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A Group Penalization Framework for Detecting Time-Lagged Microbiota-Host Associations

Abstract Number:

3149 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Emily Palmer (1), Thomas Sharpton (2), Yuan Jiang (1)

Institutions:

(1) Oregon State University, N/A, (2) Oregon State University, OR

Co-Author(s):

Thomas Sharpton  
Oregon State University
Yuan Jiang  
Oregon State University

First Author:

Emily Palmer  
Oregon State University

Presenting Author:

Emily Palmer  
Oregon State University

Abstract Text:

We present a framework to identify time-lagged associations between abundances of longitudinally sampled microbiota and a stationary response (final health outcome, disease status, etc.). We introduce a definition of the time lag by imposing a particular grouping structure on the association pattern of longitudinal microbial measurements. Using group regularization methods, we identify these time-lagged associations including their strengths, signs, and timespans. Simulation results demonstrate accurate identification of time lags and estimation of signal strengths by our approach. We apply this framework to find specific gut microbial taxa and their lagged effects associated with increased parasite worm burden in zebrafish.

Keywords:

Longitudinal data|Gut microbiome |Group penalization|Time-lagged associations|Biostatistics|Disease modeling

Sponsors:

Section on Statistics in Genomics and Genetics

Tracks:

Miscellaneous

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