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

描述(由申请人提供)：将医疗保健提供系统中患者的生物信息库与电子病历(EMRS)联系起来是高通量全基因组关联(GWA)研究的有效策略，因为对公共卫生重要的表型、协变量和暴露数据可以经济地提取并跨提供系统汇集，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关键障碍依然存在。在这个项目中，它将使用基于人群的基因组和表型数据，这些数据来自一个特征良好的人群，该人群由一个交付系统提供服务，该系统几乎捕获了其数据库中的所有医疗保健接触。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.了解有针对性的重点小组的结果，与主要利益攸关方开展协商一致进程，制定关于同意、数据共享和将研究成果返还给受试者的建议。 2.与其他网站合作，开发一个类似于癌症研究网络的虚拟数据仓库(VDW)，并将自然语言处理(NLP)扩展到病理学、放射学和临床病历笔记。 3.制定和测试策略，以确定每个基于EMR的候选表型是否足够有效，以进行GWA数据分析，并开发统计方法，明确说明地点内和地点之间的异质性表型有效性。 4.在GHC生物库和链接的生物库中执行一系列GWA分析。4A：阿尔茨海默病(AD)。4B：颈动脉粥样硬化症(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，以实现与EMR相关的生物储存库对GWA研究的难以置信的潜力，以改善对慢性病和疾病的理解、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)。这一系列研究的影响远远超出了我们选择强调的具体表型；我们预计这项工作代表着一个大型和多产企业的开始。

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.