Estimation and inference in directed acyclic graphical models for biological networks

摘要 随着生物技术的进步，生物医学研究变得更加复杂，因为高通量和 在基因组范围内收集的高维数据。最重要的是分解监管角色 基因上的遗传变异和基因间的调控关系。在此基础上，生物医学研究人员 可以识别复杂性状和神经退行性变的原因单核苷酸多态(SNPs)和基因 为阿尔茨海默病(AD)等疾病制定治疗策略。鉴于迫切需要- 根据这些疾病的进展和病因，特别是阿尔茨海默病，私人投资者建议制定统计和 精确估计和推断基因调控网络的计算工具，重点是AD和 其他复杂的特征。 该项目由两个主要部分组成：基因调控网络的估计和推断 作为工具变量(IV)的SNP。主要的重点将是因果网络的重构和推理 将静脉输液作为可能存在的无效静脉输液和隐藏的混杂因素的干预措施，并做出特别努力 对于变量数量可能超过样本大小的高维数据。关于因果关系 网络重建，该项目将开发重建基因调控网络的新方法，如 描述SNPs(干预)、基因和性状之间因果关系的有向无环图 作为AD。该项目将开发基于Modifi的似然比测试和 数据扰动方案，以解决发现过程中涉及的不确定性。此外，它将把重点放在 关于(1)多重(线性/非线性)因果关系的方向性和强度的假设检验和(2) 存在一条因果关系的路径。在计算方面，该项目将开发创新的方法和 大规模问题的算法。在应用方面，基于重构的基因调控网络，我们 fi是否会首先确定AD的原因基因和AD的风险因素，如血脂，然后推断哪些风险因素是 (推定)AD的因果关系。

Summary As biotechnology advances, biomedical investigations have become more complex due to high-throughput and high-dimensional data collected at a genomic scale. Of paramount importance is unraveling the regulatory roles of genetic variants on genes and gene-to-gene regulatory relationships. On this ground, biomedical researchers can identify causal Single-Nucleotide Polymorphisms (SNPs) and genes for complex traits and neurodegenerative diseases such as Alzheimer's disease (AD) to develop treatment strategies. Given the urgent need to under- stand the progression and etiology of these diseases, particularly AD, the PIs propose to develop statistical and computational tools for accurate estimation and inference of gene regulatory networks, with a focus on AD and other complex traits. The project consists of two major components: estimation and inference of gene regulatory networks with SNPs as instrumental variables (IVs). The main thrust will be on causal network reconstruction and inference with IVs as interventions in the possible presence of invalid IVs and hidden confounders, with particular effort on high-dimensional data, in which the number of variables may exceed the sample size. Concerning causal network reconstruction, the project will develop novel methods of reconstructing gene regulatory networks as directed acyclic graphs describing casual relationships among the SNPs (interventions), genes, and traits such as AD. The project will develop high-dimensional inferential tools based on modified likelihood ratio tests and a data perturbation scheme to account for the uncertainty involved in a discovery process. Moreover, it will focus on hypothesis testing on (1) the directionality and strength of multiple (linear/nonlinear) causal relations and (2) the presence of a pathway of causal relations. Computationally, the project will develop innovative methods and algorithms for large-scale problems. For application, based on the reconstructed gene regulatory networks, we will first identify causal genes for AD and AD's risk factors, such as lipids, then infer which of the risk factors are (putatively) causal to AD.