VGER, the Vanderbilt Genome-Electronic Records Project

VGER，范德比尔特基因组电子记录项目

• 批准号: 9134824
• 负责人: Joshua C. Denny
• 金额: $ 83.84万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9134824
• 关键词: Academic Medical Centers; Address; Affect; Area; Back; Clinical; Code; Communities; Complex; Computerized Medical Record; Coupled; Coupling; DNA; DNA Resequencing; Data; Data Security; Data Set; Databases; Disease; Disease Progression; Disease susceptibility; Educational process of instructing; Electronics; Ensure; Genes; Genetic; Genome; Genomic medicine; Genomics; Genotype; Health; Healthcare; Healthcare Systems; Hereditary Disease; Human; Individual; Investments; Knowledge; Learning; Libraries; Measures; Medical Records; Methodology; Methods; Mining; Mission; Outcome; Patient Care; Patients; Pharmaceutical Preparations; Pharmacogenomics; Phenotype; Process; Provider; Records; Research Personnel; Resources; Sampling; Site; Strategic Planning; Syndrome; System; Time; Variant; Vision; Work; base; biobank; care delivery; case control; clinical phenotype; clinically relevant; cohort; cost; data sharing; disorder subtype; exome; experience; feeding; genetic association; genetic variant; genome wide association study; genomic data; human disease; implementation science; improved; individual patient; phenome; point of care; programs; quality assurance; rare variant; response; tool; trait; treatment response; variant of unknown significance

 DESCRIPTION (provided by applicant): With their introduction into practice over the last two decades, electronic medical records (EMRs) have become increasingly recognized as platforms to not only improve delivery of care to the individual but also to understand variability in domain such as disease presentation and outcomes or quality assurance. Coupling dense genomic information to EMRs in eMERGE has provided tools for both discovery and initial implementation in genomic medicine, while raising new challenges and opportunities for using genomic data in healthcare. These include developing and mining the large datasets necessary to identify groups of patients with extreme phenotypes or rare genotypes; identifying clinically-relevant subsets of common diseases; and identifying actionable genomic variants and determining how best to deploy these in a learning healthcare system. Building on our experience and contributions to eMERGE-I and eMERGE-II, we propose here three specific aims to address these challenges. In Specific Aim 1, we will expand the network's phenotyping library by creating increasingly granular phenotype definitions that identify specific subsets of disease with predictable clinical courses or response to therapies. Genotype-phenotype relations will be studied by GWAS and advanced PheWAS methodology we have developed. In Specific Aim 2, we will identify rare variants with strong associations with human traits by resequencing 100 genes in 2,500 subjects at our center as part of the eMERGE-III 25,000 patient cohort. We propose studying genes with variants known to affect human health and drug responses, and variants that our preliminary PheWAS analysis implicates as robust markers of important human phenotypes. In Specific Aim 3, we will expand PREDICT, our pre-emptive pharmacogenomic implementation program, to develop a pipeline that will deliver actionable variants to patients and providers and to assess their response. We will collaborate across eMERGE to develop, implement, and assess tools to deliver new information, measuring impact to ensure optimal benefit to patients. By executing these discovery and implementation aims, our site and the eMERGE network will contribute importantly to advancing the vision of Genomic Medicine as a contributor to modern healthcare.

 描述（由申请人提供）：随着过去二十年的实践，电子病历 (EMR) 已越来越被认为是一种平台，不仅可以改善向个人提供的护理服务，还可以了解疾病表现和结果或质量保证等领域的变化。将密集的基因组信息与 eMERGE 中的 EMR 相结合，为基因组医学的发现和初步实施提供了工具，同时为在医疗保健中使用基因组数据带来了新的挑战和机遇。其中包括开发和挖掘识别具有极端表型或罕见基因型的患者群体所需的大型数据集；识别常见疾病的临床相关子集；识别可操作的基因组变异，并确定如何最好地在学习医疗保健系统中部署这些变异。基于我们的经验和对 eMERGE-I 和 eMERGE-II 的贡献，我们在此提出三个具体目标来应对这些挑战。在具体目标 1 中，我们将通过创建越来越细化的表型定义来扩展网络的表型库，这些表型定义可识别具有可预测的临床过程或治疗反应的特定疾病子集。基因型-表型关系将通过 GWAS 和我们开发的先进 PheWAS 方法进行研究。在具体目标 2 中，我们将通过对我们中心的 2,500 名受试者（eMERGE-III 25,000 名患者队列的一部分）的 100 个基因进行重新测序，来识别与人类特征密切相关的罕见变异。我们建议研究具有已知影响人类健康和药物反应的变异的基因，以及我们的初步 PheWAS 分析表明的变异作为重要人类表型的强有力标记。在具体目标 3 中，我们将扩展 PREDICT（我们的先发制人的药物基因组实施计划），以开发一条管道，向患者和提供者提供可操作的变异，并评估他们的反应。我们将在 eMERGE 上进行合作，开发、实施和评估工具来提供新信息，衡量影响，以确保为患者带来最佳利益。通过执行这些发现和实施目标，我们的网站和 eMERGE 网络将为推进基因组医学作为现代医疗保健贡献者的愿景做出重要贡献。