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ARS Home » Plains Area » Clay Center, Nebraska » U.S. Meat Animal Research Center » Genetics and Animal Breeding » Research » Publications at this Location » Publication #147563
Title: COVARIANCE FUNCTIONS - RANDOM REGRESSION MODELS FOR COW WEIGHT IN BEEF CATTLE

Author
item ARANGO, JESUS - UNIV. OF NEBR.-LINCOLN
item Cundiff, Larry
item Van Vleck, Lloyd

Submitted to: Journal of Animal Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/20/2003
Publication Date: 1/5/2004
Citation: ARANGO, J.A., CUNDIFF, L.V., VAN VLECK, L.D. COVARIANCE FUNCTIONS - RANDOM REGRESSION MODELS FOR COW WEIGHT IN BEEF CATTLE. JOURNAL OF ANIMAL SCIENCE. 2004. v. 82. p. 54-67.

Interpretive Summary: Energy required to attain and maintain mature weight is a major cost in beef cattle production systems. Selection to reduce or to restrict mature weight while increasing saleable weight of calves at weaning and as yearlings requires good estimates of genetic parameters and correlations among weights at different ages. This paper describes an analysis to obtain these genetic parameters with a procedure that does not require weights to be taken for all animals nor at frequent intervals. Data from the first four cycles of the Germplasm Evaluation Program at the U.S. Meat Animal Research Center were used to estimate genetic parameters such as heritabilities and genetic correlations for weights of Angus, Hereford, and F1 cows produced by crosses of 22 sire and two dam (Angus and Hereford) breeds. Four weights per year were available for cows from 2 through 8 yr of age (AY). Weights (n = 61,798) were analyzed with REML using covariance function-random regression models (CF-RRM), with regression on orthogonal Legendre polynomials of age in months. Results are that genetic correlations are high among measures of weight at all post weaning ages and that, although the linear repeatability model does not fit the data as well as more complex random regression models for cow weights, a simple repeatability model similar to those now used for routine national cattle evaluations would seem to be an acceptable approximation for practical purposes to compute National Cattle Evaluations and estimates of genetic parameters for mature weight given the simplicity and robustness of the repeatability model.

Technical Abstract: Data from the first four cycles of the Germplasm Evaluation Program at the U.S. Meat Animal Research Center were used to evaluate weights of Angus, Hereford, and F1 cows produced by crosses of 22 sire and two dam (Angus and Hereford) breeds. Four weights per year were available for cows from 2 through 8 yr of age (AY). Weights (n = 61,798) were analyzed with REML using covariance function-random regression models (CF-RRM), with regression on orthogonal Legendre polynomials of age in months (AM). Models included fixed regression on AM and effects of cow line, AY, season and interactions, year of birth and pregnancy-lactation codes. Random parts of the models were RRM coefficients for additive (a) and permanent environmental (c) effects. Estimates of CFs were used to estimate covariances among all ages. Residual effects were modeled to account for heterogeneity of variance by AY. Quadratic fixed regression was sufficient to model population trajectory. Other models varied order of fit for a and c. A parsimonious model included linear and quartic regression coefficients for a and c, respectively. Estimates of variances increased with age. Estimates for older ages disagreed with estimates using finite-dimensional models. Plots of covariances for c were smooth for intermediate, but erratic for extreme ages. Heritability estimates ranged from 0.38 (36 mo) to 0.78 (94 mo) but were erratic for extreme ages. Estimates of genetic correlations were high for most pairs of ages, with lowest estimate (0.70) between extreme ages (19 and 103 mo). Results suggest that although cow weights do not fit a repeatability model with constant variances as well as CF-RRM, a repeatability model might be an acceptable approximation for prediction of breeding values.