Statistical methods for modeling multi-omic data

多组学数据建模的统计方法

• 批准号: 9441328
• 负责人: Andrea S Foulkes
• 金额: $ 45.28万
• 依托单位: MOUNT HOLYOKE COLLEGE
• 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 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9441328
• 关键词: Academic Research Enhancement Awards; Address; Atherosclerosis; Big Data; Biological; Biological Markers; Biological Models; Biometry; Biostatistical Methods; Cells; Characteristics; Clinical Management; Code; Collaborations; Collection; Complex; Computational Science; Data; Data Science; Development; Disease; Environment; Environmental Risk Factor; Etiology; Foundations; Genes; Genetic; Genomics; Goals; Inflammatory; Institution; Intervention; Knowledge; Lead; Linkage Disequilibrium; Measures; Medicine; Mentors; Methodology; Methods; Modeling; Molecular; Morbidity - disease rate; Natural Immunity; Non-Insulin-Dependent Diabetes Mellitus; Pattern; Physiological; Process; Prognostic Marker; Proteins; Regulation; Regulator Genes; Regulatory Element; Research; Research Personnel; Rest; Sepsis; Solid; Source; Statistical Methods; Stimulus; Stress; Testing; Time; Tissues; Translational Research; Trauma; United States National Institutes of Health; Woman; big biomedical data; clinically relevant; college; data resource; experience; genetic association; genomic data; higher education; improved; insight; interdisciplinary collaboration; learning materials; mortality; novel; novel therapeutics; personalized decision; precision medicine; professor; programs; response; response biomarker; simulation; sound; therapeutic development; therapeutic target; tool; trait; transcriptome; undergraduate student; young woman

Project Summary Novel analytic paradigms allowing for a fully integrated interrogation of regulatory elements, protein-coding genes, demographic characteristics and environmental factors on evoked and dynamic traits are essential for providing new insight into the mechanistic underpinnings of genetic associations. In this proposal we aim to develop, evaluate and apply sound statistical methods for leveraging and integrating the vast amount of publicly available multi-omic data resources to improve understanding of the mechanistic relationships among genes and regulatory elements associated with complex traits. As activation of innate immunity is a fundamen- tal pathophysiological process in cardiometabolic disease, e.g., atherosclerosis and type 2 diabetes, as well as complex inflammatory disorders, e.g., response to sepsis and trauma, our understanding of the genetic under- pinnings of these evoked inflammatory biomarkers, provides clinically relevant impact toward development of novel prognostic markers and therapeutic targets in complex diseases. Advancing knowledge of the molecular and physiological underpinnings of complex diseases will deepen insight into disease etiology, while providing opportunity to develop targeted interventions and lessen disease morbidity and mortality. The Specific Aims are to: (1) Develop a novel statistical framework for inferential transcriptome association analysis using reference data for rigorous interrogation of regulatory and gene-level underpinnings of dynamic response(s) to stimulus. We will develop novel and precise estimation and hypothesis testing strategies to inves- tigate and characterize the mechanistic foundations of genomic class-level associations with biological response to inflammatory stress. (2) Extend the methodology of Aim 1 to incorporate repeatedly measured transcriptome data, multiple expression patterns, and high linkage disequilibrium within genomic classes.. We will advance the solid conceptual framework of Aim 1 to develop strategies for evaluating time varying transcriptome pro- files as well as data on multiple cell and tissue types and several genes and regulatory elements. (3) Apply and evaluate the methods of Aims 1 and 2 through leveraging multiple sources of layered -omics data, including cell and tissue specific expression. In addition to fully vetting the proposed methods and comparing to existing alternative strategies using extensive simulation studies, we will further unravel and elucidate the mechanisms of gene and regulatory element control of induced response using multiple publicly-available reference tran- scriptome data resources and repeatedly measured biomarker data arising from the GENE study. In addition to supporting rigorous and novel statistical research at the forefront of precision medicine, this NIH Academic Research Enhancement Award (AREA) Program (R15) application aims to meet the specific NIH AREA objectives by offering a unique opportunity to expose and engage underrepresented undergradu- ate students in STEM to biomedical big data science, while strengthening the research environment at Mount Holyoke College (MHC), the world's longest standing institution of higher education for women. This applica- tion launches from an extensive, decade-long and highly productive trans-disciplinary collaboration. Building on a strong research and mentoring record, the proposed research offers novel statistical research addressing pressing challenges in precision medicine, while offering an important and unique opportunity to engage young women in cutting-edge biomedical big data research.

项目摘要 新的分析范式，允许对调控元件、蛋白质编码 基因、人口学特征和环境因素对诱发和动态性状的影响是必不可少的， 为遗传关联的机制基础提供了新的见解。在本提案中，我们的目标是 开发、评估和应用合理的统计方法，以利用和整合大量的 公开可用的多组学数据资源，以提高对 与复杂性状相关的基因和调控元件。因为先天免疫的激活是基础- 心脏代谢疾病中的病理生理过程，例如，动脉粥样硬化和2型糖尿病，以及 复杂的炎症性疾病，例如，对败血症和创伤的反应，我们对遗传的理解， 这些诱发的炎症生物标志物的精确定位，提供了临床相关的影响， 复杂疾病的新的预后标志物和治疗靶点。提高分子水平 复杂疾病的生理学基础将加深对疾病病因学的了解， 有机会制定有针对性的干预措施，减少疾病发病率和死亡率。 具体目标是：（1）建立一个新的统计框架，用于推断转录组关联 使用参考数据进行分析，以严格询问动态的调控和基因水平基础 对刺激的反应。我们将开发新的和精确的估计和假设检验策略，以投资- 量化和表征基因组类别水平与生物学反应相关性的机制基础 来缓解精神压力(2)扩展目标1的方法，以纳入重复测量的转录组 数据，多种表达模式，以及基因组类别内的高度连锁不平衡。我们将推进 目标1的坚实的概念框架，以制定评估时变转录组前体的策略， 文件以及关于多种细胞和组织类型以及几种基因和调控元件的数据。(3)应用和 通过利用分层组学数据的多个来源，评估目标1和2的方法，包括 细胞和组织特异性表达。除了全面审查拟议的方法并与现有的方法进行比较外， 替代策略使用广泛的模拟研究，我们将进一步解开和阐明的机制 基因和调控元件控制的诱导反应，使用多种公众可获得的参考跨 scriptome数据资源和重复测量的生物标志物数据来自GENE研究。 除了支持精确医学前沿的严谨和新颖的统计研究外， NIH学术研究增强奖（AREA）计划（R15）申请旨在满足 通过提供一个独特的机会，暴露和参与代表性不足的本科生， 让STEM的学生学习生物医学大数据科学，同时加强芒特的研究环境。 霍利奥克学院（MHC）是世界上历史最悠久的女子高等教育机构。本申请- 概念是从一个广泛的，长达十年的，富有成效的跨学科合作中推出的。建筑 在一个强大的研究和指导记录，拟议的研究提供了新的统计研究，解决 精准医疗面临的紧迫挑战，同时提供了一个重要而独特的机会， 前沿生物医学大数据研究中的女性。