Tabet Joe-Menwer, Bussiman Fernando, Breen Vivian, Misztal Ignacy, Lourenco Daniela
Department of Animal and Dairy Science, University of Georgia, Athens, GA 30602, USA.
Cobb-Vantress, Inc., Siloam Springs, AR 72761, USA.
J Anim Sci. 2024 Jan 3;102. doi: 10.1093/jas/skae360.
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 to 2), and feet health (1 to 7) were collected for 397,974 animals in line 1 and 458,881 in line 2. A 4-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 2 lines to better assess the advantages of combining the 2 populations. Overall, the accuracy increased when the 2 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 yields 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.
将在某些时候已经分化的育种群体合并是一种常规做法，尤其是在养禽业中，因为养禽业的世代间隔短，且应经常进行遗传评估。本研究旨在评估在单步GBLUP框架内将大型、遗传关系较远的肉鸡群体合并为单一基因组评估的可行性。肉鸡品系1和品系2的系谱数据分别包含428,790只和477,488只动物，其中分别有156,088只和186,387只进行了基因分型。收集了品系1中397,974只动物和品系2中458,881只动物的体重（千克）、胴体产量（%）、死亡率（1至2）和足部健康状况（1至7）的表型数据。分析采用了四性状模型，通过不同方法处理群体间的遗传差异：引入一个额外的固定效应来考虑起源品系（模型2）或使每个固定效应针对特定起源（模型3）。将这些模型与评估中不考虑动物起源的传统模型（模型1）进行比较。在模型1、模型2和模型3中拟合未知亲本组（UPG）和元祖系（MF）以考虑遗传差异；它们根据动物的起源品系和性别来设定。使用线性回归方法，通过准确性、偏差和离散度来评估模型的性能。验证分别在各个品系内进行，并在合并两个品系后进行总体验证，以更好地评估合并两个群体的优势。总体而言，与单独评估每个品系所获得的准确性相比，合并两个群体时准确性有所提高。值得注意的是，在准确性和离散度方面，各模型之间没有明显差异。关于偏差，在合并评估中，使用带有UPG的模型2或模型3能得到偏差最小的估计值。因此，在将不同群体合并为单一基因组评估时，考虑品系间的遗传和非遗传差异可确保预测准确且偏差较小。
