Ye Yue1, Yi-Juan Hu1. 1. Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA.
Abstract
MOTIVATION: Understanding whether and which microbes played a mediating role between an exposure and a disease outcome are essential for researchers to develop clinical interventions to treat the disease by modulating the microbes. Existing methods for mediation analysis of the microbiome are often limited to a global test of community-level mediation or selection of mediating microbes without control of the false discovery rate (FDR). Further, while the null hypothesis of no mediation at each microbe is a composite null that consists of three types of null, most existing methods treat the microbes as if they were all under the same type of null, leading to excessive false positive results. RESULTS: We propose a new approach based on inverse regression that regresses the microbiome data at each taxon on the exposure and the exposure-adjusted outcome. Then the p-values for testing the coefficients are used to test mediation at both the community and individual taxon levels. This approach fits nicely into our Linear Decomposition Model (LDM) framework, so our new method LDM-med, implemented in the LDM framework, enjoys all the features of the LDM, e.g., allowing an arbitrary number of taxa to be tested simultaneously, supporting continuous, discrete, or multivariate exposures and outcomes (including survival outcomes), and so on. Using extensive simulations, we showed that LDM-med always preserved the FDR of testing individual taxa and had adequate sensitivity; LDM-med always controlled the type I error of the global test and had compelling power over existing methods. The flexibility of LDM-med for a variety of mediation analyses is illustrated by an application to a murine microbiome dataset, which identified several plausible mediating taxa. AVAILABILITY AND IMPLEMENTATION: Our new method has been added to our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Understanding whether and which microbes played a mediating role between an exposure and a disease outcome are essential for researchers to develop clinical interventions to treat the disease by modulating the microbes. Existing methods for mediation analysis of the microbiome are often limited to a global test of community-level mediation or selection of mediating microbes without control of the false discovery rate (FDR). Further, while the null hypothesis of no mediation at each microbe is a composite null that consists of three types of null, most existing methods treat the microbes as if they were all under the same type of null, leading to excessive false positive results. RESULTS: We propose a new approach based on inverse regression that regresses the microbiome data at each taxon on the exposure and the exposure-adjusted outcome. Then the p-values for testing the coefficients are used to test mediation at both the community and individual taxon levels. This approach fits nicely into our Linear Decomposition Model (LDM) framework, so our new method LDM-med, implemented in the LDM framework, enjoys all the features of the LDM, e.g., allowing an arbitrary number of taxa to be tested simultaneously, supporting continuous, discrete, or multivariate exposures and outcomes (including survival outcomes), and so on. Using extensive simulations, we showed that LDM-med always preserved the FDR of testing individual taxa and had adequate sensitivity; LDM-med always controlled the type I error of the global test and had compelling power over existing methods. The flexibility of LDM-med for a variety of mediation analyses is illustrated by an application to a murine microbiome dataset, which identified several plausible mediating taxa. AVAILABILITY AND IMPLEMENTATION: Our new method has been added to our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Robert R Jenq; Ying Taur; Sean M Devlin; Doris M Ponce; Jenna D Goldberg; Katya F Ahr; Eric R Littmann; Lilan Ling; Asia C Gobourne; Liza C Miller; Melissa D Docampo; Jonathan U Peled; Nicholas Arpaia; Justin R Cross; Tatanisha K Peets; Melissa A Lumish; Yusuke Shono; Jarrod A Dudakov; Hendrik Poeck; Alan M Hanash; Juliet N Barker; Miguel-Angel Perales; Sergio A Giralt; Eric G Pamer; Marcel R M van den Brink Journal: Biol Blood Marrow Transplant Date: 2015-05-11 Impact factor: 5.742
Authors: Emily S Charlson; Jun Chen; Rebecca Custers-Allen; Kyle Bittinger; Hongzhe Li; Rohini Sinha; Jennifer Hwang; Frederic D Bushman; Ronald G Collman Journal: PLoS One Date: 2010-12-20 Impact factor: 3.240