Improving Methods and Practices for Trans-Ethnic Genetic Studies

摘要 跨系遗传分析可以帮助发现与性状或疾病相关的基因座， 跨种群的共享和差异遗传架构，改善了因果变异的描述， 对于全球平等地提供基因组知识和精确的医疗保健至关重要。但是，当前 跨系遗传研究受到以下因素的阻碍：(1)非欧洲人口的基因组资源有限； 以及(2)有限的统计方法，可以适当地模拟和整合来自不同人口的数据。 该项目将通过以下方式应对这些挑战：(I)聚合和协调遗传数据、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2023年，拥有79.5万份非欧洲血统样本，总样本规模为150万份；以及 发展统计方法和改进做法，以整合跨人口的多血统数据 遗传结构的表征、荟萃分析、统计精细图谱和多基因预测。 具体地说，在目标1中，我们将系统地描述人类复杂特征的遗传基础 以及不同人群在变异、基因座、区域和全基因组水平上的常见疾病，以及 通过跨祖先荟萃分析发现和验证新的遗传基因座。在目标2中，我们将开发 可扩展的、健壮的、准确的和灵活的跨祖先精细作图统计方法，划定假设 一系列复杂性状和疾病的因果遗传变异，并探索它们的功能后果 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系血统 多基因预测，并以多基因评分在疾病风险预测中的临床应用为基准 不同的人群。利用大规模的生物库资源和新颖的模拟框架，我们将 此外，还能够对现有和新兴的综合分析方法进行公平和严格的比较 多世系数据，并评估各种分析选择和跨世系细微的实际考虑- 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 跨世系背景下的发展、评价和应用。

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.