Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters
Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters
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DOI：
10.1111/j.1439-0388.2007.00700.x
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发表时间：
2007-12-01
影响因子：
2.6
通讯作者：
Muir, W. M.
中科院分区：
文献类型：
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作者：
Muir, W. M.
与传统的最佳线性无偏预测 (BLUP) 相比，基于全基因组标记 (GEBV) 的估计育种值预测和基于 GEBV 的选择的准确性进行了检查，包括低遗传力、训练代数、标记密度、初始分布和有效种群规模 (N-e)。结果表明，收集基因型和表型的数据（称为训练代（TG））越多，基于 GEBV 的准确性和准确性就越好。无论初始平衡条件如何，GEBV 都擅长处理低遗传力性状，而传统的标记辅助选择则不适用于低遗传力性状。有效种群规模对于从哈代-温伯格平衡开始的种群至关重要，但对于从突变漂移平衡开始的种群则不然。与传统的 BLUP 相比，如果包含足够的 TG，GEBV 的精度可以超过 BLUP。不幸的是，选择会迅速降低 GEBV 的准确性。在所有检查的案例中，经典的 BLUP 选择超出了 GEBV 选择的可能性。即便如此，在诸如性别限制性状、测量成本昂贵或只能在亲属上测量的性状等情况下，GEBV 可能比传统的 BLUP 更具优势。建议采用一种组合方法，即利用具有第二个随机效应的混合模型来解释连锁平衡中的数量性状位点（多基因效应），作为利用这两种方法的一种方式。
Accuracy of prediction of estimated breeding values based on genome-wide markers (GEBV) and selection based on GEBV as compared with traditional Best Linear Unbiased Prediction (BLUP) was examined for a number of alternatives, including low heritability, number of generations of training, marker density, initial distributions, and effective population size (N-e). Results show that the more the generations of data in which both genotypes and phenotypes were collected, termed training generations (TG), the better the accuracy and persistency of accuracy based on GEBV. GEBV excelled for traits of low heritability regardless of initial equilibrium conditions, as opposed to traditional marker-assisted selection, which is not useful for traits of low heritability. Effective population size is critical for populations starting in Hardy-Weinberg equilibrium but not for populations started from mutation-drift equilibrium. In comparison with traditional BLUP, GEBV can exceed the accuracy of BLUP provided enough TG are included. Unfortunately selection rapidly reduces the accuracy of GEBV. In all cases examined, classic BLUP selection exceeds what was possible for GEBV selection. Even still, GEBV could have an advantage over traditional BLUP in cases such as sex-limited traits, traits that are expensive to measure, or can only be measured on relatives. A combined approach, utilizing a mixed model with a second random effect to account for quantitative trait loci in linkage equilibrium (the polygenic effect) was suggested as a way to capitalize on both methodologies.