Statistical methods for analysis of high-dimensional mediation pathways

高维中介路径分析的统计方法

• 批准号: 10582932
• 负责人: Karen Eileen Peterson
• 金额: $ 34.47万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-19 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10582932
• 关键词: Address; Adolescence; Adolescent obesity; Affect; Age; Algorithms; Area; Behavior; Biological Markers; Biological Process; Blood; Body Composition; Centers for Disease Control and Prevention (U.S.); Child; Child Health; Chronic; Chronic Disease; Clinical Pathways; Clinical Research; Complex; Computer software; DNA Methylation; Data; Data Analyses; Development; Diet; Dimensions; Disease; Disease Pathway; Disease model; Economics; Environmental Exposure; Epigenetic Process; Equation; Exposure to; Foundations; Funding; Goals; Graph; Growth and Development function; Hand; Health; Human; Intervention; Life; Life Style; Literature; Longevity; Measurement; Mediation; Mediator; Methodological Studies; Methodology; Methods; Modeling; Molecular; Nutritional; Obesity; Outcome; Parameter Estimation; Pathway interactions; Phenotype; Policies; Pregnancy; Prevention; Process; Research; Research Methodology; Risk Factors; Sample Size; Science; Scientist; Sexual Maturation; Site; Socioeconomic Status; Statistical Methods; Statistical Models; Statistical Study; Technology; Testing; Translational Research; United States National Institutes of Health; Update; adolescent health outcomes; clinical translation; cognitive function; disorder prevention; economic determinant; experimental study; health disparity; health equality; health inequalities; health practice; high dimensionality; high throughput technology; human disease; improved; innovation; interest; lipid biosynthesis; lipid metabolism; lipidome; metabolomics; methylation biomarker; molecular marker; multidimensional data; novel; obesity development; online tutorial; operation; outcome disparities; simulation; social; socioeconomics; stressor; targeted biomarker; theories; user friendly software

Abstract This proposal harnesses statistical theory and applications underlying mechanistic models to study mediation pathways involving high-dimensional omics markers on the children growth and development. This proposal aims to advance novel methodology, algorithms, and software to improve the understanding of mechanistic effects of environmental perturbations and socioeconomic stressors on biological processes related to children’s health outcomes such as adolescent obesity, cognitive function, and sexual maturation. This project is the first to systematically study the foundation of an emerging best-subset statistical estimation and inference in high-dimensional structural equation models (SEMs), and the resulting analytic toolboxes allow practitioners to simultaneously cluster, estimate, and validate key mediation pathways of clinical importance. (i) We develop a new analytic paradigm that can jointly process a large number of mediators (e.g. metabolites or DNA methylation CpG sites) to unveil mechanistic mediation pathways with well-controlled false discovery rate. The methodology innovation lies in a simultaneous operation of high-dimensional pathway clustering, parameter estimation and inference in the high-dimensional SEMs with little estimation bias and no compromise on false discovery. (ii) We develop an adaptive hypothesis testing methodology in high-dimensional SEMs to perform statistical inference for mediation pathways with a proper type I error control. This new method is deemed for significant power improvement over existing methods. (iii) We investigate mediation effects of the maternal blood lipidome and DNA methylation markers for the relationship of gestational environmental and socioeconomic exposures on children’s health outcomes. Moreover, discovered mechanistic mediation pathways will help develop potential interventions for better children’s health. (iv) We develop, test, distribute, and support freely available implementations of the proposed methods in this proposal. The developed statistical toolboxes can facilitate the translational clinical studies.

抽象的 该提议将统计理论和基础机理模型的应用都利用 研究调解途径，涉及儿童成长的高维度标记和 发展。该建议旨在将新颖的方法论，算法和软件推向 提高对环境扰动的机械影响的理解和 与儿童健康结果有关的生物学过程的社会经济压力源，例如 青少年肥胖，认知功能和性成熟。这个项目是第一个 系统地研究新兴的最符合统计估计的基础 高维结构方程模型（SEMS）的推断和所得的分析 工具箱允许从业人员简单地群集，估计和验证密钥中介 临床重要性的途径。 （i）我们开发了一个可以共同的分析范式 处理大量介体（例如代谢产物或DNA甲基化CPG位点）以揭露 机械调解途径具有良好的虚假发现率。方法 创新在于简单的高维路径聚类的操作，参数 高维SEM的估计和推断，估计偏差很少，无 错误发现妥协。 （ii）我们开发了一种自适应假设检验方法 高维SEMS以适当的 类型I错误控制。这种新方法被认为是对现有的重大功能改进 方法。 （iii）我们研究了母体血脂肪组和DNA的调解作用 妊娠环境与社会经济关系的甲基化标记 暴露于儿童健康成果。此外，发现的机械调解 途径将有助于制定潜在的干预措施，以提高儿童健康。 （iv）我们发展， 在此中测试，分发和支持提议方法的免费实施 提议。开发的统计工具箱可以促进翻译的临床研究。