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In microbiome analysis, researchers often seek to identify taxonomic featuresassociated with an outcome of interest. However, microbiome features areintercorrelated and linked by phylogenetic relationships, making it challengingto assess the association between an individual feature and an outcome.Researchers have developed global tests for the association of microbiomeprofiles with outcomes using beta diversity metrics which offer robustness toextreme values and can incorporate information on the phylogenetic treestructure. Despite the popularity of global association testing, most existingmethods for follow-up testing of individual features only consider the marginaleffect and do not provide relevant information for the design of microbiomeinterventions. This paper proposes a novel conditional association test, CAT,which can account for other features and phylogenetic relatedness when testingthe association between a feature and an outcome. CAT adopts a leave-outmethod, measuring the importance of a feature in predicting the outcome byremoving that feature from the data and quantifying how much the associationwith the outcome is weakened through the change in the coefficient ofdetermination. By leveraging global tests including PERMANOVA and MiRKAT-basedmethods, CAT allows association testing for continuous, binary, categorical,count, survival, and correlated outcomes. Our simulation and real dataapplication results illustrate the potential of CAT to inform the design ofmicrobiome interventions aimed at improving clinical outcomes.

CAT: a conditional association test for microbiome data using a leave-out approach