Multivariate analysis of microbial absolute abundance in population-based studies

项目摘要 口腔微生物组已被证明与人类健康有关。大规模流行病学 研究，包括我们正在进行的口腔感染、葡萄糖耐量和胰岛素抵抗研究 （ORIGINS）已经确定了与口腔疾病显著相关的口腔微生物群。 然而，与口腔外疾病风险（如全身性炎症和 糖尿病风险通常较低。存在的信号通常被多个衰减器衰减。 测试校正或整体数据减少程序。因此，许多潜在 由于缺乏统计能力，重要的关联仍未被发现。事实上，缺乏- 功率问题在微生物组关联研究中很常见。缺乏系统的统计数据 用于鉴定微生物失衡的方法， 对疾病的附加效应。在这项提案中，我们将开发新的统计方法， 配套软件来应对挑战。我们将在以往工作的基础上， 开发新的监督数据简化方法，以提高关联分析的能力。 该方法将首先利用临床信息来选择临床相关的分类群，然后 聚合选定的分类群以形成少量的微生物组丰度分数。评分 将用于进一步的关联分析，以减少多次测试校正的负担 并增强来自单个细菌分类群的弱信号。此外，这些方法将直接 适用于下一代测序读数计数的绝对丰度，以避免偏倚 以及与相对丰度相关的功率损耗。我们将适当地模拟计数值， 零膨胀的绝对丰度来利用测序数据中的丰富信息。新 开发的方法将应用于ORIGINS数据。我们希望能够识别口腔微生物群 与炎症和代谢生物标志物显著相关。的潜在 这一发现有望提高我们对疾病机制的理解， 炎症和代谢紊乱的诊断，并启发新的治疗方法。的 应用于其他微生物组研究将提高我们对人类的作用的理解。 微生物组在疾病发展和/或维持健康中的作用。

PROJECT SUMMARY The oral microbiome has been shown to be related to human health. Large-scale epidemiological studies, including our ongoing Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS), have identified oral microbiota that are significantly associated with oral diseases. However, the associations with extra-oral disease risks such as systemic inflammation and diabetes risk are usually much weaker. What signals exist are typically attenuated by multiple testing corrections or holistic data reduction procedures. Consequently, many potentially important associations remain undiscovered due to a lack of statistical power. In fact, the lack-of- power issue is common in microbiome association studies. There is a lack of systematic statistical approaches for identifying microbial imbalance that involves bacteria with weak but potentially additive effects on diseases. In this proposal, we will develop novel statistical methods and accompanying software to address the challenge. We will build upon our previous work and develop new supervised data reduction approaches to improve the power of association analysis. The methods will first leverage clinical information to select clinically relevant taxa, and then aggregate selected taxa to form a small number of microbiome abundance scores. The scores will be used for further association analysis to reduce the burden of multiple testing corrections and to enhance weak signals from individual bacterial taxa. Moreover, the methods will be directly applicable to absolute abundances of next-generation sequencing read counts, to avoid the bias and power loss associated with relative abundances. We will properly model the count-valued, zero-inflated absolute abundances to harness the rich information in sequencing data. The newly developed methods will be applied to the ORIGINS data. We expect to identify oral microbiota that are significantly associated with inflammatory and metabolic biomarkers. The potential discovery promises to improve our understanding of disease mechanisms, facilitate early diagnosis of inflammatory and metabolic disorders, and inspire new therapeutic approaches. The applications to other microbiome studies will improve our understanding of the role of human microbiome in the development of disease and/or maintenance of health.