Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-27T02:59:27.986Z Has data issue: false hasContentIssue false
  • English
  • Français

A definition of unknown parent groups based on bull usage patterns across herds

Published online by Cambridge University Press:  29 October 2010

A. Bouquet ,
G. Renand  and
F. Phocas
A. Bouquet*
Affiliation:
INRA, UMR1313 Génétique Animale et Biologie Intégrative, F-78352 Jouy-en-Josas, France AgroParisTech, UMR1313 Génétique Animale et Biologie Intégrative, F-75231 Paris Cedex 05, France
G. Renand
Affiliation:
INRA, UMR1313 Génétique Animale et Biologie Intégrative, F-78352 Jouy-en-Josas, France
F. Phocas
Affiliation:
INRA, UMR1313 Génétique Animale et Biologie Intégrative, F-78352 Jouy-en-Josas, France
*
†E-mail: alban.bouquet@gmail.com
Get access

Abstract

In genetic evaluations, the definition of unknown parent groups (UPG) is usually based on time periods, selection path and flows of foreign founders. The definition of UPG may be more complex for populations presenting genetic heterogeneity due to both, large national expansion and coexistence of artificial insemination (AI) and natural service (NS). A UPG definition method accounting for beef bull flows was proposed and applied to the French Charolais cattle population. It assumed that, at a given time period, unknown parents belonged to the same UPG when their progeny were bred in herds that used bulls with similar origins (birth region and reproduction way). Thus, the birth period, region and AI rate of a herd were pointed out to be the three criteria reflecting genetic disparities at the national level in a beef cattle population. To deal with regional genetic disparities, 14 regions were identified using a factorial approach combining principal component analysis and Ward clustering. The selection nucleus of the French cattle population was dispersed over three main breeding areas. Flows of NS bulls were mainly carried out within each breeding area. On the contrary, the use and the selection of AI bulls were based on a national pool of candidates. Within a time period, herds of different regions were clustered together when they used bulls coming from the same origin and with an estimated difference of genetic level lower than 20% of genetic standard deviation (σg) for calf muscle and skeleton scores (SS) at weaning. This led to the definition of 16 UPG of sires, which were validated as robust and relevant in a sire model, meaning numerically stable and corresponding to distinct genetic subpopulations. The UPG genetic levels were estimated for muscle and SS under sire and animal models. Whatever the trait, differences between bull UPG estimates within a time period could reach 0.5 σg across regions. For a given time period, bull UPG estimates for muscle and SS were generally larger by 0.30 to 0.75 σg than those of cows. Including genetic groups in the evaluation model increased the estimated genetic trends by 20% to 30%. It also provoked re-ranking in favor of bulls and cows without pedigree.

Keywords

beef cattleunknown parentsgenetic evaluationgenetic groupsgenetic trend
Type
Full Paper
Information
animal , Volume 5 , Issue 3 , March 2011 , pp. 337 - 346
Copyright
Copyright © The Animal Consortium 2011

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

Akaike, H 1973. Information theory as an extension of the maximum likelihood principle. In Proceedings of the 2nd International Symposium on Information Theory (ed. BN Petrov and F Csaki), pp. 267–281. Akademiai Kiado, Budapest, Hungary.Google Scholar
Banos, G, Schaeffer, LR, Burnside, EB 1991. Genetic relationships and linear model comparisons between United States and Canadian Ayrshire and Jersey bull populations. Journal of Dairy Science 74, 1060–1068.CrossRefGoogle Scholar
Bougler, J, Le Liboux, H, Hocde, H, Vissac, B 1973. Performances de production. In La race Charolaise – resultats français (ed. J Bougler, B Coudurier, JM Duplan, H Hocde, H Le Liboux, R Tondu and B Vissac B), pp. 265–339. INRA, Jouy-en-Josas, France.Google Scholar
Bouquet, A, Renand, G, Phocas, F 2009. Evolution de la diversité génétique des populations françaises de bovins allaitants spécialisés de 1979 à 2008. Productions Animales 22, 317–330.CrossRefGoogle Scholar
De Jong, G 2003. MACE – Options for improvement. Interbull Bulletin 30, 112–116.Google Scholar
Fikse, F 2003. Approaches to incorporate genetic groups in MACE. Conference at the Interbull Workshop, Beltsville, MD, USA. Retrieved October 8, 2008, from http://www-interbull.slu.se/bulletins/framesida-pub.htmGoogle Scholar
Gilmour, AR, Gogel, BJ, Cullis, BR, Welham, SJ, Thompson, R 2002. ASREML user guide, release 1.0. VSN International Ltd, Hemel Hempstead, HP1 1ES, UK.Google Scholar
Henderson, CR 1973. Sire evaluation and genetic trends. In Proceedings of the animal breeding and genetics symposium in honor of Dr. Jay L. Lush. Journal of Animal Science 36 (Symposium), 10–41.CrossRefGoogle Scholar
Interbull 2001. Use of phantom parent groups. In Interbull guidelines for national and international genetic evaluation systems in dairy cattle with focus on production traits (ed. H Jorjani, J Philipsson and JC Moquot), pp. 16–17. Interbull, Uppsala, Sweden.Google Scholar
Journaux, J, Berrechet, P, Ledos, H 2006. Etude d’un dispositif déclaratif des filiations en bovins et ses conséquences sur la fiabilité des parentés et l’organisation collective. Technical Bulletin 010678012. Institut de l’Elevage, Paris, France, 56 pp. Retrieved December 13, 2006, from www.inst-elevage.asso.fr/html1/IMG/pdf/QUAPI_CR_Final_n010678012-1.pdfGoogle Scholar
Kennedy, BW 1981. Bias and mean standard error from ignoring genetic groups in mixed model sire evaluation. Journal of Dairy Science 64, 689–697.CrossRefGoogle Scholar
Phocas, F, Laloë, D 2004a. Should genetic groups be fitted in BLUP evaluation? Practical answer for the French AI beef sire evaluation. Genetics Selection Evolution 36, 325–345.CrossRefGoogle ScholarPubMed
Phocas, F, Laloë, D 2004b. Genetic parameters for birth and weaning traits in French specialized beef cattle breeds. Livestock Production Science 89, 121–128.CrossRefGoogle Scholar
Quaas, RL 1988. Additive genetic model with groups and relationships. Journal of Dairy Science 71, 1338–1345.CrossRefGoogle Scholar
Robinson, GK 1986. Group effects and computing strategies for models for estimating breeding values. Journal of Dairy Science 69, 3106–3111.CrossRefGoogle Scholar
Statistical Analysis Systems Institute Inc. 2004. Base SAS 9.1 procedures guide. SAS Institute Inc., Cary, NC, USA.Google Scholar
Ward, EJ 2008. A review and comparison of four commonly used Bayesian and maximum likelihood model selection tools. Ecological Modelling 211, 1–10.CrossRefGoogle Scholar
Westell, RA, Quaas, RL, Van Vleck, LD 1988. Genetic groups in an animal model. Journal of Dairy Science 71, 1310–1318.CrossRefGoogle Scholar