Leveraging pleiotropy to develop polygenic risk scores for cardiometabolic diseases

摘要/摘要 心脏代谢性疾病(CMD)是世界范围内主要的死亡原因。在此应用程序中，我们将探索 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，当一个遗传位点影响不止一个性状时，就会发生多效性 可能是观察到的交叉表型关联的潜在原因。相对于多效性、多基因风险 通过将与GWAS相关的变量组合成综合分数来估计分数(PR)可能会更好 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有不同的祖先时，PR翻译得很差。组合 多效性和PRS在发现与CMD相关的新的风险变异方面具有巨大的潜力。我们利用 我们所有人研究计划和美国国立卫生研究院资助的非洲心脏代谢紊乱的大型数据集- 祖先群体(红衣主教)研究地点，以确定多效性变异并为2型发展PR 糖尿病、冠状动脉疾病、高血压和血脂异常，在不同血统的人群中。所有的 US数据集是对复杂疾病病因产生新的生物学见解的理想选择，在全球范围内得到应用 人口。

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.