Word-based GWAS harnesses the rich potential of genomic data for E. coli quinolone resistance - Malekian, Negin; Sainath, Srividhya; Al-Fatlawi, Ali; Schroeder, Michael (Frontiers in Microbiology, 2023)
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 E. coli 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 gyrA and parC genes and the results of our variant-based GWAS but also revealed mutations in novel genes such as mdfA, the AcrEF-TolC multidrug efflux system, ptrB, and hisI, implicated in antibiotic resistance. Furthermore, our study identified joint mutations in the known gyrA gene and 14 other new genes, 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.