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

表观基因组位于基因(DNA序列)的顶端，控制着基因的作用或不作用。例如，它解释了为什么同卵双胞胎的行为和健康结果不同，比如他们的血压。科学家们对表观遗传学越来越感兴趣，表观遗传学是对表观基因组的研究，以更好地了解行为(如吸烟)、基因和疾病之间的联系。已知表观遗传模式会随着时间的推移而改变，这可能部分是由于环境因素(如污染)、特征(如我们的血压)和行为(如吸烟)的影响。此外，随着年龄的增长，表观遗传模式似乎也会发生变化。能够理解表观基因组的哪一部分随着时间的推移发生变化，以及它是如何以及何时发生变化的，对于理解风险因素如何与基因相互作用导致疾病，以及随着我们年龄的增长健康状况普遍下降可能是重要的。目前，由于表观遗传学数据的复杂性，我们没有很好的统计方法来进行这项研究。第一个问题是，每个人都有大量的甲基化(表观遗传)位点-45万个，使用的是一种常用技术来测量这些位点。这意味着，很难确定其中一小部分与特定环境因素、特征或健康结果有关的地点。其次，确定表观遗传位点如何随时间变化并非易事，因为这些变化的测量方式使得很难知道这些变化是由于少数几个站点之间的巨大变化还是由于大量站点之间的小变化。第三，表观遗传位点在我们基因组的区域内聚集(组合在一起)，因此来自同一区域的两个站点可能比来自不同区域的两个站点更相似。在这个项目中，我们的目标是开发复杂的统计方法来识别随着时间的推移甲基化发生变化的位点，并将这些变化与风险因素和以后的健康结果联系起来。这将确保最好地利用这项新兴技术来研究环境和生活方式如何与基因相互作用导致疾病。我们将确保我们的新方法能够在常用的统计软件包中发挥作用，并向所有科学家免费提供。

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.