Abstract
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.
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References
Agerholm JS, Bendixen C, Andersen O, Arnbjerg J (2001) Complex vertebral malformation in Holstein calves. J Vet Diagn Investig 13:283–289
Baes CF, Makanjuola BO, Miglior F, Marras G, Howard JT, Fleming A, Maltecca C (2019) Symposium review: the genomic architecture of inbreeding: how homozygosity affects health and performance. J Dairy Sci 102:2807–2817
Banos G, Brotherstone S, Coffey MP (2004) Evaluation of body condition score measured throughout lactation as an indicator of fertility in dairy cattle. J Dairy Sci 87:2669–2676
Berghof TVL, Poppe M, Mulder HA (2019) Opportunities to improve resilience in animal breeding programs. Front Genet 9
Berry D, Wall E, Pryce J (2014) Genetics and genomics of reproductive performance in dairy and beef cattle. Animal 8:105–121
Bikker JP, Van Laar H, Rump P, Doorenbos J, Van Meurs K, Griffioen GM, Dijkstra J (2014) Technical note: evaluation of an ear-attached movement sensor to record cow feeding behavior and activity. J Dairy Sci 97:2974–2979
Bishop SC, Woolliams JA (2010) On the genetic interpretation of disease data. PLoS One 5:e8940
Bjelland DW, Weigel KA, Vukasinovic N, Nkrumah JD (2013) Evaluation of inbreeding depression in Holstein cattle using whole-genome SNP markers and alternative measures of genomic inbreeding. J Dairy Sci 96:4697–4706
Boichard D, Brochard M (2012) New phenotypes for new breeding goals in dairy cattle. Animal 6:544–550
Brito LF, Oliveira HR, Mcconn BR, Schinckel AP, Arrazola A, Marchant-Forde JN, Johnson JS (2020) Large-scale phenotyping of livestock welfare in commercial production systems: A new frontier in animal breeding. Front Genet 11:793–793
Broom DM (1991) Animal welfare: concepts and measurement2. J Anim Sci 69:4167–4175
Buch LH, Sørensen AC, Lassen J, Berg P, Eriksson J-Å, Jakobsen J, Sørensen MK (2011) Hygiene-related and feed-related hoof diseases show different patterns of genetic correlations to clinical mastitis and female fertility. J Dairy Sci 94:1540–1551
Byrne TJ, Santos BFS, Amer PR, Martin-Collado D, Pryce JE, Axford M (2016) New breeding objectives and selection indices for the Australian dairy industry. J Dairy Sci 99:8146–8167
Carabaño MJ, Ramón M, Menéndez-Buxadera A, Molina A, Díaz C (2019) Selecting for heat tolerance. Anim Front 9:62–68
Colditz IG, Hine BC (2016) Resilience in farm animals: biology, management, breeding and implications for animal welfare. Anim Prod Sci 56:1961–1983
Cole JB, Vanraden PM (2018) Symposium review: possibilities in an age of genomics: the future of selection indices. J Dairy Sci 101:3686–3701
Cole JB, Eaglen SAE, Maltecca C, Mulder HA, Pryce JE (2020) The future of phenomics in dairy cattle breeding. Anim Front 10:37–44
DairyNZ (2022) Economic values (EV’S) are an estimate of the dollar value to a NZ dairy farmer of a unit change in each trait. https://www.dairynz.co.nz/animal/animal-evaluation/interpreting-the-info/economic-values/. Accessed 8 December 2022
Davis SR, Spelman RJ, Littlejohn MD (2017) Breeding and genetics symposium: breeding heat tolerant dairy cattle: the case for introgression of the “slick” prolactin receptor variant into Bos taurus dairy breeds. J Anim Sci 95:1788–1800
De La Paz JM (2008) Using antibody and cell-mediated immune response to test antigens in periparturient dairy cows as a measure of disease resistance. University of Florida, Gainesville, FL
De Marchi M, Toffanin V, Cassandro M, Penasa M (2014) Invited review: mid-infrared spectroscopy as phenotyping tool for milk traits. J Dairy Sci 97:1171–1186
Doublet A-C, Croiseau P, Fritz S, Michenet A, Hozé C, Danchin-Burge C, Laloë D, Restoux G (2019) The impact of genomic selection on genetic diversity and genetic gain in three French dairy cattle breeds. Genet Sel Evol 51:52
Eggen A (2012) The development and application of genomic selection as a new breeding paradigm. Anim Front 2:10–15
Egger-Danner C, Nielsen P, Fiedler A, Müller K, Fjeldaas T, Döpfer D, Daniel V, Bergsten C, Cramer G, Christen A (2014) ICAR claw health atlas. ICAR Technical Series
Egger-Danner C, Cole J, Pryce J, Gengler N, Heringstad B, Bradley A, Stock K (2015) Invited review: overview of new traits and phenotyping strategies in dairy cattle with a focus on functional traits. Animal 9:191–207
Elgersma GG, De Jong G, Van Der Linde R, Mulder HA (2018) Fluctuations in milk yield are heritable and can be used as a resilience indicator to breed healthy cows. J Dairy Sci 101:1240–1250
Fahrenkrug SC, Carlson DF (2014) Hornless livestock. Google Patents
Fuerst-Waltl B, Fuerst C, Obritzhauser W, Egger-Danner C (2016) Sustainable breeding objectives and possible selection response: finding the balance between economics and breeders’ preferences. J Dairy Sci 99:9796–9809
García-Ruiz A, Cole JB, Vanraden PM, Wiggans GR, Ruiz-López FJ, Van Tassell CP (2016) Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proc Natl Acad Sci 113:E3995–E4004
Garner JB, Douglas ML, Williams SRO, Wales WJ, Marett LC, Nguyen TTT, Reich CM, Hayes BJ (2016) Genomic selection improves heat tolerance in dairy cattle. Sci Rep 6:34114
Goddard ME, Hayes BJ (2009) Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat Rev Genet 10:381–391
Hansen PJ (2020) Prospects for gene introgression or gene editing as a strategy for reduction of the impact of heat stress on production and reproduction in cattle. Theriogenology 154:190–202
Harris DL, Arboleda CR, Stewart TS (1984) Animal breeding programs: a systematic approach to their design. AGRIS
Haskell MJ, Simm G, Turner SP (2014) Genetic selection for temperament traits in dairy and beef cattle. Front Genet 5
Haskell MJ, Vasseur E, Mosconi C, Egger-Danner C (2019) Mapping the global use of welfare indicators for dairy cows. In: Proceedings of the 43rd ICAR Conference, Prague 2019
Hazel LN (1943) The genetic basis for constructing selection indexes. Genetics 28:476–490
Heringstad B, Østerås O (2013) More than 30 years of health recording in Norway. ICAR Technical Series no. 17, 39
Heringstad B, Gianola D, Chang Y, Ødegård J, Klemetsdal G (2006) Genetic associations between clinical mastitis and somatic cell score in early first-lactation cows. J Dairy Sci 89:2236–2244
Heringstad B, Egger-Danner C, Charfeddine N, Pryce JE, Stock KF, Kofler J, Sogstad AM, Holzhauer M, Fiedler A, Müller K, Nielsen P, Thomas G, Gengler N, De Jong G, Ødegård C, Malchiodi F, Miglior F, Alsaaod M, Cole JB (2018) Invited review: genetics and claw health: opportunities to enhance claw health by genetic selection. J Dairy Sci 101:4801–4821
Kipp S, Segelke D, Schierenbeck S, Reinhardt F, Reents R, Wurmser C, Pausch H, Fries R, Thaller G, Tetens J, Pott J, Haas D, Raddatz BB, Hewicker-Trautwein M, Proios I, Schmicke M, Grünberg W (2016) Identification of a haplotype associated with cholesterol deficiency and increased juvenile mortality in Holstein cattle. J Dairy Sci 99:8915–8931
König S, May K (2019) Invited review: phenotyping strategies and quantitative-genetic background of resistance, tolerance and resilience associated traits in dairy cattle. Animal 13:897–908
Lasser J, Matzhold C, Egger-Danner C, Fuerst-Waltl B, Steininger F, Wittek T, Klimek P (2021) Integrating diverse data sources to predict disease risk in dairy cattle. bioRxiv. https://doi.org/10.1101/2021.03.25.436798
Lopez-Villalobos N, Garrick DJ, Holmes CW, Blair HT, Spelman RJ (2000) Profitabilities of some mating systems for dairy herds in New Zealand. J Dairy Sci 83:144–153
Lucy M (2001) Reproductive loss in high-producing dairy cattle: where will it end? J Dairy Sci 84:1277–1293
Luke TDW, Nguyen TTT, Rochfort S, Wales WJ, Richardson CM, Abdelsayed M, Pryce JE (2019a) Genomic prediction of serum biomarkers of health in early lactation. J Dairy Sci 102:11142–11152
Luke TDW, Rochfort S, Wales WJ, Bonfatti V, Marett L, Pryce JE (2019b) Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra. J Dairy Sci 102:1747–1760
Makanjuola BO, Miglior F, Abdalla EA, Maltecca C, Schenkel FS, Baes CF (2020) Effect of genomic selection on rate of inbreeding and coancestry and effective population size of Holstein and Jersey cattle populations. J Dairy Sci 103:5183–5199
Martin-Collado D, Byrne T, Amer P, Santos B, Axford M, Pryce J (2015) Analyzing the heterogeneity of farmers’ preferences for improvements in dairy cow traits using farmer typologies. J Dairy Sci 98:4148–4161
McParland S, Kearney JF, Rath M, Berry DP (2007) Inbreeding effects on milk production, calving performance, fertility, and conformation in Irish Holstein-Friesians. J Dairy Sci 90:4411–4419
Medugorac I, Seichter D, Graf A, Russ I, Blum H, Göpel KH, Rothammer S, Förster M, Krebs S (2012) Bovine polledness–an autosomal dominant trait with allelic heterogeneity. PLoS One 7:e39477
Miglior F, Fleming A, Malchiodi F, Brito LF, Martin P, Baes CF (2017) A 100-year review: identification and genetic selection of economically important traits in dairy cattle. J Dairy Sci 100:10251–10271
Mueller ML, Cole JB, Sonstegard TS, Van Eenennaam AL (2019) Comparison of gene editing versus conventional breeding to introgress the POLLED allele into the US dairy cattle population. J Dairy Sci 102:4215–4226
Nguyen TTT, Bowman PJ, Haile-Mariam M, Pryce JE, Hayes BJ (2016) Genomic selection for tolerance to heat stress in Australian dairy cattle. J Dairy Sci 99:2849–2862
Nielsen H-M, Christensen LG, Groen A (2005) Derivation of sustainable breeding goals for dairy cattle using selection index theory. J Dairy Sci 88:1882–1890
Ødegård C, Svendsen M, Heringstad B (2013) Genetic analyses of claw health in Norwegian red cows. J Dairy Sci 96:7274–7283
OIE (2022) Terrestrial code online access. https://www.oie.int/en/what-we-do/standards/codes-and-manuals/terrestrial-code-online-access/. Accessed 8 December 2022
Poppe M, Bonekamp G, Van Pelt ML, Mulder HA (2021) Genetic analysis of resilience indicators based on milk yield records in different lactations and at different lactation stages. J Dairy Sci 104:1967–1981
Pryce JE, Harris BL (2006) Genetics of body condition score in New Zealand dairy cows. J Dairy Sci 89:4424–4432
Pryce JE, Parker Gaddis KL, Koeck A, Bastin C, Abdelsayed M, Gengler N, Miglior F, Heringstad B, Egger-Danner C, Stock KF, Bradley AJ, Cole JB (2016) Invited review: opportunities for genetic improvement of metabolic diseases. J Dairy Sci 99:6855–6873
Rauw W, Kanis E, Noordhuizen-Stassen E, Grommers F (1998) Undesirable side effects of selection for high production efficiency in farm animals: a review. Livest Prod Sci 56:15–33
Simm G, Pollott G, Mrode R, Houston R, Marshall K (2021) Genetic improvement of farmed animals. CABI, Wallingford
Stock ML, Baldridge SL, Griffin D, Coetzee JF (2013) Bovine dehorning: assessing pain and providing analgesic management. Vet Clin N Am Food Anim Pract 29:103–133. https://doi.org/10.1016/j.cvfa.2012.11.001
Thompson-Crispi K, Hine B, Quinton M, Miglior F, Mallard B (2012) Short communication: association of disease incidence and adaptive immune response in Holstein dairy cows. J Dairy Sci 95:3888–3893
Van Den Berg I, Ho PN, Luke TDW, Haile-Mariam M, Bolormaa S, Pryce JE (2021) The use of milk mid-infrared spectroscopy to improve genomic prediction accuracy of serum biomarkers. J Dairy Sci 104:2008–2017
Van Der Waaij E, Holzhauer M, Ellen E, Kamphuis C, De Jong G (2005) Genetic parameters for claw disorders in Dutch dairy cattle and correlations with conformation traits. J Dairy Sci 88:3672–3678
Van Eenennaam AL (2019) Application of genome editing in farm animals: cattle. Transgenic Res 28:93–100
Vanraden PM, Olson KM, Wiggans GR, Cole JB, Tooker ME (2011) Genomic inbreeding and relationships among Holsteins, jerseys, and Brown Swiss. J Dairy Sci 94:5673–5682
Von Keyserlingk MAG, Weary DM (2017) A 100-year review: animal welfare in the journal of dairy science—the first 100 years. J Dairy Sci 100:10432–10444
Weary DM, Droege P, Braithwaite VA (2017) Chapter two - Behavioral evidence of felt emotions: approaches, inferences, and refinements. In: Naguib M, Podos J, Simmons LW, Barrett L, Healy SD, Zuk M (eds) Advances in the study of behavior. Academic Press, New York
Whay HR, Shearer JK (2017) The impact of lameness on welfare of the dairy cow. Vet Clin North Am Food Anim Pract 33:153–164
Zhang X, Amer P (2021) A new selection index percent emphasis method using subindex weights and genetic evaluation accuracy. J Dairy Sci 104:5827–5842
Zwald NR, Weigel KA, Fikse WF, Rekaya R (2003) Identification of factors that cause genotype by environment interaction between herds of Holstein cattle in seventeen countries. J Dairy Sci 86:1009–1018
Acknowledgements
Jennie Pryce thanks DairyBio (a programme funded by Dairy Australia, the State Government of Victoria and The Gardiner Foundation, Melbourne) for financial support and colleagues from Agriculture Victoria for inspirational conversations.
Christa Egger-Danner was supported by the COMET project D4Dairy—Digitalisation, Data Integration, Detec[1]tion, Decision Support in Dairying in Austria (Vienna). These projects are supported by BMK (Federal Minis[1]try Republic of Austria Climate Action, Environment, Energy, Mobility, Innovation and Technology), BMDW (Federal Ministry Republic of Austria Digital and Eco[1]nomic Affairs, Vienna), the province of Lower Austria, and the city of Vienna in the framework of COMET—Competence Centers for Excellent Technologies. The COMET programme is handled by the FFG (Austrian Research Promotion Agency, Vienna).
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Pryce, J.E., Egger-Danner, C., Simm, G. (2023). Strategies and Tools for Genetic Selection in Dairy Cattle and Their Application to Improving Animal Welfare. In: Haskell, M. (eds) Cattle Welfare in Dairy and Beef Systems. Animal Welfare, vol 23. Springer, Cham. https://doi.org/10.1007/978-3-031-21020-4_12
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