EHR-based Genomic Risk Assessment and Management for Diverse Populations

项目摘要/摘要 最近，大规模的全基因组关联研究提供了大量多基因的证据 增加了许多常见的复杂疾病的风险。然而，这些研究大多是在 欧洲人，而新的数据和方法是必要的，以适应非欧洲人的多基因风险预测，以 确保基因组分层不会进一步加剧健康差距。的首要目标是 Emerge-IV网络是利用不同人群的遗传和电子健康记录(EHR)数据来 设计、验证和测试为常见疾病量身定做的多基因风险评分的临床实用性。作为一名 哥伦比亚大学目前是Emerge网络的成员，已经显著推进了其目标， 招募了2500多名不同的患者进行测序并返回可操作的结果，领导了这一努力 将网络过渡到OMOP公共数据模型，以提高效率、准确性、重复性和 电子表型的可移植性，并为构建基因测试提供了一个广泛采用的XML解析器 报告。自从我们上一次申请以来，哥伦比亚精密医学计划也得到了发展，现在包括 参与若干国家倡议，如我们所有人方案，在该方案中，我们展示了我们的 有能力迅速招募生物医学研究中代表性不足的患者。我们的科学专长结合在一起 凭借我们以患者为中心的研究和社区参与社会经济学的强大传统， 曼哈顿北部的种族和民族多元化社区，使我们能够成功地为 增强eEMERGE-IV网络临床站点的多样性。我们将利用我们之前的经验 从参与其他国家精准医学中获得的新兴、科学专业知识和知识 开发、优化、验证和传播针对祖先定制的基因组风险评估和临床的倡议 管理工具。在目标1中，我们将继续推进电子表型，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 互操作性。在目标2中，我们将开发和优化针对精确祖先定制的全基因组多基因 将它们与临床风险预测相结合，并测试它们在不同人群中的表现。在AIM 3，我们将调查与向不同患者返回健康风险预测相关的ELSI问题，通过确定 患者、临床医生和IRB成员通过焦点小组的观点。在目标4中，我们将开发便携式电子病历 使用集成的基因组数据、家族来促进预期风险沟通和管理的插件 病史和临床数据。在目标5中，我们将招募2500名不同的患者，并使用随机对照试验 评估基因组预测回报对风险感知、健康的准确性的影响的设计 监测和降低风险的措施。这项提案将解决遗传风险方面的主要知识差距。 对不同人群的评估，以及所获得的解决办法和知识将广泛适用于 跨多个临床专科的常见复杂性状的精准医学。

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.