Polygenic Risk of Disease in Populations of Diverse Ancestry

不同血统人群的多基因疾病风险

• 批准号: 10210804
• 负责人: Iftikhar J Kullo
• 金额: $ 68.54万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-08 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10210804
• 关键词: Address; Admixture; Adult; All of Us Research Program; Asians; Categories; Clinic; Clinical; Collaborations; Complex; Computing Methodologies; Coronary heart disease; Data; Data Element; Data Set; Development; Diabetes Mellitus; Disease; Electronic Health Record; Electronic Medical Records and Genomics Network; Ensure; Equation; European; Gene Frequency; Generations; Genetic; Genetic Diseases; Genetic Variation; Genomic medicine; Genotype; Goals; Grant; Health; Heritability; Hypertension; Individual; International; Lead; Linkage Disequilibrium; Metadata; Methods; Modeling; Obesity; Ontology; Outcome; Pattern; Performance; Phase; Phenotype; Population; Population Genetics; Population Heterogeneity; Prevention; Public Health; Randomized Clinical Trials; Risk; Risk Estimate; Risk Factors; Sample Size; Site; Source; South Asian; Statistical Methods; Testing; Therapeutic; Variant; Veterans; Weight; Work; base; biobank; clinical decision-making; clinical practice; clinical risk; cloud platform; cohort; data harmonization; data modeling; data standards; database of Genotypes and Phenotypes; disorder risk; electronic data; genome wide association study; genomic data; health disparity; heart disease risk; hypercholesterolemia; improved; innovation; large datasets; novel; phenotypic data; polygenic risk score; precision medicine; programs; risk prediction; risk stratification; risk variant; statistics; working group

PROJECT SUMMARY In this application we propose to build on our prior work on polygenic risk scores (PRSs) to extend these to diverse ancestry groups. By improving risk stratification, PRSs for common diseases have the potential to transform clinical practice. However, such PRSs must be available for diverse ancestry groups to ensure equitable implementation of genomic medicine and reduce the potential exacerbation of health disparities in the context of genomic medicine. Our application aims to address the critical need to develop PRSs for diverse ancestry groups and will focus on coronary heart disease (CHD) and its risk factors: hypertension, diabetes, obesity and hypercholesterolemia, collectively an enormous health burden world-wide. CHD is the prototypical complex disease for the use of PRSs given available validated risk prediction equations that bin individuals into risk categories and substantial reclassification across these categories by a PRS with consequent therapeutic implications. As part of the PRS Diversity Consortium (PRS-DC), we will develop methods to generate PRSs for populations of diverse ancestry using existing and new datasets with genomic and phenotype data for CHD and its risk factors. We will harmonize data elements across these data sets. The methods we develop will be applicable towards the generation of PRSs for a broad range of common diseases across diverse populations. The investigative team is part of the Mayo eMERGE IV application and will serve as a bridge between the PRS-DC and eMERGE. To generate PRSs for diverse ancestries, we will use data from the eMERGE consortium, Million Veteran’s Program (MVP), the All of US (AoU) program, dbGAP, PRS-DC sites, UK Biobank, and collaborations with several international groups representing the Middle Eastern, South Asian and East Asian cohorts. Our application includes several innovations to enable the use of PRSs for risk stratification and prevention of CHD in individuals belonging to diverse ancestries. Our specific aims are: Specific aim 1. Integrate and harmonize phenotype data from heterogeneous sources to enable cross platform phenotyping and generation of PRSs for common diseases in diverse ancestry groups. Specific aim 2. Develop PRSs for CHD and its major risk factors (hypertension, diabetes, obesity, hypercholesterolemia) in populations of diverse ancestry. Specific aim 3. Develop novel statistical and computational methods to account for diverse genetic ancestry and admixture in models of polygenic risk. Specific aim 4. Develop ‘clinic ready’ PRSs for diverse ancestry groups by creating reference distributions of a PRSCHD and integrate it with clinical information to compute absolute risk estimates.

项目总结