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Abstract:
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High-throughput sequencing technologies have enabled large-scale studies of the role of human microbiome in health and disease. Microbiome association test, as a critical step to establish the connection between the microbiome and an outcome of interest, has now been routinely performed in microbiome data analysis. It has become increasingly common for a microbiome study to collect multiple, possibly related, outcomes to maximize the power of discovery. As these outcomes may share common mechanisms, jointly analyzing these outcomes can amplify the association signal and improve statistical power to detect potential associations. We propose a multivariate microbiome regression-based kernel association test (MMiRKAT) for testing association between multiple outcomes and microbiome composition. MMiRKAT directly regresses all outcomes on the microbiome profiles via kernel machine regression, which allows for covariate adjustment. A novel small-sample correction procedure is implemented in MMiRKAT. The proposed MMiRKAT is demonstrated via simulation studies and an application to a real data set examining the association between host gene expression and mucosal microbiome composition.
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