Microbes are associated with all complex organisms, influencing host fitness, local ecologyand evolutionary trajectories. Thus, there is burgeoning interest in engineering microbiomes for practical applications, such as sustainable agriculture and precision medicine. However, the emergent phenotype from confounding host, microbe, and environmental interactions within diverse microbiomes proves challenging to characterize, let alone engineer. One method of microbiome characterization is to quantify differential abundances of distinct bacteria taxonomic groups among hosts. The development of high throughput sequencing facilitates this type of characterization using variable regions of marker genes to taxonomically group the microbes. However, the canonical 16S v5-v7 gene region used to assess plant microbiomes is relatively constrained, effectively grouping distinct bacteria into higher levels and potentially masking host-microbe interactions. Here, I propose decomposing taxonomic groups to lower levels, facilitating finer groupingsof bacteria to more accurately describe the respective microbe-microbe interactions and subsequently investigate host-genotype effects on the microbiome abundance phenotype. I first describe the collection and classification of natural bacteria isolates collected from Arabidopsis thaliana plants from the field. In the next chapter, I describe the development of a new marker gene database using gyrase subunit-β (gyrB). Using the isolates from the previous chapter in combination with published data sets of A. thaliana microbiomes, I show that gyrB provides both finer taxonomic resolution and stronger correlations between genetic and genomic distances compared to the canonical 16S v5-v7 marker gene. Lastly, I apply gyrB sequencing to leaf microbiome community abundances from replicated A. thaliana field experiments. I show that using gyrB, compared to 16S v5-v7, and including microbe-microbe interactions improves model fits for broad-sense heritability estimates. I use these data to perform Genome Wide Association Studies (GWAS), and identify host gene-candidates potentially shaping the microbiome.