Improving Methods and Practices for Trans-Ethnic Genetic Studies

改进跨种族遗传研究的方法和实践

• 批准号: 10584152
• 负责人: Tian Ge
• 金额: $ 45.88万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-18 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10584152
• 关键词: All of Us Research Program; Architecture; Benchmarking; Biological; Calibration; Clinical; Complex; Computer software; Data; Data Aggregation; Data Linkages; Data Set; Disease; Electronic Health Record; Ethnic Origin; Evaluation; Future; Genes; Genetic; Genetic Research; Genetic study; Genomics; Haplotypes; Health; Healthcare Systems; Human; Japan; Knowledge; Koreans; Laboratories; Link; Linkage Disequilibrium; Maps; Measures; Medicine; Meta-Analysis; Methods; Modeling; Pattern; Population; Population Genetics; Population Heterogeneity; Research Design; Resolution; Resources; Sample Size; Sampling; Statistical Methods; Taiwan; Testing; Training; United States; Universities; Validation; Variant; Weight; Work; biobank; biomarker performance; causal variant; comparative; data harmonization; data integration; disorder risk; epidemiology study; experience; flexibility; genetic analysis; genetic architecture; genetic predictors; genetic variant; genome resource; genome wide association study; genome-wide; genomic locus; improved; large scale data; method development; novel; open source; personalized health care; polygenic risk score; population based; portability; risk prediction; simulation; statistics; trait

ABSTRACT Trans-ancestry genetic analysis can facilitate the discovery of trait- or disease-associated loci, characterize shared and differential genetic architectures across populations, improve the delineation of causal variants, and is critical for equal delivery of genomic knowledge and precision healthcare globally. However, current trans-ancestry genetic research is impeded by (i) limited genomic resources for non-European populations; and (ii) limited statistical methods that can appropriately model and integrate data from diverse populations. This project will address these challenges by (i) aggregating and harmonizing genetic data, physical measures, laboratory tests and disease information from global biobanks and multiple health care systems in the United States, with >795K samples of non-European ancestry and a total sample size >1.5M by 2023; and (ii) developing statistical methods and improving practices to integrate multi-ancestry data for cross-population characterization of genetic architectures, meta-analysis, statistical fine-mapping and polygenic prediction. Specifically, in Aim 1, we will systematically characterize the genetic underpinnings of human complex traits and common diseases at variant, locus, regional and genome-wide levels across diverse populations, and discover and validate novel genetic loci through trans-ancestry meta-analysis. In Aim 2, we will develop scalable, robust, accurate and flexible statistical methods for trans-ancestry fine-mapping, delineate putative causal genetic variants for a range of complex traits and diseases, and explore their functional consequences and biological mechanisms. In Aim 3, we will develop haplotype-based methods for improved trans-ancestry polygenic prediction, and benchmark the clinical utility of polygenic scores in disease risk prediction across diverse populations. Leveraging large-scale biobank resources and novel simulation frameworks, we will additionally enable fair and rigorous comparisons of existing and emerging methods for the integrative analysis of multi-ancestry data, and assess various analysis choices and practical considerations in trans-ancestry fine- mapping and genetic prediction in order to inform future study design and analysis plan, as well as methods development, evaluation and application in trans-ancestry settings.

抽象的 跨诊所遗传分析可以促进特征性特征或疾病相关的基因座的发现 跨种群的共享和差异遗传体系结构，改善因果变异的描述， 对于全球基因组知识和精确保健的平等提供至关重要。但是，当前 （i）非欧洲人群的基因组资源有限，跨学历的遗传研究受到了阻碍； （ii）有限的统计方法可以适当地模拟和整合来自不同人群的数据。 该项目将通过（i）汇总和协调遗传数据，物理措施， 联合国全球生物库和多个医疗保健系统的实验室测试和疾病信息 国家，有> 795k的非欧洲血统样本，到2023年，总样本量> 150万； （ii） 开发统计方法和改进实践以整合多功能数据以进行交叉人口 遗传体系结构，荟萃分析，统计精细图和多基因预测的表征。 具体而言，在AIM 1中，我们将系统地表征人类复杂性状的遗传基础 以及不同人群中的变体，基因座，区域和全基因组水平的常见疾病，以及 通过反式荟萃分析发现并验证新的遗传基因座。在AIM 2中，我们将发展 可扩展，健壮，准确且灵活的统计方法，用于跨务手映射，划定推定 一系列复杂性状和疾病的因果遗传变异，并探索其功能后果 和生物学机制。在AIM 3中，我们将开发基于单倍型的方法，以改善跨疗法 多基因预测，并基准测试多基因评分在整个疾病风险预测中的临床实用性 多样化的人群。利用大型生物库资源和新颖的模拟框架，我们将 此外，还可以对现有和新兴方法进行公平和严格的比较，以进行集成分析 多功能数据的数据，并评估跨委员会精细的各种分析选择和实际考虑因素 映射和遗传预测，以告知未来的研究设计和分析计划以及方法 在跨学院设置中的开发，评估和应用。