Biology subjects

McDonald, B. A.

Publications and source records attributed to McDonald, B. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Genetic mapping of trait plasticity in a plant pathogenic fungus reveals genetic architecture and candidate genes for plasticity

Understanding how plant pathogens respond to environmental change is needed to better manage plant diseases. Phenotypic plasticity, the ability of a single genotype to produce different phenotypes across different environments, can influence pathogen adaptation and host-pathogen dynamics. Few studies have investigated the mechanisms underlying phenotypic plasticity in plant pathogens. Here we used phenotypic and genotypic data collected over >15 years and across multiple environments to perform genetic mapping of plasticity traits in the wheat pathogen Zymoseptoria tritici. Most (75%) of the QTL for plasticity (plQTL) overlapped with their corresponding mean QTL (mnQTL), suggesting that plasticity is controlled mainly by pleiotropic genes or tightly linked genes. 25% of the plQTL mapped to genomic locations separate from the mnQTL, suggesting that plasticity in these cases results from epistasis between unlinked loci. In several cases plasticity measured across different environmental gradients mapped to the same genomic positions, suggesting a shared control of plasticity for unrelated factors. These cases of shared control could be due to master regulators of plasticity or gene clusters. This mapping study provide unprecedented insights into the genetic architecture of plasticity in fungal plant pathogens.

evolutionary biology↗

bioRxiv2026

Contrasting transcriptional responses and genetic determinants underlie Zymoseptoria tritici adaptation mechanisms to simulated host defense environments

Successful colonization of the wheat apoplast requires that Zymoseptoria tritici tolerate host-derived stresses, but the mechanisms underlying this adaptation remain poorly understood. We combined phenotypic assays, transcriptomics, and genome-wide association analyses to characterize fungal responses to acidic pH, salicylic acid, gibberellic acid, and oxidative stress. Exposure to salicylic acid inhibited in vitro growth across a global collection of 411 Z. tritici strains, whereas acidic pH promoted growth, illustrating contrasting effects on pathogen performance of environments simulating host-defense responses. At the transcriptional level, acidic pH and oxidative stress induced the strongest and most similar responses, while salicylic acid elicited a more distinct transcriptional program and gibberellic acid caused only limited transcriptional changes. Although the sets of differentially expressed genes were largely condition specific, overlapping enrichment of transport- and redox-related functions across conditions indicated shared transcriptional responses. K-mer based genome-wide association mapping identified five candidate loci associated with growth under acidic pH, gibberellic acid and salicylic acid, including four loci specific to a single growth condition. These loci colocalized with genes implicated in cell wall remodeling, nitrogen metabolite regulation, proteostasis, and ubiquitin-related processes. This study highlights the multifaceted strategies employed by Z. tritici to navigate environments simulating host-defense responses, involving shared and environment-specific adaptations. We provide new insights into the genetic and molecular basis of fungal resilience, with implications for understanding pathogen-host interactions.

genomics↗

bioRxiv2026

A Novel Phenotyping Approach for Reconciling Precision and Variance in Disease Severity Estimates from High-resolution Imaging

1Accurate quantification of plant disease is essential for resistance breeding, variety testing, and precision agriculture, yet visual ratings are limited by subjectivity, low precision, and restricted throughput. Image-based phenotyping can address these limitations, but field applications face substantial challenges due to spatial heterogeneity, symptom-level diagnostic requirements, and the need for very high-resolution imagery with limited spatial coverage. This introduces a fundamental trade-off: high-resolution images provide precise local measurements of disease, but spot-level estimates can be highly variable within experimental units. We analyzed a large image data set of wheat foliar diseases to characterize the distribution, spatial dependence, and aggregation behavior of spot-level severity estimates in plots. We combined high-resolution macro-scale imaging with focus bracketing to increase the sampled leaf area. Our results highlight focus bracketing as a promising approach for simultaneous diagnosis and quantification of disease in field plots. Autocorrelation in severity estimates both within focal image stacks and across plot positions was comparable, with 10 focal stack images or 10 positions per plot contributing approximately 2.5 independent observations each. Modeling plot-level severity as a latent Beta-distributed variable enabled robust estimation of mean severity and associated uncertainty. This supports both hypothesis testing and efficient sampling across the full range of disease severity associated with genotypic diversity and seasonality of developing epidemics. The proposed imaging approach is non-invasive and, in principle, transferrable to autonomous ground-based phenotyping platforms, offering the potential to shift the dominant source of uncertainty in estimating disease severity from measurement-related limitations toward biologically and environmentally driven variability in disease expression.

plant biology↗

bioRxiv2025

Tracking Lesion Growth in the Field: Imaging and Deep Learning Reveal Components of Quantitative Resistance

O_LIMeasuring individual components of pathogen reproduction is key to understanding mechanisms underlying rate-reducing quantitative resistance (QR). Simulation models predict that lesion expansion plays a key role in seasonal epidemics of foliar diseases, but measuring lesion growth with sufficient precision and scale to test these predictions under field conditions has remained impractical. C_LIO_LIWe used deep learning-based image analysis to track 6889 individual lesions caused by Zymoseptoria tritici on 14 wheat cultivars across two field seasons, enabling 27,218 precise and objective measurements of lesion growth in the field. C_LIO_LILesion appearance traits reflecting specific interactions between particular host and pathogen genotypes were consistently associated with lesion growth, whereas overall effects of host genotype and environment were modest. Both host cultivar and cultivar-by-environment interaction effects on lesion growth were highly significant and moderately heritable (h2 [≥] 0.40). After excluding a single outlier cultivar, a strong and statistically significant association between lesion growth and overall QR was found. C_LIO_LILesion expansion appears to be an important component of QR to STB in most--but not all--wheat cultivars, underscoring its potential as a selection target. By facilitating the dissection of individual resistance components, our approach can support more targeted, knowledge-based breeding for durable QR. C_LI

plant biology↗

bioRxiv2025

From Canopy Images to Organ-Level Disease Assessments: A Scalable Approach to Measure Quantitative Resistance in the Field

Breeding for quantitative, polygenic resistance is widely considered the most durable, cost-effective, and environmentally safe approach to crop disease control. However, progress in resistance breeding is hindered by the limited capability of current approaches to measure highly quantitative disease phenotypes under field conditions with high precision and sufficient throughput. Here, we present an imaging protocol and a modular image processing pipeline that enables wheat disease detection and severity estimation directly from very-high-resolution canopy imagery, eliminating the need for physical interaction with the monitored plants as required in previously proposed sensor-based methods capable of symptom-level diagnosis. The pipeline combines deep-learning-based semantic segmentation, keypoint detection, and depth estimation to diagnose and quantify disease symptoms and extract the analyzable reference plant surfaces for severity estimation. By leveraging estimated relative depth and analyzing image texture, well-focused areas with sufficient quality were accurately segmented. Despite the challenging nature of canopy images and frequent symptom ambiguity, symptom detection and segmentation models trained on a new dataset reached a similar performance as already described in more simplified scenarios where detached, flattened leaves were analyzed. Plot-level severity estimates of Septoria Tritici Blotch, a major wheat disease, obtained using the new method and a precise but more laborious reference method were highly correlated (Pearson R = 0.83) across a range of morphologically contrasting cultivars. Validation of the new method on data collected by different operators at different sites demonstrated the robustness of the approach. The ability of the method to process imagery acquired in a contact-free manner can enable deployment on autonomous ground vehicles, paving the way for automated, scalable phenotype acquisition.

biochemistry↗

bioRxiv2024

Responses to temperature shocks in Zymoseptoria tritici reveal specific transcriptional reprogramming and novel candidate genes for thermal adaptation

Pathogens responses to sudden temperature fluctuations, spanning various temporal scales, are critical determinants of their survival, growth, reproduction, and homeostasis. Here, we combined phenotyping, transcriptomics, and genome-wide association approaches to investigate how the wheat pathogen Zymoseptoria tritici responds to and recovers from temperature shocks. Survival emerged as the most significantly affected trait immediately following temperature shocks across 122 geographically diverse strains. In contrast, post-recovery phenotypic traits, including growth rate and melanization, showed no significant deviations from control conditions. Transcriptomic analyses of a reference strain revealed temperature stress-specific gene expression patterns, with genes involved in protein folding, redox homeostasis, membrane stabilization, and cell-wall remodeling playing central roles in the response. A multi-reference k-mer genome-wide association study (GWAS) identified six loci significantly associated with cold shock responses. Among these, two loci emerged as strong candidates for near-freezing temperature adaptation, including a 60S ribosomal protein gene involved in protein synthesis and stress recovery, and an NADH oxidoreductase gene implicated in redox homeostasis and oxidative stress tolerance. These findings shed light on the distinct molecular strategies Z. tritici employs to adapt to temperature stress and provide novel insights into fungal resilience under dynamic environmental conditions. Author summaryTemperature fluctuations, an inherent aspect of natural environments, are increasingly exacerbated by climate change, intensifying challenges for organisms to maintain homeostasis amid more frequent and severe extreme weather events. This study reveals distinct phenotypic, transcriptomic, and genetic mechanisms underlying Z. triticis responses to short-term temperature shocks. Survival-related phenotypic traits were significantly reduced by heat and cold shocks, while other traits measured after a recovery period demonstrated the resilience of Z. tritici strains to temperature stress, reflecting efficient recovery mechanisms. Transcriptomic analyses uncovered temperature-specific gene expression patterns, emphasizing unique regulatory strategies, which mostly return to baseline levels after a recovery period. The discovery of novel loci associated with cold shock responses provides valuable insights into the genetic basis of resilience to short-term temperature stress, offering a foundation for future research on pathogen adaptation to fluctuating environments.

genomics↗

bioRxiv2024

Zymoseptoria tritici stealth infection is facilitated by stage-specific down-regulation of a β-glucanase

Plant cell walls constitute a major defence barrier against pathogens, although it is unclear how specific cell wall components impact pathogen colonisation. Pathogens secrete cell wall degrading enzymes (CWDEs) to facilitate plant colonisation, but damaged, infected cells are often a source of cell wall-derived oligosaccharides that trigger host immunity. The mechanisms by which pathogens minimize the release of cell wall-derived oligosaccharides while colonizing the host remain to be elucidated. We combined biochemical, molecular genetics and transcriptomic analyses to functionally characterize a glycoside hydrolase (ZtGH45) from the wheat pathogen Zymoseptoria tritici. ZtGH45 gene is expressed during the necrotrophic phase of the fungus, coinciding with an accumulation of wheat {beta}-1,3/1,4-mixed-linked glucan (MLG)-derived oligosaccharides. We show that overexpression of ZtGH45 enhances {beta}-1,3/1,4-glucan hydrolysis and the derived oligosaccharides trigger an immune response in wheat, which hinders Z. tritici virulence. The results demonstrate that tight regulation of ZtGH45 is critical for the infection process to prevent early accumulation of MLG oligosaccharides that would prematurely induce host immunity counterbalancing fungal virulence. We suggest that the balance between plant cell wall degradation by fungal CWDE and the release of immunogenic wall-derived oligosaccharides governs the outcome of host invasion by pathogens.

plant biology↗

bioRxiv2024

Mapping genomic regions associated with temperature stress in the wheat pathogen Zymoseptoria tritici

Climate change can alter interactions between plants and their pathogens, which could adversely affect crop production. To better understand the molecular mechanisms underlying the responses of pathogenic fungal to temperature stress, we conducted a quantitative trait loci (QTL) mapping study in the wheat pathogen Zymoseptoria tritici to identify genomic regions associated with colony growth and melanisation at three temperatures (10{degrees}C, 18{degrees}C, 27{degrees}C). We then identified likely candidate genes for thermal adaptation within these intervals by combining information regarding gene function, GO annotation enrichment, transcriptional profile, and results from previous genome wide association studies (GWAS) investigating responses to climate, temperature and thermal adaptation. The QTL mapping, conducted for two separate crosses involving four Swiss parents, found significant QTL uniquely associated with traits measured in high and low temperatures. These intervals contained many genes known to regulate responses to temperature stress, including heat shock proteins (HSPs) and proteins involved in the mitogen-activated protein kinase (MAPK) pathways, and were enriched for genes with a zinc ion binding GO annotation. We highlight the most promising candidate genes for thermal adaptation, including an ammonium transporter gene, a stress response factor (Whi1) and two MAPK pathway genes - SSk2 and Opy2. Future validation work on these candidate genes could provide novel insight into the molecular mechanisms underlying temperature adaptation in this important wheat pathogen.

genetics↗

bioRxiv2024

Thermal adaptation in worldwide collections of a major fungal pathogen

Adaptation to new climates poses a significant challenge for plant pathogens during range expansion, highlighting the importance of understanding their response to climate to accurately forecast future disease outbreaks. The wheat pathogen Zymoseptoria tritici is ubiquitous across most wheat production regions distributed across diverse climate zones. We explored the genetic architecture of thermal adaptation using a global collection of 411 Z. tritici strains that were phenotyped across a wide range of temperatures and then included in a genome-wide association study. Our analyses provided evidence for local thermal adaptation in Z. tritici populations worldwide, with a significant positive correlation between bioclimatic variables and optimal growth temperatures. We also found a high variability in thermal performance among Z. tritici strains coming from the same field populations, reflecting the high evolutionary potential of this pathogen at the field scale. We identified 69 genes putatively involved in thermal adaptation, including one high-confidence candidate potentially involved in cold adaptation. These results highlight the complex polygenic nature of thermal adaptation in Z. tritici and suggest that this pathogen is likely to adapt well when confronted with climate change.

genomics↗

bioRxiv2024

A genome-wide association study identifies markers and candidate genes affecting tolerance to the wheat pathogen Zymoseptoria tritici

Plants defend themselves against pathogens using either resistance, measured as the hosts ability to limit pathogen multiplication, or tolerance, measured as the hosts ability to reduce the negative effects of infection. Tolerance is a promising trait for crop breeding, but its genetic basis has rarely been studied and remains poorly understood. Here, we reveal the genetic basis of leaf tolerance to the fungal pathogen Zymoseptoria tritici that causes the globally important septoria tritici blotch disease on wheat. Leaf tolerance to Z. tritici is a quantitative trait that was recently discovered in wheat by using automated image analyses that quantified the symptomatic leaf area and counted the number of pycnidia found on the same leaf. A genome-wide association study identified four chromosome intervals associated with tolerance and a separate chromosome interval associated with resistance. Within these intervals, we identified candidate genes, including wall-associated kinases similar to Stb6, the first cloned STB resistance gene. Our analysis revealed a strong negative genetic correlation between tolerance and resistance to STB, indicative of a trade-off. Such a trade-off between tolerance and resistance would hinder breeding simultaneously for both traits, but our findings suggest a way forward using marker-assisted breeding. We expect that the methods described here can be used to characterize tolerance to other fungal diseases that produce visible fruiting bodies, such as speckled leaf blotch on barley, potentially unveiling conserved tolerance mechanisms shared among plant species.

plant biology↗

bioRxiv2024

Cultivar-specific fungicide resistance emerges during a growing season in field populations of Zymoseptoria tritici

Zymoseptoria tritici causes the most damaging wheat disease in Europe, septoria tritici blotch (STB). In Europe, STB is controlled mainly by fungicides and fungicide resistance is frequently reported. While fungicide resistance is thought to emerge mainly from standing genetic variation within field populations of Z. tritici, few studies have attempted to quantify the degree of fungicide resistance occurring at the field scale and to measure changes in frequencies of resistant strains following fungicide applications during a single growing season. Even fewer studies have considered the effects of different wheat cultivars on the emergence of fungicide resistance. We measured EC50 values for 1005 strains of Z. tritici sampled at two time points from 17 different wheat cultivars growing in a replicated field experiment that was treated with combinations of five different fungicides three times during the growing season. We found that field populations of Z. tritici can maintain a very high diversity in fungicide sensitivity phenotypes despite three fungicide treatments, with as much diversity found within a single field during a single growing season as has been described across all of Europe over several years. Multidrug resistance to two or more fungicides was found in 18.9% of the tested strains. We discovered that wheat cultivars that were more resistant to STB tended to be colonized by Z. tritici strains that exhibited higher fungicide resistance. We also found that specific wheat cultivars selected for resistance to specific active ingredients. Overall, our findings illustrate the many challenges associated with designing fungicide treatment programs that aim to reduce selection for fungicide resistance when confronted with a pathogen like Z. tritici that has a very high evolutionary potential.

microbiology↗

bioRxiv2024

Towards high throughput in-field detection and quantification of wheat foliar diseases with deeplearning

1Reliable, quantitative information on the presence and severity of crop diseases is critical for site-specific crop management and resistance breeding. Successful analysis of leaves under naturally variable lighting, presenting multiple disorders, and across phenological stages is a critical step towards high-throughput disease assessments directly in the field. Here, we present a dataset comprising 422 high resolution images of flattened leaves captured under variable outdoor lighting with polygon annotations of leaves, leaf necrosis and insect damage as well as point annotations of Septoria tritici blotch (STB) fruiting bodies (pycnidia) and rust pustules. Based on this dataset, we demonstrate the capability of deep learning for keypoint detection of pycnidia (F 1 = 0.76) and rust pustules (F 1 = 0.77) combined with semantic segmentation of leaves (IoU = 0.96), leaf necrosis (IoU = 0.77) and insect damage(IoU = 0.69) to reliably detect and quantify the presence of STB, leaf rusts, and insect damage under natural outdoor conditions. An analysis of intra- and inter-annotator agreement on selected images demonstrated that the proposed method achieved a performance close to that of annotators in the majority of the scenarios. We validated the generalization capabilities of the proposed method by testing it on images of unstructured canopies acquired directly in the field and with-out manual interaction with single leaves. The corresponding imaging procedure can be adapted to support automated data acquisition. Model predictions were in good agreement with visual assessments of in-focus regions in these images, despite the presence of new challenges such as variable orientation of leaves and more complex lighting. This underscores the principle feasibility of diagnosing and quantifying the severity of foliar diseases under field conditions using the proposed imaging setup and image processing methods. By demonstrating the ability to diagnose and quantify the severity of multiple diseases in highly natural complex scenarios, we lay out the groundwork for a significantly more efficient, non-invasive in-field analysis of foliar diseases that can support resistance breeding and the implementation of core principles of precision agriculture.

plant biology↗

bioRxiv2023

Molecular epidemiology of Cercospora leaf spot on resistant and susceptible sugar beet hybrids

Cercospora leaf spot (CLS), caused by Cercospora beticola, is a major foliar disease impacting sugar beet production worldwide. The development of new resistant sugar beet hybrids is a powerful tool to better manage the disease, but it is unclear how these hybrids affect CLS epidemiology. We used a molecular epidemiology approach to study natural epidemics of CLS affecting two susceptible and two resistant sugar beet hybrids at two field sites. Infected plants were geotagged on a weekly basis. Isolations of C. beticola were made from infected leaves and genotyped using six simple sequence repeat loci to identify clones. We determined that CLS epidemics had a later onset in plots planted to resistant hybrids, but once the pathogen established an infection, there was little difference between resistant and susceptible hybrids in the probability of localized spread and dispersal. We found that different clones often infected the same leaf and that clusters of infected plants were often colonized by a mixture of clones. There was little overall difference in genetic diversity between resistant and susceptible hybrids, however genotypic evenness differed at one site. In this site we found one genotype restricted to the resistant cultivars at a high frequency. At the end of the epidemic infections were not randomly distributed across the fields and we found that a single clone could spread over a distance of 100 m during a growing season.

pathology↗

bioRxiv2023

Recurrent chromosome destabilization through repeat-mediated rearrangements in a fungal pathogen

Genomic instability caused by chromosomal rearrangements has severe consequences for organismal fitness and progression of cancerous cell lines. The triggers of destabilized chromosomes remain poorly understood but likely co-locate with fragile sites. Here, we retrace a runaway chromosomal degeneration process observed in a fungal pathogen using telomere-to-telomere assemblies across an experimental progeny. We show that the same fragile sites triggered reproducible, large-scale rearrangements through non-allelic recombination. Across our four-generation progeny, chromosomal rearrangements were accompanied by non-disjunction events leading to aneuploid progeny with up to four chromosomal copies. We identify a specific transposable element family co-locating with fragile sites, likely triggering ongoing repeated chromosomal degeneration. The element has recently been associated with lower virulence of the pathogen and has undergone an expansion of copy numbers across the genome. Chromosome sequences are also targeted by repeat-induced point mutation, a genome defense mechanism actively leading to hypermutation on duplicated sequences. Our work identifies the exact sequence triggers that initiate chromosome instability and perpetuate degenerative cycles. Dissecting proximate causes leading to runaway chromosomal degeneration could expand our understanding of chromosomal evolution beyond fungal pathogens.

genomics↗

bioRxiv2023

Zymoseptoria tritici suppresses the host immune response and facilitates the success of avirulent strains in mixed infections

Plants interact with a plethora of pathogenic microorganisms in nature. Pathogen-plant interaction experiments focus mainly on single-strain infections, typically ignoring the complexity of multi-strain infections even though mixed infections are common and critical for the infection outcome. The wheat pathogen Zymoseptoria tritici forms highly diverse fungal populations in which several pathogen strains often colonize the same leaf. Despite the importance of mixed infections, the mechanisms governing interactions between a mixture of pathogen strains within a plant host remain largely unexplored. Here we demonstrate that avirulent pathogen strains benefit from being in mixed infections with virulent strains. We show that virulent strains suppress the wheat immune response, allowing the avirulent strain to colonize the apoplast and to reproduce. Our experiments indicate that virulent strains in mixed infections can challenge the plant immune system both locally and systemically, providing a mechanistic explanation for the persistence of avirulent pathogen strains in fields planted to resistant host plants.

plant biology↗

bioRxiv2022

A thousand-genome panel retraces the global spread and climatic adaptation of a major crop pathogen

Human activity impacts the evolutionary trajectories of many species worldwide. Global trade of agricultural goods contributes to the dispersal of pathogens reshaping their genetic makeup and providing opportunities for virulence gains. Understanding how pathogens surmount control strategies and cope with new climates is crucial to predicting the future impact of crop pathogens. Here, we address this by assembling a global thousand-genome panel of Zymoseptoria tritici, a major fungal pathogen of wheat reported in all production areas worldwide. We identify the global invasion routes and ongoing genetic exchange of the pathogen among wheat-growing regions. We find that the global expansion was accompanied by increased activity of transposable elements and weakened genomic defenses. Finally, we find significant standing variation for adaptation to new climates encountered during the global spread. Our work shows how large population genomic panels enable deep insights into the evolutionary trajectory of a major crop pathogen.

genomics↗

bioRxiv2022

Quantitative trait locus mapping of osmotic stress response in the wheat pathogen Zymoseptoria tritici

Osmotic stress is a ubiquitous and potent stress for all living organisms, but few studies have investigated the genetic basis of salt tolerance in filamentous fungi. The main aim of this study was to identify regions of the genome associated with tolerance to potassium chloride in the wheat pathogen Zymoseptoria tritici. A secondary aim was to identify candidate genes affecting salt tolerance within the most promising chromosomal regions. We achieved these aims with a quantitative trait locus (QTL) mapping study using offspring from two crosses grown in vitro in the presence or absence of osmotic stress imposed by 0.75M KCl. We identified significant QTL for most of the traits in both crosses. Several QTL overlapped with QTL identified in earlier studies for other traits and some QTL explained trait variation in both the control and salt stress environments. A significant QTL on chromosome 3 explained variation in colony radius at 8 days post inoculation (dpi) in the KCl environment as well as colony radius KCl tolerance at 8 dpi. The QTL peak had a high LOD and encompassed an interval containing only 36 genes. Six of these genes present promising candidates for functional analyses. A GO enrichment analysis of QTL unique to the KCl environment found evidence for enrichment of functions involved in osmotic stress responses.

genetics↗

bioRxiv2022

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

BackgroundMicrobial 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. ResultsWe 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. ConclusionsOur 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.

genomics↗