Statistical methods for analysis of high-dimensional mediation pathways

摘要 这一建议利用统计理论和基础机制模型的应用来 研究高维组学标志物对儿童生长发育的调节作用 发展。该提案旨在推进新的方法、算法和软件，以 提高对环境扰动的机理影响的理解，并 与儿童健康结果有关的生物过程的社会经济应激源，如 青少年肥胖、认知功能和性成熟。这个项目是第一个 系统地研究了一种新兴的最佳子集统计估计的基础 高维结构方程模型(SEM)中的推断及其结果分析 通过工具箱，从业人员可以同时对关键中介进行集群、评估和验证 临床上重要的途径。(一)我们开发了一种新的分析范式，可以共同 处理大量的介体(例如代谢物或DNA甲基化CpG位点)以揭示 错误发现率控制良好的机械化调解路径。方法论 创新之处在于同时运行高维路径聚类、参数 估计偏差小且无偏倚的高维自回归模型中的估计和推断 在错误发现上妥协。(Ii)我们开发了一种自适应假设检验方法 高维SEM对中介路径进行统计推断 I类错误控制。这种新的方法被认为比现有的方法有显著的功率改进 方法：研究方法。(Iii)研究母体血脂和DNA的调节作用 妊娠环境与社会经济关系的甲基化标记物 关于儿童健康后果的暴露。此外，还发现了机械化的调解 路径将有助于开发潜在的干预措施，以改善儿童的健康。(Iv)我们发展， 测试、分发和支持建议的方法的免费实现 求婚。所开发的统计工具箱可以方便地进行翻译临床研究。

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.