The human microbiome studies have been accelerated by the advances in next-generation sequencing technologies. There has also been increasing interest in discovering microbial taxa that are associated with diverse host phenotypes, environmental factors or clinical interventions. Here, I first describe unique features of microbiome data and the resulting demand for adaptive association analysis which robustly suits different association patterns, while providing valid statistical inferences. Then, I introduce two adaptive microbiome association tests as follows.
My first method, namely, optimal microbiome-based association test (OMiAT), relates microbial composition with continuous (e.g., body mass index) or binary (e.g., disease status) traits. OMiAT is a data-driven adaptive testing method which approximates to the most powerful performance among different candidate tests from the sum of powered score tests (SPU) and microbiome regression-based kernel association test (MiRKAT). I illustrate that OMiAT robustly discovers underlying association signals arising from highly imbalanced microbial abundances and phylogenetic tree structure, while correctly controlling type I error rates. I also propose a way to apply it to fine association mapping of diverse higher-level taxa at different taxonomic levels within a newly introduced microbial taxa discovery framework, microbiome comprehensive association mapping (MiCAM).
My second method, namely, optimal microbiome-based survival analysis (OMiSA), relates microbial composition with survival (i.e., time to event) traits. OMiSA approximates to the most powerful association test within two test domains, 1) microbiome-based survival analysis using linear and non-linear bases of OTUs (MiSALN) and 2) microbiome-based kernel association test for survival traits (MiRKAT-S). I illustrate that OMiSA powerfully discovers underlying associated lineages whether they are rare or abundant and phylogenetically related or not, while correctly controlling type I error rates.
OMiAT and OMiSA are attractive in practice due to the high complexity of microbiome data and the unknown true nature of the state. MiCAM also provides a hierarchical microbiome association map through a breadth of taxonomic levels, which can be used as a guideline for further investigation on the roles of discovered taxa in human health or disease.
|Commitee:||Blaser, Martin J., Goldberg, Judith D., Shao, Yongzhao, Troxel, Andrea B.|
|School:||New York University|
|Department:||Environmental Health Science|
|School Location:||United States -- New York|
|Source:||DAI-B 79/12(E), Dissertation Abstracts International|
|Keywords:||Adaptive association analysis, Compositional data analysis, High-dimensional statistics, Microbial group analysis, Microbiome association studies, Phylogenetics|
Copyright in each Dissertation and Thesis is retained by the author. All Rights Reserved
dissertation or thesis. The supplemental files are provided "AS IS" without warranty. ProQuest is not responsible for the
content, format or impact on the supplemental file(s) on our system. in some cases, the file type may be unknown or
may be a .exe file. We recommend caution as you open such files.
supplemental files is subject to the ProQuest Terms and Conditions of use.