Genetic and phenotypic parameters and responses in index component traits for breeds of sheep selected for lean tissue growth

The aims of this experiment were to estimate genetic and phenotypic parameters, and responses in vivo for selection index component traits, i.e. live weight, fat depth and muscle depth. Performance data sets were available from lean tissue growth selection experiments for Border Leicester (n = 613), Coopworth (n = 1711) and Dorset Down (n = 820) ram flocks and from a Corriedale progeny testing ram flock (n = 722). Lean tissue growth flocks had been selected for an increased aggregate breeding value incorporating predicted lean and fat weights with positive and negative economic weightings, respectively, over the period 1986–1992 inclusive. Estimates of genetic and phenotypic parameters and responses in index component traits were derived from multivariate AIREML analyses, fitting an individual animal model including animal as a random variable, birth rank, year and age of dam fitted as fixed effects and age at measurement as a covariable. Rates of response for each index component were obtained by regressing best linear unbiased prediction (BLUP) breeding values (BV) on time. Index component traits had medium to high (0.20 ± 0.04 to 0.45 ± 0.08) heritability estimates. Estimates of genetic and phenotypic correlations were predominantly low to moderate (0.20–0.60), although a few were high (>0.60). Live weight (+0.24 ± 0.09 kg per year) and muscle depth (+0.05 ± 0.02mm per year) increased and fat depth (−0.03 ± 0.02mm per year) decreased under selection. Responses, adjusted for animal size were greatest in fat depth (−0.90±0.63% per year) and least in muscle depth (+0.18±0.06% per year) with live weight being intermediate (+0.45±0.12% per year). Although not significantly different (P > 0.05), estimates of genetic and phenotypic parameters and responses to selection varied between breeds. Variation in genetic and phenotypic parameters led to variation in predicted responses for lean and fat weights between breeds, suggesting that in order to design more optimal breeding programmes breed specific parameters should be used. © 2003 Elsevier B.V. All rights reserved