DMS/NIGMS 2: Unraveling the Role of the Human Microbiome to Advance Precision Medicine

精准医学旨在通过根据个人的独特特征定制治疗方案来优化医疗保健质量。目前的精准医疗实践主要是利用个体基因组学信息。新的测序技术使我们能够量化人类微生物组——人体内的全部微生物。微生物组中丰富的信息对推进精准医疗具有巨大的潜力。然而，适应微生物组数据独特特征的稳健有效的统计方法的发展已经严重落后。例如，微生物的丰度是以分数来测量的，它们的实际丰度无法在测序实验中恢复。用标准方法一次分析一种微生物会导致许多错误的发现和不可复制的结果。在这个项目中，pi将开发用于精准医疗的多种微生物联合分析方法。具体来说，pi将开发新的统计方法和计算机软件，利用微生物组数据来表征疾病病理和亚型，为个性化饮食或药物摄入指定微生物生物标志物，并识别疾病进展因果途径中的微生物。pi还计划为各级学生提供培训，从代表性不足的群体中招募研究助理，并开发新的跨学科课程。该项目旨在开发新的统计方法，适用于分析精密医学应用中的组成，高维和过度分散的微生物组数据。特别是，将开发一种新的模型，以利用广义狄利克雷多项分布的混合对协变量条件下的学科微生物组时间序列进行聚类。这种混合模型使研究人员能够灵活地捕捉微生物组组成的复杂时间变异性，并产生有意义的疾病亚型。主题聚类和协变量选择同时进行，提高了两个分析任务的性能。该研究还将开发一个新的框架，用于学习基于梯度增强树方法的微生物组信息个性化治疗规则（PTR）。此外，将采用一种新颖的仿制品生成方法来选择与PTR相关的微生物，并适当地控制错误发现率。开发的框架将能够强有力地选择对个性化治疗或个性化饮食重要的微生物。最后，本研究将引入一种新的系统发育树辅助局部中介模型，以确定疾病进展的重要中介类群。本提案中开发的所有方法将被实施到高效且用户友好的R包中，以推进精准医学和微生物组研究。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Precision medicine seeks to optimize health care quality by tailoring treatments to a person’s unique characteristics. The current practice of precision medicine mainly utilizes individuals’ genomics information. The new sequencing technology allows us to quantify the human microbiome – the full array of microorganisms in the human body. The rich information in the microbiome holds great potential to advance precision medicine. However, the development of robust and efficient statistical methods that adapt to the unique features of the microbiome data has seriously fallen behind. For instance, the abundance of microbes is measured in fractions and their actual abundance cannot be recovered in sequencing experiments. Applying standard methods to analyze one microbe at a time will lead to many false discoveries and irreproducible results. In this project, the PIs will develop methods to jointly analyze many microbes for precision medicine. Specifically, the PIs will develop novel statistical methods and computer software to characterize disease pathology and subtypes using microbiome data, nominate microbial biomarkers for personalized diet or drug intake, and identify microbes in the causal pathways of disease progression. The PIs also plan to provide training to students at all levels, recruit research assistants from under-represented groups, and develop new interdisciplinary courses. This project aims to develop new statistical methods that are suitable to analyze compositional, high-dimensional, and overdispersed microbiome data for precision medicine applications. In particular, a novel model will be developed to cluster subject-wise microbiome time-series conditional on covariates using the mixture of generalized Dirichlet multinomial distribution. This mixture model enables researchers to flexibly capture the complex temporal variability of microbiome compositions and produce meaningful disease subtypes. The subject clustering and covariate selection are performed simultaneously, which improves the performance of both analysis tasks. The research will also develop a new framework for learning microbiome-informed personalized treatment rule (PTR) based on the gradient boosting tree method. Furthermore, a novel knockoff generating method will be used to select the microbe relevant to the PTR with proper false discovery rate control. The developed framework will enable robustly and powerfully selecting microbes that are important for personalized treatment or personalized diet. Lastly, the research will introduce a novel phylogenetic-tree-assisted local mediation model to identify vital mediating taxa for disease progression. All methods developed in this proposal will be implemented into efficient and user-friendly R packages to advance precision medicine and microbiome research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.