Methodology Development and Implementation for Microbiome Sequencing Data: Hierarchical Modeling on Clustered Taxa Counts with Repeated Measures

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The technological advances in next generation sequencing have enabled researchers to unveil the wide variability in microbial communities and their relationships with different diseases. Therefore, it is becoming critical to understand both environmental and host genetic factors that impact the composition of the microbiome. However, robust and powerful methods in this area are underdeveloped due to the complexity of microbiome sequencing data, which includes: a) microbial taxa data are usually grouped into operational taxonomic units (OTUs) and these counts are often highly skewed, over-dispersed, and zero inflated, b) OTU counts within a taxonomic hierarchical cluster are often highly correlated, but this multivariate nature is usually ignored, c) the study designs often involve repeated measures taken from related family members, thus inducing temporal and familial correlations. In this proposal, I will develop powerful bioinformatics, statistical, and computational methods to overcome these challenges. Specifically, I propose to use the latent variable (LV) methodology to jointly model multiple taxa from hierarchical taxonomic clusters within a longitudinal family study framework. The LV framework represents the underlying conceptual traits of the cluster and explains the correlations among different taxa. To address the over-dispersed and zero inflated features of the taxa counts, I will apply both zero-inflated and hurdle models on the multivariate OTU outcomes.