Clusters of Epigenetic Networks at Birth and Asthma Incidence in Children

这是一项关于儿童和青年哮喘表观遗传起源的流行病学研究。已经 越来越多的人认识到，哮喘是基因间联合活动的结果，而不是单个基因的结果。 独立贡献。检查CpG对哮喘发展的个体贡献是生物学上的 没有根据，我们很有可能得出不完整和/或误导性的结论。这种限制已经 广泛同意，并提出了解决基因间联合活动的方法，包括 以鉴定差异甲基化区域（DMR）和用于检测基因网络的区域。然而，DMR 这种方法可能存在缺陷，因为几乎所有的DMR都是基于每个单独CpG的影响而推断的， 而不是CpG的联合效应。定义基因或CpG之间的网络可以更好地理解它们的协同作用。 方面的影响.然而，目前的网络建设要么侧重于整个人口，要么侧重于特定群体。 为一般人群构建一个网络缺乏纯粹性，因为它忽略了 受试者（例如，哮喘风险的异质性）。虽然为一组疾病患者建立网络， 限制了预测和预防疾病的能力，这是由于基因活动的潜在反向因果关系。当前没有任何 在构建网络的同时解决异质性的方法是可用的。我们提出了一种方法来检测不同的 表观遗传网络（DENs），每个DENs通过使用DNA甲基化的网络聚类对一组受试者是唯一的 （DNAm）数据，例如，与儿童哮喘的关系。网络构建在 每个聚类将代表同质受试者组的独特表观遗传特征。区分 在不同条件下构建的网络支持网络聚类在一般人群中的可行性。 此外，在哮喘研究中，我们将研究出生时DEN与哮喘发病率的纵向关系， 儿童、后青春期和青年期，评估年龄、性别和种族在这种关联中的作用，并检查 评估联合而非个体CpG活性对哮喘发病率的益处。这项研究的结果 将告知CpG在多大程度上联合工作并有助于哮喘的发展，并将有利于未来 对不同性别和不同种族哮喘发病的早期预测研究。此外，该方法将 所开发的技术将容易地应用于具有不同于DNAm的其他组学数据的其他健康状况。创新实施 这项重要的研究在三个出生队列中进行，两个白人队列（IOWBC和ALSPAC出生队列侧重于 哮喘史）和一个队列（NEST出生队列），其中一个队列有白色和黑人参与者。这三个出生 队列研究将使我们能够发现、复制和评估DEN的区域和种族特异性及其流行病学特征。 与哮喘发病率的关系。对于网络模块中的CpG，显示与 在一个或多个生命阶段的哮喘发病率，我们将通过孟德尔遗传学评估CpG的潜在因果关系。 使用基于MR的网络工具进行随机化（MR）测试。对于检测DEN的统计方法，我们将建立 方法转换成一个公开可用的R包，并附有详细的手册和具体的例子。

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.