School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia.
Genet Sel Evol. 2011 May 17;43(1):18. doi: 10.1186/1297-9686-43-18.
The theory of genomic selection is based on the prediction of the effects of quantitative trait loci (QTL) in linkage disequilibrium (LD) with markers. However, there is increasing evidence that genomic selection also relies on "relationships" between individuals to accurately predict genetic values. Therefore, a better understanding of what genomic selection actually predicts is relevant so that appropriate methods of analysis are used in genomic evaluations.
Simulation was used to compare the performance of estimates of breeding values based on pedigree relationships (Best Linear Unbiased Prediction, BLUP), genomic relationships (gBLUP), and based on a Bayesian variable selection model (Bayes B) to estimate breeding values under a range of different underlying models of genetic variation. The effects of different marker densities and varying animal relationships were also examined.
This study shows that genomic selection methods can predict a proportion of the additive genetic value when genetic variation is controlled by common quantitative trait loci (QTL model), rare loci (rare variant model), all loci (infinitesimal model) and a random association (a polygenic model). The Bayes B method was able to estimate breeding values more accurately than gBLUP under the QTL and rare variant models, for the alternative marker densities and reference populations. The Bayes B and gBLUP methods had similar accuracies under the infinitesimal model.
Our results suggest that Bayes B is superior to gBLUP to estimate breeding values from genomic data. The underlying model of genetic variation greatly affects the predictive ability of genomic selection methods, and the superiority of Bayes B over gBLUP is highly dependent on the presence of large QTL effects. The use of SNP sequence data will outperform the less dense marker panels. However, the size and distribution of QTL effects and the size of reference populations still greatly influence the effectiveness of using sequence data for genomic prediction.
基因组选择的理论基于预测与标记处于连锁不平衡（LD）的数量性状位点（QTL）的效应。然而，越来越多的证据表明，基因组选择也依赖于个体之间的“关系”来准确预测遗传值。因此，更好地理解基因组选择实际上预测的内容，对于在基因组评估中使用适当的分析方法非常重要。
模拟被用来比较基于系谱关系（最佳线性无偏预测，BLUP）、基因组关系（gBLUP）和基于贝叶斯变量选择模型（Bayes B）的估计育种值的估计值的性能，以估计在不同遗传变异的基本模型下的育种值。还检查了不同标记密度和不同动物关系的影响。
本研究表明，基因组选择方法可以预测遗传变异由常见数量性状位点（QTL 模型）、稀有位点（稀有变异模型）、所有位点（微小模型）和随机关联（多基因模型）控制时，一部分的加性遗传值。在替代标记密度和参考群体下，Bayes B 方法在 QTL 和稀有变异模型下比 gBLUP 更能准确地估计育种值。在微小模型下，Bayes B 和 gBLUP 方法具有相似的准确性。
我们的结果表明，Bayes B 比 gBLUP 更能从基因组数据中估计育种值。遗传变异的基本模型极大地影响了基因组选择方法的预测能力，Bayes B 优于 gBLUP 的优势高度依赖于大 QTL 效应的存在。使用 SNP 序列数据将优于密度较低的标记面板。然而，QTL 效应的大小和分布以及参考群体的大小仍然极大地影响了使用序列数据进行基因组预测的有效性。
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