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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 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 new (mutated) genes such as mdfA, the AcrEF-TolC multidrug efflux system, ptrB, and hisI, implicated in antibiotic resistance. Furthermore, our study identified joint mutations in 14 genes including the known gyrA 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.
摘要：
喹诺酮耐药性对全球健康构成了日益严重的威胁。我们采用基于单词的GWAS来探索基因组数据，旨在提高我们对这一现象的认识。与传统的基于变体的GWAS分析不同，这种方法同时捕获多个基因组因子，包括单个和相互作用的抗性突变和基因。分析来自德累斯顿废水处理厂的92个基因组大肠杆菌样本的数据集，我们鉴定出54个与喹诺酮耐药显著相关的DNA单位。值得注意的是,我们的分析不仅验证了gyrA和parC基因中的已知突变以及我们基于变体的GWAS的结果，而且还揭示了新的（突变的）基因，如mdfA，AcrEF-TolC多药外排系统，ptrB,而HisI,与抗生素耐药性有关。此外，我们的研究确定了14个基因的联合突变，包括已知的gyrA基因，提供对导致喹诺酮耐药的潜在协同效应的见解。这些发现展示了基于单词的GWAS在解开喹诺酮耐药的复杂基因组基础方面的卓越能力。
