EHR-based Genomic Risk Assessment and Management for Diverse Populations

基于 EHR 的不同人群基因组风险评估和管理

• 批准号: 10201799
• 负责人: Wendy K Chung
• 金额: $ 12.13万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 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 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10201799
• 关键词: Address; Adopted; Adoption; All of Us Research Program; Behavior; Biomedical Research; Chronic Disease; Clinical; Clinical Data; Clinical Management; Clinical Research; Collaborations; Colon Carcinoma; Communication; Communities; Complex; Coronary Arteriosclerosis; Cost Analysis; Data; Development; Diagnostic; Disease; Education; Electronic Health Record; Electronic Medical Records and Genomics Network; Engineering; Ensure; Ethnic group; European; Extensible Markup Language; Family; Focus Groups; Funding; Genetic; Genetic Risk; Genetic Structures; Genomic medicine; Genomics; Goals; Health; Health Status; Hospitals; Individual; Informatics; Institutional Review Boards; Kidney; Knowledge; Link; Measures; Medical Genetics; Medical center; Methods; Natural Language Processing; New York City; Participant; Patient Preferences; Patient Recruitments; Patients; Performance; Phenotype; Population Heterogeneity; Positioning Attribute; Precision Medicine Initiative; Prevention strategy; Primary Prevention; Provider; Public Health; Randomized Controlled Trials; Recommendation; Recording of previous events; Reporting; Reproducibility; Research; Risk; Risk Assessment; Risk Estimate; Risk Factors; Risk Management; Stratification; Systems Biology; Technology; Testing; Text; Translational Research; Universities; Variant; Washington; base; clinical research site; clinical risk; cost effectiveness; data modeling; design; discrete data; diverse data; ethnic diversity; experience; genetic risk assessment; genetic testing; genetic variant; genome wide association study; genome-wide; genomic data; health disparity; high risk; improved; individual patient; interoperability; literacy; malignant breast neoplasm; mathematical ability; medical specialties; medically underserved; member; patient oriented; polygenic risk score; portability; precision medicine; programs; prospective; public health relevance; racial and ethnic; racial diversity; rare variant; recruit; risk perception; screening; socioeconomics; structural genomics; tailored health care; tool; trait; trial design; user centered design; validation studies

PROJECT SUMMARY/ABSTRACT Recently, large-scale genome-wide association studies (GWAS) provide evidence for a substantial polygenic contribution to the risk of many common complex diseases. However, most of these studies were performed in Europeans, and new data and methods are necessary to tailor polygenic risk prediction to non-Europeans, to ensure that genomic stratification does not further exacerbate health disparities. The overarching goal of the eMERGE-IV network is to leverage genetic and electronic health record (EHR) data for diverse populations to design, validate and test the clinical utility of ancestry-tailored polygenic risk scores for common diseases. As a current member of the eMERGE network, Columbia University has significantly advanced its goals, having recruited over 2,500 diverse patients for sequencing and return of actionable findings, leading the effort to transition the network to the OMOP Common Data Model to improve the efficiency, accuracy, reproducibility and portability of electronic phenotypes, and contributing a widely-adopted XML parser for structuring genetic test reports. Since our last application, the Columbia Precision Medicine Initiative has also grown and now includes participation in several national initiatives, such as the All-of-Us program, in which we have demonstrated our ability to rapidly recruit patients under-represented in biomedical research. Our scientific expertise combined with our strong tradition of patient-centered research and community engagement in a socioeconomically, racially, and ethnically diverse community of Northern Manhattan, positions us to successfully contribute as the Enhanced Diversity Clinical Site of the eEMERGE-IV network. We will leverage our prior experience with eMERGE, scientific expertise, and knowledge gained from participation in other national precision medicine initiatives to develop, optimize, validate and disseminate ancestry-tailored genomic risk assessment and clinical management tools. In Aim 1, we will continue to advance electronic phenotyping by contributing sharable natural language processing tools for converting clinical text into OMOP-based discrete data and facilitating phenotype interoperability. In Aim 2, we will develop and optimize accurate ancestry-tailored genome-wide polygenic predictors, integrate them with clinical risk predictions, and test their performance in diverse populations. In Aim 3, we will investigate ELSI issues related to the return of health risk predictions to diverse patients by ascertaining patients’, clinicians’, and IRB members’ views through focus groups. In Aim 4, we will develop portable EHR plug-ins to facilitate prospective risk communication and management using integrated genomic data, family history, and clinical data. In Aim 5, we will recruit 2,500 diverse patients and use a randomized controlled trial design to assess the impact of return of genomic prediction on the accuracy of risk perception, health surveillance, and risk reducing measures. This proposal will address major knowledge gaps in genetic risk assessment for diverse populations, and the solutions and knowledge gained will be broadly applicable to precision medicine for common complex traits across many clinical specialties.

项目总结/摘要 最近，大规模的全基因组关联研究（GWAS）提供了一个实质性的多基因的证据， 导致许多常见复杂疾病的风险。然而，这些研究中的大多数都是在 欧洲人，新的数据和方法是必要的，以适应多基因风险预测非欧洲人， 确保基因组分层不会进一步加剧健康差距。的首要目标 eMERGE-IV网络将利用不同人群的遗传和电子健康记录（EHR）数据， 设计、验证和测试针对常见疾病的祖先定制的多基因风险评分的临床效用。作为 作为eMERGE网络的现任成员，哥伦比亚大学大大推进了其目标， 招募了2,500多名不同的患者进行测序，并返回可操作的发现， 将网络过渡到OMOP通用数据模型，以提高效率、准确性、可重复性和 电子表型的可移植性，并提供了一个广泛采用的XML解析器，用于结构化遗传测试 报道自我们上次申请以来，哥伦比亚精准医学计划也在不断发展，现在包括 参与了几项国家倡议，例如我们所有人的计划，我们在其中展示了我们的 快速招募在生物医学研究中代表性不足的患者的能力。我们的科学专长结合了 凭借我们以患者为中心的研究和社区参与社会经济的悠久传统， 种族和种族多元化的社区北方曼哈顿，使我们能够成功地作出贡献， eEMERGE-IV网络的增强多样性临床站点。我们将利用我们以前的经验， eMERGE、科学专业知识和参与其他国家精准医疗所获得的知识 制定、优化、验证和传播针对祖先的基因组风险评估和临床 管理工具。在目标1中，我们将继续通过贡献可共享的自然 用于将临床文本转换为基于OMOP的离散数据并促进表型的语言处理工具 互用性在目标2中，我们将开发和优化精确的祖先定制的全基因组多基因 预测因子，将其与临床风险预测相结合，并在不同人群中测试其性能。在Aim中 3，我们将调查ELSI问题有关的健康风险预测的回报，以不同的病人，通过确定 通过焦点小组收集患者、临床医生和IRB成员的意见。在目标4中，我们将开发便携式EHR 使用集成基因组数据、家族、 历史和临床数据。在目标5中，我们将招募2,500名不同的患者，并使用随机对照试验 设计以评估基因组预测的回报对风险感知的准确性、健康 监测和降低风险的措施。该提案将解决遗传风险方面的主要知识空白 对不同人群的评估，以及获得的解决方案和知识将广泛适用于 针对许多临床专业常见复杂特征的精准医学。