The application of random regression models in the genetic analysis of monthly egg production in turkeys and a comparison with alternative longitudinal models.

Random regression models (RR) have become a popular methodology for the genetic study of longitudinal data since the last decade. The first objective of the current study was to investigate the application of RR models for the genetic analysis of egg production in turkeys. Data collected from a heavy dam line were used to estimate genetic parameters with 2 RR models, one having second-order Legendre polynomials as regression over time (RR2) and another with third-order polynomials (RR3). The second objective was to benchmark the performance of RR models with more conventional methods, so genetic parameters were reestimated using a multitrait (MT) and a repeatability model. To assess the model efficiency of predicting missing values, a reduced data set was used, and for each model, the predicted values of the deleted records were compared with the true values. The RR models were further compared against each other by eliminating the last period and estimating the MS error of the predictions for both models. The repeatability model had the poorest performance in predicting missing values. Heritability estimates from RR2 and MT models were close, whereas the RR3 model estimates were different. Both RR models demonstrated better prediction ability than the MT model. However, when RR models were compared solely, the RR2 model resulted in the smallest MS error. The results indicated that the RR3 model overfitted the data, suggesting that the choice of the appropriate polynomial order requires careful consideration. The present study illustrated that the application of RR models for the genetic analysis of egg production in turkeys is not only feasible but also offers a high accuracy of prediction.