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

Oregon Convention Center 

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.

Longitudinal data

Gut microbiome

Group penalization

Time-lagged associations

Biostatistics

Disease modeling 

Section on Statistics in Genomics and Genetics