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。大规模的流行病学研究， 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）， 确定口腔微生物群与口腔疾病显著相关。然而，与 口腔外疾病的风险，如全身炎症和糖尿病的风险通常要弱得多。什么 存在的信号通常通过多次测试校正或整体数据简化过程而衰减。 因此，由于缺乏统计能力，许多潜在的重要关联仍未被发现。 事实上，缺乏动力的问题在微生物组关联研究中很常见。缺乏系统的 用于鉴定微生物不平衡的统计方法，所述微生物不平衡涉及具有弱但潜在的 对疾病的附加效应。在本提案中，我们将开发新的统计方法和配套的 软件来应对挑战。我们将在以前工作的基础上开发新的监督数据 减少方法，以提高关联分析的能力。这些方法将首先利用临床 信息以选择临床相关的分类群，然后聚集所选择的分类群以形成少量的 微生物丰度得分。这些分数将用于进一步的关联分析，以减少 多重测试校正和增强来自单个细菌分类群的弱信号。此外，方法 将直接适用于下一代测序读段计数的绝对丰度，以避免偏倚 以及与相对丰度相关的功率损耗。我们将正确地建模计数值，零膨胀 绝对丰度来利用测序数据中的丰富信息。新开发的方法将是 应用于ORIGINS数据。我们希望识别与以下疾病显着相关的口腔微生物群 炎症和代谢生物标志物。这一潜在的发现有望提高我们对 疾病机制，促进炎症和代谢紊乱的早期诊断，并激发新的 治疗方法。其他微生物组研究的应用将提高我们对微生物组的理解。 人类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.