Combining large broiler populations into a single genomic evaluation: Dealing with genetic divergence1.

Abstract

Combining breeding populations that have diverged at some point is a conventional practice, particularly in the poultry industry, where generation intervals are short and genetic evaluations should be frequently available. This study aimed to assess the feasibility of combining large, distantly genetically connected broiler populations into a single genomic evaluation within the single-step GBLUP framework. The pedigree data for broiler lines 1 and 2 consisted of 428,790 and 477,488 animals, being 156,088 and 186,387 genotyped, respectively. Phenotypic data for Body weight (kg), Carcass Yield (%), Mortality (1-2), and Feet Health (1-7) were collected for 397,974 animals in line 1 and 458,881 in line 2. A four-trait model was employed for the analyses, and genetic differences between the populations were addressed through different approaches: introducing an additional fixed effect accounting for the line of origin (M2) or making each fixed effect origin-specific (M3). Those models were compared against a conventional model (M1) that did not account for animal origin in the evaluation. Unknown parent groups (UPG) and Metafounders (MF) were fit to account for the genetic differences in M1, M2, and M3; they were set based on the animal's line of origin and sex. Accuracy, bias, and dispersion were used to assess the performances of the models using the Linear Regression method. Validations were performed separately within individual lines and collectively after combining the two lines to better assess the advantages of combining the two populations. Overall, the accuracy increased when the two populations were combined compared to the accuracies obtained from evaluating each line individually. Notably, there were no apparent differences among the models regarding accuracy and dispersion. Regarding bias, using models M2 or M3 with UPG yielding the least biased estimates in the combined evaluation. Thus, when combining different populations into a single genomic evaluation, accounting for the genetic and non-genetic differences among the lines ensures accurate and less biased predictions.