FindAPhD LIVE! Study Fair

Edinburgh | Oxford | Leeds

University of Warwick Featured PhD Programmes
National University of Ireland, Galway Featured PhD Programmes
Norwich Research Park Featured PhD Programmes
Imperial College London Featured PhD Programmes
European Molecular Biology Laboratory (Heidelberg) Featured PhD Programmes

Using Mendelian Randomization to infer causality in life course epidemiology

This project is no longer listed in the FindAPhD
database and may not be available.

Click here to search the FindAPhD database
for PhD studentship opportunities
  • Full or part time
    Dr L Howe
    Prof K Tilling
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

About This PhD Project

Project Description

Observational associations in epidemiology may suffer from residual confounding, even after adjustment for measured variables.
Mendelian Randomization (MR) is a tool used to improve the strength of causal inference from observational data. In MR, genetic variants related to the exposure of interest are used to give a causal, unconfounded estimate of the exposure-outcome association. MR has successfully been applied to many research questions in epidemiology. However, to date, applications have focused on the situation where there is a single measure of the exposure and a single measure of the outcome.
In life course epidemiology, the focus is on assessing the influence of exposures throughout the life course on later health and disease; this often requires the analysis of longitudinal data. Using appropriate longitudinal analysis methods can help to understand these relationships, but applying MR to such questions would strengthen causal inference.

Aims & Objectives
The aim of this PhD is to extend the framework of MR to situations where either the exposure of interest, the outcome, or both are measured repeatedly.

Depending on the interests and strengths of the student, this PhD could be an epidemiological PhD, applying MR to problems of interest, or it could be a more statistical PhD, developing MR methodology, or it could be a combination of epidemiology and statistics.

Depending on your interests, you could focus on various areas; some examples are:
1. A single measure of the exposure, repeated measures of an outcome:
Observational data suggests that maternal smoking is associated with blood pressure during pregnancy. MR using longitudinal models of maternal blood pressure over pregnancy as the outcome could help to understand whether the association changes in different periods of pregnancy.
2. Repeated measures of the exposure, a single measure of the outcome:
Evidence from MR studies suggests that height has a causal effect on cognitive and behavioural outcomes. This analysis could be extended to determine whether height growth in specific periods represents a sensitive period in the development of the outcomes
3. Repeated measures of both exposure and outcome:
MR studies have attempted to untangle the causality of observed associations between obesity and depression. MR using trajectories of fatness and mental health could help to untangle this.


Davey Smith G, Ebrahim S. What can mendelian randomisation tell us about modifiable behavioural and environmental exposures? BMJ. 2005;330(7499):1076-1079.

Let us know you agree to cookies

We use cookies to give you the best online experience. By continuing, we'll assume that you're happy to receive all cookies on this website. To read our privacy policy click here