# Genome analysis:  statistical methods and applications

> **NIH NIH R56** · UNIVERSITY OF CHICAGO · 2023 · $292,000

## Abstract

Project Summary
The overall goal of this project is to develop new statistical methods that address important problems in ge-
nomics, to implement them in user-friendly open source software, and to make them available to scientists to
facilitate new biological discoveries. To this end, the proposed work tackles three important problems arising in
genomics where existing statistical methods are lacking, and where new, improved methods could accelerate the
pace of scientific discovery: fine-mapping of functional traits; gene set enrichment analysis (GSEA); and discovering
overlapping cluster structure in genomic data.
 The work on fine-mapping will enable the identification of genetic variants influencing common sequencing
assays such as ATAC-seq and ChIP-seq, without pre-specifying the locations of potential effects. This unbiased
approach will help identify regulatory genetic variants and interacting parts of the regulatory genome.
 The work on GSEA will provide a new more effective set of tools for researchers who use GSEA to set new
findings in the context of known biology. The proposed work uses recently-developed statistical techniques
to substantially reduce the redundancy of enriched gene sets, and will provide researchers with succinct and
precise results that better highlight the full range of known biological factors that are relevant to a new set of
findings.
 The work on overlapping cluster structure will provide new generally-applicable methods for understanding
complex layered and hierarchical relationships that occur commonly in genomics applications (e.g. cell sub-
types nested within cell types, layered on top of patient effects).
 The tools developed here will help scientists tackle a diverse range of analysis problems that arise in ge-
nomics, ultimately helping them better understand the biology of disease, with the eventual goal of improving
therapies and treatment strategies.

## Key facts

- **NIH application ID:** 10861108
- **Project number:** 2R56HG002585-17
- **Recipient organization:** UNIVERSITY OF CHICAGO
- **Principal Investigator:** MATTHEW STEPHENS
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $292,000
- **Award type:** 2
- **Project period:** 2002-09-20 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10861108

## Citation

> US National Institutes of Health, RePORTER application 10861108, Genome analysis:  statistical methods and applications (2R56HG002585-17). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10861108. Licensed CC0.

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