Development of Polygenic Risk Scores for Diabetes and Complications across the Life-Span in Populations of Diverse Ancestry

摘要 大规模全基因组关联研究已经确定了大量的遗传变异 与复杂的疾病有关。所有已知导致这种疾病的变异的集合 以多基因风险评分(PR)的形式改进了对一系列复杂疾病的预测。大多数PR 是在欧洲血统研究样本中开发的，并在其他方面表现不佳 种族/民族群体，进一步夸大了祖先之间的健康差距。随着遗传技术的发展 精准医疗变得更受欢迎，迫切需要应对和积极扩大准入 为了精准的数据传输服务。具体地说，糖尿病及其相关并发症是全球最大的健康问题之一 21世纪的问题。事实上，1型和2型糖尿病(T1D和T2D)、妊娠期糖尿病(GDM)和 相关并发症是研究PrS预测异型和异型血管疾病的良好疾病模型 在存在明显的种族/族裔和社会经济差异的情况下，造成复杂的健康后果。不仅 PR对于预测T1D和T2D有用，但它们可以区分T1D和T2D以及T2D 子类型。来自糖尿病亚型、并发症和糖尿病的现有跨代血统数据的财富 最近产生的数量性状为构建高度可转移的PR提供了独特的机会 在不同人群之间。为了解决不同祖先之间的差异，我们组建了一个多学科的 团队收集和分析180多万人(35%的非 欧洲)有T1D、T2D、GDM和血糖相关的并发症和数量性状以改善PRS 在不同祖先中预测糖尿病和跨寿命进展的目的如下：(1)收集， 具有糖尿病特征的大规模、多祖先队列在整个生命周期和 用于发展、培训和测试不同祖先的PR的基因组学；(2)开发方法 通过使用贝叶斯方法改进非欧洲人群的PRS预测，从而允许整合 来自几个祖先的链接不平衡和汇总统计。(3)开发、测试、比较 针对每个性状的PR性能，开发集成临床和遗传风险的风险预测工具 因素，以及对减贫方案改进预测的情景的评估。实现这项建议的目标 将展示基因组数据如何为医疗保健领域更有效和更有针对性的预防策略提供信息 在不同的系统和不同种族的人群中。这一发现有望促进对患者的精准护理 在不同祖先背景的人中患有糖尿病和相关疾病，并作为 许多其他复杂的疾病。

Abstract Large-scale genome wide association studies (GWAS) have identified a large number of genetic variants associated with complex diseases. The aggregation of all the variants that are known to contribute to the disease in the form of polygenic risk scores (PRS) improves the prediction of a range of complex diseases. Most PRS have been developed within European ancestry study samples and have shown to perform poorly in other race/ethnic groups, further exaggerating health disparities across ancestries. As genetic approaches for precision medicine become more popular, there is a critical need to responsively and pro-actively expand access to accurate PRS. Specifically, diabetes, and its associated complications are one of the biggest global health problems of the 21st century. In fact, type 1 and type 2 diabetes (T1D and T2D), gestational diabetes (GDM) and related complications are excellent disease models to study the utility of PRS for predicting heterogenous and complex health outcomes in a setting where dramatic racial/ethnic and socioeconomic disparities exist. Not only are PRS useful to predict T1D and T2D, but they can distinguish between T1D and T2D, and between T2D subtypes. The wealth of existing trans-ancestry GWAS data from diabetes subtypes, complications, and quantitative traits recently generated provides a unique opportunity for constructing highly transferable PRS across populations. To address the disparities in PRS across ancestries, we have assembled a multi-disciplinary team to aggregate and analyze the largest existing genetic data from more than 1.8 M individuals (35% non- European) with T1D, T2D, GDM and glycemia-related complications and quantitative traits to improve the PRS prediction of diabetes and progression across lifespan in diverse ancestries with these Aims: (1) Collection, harmonization and integration of large-scale, multi-ancestry cohorts with diabetes traits across the life-span and genomics for development, training and testing PRS for diverse ancestries; (2) Development of methods to improve PRS prediction in non-European populations by using Bayesian approaches that allow integration of linkage disequilibrium and summary statistics from several ancestries. (3) Development, testing, and comparing performance of PRS for each trait, development of risk prediction tools that integrate clinical and genetic risk factors, and assessment of scenarios where PRS improve the prediction. Accomplishing the aims of this proposal will demonstrate how genomic data can inform more efficient and targeted preventive strategies within healthcare systems and across ethnically diverse populations. Findings are expected to advance precision care of patients with diabetes and related conditions in people of diverse ancestral background and serve as a paradigm for many other complex diseases.