Harnessing genetics of DNA methylation to elucidate molecular mechanisms underlying human phenotypes.

Rationale

GWAS studies have discovered many genetic associations for traits and diseases, but it has been difficult to elucidate the functional consequences of these variants. One of the reasons for this is linkage disequilibrium (co-inheritance of many variants with the disease variant). Another reason is that most GWAS signals reside in non-coding regions (outside genes), and it is likely that GWAS variants confer their effects through modulating of regulatory mechanisms. Molecular traits (such as gene expression, DNA methylation, proteins) have increasingly been used to address this question because they are often dysregulated in disease and can act as an intermediate phenotype.

DNA methylation levels can be routinely assayed at a large scale using micro-arrays. Multiple studies have identified genetic variants associated with DNA methylation (mQTL: methylation quantitative trait locus) by combining genome wide genotype information with DNA methylation levels. mQTLs can be defined as cis, which are typically nearby a DNA methylation site and can be found in small sample sizes. Trans mQTLs (defined as associations which are further away or on different chromosomes) have smaller effects and large sample sizes are needed. The Genetics of DNA methylation Consortium (GoDMC, http://www.godmc.org.uk/) brought together a large number of cohorts to identify these small effects 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 DNA methylation (Min JL et al.).

DNA methylation might be on the causal pathway only in the trait-relevant cell-type or context. However, it is unknown to what extent cell-type specific DNA methylation signals differ from bulk tissue (eg. blood has many cell-types and each cell-type might have a different methylation level), and whether cell-type specific DNAm signals are on a causal path to disease as genome-wide mQTL resources across multiple ancestries, tissues and cell types are not currently available. This studentship will provide cross-disciplinary training in state-of-the-art genetic and genomic epidemiological approaches (under the supervision of Dr. Josine Min and Prof Caroline Relton at the Medical Research Council Integrative Epidemiology Unit at the University of Bristol and Prof Jonathan Mill and Dr. Eilis Hannon at the University of Exeter Medical School) 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 & Objectives

The overall aim of this PhD is to compile a mQTL catalogue of uniformly processed mQTLs across a wide range of tissues, cell types, ancestries and across the lifecourse. The use of the catalog for this project would depend on the candidate’s research interest. It could be used for Mendelian randomization analyses to gain insights into the regulation of GWAS signals. GoDMC has collected genetic and DNA methylation data across multiple cohorts offering the student an excellent platform for these analyses.

Methods

1) mQTL analysis will be conducted followed by meta-analyses. There will be several challenges with this type of analysis including heterogeneity of datasets (eg. age, sex).

2) Novel methodology can be used (and potentially developed) to combine mQTLs across different studies, ethnicities, tissues and timepoints.

3) Cellular deconvolution approaches and analysis of cell-types from bulk tissue will be used 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.

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.