Due to the ever-increasing data collected in genomic breeding programs, there is a need for genomic prediction models that can deal better with big data. For this reason, here we propose a Maximum a posteriori Threshold Genomic Prediction (MAPT) model for ordinal traits that is more efficient than the conventional Bayesian Threshold Genomic Prediction model for ordinal traits. The MAPT performs the predictions of the Threshold Genomic Prediction model by using the maximum a posteriori estimation of the parameters, that is, the values of the parameters that maximize the joint posterior density. We compared the prediction performance of the proposed MAPT to the conventional Bayesian Threshold Genomic Prediction model, the multinomial Ridge regression and support vector machine on 8 real data sets. We found that the proposed MAPT was competitive with regard to the multinomial and support vector machine models in terms of prediction performance, and slightly better than the conventional Bayesian Threshold Genomic Prediction model. With regard to the implementation time, we found that in general the MAPT and the support vector machine were the best, while the slowest was the multinomial Ridge regression model. However, it is important to point out that the successful implementation of the proposed MAPT model depends on the informative priors used to avoid underestimation of variance components.

由于基因组育种计划中收集的数据不断增加，因此需要能够更好地处理大数据的基因组预测模型。出于这个原因，在此我们针对序性提出了最大后验阈值基因组预测（MAPT）模型，该模型比常规的针对序性状的贝叶斯阈值基因组预测模型更为有效。MAPT通过使用最大后验来执行阈值基因组预测模型的预测参数估计，即使关节后部密度最大化的参数值。我们将提出的MAPT的预测性能与传统的贝叶斯阈值基因组预测模型，多项式Ridge回归和支持向量机在8个真实数据集上进行了比较。我们发现，就预测性能而言，所提出的MAPT在多项式和支持向量机模型方面具有竞争力，并且比传统的贝叶斯阈值基因组预测模型略胜一筹。关于执行时间，我们发现一般而言，MAPT和支持向量机是最好的，而最慢的是多项式Ridge回归模型。然而，