Genetic parameters and evaluation of mortality and slaughter rate in Holstein and Jersey cows.

The longevity of dairy cattle has economic, animal welfare, and health implications and is influenced by the frequency of mortality on the farm and sale for slaughter. In this study cows removed from the herd due to death or slaughter during the lactation were coded 1 and cows that were not terminated were coded 0. Genetic parameters for mortality rates (MR) and slaughter rates (SR) were estimated for Holstein (H) and Jersey (J) breeds by applying both linear (LM) and threshold (TM) sire models using about 1.2 million H and 286,000 J cows. Estimated breeding values (EBV) for MR and SR were predicted using animal models to assess the opportunity for selection and genetic trends. Cow termination data, recorded between 1990 and 2020 on a voluntary basis by Australian dairy farmers, were analyzed. Cow MR has increased from below 1% in the 1990s to 4.1% and 3.6% in recent years in H and J cows, respectively. Most dead cows (∼36%) left the herd before 120 d of lactation, while cows that were slaughtered left the herd toward the end of the lactation. Using the LM, heritability (h2) estimates for MR were lower (1%) than those for SR (2%-3.5%). When h2 were estimated using a TM, the estimates for both traits varied between 4% and 20%, suggesting that the difference in incidence level is one of the reasons for the difference in the h2 values between MR and SR. Early test-day milk yield (MY) and 305-d MY (305-d MY) have unfavorable genetic correlations (0.32-0.41) with MR in both breeds. The genetic correlations of calving interval with MR were stronger (0.54-0.68) than with SR (0.28-0.45) suggesting that poor fertility can serve as an early indicator of poor cow health that may lead to increased risk of death. High early test-day somatic cell count is genetically associated with increased likelihood of slaughter (0.24-0.46), but not with increased likelihood of death. In H, 305-d protein yield (PY) had the strongest genetic correlation (-0.34 to -0.40) with SR whereas in J, both 305-d PY and fat yield showed high genetic (-0.64 to -0.70) and moderate environmental (-0.35 to -0.37) correlations with SR. The genetic correlation of removal from the herd due to death and slaughter was negative (-0.3) in J and zero in H. Strong selection for improved fertility and survival and less selection emphasis for MY, has led to an improvement in the genetic trend for cow MR in H and the trend in J has stabilized. Although genetic evaluations for cow MR are feasible, the reliabilities of the EBV are low and the level of cow MR in Australia are relatively low compared with similar countries. Therefore, genetic evaluation for survival based on mortality and slaughter data could be sufficient in the current selection circumstances where breeding objectives are broadly defined. Nevertheless, all Australian farmers should be encouraged to continue recording mortality and slaughter data for monitoring of the trends and for future development of genetic evaluations.

Erratum to "Evaluation of updated Feed Saved breeding values developed in Australian Holstein dairy cattle" (JDS Commun. 3:114-119).

[This corrects the article DOI: 10.3168/jdsc.2021-0150.].

Evaluation of updated Feed Saved breeding values developed in Australian Holstein dairy cattle.

Although selection for increased milk production traits has led to a genetic increase in body weight (BW), the genetic gain in milk production has exceeded the gain in BW, so gross feed efficiency has improved. Nonetheless, greater gains may be possible by directly selecting for a measure of feed efficiency. Australia first introduced Feed Saved (FS) estimated breeding value (EBV) in 2015. Feed Saved combines residual feed intake (RFI) genomic EBV and maintenance requirements calculated from mature BW EBV. The FS EBV was designed to enable the selection of cows for reduced energy requirements with similar milk production. In this study, we used a reference population of 3,711 animals in a multivariate analysis including Australian heifers (AUSh), Australian cows (AUSc), and overseas cows (OVEc) to update the Australian EBV for lifetime RFI (i.e., a breeding value that incorporated RFI in growing and lactating cows) and to recalculate the FS EBV in Australian Holstein bulls (AUSb). The estimates of genomic heritabilities using univariate (only AUSc or AUSh) to trivariate (including the OVEc) analyses were similar. Genomic heritabilities for RFI were estimated as 0.18 for AUSc, 0.27 for OVEc, and 0.36 for AUSh. The genomic correlation for RFI between AUSc and AUSh was 0.47 and that between AUSc and OVEc was 0.94, but these estimates were associated with large standard errors (range: 0.18-0.28). The reliability of lifetime RFI (a component of FS) in the trivariate analysis (i.e., including OVEc) increased from 11% to 20% compared with the 2015 model and was greater, by 12%, than in a bivariate analysis in which the reference population included only AUSc and AUSh. By applying the prediction equation of the 2020 model, the average reliability of the FS EBV in 20,816 AUSb that were born between 2010 and 2020 improved from 33% to 43%. Previous selection strategies-that is, using the predecessor of the Balanced Performance Index (Australian Profit Ranking index) that did not include FS-have resulted in an unfavorable genetic trend in FS. However, this unfavorable trend has stabilized since 2015, when FS was included in the Balanced Performance Index, and is expected to move in a favorable direction with selection on Balanced Performance Index or the Health Weighted Index. Doubling the reference population, particularly by incorporating international data for feed efficiency, has improved the reliability of the FS EBV. This could lead to increased genetic gain for feed efficiency in the Australian industry.

Machine learning approaches for the prediction of lameness in dairy cows.

Lameness is one of the costliest health problems, as well as a welfare concern in dairy cows. However, it is difficult to detect cows with possible lameness, or the ones that are at risk of becoming lame e.g. in the next week or so. In this study, we investigated the ability of three machine learning algorithms, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP), to predict cases of lameness using milk production and conformation traits. The performance of these algorithms was compared with logistic regression (LR) as the gold standard approach for binary classification. We had a total of 2 535 lameness scores (2 248 sound and 287 unsound) and 29 predictor features from nine dairy herds in Australia to predict lameness incidence. Training was done on 80% of the data within each herd with the remainder used as validation set. Our results indicated that in terms of area under curve of receiver operating characteristics, there were negligible differences between LR (0.67) and NB (0.66) while MLP (0.62) and RF (0.61) underperformed compared to the other two methods. However, the F1-score in NB (27%) outperformed LR (1%), suggesting that NB could potentially be a more reliable method for the prediction of lameness in practice, given enough relevant data are available for proper training, which was a limitation in this study. Considering the small size of our dataset, lack of information about environmental conditions prior to the incidence of lameness, management practices, short time gap between production records and lameness scoring, and farm information, this study proved the concept of using machine learning predictive models to predict the incidence of lameness a priori to its occurrence and thus may become a valuable decision support system for better lameness management in precision dairy farming.

Including milk production, conformation, and functional traits in multivariate models for genetic evaluation of lameness.

Lameness is a serious health and welfare issue that can negatively affect the economic performance of cows, especially on pasture-based dairy farms. However, most genetic predictions (GP) of lameness have low accuracy because lameness data are often incomplete as data are collected voluntarily by farmers in countries such as Australia. The objective of this study was to find routinely measured traits that are correlated with lameness and use them in multivariate evaluation models to improve the accuracy of GP for lameness. We used health events and treatments associated with lameness recorded by Australian farmers from 2002 to early 2019. The lameness incidence rates in Holstein and Jersey cows were 3.3% and 4.6%, respectively. We analyzed the records of 36 other traits (milk production, conformation, fertility, and survival traits) to estimate genetic correlations with lameness. The estimated heritability ± standard error (and repeatability ± standard error) for lameness in both Holstein and Jersey breeds were very low: 0.007 ± 0.002 (and 0.029 ± 0.002) and 0.005 ± 0.003 (and 0.027 ± 0.006), respectively, in univariate sire models. For the GP models, we tested including measurements of overall type to prediction models for Holsteins, stature and body length for Jersey, and milk yield and fertility traits for both breeds. The average accuracy of GP, calculated from prediction error variances, were 0.38 and 0.24 for Holstein and Jersey sires, respectively, when estimated using univariate sire models and both increased to 0.43 using multivariate sire models. In conclusion, we found that the accuracy of GP for lameness could be improved by including genetically correlated traits in a multivariate model. However, to further improve the accuracy of predictions of lameness, precise identification and recording incidences of hoof or leg disorder, or large-scale recording of locomotion and claw scores by trained personnel should be considered.

Gene expression analysis of blood, liver, and muscle in cattle divergently selected for high and low residual feed intake.

Improving feed efficiency in cattle is important because it increases profitability by reducing costs, and it also shrinks the environmental footprint of cattle production by decreasing manure and greenhouse gas emissions. Residual feed intake (RFI) is 1 measurement of feed efficiency and is the difference between actual and predicted feed intake. Residual feed intake is a complex trait with moderate heritability, but the genes and biological processes associated with its variation still need to be found. We explored the variation in expression of genes using RNA sequencing to find genes whose expression was associated with RFI and then investigated the pathways that are enriched for these genes. In this study, we used samples from growing Angus bulls (muscle and liver tissues) and lactating Holstein cows (liver tissue and white blood cells) divergently selected for low and high RFI. Within each breed-tissue combination, the correlation between the expression of genes and RFI phenotypes, as well as GEBV, was calculated to determine the genes whose expression was correlated with RFI. There were 16,039 genes expressed in more than 25% of samples in 1 or more tissues. The expression of 6,143 genes was significantly associated with RFI phenotypes, and expression of 2,343 genes was significantly associated with GEBV for RFI ( < 0.05) in at least 1 tissue. The genes whose expression was correlated with RFI phenotype (or GEBV) within each breed-tissue combination were enriched for 158 (78) biological processes (Fisher Exact Statistics for gene-enrichment analysis, EASE score < 0.1) and associated with 13 (13) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways ( < 0.05 and fold enrichment > 2). These biological processes were related to regulation of transcription, translation, energy generation, cell cycling, apoptosis, and proteolysis. However, the direction of the correlation between RFI and gene expression in some cases reversed between tissues. For instance, low levels of proteolysis in muscle were associated with high efficiency in growing bulls, but high levels of proteolysis in white blood cells were associated with efficiency of milk production in lactating cows.

Genomewide association study of methane emissions in Angus beef cattle with validation in dairy cattle.

Methane (CH) is a product of enteric fermentation in ruminants, and it represents around 17% of global CH emissions. There has been substantial effort from the livestock scientific community toward tools that can help reduce this percentage. One approach is to select for lower emitting animals. To achieve this, accurate genetic parameters and identification of the genomic basis of CH traits are required. Therefore, the objectives of this study were 1) to perform a genomewide association study to identify SNP associated with several CH traits in Angus beef cattle (1,020 animals) and validate them in a lactating Holstein population (population 1 [POP1]; 205 animals); 2) to validate significant SNP for DMI and weight at test (WT) from a second Holstein population, from a previous study (population 2 [POP2]; 903 animals), in an Angus population; and 3) to evaluate 2 different residual CH traits and determine if the genes associated with CH also control residual CH traits. Phenotypes calculated for the genotyped Angus population included CH production (MeP), CH yield (MeY), CH intensity (MI), DMI, and WT. The Holstein population (POP1) was multiparous, with phenotypes on CH traits (MeP, MeY, and MI) plus genotypes. Additionally, 2 CH traits, residual genetic CH (RGM) and residual phenotypic CH (RPM), were calculated by adjusting MeP for DMI and WT. Estimated heritabilities in the Angus population were 0.30, 0.19, and 0.15 for MeP, RGM, and RPM, respectively, and genetic correlations of MeP with DMI and WT were 0.83 and 0.80, respectively. Estimated heritabilities in Holstein POP1 were 0.23, 0.30, and 0.42 for MeP, MeY, and MI, respectively. Strong associations with MeP were found on chromosomes 4, 12, 14, 20, and 30 at < 0.001, and those chromosomes also had significant SNP for DMI in Holstein POP1. In the Angus population, the number of significant SNP for MeP at < 0.005 was 3,304, and approximately 630 of those SNP also were important for DMI and WT. When a set (approximately 3,300) of significant SNP for DMI and WT in the Angus population was used to estimate genetic parameters for MeP and MeY in Holstein POP1, the genetic variance and, consequently, the heritability slightly increased, meaning that most of the genetic variation is largely captured by these SNP. Residual traits could be a good option to include in the breeding goal, as this would facilitate selection for lower emitting animals without compromising DMI and WT.

Estimation of genomic breeding values for residual feed intake in a multibreed cattle population.

Residual feed intake (RFI) is a measure of the efficiency of animals in feed utilization. The accuracies of GEBV for RFI could be improved by increasing the size of the reference population. Combining RFI records of different breeds is a way to do that. The aims of this study were to 1) develop a method for calculating GEBV in a multibreed population and 2) improve the accuracies of GEBV by using SNP associated with RFI. An alternative method for calculating accuracies of GEBV using genomic BLUP (GBLUP) equations is also described and compared to cross-validation tests. The dataset included RFI records and 606,096 SNP genotypes for 5,614 Bos taurus animals including 842 Holstein heifers and 2,009 Australian and 2,763 Canadian beef cattle. A range of models were tested for combining genotype and phenotype information from different breeds and the best model included an overall effect of each SNP, an effect of each SNP specific to a breed, and a small residual polygenic effect defined by the pedigree. In this model, the Holsteins and some Angus cattle were combined into 1 "breed class" because they were the only cattle measured for RFI at an early age (6-9 mo of age) and were fed a similar diet. The average empirical accuracy (0.31), estimated by calculating the correlation between GEBV and actual phenotypes divided by the square root of estimated heritability in 5-fold cross-validation tests, was near to that expected using the GBLUP equations (0.34). The average empirical and expected accuracies were 0.30 and 0.31, respectively, when the GEBV were estimated for each breed separately. Therefore, the across-breed reference population increased the accuracy of GEBV slightly, although the gain was greater for breeds with smaller number of individuals in the reference population (0.08 in Murray Grey and 0.11 in Hereford for empirical accuracy). In a second approach, SNP that were significantly (P < 0.001) associated with RFI in the beef cattle genomewide association studies were used to create an auxiliary genomic relationship matrix for estimating GEBV in Holstein heifers. The empirical (and expected) accuracy of GEBV within Holsteins increased from 0.33 (0.35) to 0.39 (0.36) and improved even more to 0.43 (0.50) when using a multibreed reference population. Therefore, a multibreed reference population is a useful resource to find SNP with a greater than average association with RFI in 1 breed and use them to estimate GEBV in another breed.

The Italian National Registry of severe acquired brain injury: epidemiological, clinical and functional data of 1469 patients.

BACKGROUND: The lack of knowledge about epidemiological and clinical data of patients with Acquired Brain Injury (ABI) admitted to Rehabilitation facilities in Italy led to the creation, in June 2008, of a data collection on-line registry. AIM: To collect epidemiological and clinical data and to evaluate functional outcome of patients with severe traumatic and non-traumatic ABI admitted to Rehabilitation facilities in Italy between June 2008 and December 2011 and to compare data of patients with ABI of different aetiologies. DESIGN: Observational retrospective study. SETTING: The study involved 29 Italian Rehabilitation facilities. POPULATION: The study enrolled 1469 patients with severe traumatic (TBI) and non-traumatic ABI (NTBI). METHODS: Data collected included demographic (number of patients with TBI and NTBI, gender, age) and clinical characteristics (provenience, number of days elapsed between onset and rehabilitation admission, rehabilitation length of stay, discharge destination, death and vegetative state diagnosis, presence of percutaneous endoscopic gastrostomy, tracheostomy, pressure sores and paraosteoarthropathies). Functional outcome was evaluated using the Disability Rating Scale. RESULTS: Of the whole population studied, 44.31% and 55.69% patients had suffered a TBI and a NTBI, respectively. In the NTBI group 40.09% had a cerebrovascular injury, 12.04% an anoxic brain damage, 3.6% had a brain injury of other causes. The mean age was 43.67 and 56.68 for subjects with TBI and NTBI, respectively. Patients with TBI showed a lower onset-admission interval (OAI), compared with NTBI group; no difference in rehabilitation length of stay (LOS) was recorded between groups. Patients with TBI presented a lower DRS score at admission and discharge and returned home more frequently than NTBI group. CONCLUSIONS: The creation of a National registry allows the collection of data about patients with ABI in order to study the clinical course, the functional outcome and to establish a basis for comparison with other data sources. Clinical Rehabilitation Impact. Data collection could be useful in the evaluation and planning of rehabilitation pathways, and to assess the allocation of healthcare and rehabilitative resources.

Use of early indicators in rehabilitation process to predict functional outcomes in subjects with acquired brain injury.

BACKGROUND: The use of Evaluation Scales in ABI is necessary for measure of outcome, but not always they are used as predictor factors for rehabilitation processes and organization. AIM: The aim of this study was to evaluate the effectiveness and efficiency of an inpatient rehabilitation program for patients with traumatic brain injury through the use of selected indicators and to identify predictive factors for functional outcome. DESIGN: This was a retrospective database analysis. SETTING: Patients admitted to an Intensive Rehabilitation Unit as inpatient (Sacro Cuore-Don Calabria Hospital, Negrar-Verona). POPULATION: The population included patients with traumatic brain injury. METHODS: The study enrolled 175 patients admitted to an Intensive Rehabilitation Unit between 2004 and 2007. Data collected included demographic characteristics, first 24-hours worst GCS, length of acute and rehabilitative stay at admission and discharge FIM, DRS, LCF and GOS. RESULTS: There was a statistically significant recovery over the course of admission for all assessment tools (P<0.000). When patients were subdivided on the basis of admission DRS categories a linear correlation among variables could be observed, with most disabled patients showing the longest acute and rehabilitation stays and the lowest functional gains. Within each DRS category age appeared to affect improvement (P<0.005) while final outcome was influenced not only by age but also by initial functional status (P<0.000) and time from injury to admission (P<0.004). CONCLUSIONS AND CLINICAL REHABILITATION IMPACT: Systematic data collection in intensive rehabilitation is of great importance to monitor recovery and plan appropriate programs on the basis of admission functional status.