Genomic Approaches to Population Health in Multi-Ethnic Hospital Systems

项目摘要 基因组医学是一门迅速崛起的医学学科，它结合了基因组信息在 病人护理。了解个人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， 推动这些决定的基因组数据集通常专注于欧洲血统的人群。当这些 有限的发现推动了基因组医学，未被研究的群体往往是最后受益的 研究、技术和临床最佳实践方面的进展。为了真正被采用，精准医疗需要 考虑到现代卫生系统固有的基因组多样性。 为了解决在现代社会中了解细微人群中疾病风险的重要性 卫生系统，并促进在不同人群中推进基因组医学的机会，我们有 汇集了来自五个国际生物库的100多万名基因分型个人的多种族队列 与电子病历链接的医疗系统。利用我们研究所的这一独特的研究队列， 我们将为人口健康设计精细的人口检测和监测，由NOVICE提供支持 统计学和群体遗传学方法。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所帮助 描述罕见(通过表型评分)和常见(通过多基因评分)特征的风险概况，a 在现实的、现代化的多民族医院环境中工作的必要步骤。这些目标得到了落实 通过三个具体目标： 目标1：实施小规模人群疾病负担差异监测系统 通过从跨多个生物库的全基因组数据推断的按血统身份(IBD)定义。在此过程中，我们 将应用一种高通量、便携的方法来改进精细的祖先，并用它来改善疾病和 跨多个卫生系统的特征监测。 目标2：通过改进改进我们的临床变异致病性、外显性和表现性的特征 通过目标1中确定的精细群体进行等位基因频率检查。 目的3：通过改进的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中确定的细小群体的共同性状，我们将开发改进的反式- 种族风险模型，并展示了它们在改善我们对疾病的基于人群的理解方面的效用 结果。 我们长期合作的跨学科团队，包括临床、统计学和人口遗传学家， 已经产生的初步数据不仅表明成功的可能性很高，而且还有愿望和 将成果转化为临床护理实施变革的能力。这个项目将推动一个新的 了解人类疾病以及新的保健干预措施的机会，特别是 目前对少数民族人口的研究不足，从而改善了所有人的精准医疗。

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.