genetic investigation of various growth models to describe growth of lambs of two contrasting breeds

• Published in: Journal of animal science Vol. 84; no. 10; pp. 2642 - 2654
• Main Authors: Lambe, N.R, Navajas, E.A, Simm, G, Bünger, L
• Format: Journal Article
• Language: English
• Published: Savoy, IL American Society of Animal Science 01.10.2006; Am Soc Animal Sci
• Subjects: Age Factors; animal growth; Animal productions; Animals; artificial selection; Biological and medical sciences; body weight; Body Weight - physiology; breed differences; Breeding; Female; Fundamental and applied biological sciences. Psychology; genetic correlation; growth models; heritability; lambs; Least-Squares Analysis; Likelihood Functions; liveweight gain; Logistic Models; Male; mathematical models; Models, Biological; Models, Genetic; Nonlinear Dynamics; Phenotype; Regression Analysis; Scottish Blackface; selection criteria; Sheep - genetics; Sheep - growth & development; Terrestrial animal productions; Texel; Time Factors; Vertebrates; Farming animal; genetic parameter; Growth; random regression; Lamb; Vertebrata; growth function; Meat animals; Mammalia; Sheep; Genetics; Models; Artiodactyla; Ungulata
• ISSN: 0021-8812; 1525-3163
• DOI: 10.2527/jas.2006-041

This study compared the use of various models to describe growth in lambs of 2 contrasting breeds from birth to slaughter. Live BW records (n = 7559) from 240 Texel and 231 Scottish Blackface (SBF) lambs weighed at 2-wk intervals were modeled. Biologically relevant variables were estimated for each lamb from modified versions of the logistic, Gompertz, Richards, and exponential models, and from linear regression. In both breeds, all nonlinear models fitted the data well, with an average coefficient of determination (R²) of >0.98. The linear model had a lower average R² than any of the nonlinear models (<0.94). The variables used to describe the best 3 models (logistic, Gompertz, and Richards) included estimated final BW (A); maximum ADG (B); age at maximum ADG (C); position of point of inflection in relation to A (D, for Richards only). The Richards and Gompertz models provided the best fit (average R² = 0.986 to 0.989) in both breeds. Richards estimated an extra variable, allowing increased flexibility in describing individual growth patterns, but the Akaike's information criteria value (which weighs log-likelihood by number of parameters estimated) was similar to that of the Gompertz model. Variables A, B, C, and D were moderately to highly heritable in Texel lambs (h² = 0.33 to 0.87), and genetic correlations between variables within-model ranged from -0.80 to 0.89, suggesting some flexibility to change the shape of the growth curve when selecting for different variables. In SBF lambs, only variables from the logistic and Gompertz models had moderate heritabilities (0.17 to 0.56), but with high genetic correlations between variables within each model (<-0.88 or >0.92). Selection on growth variables seems promising (in Texel more than SBF), but high genetic correlations between variables may restrict the possibilities to change the growth curve shape. A random regression model was also fitted to the data to allow predictions of growth rates at relevant time points. Heritabilities for growth rates differed markedly at various stages of growth and between the 2 breeds (Texel: 0.14 to 0.74; SBF: 0.07 to 0.34), with negative correlations between growth rate at 60 d of age and growth rate at finishing. Following these results, future studies should investigate genetic relationships between relevant growth curve variables and other important production traits, such as carcass composition and meat quality.