Abstract
As many diseases are known to be related to microbes, interests in statistical methods for Microbiome-Wide Association Studies (MWAS) are also increasing. In this respect, we systematically investigate the properties of statistical methods for MWAS and compare their performances using simulation data generated from Human Microbiome Project data. We first assessed the type I error rates of eight commonly used methods over different levels of sparsity. Among these, ANCOM and metagenomeSeq methods yielded the well preserved type I error rates regardless of sparsity. In the power comparison study, metagenomeSeq showed higher power than ANCOM. In conclusion, we recommend using metagenomeSeq for the analysis of metagenome data.