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.

中文翻译：

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将大量肉鸡种群合并到单个基因组评估中：处理遗传差异1


合并在某个时候分化的育种种群是一种常规做法，特别是在家禽业，那里的世代间隔很短，应该经常进行遗传评估。本研究旨在评估在单步 GBLUP 框架内将大型、遗传连接的肉鸡种群合并到单个基因组评估中的可行性。肉鸡品系 1 和 2 的系谱数据包括 428,790 头和 477,488 头动物，分别是 156,088 头和 186,387 头基因分型。收集了第 1 行 397,974 只动物和第 2 行 458,881 只动物的体重 （kg）、胴体产量 （%）、死亡率 （1-2） 和足部健康 （1-7） 的表型数据。采用四性状模型进行分析，并通过不同的方法解决种群之间的遗传差异：引入额外的固定效应来解释起源线 （M2） 或使每个固定效应起源特异性 （M3）。将这些模型与在评估中未考虑动物来源的常规模型 （M1） 进行了比较。未知亲本组 （UPG） 和 Metafounders （MF） 适合解释 M1 、 M2 和 M3 的遗传差异;它们是根据动物的起源和性别设定的。使用线性回归方法使用准确度、偏差和离散度来评估模型的性能。在合并两个细胞系后，在单个细胞系内和集体进行验证，以更好地评估结合两个细胞群的优势。总体而言，与单独评估每条线获得的准确性相比，将两个群体合并时的准确性有所提高。 值得注意的是，模型之间在准确度和离散度方面没有明显差异。关于偏倚，使用模型 M2 或 M3 与 UPG 在组合评估中产生的偏差估计值最小。因此，当将不同的群体组合到单个基因组评估中时，考虑品系之间的遗传和非遗传差异可确保准确且偏差较小的预测。