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

• 批准号: 10474584
• 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
• 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/10474584
• 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 group; 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; Institutes; Institution; International; Intervention; Investigation; Investments; Knowledge; Link; Measures; Medical; Medicine; Mendelian disorder; 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; base; 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; multi-ethnic; new technology; novel; patient population; polygenic risk score; population based; population health; portability; precision medicine; racial and ethnic; rare genetic disorder; repository; success; tool; trait; whole genome

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.

项目总结