Evaluation of Egg Production in Layers Using Random Regression Models
The objectives of this study were to estimate genetic parameters for egg production over the age trajectory in three commercial layer breeding lines, which represent different biotypes for egg production, and to validate the use of breeding values for slope as a measure of persistency to be used in the selection program. Egg production data of over 26,000 layers per line from six consecutive generations were analyzed. Daily records were cumulated into biweekly periods. Data were analyzed with a random regression model with linear polynomials on period for random additive genetic and permanent environmental effects. In all lines, a nonzero genetic variance for mean and slope and a positive genetic correlation between mean and slope were estimated. Breeding values for slope well reflected the shape of the egg production curve and can be used to select for persistency of egg production. The model proposed in this study appealing for implementation in large and multiple populations under commercial conditions by breeding companies or other breeding organizations.
Keywords: ASL R2622
How to Cite:
Wolc, A., Arango, J., Settar, P., O'Sullivan, N. P. & Dekkers, J. C., (2011) “Evaluation of Egg Production in Layers Using Random Regression Models”, Iowa State University Animal Industry Report 8(1). doi: https://doi.org/10.31274/ans_air-180814-774