Linking Complex Disease and Exposure Data to Established Data Standards

项目摘要/摘要 这项拟议研究的目标是将复杂疾病和暴露数据与已建立的数据联系起来 标准“是使用以社区为基础的数据标准，表型和共识措施 Exposures(PhenX)链接现有研究，并通过该链接将语义标准扩展到 造成复杂疾病和状况的环境因素。我们的方法将解决记录在案的 标准差距，执行试点分析，增加开创性研究的可重用性，并作为 未来数据驱动的标准努力。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不断变化的需求 环境和人类健康研究界，并解决大数据对知识的需求 (BD2K)基于社区的数据和元数据标准。我们建议扩大现有的适用范围。 确定环境暴露和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措施将变量联系起来并找出差距 我们对四项开创性研究的合作分析：海湾地区对妇女健康的韧性研究， 海湾长期随访研究、动脉粥样硬化多种族研究和哥伦比亚儿童中心 环境健康队列。我们将(A)确定研究变量与PhenX数据标准的联系， 四项研究中的共同变量和潜在的交叉研究问题，以及(B)执行 试点交叉研究分析表明，数据统一显示了新的结果。2：扩展现有 暴露和复杂疾病的形式化本体，以进一步支持变量关联和数据 协调工作，并开始模拟暴露与疾病的相互作用。(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(ECTO)以 包括与导致复杂疾病的环境因素有关的概念，如接触途径和 健康结果。(B)使用本体提供的复杂层次结构，以便于进一步 统一和评估到标准数据元素的映射。(C)建造和提供一个 与环境暴露和复杂疾病相关的概念的社区本体论。3： 创建逻辑观测标识名称和代码(LOINC)以简化电子交换 关于结果的。LOINC，实验室和临床观察系统中广泛采用的标准，将促进 将识别的、关联的数据元素集成到现有的临床信息学管道中。通过这些 这项拟议的工作有可能对科学研究产生重大影响，因为 当前公共卫生关切和积极研究领域的数据整合工具：环境 造成复杂疾病发生和后果的因素。

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.