Complex diseases are typically caused by a mixture of genetic, environmental and epigenetic effects. A number of prenatal risk factors, including intra-uterine growth restriction (IUGR) and gestational diabetes mellitus (GDM), have been shown to determine signatures in the blood methylome. However, most studies on this topic fall short are underpowered, or fail to take genetic effects into account to investigate causality. Birth cohorts with multiple tissue samples and deep phenotyping offer an opportunity to investigate the interplay of genetics, epigenetics and the environment affecting foetal and child growth. Moreover, my project aims to specifically determine which changes in DNA methylation are causal to subsequent disease states by combining genetic and epigenetic data in Mendelian Randomization experiments. The discovered results can then be used to develop biological tools to detect children at risk stratify risk groups and develop prevention strategies.

The work I propose to undertake follows on from my recent research in cross-sectional epigenome-wide association studies (EWAS) for T2D and obesity In adults (Wahl*, Drong* et al. under review). However, I have found previously that most of the strongest signals of methylations associations display a reverse causal relationship. I am thus interested to investigate the influence of early-life events, such as foetal growth and gestational diabetes (GDM) on patterns of DNA methylation. To achieve this, I aim to employ quantitative skills to develop robust methodology to take into effect confounding effects from experimental confounders (mixture of foetal/maternal tissues), maternal/paternal genotypes and to develop a robust, reusable pipeline for Mendelian Randomization in birth cohorts. Ultimately, I aim to link epigenetic markers both identified for GDM and IUGR with future health outcomes, and develop biomarkers for the effects.

Firstly, I aim to apply my analysis methodology to utilise the rich data sets curated by the proposed research sponsors to detect associations of DNA methylation with a number of phenotypes. I will perform a large scale EWAS case/control studies for GDM. Secondly, I will lead for the analysis of epigenetic data from biological samples for IGUR in the INTERBIO-21 study. This includes development of the experimental designs for large-scale epigenome-wide association scans. Lastly, I will be utilizing genetic variants as instrumental variables in two-step Mendelian Randomization to determine whether epigenetic markers are in causal pathways linking intra-uterine events to the child's phenotypes. Thus I will apply an informed approach about causality to separately detect genetic associations to avoid bias and will be able to detect maternal genetic and epigenetic confounding.

This will allow me to be in a unique position by extending the scope of simple cross-sectional EWAS to include not only causal analysis, but also a robust characterisation with respect to biological and technical confounders.
By focussing my hypothesis on causal pathways, my work can filter out reverse-causal confounders facilitate personalised prevention and novel drug target discovery. If successful, my research will provide accurate tools to guide childhood interventions through early biomarkers of the intrauterine environment.