Word-based GWAS harnesses the rich potential of genomic data for E. coli quinolone resistance
Negin Malekian,
Srividhya Sainath,
Ali Al-Fatlawi
et al.
Abstract: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 re… Show more
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