Statistical methods for modeling multi-omic data

项目摘要 新的分析范例，允许对调节元件、蛋白质编码进行完全集成的询问 基因、人口统计特征和环境因素对诱发和动态特征是必不可少的 为基因关联的机制基础提供了新的见解。在这项提案中，我们的目标是 开发、评估和应用合理的统计方法，以利用和整合海量的 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。因为激活先天免疫是一个基础-- 心脏代谢疾病的TAL病理生理过程，例如动脉粥样硬化和2型糖尿病，以及 fl炎症性疾病的复杂性，例如，对脓毒症和创伤的反应，我们对遗传不足的理解- 在fl炎症生物标记物中诱发的这些因子的结合，提供了临床上相关的影响 复杂疾病的新预后标记物和治疗靶点。关于分子的前沿知识 复杂疾病的生理基础将加深对疾病病因的洞察，同时提供 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。 SPECIfic的目的是：(1)开发一种新的用于推断转录组关联的统计框架 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的基础 对刺激的反应(S)。我们将开发新的和精确的估计和假设检验策略来投资- 提炼和描述基因组类水平与生物反应相关的机制基础 在fl中应激变性应激。(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，以及基因组类内的高连锁不平衡。我们将继续前进 目标1的坚实的概念框架，以制定评估时变转录组PRO的策略。 fiLES以及关于多种细胞和组织类型以及几个基因和调控元件的数据。(3)申请及 通过利用分层组学数据的多种来源，评估目标1和目标2的方法，包括 细胞和组织特异性fic的表达。除了全面审查建议的方法并与现有方法进行比较 替代策略使用广泛的模拟研究，我们将进一步解开和阐明其机制 利用多个可公开获得的参考转基因技术控制诱导反应的基因和调控元件 来自基因研究的脚本数据资源和重复测量的生物标记物数据。 除了支持精确医学前沿的严谨和新颖的统计研究外，这 美国国立卫生研究院学术研究增强奖(AREA)计划(R15)的申请旨在满足fic NIH的地区目标，通过提供一个独特的机会来揭露和接触代表不足的弱势群体- 让STEM的学生了解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)，世界上历史最悠久的女子高等教育机构。本申请书- Ttion通过长达十年的广泛、高效的跨学科合作启动。建房 在强大的研究和指导记录上，拟议的研究提供了新的统计研究解决方案 精准医学面临的紧迫挑战，同时提供了吸引年轻人参与的重要和独特的机会 尖端生物医学大数据研究领域的女性。

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.