Variance Component Selection With Applications to Microbiome Taxonomic Data.

ABSTRACT: High-throughput sequencing technology has enabled population-based studies of the role of the human microbiome in disease etiology and exposure response. Microbiome data are summarized as counts or composition of the bacterial taxa at different taxonomic levels. An important problem is to identify the bacterial taxa that are associated with a response. One method is to test the association of specific taxon with phenotypes in a linear mixed effect model, which incorporates phylogenetic information among bacterial communities. Another type of approaches consider all taxa in a joint model and achieves selection via penalization method, which ignores phylogenetic information. In this paper, we consider regression analysis by treating bacterial taxa at different level as multiple random effect

SUBMITTER: Zhai J 

PROVIDER: S-EPMC5883493 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature