4-year PhD Studentship: Harnessing genetics of DNA methylation to understand molecular mechanisms underlying human traits and diseases.

GWAS studies have discovered many genetic associations for traits and diseases, but the functional consequences of these variants remain elusive. DNA methylation (DNAm) plays a central role in gene regulation and provides a candidate mechanism underlying GWAS associations. However, DNAm is cell type specific, can be dynamic and is influenced by genetic and environmental factors.

Multiple studies have identified genetic variants associated with DNAm (mQTL: methylation quantitative trait locus) by combining genome wide genotype information with DNAm levels. The Genetics of DNA methylation Consortium brought together a large number of cohorts to identify mQTLs in blood and investigated whether the mQTLs play a role in disease etiology. Using several causal inference approaches, they discovered a small number of causal relationships between trait and DNAm(1). However, blood has a variety of cell-types obscuring cell-type specific DNAm differences and mQTL resources across tissues and cell-types are not available(2). Therefore it is unknown to what extent cell-type specific DNAm signals contribute to traits. This studentship will provide cross-disciplinary training in state-of-the-art genetic epidemiological approaches to address questions about the human genetic basis of DNA methylation variation. The student will combine epigenetic, genetic, cellular deconvolution and causal inference analyses in large-scale epidemiological datasets.

Aims and objectives

The overall aim of this PhD is to compile a catalogue of DNA methylation QTLs (mQTLs) across a wide range of tissues, cell types, ancestries and across the lifecourse. The project would then involve using the catalog to uncover mechanisms of disease - the focus could be adapted to the particular interests of the student. The Genetics of DNA Methylation Consortium (GoDMC; http://www.godmc.org.uk/) has collected genetic and DNA methylation data across multiple cohorts offering the student an excellent platform for these analyses. The project could include the application of causal inference methods (Mendelian randomization).

Methodology

The following methods will be used:

1. mQTL analysis will be conducted followed by meta-analyses.
2. Novel methodology can be used (and potentially developed) to combine mQTLs across different studies, ethnicities, tissues and timepoints.
3. Novel computational approaches can be used to identify cell-type-specific DNA methylation signals from bulk tissue ("cellular deconvolution") and to understand whether mQTLs operate in a cell-type specific manner.
4. Mendelian Randomization analysis will be used to investigate causal relationships between DNA methylation and diseases/risk factors.

The student will be part of the GoDMC consortium core team and work with researchers across different disciplines ((epi)genetic epidemiology, statistical genetics, epidemiology). The candidate will develop a wide range of interdisciplinary skills (statistics, epidemiology and computer science) and will analyse large genetic and epigenetic datasets. The student will be offered an excellent training in (epi)genetic epidemiology and will develop a unique range of skills incorporating (epi)genetic, cellular deconvolution, causal inference and mediation analyses which are highly relevant in the era of big data science. The candidate will use unique blood and brain DNA methylation datasets from University of Exeter Medical School.

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.

Please visit the Faculty of Health Sciences website for details of how to apply