Genetic Discovery and Application in a Clinical Setting Continuing a Partnership

描述（由申请人提供）：西雅图涌现项目旨在利用广泛的电子医疗记录（EMR）和团体健康合作社（GH）的生物储存库，包括33年的药房数据库和老龄化人口的纵向数据，将个人基因组学带到实践环境中。在eMERGE I中开发的算法将用于将全基因组关联研究与从emr中挖掘的表型相结合，以发现新的多态性-表型关系。目标表型是传染病易感性，特别是对艰难梭菌腹泻、水痘带状疱疹病毒引起的带状疱疹和真菌指甲感染，对降压药、血清素特异性再摄取抑制剂和他汀类药物的反应，包括不良事件。一种新的算法将跟踪纵向血糖和红细胞压积轨迹，一种新的自动化方法将检测核型异常，以评估与骨髓增生异常和白血病的相关性。数据也将支持在其他eMERGE站点调查的表型。为了创建一个将基因组学引入临床实践的模型，eMERGE I的成功需求评估方法将使利益相关者参与指导临床决策支持格式的原型EMR用户界面的开发。测试案例将是针对药物不良反应的人类白细胞抗原分型，环境将是GH开发的以患者为中心的医疗家庭护理模式。该提案为eMERGE网络及其合作者提供了西雅图团队在使用自然语言处理（NLP）从emr中提取信息方面的独特专业知识，并协助采用NLP方法。为了传播eMERGE的成果并促进合作，它利用了研究人员的领导地位，包括eMERGE内部的合作伙伴、其他联盟和HMO研究网络，特别是NIH主任共同基金在生物银行和大流行病学方面支持的发展潜力。这些目标的完成将揭示新的医学上有用的标记，改进高通量基因组方法与电子病历数据的联系，并制定政策和做法，将个体化循证医学带到社区。

DESCRIPTION (provided by applicant): The Seattle eMERGE project aims to bring personal genomics to practice settings by taking advantage of the extensive electronic medical record (EMR) and biorepository of Group Health Cooperative (GH), including a 33-year pharmacy database and longitudinal data on an aging population. Algorithms developed in eMERGE I will be used to combine genome-wide association studies with phenotypes mined from EMRs to discover new polymorphism-phenotype relationships. Target phenotypes are infectious disease susceptibility, specifically to Clostridium difficile diarrhea, shingles from varicella zoster virus, and fungal nail infection, responses to antihypertensive drugs, serotonin-specific reuptake inhibitors, and statins, including adverse events. A new algorithm will follow longitudinal glycemia and hematocrit trajectories, and a novel automated method will detect karyotype abnormalities for assessing correlation to myelodysplasia and leukemia. Data will also support phenotypes investigated at other eMERGE sites. To create a model for introducing genomics into clinical practice, successful needs assessment methods from eMERGE I will engage stakeholders in guiding development of prototype EMR user interfaces in a clinical decision support format. The test case will be human leukocyte antigen-typing for an adverse drug reaction and the setting will be the patient-centered medical home care model developed at GH. This proposal provides the eMERGE network and its collaborators with the Seattle team's unique expertise in using natural language processing (NLP) to extract information from EMRs, and assisting in adoption of NLP methods. To disseminate eMERGE results and foster collaborations, it takes advantage of leadership positions of the investigators, including partners within eMERGE, other consortia and the HMO Research network, especially the potential for developments supported by the NIH Director's Common Fund in biobanking and megaepidemiology. Completion of the aims will reveal new, medically useful markers, improve the linking of high-throughput genomic methods to EMR data, and develop policies and practices for bringing individualized evidence-based medicine to communities. RELEVANCE (See instrucfions): To advance personalized medicine-treatment and preventive care based on individual traits; this project matches small differences in DNA to infectious disease susceptibility and response to statins, serotonin- specific reuptake inhibitors (SSRIs) and blood pressure medications. Methods to use these results in clinical care will be guided by focus groups of patients and caregivers in the patient-centered Group Health system.