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

开发多层次混合模型框架，用于模拟表观遗传随时间的变化（重新提交）

• 批准号: MR/M025020/1
• 负责人: Kate Tilling
• 金额: $ 37.77万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FM025020%2F1
• 关键词: Development; multilevel; mixture; model; framework

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.

表观基因组位于基因（DNA序列）之上，控制基因是否起作用。例如，它解释了为什么“同卵”双胞胎的行为和健康结果（如血压）不同。科学家们对表观遗传学越来越感兴趣，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（例如污染），特征（例如我们的血压）和行为（例如吸烟）的影响。此外，表观遗传模式似乎随着我们年龄的增长而改变。能够了解表观基因组的哪一部分随着时间的推移而变化，以及它如何以及何时变化，对于了解风险因素如何与基因相互作用导致疾病以及随着年龄的增长健康状况普遍下降可能很重要。由于表观遗传学数据非常复杂，目前我们还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-450，000个用于测量这些的常见技术之一。这意味着很难确定少数与特定环境因素、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不简单，因为测量这些位点的方式使得很难知道随时间变化是因为少量位点之间的大变化还是大量位点之间的小变化。第三，表观遗传位点在我们基因组的区域内聚集（聚集在一起），因此来自同一区域的两个位点可能比来自不同区域的两个位点更相似。在这个项目中，我们的目标是开发复杂的生物学方法来识别甲基化随时间变化的位点，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一新兴技术，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可以在常用的统计软件包中工作，并免费提供给所有科学家。

项目成果

DNA methylation signatures of adolescent victimization: analysis of a longitudinal monozygotic twin sample.

• DOI: 10.1080/15592294.2020.1853317
• 发表时间: 2021-11
• 期刊: Epigenetics
• 影响因子: 3.7
• 作者: Kandaswamy R;Hannon E;Arseneault L;Mansell G;Sugden K;Williams B;Burrage J;Staley JR;Pishva E;Dahir A;Roberts S;Danese A;Mill J;Fisher HL;Wong CCY
• 通讯作者: Wong CCY

Longitudinal analysis strategies for modelling epigenetic trajectories.

• DOI: 10.1093/ije/dyy012
• 发表时间: 2018-04-01
• 期刊: International journal of epidemiology
• 影响因子: 7.7
• 作者: Staley JR;Suderman M;Simpkin AJ;Gaunt TR;Heron J;Relton CL;Tilling K
• 通讯作者: Tilling K

A robust mean and variance test with application to high-dimensional phenotypes.

• DOI: 10.1007/s10654-021-00805-w
• 发表时间: 2022-04
• 期刊: EUROPEAN JOURNAL OF EPIDEMIOLOGY
• 影响因子: 13.6
• 作者: Staley, James R.;Windmeijer, Frank;Suderman, Matthew;Lyon, Matthew S.;Davey Smith, George;Tilling, Kate
• 通讯作者: Tilling, Kate

Methods for Dealing With Missing Covariate Data in Epigenome-Wide Association Studies.

表观基因组范围关联研究中缺失协变量数据的处理方法。

• DOI: 10.1093/aje/kwz186
• 发表时间: 2019
• 期刊: American journal of epidemiology
• 影响因子: 5
• 作者: Mills HL