Active Genetics & Molecular Biology Cells, Biochemistry & Physiology

Understanding gene regulation utilising high-resolution gene interaction data generated with Micro Capture-C

Funder Wellcome Charity-funded
Recipient organisation University of Oxford
Funding Not disclosed
Period Oct 2025 — Sept 2028

In plain English

AI plain-English summary

Most disease-linked genetic variants lie not in genes themselves but in the DNA stretches that switch genes on and off—and this project will map those control points with unprecedented precision. The problem is that scientists know where thousands of disease-associated DNA variants sit in the genome, but they cannot tell which ones actually matter. Many fall in non-coding regions that regulate gene activity, and their effects depend on the specific cell type involved. Existing methods struggle to identify which variants are causal, especially in rare or hard-to-study cells. This project uses Micro Capture-C, a technique that captures physical contacts between regulatory DNA elements at the level of single base pairs—far sharper than previous methods. If successful, the work will produce three practical tools: a statistical method to prioritise which non-coding variants from genome-wide association studies are worth testing; a deep-learning model that can generate synthetic cell-type-specific interaction maps from bulk tissue data; and improved “footprinting” to pinpoint where transcription factors actually bind. These could accelerate the hunt for causal variants in common diseases, helping researchers focus functional experiments on the handful of DNA changes that truly drive disease risk, rather than the thousands that merely correlate with it.

View original technical description
Micro Capture-C (MCC) is a high-resolution chromatin conformation capture (3C)-based method that captures the physical contacts between interacting regulatory elements at base-pair resolution and thereby provides unique insights into gene regulation. The majority of disease-associated variants identified via genome-wide association studies (GWAS) lie within the non- coding regions of the genome, but the mechanisms governing gene regulation and transcription factor binding are not well understood. Limitations in exploring cell-type-specific interactions between regulatory elements in rarer cell types restrict our ability to understand gene regulation in a disease-relevant environment, while existing computational methods in post-GWAS analyses can struggle to accurately prioritise causal non-coding SNPs for functional validation. In this project, I will utilise the high-resolution interaction data generated via MCC experiments to investigate cell-type-specific genomic regulation mechanisms and their implications for disease. To achieve this aim, we will take a three-fold approach: we shall develop methods improving probabilistic prioritisation of non-coding SNPs from GWAS; we shall develop a deep learning model to deconvolute bulk tissue MCC to generate synthetic, cell-type-specific MCC utilising cell-type-specific open chromatin; we shall refine MCC footprinting methods to improve transcription factor binding identification.

View the original record at the funder ↗

Researchers

Anne Marie Delaney (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Harnessing genome characterization to uncover disease mechanisms
Machine-learning to create predictive models of genetic regulation
Context-specific mapping of genetic variants to function
Characterisation of allele-specific regulatory elements and identification of causal non-coding SNPs related to CHD and CHD risk traits
Three-dimensional interrogation of gene regulation using next generation chromatin conformation capture and super-resolution imaging.

Original classification

PhD Studentship (Basic)

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