Estimation and Inference of Gene Regulatory Networks

基因调控网络的估计和推断

• 批准号: 10001547
• 负责人: Wei Pan
• 金额: $ 32.82万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10001547
• 关键词: Address; Algorithms; Alzheimer' s Disease; Alzheimer' s disease patient; Biological; Code; Communities; Complex; Computer software; Computing Methodologies; DNA sequencing; Data; Data Analyses; Data Set; Development; Diagnosis; Dimensions; Disease; Etiology; Gene Expression; Genes; Genetic; Genomics; Genotype; Graph; Human; Knowledge; Light; Methods; Modeling; Neurodegenerative Disorders; Pathway Analysis; Patients; Population; Prevention; Prevention strategy; Public Domains; Quantitative Trait Loci; Regulator Genes; Research; Research Personnel; Role; Sample Size; Single Nucleotide Polymorphism; Specific qualifier value; Statistical Methods; Structure; Techniques; Testing; Transcript; base; combinatorial; computational platform; computerized tools; genetic variant; genome wide association study; genome-wide; genome-wide analysis; high dimensionality; human disease; innovation; interest; multidimensional data; non-Gaussian model; novel; reconstruction; shared memory; sharing platform; software development; theories; tool; trait; treatment strategy

Project Summary Alzheimer’s disease (AD) is the most common neurodegenerative disease without a cure, and most cases are often diagnosed in the late stage of the disease. To advance our understanding of the initiation, progression, and etiology of such a disease, many genetic and genomic studies, including genome-wide association studies (GWAS), have been conducted, successfully identifying some associated single-nucleotide polymorphisms (SNPs). However, since many of these associated SNPs are either close to multiple genes or far away from any genes, identifying the responsible genes and thus understanding biological mechanisms with the disease remain most challenging. Of paramount importance is unraveling and understanding the regulatory roles of susceptible SNPs and genes for complex human diseases such as AD, so that treatment and prevention strategies can be developed. Given the urgent need of understanding the biological mechanisms of the disease, the PIs propose to develop powerful statistical and computational tools for accurate inference of gene regulatory networks for AD patients and healthy subjects. The proposed project consists of two interconnected components: reconstruction and inference of regulatory networks of the genes and SNPs. It will be centered on structures of regulatory networks, with particular effort focused on the accuracy of discovery and unbiased inference in high-dimensional situations, where model pa- rameters describing regulatory networks may greatly exceed the sample size. With regard to reconstruction of regulatory networks, the project will develop the constrained maximum likelihood method to identify directionality as well as strengths of pairwise causal relations, modeled by directed acyclic graphs (DAGs) among the genes and SNPs. With regard to network inference, the project will develop novel tests as formal inference methods for DAGs. On this ground, high-dimensional inference tools will be developed for detecting structural changes of multi- ple networks using the constrained likelihood tests. Moreover, linear and nonlinear causal relations will be studied. Computationally, innovative strategies will be developed based on the state-of-the-art optimization techniques, and be used for scalable analysis of large-scale networks.

项目概要 阿尔茨海默病 (AD) 是最常见的神经退行性疾病，无法治愈，大多数病例 往往在疾病晚期才被诊断出来。为了增进我们对起始、进展的理解， 和此类疾病的病因学，许多遗传和基因组研究，包括全基因组关联研究 （GWAS）已进行，成功鉴定了一些相关的单核苷酸多态性（SNP）。 然而，由于许多相关的 SNP 要么靠近多个基因，要么远离任何基因， 识别负责的基因，从而了解该疾病的生物学机制仍然是最重要的 具有挑战性的。最重要的是阐明和理解易受影响的 SNP 的调节作用 以及 AD 等复杂人类疾病的基因，以便制定治疗和预防策略。 鉴于迫切需要了解该疾病的生物学机制，PI 建议开发 强大的统计和计算工具，可准确推断 AD 患者的基因调控网络 健康的受试者。 拟议的项目由两个相互关联的部分组成：监管的重建和推断 基因和 SNP 的网络。它将以监管网络的结构为中心，并特别努力 专注于高维情况下发现和无偏推理的准确性，其中模型 描述监管网络的参数可能大大超出样本量。关于重建 监管网络，该项目将开发约束最大似然法来识别方向性 以及成对因果关系的强度，通过基因之间的有向无环图 (DAG) 建模 和 SNP。关于网络推理，该项目将开发新的测试作为正式的推理方法 DAG。在此基础上，将开发高维推理工具来检测多维结构的变化。 使用约束似然检验的 ple 网络。此外，还将研究线性和非线性因果关系。 在计算方面，将基于最先进的优化技术开发创新策略，并且 可用于大规模网络的可扩展分析。