# Systematic mapping and prediction of gene-enhancer connections

> **NIH NIH R00** · STANFORD UNIVERSITY · 2020 · $249,000

## Abstract

A fundamental challenge in modern biology is to identify the noncoding regulatory elements (REs) that control
gene expression, which could inform the interpretation of the thousands of noncoding genetic variants
associated with human diseases through genome-wide association studies (GWAS). Interpreting the
functions of REs and noncoding genetic variants has been challenging because we have lacked the ability
to systematically perturb REs in their native locations in the genome. To address this challenge, I recently
developed a high-throughput method to map the functions of thousands of REs in their native genomic
contexts and measure their quantitative effects on gene expression (CRISPRi tiling). I also developed a novel
analytical approach to model and predict gene-RE connections based on maps of chromatin state and 3D
folding. Together, these advances motivate a strategy to allow systematic mapping of all of the REs that
control any given gene in any given cell type. In the K99 phase, I propose to: (i) apply CRISPRi tiling to map
~6,000 additional gene-RE connections, and (ii) use these data to extend and optimize a model to predict
gene-RE connections from chromatin state. I will use human immune cells as a model system to compare
predictions across cell types. In the R00 phase, I will apply these tools to (iii) characterize the network
architecture of gene-RE connections across hundreds of cell types, and (iv) edit single-nucleotide variants
identified by the model in cellular models to characterize their effects on gene expression. Together, these
aims will provide insights into the mechanisms and architecture of gene-RE connectivity, generate tools for
mapping gene-RE connectivity in any cell type, and reveal mechanisms underlying common diseases.
Stanford University is an ideal environment for my independent laboratory, providing all of the facilities
needed for the proposed research and a rich interdisciplinary environment for collaborative studies. Together,
these aims will launch my independent scientific career at the interface of regulatory genomics and disease
genetics.

## Key facts

- **NIH application ID:** 10134673
- **Project number:** 4R00HG009917-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** JESSE M ENGREITZ
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $249,000
- **Award type:** 4N
- **Project period:** 2020-05-01 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10134673

## Citation

> US National Institutes of Health, RePORTER application 10134673, Systematic mapping and prediction of gene-enhancer connections (4R00HG009917-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10134673. Licensed CC0.

---

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