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Activity Number: 71
Type: Contributed
Date/Time: Sunday, August 3, 2014 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract #311144 View Presentation
Title: An Integrated Method for Associating Microbiome Composition with Longitudinal Trait
Author(s): Yilong Zhang*+ and Laura Cox and Martin Blaser and Huilin Li
Companies: and NYU Lagone Medical Center and NYU Lagone Medical Center and New York University
Keywords: microbiome ; phylogenetics ; longitudinal data ; latent class
Abstract:

The human microbiome can have a major impact on health by promoting beneficial interactions or by damaging host tissues to produce disease. New sequencing technologies provide an unprecedented opportunity to explore the relationships between the microbiome and human physiology. However, analyzing the microbiome data is challenging because the data are: 1) high dimensional (number of taxa is much larger than the number of subjects) and with constraints as compositional data; 2) the species abundance count data are over-dispersed with many zeros; and 3) the data analysis should incorporate phylogenetic structure which includes important prior knowledge of evolutionary relationships among bacterial taxa. In this paper, we propose a novel method to study the association between microbiome composition and longitudinal traits, such as weight change. In this method, we combined the phylogenetic tree information into a latent linear mixed model to identify which bacterial taxa are related to the weight change trends. A data set involving longitudinal sampling of the murine gut microbiota is used to evaluate the method.


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