A multivariate distance‐based analytic framework for microbial interdependence association test in longitudinal study. Issue 8 (5th September 2017)

• Record Type: Journal Article
• Authors: Zhang, Yilong; Han, Sung Won; Cox, Laura M.; Li, Huilin
• Is Part Of: Genetic epidemiology. Volume 41:Issue 8(2017)
• Page Start: 769
• Page End: 778
• Publication Date: 2017-09-05
• Subjects: longitudinal data -- MANOVA -- microbial interdependence -- nonparametric test; Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.22065
• Languages: English
• ISSNs: 0741-0395

Abstract

ABSTRACT: Human microbiome is the collection of microbes living in and on the various parts of our body. The microbes living on our body in nature do not live alone. They act as integrated microbial community with massive competing and cooperating and contribute to our human health in a very important way. Most current analyses focus on examining microbial differences at a single time point, which do not adequately capture the dynamic nature of the microbiome data. With the advent of high‐throughput sequencing and analytical tools, we are able to probe the interdependent relationship among microbial species through longitudinal study. Here, we propose a multivariate distance‐based test to evaluate the association between key phenotypic variables and microbial interdependence utilizing the repeatedly measured microbiome data. Extensive simulations were performed to evaluate the validity and efficiency of the proposed method. We also demonstrate the utility of the proposed test using a well‐designed longitudinal murine experiment and a longitudinal human study. The proposed methodology has been implemented in the freely distributed open‐source R package and Python code.