# Project 4: Evaluating mediation effects of the microbiome and epigenetics using high dimensional assays

> **NIH NIH P20** · DARTMOUTH COLLEGE · 2020 · $249,832

## Abstract

PROJECT 4 ABSTRACT 
High dimensional human microbiome and DNA methylation data offer great promise to contributing to the 
understanding of the underlying etiology of a myriad of human diseases. Mediation modeling is a critical tool 
used in molecular epidemiology to infer causal pathways for biological processes. As yet, mediation modeling 
has not been extensively applied to studies of the microbiome and epigenome even though it is likely to clarify 
their critical roles in disease pathogenesis. To our knowledge, there are no available mediation models to test 
whether the human microbiome mediates disease occurrence. Impediments to using mediation modeling arise 
from the compositional, phylogenetically hierarchical, sparse, and high dimensional structure of microbiome 
data. Another level of complexity is that mediations can occur through changes in individual microbes or 
through alterations to the overall community structure of the microbiome. For DNA methylation data, models 
exist for analyzing mediational effects; however, current methods rely on reference data to adjust for cell- 
composition effects. Yet reference data are often not available and are costly to obtain. Reference-free 
approaches have been proposed for association analyses to resolve this issue, but these have not been 
applied to mediation analyses. To address these critical challenges, we will develop new mediation methods to 
analyze high-dimensional data on the human microbiome and DNA methylation as complex mediators in 
disease causing pathways. We will apply our models to test the effects of the infant gut microbiome, breast 
milk microbiome, cord blood DNA methylome, and breast milk DNA methylome in mediating the associations 
between prenatal exposures (e.g. arsenic exposure) and childhood infections and allergy/atopy in the first year 
of life using the rich data from the large ongoing longitudinal molecular epidemiologic New Hampshire Birth 
Cohort Study. R packages will be developed to implement these two models. These methods will enable the 
identification of complex mediators of disease pathways to highlight opportunities for designing interventions to 
support children's health and development.

## Key facts

- **NIH application ID:** 9843723
- **Project number:** 5P20GM104416-08
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** Zhigang Li
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $249,832
- **Award type:** 5
- **Project period:** 2020-02-01 → 2020-02-02

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9843723

## Citation

> US National Institutes of Health, RePORTER application 9843723, Project 4: Evaluating mediation effects of the microbiome and epigenetics using high dimensional assays (5P20GM104416-08). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9843723. Licensed CC0.

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