用混合图马尔可夫模型映射eQTL网络
Mapping eQTL networks with mixed graphical Markov models
摘要：
由于基因表达性状的高维多变量性质，表达数量性状基因座（eQTL）定位构成了一个具有挑战性的问题。除了混杂因素和其他不需要的变异来源所产生的表达异质性之外，由于遗传、分子和环境的扰动，间接效应遍及基因。从多变量的角度来看，人们希望调整所有这些因素的影响，最终形成一个连接基因型与表型路径的直接关联网络。在本文中，我们接近这个挑战与混合图形马尔可夫模型，高阶条件独立和q阶相关图。这些模型表明，加性遗传效应作为基因-基因相关性的函数通过网络传播。我们的eQTL网络的估计基础上的一个充分研究的酵母数据集导致一个稀疏的结构，更直接的遗传和监管协会，使一个简单的比较遗传控制的基因表达跨染色体。有趣的是，它还揭示了eQTL解释了网络中心基因的大部分表达变异。
英文摘要：
Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to, among other reasons, the high-dimensional multivariate nature of gene-expression traits. Next to the expression heterogeneity produced by confounding factors and other sources of unwanted variation, indirect effects spread throughout genes as a result of genetic, molecular and environmental perturbations. From a multivariate perspective one would like to adjust for the effect of all of these factors to end up with a network of direct associations connecting the path from genotype to phenotype. In this paper we approach this challenge with mixed graphical Markov models, higher-order conditional independences and q-order correlation graphs. These models show that additive genetic effects propagate through the network as function of gene-gene correlations. Our estimation of the eQTL network underlying a well-studied yeast data set leads to a sparse structure with more direct genetic and regulatory associations that enable a straightforward comparison of the genetic control of gene expression across chromosomes. Interestingly, it also reveals that eQTLs explain most of the expression variability of network hub genes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
