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 跨种族遗传分析可以促进性状或疾病相关基因座的发现，表征 跨人群共享和差异的遗传结构，改善因果变异的描述， 对于全球基因组知识的平等传播和精准医疗保健至关重要。然而，目前 跨种族遗传研究受到以下因素的阻碍：(i) 非欧洲人群的基因组资源有限；和 (ii) 能够适当建模和整合来自不同人群的数据的统计方法有限。这 项目将通过以下方式应对这些挑战：(1) 汇总和协调遗传数据、物理测量、 来自全球生物库和美国多个医疗保健系统的实验室测试和疾病信息 到 2022 年，拥有超过 68 万个非欧洲血统样本，总样本量超过 140 万个；和（2） 开发统计方法和最佳实践，整合多种族数据，以改善跨人群 遗传结构的表征、荟萃分析、统计精细绘图和多基因预测。 具体来说，在目标 1 中，我们将系统地描述物理的比较遗传结构。 内部和外部变异、基因座和全基因组水平的测量、生物标志物和疾病表型 大陆人口，并通过跨种族荟萃分析发现新的遗传位点。在目标 2 中，我们将 开发新的统计方法并建立跨种族精细绘图的最佳实践，描绘假定的 一系列复杂性状和疾病的因果遗传变异，并探索其生物学机制 精细映射的变体。在目标 3 中，我们将开发新的基于单倍型的跨种族多基因方法 预测，综合评估可能影响多基因风险评分可转移性的因素 (PRS) 并对生物标志物 PRS 在不同疾病风险预测中的临床效用进行基准测试 人口。我们致力于资源共享，将公开发布全基因组关联 在此生成的汇总统计数据、参考面板、精细绘图结果和多基因预测管道 项目。本项目开发的所有统计方法和生物信息学工具将作为公开传播 可用的软件包。