Shi Shaolei, Li Xiujin, Fang Lingzhao, Liu Aoxing, Su Guosheng, Zhang Yi, Luobu Basang, Ding Xiangdong, Zhang Shengli
National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China.
Guangdong Provincial Key Laboratory of Waterfowl Healthy Breeding, College of Animal Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, China.
Front Genet. 2021 Apr 15;12:628205. doi: 10.3389/fgene.2021.628205. eCollection 2021.
Bayesian regression models are widely used in genomic prediction for various species. By introducing the global parameter τ, which can shrink marker effects to zero, and the local parameter λ , which can allow markers with large effects to escape from the shrinkage, we developed two novel Bayesian models, named BayesHP and BayesHE. The BayesHP model uses Horseshoe+ prior, whereas the BayesHE model assumes local parameter λ , after a half-t distribution with an unknown degree of freedom. The performances of BayesHP and BayesHE models were compared with three classical prediction models, including GBLUP, BayesA, and BayesB, and BayesU, which also applied global-local prior (Horseshoe prior). To assess model performances for traits with various genetic architectures, simulated data and real data in cattle (milk production, health, and type traits) and mice (type and growth traits) were analyzed. The results of simulation data analysis indicated that models based on global-local priors, including BayesU, BayesHP, and BayesHE, performed better in traits with higher heritability and fewer quantitative trait locus. The results of real data analysis showed that BayesHE was optimal or suboptimal for all traits, whereas BayesHP was not superior to other classical models. For BayesHE, its flexibility to estimate hyperparameter automatically allows the model to be more adaptable to a wider range of traits. The BayesHP model, however, tended to be suitable for traits having major/large quantitative trait locus, given its nature of the "U" type-like shrinkage pattern. Our results suggested that auto-estimate the degree of freedom (e.g., BayesHE) would be a better choice other than increasing the local parameter layers (e.g., BayesHP). In this study, we introduced the global-local prior with unknown hyperparameter to Bayesian regression models for genomic prediction, which can trigger further investigations on model development.
贝叶斯回归模型在各种物种的基因组预测中被广泛应用。通过引入可将标记效应收缩至零的全局参数τ以及可使具有大效应的标记避免收缩的局部参数λ，我们开发了两种新颖的贝叶斯模型，分别命名为BayesHP和BayesHE。BayesHP模型使用马蹄形加先验，而BayesHE模型在具有未知自由度的半t分布之后假定局部参数λ。将BayesHP和BayesHE模型的性能与三种经典预测模型进行了比较，这三种经典模型包括GBLUP、BayesA和BayesB，以及同样应用全局-局部先验（马蹄形先验）的BayesU。为了评估不同遗传结构性状的模型性能，对牛（产奶量、健康和体型性状）和小鼠（体型和生长性状）的模拟数据和真实数据进行了分析。模拟数据分析结果表明，基于全局-局部先验的模型，包括BayesU、BayesHP和BayesHE，在遗传力较高且数量性状位点较少的性状上表现更好。真实数据分析结果表明，BayesHE在所有性状上都是最优或次优的，而BayesHP并不优于其他经典模型。对于BayesHE，其自动估计超参数的灵活性使该模型能够更好地适应更广泛的性状。然而，鉴于其“U”型收缩模式的性质，BayesHP模型倾向于适用于具有主效/大效应数量性状位点的性状。我们的结果表明，自动估计自由度（例如BayesHE）比增加局部参数层（例如BayesHP）是更好的选择。在本研究中，我们将具有未知超参数的全局-局部先验引入用于基因组预测的贝叶斯回归模型，这可以引发对模型开发的进一步研究。
