Novel Statistical Methods for Oral Microbiome Data Analysis

项目摘要 口腔中含有多种多样的微生物群，对人类健康起着重要作用。研究 表明口腔微生物组的改变与牙周炎等口腔疾病密切相关， 龋齿以及许多口腔外疾病，如糖尿病和心血管疾病。大- 规模流行病学研究，包括我们的动机口腔感染，葡萄糖不耐症和胰岛素 抗性研究（ORIGINS）收集了丰富的数据，以进一步研究两者之间的关联和机制。 口腔微生物群落和健康结果之间的途径。然而，微生物组数据 复杂的，受到约束，如组合，零通货膨胀，过度分散，分类层次， 高维度。大多数现有的统计分析依赖于临时的数据操作和转换 绕过这些限制的方法，往往导致无法解释和不可复制的结果。有一个 迫切需要更有原则的统计方法。这一建议将开发新的协会和调解 这些方法完全适应独特的数据特征，并利用微生物组数据的力量。我们将 建立在一个新生的相对偏移回归框架之上，该框架提供了一种高度直观和可解释的方法， 模拟成分数据。在目标1中，我们将通过以下方式显著扩展相对位移模型的范围： 将其推广到非高斯响应和非线性关系。新方法可以更好地描述 微生物群和不同表型之间的复杂关联。在目标2中，我们将开发新型调解 微生物暴露（子目标2a）和微生物介质（子目标2b）的方法。这些方法将提供 为量化和测试与微生物组数据有关的中介作用奠定了坚实的基础。拟议 方法将应用于ORIGINS数据，以确定与口腔/口腔外相关的关键口腔微生物群。 表型和描绘调解途径。这些方法和附带的软件包将 在其他微生物组研究中也有普遍的效用，并有望提高我们对微生物的理解。 微生物组在人类健康中的关键作用。

PROJECT SUMMARY The oral cavity harbors a great diversity of microbiota that play an important role in human health. Studies have shown that alterations in the oral microbiome are closely associated with oral diseases such as periodontitis and dental caries, as well as numerous extra-oral disorders such as diabetes and cardiovascular diseases. Large- scale epidemiological studies, including our motivating Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS), have collected rich data to further investigate the association and mechanistic pathways between the oral microbial communities and health outcomes. However, microbiome data are complex, subject to constraints such as compositionality, zero inflation, overdispersion, taxonomic hierarchy, and high dimensionality. Most existing statistical analyses rely on ad hoc data manipulation and transformation approaches to bypass these constraints, often leading to uninterpretable and irreproducible results. There is a pressing need for more principled statistical methods. This proposal will develop novel association and mediation methods that fully accommodate the unique data features and harness the power of microbiome data. We will build upon a nascent relative-shift regression framework that provides a highly intuitive and interpretable way of modeling compositional data. In Aim 1, we will significantly extend the scope of the relative-shift model by generalizing it to non-Gaussian responses and nolinear relationships. The new methods can better depict the complex association between microbiota and diverse phenotypes. In Aim 2, we will develop novel mediation methods with microbial exposures (Sub-Aim 2a) and microbial mediators (Sub-Aim 2b). The methods will provide a solid foundation for quantifying and testing mediation effects pertaining to microbiome data. The proposed methods will be applied to the ORIGINS data to identify crucial oral microbiota associated with oral/extra-oral phenotypes and to delineate the mediation pathways. The methods and an accompanying software package will also have general utilities in other microbiome studies and promise to enhance our understanding of the microbiome’s critical role in human health.