[en] Genetic improvement of udder health in dairy cows is of high relevance as mastitis is one of the most prevalent diseases. Since it is known that the heritability of mastitis is low and direct data on mastitis cases are often not available in large numbers, auxiliary traits, such as somatic cell count (SCC) are used for the ge
Disciplines :
Animal production & animal husbandry Genetics & genetic processes
Author, co-author :
Rienesl, Lisa ; Institute of Livestock Sciences, University of Natural Resources and Life Sciences, Vienna, Austria
Fuerst-Waltl, Birgit; Institute of Livestock Sciences, University of Natural Resources and Life Sciences, Vienna, Austria
Mészáros, |; Institute of Livestock Sciences, University of Natural Resources and Life Sciences, Vienna, Austria
Koeck, Astrid; ZuchtData EDV-Dienstleistungen GmbH, Vienna, Austria
Egger-Danner, Christa; ZuchtData EDV-Dienstleistungen GmbH, Vienna, Austria
Gengler, Nicolas ; Université de Liège - ULiège > TERRA Research Centre > Animal Sciences (AS)
Grelet, Clément; Walloon Agricultural Research Center, CRA-W), Gembloux, Belgium
Sölkner, Johann ; Institute of Livestock Sciences, University of Natural Resources and Life Sciences, Vienna, Austria
Language :
English
Title :
Genetic parameters for mid-infrared-spectroscopypredicted mastitis phenotypes and related traits
Ali, A. K. A., & Shook, G. E. (1980). An optimum transformation for somatic cell concentration in Milk. Journal of Dairy Science, 63, 487–490. https://doi.org/10.3168/jds.S0022-0302(80)82959-6
Arnould, V. M. R., Soyeurt, H., Gengler, N., Colinet, F. G., Georges, M. V., Bertozzi, C., Portetelle, D., & Renaville, R. (2009). Genetic analysis of lactoferrin content in bovine milk. Journal of Dairy Science, 92, 2151–2158. https://doi.org/10.3168/jds.2008-1255
Belay, T. K., Svendsen, M., Kowalski, Z. M., & Ådnøy, T. (2017). Genetic parameters of blood β-hydroxybutyrate predicted from milk infrared spectra and clinical ketosis, and their associations with milk production traits in Norwegian red cows. Journal of Dairy Science, 100, 6298–6311. https://doi.org/10.3168/jds.2016-12458
Benedet, A., Costa, A., De Marchi, M., & Penasa, M. (2020). Heritability estimates of predicted blood β-hydroxybutyrate and nonesterified fatty acids and relationships with milk traits in early-lactation Holstein cows. Journal of Dairy Science, 103, 6354–6363. https://doi.org/10.3168/jds.2019-17916
Cheng, J. B., Wang, J. Q., Bu, D. P., Liu, G. L., Zhang, C. G., Wei, H. Y., Zhou, L. Y., & Wang, J. Z. (2008). Factors affecting the lactoferrin concentration in bovine milk. Journal of Dairy Science, 91, 970–976. https://doi.org/10.3168/jds.2007-0689
Dal Zotto, R., De Marchi, M., Dalvit, C., Cassandro, M., Gallo, L., Carnier, P., & Bittante, G. (2007). Heritabilities and genetic correlations of body condition score and calving interval with yield, somatic cell score, and linear type traits in brown swiss cattle. Journal of Dairy Science, 90, 5737–5743. https://doi.org/10.3168/jds.2007-0280
Dale, L., & Werner, A. (2017). “MastiMIR”-Ein Mastitis-Frühwarnsystem basierend auf MIR-Spektren. In Proceedings of the Vortragstagung der DCfZ und GfT, Stuttgart, Germany, 20–21 September 2017.
De Haas, Y., Barkema, H. W., & Veerkamp, R. F. (2002). The effect of pathogen-specific clinical mastitis on the lactation curve for somatic cell count. Journal of Dairy Science, 85, 1314–1323. https://doi.org/10.3168/jds.S0022-0302(02)74196-9
Egger-Danner, C., Fuerst-Waltl, B., Obritzhauser, W., Fuerst, C., Schwarzenbacher, H., Grassauer, B., Mayerhofer, M., & Koeck, A. (2012). Recording of direct health traits in Austria-experience report with emphasis on aspects of availability for breeding purposes. Journal of Dairy Science, 95, 2765–2777. https://doi.org/10.3168/jds.2011-4876
Fleming, A., Schenkel, F. S., Ali, R. A., Corredig, M., Carta, S., Gregu, C. M., Malchiodi, F., Macciotta, N. P. P., & Miglior, F. (2019). Phenotypic investigation of fine milk components in bovine milk and their prediction using mid-infrared spectroscopy. Canadian Journal of Animal Science, 99, 218–227. https://doi.org/10.1139/cjas-2018-0058
Fuerst, C., Koeck, A., Egger-Danner, C., & Fuerst-Waltl, B. (2011). Routine genetic evaluation for direct health traits in Austria and Germany. Interbull Bulletin, 44, 210–215.
Fürst, C., Dodenhoff, J., Egger-Danner, C., Emmerling, R., Hamann, H., Krogmeier, D., & Schwarzenbacher, H. (2021). Zuchtwertschätzung Beim Rind - Grundlagen, Methoden, Interpretation. ZuchtData EDV-Dienstleistungen GmbH, ed. Vienna.
Gengler, N., Soyeurt, H., Dehareng, F., Bastin, C., Colinet, F., Hammami, H., Vanrobays, M. L., Lainé, A., Vanderick, S., Grelet, C., Vanlierde, A., Froidmont, E., & Dardenne, P. (2016). Capitalizing on fine milk composition for breeding and management of dairy cows. Journal of Dairy Science, 99, 4071–4079. https://doi.org/10.3168/jds.2015-10140
Græsbøll, K., Kirkeby, C., Nielsen, S. S., Halasa, T., Toft, N., & Christiansen, L. E. (2016). Models to estimate lactation curves of milk yield and somatic cell count in dairy cows at the herd level for the use in simulations and predictive models. Frontiers in Veterinary Science, 3, 1–10. https://doi.org/10.3389/fvets.2016.00115
Grelet, C., Bastin, C., Gelé, M., Davière, J. B., Johan, M., Werner, A., Reding, R., Fernandez Pierna, J. A., Colinet, F. G., Dardenne, P., Gengler, 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, 4816–4825. https://doi.org/10.3168/jds.2015-10477
Grelet, C., Dardenne, P., Soyeurt, H., Fernandez, J. A., Vanlierde, A., Stevens, F., Gengler, N., & Dehareng, F. (2021). Large-scale phenotyping in dairy sector using milk MIR spectra: Key factors affecting the quality of predictions. Methods, 186, 97–111. https://doi.org/10.1016/j.ymeth.2020.07.012
Grelet, 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, 2150–2160. https://doi.org/10.3168/jds.2014-8764
Grelet, C., Pierna, J. A. F., Dardenne, P., Soyeurt, H., Vanlierde, A., Colinet, F., Bastin, C., Gengler, N., Baeten, V., & Dehareng, F. (2017). Standardization of milk mid-infrared spectrometers for the transfer and use of multiple models. Journal of Dairy Science, 100, 7910–7921. https://doi.org/10.3168/jds.2017-12720
Groeneveld, E., Kovac, M., & Mielenz, N. (2010). VCE user's guide and reference manual version 6.0.
Heringstad, B., Klemetsdal, G., & Ruane, J. (2000). Selection for mastitis resistance in dairy cattle: A review with focus on the situation in the Nordic countries. Livestock Production Science, 64, 95–106. https://doi.org/10.1016/S0301-6226(99)00128-1
ICAR. (2022). ICAR Guidlines. Section 2 - guidelines for dairy cattle Milk recording. I.C. on A. Recording, ed. Utrecht.
Interbull. (2022). National Genetic Evaluation Forms Provided by Countries. https://interbull.org/ib/geforms
Kirkeby, C., Toft, N., Schwarz, D., Farre, M., Nielsen, S. S., Zervens, L., Hechinger, S., & Halasa, T. (2020). Differential somatic cell count as an additional indicator for intramammary infections in dairy cows. Journal of Dairy Science, 103, 1759–1775. https://doi.org/10.3168/jds.2019-16523
Koeck, A., Heringstad, B., Egger-Danner, C., Fuerst, C., & Fuerst-Waltl, B. (2010). Comparison of different models for genetic analysis of clinical mastitis in Austrian Fleckvieh dual-purpose cows. Journal of Dairy Science, 93, 4351–4358. https://doi.org/10.3168/jds.2009-2955
Koeck, A., Heringstad, B., Egger-Danner, C., Fuerst, C., Winter, P., & Fuerst-Waltl, B. (2010). Genetic analysis of clinical mastitis and somatic cell count traits in Austrian Fleckvieh cows. Journal of Dairy Science, 93, 5987–5995. https://doi.org/10.3168/jds.2010-3451
Koeck, A., Miglior, F., Kelton, D. F., & Schenkel, F. S. (2012). Health recording in Canadian Holsteins: Data and genetic parameters. Journal of Dairy Science, 95, 4099–4108. https://doi.org/10.3168/jds.2011-5127
Lean, I. J., LeBlanc, S. J., Sheedy, D. B., Duffield, T., Santos, J. E. P., & Golder, H. M. (2022). Associations of parity with health disorders and blood metabolite concentrations in Holstein cows in different production systems. Journal of Dairy Science, 106, 500–518. https://doi.org/10.3168/jds.2021-21673
Li, G. H., Zhang, Y., Sun, D. X., & Li, N. (2004). Study on the polymorphism of bovine lactoferrin gene and its relationship with mastitis. Animal Biotechnology, 15, 67–76. https://doi.org/10.1081/ABIO-120037899
Lopez-Villalobos, N., Davis, R. D., Beattie, E. M., Melis, J., Berry, S., Holroyd, S., Spelman, R. J., & Snell, R. (2009). Breed effects for lactoferrin concentration determined by Fourier transform infrared spectroscopy. Proceedings of the New Zealand Society of Animal Production, 69, 60–64.
Luttinen, A., & Juga, J. (1997). Genetic relationships between milk yield, somatic cell count, mastitis, milkability and leakage in Finnish dairy cattle population. Interbull Bulletin, 0, 78.
McParland, S., Kennedy, E., Lewis, E., Moore, S. G., McCarthy, B., O'Donovan, M., & Berry, D. P. (2015). Genetic parameters of dairy cow energy intake and body energy status predicted using mid-infrared spectrometry of milk. Journal of Dairy Science, 98, 1310–1320. https://doi.org/10.3168/jds.2014-8892
Mrode, R. A., & Swanson, G. J. T. (1998). Genetic and statistical properties of somatic cell count and its suitability as an indirect means of reducing the incidence of mastitis in dairy cattle. Animal Breeding, 64, 847–857.
Nayeri, S., Schenkel, F. S., Martin, P., Fleming, A., Jamrozik, J., Malchiodi, F., Brito, L. F., Baes, C. F., Sargolzaei, M., & Miglior, F. (2020). Estimation of genetic parameters for mid-infrared–predicted lactoferrin and milk fat globule size in Holstein cattle. Journal of Dairy Science, 103, 2487–2497. https://doi.org/10.3168/jds.2019-16850
Philipsson, J., & Lindhé, B. (2003). Experiences of including reproduction and health traits in Scandinavian dairy cattle breeding programmes. Livestock Production Science, 83, 99–112. https://doi.org/10.1016/S0301-6226(03)00047-2
Rienesl, L., Khayatzdadeh, N., Köck, A., Egger-Danner, C., Gengler, N., Grelet, C., Dale, L. M., Werner, A., Auer, F. J., Leblois, J., & Sölkner, J. (2022). Prediction of acute and chronic mastitis in dairy cows based on somatic cell score and mid-infrared spectroscopy of Milk. Animals, 12, 1–13. https://doi.org/10.3390/ani12141830
Rienesl, L., Marginter, M., Stückler, P., Köck, A., Egger-Danner, C., & Sölkner, J. (2022). Use of differential somatic cell count, somatic cell score, and milk mid-infrared spectral analysis for monitoring mastitis in dairy cows during routine milk recording. Livestock Science, 264, 105050. https://doi.org/10.1016/j.livsci.2022.105050
Schukken, Y. H., Wilson, D. J., Welcome, F., Garrison-Tikofsky, L., & Gonzalez, R. N. (2003). Monitoring udder health and milk quality using somatic cell counts. Veterinary Research, 34, 579–596. https://doi.org/10.1051/vetres:2003028
Shimazaki, K., & Kawai, K. (2017). Advances in lactoferrin research concerning bovine mastitis. Biochemistry and Cell Biology, 95, 69–75. https://doi.org/10.1139/bcb-2016-0044
Sordillo, L. M., & Streicher, K. L. (2002). Mammary gland immunity and mastitis susceptibility. Journal of Mammary Gland Biology and Neoplasia, 7, 135–146. https://doi.org/10.1023/A:1020347818725
Soyeurt, H., Bastin, C., Colinet, F. G., Arnould, V. M.-R., Berry, D. P., Wall, E., Dehareng, F., Nguyen, H. N., Dardenne, 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, 1830–1838. https://doi.org/10.1017/s1751731112000791
Soyeurt, H., Colinet, F. G., Arnould, V. M.-R., Dardenne, P., Bertozzi, C., Renaville, R., Portetelle, D., & Gengler, N. (2007). Genetic variability of lactoferrin content estimated by mid-infrared spectrometry in bovine Milk. Journal of Dairy Science, 90, 4443–4450. https://doi.org/10.3168/jds.2006-827
Soyeurt, H., Grelet, C., McParland, S., Calmels, M., Coffey, M., Tedde, A., Delhez, P., Dehareng, F., & Gengler, N. (2020). A comparison of 4 different machine learning algorithms to predict lactoferrin content in bovine milk from mid-infrared spectra. Journal of Dairy Science, 103, 11585–11596. https://doi.org/10.3168/jds.2020-18870
Suntinger, M., Fuerst-Waltl, B., Obritzhauser, W., Firth, C. L., Köck, A., & Egger-Danner, C. (2022). Usability of bacteriological milk analyses for genetic improvement of udder health in Austrian Fleckvieh cows. Journal of Dairy Science, 105, 5167–5177. https://doi.org/10.3168/jds.2021-20832
Tiplady, K. M., Lopdell, T. J., Littlejohn, M. D., & Garrick, D. J. (2020). The evolving role of Fourier-transform mid-infrared spectroscopy in genetic improvement of dairy cattle. Journal of Animal Science and Biotechnology, 11, 1–13. https://doi.org/10.1186/s40104-020-00445-2
Van den Berg, I., Ho, P. N., Haile-Mariam, M., & Pryce, J. E. (2021). Genetic parameters for mid-infrared spectroscopy–predicted fertility. JDS Communication, 2, 361–365. https://doi.org/10.3168/jdsc.2021-0141
Van der Werf, J. H. J. (2018). MTINDEX-multiple trait selection index. https://jvanderw.une.edu.au/software.Htm
Wiggans, G. R., & Shook, G. E. (1987). A lactation measure of somatic cell count. Journal of Dairy Science, 70, 2666–2672. https://doi.org/10.3168/jds.S0022-0302(87)80337-5
Zavadilová, L., Kašná, E., Krupová, Z., & Klímová, A. (2021). Health traits in current dairy cattle breeding: A review. Czech Journal of Animal Science, 66, 235–250. https://doi.org/10.17221/163/2020-CJAS
