Clusters of Epigenetic Networks at Birth and Asthma Incidence in Children

这是一项关于儿童和青壮年哮喘的表观遗传起源的流行病学研究。一直以来 越来越多的人认识到哮喘是基因之间共同活动的结果，而不是单个基因的结果 独立捐款。从生物学角度考察CPGS在哮喘发病中的个体贡献 没有根据，我们有很高的风险得出不完整和/或误导性的结论。这样的限制一直是 已经提出了解决基因间联合活动的方法，包括方法 鉴定差异甲基化区域(DMRS)和那些用于检测基因网络的区域。然而，DMR 方法可能有缺陷，因为几乎所有的DMR都是基于每个单独的CpG的影响而推断的 而不是CPGS的联合作用。定义基因或CPG之间的网络可以更好地理解它们之间的协调 效果。然而，目前的网络建设要么关注整个人群，要么关注特定群体。 为普通人群构建一个网络缺乏纯粹性，因为它忽略了不同群体之间潜在的异质性 受试者(例如哮喘风险的异质性)。在为一组疾病患者建立网络的同时， 由于基因活动的潜在反向原因，限制了预测和预防疾病的能力。目前，没有 在构建网络时解决异构性的方法是可用的。我们提出一种方法来检测不同的 通过使用DNA甲基化的网络聚类，每个表观遗传网络(DENS)对于一组受试者是唯一的 (DNaM)疾病表现之前的数据，例如出生时dNaM和儿童哮喘。在中国构建的网络 每一组将代表同一组受试者的独特表观遗传学特征。区别于 在不同条件下构建的网络支持在一般人群中进行网络聚类的可行性。 此外，在哮喘研究中，我们将研究出生时洞穴与哮喘发病率的纵向联系。 儿童、青春期后和成年后，评估年龄、性别和种族在这种联系中的作用，并检查 评估CPGS联合活动而不是单独活动对哮喘发病率的益处。这项研究的发现 将告知CPGS在多大程度上共同工作并对哮喘的发展做出贡献，并将有益于未来 不同性别、不同种族哮喘发病早期预测研究。此外，该方法将是 与dNaM不同的其他组学数据将很容易应用于其他健康状况。我们会带着 在三个出生队列中进行了这项重要的研究，其中两个队列中有白人(IOWBC和ALSPAC关注出生队列 哮喘史)和一个同时有白人和黑人参与者的队列(Nest出生队列)。这三个孩子的出生 队列将使我们能够发现、复制和评估洞穴的区域和种族特异性及其流行病学 与哮喘发病率的关系。对于一个或多个网络模块中的CPG，显示与 哮喘发病率在生命的一个或多个阶段，我们将通过孟德尔评估CPGS的潜在因果关系 使用MR-BASE网络工具进行随机化(MR)测试。对于检测窝点的统计方法，我们将构建 方法并入公开可用的R包中，并附带有具体示例的详细手册。

This is an epidemiological study on the epigenetic origins of asthma in children and young adulthood. It has been increasingly recognized that asthma is a consequence of joint activities among genes rather than of individual genes’ independent contributions. Examining individual contributions of CpGs on asthma development is biologically ungrounded and we take a high risk of concluding incomplete and/or misleading findings. Such limitations have been widely agreed upon, and methods to address joint activities among genes have been proposed, including approaches to identify differentially methylated regions (DMRs) and those for detection of gene networks. However, the DMR approach is potentially flawed, since almost all DMRs are inferred based on effects of each individual CpG rather than joint effects of CpGs. Defining networks amongst genes or CpGs allow better understanding of their concerted effects. Current network constructions, however, either focus on a population as a whole or on a specific group. Constructing one network for a general population lacks purity, since it overlooks underlying heterogeneity among subjects (e.g., heterogeneity in asthma risk). Whilst building a network for a group of disease patients will substantially limit the ability to predict and prevent disease, due to potential reverse-causation on gene activities. Currently, no methods are available that address heterogeneity while building networks. We propose an approach to detect distinct epigenetic networks (DENs) with each unique to a group of subjects via network clustering using DNA methylation (DNAm) data before disease manifestation, e.g., at-birth DNAm and childhood asthma. The constructed network in each cluster will represent unique epigenetic features of a homogeneous group of subjects. Differentiation between networks constructed under different conditions support the feasibility of network clustering in a general population. In addition, in asthma studies, we will study the longitudinal association of DENs at birth with asthma incidence in children, post-adolescence, and young adulthood, assess the role of age, sex, and race in this association, and examine the benefit of evaluating joint rather than individual activities of CpGs on asthma incidence. The findings of this study will inform to what extent CpGs work jointly and contribute to the development of asthma, and will benefit future studies on early prediction of asthma acquisition for each sex and different races. Furthermore, the method to be developed will be readily applied to other health conditions with other omics data different from DNAm. We will carry out this important study in three birth cohorts, two cohorts with whites (the IOWBC and ALSPAC birth cohorts focusing on asthma history) and one cohort with both white and black participants (the NEST birth cohort). These three birth cohorts will allow us to discover, replicate, and assess region and race specificity of DENs and their epidemiological association with asthma incidence. For CpGs in modules of a network or networks showing association with asthma incidence at one or more stages of life, we will evaluate the potential causality of the CpGs via Mendelian randomization (MR) tests using the MR-base webtool. For the statistical methods to detect DENs, we will build the method into a publicly available R package accompanied by a detailed manual with concrete examples.