Genomic Approaches to Population Health in Multi-Ethnic Hospital Systems

多民族医院系统中人口健康的基因组方法

基本信息

批准号：
10676210
负责人：
Valerie A Arboleda
金额：
$ 76.86万
依托单位：
UNIVERSITY OF COLORADO DENVER
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：
2020-09-16 至 2024-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10676210
关键词：
Address Adoption African American population Architecture Asthma Automobile Driving Biological Markers Categories Chronic Disease Clinical Complex Computerized Medical Record Coupled Data Databases Decision Making Detection Diabetes Mellitus Diagnosis Diagnostic Discipline Disease Disease Outcome Electronic Health Record Engineering Ensure Ethnic Origin Ethnic Population European Evaluation Family history of Fostering Gene Frequency Genetic Genetic Databases Genomic approach Genomic medicine Genomics Genotype Goals Health Health system Healthcare Healthcare Systems Hospitals Hypertension Hypertrophic Cardiomyopathy Individual Institution International Intervention Investigation Investments Knowledge Link Measures Medical Medicine Methods Modeling Modernization Monitor Outcome Pathogenicity Patient Care Patients Pattern Penetrance Performance Persons Phenotype Policy Making Population Population Genetics Population Heterogeneity Prevalence Race Rare Diseases Research Risk Risk Assessment Science Statistical Methods Stevens-Johnson Syndrome System Taiwan Technology Testing Therapeutic Translating Translational Research Variant Work biobank burden of illness clinical care clinical database clinical phenotype clinical practice clinical risk cohort disorder prevention disorder risk ethnic minority population genetic information genetic risk factor genome-wide genomic data health care service utilization health disparity human disease identity by descent implementation barriers improved interdisciplinary collaboration multi-ethnic new technology novel patient population polygenic risk score population based population health portability precision medicine racial population rare genetic disorder rare mendelian disorder repository success tool trait whole genome

项目摘要

Project Summary Genomic medicine is a rapidly emerging medical discipline that incorporates the use of genomic information in patient care. Understanding an individual's genetic information holds the potential to improve diagnostic and therapeutic decision-making in clinical care, impact health outcomes and inform policy making. Yet the genomic datasets driving these decisions are often focused on populations of European descent. When these limited discoveries drive genomic medicine, understudied groups are frequently the last to benefit from advances in research, technology and clinical best practices. For true adoption, precision medicine needs to account for genomic diversity inherent to modern health systems. To address the importance of understanding disease risk in fine-scale populations present in modern health systems, and foster opportunities for advancement of genomic medicine in diverse populations, we have assembled a multi-ethnic cohort of over one million genotyped individuals from five international biobanks in health systems linked to electronic medical records. Leveraging this unique research cohort from our institutes, we will engineer fine-scale population detection and monitoring for population health powered by novel statistical and population genetics methods. These in turn can help us understand disease prevalence and refine our understanding of clinical variant pathogenicity. The systems we develop within hospitals will help characterize risk profiles for both rare (via Phenotype Scores) and common (via Polygenic Scores) traits, a necessary step to work in realistic, modern multi-ethnic hospital settings. These goals are implemented through three specific aims: Aim 1: Implement a monitoring system for differences in disease burden between fine-scale populations defined via identity-by-descent (IBD) inferred from genome-wide data across multiple biobanks. In so doing, we will apply a high-throughput, portable method to improve fine-scale ancestry and use it to improve disease and trait monitoring across multiple health systems. Aim 2: Improve our characterization of clinical variant pathogenicity, penetrance and expressivity via improved allele frequency examination through the fine-scale populations determined in Aim 1. Aim 3: Model risk via improved phenotype risk score (PheRS) for rare disease and polygenic risk score (PRS) for common traits across the fine-scale populations determined in Aim 1. We will develop improved trans- ethnic risk models and demonstrate their utility in improving our population-based understanding of disease outcomes. Our long-collaborating interdisciplinary team including clinical, statistical, and population geneticists has already produced preliminary data demonstrating not only a high likelihood of success, but also a desire and capacity to translate results into implemented changes in clinical care. This project will drive a new understanding of human disease, as well as opportunities for new health care interventions, particularly for currently understudied ethnic minority populations, thereby improving precision medicine for all.

项目概要基因组医学是一门快速新兴的医学学科，它将基因组信息的使用融入到病人护理。了解个体的遗传信息有可能改善诊断和治疗临床护理中的治疗决策影响健康结果并为政策制定提供信息。然而推动这些决策的基因组数据集通常关注欧洲血统人群。当这些有限的发现推动了基因组医学的发展，而未被研究的群体往往是最后受益的群体研究、技术和临床最佳实践的进步。为了真正采用，精准医疗需要解释现代卫生系统固有的基因组多样性。解决了解现代小规模人群疾病风险的重要性卫生系统，并为不同人群的基因组医学发展创造机会，我们有汇集了来自五个国际生物库的超过 100 万基因分型个体的多种族队列与电子病历相关的卫生系统。利用我们研究所独特的研究团队，我们将设计由新颖技术支持的精细人口检测和人口健康监测统计和群体遗传学方法。这些反过来又可以帮助我们了解疾病的流行情况和完善我们对临床变异致病性的理解。我们在医院内开发的系统将有助于描述罕见（通过表型评分）和常见（通过多基因评分）性状的风险概况，在现实的、现代的多民族医院环境中工作的必要步骤。这些目标得到落实通过三个具体目标：目标 1：建立小规模人群疾病负担差异监测系统通过从多个生物库的全基因组数据推断出的血统身份（IBD）来定义。这样做，我们将应用高通量、便携式方法来改善精细祖先，并用它来改善疾病和跨多个卫生系统的特征监测。目标 2：通过改进临床变异致病性、外显率和表达性的表征通过目标 1 中确定的精细规模群体进行等位基因频率检查。目标 3：通过改进罕见疾病表型风险评分 (PheRS) 和多基因风险评分 (PRS) 建立风险模型对于目标 1 中确定的精细规模群体的共同特征。我们将开发改进的反式种族风险模型并展示其在提高我们对疾病的基于人群的理解方面的效用结果。我们的长期合作跨学科团队包括临床、统计和群体遗传学家已经产生了初步数据，不仅表明成功的可能性很高，而且还表明了一种愿望和将结果转化为临床护理实施变革的能力。该项目将带动新对人类疾病的了解，以及新的卫生保健干预措施的机会，特别是对于目前对少数民族人口的研究不足，从而改善所有人的精准医疗。