# Statistical methods for analysis of high-dimensional mediation pathways

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $344,676

## Abstract

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.

## Key facts

- **NIH application ID:** 10829856
- **Project number:** 5R01ES033656-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Karen Eileen Peterson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $344,676
- **Award type:** 5
- **Project period:** 2023-04-19 → 2027-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10829856

## Citation

> US National Institutes of Health, RePORTER application 10829856, Statistical methods for analysis of high-dimensional mediation pathways (5R01ES033656-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10829856. Licensed CC0.

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