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-l) 网络第一阶段的五个站点之一。 VGER 团队为 eMERGE-l 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上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返还活动；制定解决数据访问和个人隐私之间紧张关系的方法；并为该领域开发新的软件工具，包括去识别和全表组关联研究范式（“PheWAS”）。范德比尔特大学已将个性化医疗保健确定为整个机构投资基本发现、转化和实施的关键战略重点；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，其中包括超过 170 万名患者的数据； BioVU，DNA 存储库，可将超过 100,000 个 DNA 样本链接到 EMR 的去识别化图像； PREDICT 项目创建了一个评估基因型-表型关系的框架，并将临床上可行的基因型存入 EMR。 BioVU 目前包含 5,186 个带有 GWAS 数据的样本，预计到 2011 年秋季将超过 16,500 个。目前作为 eMERGE-ll 站点参与的提议建立在这一成就记录和机构投资的基础上。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) 加速从电子病历中提取表型的算法的开发和验证。 (2) 利用 BioVU 和其他数据集中的 GWAS 结果来识别高度预测疾病或药物反应结果的基因型组合。 (3) 在 PREDICT 中实施前瞻性临床基因分型时吸引患者参与。 (4) 开发新工具，最大限度地提高我们有效共享基因组信息并确保患者机密的能力。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推动医疗保健，而 VGER 和 eMERGE-ll 代表了朝这个方向迈出的重要一步。 相关性：对遗传变异如何决定疾病易感性或药物反应等临床重要状况的变异性的描述代表了人类基因组计划的第一个成果。该提案所解决的一个挑战是如何分析和使用大量信息来改善人类健康。 我们加入 eMERGE-ll 网络的建议通过识别对人类健康重要的遗传变异并开始在个性化医疗保健系统方法中使用这些变异来应对这一挑战，该系统方法在面对临床相关数据的数量和复杂性不断增加时具有稳健性和可扩展性。

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.