A SPARSE CONDITIONAL GAUSSIAN GRAPHICAL MODEL FOR ANALYSIS OF GENETICAL GENOMICS DATA.
A SPARSE CONDITIONAL GAUSSIAN GRAPHICAL MODEL FOR ANALYSIS OF GENETICAL GENOMICS DATA.
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DOI：
10.1214/11-aoas494
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发表时间：
2011-12
期刊：
影响因子：
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通讯作者：
Li H
中科院分区：
文献类型：
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作者：
Yin J;Li H
遗传基因组学实验现在已经被常规地进行，以测量相同受试者的遗传标记和基因表达数据。基因表达水平通常被视为数量性状，并进行标准遗传分析，以确定基因表达数量基因座（eQTL）。然而，许多基因表达的遗传结构可能是复杂的，并且估计不佳的遗传结构可能会损害在转录水平上对基因的依赖性结构的推断。在本文中，我们引入了一个稀疏的条件高斯图形模型，用于研究一组基因表达之间的条件独立关系，调整可能的遗传效应，其中基因表达与看似无关的回归建模。我们提出了一种有效的坐标下降算法来获得回归系数和稀疏浓度矩阵的惩罚估计。相应的图可用于确定一组基因之间的条件独立性，同时调整共享的遗传效应。仿真实验、渐进收敛率和稀疏性用于证明我们提出的方法的合理性。稀疏性是指所有为零的参数实际上都被估计为零，概率趋于1的性质。我们将我们的方法应用于酵母eQTL数据集的分析，并证明了条件高斯图形模型比仅基于基因表达数据的标准高斯图形模型导致更可解释的基因网络。
Genetical genomics experiments have now been routinely conducted to measure both the genetic markers and gene expression data on the same subjects. The gene expression levels are often treated as quantitative traits and are subject to standard genetic analysis in order to identify the gene expression quantitative loci (eQTL). However, the genetic architecture for many gene expressions may be complex, and poorly estimated genetic architecture may compromise the inferences of the dependency structures of the genes at the transcriptional level. In this paper, we introduce a sparse conditional Gaussian graphical model for studying the conditional independent relationships among a set of gene expressions adjusting for possible genetic effects where the gene expressions are modeled with seemingly unrelated regressions. We present an efficient coordinate descent algorithm to obtain the penalized estimation of both the regression coefficients and sparse concentration matrix. The corresponding graph can be used to determine the conditional independence among a group of genes while adjusting for shared genetic effects. Simulation experiments and asymptotic convergence rates and sparsistency are used to justify our proposed methods. By sparsistency, we mean the property that all parameters that are zero are actually estimated as zero with probability tending to one. We apply our methods to the analysis of a yeast eQTL data set and demonstrate that the conditional Gaussian graphical model leads to more interpretable gene network than standard Gaussian graphical model based on gene expression data alone.