microGWAS: a computational pipeline to perform large scale bacterial genome-wide association studies

Identifying genetic variants associated with bacterial phenotypes, such as virulence, host preference, and antimicrobial resistance, has great potential for a better understanding of the mechanisms involved in these traits. The availability of large collections of bacterial genomes has made genome-wide association studies (GWAS) a common approach for this purpose. The need to employ multiple software tools for data pre- and post-processing limits the application of these methods by experienced bioinformaticians. To address this issue, we have developed a pipeline to perform bacterial GWAS from a set of assemblies and annotations, with multiple phenotypes as targets. The associations are run using five sets of genetic variants: unitigs, gene presence/absence, rare variants ( i . e . gene burden test), gene cluster specific k-mers, and all unitigs jointly. All variants passing the association threshold are further annotated to identify overrepresented biological processes and pathways. The results can be further augmented by generating a phylogenetic tree and by predicting the presence of antimicrobial resistance and virulence associated genes. We tested the microGWAS pipeline on a previously reported dataset on E. coli virulence, successfully identifying the causal variants, and providing further interpretation on the association results. The microGWAS pipeline integrates the state-of-the-art tools to perform bacterial GWAS into a single, user-friendly, and reproducible pipeline, allowing for the democratization of these analyses. The pipeline can be accessed, together with its documentation, at: https://github.com/microbial-pangenomes-lab/microGWAS .