Verbyla Klara L, Hayes Ben J, Bowman Philip J, Goddard Michael E
Biosciences Research Division, Department of Primary Industries Victoria, Bundoora, Australia.
Genet Res (Camb). 2009 Oct;91(5):307-11. doi: 10.1017/S0016672309990243.
Genomic selection describes a selection strategy based on genomic breeding values predicted from dense single nucleotide polymorphism (SNP) data. Multiple methods have been proposed but the critical issue is how to decide whether an SNP should be included in the predictive set to estimate breeding values. One major disadvantage of the traditional Bayes B approach is its high computational demands caused by the changing dimensionality of the models. The use of stochastic search variable selection (SSVS) retains the same assumptions about the distribution of SNP effects as Bayes B, while maintaining constant dimensionality. When Bayesian SSVS was used to predict genomic breeding values for real dairy data over a range of traits it produced accuracies higher or equivalent to other genomic selection methods with significantly decreased computational and time demands than Bayes B.
基因组选择描述了一种基于从密集单核苷酸多态性（SNP）数据预测的基因组育种值的选择策略。已经提出了多种方法，但关键问题是如何决定一个SNP是否应包含在预测集中以估计育种值。传统贝叶斯B方法的一个主要缺点是其模型维度变化导致的高计算需求。随机搜索变量选择（SSVS）的使用保留了与贝叶斯B相同的关于SNP效应分布的假设，同时保持维度不变。当使用贝叶斯SSVS来预测一系列性状的真实奶牛数据的基因组育种值时，它产生的准确性高于或等同于其他基因组选择方法，且计算和时间需求比贝叶斯B显著降低。
