A systems based approach to integrating genetic and longitudinal omics data to support diagnosis and prediction of common chronic disease

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New technologies are providing opportunities to measure health and disease in many novel ways. The data produced is complex and hard to decipher even by clinicians and health workers. This proposal will investigate how we can use modern molecular techniques which measure in blood the activity and expression of genes and the signatures of chemical reactions (metabolites) in the cell to help predict early disease. To do this we need to explore how global gene expression and metabolites alter over time and how these longitudinal changes along with other new molecular and genetic techniques (called omics) can be used to explore disease mechanisms and susceptibility in ageing populations. To explore the biology of "omic" variability, and to lay the foundation for the clinical integration of genetic and genomic data, we will investigate the longitudinal relationships of cellular and genomic phenotypes, including global gene expression and metabolites, in 700 twins over 7 years, measured at three time-periods. The study subjects derive from the TwinsUK cohort on whom there is already extensive clinical information and cross-sectional genetic and genomic data. Building on these existing data, and making use of the specific methodological opportunities and advantages afforded by the twin design, we will explore how these genomic traits track and vary over time, determine how such variation relates to underlying genetic variation, and explore the joint contribution of genetic and genomic data to disease risk and onset. We will also explore the potential value of monitoring changes in these and other situations within an integrated personalised medicine framework. We will use and develop new analysis approaches to integrate these complex data sets and suggest which changes might play a role in clinically relevant tratis and disease itself. This study will provide novel insights into disease understanding and stimulate larger-scale efforts to combine modern genetic and genomic data for clinical benefit in the future. These studies will pave the way for individualised medicine.