Vanderbilt Genome Electronic Records Project

描述（由申请人提供）：范德比尔特基因组-电子记录项目是电子病历和基因组学（eMERGE-1）网络第一阶段的五个研究中心之一。VGER团队为eMERGE-I多个领域的进展做出了重要贡献，包括在范德比尔特和整个网络中开发和部署表型算法;开发和管理网络的基因型质量控制管道;在范德比尔特和跨网络数据集中发现新的基因型-表型关系;积极参与社区咨询和结果回报计划;开发解决数据访问和个人隐私之间的紧张关系的方法;以及为该领域开发新的软件工具，包括去识别和全表型关联研究范式（"PheWAS"）。范德比尔特已将个性化医疗保健确定为在整个机构投资基础发现、转化和实施的关键战略优先事项;范德比尔特开发的资源（由VGER团队成员指导）包括StarChart，这是一种电子病历（EMR）系统，具有全面的患者特定临床决策支持功能，包括超过170万患者的数据; BioVU是一个DNA储存库，将超过100，000个DNA样本与EMR的去识别图像联系起来; PREDICT项目创建了一个评估基因型-表型关系的框架，并将临床可操作的基因型存入EMR。BioVU目前包括5,186个具有GWAS数据的样本，预计到2011年秋季将超过16,500个。目前关于作为一个地点参加eMERGE-II的提议是以这一成就记录和机构投资为基础的。我们提出了4个具体目标，这些目标将通过我们网站和整个网络中具有不同学科（临床医学，基础科学，基因组学，统计学，信息学，隐私科学和伦理学）专业知识的科学家之间的合作来实现：（1）加速开发和验证从EMR中提取表型的算法。(2)利用BioVU和其他数据集中的GWAS结果，以识别高度预测疾病或药物反应结局的基因型组合。(3)在PREDICT中实施前瞻性临床基因分型时，让患者参与。(4)开发新的工具，以最大限度地提高我们有效共享基因组信息的能力，并确保患者的保密性。我们赞同个性化医疗的愿景，其中基因组和其他患者特定信息驱动医疗保健，VGER和eMERGE-II代表了该方向的重要步骤。 相关性：关于遗传变异如何决定疾病易感性或药物反应等临床重要条件的变异性的描述代表了人类基因组计划的第一批成果。这项提案所面临的一个挑战是如何分析和利用这一信息洪流来改善人类健康。 我们加入eMERGE-II网络的提议通过识别对人类健康重要的遗传变异来应对这一挑战，并开始将这些遗传变异用于个性化医疗保健的系统方法中，该方法在临床相关数据的数量和复杂性不断增加的情况下是稳健和可扩展的。

DESCRIPTION (provided by applicant): The Vanderbilt Genome-Electronic Records project was one of five sites in the first phase of the Electronic Medical Records and Genomics (eMERGE-l) network. The VGER team contributed importantly to progress in multiple areas across eMERGE-l, including developing and deploying algorithms for phenotypes at Vanderbilt and across the network; developing and managing the genotype quality control pipeline for the network; discovering new genotype-phenotype relations in Vanderbilt and cross-network datasets; actively participating in the community consultation and return of results initiatives; developing methods to resolve tensions between data access and individual privacy; and developing new software tools for the field, including for de-identification and for the phenome-wide association study paradigm ("PheWAS"). Vanderbilt has identified Personalized Healthcare as a key strategic priority for investment in basic discovery, translation, and implementation across the institution; resources developed at Vanderbilt (directed by VGER team members) include StarChart, an electronic medical record (EMR) system with comprehensive patient-specific clinical decision support functionality that includes data on >1.7 million patients; BioVU, the DNA repository that links >100,000 DNA samples to a de-identified image of the EMR; and the PREDICT project which has created a framework for evaluating genotype-phenotype relations and is depositing clinically actionable genotypes into the EMR. BioVU currently includes 5,186 samples with GWAS data, projected to >16,500 by fall 2011. The present proposal to participate as a site in eMERGE-ll builds on this record of accomplishment and on institutional investments. We propose 4 specific aims that will be accomplished by collaborations among scientists with expertise in diverse disciplines (clinical medicine, basic science, genomics, statistics, informatics, privacy science, and ethics) at our site and across the network: (1) to accelerate development and validation of algorithms for phenotype extraction from EMRs. (2) To exploit the results of GWAS in BioVU and other datasets to identify combinations of genotypes highly predictive of disease or drug response outcomes. (3) To engage patients as we implement prospective clinical genotyping in PREDICT. (4) To develop new tools to maximize our ability to effectively share genomic information and ensure patient confidentiality. We subscribe to a vision of Personalized Medicine in which genomic and other patient-specific information drives healthcare, and VGER and eMERGE-ll represent important steps in that direction. RELEVANCE: Descriptions of how genetic variation determines variability in clinically important conditions like disease susceptibility or drug responses represent the first fruits of the Human Genome Project. A challenge - that this proposal addresses - is how to analyze and use this torrent of information to improve human health. Our proposal to join the eMERGE-ll network addresses this challenge by identifying genetic variants important for human health and beginning to use these in a systems approach to personalized healthcare that is robust and scalable in the face of the escalating volume and complexity of clinically relevant data.