Belay Tesfaye K, Gjuvsland Arne B, Jenko Janez, Eikje Leiv S, Svendsen Morten, Meuwissen Theo
Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Ås, Norway.
Geno SA, Hamar, Norway.
J Anim Breed Genet. 2025 Apr 2. doi: 10.1111/jbg.12939.
The objective of this study was to examine the effects of different methods for handling missing pedigree data on biases, stability, relative increase in accuracy, and genetic trends using national data from Norwegian Red (NRF) cattle. The dataset comprised 8,402,773 milk yield records from 3,896,116 NRF cows, a pedigree with 4,957,544 animals, and a genomic dataset from 170,293 animals with 121,741 SNPs. Missing parents were modelled using three approaches: unknown parent groups (UPG), metafounders (MF), and "Q-Q" methods. The UPG method is routinely used for genetic evaluations of NRF cattle by including 52 fixed UPG in the pedigree. In the MF method, two MF were defined: MF14 and MF52, with MF treated as random effects. The MF14 included 6 MF defined by birth year intervals for NRF breed and 8 MF defined by breed origins for other breeds. The MF52 classification included all the 52 UPG as MF considering relationships among them. The "Q-Q" approach corrects for the combined effects of UPG and "J factor" in non-genotyped animals while avoiding such corrections in genotyped animals. The three approaches, combined with different G matrices (G matrix constructed with a 0.5 allele frequency (AF) and 10% weight (w) on A, G constructed using AF = 0.5 and w = 0.0, and G constructed with observed AF and w = 0.0), led to eight ssGBLUP models being tested. This included one UPG model (using G), four MF models (MF14 and MF52 using G or G), and three Q-Q+ models (using G, G, or G). The models were evaluated through cross-validation by masking the phenotypes of 5000 genotyped young cows. Results showed that the Q-Q models using the G or G matrix had significantly (p < 0.05) lower level biases and higher genetic trends than all other models. MF models with 14 or 52 groups using G were second best for level bias and performed similarly or slightly better than Q-Q+ models regarding inflation bias and stability. Increasing the number of MF from 14 to 52 had minimal effects on biases but significantly improved stability and genetic trend estimates. Models with G had slightly higher gain in accuracy from adding phenotypic data (2.01%) than G (1.18%), but pedigree-based models showed the highest improvement in accuracy due to adding phenotypic (26%) or genomic (47%) data to the partial dataset. Overall, all models with G showed the least bias (with a small standard error) and most stable predictions, while models using G introduced biases and instability. Thus, the Q-Q and MF models combined with G and Q-Q with G are recommended for their improved validation results and genetic trends.
本研究的目的是利用挪威红牛（NRF）的全国数据，检验处理缺失系谱数据的不同方法对偏差、稳定性、准确性的相对提高以及遗传趋势的影响。数据集包括来自3896116头NRF奶牛的8402773条产奶量记录、一个包含4957544只动物的系谱，以及来自170293只动物的包含121741个单核苷酸多态性（SNP）的基因组数据集。缺失的亲本采用三种方法建模：未知亲本组（UPG）、元奠基者（MF）和“Q-Q”方法。UPG方法通过在系谱中纳入52个固定的UPG，常规用于NRF奶牛的遗传评估。在MF方法中，定义了两个MF：MF14和MF52，将MF视为随机效应。MF14包括根据NRF品种的出生年份间隔定义的6个MF和根据其他品种的品种起源定义的8个MF。MF52分类将所有52个UPG视为MF，并考虑它们之间的关系。“Q-Q”方法校正未基因分型动物中UPG和“J因子”的综合效应，同时避免对基因分型动物进行此类校正。这三种方法与不同的G矩阵（使用0.5等位基因频率（AF）和10%权重（w）构建的G矩阵，基于A构建；使用AF = 0.5和w = 0.0构建的G矩阵；以及使用观察到的AF和w = 0.0构建的G矩阵）相结合，导致测试了八个单步基因组最佳线性无偏预测（ssGBLUP）模型。这包括一个UPG模型（使用G）、四个MF模型（MF14和MF52使用G或G）和三个Q-Q+模型（使用G、G或G）。通过对5000头基因分型的年轻奶牛的表型进行掩码处理，对模型进行交叉验证评估。结果表明，使用G或G矩阵的Q-Q模型比所有其他模型具有显著更低（p < 0.05）的水平偏差和更高的遗传趋势。使用G的14组或52组MF模型在水平偏差方面排名第二，在膨胀偏差和稳定性方面的表现与Q-Q+模型相似或略好。将MF的数量从14增加到52对偏差影响最小，但显著提高了稳定性和遗传趋势估计。使用G的模型从添加表型数据中获得的准确性提高（2.01%）略高于使用G的模型（1.18%），但基于系谱的模型由于向部分数据集添加表型（26%）或基因组（47%）数据而在准确性方面显示出最高的提高。总体而言，所有使用G的模型显示出最小的偏差（标准误差小）和最稳定的预测，而使用G的模型引入了偏差和不稳定性。因此，推荐将Q-Q和MF模型与G相结合以及将Q-Q与G相结合，因为它们具有改进的验证结果和遗传趋势。
