Table_5_Comparing Alternative Single-Step GBLUP Approaches and Training Population Designs for Genomic Evaluation of Crossbred Animals.docx

As crossbreeding is extensively used in some livestock species, we aimed to evaluate the performance of single-step GBLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) methods to predict Genomic Estimated Breeding Values (GEBVs) of crossbred animals. Different training population scenarios were evaluated: (SC1) ssGBLUP based on a single-trait model considering purebred and crossbred animals in a joint training population; (SC2) ssGBLUP based on a multiple-trait model to enable considering phenotypes recorded in purebred and crossbred training animals as different traits; (SC3) WssGBLUP based on a single-trait model considering purebred and crossbred animals jointly in the training population (both populations were used for SNP weights' estimation); (SC4) WssGBLUP based on a single-trait model considering only purebred animals in the training population (crossbred population only used for SNP weights' estimation); (SC5) WssGBLUP based on a single-trait model and the training population characterized by purebred animals (purebred population used for SNP weights' estimation). A complex trait was simulated assuming alternative genetic architectures. Different scaling factors to blend the inverse of the genomic (G−1) and pedigree (A22-1) relationship matrices were also tested. The predictive performance of each scenario was evaluated based on the validation accuracy and regression coefficient. The genetic correlations across simulated populations in the different scenarios ranged from moderate to high (0.71–0.99). The scenario mimicking a completely polygenic trait (hQTL2= 0) yielded the lowest validation accuracy (0.12; for SC3 and SC4). The simulated scenarios assuming 4,500 QTLs affecting the trait and hQTL2=h2 resulted in the greatest GEBV accuracies (0.47; for SC1 and SC2). The regression coefficients ranged from 0.28 (for SC3 assuming polygenic effect) to 1.27 (for SC2 considering 4,500 QTLs). In general, SC3 and SC5 resulted in inflated GEBVs, whereas other scenarios yielded deflated GEBVs. The scaling factors used to combine G−1 and A22-1 had a small influence on the validation accuracies, but a greater effect on the regression coefficients. Due to the complexity of multiple-trait models and WssGBLUP analyses, and a similar predictive performance across the methods evaluated, SC1 is recommended for genomic evaluation in crossbred populations with similar genetic structures [moderate-to-high (0.71–0.99) genetic correlations between purebred and crossbred populations].

鉴于杂交育种在部分畜禽品种中应用广泛，本研究旨在评估单步基因组最佳线性无偏预测（single-step GBLUP，ssGBLUP）与加权单步基因组最佳线性无偏预测（weighted ssGBLUP，WssGBLUP）两种方法对杂交动物基因组估计育种值（Genomic Estimated Breeding Values，GEBVs）的预测性能。本研究设置了多种训练群体场景进行评估：（SC1）基于单性状模型的ssGBLUP，将纯种与杂交动物纳入联合训练群体；（SC2）基于多性状模型的ssGBLUP，可将纯种与杂交训练动物的表型记录视为不同性状；（SC3）基于单性状模型的WssGBLUP，联合使用纯种与杂交动物构建训练群体（两类群体均用于单核苷酸多态性（Single Nucleotide Polymorphism，SNP）权重估计）；（SC4）基于单性状模型的WssGBLUP，仅以纯种动物构建训练群体（杂交群体仅用于SNP权重估计）；（SC5）基于单性状模型的WssGBLUP，训练群体仅包含纯种动物（纯种群体用于SNP权重估计）。本研究模拟了一种复杂性状，并设定了不同的遗传架构，同时测试了用于融合基因组亲缘关系逆矩阵（G⁻¹）与系谱亲缘关系逆矩阵（A22⁻¹）的不同缩放因子。各场景的预测性能通过验证准确性与回归系数进行评估。不同场景下模拟群体间的遗传相关介于中等至较高水平（0.71~0.99）。仅受多基因调控的性状（hQTL²=0）对应的验证准确性最低，在SC3与SC4场景下为0.12。当模拟性状受4500个数量性状基因座（Quantitative Trait Locus，QTL）调控且hQTL²=h²时，各场景的GEBV准确性最高，在SC1与SC2场景下为0.47。回归系数的范围为0.28（多基因效应下的SC3场景）至1.27（受4500个QTL调控的SC2场景）。总体而言，SC3与SC5场景会导致GEBV被高估，其余场景则会使GEBV被低估。用于组合G⁻¹与A22⁻¹的缩放因子对验证准确性的影响较小，但对回归系数的影响更为显著。考虑到多性状模型与WssGBLUP分析的复杂性，且本研究所评估的方法整体预测性能相近，推荐在遗传结构相似[纯种与杂交群体间遗传相关中等至较高（0.71~0.99）]的杂交群体中使用SC1进行基因组评估。