High Dimensional Mediation Analysis with Multi-Omics Data

多组学数据的高维中介分析

• 批准号: 1712933
• 负责人: Bhramar Mukherjee
• 金额: $ 18万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1712933&HistoricalAwards=false
• 关键词: Dimensional; Mediation; Analysis; Multi; Omics

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.

各种高通量生物技术的快速发展彻底改变了基因组学领域。各种基因组研究通过测量基因表达水平并表征 DNA 和组蛋白的各种共价修饰来产生分子水平的性状。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，并且是了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并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的测量，这些环境随生命过程动态变化。 本项目将开发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。所提出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衡量）如何影响内部环境的组学测量，进而导致不良的健康结果。从技术上讲，组学研究的分子水平特征将被视为一组多元的高维中介因素，用于综合分析。将开发一种新颖的高维中介分析框架来同时处理多个暴露和多个中介。所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来扩展现有的中介分析方法，从处理单变量中介和/或单变量暴露到高维设置，以实现模型可识别性。虽然问题是在因果推理框架中提出的，但推理将使用贝叶斯变量选择框架来进行，该框架同时识别重要的暴露和中介。该研究以统一的方式结合了方差成分评分测试、贝叶斯变量选择和因果推理的思想，从而得出了直接和间接影响估计的新理论见解。方法论也将得到扩展，以基于共享汇总统计数据进行中介分析，这在遗传学研究中变得越来越普遍。新开发的方法将应用于大型正在进行的队列/病例对照研究，并将开发软件以扩展实施所提出的方法。

项目成果

Does Information on Blood Heavy Metals Improve Cardiovascular Mortality Prediction?

• DOI: 10.1161/jaha.119.013571
• 发表时间: 2019-10
• 期刊: Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease
• 作者: Xin Wang;B. Mukherjee;S. Park

A Review of Statistical Methods for Identifying Trait-Relevant Tissues and Cell Types.

• DOI: 10.3389/fgene.2020.587887
• 发表时间: 2020
• 期刊: Frontiers in genetics
• 影响因子: 3.7
• 作者: Zhu H;Shang L;Zhou X
• 通讯作者: Zhou X

Interaction analysis under misspecification of main effects: Some common mistakes and simple solutions.

主效应错误指定下的交互分析：一些常见错误和简单的解决方案。

• DOI: 10.1002/sim.8505
• 发表时间: 2020
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Zhang,Min;Yu,Youfei;Wang,Shikun;Salvatore,Maxwell;GFritsche,Lars;He,Zihuai;Mukherjee,Bhramar
• 通讯作者: Mukherjee,Bhramar

DNA Methylation Mediates the Association Between Individual and Neighborhood Social Disadvantage and Cardiovascular Risk Factors.

• DOI: 10.3389/fcvm.2022.848768
• 发表时间: 2022
• 期刊: Frontiers in cardiovascular medicine
• 影响因子: 3.6

Group Inverse-Gamma Gamma Shrinkage for Sparse Linear Models with Block-Correlated Regressors

• DOI: 10.1214/23-ba1371
• 发表时间: 2024-09-01
• 期刊: BAYESIAN ANALYSIS
• 影响因子: 4.4
• 作者: Boss，Jonathan;Datta，Jyotishka;Mukherjee，Bhramar
• 通讯作者: Mukherjee，Bhramar