在全基因组预测中引入高斯协方差图模型
Introducing Gaussian covariance graph models in genome-wide prediction
摘要：
在全基因组预测中使用的几种统计模型假设标记等位基因置换效应是独立的，但已知这些效应可能是相关的。在统计学中，图形模型已被确定为一个有用的工具，在高维问题的协方差估计，它是一个领域，最近经历了一个很大的扩张。在高斯协方差图模型（GCovGM）中，假设一组随机变量的联合分布为高斯分布，协方差矩阵的零点模式用无向图G进行编码。在这项研究中，开发了将GCovGM理论应用于全基因组预测的方法（Bayes GCov，Bayes GCov-KR和Bayes GCov-H）。在模拟和真实的数据集，改进的相关性表型和预测育种值和准确性的预测育种values. We的模型占标记效应的相关性，并允许容纳一般结构，而不是在以前的研究中提出的模型只考虑空间相关性。此外，它们允许在预测过程中通过在构建图G时使用生物信息来并入生物信息，并且它们对多等位基因位点情况的扩展是直接的。
英文摘要：
Several statistical models used in genome-wide prediction assume independence of marker allele substitution effects, but it is known that these effects might be correlated. In statistics, graphical models have been identified as a useful tool for covariance estimation in high dimensional problems and it is an area that has recently experienced a great expansion. In Gaussian covariance graph models (GCovGM), the joint distribution of a set of random variables is assumed to be Gaussian and the pattern of zeros of the covariance matrix is encoded in terms of an undirected graph G. In this study, methods adapting the theory of GCovGM to genome-wide prediction were developed (Bayes GCov, Bayes GCov-KR and Bayes GCov-H). In simulated and real datasets, improvements in correlation between phenotypes and predicted breeding values and accuracies of predicted breeding values were found. Our models account for correlation of marker effects and permit to accommodate general structures as opposed to models proposed in previous studies which consider spatial correlation only. In addition, they allow incorporation of biological information in the prediction process through its use when constructing graph G, and their extension to the multiallelic loci case is straightforward.
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