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.

项目概要/摘要 基因发现彻底改变了医学遗传学，但对复杂疾病的理解仍存在差距 在少数群体中，特别是具有混合血统的“混合”群体。混合人群有系统地 被排除在基因组研究之外，很大程度上是因为缺乏可以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，占总人口的三分之一以上 美国民众罹患一些常见复杂疾病的比例较高，包括心血管疾病、糖尿病、 一些癌症和创伤后应激障碍（PTSD）——这些疾病是全球导致残疾多年的主要原因之一。 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严重差异 在基因组学中。为了从包括我们所有人在内的现有和正在进行的努力中获得充分和公平的利益，还有一个未满足的目标 需要开发能够研究混血民族复杂特征的工具。 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一紧迫问题， 软件包和分析资源将使基因组学更具包容性。在这5年的R01奖中，她 将： 1a）建立一种新颖的统计方法，以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这种新方法，跨不同的表型 “我们所有人研究计划”中的遗传结构； 1c）利用混合中的连锁不平衡 个人改善顶级基因座的精细作图。我们还将开发新颖的软件工具来 2a) 真实地模拟 多样化的群体；我们将用它来 2b) 定义多祖先阶段、插补的最佳实践建议， 以及当地血统推断； 2c) 评估不同馆藏的通用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来量化 局部和远端的基因-基因相互作用； 3b) 描述等位基因效应大小差异的趋势 具有对环境的控制能力的祖先组件。这些努力填补了现有资源和 消除将代表性不足的人群纳入医学基因组学的障碍。这项工作直接符合 NIH/NHGRI 的战略使命是关注包容性和多样性，强调关键性和及时性 拟议项目的。