Linking Complex Disease and Exposure Data to Established Data Standards

项目总结/摘要 这项拟议研究的目标是“将复杂疾病和暴露数据与既定数据联系起来”， 标准”是使用基于社区的数据标准共识措施， eXposures（PhenX）链接现有的研究，并通过这种联系，扩展语义标准， 复杂疾病和病症的环境因素。我们的方法将解决一个记录在案的 标准差距，进行试点分析，提高可重用性的开创性研究，并作为一个模型， 未来的数据驱动标准。为了实现这一目标，我们提供了一个独特的跨学科团队， 标准开发人员和研究人员。拟议的三年研究计划将满足以下方面不断变化的需求： 环境和人类健康研究社区，并解决大数据知识的需求 （BD 2K）社区数据和元数据标准。我们建议扩大现有的 确定环境暴露与复杂疾病之间多方面相互作用的标准 通过以下具体目标。1：使用PhenX措施链接变量，并确定差距 我们对四项开创性研究的合作分析：海湾妇女健康恢复力研究， 海湾长期随访研究、多种族动脉粥样硬化研究和哥伦比亚儿童中心 环境卫生队列。我们将（a）确定研究变量链接到PhenX数据标准， 四项研究中的共同变量，以及潜在的交叉研究问题和（B）执行a 试验性交叉研究分析，以证明数据统一揭示了新的结果。2：扩展现有 暴露和复杂疾病中的正式本体，以进一步支持变量联系和数据 协调努力，并开始建模疾病的相互作用。(a)使用已确定的联系 评估和扩展环境条件、治疗和暴露本体论（ECTO）， 包括与复杂疾病环境因素有关的概念，如接触途径， 健康成果。(b)使用本体提供的复杂的层次结构， 协调和评价标准数据元素的映射。(c)建立并提供一个 与环境暴露和复杂疾病相关的概念的基于社区的本体。第三章： 创建逻辑观测标识符名称和代码（LOINC）以简化电子交换 的结果。LOINC是实验室和临床观察系统中广泛采用的标准， 将已识别的链接数据元素整合到现有的临床信息学管道中。通过这些 目标，拟议的工作有可能通过推进科学调查产生巨大影响。 在当前公共卫生关注和积极研究的一个领域中进行数据整合的工具：环境 导致复杂疾病的发生和结果。

PROJECT SUMMARY/ABSTRACT The goals of this proposed study, “Linking Complex Disease and Exposure Data to Established Data Standards,” are to use the community-based data standard consensus measures for Phenotypes and eXposures (PhenX) to link existing studies and, through that linkage, to extend semantic standards to environmental contributors to complex diseases and conditions. Our approach will address a documented standards gap, perform a pilot analysis, increase the reusability of seminal studies, and serve as a model for future data-driven standards efforts. To accomplish this, we offer a unique cross-disciplinary team of standards developers and researchers. The proposed 3-year research plan will meet the evolving needs of the environmental and human health research communities and address the need for Big Data to Knowledge (BD2K) Community-Based Data and Metadata Standards. We propose extending the application of existing standards to identify multifaceted interactions between environmental exposures and complex diseases through the following specific aims. 1: Use PhenX measures to link variables and identify gaps within our collaborative analysis of the four seminal studies: the Gulf Resilience on Women's Health Study, Gulf Long-term Follow-up Study, Multi-Ethnic Study of Atherosclerosis, and Columbia Center for Children's Environmental Health cohort. We will (a) identify study variable linkages to the PhenX data standard, common variables among the four studies, and potential cross-cutting research questions and (b) perform a pilot cross-study analysis to demonstrate that data harmonization revealed new results. 2: Extend existing formal ontologies in exposures and complex diseases to further support variable linkage and data harmonization efforts and to begin modeling exposure-disease interactions. (a) Use identified linkages to evaluate and extend the Environmental Conditions, Treatments, and Exposures Ontology (ECTO) to include concepts relevant to environmental contributors to complex disease, such as route of exposure and health outcomes. (b) Use the complex hierarchical structure provided by the ontology to facilitate further harmonization and evaluation of mappings to standard data elements. (c) Build and make available a community-based ontology of concepts relevant to environmental exposures and complex diseases. 3: Create Logical Observation Identifiers Names and Codes (LOINC) to simplify the electronic exchange of results. LOINC, a widely adopted standard in laboratory and clinical observation systems, will facilitate the integration of identified, linked data elements into existing clinical informatics pipelines. Through these aims, the proposed work has the potential to have a dramatic impact on the scientific inquiries by advancing tools for data integration in an area of current public health concern and active research: environmental contributors to complex disease occurrences and outcomes.