Genetic & Social Determinants of Health: Center for Admixture Science and Technology

遗传

• 批准号: 10599760
• 负责人: KELLY A FRAZER
• 金额: $ 21.18万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-22 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10599760
• 关键词: Admixture; Affect; African; Algorithmic Software; Algorithms; All of Us Research Program; American; American Indians; Asian population; Awareness; Behavior; Benchmarking; Biology; Cardiovascular Diseases; Cardiovascular system; Characteristics; Clinical; Complex; Data; Diagnosis; Disease model; Electronic Health Record; Environment; Ethnic Origin; Ethnic group; European; Genes; Genetic; Genetic Determinism; Genetic Variation; Genome; Genomic medicine; Genomics; Goals; Health; Health Surveys; Healthcare; Heart; Incidental Findings; Individual; Intervention; Joints; Language; Major Histocompatibility Complex; Malignant Neoplasms; Maps; Medical; Medical Genetics; Methods; Minority; Minority Groups; Modeling; Morbidity - disease rate; Native Americans; Natural Language Processing; Outcome; Participant; Pathogenicity; Persons; Phenotype; Policies; Population; Population Heterogeneity; Poverty; Privacy; Prognosis; Publishing; Race; Reporting; Research; Research Personnel; Risk; Science; Single Nucleotide Polymorphism; Site; Socioeconomic Factors; Socioeconomic Status; Source; Tandem Repeat Sequences; Techniques; Technology; United States; Variant; Veterans; algorithm development; base; cardiometabolism; cluster computing; cohort; data privacy; data sharing; design; ethnic minority population; follow-up; genetic information; genome sciences; genome wide association study; health disparity; health outcome disparity; improved; innovation; large datasets; medical schools; mortality; novel; novel strategies; polygenic risk score; prevent; programs; prospective; racial minority; repository; risk prediction; social determinants; social factors; social health determinants; socioeconomics; statistics; trait

PROJECT SUMMARY It is imperative to understand the underlying sources of the large health disparities among individuals from different racial and ethnic groups living in the United States (US). Complex relationships between genetics and social factors influence health outcomes. Approximately 33% of people in the US belong to an ethnic minority group and ~12.5% live below the federal poverty line. Historical and recent mixing of Europeans, Native Americans, Africans and Asians resulted in the US population having a relatively large number of admixed individuals who carry ancestry from outside their self-identified race. The All of Us (AoU) Program and the Million Veterans Program (MVP) include genetic, health and socioeconomic information on all participants, and therefore provide an opportunity to identify factors contributing to health disparities. However, the AoU program and MVP require their data to stay within local hosting sites, therefore conducting joint analyses on these cohorts requires the development of algorithms that enable privacy-protecting distributed computing (i.e., without revealing individual-level data). There are three important gaps in understanding genetic determinants of health: 1) most studies have been dominated by European individuals, and while they control for global ancestry, there is no attempt to model the patchwork of local ancestry characteristic of admixed individuals; 2) GWAS are primarily conducted using SNPs, while important sources of ancestry-specific genetic variation (tandem repeats (TRs) and the major histocompatibility complex (MHC) interval) are not assayed; and 3) most GWAS do not adjust for socioeconomic factors. The American College of Medical Genetics and Genomics (ACMG) has published a list of medically actionable cancer and cardiovascular genes recommended for return of incidental findings of pathogenic variants to reduce morbidity and mortality, but having minorities excluded from healthcare follow up due to common barriers (e.g., language and access) makes it difficult to distinguish between the genetic and socioeconomic factors that contribute to disparate health outcomes. The goal of the CAST (Center for Admixture Science and Technology) program is to improve the clinical utility of genetic information for all populations living in the US. In Aim 1, we will develop and apply multivariate models of disease risk prediction that incorporate local ancestry, complex variants (TRs and HLA types). In Aim 2, we will conduct scalable distributed computing using data from millions of individuals across the AoU and MVP compute enclaves. In Aim 3, we will develop new approaches to characterize phenotypes using electronic health records and surveys from AoU and MVP, assess the impact of including social determinants of health in our models, and prospectively evaluate them with new AoU and MVP participants. To achieve these goals, we assembled a highly interdisciplinary group of researchers with expertise in Genetics, Genome Biology, Data Sharing Policy and Technology, Health Disparities, Phenotyping, and Statistics.

项目概要 必须了解不同种族之间巨大健康差异的根本原因 居住在美国 (US) 的不同种族和族裔群体。遗传与基因之间的复杂关系 社会因素影响健康结果。大约 33% 的美国人属于少数族裔 约 12.5% 的人生活在联邦贫困线以下。历史上和近代欧洲人、本土人的混合 美国人、非洲人和亚洲人导致美国人口中混血数量较多 具有来自其自我认定的种族之外的血统的个人。我们所有人 (AoU) 计划和百万 退伍军人计划 (MVP) 包括所有参与者的遗传、健康和社会经济信息，以及 因此，提供了一个机会来确定造成健康差异的因素。然而，AoU 计划 和 MVP 要求他们的数据保留在本地托管站点内，因此对这些群体进行联合分析 需要开发能够保护隐私的分布式计算的算法（即，无需 揭示个人层面的数据）。在理解健康的遗传决定因素方面存在三个重要差距： 1）大多数研究以欧洲个体为主，虽然他们控制了全球血统，但 并不是试图模拟混合个体的当地血统特征的拼凑； 2) GWAS 是 主要使用 SNP 进行，而祖先特异性遗传变异的重要来源（串联重复序列） (TR) 和主要组织相容性复合物 (MHC) 区间) 未进行测定； 3) 大多数 GWAS 不 调整社会经济因素。美国医学遗传学与基因组学学院 (ACMG) 发布了一份可医学上采取行动的癌症和心血管基因列表，建议返回意外事件 致病变异的发现可降低发病率和死亡率，但将少数群体排除在医疗保健之外 由于共同的障碍（例如语言和访问），后续工作很难区分遗传因素 以及导致不同健康结果的社会经济因素。 CAST（中心）的目标 混合物科学与技术）计划旨在提高遗传信息对所有人的临床效用 居住在美国的人口。在目标 1 中，我们将开发并应用疾病风险预测的多变量模型 融合了当地血统、复杂变异（TR 和 HLA 类型）。在目标 2 中，我们将开展可扩展的 使用 AoU 和 MVP 计算飞地中数百万个人的数据进行分布式计算。瞄准 3、我们将开发新的方法来利用电子健康记录和调查来表征表型 AoU 和 MVP，评估将健康的社会决定因素纳入我们的模型的影响，并前瞻性地 与新的 AoU 和 MVP 参与者一起评估他们。为了实现这些目标，我们组建了一支高度 由在遗传学、基因组生物学、数据共享政策和 技术、健康差异、表型分析和统计。