Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies.

The quest for genes associated with different phenotypes and diseases is a central theme of research in human genetics. Several human phenotypes/diseases including height, myopia, diabetes, and others show complex pattern of inheritance where many genes together with environmental factors influence the intensity of a phenotype or disease. The genome-wide association study (GWAS) has been successful in mapping loci associated to variety of human traits and diseases. GWAS test for association of single nucleotide polymorphism (SNP) markers with a phenotype of interest at the genome-wide scale, this hence requires a severe multiple testing correction for statistical significance (p-value < 5 × 10-8). A single marker association approach has limited power in finding disease susceptibility genes especially in situations where the risk architecture of a gene is defined by many SNPs rather than one or two. This issue can be addressed through gene-based association approaches where the combined (or mean) statistics of a number of markers in a gene is tested for association with the trait of interest. Gene-based association approaches are established as a complementary approach to GWAS. It is well established that genes work in concert as molecular networks and cellular pathways to perform biological processes. Individual tests for association of a gene (or a SNP in a gene) with phenotype may have limited power in identifying interacting genes with small effects. SNP or gene-based tests are limited in how much they can reveal of the biological mechanism behind phenotypic variation. Pathway-based association approaches, where gene-based tests statistics are combined into gene sets and tested for association, have been developed to overcome these limitations. Each gene set represents a biological pathway or a process, based on prior biological knowledge. Various techniques have been proposed to perform pathway-based association tests on GWAS data, including ALIGATOR, MAGENTA, and INRICH. These packages show several limitations, which can be addressed by the development of a new pathway-based association approach. The aim of this thesis is to demonstrate the analytical approaches (GWAS and post-GWAS) to identify genes and pathways associated with complex ophthalmology traits and report methods for gene- and pathways-based analysis of GWAS summary data. Chapter 1 introduces the basic principles and strategies involved in GWAS analysis. Further, it extensively reviews the popular methods available for gene-based and pathway-based association tests. Chapter 2 describes an initial GWAS on corneal curvature, a phenotype of interest to diseases such as myopia and keratoconus, in Australians and further meta-analysis of two cohorts comprising of 1788 Australian twins and their families and 1013 individuals from a birth cohort from Western Australia. It also reports on replication in Australians of Northern European ancestry of the previously reported association of SNPs in PDGFRA and FRAP1 genes with corneal curvature in an Asian population. Chapter 3 is a demonstration of GWAS and pathway-based analysis. It reports the first GWAS and meta-analysis for corneal astigmatism in two Australian cohort studies, total sample size ~2,700. This chapter describes the application of Pathway-VEGAS, an extension of the gene-based analysis tool Versatile Gene-based Association Study (VEGAS) program for pathway-based analysis. Chapter 4 reports the successful application of VEGAS and Pathway-VEGAS to GWAS meta-analysis data. As part of the international glaucoma genetics consortium (IGGC), I participated in genetic analysis of the vertical cup disc ratio (VCDR) phenotype. This manuscript describes the meta-analysis of GWAS on a discovery sample of 21,094 individuals from ten cohorts of northern European ancestry and a replication sample of 6,784 individuals from four Asian cohorts. Gene-based and pathway-based tests were performed using the VEGAS and Pathway-VEGAS programs separately for northern European ancestry samples and Asian ancestry samples, and respective results were meta-analysed. This chapter demonstrates the advantages of using different approaches such as single marker GWAS, gene-based association approach and pathway based association approach. Chapter 5 presents the VEGAS2 software. VEGAS is one of the most popular gene-based association software. VEGAS however has some limitations such as the inability to perform a gene-based tests on the X chromosome, dependence on HapMap2 data to model the correlation between SNPs and inflexibility in the selection of gene boundary. The VEGAS2 software is an extension of VEGAS, redeveloped and upgraded to overcome these limitations. Chapter 6 presents the VEGAS2Pathway approach for pathway analysis of GWAS summary data. This chapter describes the shortcomings of many popular pathway-based association approaches, and how VEGAS2-Pathway overcomes these limitations. Further it demonstrates the application of VEGAS2Pathway on the endometriosis GWAS summary data. The last chapter provides the contributions this thesis makes to the literature and discusses its implications, limitations and future directions. In conclusion, my thesis provides the methodological and analytical approaches to further our knowledge in human genetics.

ObjectiveThe power of genome-wide association studies (GWAS) to identify common disease variants depends primarily on the number of included samples. The availability of formalin-fixed paraffin-embedded (FFPE) samples in pathology institutes provides a valuable resource for GWAS, but the use of this material poses significant challenges. To explore the suitability of utilizing FFPE tissue for GWAS, we analysed the genotyping concordance between corresponding FFPE and blood samples. We evaluated both microarray technology and low-coverage whole-genome sequencing (lcWGS) to determine whether there were differences between genotyping methods.ResultsIn our concordance study, FFPE tissue showed high recall and precision values across both genotyping methods when compared to matched blood samples for single nucleotide polymorphisms. This demonstrates that FFPE samples are suitable for GWAS and that both methods are viable options for genotyping. However, microarray technology outperformed lcWGS, as evidenced by significantly higher recall (p = 0.005) and precision (p = 0.003) values. This, together with the lower cost of genotyping and computational efficiency, makes microarray technology currently the superior method for GWAS using FFPE tissue. Nevertheless, lcWGS has shown reliable results and holds the potential to provide more comprehensive and unbiased genetic variant analysis across diverse populations in the future. Our results show that the large number of FFPE samples stored in pathology institutes can significantly increase the power of future GWAS.

Host Genetic Factors for COVID-19 Severity and Outcome in Western Indian Population

Background: Despite the recent success of genome-wide association studies (GWAS) in identifying 90 independent risk loci for Parkinson’s disease (PD), the genomic underpinning of PD is still largely unknown. At the same time, accurate and reliable predictive models utilizing genomic or demographic features are desired in the clinic for predicting the risk of Parkinson’s disease.Methods: To identify influential demographic and genomic factors associated with PD and to further develop predictive models, we utilized demographic data, incorporating 200 variables across 33,473 participants, along with genomic data involving 447,089 SNPs across 8,840 samples, both derived from the Fox Insight online study. We first applied correlation and GWAS analyses to find the top demographic and genomic factors associated with PD, respectively. We further developed and compared a variety of machine learning (ML) models for predicting PD. From the developed ML models, we performed feature importance analysis to reveal the predictability of each demographic or the genomic input feature for PD. Finally, we performed gene set enrichment analysis on our GWAS results to identify PD-associated pathways.Results: In our study, we identified both novel and well-known demographic and genetic factors (along with the enriched pathways) related to PD. In addition, we developed predictive models that performed robustly, with AUC = 0.89 for demographic data and AUC = 0.74 for genomic data. Our GWAS analysis identified several novel and significant variants and gene loci, including three intron variants in LMNA (p-values smaller than 4.0e-21) and one missense variant in SEMA4A (p-value = 1.11e-26). Our feature importance analysis from the PD-predictive ML models highlighted some significant and novel variants from our GWAS analysis (e.g., the intron variant rs1749409 in the RIT1 gene) and helped identify potentially causative variants that were missed by GWAS, such as rs11264300, a missense variant in the gene DCST1, and rs11584630, an intron variant in the gene KCNN3.Conclusion: In summary, by combining a GWAS with advanced machine learning models, we identified both known and novel demographic and genomic factors as well as built well-performing ML models for predicting Parkinson’s disease.

Purpose: Trastuzumab has been administered to HER2-positive breast or gastric cancer patients as an effective treatment, however, the cardiotoxicity is identified as one of the life-threatening side effects. To identify a novel genetic marker(s) determining the risk of trastuzumab-induced cardiotoxicity, we performed a genome-wide association study (GWAS).Experimental Design: We carried out a GWAS using 11 cases (with trastuzumab-induced cardiotoxicity) and 257 controls (showing no sign of trastuzumab-induced cardiotoxicity). Top 100 single nucleotide polymorphisms (SNPs) which revealed smallest P value in GWAS (P = 1.61 x 10-7 -1.97 x 10-4) were further examined using replication samples consisted of 14 cases and 199 controls. Results: The combined analysis of the GWAS and replication study indicated possible association of five loci with trastuzumab-induced cardiotoxicity (chromosome 13q14.3, 4q25, 15q26.3, 17q25.3 and another 15q26.3, Pcombined = 6.00 x 10-6, 8.60 x 10-5, 1.01 x 10-4, 1.07 x 10-4, 1.60 x 10-4, respectively). Furthermore, we developed a risk prediction model for trastuzumab-induced cardiotoxicity, using five SNPs which revealed smallest P value at each locus. The incidence of trastuzumab-induced cardiotoxicity in patients with risk score ≥5 was 42.5% (P =8.69 x 10-15) . This scoring system using five candidate loci could be used to predict the risk of the adverse event before administration of trastuzumab.Conclusion: We identified five novel loci, which could be associated with trastuzumab-induced cardiotoxicity. These findings provide new insights into personalized trastuzumab therapy for patients with HER2-positive cancer.Citation Format: Chihiro Udagawa, Mari Hara, Arata Shimo, Yasuyuki Kojima, Reiko Yoshie, Hisamitsu Zaha, Norie Abe, Tokiwa Motonari, Mikiko Unesoko, Kenji Tamura, Tatsunori Shimoi, Masayuki Yoshida, Teruhiko Yoshida, Hiroshi Okamura, Taisei Mushiroda, Koichiro Tsugawa, Hitoshi Zembutsu. A genome-wide association study identifies five novel genetic markers for trastuzumab-induced cardiotoxicity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2951.

This study aimed to investigate the effects of incidence rate, heritability, and polygenic variance on the statistical power of genome-wide association studies (GWAS) for threshold traits. Different incidence rates of threshold trait (1, 3, 5, 10, 25, 40, 50, 60, 75 and 90%), heritability (10 and 25%), and polygenic variance ratio (0 and 25%) were simulated separately for common (MAF ≥ 0.05), low-frequency (0.05 > MAF ≥ 0.01), and rare (MAF < 0.01) variants. Association studies were performed by logistic and linear mixed models. The highest statistical powers were observed in common and low-frequency variants with an incidence of 25-50% and 10-40%, respectively, but for rare variants, the highest statistical power was observed at low incidence. For all causal variant frequencies, the estimated heritability decline with an increase in incidence rate. We found high statistical power for traits with high heritability. In contrast, those with a high polygenic variance ratio have lower statistical power to detect common causal variants using a linear mixed model. These results demonstrate that the incidence rate of threshold traits, heritability, and polygenic variance may affect the statistical power of GWAS. Therefore, it is recommended that the effect of incidence rate, heritability, and polygenic variance be considered in designing GWAS for threshold traits.

Genome-wide association study (GWAS) has become a widely accepted strategy for decoding genotype phenotype associations in many species thanks to advances in next-generation sequencing (NGS) technologies. Maize is an ideal crop for GWAS and significant progress has been made in the last decade. This review summarizes current GWAS efforts in maize functional genomics research and discusses future prospects in the omics era. The general goal of GWAS is to link genotypic variations to corresponding differences in phenotype using the most appropriate statistical model in a given population. The current review also presents perspectives for optimizing GWAS design and analysis. GWAS analysis of data from RNA, protein, and metabolite-based omics studies is discussed, along with new models and new population designs that will identify causes of phenotypic variation that have been hidden to date. The detailed that low temperature in maize seedlings altogether restricts germination and seedlings' development and destabilizes the cancer prevention agent safeguard component. Cold pressure adversely influences root morphology, photosystem II (PS II) effectiveness, chlorophyll substance, and leaf region. A short scene of low temperature stress (for example, under 10 °C for 7 days) during the V6–V9 maize development stages can fundamentally defer the anthesis commencement. Among the morphological reactions by focused on maize plants, low temperature stress causes strange tuft development in maize, along these lines influencing the fertilization and grain filling measures. Hence, problematic temperatures can cause a genuine yield decrease if happening at basic conceptive stages, as plants allocate over half of their photosynthesis to foster grains during this stage until physiological development. Low temperature stress fundamentally diminishes the plant stature and absolute yield biomass of maize. Leaf improvement turns out to be delayed in chilly focused on plants because of a drawn-out cell cycle and diminished pace of mitosis. The joint and continuous efforts of the whole community will enhance our understanding of maize quantitative traits and boost crop molecular breeding designs.

ObjectivesIdentification of patient subpopulations susceptible to develop myocardial infarction (MI) or, conversely, those displaying either intrinsic cardioprotective phenotypes or highly responsive to protective interventions remain high-priority knowledge gaps. We sought...

Genome-wide association studies (GWAS) have led to the discovery of many genetic variants associated with diseases, with more than 50 000 associations described over the past 10 years.1 However, these variants explain only part of the risk of presenting these conditions, since neurodegenerative diseases may be related to a large number of variants whose effects are too subtle to be detected; therefore, they can only be observed in studies with large sample sizes or in meta-analyses. Furthermore, many of the variants identified by GWAS occur frequently in the general population,2 giving way to doubts regarding their relationship with the disease in question: in principle, if a variant is frequent, it is unlikely to have a differentiated functional impact causing the disease, since a large portion of the population does not present the pathology. In recent years, many articles have discussed potential biases affecting GWAS findings, especially methodological biases resulting from the advancement of detection techniques or the statistical analysis of the data obtained,3 as well as the influence of environmental factors.4 GWAS are case-control studies, and may therefore be subject to biases in terms of their design and performance, as well as to confounding factors hindering attempts to establish causation; many such factors are inherent to the populations analyzed.5 However, to a lesser extent, the literature has also reported that if a variant is related to the disease, this may in some way influence its clinical manifestation, leading to a different progression or specific profile, as we would expect if the variant is related to a functional effect. Demonstrating that a variant causes a functional alteration is one of the criteria for establishing its pathogenicity.6 In this issue of CNS Neuroscience & Therapeutics, Shen et al7 report a study aiming to observe the functional effect of a variant identified in GWAS in patients with Parkinson's disease (PD). The study belongs to the field of neuroimaging genetics, which has grown rapidly in recent years and whose application to neurodegenerative diseases may be a key to improving our knowledge of the function of certain genetic variants. The rs4698412 variant consists of a G-to-A substitution in the gene encoding the bone marrow stromal cell antigen 1 (BST1) protein, located on chromosome 4. BST1 is a glycosyl-phosphatidylinositol-anchored glycoprotein of bone marrow stromal cell lines that facilitates pre-B-cell growth.8 This variant is frequent in the general population, with an allele frequency of 40%. Furthermore, this is an intronic variant which is unlikely to cause functional effects, since it should not produce an anomalous transcript, and is not considered a variant of uncertain significance according to genomic criteria. Several GWAS studies have shown an association between BTS1 and PD, although other studies have found no clear association (Table 1). Likewise, this is not the only variant of the BST1 gene observed in GWAS studies (Table 2); however, given the noncoding character of these variants and the hypothesis that they act on a regulatory network, a shared mechanism may be involved.9 Shen et al7 analyze the pathogenicity of this variant, attempting to identify a specific clinical and functional neuroimaging profile in patients, according to the allelic variant of the gene. Other authors have previously attempted to establish a relationship between the disease and presence of the variant using the Unified Parkinson's Disease Rating Scale and the Hoehn-Yahr Stage, but rs4698412 was excluded from the analysis for methodological reasons.10 11931532 12645693 4698412 4538475 11931532 12502586 12645693 4698412 4538475 12646913 11931532, 12645693, 4698412 12502586 11931532 12645693, 11724635 The study by Shen et al7 is one of few studies that attempt to validate the detected variants through profiles and the first to include rs4698412. The authors observe a clinical profile associated with gait deficits and a functional magnetic resonance imaging profile that suggests that BST1 variants may affect stability in patients with PD. Regardless of whether these data should be reproduced, the findings of Shen et al7 support the idea that the variants identified in genetic association studies should be systematically validated by studying clinical profiles and, in the case of such neurodegenerative diseases as PD, neuroimaging profiles; data from these profiles should therefore be considered when analyzing the pathogenicity of these variants. Therefore, observing a higher frequency among patients of a variant whose incidence is high in the general population must be accompanied by other criteria if we are to consider that it has a role in the development of a disease, whether in its incidence or its progression. Therefore, observing a deleterious effect on the gene or gene product6 and differences between patients who do and who do not present the variant, may be a factor associated with its pathogenicity.

Populations that have experienced a bottleneck are regularly used in Genome Wide Association Studies (GWAS) to investigate variants associated with complex traits. It is generally understood that these isolated sub-populations may experience high frequency of otherwise rare variants with large effect size, and therefore provide a unique opportunity to study said trait. However, the demographic history of the population under investigation affects all SNPs that determine the complex trait genome-wide, changing its heritability and genetic architecture. We use a simulation based approach to identify the impact of the demographic processes of drift, expansion, and migration on the heritability of complex trait. We show that demography has considerable impact on complex traits. We then investigate the power to resolve heritability of complex traits in GWAS studies subjected to demographic effects. We find that demography is an important component for interpreting inference of complex traits and has a nuanced impact on the power of GWAS. We conclude that demographic histories need to be explicitly modelled to properly quantify the history of selection on a complex trait.

BackgroundGray Leaf Spot (GLS causal agents Cercospora zeae-maydis and Cercospora zeina) is one of the most important foliar diseases of maize in all areas where the crop is being cultivated. Although in the USA the situation with GLS severity is not as critical as in sub-Saharan Africa or Brazil, the evidence of climate change, increasing corn monoculture as well as the narrow genetic base of North American resistant germplasm can turn the disease into a serious threat to US corn production. The development of GLS resistant cultivars is one way to control the disease. In this study we combined the high QTL detection power of genetic linkage mapping with the high resolution power of genome-wide association study (GWAS) to precisely dissect QTL controlling GLS resistance and identify closely linked molecular markers for robust marker-assisted selection and trait introgression.ResultsUsing genetic linkage analysis with a small bi-parental mapping population, we identified four GLS resistance QTL on chromosomes 1, 6, 7, and 8, which were validated by GWAS. GWAS enabled us to dramatically increase the resolution within the confidence intervals of the above-mentioned QTL. Particularly, GWAS revealed that QTLGLSchr8, detected by genetic linkage mapping as a locus with major effect, was likely represented by two QTL with smaller effects. Conducted in parallel, GWAS of days-to-silking demonstrated the co-localization of flowering time QTL with GLS resistance QTL on chromosome 7 indicating that either QTLGLSchr7 is a flowering time QTL or it is a GLS resistance QTL that co-segregates with the latter. As a result, this genetic linkage – GWAS hybrid mapping system enabled us to identify one novel GLS resistance QTL (QTLGLSchr8a) and confirm with more refined positions four more previously mapped QTL (QTLGLSchr1, QTLGLSchr6, QTLGLSchr7, and QTLGLSchr8b). Through the novel Single Donor vs. Elite Panel method we were able to identify within QTL confidence intervals SNP markers that would be suitable for marker-assisted selection of gray leaf spot resistant genotypes containing the above-mentioned GLS resistance QTL.ConclusionThe application of a genetic linkage – GWAS hybrid mapping system enabled us to dramatically increase the resolution within the confidence interval of GLS resistance QTL by-passing labor- and time-intensive fine mapping. This method appears to have a great potential to accelerate the pace of QTL mapping projects. It is universal and can be used in the QTL mapping projects in any crops.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2171-3) contains supplementary material, which is available to authorized users.

Breast cancer is the second largest cause of cancer death among U.S. women and the leading cause of cancer death among women worldwide. Genome-wide association studies (GWAS) have identified several genetic variants associated with susceptibility to breast cancer, but these still explain less than half of the estimated genetic contribution to the disease. Combinations of variants (i.e. genetic interactions) may play an important role in breast cancer susceptibility. However, due to a lack of statistical power, the current tests for genetic interactions from GWAS data mainly leverage prior knowledge to focus on small sets of genes or SNPs that are known to have an association with breast cancer. Thus, many genetic interactions, particularly among novel variants, remain understudied. Reverse-genetic interaction screens in model organisms have shown that genetic interactions frequently cluster into highly structured motifs, where members of the same pathway share similar patterns of genetic interactions. Based on this key observation, we recently developed a method called BridGE to search for such structured motifs in genetic networks derived from GWAS studies and identify pathway-level genetic interactions in human populations. We applied BridGE to six independent breast cancer cohorts and identified significant pathway-level interactions in five cohorts. Joint analysis across all five cohorts revealed a high confidence consensus set of genetic interactions with support in multiple cohorts. The discovered interactions implicated the glutathione conjugation, vitamin D receptor, purine metabolism, mitotic prometaphase, and steroid hormone biosynthesis pathways as major modifiers of breast cancer risk. Notably, while many of the pathways identified by BridGE show clear relevance to breast cancer, variants in these pathways had not been previously discovered by traditional single variant association tests, or single pathway enrichment analysis that does not consider SNP-SNP interactions.

BackgroundThe lack of integrated analysis of genome-wide association studies (GWAS) and 3D epigenomics restricts a deep understanding of the genetic mechanisms of meat-related traits. With the application of techniques as ChIP-seq and Hi-C, the annotations of cis-regulatory elements in the pig genome have been established, which offers a new opportunity to elucidate the genetic mechanisms and identify major genetic variants and candidate genes that are significantly associated with important economic traits. Among these traits, loin muscle depth (LMD) is an important one as it impacts the lean meat content. In this study, we integrated cis-regulatory elements and genome-wide association studies (GWAS) to identify candidate genes and genetic variants regulating LMD.ResultsFive single nucleotide polymorphisms (SNPs) located on porcine chromosome 17 were significantly associated with LMD in Yorkshire pigs. A 10 kb quantitative trait locus (QTL) was identified as a candidate functional genomic region through the integration of linkage disequilibrium and linkage analysis (LDLA) and high-throughput chromosome conformation capture (Hi-C) analysis. The BMP2 gene was identified as a candidate gene for LMD based on the integrated results of GWAS, Hi-C meta-analysis, and cis-regulatory element data. The identified QTL region was further verified through target region sequencing. Furthermore, through using dual-luciferase assays and electrophoretic mobility shift assays (EMSA), two SNPs, including SNP rs321846600, located in the enhancer region, and SNP rs1111440035, located in the promoter region, were identified as candidate SNPs that may be functionally related to the LMD.ConclusionsBased on the results of GWAS, Hi-C, and cis-regulatory elements, the BMP2 gene was identified as an important candidate gene regulating variation in LMD. The SNPs rs321846600 and rs1111440035 were identified as candidate SNPs that are functionally related to the LMD of Yorkshire pigs. Our results shed light on the advantages of integrating GWAS with 3D epigenomics in identifying candidate genes for quantitative traits. This study is a pioneering work for the identification of candidate genes and related genetic variants regulating one key production trait (LMD) in pigs by integrating genome-wide association studies and 3D epigenomics.

Strong evidence suggests a genetic susceptibility to suicidal behavior, including familial heritability and common occurrence in twins. In the recent advent of scientific research, the genome-wide association study (GWAS), an alternative to the candidate-gene approach, is widely utilized to examine hundreds of thousands of SNPs by high-throughput genotyping technologies. In addition to the candidate-gene approach, the GWAS approach has recently been employed to study the determinants of suicidal behavior. Several recent findings have demonstrated that some SNPs and genes are closely associated with suicidal behavior. This chapter addresses recent molecular genetic studies in suicidal behavior. First, we surveyed the SNPs and genes identified as genetic markers that are correlated and associated with suicidal behavior in the candidate-gene association studies. Next, we reviewed the SNPs and genes that have been suggested as contributors to suicidal behavior in the GWAS studies. Finally, we summarized the limitations and future directions. Future research with independent replication in large sample sizes is needed to confirm the role of the SNPs and genes identified in the candidate-gene and GWAS studies in suicidal behavior.

Microbial pathogens often harbor substantial functional diversity driven by structural genetic variation. Rapid adaptation from such standing variation threatens global food security and human health. Genome-wide association studies (GWAS) provide a powerful approach to identify genetic variants underlying recent pathogen adaptation. 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. 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 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 and revealed functionally confirmed loci that classic GWAS approaches failed to map. The power of GWAS in microbial pathogens can be significantly enhanced by comprehensively capturing structural genetic variation. Our approach is generalizable to a large number of species and will uncover novel mechanisms driving rapid adaptation of pathogens.