Multivariate analysis of microbial absolute abundance in population-based studies.

基于人群的研究中微生物绝对丰度的多变量分析。

• 批准号: 9508100
• 负责人: Ryan T. Demmer
• 金额: $ 17.19万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2020-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9508100
• 关键词: Address; Anatomy; Attenuated; Bacteria; Biological Markers; Clinical; Complex; Computer software; Data; Development; Diabetes Mellitus; Dimensions; Disease; Early Diagnosis; Ecology; Factor Analysis; Glucose Intolerance; Goals; Health; Heart Diseases; Human; Human Microbiome; Impairment; Individual; Inflammation; Inflammatory; Insulin Resistance; Investigation; Longitudinal Studies; Maintenance; Malignant Neoplasms; Measures; Mental Depression; Metabolic; Metabolic Diseases; Metabolic Marker; Methods; Microbe; Modeling; Motivation; Mouth Diseases; Multivariate Analysis; Oral; Outcome; Periodontal Diseases; Periodontitis; Play; Population Study; Prevalence; Procedures; Research; Ribosomal RNA; Risk; Role; Sampling; Secondary to; Signal Transduction; Statistical Methods; Supervision; Techniques; Technology; Testing; Theoretical Studies; Therapeutic; Vagina; Work; base; blood glucose regulation; clinical biomarkers; clinically relevant; data reduction; diabetes risk; disease phenotype; disorder risk; dysbiosis; epidemiology study; improved; inflammatory marker; insight; microbial; microbial community; microbiome; microbiome analysis; microbiome research; next generation sequencing; novel; novel therapeutic intervention; oral infection; oral microbial community; oral microbiome; recruit; tool; young adult

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.

项目摘要 口服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，葡萄糖不耐症和胰岛素抵抗研究（起源），具有 鉴定出与口腔疾病显着相关的口服微生物群。但是，与 系统性炎症和糖尿病风险等口腔外疾病的风险通常要弱得多。什么 存在信号通常会通过多个测试校正或整体数据减少程序来减弱。 因此，由于缺乏统计能力，许多潜在的重要关联仍未发现。 实际上，在微生物组关联研究中，缺乏功率问题很常见。缺乏系统 识别涉及弱但潜在的细菌的微生物失衡的统计方法 对疾病的添加作用。在此提案中，我们将开发新颖的统计方法和随附 解决挑战的软件。我们将基于我们以前的工作并制定新的监督数据 减少方法以提高关联分析的能力。该方法将首先利用临床 信息以选择临床相关的分类单元，然后汇总选定的分类单元以形成少数 微生物组丰度得分。该分数将用于进一步的关联分析，以减轻 多次测试校正并增强单个细菌类群的弱信号。而且，这些方法 将直接适用于绝对丰富的下一代测序读数计数，以避免偏见 和与相对丰度相关的功率损失。我们将正确建模计数值，零充气 在测序数据中利用丰富信息的绝对丰富。新开发的方法将是 应用于原始数据。我们希望鉴定出与 炎症和代谢生物标志物。潜在的发现有望提高我们对 疾病机制，促进炎症和代谢性疾病的早期诊断，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 人类微生物组在疾病发展和/或维持健康中的作用。