Empowering gene discovery and accelerating clinical translation for diverse admixed populations

促进基因发现并加速不同混合人群的临床转化

• 批准号: 10584936
• 负责人: Elizabeth Grace Atkinson
• 金额: $ 78.2万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-11 至 2028-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10584936
• 关键词: Acceleration; Accounting; Address; Admixture; Affect; African American; All of Us Research Program; Alleles; American; Asthma; Award; Awareness; Benchmarking; Cardiovascular Diseases; Clinical; Collection; Complex; Computer software; Computing Methodologies; DNA; Data; Data Collection; Development; Development Plans; Diabetes Mellitus; Disease; Distal; Ensure; Environment; Equity; European; Exclusion; Face; Frequencies; Functional disorder; Genes; Genetic; Genetic Drift; Genetic Variation; Genetic study; Genome; Genomic medicine; Genomics; Goals; Guidelines; Haplotypes; Individual; Latinx population; Linkage Disequilibrium; Malignant Neoplasms; Maps; Medical; Medical Genetics; Methodology; Methods; Minority; Minority Groups; Mission; Modeling; Molecular; National Human Genome Research Institute; Nature; Outcome; Outcome Study; Persons; Phase; Phenotype; Population; Population Heterogeneity; Positioning Attribute; Post-Traumatic Stress Disorders; Recommendation; Research; Research Personnel; Resolution; Resources; Sampling; Signal Transduction; Site; Software Tools; Statistical Methods; Structure; Testing; Training; Underrepresented Populations; United States National Institutes of Health; Variant; Work; biobank; career; clinical translation; cloud based; cohort; computer framework; diversity and inclusion; empowerment; experience; flexibility; gene discovery; gene interaction; genetic architecture; genetic element; genome wide association study; genomic tools; health disparity; improved; in silico; innovation; large scale data; molecular targeted therapies; novel; population stratification; rare variant; simulation; statistics; success; tool; tool development; trait; trend; years lived with disability

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.

项目摘要/摘要 基因发现使医学遗传学发生了革命性的变化，但对复杂疾病的理解仍然存在差距。 在少数群体中，特别是混合血统的“混合”群体。混合种群是有系统的 被排除在基因组研究之外，主要是因为缺乏能够解释其基因组的分析方法 多样性。混血人口，包括非裔美国人和拉丁裔人，占美国人口总数的三分之一以上 美国人，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(PTSD)是导致残疾寿命的全球最大因素之一。 然而，由于代表性严重不足，这些群体在健康研究和治疗方面面临着严重的差距。 在基因组学方面。为了从包括我们所有人在内的现有和持续的努力中获得充分和公平的好处，有一个未得到满足的 需要开发工具来研究混血人群的复杂特征。 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，这将使基因组学更具包容性。在这个为期5年的R01奖项中，她 将：1a)建立一种新的统计方法，允许将混合个体整合到稀有变异关联中 研究；1b)在可扩展的基于云的软件实施中测试此新方法 我们所有人研究计划中的遗传结构；以及1c)利用混合基因中的连锁不平衡 改善顶端基因座的精细定位。我们还将开发新的软件工具来逼真地模拟 不同的队列；我们将使用这些队列来2b)定义多祖先分期、归责、 和地方血统推断；以及2c)评估针对不同收集的常见分析策略对基因的影响 发现结果。最后，我们将利用混合来3a)扩展我们的祖先信息框架以量化 局部和远端的基因-基因相互作用；以及3b)表征等位基因效应大小差异的趋势 具有环境控制功能的祖先组件。这些努力填补了现有资源的主要缺口，并 消除将代表性不足的人群纳入医学基因组学的障碍。这项工作与 NIH/NHGRI的战略使命侧重于包容性和多样性，突出了关键和及时的性质 所提议的项目。