BACKGROUND

This project focuses on developing methodological applications for analysing "omics" data resources (genome-wide genotypes, metabolomics, the epigenome), an area that has exciting prospects for observational epidemiology (Brion et al., Curr Epidem Rep 2014). We have previously published work indicating that genome-wide allelic scores can be used to data-mine large numbers of associations between biological intermediates and disease-related outcomes and screen for potentially causal relationships (Evans, Brion et al., PLoS Genetics 2013). We would like to build on this work and investigate the use of similar allelic scores in studies based on metabolomic and epigenetic measures. In addition, we would like to develop and implement novel applications of Mendelian Randomization to these genome-wide genotype, epigenetic and metabolomic measures, such as by implementing bidirectional MR (Welsh et al., J Clin Endocrin Metab, 2010) and MR for mediation (Relton & Davey Smith, IJE 2012). We would then implement these methods to test causal relationships involving blood methylation (epigenetic) markers and metabolomic measures.

AIMS

1.To develop and test novel epidemiological approaches, such as Mendelian Randomization and data-mining approaches, that exploit the availability of high throughput biological data (genome-wide single nucleotide polymorphism (SNP) data, epigenetic, metabolomic).

2.To apply these novel methods to infer the causality between risk factors, such as environmental exposures and biomarkers, with cardiovascular and psychological outcomes, through potentially mediating epigenomic and metabolomic pathways.