|Title||Modeling genetic differences of combined broiler chicken populations in single-step GBLUP.|
|Publication Type||Journal Article|
|Year of Publication||2021|
|Authors||Bermann, M, Lourenco, D, Breen, V, Hawken, R, F Lopes, B, Misztal, I|
|Journal||J Anim Sci|
|Date Published||2021 Feb 27|
The introduction of animals from a different environment or population is a common practice in commercial livestock populations. In this study we modeled the inclusion of a group of external birds into a local broiler chicken population for the purpose of genomic evaluations. The pedigree was composed by 242,413 birds and genotypes were available for 107,216 birds. A five-trait model that included one growth, two yield, and two efficiency traits was used for the analyses. The strategies to model the introduction of external birds were to include a fixed effect representing the of origin of parents and to use UPG or metafounders. Genomic estimated breeding values (GEBV) were obtained with single-step GBLUP (ssGBLUP) using the Algorithm for Proven and Young (APY). Bias, dispersion, and accuracy of GEBV for the validation birds, i.e., from the most recent generation, were computed. The bias and dispersion were estimated with the LR-method, whereas accuracy was estimated by the LR-method and predictive ability. When fixed UPG were fit without estimated inbreeding, the model did not converge. In contrast, models with fixed UPG and estimated inbreeding or random UPG converged and resulted in similar GEBV. The inclusion of an extra fixed effect in the model made the GEBV unbiased and reduced the inflation. Genomic predictions with metafounders were slightly biased and inflated due to the unbalanced number of observations assigned to each metafounder. When combining local and external populations, the greatest accuracy can be obtained by adding an extra fixed effect to account for origin of parents plus UPG with estimated inbreeding or random UPG. To estimate the accuracy, the LR-method is more consistent among scenarios, whereas predictive ability greatly depends on the model specification.
|Alternate Journal||J Anim Sci|