A0450
Title: CAT: A conditional association test for microbiome data using a permutation approach
Authors: Yushu Shi - Weill Cornell Medicine (United States) [presenting]
Liangliang Zhang - Case Western Reserve University (United States)
Christine Peterson - The University of Texas MD Anderson Cancer Center (United States)
Robert Jenq - University of Texas MD Anderson Cancer Center (United States)
Kim-Anh Do - University of Texas MD Anderson Cancer Center (United States)
Abstract: In microbiome analysis, researchers often seek to identify taxonomic features associated with an outcome of interest. However, microbiome features are intercorrelated and linked by phylogenetic relationships, making it challenging to assess the association between an individual feature and an outcome. A novel conditional association test is proposed, CAT, which can account for other features and phylogenetic relatedness when testing the association between a feature and an outcome. CAT adopts a permutation approach, measuring the importance of a feature in predicting the outcome by permuting OTU/ASV counts belonging to that feature from the data and quantifying how much the association with the outcome is weakened through the change in the coefficient of determination. Compared with marginal association tests, it focuses on the added value of a feature in explaining outcome variation that is not captured by other features. By leveraging global tests, including PERMANOVA and MiRKAT-based methods, CAT allows association testing for continuous, binary, categorical, count, survival, and correlated outcomes. It is demonstrated through simulation studies that CAT can provide a direct quantification of feature importance that is distinct from that of marginal association tests and illustrate CAT with applications to two real-world studies on the microbiome in melanoma patients.
