Animal Science Department, Lanzhou Institute of Husbandry and Pharmaceutical Sciences, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China.
Sheep Breeding Engineering Technology Center, Chinese Academy of Agricultural Sciences, Lanzhou 730050, China.
G3 (Bethesda). 2021 Oct 19;11(11). doi: 10.1093/g3journal/jkab206.
The marker density, the heritability level of trait and the statistical models adopted are critical to the accuracy of genomic prediction (GP) or selection (GS). If the potential of GP is to be fully utilized to optimize the effect of breeding and selection, in addition to incorporating the above factors into simulated data for analysis, it is essential to incorporate these factors into real data for understanding their impact on GP accuracy, more clearly and intuitively. Herein, we studied the GP of six wool traits of sheep by two different models, including Bayesian Alphabet (BayesA, BayesB, BayesCπ, and Bayesian LASSO) and genomic best linear unbiased prediction (GBLUP). We adopted fivefold cross-validation to perform the accuracy evaluation based on the genotyping data of Alpine Merino sheep (n = 821). The main aim was to study the influence and interaction of different models and marker densities on GP accuracy. The GP accuracy of the six traits was found to be between 0.28 and 0.60, as demonstrated by the cross-validation results. We showed that the accuracy of GP could be improved by increasing the marker density, which is closely related to the model adopted and the heritability level of the trait. Moreover, based on two different marker densities, it was derived that the prediction effect of GBLUP model for traits with low heritability was better; while with the increase of heritability level, the advantage of Bayesian Alphabet would be more obvious, therefore, different models of GP are appropriate in different traits. These findings indicated the significance of applying appropriate models for GP which would assist in further exploring the optimization of GP.
标记密度、性状的遗传力水平和所采用的统计模型对基因组预测（GP）或选择（GS）的准确性至关重要。如果要充分利用 GP 的潜力来优化选育效果，除了将上述因素纳入模拟数据进行分析外，还必须将这些因素纳入真实数据，以更清楚、更直观地了解它们对 GP 准确性的影响。在此，我们通过两种不同的模型（贝叶斯字母（BayesA、BayesB、BayesCπ 和贝叶斯 LASSO）和基因组最佳线性无偏预测（GBLUP））对绵羊的六个羊毛性状进行了 GP 研究。我们采用五重交叉验证方法，基于阿尔卑斯梅里诺羊的基因型数据（n=821）进行准确性评估。主要目的是研究不同模型和标记密度对 GP 准确性的影响和交互作用。六个性状的 GP 准确性通过交叉验证结果显示在 0.28 到 0.60 之间。我们表明，通过增加标记密度可以提高 GP 的准确性，这与所采用的模型和性状的遗传力水平密切相关。此外，基于两种不同的标记密度，得出 GBLUP 模型对遗传力较低的性状的预测效果更好；随着遗传力水平的提高，贝叶斯字母的优势更加明显，因此，不同的 GP 模型适用于不同的性状。这些发现表明，为 GP 应用适当的模型非常重要，这将有助于进一步探索 GP 的优化。
