In microbiome studies, it is important to detect taxa which are associated with pathological outcomes at the lowest definable taxonomic rank, such as genus or species. Traditionally, taxa at the target rank are tested for association individually, and then the Benjamini-Hochberg (BH) procedure is applied to control for false discovery rate (FDR). However, this approach neglects the dependence structure among taxa and may lead to conservative results. We propose a two-stage microbial association mapping framework (massMap) which uses prior grouping information from the taxonomic tree to strengthen statistical power at the target rank. MassMap first screens the association of taxonomic groups at a pre-selected higher taxonomic rank using a powerful microbial group test OMiAT. Then it proceeds to test the association for each candidate taxon at the target rank within the significant taxonomic groups identified in the first stage. Hierarchical BH and selected subset testing procedures are evaluated to control the FDR for the two-stage structured tests. Extensive simulations and real data analyses have shown that massMap achieves higher statistical power and detects more taxa.