Development of a multilevel and mixture-model framework for modelling epigenetic changes over time (resubmission)

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The epigenome sits on top of genes (DNA sequences) and controls whether genes are act or do not. It explains for example why 'identical' twins differ in their behaviours and health outcomes like their blood pressure. Scientists are increasingly interested in epigenetics, the study of the epigenome, to better understand the links between behaviours (such as smoking), genes and disease. Epigenetic patterns are known to change over time, which may partly be due to the influence of environmental factors (e.g. pollution), characteristics (such as our blood pressure) and behaviours (like smoking). In addition epigenetic patterns seem to change as we get older. Being able to understand which part of the epigenome changes over time, and how and when it changes could be important for understanding how risk factors interact with genes to cause disease and the general decline in health as we get older. At the moment we do not have good statistical methods for doing this research because of how complex epigenetic data are. The first issue is that there is a large number of methylation (epigenetic) sites for each person - 450,000 with one of the common technologies used to measure these. This means that identifying a small number of these sites that are related to a given environmental factor, characteristic or health outcome is difficult. Secondly, identifying how epigenetic sites changes over time is not straightforward because the way in which these are measured which makes it difficult to know whether change over time is because of large change between a small number of sites or small changes between a large number of sites. Thirdly, epigenetic sites are clustered (group together) within regions of our genome, and thus two sites from the same region may be more similar than two sites from different regions.In this project, we aim to develop sophisticated statisticalmethods for identifying sites which show change in methylation over time, and relating those changes to risk factors and later health outcomes. This will ensure the best possible use of this emerging technology in investigating how the environment and lifestyle interact with genes to cause disease. We will make sure our new methods can work in commonly used statistical packages and make them freely available to all scientists.