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: 9894963
• 负责人: Joshua C. Denny
• 金额: $ 70.42万
• 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 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9894963
• 关键词: Academic Medical Centers; Address; Affect; Area; Back; Case-Control Studies; Clinical; Code; Communities; Complex; Computerized Medical Record; Coupled; Coupling; DNA; DNA Resequencing; Data; Data Security; Data Set; Disease; Disease Progression; Disease susceptibility; Electronic Medical Records and Genomics Network; Ensure; Genes; Genetic; Genetic Diseases; Genome; Genomic medicine; Genomics; Genotype; Health; Healthcare; Healthcare Systems; Human; Individual; Investments; Knowledge; Learning; Libraries; Measures; Medical Records; Methodological Studies; Methods; Mining; Mission; Modernization; Outcome; Patient Care; Patients; Pharmaceutical Preparations; Pharmacogenomics; Phenotype; Process; Provider; Records; Research Personnel; Resources; Sampling; Site; Strategic Planning; Syndrome; System; Time; Variant; Vision; actionable mutation; base; biobank; care delivery; clinical decision support; clinical phenotype; clinical predictors; 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; pleiotropism; point of care; programs; public health relevance; 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）：随着电子病历（EMR）在过去二十年中的引入实践，电子病历（EMR）已越来越多地被认为是一种平台，不仅可以改善对个人的护理，还可以了解疾病表现和结局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，同时也为在医疗保健中使用基因组数据提出了新的挑战和机遇。这些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;识别临床相关的常见疾病子集;识别可操作的基因组变异并确定如何在学习型医疗保健系统中最好地部署这些变异。基于我们在eMERGE-I和eMERGE-II方面的经验和贡献，我们在此提出了应对这些挑战的三个具体目标。在具体目标1中，我们将通过创建越来越细粒度的表型定义来扩展网络的表型库，这些表型定义可以识别具有可预测临床过程或对治疗反应的疾病的特定子集。将通过GWAS和我们开发的先进PheWAS方法研究基因型-表型关系。在具体目标2中，我们将通过对我们中心2,500名受试者的100个基因进行重新测序，识别与人类特征密切相关的罕见变异，作为eMERGE-III 25,000名患者队列的一部分。我们建议研究已知会影响人类健康和药物反应的变异基因，以及我们初步PheWAS分析表明是重要人类表型的强大标记的变异基因。在具体目标3中，我们将扩展我们的先发制人的药物基因组学实施计划PREDICT，以开发一个管道，为患者和提供者提供可操作的变体并评估他们的反应。我们将在eMERGE中合作开发、实施和评估工具，以提供新信息，衡量影响，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。

项目成果

Arrhythmia genetics: Not dark and lite, but 50 shades of gray.

心律失常遗传学：不是黑暗和淡雅，而是50度灰色。

• DOI: 10.1016/j.hrthm.2018.04.031
• 发表时间: 2018
• 期刊: Heart rhythm
• 影响因子: 5.5
• 作者: Roden,DanM;Glazer,AndrewM;Kroncke,Brett
• 通讯作者: Kroncke,Brett

Growing Pains in Cardiovascular Genetics.

心血管遗传学中的“成长的烦恼”。

• DOI: 10.1161/circulationaha.118.035933
• 发表时间: 2018
• 期刊: Circulation
• 影响因子: 37.8
• 作者: Roden,DanM

Using Human 'Experiments of Nature' to Predict Drug Safety Issues: An Example with PCSK9 Inhibitors.

• DOI: 10.1007/s40264-017-0616-0
• 发表时间: 2018-03
• 期刊: Drug safety
• 影响因子: 4.2
• 作者: Jerome RN;Pulley JM;Roden DM;Shirey-Rice JK;Bastarache LA;R Bernard G;B Ekstrom L;Lancaster WJ;Denny JC
• 通讯作者: Denny JC

Influence of Human Leukocyte Antigen (HLA) Alleles and Killer Cell Immunoglobulin-Like Receptors (KIR) Types on Heparin-Induced Thrombocytopenia (HIT).

• DOI: 10.1002/phar.1983
• 发表时间: 2017-09
• 期刊: Pharmacotherapy
• 影响因子: 4.1
• 作者: Karnes JH;Shaffer CM;Cronin R;Bastarache L;Gaudieri S;James I;Pavlos R;Steiner HE;Mosley JD;Mallal S;Denny JC;Phillips EJ;Roden DM
• 通讯作者: Roden DM

What Results Should Be Returned from Opportunistic Screening in Translational Research?

• DOI: 10.3390/jpm10010013
• 发表时间: 2020-03-01
• 期刊: JOURNAL OF PERSONALIZED MEDICINE
• 作者: Halverson, Colin M. E.;Jones, Sarah H.;Clayton, Ellen W.
• 通讯作者: Clayton, Ellen W.