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Activity Number:
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91
- High Dimensional Data, Causal Inference, Biostats Education, and More
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Type:
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Contributed
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Date/Time:
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Monday, August 9, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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ENAR
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Abstract #318060
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Title:
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Information-Based Mediation Analysis on High-Dimensional Metagenomic Data
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Author(s):
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Lingling An* and Kyle Carter and Meng Lu and Hongmei Jiang
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Companies:
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University of Arizona and University of Arizona and University of Arizona and Northwestern University
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Keywords:
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microbiome;
mediation analysis;
metagenome;
integrative analysis
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Abstract:
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The 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 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.
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Authors who are presenting talks have a * after their name.
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