# Computational Methods for Investigating the Genetics of Gene Regulation

> **NIH NIH R35** · DUKE UNIVERSITY · 2024 · $366,714

## Abstract

Abstract
Genetic diagnosis of complex disease is an important challenge in modern medicine. As a majority of GWAS
hits implicate noncoding regions of the human genome, regulatory elements such as enhancers have become a
major focus in the search for causal mechanisms. This proposal focuses on the development of computational
methods for analyzing experimental data relevant to gene regulatory mechanisms and the variants that can
perturb them, leading to disease. My lab is well positioned to have a sizeable impact on the experimental science
in gene regulation ongoing at Duke and elsewhere, via existing collaborations and memberships in multiple
consortia. In particular, my lab has been developing statistical models for detecting allele-specific gene
expression in individuals and trios, to identify genes that may be under dysregulation. My lab also continues to
develop methods for analyzing genetic variant data from massively parallel reporter assays, which was a focus
of my Ph.D. thesis. And my lab has begun developing statistical methods for analyzing CRISPRi perturbations
in single-cell data to identify gene-enhancer relationships. We expect these synergistic projects to result in more
effective identification of causal variants and a higher diagnosis rate for currently undiagnosed patients of a wide
variety of diseases, as well as potential leads toward the design of therapeutics.

## Key facts

- **NIH application ID:** 10902091
- **Project number:** 5R35GM150404-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** William Majoros
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $366,714
- **Award type:** 5
- **Project period:** 2023-08-15 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10902091

## Citation

> US National Institutes of Health, RePORTER application 10902091, Computational Methods for Investigating the Genetics of Gene Regulation (5R35GM150404-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10902091. Licensed CC0.

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