Causal network inference with application to breast cancer

复杂疾病通常涉及DNA序列、转录和表观遗传过程的变化，例如 甲基化。这些变化会导致广泛的症状或同一疾病的多个亚型。在……里面 为了开发针对不同疾病亚型的更有效的治疗方法，我们需要更好地了解 驱动这些差异的基因和过程(即转录和甲基化)。不幸的是， 疾病背后的基因和过程的识别通常受到基于以下因素的推断的影响 相关性，而不是因果关系。我们的长期目标是开发计算方法来推断基因调控 使用复杂疾病的基因组和临床数据对多种临床表型有因果关系的网络。 在这个项目中，我们将开发和测试新的统计方法，以确定涉及这两个方面的监管网络 可能导致疾病亚型的转录和甲基化。我们的策略是使用 孟德尔随机化原理。这假设遗传变异的等位基因是随机分配的。 种群中的个体，类似于自然扰动实验。鉴于大多数现有的方法 为了研究基因之间的相互作用，看看相关性(或关联性)，这一原理允许我们分离 因因果关系而非因因果关系。我们将通过三个方面来发展我们的方法 具体目标和将使用乳腺癌作为疾病模型：(1)开发因果网络模型 单基因的基因类型、表达和甲基化。(2)开发因果网络模型来识别 转录或甲基化导致多种临床表型的个别基因。(3)发展 用于识别其转录或甲基化是原因的基因组合的因果网络模型 多种临床表型。本提案中开发的模型和算法将使我们能够 在多个基因上关于这两个过程的因果陈述，并解释了混淆变量，两者都不是 这一点以前在类似的研究中得到了检验。这些模型将识别特定乳房的关键基因 当多个基因参与时，癌症亚型和转录和甲基化的作用，导致 更好地诊断和开发新的药物靶点。

Complex diseases often involve changes in DNA sequence, transcription, and epigenetic processes such as methylation. These changes lead to a wide range of symptoms or multiple subtypes of the same disease. In order to develop more effective treatments for different disease subtypes, we need to better understand the genes and processes (i.e., transcription and methylation) that drive these differences. Unfortunately, identification of genes and processes that underlie a disease is often compromised by inference based on correlation, not causation. Our long-term goal is to develop computational methods to infer gene regulatory networks that are causal for multiple clinical phenotypes using genomic and clinical data of complex diseases. In this project, we will develop and test new statistical approaches to identify regulatory networks involving both transcription and methylation that are potentially causal for disease subtypes. Our strategy is to use the principle of Mendelian randomization. This assumes that the alleles of a genetic variant are randomly assigned to individuals in a population, analogous to a natural perturbation experiment. Whereas most existing methods for studying interactions among genes look at correlation (or association), this principle allows us to separate correlation due to causation from correlation not due to causation. We will develop our approaches via three specific aims and will use breast cancer as the disease model: (1) Develop a causal network model using genotypes, expression and methylation of single genes. (2) Develop a causal network model to identify individual genes whose transcription or methylation is causal for multiple clinical phenotypes. (3) Develop a causal network model to identify combinations of genes whose transcription or methylation are causal for multiple clinical phenotypes. The models and algorithms developed in this proposal will allow us to make causal statements about the two processes at multiple genes and account for confounding variables, neither of which has been examined before in similar studies. These models will identify key genes for specific breast cancer subtypes and the roles for transcription and methylation when many genes are involved, leading to better diagnoses and development of novel drug targets.