Microbial strains within a single species can exhibit distinct functional characteristics due to variations in gene content and often show individual specificity, which can obscure unbiased associations and hinder deductive research. Here, we comprehensively define the human gut microbiota at a consistently annotated operational subspecies unit (OSU) resolution in an unbiased, cohort-independent manner, demonstrating that this approach can generalize across diverse global populations while maintaining specificity and improving interstudy reproducibility. We develop panhashome-a sketching-based method for rapid subspecies and species quantification and identification of genes that drive intraspecies variations-and show that subspecies carry implicit information undetectable at the species level. We identify subspecies associated with colorectal cancer (CRC) whose sibling subspecies or species are not, while a machine-learning CRC diagnostic algorithm based on subspecies outperformed species-level methods. This subspecies catalog allows identification of genes that drive functional differences between subspecies as a fundamental step in mechanistically understanding microbiome-phenotype interactions.