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

DMS/NIGMS 2：揭示人类微生物组在推进精准医学中的作用

• 批准号: 2054346
• 负责人: Guanhua Chen
• 金额: $ 60万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2054346&HistoricalAwards=false
• 关键词: DMS; NIGMS; Unraveling; Role; Human

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.

精准医疗寻求通过根据个人的独特特征量身定做治疗方法来优化医疗质量。目前的精准医学实践主要是利用个体基因组信息。新的测序技术使我们能够量化人类微生物群--人体内的全部微生物。微生物组中丰富的信息为推进精准医学提供了巨大的潜力。然而，适应微生物组数据的独特特征的稳健和有效的统计方法的开发严重落后。例如，微生物的丰度是以分数来测量的，它们的实际丰度在测序实验中无法恢复。应用标准方法一次分析一种微生物将导致许多错误的发现和不可重现的结果。在这个项目中，PI将开发方法来联合分析精准医学中的许多微生物。具体地说，PIS将开发新的统计方法和计算机软件，以利用微生物组数据描述疾病病理和亚型，提名用于个性化饮食或药物摄取的微生物生物标记物，并识别疾病发展原因路径中的微生物。PIS还计划为所有级别的学生提供培训，从代表性不足的群体中招聘研究助理，并开发新的跨学科课程。该项目旨在开发新的统计方法，适用于分析精确医学应用中的成分、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，以协变量为条件，使用广义Dirichlet多项式分布的混合来聚类按主题分类的微生物组时间序列。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，还将使用一种新的假冒产品生成方法来选择与PTR相关的微生物，并适当控制误发率。开发的框架将使我们能够有力地选择对个性化治疗或个性化饮食至关重要的微生物。最后，本研究将引入一种新的系统发育树辅助的局部调解模型来识别影响疾病发展的重要中介类群。本提案中开发的所有方法都将被实施到高效和用户友好的R包中，以促进精确医学和微生物组研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Independence Weights for Causal Inference with Continuous Treatments

连续治疗因果推理的独立权重

• DOI: 10.1080/01621459.2023.2213485
• 发表时间: 2023
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Huling, Jared D.;Greifer, Noah;Chen, Guanhua
• 通讯作者: Chen, Guanhua