Associations between exposure to bovine herpesvirus 1 (BoHV-1) and milk production,reproductive performance,and mortality in Irish dairy herds

As cost-benefit analyses are required to prioritize and promote disease control and eradication programs within a jurisdiction, national data relating to disease-related production losses are particularly useful. The objectives of the current study were to use Irish bovine herpesvirus 1 (BoHV-1) prevalence data in dairy herds, obtained by bulk milk sampling on 4 occasions over the 2009 lactation, to document associations between milk production, fertility performance, mortality, and BoHV-1 herd status. Bulk milk (n = 305) antibody ELISA was used to classify farms as positive or negative in terms of endemic BoHV-1. Cow-level (milk parameters only) and herd-level performance data were sourced from the Irish Cattle Breeding Federation. Ordinary linear and negative binomial regressions were used to investigate associations between milk, fertility, and mortality performance and herd-level BoHV-1 results (both categorical and continuous variables). Only slight effects on the rates of carryover cows, nonpregnant cows, and total deaths were highlighted with increasing ELISA sample/positive (%) values (incidence rate ratio = 1.001). Multiparous cows in herds BoHV-1 bulk milk antibody positive recorded a reduction in milk yield per cow per year of 250.9 L in the multivariable linear model. Milk fat and protein yields were also affected by herd BoHV-1 status, again highlighting sub-optimal milk production in BoHV-1 bulk milk-positive herds. The current study has highlighted an economical method of investigating losses due to endemic infection using repeated bulk milk sampling over a single lactation. These data can contribute to analyzing the cost-benefit of applying BoHV-1 control strategies both on farm and at a national level.     

Cross-sectional analyses of a national database to determine if superior genetic merit translates to superior dairy cow performance

Various studies have validated that genetic divergence in dairy cattle translates to phenotypic differences; nonetheless, many studies that consider the breeding goal, or associated traits, have generally been small scale, often undertaken in controlled environments, and they lack consideration for the entire suite of traits included in the breeding goal. Therefore, the objective of the present study was to fill this void, and in doing so, provide producers with confidence that the estimated breeding values (EBV) included in the breeding goal do (or otherwise) translate to desired changes in performance among commercial cattle; an additional outcome of such an approach is the identification of potential areas for improvements. Performance data on 536,923 Irish dairy cows (and their progeny) from 13,399 commercial spring-calving herds were used. Association analyses between the cow's EBV of each trait included in the Irish total merit index for dairy cows (which was derived before her own performance data accumulated) and her subsequent performance were undertaken using linear mixed models; milk production, fertility, calving, maintenance (i.e., liveweight), beef, health, and management traits were all considered in the analyses. Results confirm that excelling in EBV for individual traits, as well as on the total merit index, generally delivers superior phenotypic performance; examples of the improved performance for genetically elite animals include a greater yield and concentration of both milk fat and milk protein, despite a lower milk volume, superior reproductive performance, better survival, improved udder and hoof health, lighter cows, and fewer calving complications; all these gains were achieved with minimal to no effect on the beef merit of the dairy cow's progeny. The associated phenotypic change in each performance trait per unit change in its respective EBV was largely in line with the direction and magnitude of expectation, the exception being for calving interval. Per unit change in calving interval EBV, the direction of phenotypic response was as anticipated but the magnitude of the response was only half of what was expected. Despite the deviation from expectation between the calving interval EBV and its associated phenotype, a superior total merit index or a superior fertility EBV was indeed associated with an improvement in all detailed fertility performance phenotypes investigated. Results substantiate that breeding is a sustainable strategy of improving phenotypic performance in commercial dairy cattle and, by extension, profit.     

Components of the covariances between reproductive performance traits and milk protein concentration and milk yield in dairy cows

Reproductive performance in dairy cows can be improved through genetic selection and herd management. Milk protein concentration is strongly associated with various measures of reproductive performance, but the relative importance of genetic and environmental components of these associations have not been defined. The primary objective of this study was to estimate the magnitudes of correlations and covariances between 9 reproductive performance traits in dairy cows and each of milk protein concentration and milk yield at 4 levels: genetic, permanent environmental effects of cow, herd-year-season, and residual levels. A retrospective single cohort study was conducted using data collected from seasonally and split calving dairy herds. We used animal models to partition covariances for the relationships between 9 fertility traits and each of milk protein concentration and milk yield at lactation level, with up to 80,203 lactations from 27,244 cows that were 780 herd-year-seasons in 65 herds. For the fertility traits, of the explained covariance with milk protein concentration, between 33 and 79% (median 53%) was genetic and 21 to 67% (median 47%) was nongenetic. We concluded that research should be conducted to identify management strategies that capture the nongenetic components of relationships between milk protein concentration and reproductive performance. Genetic correlations with milk protein concentration were generally similar to genetic correlations with milk yield, but the correlation with milk protein concentration was closer (i.e., the absolute value of the correlation coefficient was nearer to 1) for pregnant by wk 6, a key trait for seasonally and split calving dairy herds (correlation coefficient ± standard error = 0.28 ± 0.05 and ?0.17 ± 0.07 for milk protein concentration and milk yield, respectively). As the associations also have substantial genetic components, it is possible that reliabilities of estimated breeding values for fertility may be improved by including milk protein concentration in multitrait genetic evaluation models for fertility traits. From our preliminary analyses, reliabilities were only slightly higher when pregnancy by wk 6 of the breeding period was analyzed with milk protein concentration rather than alone or with milk yield, but further research should be considered to assess this question. Importantly, the benefits of these strong relationships can only be fully harnessed through joint use of both management strategies and genetic strategies.     

Genetic analysis of leukosis incidence in United States Holstein and Jersey populations

Bovine leukosis (BL) is a retroviral disease caused by the bovine leukosis virus that affects only cattle. It is associated with decreased milk production and increased cull rates due to development of lymphosarcoma. The virus also affects the immune system. Infected cows display a weak response to some vaccinations. It is important to determine if the heritability of BL susceptibility is greater than zero, or if the environment is the only factor that can be used to reduce the transmission and incidence of the disease. Accordingly, the aim of this study was to estimate the heritability for BL incidence and the genetic merit of sires for leukosis resistance in Holstein and Jersey cattle. Continuous scores and binary milk ELISA results for 13,217 Holstein cows from 114 dairy herds across 16 states and 642 Jersey cows from 8 dairy herds were considered. Data were obtained from commercial testing records at Antel BioSystems (Lansing, MI). Out of the 13,859 animals tested, 38% were found to be infected with the disease. Linear and threshold animal models were used to analyze the continuous and binary data, respectively. Results from both models were similar in terms of estimated breeding values and variance components in their respective scales. Estimates of heritability obtained with the 2 approaches were approximately 8% for both breeds, indicating a considerable genetic component underlying BL disease incidence. The correlation between the estimated breeding values from the 2 models was larger than 0.90, and the lists of top 10% bulls selected from each model had about 80% overlap for both breeds. In summary, results indicate that a simple linear model using the continuous ELISA scores as the response variable was a reasonable approach for the genetic analysis of BL incidence in cattle. In addition, the levels of heritability found indicate that genetic selection could also be used to decrease susceptibility to bovine leukosis virus infection in Holstein and Jersey cattle. Further research is necessary to investigate the genetic correlations of BL with other production and reproduction traits, and to search for potential genomic regions harboring major genes affecting BL susceptibility.     

Genetic parameters for lactose and its correlation with other milk production traits and fitness traits in pasture-based production systems

Lactose is a major component of milk (typically around 5% of composition) that is not usually directly considered in national genetic improvement programs of dairy cattle. Daily test-day lactose yields and percentage data from pasture-based seasonal calving herds in Australia were analyzed to assess if lactose content can be used for predicting fitness traits and if an additional benefit is achieved by including lactose yield in selecting for milk yield traits. Data on lactose percentage collected from 2007 to 2014, from about 600 herds, were used to estimated genetic parameters for lactose percentage and lactose yield and correlations with other milk yield traits, somatic cell count (SCC), calving interval (CIV), and survival. Daily test-day data were analyzed using bivariate random regression models. In addition, multi-trait models were also performed mainly to assess the value of lactose to predict fitness traits. The heritability of lactose percentage (0.25 to 0.37) was higher than lactose yield (0.11 to 0.20) in the first parity. Genetically, the correlation of lactose percentage with protein percentage varied from 0.3 at the beginning of lactation to ?0.24 at the end of the lactation in the first parity. Similar patterns in genetic correlations were also observed in the second and third parity. At all levels (i.e., genetic, permanent environmental, and residual), the correlation between milk yield and lactose yield was close to 1. The genetic and permanent environmental correlations between lactose percentage and SCC were stronger in the second and third parity and toward the end of the lactation (?0.35 to ?0.50) when SCC levels are at their maximum. The genetic correlation between lactose percentage in the first 120 d and CIV (?0.23) was similar to correlation of CIV with protein percentage (?0.28), another component trait with the potential to predict fertility. Furthermore, the correlations of estimated breeding values of lactose percentage and estimated breeding values of traits such as survival, fertility, SCC, and angularity suggest that the value of lactose percentage as a predictor of fitness traits is weak. The results also suggest that including lactose yield as a trait into the breeding objective is of limited value due to the high positive genetic correlation between lactose yield and protein yield, the trait highly emphasized in Australia. However, recording lactose percentage as part of the routine milk recording system will enable the Australian dairy industry to respond quickly to any future changes and market signals.     

How herd best linear unbiased estimates affect the progress achievable from gains in additive and nonadditive genetic merit

Sustainable dairy cow performance relies on coevolution in the development of breeding and management strategies. Tailoring breeding programs to herd performance metrics facilitates improved responses to breeding decisions. Although herd-level raw metrics on performance are useful, implicitly included within such statistics is the mean herd genetic merit. The objective of the present study was to quantify the expected response from selection decisions on additive and nonadditive merit by herd performance metrics independent of herd mean genetic merit. Performance traits considered in the present study were age at first calving, milk yield, calving to first service, number of services, calving interval, and survival. Herd-level best linear unbiased estimates (BLUE) for each performance trait were available on a maximum of 1,059 herds, stratified as best, average, and worst for each performance trait separately. The analyses performed included (1) the estimation of (co)variance for each trait in the 3 BLUE environments and (2) the regression of cow-level phenotypic performance on either the respective estimated breeding value (EBV) or the heterosis coefficient of the cow. A fundamental assumption of genetic evaluations is that 1 unit change in EBV equates to a 1 unit change in the respective phenotype; results from the present study, however, suggest that the realization of the change in phenotypic performance is largely dependent on the herd BLUE for that trait. Herds achieving more yield, on average, than expected from their mean genetic merit, had a 20% greater response to changes in EBV as well as 43% greater genetic standard deviation relative to herds within the worst BLUE for milk yield. Conversely, phenotypic performance in fertility traits (with the exception of calving to first service) tended to have a greater response to selection as well as a greater additive genetic standard deviation within the respective worst herd BLUE environments; this is suggested to be due to animals performing under more challenging environments leading to larger achievable gains. The attempts to exploit nonadditive genetic effects such as heterosis are often the basis of promoting cross-breeding, yet the results from the present study suggest that improvements in phenotypic performance is largely dependent on the environment. The largest gains due to heterotic effects tended to be within the most stressful (i.e., worst) BLUE environment for all traits, thus suggesting the heterosis effects can be beneficial in mitigating against poorer environments.     

Milk protein concentration,estimated breeding value for fertility,and reproductive performance in lactating dairy cows

Milk protein concentration in dairy cows has been positively associated with a range of measures of reproductive performance, and genetic factors affecting both milk protein concentration and reproductive performance may contribute to the observed phenotypic associations. It was of interest to assess whether these beneficial phenotypic associations are accounted for or interact with the effects of estimated breeding values for fertility. The effects of a multitrait estimated breeding value for fertility [the Australian breeding value for daughter fertility (ABV fertility)] on reproductive performance were also of interest. Interactions of milk protein concentration and ABV fertility with the interval from calving date to the start of the herd's seasonally concentrated breeding period were also assessed. A retrospective single cohort study was conducted using data collected from 74 Australian seasonally and split calving dairy herds. Associations between milk protein concentration, ABV fertility, and reproductive performance in Holstein cows were assessed using random effects logistic regression. Between 52,438 and 61,939 lactations were used for analyses of 4 reproductive performance measures. Milk protein concentration was strongly and positively associated with reproductive performance in dairy cows, and this effect was not accounted for by the effects of ABV fertility. Increases in ABV fertility had important additional beneficial effects on the probability of pregnancy by wk 6 and 21 of the herd's breeding period. For cows calved before the start of the breeding period, the effects of increases in both milk protein concentration and ABV fertility were beneficial regardless of their interval from calving to the start of the breeding period. These findings demonstrate the potential for increasing reproductive performance through identifying the causes of the association between milk protein concentration and reproductive performance and then devising management strategies to capitalize on them. Research should be conducted to understand the component of the relationship not captured by ABV fertility.     

Genetic evaluation of fertility traits of dairy cattle using a multiple-trait animal model

A genetic evaluation system was developed for 5 fertility traits of dairy cattle: interval from first to successful insemination and nonreturn rate to 56 d of heifers, and interval from calving to first insemination, nonreturn rate to 56 d, and interval first to successful insemination of cows. Using the 2 interval traits of cows as components, breeding values for days open were derived. A multiple-trait animal model was applied to evaluate these fertility traits. Fertility traits of later lactations of cows were treated as repeated measurements. Genetic parameters were estimated by REML. Mixed model equations of the genetic evaluation model were solved with preconditioned conjugate gradients or the Gauss-Seidel algorithm and iteration on data techniques. Reliabilities of estimated breeding values were approximated with a multi-trait effective daughter contribution method. Daughter yield deviations and associated effective daughter contributions were calculated with a multiple trait approach. The genetic evaluation software was applied to the insemination data of dairy cattle breeds in Germany, Austria, and Luxembourg, and it was validated with various statistical methods. Genetic trends were validated. Small heritability estimates were obtained for all the fertility traits, ranging from 1% for nonreturn rate of heifers to 4% for interval calving to first insemination. Genetic and environmental correlations were low to moderate among the traits. Notably, unfavorable genetic trends were obtained in all the fertility traits. Moderate to high correlations were found between daughter yield-deviations and estimated breeding values (EBV) for Holstein bulls. Because of much lower heritabilities of the fertility traits, the correlations of daughter yield deviations with EBV were significantly lower than those from production traits and lower than the correlations from type traits and longevity. Fertility EBV were correlated unfavorably with EBV of milk production traits but favorably with udder health and longevity. Integrating fertility traits into a total merit selection index can halt or reverse the decline of fertility and improve the longevity of dairy cattle.     

Genetic parameters for atypical reproductive patterns in dairy cows estimated from in-line milk progesterone profiles

Our aim was to estimate genetic parameters of atypical reproductive patterns and estimate their genetic correlation with milk production and classical fertility traits for commercial dairy cows. In contrast with classical fertility traits, atypical reproductive patterns based on in-line milk progesterone profiles might have higher heritability and lower genetic correlation with milk production. We had in-line milk progesterone profiles available for 12,046 cycles in 4,170 lactations of 2,589 primiparous and multiparous cows (mainly Holstein Friesian) from 14 herds. Based on progesterone profiles, 5 types of atypical reproductive patterns in a lactation were defined: delayed ovulation types I and II, persistent corpus luteum types I and II, and late embryo mortality. These atypical patterns were detected in 14% (persistent corpus luteum type II) to 21% (persistent corpus luteum type I) of lactations. In 47% of lactations, at least 1 atypical pattern was detected. Threshold model heritabilities for atypical reproduction patterns ranged between 0.03 and 0.14 and for most traits were slightly higher compared with classical fertility traits. The genetic correlation between milk yield and calving interval was 0.56, whereas genetic correlations between milk yield and atypical reproductive patterns ranged between ?0.02 and 0.33. Although most of these correlations between milk yield and atypical reproductive patterns are still unfavorable, they are lower compared with the correlations between classical fertility traits and milk yield. Therefore selection against atypical reproductive patterns may relax some constraints in current dairy breeding programs, to enhance genetic progress in both fertility and milk yield at a steady pace. However, as long as the target trait for fertility is calving interval, atypical reproductive patterns will not add additional value to the breeding goal in the near future due to the low number of available records.     

Effects of animal selection on milk composition and processability

One goal of animal breeding is to increase the economic output through increased production, improved milk quality, and cow health. The objective of this study was to evaluate genetic progress in relation to milk composition, processability, and yield as a correlated response to selection for the Swedish breeding objective. Dairy cows with high genetic merit, classified as elite dairy cows, of the Swedish Red and Swedish Holstein breeds were used. Milk samples were collected on the farm level in winter and summer from a research herd at Nötcenter Viken, a bovine research farm in Sweden. Comparisons were made with milk from a Swedish Red herd, a Swedish Holstein herd, and a Swedish dairy processor in the same geographical area. Protein, lipid, and carbohydrate profiles as well as minerals were analyzed, and technological properties, including rennet-induced gelation characteristics, lipid oxidation, total antioxidant capacity, and fat globule size, were determined. Higher yields were found for elite cows for components of the protein, lipid, and carbohydrate profiles as well as for minerals, implying genetic progress in relation to milk yield; however, the content of some milk components (e.g., lipid and whey protein contents) had decreased on average. Milk from the elite cows had good gelation characteristics, but was more susceptible to lipid autooxidation and had a lower total antioxidant capacity. These results demonstrate that milk composition and processing characteristics could be used to adjust breeding practices to optimize the quality and stability of milk and dairy products.     

Evidence of genetic resistance of cattle to infection with Mycobacterium bovis

Anecdotal evidence points to genetic variation in resistance of cattle to infection with Mycobacterium bovis, the causative agent of bovine tuberculosis (BTB), and published experimental evidence in deer and cattle suggests significant genetic variation in resistance and reactivity to diagnostic tests. However, such genetic variation has not been properly quantified in the United Kingdom dairy cattle population; it is possible that it exists and may be a factor influencing the occurrence of BTB. Using models based on the outcome of the process of diagnosis (ultimate fate models) and on the outcome of a single stage of diagnosis (continuation ratio models, herd test-date models), this study shows that there is heritable variation in individual cow susceptibility to BTB, and that selection for milk yield is unlikely to have contributed to the current epidemic. Results demonstrate that genetics could play an important role in controlling BTB by reducing both the incidence and the severity of herd breakdowns.     

Derivation of sustainable breeding goals for dairy cattle using selection index theory

The objective was to present 2 methods for the derivation of nonmarket values for functional traits in dairy cattle using deterministic simulation and selection index theory. A nonmarket value can be a value representing animal welfare and societal influences for animal production, which can be added to market economic values in the breeding goal to define sustainable breeding goals. The first method was restricted indices. A consequence of adding a nonmarket value to a market economic value for a given functional trait is less selection emphasis on milk yield. In the second method, the loss in selection response in milk resulting from greater emphasis on functional traits was quantified. The 2 methods were demonstrated using a breeding goal for dairy cattle with 4 traits (milk yield, mastitis resistance, conception rate, and stillbirth). Nonmarket values derived separately using restricted indices were 0.4 and 2.6 times the value of market economic values for mastitis resistance and conception rate, respectively. Nonmarket values for mastitis resistance and conception rate were both lower when derived simultaneously than when derived separately. This was due to the positive genetic correlation between mastitis resistance and conception rate, and because both traits are negatively correlated with milk yield. Using the second method and accepting a 5% loss in selection response for milk yield, nonmarket values for mastitis, conception rate, and stillbirth were 0.3, 1.4, and 2.9 times the market economic values. It was concluded that the 2 methods could be used to derive nonmarket values for functional traits in dairy cattle.     

Disease resistance and immune response genes in cattle: strategies for their detection and evidence of their existence

The possibility of breeding or genetically engineering cattle for resistance to disease has tremendous potential for increasing the efficiency of milk and meat production. In cattle and other species, major genes that control humoral and cellular immune responses to a variety of antigens have been mapped to a chromosomal region known as the major histocompatibility complex. However, resistance or susceptibility to viral, bacterial, and parasitic diseases in noninbred species is often a complex phenotype, with age, stress, and physiologic status all being important factors in the outcome of infection. This paper reviews the function of major histocompatibility complex gene products and the relationship between polymorphism of these genes and infectious diseases. A discussion of strategies for detecting immune response genes and disease associations is presented, with particular reference to the problems and advantages of working with cattle. The present knowledge of the bovine major histocompatibility complex and its relationship to immune responsiveness and disease resistance are also reviewed, with special consideration given to enzootic bovine leukosis because of the significant relationship between alleles of the bovine lymphocyte antigen system and resistance or susceptibility to subclinical progression of bovine leukemia virus infection. Finally, potential applications of this research to genetic improvement and animal health are considered.     

Invited review: Beef-on-dairy—The generation of crossbred beef × dairy cattle

Because a growing proportion of the beef output in many countries originates from dairy herds, the most critical decisions about the genetic merit of most carcasses harvested are being made by dairy producers. Interest in the generation of more valuable calves from dairy females is intensifying, and the most likely vehicle is the use of appropriately selected beef bulls for mating to the dairy females. This is especially true given the growing potential to undertake more beef × dairy matings as herd metrics improve (e.g., reproductive performance) and technological advances are more widely adopted (e.g., sexed semen). Clear breed differences (among beef breeds but also compared with dairy breeds) exist for a whole plethora of performance traits, but considerable within-breed variability has also been demonstrated. Although such variability has implications for the choice of bull to mate to dairy females, the fact that dairy females themselves exhibit such genetic variability implies that “one size fits all” may not be appropriate for bull selection. Although differences in a whole series of key performance indicators have been documented between beef and beef-on-dairy animals, of particular note is the reported lower environmental hoofprint associated with beef-on-dairy production systems if the environmental overhead of the mature cow is attributed to the milk she eventually produces. Despite the known contribution of beef (i.e., both surplus calves and cull cows) to the overall gross output of most dairy herds globally, and the fact that each dairy female contributes half her genetic merit to her progeny, proxies for meat yield (i.e., veal or beef) are not directly considered in the vast majority of dairy cow breeding objectives. Breeding objectives to identify beef bulls suitable for dairy production systems are now being developed and validated, demonstrating the financial benefit of using such breeding objectives over and above a focus on dairy bulls or easy-calving, short-gestation beef bulls. When this approach is complemented by management-based decision-support tools, considerable potential exists to improve the profitability and sustainability of modern dairy production systems by exploiting beef-on-dairy breeding strategies using the most appropriate beef bulls.     

Invited review: Milk lactose—Current status and future challenges in dairy cattle

Lactose is the main carbohydrate in mammals' milk, and it is responsible for the osmotic equilibrium between blood and alveolar lumen in the mammary gland. It is the major bovine milk solid, and its synthesis and concentration in milk are affected mainly by udder health and the cow's energy balance and metabolism. Because this milk compound is related to several biological and physiological factors, information on milk lactose in the literature varies from chemical properties to heritability and genetic associations with health traits that may be exploited for breeding purposes. Moreover, lactose contributes to the energy value of milk and is an important ingredient for the food and pharmaceutical industries. Despite this, lactose has seldom been included in milk payment systems, and it has never been used as an indicator trait in selection indices. The interest in lactose has increased in recent years, and a summary of existing information about lactose in the dairy sector would be beneficial for the scientific community and the dairy industry. The present review collects and summarizes knowledge about lactose by covering and linking several aspects of this trait in bovine milk. Finally, perspectives on the use of milk lactose in dairy cattle, especially for selection purposes, are outlined.     

Comparison of cattle derived from in vitro fertilization,multiple ovulation embryo transfer,and artificial insemination for milk production and fertility traits

The use of assisted-reproduction technologies such as in vitro fertilization (IVF) is increasing, particularly in dairy cattle. The question of consequences in later life has not yet been directly addressed by studies on large animal populations. Studies on rodents and early data from humans and cattle suggest that in vitro manipulation of gametes and embryos could result in long-term alteration of metabolism, growth, and fertility. Our goal was to better describe these presumed consequences in the population of dairy cows produced by IVF in Québec (Canada) and to compare them to animals conceived by artificial insemination (AI) or multiple ovulation embryo transfer (MOET). To do so, we leveraged a large phenotypic database (2.5 million animals and 4.5 million lactations) from milk records in Québec aggregated by Lactanet (Sainte-Anne-de-Bellevue, QC, Canada) and spanning 2012 to 2019. We identified 304,163, 12,993, and 732 cows conceived by AI, MOET, and IVF, respectively, for a total of 317,888 Holstein animals from which we retrieved information for 576,448, 24,192, and 1,299 lactations (total = 601,939), respectively. Genetic energy-corrected milk yield (GECM) and Lifetime Performance Index (LPI) of the parents of cows were used to normalize for genetic potential across animals. When compared with the general Holstein population, MOET and IVF cows outperformed AI cows. However, when comparing those same MOET and IVF cows with only herdmates and accounting for their higher GECM in the models, we found no statistical difference between the conception methods for milk production across the first 3 lactations. We also found that the rate of Lifetime Performance Index improvement of the IVF population during the 2012 to 2019 period was less than the rate observed in the AI population. Fertility analysis revealed that MOET and IVF cows also scored 1 point lower than their parents on the daughter fertility index and had a longer interval from first service to conception, with an average of 35.52 d compared with 32.45 for MOET and 31.87 for AI animals. These results highlight the challenges of elite genetic improvement while attesting to the progress the industry has made in minimizing epigenetic disturbance during embryo production. Nonetheless, additional work is required to ensure that IVF animals can maintain their performance and fertility potential.     

Genomic-based genetic parameters for resilience across lactations in North American Holstein cattle based on variability in daily milk yield records

Considering the increasing challenges imposed by climate change and the need to improve animal welfare, breeding more resilient animals capable of better coping with environmental disturbances is of paramount importance. In dairy cattle, resilience can be evaluated by measuring the longitudinal occurrences of abnormal daily milk yield throughout lactation. Aiming to estimate genetic parameters for dairy cattle resilience, we collected 5,643,193 daily milk yield records on automatic milking systems (milking robots) and milking parlors across 21,350 lactations 1 to 3 of 11,787 North American Holstein cows. All cows were genotyped with 62,029 SNPs. After determining the best fitting models for each of the 3 lactations, daily milk yield residuals were used to derive 4 resilience indicators: weighted occurrence frequency of yield perturbations (wfPert), accumulated milk losses of yield perturbations (dPert), and log-transformed variance (LnVar) and lag-1 autocorrelation (r_auto) of daily yield residuals. The indicator LnVar presented the highest heritability estimates (±standard error), ranging from 0.13 ± 0.01 in lactation 1 to 0.15 ± 0.02 in lactation 2; the other 3 indicators had relatively lower heritabilities across the 3 lactations (0.01–0.06). Based on bivariate analyses of each resilience indicator across lactations, stronger genetic correlations were observed between lactations 2 and 3 (0.88–0.96) than between lactations 1 and 2 or 3 (0.34–0.88) for dPert, LnVar, and r_auto. For the pairwise comparisons of different resilience indicators within each lactation, dPert had the strongest genetic correlations with wfPert (0.64) and r_auto (0.53) in lactation 1, whereas the correlations in lactations 2 and 3 were more variable and showed relatively high standard errors. The genetic correlation results indicated that different resilience indicators across lactations might capture additional biological mechanisms and should be considered as different traits in genetic evaluations. We also observed favorable genetic correlations of these resilience indicators with longevity and Net Merit index, but further biological validation of these resilience indicators is needed. In conclusion, this study provided genetic parameter estimates for different resilience indicators derived from daily milk yields across the first 3 lactations in Holstein cattle, which will be useful when potentially incorporating these traits in dairy cattle breeding schemes.     

Comparing microRNA in milk small extracellular vesicles among healthy cattle and cattle at high risk for bovine leukemia virus transmission

Enzootic bovine leukosis (EBL) is a B-cell lymphosarcoma caused by bovine leukemia virus (BLV) infection. In Japan, cattle diagnosed with EBL are not permitted for human consumption by the law, thereby causing serious economic losses to farmers. The prevalence of BLV is high in Japan (40.9% in dairy cattle and 28.7% in beef cattle, respectively), which makes it difficult to perform the test-and-slaughter of BLV-infected cattle. This necessitates preventing the spread of BLV infection in cattle by early detection, segregation, and the removal of BLV-infected cattle with high proviral load, which are considered high risk for BLV transmission. We aimed to identify cattle that were at high risk for BLV transmission by comparing microRNA (miRNA) profiles in milk small extracellular vesicles (sEV). At first, miRNA profiles in sEV were compared among 4 uninfected cattle and 4 BLV-infected cattle with high proviral load by using a microarray containing mixed probes for miRNA of cattle and humans. Significantly lower amounts of hsa-miR-557 and hsa-miR-19b-1-5p, and insignificantly but higher amounts of hsa-miR-424-5p were observed in milk sEV from BLV-infected cattle than those from uninfected cattle. Next, to evaluate the utility of the aforementioned miRNAs for the identification of cattle that were at high risk for BLV transmission, we performed quantitative real-time PCR using milk sEV newly collected from 5 uninfected cattle and 17 BLV-infected cattle with high proviral load. The cycle threshold value of hsa-miR-424-5p was significantly lower in milk sEV from BLV-infected cattle. The PCR detection was unavailable or a significant difference was not observed for hsa-miR-557 and hsa-miR-19b-1-5p, respectively. These results suggest that the amount of hsa-miR-424-5p was higher in milk sEV from BLV-infected cattle and increasing the hsa-miR-424-5p in milk sEV could be one of the characteristic trends in cattle that are high risk for BLV transmission. Moreover, assessing characteristic miRNA amounts in milk sEV, which can be recovered twice a day by milking, could be useful for the routine monitoring of cattle in dairy herds instead of blood collection.     

Genetic correlation between female fertility and milk yield in Lacaune sheep

A total of 416,670 lactations for 189,101 ewes from 3,603 sires and distributed across 1,978 herd-year groups were used to estimate genetic and environmental parameters of standardized milk yield (SMY_T), fertility in ewe lambs (PR₁), and fertility in adult ewes (PR_A). Parameters were estimated with a multiple-trait sire linear model. Heritabilities for SMY_T, PR₁, and PR_A were 0.27 (0.009), 0.04 (0.004), and 0.05 (0.004), respectively. These results were in accordance with the literature. The genetic correlation between PR₁ and PR_A was 0.55, indicating that fertility is not the same trait in ewe lambs and adult ewes. The genetic correlation between milk yield and lamb fertility was not significantly different from zero. The genetic correlation between milk yield and fertility in adult ewe (−0.23) was in the range of antagonistic correlations reported in dairy cattle. Consequently, these results show that selection for milk yield can induce an indirect decrease in fertility. Nevertheless, no phenotypic decrease in fertility in artificial insemination matings has been observed in this population. This is the first time that correlation between milk yield and fertility is reported in sheep and further investigations are needed to confirm this result.