Development and Use of Network Infrastructure for High-Throughput GWA Studies

描述（由申请人提供）：将医疗保健输送系统中的患者生物储存库与电子病历（EMR）联系起来是高通量全基因组关联（GWA）研究的有效策略，因为可以经济地提取并汇集输送系统中具有公共卫生重要性的表型、协变量和暴露数据，以促进每种表型的GWA研究所需的大量受试者。这一战略取得成功的主要障碍仍然存在。在这个项目中，将使用基于人口的基因组和表型数据，这些数据来自一个由一个传递系统提供服务的人口，该系统在其数据库中捕获了几乎所有的医疗保健服务。来自团体健康合作社健康研究中心、华盛顿大学和弗雷德哈钦森癌症研究中心的研究人员将通过追求以下具体目标来解决这些障碍： 1.根据目标焦点小组的结果，与关键利益相关者实施共识流程，以制定有关同意、数据共享和将研究结果返还给受试者的建议。 2.与其他网络站点合作，开发类似于癌症研究网络中使用的虚拟数据仓库（VDW），并将自然语言处理（NLP）扩展到病理学，放射学和临床图表注释。 3.开发和测试策略，以确定每个候选的基于EMR的表型是否足够有效，以进行GWA数据的分析，并开发统计方法，明确说明研究中心内部和之间的异质表型有效性。 4.在GHC生物储存库和连接的生物储存库中进行一系列GWA分析。4a：阿尔茨海默病（AD）。4 b：颈动脉粥样硬化性疾病（CAAD）。4c：使用他汀类药物的并发症，包括CPK升高和肌肉疼痛。 通过与其他研究人员和NHGRI的合作，这项工作将促进政策和程序的制定，以实现EMR相关生物储存库在GWA研究中的巨大潜力，以提高对慢性疾病和疾病的理解，预防和治疗。具体的GWA研究将使我们能够探索病因学研究（AD和CAAD进展）和药物遗传学（他汀类药物治疗）。这一系列研究的意义远远超出了我们选择强调的特定表型;我们预计这项工作代表了一个大型和富有成效的企业的开始。

DESCRIPTION (provided by applicant): Linking biorepositories of patients in healthcare delivery systems with electronic medical records (EMRs) is an efficient strategy for high-throughput genome wide association (GWA) studies, as phenotype, covariable and exposure data of public health importance can be economically abstracted and pooled across delivery systems to facilitate the large numbers of subjects needed for GWA studies of each phenotype. Key obstacles to the success of this strategy remain. In this project, which will use population-based genomic and phenotype data from a well characterized population served by a delivery system which captures virtually all health care encounters in its data bases. Researchers from Group Health Cooperative's Center for Health Studies, the University of Washington, and the Fred Hutchinson Cancer Research Center will address these obstacles by pursuing the following specific aims: 1. Informed by results from targeted focus groups, implement a consensus process with key stakeholders to develop recommendations concerning consent, data sharing, and return of research results to subjects. 2. Work together with other network sites to develop a virtual data warehouse (VDW) analogous to that used in the Cancer Research Network, and extend natural language processing (NLP) to pathology, radiology, and clinical chart notes. 3. Develop and test strategies to determine whether each candidate EMR-based phenotype is sufficiently valid to pursue analyses of GWA data, and develop statistical methods that explicitly account for heterogeneous phenotype validity within and between sites. 4. Perform a series of GWA analyses in the GHC biorepository and linked biorepositories. 4a: Alzheimer's disease (AD). 4b: Carotid artery atherosclerotic disease (CAAD). 4c: Complications of statin use, including elevations of CPK and muscle pain. Through cooperation with other investigators and the NHGRI, this work will facilitate development of policies and procedures to realize the incredible potential of EMR-linked biorepositories for GWA studies to improve understanding, prevention and treatment of chronic diseases and illnesses. Specific GWA research will allow us to explore both etiologic research (AD and CAAD progression) and pharmacogenetics (statin therapy). The implications of this portfolio of research extend far beyond the specific phenotypes we have chosen to emphasize; we expect this work represents the beginning of a large and productive enterprise.