Linking microbiome genetic variants with cardiovascular phenotypes in 50,000 individuals

将 50,000 名个体的微生物组遗传变异与心血管表型联系起来

• 批准号: 10516693
• 负责人: KATHERINE S. POLLARD
• 金额: $ 70.73万
• 依托单位: J. DAVID GLADSTONE INSTITUTES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10516693
• 关键词: Accounting; Address; Algorithms; Alleles; Animal Model; Atherosclerosis; Bacterial Genes; Benchmarking; Biological Markers; Biology; Cardiovascular Diagnostic Techniques; Cardiovascular Diseases; Cardiovascular system; Cells; Code; Communities; Complex; Complex Mixtures; Copy Number Polymorphism; Country; DNA; Data; Data Set; Development; Diet; Disease; Drug Targeting; Ensure; Gene Dosage; Gene Family; Gene Frequency; Genes; Genetic; Genetic Recombination; Genetic Variation; Genome; Genotype; Goals; Haploidy; Health; Heart Diseases; Home; Human; Human Genetics; Human Microbiome; Human body; In Situ; Individual; Inflammation; Investigation; Israel; Label; Levocarnitine; Life Style; Linear Models; Link; Measurement; Measures; Mediating; Medical Records; Metabolic; Metagenomics; Methods; Microbe; Modeling; Outcome; Pan Genus; Pathway interactions; Pattern; Persons; Pharmaceutical Preparations; Phenotype; Phylogenetic Analysis; Physiology; Population; Population Genetics; Prevention; Probiotics; Process; Research; Resolution; Running; Sampling; Shotgun Sequencing; Shotguns; Single Nucleotide Polymorphism; Statistical Models; Structure; Taxonomy; Testing; United States; Unmarried person; Variant; acute coronary syndrome; base; bioinformatics tool; cardiovascular disorder risk; cohort; computational platform; cost; design; drug metabolism; genetic approach; genetic variant; gut microbes; gut microbiome; heart function; host-microbe interactions; insight; metabolomics; metagenome; metagenomic sequencing; microbial; microbiome; microorganism; novel; open source; personalized medicine; population genetic structure; prebiotics; precision medicine; simulation; therapeutic target; tool; trait

PROJECT SUMMARY / ABSTRACT The human body is home to a complex community of microorganisms (“microbiome”) that differs in composition between people, with numerous correlates to cardiovascular disease (CVD). Any two people will harbor different strains of a given species, which can be more genetically different than a human and chimpanzee with <60% of their genes shared. Even within a single person, each microbiome species may be a complex mixture of strains with different genomes and functional capabilities. This striking within-species genetic diversity has functional consequences for CVD, because gene loss and gain modify how strains process our diet, metabolize drugs, and stimulate inflammation. Hence, a population genetic approach is essential for revealing causal links between the microbiome and CVD. We have compiled a deeply phenotyped cohort of ~50,000 individuals with metagenomic sequencing of their gut microbiomes. This dataset includes ~8,000 people with atherosclerosis, thousands with measurements of heart function and metabolic health, and hundreds with acute coronary syndrome. This cohort is a unique and ideal setting to perform a well-powered CVD metagenome-wide association study (MWAS). Several barriers must be overcome before MWAS can be deployed at this scale. First, we must reduce the infeasible computational cost of genotyping thousands of microbiome species across ~50,000 people. Second, to ensure that statistical tests for associations do not have high false positive rates we need statistical models that adjust for microbial population structure within and across hosts. The goal of this proposal is to create a research toolbox to address these challenges as well as to identify putative mechanistic links between microbiome and CVD. We will develop data structures and query algorithms for accelerated genotype estimation and mixed effects models for accurate association tests. All code and methods will be open source and designed to be easily extended to other microbiome cohorts. Applying these tools to our cohort, we aim to identify specific microbial genes and pathways responsible for known associations between microbes and CVD. We also expect to discover new associations that were missed because cohorts were too small or they were analyzed with methods that ignore differences in gene content across strains. These findings will be used to identify microbial biomarkers for CVD diagnosis and personalized treatments or to design microbiome targeted drugs, prebiotics, and probiotics to treat heart disease.

项目概要/摘要 人体是一个复杂的微生物群落（“微生物组”）的家园，其组成不同 人与人之间的差异，与心血管疾病 (CVD) 有许多相关性。任何两个人都会怀有不同的 特定物种的品系，其基因差异可能比人类和黑猩猩更大，其遗传差异<60% 他们的基因是共享的。即使在一个人体内，每个微生物组物种也可能是菌株的复杂混合物 具有不同的基因组和功能。这种惊人的种内遗传多样性具有功能性 CVD 的后果，因为基因丢失和获得改变了菌株处理我们的饮食、代谢药物和 刺激炎症。因此，群体遗传学方法对于揭示不同物种之间的因果关系至关重要。 微生物组和 CVD。 我们编制了约 50,000 名个体的深度表型队列，并对他们的肠道进行了宏基因组测序 微生物组。该数据集包括约 8,000 名动脉粥样硬化患者，其中数千人进行了心脏测量 功能和代谢健康，以及数百名急性冠状动脉综合征患者。这个群体是一个独特而理想的群体 设置进行功能强大的 CVD 宏基因组范围关联研究 (MWAS)。 在大规模部署 MWAS 之前，必须克服一些障碍。首先，我们必须减少 对约 50,000 人的数千个微生物组物种进行基因分型的计算成本不可行。第二， 为了确保关联的统计测试不会出现高误报率，我们需要统计模型 调整宿主内部和宿主之间的微生物种群结构。该提案的目标是创建一个 研究工具箱来应对这些挑战以及确定之间假定的机制联系 微生物组和 CVD。我们将开发数据结构和查询算法以加速基因型估计 以及用于准确关联测试的混合效应模型。所有代码和方法都将开源并设计 可以轻松扩展到其他微生物群。 将这些工具应用于我们的队列，我们​​的目标是确定负责的特定微生物基因和途径 微生物与 CVD 之间的已知关联。我们还期望发现被遗漏的新关联 因为队列太小或者他们的分析方法忽略了基因内容的差异 跨菌株。这些发现将用于识别微生物生物标志物，用于 CVD 诊断和个性化治疗 治疗或设计微生物组靶向药物、益生元和益生菌来治疗心脏病。