Data from: Bayesian methods for estimating GEBVs of threshold traits

Estimation of genomic breeding values is the key step in genomic selection. Many methods have been proposed for continuous traits, but methods for threshold traits are still scarce. Here we introduced threshold model to the framework of genomic selection, and specifically we extended the three Bayesian methods BayesA, BayesB and BayesCπ based on threshold model for estimating genomic breeding values of threshold traits, and the extended methods are correspondingly termed BayesTA, BayesTB and BayesTCπ. Computing procedures of the three BayesT methods using Markov Chain Monte Carlo (MCMC) algorithm were derived. A simulation study was performed to investigate the benefit of the presented methods in accuracy of genomic estimated breeding values (GEBVs) for threshold traits. Factors affecting the performance of the three BayesT methods were addressed. As expected, the three BayesT methods generally performed better than the corresponding normal Bayesian methods, in particular when the number of phenotypic categories was small. In the standard scenario (No. categories = 2, incidence = 30%, No. QTL = 50, h2 = 0.3), the accuracies were improved by 30.4, 2.4, and 5.7 percentage points, respectively. In most scenarios, BayesTB and BayesTCπ generated similar accuracies and both performed better than BayesTA. In conclusion, our work proved that threshold model fits well for predicting GEBVs of threshold traits, and BayesTCπ is supposed to be the method of choice for genomic selection of threshold traits.

基因组育种值（genomic breeding value）的估计是基因组选择（genomic selection）的核心环节。目前针对连续性性状已提出诸多分析方法，但针对阈性状（threshold trait）的相关方法仍较为匮乏。本研究将阈模型（threshold model）引入基因组选择框架，针对阈性状基因组育种值的估计任务，基于阈模型对三种贝叶斯方法——BayesA、BayesB及BayesCπ进行扩展，扩展后的方法分别命名为BayesTA、BayesTB及BayesTCπ。本研究推导了基于马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）算法的三种BayesT方法的计算流程。通过模拟试验，本研究评估了所提方法在阈性状基因组估计育种值（genomic estimated breeding value, GEBV）估计准确性上的优势，并分析了影响三种BayesT方法性能的各类因素。正如预期，三种BayesT方法的整体表现均优于对应的常规贝叶斯方法，尤其在表型分类数较少时优势更为显著。在标准情景（分类数=2，发病率=30%，数量性状基因座（quantitative trait locus, QTL）数量=50，遗传力h²=0.3）下，三种方法的估计准确性分别提升了30.4、2.4及5.7个百分点。在多数模拟情景中，BayesTB与BayesTCπ的估计准确性相近，且二者的表现均优于BayesTA。综上，本研究证实阈模型可很好地适配阈性状基因组估计育种值的预测任务，且BayesTCπ可作为阈性状基因组选择的首选方法。