HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Arango, J. Articles by Herring, W. Search for Related Content PubMed PubMed Citation Articles by Arango, J. Articles by Herring, W.

Estimation of variance components including competitive effects of Large White growing gilts¹

J. Arango^*,2,3, I. Misztal^*, S. Tsuruta^*, M. Culbertson and W. Herring

^* Department of Animal and Dairy Science, University of Georgia, Athens 30602-2771; and and Smithfield Premium Genetics, Roanoke Rapids, NC 27870

² Correspondence: 306 Dept. of Anim. and Dairy Sci (phone: 706-583-0250; fax: 706-583-0274; e-mail: arangoj{at}uga.edu).

Records of on-test ADG of Large White gilts were analyzed to estimate variance components of direct and associative genetic effects. Models included the effects of contemporary group (farm-barn-batch), birth litter, pen group, and direct and associative additive genetic effects. The area of each pen was 14 m². The additive genetic variance was a function of the number of competitors in a group, the additive relationships between the animal performing the record and its pen mates, and the additive relationships between pen mates. To partially account for differences in the number of pen mates, a covariable (q_i = 1, 1/n, or 1/n) was added to the associative genetic effect. There were 4,946 records from 2,409 litters and 362 pen groups. Pen group size ranged from 12 to 16 gilts. Analyses by REML converged very slowly. A grid search showed that the likelihood function was almost flat when the additive genetic associative effect was fitted. Estimates of direct and associative heritability were 0.15 and 0.03, respectively. Within the BLUPF90 family of programs, the mixed-model equations can be set up directly. For variance component estimation, simple programs (REMLF90 and GIBBSF90) worked without modifications, but more optimized programs did not. Estimates obtained using the three values of q_i were similar. With the data structure available for this study and under an environment with relative low competition among animals, accurate estimation of associative genetic effects was not possible. Estimation of competitive effects with large pen size is difficult. The magnitude of competition effects may be larger in commercial populations, where housing is denser and food is limited.

Key Words: Competition • Genetic Effects • Growth • Swine

This article has been cited by other articles:

				C. Y. Chen, R. K. Johnson, S. Newman, S. D. Kachman, and L. D. Van Vleck Effects of social interactions on empirical responses to selection for average daily gain of boars J Anim Sci, March 1, 2009; 87(3): 844 - 849. [Abstract] [Full Text] [PDF]

				C. Y. Chen, S. D. Kachman, R. K. Johnson, S. Newman, and L. D. Van Vleck Estimation of genetic parameters for average daily gain using models with competition effects J Anim Sci, October 1, 2008; 86(10): 2525 - 2530. [Abstract] [Full Text] [PDF]

				R. Bergsma, E. Kanis, E. F. Knol, and P. Bijma The Contribution of Social Effects to Heritable Variation in Finishing Traits of Domestic Pigs (Sus scrofa) Genetics, March 1, 2008; 178(3): 1559 - 1570. [Abstract] [Full Text] [PDF]

				L. D. Van Vleck, L. V. Cundiff, and R. M. Koch Effect of competition on gain in feedlot bulls from Hereford selection lines J Anim Sci, July 1, 2007; 85(7): 1625 - 1633. [Abstract] [Full Text] [PDF]