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.

摘要 跨祖先遗传分析可以促进性状或疾病相关基因座的发现， 群体间共享和差异遗传结构，改善因果变异的描绘， 这对全球平等提供基因组知识和精准医疗至关重要。但目前的 跨祖先遗传研究受到以下因素的阻碍：（i）非欧洲人口的基因组资源有限; 以及（ii）有限的统计方法可以适当地建模和整合来自不同人群的数据。 该项目将通过以下方式应对这些挑战：（一）汇总和统一遗传数据、实物测量数据， 来自全球生物库和美国多个卫生保健系统的实验室测试和疾病信息 到2023年，非欧洲血统样本超过79.5万，总样本量超过150万;以及（ii） 制定统计方法和改进做法，以整合跨人群的多祖先数据 遗传结构表征、荟萃分析、统计精细作图和多基因预测。 具体来说，在目标1中，我们将系统地描述人类复杂性状的遗传基础 和不同人群中变异、基因座、区域和全基因组水平的常见疾病， 通过跨祖先荟萃分析发现和验证新的遗传位点。在目标2中，我们将开发 可扩展的，强大的，准确的和灵活的统计方法，用于跨祖先精细映射，描绘推定的 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 和生物机制。在目标3中，我们将开发基于单倍型的方法来改进跨祖先 多基因预测，并基准多基因评分在疾病风险预测中的临床效用， 不同的人群。利用大规模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的综合分析方法进行公平和严格的比较， 的多祖先数据，并评估各种分析选择和跨祖先精细的实际考虑， 作图和遗传预测，以便为未来的研究设计和分析计划以及方法提供信息 在跨祖先背景下的开发、评估和应用。