Elucidating the ancestry-specific genetic and environmental architecture of cardiometabolic traits across All of Us ethnic groups

项目摘要/摘要 我们所有人都是了解人类遗传、环境和社会决定因素的独特资源。 在广泛多样的美国人口中具有复杂的特征。我们所有人都包括被低估的性和 性别、种族和少数民族，生活在城市和农村地区。目前全基因组序列数据 (WGS)可用于近10万名受试者，预计到夏季将有25万名受试者。 人类疾病是基因和环境之间复杂的相互作用。更好地了解如何 疾病是通过遗传与环境、生活方式和治疗因素以及年龄的相互作用而改变的， 性和血统将为预防、早期干预和潜在的治疗策略提供见解 减轻疾病负担和健康差距。 心脏代谢特征，如BMI、血糖水平、血脂水平、血压是重要的决定因素 心血管疾病。了解不同种族之间的遗传和环境差异是一种 了解疾病流行的基本要素。我们知道性状可以有不同的遗传力 在几千年的独立进化中形成的跨种族群体。这一演变在所有方面都可以看到。 其中约12%的标记是多等位基因，因此代表不同的交替等位基因 出现在不同的种群中，每个种群都有潜在的不同生物效应。 能够理解复杂性状的遗传和环境结构在所有 对于我们这些种族群体，我们需要研究员工作台中的工作流，可以对不同来源的 遗传变异从方差成分模型到全基因组与环境和不环境的关联 互动。为了实现这一理解，我们提出了三个目标。 在目标1中，我们开发了可以包含多个特征、多个暴露和多个种族阶层的模型 能够量化种族群体内部和群体之间的特征、遗传和环境建筑。 在目标2中，我们为目标1中开发的工具开发并实施了经过严格测试的工作流到所有 美国研究员工作台。工作流程将由提供大量培训材料的支持，这些培训材料将 包括视频教程、用户手册和示例数据集以及如何分析它们的说明。 在目标3中，我们应用我们所有的研究人员工作台工作流来调查遗传和环境 我们所有种族的心脏代谢特征的结构。这些特征包括血脂、血糖、 血压和体重指数。环境暴露将包括吸烟、教育和体育活动。 我们的研究人员工作台工作流将使研究人员能够将相同的模型应用于广泛的列表 我们所有人都可以获得临床表型和暴露，以解决重要健康差距的根源。

PROJECT SUMMARY/ABSTRACT All of Us represents a unique resource to understand the genetic, environmental, and social determinants of complex traits across the broad diverse US population. All of Us encompasses underrepresented sexual and gender, racial and ethnic minorities, living in urban and rural areas. Currently whole-genome sequence data (WGS) is available on close to 100,000 subjects with 250,000 subjects expected by summer. Human disease is a complex interplay between both genes and environment. A better understanding of how disease is modified by genetic interactions with environmental, lifestyle and treatment factors as well as age, sex, and ancestry, will provide insights into prevention, early intervention, and potential therapeutic strategies to reduce the burden of disease and health disparities. Cardiometabolic traits such a BMI, glucose levels, lipid levels, blood pressure are important determinants cardiovascular disease. Understanding the genetic and environmental differences between ethnic groups is an essential component to understanding disease prevalence. We know traits can have different heritability across ethnic groups, shaped by thousands of years of independent evolution. This evolution is seen in the All of US WGS where ~12% of the markers are multi-allelic, thus, representing different alternate alleles that arose in different populations, each with a potentially different biological effect. To be able to understand how the genetic and environmental architecture of complex traits differs between All of Us ethnic groups we need workflows in the Researcher Workbench that can model diverse sources of genetic variation from variance component models to genome-wide association with and without environmental interactions. To achieve this understanding, we propose three aims. In Aim 1 we develop models that can incorporate multiple traits, multiple exposures, and multiple ethnic strata to be able to quantify the trait genetic and environmental architecture within and between ethnic groups. In Aim 2 we develop and implement rigorously tested workflows for the tools developed in Aim 1 into the All of Us Researcher Workbench. The workflows will be supported by with extensive training materials that will include video tutorials, user manuals, and example data sets and instructions how to analyze them. In Aim 3 we apply our All of Us Researcher Workbench workflows to investigate the genetic and environmental architecture of cardiometabolic traits across All of Us ethnic groups. These traits will include lipids, glucose, blood pressure and BMI. Environmental exposures will include smoking, education, and physical activity. Our Researcher Workbench workflows will enable researchers to apply the same models to the extensive list of clinical phenotypes and exposures available in All of Us to address sources of important health disparities.