Characterizing the Genomic Basis of Immune-Mediated Resilience Against Tuberculosis in an Admixed Peruvian Population

项目总结/摘要 结核病（TB）仍然是全球主要的传染性病原体死亡原因。理解 为什么有些患者出现早期症状，而另一些患者出现潜伏感染， 这种病。虽然已知环境因素会增加早期进展的风险，但最近的一项基因组研究表明， 广泛关联研究表明，宿主遗传因素也发挥作用，具有早期进展遗传性 接近20%。然而，以欧洲人为重点的结核病遗传学研究一直在该领域占据主导地位， 连锁不平衡模糊了致病基因座，未能集中在结核病患者最多的人群。通过 相比之下，本项目将重点关注来自秘鲁利马的结核病高负担和混合血统的人群， 这将揭示更广泛的遗传变异，并使更具体的映射的影响基因座。早期 进展风险可能由免疫应答（主要宿主-病原体界面）决定。因此，我们认为， 使用单核细胞（大量）和T细胞（作为单细胞）的转录谱分析与基因分型的组合 和环境协变量数据，该项目的目的是表征免疫表型改变， 基因组背景影响结核病进展风险。具体而言，本项目将：目标1）识别基因 受遗传祖先影响的表达免疫表型，目的2）创建一种新的单细胞分析 方法，并使用它来鉴定受遗传祖先影响的细胞群体免疫表型，以及目的3） 确定改变免疫表型和影响TB进展风险的特定基因组变异。如果成功， 这项工作将阐明影响结核病感染结果的基因组机制，并加深我们对结核病的认识 免疫生理学的研究。防止早期进展的遗传因素的知识 可以为治疗方法和疫苗的开发提供信息。这项工作也将产生一个强大的方法， 单细胞数据集的关联测试，协变邻域分析，可以定义与伟大的 其丰度被感兴趣的临床表型改变的灵活性和粒度细胞群体。 通过奖学金培训计划，申请人将发展：复杂性状遗传学的专业知识， 流利的应用和生物信息学方法的发展，社会如何理解 和环境因素也有助于传染病的结果，熟悉免疫 传染病管理的生理学和临床方面。理想的训练环境， 由免疫介导疾病和单细胞方法的复杂性状遗传学专家指导 发展，加上具有互补科学专业知识和职业生涯的顾问网络的支持 学术医学经验，结合接触相关课程和会议，将 使申请人能够在这项研究计划中茁壮成长。在本奖学金结束时，申请人将 准备解决计算免疫遗传学中的关键问题，并将其应用于传染病 和全球健康，并通过在学术医学的职业生涯桥梁临床和计算世界。

Project Summary/Abstract Tuberculosis (TB) remains the global leading cause of death from an infectious agent. Understanding why some patients present with early symptoms while others develop latent infections is critical for combating this illness. While environmental factors are known to elevate the risk of early progression, a recent Genome Wide Association Study revealed that host genetic factors also play a role, with early progression heritability near 20%. However, TB genetics studies focusing on Europeans, which have dominated the field, suffer from linkage disequilibrium obscuring causal loci and fail to center on the populations who suffer most from TB. By contrast, this project will focus on a population from Lima, Peru with high TB burden and admixed ancestry, which will reveal a broader range of genetic variants and enable more specific mapping of impactful loci. Early progression risk is likely dictated by the immune response, the primary host-pathogen interface. Therefore, using transcriptional profiling of monocytes (in bulk) and T cells (as single cells) in combination with genotyping and environmental covariate data, the objective of this project is to characterize immune phenotypes altered by genomic background that influence TB progression risk. Specifically, this project will: Aim 1) identify gene expression immune phenotypes influenced by genetic ancestry, Aim 2) create a novel single-cell analysis method and use it to identify cell population immune phenotypes influenced by genetic ancestry, and Aim 3) identify specific genomic variants that alter immune phenotypes and impact TB progression risk. If successful, this work will elucidate genomic mechanisms influencing TB infection outcomes and deepen our understanding of immune physiology in a Peruvian population. Knowledge of genetic factors that prevent early progression can inform the development of therapeutics and vaccines. This work will also produce a powerful method for association testing in single cell datasets, Covarying Neighborhood Analysis, that can define with great flexibility and granularity cell populations whose abundance is altered by a clinical phenotype of interest. Through the fellowship training plan, the applicant will develop: expertise in complex trait genetics, fluency in the application of and development of bioinformatic methodologies, an understanding of how social and environmental factors also contribute to infectious disease outcomes, and familiarity with immune physiology and clinical aspects of infectious disease management. An ideal training environment of close mentorship by an expert in complex trait genetics of immune-mediated diseases and single-cell methods development, plus support from a network of advisors with complementary scientific expertise and career experience in academic medicine, in combination with exposure to relevant coursework and meetings, will enable the applicant to thrive in this program of study. At the conclusion of this fellowship, the applicant will be poised to tackle critical problems in computational immune genetics with applications to infectious diseases and global health, and to bridge the clinical and computational worlds through a career in academic medicine.