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Abstract:
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The role of the microbiome in human health and disease has been increasingly studied, gathering momentum through the use of high-throughput technologies. Further identification of microbial culprits is necessary to better understand the mechanisms involved in microbiome-induced phenotypes or diseases. Accordingly, not only has the entire microbial community or individual microorganisms, but also the upper-level taxa in different taxonomic ranks (e.g., phylum, class, order, family, and genus) been highlighted as key microbial biomarkers. For assessing upper-level taxa, a conventional ecological method, which is based on the aggregates of microbial abundances in lower-level lineages, has been most commonly used. However, this approach is inefficient by neglecting detailed information about diverse association patterns from nested microorganisms. Here, we investigate different group analytic methods from diverse angles for more robust and powerful testing. We introduce a hierarchical framework, namely, the microbiome comprehensive association mapping (MiCAM), which uses different configurations to fine-map diverse microbial taxa throughout all different taxonomic ranks.
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