High Precision Binary Trait Association on Phylogenetic Trees

Understanding how bacterial genes associate with traits and with one another is essential for predicting disease outcomes, antibiotic resistance, and future evolution. However, identifying these interactions is challenging because shared ancestry creates false correlations. SimPhyNI overcomes this through an ancestry-informed statistical simulation process, achieving near-zero false positive rates while maintaining computational efficiency for large scale analyses. This efficiency enables systematic mapping of gene-gene interaction networks across large datasets containing thousands of genes and genomes. As microbial genomic datasets continue to expand, SimPhyNI's scalability and precision will accelerate discovery of the mechanistic principles underlying infectious disease, microbiome function, and microbial evolution and ecology.