Combined reference-free and multi-reference approaches uncover cryptic variation underlying rapid adaptation in microbial pathogens

Background Microbial species often harbor substantial functional diversity driven by structural genetic variation. Rapid adaptation from such standing variation in pathogens threatens global food security and human health. Genome wide association studies (GWAS) provide a powerful approach to identify genetic variants underlying recent pathogen evolution. However, the reliance on single reference genomes and single nucleotide polymorphisms (SNPs) obscures the true extent of adaptive genetic variation. Here, we show quantitatively how a combination of multiple reference genomes and reference-free approaches captures substantially more relevant genetic variation compared to single reference mapping. Results We performed reference-genome based association mapping across 19 reference-quality genomes covering the diversity of the species. We contrasted the results with a reference-free (i.e., K-mer) approach using raw whole genome sequencing data. We assessed the relative power of these GWAS approaches in a panel of 145 strains collected across the global distribution range of the fungal wheat pathogen Zymoseptoria tritici . We mapped the genetic architecture of 49 life history traits including virulence, reproduction and growth in multiple stressful environments. The inclusion of additional reference genome SNP datasets provides a nearly linear increase in additional loci mapped through GWAS. Variants detected through the K-mer approach explained a higher proportion of phenotypic variation than a reference genome based approach, illustrating the benefits of including genetic variants beyond SNPs. Conclusions Our study demonstrates how the power of GWAS in microbial species can be significantly enhanced by comprehensively capturing functional genetic variation. Our approach is generalizable to a large number of microbial species and will uncover novel mechanisms driving rapid adaptation in microbial populations.