Multivariate analysis of microbial absolute abundance in population-based studies

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.