High Dimensional Mediation Analysis with Multi-Omics Data

各种高通量生物技术的快速发展使基因组学领域发生了革命性的变化。各种基因组研究通过测量基因表达水平和描述DNA和组蛋白的各种共价修饰来产生分子水平的特征。测量的分子水平性状，包括基因表达和甲基化水平，被认为介导DNA和/或环境对许多性状和疾病的影响，是理解疾病易感性和表型变异的遗传和环境基础的关键。特别是，这些高维生物标志物吸收和反映环境对基因组的损害，并作为个体内部分子和细胞环境随生命时间进程动态变化的衡量标准。在本项目中，将发展统计方法进行高维中介分析，以进一步了解疾病易感性和表型变异的分子基础，并促进组学研究中各种分子水平性状的综合分析。拟议的统计方法将用于研究遗传DNA环境和外部环境（通过环境毒物、社会经济条件、邻里特征、社会心理压力和其他生活事件来测量）如何影响内部环境的组学测量，并反过来导致不利的健康结果。从技术上讲，来自组学研究的分子水平性状将被视为一组多变量的高维介质进行综合分析。本文将开发一种新的高维中介分析框架，以同时处理多个暴露和多个中介。提出的高维中介分析方法将通过对中介和暴露的影响进行额外的建模假设，以实现模型的可识别性，将现有的中介分析方法从处理单变量中介和/或单变量暴露扩展到高维设置。虽然问题是在因果推理框架中制定的，但推理将使用贝叶斯变量选择框架进行，该框架同时识别重要的暴露和中介。本研究将方差成分得分检验、贝叶斯变量选择和因果推理的思想统一起来，对直接效应和间接效应的估计提出了新的理论见解。还将进行方法扩展，以在遗传学研究中日益普遍的共享汇总统计数据的基础上进行中介分析。新开发的方法将应用于正在进行的大型队列/病例对照研究，并将开发软件以可扩展地实施所提议的方法。

Rapid development of various high-throughput biological technologies has revolutionized the field of genomics. Various genomic studies produce molecular-level traits by measuring gene expression levels and characterizing various covalent modifications of DNA and histone proteins. The measured molecular-level traits, including gene expression and methylation levels, are thought to mediate the effects of DNA and/or the environment on many traits and diseases, and hold the key to understanding the genetic and environmental basis of disease susceptibility and phenotypic variation. In particular, these high-dimensional biomarkers absorb and reflect environmental insults to the genome and serve as measures of an individual's internal molecular and cellular environment that change dynamically over the time course of life. In this project, statistical methods will be developed to perform high-dimensional mediation analysis, in order to further our understanding of the molecular basis of disease susceptibility and phenotypic variation, and facilitate the integrative analysis of various molecular-level traits from omics studies. The proposed statistical methods will be used to study how the inherited DNA environment and the external environment, as measured through environmental toxicants, socioeconomic conditions, neighborhood characteristics, psychosocial stress and other life events, influence omics measures of the internal environment, and in turn lead to adverse health outcomes. Technically, the molecular-level traits from omics studies will be treated as a multivariate set of high-dimensional mediators for integrative analysis. A novel high-dimensional mediation analysis framework will be developed to handle multiple exposures and multiple mediators simultaneously. The proposed high-dimensional mediation analysis methods will extend existing mediation analysis methods from handling univariate mediator and/or univariate exposure to a high-dimensional setting by making additional modeling assumptions on the effects of mediators and exposures to enable model identifiability. While the problem is formulated in a causal inference framework, inference will be conducted using a Bayesian variable selection framework that identifies important exposures and mediators simultaneously. The research combines ideas from variance component score tests, Bayesian variable selection, and causal inference in a unified manner to lead to new theoretical insights on estimation of direct and indirect effects. Methodological extensions will also be made to conduct mediation analysis based on sharing summary statistics that are becoming increasingly common in genetics studies. The newly developed methods will be applied to large ongoing cohort/case-control studies, and software will be developed for scalable implementation of the proposed methods.