Methods for integrated analysis of multi-level omics data

多层次组学数据综合分析方法

• 批准号: 9897639
• 负责人: Andrea S Foulkes
• 金额: $ 41.76万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-01 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9897639
• 关键词: Address; Big Data; Biological Markers; Biological Models; Biostatistical Methods; Cells; Clinical; Clinical Management; Code; Collaborations; Collection; Complex; Coupling; Data; Development; Disease; Elements; Etiology; Event; Expression Profiling; Foundations; Genes; Genetic Enhancer Element; Genome; Genomic medicine; Genomics; Genotype; Goals; Inflammatory; Institutes; Investigation; Knowledge; Linkage Disequilibrium; Mathematics; Measures; Mediating; Mediation; Medicine; Mentors; Methods; Molecular; Outcome; Performance; Phenotype; Physiological; Prognostic Marker; Protein Isoforms; Proteins; RNA; RNA Splicing; Regulatory Element; Reporting; Research; Research Personnel; Resources; Role; Statistical Methods; Stress; Structure; Taxonomy; Testing; Time; Tissues; Translating; Translational Research; Uncertainty; base; clinically relevant; cohort; data resource; expectation; genetic association; genetic element; genome wide association study; genomic data; high throughput technology; improved; innovation; insight; interdisciplinary collaboration; interest; multilevel analysis; novel; personalized decision; precision medicine; professor; response biomarker; simulation; sound; statistics; structural genomics; structured data; survival outcome; therapeutic target; trait; transcriptome; transcriptomics

Project Summary Novel analytic paradigms allowing for fully integrated interrogation of independent genomics data resources is expected to reveal substantial new knowledge regarding the mechanistic foundations of genetic associations. In this proposal we aim to develop, evaluate and apply sound statistical methods for leveraging and integrat- ing the vast amount of publicly available transcriptome and genomics resources to improve understanding of the mechanistic relationships among genes and regulatory elements associated with complex traits. Ultimately, methods for uncovering the molecular and physiological underpinnings of complex diseases will provide clin- ically relevant impact toward development of novel prognostic markers and therapeutic targets. The Specific Aims are to: (1) Develop a likelihood-based framework for integrated analysis of genomic elements, expression pro- files and phenotypes. An overarching challenge in this setting is that transcriptomics data, composed of genotypes and expression profiles, and GWA data, composed of genotypes and complex traits, are only generally available for independent cohorts. We propose combining these two data resources and framing the analysis in terms of a missing data problem. The unobserved expression profiles in the GWA data are treated as missing and an expectation-maximization (EM) approach is proposed. Methods for efficient implementation and inference, as well as an alternative Bayesian MCMC approach, are also described. (2) Extend the methods of Aim 1 for alternative data structures and types. The framework of Aim 1 will be further developed to: (a) account for complex linkage disequilibrium (LD) structures within and across genes; (b) address disparities across genotyping platforms; (c) provide for simultaneous investigation of multiple cell and tissue compartments, multiple isoforms, and multiple genes and regulatory elements; and (d) accommodate time-varying biomarker profiles and time-to-event outcomes. (3) Apply and evaluate performance of the methods developed in Aims 1 and 2. In addition to fully vetting the proposed methods and comparing to alternative strategies using extensive simulation studies, we will further unravel and elucidate the mechanisms of gene and regulatory element control of complex traits using multiple publicly-available reference transcriptome data resources, repeatedly measured biomarker data arising from the GENE study, and clinical outcomes from the CRIC study (see Section C). This application launches from an extensive, decade-long and highly productive trans-disciplinary collabora- tion. Building on a strong research and mentoring record, the proposed research offers novel statistical research addressing pressing challenges in precision medicine.

项目概要 新颖的分析范式允许对独立基因组数据资源进行完全集成的询问 预计将揭示有关遗传关联机制基础的大量新知识。 在本提案中，我们的目标是开发、评估和应用健全的统计方法来利用和整合 收集大量公开可用的转录组和基因组学资源，以提高对 最终，与复杂性状相关的基因和调控元件之间的机械关系。 揭示复杂疾病的分子和生理基础的方法将为临床提供 对新型预后标志物和治疗靶点的开发具有重要影响。 具体目标是： (1) 开发一个基于可能性的框架，用于基因组元件、表达亲和的综合分析 文件和表型的首要挑战是转录组数据，由以下部分组成。 基因型和表达谱以及由基因型和复杂性状组成的 GWA 数据只是 我们建议将这两种数据资源和框架结合起来。 根据缺失数据问题进行的分析是 GWA 数据中未观察到的表达谱。 被视为缺失，并提出了一种有效的期望最大化（EM）方法。 还描述了实现和推理，以及替代的贝叶斯 MCMC 方法。 (2) 将目标 1 的方法扩展为替代数据结构和类型 目标 1 的框架将是。 进一步发展为：（a）解释内部和外部的复杂连锁不平衡（LD）结构 (b) 解决基因分型平台之间的差异； (c) 提供同时调查 多个细胞和组织区室、多种亚型以及多个基因和调控元件； (d) 适应随时间变化的生物标志物概况和事件发生时间的结果。 (3) 应用和评估目标 1 和 2 中开发的方法的性能。除了充分审查之外 所提出的方法并通过广泛的模拟研究与替代策略进行比较，我们将 进一步揭示和阐明基因和调控元件控制复杂性状的机制 使用多个公开的参考转录组数据资源，重复测量生物标志物 GENE 研究产生的数据以及 CRIC 研究的临床结果（参见 C 节）。 该应用程序源自广泛、长达十年且高效的跨学科合作 拟议的研究以强大的研究和指导记录为基础，提供了新颖的统​​计研究。 应对精准医疗的挑战。