Detecting sparse microbial association signals adaptively from longitudinal microbiome data based on generalized estimating equations

• Published in: Briefings in bioinformatics Vol. 23; no. 5
• Main Authors: Sun, Han, Huang, Xiaoyun, Huo, Ban, Tan, Yuting, He, Tingting, Jiang, Xingpeng
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 20.09.2022; Oxford Publishing Limited (England)
• Subjects: Biomarkers; Crohn's disease; Error correction; Intestinal microflora; Mathematical analysis; Microbial activity; Microbiomes; Microorganisms; Phenotypes; Statistical methods; Statistics; longitudinal microbiome data; microbiome-based association test; higher criticism; sparse microbial association signals; generalized estimating equations
• ISSN: 1467-5463; 1477-4054; 1477-4054
• DOI: 10.1093/bib/bbac149

Abstract The association between the compositions of microbial communities and various host phenotypes is an important research topic. Microbiome association research addresses multiple domains, such as human disease and diet. Statistical methods for testing microbiome–phenotype associations have been studied recently to determine their ability to assess longitudinal microbiome data. However, existing methods fail to detect sparse association signals in longitudinal microbiome data. In this paper, we developed a novel method, namely aGEEMIHC, which is a data-driven adaptive microbiome higher criticism analysis based on generalized estimating equations to detect sparse microbial association signals from longitudinal microbiome data. aGEEMiHC adopts generalized estimating equations framework that fully considers the correlation among different observations from the same subject in longitudinal data. To be robust to diverse correlation structures for longitudinal data, aGEEMiHC integrates multiple microbiome higher criticism analyses based on generalized estimating equations with different working correlation structures. Extensive simulation experiments demonstrate that aGEEMiHC can control the type I error correctly and achieve superior performance according to a statistical power comparison. We also applied it to longitudinal microbiome data with various types of host phenotypes to demonstrate the stability of our method. aGEEMiHC is also utilized for real longitudinal microbiome data, and we found a significant association between the gut microbiome and Crohn’s disease. In addition, our method ranks the significant factors associated with the host phenotype to provide potential biomarkers.