Microbiome association studies are growing increasingly complex, often combining several omics data sources (host gene expression, metabolomics, and others) and following subjects across time. These study design characteristics attempt to provide some understanding of the mechanism of association between the microbiome and host phenotypes, but few statistical methods are available for testing associations between the microbiome and other structured high-dimensional data types longitudinally. We evaluate and compare several approaches to testing longitudinal associations between the microbiota and genomic features, such as host genetic variants or gene expression, by leveraging existing methodology. We then propose a combined kernel RV approach. The method is tested on both simulated and real microbiome data.