Improving Methods and Practices for Trans-Ethnic Genetic Studies

摘要 跨种族遗传分析可以促进发现与性状或疾病相关的基因座，表征 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 跨种族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)能够适当模拟和综合来自不同人口的数据的统计方法有限。这 该项目将通过以下方式解决这些挑战：(1)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2022年，拥有68万份非欧洲血统样本，总样本量为140万份； 制定统计方法和最佳做法，以整合多民族数据，以改善跨人口 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，我们将系统地描述物理的比较遗传结构。 在变异、基因座和全基因组水平上的测量、生物标志物和疾病表型 并通过跨种族荟萃分析发现新的遗传基因座。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做法，划定假定的 一系列复杂性状和疾病的因果遗传变异，并探索 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检测方法 预测，综合评估可能影响多基因风险分数可转移性的因素 (Prs)和基准生物标记物prs在不同领域的疾病风险预测中的临床应用 人口。我们致力于资源共享，并将公开发布全基因组关联 汇总统计数据、参考小组、精细绘图结果和多基因预测管道 项目。在该项目中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。

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.