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Abstract:
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Most existing microbiome association analyses focus on the association between microbiome and conditional mean of health or disease-related outcomes. However, these methods tend to be limited either when the underlying microbiome-outcome association occurs somewhere other than the mean level, or when distribution of the outcome variable is irregular. We address this gap by investigating association analysis between microbiome and conditional outcome quantiles. We introduce a new association analysis tool named MiRKAT-IQ to examine the association between microbiome and the distribution of outcome. For an individual quantile, we utilize the existing kernel machine regression framework to examine the association between that conditional outcome quantile and a group of microbial features. Then, the goal of examining microbiome association with the whole outcome distribution is achieved by integrating all outcome conditional quantiles, and thus our new MiRKAT-IQ test is robust to both the location of association signals and the distribution of the outcome. We demonstrate the potential usefulness of MiRKAT-IQ with extensive simulations and applications to actual biological data.
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