Word-based GWAS Harnesses the Rich Potential of Genomic Data for Quinolone Resistance

Quinolone resistance presents a growing global health threat. We employed word-based GWAS to explore genomic data, aiming to enhance our understanding of this phenomenon. Unlike traditional variant-based GWAS analyses, this approach simultaneously captures multiple genomic factors, including single and interacting resistance mutations and genes. Analyzing a dataset of 92 genomic samples from a wastewater treatment plant in Dresden, we identified 54 DNA unitigs significantly associated with quinolone resistance. Remarkably, our analysis not only validated known mutations in and genes and the results of our variant-based GWAS but also revealed new (mutated) genes such as , the AcrEF-TolC multidrug efflux system, , and , implicated in antibiotic resistance. Furthermore, our study identified joint mutations in 14 genes including the known gene, providing insights into potential synergistic effects contributing to quinolone resistance. These findings showcase the exceptional capabilities of word-based GWAS in unraveling the intricate genomic foundations of quinolone resistance.