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

摘要 大规模的全基因组关联研究（GWAS）已经确定了大量的遗传变异 与复杂疾病有关。所有已知导致疾病的变异的聚集 以多基因风险评分（PRS）的形式改进了对一系列复杂疾病的预测。大多数PRS 已经在欧洲血统研究样本中开发，并且在其他研究中表现不佳。 种族/族裔群体，进一步扩大了各祖先之间的健康差距。作为遗传方法， 精准医疗越来越受欢迎，迫切需要响应和积极主动地扩大获取 准确的PRS。具体来说，糖尿病及其相关并发症是全球最大的健康问题之一， 21世纪世纪的问题事实上，1型和2型糖尿病（T1 D和T2 D），妊娠糖尿病（GDM）和 相关并发症是研究PRS预测异质性和 在种族/族裔和社会经济存在巨大差异的环境中，不仅 PRS对预测T1 D和T2 D有用吗？但它们可以区分T1 D和T2 D以及T2 D 亚型从糖尿病亚型、并发症和 最近产生的数量性状为构建高度可转移的PRS提供了独特的机会 在人群中。为了解决不同祖先在减贫战略方面的差异，我们组织了一个多学科的 团队聚集和分析来自超过180万个体的最大现有遗传数据（35%非 欧洲）与T1 D、T2 D、GDM和糖尿病相关并发症和数量性状，以改善PRS 预测不同祖先的糖尿病和寿命的进展，目的如下：（1）收集， 协调和整合具有糖尿病特征的大规模、多祖先队列， 基因组学促进发展，培训和测试不同祖先的PRS;（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， 连锁不平衡和几个祖先的汇总统计。(3)开发、测试和比较 对每个特征进行PRS，开发整合临床和遗传风险的风险预测工具 因素，并评估减贫战略改进预测的情景。实现本提案的目标 将展示基因组数据如何为医疗保健提供更有效和更有针对性的预防策略 系统和不同种族的人口。研究结果有望推动患者的精确护理 糖尿病和相关疾病的人不同的祖先背景，并作为一个范例， 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.