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Abstract:
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Association testing for microbiome data with diseases is challenging. First, microbiome data needs to be normalized because of differences in read depths. There are many different resampling or scaling normalization methods, and the performance of existing approaches are highly dependent on the choices, which impedes the comparison among various studies. Second, microbiome data is complex, usually zero-inflated, dispersed and high-dimensional. It is hopeless to determine a one-size-fit-all parametric method for all taxa; also, these mean-based methods are insufficient to detect the heterogeneous association between the disease condition and microbial abundance. We propose to use a quantile rank-score based test (ZIQRank) under a two-part quantile regression model, which is a powerful and robust method to deal with heterogeneity. We applied ZIQRank to study the association between gut microbiota and high blood pressure. Compared with existing methods, it increases the power with well-controlled Type I error and is robust to the normalization method. Moreover, it complemented the competing approaches with finding additional taxa on which hypertension has heterogeneous effects.
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