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

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• 批准号: 10307040
• 负责人: KELLY A FRAZER
• 金额: $ 251.08万
• 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/10307040
• 关键词: Admixture; Affect; African; Algorithmic Software; Algorithms; All of Us Research Program; American; American Indians; Asians; 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; 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; 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.

项目总结