Abstract

Information on mature weight, hip height, and body condition score from Angus cows was analyzed to estimate variance components and compare prediction models. Observations from repeated measures were analyzed with animal models with or without condition score as a covariate and with or without an effect for permanent environment. Heritability (repeatability) estimates for mature weight, hip height, and condition score from Method R procedures were 0.40 (0.77), 0.62 (0.81), and 0.11 (0.38), respectively, from animal models containing a permanent environmental effect but without a covariate for condition score. Heritability estimates from animal models without a permanent environmental effect were similar to repeatability estimates from animal models with it, suggesting inflated estimates of genetic variance from models not containing a permanent environmental effect. Regressing mature weight on condition score reduced both additive genetic variance and permanent environmental variance, increasing the heritability estimate of mature weight to 0.54 and altering the biological interpretation of the trait. The covariate for condition score had little effect on hip height. Regressions of mature weight and hip height on condition score were 25.9 kg/unit of body condition score and 0.4 cm/unit, respectively. Least-squares means for mature weight and hip height tended to increase until 7 and 5 yr of age, respectively. Condition score tended to increase until 6 yr of age and decrease after 8 yr of age. Correlations between breeding value solutions for the same trait were high whether or not prediction models included a permanent environmental effect or a covariate for condition score, and whether or not the variance components used were derived from models containing a covariate for condition score. Results suggest the importance of including a permanent environmental effect in genetic prediction models for these traits. Whether mature weight should be adjusted for body condition is arguable, depending on availability of condition score predictions and tools for analyzing mature weight and condition score predictions in an environment-specific context.

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