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Leveraging pleiotropy to develop polygenic risk scores for cardiometabolic diseases

利用多效性开发心脏代谢疾病的多基因风险评分

基本信息

批准号：
10797389
负责人：
Sally Nneoma Adebamowo
金额：
$ 15.45万
依托单位：
UNIVERSITY OF MARYLAND BALTIMORE
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-09-10 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10797389
关键词：
Adult Affect African African American population African ancestry All of Us Research Program American Biological Body mass index Cardiometabolic Disease Catalogs Cause of Death Chronic Disease Complex Coronary Arteriosclerosis Data Data Set Databases Development Disease Dyslipidemias Environmental Risk Factor Ethnic Origin Etiology European European ancestry Funding Gene Frequency Genetic Genetic Risk Genetic Transcription Genome Genomics Health Policy Human Hypertension Incidence Individual Investigation Lead Link Linkage Disequilibrium Mendelian randomization Minority Groups Morbidity - disease rate National Human Genome Research Institute Non-Insulin-Dependent Diabetes Mellitus Outcome Participant Pathway interactions Phenotype Population Population Heterogeneity Population Study Prevalence Prevention strategy Public Health Reporting Research Resources Risk Risk Assessment Risk Factors Scoring Method Signal Transduction Site Target Populations Translating United States National Institutes of Health Validation Variant Veterans burden of illness cardiometabolism causal variant clinical risk diagnostic strategy disorder risk ethnic minority population genetic variant genome wide association study genomic locus genomic variation health inequalities insight large datasets mortality novel pleiotropism polygenic risk score programs racial minority population risk prediction risk variant statistics trait whole genome

项目摘要

Abstract/Summary Cardiometabolic diseases (CMD) are the leading causes of death worldwide. In this application we explore the genomic risk for common CMD, including type 2 diabetes, coronary artery disease, hypertension, and dyslipidemia, across non-European ancestry populations. Recent studies have shown that these complex human traits are genetically correlated by pleiotropy, which occurs when a genetic locus affects more than one trait and is a possible underlying cause for observed cross-phenotype associations. Relative to pleiotropy, polygenic risk scores (PRS) which are estimated by combining GWAS-associated variants into a composite score, may better explain the correlation between complex traits, by determining a subset of risk variants for each outcome. However, PRS translate poorly when the discovery and target populations have differential ancestry. Combining pleiotropy and PRS has great potential to uncover novel risk variants associated with CMD. We leverage the large dataset in the All of Us research program and the NIH-funded CARdiometabolic Disorders IN African- ancestry PopuLations (CARDINAL) study site, to identify pleiotropic variants and develop PRS for type 2 diabetes, coronary artery disease, hypertension, and dyslipidemia, in populations of diverse ancestry. The All of Us dataset is ideal for generating novel biologic insights into complex disease etiology, with applications in global populations.

摘要/概要心脏代谢性疾病（CMD）是世界范围内的主要死亡原因。在这个应用程序中，我们探索常见CMD的基因组风险，包括2型糖尿病、冠状动脉疾病、高血压和非欧洲血统人群中的血脂异常。最近的研究表明，这些复杂的人类性状通过多效性在遗传上相关，多效性发生在一个遗传基因座影响一个以上的性状时，是观察到的跨表型关联的可能潜在原因。相对于多效性，多基因风险通过将GWAS相关变体组合成综合评分来估计的PRS评分（PRS）可能更好地通过确定每个结果的风险变量子集，解释复杂特征之间的相关性。然而，当发现人群和目标人群具有不同的祖先时，PRS的翻译效果很差。结合多效性和PRS具有发现与CMD相关的新风险变体的巨大潜力。我们利用我们所有人研究计划和NIH资助的非洲心脏代谢疾病的大型数据集- 祖先群体（CARDINAL）研究中心，以确定多效性变体并开发2型PRS 糖尿病、冠状动脉疾病、高血压和血脂异常。的全部美国数据集是产生复杂疾病病因学的新生物学见解的理想选择，人口。