Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-23T05:08:19.906Z Has data issue: false hasContentIssue false
  • English
  • Français

Estimation of additive and dominance genetic variance components for female fertility traits in Iranian Holstein cows

Published online by Cambridge University Press:  04 July 2018

H. Ghiasi ,
R. Abdollahi-Arpanahi ,
M. Razmkabir ,
M. Khaldari  and
R. Taherkhani
H. Ghiasi*
Affiliation:
Department of Animal Science, Faculty of Agricultural Science, Payame Noor University, Tehran, Iran
R. Abdollahi-Arpanahi
Affiliation:
Department of Animal and Poultry Science, College of Aburaihan, University of Tehran, 465 Pakdasht, Iran
M. Razmkabir
Affiliation:
Department of Animal Science, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran
M. Khaldari
Affiliation:
Department of Animal Science, Faculty of Agriculture, Lorestan University, PO Box 465, 68137-1713, Khorram-Abad, Iran
R. Taherkhani
Affiliation:
Department of Animal Science, Faculty of Agricultural Science, Payame Noor University, Tehran, Iran
*
Author for correspondence: H. Ghiasi, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The aim of the current study was to estimate additive and dominance genetic variance components for days from calving to first service (DFS), a number of services to conception (NSC) and days open (DO). Data consisted of 25 518 fertility records from first parity dairy cows collected from 15 large Holstein herds of Iran. To estimate the variance components, two models, one including only additive genetic effects and another fitting both additive and dominance genetic effects together, were used. The additive and dominance relationship matrices were constructed using pedigree data. The estimated heritability for DFS, NSC and DO were 0.068, 0.035 and 0.067, respectively. The differences between estimated heritability using the additive genetic and additive-dominance genetic models were negligible regardless of the trait under study. The estimated dominance variance was larger than the estimated additive genetic variance. The ratio of dominance variance to phenotypic variance was 0.260, 0.231 and 0.196 for DFS, NSC and DO, respectively. Akaike's information criteria indicated that the model fitting both additive and dominance genetic effects is the best model for analysing DFS, NSC and DO. Spearman's rank correlations between the predicted breeding values (BV) from additive and additive-dominance models were high (0.99). Therefore, ranking of the animals based on predicted BVs was the same in both models. The results of the current study confirmed the importance of taking dominance variance into account in the genetic evaluation of dairy cows.

Keywords

Dairy cowdominance variancefertilitynon-additive
Type
Animal Research Paper
Information
The Journal of Agricultural Science , Volume 156 , Issue 4 , May 2018 , pp. 565 - 569
Copyright
Copyright © Cambridge University Press 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aliloo, H, Pryce, JE, González-Recio, O, Cocks, BG and Hayes, BJ (2016) Accounting for dominance to improve genomic evaluations of dairy cows for fertility and milk production traits. Genetics Selection Evolution 48, article number 8, 1–11. doi: 10.1186/s12711-016-0186-0.Google Scholar
Beckett, RC, Ludwick, TM, Rader, ER, Hines, HC and Pearson, R (1979) Specific and general combining abilities for production and reproduction among lines of Holstein cattle. Journal of Dairy Science 62, 613–620.Google Scholar
Bolormaa, S, Pryce, JE, Zhang, Y, Reverter, A, Barendse, W, Hayes, BJ and Goddard, ME (2015) Non-additive genetic variation in growth, carcass and fertility traits of beef cattle. Genetics Selection Evolution 47, article number 26, 1–12. Available at https://doi.org/10.1186/s12711-015-0114-8.Google Scholar
Butler, ST (2013) Genetic control of reproduction in dairy cows. Reproduction, Fertility and Development 26, 1–11.Google Scholar
Charlesworth, D and Willis, JH (2009) The genetics of inbreeding depression. Nature Review Genetics 10, 783–796.Google Scholar
Fisher, RA (1930) The Genetical Theory of Natural Selection. Oxford, UK: Clarendon Press.Google Scholar
Fuerst, C and Solkner, J (1994) Additive and non-additive genetic variances for milk yield, fertility, and life performance traits of dairy cattle. Journal of Dairy Science 77, 1114–1125.Google Scholar
Ghiasi, H, Pakdel, A, Nejati-Javaremi, A, Mehrabani-Yeganeh, H, Honarvar, M, González-Recio, O, Carabaño, MJ and Alenda, R (2011) Genetic variance components for female fertility in Iranian Holstein cows. Livestock Science 139, 277–280.Google Scholar
Gilmour, AR, Gogel, BJ, Cullis, BR and Thompson, R (2009) ASReml User Guide Release 3.0. Hemel Hempstead, UK: VSN International Ltd.Google Scholar
González-Recio, O and Alenda, R (2005) Genetic parameters for female fertility traits and a fertility index in Spanish dairy cattle. Journal of Dairy Science 88, 3282–3289.Google Scholar
González-Recio, O, Lopez De Maturana, E and Gutiérrez, JP (2007) Inbreeding depression on female fertility and calving ease in Spanish dairy cattle. Journal of Dairy Science 90, 5744–5752.Google Scholar
Hoeschele, I (1991) Additive and non-additive genetic variance in female fertility of Holsteins. Journal of Dairy Science 74, 1743–1752.Google Scholar
Jamrozik, J, Fatehi, J, Kistemaker, GJ and Schaeffer, LR (2005) Estimates of genetic parameters for Canadian Holstein female reproduction traits. Journal of Dairy Science 88, 2199–2208.Google Scholar
Jones, JS (1987) The heritability of fitness: bad news for good genes? Trends in Ecology and Evolution 2, 35–38.Google Scholar
Lawlor, TJ, Weigel, KA and Misztal, I (1993) Implications of incorporating inbreeding information into animal model evaluations for type (abstract). Journal of Dairy Science 76, 292.Google Scholar
Miglior, F, Burnside, EB and Kennedy, BW (1995) Production traits of Holstein cattle: estimation of non-additive genetic variance components and inbreeding depression. Journal of Dairy Science 78, 1174–1180.Google Scholar
Misztal, I (2001) New models and computations in animal breeding. 50th Annual National Breeders Roundtable (Poultry Science Association). St. Louis, Missouri, USA: Poultry Science Association, May 3–4. pp. 32–42.Google Scholar
Nagy, I, Farkas, J, Curik, I, Gorjanc, G, Gyovai, P and Szendrő, Z (2014) Estimation of additive and dominance variance for litter size components in rabbits. Czech Journal of Animal Science 59, 182–189.Google Scholar
Palucci, V, Schaeffer, LR, Miglior, F and Osborne, V (2007) Non-additive genetic effects for fertility traits in Canadian Holstein cattle. Genetics Selection Evolution 39, 181–193.Google Scholar
Pryce, JE, Haile-Mariam, M, Goddard, ME and Hayes, BJ (2014) Identification of genomic regions associated with inbreeding depression in Holstein and Jersey dairy cattle. Genetics Selection Evolution 46, article number 71, 1–14. doi: 10.1186/s12711-014-0071-7.Google Scholar
Refsdal, AO (2007) Reproductive performance of Norwegian cattle from 1985 to 2005: trends and seasonality. Acta Veterinaria Scandinavica 49, article number 5, 1–7. doi: 10.1186/1751-0147-49-5.Google Scholar
Serenius, T, Stalder, KJ and Puonti, M (2006) Impact of dominance effects on sow longevity. Journal of Animal Breeding and Genetics 123, 355–361.Google Scholar
Tempelman, RJ and Burnside, EB (1990) Additive and nonadditive genetic variation for production traits in Canadian Holsteins. Journal of Dairy Science 73, 2206–2213.Google Scholar
Toro, MA and Varona, L (2010) A note on mate allocation for dominance handling in genomic selection. Genetics Selection Evolution 42, article number 33, 1–9. Available at https://doi.org/10.1186/1297-9686-42-33.Google Scholar
Van Der Werf, JHJ and De Boer, W (1989) Influence of nonadditive effects on estimation of genetic parameters in dairy cattle. Journal of Dairy Science 72, 2606–2614.Google Scholar
Van Tassell, CP, Misztal, I and Varona, L (2000) Method R estimates of additive genetic, dominance genetic, and permanent environmental fraction of variance for yield and health traits of Holsteins. Journal of Dairy Science 83, 1873–1877.Google Scholar
Wei, M and Van Der Werf, JH (1993) Animal model estimation of additive and dominance variances in egg production traits of poultry. Journal of Animal Science 71, 57–65.Google Scholar
Wolak, ME (2012) Nadiv: an R package to create relatedness matrices for estimating non-additive genetic variances in animal models. Methods in Ecology and Evolution 3, 792–796.Google Scholar