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的患者生物库与电子医疗记录（emr）联系起来是高通量基因组全关联（GWA）研究的有效策略，因为表型、协变量和对公共卫生具有重要意义的暴露数据可以经济地抽象出来，并汇集在整个提供系统中，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，它将使用基于人群的基因组和表型数据，这些数据来自一个提供系统，该系统在其数据库中捕获了几乎所有的卫生保健遭遇。来自团体健康合作健康研究中心、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：

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.