Methods for Integrative Genomic Data Analysis

综合基因组数据分析方法

• 批准号: 10188561
• 负责人: Hongzhe Lee
• 金额: $ 43.08万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10188561
• 关键词: Address; Algorithmic Software; Alzheimer' s Disease; Area; Biological; Biological Process; Charge; Chronic Kidney Failure; Collaborations; Complex; Computer software; Computing Methodologies; Data; Data Analyses; Data Set; Dependence; Development; Disease; Disease Pathway; Documentation; Functional disorder; Gene Expression; Genes; Genetic; Genomics; Genotype-Tissue Expression Project; Grant; Graph; High-Throughput Nucleotide Sequencing; Individual; Kidney; Kidney Diseases; Lead; Measurement; Measures; Mediating; Mediation; Mendelian randomization; Methodology; Methods; Modeling; Network-based; Pathway interactions; Pennsylvania; Phenotype; Play; Public Health; Regulator Genes; Research Personnel; Sample Size; Software Tools; Statistical Methods; Statistical Models; Structure; Syndrome; System; Technology; Universities; Work; base; biological systems; cardiometabolism; causal variant; computerized tools; data complexity; disease phenotype; disorder risk; epigenomics; experimental study; gene function; gene interaction; genetic analysis; genetic association; genetic variant; genome wide association study; genome-wide; genomic data; high dimensionality; high throughput technology; human disease; insight; interest; metabolomics; multidimensional data; next generation sequencing; novel; programs; simulation; statistics; theories; treatment response

Abstract The broad, long-term objective of this project concerns the development of novel statistical methods, theory and computational tools for statistical modeling of large-scale multiple high-dimensional genomic data motivated by important biological questions and experiments. New high-throughput technologies and next generation sequencing are generating various types of very high-dimensional genetics, genomic, epigenomics, metabolomics data in order to obtain an integrative understanding of various complex phenotypes. As the types and complexity of the data increase and as the questions being addressed become more sophisticated, statistical methods that can both integrate these genomic data and incorporate information about gene function and pathways are required in order to draw valid statistical and biological inferences. The specific aims of the current project are to develop new statistical models and methods for causal integrative analysis of eQTL data with genome wide genetic association data (GWAS) in order to identify the possible causal genes and pathways for disease phenotypes. Motivated by analysis of diverse genomic data, the first aim is to develop novel causal mediation analysis methods to identify the genes that mediate the effects of genetic variants on disease phenotypes by constructing gene regulatory networks based on eQTL data. Aim 2 is to develop high-dimensional instrumental variables (HDIV) regression models in order to identify the phenotype-causing genes using eQTLs as possible instrumental variables. Aims 3 develops methods for estimating the genetic relatedness between disease phenotype and gene expressions in order to identify the possible disease causing genes and biological pathways. Finally, Aim 4 is to develop statistical methods that can effectively integrate GTEx data with GWAS association summary statistics in order to identify possible causal disease genes and pathways. These methods hinge on novel integration of methods for multiple related high-dimensional regressions and high-dimensional causal inference. The new methods can be applied to different types of genomic data and will ideally help facilitate the identification of genes and their complex interactions as well as the biological pathways underlying various complex human diseases. The work proposed here will contribute statistical methodology and theory for modeling high-dimensional genomic data and to studying complex phenotypes and biological systems and o er insights into each of the biological areas represented by the various data sets, including Alzheimer's disease, cardiometabolic syndrome, and chronic kidney disease. All algorithms and software tools developed under this grant and detailed documentation will be made available free-of-charge to interested researchers.

摘要 该项目的广泛的长期目标涉及新的统计方法，理论， 和计算工具，用于大规模多个高维基因组数据的统计建模， 重要的生物学问题和实验。新的高通量技术和下一代测序正在产生各种类型的非常高维的遗传学、基因组学、表观基因组学、代谢组学数据 以获得对各种复杂表型的综合理解。由于其类型和复杂性， 随着数据的增加，以及所处理的问题变得越来越复杂， 整合这些基因组数据，并纳入有关基因功能和途径的信息， 以得出有效的统计学和生物学推论。本项目的具体目标是开发新的 具有全基因组遗传关联eQTL数据因果综合分析的统计模型和方法 数据（GWAS），以确定可能的致病基因和疾病表型的途径。出于 分析不同的基因组数据，第一个目标是开发新的因果中介分析方法，以确定 通过构建基因调控网络介导遗传变异对疾病表型影响的基因 基于eQTL数据。目的2是建立高维工具变量（HDIV）回归模型， 利用eQTL作为工具变量，对表型相关基因进行鉴定。目标3开发 评估疾病表型和基因表达之间遗传相关性的方法，以鉴定可能的致病基因和生物学途径。最后，目标4是开发统计方法 它可以有效地将GTEx数据与GWAS关联汇总统计数据集成在一起， 致病基因和途径。这些方法取决于多个相关方法的新集成 高维回归和高维因果推理。新方法可应用于不同的 类型的基因组数据，并将理想地帮助促进基因及其复杂的相互作用， 以及各种复杂的人类疾病的生物学途径。本文提出的工作将为高维基因组数据的建模和复杂基因组的研究提供统计方法和理论 表型和生物系统，以及对各种生物学领域所代表的每一个生物学领域的深入了解。 数据集，包括阿尔茨海默病，心脏代谢综合征和慢性肾脏疾病。所有算法 根据这项赠款开发的软件工具和详细的文件将免费提供给 感兴趣的研究人员。