Data from: Factors affecting GEBV accuracy with single-step Bayesian models

A single-step approach to obtain genomic prediction was firstly proposed in 2009. Many studies have investigated the components of GEBV accuracy in genomic selection. However, it is still unclear how the population structure and the relationships between training and validation populations influence GEBV accuracy in term of single-step analysis. Here, we explored the components of GEBV accuracy in single-step Bayesian analysis with a simulation study. Three scenarios with various numbers of QTL (5, 50 and 500) were simulated. Three models were implemented to analyze the simulated data: single-step GBLUP (SSGBLUP), single-step BayesA (SS-BayesA) and single-step BayesB (SS-BayesB). According to our results, GEBV accuracy was influenced by the relationships between the training and validation populations more significantly for ungenotyped animals than that for genotyped animals. SS-BayesA/BayesB showed an obvious advantage over SSGBLUP with the scenarios of 5 and 50 QT L. SS-BayesB model obtained the lowest accuracy with the 500 QTL in the simulation. SS-BayesA model was the most efficient and robust considering all QTL scenarios. Generally, both the relationships between training and validation populations and LD between markers and QTL contributed to GEBV accuracy in the single-step analysis, and the advantages of single-step Bayesian models were more apparent when the trait is controlled by fewer QTL.

单步基因组预测方法于2009年首次被提出。诸多研究已针对基因组选择中基因组估计育种值（Genomic Estimated Breeding Value，GEBV）的准确性影响因素展开了探究。然而，在单步分析框架下，群体结构以及训练群体与验证群体间的亲缘关系如何影响GEBV准确性，目前仍未明确。本研究通过模拟试验，探究了单步贝叶斯分析中GEBV准确性的影响因素。试验设置了3种不同数量性状基因座（Quantitative Trait Locus，QTL）数量的模拟场景，QTL数量分别为5、50和500；并采用3种模型对模拟数据进行分析：单步基因组最佳线性无偏预测（single-step Genomic Best Linear Unbiased Prediction，SSGBLUP）、单步BayesA（SS-BayesA）以及单步BayesB（SS-BayesB）。研究结果显示，与已分型个体相比，训练群体与验证群体间的亲缘关系对未分型个体的GEBV准确性影响更为显著。在QTL数量为5和50的模拟场景中，SS-BayesA与SS-BayesB模型的表现显著优于SSGBLUP。当QTL数量为500时，SS-BayesB模型的预测准确性最低。综合所有QTL模拟场景来看，SS-BayesA模型的效率与稳健性最佳。总体而言，在单步分析中，训练群体与验证群体间的亲缘关系以及标记与QTL间的连锁不平衡（Linkage Disequilibrium，LD）均会对GEBV准确性产生影响；且当性状由较少数量的QTL控制时，单步贝叶斯模型的优势更为显著。