Harnessing the genetics of DNA methylation to understand context-specific gene regulation in disease

Project Background

Genome wide association studies (GWAS) have discovered many genetic associations with health outcomes. However, most GWAS signals reside in non-coding regions and it is likely that GWAS variants confer their effects through a regulatory mechanism. DNA methylation (DNAm) plays a central role in gene regulation. Genetic variants associated with DNAm (methylation quantitative trait loci) have been used to provide a candidate mechanism underlying GWAS associations. So far only a small proportion of GWA signals have been causally linked to mQTLs. One explanation for this could be that mQTLs are context specific. For example, most mQTL studies have identified mQTLs in blood. However, blood has a variety of cell-types which may obscure cell-type specific DNAm differences.

Many other contexts (eg. sex, age or smoking habits) can also have an influence. These context-specific effects may operate by changing your DNAm levels and inducing interactions between genetics and DNAm. These interactions may then contribute to the development of disease in individuals with a genetic predisposition. The effect of context-specific mQTLs on health outcomes is unknown.

Project Aims

The aim is to get a better understanding of context-specific gene regulation and to identify causal influences between context-specific mQTLs and disease. The following are examples of research questions that the student may wish to address as part of their PhD.

1. Identify genetic factors for cell-type interacting DNA methylation variation

2. Identify genetic factors for sex, age, BMI and smoking dependent DNA methylation variation

3. identify infection dependent DNA methylation variation

4. Identify causal associations between context-dependent mQTLs and health outcomes 

Project Methods

This studentship will provide cross-disciplinary training in state-of-the-art epigenetic, genetic and causal inference analyses.

The student will use innovative software and methods to analyse genetic and DNAm data by collaborating with academic centres that participate in the Genetics of DNA Methylation Consortium (GoDMC; http://www.godmc.org.uk/). The student will identify context-specific mQTLs by modelling genotype environmental interactions where cell type, smoking, sex or BMI and can be taken as a proxy for the environment. GoDMC promotes a federated analysis protocol which means that the PhD student has an excellent platform to develop analysis skills for genetic and DNAm analysis and develop his/her own research questions.

The student will apply Mendelian randomization (MR) analysis to identify novel causal factors influencing chronic diseases(2). Mendelian Randomization is a genetic epidemiological approach that uses genetic variants as proxies to interrogate potential causal links between exposure (eg cell counts) and outcome (disease). 

How to apply for this project

This project will be based in Bristol Medical School - Population Health Sciences in the Faculty of Health Sciences at the University of Bristol.

If you have secured your own sponsorship or can self-fund this PhD please visit our information page here for further information on the department of Population Health Science and how to apply.