respectively. Like the order correlations (Spearman) which were also high for the three breeds analyzed (equal to or above 0.92). Thus, the presence of HV had a smaller impact for L305 and did not affect the genetic evaluation of sires.

Invited review: From heat stress to disease-Immune response and candidate genes involved in cattle thermotolerance

Heat stress implies unfavorable effects on primary and functional traits in dairy cattle and, in consequence, on the profitability of the whole production system. The increasing number of days with extreme hot temperatures suggests that it is imperative to detect the heat stress status of animals based on adequate measures. However, confirming the heat stress status of an individual is still challenging, and, in consequence, the identification of novel heat stress biomarkers, including molecular biomarkers, remains a very relevant issue. Currently, it is known that heat stress seems to have unfavorable effects on immune system mechanisms, but this information is of limited use in the context of heat stress phenotyping. In addition, there is a lack of knowledge addressing the molecular mechanisms linking the relevant genes to the observed phenotype. In this review, we explored the potential molecular mechanisms explaining how heat stress affects the immune system and, therefore, increases the occurrence of immune-related diseases in cattle. In this regard, 2 relatively opposite hypotheses are under focus: the immunosuppressive action of cortisol, and the proinflammatory effect of heat stress. In both hypotheses, the modulation of the immune response during heat stress is highlighted. Moreover, it is possible to link candidate genes to these potential mechanisms. In this context, immune markers are very valuable indicators for the detection of heat stress in dairy cattle, broadening the portfolio of potential biomarkers for heat stress.

Genome-wide expression analysis reveals different heat shock responses in indigenous (Bos indicus) and crossbred (Bos indicus X Bos taurus) cattle

Environmental heat stress in dairy cattle leads to poor health, reduced milk production and decreased reproductive efficiency. Multiple genes interact and coordinate the response to overcome the impact of heat stress. The present study identified heat shock regulated genes in the peripheral blood mononuclear cells (PBMC). Genome-wide expression patterns for cellular stress response were compared between two genetically distinct groups of cattle viz., Hariana (B. indicus) and Vrindavani (B. indicus X B. taurus). In addition to major heat shock response genes, oxidative stress and immune response genes were also found to be affected by heat stress. Heat shock proteins such as HSPH1, HSPB8, FKB4, DNAJ4 and SERPINH1 were up-regulated at higher fold change in Vrindavani compared to Hariana cattle. The oxidative stress response genes (HMOX1, BNIP3, RHOB and VEGFA) and immune response genes (FSOB, GADD45B and JUN) were up-regulated in Vrindavani whereas the same were down-regulated in Hariana cattle. The enrichment analysis of dysregulated genes revealed the biological functions and signaling pathways that were affected by heat stress. Overall, these results show distinct cellular responses to heat stress in two different genetic groups of cattle. This also highlight the long-term adaptation of B. indicus (Hariana) to tropical climate as compared to the crossbred (Vrindavani) with mixed genetic makeup (B. indicus X B. taurus).

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

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

Exploring milk loss and variability during environmental perturbations across lactation stages as resilience indicators in Holstein cattle

Genetic selection for resilience is essential to improve the long-term sustainability of the dairy cattle industry, especially the ability of cows to maintain their level of production when exposed to environmental disturbances. Recording of daily milk yield provides an opportunity to develop resilience indicators based on milk losses and fluctuations in daily milk yield caused by environmental disturbances. In this context, our study aimed to explore milk loss traits and measures of variability in daily milk yield, including log-transformed standard deviation of milk deviations (Lnsd), lag-1 autocorrelation (Ra), and skewness of the deviations (Ske), as indicators of general resilience in dairy cows. The unperturbed dynamics of milk yield as well as milk loss were predicted using an iterative procedure of lactation curve modeling. Milk fluctuations were defined as a period of at least 10 successive days of negative deviations in which milk yield dropped at least once below 90% of the expected values. Genetic parameters of these indicators and their genetic correlation with economically important traits were estimated using single-trait and bivariate animal models and 8,935 lactations (after quality control) from 6,816 Chinese Holstein cows. In general, cows experienced an average of 3.73 environmental disturbances with a milk loss of 267 kg of milk per lactation. Each fluctuation lasted for 19.80 ± 11.46 days. Milk loss traits are heritable with heritability estimates ranging from 0.004 to 0.061. The heritabilities differed between Lnsd (0.135-0.250), Ra (0.008-0.058), and Ske (0.001-0.075), with the highest heritability estimate of 0.250 ± 0.020 for Lnsd when removing the first and last 10 days in milk in a lactation (Lnsd2). Based on moderate to high genetic correlations, lower Lnsd2 is associated with less milk losses, better reproductive performance, and lower disease incidence. These findings indicate that among the variables evaluated, Lnsd2 is the most promising indicator for breeding for improved resilience in Holstein cattle.

Animal board invited review: Genomic-based improvement of cattle in response to climate change

Climate change brings challenges to cattle production, such as the need to adapt to new climates and pressure to reduce greenhouse emissions (GHG). In general, the improvement of traits in current breeding goals is favourably correlated with the reduction of GHG. Current breeding goals and tools for increasing cattle production efficiency have reduced GHG. The same amount of production can be achieved by a much smaller number of animals. Genomic selection (GS) may offer a cost-effective way of using an efficient breeding approach, even in low- and middle-income countries. As climate change increases the intensity of heatwaves, adaptation to heat stress leads to lower efficiency of production and, thus, is unfavourable to the goal of reducing GHG. Furthermore, there is evidence that heat stress during cow pregnancy can have many generation-long lowering effects on milk production. Both adaptation and reduction of GHG are among the difficult-to-measure traits for which GS is more efficient and suitable than the traditional non-genomic breeding evaluation approach. Nevertheless, the commonly used within-breed selection may be insufficient to meet the new challenges; thus, cross-breeding based on selecting highly efficient and highly adaptive breeds may be needed. Genomic introgression offers an efficient approach for cross-breeding that is expected to provide high genetic progress with a low rate of inbreeding. However, well-adapted breeds may have a small number of animals, which is a source of concern from a genetic biodiversity point of view. Furthermore, low animal numbers also limit the efficiency of genomic introgression. Sustainable cattle production in countries that have already intensified production is likely to emphasise better health, reproduction, feed efficiency, heat stress and other adaptation traits instead of higher production. This may require the application of innovative technologies for phenotyping and further use of new big data techniques to extract information for breeding.

Positive Welfare Indicators in Dairy Animals

Nowadays, there is growing interest in positive animal welfare not only from the view of scientists but also from that of society. The consumer demands more sustainable livestock production, and animal welfare is an essential part of sustainability, so there is interest in incorporating positive welfare indicators into welfare assessment schemes and legislation. The aim of this review is to cite all the positive welfare indicators that have been proposed for dairy animals in theory or practice. In total, twenty-four indicators were retrieved. The most promising are exploration, access to pasture, comfort and resting, feeding, and behavioral synchronicity. Qualitative behavioral assessment (QBA), social affiliative behaviors, play, maternal care, ear postures, vocalizations, visible eye white, nasal temperature, anticipation, cognitive bias, laterality, and oxytocin have been also studied in dairy ruminants. QBA is the indicator that is most often used for the on-farm welfare assessment. Among all dairy animals, studies have been performed mostly on cattle, followed by sheep and goats, and finally buffaloes. The research on camel welfare is limited. Therefore, there is a need for further research and official assessment protocols for buffaloes and especially camels.

Immune mechanisms, resistance genes, and their roles in the prevention of mastitis in dairy cows

Mastitis is one of the most important diseases of the mammary gland. The increased incidence of this disease in cows is due to the breeding of dairy cattle for higher yields, which is accompanied by an increased susceptibility to mastitis. Therefore, the difficulty involved with preventing this disease has increased. An integral part of current research is the elimination of mastitis in order to reduce the consumption of antibiotic drugs, thereby reducing the resistance of microorganisms and decreasing companies' economic losses due to mastitis (i.e. decreased milk yield, increased drug costs, and reduced milk supply). Susceptibility to mastitis is based on dairy cows' immunity, health, nutrition, and welfare. Thus, it is important to understand the immune processes in the body in order to increase the resistance of animals. Recently, various studies have focused on the selection of mastitis resistance genes. An important point is also the prevention of mastitis. This publication aims to describe the physiology of the mammary gland along with its immune mechanisms and to approximate their connection with potential mastitis resistance genes. This work describes various options for mastitis elimination and focuses on genetic selection and a closer specification of resistance genes to mastitis. Among the most promising resistance genes for mastitis, we consider CD14, CXCR1, lactoferrin, and lactoglobulin.

Emphasis on resilience in dairy cattle breeding: Possibilities and consequences

This study aimed to investigate dairy cattle breeding goals with more emphasis on resilience. We simulated the consequences of increasing weight on resilience indicators and an assumed true resilience trait (TR). Two environments with different breeding goals were simulated to represent the variability of production systems across Europe. Ten different scenarios were stochastically simulated in a so-called pseudogenomic simulation approach. We showed that many modern dairy cattle breeding goals most likely have negative genetic gain for TR and promising resilience indicators such as the log-transformed, daily deviation from the lactation curve (LnVAR). In addition, there were many ways of improving TR by increasing the breeding goal weight of different resilience indicators. The results showed that adding breeding goal weight to resilience indicators, such as body condition score and LnVAR, could reverse the negative trend observed for resilience indicators. Loss in the aggregate genotype calculated with only current breeding goal traits was 12 to 76%. This loss was mainly due to a reduction in genetic gain in milk production. We observed higher genetic gain in beef production, fertility, and udder health when breeding for more resilience, but from an economical point of view, this was not high enough to compensate for the reduction in genetic gain in milk production. The highest genetic gain in TR was obtained when adding the highest breeding goal weight to LnVAR or TR, both with 0.29 genetic standard deviation units. The indicators we used, body condition score and LnVAR, can be measured on a large scale today with relatively cheap methods, which is crucial if we want to improve these traits through breeding. Economic values for resilience have to be estimated to find the most optimal breeding goal for a more resilient dairy cow in the future.

Genome-wide associations for heat stress response suggest potential candidate genes underlying milk fatty acid composition in dairy cattle

Contents of milk fatty acids (FA) display remarkable alterations along climatic gradients. Detecting candidate genes underlying such alterations might be beneficial for the exploration of climate sensitivity in dairy cattle. Consequently, we aimed on the definition of FA heat stress indicators, considering FA breeding values in response to temperature-humidity index (THI) alterations. Indicators were used in GWAS, in ongoing gene annotations and for the estimation of chromosome-wide variance components. The phenotypic data set consisted of 39,600 test-day milk FA records from 5,757 first-lactation Holstein dairy cows kept in 16 large-scale German cooperator herds. The FA traits were C18:0, polyunsaturated fatty acids (PUFA), saturated fatty acids (SFA), and unsaturated fatty acids (UFA). After genotype quality control, 40,523 SNP markers from 3,266 cows and 930 sires were considered. Meteorological data from the weather station in closest herd distance were used for the calculation of maximum hourly daily THI, which were allocated to 10 different THI classes. The same FA from 3 stages of lactation were considered as different, but genetically correlated traits. Consequently, a 3-trait reaction norm model was used to estimate genetic parameters and breeding values for FA along THI classes, considering either pedigree (A) or genomic (G) relationship matrices. De-regressed proofs and genomic estimated breeding values at the intermediate THI class 5 and at the extreme THI class 10 were used as pseudophenotypes in ongoing genomic analyses for thermoneutral (TNC) and heat stress conditions (HSC), respectively. The differences in de-regressed proofs and in genomic estimated breeding values from both THI classes were pseudophenotypes for heat stress response (HSR). Genetic correlations between the same FA under TNC and HSC were smallest in the first lactation stage and ranged from 0.20 for PUFA to 0.87 for SFA when modeling with the A matrix, and from 0.35 for UFA to 0.86 for SFA when modeling with the G matrix. In the first lactation stage, larger additive genetic variances under HSC compared with TNC indicate climate sensitivity for C18:0, PUFA, and UFA. Climate sensitivity was also reflected by pronounced chromosome-wide genetic variances for HSR of PUFA and UFA in the first stage of lactation. For all FA under TNC, HSC, and HSR, quite large genetic variance proportions were explained by BTA14. In GWAS, 30 SNP (within or close to 38 potential candidate genes) overlapped for HSR of the different FA. One unique potential candidate gene (AMFR) was detected for HSR of PUFA, 15 for HSR of SFA (ADGRB1, DENND3, DUSP16, EFR3A, EMP1, ENSBTAG00000003838, EPS8, MGP, PIK3C2G, STYK1, TMEM71, GSG1, SMARCE1, CCDC57, and FASN) and 3 for HSR of UFA (ENSBTAG00000048091, PAEP, and EPPK1). The identified unique genes play key roles in milk FA synthesis and are associated with disease resistance in dairy cattle. The results suggest consideration of FA in combination with climatic responses when inferring genetic mechanisms of heat stress in dairy cows.

The Transition Period Updated: A Review of the New Insights into the Adaptation of Dairy Cows to the New Lactation

Recent research on the transition period (TP) of dairy cows has highlighted the pivotal role of immune function in affecting the severity of metabolic challenges the animals face when approaching calving. This suggests that the immune system may play a role in the etiology of metabolic diseases occurring in early lactation. Several studies have indicated that the roots of immune dysfunctions could sink way before the “classical” TP (e.g., 3 weeks before and 3 weeks after calving), extending the time frame deemed as “risky” for the development of early lactation disorders at the period around the dry-off. Several distressing events occurring during the TP (i.e., dietary changes, heat stress) can boost the severity of pre-existing immune dysfunctions and metabolic changes that physiologically affect this phase of the lactation cycle, further increasing the likelihood of developing diseases. Based on this background, several operational and nutritional strategies could be adopted to minimize the detrimental effects of immune dysfunctions on the adaptation of dairy cows to the new lactation. A suitable environment (i.e., optimal welfare) and a balanced diet (which guarantees optimal nutrient partitioning to improve immune functions in cow and calf) are key aspects to consider when aiming to minimize TP challenges at the herd level. Furthermore, several prognostic behavioral and physiological indicators could help in identifying subjects that are more likely to undergo a “bad transition”, allowing prompt intervention through specific modulatory treatments. Recent genomic advances in understanding the linkage between metabolic disorders and the genotype of dairy cows suggest that genetic breeding programs aimed at improving dairy cows’ adaptation to the new lactation challenges (i.e., through increasing immune system efficiency or resilience against metabolic disorders) could be expected in the future. Despite these encouraging steps forward in understanding the physiological mechanisms driving metabolic responses of dairy cows during their transition to calving, it is evident that these processes still require further investigation, and that the TP—likely extended from dry-off—continues to be “the final frontier” for research in dairy sciences.

A data-driven prediction of lifetime resilience of dairy cows using commercial sensor data collected during first lactation

Reliable prediction of lifetime resilience early in life can contribute to improved management decisions of dairy farmers. Several studies have shown that time series sensor data can be used to predict lifetime resilience rankings. However, such predictions generally require the translation of sensor data into biologically meaningful sensor features, which involve proper feature definitions and a lot of preprocessing. The objective of this study was to investigate the hypothesis that data-driven random forest algorithms can equal or improve the prediction of lifetime resilience scores compared with ordinal logistic regression, and that these algorithms require considerably less effort for data preprocessing. We studied this by developing prediction models that forecast lifetime resilience of a cow early in her productive life using sensor data from the first lactation. We used an existing data set from a Dutch experimental herd, with data of culled cows for which birth dates, insemination dates, calving dates, culling dates, and health treatments were available to calculate lifetime resilience scores. Moreover, 4 types of first-lactation sensor data, converted to daily aggregated values, were available: milk yield, body weight, activity, and rumination. For each sensor, 14 sensor features were calculated, of which part were based on absolute daily values and part on relative to herd average values. First, we predicted lifetime resilience rank with stepwise logistic regression using sensor features as predictors and a P-value of <0.2 as the cut-off. Next, we applied a random forest with the 6 features that remained in the final logistic regression model. We then applied a random forest with all sensor features, and finally applied a random forest with daily aggregated values as features. All models were validated with stratified 10-fold cross-validation with 90% of the records in the training set and 10% in the validation set. Model performances expressed in percentage of correctly classified cows (accuracy) and percentage of cows being critically misclassified (i.e., high as low and vice versa) ± standard deviation were 45.1 ± 8.1% and 10.8% with the ordinal logistic regression model, 45.7 ± 8.4% and 16.0% with the random forest using the same 6 features as the logistic regression model, 48.4 ± 6.7% and 10.0% for the random forest with all sensor features, and 50.5 ± 6.3% and 8.4% for the random forest with daily sensor values. This random forest also revealed that data collected in early and late stages of first lactation seem to be of particular importance in the prediction compared with that in mid lactation. Accuracies of the models were not significantly different, but the percentage of critically misclassified cows was significantly higher for the second model than for the other models. We concluded that a data-driven random forest algorithm with daily aggregated sensor data as input can be used for the prediction of lifetime resilience classification with an overall accuracy of ~50%, and provides at least as good prediction as models with sensor features as input.

The future of smallholder farming in developing countries in the face of climate change: a perspective with a focus on Pakistan

The fragile balance in the world’s carbon equilibrium through the discovery of cheap carbon-based fuels in the nineteenth century has led to mass industrialisation and an explosion in the world human population, including that of Pakistan. Farmers worldwide will need to adapt their production systems to accommodate global warming and increased climate extremes resulting from these man-made environmental changes. The focus will need to be on smallholder farmers who generate 53% of the world’s food but who are least equipped to accommodate climate change. The most major limitation will be fresh water supply, no more exemplified than in Pakistan as Himalayan snowfall decreases and peak snow melt comes earlier in spring, limiting irrigation water for summer C4 crops such as corn, millet, sorghum and sugarcane. These are destined to replace the traditional C3 crops of wheat and rice, which will not be as suited to climate change conditions resulting from a projected mean 2°C rise in ambient temperature. Smallholder farmers will need to access superior-quality seed for crop cultivars for both human food and animal forage bred to withstand climatic change. Quantitative genetic selection programs for tropically adapted livestock must be implemented with a major focus on Pakistan’s Nili Ravi and Kundhi buffalo, together with Sahiwal cattle servicing the milk consumption needs of Pakistan’s burgeoning population of 211 million. The quality of forage available for livestock emanating largely from crop residues needs to be improved to meet the country’s greenhouse-gas production targets in line with international expectation. The challenge remains for governments to sustain marketing chains that allow them to be profitable when operating in an increasingly hostile environment. The conservation of soil fertility through increased carbon sequestration will be an important imperative. It is likely that females will play a more important role in directing adaptation in these communities. Successful adjustment will be dependent on effective extension programs working with all sectors of the community including males, females and children from all walks of life in both rural and urban environments. Failure to do so will lead to rapid increases in climate refugee numbers, which the world can ill-afford.

The Whole and the Parts—A New Perspective on Production Diseases and Economic Sustainability in Dairy Farming

The levels of production diseases (PD) and the cow replacement rate are high in dairy farming. They indicate excessive production demands on the cow and a poor state of animal welfare. This is the subject of increasing public debate. The purpose of this study was to assess the effect of production diseases on the economic sustainability of dairy farms. The contributions of individual culled cows to the farm’s economic performance were calculated, based on milk recording and accounting data from 32 farms in Germany. Cows were identified as ‘profit cows’ when they reached their individual ‘break-even point’. Data from milk recordings (yield and indicators for PD) were used to cluster farms by means of a principal component and a cluster analysis. The analysis revealed five clusters of farms. The average proportion of profit cows was 57.5%, 55.6%, 44.1%, 29.4% and 19.5%. Clusters characterized by a high proportion of cows with metabolic problems and high culling and mortality rates had lower proportions of profit cows, somewhat irrespective of the average milk-yield per cow. Changing the perception of PD from considering it as collateral damage to a threat to the farms’ economic viability might foster change processes to reduce production diseases.

Genetic analysis of lifetime productivity traits in goats

As part of the development of a breeding programme for dairy goats to support sustainable production in organic farming, the overall aim of the present study was to identify traits that can be used as selection criteria for lifetime productivity. The breeding goal is high lifetime productivity with a good milk composition and a good level of robustness in the animals, especially within grazing systems. The lifetime productivity traits analysed in this study were the length of the animal's productive life (LPL), the lifetime efficiency (LEF), and the animal's milk yield efficiency with respect the total number of lactating days (EDM); the average fat and protein content over the animal's lifetime, the fat-to-protein ratio (FPR), and the urea content (UC) were also included as indirect health traits and potential indicators of robustness in dairy goats. The traits' influencing factors, phenotypic and genetic correlations, and heritability were examined. Furthermore, factors influencing milk yield in the first 120 d of lactation during the animal's first lactation were analysed. The aim of investigating milk yield during the first lactation was to consider a connection between early performance recoding in the life of an animal and LPL, LEF, and EDM. In total, lactation and pedigree data from 9192 dairy goats of the common German Fawn (GF) and German White (GW) dairy breeds were used. Prerequisites were that the investigated birth cohorts had to have definitively completed their lifetime production, and a high proportion of goats had to have completed extended lactation. The data analysis showed that breed did not influence milk yield. The age at first kidding, the average number of kids born during the animal's lifetime, and the lactation length did influence the milk yield. This applies to the milk yield during the first 120 d of the first lactation as well as over the lifetime of an animal. Considering the influencing factors, the results showed that LPL was genetically and positively correlated with LEF and EDM ( r g = 0.65 ± 0.06 and 0.29 ± 0.07 respectively). The heritability of LPL, LEF, and EDM was 0.22 ± 0.02 , 0.29 ± 0.03 , and 0.44 ± 0.03 respectively. Regarding the lifetime milk composition, the heritability of protein and fat content, FPR, and UC was 0.63 ± 0.02 , 0.52 ± 0.02 , 0.32 ± 0.03 , and 0.47 ± 0.04 respectively. The heritability regarding the milk yield during the first 120 d of the first lactation was 0.34 ± 0.03 . We found that the milk yield during the first 120 d of the first lactation showed a genetic correlation with LPL, LEF, and EDM of 0.30 ± 0.08 , 0.82 ± 0.04 , and 0.89 ± 0.03 respectively. In summary, LPL, LEF, and EDM are suitable traits to indicate lifetime productivity in dairy goats. An additional indicator for lifetime productivity could be the milk yield during the first 120 d of the first lactation. Moreover, FPR and UC appear to be promising indicator traits for the health and robustness of dairy goats. The present study showed the importance of considering extended lactation in selective breeding programmes as well as the importance of modelling extended lactation in the breeding value estimation.

Digital Phenotyping in Livestock Farming

Currently, large volumes of data are being collected on farms using multimodal sensor technologies. These sensors measure the activity, housing conditions, feed intake, and health of farm animals. With traditional methods, the data from farm animals and their environment can be collected intermittently. However, with the advancement of wearable and non-invasive sensing tools, these measurements can be made in real-time for continuous quantitation relating to clinical biomarkers, resilience indicators, and behavioral predictors. The digital phenotyping of humans has drawn enormous attention recently due to its medical significance, but much research is still needed for the digital phenotyping of farm animals. Implications from human studies show great promise for the application of digital phenotyping technology in modern livestock farming, but these technologies must be directly applied to animals to understand their true capacities. Due to species-specific traits, certain technologies required to assess phenotypes need to be tailored efficiently and accurately. Such devices allow for the collection of information that can better inform farmers on aspects of animal welfare and production that need improvement. By explicitly addressing farm animals' individual physiological and mental (affective states) needs, sensor-based digital phenotyping has the potential to serve as an effective intervention platform. Future research is warranted for the design and development of digital phenotyping technology platforms that create shared data standards, metrics, and repositories.

Heat stress during late pregnancy and postpartum influences genetic parameter estimates for birth weight and weight gain in dual-purpose cattle offspring generations

Impact of direct heat stress (HS) on genetic parameter estimates, i.e., HS close to the trait recording date, was verified in several previous studies conducted in dairy and beef cattle populations. The aim of the present study was to analyze the impact of time-lagged HS at different recording periods during late pregnancy (a.p.) and postpartum (p.p.) on genetic parameter estimates for birth weight (BWT) and weight gain traits (200 d- and 365 d-weight gain (200dg, 365dg)) in offspring of the dual-purpose cattle breed "Rotes Höhenvieh" (RHV). Furthermore, we estimated genetic correlations within traits across time-lagged climatic indicators, in order to proof possible genotype by environment interactions (G×E). Trait recording included 5,434 observations for BWT, 3,679 observations for 200dg and 2,998 observations for 365dg. Time-lagged climatic descriptors were classes for the mean temperature humidity index (mTHI) and number of HS days (nHS) from the following periods: 7 d-period a.p. (BWT), 56 d-period a.p., and 56 d-period p.p. (200dg and 365dg). Genetic parameters were estimated via 2-trait animal models, i.e., defining the same trait in different climatic environments as different traits. Genetic variances and heritabilities for all traits increased with increasing mTHI- and nHS-classes for all recording periods, indicating pronounced genetic differentiation with regard to time-lagged in utero HS and HS directly after birth. Similarly, in low mTHI- and nHS-classes indicating cold stress, genetic variances, and heritabilities were larger than for temperate climates. Genetic correlations substantially smaller than 0.80 indicating G × E were observed when considering same traits from mTHI- and nHS-classes in greater distance. Estimated breeding values (EBV) of the 10 most influential sires with the largest number of offspring records fluctuated across mTHI- and nHS-classes. Correlations between sire EBV for same traits from distant climatic classes confirmed the genetic correlation estimates. Sires displaying stable EBV with climatic alterations were also identified. Selection of those sires might contribute to improved robustness in the RHV outdoor population genetically.

Genomic Analysis, Progress and Future Perspectives in Dairy Cattle Selection: A Review

Genomics comprises a set of current and valuable technologies implemented as selection tools in dairy cattle commercial breeding programs. The intensive progeny testing for production and reproductive traits based on genomic breeding values (GEBVs) has been crucial to increasing dairy cattle productivity. The knowledge of key genes and haplotypes, including their regulation mechanisms, as markers for productivity traits, may improve the strategies on the present and future for dairy cattle selection. Genome-wide association studies (GWAS) such as quantitative trait loci (QTL), single nucleotide polymorphisms (SNPs), or single-step genomic best linear unbiased prediction (ssGBLUP) methods have already been included in global dairy programs for the estimation of marker-assisted selection-derived effects. The increase in genetic progress based on genomic predicting accuracy has also contributed to the understanding of genetic effects in dairy cattle offspring. However, the crossing within inbred-lines critically increased homozygosis with accumulated negative effects of inbreeding like a decline in reproductive performance. Thus, inaccurate-biased estimations based on empirical-conventional models of dairy production systems face an increased risk of providing suboptimal results derived from errors in the selection of candidates of high genetic merit-based just on low-heritability phenotypic traits. This extends the generation intervals and increases costs due to the significant reduction of genetic gains. The remarkable progress of genomic prediction increases the accurate selection of superior candidates. The scope of the present review is to summarize and discuss the advances and challenges of genomic tools for dairy cattle selection for optimizing breeding programs and controlling negative inbreeding depression effects on productivity and consequently, achieving economic-effective advances in food production efficiency. Particular attention is given to the potential genomic selection-derived results to facilitate precision management on modern dairy farms, including an overview of novel genome editing methodologies as perspectives toward the future.

Differential haptoglobin responsiveness to a Mannheimia haemolytica challenge altered immunologic, physiologic, and behavior responses in beef steers

Indicator traits associated with disease resiliency would be useful to improve the health and welfare of feedlot cattle. A post hoc analysis of data collected previously (Kayser et al., 2019a) was conducted to investigate differences in immunologic, physiologic, and behavioral responses of steers (N = 36, initial BW = 386 ± 24 kg) that had differential haptoglobin (HPT) responses to an experimentally induced challenge with Mannheimia haemolytica (MH). Rumen temperature, DMI, and feeding behavior data were collected continuously, and serial blood samples were collected following the MH challenge. Retrospectively, it was determined that 9 of the 18 MH-challenged steers mounted a minimal HPT response, despite having similar leukocyte and temperature responses to other MH-challenged steers with a greater HPT response. Our objective was to examine differences in behavioral and physiological responses between MH-challenged HPT responsive (RES; n = 9), MH-challenged HPT nonresponsive (NON; n = 9), and phosphate-buffered saline-inoculated controls (CON; n = 18). Additionally, 1H NMR analysis was conducted to determine whether the HPT-responsive phenotype affected serum metabolite profiles. The RES steers had lesser (P < 0.05) cortisol concentrations than NON and CON steers. The magnitude of the increases in neutrophil concentrations and rumen temperature, and the reduction in DMI following the MH challenge were greatest (P < 0.05) in RES steers. Univariate analysis of serum metabolites indicated differences between RES, NON, and CON steers following the MH challenge; however, multivariate analysis revealed no difference between HPT-responsive phenotypes. Prior to the MH challenge, RES steers had longer (P < 0.05) head down and bunk visit durations, slower eating rates (P < 0.01) and greater (P < 0.05) daily variances in bunk visit frequency and head down duration compared with NON steers, suggesting that feeding behavior patterns were associated with the HPT-responsive phenotype. During the 28-d postchallenge period, RES steers had decreased (P < 0.05) final BW, tended (P = 0.06) to have lesser DMI, and had greater (P < 0.05) daily variances in head down and bunk visit durations compared with NON steers, which may have been attributed to their greater acute-phase protein response to the MH challenge. These results indicate that the HPT-responsive phenotype affected feeding behavior patterns and may be associated with disease resiliency in beef cattle.

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

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

Large-Scale Phenotyping of Livestock Welfare in Commercial Production Systems: A New Frontier in Animal Breeding

Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and indirect biomarkers at the cellular and physiological levels. The development of novel traits coupled with genomic selection schemes for improved welfare in livestock can be feasible and optimized based on recently developed (or developing) technologies. Efficient implementation of genetic and genomic selection for improved animal welfare also requires the integration of a multitude of scientific fields such as cell and molecular biology, neuroscience, immunology, stress physiology, computer science, engineering, quantitative genomics, and bioinformatics.

Modelling the shape of the pig scapula

BACKGROUND

The shape of pig scapula is complex and is important for sow robustness and health. To better understand the relationship between 3D shape of the scapula and functional traits, it is necessary to build a model that explains most of the morphological variation between animals. This requires point correspondence, i.e. a map that explains which points represent the same piece of tissue among individuals. The objective of this study was to further develop an automated computational pipeline for the segmentation of computed tomography (CT) scans to incorporate 3D modelling of the scapula, and to develop a genetic prediction model for 3D morphology.

RESULTS

The surface voxels of the scapula were identified on 2143 CT-scanned pigs, and point correspondence was established by predicting the coordinates of 1234 semi-landmarks on each animal, using the coherent point drift algorithm. A subsequent principal component analysis showed that the first 10 principal components covered more than 80% of the total variation in 3D shape of the scapula. Using principal component scores as phenotypes in a genetic model, estimates of heritability ranged from 0.4 to 0.8 (with standard errors from 0.07 to 0.08). To validate the entire computational pipeline, a statistical model was trained to predict scapula shape based on marker genotype data. The mean prediction reliability averaged over the whole scapula was equal to 0.18 (standard deviation = 0.05) with a higher reliability in convex than in concave regions.

CONCLUSIONS

Estimates of heritability of the principal components were high and indicated that the computational pipeline that processes CT data to principal component phenotypes was associated with little error. Furthermore, we showed that it is possible to predict the 3D shape of scapula based on marker genotype data. Taken together, these results show that the proposed computational pipeline closes the gap between a point cloud representing the shape of an animal and its underlying genetic components.

Symposium review: Genomic selection for reducing environmental impact and adapting to climate change

The world has been warming as greenhouse gases accumulate. Worldwide from 1880 to 2012, the average surface temperature has increased by about 0.85°C and by 0.12°C per decade since 1951. The world's cattle population is a contributor to atmospheric methane, a potent greenhouse gas, in addition to suffering from high temperatures combined with humidity. This makes research into reducing the global footprint of dairy cows of importance on a long-term horizon, while improving tolerance to heat could alleviate the effects of rising temperatures. In December 2017, genomic estimated breeding values for heat tolerance in dairy cattle were released for the first time in Australia. Currently, heat tolerance is not included in the Balanced Performance Index (Australia's national selection index), and the correlation between heat tolerance breeding values and Balanced Performance Index is -0.20, so over time, heat tolerance has worsened due to lack of selection pressure. However, in contrast, sizable reductions in greenhouse gas emissions have been achieved as a favorable response to selecting for increased productivity, longevity, and efficiency, with opportunities for even greater gains through selecting for cow emissions directly. Internationally considerable research effort has been made to develop breeding values focused on reducing methane emissions using individual cow phenotypes. This requires (1) definition of breeding objectives and selection criteria and (2) assembling a sufficiently large data set for genomic prediction. Selecting for heat tolerance and reduced emissions directly may improve resilience to changing environments while reducing environmental impact.

Genetic correlations between energy status indicator traits and female fertility in primiparous Nordic Red Dairy cattle

Inclusion of feed efficiency traits into the dairy cattle breeding programmes will require considering early lactation energy status to avoid deterioration in health and fertility of dairy cows. In this regard, energy status indicator (ESI) traits, for example, blood metabolites or milk fatty acids (FAs), are of interest. These indicators can be predicted from routine milk samples by mid-IR reflectance spectroscopy (MIR). In this study, we estimated genetic variation in ESI traits and their genetic correlation with female fertility in early lactation. The data consisted of 37 424 primiparous Nordic Red Dairy cows with milk test-day records between 8 and 91 days in milk (DIM). Routine test-day milk samples were analysed by MIR using previously developed calibration equations for blood plasma non-esterified FA (NEFA), milk FAs, milk beta-hydroxybutyrate (BHB) and milk acetone concentrations. Six ESI traits were considered and included: plasma NEFA concentration (mmol/l) either predicted by multiple linear regression including DIM, milk fat to protein ratio (FPR) and FAs C10:0, C14:0, C18:1 cis-9, C14:0 * C18:1 cis-9 (NEFAFA) or directly from milk MIR spectra (NEFAMIR), C18:1 cis-9 (g/100 ml milk), FPR, BHB (mmol/l milk) and acetone (mmol/l milk). The interval from calving to first insemination (ICF) was considered as the fertility trait. Data were analysed using linear mixed models. Heritability estimates varied during the first three lactation months from 0.13 to 0.19, 0.10 to 0.17, 0.09 to 0.14, 0.07 to 0.10, 0.13 to 0.17 and 0.13 to 0.18 for NEFAMIR, NEFAFA, C18:1 cis-9, FPR, milk BHB and acetone, respectively. Genetic correlations between all ESI traits and ICF were from 0.18 to 0.40 in the first lactation period (8 to 35 DIM), in general somewhat lower (0.03 to 0.43) in the second period (36 to 63 DIM) and decreased clearly (-0.02 to 0.19) in the third period (64 to 91 DIM). Our results indicate that genetic variation in energy status of cows in early lactation can be determined using MIR-predicted indicators. In addition, the markedly lower genetic correlation between ESI traits and fertility in the third lactation month indicated that energy status should be determined from the first test-day milk samples during the first 2 months of lactation.

Preventing postpartum uterine disease in dairy cattle depends on avoiding, tolerating and resisting pathogenic bacteria

Up to forty percent of dairy cows develop metritis or endometritis when pathogenic bacteria infect the uterus after parturition. However, resilient cows remain healthy even when exposed to the same pathogens. Here, we provide a perspective on the mechanisms that dairy cows use to prevent postpartum uterine disease. We suggest that resilient cows prevent the development of uterine disease using the three complementary defensive strategies of avoiding, tolerating and resisting infection with pathogenic bacteria. Avoidance maintains health by limiting the exposure to pathogens. Avoidance mechanisms include intrinsic behaviors to reduce the risk of infection by avoiding pathogens or infected animals, perhaps signaled by the fetid odor of uterine disease. Tolerance improves health by limiting the tissue damage caused by the pathogens. Tolerance mechanisms include neutralizing bacterial toxins, protecting cells against damage, enhancing tissue repair, and reprogramming metabolism. Resistance improves health by limiting the pathogen burden. Resistance mechanisms include inflammation driven by innate immunity and adaptive immunity, with the aim of killing and eliminating pathogenic bacteria. Farmers can also help cows prevent the development of postpartum uterine disease by avoiding trauma to the genital tract, reducing stress, and feeding animals appropriately during the transition period. Understanding the mechanisms of avoidance, tolerance and resistance to pathogens will inform strategies to generate resilient animals and prevent uterine disease.

Genome-enable prediction for health traits using high-density SNP panel in US Holstein cattle

The objective of this study was to compare accuracies of different Bayesian regression models in predicting molecular breeding values for health traits in Holstein cattle. The dataset was composed of 2505 records reporting the occurrence of retained fetal membranes (RFM), metritis (MET), mastitis (MAST), displaced abomasum (DA), lameness (LS), clinical endometritis (CE), respiratory disease (RD), dystocia (DYST) and subclinical ketosis (SCK) in Holstein cows, collected between 2012 and 2014 in 16 dairies located across the US. Cows were genotyped with the Illumina BovineHD (HD, 777K). The quality controls for SNP genotypes were HWE P‐value of at least 1 × 10⁻¹⁰; MAF greater than 0.01 and call rate greater than 0.95. The fimpute program was used for imputation of missing SNP markers. The effect of each SNP was estimated using the Bayesian Ridge Regression (BRR), Bayes A, Bayes B and Bayes Cπ methods. The prediction quality was assessed by the area under the curve, the prediction mean square error and the correlation between genomic breeding value and the observed phenotype, using a leave‐one‐out cross‐validation technique that avoids iterative cross‐validation. The highest accuracies of predictions achieved were: RFM [Bayes B (0.34)], MET [BRR (0.36)], MAST [Bayes B (0.55), DA [Bayes Cπ (0.26)], LS [Bayes A (0.12)], CE [Bayes A (0.32)], RD [Bayes Cπ (0.23)], DYST [Bayes A (0.35)] and SCK [Bayes Cπ (0.38)] models. Except for DA, LS and RD, the predictive abilities were similar between the methods. A strong relationship between the predictive ability and the heritability of the trait was observed, where traits with higher heritability achieved higher accuracy and lower bias when compared with those with low heritability. Overall, it has been shown that a high‐density SNP panel can be used successfully to predict genomic breeding values of health traits in Holstein cattle and that the model of choice will depend mostly on the genetic architecture of the trait.

Inflammation: friend or foe of bovine reproduction?

Inflammation is not only the first line of defense of the organism but is also required in many reproductive processes such as ovulation, corpus luteum development, luteolysis, uterine clearance after insemination and post partum. Nevertheless, if excessive or persistent, inflammation can switch from a positive mechanism to a deleterious process, impairing oocyte quality and embryo development. Not only uterine but also non genital inflammatory sites can depreciate reproductive performances, with a carry over effect of 2 to 4 months. Since the metabolic challenges of the peripartum transition period make difficult for the cow to control inflammation, dairy cows are frequently in a pro-inflammatory stage, suggesting that inflammation, rather than infection, is a limiting factor of fertility in modern dairy cows. Within the first week after calving, cows have to mount an intense inflammatory response to the bacterial invasion of the uterine cavity with the challenge of being able to switch it off in no more than 5-6 weeks. The absence of neutrophils on endometrial smear is associated with the highest success rate at insemination. Since a fine tuning – rather than an absence - of inflammation is required along the reproductive cycle, anti-inflammatory drugs do not allow any improvement of pregnancy rate, except in the specific case of embryo transfer. Appropriate management of the transition period (especially nutritional) and in a long term perspective, genetic selection contribute to improve the aptitude of cows to controls the intensity of inflammatory process.