Vanderbilt Genome-Electronic Records (VGER) Project

范德比尔特基因组电子记录 (VGER) 项目

• 批准号: 10207727
• 负责人: DAN M RODEN
• 金额: $ 144.81万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 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/10207727
• 关键词: Academic Medical Centers; Address; Assessment tool; Bioethics; Biological Markers; Biomedical Research; Caring; Chronic Kidney Failure; Clinical; Clinical Medicine; Colorectal Cancer; Communities; 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; Family; Family health status; Focus Groups; Foundations; Future; Genome; Genomic medicine; Genomics; Goals; Growth; Health; Health Personnel; Healthcare; Heritability; Human; Individual; Informatics; Information Sciences; Intervention; Knowledge; Link; Maps; Methods; Modeling; Modernization; Non-Insulin-Dependent Diabetes Mellitus; Outcome Study; Participant; Pathogenicity; Patients; Persons; Pharmaceutical Preparations; Pharmacogenomics; Phenotype; Physiological; Population; Predisposition; Prevention; Prevention approach; Provider; Recording of previous events; Records; Research; Resources; Risk; Risk Assessment; Risk Management; Sampling; Science; Site; Subgroup; Testing; Underrepresented Populations; United States National Institutes of Health; Uterine Fibroids; Validation; Variant; Vision; 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

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.

精准医学的概念建立在人类疾病各不相同的古老理解之上 易感性、表现、进展和治疗反应。现代大数据分析表明， 我们中的大多数人对大多数疾病具有“平均”易感性，但我们每个人对少数疾病具有高风险。这一发现提供 机遇和挑战--通过eMERGE基因组风险评估和管理解决 （eMERGEgram）倡议-识别易患常见疾病的高危人群，以促进预防或及早 治疗我们在这里提出一个计划，建立在十多年的增长和知识的关键使能 学科包括信息学，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，作为eMERGE的富有成效的参与者，在特定 目标1，我们将开发和验证基因组风险评估工具，以识别高风险人群， 疾病基因组风险评估将包括多基因风险评分、家族健康史和 临床疾病预测因子。我们建议eMERGEGram指导委员会选择以下疾病： 遗传性;在不同祖先中显示出不同的影响;与现有的早期检测、预防或 治疗干预;其中大型多祖先全基因组关联研究可用于 制定多基因风险评分。使用这些标准，我们提供的数据支持对冠状动脉的关注 糖尿病、慢性肾病、2型糖尿病、子宫肌瘤和结直肠癌。在具体目标2中， 我们将利用eMERGE-3、All of Us和我们NIH支持的招募创新中心的经验， 执行一项计划，将吸引、招募和保留2,500名受试者（>35%来自代表性不足的人群 在生物医学研究中），包括家庭二人组或三人组。我们将计算网络的基因组风险评估- 选定的目标条件;将结果返回给参与者、其医疗保健提供者及其电子健康 记录;并跟踪医疗保健结果，包括疾病检测或医疗保健利用，并将其提供给 协调中心。在具体目标3中，我们将使用eMERGEgram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独立的信息， 人口和亚组内。我们将使用一个共享的DNA片段地图，在我们的 生物库，以发展遗传信息家族史的概念，这是一种可以告知健康风险的工具， 没有大的血统，就像小家庭或收养家庭一样。其他研究目标将是 由参与者驱动，通过第1-2年焦点小组的社区参与开发， 参与者在整个项目中的互动。