# Statistical methods to enhance reproducible microbiome discovery

> **NIH NIH R35** · UNIVERSITY OF WASHINGTON · 2024 · $403,273

## Abstract

PROJECT SUMMARY
The microbiome plays an important role in many human disorders and diseases, including
cancer, autoimmune diseases and sexually transmitted infections. Microbial communities are
usually studied via their genetic sequences, and a major challenge in modern microbiome
science is that microbial sequencing introduces both bias and variability. The magnitude of
sample- and study- specific variation in sample measurements can exceed the magnitude of
variation due to treatment or disease status, which impedes the diagnosis and treatment of
complex diseases. To support low-cost, rigorous and reproducible microbiome research, we will
develop statistical tools to guide researchers in decision-making in the presence of
measurement error in microbiome studies. We will focus specifically on robust approaches to
differential abundance, integrated models for multiple biological units (including both within- and
across-kingdom interactions), and methods for emerging data structures (such as samples with
spike-in cells or communities). We will also develop recommendations on the experimental
design of microbiome studies, focusing on maximizing statistical power to make true discoveries
while minimizing sequencing and labor costs. Our methods apply to a broad range of microbial
questions, including ecology, metabolism, evolution, and community assembly. Our methods
will be accompanied by freely available, open-source software, as well as detailed tutorials and
forums for user questions. The long-term goal of this research is to improve the efficiency with
which microbiome science can have a positive impact on the etiology and treatment of human
disease and infection.

## Key facts

- **NIH application ID:** 10839570
- **Project number:** 2R35GM133420-06
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Amy D Willis
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $403,273
- **Award type:** 2
- **Project period:** 2019-09-01 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10839570

## Citation

> US National Institutes of Health, RePORTER application 10839570, Statistical methods to enhance reproducible microbiome discovery (2R35GM133420-06). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10839570. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*