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Genetic (co)variance for sire fertility estimated by additive, non-additive and longitudinal models in Holstein–Zebu cross-bred cows

Published online by Cambridge University Press:  11 December 2012

A. Menéndez Buxadera  and
Y. Ayrado
A. Menéndez Buxadera
Affiliation:
National Recording Centre, Ministry of Agriculture, Conill and Boyeros, C. de La Habana 10400, Cuba
Y. Ayrado
Affiliation:
National Recording Centre, Ministry of Agriculture, Conill and Boyeros, C. de La Habana 10400, Cuba
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Abstract

For this study, we used the results of sire fertility (SF) measured on a monthly basis by the ratio between the number of pregnant cows and the number of inseminations, from a total of 905 140 inseminations carried out in Cuba between 1994 and 2003. These artificial inseminations were made using 815 sires in 3249 herds throughout the country, and were analysed using additive, non-additive linear models and a random regression model (RRM). The additive genetic (add), heterosis (het) and recombination loss (rec) coefficients were estimated according to the proportion of Zebu (Z) and Holstein (H) blood from the paternal and maternal origin of each cow. The mean level of SF was 48.8%, whereas het and rec were 9.6% and −8.4%, respectively. The heritability (h2) of a single insemination ranged from h2 = 0.011 to h2 = 0.030 for females from 0% to 100% of H genes. The additive multi-trait and RRM analyses showed the existence of heterogeneous genetic (co)variance components, as the proportion of Holstein genes in the inseminated cow increased. We found low genetic correlations for SF recorded in pure-bred and cross-bred females, with over 50% of breed differences in their additive genetic composition. The use of a RRM allows us to identify the changes in genetic (co)variance and estimated breeding values in the whole trajectory of the different proportions of Bos taurus × Bos indicus blood.

Keywords

sire fertilityHolsteinZebu and cross-bred cowsgenetic (co)variance componentsrandom regression model
Type
Breeding and genetics
Information
animal , Volume 7 , Issue 5 , May 2013 , pp. 721 - 728
Copyright
Copyright © The Animal Consortium 2012

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References

Averill, TA, Rekaya, R, Weigel, K 2004. Genetic analysis of male and female fertility using longitudinal binary data. Journal of Dairy Science 87, 3947–3952.Google Scholar
Berry, DP, Evans, RD, McParland, S 2011. Evaluation of bull fertility in dairy and beef cattle using cow field data. Theriogenology 75, 172–181.Google Scholar
Biscarini, F, Bovenhuis, H, van Arendonk, JAM 2008. Estimation of variance components and prediction of breeding values using pooled data. Journal of Animal Science 86, 2845–2852.Google Scholar
Biscarini, F, Bovenhuis, H, Ellen, ED, Addo, S, van Arendonk, JAM 2010. Estimation of heritability and breeding values for early egg production in laying hens from pooled data. Poultry Science 89, 1842–1849.CrossRefGoogle ScholarPubMed
Clay, JS, McDaniel, BT 2001. Computing mating bull fertility from DHI non return data. Journal of Dairy Science 84, 1238–1245.Google Scholar
Cooper, AJ, Ferrell, CL, Cundiff, LV, Van Vleck, LD 2010. Prediction of genetic values for feed intake from individual body weight gain and total feed intake of the pen. Journal of Animal Science 88, 1967–1972.Google Scholar
Cunningham, EP, Syrstad, O 1987. Crossbreeding B. indicus and B. taurus for milk production in the tropics. FAO Animal Production and Health, paper No. 68. Rome, Italy, 90pp.Google Scholar
De Haas, Y, Janss, LLG, Kadarmideen, HN 2007. Genetic correlations between body condition scores and fertility in dairy cattle using bivariate random regression models. Journal of Animal Breeding and Genetics 124, 277–285.Google Scholar
Demeke, S, Neser, FWC, Schofman, SJ 2004. Estimates of genetic parameters for Boran, Friesian and crosses of Friesian and Jersey with Boran cattle in the tropical highlands of Ethiopia: reproduction data. Journal of Animal Breeding and Genetics 121, 57–65.Google Scholar
Elzo, M, Bradford, GE 1985. Multibreed sire evaluation procedures across countries. Journal of Animal Science 60, 953–963.CrossRefGoogle Scholar
Elzo, M, Famula, TR 1985. Multibreed sire evaluation procedures within a country. Journal of Animal Science 60, 942–952.Google Scholar
Elzo, M, Manrique, C, Ossa, G, Acosta, O 1998. Additive and non-additive genetic variability for growth traits in the Turipaná Romosinuano-Zebu multibreed herd. Journal of Animal Science 76, 1539–1549.Google Scholar
Foulley, JL, Robert-Granie, C 2002. Basic tools for the statistical analysis of longitudinal data via mixed model. In Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, August 18, Montpellier, France, Special Brochure, 159pp.Google Scholar
Gilmour, AR, Cullis, BR, Welham, SJ, Thompson, R 2000. ASREML Reference Manual. NSW Agric Biom Bull NSW Agriculture, Orange, NSW, Australia.Google Scholar
Hickey, JM, Calus, MPL, Cromie, AR, Keane, MG, Brophy, P, Verkamp, RF 2006. Accounting for heterogeneous variance components in multiple breed evaluation of beef traits in Black and White Cattle. In Proceedings of the 8th World Congress on Genetic Apply to Livestock Production, Belo Horizonte, August 13–18, Brazil, Session 33-11, 4pp.Google Scholar
Jamrozik, JL, Schaeffer, LR 1997. Estimates of genetic parameters for a test day model with random regression for production of first lactation. Journal of Dairy Science 80, 762–770.Google Scholar
Kahi, AK, Thorpe, W, Nitter, G, Baker, RL 2000. Crossbreeding for daily production in the lowland tropics of Kenya: I. Estimation of individual crossbreeding effects on milk production and reproduction traits and on cow live weight. Livestock Production Science 63, 39–54.Google Scholar
Kirkpatrick, M, Lofsvold, D, Bulmer, M 1990. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124, 979–993.Google Scholar
Kuhn, MT, Hutchison, JL, Norman, HD 2008. Modeling nuisance variables for prediction of service sire fertility. Journal of Dairy Science 91, 2823–2835.Google Scholar
Lidauer, M, Mantysaari, EA, Strandén, I, Poso, J, Pedersen, J, Nielsen, US, Johansson, K, Ericksson, JA, Madsen, P, Aumand, GP 2006. Random heterosis and recombination loss effects in a multibreed evaluation for Nordic red Dairy cattle. In proceedings of the 8th World Congress on Genetic Apply to Livestock Production, Belo Horizonte, August 13–18, Brazil, Session 24-14, 4pp.Google Scholar
Mackinnon, MJ, Thorpe, W, Baker, RL 1996. Sources of genetic variation for milk production in a crossbred herd in the tropics. Animal Production 62, 5–16.Google Scholar
Menéndez-Buxadera, A, Rodriguez, M 1996. Genetic and environmental factors affecting Holstein sire fertility in Cuba. Cuban Journal of Agricultural Science 27, 11–18.Google Scholar
Menéndez-Buxadera, A, Mandonnet, N 2006. The role and importance of genotype–environment interaction for animal breeding in the tropics. Animal Breeding Abstract 74, 1–14.Google Scholar
Menéndez-Buxadera, A, Caunedo Sibori, J, Fernández Santibañez, M 2004. Relacion entre el porciento de vacas en ordeño y la produccion lactea del rebaño. Cuban Journal of Agricultural Science 38, 361–367.Google Scholar
Menéndez-Buxadera, A, Morales, JR, Dora, J, Iglesias, C, Chávez, H 1976. Resultados de los servicios de I.A. en ganado Holstein, Cebú y sus mestizos en las condiciones de Cuba. Revista Cubana de Reproducción Animal 2, 38–57.Google Scholar
Newman, S, Reverter, A, Johnston, DJ 2002. Purebred–crossbred performance and genetic evaluation of postweaning growth and carcass traits in Bos indicus × Bos taurus crosses in Australia. Journal of Animal Science 80, 1801–1808.Google Scholar
Olson, KM, Garrick, DJ, Enns, RM 2006. Predicting breeding values and accuracies from group in comparison to individual. Journal of Animal Science 84, 88–92.CrossRefGoogle ScholarPubMed
Rutledge, JJ 2001. Greek temples, tropical kine and recombination load. Livestock Production Science 68, 171–179.CrossRefGoogle Scholar
Thaller, G 1998. Genetics and breeding for fertility. Interbull Bulletin No. 18, Grub, Germany, November 1997, pp. 55–61.Google Scholar
Thibier, M, Wagner, HG 2002. World statistics for artificial insemination in cattle. Livestock Production Science 74, 203–212.Google Scholar
van Arendonk, JAM 2011. The role of reproductive technologies in breeding schemes for livestock populations in developing countries. Livestock Science 136, 29–37.Google Scholar
Van der Werf, JHJ 1990. Models to estimate genetic parameters on crossbred dairy cattle populations under selection. PhD, Wageningen Agricultural University, The Netherlands.Google Scholar
van der Werf, J, Schaeffer, LR 1997. Random Regression in Animal Breeding. Course Notes, CGIL, Guelph, Canada, June 25–28, 58pp.Google Scholar
Vishwanath, R 2003. Artificial insemination: the state of the art. Theriogenology 59, 571–584.Google Scholar
Wall, E, Brotherstone, S, Kearney, JF, Woolliams, JA, Coffey, MP 2005. Impact of nonadditive genetic effects in the estimation of breeding values for fertility and correlated traits. Journal of Dairy Science 88, 376–385.Google Scholar
Wei, M 1992. Combined crossbred and purebred selection in animal breeding. PhD, Wageningen Agricultural University, The Netherlands.Google Scholar
Weigel, KA 2004. Improving the reproductive efficiency of dairy cattle through genetic selection. Journal of Dairy Science 87 (suppl.), E86–E92.Google Scholar