Integrating biological knowledge for mechanistic inference in the host-associated microbiome

Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.

Molecular autopsy for fetal structural anomaly: diagnostic and clinical utility of multidisciplinary team approach

OBJECTIVES

In the West Midlands Regional Genetic Service, cases of perinatal death with a possible genetic diagnosis are evaluated by the Perinatal Pathology Genetic Multidisciplinary Team (MDT). The MDT assessed autopsy findings and considered genomic assessments. The objective of this retrospective service evaluation was to determine the clinical utility of the MDT. This is the first evaluation since the introduction of whole genome and whole exome sequencing in routine clinical care.

METHOD

The outcomes for all the perinatal MDT cases from January 2021 to December 2021 were examined. All cases received a full or partial post-mortem examination (PM) and a chromosomal microarray. Demographics, phenotype, MDT recommendations, genetic testing, diagnoses, outcomes, impact of PM and impact of genetic testing were collected from patient case notes.

RESULTS

One hundred and twenty-three cases were discussed at the MDT meeting in 2021. Genetic evaluation was recommended in 84 cases and accepted in 64 cases. A range of genetic tests were requested according to indication and availability. Thirty diagnoses were identified in 29 cases from 26 unrelated families. The diagnostic yield was 24% (29/123) of all cases or 45% (29/64) of the cases with a suspected genetic diagnosis who underwent genetic testing. PM examination added clinically actionable phenotype data in 79% of cases. A genetic diagnosis enabled accurate counselling of recurrence risk and provision of appropriate follow-up, including prenatal testing and preimplantation diagnosis for patients with inherited conditions.

CONCLUSIONS

Genomic testing was a clinically useful addition to (but not a substitute for) PM examination in perinatal cases associated with structural anomalies. The MDT model helped assess cases and plan appropriate follow-up. Expedited whole genome sequencing or panel-agnostic analysis were most appropriate for heterogeneous presentations. This broad approach can also expand prenatal phenotypes and detect novel disease genes and should be a priority for future research. This article is protected by copyright. All rights reserved.

Lactational performance, enteric methane emission, and nutrient utilization of dairy cows supplemented with botanicals

The objective of this study was to evaluate lactational performance, enteric gas emissions, ruminal fermentation, nutrient use efficiency, milk fatty acid profile, and energy and inflammatory markers in blood of peak-lactation dairy cows fed diets supplemented with Capsicum oleoresin or a combination of Capsicum oleoresin and clove oil. A 10-wk randomized complete block design experiment was conducted with 18 primiparous and 30 multiparous Holstein cows. Cows were blocked based on parity, days in milk, and milk yield (MY), and randomly assigned to 1 of 3 treatments (16 cows/treatment): (1) basal diet (CON); (2) basal diet supplemented with 300 mg/cow per day of Capsicum oleoresin (CAP); and (3) basal diet supplemented with 300 mg/cow per day of a combination of Capsicum oleoresin and clove oil (CAPCO). Premixes containing ground corn (CON), CAP, or CAPCO were mixed daily with the basal diet at 0.8% of dry matter intake (DMI). Supplementation of the diet with CAP or CAPCO did not affect DMI, MY, milk components, and feed efficiency of the cows. Body weight (BW) was increased during the last 2 wk of the experiment by CAP and CAPCO, compared with CON. The botanicals improved BW gain (0.85 and 0.66 kg/d for CAP and CAPCO, respectively, compared with -0.01 kg/d for CON) and CAP enhanced the efficiency of energy utilization, compared with CON (94.5% vs. 78.4%, respectively). Daily CH4 emission was not affected by treatments, but CH4 emission yield (per kg of DMI) and intensity (per kg of MY) were decreased by up to 11% by CAPCO supplementation, compared with CON and CAP. A treatment × parity interaction indicated that the CH4 mitigation effect was pronounced in primiparous but not in multiparous cows. Ruminal molar proportion of propionate was decreased by botanicals, compared with CON. Concentrations of trans-10 C18:1 and total trans fatty acids in milk fat were decreased by CAP and tended to be decreased by CAPCO, compared with CON. Total-tract apparent digestibility of nutrients was not affected by treatments, except for a tendency for decreased starch digestibility in cows supplemented with botanicals. Blood concentrations of β-hydroxybutyrate, total fatty acids, and insulin were not affected by botanicals. Blood haptoglobin concentration was increased by CAP in multiparous but not in primiparous cows. Lactational performance of peak-lactation dairy cows was not affected by the botanicals in this study, but they appeared to improve efficiency of energy utilization and partitioned energy toward BW gain. In addition, CH4 yield and intensity were decreased in primiparous cows fed CAPCO, suggesting a potential positive environmental effect of the combination of Capsicum oleoresin and clove oil supplementation.

Multiorgan MRI findings after hospitalisation with COVID-19 in the UK (C-MORE): a prospective, multicentre, observational cohort study

INTRODUCTION

The multiorgan impact of moderate to severe coronavirus infections in the post-acute phase is still poorly understood. We aimed to evaluate the excess burden of multiorgan abnormalities after hospitalisation with COVID-19, evaluate their determinants, and explore associations with patient-related outcome measures.

METHODS

In a prospective, UK-wide, multicentre MRI follow-up study (C-MORE), adults (aged ≥18 years) discharged from hospital following COVID-19 who were included in Tier 2 of the Post-hospitalisation COVID-19 study (PHOSP-COVID) and contemporary controls with no evidence of previous COVID-19 (SARS-CoV-2 nucleocapsid antibody negative) underwent multiorgan MRI (lungs, heart, brain, liver, and kidneys) with quantitative and qualitative assessment of images and clinical adjudication when relevant. Individuals with end-stage renal failure or contraindications to MRI were excluded. Participants also underwent detailed recording of symptoms, and physiological and biochemical tests. The primary outcome was the excess burden of multiorgan abnormalities (two or more organs) relative to controls, with further adjustments for potential confounders. The C-MORE study is ongoing and is registered with ClinicalTrials.gov, NCT04510025.

FINDINGS

Of 2710 participants in Tier 2 of PHOSP-COVID, 531 were recruited across 13 UK-wide C-MORE sites. After exclusions, 259 C-MORE patients (mean age 57 years [SD 12]; 158 [61%] male and 101 [39%] female) who were discharged from hospital with PCR-confirmed or clinically diagnosed COVID-19 between March 1, 2020, and Nov 1, 2021, and 52 non-COVID-19 controls from the community (mean age 49 years [SD 14]; 30 [58%] male and 22 [42%] female) were included in the analysis. Patients were assessed at a median of 5·0 months (IQR 4·2-6·3) after hospital discharge. Compared with non-COVID-19 controls, patients were older, living with more obesity, and had more comorbidities. Multiorgan abnormalities on MRI were more frequent in patients than in controls (157 [61%] of 259 vs 14 [27%] of 52; p<0·0001) and independently associated with COVID-19 status (odds ratio [OR] 2·9 [95% CI 1·5-5·8]; padjusted=0·0023) after adjusting for relevant confounders. Compared with controls, patients were more likely to have MRI evidence of lung abnormalities (p=0·0001; parenchymal abnormalities), brain abnormalities (p<0·0001; more white matter hyperintensities and regional brain volume reduction), and kidney abnormalities (p=0·014; lower medullary T1 and loss of corticomedullary differentiation), whereas cardiac and liver MRI abnormalities were similar between patients and controls. Patients with multiorgan abnormalities were older (difference in mean age 7 years [95% CI 4-10]; mean age of 59·8 years [SD 11·7] with multiorgan abnormalities vs mean age of 52·8 years [11·9] without multiorgan abnormalities; p<0·0001), more likely to have three or more comorbidities (OR 2·47 [1·32-4·82]; padjusted=0·0059), and more likely to have a more severe acute infection (acute CRP >5mg/L, OR 3·55 [1·23-11·88]; padjusted=0·025) than those without multiorgan abnormalities. Presence of lung MRI abnormalities was associated with a two-fold higher risk of chest tightness, and multiorgan MRI abnormalities were associated with severe and very severe persistent physical and mental health impairment (PHOSP-COVID symptom clusters) after hospitalisation.

INTERPRETATION

After hospitalisation for COVID-19, people are at risk of multiorgan abnormalities in the medium term. Our findings emphasise the need for proactive multidisciplinary care pathways, with the potential for imaging to guide surveillance frequency and therapeutic stratification.

FUNDING

UK Research and Innovation and National Institute for Health Research.

Complete Genome Sequence of a Staphylococcus epidermidis Isolate from Healthy Skin with Upregulation of Protease EcpA

Staphylococcus epidermidis is a ubiquitous skin commensal that has the potential to become pathogenic and cause disease. Here, we report the complete genome sequence of a S. epidermidis strain isolated from adult healthy skin that shows high expression of the virulence factor extracellular cysteine protease A (EcpA).

A retrospective cohort study of the characteristics of unsuccessful operative vaginal deliveries

INTRODUCTION

Unsuccessful operative vaginal delivery (OVD) is associated with high rates of materno-fetal morbidity. We aimed to examine institutional rates of unsuccessful OVDs (uOVD) and compare them with successful OVD (sOVD) in order to identify factors to aid patient selection and education.

METHODS

A 6-month retrospective cohort study was performed on all unsuccessful and successful OVDs in a tertiary level maternity hospital in the Republic of Ireland. Maternal demographics and obstetric factors were assessed to evaluate potential underlying risk factors for unsuccessful operative vaginal delivery versus successful vaginal delivery.

RESULTS

There were 4,191 births during the study period with an OVD rate of 14.2% (n = 595) with 28 (4.7% of OVDs) being unsuccessful. Unsuccessful OVD were predominately nulliparous (25; 89.2%) with a mean maternal age of 30.1 years (range 20-42), with more than half (n = 15, 53.5%) being induced. The most common indication for induction was prolonged rupture of membranes (PROM) (n = 7, 25%) which was significantly different from the successful OVD group. A senior obstetrician was significantly more likely to be the primary operator in uOVD when compared to sOVD. (82.1 % V 54.1% p < 0.01). The majority of unsuccessful OVD were vacuum deliveries (n = 17; 60.7%), with a significantly higher mean birthweight when compared to successful OVD (3.695 kg V 3.483 kg; p < 0.01). Following an unsuccessful OVD, women were more likely to have a postpartum haemorrhage (64.2 % V 31.5% p < 0.01) and their infant was more likely to require admission to the neonatal intensive care unit (NICU) (32.1 % V 5.8% p < 0.01) when compared with successful OVD.

CONCLUSION

Risk factors for unsuccessful OVD were higher birth weight and induction of labour. There was a higher incidence of postpartum haemorrhage and NICU admission when compared with successful OVD.

Correlations of milk and serum element concentrations with production and management traits in dairy cows

The present study investigated the potential consequences, positive or negative, that selection for favorable production-related traits may have on concentrations of vitamin B₁₂ and key chemical elements in dairy cow milk and serum and the possible impact on milk healthiness, and associated benefits, for the dairy product consumer. Milk and serum samples (950 and 755, respectively) were collected from Holstein-Friesian dairy cows (n = 479) on 19 occasions over a 59-mo period, generating 34,258 individual records, and analyzed for concentrations of key trace and quantity elements, heavy metals, and milk vitamin B₁₂. These data were then matched to economically important production data (milk, fat, and protein yield) and management data (dry matter intake, liveweight, and body condition score). Multivariate animal models, including full pedigree information, were used to analyze data and investigate relationships between traits of interest. Results highlighted negative genetic correlations between many quantity and trace elements in both milk and serum with production and management traits. Milk yield was strongly negatively correlated with the milk quantity elements Mg and Ca (genetic correlation between traits, r_a = -0.58 and -0.63, respectively) as well as the trace elements Mn, Fe, Ni, Cu, Zn, and Mo (r_a = -0.32, -0.58, -0.52, -0.40, -0.34, and -0.96, respectively); and in serum, Mg, Ca, Co, Fe, and Zn (r_a = -0.50, -0.36, -0.68, -0.54, and -0.90, respectively). Strong genetic correlations were noted between dry matter intake with V (r_a = 0.97), Fe (r_a = -0.69), Ni (r_a = -0.81), and Zn (r_a = -0.75), and in serum, strong negative genetic correlations were observed between dry matter intake with Ca and Se (r_a = -0.95 and -0.88, respectively). Body condition score was negatively correlated with serum P, Cu, Se, and Pb (r_a = -0.45, -0.35, -0.51, and -0.64, respectively) and positively correlated with Mn, Fe, and Zn (r_a = 0.40, 0.71, and 0.55, respectively). Our results suggest that breeding strategies aimed at improving economically important production-related traits would most likely result in a negative impact on levels of beneficial nutrients within milk for human consumption (such as Mg, Ca, Fe, Zn, and Se).

149 A PROFILE OF THE COMMUNICATION NEEDS OF NEW PATIENTS ATTENDING A MEMORY ASSESSMENT AND SUPPORT SERVICE

Background

Communication is a key factor in maintaining quality of life and well-being. This is of core importance for people with dementia who experience changes to their communication abilities. The role of the Speech and Language Therapist working with people with dementia is well documented. Speech and Language Therapists support people with communication and swallowing disorders however in practice the focus is often on swallowing disorders with less emphasis on communication. The recommended allocation of Speech and Language Therapy (SLT) for a Memory and Support Service (MASS) is 0.5 Senior Speech and Language Therapist. A pilot SLT service was trialled in a MASS to identify the SLT service needs and the benefit of introducing SLT earlier for people with Dementia.

Methods

A retrospective caseload review was completed of ten patients seen by SLT as part of the MASS assessment. Communication profiles and SLT interventions were analysed.

Results

The majority of the ten patients reviewed self-reported communication and speech and language changes, such as word finding difficulties and difficulties understanding conversations. Patient's self-report often corresponded with formal assessment results. On formal assessment 30% of patients presented with slight cognitive-communicative changes and 30% of patients presented with slight-mild cognitive-communicative changes. 10% of patients presented with mild cognitive-communicative changes and 30% of patients presented with mild-moderate cognitive-communicative changes. SLT interventions included conversational strategies, language strategies, language tasks and conversation partner training. 50% of patients were referred to Primary Care SLT.

Conclusion

SLT interventions support patients’ cognitive-communication abilities in dementia. SLT within a MASS adds clinical value by supporting assessment and diagnosis of dementia and developing patients’ communication profiles to highlight communicative abilities. Furthermore, SLT input supports developing communication strategies for the patient and communication partner training. In summary these interventions support improved quality of life and well-being for the person with dementia and their family.

Clinical characteristics with inflammation profiling of long COVID and association with 1-year recovery following hospitalisation in the UK: a prospective observational study

BACKGROUND

No effective pharmacological or non-pharmacological interventions exist for patients with long COVID. We aimed to describe recovery 1 year after hospital discharge for COVID-19, identify factors associated with patient-perceived recovery, and identify potential therapeutic targets by describing the underlying inflammatory profiles of the previously described recovery clusters at 5 months after hospital discharge.

METHODS

The Post-hospitalisation COVID-19 study (PHOSP-COVID) is a prospective, longitudinal cohort study recruiting adults (aged ≥18 years) discharged from hospital with COVID-19 across the UK. Recovery was assessed using patient-reported outcome measures, physical performance, and organ function at 5 months and 1 year after hospital discharge, and stratified by both patient-perceived recovery and recovery cluster. Hierarchical logistic regression modelling was performed for patient-perceived recovery at 1 year. Cluster analysis was done using the clustering large applications k-medoids approach using clinical outcomes at 5 months. Inflammatory protein profiling was analysed from plasma at the 5-month visit. This study is registered on the ISRCTN Registry, ISRCTN10980107, and recruitment is ongoing.

FINDINGS

2320 participants discharged from hospital between March 7, 2020, and April 18, 2021, were assessed at 5 months after discharge and 807 (32·7%) participants completed both the 5-month and 1-year visits. 279 (35·6%) of these 807 patients were women and 505 (64·4%) were men, with a mean age of 58·7 (SD 12·5) years, and 224 (27·8%) had received invasive mechanical ventilation (WHO class 7-9). The proportion of patients reporting full recovery was unchanged between 5 months (501 [25·5%] of 1965) and 1 year (232 [28·9%] of 804). Factors associated with being less likely to report full recovery at 1 year were female sex (odds ratio 0·68 [95% CI 0·46-0·99]), obesity (0·50 [0·34-0·74]) and invasive mechanical ventilation (0·42 [0·23-0·76]). Cluster analysis (n=1636) corroborated the previously reported four clusters: very severe, severe, moderate with cognitive impairment, and mild, relating to the severity of physical health, mental health, and cognitive impairment at 5 months. We found increased inflammatory mediators of tissue damage and repair in both the very severe and the moderate with cognitive impairment clusters compared with the mild cluster, including IL-6 concentration, which was increased in both comparisons (n=626 participants). We found a substantial deficit in median EQ-5D-5L utility index from before COVID-19 (retrospective assessment; 0·88 [IQR 0·74-1·00]), at 5 months (0·74 [0·64-0·88]) to 1 year (0·75 [0·62-0·88]), with minimal improvements across all outcome measures at 1 year after discharge in the whole cohort and within each of the four clusters.

INTERPRETATION

The sequelae of a hospital admission with COVID-19 were substantial 1 year after discharge across a range of health domains, with the minority in our cohort feeling fully recovered. Patient-perceived health-related quality of life was reduced at 1 year compared with before hospital admission. Systematic inflammation and obesity are potential treatable traits that warrant further investigation in clinical trials.

FUNDING

UK Research and Innovation and National Institute for Health Research.

Preferences of European dairy stakeholders in breeding for resilient and efficient cattle: A best-worst scaling approach

Including resilience in the breeding objective of dairy cattle is gaining increasing attention, primarily as anticipated challenges to production systems, such as climate change, may make some perturbations more difficult to moderate at the farm level. Consequently, the underlying biological mechanisms by which resilience is achieved are likely to become an important part of the system itself, increasing value on the animal's ability to be unperturbed by variable production circumstances, or to quickly return to pre-perturbed levels of productivity and health. However, because the value of improving genetic traits to a system is usually based on known profit functions or bioeconomic models linked to current production conditions, it can be difficult to define longer-term value, especially under uncertain future production circumstances and where nonmonetary values may be progressively more important. We present the novel application of a discrete choice experiment, used to investigate potential antagonisms in the values of genetic improvements for 8 traits to dairy cattle system stakeholders in Europe when the production goal was either efficiency or resilience. A latent class model was used to identify heterogeneous preferences within each production goal, and postestimation was used to identify associations between these preferences and sociodemographic characteristics of respondents. Results suggested 3 distinct latent preference classes for each production goal. For the efficiency goal, yield and feed efficiency traits were generally highly valued, whereas for the resilience goal, health and robustness traits were generally highly valued. In both cases, these traits generally carried a low value in the other production scenario. Overall, in both scenarios, longevity was highly valued; however, the value of this trait in terms of resilience will depend on phenotyping across diverse environments to sufficiently capture performance under various anticipated system challenges. Additionally, results showed significant associations between membership of latent preference classes with education level and profession. In conclusion, as resilience becomes increasingly important, it is likely that a continued reliance on the short-term economic value of traits alone will lead decision makers to misrepresent the importance of some traits, including those with substantial contextual values in terms of resilience.

Predicting pregnancy status from mid-infrared spectroscopy in dairy cow milk using deep learning

Accurately identifying pregnancy status is imperative for a profitable dairy enterprise. Mid-infrared (MIR) spectroscopy is routinely used to determine fat and protein concentrations in milk samples. Mid-infrared spectra have successfully been used to predict other economically important traits, including fatty acid content, mineral content, body energy status, lactoferrin, feed intake, and methane emissions. Machine learning has been used in a variety of fields to find patterns in vast quantities of data. This study aims to use deep learning, a sub-branch of machine learning, to establish pregnancy status from routinely collected milk MIR spectral data. Milk spectral data were obtained from National Milk Records (Chippenham, UK), who collect large volumes of data continuously on a monthly basis. Two approaches were followed: using genetic algorithms for feature selection and network design (model 1), and transfer learning with a pretrained DenseNet model (model 2). Feature selection in model 1 showed that the number of wave points in MIR data could be reduced from 1,060 to 196 wave points. The trained model converged after 162 epochs with validation accuracy and loss of 0.89 and 0.18, respectively. Although the accuracy was sufficiently high, the loss (in terms of predicting only 2 labels) was considered too high and suggested that the model would not be robust enough to apply to industry. Model 2 was trained in 2 stages of 100 epochs each with spectral data converted to gray-scale images and resulted in accuracy and loss of 0.97 and 0.08, respectively. Inspection on inference data showed prediction sensitivity of 0.89, specificity of 0.86, and prediction accuracy of 0.88. Results indicate that milk MIR data contains features relating to pregnancy status and the underlying metabolic changes in dairy cows, and such features can be identified by means of deep learning. Prediction equations from trained models can be used to alert farmers of nonviable pregnancies as well as to verify conception dates.

Genetic parameters for carcase measurements and age at slaughter in commercial cattle

Genetic parameters were estimated for cold carcase weight (CCW), carcase conformation (CON), carcase fat class (FAT), age at slaughter (AGE) and average daily carcase gain (ADCG) in 14 common UK breeds of cattle. These included crossbred animals but purebred datasets were also analysed for the most populous sire-breeds. Heritability estimates for beef breeds that were significant ranged from 0.24 to 0.44, 0.12 to 0.35, 0.12 to 0.36, 0.15 to 0.38 and 0.26 to 0.43 for CCW, CON, FAT, AGE and ADCG, respectively. For Holstein-Friesian, a dairy breed, heritability estimates were consistently lower than most beef breeds with estimates of 0.12, 0.13, 0.13, 0.06 and 0.15 for CCW, CON, FAT, AGE and ADCG, respectively. In all breed groups, genetic correlations were positive between CCW, CON and ADCG. In general, genetic correlations were moderate between CCW and CON (0.13 to 0.77), moderate to strong between CCW and ADCG (0.57 to 0.98) and weak or moderate between CON and ADCG (0.12 to 0.82). Genetic correlations for FAT with CCW (- 0.20 to - 0.42) and CON (- 0.16 to - 0.52) tended to be negative in the beef breed but were positive in the dairy breed, although not significant between CCW and FAT. For most beef breeds genetic correlations between AGE and carcase traits were not significant with the exceptions of AGE and CCW for Simmental (- 0.15) and Salers (- 0.24), AGE and CON for Limousin (0.15) and Simmental (0.14) and AGE and FAT from three sire-breeds (- 0.17 to - 0.35). However, the correlation between AGE and ADCG was negative and moderate to strong in magnitude (- 0.23 to - 0.67) in all beef breeds as expected since faster-growing animals reach slaughter age earlier. For Holstein-Friesian, all genetic correlations with AGE were negative and moderate to strong. Genetic correlations indicate that selection for increased carcase weight should simultaneously increase growth rate and improve conformation in all breeds and reduce carcase fatness in the majority of beef breeds. The results indicate that there is genetic variation in all five traits suitable for undertaking genetic improvement of carcase traits and age at slaughter; however, there are apparent breed differences. The use of abattoir-derived phenotypes for undertaking genetic improvement is an example where the supply chain can work together to share information to enable the cattle industry to move forward.

Predicting bovine tuberculosis status of dairy cows from mid-infrared spectral data of milk using deep learning

Bovine tuberculosis (bTB) is a zoonotic disease in cattle that is transmissible to humans, distributed worldwide, and considered endemic throughout much of England and Wales. Mid-infrared (MIR) analysis of milk is used routinely to predict fat and protein concentration, and is also a robust predictor of several other economically important traits including individual fatty acids and body energy. This study predicted bTB status of UK dairy cows using their MIR spectral profiles collected as part of routine milk recording. Bovine tuberculosis data were collected as part of the national bTB testing program for Scotland, England, and Wales; these data provided information from over 40,500 bTB herd breakdowns. Corresponding individual cow life-history data were also available and provided information on births, movements, and deaths of all cows in the study. Data relating to single intradermal comparative cervical tuberculin (SICCT) skin-test results, culture, slaughter status, and presence of lesions were combined to create a binary bTB phenotype labeled 0 to represent nonresponders (i.e., healthy cows) and 1 to represent responders (i.e., bTB-affected cows). Contemporaneous individual milk MIR spectral data were collected as part of monthly routine milk recording and matched to bTB status of individual animals on the single intradermal comparative cervical tuberculin test date (±15 d). Deep learning, a sub-branch of machine learning, was used to train artificial neural networks and develop a prediction pipeline for subsequent use in national herds as part of routine milk recording. Spectra were first converted to 53 × 20-pixel PNG images, then used to train a deep convolutional neural network. Deep convolutional neural networks resulted in a bTB prediction accuracy (i.e., the number of correct predictions divided by the total number of predictions) of 71% after training for 278 epochs. This was accompanied by both a low validation loss (0.71) and moderate sensitivity and specificity (0.79 and 0.65, respectively). To balance data in each class, additional training data were synthesized using the synthetic minority over sampling technique. Accuracy was further increased to 95% (after 295 epochs), with corresponding validation loss minimized (0.26), when synthesized data were included during training of the network. Sensitivity and specificity also saw a 1.22- and 1.45-fold increase to 0.96 and 0.94, respectively, when synthesized data were included during training. We believe this study to be the first of its kind to predict bTB status from milk MIR spectral data. We also believe it to be the first study to use milk MIR spectral data to predict a disease phenotype, and posit that the automated prediction of bTB status at routine milk recording could provide farmers with a robust tool that enables them to make early management decisions on potential reactor cows, and thus help slow the spread of bTB.

Genetic analyses of live weight and carcass composition traits in purebred Texel, Suffolk and Charollais lambs

Lamb live weight is one of the key drivers of profitability on sheep farms. Previous studies in Ireland have estimated genetic parameters for live weight and carcass composition traits using a multi-breed population rather than on an individual breed basis. The objective of the present study was to undertake genetic analyses of three lamb live weight and two carcass composition traits pertaining to purebred Texel, Suffolk and Charollais lambs born in the Republic of Ireland between 2010 and 2017, inclusive. Traits (with lamb age range in parenthesis) considered in the analyses were: pre-weaning weight (20 to 65 days), weaning weight (66 to 120 days), post-weaning weight (121 to 180 days), muscle depth (121 to 180 days) and fat depth (121 to 180 days). After data edits, 137 402 records from 50 372 lambs across 416 flocks were analysed. Variance components were derived using animal linear mixed models separately for each breed. Fixed effects included for all traits were contemporary group, age at first lambing of the dam, parity of the dam, a gender by age of the lamb interaction and a birth type by rearing type of the lamb interaction. Random effects investigated in the pre-weaning and weaning weight analyses included animal direct additive genetic, dam maternal genetic, litter common environment, dam permanent environment and residual variances. The model of analysis for post-weaning, muscle and fat depth included an animal direct additive genetic and litter common environment effect only. Significant direct additive genetic variation existed in all cases. Direct heritability for pre-weaning weight ranged from 0.14 to 0.30 across the three breeds. Weaning weight had a direct heritability ranging from 0.17 to 0.27 and post-weaning weight had a direct heritability ranging from 0.15 to 0.27. Muscle and fat depth heritability estimates ranged from 0.21 to 0.31 and 0.15 to 0.20, respectively. Positive direct correlations were evident for all traits. Results revealed ample genetic variation among animals for the studied traits and significant differences between breeds to suggest that genetic evaluations could be conducted on a per-breed basis.

Heteropaternal superfecundation frequently occurs in multiple-bearing mob-mated sheep

Heteropaternal superfecundation may be defined as the fertilisation of two or more ova during the same oestrus cycle as a result of more than one coital act from different males; this results in foetuses being born in the same litter of the same age but different paternity. Heteropaternal superfecundation is more likely to occur in poly-ovulatory species like sheep; moreover, female sheep are often mob-mated with several rams concurrently, thus providing an opportunity for a given female to be served by multiple males during the same oestrus cycle. The objective of the present study was to determine the frequency of heteropaternal superfecundation in six sheep flocks where most of the ewes, lambs and rams were genotyped. A total of 685 multiple-birth litters were available where the sire, dam and all lambs were genotyped. Of the 539 pairs of twins included in the analysis, 160 (i.e. 30%) were sired by two different rams. Of the 137 sets of triplets included in the analysis, 73 (i.e. 53%) were sired by more than one ram. Of the nine sets of quadruplets, eight were sired by two rams with the remaining litter being mono-paternal. The overall incidence of heteropaternal superfecundation among litters was therefore 35%. Given that the incidence of multiple births in these flocks was 65%, heteropaternal superfecundation is expected to be relatively common in sheep; this is especially true as all but two of the litter-mates were polyzygotic. Genotyping of progeny is one practical solution to identity such individuals.

Energy profiling of dairy cows from routine milk mid-infrared analysis

The balance of body energy within and across lactations can have health and fertility consequences for the dairy cow. This study aimed to create a large calibration data set of dairy cow body energy traits across the cow's productive life, with concurrent milk mid-infrared (MIR) spectral data, to generate a prediction tool for use in commercial dairy herds. Detailed phenotypic data from 1,101 Holstein Friesian cows from the Langhill research herd (SRUC, Scotland) were used to generate energy balance (EB) and effective energy intake (EI), both in megajoules per day. Pretreatment of spectral data involved standardization to account for drift over time and machine. Body energy estimates were aligned with their spectral data to generate a prediction of these traits based on milk MIR spectroscopy. After data edits, partial least squares analysis generated prediction equations with a coefficient of determination from split sample 10-fold cross validation of 0.77 and 0.75 for EB and EI, respectively. These prediction equations were applied to national milk MIR spectra on over 11 million animal test dates (January 2013 to December 2016) from 4,453 farms. The predictions generated from these were subject to phenotypic analyses with a fixed regression model highlighting differences between the main dairy breeds in terms of energy traits. Genetic analyses generated heritability estimates for EB and EI ranging from 0.12 to 0.17 and 0.13 to 0.15, respectively. This study shows that MIR-based predictions from routinely collected national data can be used to generate predictions of dairy cow energy turnover profiles for both animal management and genetic improvement of such difficult and expensive-to-record traits.

Short communication: Heritability of methane production and genetic correlations with milk yield and body weight in Holstein-Friesian dairy cows

Greenhouse gases originating from the dairy sector, including methane (CH₄), contribute to global warming. A possible strategy to reduce CH₄ production is to use genetic selection. This requires genetic parameters for CH₄ production and correlations with production traits. Data were available on 184 Holstein-Friesian cows. Methane production was measured in the milking robot during milking from December 2009 to April 2010. In total 2,456 observations for CH₄ production were available. Milk yield (MY) and body weight (BW) were obtained at every milking from November 2008 to October 2010. In total 4,567 observations for milk yield and 4,570 observations for BW were available. Restricted maximum likelihood, using random regression models, was used to analyze the data. Heritability (standard error given in parentheses) for CH₄ production ranged from 0.12 (0.16) to 0.45 (0.11), and genetic correlations with MY ranged from 0.49 (0.12) to 0.54 (0.26). The positive genetic correlation between CH₄ production and milk yield indicates that care needs to be taken when genetically selecting for lower CH₄ production, to avoid a decrease in MY at the animal level. However, this study shows that CH₄ production is moderately heritable and therefore progress through genetic selection is possible.

Impact of alternative definitions of contemporary groups on genetic evaluations of traits recorded at lambing

The objective of this study was to quantify the impact of alternative contemporary group definitions for lambing traits on genetic evaluations in the Irish multibreed sheep population. Three lambing traits were considered for analysis: lambing difficulty, birth weight, and survival. Eight alternative contemporary group definitions were investigated for each lambing trait; all contemporary groups were formed within flock of lambing and included (flock by) week of lambing, week of lambing by litter size (i.e., singles vs. multiples), 2-wk interval (i.e., fortnight) of lambing, fortnight of lambing by litter size, month of lambing, and month of lambing by litter size or were based on an optimized algorithm that creates contemporary groups based on animals from the same flock that are born in close proximity of date. Three alternative scenarios were modeled for each of the lambing traits using the contemporary group definitions: the first scenario (termed Current Scenario) represented the editing criteria currently employed in the Irish national genetic evaluations; the second scenario (No Restriction Scenario) removed any restriction on number of records per contemporary group, and the final scenario (Variation Scenario) included only data from contemporary groups with some variability in the dependent variable. Variance components and EBV for each of the 3 lambing traits were estimated using linear mixed models. The direct heritability estimates ranged from 0.09 ± 0.02 to 0.29 ± 0.02 for lambing difficulty, 0.11 ± 0.01 to 0.24 ± 0.01 for birth weight, and 0.05 ± 0.02 to 0.10 ± 0.02 for lamb survival. Irrespective of lambing trait, greater estimated accuracy of the sire EBV was achieved with the No Restriction Scenario. Results for the ability to predict future lambing characteristics, based on only the direct and maternal EBV, revealed that the area under the receiver operator characteristic curve for the dichotomized lambing assistance phenotype varied from 0.56 to 0.66; a lambing event predicted to be in the worst 10% risk category of a difficult lambing on the basis of genetic merit alone was 5.48 times (95% CI: 3.94 to 7.61; < 0.001) more likely to require assistance at lambing compared to a lambing event in the best 10%. Results show that the use of contemporary groups formed over short time periods, coupled with moderate editing of the data, yielded superior predictions for all lambing traits.

Impact of birth and rearing type, as well as inaccuracy of recording, on pre-weaning lamb phenotypic and genetic merit for live weight

The objective of the present study was to quantify the impact of the systematic environmental effects of both birth and rearing type on pre-weaning lamb live weight, and to evaluate the repercussions of inaccurate recording of birth and rearing type on subsequent genetic evaluations. A total of 32,548 birth weight records, 35,770 forty-day weight records and 32,548 records for average daily gain (ADG) between birth and 40-day weight from the Irish national sheep database were used. For each lamb, a new variable, birth-rearing type, reflecting both the birth and rearing type of a lamb was generated by concatenating both parameters. The association between birth-rearing type and birth weight, 40-day weight, and ADG was estimated using linear mixed models. The repercussions of inaccurate recording of birth type were determined by quantifying the impact on sire estimated breeding value (EBV; with an accuracy of ≥ 35%), where one of the lambs born in a selection of twin litter births was assumed to have died at birth but the farmer recorded the birth and rearing type as a singleton. The heaviest mean birth weight was associated with lambs born and subsequently reared as singles (5.47 kg); the lightest mean birth weight was associated with lambs born and reared as triplets (4.10 kg). The association between birth-rearing type and 40-day weight differed by dam parity (P < 0.001). Lambs reared by first parity dams as singles, irrespective of birth type were, on average, heavier at 40-day weighing than lambs reared as multiples, but as parity number increased, single-born lambs reared as twins outperformed triplet-born lambs reared as singles. Irrespective of the trait evaluated, the correlation between sire EBV estimated from the accurately recorded data and sire EBV estimated from the data with recording errors was strong ranging from 0.93 (birth weight) to 0.97 (ADG). The EBV for sires with progeny data manipulated were 0.14 kg, 0.34 kg and 5.56 g/d less for birth weight, 40-day weight and ADG, respectively, compared to their equivalent EBV calculated using accurately recorded data. Results from this study highlight the importance of precise recording of birth-rearing type by producers for the generation of accurate genetic evaluations.

Genetic parameters for predicted methane production and laser methane detector measurements

Enteric ruminant methane is the most important greenhouse gas emitted from the pastoral agricultural systems. Genetic improvement of livestock provides a cumulative and permanent impact on performance, and using high-density SNP panels can increase the speed of improvement for most traits. In this study, a data set of 1,726 dairy cows, collected since 1990, was used to calculate a predicted methane emission (PME) trait from feed and energy intake and requirements based on milk yield, live weight, feed intake, and condition score data. Repeated measurements from laser methane detector (LMD) data were also available from 57 cows. The estimated heritabilities for PME, milk yield, DMI, live weight, condition score, and LMD data were 0.13, 0.25, 0.11, 0.92, 0.38, and 0.05, respectively. There was a high genetic correlation between DMI and PME. No SNP reached the Bonferroni significance threshold for the PME traits. One SNP was within the 3 best SNP for PME at wk 10, 20, 30, and 40. Genomic prediction accuracies between dependent variable and molecular breeding value ranged between 0.26 and 0.30. These results are encouraging; however, more work is required before a PME trait can be implemented in a breeding program.

Selection for 'environmentaI fit' from existing domesticated species

The selection of farm animals through breeding for human benefit has a very long history. In more recent times the practice of animal breeding has become highly sophisticated and the speed of change in 'production traits' such as rate of growth and milk yield has correspondingly increased dramatically. This narrow focus on production traits led to a number of well-documented examples of 'unfavourable' correlated responses such as negative fertility and health issues in high-yielding dairy cattle, with concerns that animal breeding is inherently antagonistic to animal welfare. In this paper the authors explore some of the questions surrounding breeding and welfare and, specifically, how to conceptualise and improve the 'fit' between the selected animal and the environment, or system, in which the animal is reared and managed. The authors conclude that there is a need for a better understanding of genotype x environment effects on health and welfare traits in order to inform the development of breeding programmes that lead to improved environmental fit in animals. They also see the need for the development of valid traits for assessing health and welfare, greater consideration of early life effects that can also potentially affect environmental fit and a need to consider the impacts of climate change on breeding programmes.

Imputation of genotypes from low density (50,000 markers) to high density (700,000 markers) of cows from research herds in Europe, North America, and Australasia using 2 reference populations

Combining data from research herds may be advantageous, especially for difficult or expensive-to-measure traits (such as dry matter intake). Cows in research herds are often genotyped using low-density single nucleotide polymorphism (SNP) panels. However, the precision of quantitative trait loci detection in genome-wide association studies and the accuracy of genomic selection may increase when the low-density genotypes are imputed to higher density. Genotype data were available from 10 research herds: 5 from Europe [Denmark, Germany, Ireland, the Netherlands, and the United Kingdom (UK)], 2 from Australasia (Australia and New Zealand), and 3 from North America (Canada and the United States). Heifers from the Australian and New Zealand research herds were already genotyped at high density (approximately 700,000 SNP). The remaining genotypes were imputed from around 50,000 SNP to 700,000 using 2 reference populations. Although it was not possible to use a combined reference population, which would probably result in the highest accuracies of imputation, differences arising from using 2 high-density reference populations on imputing 50,000-marker genotypes of 583 animals (from the UK) were quantified. The European genotypes (n=4,097) were imputed as 1 data set, using a reference population of 3,150 that included genotypes from 835 Australian and 1,053 New Zealand females, with the remainder being males. Imputation was undertaken using population-wide linkage disequilibrium with no family information exploited. The UK animals were also included in the North American data set (n=1,579) that was imputed to high density using a reference population of 2,018 bulls. After editing, 591,213 genotypes on 5,999 animals from 10 research herds remained. The correlation between imputed allele frequencies of the 2 imputed data sets was high (>0.98) and even stronger (>0.99) for the UK animals that were part of each imputation data set. For the UK genotypes, 2.2% were imputed differently in the 2 high-density reference data sets used. Only 0.025% of these were homozygous switches. The number of discordant SNP was lower for animals that had sires that were genotyped. Discordant imputed SNP genotypes were most common when a large difference existed in allele frequency between the 2 imputed genotype data sets. For SNP that had ≥ 20% discordant genotypes, the difference between imputed data sets of allele frequencies of the UK (imputed) genotypes was 0.07, whereas the difference in allele frequencies of the (reference) high-density genotypes was 0.30. In fact, regions existed across the genome where the frequency of discordant SNP was higher. For example, on chromosome 10 (centered on 520,948 bp), 52 SNP (out of a total of 103 SNP) had ≥ 20% discordant SNP. Four hundred and eight SNP had more than 20% discordant genotypes and were removed from the final set of imputed genotypes. We concluded that both discordance of imputed SNP genotypes and differences in allele frequencies, after imputation using different reference data sets, may be used to identify and remove poorly imputed SNP.

Direct and maternal genetic relationships between calving ease, gestation length, milk production, fertility, type, and lifespan of Holstein-Friesian primiparous cows

As the emphasis in cattle breeding is shifting from traits that increase income toward traits that reduce costs, national breeding indices are expanding to include functional traits such as calving ease (CE). However, one issue is the lack of knowledge of genetic relationships between CE and other dairy traits. The same can be said about gestation length (GL), a potential novel selection trait with considerable heritabilities and possible genetic relationships with the calving process. This study aimed to estimate the genetic relationships between CE, GL, and other dairy traits of interest using a national data set of 31,053 primiparous cow performance records, as well as to separate direct and maternal genetic effects. Chosen dairy traits included fertility (calving interval, days to first service, nonreturn rate after 56 d, number of inseminations per conception), milk production (milk yield at d 110 in milk, accumulated 305-d milk yield, accumulated 305-d fat yield, accumulated 305-d protein yield), type (udder depth, chest width, rump width, rump angle, mammary composition, stature, body depth), and lifespan traits (functional days of productive life). To allow the separation of direct and maternal genetic effects, a random sire of the calf effect was included in the multi-trait linear trivariate sire models fitted using ASReml. Significant results showed that easily born individuals were genetically prone to high milk yield and reduced fertility in first lactation. Difficult calving primiparous cows were likely associated with being high-producing, wide and deep animals, with a reduced ability to subsequently conceive. Individuals that were born relatively early were associated with good genetic merit for milk production. Finally, individuals carrying their offspring longer were genetically associated with being wide and large animals that were themselves born relatively early. The study shows that it is feasible and valuable to separate direct and maternal effects when estimating genetic correlations between calving and other dairy traits. Furthermore, gestation length is best used as an indicator trait for lowly heritable calving traits, rather than as a novel selection trait. As estimated direct and maternal genetic correlations differ, we can conclude that genetic relationships between CE, GL, and traits of interest are present, but caution is required if these traits are implemented in national breeding indices.

Understanding the genetics of survival in dairy cows

Premature mortality and culling causes great wastage in the dairy industry, as a large number of heifers born never become productive or are culled before their full lactation potential is reached. The objectives of this study were to characterize survival and estimate genetic parameters for alternative longevity traits that considered (1) the survival of replacement heifers and (2) functional longevity of milking cows in the UK Holstein Friesian population, using combined information from the British Cattle Movement Service and milk recording organizations. Mortality of heifers was highest in the first month of life and was proportionately highest in calves born during winter months. Heifer mortality tended to decrease with age until about 16 mo onward; it then gradually increased, expected to be associated with culls due to reproductive failure or problems during pregnancy and calving. In milking cows, days of productive life (DPL) was analyzed as an alternative to the current trait lifespan score. Cows that died in 2009 on average lived for 6.8 yr with an average production of 4.3 yr. Heritability estimates were low for both heifer and cow survival and were ~0.01 and ~0.06, respectively. The positive genetic correlation between heifer survival with lifespan score (0.31) indicates that bulls that sire daughters with longer productive lives are also likely to have calves that survive and become replacement heifers. However, the magnitude of the genetic correlation suggests that survival in the rearing period and the milking herd are different traits. Genetic correlations were favorable between DPL with somatic cell count and fertility traits indicating that animals with a longer productive life tend to have lower somatic cell count, a shorter calving interval, fewer days to first service, and require fewer inseminations. However, an antagonistic relationship existed between DPL with milk and fat yield traits.

Validation of mid-infrared spectrometry in milk for predicting body energy status in Holstein-Friesian cows

Cow energy balance is known to be associated with cow health and fertility; therefore, routine access to data on energy balance can be useful in both management and breeding decisions to improve cow performance. The objective of this study was to determine if individual cow milk mid-infrared spectra (MIR) could be useful to predict cow energy balance across contrasting production systems. Direct energy balance was calculated as the differential between energy intake and energy output in milk and maintenance (maintenance was predicted using body weight). Body energy content was calculated from (change in) body weight and body condition score. Following editing, 2,992 morning, 2,742 midday, and 2,989 evening milk MIR records from 564 lactations on 337 Scottish cows, managed in a confinement system on 1 of 2 diets, were available. An additional 844 morning and 820 evening milk spectral records from 338 lactations on 244 Irish cows offered a predominantly grazed grass diet were also available. Equations were developed to predict body energy status using the milk spectral data and milk yield as predictor variables. Several different approaches were used to test the robustness of the equations calibrated in one data set and validated in another. The analyses clearly showed that the variation in the validation data set must be represented in the calibration data set. The accuracy (i.e., square root of the coefficient of multiple determinations) of predicting, from MIR, direct energy balance, body energy content, and energy intake was 0.47 to 0.69, 0.51 to 0.56, and 0.76 to 0.80, respectively. This highlights the ability of milk MIR to predict body energy balance, energy content, and energy intake with reasonable accuracy. Very high accuracy, however, was not expected, given the likely random errors in the calculation of these energy status traits using field data.

Improved accuracy of genomic prediction for dry matter intake of dairy cattle from combined European and Australian data sets

With the aim of increasing the accuracy of genomic estimated breeding values for dry matter intake (DMI) in dairy cattle, data from Australia (AU), the United Kingdom (UK), and the Netherlands (NL) were combined using both single-trait and multi-trait models. In total, DMI records were available on 1,801 animals, including 843 AU growing heifers with records on DMI measured over 60 to 70 d at approximately 200 d of age, and 359 UK and 599 NL lactating heifers with records on DMI during the first 100 d in milk. The genotypes used in this study were obtained from the Illumina Bovine 50K chip (Illumina Inc., San Diego, CA). The AU, UK, and NL genomic data were matched using the single nucleotide polymorphism (SNP) name. Quality controls were applied by carefully comparing the genotypes of 40 bulls that were available in each data set. This resulted in 30,949 SNP being used in the analyses. Genomic predictions were estimated with genomic REML, using ASReml software. The accuracy of genomic prediction was evaluated in 11 validation sets; that is, at least 3 validation sets per country were defined. The reference set (in which animals had both DMI phenotypes and genotypes) was either AU or Europe (UK and NL) or a multi-country reference set consisting of all data except the validation set. When DMI for each country was treated as the same trait, use of a multi-country reference set increased the accuracy of genomic prediction for DMI in UK, but not in AU and NL. Extending the model to a bivariate (AU-EU) or trivariate (AU-UK-NL) model increased the accuracy of genomic prediction for DMI in all countries. The highest accuracies were estimated for all countries when data were analyzed with a trivariate model, with increases of up to 5.5% compared with univariate models within countries.

Genomic associations with somatic cell score in first-lactation Holstein cows

This genome-wide association study aimed to identify loci associated with lactation-average somatic cell score (LASCS) and the standard deviation of test-day somatic cell score (SCS-SD). It is one of the first studies to combine detailed phenotypic and genotypic cow data from research dairy herds located in different countries. The combined data set contained up to 52 individual test-days per lactation and thereby aimed to capture temporary increases in somatic cell score associated with infection. Phenotypic data for analysis consisted of 46,882 test-day records on 1,484 cows, and genotypic data consisted of 37,590 single nucleotide polymorphisms (SNP). Using an animal model, the associations between each individual SNP and the phenotypic data were estimated. To account for the risk of false positives, a false discovery rate threshold of 0.20 was set. The analyses showed that LASCS was significantly associated with a SNP on Bos taurus autosome (BTA) 4 and a SNP on BTA18. Likewise, SCS-SD was associated with this SNP on BTA18. In addition, SCS-SD significantly associated with a SNP on BTA6. Relatively few associations were found, suggesting that LASCS and SCS-SD are controlled by multiple loci distributed across the genome, each with a relatively small effect. Increased knowledge on genetic regulation of LASCS and SCS-SD may aid in identification of genes that play a role in mastitis resistance. Such knowledge helps us understand the genetic mechanisms leading to mastitis and in discovery of targets for mastitis therapeutics.

Phenotypic effects of calving ease on the subsequent fertility and milk production of dam and calf in UK Holstein-Friesian heifers

The effect of calving ease on the fertility and production performance of both dam and calf was studied in approximately 50,000 and 10,000 UK Holstein-Friesian heifers and heifer calves, respectively. The first objective of this study was to estimate the effect of a difficult calving on the subsequent first-lactation milk production by estimating lactation curves using cubic splines. This methodology allows the estimation of daily milk, protein, and fat yields following calvings of differing degrees of difficulty. Losses in milk yield after a difficult calving have been quantified previously; however, estimates are generally restricted to the accumulated yields at specific days in lactation. By fitting cubic splines, gaps (in which the shape of the lactation curve can be merely guessed) between estimations were avoided. The second objective of this study was to estimate the effect of a difficult birth on the subsequent performance of the calf as an adult animal. Even though the calving process is known to involve cooperation between dam and calf, the effect of a difficult calving has, until now, only been estimated for the subsequent performance of the dam. Addressing the effects of a difficult birth on the adult calf strengthens the importance of calving ease as a selection trait because it suggests that the benefit of genetic improvement may currently be underestimated. The effect of calving ease on the subsequent reproductive performance of dam and calf was analyzed using linear regression and with calving ease score fitted as a fixed effect. Dams with veterinary-assisted calvings required 0.7 more services to conception and 8 more days to first service and experienced a 28-d longer calving interval in first lactation compared with dams that were not assisted at calving. Effects of calving ease on the reproductive performance of the adult calf in first lactation were not detected. Losses in milk yield of the dam were significant between d 9 to 90 in milk subsequent to a veterinary-assisted calving, creating a loss of approximately 2 kg of milk per day, compared with a nonassisted calving. Calves being born with difficulties showed a significant reduction in milk yield in first lactation, demonstrating the lifelong effect of a difficult birth. Compared with nonassisted calves, veterinary-assisted calves showed a loss of 710 kg in accumulated 305-d milk yield, which was significant from 129 to 261 d in milk. This suggests that from birth to production, physiological effects of a bad calving are not negated. Results furthermore suggest a beneficial effect of farmer assistance at calving on the milk yield of both dam and calf, when moderate difficulties occurred.

The use of mid-infrared spectrometry to predict body energy status of Holstein cows

Energy balance, especially in early lactation, is known to be associated with subsequent health and fertility in dairy cows. However, its inclusion in routine management decisions or breeding programs is hindered by the lack of quick, easy, and inexpensive measures of energy balance. The objective of this study was to evaluate the potential of mid-infrared (MIR) analysis of milk, routinely available from all milk samples taken as part of large-scale milk recording and milk payment operations, to predict body energy status and related traits in lactating dairy cows. The body energy status traits investigated included energy balance and body energy content. The related traits of body condition score and energy intake were also considered. Measurements on these traits along with milk MIR spectral data were available on 17 different test days from 268 cows (418 lactations) and were used to develop the prediction equations using partial least squares regression. Predictions were externally validated on different independent subsets of the data and the results averaged. The average accuracy of predicting body energy status from MIR spectral data was as high as 75% when energy balance was measured across lactation. These predictions of body energy status were considerably more accurate than predictions obtained from the sometimes proposed fat-to-protein ratio in milk. It is not known whether the prediction generated from MIR data are a better reflection of the true (unknown) energy status than the actual energy status measures used in this study. However, results indicate that the approach described may be a viable method of predicting individual cow energy status for a large scale of application.