A0218
Title: Melody: Meta-analysis of microbiome association studies for discovering generalizable microbial signatures
Authors: Zhengzheng Tang - University of Wisconsin-Madison (United States) [presenting]
Abstract: With the increasing number of microbiome association studies, the field aims to establish standards for data sharing, harmonization, and meta-analysis to accurately identify microbial signatures that are generalizable across cohorts and populations. Standard meta-analysis protocols fall short for microbiome data due to their unique characteristics, particularly their compositional structure. To address this issue, Melody is introduced, a framework that generates, harmonizes, and combines summary association statistics across studies to identify microbial signatures in meta-analysis. Comprehensive and realistic simulations demonstrate that Melody substantially outperforms existing approaches in prioritizing true signatures. The utility of Melody is illustrated in real data applications.
