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: 10661266
• 负责人: Tian Ge
• 金额: $ 47.47万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-12 至 2023-05-17
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10661266
• 关键词: Academic Medical Centers; Accounting; Address; Affect; All of Us Research Program; Benchmarking; Biological; Biological Markers; Calibration; Clinical; Communities; Complex; Computer software; Data; Data Aggregation; Data Set; Disease; Electronic Health Record; Genes; Genetic; Genetic Research; Genetic study; Genomics; Haplotypes; Health; Healthcare Systems; Japan; Knowledge; Koreans; Laboratories; Link; Linkage Disequilibrium; Maps; Measures; Meta-Analysis; Methods; Modeling; Pattern; Population; Population Heterogeneity; Resolution; Resource Sharing; Resources; Sample Size; Sampling; Statistical Methods; Taiwan; Testing; Training; United States; Variant; Weight; Work; base; biobank; bioinformatics tool; biomarker performance; causal variant; comparative; data harmonization; disease phenotype; disorder risk; diverse data; experience; genetic analysis; genetic architecture; genetic variant; genome wide association study; genome-wide; genomic locus; improved; large scale data; multi-ethnic; novel; open source; personalized health care; polygenic risk score; population based; rare variant; risk prediction; statistics; trait

ABSTRACT Trans-ethnic 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-ethnic 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 (1) 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 >680K samples of non-European ancestry and a total sample size >1.4M by 2022; and (2) developing statistical methods and best practices to integrate multi-ethnic data for improved cross-population characterization of genetic architectures, meta-analysis, statistical fine-mapping and polygenic prediction. Specifically, in Aim 1, we will systematically characterize the comparative genetic architectures of physical measures, biomarkers and disease phenotypes at variant, locus and genome-wide levels within and across continental populations, and discover novel genetic loci through trans-ethnic meta-analysis. In Aim 2, we will develop novel statistical methods and establish best practices for trans-ethnic fine-mapping, delineate putative causal genetic variants for a range of complex traits and diseases, and explore the biological mechanisms of fine-mapped variants. In Aim 3, we will develop novel haplotype-based methods for trans-ethnic polygenic prediction, comprehensively assess the factors that might affect the transferability of polygenic risk scores (PRS) and benchmark the clinical utility of biomarker PRS in disease risk prediction across diverse populations. We are committed to resource sharing and will publicly release genome-wide association summary statistics, reference panels, fine-mapping results, and polygenic prediction pipelines produced in this project. All statistical methods and bioinformatic tools developed in this project will be disseminated as publicly available software packages.

抽象的 跨种族的遗传分析可以促进发现性状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 跨种族遗传研究受到（i）非欧洲人群的基因组资源有限的影响；和 （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。这 项目将通过（1）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> 680k的非欧洲血统样本，到2022年，总样本量> 140万； （2） 开发统计方法和最佳实践，以整合多种族数据以改善交叉人群 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，我们将系统地表征物理的比较遗传体系结构 在变体，基因座和整个基因组范围内的措施，生物标志物和疾病表型 大陆种群，并通过跨种族荟萃分析发现新的遗传基因座。在AIM 2中，我们将 开发新颖的统计方法并建立用于跨种族精细图的最佳实践，划定推定 各种复杂性状和疾病的因果遗传变异，并探索生物学机制 精细映射变体。在AIM 3中，我们将开发基于新型单倍型的跨种族多基因的方法 预测，全面评估可能影响多基因风险评分转移性的因素 （PRS）和基准测试生物标志物PR在疾病风险预测中的临床实用性 人群。我们致力于资源共享，并将公开发布全基因组协会 摘要统计数据，参考面板，精细映射结果和在此产生的多基因预测管道 项目。该项目中开发的所有统计方法和生物信息学工具将被公开传播 可用的软件包。