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On-farm net benefit of genotyping candidate female replacement cattle and sheep

Published online by Cambridge University Press:  27 February 2020

J. E. Newton  and
D. P. Berry Open the ORCID record for D. P. Berry [Opens in a new window]
J. E. Newton
Affiliation:
Teagasc Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland
D. P. Berry*
Affiliation:
Teagasc Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland
*
†E-mail: [email protected]
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Abstract

The net benefit from investing in any technology is a function of the cost of implementation and the expected return in revenue. The objective of the present study was to quantify, using deterministic equations, the net monetary benefit from investing in genotyping of commercial females. Three case studies were presented reflecting dairy cows, beef cows and ewes based on Irish population parameters; sensitivity analyses were also performed. Parameters considered in the sensitivity analyses included the accuracy of genomic evaluations, replacement rate, proportion of female selection candidates retained as replacements, the cost of genotyping, the sire parentage error rate and the age of the female when it first gave birth. Results were presented as an annualised monetary net benefit over the lifetime of an individual, after discounting for the timing of expressions. In the base scenarios, the net benefit was greatest for dairy, followed by beef and then sheep. The net benefit improved as the reliability of the genomic evaluations improved and, in fact, a negative net benefit of genotyping was less frequent when the reliability of the genomic evaluations was high. The impact of a 10% point increase in genomic reliability was, however, greatest in sheep, followed by beef and then dairy. The net benefit of genotyping female selection candidates reduced as replacement rate increased. As genotyping costs increased, the net benefit reduced irrespective of the percentage of selection candidates kept, the replacement rate or even the population considered. Nonetheless, the association between the genotyping cost and the net benefit of genotyping differed by the percentage of selection candidates kept. Across all replacement rates evaluated, retaining 25% of the selection candidates resulted in the greatest net benefit when genotyping cost was low but the lowest net benefit when genotyping cost was high. Genotyping breakeven cost was non-linearly associated with the percentage of selection candidates retained, reaching a maximum when 50% of selection candidates were retained, irrespective of replacement rate, genomic reliability or the population. The genotyping breakeven cost was also non-linearly associated with replacement rate. The approaches outlined within provide the back-end framework for a decision support tool to quantify the net benefit of genotyping, once parameterised by the relevant population metrics.

Keywords

genomic testcost-benefitdairybeefgenotype
Type
Research Article
Information
animal , Volume 14 , Issue 8 , August 2020 , pp. 1565 - 1575
Copyright
© The Animal Consortium 2020

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References

Amer, PR, Simm, G, Keane, MG, Diskin, MG and Wickham, BW 2001. Breeding objectives for beef cattle in Ireland. Livestock Production Science 67, 223–239.CrossRefGoogle Scholar
Berry, D, Wall, E and Pryce, J 2014. Genetics and genomics of reproductive performance in dairy and beef cattle. Animal 8 (suppl. 1), 105–121. doi: 10.1017/S1751731114000743CrossRefGoogle ScholarPubMed
Berry, DP 2019. Genomic information in livestock has multiple uses in precision breeding and management. Livestock 24, 30–33.CrossRefGoogle Scholar
Berry, DP, Garcia, JF and Garrick, DJ 2016. Development and implementation of genomic predictions in beef cattle. Animal Frontiers 6, 32–38.CrossRefGoogle Scholar
Berry, DP, Wolfe, A, Byrne, N, Sayers, R, Dodds, KG, McEwan, JC, O’Connor, RE, McClure, M and Purfield, D 2017. Characterization of an X-chromosomal non-mosaic monosomy (59,XO) dairy heifer detected using routinely available single nucleotide polymorphism genotype data. Journal of Animal Science 95, 1042–1049.Google Scholar
Bijma, P 2012. Accuracies of estimated breeding values from ordinary genetic evaluations do not reflect the correlation between true and estimated breeding values in selected populations. Journal of Animal Breeding and Genetics 129, 345–358.CrossRefGoogle Scholar
Bohan, A, Shalloo, L, Creighton, P, Berry, DP, Boland, TM, O’Brien, AC, Pabiou, T, Wall, E, McDermott, K and McHugh, N 2019. Deriving economic values for national sheep breeding objectives using a bio-economic model. Livestock Science 227, 44–54.CrossRefGoogle Scholar
Boichard, D, Dassonneville, R, Mattalia, S, Ducrocq, V, and Fritz, S 2013. All cows are worth to be genotyped. Interbull Bulletin 47, 256–260.Google Scholar
Calus, MP, Bijma, P and Veerkamp, RF 2015. Evaluation of genomic selection for replacement strategies using selection index theory. Journal of Dairy Science 98, 6499–6509.CrossRefGoogle ScholarPubMed
Carthy, TR, McCarthy, J and Berry, DP. 2019. A mating advice system in dairy cattle incorporating genomic information. Journal of Dairy Science 102:8210–8220.CrossRefGoogle ScholarPubMed
Crowley, JJ, McGee, M, Kenny, DA, Crews, DH Jr, Evans, RD and Berry, DP 2010. Phenotypic and genetic parameters for different measures of feed efficiency in different breeds of Irish performance tested beef bulls. Journal of Animal Science 88, 885–894.CrossRefGoogle ScholarPubMed
Dekkers, JCM 1992. Asymptotic response to selection on best linear unbiased predictors of breeding values. Animal Science 54, 351–360.CrossRefGoogle Scholar
Dodds, KG, McEwan, JC, Brauning, R, Anderson, RM, van Stijn, TC, Kristjánsson, T and Clarke, SM 2015. Construction of relatedness matrices using genotyping-by-sequencing data. BMC Genomics 16:1047.CrossRefGoogle ScholarPubMed
García-Ruiz, A, Cole, JB, VanRaden, PM, Wiggans, GR, Ruiz-López, FJ, and Van Tassell, CP. 2016. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection. Proceedings of the National Academy of Science 113, E3995–E4004.CrossRefGoogle ScholarPubMed
Irish Cattle Breeding Federation (ICBF) (2018) Beef Calving Statistics – National Averages 2017. Accessed on 20 May 2019 from https://www.icbf.com.Google Scholar
Judge, MM, Kelleher, MM, Kearney, JF, Sleator, RD and Berry, DP 2017. Ultra-low-density genotype panels for breed assignment of Angus and Hereford cattle. Animal 11, 938–947.CrossRefGoogle ScholarPubMed
McHugh, N, Evans, RD, Amer, PR, Fahey, AG and Berry, DP 2011. Genetic parameters for cattle price and body weight from routinely collected data at livestock auctions and commercial farms. Journal of Animal Science 89, 29–39.CrossRefGoogle Scholar
McHugh, N, Pabiou, T, McDermott, K, Wall, E and Berry, DP 2017. Impact of birth and rearing type, as well as inaccuracy of recording, on pre-weaning lamb phenotypic and genetic merit for live weight. Translational Animal Science 1:137–145.CrossRefGoogle ScholarPubMed
McParland, S, Kearney, JF, MacHugh, DE and Berry, DP 2008. Inbreeding effects on postweaning production traits, conformation, and calving performance in Irish beef cattle. Journal of Dairy Science 86, 3338–3347.Google ScholarPubMed
McParland, S, Kearney, JF, Rath, M and Berry, DP 2007. Inbreeding effects on milk production, calving performance, fertility, and conformation in Irish Holstein-Friesians. Journal of Dairy Science 90, 4411–4419.CrossRefGoogle Scholar
Newton, JE, Hayes, BJ and Pryce, JE. 2018. The cost-benefit of genomic testing of heifers and using sexed semen in pasture-based dairy herds. Journal of Dairy Science 101, 6159–6173.CrossRefGoogle ScholarPubMed
Norberg, E and Sørensen, AC. 2007. Inbreeding trend and inbreeding depression in the Danish populations of Texel, Shropshire, and Oxford Down. Journal of Animal Science 85, 299–304.CrossRefGoogle ScholarPubMed
Pryce, JE and Hayes, BJ. 2012. A review of how dairy farmers can use and profit from genomic technologies Animal Production Science 52, 180–184.CrossRefGoogle Scholar
Rendel, J and Robertson, A 1950. Estimation of genetic gain in milk yield by selection in a closed herd of dairy cattle. Journal of Genetics 50, 1–8.CrossRefGoogle Scholar
Rupp, R, Mucha, S, Larroque, H, McEwan, J and Conington, J 2016. Genomic application in sheep and goat breeding. Animal Frontiers 6, 39–44.CrossRefGoogle Scholar
Rutten, CJ, Steeneveld, W, Inchaisri, C and Hogeveen, H. 2014. An ex ante analysis on the use of activity meters for automated estrus detection: To invest or not to invest? Journal of Dairy Science 97, 6869–6887.CrossRefGoogle Scholar
Santos, BFS, Amer, PR, Granleese, T, Byrne, TJ, Hogan, L, Gibson, JP and van der Werf, JHJ 2018. Assessment of the genetic and economic impact of performance recording and genotyping in Australian commercial sheep operations. Journal of Animal Breeding and Genetics 135, 221–237.Google ScholarPubMed
Santos, BFS, McHugh, N, Byrne, TJ, Berry, DP and Amer, PR 2015. Comparison of breeding objectives across countries with application to sheep indexes in New Zealand and Ireland. Journal of Animal Breeding and Genetics 132, 144–154.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
Van Eenennaam, AL and Drake, DJ 2012. Where in the beef-cattle supply chain might DNA tests generate value? Animal Production Science 52, 185–196.CrossRefGoogle Scholar
Van Tassell, CP and Van Vleck, LD 1991. Estimates of genetic selection differentials and generation intervals for four paths of selection. Journal of Dairy Science 74, 1078–1086.CrossRefGoogle Scholar
Visscher, PM, Woolliams, JA, Smith, D and Williams, JL 2002. Estimation of pedigree errors in the UK dairy population using microsatellite markers and the impact on selection. Journal of Dairy Science 85, 2368–2375.CrossRefGoogle ScholarPubMed
Weigel, KA, Hoffman, PC, Herring, W, and Lawlor, TJ Jr. 2012. Potential gains in lifetime net merit from genomic testing of cows, heifers, and calves on commercial dairy farms. Journal of Dairy Science 95, 2215–2225.CrossRefGoogle ScholarPubMed
Wiggans, GR, Cole, JB, Hubbard, SM, and Sonstegard, TS. 2017. Genomic selection in dairy cattle: The USDA experience. Annual Reviews in Animal Bioscience 8, 309–327.CrossRefGoogle Scholar
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