Disentangling molecular relationships with a causal inference test.
Disentangling molecular relationships with a causal inference test.
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DOI：
10.1186/1471-2156-10-23
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发表时间：
2009-05-27
期刊：
影响因子：
2.9
通讯作者：
Schadt EE
中科院分区：
文献类型：
--
作者：
Millstein J;Zhang B;Zhu J;Schadt EE
在过去的几十年里，人们付出了巨大的努力来确定数量性状背后的基因座，作为阐明复杂疾病病因过程中的关键一步。最近，人们在合并无偏高通量数据方面做出了一些努力，例如高密度基因分型和全基因组 RNA 表达，以促进对疾病分子基础的理解。然而，如何利用这些信息来识别解释数量性状基因座（QTL）的分子机制一直是一个难题。我们开发了正式的统计假设检验，得出 p 值，以量化与测量因素相关的因果推断的不确定性，例如一种分子物种，可能介导基因座和数量性状之间已知的因果关系。我们将因果推理视为一条数学条件“链”，必须满足这些条件才能得出潜在中介因素与特征有因果关系的结论，其中推理的好坏取决于链中最薄弱的一环。 P 值是针对组件条件计算的，其中包括关联测试和条件独立性测试。然后采用交叉联合测试（Intersection-Union Test）将一系列统计测试组合起来形成综合测试，以生成总体测试结果。使用计算机模拟的小鼠杂交，我们表明，在包括隐藏变量和反应途径的各种条件下，I 型错误都很低。我们证明，简单因果模型下的功效与其他模型选择技术以及贝叶斯网络重建方法相当。我们进一步凭经验表明，该方法在酵母中重建转录调控网络方面优于贝叶斯网络重建方法，恢复了 8 个经实验验证的调控因子中的 7 个。在这里，我们提出了一种新颖的统计框架，其中现有的因果中介概念被形式化为假设检验，从而以 p 值的形式提供不确定性的标准定量度量。该方法在理论上和计算上都是可行的，并且所提供的软件可能被证明是解开分子关系的有用工具。
There has been intense effort over the past couple of decades to identify loci underlying quantitative traits as a key step in the process of elucidating the etiology of complex diseases. Recently there has been some effort to coalesce non-biased high-throughput data, e.g. high density genotyping and genome wide RNA expression, to drive understanding of the molecular basis of disease. However, a stumbling block has been the difficult question of how to leverage this information to identify molecular mechanisms that explain quantitative trait loci (QTL). We have developed a formal statistical hypothesis test, resulting in a p-value, to quantify uncertainty in a causal inference pertaining to a measured factor, e.g. a molecular species, which potentially mediates a known causal association between a locus and a quantitative trait. We treat the causal inference as a 'chain' of mathematical conditions that must be satisfied to conclude that the potential mediator is causal for the trait, where the inference is only as good as the weakest link in the chain. P-values are computed for the component conditions, which include tests of linkage and conditional independence. The Intersection-Union Test, in which a series of statistical tests are combined to form an omnibus test, is then employed to generate the overall test result. Using computer simulated mouse crosses, we show that type I error is low under a variety of conditions that include hidden variables and reactive pathways. We show that power under a simple causal model is comparable to other model selection techniques as well as Bayesian network reconstruction methods. We further show empirically that this method compares favorably to Bayesian network reconstruction methods for reconstructing transcriptional regulatory networks in yeast, recovering 7 out of 8 experimentally validated regulators. Here we propose a novel statistical framework in which existing notions of causal mediation are formalized into a hypothesis test, thus providing a standard quantitative measure of uncertainty in the form of a p-value. The method is theoretically and computationally accessible and with the provided software may prove a useful tool in disentangling molecular relationships.
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