Vanderbilt Genome-Electronic Records (VGER) Project

范德比尔特基因组电子记录 (VGER) 项目

基本信息

批准号：
10659136
负责人：
DAN M RODEN
金额：
$ 136.74万
依托单位：
VANDERBILT UNIVERSITY MEDICAL CENTER
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10659136
关键词：
Academic Medical Centers Address Assessment tool Bioethics Biological Markers Biomedical Research Caring Chronic Kidney Failure Clinical Clinical Medicine Colorectal Cancer Computerized Medical Record Coronary Arteriosclerosis Coupled DNA Data Data Analytics Data Set Detection Development Discipline Disease Disease Progression Disease susceptibility Early Diagnosis Early identification Early treatment Electronic Health Record Electronic Medical Records and Genomics Network Electronics Family Family health status Focus Groups Future Genome Genomic medicine Genomics Goals Growth Health Health Personnel Healthcare Heritability Human Individual Informatics Information Sciences Intervention Knowledge Learning Link Maps Methods Modeling Modernization National Center for Advancing Translational Sciences Non-Insulin-Dependent Diabetes Mellitus Outcome Study Participant Pathogenicity Patients Persons Pharmaceutical Preparations Pharmacogenomics Phenotype Physiological Population Predisposition Prevention Prevention approach Productivity Provider Recording of previous events Records Research Resources Risk Risk Assessment Risk Management Sampling Science Site Strategic vision Subgroup Testing Underrepresented Populations United States National Institutes of Health Uterine Fibroids Validation Variant analytical method biobank care outcomes clinical decision support clinical phenotype cohort community engagement disorder risk experience genetic pedigree genetic variant genome wide association study health care service utilization high risk human old age (65+)improved innovation large datasets meetings novel patient engagement personalized approach personalized care phenome polygenic risk score precision medicine programs recruit response tool trait treatment response uptake

Academic Medical Centers Address Assessment tool Bioethics Biological Markers Biomedical Research Caring Chronic Kidney Failure Clinical Clinical Medicine Colorectal Cancer Computerized Medical Record Coronary Arteriosclerosis Coupled DNA Data Data Analytics Data Set Detection Development Discipline Disease Disease Progression Disease susceptibility Early Diagnosis Early identification Early treatment Electronic Health Record Electronic Medical Records and Genomics Network Electronics Family Family health status Focus Groups Future Genome Genomic medicine Genomics Goals Growth Health Health Personnel Healthcare Heritability Human Individual Informatics Information Sciences Intervention Knowledge Learning Link Maps Methods Modeling Modernization National Center for Advancing Translational Sciences Non-Insulin-Dependent Diabetes Mellitus Outcome Study Participant Pathogenicity Patients Persons Pharmaceutical Preparations Pharmacogenomics Phenotype Physiological Population Predisposition Prevention Prevention approach Productivity Provider Recording of previous events Records Research Resources Risk Risk Assessment Risk Management Sampling Science Site Strategic vision Subgroup Testing Underrepresented Populations United States National Institutes of Health Uterine Fibroids Validation Variant analytical method biobank care outcomes clinical decision support clinical phenotype cohort community engagement disorder risk experience genetic pedigree genetic variant genome wide association study health care service utilization high risk human old age (65+)improved innovation large datasets meetings novel patient engagement personalized approach personalized care phenome polygenic risk score precision medicine programs recruit response tool trait treatment response uptake

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项目摘要

The concept of Precision Medicine builds on the age-old understanding that humans vary in their disease susceptibility, presentation, progression, and therapeutic response. Modern large data analytics reveal that most of us have “average” susceptibility for most diseases, but each of us has high risk for a few. This finding provides the opportunity and challenge – addressed by this eMERGE Genomic Risk Assessment and Management (eMERGEgram) initiative – to identify people at high risk for common diseases to promote prevention or early treatment. We propose here a program that builds on over a decade of growth and knowledge in key enabling disciplines including informatics, genomics, bioethics, participant and community engagement, and clinical medicine, and of experience as productive participants in eMERGE since the network's inception. In Specific Aim 1, we will develop and validate Genomic Risk Assessment tools to identify people at high risk for common diseases. The Genomic Risk Assessments will incorporate polygenic risk scores, family health history, and clinical disease predictors. We propose that the eMERGEgram Steering Committee select diseases that are heritable; display variable impact across ancestries; are associated with available early detection, prevention or treatment interventions; and in which large multi-ancestry genome wide association studies are available to develop polygenic risk scores. Using these criteria, we present data that support a focus on coronary artery disease, chronic kidney disease, type 2 diabetes, uterine fibroids, and colorectal cancer. In Specific Aim 2, we will build on experience in eMERGE-3, All of Us, and our NIH-supported Recruitment Innovation Center to execute a program that will engage, recruit, and retain 2,500 subjects (>35% from populations under-represented in biomedical research), including family dyads or trios. We will compute Genomic Risk Assessments for network- selected target conditions; return results to participants, their healthcare providers, and their electronic health records; and track healthcare outcomes including disease detection or healthcare utilization and deliver these to the Coordinating Center. In Specific Aim 3, we will use the eMERGEgram experience to improve our ability to deliver Genomic Risk Assessment. We will assess the uptake and impact of Genomic Risk Assessments and the extent to which the components of the Genomic Risk Assessment provide independent information across populations and within subgroups. We will use a shared DNA segment map across >100,000 records in our biobank to develop the concept of a genetically-informed family history, a tool that can inform health risk in the absence of a large pedigree, as is the case for small or adoptive families. Additional research goals will be driven by participants, developed through community engagement by focus groups in Years 1-2 and highly intentional participant interaction throughout the project.

精确医学的概念是基于人类疾病有所不同的古老的理解敏感性，表现，进展和热反应。现代大数据分析表明，大多数我们中的大多数疾病都具有“平均”敏感性，但是我们每个人都有少数疾病的风险。这一发现提供了机会和挑战 - 由这种出现的基因组风险评估和管理解决（出版物）主动性 - 确定普通疾病高风险的人以促进预防或早期治疗。我们在这里提出一个计划，该计划以十多年的增长和知识为基础学科包括信息信息，基因组学，生物伦理学，参与者和社区参与以及临床自网络成立以来，医学和作为产品参与者的经验。具体 AIM 1，我们将开发和验证基因组风险评估工具，以识别具有共同风险的人疾病。基因组风险评估将包括多基因风险评分，家庭健康病史和临床疾病预测因子。我们建议出现的指导委员会选择的疾病遗传;在祖先展示可变影响；与可用的早期检测，预防或治疗干预措施；在其中可以使用大量多功能基因组的研究研究发展多基因风险评分。使用这些标准，我们提供支持关注冠状动脉的数据疾病，慢性肾脏疾病，2型糖尿病，子宫纤维和大肠癌。在特定的目标2中，我们我们所有人都将基于Emerge-3的经验以及我们的NIH支持的招聘创新中心执行将参与，招募和保留2,500名受试者的计划（人口不足的人口> 35％在生物医学研究中），包括家庭二元组或三重奏。我们将计算网络基因组风险评估 - 选定的目标条件；退回给参与者，其医疗保健提供者及其电子健康的结果记录；并跟踪医疗保健结果，包括疾病检测或医疗保健利用，并将其交付给协调中心。在特定目标3中，我们将利用出现的经验来提高我们的能力提供基因组风险评估。我们将评估基因组风险评估的吸收和影响基因组风险评估的组成部分在多大程度上提供了跨越的独立信息种群和亚组内。我们将在我们的> 100,000个记录中使用共享的DNA段图生物库发展了一般信息的家族史的概念，该工具可以为健康风险提供信息小家族或适应性家庭的情况也没有大的谱系。其他研究目标将是由参与者驱动，通过焦点小组在1 - 2年内通过社区参与而发展，高度在整个项目中有意参与互动。