Strategies and tools for genetic selection in dairy cattle and their application to improving animal welfare

Genetic improvement of farm animals, especially selection within breeds focussed on high production and efficiency, is often cited as a potential threat to animal welfare. However, many animal welfare issues can be addressed, at least partially, by animal breeding and genetics. In this chapter, we explore the relationship between genetic selection and animal welfare, the strategies and tools for genetic improvement and how they can contribute to improved animal welfare. A growing public awareness of animal welfare and environmental issues has led to breeding goals being broadened beyond farmer profitability. As animal welfare and behaviour are complex and multi-factorial, so the emergence of selection indices that include a large number of traits to optimise animal welfare in a way that is consistent with enterprise sustainability for the farmer is necessary. This trend is likely to continue and will be aided by the advent of new technologies for measuring animal welfare in conjunction with DNA-based predictions of genetic merit (genomic selection). The dairy cattle industry has been exemplary for the application of genomic selection, in addition to enabling selection decisions to be made earlier in life, it can be used to select for traits where it was not possible to select for previously. These include important welfare-related traits, such as improved disease resistance and heat tolerance. Dairy cattle breeding is a very international activity with just a few breeding companies dominating the market in semen for the most numerous breeds, especially the Holstein. Consequently, genetic diversity within breeds is diminishing and although genetic gain has been significant, the rate of inbreeding now presents itself as a threat to the future success of breeding programmes. A greater emphasis on diversity in breeding programmes and the traits under selection is needed as major themes in research and application. Innovation in methods to measure these new traits, (e.g. molecular phenotyping, sensor development, digitalisation data science, etc.) could dramatically transform selection for animal welfare, as these technologies can enable large-scale objective measurements of animal behaviours. In addition to animal-based outcome measures, factors like housing, feeding, specific management practices pose other risks to welfare. Risk factors and their interactions have an impact on the development of diseases or other challenges to welfare. Collaborative efforts between animal behaviour scientists, geneticists, engineers, data scientists, and others will potentially provide solutions to these challenges

On some invariant ideals, and on extension of differentiations to seminormalization

AbstractLet A be a noetherian integral domain, D=(1,D1,…,Di…) be a differentation of A, and B be a ring such that A⊂B⊂Ā. In the paper we mainly prove (whenever Ā is finite over A): (a) if α is the conductor of A in B, then A√α is D-invariant. (b) D extends to the seminormalization +A of A in Ā

Prediction of pregnancy state from milk mid-infrared (MIR) spectroscopy in dairy cows

Submitted 2020-07-14 | Accepted 2020-08-18 | Available 2020-12-01https://doi.org/10.15414/afz.2020.23.mi-fpap.224-232Pregnancy assessment is a very important tool for the reproductive management in efficient and profitable dairy farms. Nowadays, mid-infrared (MIR) spectroscopy is the method of choice in the routine milk recording system for quality control and to determine standard milk components. Since it is well known that there are changes in milk yield and composition during pregnancy, the aim of this study was to develop a discriminant model to predict the pregnancy state from routinely recorded MIR spectral data. The data for this study was from the Austrian milk recording system. Test day records of Fleckvieh, Brown Swiss and Holstein Friesian cows between 3 and 305 days of lactation were included in the study. As predictor variables, the first derivative of 212 selected MIR spectral wavenumbers were used. The data set contained roughly 400,000 records from around 40,000 cows and was randomly split into calibration and validation set by farm. Prediction was done with Partial Least Square Discriminant Analysis. Indicators of model fit were sensitivity, specificity, balanced accuracy and Area Under Receiver Operating Characteristic Curve (AUC). In a first approach, one discriminant model for all cows across the whole lactation and gestation lengths was applied. The sensitivity and specificity of this model in validation were 0.856 and 0.836, respectively. Splitting up the results for different lactation stages showed that the model was not able to predict pregnant cases before the third month of lactation and vice versa not able to predict non-pregnancy after the third month of lactation. Consequently, in the second approach a prediction model for each different (expected) pregnancy stage and lactation stage was developed. Balanced accuracies ranged from 0.523 to 0.918. Whether prediction accuracies from this study are sufficient to provide farmers with an additional tool for fertility management, it needs to be explored in discussions with farmers and breeding organizations.Keywords: MIR spectroscopy, pregnancy prediction, dairy cow, PLSReferencesBalhara, A. K., Gupta, M., Singh, S., Mohanty, A. K., & Singh, I. (2013). Early pregnancy diagnosis in bovines: Current status and future directions. The Scientific World Journal, 2013. hhttps://doi.org/10.1155/2013/958540Bekele, N., Addis, M., Abdela, N., & Ahmed, W. M. (2016). Pregnancy Diagnosis in Cattle for Fertility Management: A Review. Global Veterinaria, 16(4), 355–364. https://doi.org/10.5829/idosi.gv.2016.16.04.103136Benedet, A., Franzoi, M., Penasa, M., Pellattiero, E., & De Marchi, M. (2019). Prediction of blood metabolites from milk mid-infrared spectra in early-lactation cows. Journal of Dairy Science, 102(12), 11298–11307. https://doi.org/10.3168/jds.2019-16937Delhez, P., Ho, P. N., Gengler, N., Soyeurt, H., & Pryce, J. E. (2020). Diagnosing the pregnancy status of dairy cows: How useful is milk mid-infrared spectroscopy? Journal of Dairy Science, 103(4), 3264–3274. https://doi.org/10.3168/jds.2019-17473Egger-Danner, C., Fürst, C., Mayerhofer, M., Rain, C., & Rehling, C. (2018). ZuchtData Jahresbericht 2018. Vienna. [Online]. Available at: https://zar.at/Downloads/Jahresberichte/ZuchtData-Jahresberichte.html. [Accessed: 2020, May 15].Gengler, N., Tijani, A., Wiggans, G. R., & Misztal, I. (1999). Estimation of (Co)variance function coefficients for test day yield with a expectation-maximization restricted maximum likelihood algorithm. Journal of Dairy Science, 82(8), 1849.e1-1849.e23. https://doi.org/10.3168/jds.S0022-0302(99)75417-2Grelet, C., Fernández Pierna, J. A., Dardenne, P., Baeten, V., & Dehareng, F. (2015). Standardization of milk mid-infrared spectra from a European dairy network. Journal of Dairy Science, 98(4), 2150–2160. https://doi.org/10.3168/jds.2014-8764Grelet, C., Bastin, C., Gelé, M., Davière, J. B., Johan, M., Werner, A., Reding, R., Fernandes Pierna, J. A., Colinet, F. G., Dardenne, P., Gendler, N., Soyeurt, H. & Dehareng, F. (2016). Development of Fourier transform mid-infrared calibrations to predict acetone, β-hydroxybutyrate, and citrate contents in bovine milk through a European dairy network. Journal of Dairy Science, 99(6), 4816–4825. https://doi.org/10.3168/jds.2015-10477Hirpa, A., Yehualaw, B., Wube, A., Asnake, A., Jemberu, A., Medicine, V., & Box, P. O. (2018). Review on Pregnancy Diagnosis in Dairy Cows, 9(2), 45–55. https://doi.org/10.5829/idosi.jri.2018.45.55Ho, P. N., Bonfatti, V., Luke, T. D. W., & Pryce, J. E. (2019). Classifying the fertility of dairy cows using milk mid-infrared spectroscopy. Journal of Dairy Science. https://doi.org/10.3168/jds.2019-16412Humblot, P. (2001). Monitor Pregnancy and Determine the Timing , Frequencies and Sources of Embryonic Mortality in Ruminants. Theriogenology, 56(01), 1417–1433.Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 28(5), 1–26.Lainé, A., Bel Mabrouk, H., Dale, L. M., Bastin, C., & Gengler, N. (2014). How to use mid-infrared spectral information from milk recording system to detect the pregnancy status of dairy cows. Communications in Agricultural and Applied Biological Sciences, 79(1), 33–38.Lainé, A., Bastin, C., Grelet, C., Hammami, H., Colinet, F. G., Dale, L. M., Gillon, A., Vandenplas, J., Deharend, F. & Gengler, N. (2017). Assessing the effect of pregnancy stage on milk composition of dairy cows using mid-infrared spectra. Journal of Dairy Science, 100(4), 2863–2876.https://doi.org/10.3168/jds.2016-11736Lantz, B. (2015). Machine Learning with R. Machine Learning (Second Edi). Packt Publishing Ltd. https://doi.org/10.1002/9781119642183.ch14Mineur, A., Köck, A., Grelet, C., Gengler, N., Egger-Danner, C., & Sölkner, J. (2017). First Results in the Use of Milk Mid-infrared Spectra in the Detection of Lameness in Austrian Dairy Cows Genomic evaluation View project MACSUR View project. Agriculturae Conspectus Scientifi Cus, Vol. 82(No. 2 (163-166)), (163-166). Retrieved from https://www.researchgate.net/publication/325450513Olori, V. E., Brotherstone, S., Hill, W. G., & McGuirk, B. J. (1997). Effect of gestation stage on milk yield and composition in Holstein Friesian dairy cattle. Livestock Production Science, 52(2), 167–176. https://doi.org/10.1016/S0301-6226(97)00126-7Pohler, K. G., Franco, G. A., Reese, S. T., Dantas, F. G., Ellis, M. D., & Payton, R. R. (2016). Past, present and future of pregnancy detection methods. Applied Reproductive Strategies in Beef Cattle 7-8 September 2016, 251–259.Rienesl, L., Khayatzadeh, N., Köck, A., Dale, L., Werner, A., Grelet, C., Gengler, N., Auer, F-J., Egger-Danner, C., Massart, X. & Sölkner, J. (2019). Mastitis detection from milk mid-infrared (MIR) spectroscopy in dairy cows. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 67(5), 1221–1226. https://doi.org/10.11118/actaun201967051221Santos, J. E. P., Thatcher, W. W., Chebel, R. C., Cerri, R. L. A., & Galvão, K. N. (2004). The effect of embryonic death rates in cattle on the efficacy of estrus synchronization programs. Animal Reproduction Science, 82–83, 513–535. https://doi.org/10.1016/j.anireprosci.2004.04.015SAS Institute Inc. (2017). SAS software 9.4. SAS Institute Inc., Cary, NC, USA.Soyeurt, H., Dehareng, F., Gengler, N., McParland, S., Wall, E., Berry, D. P., Coffey, P. & Dardenne, P. (2011). Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries. Journal of Dairy Science, 94(4), 1657–1667. https://doi.org/10.3168/jds.2010-3408Soyeurt, H., Bastin, C., Colinet, F. G., Arnould, V. M.-R., Berry, D. P., Wall, E., Dehareng, F., Nguyen, H. N., Pardenne, P., Schefers, J., Vandenplas, J., Weigel, K., Coffey, M., Théron, L., Detilleux, J., Reding, E., Gengler, N. & McParland, S. (2012). Mid-infrared prediction of lactoferrin content in bovine milk: potential indicator of mastitis. Animal, 6(11), 1830–1838. https://doi.org/10.1017/s1751731112000791Toffanin, V., De Marchi, M., Lopez-Villalobos, N., & Cassandro, M. (2015). Effectiveness of mid-infrared spectroscopy for prediction of the contents of calcium and phosphorus, and titratable acidity of milk and their relationship with milk quality and coagulation properties. International Dairy Journal, 41, 68–73. https://doi.org/10.1016/j.idairyj.2014.10.002Vanlierde, A., Vanrobays, M.-L., Dehareng, F., Froidmont, E., Soyeurt, H., McParland, S., S., Lewis, E., Deighton, M. H., Grandl, F., Kreuzer, M., Gredler, B., Dardenne, P. & Gengler, N. (2015). Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra. Journal of Dairy Science, 98(8), 5740–5747. https://doi.org/10.3168/jds.2014-8436Vanlierde, A., Soyeurt, H., Gengler, N., Colinet, F. G., Froidmont, E., Kreuzer, M., Grandl, F., Bell, M., Lund, P., Olijhoek, D. W., Eugéne M., Martin, C., Kuhla, B. & Dehareng, F. (2018). Short communication: Development of an equation for estimating methane emissions of dairy cows from milk Fourier transform mid-infrared spectra by using reference data obtained exclusively from respiration chambers. Journal of Dairy Science, 101(8). https://doi.org/10.3168/jds.2018-14472

Body weight prediction using body size measurements in Fleckvieh, Holstein, and Brown Swiss dairy cows in lactation and dry periods

The objective of this study was to predict cows' body weight from body size measurements and other animal data in the lactation and dry periods. During the whole year 2014, 6306 cows (on 167 commercial Austrian dairy farms) were weighed at each routine performance recording and body size measurements like heart girth (HG), belly girth (BG), and body condition score (BCS) were recorded. Data on linear traits like hip width (HW), stature, and body depth were collected three times a year. Cows belonged to the genotypes Fleckvieh (and Red Holstein crosses), Holstein, and Brown Swiss. Body measurements were tested as single predictors and in multiple regressions according to their prediction accuracy and their correlations with body weight. For validation, data sets were split randomly into independent subsets for estimation and validation. Within the prediction models with a single body measurement, heart girth influenced relationship with body weight most, with a lowest root mean square error (RMSE) of 39.0&thinsp;kg, followed by belly girth (39.3&thinsp;kg) and hip width (49.9&thinsp;kg). All other body measurements and BCS resulted in a RMSE of higher than 50.0 kg. The model with heart and belly girth (ModelHG BG) reduced RMSE to 32.5&thinsp;kg, and adding HW reduced it further to 30.4&thinsp;kg (ModelHG BG HW). As RMSE and the coefficient of determination improved, genotype-specific regression coefficients for body measurements were introduced in addition to the pooled ones. The most accurate equations, ModelHG BG and ModelHG BG HW, were validated separately for the lactation and dry periods. Root mean square prediction error (RMSPE) ranged between 36.5 and 37.0&thinsp;kg (ModelHG BG HW, ModelHG BG, lactation) and 39.9 and 41.3&thinsp;kg (ModelHG BG HW, ModelHG BG, dry period). Accuracy of the predictions was evaluated by decomposing the mean square prediction error (MSPE) into error due to central tendency, error due to regression, and error due to disturbance. On average, 99.6&thinsp;% of the variance between estimated and observed values was caused by disturbance, meaning that predictions were valid and without systematic estimation error. On the one hand, this indicates that the chosen traits sufficiently depicted factors influencing body weight. On the other hand, the data set was very heterogeneous and large. To ensure high prediction accuracy, it was necessary to include body girth traits for body weight estimation.</p

Association of plasma microRNA expression with age, genetic background and functional traits in dairy cattle

Abstract A number of blood circulating microRNAs (miRNAs) are proven disease biomarkers and have been associated with ageing and longevity in multiple species. However, the role of circulating miRNAs in livestock species has not been fully studied. We hypothesise that plasma miRNA expression profiles are affected by age and genetic background, and associated with health and production traits in dairy cattle. Using PCR arrays, we assessed 306 plasma miRNAs for effects of age (calves vs mature cows) and genetic background (control vs select lines) in 18 animals. We identified miRNAs which were significantly affected by age (26 miRNAs) and genetic line (5 miRNAs). Using RT-qPCR in a larger cow population (n = 73) we successfully validated array data for 12 age-related miRNAs, one genetic line-related miRNA, and utilised expression data to associate their levels in circulation with functional traits in these animals. Plasma miRNA levels were associated with telomere length (ageing/longevity indicator), milk production and composition, milk somatic cell count (mastitis indicator), fertility, lameness, and blood metabolites linked with body energy balance and metabolic stress. In conclusion, circulating miRNAs could provide useful selection markers for dairy cows to help improve health, welfare and production performance

A meta-analysis of N-acetylcysteine in contrast-induced nephrotoxicity: unsupervised clustering to resolve heterogeneity

<p>Abstract</p> <p>Background</p> <p>Meta-analyses of N-acetylcysteine (NAC) for preventing contrast-induced nephrotoxicity (CIN) have led to disparate conclusions. Here we examine and attempt to resolve the heterogeneity evident among these trials.</p> <p>Methods</p> <p>Two reviewers independently extracted and graded the data. Limiting studies to randomized, controlled trials with adequate outcome data yielded 22 reports with 2746 patients.</p> <p>Results</p> <p>Significant heterogeneity was detected among these trials (<it>I</it>²= 37%; <it>p </it>= 0.04). Meta-regression analysis failed to identify significant sources of heterogeneity. A modified L'Abbé plot that substituted groupwise changes in serum creatinine for nephrotoxicity rates, followed by model-based, unsupervised clustering resolved trials into two distinct, significantly different (<it>p </it>< 0.0001) and homogeneous populations (<it>I</it>²= 0 and <it>p </it>> 0.5, for both). Cluster 1 studies (<it>n </it>= 18; 2445 patients) showed no benefit (relative risk (RR) = 0.87; 95% confidence interval (CI) 0.68–1.12, <it>p </it>= 0.28), while cluster 2 studies (<it>n </it>= 4; 301 patients) indicated that NAC was highly beneficial (RR = 0.15; 95% CI 0.07–0.33, <it>p </it>< 0.0001). Benefit in cluster 2 was unexpectedly associated with NAC-induced decreases in creatinine from baseline (<it>p </it>= 0.07). Cluster 2 studies were relatively early, small and of lower quality compared with cluster 1 studies (<it>p </it>= 0.01 for the three factors combined). Dialysis use across all studies (five control, eight treatment; <it>p </it>= 0.42) did not suggest that NAC is beneficial.</p> <p>Conclusion</p> <p>This meta-analysis does not support the efficacy of NAC to prevent CIN.</p

Non-AIDS defining cancers in the D:A:D Study-time trends and predictors of survival : a cohort study

BACKGROUND:Non-AIDS defining cancers (NADC) are an important cause of morbidity and mortality in HIV-positive individuals. Using data from a large international cohort of HIV-positive individuals, we described the incidence of NADC from 2004-2010, and described subsequent mortality and predictors of these.METHODS:Individuals were followed from 1st January 2004/enrolment in study, until the earliest of a new NADC, 1st February 2010, death or six months after the patient's last visit. Incidence rates were estimated for each year of follow-up, overall and stratified by gender, age and mode of HIV acquisition. Cumulative risk of mortality following NADC diagnosis was summarised using Kaplan-Meier methods, with follow-up for these analyses from the date of NADC diagnosis until the patient's death, 1st February 2010 or 6 months after the patient's last visit. Factors associated with mortality following NADC diagnosis were identified using multivariable Cox proportional hazards regression.RESULTS:Over 176,775 person-years (PY), 880 (2.1%) patients developed a new NADC (incidence: 4.98/1000PY [95% confidence interval 4.65, 5.31]). Over a third of these patients (327, 37.2%) had died by 1st February 2010. Time trends for lung cancer, anal cancer and Hodgkin's lymphoma were broadly consistent. Kaplan-Meier cumulative mortality estimates at 1, 3 and 5 years after NADC diagnosis were 28.2% [95% CI 25.1-31.2], 42.0% [38.2-45.8] and 47.3% [42.4-52.2], respectively. Significant predictors of poorer survival after diagnosis of NADC were lung cancer (compared to other cancer types), male gender, non-white ethnicity, and smoking status. Later year of diagnosis and higher CD4 count at NADC diagnosis were associated with improved survival. The incidence of NADC remained stable over the period 2004-2010 in this large observational cohort.CONCLUSIONS:The prognosis after diagnosis of NADC, in particular lung cancer and disseminated cancer, is poor but has improved somewhat over time. Modifiable risk factors, such as smoking and low CD4 counts, were associated with mortality following a diagnosis of NADC