Estimation and inference in directed acyclic graphical models for biological networks

生物网络有向无环图模型的估计和推理

• 批准号: 10595510
• 负责人: Wei Pan
• 金额: $ 62.36万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10595510
• 关键词: Algorithms; Alzheimer' s Disease; Alzheimer' s disease risk; Biological; Biological Models; Biotechnology; Communities; Complex; Computer software; Computing Methodologies; Data; Data Analyses; Development; Disease; Documentation; Early Diagnosis; Environment; Equation; Etiology; Gene Expression; Genes; Genetic Diseases; Genomics; Genotype; Graph; Intervention; Investigation; Least-Squares Analysis; Lipids; Literature; Mendelian randomization; Methodology; Methods; Modeling; Neurodegenerative Disorders; Pathway interactions; Prevention; Process; Public Domains; Pythons; Regulator Genes; Regulatory Pathway; Research; Research Personnel; Risk Factors; Role; Sample Size; Scheme; Single Nucleotide Polymorphism; Statistical Computing; Statistical Methods; Testing; Uncertainty; Writing; causal variant; computerized tools; deep neural network; gene regulatory network; genetic variant; genome wide association study; genome-wide; high dimensionality; innovation; instrument; interest; multidimensional data; neural; novel; phenotypic data; pleiotropism; power analysis; programs; reconstruction; software development; theories; therapeutic development; tool; trait; transcriptome; treatment strategy; web site

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.

概括 随着生物技术的进步，生物医学研究由于高通量和高通量而变得更加复杂。 在基因组规模收集的高维数据。最重要的是阐明监管角色 基因的遗传变异和基因间的调控关系。基于此，生物医学研究人员 可以识别复杂性状和神经退行性疾病的因果单核苷酸多态性 (SNP) 和基因 阿尔茨海默病 (AD) 等疾病制定治疗策略。鉴于迫切需要 由于了解这些疾病（特别是 AD）的进展和病因，PI 建议制定统计和 用于准确估计和推断基因调控网络的计算工具，重点关注 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 SNP 作为工具变量 (IV)。主要重点是因果网络重建和推理 将 IV 作为可能存在无效 IV 和隐藏混杂因素的干预措施，并特别努力 对于高维数据，其中变量的数量可能超过样本量。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 有向无环图描述 SNP（干预措施）、基因和性状之间的因果关系 作为广告。该项目将开发基于修正似然比检验和 数据扰动方案来解释发现过程中涉及的不确定性。此外，还将重点 对 (1) 多重（线性/非线性）因果关系的方向性和强度以及 (2) 进行假设检验 因果关系路径的存在。在计算方面，该项目将开发创新方法和 大规模问题的算法。对于应用，基于重建的基因调控网络，我们 首先会识别 AD 的致病基因和 AD 的危险因素（例如血脂），然后推断哪些危险因素是 （推定）AD 的因果关系。