PAGE III: Population Architecture using Genomics and Epidemiology

一些大规模的全基因组测序(WGS)和组学工作正在进行中。尽管这些 尽管项目正在朝着包括祖先多样化人口的方向迈进，但仍存在显著的差距 对这些数据的分析利用多样性来增强发现能力并提高我们对 所有种群的遗传型和表型结构。欧洲人的严重偏见持续存在 大规模的WGS和组学工作。祖先不同群体之间的遗传变异差异是 众所周知，尽管数据非常有限，但基因表达、甲基化和代谢物也不同 在祖先不同的种群中。自2008年以来，PAGE研究一直得到美国国立卫生研究院的资助 研究基因组变异以促进我们对复杂遗传性状的群体结构的理解 和疾病，特别是在存在祖先多样性的情况下。我们已经在……的前沿建立了佩奇。 40个主要和200个次要复杂性状和疾病的发现和精细定位 多样化人口，并在阵列方面走在了前列(例如，多种族全球阵列、全球筛选阵列) 以及针对这些不同人群开发的统计方法。此外，佩奇还充当了 对科学界有价值的资源，高度重视快速传播等位基因频率 数据、Gwas汇总统计数据、研究结果和分析软件。页面第三阶段(页面III)将包括 现有的遗传学和基因组学数据来自6个具有良好特征的12万多个不同的个体 队列/生物库。在第三页中，我们建议将我们迄今宝贵的工作扩展并继续1)确定 并对影响祖先不同个体的复杂特征和疾病的遗传变异进行表征 使用WGS数据(n=50,000)和推定的基因分型数据(n=124,000)，2)整合关于 序列变异和组学以更好地了解不同物种复杂性状的遗传基础 PAGE参与者，以及3)表征潜在疾病风险的生物途径 人口。对于目标1，我们将使用最近生成的序列数据来推算不同的 每个参与者都被认为是复杂性状的遗传学。对于目标2，我们将使用新的现有数据来 将基因表达、DNA甲基化和代谢组学数据归因于剩余的PAGE样本， 将涉及当前方法的扩展和发展，以确保适合于祖先的多样性 人口。目标3将专注于整合所有组学数据，以发现新的途径和 复杂疾病的遗传基础，并阐明各种疾病病因的分子驱动因素 页面群。作为我们上一页开创性的、非常成功的工作的继续 十年来，我们的建议在理解遗传和基因组成分方面取得了重大进展 疾病的潜在生物学机制，以及转化医学在不利影响和 对不同群体的研究不足。

Several large-scale whole genome sequencing (WGS) and omics efforts are underway. Although these programs are making strides towards including ancestrally diverse populations, there is still a notable gap in analyses of these data that leverage diversity to empower discovery and improve our understanding of genotypic and phenotypic architecture across all populations. Substantial European bias persists in ongoing large scale WGS and omics efforts. Differences in genetic variation among ancestrally diverse groups are well-known, and although data are very limited, gene expression, methylation, and metabolites also differ among ancestrally diverse populations. The PAGE Study has been continuously funded by the NIH since 2008 to study genomic variation to advance our understanding of population architecture of complex genetic traits and diseases, particularly in the presence of ancestral diversity. We have established PAGE at the forefront of discovery and fine-mapping across 40 primary and >200 secondary complex traits and diseases in ancestrally diverse populations, and have led the way in array (e.g. the Multi-Ethnic Global Array, Global Screening Array) and statistical methods development specific to these diverse populations. Additionally, PAGE has served as a valuable resource to the scientific community, placing a high priority on quickly disseminating allele frequency data, GWAS summary statistics, study findings, and analytical software. PAGE Phase III (PAGE III) will include existing genetics and genomics data from more than 120,000 diverse individuals from six well characterized cohorts/biobanks. In PAGE III, we propose to extend and continue our invaluable work to date to 1) identify and characterize genetic variants that influence complex traits and diseases in ancestrally diverse individuals using both WGS data (n=>50,000) and imputed genotyping data (n=124,000), 2) integrate information on sequence variation and omics to better understand the genetic underpinnings of complex traits in the diverse PAGE participants, and 3) characterize biological pathways underlying disease risk both within and between populations. For Aim 1, we will use recently generated sequence data to impute the largest sample of diverse participants every considered for the genetics of complex traits. For Aim 2, we will use newly existing data to impute gene expression, DNA methylation and metabolomics data into the remainder of PAGE samples, which will involve both extension and development of current methodology to ensure suitability for ancestrally diverse populations. Aim 3 will focus on integration of all omics data to inform discovery of novel pathways and the genetic basis of complex diseases and elucidate the molecular drivers of disease etiologies across diverse PAGE populations. As a continuation of our ground-breaking highly-successful work in PAGE over the last decade, our proposal offers major advances towards understanding the genetic and genomic components underlying biological mechanisms of disease, and translational medicine in adversely affected and understudied diverse populations.