Improving Methods and Practices for Trans-Ethnic Genetic Studies

摘要 跨种族遗传分析可以促进发现性状或疾病相关的基因座， 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 跨种族遗传研究受到以下因素的阻碍：㈠非欧洲人口的基因组资源有限; (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 该项目将通过以下方式应对这些挑战：（1）汇总和统一遗传数据、物理措施， 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2022年，非欧洲血统样本超过68万，总样本量超过140万;以及（2） 制定统计方法和最佳做法，以整合多族裔数据，改善人口交叉 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，我们将系统地描述物理遗传结构的比较特征， 在变异、基因座和全基因组水平上， 大陆人群，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 开发新的统计方法，建立跨种族精细绘图的最佳实践， 因果遗传变异的一系列复杂的性状和疾病，并探讨生物学机制， 精细映射的变体。在目标3中，我们将开发新的基于单体型的跨种族多基因多态性分析方法。 预测，综合评估可能影响多基因风险评分可传递性的因素 (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，并将公开发布全基因组关联 摘要统计，参考面板，精细定位结果和多基因预测管道在此产生 项目本项目中开发的所有统计方法和生物信息工具都将公开传播， 可用的软件包。

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.