Contextualizing and Addressing Population-Level Bias in Social Epigenomics Study of Asthma in Childhood

摘要 美国目前有610万儿童患有哮喘，使其成为最常见的慢性病 在童年时期经历过。非洲裔美国人(AA)儿童存在显著的种族和民族差异 与非西班牙裔白人儿童相比，死于哮喘的可能性高出8倍。遗传、环境和 心理社会因素被认为是通过影响与哮喘相关的生物学途径共同导致这种疾病的。 病理生理学。在我们的父母R01奖(5R01MD015409)-缩写为“压力，表观基因组和 哮喘“(SEA)研究中，我们假设儿童时期暴露在心理社会压力下可能以一种机械性的方式起作用 (生物学的)水平通过表观遗传修饰影响我们基因组的功能。为了检验我们的假设，我们 在哮喘AA儿童/家庭的前瞻性社会表观基因组学研究中收集了大量数据 包括高分辨率表观遗传学概况、全面的健康社会决定因素(SDOH)和慢性 压力信息。虽然我们建议在父级奖项中为下游做好数据集的准备 AI/ML方法我们认识到还需要为类似的SDOH和慢性应激数据做准备 但不在母公司裁决范围之外的申请。具体地说，我们认为海洋研究 数据将极大地受益于AI/ML技术的使用，例如能够天真地 捕获不同功能组合的不同结果。然而，考虑到长期接触 压力源与孩子的社会环境有关，要想建立可靠的模式需要付出巨大的努力 准备收集的数据并将其与环境联系起来。我们假设这可以通过以下方式实现 使用不同的人口水平数据集收集社会和临床数据。我们的增刊将针对两个目标： 1)我们将开发新的量化方法来定义研究参与者数据的代表性。通过 利用可公开获得的人口水平数据(例如人口普查数据)，我们将开发一个框架来比较 研究参与者的社会人口学概况与预期的个人地理分布 参照区。通过这样做，确定可能与结果所在的社区不一致的子组 预计将一概而论。通过进一步将这种对齐与数据质量度量(例如，遗漏)相关联，我们可以 创建标准化工具来传达数据集对人口子集的内在偏差，以帮助设计 分析和解释AI/ML模型的结果；2)扩展传统的AI/ML推算前处理 方法考虑社会经济因素。了解慢性压力与 儿童的社会环境，以及抽样不按地理区域平衡，目前的归咎 对失配程度较高的子组中数据的估计将主要受关系驱动 在拥有更完整信息的队列中发现。我们假设，人口水平的数据可以被整合 引入了用于多个补偿模型的新的加权技术，以更好地解释 病人。反过来，为较小的人口亚群提供更准确的缺失数据估计。

SUMMARY 6.1 million children in the US currently suffer from asthma, making it the most common chronic disease experienced during childhood. Significant racial and ethnic disparities exist with African American (AA) children being 8 times more likely to die of asthma relative to non-Hispanic white children. Genetic, environmental, and psychosocial factors are believed to jointly cause the disease by affecting biological pathways related to asthma pathophysiology. Within our parent R01 award (5R01MD015409) – abbreviated as the “Stress, Epigenome and Asthma” (SEA) study, we hypothesize that exposure to psychosocial stress in childhood may act at a mechanistic (biological) level impacting the function of our genome by epigenetic modifications. To test our hypothesis, we are collecting large amounts of data in a prospective social epigenomics study of asthmatic AA children/families including high-resolution epigenetic profiles, comprehensive social determinants of health (SDOH), and chronic stress information. While we propose within the parent award to make the ‘omics’ dataset ready for downstream AI/ML approaches we recognize the need to also prepare our SDOH and chronic stress data for similar applications which is however outside of the scope of the parent award. Specifically, we argue the SEA study data will greatly benefit from use of AI/ML techniques such as ensemble models that are capable of naively capturing differential outcomes across combinations of features. However, given that exposure to chronic stressors is tied to a child’s social environment, to develop reliable models will require significant efforts to prepare and contextualize the collected data. We hypothesize this can be accomplished through the linking of collected social and clinical data with disparate population level datasets. Our supplement will address two aims: 1) We will develop novel quantitative measures to define the representativeness of study participant data. By utilizing publicly available population-level data (e.g., Census data) we will develop a framework to compare the sociodemographic profile of study participations against an expected distribution of individuals in a geographic reference area. And, by doing so, identify subgroups that may misaligned to the community on which results are expected to generalize. By further linking this alignment to data quality measures (e.g., missingness), we can create a standardized tool to convey the dataset’s intrinsic biases on population subsets to aid in designing analyses and interpreting AI/ML model results; and 2) We will extend traditional AI/ML imputation preprocessing methods to account for socioeconomic factors. Understanding that chronic stress is deeply interconnected with children’s social environment and that sampling is not balanced by geographic region, current imputation estimates for data in subgroups with a high degree of missingness, would be primarily driven by relationships found in cohorts with more complete information. We hypothesize, that population-level data can be integrated into novel weighting techniques for multiple imputation models to better account for socioeconomic similarity of patients. In turn, providing more precise estimates of missing data for smaller population subgroups.