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，由于高通量和 高维数据以基因组量表收集。至关重要的是揭开监管角色 基因和基因对基因调节关系的遗传变异。以此为基础，生物医学研究人员 可以鉴定复杂性状和神经退行性的因果单核苷酸多态性（SNP）和基因 阿尔茨海默氏病（AD）等疾病以制定治疗策略。考虑到迫切需要不足 具有这些疾病的进展和病因，尤其是AD的PIS提议，以发展统计和 用于准确估计和推断基因调节网络的计算工具，重点是AD和 其他复杂的特征。 该项目由两个主要组成部分组成：基因调节网络的估计和推断 SNP作为仪器变量（IVS）。主要推力将在因果网络重建和推理上 在可能存在无效的IV和隐藏的混杂因素的情况下，将IV作为干预措施，特别努力 在高维数据上，其中变量的数量可能超过样本量。关于催化 网络重建，该项目将开发新的方法，将基因调节网络重建为 定向的无环图描述了SNP（干预措施），基因和特质之间的休闲关系 作为广告。该项目将根据修改的似然比测试和A开发高维的推论工具 数据扰动方案说明发现过程中涉及的不确定性。而且，它将集中 关于（1）多个（线性/非线性）因果关系的定向性和强度的假设检验和（2） 因果关系途径的存在。在计算上，该项目将开发创新的方法和 大规模问题的算法。对于应用，基于重建的基因调节网络，我们 将首先确定AD和AD危险因素（例如脂质）的因果基因，然后推断哪些危险因素是 （推定）AD的因果。