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 大规模基因组宽结合研究（GWAS）已经确定了大量遗传变异 与复杂疾病有关。已知有助于该疾病的所有变体的聚集 以多基因风险评分（PR）的形式改善了一系列复杂疾病的预测。大多数PR 已经在欧洲血统研究样本中开发，并且在其他方​​面表现出色 种族/族裔群体，进一步夸大了祖先的健康差异。作为遗传方法 精密药物变得更加流行，至关重要的是，响应和积极地扩展访问 准确的PR。具体而言，糖尿病及其相关并发症是全球最大的健康之一 21世纪的问题。实际上，类型1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在预测异质和 在存在戏剧性的种族/种族和社会经济差异的环境中，复杂的健康结果。不仅 PRS可用于预测T1D和T2D，但它们可以区分T1D和T2D，并且在T2D之间 亚型。来自糖尿病，并发症和并发症和 最近产生的定量性状为构建高度可转移的PR提供了独特的机会 跨种群。为了解决跨祖先的PR的差异，我们组装了一个多学科 团队汇总和分析来自1.8 m以上个体的最大遗传数据（35％的非 - 欧洲）具有T1D，T2D，GDM和与糖症有关的并发症和定量特征，以改善PRS 以这些目的的不同祖先的糖尿病和寿命的进展预测：（1）收集， 大规模的多项式队列的统一和整合与生命中的糖尿病特征 用于开发，培训和测试PR的基因组学针对各种祖先； （2）开发方法 通过使用允许整合的贝叶斯方法来改善非欧洲人群的PR预测 连锁不平衡和来自几个祖先的汇总统计数据。 （3）开发，测试和比较 每个特征的PR的性能，开发整合临床风险和遗传风险的风险预测工具 因素以及评估PRS改善预测的方案。实现该提议的目标 将证明基因组数据如何为医疗保健中的更有效和有针对性的预防策略提供信息 系统以及跨种族多样化的人群。期望发现患者的精确护理 患有多种祖先背景的人的糖尿病和相关状况，并作为范式 许多其他复杂疾病。

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