Genome-wide association study (GWAS) methods applied to bacterial genomes have shown promising results for genetic marker discovery or detailed assessment of marker effect. Recently, alignment-free methods based on k-mer composition have proven their ability to explore the accessory genome. However, they lead to redundant descriptions and results which are sometimes hard to interpret. Here we introduce DBGWAS, an extended k-mer-based GWAS method producing interpretable genetic variants associated with distinct phenotypes. Relying on compacted De Bruijn graphs (cDBG), our method gathers cDBG nodes, identified by the association model, into subgraphs defined from their neighbourhood in the initial cDBG. DBGWAS is alignment-free and only requires a set of contigs and phenotypes. In particular, it does not require prior annotation or reference genomes. It produces subgraphs representing phenotype-associated genetic variants such as local polymorphisms and mobile genetic elements (MGE). It offers a graphical framework which helps interpret GWAS results. Importantly it is also computationally efficient-experiments took one hour and a half on average. We validated our method using antibiotic resistance phenotypes for three bacterial species. DBGWAS recovered known resistance determinants such as mutations in core genes in Mycobacterium tuberculosis, and genes acquired by horizontal transfer in Staphylococcus aureus and Pseudomonas aeruginosa-along with their MGE context. It also enabled us to formulate new hypotheses involving genetic variants not yet described in the antibiotic resistance literature. An open-source tool implementing DBGWAS is available at https://gitlab.com/leoisl/dbgwas. Genome-wide association studies (GWAS) help explore the genetic bases of phenotype variation in a population. Our objective is to make GWAS amenable to bacterial genomes. These genomes can be too different to be aligned against a reference, even within a single species, making the description of their genetic variation challenging. We test the association between the phenotype and the presence in the genomes of DNA subsequences of length k-the so-called k-mers. These k-mers provide a versatile descriptor, allowing to capture genetic variants ranging from local polymorphisms to insertions of large mobile genetic elements. Unfortunately, they are also redundant and difficult to interpret. We rely on the compacted De Bruijn graph (cDBG), which represents the overlaps between k-mers. A single cDBG is built across all genomes, automatically removing the redundancy among consecutive k-mers, and allowing for a visualisation of the genomic context of the significant ones. We provide a computationally efficient and user-friendly implementation, enabling non-bioinformaticians to carry out GWAS on thousands of isolates in a few hours. This approach was effective in catching the dynamics of mobile genetic elements in Staphylococcus aureus and Pseudomonas aeruginosa genomes, and retrieved known local polymorphisms in Mycobacterium tuberculosis genomes.