A fast and agnostic method for bacterial genome-wide association studies: Bridging the gap between k-mers and genetic events

Abstract

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.

Contrasting approaches to genome-wide association studies impact the detection of resistance mechanisms in Staphylococcus aureus

“…We also found difficulty in reconciling gene family groupings produced by automated gene clustering, which often split known resistance determinants into multiple families, weakening their association with phenotype and often strengthening their association with specific lineages. The use of unitigs (Jaillard et al 2018) and read-mapping approaches (Hunt et al 2017) can address these shortcomings, but interpretation of results is less clear-cut.…”

Diverse conjugative elements silence natural transformation inLegionellaspecies

“…We thus converted the transformation scores into a binary phenotype (transformable T, score 1-3 or nontransformable NT, score 0-0.5) ( Fig. S1A) and conducted a genome-wide association studies (GWAS) using DBGWAS (42). DBGWAS provides a graphical output that can visually distinguish genetic determinants associating with phenotypes that correspond to single-nucleotide polymorphism (SNP) or to horizontal gene transfer (HGT) events.…”

Improved inference and prediction of bacterial genotype-phenotype associations using pangenome-spanning regressions

“…We also follow the method used in DBGWAS (39) , which after counting fixed-length k-mers constructs a de Bruijn graph of the population. Nodes in this graph are extensions of k-mers with the same population frequency vector, and whose sequence is referred to as unitigs.…”

“…We follow the same method as step 1 of the DBGWAS package, which uses the GATB library to construct a compressed de Bruijn graph (40) , and then report frequency vectors of each unitig/node and unique pattern in a format readable by pyseer. We use a k-mer length of 31 throughout to count unitigs, as this was previously shown to maximise association power (39) . This length can also be set by the user.…”