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

项目总结/摘要 人体是一个复杂的微生物群落（“微生物组”）的家园， 人与人之间，与心血管疾病（CVD）有许多相关性。任何两个人都会有不同的 给定物种的菌株，其可以比人类和黑猩猩具有<60%的遗传差异， 他们的基因是相同的。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 不同的基因组和功能。这种惊人的物种内遗传多样性具有功能性 因为基因的丢失和获得改变了菌株如何处理我们的饮食，代谢药物， 刺激炎症。因此，群体遗传学方法对于揭示遗传学之间的因果关系至关重要。 微生物组和CVD。 我们已经编制了一个深度表型队列，约50，000人，他们的肠道宏基因组测序 微生物群落该数据集包括约8，000名动脉粥样硬化患者，数千名心脏测量结果 功能和代谢健康，以及数百名急性冠脉综合征患者。这个群体是一个独特的理想 进行了一项动力良好的CVD宏基因组关联研究（MWAS）。 在MWAS能够以这种规模部署之前，必须克服几个障碍。首先，我们必须减少 在约50，000人中对数千种微生物组物种进行基因分型的不可行的计算成本。第二、 为了确保关联的统计检验不会有很高的假阳性率，我们需要统计模型 调节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，以应对这些挑战，并确定假定的机械联系之间 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 以及用于精确关联检验的混合效应模型。所有的代码和方法都将是开源的， 很容易扩展到其他微生物组群。 将这些工具应用于我们的队列，我们的目标是确定负责 微生物和CVD之间的已知关联。我们还希望发现新的关联， 因为队列太小，或者他们的分析方法忽略了基因含量的差异， 跨菌株。这些发现将用于确定CVD诊断和个性化的微生物生物标志物。 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。

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.