Genetic Evaluation of Pure Line Laying Hens Based on Crossover Testing Data From Simulated Populations

Commercial laying hen breeding programs aim to maximize the performance of crossbred animals. However, selection is usually based on data from pure lines raised in controlled nucleus environments, which may not fully predict the yields under commercial conditions. This study assessed the impact of incorporating crossbred information into genetic evaluations of pure lines. Using stochastic simulations of a two-way crossbreeding scheme, performance in pure lines and crossbred animals was modelled as different traits with an assumed genetic correlation. Nine scenarios were simulated by varying heritability and the genetic correlation between populations. Six levels of crossbred information were tested, ranging from no crossbred data to full use of individual phenotypes and genotypes, as well as cost-effective options based on pooled phenotypes and pooled genotypes. Results showed that strategies using both individual phenotypic and genotypic information achieved the highest response, particularly when the genetic correlation between populations was low and heritability was high. At intermediate correlations, pooling approaches offered a useful balance between performance and cost, while at high correlations, pure line selection performed almost as well as the most informative strategies. These findings highlight the importance of estimating the genetic correlation between pure and crossbred populations and considering cost-effective ways to integrate crossbred information, such as pooled data, to optimize genetic gain in laying hen breeding programs.