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

不同血统人群终生糖尿病和并发症的多基因风险评分的制定

• 批准号: 10612985
• 负责人: Alisa Knodle Manning
• 金额: $ 96.68万
• 依托单位: BROAD INSTITUTE, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-08 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10612985
• 关键词: Address; Bayesian Method; Biological Process; Collection; Complex; Coupled; Data; Development; Diabetes Mellitus; Diabetic Nephropathy; Diabetic Retinopathy; Disease; Disease model; Disparity; Electronic Health Record; Ethnic Population; European ancestry; Genetic; Genomics; Genotype; Gestational Diabetes; Goals; Health; Healthcare Systems; Individual; Informatics; Insulin-Dependent Diabetes Mellitus; Life Cycle Stages; Linkage Disequilibrium; Longevity; Methods; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Patient Care; Performance; Persons; Phenotype; Population; Population Heterogeneity; Prevention strategy; Process; Research; Risk Factors; Sampling Studies; Scoring Method; Secure; Societies; System; Testing; Training; Variant; Visualization; Weight; Work; biobank; clinical risk; cohort; data harmonization; data sharing; diabetes risk; disorder risk; disorder subtype; diverse data; ethnic disparity; ethnic diversity; genetic approach; genetic risk factor; genetic variant; genome wide association study; genome-wide; genomic data; global health; health disparity; improved; large datasets; method development; multidisciplinary; novel; personalized care; phenotypic data; polygenic risk score; precision medicine; predictive tools; racial disparity; racial population; risk prediction; socioeconomic disparity; statistics; trait; type I and type II diabetes

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.

抽象的 大规模全基因组关联研究（GWAS）已鉴定出大量遗传变异 与复杂的疾病有关。已知导致疾病的所有变异的聚合 多基因风险评分 (PRS) 的形式提高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出来，并且在其他研究中表现不佳 种族/族裔群体，进一步夸大了不同血统之间的健康差异。随着遗传方法 精准医疗变得越来越流行，迫切需要积极主动地扩大准入范围 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世纪的问题。事实上，1 型和 2 型糖尿病（T1D 和 T2D）、妊娠糖尿病 (GDM) 和 相关并发症是研究 PRS 预测异质性和异质性的效用的绝佳疾病模型。 在存在巨大种族/族裔和社会经济差异的环境中，会产生复杂的健康结果。不仅 PRS 对预测 T1D 和 T2D 有用，但它们可以区分 T1D 和 T2D，以及 T2D 亚型。来自糖尿病亚型、并发症和疾病的现有跨祖先 GWAS 数据的丰富性 最近生成的数量性状为构建高度可转移的 PRS 提供了独特的机会 跨人群。为了解决不同血统的 PRS 差异，我们组建了一个多学科团队 团队汇总并分析了来自超过 180 万个体（35% 非非个体）的最大现有遗传数据 欧洲）患有T1D、T2D、GDM和血糖相关并发症和数量性状以改善PRS 预测不同血统的糖尿病和整个生命周期的进展，目标如下：(1) 收集， 协调和整合具有糖尿病特征的大规模、多祖先群体的整个生命周期 用于开发、训练和测试不同血统的 PRS 的基因组学； (2) 开发方法 通过使用允许整合的贝叶斯方法来改进非欧洲人群的 PRS 预测 连锁不平衡和来自几个祖先的汇总统计。 (3) 开发、测试和比较 每个性状的PRS表现，开发整合临床和遗传风险的风险预测工具 因素，以及 PRS 改进预测的场景评估。实现本提案的目标 将展示基因组数据如何为医疗保健领域提供更有效、更有针对性的预防策略 系统和不同种族的人群。研究结果有望促进患者的精准护理 不同祖先背景的人患有糖尿病和相关疾病，并作为一个范例 许多其他复杂的疾病。

项目成果

Predicting diabetes risk in diverse populations: what next?

• DOI: 10.1016/s2213-8587(21)00287-4
• 发表时间: 2021-12
• 期刊: The lancet. Diabetes & endocrinology
• 作者: Mercader JM;Ng MCY;Manning AK;Rich SS
• 通讯作者: Rich SS

Earlier Age at Type 2 Diabetes Diagnosis Is Associated With Increased Genetic Risk of Cardiovascular Disease.

2 型糖尿病诊断年龄越早与心血管疾病遗传风险增加相关。

• DOI: 10.2337/dc22-2144
• 发表时间: 2023
• 期刊: Diabetes care
• 影响因子: 16.2
• 作者: Lee,Hyunsuk;Choi,Jaewon;Kim,NaYeon;Kim,Jong-Il;Moon,MinKyong;Lee,Seunggeun;Park,KyongSoo;Kwak,SooHeon
• 通讯作者: Kwak,SooHeon