An Information-Based Approach for Mediation Analysis on High-Dimensional Metagenomic Data
AffiliationUniv Arizona, Interdiciplanary Program Stat & Data Sci
Univ Arizona, Dept Epidemiol & Biostat
Univ Arizona, Dept Biosyst Engn
MetadataShow full item record
PublisherFRONTIERS MEDIA SA
CitationCarter KM, Lu M, Jiang H and An L (2020) An Information-Based Approach for Mediation Analysis on High-Dimensional Metagenomic Data. Front. Genet. 11:148. doi: 10.3389/fgene.2020.00148
JournalFRONTIERS IN GENETICS
Rights© 2020 Carter, Lu, Jiang and An. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractThe human microbiome plays a critical role in the development of gut-related illnesses such as inflammatory bowel disease and clinical pouchitis. A mediation model can be used to describe the interaction between host gene expression, the gut microbiome, and clinical/health situation (e.g., diseased or not, inflammation level) and may provide insights into underlying disease mechanisms. Current mediation regression methodology cannot adequately model high-dimensional exposures and mediators or mixed data types. Additionally, regression based mediation models require some assumptions for the model parameters, and the relationships are usually assumed to be linear and additive. With the microbiome being the mediators, these assumptions are violated. We propose two novel nonparametric procedures utilizing information theory to detect significant mediation effects with high-dimensional exposures and mediators and varying data types while avoiding standard regression assumptions. Compared with available methods through comprehensive simulation studies, the proposed method shows higher power and lower error. The innovative method is applied to clinical pouchitis data as well and interesting results are obtained.
NoteOpen access journal
VersionFinal published version
Except where otherwise noted, this item's license is described as © 2020 Carter, Lu, Jiang and An. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
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