139 Genome-wide analysis in E. coli unravels homoplasy associated with cefotaxime resistance association analysis limits phylogenetic bias by correcting for genetic relatedness of strains with the same phenotype, thereby increasing statistical power to find true associations (Ruesen et al. 2018). Taking this into account, the use of homoplasy-based association analysis seems viable to relate polymorphic sites to phenotypic traits in bacteria. Still, studies on other genera than mycobacteria are scarce. To our knowledge, no homoplasy studies have used this method on E. coli. The increase of 3GC resistance imposes a clinical threat by restricting treatment options and it is essential to understand the underlying resistance mechanisms. To be able to explore these mechanisms, we selected primarily clinical E. coli strains. The current study is directed on exploring AmpC-mediated CTX resistance. Therefore, we included isolates that are already suspected for increased AmpC production based on elevated FOX resistance. Since a random sample of E. coli would limit finding homoplasy-based associated promoter mutations with CTX resistance. A downside of these selection criteria might be that we over-estimated certain genetic variants associated with the trait, as we do not know the frequency of these variants in the general population. Despite the fact that the spontaneous mutation rate in E. coli is relatively low (H. Lee et al. 2012), it is still likely that this particular mutation occurs often in the general population, given the vast amounts of E. coli in the environment (Tenaillon et al. 2010), providing ample opportunities for adaptation to antibiotics and arguing for antibiotics of which genomic adaptation requires multiple mutations in order to develop resistance. The findings of this study have a number of implications for future practice. This study not only grants insights into how chromosome-encoded antibiotic resistance evolves, but also provides potential strategies for future homoplasy-based association studies. Furthermore, the use of genome-wide homoplasy-based analysis could be applied to optimize outbreak analysis. Prior studies have optimized outbreak analysis by removing recombinant regions (Escobar-Páramo et al. 2004; L. B. Price et al. 2013). Homoplasy events disturbs the true phylogeny; hence, removing genomic positions that are heavily affected by homoplasy could improve tree topology, thereby refining outbreak analysis, although this strategy is still under debate (Hedge and Wilson 2014). 7
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