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New phenotypes for new breeding goals in dairy cattle

Published online by Cambridge University Press:  17 January 2012

D. Boichard  and
M. Brochard
D. Boichard*
Affiliation:
INRA, UMR1313 Animal Genetics and Integrative Biology, 78350 Jouy en Josas, France
M. Brochard
Affiliation:
Institut de l'Elevage, UMR1313 Animal Genetics and Integrative Biology, 78350 Jouy en Josas, France
*
† E-mail: didier.boichard@jouy.inra.fr
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Abstract

Cattle production faces new challenges regarding sustainability with its three pillars – economic, societal and environmental. The following three main factors will drive dairy cattle selection in the future: (1) During a long period, intensive selection for enhanced productivity has deteriorated most functional traits, some reaching a critical point and needing to be restored. This is especially the case for the Holstein breed and for female fertility, mastitis resistance, longevity and metabolic diseases. (2) Genomic selection offers two new opportunities: as the potential genetic gain can be almost doubled, more traits can be efficiently selected; phenotype recording can be decoupled from selection and limited to several thousand animals. (3) Additional information from other traits can be used, either from existing traditional recording systems at the farm level or from the recent and rapid development of new technologies and precision farming. Milk composition (i.e. mainly fatty acids) should be adapted to better meet human nutritional requirements. Fatty acids can be measured through a new interpretation of the usual medium infrared spectra. Milk composition can also provide additional information about reproduction and health. Modern milk recorders also provide new information, that is, on milking speed or on the shape of milking curves. Electronic devices measuring physiological or activity parameters can predict physiological status like estrus or diseases, and can record behavioral traits. Slaughterhouse data may permit effective selection on carcass traits. Efficient observatories should be set up for early detection of new emerging genetic defects. In the near future, social acceptance of cattle production could depend on its capacity to decrease its ecological footprint. The first solution consists in increasing survival and longevity to reduce replacement needs and the number of nonproductive animals. At the individual level, selection on rumen activity may lead to decreased methane production and concomitantly to improved feed efficiency. A major effort should be dedicated to this new field of research and particularly to rumen flora metagenomics. Low input in cattle production is very important and tomorrow's cow will need to adapt to a less intensive production environment, particularly lower feed quality and limited care. Finally, global climate change will increase pathogen pressure, thus more accurate predictors for disease resistance will be required.

Keywords

dairy cattlephenotypebreeding objectiveselection
Type
Full Paper
Information
animal , Volume 6 , Issue 4 , April 2012 , pp. 544 - 550
Copyright
Copyright © The Animal Consortium 2012

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References

Agerholm, JS 2007. Inherited disorders in Danish cattle. Acta Pathologica, Microbiologica and Immunologica Scandinavica 115 (suppl. s122), 1–76.CrossRefGoogle Scholar
Assie, S, Seegers, H, Makoschey, B, Desire-Bousquie, L, Bareille, N 2009. Exposure to pathogens and incidence of respiratory disease in young bulls on their arrival at fattening operations in France. Veterinary Record 165, 195–199.CrossRefGoogle ScholarPubMed
Banos, G 2010. Past, present and future of international genetic evaluations of dairy bulls. In Proceedings of the 9th World Congress of Genetics Applied to Livestock Production, Paper 0033. German Society for Animal Science, Leipzig, Germany.Google Scholar
Bertrand, S, Barnett, J 2011. Standard method for determining the carbon footprint of dairy products reduces confusion. Animal Frontiers 1, 14–18.CrossRefGoogle Scholar
Bewley, JM, Peacock, AM, Lewis, O, Boyce, RE, Roberts, DJ, Coffey, MP, Kenyon, SJ, Schutz, MM 2008. Potential for estimation of body condition scores in dairy cattle from digital images. Journal of Dairy Science 91, 3439–3453.CrossRefGoogle ScholarPubMed
Boichard, D, Maignel, L, Verrier, E 1996. Analyse généalogique des races bovines laitières françaises. INRA Productions Animales 9, 323–335.CrossRefGoogle Scholar
Brochard, M, Faucon, F, Barillet, F, Bolard, M, Brunschwig, P, Duhem, K, Eggen, A, Esvan, S, Ferrand, M, Fritz, S, Gastinel, PL, Guerin, JL, Journaux, L, Krychowski, T, Lagriffoul, G, Larroque, H, Lecomte, C, Leray, O, Leroux, C, Leverrier, C, Martin, P, Mattalia, S, Miranda, G, Palhiere, I, Peyraud, JL, Boichard, D 2009. PhenoFinLait: a French national project to detect QTL or major genes affecting the fine composition of dairy ruminants’ milk. In 16èmes Rencontres Recherches Ruminants, (in French) INRA and Institut de l'Elevage, Paris, France, 423p.Google Scholar
Capel, C, Duchesne, A 2010. Du nouveau au sein de l'Observatoire National des Anomalies Bovines. Bulletin Technique de l'Insémination Animale 132, 40.Google Scholar
Capitan, A, Grohs, C, Weiss, B, Rossignol, MN, Reversé, P, Eggen, A 2011. A newly described bovine type2 scurs syndrome segregates with a frame-shift mutation in TWIST1. PLoS One 6, e22242.CrossRefGoogle Scholar
Cecchinato, A, De Marchi, M, Gallo, L, Bittante, G, Carnier, P 2009. Mid-infrared spectroscopy predictions as indicator traits in breeding programs for enhanced coagulation properties of milk. Journal of Dairy Science 92, 5304–5313.CrossRefGoogle Scholar
Charlier, C, Coppieters, W, Rollin, F, Desmecht, D, Agerholm, JS, Cambisano, N, Carta, E, Dardano, S, Dive, M, Fasquelle, C, Frennet, JC, Hanset, R, Hubin, X, Jorgensen, C, Karim, L, Kent, M, Harvey, K, Pearce, BR, Simon, P, Tama, N, Nie, H, Vandeputte, S, Lien, S, Longeri, M, Fredholm, M, Harvey, RJ, Georges, M 2008. Highly effective SNP-based association mapping and management of recessive defects in livestock. Nature Genetics 40, 449–454.CrossRefGoogle ScholarPubMed
Collins, MT, Manning, E 2005. Johne's disease diagnosis. Retrieved March, 2010, from http://www.johnes.org/general/diagnosis.htmlGoogle Scholar
Cramer, G, Lissemore, KD, Guard, CL, Leslie, KE, Kelton, DF 2008. Herd- and cow-level prevalence of foot lesions in Ontario dairy cattle. Journal of Dairy Science 91, 3888–3895.CrossRefGoogle ScholarPubMed
Dal Zotto, R, De Marchi, M, Cecchinato, A, Penasa, M, Cassandro, M, Carnier, P, Gallo, L, Bittante, G 2008. Reproducibility and repeatability of measures of milk coagulation properties and predictive ability of mid-infrared reflectance spectroscopy. Journal of Dairy Science 91, 4103–4112.CrossRefGoogle ScholarPubMed
De Marchi, M, Fagan, CC, O'Donnell, CP, Cecchinato, A, Dal Zotto, R, Cassandro, M, Penasaand, M, Bittante, G 2009. Prediction of coagulation properties, titratable acidity, and pH of bovine milk using mid-infrared spectroscopy. Journal of Dairy Science 92, 423–432.CrossRefGoogle ScholarPubMed
Druet, T, Jaffrezic, F, Boichard, D, Ducrocq, V 2003. Estimation of genetic parameters for first parity lactation curves of French Holstein cows. Journal of Dairy Science 86, 2480–2490.CrossRefGoogle Scholar
Eding, E, de Haas, Y, de Jong, G 2009. Predicting mastitis resistance breeding values from somatic cell count indicator traits. In Proceedings of the Interbull Meeting, vol. 40, pp. 21–25. Interbull, Uppsala, Sweden.Google Scholar
Egger-Danner, C, Obritzhauser, W, Fuerst-Waltl, B, Grassauer, B, Janacek, R, Schallerl, F, Litzllachner, C, Koeck, A, Mayerhofer, M, Miesenberger, M, Schoder, G, Sturmlechner, F, Wagner, A, Zottl, K 2010. Registration of health traits in Austria: experience review. In Proceedings of the 37th Conference of the International Committee for Animal Recording, Riga, Latvia, pp. 69–78.Google Scholar
Eriksson, JA 2006. Swedish sire evaluation of hoof diseases based on hoof trimming records. In Proceedings of the Interbull Meeting, vol. 35, pp. 49–50. Interbull, Uppsala, Sweden.Google Scholar
Food and Agriculture Organization (FAO) 2006. Livestock's long shadow. FAO, Rome, Italy (http://www.fao.org/docrep/010/a0701e/a0701e00.HTM).Google Scholar
Ferrand, M, Huquet, B, Barbey, S, Barillet, F, Faucon, F, Larroque, H, Leray, O, Trommenschlager, JM, Brochard, M 2011. Determination of fatty acid profile in cow's milk using mid-infrared spectrometry: interest of applying a variable selection by genetic algorithms before a PLS regression. Chemometrics and Intelligent Laboratory Systems 106, 183–189.CrossRefGoogle Scholar
Fourichon, C, Seegers, H, Bareille, N, Beaudeau, F 2001. Assessment of economic losses and total costs consecutive to production diseases in dairy herds. In 8èmes Rencontres Recherches Ruminants, pp. 137–143 (in French). INRA and Institut de l'Elevage, Paris, France.Google Scholar
Friggens, NC, Chagunda, MGG, Bjerring, M, Ridder, C, Hojsgaard, S, Larsen, T 2007. Estimating degree of mastitis from time-series measurements in milk: a test of a model based on lactate dehydrogenase measurements. Journal of Dairy Science 90, 5415–5427.CrossRefGoogle Scholar
Gonda, MG, Chang, YM, Shook, GE, Collins, MT, Kirkpatrick, B 2006. Genetic variation of Mycobacterium avium ssp. paratuberculosis infection in US Holsteins. Journal of Dairy Science 89, 1804–1812.CrossRefGoogle ScholarPubMed
Guillemin, N, Jurie, C, Cassar-Malek, I, Hocquette, JF, Renand, G, Picard, B 2011. Variations in the abundance of 24 protein biomarkers of beef tenderness according to muscle and animal type. Animal 5, 885–894.CrossRefGoogle ScholarPubMed
Halachmi, I, Polak, P, Roberts, DJ, Klopcic, M 2008. Cow body shape and automation of condition scoring. Journal of Dairy Science 91, 4444–4451.CrossRefGoogle ScholarPubMed
Hayes, B, Bowman, P, Chamberlain, A, Goddard, M 2009. Genomic selection in dairy cattle: progress and challenges. Journal of Dairy Science 92, 433–443.CrossRefGoogle ScholarPubMed
Hegarty, RS, Goopy, JP, Herd, RM, McCorkell, B 2007. Cattle selected for lower residual feed intake have reduced daily methane production. Journal of Animal Science 85, 1479–1486.CrossRefGoogle ScholarPubMed
Hojsgaard, S, Friggens, NC 2010. Quantifying degree of mastitis from common trends in a panel of indicators for mastitis in dairy cows. Journal of Dairy Science 93, 582–592.CrossRefGoogle Scholar
Hughes, LM, Bao, J, Hu, ZL, Honavar, V, Reecy, JM 2008. Animal Trait Ontology (ATO): the importance and usefulness of a unified trait vocabulary for animal species. Journal of Animal Science 86, 1485–1491.CrossRefGoogle ScholarPubMed
Hurtaud, C, Bugeon, J, Dameron, O, Fatet, A, Hue, I, Meunier-Salaün, MC, Reichtadt, M, Valancogne, A, Vernet, J, Reecy, J, Park, C, Le Bail, PY 2011. ATOL: a new ontology for livestock. In Conference of the International Committee for Animal Recording, Bourg-en-Bresse, France.Google Scholar
Koskinen, MT, Holopainen, J, Pyörälä, S, Bredbacka, P, Pitkälä, A, Barkema, HW, Bexiga, R, Roberson, J, Sølverød, L, Piccinini, R, Kelton, D, Lehmusto, H, Niskala, S, Salmikivi, L 2009. Analytical specificity and sensitivity of a real-time polymerase chain reaction assay for identification of bovine mastitis pathogens. Journal of Dairy Science 92, 952–959.CrossRefGoogle ScholarPubMed
Lassen, J, Løvendahl, P, Madsen, J 2010. Experiences with large scale individual measurements of methane emission in dairy cattle using a Fourier Transform Infrared (FTIR) measuring unit. In Proceedings of the 9th World Congress of Genetics Applied to Livestock Production, Paper 0247. German Society for Animal Science, Leipzig, Germany.Google Scholar
Leray, O, Ferrand, M, Larroque, H, Astruc, JM, Douguet, M, Brochard, M, Duhem, K 2011. Harmonisation of milk analysers for fatty acid determination by FTMIR: an essential step prior to collective data use. In Conference of the International Committee for Animal Recording, Bourg-en-Bresse, France.Google Scholar
Madsen, J, Bjerg, BS, Hvelplund, T, Weisbjerg, MR, Lund, P 2010. Methane and carbon dioxide ratio in excreted air for quantification of the methane production from ruminants. Livestock Science 129, 223–227.CrossRefGoogle Scholar
Martin, C, Morgavi, DP, Doreau, M 2010. Methane mitigation in ruminants: from microbe to the farm scale. Animal 4, 351–365.CrossRefGoogle Scholar
McParland, S, Banos, G, Wall, E, Coffey, MP, Soyeurt, H, Veerkamp, RF, Berry, DP 2011. The use of mid-infrared spectrometry to predict body energy status of Holstein cows. Journal of Dairy Science 94, 3651–3661.CrossRefGoogle ScholarPubMed
Miglior, F 2000. Impact of inbreeding – managing a declining Holstein gene pool. In Proceedings of the 10th World Holstein Friesian Federation Conference, Sydney, Australia, pp. 108–113.Google Scholar
Minozzi, G, Buggiotti, L, Stella, A, Strozzi, F, Luini, M, Williams, JL 2010. Genetic loci involved in antibody response to Mycobacterium avium ssp. paratuberculosis in cattle. PLoS One 5, e11117.CrossRefGoogle ScholarPubMed
Mitloehner, F 2009. Clearing the air: livestock's contributions to climate change. In Proceedings of the GABS II Conference, UC Davis, http://airquality.ucdavis.edu/pages/events/2009/greenacres/DairyMitloehner.pdfGoogle Scholar
Pabiou, T, Fikse, WF, Cromie, AR, Keane, MG, Nasholm, A, Berry, DP 2011. Use of digital images to predict carcass cut yields in cattle. Livestock Science 137, 130–140.CrossRefGoogle Scholar
Pfimlin, A 2008. L'ombre de l’élevage sur la planète: analyse du rapport FAO et perspectives européennes. Retrieved January 10, 2012, from http://www.inst-elevage.asso.fr/IMG/pdf_CR_190810001.pdfGoogle Scholar
Pinares-Patiño, CS, Baumont, R, Martin, C 2003. Methane emissions by Charolais cows grazing a monospecific pasture of timothy at four stages of maturity. Canadian Journal of Animal Science 83, 769–777.CrossRefGoogle Scholar
Roche, JR, Friggens, NC, Kay, JK, Fisher, MW, Stafford, KJ, Berry, DP 2009. Body condition score and its association with dairy cow productivity, health and welfare. Journal of Dairy Science 92, 5769–5801.CrossRefGoogle ScholarPubMed
Schaeffer, LR 2006. Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics 123, 218–223.CrossRefGoogle ScholarPubMed
Soyeurt, H, Dardenne, P, Dehareng, F, Lognay, G, Veselko, D, Marlier, M, Bertozzi, C, Mayeres, P, Gengler, N 2006. Estimating fatty acid content in cow milk using mid-infrared spectrometry. Journal of Dairy Science 89, 3690–3695.CrossRefGoogle ScholarPubMed
Soyeurt, H, Gillon, A, Vanderick, S, Mayeres, P, Bertozzi, C, Gengler, N 2007a. Estimation of heritability and genetic correlations for the major fatty acids in bovine milk. Journal of Dairy Science 90, 4435–4442.CrossRefGoogle ScholarPubMed
Soyeurt, H, Colinet, FG, Arnould, VMR, Dardenne, P, Bertozzi, C, Renaville, R, Portetelle, D, Gengler, N 2007b. Genetic variability of lactoferrin content estimated by mid-infrared spectrometry in bovine milk. Journal of Dairy Science 90, 4443–4450.CrossRefGoogle ScholarPubMed
Soyeurt, H, Dardenne, P, Dehareng, F, Bastin, C, Gengler, N 2008. Genetic parameters of saturated and monounsaturated fatty acid content and the ratio of saturated to unsaturated fatty acids in bovine milk. Journal of Dairy Science 91, 3611–3626.CrossRefGoogle ScholarPubMed
Soyeurt, H, Dehareng, F, Mayeres, P, Bertozzi, C, Gengler, N 2009a. Variation of delta(9)-desaturase activity in dairy cattle. Journal of Dairy Science 91, 3211–3224.CrossRefGoogle Scholar
Soyeurt, H, Bruwier, D, Romnee, JM, Gengler, N, Bertozzi, C, Veselko, D, Dardenne, P 2009b. Potential estimation of major mineral contents in cow milk using mid-infrared spectrometry. Journal of Dairy Science 92, 2444–2454.CrossRefGoogle ScholarPubMed
Stoop, WM, Van Arendonk, JAM, Heck, JML, Van Valenberg, HJF, Bovenhuis, H 2008. Genetic parameters for major milk fatty acids and milk production traits of Dutch Holstein Friesians. Journal of Dairy Science 91, 385–394.CrossRefGoogle ScholarPubMed
Stoop, M, de Jong, G, van Pelt, M, van der Linde, C 2010. Implementation of a claw health index in The Netherlands. In Proceedings of the Interbull Meeting, vol. 42, pp 95–99. Interbull, Uppsala, Sweden.Google Scholar
Van Doormaal, B 2009. Health data recording in Canada. Retrieved January 10, 2012, from http://www.cdn.ca/document.php?id=160Google Scholar