multiMiAT: An optimal microbiome-based association test for multicategory phenotypes

Abstract

Microbes affect the metabolism, immunity, digestion and other aspects of the human body incessantly, and dysbiosis of the microbiome drives not only the occurrence but also the development of disease (i.e., multiple statuses of disease). Recently, microbiome-based association tests have been widely developed to detect the association between the microbiome and host phenotype. However, existing methods have not achieved satisfactory performance in testing the association between the microbiome and ordinal/nominal multicategory phenotypes (e.g., disease severity and tumor subtype). In this paper, we propose an optimal microbiome-based association test for multicategory phenotypes, namely, multiMiAT. Specifically, under the multinomial logit model framework, we first introduce a microbiome regression-based kernel association test (multiMiRKAT). As a data-driven optimal test, multiMiAT then integrates multiMiRKAT, score test and MiRKAT-MC to maintain excellent performance in diverse association patterns. Massive simulation experiments prove the excellent performance of our method. multiMiAT is also applied to real microbiome data experiments to detect the association between the gut microbiome and clinical statuses of colorectal cancer development and the association between the gut microbiome and diverse development statuses of Clostridium difficile infections.