Empowering gene discovery and accelerating clinical translation for diverse admixed populations

项目总结/摘要 基因发现已经彻底改变了医学遗传学，但在理解复杂疾病方面仍然存在差距 在少数族裔群体中，特别是混合血统的“混合”群体。混合种群系统地 被排除在基因组研究之外，主要是因为缺乏可以解释其基因组的分析方法。 多样性包括非洲裔美国人和拉丁裔人在内的混血人口占美国人口的三分之一以上。 美国人口和一些常见的复杂疾病，包括心血管疾病，糖尿病， 一些癌症和创伤后应激障碍--这些疾病在全球范围内是导致残疾的主要原因。 然而，由于这些群体的代表性严重不足，他们在健康研究和治疗方面面临严重的差距 在基因组学中。为了从包括"我们大家"在内的现有和正在进行的努力中获得充分和公平的利益， 需要开发工具，允许研究混合人群的复杂特征。 阿特金森博士建议通过开发一套创新的统计方法来解决这个紧迫的问题， 软件包和分析资源，使基因组学更具包容性。在这5年的R01奖，她 将：1a）建立一种新的统计方法，以允许将混合个体整合到罕见变异关联中 研究; 1b）在可扩展的基于云的软件实现中测试这种新方法， 遗传结构在我们所有的研究计划;和1c）利用混合的连锁不平衡 个体以改善顶部基因座的精细定位。我们还将开发新的软件工具， 不同的队列;我们将使用这些队列2b）定义多祖先分期，插补， 和地方祖先推断;以及2c）评估不同集合的共同分析策略对基因表达的影响。 发现成果。最后，我们将利用混合来3a）扩展我们的祖先知情框架，以量化 局部和远端的基因-基因相互作用;和3b）表征等位基因效应大小差异的趋势， 祖先组件控制环境。这些努力填补了现有资源的一个重大缺口， 消除将代表性不足的人群纳入医学基因组学的障碍。这项工作是直接符合 NIH/NHGRI的战略任务是关注包容性和多样性，强调其重要性和及时性。 的拟议项目。

PROJECT SUMMARY/ABSTRACT Gene discovery has revolutionized medical genetics, but gaps remain in the understanding of complex disorders in minority populations, in particular “admixed” groups of mixed ancestry. Admixed populations are systematically excluded from genomic studies due largely to the lack of analytic approaches that can account for their genomic diversity. Admixed populations, including African American and Latinx individuals, make up more than a third of the U.S. populace and have higher rates of some common complex disorders including cardiovascular disease, diabetes, some cancers, and PTSD – disorders which rank amongst the top global contributors to years lived with disability. Yet, these groups face severe disparities in health research and treatment due to being so sorely underrepresented in genomics. To reap full and equitable benefits from existing and ongoing efforts including All of Us, there is an unmet need for the development of tools permitting the study of complex traits in admixed peoples. Dr. Atkinson proposes to address this pressing issue by developing a suite of innovative statistical methods, software packages, and analytical resources that will make genomics more inclusive. In this 5-year R01 award, she will: 1a) build a novel statistical method to allow for the integration of admixed individuals into rare variant association studies; 1b) test this new method in a scalable cloud-based software implementation across phenotypes of varying genetic architectures in the All of Us Research Program; and 1c) leverage the linkage disequilibrium in admixed individuals to improve fine-mapping of top loci. We will also develop novel software tools to 2a) realistically simulate diverse cohorts; which we will use to 2b) define best practice recommendations for multi-ancestry phasing, imputation, and local ancestry inference; and 2c) assess the impact of common analytic strategies for diverse collections on gene discovery outcomes. Finally, we will leverage admixture to 3a) extend our ancestry-informed frameworks to quantify gene-gene interactions both locally and distally; and 3b) characterize trends in allelic effect size differences across ancestry components with control over the environment. These efforts fill a major gap in existing resources and remove barriers to the inclusion of underrepresented populations in medical genomics. This work is in direct line with the strategic missions of the NIH/NHGRI to focus on inclusion and diversity, highlighting the crucial and timely nature of the proposed project.