Data from: Genomic BLUP decoded: a look into the black box of genomic prediction

Genomic best linear unbiased prediction (BLUP) is a statistical method that uses relationships between individuals calculated from single-nucleotide polymorphisms (SNPs) to capture relationships at quantitative trait loci (QTL). We show that genomic BLUP exploits not only linkage disequilibrium (LD) and additive-genetic relationships, but also cosegregation to capture relationships at QTL. Simulations were used to study the contributions of those types of information to accuracy of genomic estimated breeding values (GEBVs), their persistence over generations without retraining, and their effect on the correlation of GEBVs within families. We show that accuracy of GEBVs based on additive-genetic relationships can decline with increasing training data size and speculate that modeling polygenic effects via pedigree relationships jointly with genomic breeding values using Bayesian methods may prevent that decline. Cosegregation information from half sibs contributes little to accuracy of GEBVs in current dairy cattle breeding schemes but from full sibs it contributes considerably to accuracy within family in corn breeding. Cosegregation information also declines with increasing training data size, and its persistence over generations is lower than that of LD, suggesting the need to model LD and cosegregation explicitly. The correlation between GEBVs within families depends largely on additive-genetic relationship information, which is determined by the effective number of SNPs and training data size. As genomic BLUP cannot capture short-range LD information well, we recommend Bayesian methods with t-distributed priors.

基因组最佳线性无偏预测（Genomic best linear unbiased prediction, BLUP）是一种统计学方法，其借助由单核苷酸多态性（single-nucleotide polymorphisms, SNPs）计算得到的个体间亲缘关系，来捕捉数量性状基因座（quantitative trait loci, QTL）上的亲缘关联。我们的研究证实，基因组BLUP不仅可利用连锁不平衡（linkage disequilibrium, LD）与加性遗传关系，还可通过共分离效应捕捉数量性状基因座上的亲缘关联。本研究通过模拟实验，分析了上述几类信息对基因组估计育种值（genomic estimated breeding values, GEBVs）准确性的贡献、未经重新训练时育种值准确性在多代间的维持能力，以及其对家系内基因组估计育种值相关性的影响。研究发现，基于加性遗传关系的基因组估计育种值准确性会随训练数据集规模扩大而下降；据此推测，采用贝叶斯方法将系谱关系构建的多基因效应与基因组育种值联合建模，或可避免这一准确性衰减问题。在当前奶牛育种体系中，半同胞携带的共分离信息对基因组估计育种值准确性的贡献较为有限；而在玉米育种中，全同胞携带的共分离信息可显著提升家系内的育种值准确性。共分离信息的准确性同样会随训练数据集规模扩大而下降，且其在多代间的维持能力弱于连锁不平衡信息，这提示我们需要显式建模连锁不平衡与共分离效应。家系内基因组估计育种值的相关性主要取决于加性遗传关系信息，而该信息由有效单核苷酸多态性数量与训练数据集规模共同决定。鉴于基因组BLUP无法很好地捕捉短程连锁不平衡信息，我们推荐采用带有t分布先验的贝叶斯方法。