Land and Food Systems, Faculty of
DSpace
UBCV
Singh, Sharandeep
Master of Science - MSc
University of British Columbia
Fusarium head blight (FHB) is an important fungal disease affecting the yield and quality of wheat. Deploying genetic resistance in wheat is an essential component of an integrated strategy for reducing the adverse effects of the disease. Most previous studies have mapped FHB resistance from Chinese or Brazilian germplasm. In preliminary research, 12 Watkins wheat landraces with MR/R resistance to FHB severity and deoxynivalenol (DON) toxin accumulation were identified. In the present study, we are utilizing one of the 12 Watkins landraces identified, namely, Wat.1190580 (origin: Iran) to identify gene(s)/QTL for FHB and DON resistance. Wat.1190580 has a resistant reaction to FHB and DON accumulation and is used as a male/resistance donor parent to develop recombinant inbred lines (RIL) population: Paragon X Wat.1190580 (F8, n=75). Paragon is a European spring wheat cultivar susceptible to FHB. The mapping population was screened at the UBC Greenhouse in (2021) and in field nurseries in Morden (2021, 2022, 2023), and Carman (2021, 2022) for FHB infection: incidence (INC), severity (SEV), Fusarium damaged kernels (FDKs), and DON and phenological traits: plant height (PHT), days to anthesis (DTA). At first, Paragon X Wat.1190580 was genotyped using a Wheat 90K iSelect SNP array. Our results from 90K genotyping resulted in genetic maps with large intervals between skeleton markers (absence of markers in most regions across the entire genome) indicating allelic diversity in landraces not represented by the Wheat 90K iSelect SNP array. As 90K is identified as unsuitable for genotyping of the RILs derived from Watkin landraces, we genotyped the population using a skim-sequencing approach at 0.1x coverage and generated genome assembly of Wat.1190580 (20x coverage). QTL regions associated with INC (2B, 5A), SEV (2B, 3B, 5B), DON (5A), FDKs (5A) resistance, and PHT (3A, 5A), and DTA (5A) were successfully mapped using the phenotypic data and the skim-sequencing data.
Genetic mapping of resistance to Fusarium head blight and DON accumulation in Watkins landrace Wat.1190580 by Sharandeep Singh B.Sc., Punjab Agricultural University, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Plant Science) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April, 2024 © Sharandeep Singh, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Genetic mapping of resistance to Fusarium head blight and DON accumulation in Watkins landrace Wat.1190580 submitted by Sharandeep Singh in partial fulfilment of the requirements for the degree of Master of Science in Plant Science Examining Committee: Dr. Andrew Riseman, Associate Professor, Applied Biology, UBC Supervisor Dr. Gurcharn S. Brar, Assistant Professor, Wheat Breeding and Genetics, University of Alberta Supervisory Committee Member Dr. Harpinder Randhawa, Research Scientist, Spring Wheat Breeding, Agriculture and Agri-Food Canada Additional Examiner Additional Supervisory Committee Members: Dr. Rishi R. Burlakoti, Research Scientist, Plant Pathology, Agriculture and Agri-Food Canada Supervisory Committee Member iii Abstract Fusarium head blight (FHB) is an important fungal disease affecting the yield and quality of wheat. Deploying genetic resistance in wheat is an essential component of an integrated strategy for reducing the adverse effects of the disease. Most previous studies have mapped FHB resistance from Chinese or Brazilian germplasm. In preliminary research, 12 Watkins wheat landraces with MR/R resistance to FHB severity and deoxynivalenol (DON) toxin accumulation were identified. In the present study, we are utilizing one of the 12 Watkins landraces identified, namely, Wat.1190580 (origin: Iran) to identify gene(s)/QTL for FHB and DON resistance. Wat.1190580 has a resistant reaction to FHB and DON accumulation and is used as a male/resistance donor parent to develop recombinant inbred lines (RIL) population: Paragon X Wat.1190580 (F8, n=75). Paragon is a European spring wheat cultivar susceptible to FHB. The mapping population was screened at the UBC Greenhouse in (2021) and in field nurseries in Morden (2021, 2022, 2023), and Carman (2021, 2022) for FHB infection: incidence (INC), severity (SEV), Fusarium damaged kernels (FDKs), and DON and phenological traits: plant height (PHT), days to anthesis (DTA). At first, Paragon X Wat.1190580 was genotyped using a Wheat 90K iSelect SNP array. Our results from 90K genotyping resulted in genetic maps with large intervals between skeleton markers (absence of markers in most regions across the entire genome) indicating allelic diversity in landraces not represented by the Wheat 90K iSelect SNP array. As 90K is identified as unsuitable for genotyping of the RILs derived from Watkin landraces, we genotyped the population using a skim-sequencing approach at 0.1x coverage and generated genome assembly of Wat.1190580 (20x coverage). QTL regions associated with INC (2B, 5A), SEV (2B, 3B, 5B), DON (5A), FDKs (5A) resistance, and PHT (3A, 5A), and DTA (5A) were successfully mapped using the phenotypic data and the skim-sequencing data. iv Lay Summary This study aimed at identifying the genetic regions underlying resistance to Fusarium head blight (FHB) in the Watkins landrace Wat.1190580 through QTL mapping analysis. By mapping QTL regions associated with incidence (2B, 5A), severity (2B, 3B, 5B), deoxynivalenol (5A), Fusarium damaged kernels (5A) resistance, plant height (3A, 5A), and days to anthesis (5A) from Paragon X Wat.1190580, this study contributed to the knowledge of FHB resistance loci carried within the landrace where developmental traits: plant height and days to anthesis, have an influence on the disease incidence. Whether developmental traits help in disease escape or are in linkage with resistance loci is not clear yet. It also emphasizes using the skim sequencing technique in efficient and cost-effective library preparation, RIL sequencing (0.1x coverage), resulting in dense SNP-rich regions. This approach enhances the feasibility of employing skim sequencing in QTL mapping studies, particularly in bi-parental crosses involving Watkins landraces where other methods such as the Wheat 90K iSelect SNP array may prove less suitable. v Preface The research presented in this thesis is one of the research objectives listed in the Agriculture Development Fund (ADF) of Saskatchewan Ministry of Agriculture project #20200205: “Mapping novel Fusarium head blight (FHB) and stripe rust resistance genes in wheat”. This project was developed under the guidance of Dr. Gurcharn S. Brar. The plant material generation advancement and seed bulking experiments were conducted at the Greenhouses and the Totem Plant Science Research Station of the University of British Columbia (UBC), Vancouver campus. Ms. Melina Biron (Manager, Plant Care Services) managed the technical support for the compartment's programmable temperature and LED lighting. Phenotypic screening of RILs was conducted at Morden and Carman FHB disease screening nurseries with assistance from Dr. Maria Antonia-Henriquez and Dr. Curt McCartney, respectively. DON data was provided by the Cereal and Flax Pathology group of Dr. Hadley Kutcher at the University of Saskatchewan. Dr. Brar provided the research stipend, experimental workspace, plant materials, and manuscript feedback. Administrative support was provided by Dr. Andrew Riseman and Dr. Gurcharn S. Brar. The remaining portion of the project comprises original, unpublished, independent contributions by the author, Sharandeep Singh. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................ ix List of Figures .................................................................................................................................x List of Abbreviations ................................................................................................................... xi Acknowledgements .................................................................................................................... xiii Dedication .....................................................................................................................................xv Chapter 1: Introduction ................................................................................................................1 1.1 Wheat domestication ....................................................................................................... 1 1.1.1 Evolution ..................................................................................................................... 1 1.2 Biotic stresses associated with wheat cultivation in Canada .......................................... 2 1.3 Fusarium head blight (FHB): causal agent and disease epidemiology ........................... 2 1.3.1 FHB management strategies ....................................................................................... 4 1.3.2 Host resistance ............................................................................................................ 6 1.3.3 Molecular markers and genetic improvement ............................................................. 7 1.3.4 Skim sequencing ......................................................................................................... 9 1.3.5 Breeding for resistance against FHB in Canada ....................................................... 10 1.3.6 Landrace cultivars: importance for genetic diversity................................................ 12 1.4 Research objectives ....................................................................................................... 13 vii Chapter 2: Genetic mapping of resistance to Fusarium head blight and DON accumulation in Watkins landrace Wat.1190580..............................................................................................14 2.1 Introduction ................................................................................................................... 14 2.2 Materials and methods .................................................................................................. 17 2.2.1 Plant material ............................................................................................................ 17 2.2.2 Greenhouse FHB evaluation ..................................................................................... 17 2.2.3 Field FHB evaluations .............................................................................................. 18 2.2.4 FDK assessment ........................................................................................................ 20 2.2.5 DON quantification ................................................................................................... 21 2.2.6 Statistical analyses .................................................................................................... 23 2.2.7 Genotyping and linkage map construction ............................................................... 24 2.2.8 Skim-sequencing ....................................................................................................... 25 2.2.9 Sequence alignment and linkage map construction .................................................. 27 2.3 Results ........................................................................................................................... 29 2.3.1 Phenotypic results ..................................................................................................... 29 2.3.2 Genotyping results .................................................................................................... 31 2.3.2.1 Linkage map...................................................................................................... 31 2.3.3 Skim-sequencing results ........................................................................................... 32 2.3.3.1 Linkage map...................................................................................................... 32 2.3.3.2 QTL mapping .................................................................................................... 33 2.4 Discussion ..................................................................................................................... 34 2.4.1 Premature ripening above the point of inoculation ................................................... 34 viii 2.4.2 Wheat 90K iSelect SNP genotyping may not represent the genetic diversity in Watkins landraces ................................................................................................................. 35 2.4.3 QTL mapping for FHB resistance in PXW580......................................................... 38 2.5 Future work and conclusions ........................................................................................ 42 2.6 Tables and figures ......................................................................................................... 43 References .....................................................................................................................................48 Appendices ....................................................................................................................................57 Appendix A ............................................................................................................................... 57 Appendix B ............................................................................................................................... 58 Appendix C ............................................................................................................................... 59 Appendix D ............................................................................................................................... 60 Appendix E ............................................................................................................................... 61 Appendix F................................................................................................................................ 62 Appendix G ............................................................................................................................... 63 ix List of Tables Table 1. Analysis of variance (ANOVA) for phenotypic field ariables………………………...43 Table 2. Summary of the genetic map constructed with 75 RILs from ‘Paragon X Wat.1190580’ (PXW580) cross using a skim sequencing approach …………………………………………...44 Table 3. Quantitative trait loci (QTL) associated with FHB incidence (INC), severity (SEV), visual rating index (VRI), fusarium damaged kernels (FDKs), DON accumulation (DON), plant height (PHT), and days to anthesis (DTA), in the Paragon X Wat.1190580 (PXW580) recombinant inbred line (RIL) population………………………………………………………45 x List of Figures Figure 1. Chromosomal location of QTL (regions overlapping are shown in separate chromosomes) …………………………………………………………………………………...46 Figure 2. Chromosomal location of QTL (regions overlapping are shown in separate chromosomes, for example 5A)…………………………………………………………………47 xi List of Abbreviations 15ADON – 15-Acetyl Deoxynivalenol 3ADON – 3-Acetyl Deoxynivalenol ADF - Agriculture Development Fund ANOVA – Analysis of Variance AUDPC – Area Under the Disease Progress Curve CAAS – Chinese Academy of Agricultural Sciences CR – Carman CTAB - Cetyl Trimethyl Ammonium Bromide DArT – Diversity Array Technology DMI - Demethylation Inhibitor Group DON – Deoxynivalenol DTA – Days to Anthesis FDKs – Fusarium Damaged Kernels FHB – Fusarium head blight GBS – Genotyping-by-Sequencing GH_UBC21 – Greenhouse trial, UBC, 2021 GXE – Genotype X Environment interaction H2 – Broad-sense heritability His – Histidine-rich calcium-binding protein INC – Incidence LC-MS/MS - Liquid Chromatography-Tandem Mass Spectrometry LCs – Landrace Cultivars xii MAS – Marker-Assisted Selection MR- Morden PCR – Polymerase Chain Reaction PFT – Pore-Forming Toxin-like PHT- Plant Height PVE – Percentage Variance Explained PXW580 – ‘Paragon X Wat.1190580’ QTL – Quantitative Trait Loci RILs – Recombinant Inbred Lines SAS – Statistical Analysis Software SEV – Severity SNP – Single Nucleotide Polymorphism VRI – Visual Rating Index xiii Acknowledgements I want to give a huge shout-out to and Dr. Andrew Riseman and Dr. Gurcharn S. Brar. Dr. Brar, you've been like a guiding light (with a lot of sparks), always there with your top-notch advice that made my research journey smoother than silk. And Dr. Riseman, your positivity has been contagious, especially during those last few months when I needed it the most. Big thanks to Dr. Santosh Kumar, Dr. Juli Carrillo, and Dr. Rishi Burlakoti. Your feedback and suggestions were like gold nuggets for my research experiments and manuscripts. I can not forget to mention the Cereal Breeding Lab, Dr. Samuel Holden, your data analysis skills are next level (that tuk tuk noise on your keyboard), and your comments on my manuscripts were spot-on. And a big thanks to Dr. Meng Li for being so patient and knowledgeable during those DNA-extraction, qubit reading and library prep marathons. Hats off to Ana Sofia Alfonso, Tiffany Li, Ivy Chiu, Hanni Liu, Jingwin Wang, and Dr. Ishwin Kamboj for their help with threshing never ending wheat samples. And let's not forget Bharat Yadav and Dr. Ana Achilli for their assistance with phenotyping data analysis. Gurcharn Brar, Raman Brar, Bharat Yadav, Ishwin Kamboj, and Naveen Bishnoi, your homemade meals were a lifesaver! My mom sends her blessings for keeping her son well-fed. Thank you for your kindness and delicious dishes – they made my days brighter! xiv And to Naveen, Vincent, Ana, and Jonathan – you guys are the real MVPs for always keeping the vibes positive in the workplace. Last but not least, a big, warm hug to Yishan Zhang for being an amazing friend and support system, both at work and in life. You're the reason I survived all these years, much love to you! xv Dedication I dedicate this work to the one creator, Guru Nanak (Ik Onkar) And to my parents, my idols, my inspiration., Dharminder Singh and Jasveer Kaur 1 Chapter 1: Introduction 1.1 Wheat domestication Over 10,000 years ago, humans led a nomadic lifestyle, relying on wild animals for food and collecting wild plants for sustenance (Faris, 2014). The change from this nomadic lifestyle to an agrarian lifestyle is a significant historical turning point known as the ‘Neolithic Revolution’. Fertile Crescent is believed to be the geographical origin of this transition and its spread in the Middle East and encompasses a region extending from Jordan, Israel, Lebanon, and Syria. Our ancestors engaged in the process of selection, favoring traits in wild grasses such as a non-shattering rachis, which limited the natural seed dispersal mechanisms of the wild forms and allowed early farmers to harvest the grain much more. In addition, other early domestication traits included reduced seed dormancy, enhanced grain quality, and free threshing nature (Harlan et al., 1973). These “selections” became the domesticated forms that were a part of the large-scale shift to an agrarian lifestyle. 1.1.1 Evolution In my opinion, the evolution of wheat is far more interesting than any other crop species and is well explained by Charmet (2011). The grass family (Poaceae) is believed to have originated from a common ancestor with approximately five pairs of chromosomes. A diploid ancestor within the Triticeae subtribe, with a total of seven chromosomes, emerged from the same ancestral species. Around 150,000 years ago, a hybridization (polyploid) event involving Triticum urartu (2n = 2x = 14; AA) and an extinct species related to Aegilops speltoides (2n = 2x = 14; BB) resulted in the emergence of Triticum turgidum L. ssp diccocoides (2n = 4x = 28; 2 AABB), an amphi-tetraploid species with 14 pairs of chromosomes. And nearly 10,000 years ago, another polyploidy event between domesticated tetraploid Triticum turgidum L. ssp diccocoides (AABB) and a wild diploid species, T. tauschii (2n = 2x = 14; DD) gave birth to the present bread wheat, T. aestivum L., (2n = 6x = 42; AABBDD), a hexaploid species with 21 chromosome pairs and the most economically important crop in the world. 1.2 Biotic stresses associated with wheat cultivation in Canada Fungal diseases (called the priority one diseases in Canada) are among the major biotic stressors of wheat production in Canada (Aboukhaddour et al., 2020). Priority-one diseases in the Canadian agriculture context are defined as those which are considered to cause harm significant enough to warrant regulation through the cultivar registration process. Priority one diseases include Fusarium head blight [FHB, Fusarium graminearum Schw. (Petch)], leaf rust (Puccinia triticina Eriks.), stem rust (Puccinia graminis Pers.: Pers f. sp. tritici Eriks. E. Henn), stripe rust (Puccinia striiformis Westend f. sp. tritici Eriks.) and common bunt (Tilletia laevis Bjerk. and Tilletia laevis Bjerk.). Among these, FHB has always been a top concern for which wheat breeders must achieve at least an intermediate level of resistance to register a new cultivar (Brar et al., 2019). 1.3 Fusarium head blight (FHB): causal agent and disease epidemiology Also known as head scab or simply scab, FHB is caused by multiple species in the ascomycetous genus Fusarium. However, Fusarium graminearum (Schwabe) (teleomorph Gibberella zeae (Schwein.) Petch) is most prevalent in cereal growing regions of Canada (Gilbert and Haber, 3 2013). FHB infects the wheat plant at the flowering stage and appears as pre-mature bleaching of the spike. Symptoms include shrunken, chalky white kernels known as Fusarium damaged kernels (FDKs), lower wheat yields (light weight kernels), and diminished grain quality. Reduced grain quality is primarily associated with production of the trichothecene class of mycotoxins mainly deoxynivalenol (DON) and nivalenol (NIV). A better understanding of symptoms lies in the infection process of the pathogen. The pathogen F. graminearum is a hemibiotroph, that has an acute biotrophic phase which is followed by the necrotrophic phase. In the biotrophic phase (under conducive environment), fungal ascospores (sexual) and conidiospores (asexual) form germ tube within 12 hours of the initial contact that gives rise to hyphae (Gunupuru et al., 2017). Plant openings such as stomata and other susceptible sites facilitate the entrance of the pathogen wherein it produces dense mycelial networks. This phase lasts from 24 to 32 hours post-infection and fungus feeds off the living host cells. The growing mycelia block the vascular bundles in rachis, halting the movement of water and nutrients to head, resulting in classic phenotypic symptoms: pre-mature bleaching of spike and shrivelling of grains. Other symptoms include contamination of grains with DON. It is important to mention here that DON is not just a contamination factor but a fungal virulence factor as DON helps the fungal necrotrophic phase. Production of DON induces the plant defense mechanisms such as reactive oxidative species production resulting in programmed cell death. The programmed cell death in turn facilitates the necrotrophic phase, therefore, increased levels of DON production correlates with increased infection levels. F. graminearum population encompasses two chemotypes (chemotype simply means chemically diverse forms of 4 mycotoxins produced by the fungal isolates or races), 3-acetylated DON (3-ADON) and 15-acetylated DON (15-ADON), of which former is more aggressive and produces more DON. This heightened aggressiveness signifies the chemotype's ability to propagate the infection at an accelerated rate. In regions such as western Canada, where a substantial portion of wheat is grown, the prevalence of the 3ADON chemotype is abundant. Higher DON accumulation in wheat not only raises concerns for human health but also carries economic implications on Canadian wheat industry, therefore, DON resistance is an active component of FHB resistance. 1.3.1 FHB management strategies Alterations in agricultural practices have been shown to reduce FHB pressure and risks to crop loss. Soil and crop residues serve as an effective refuge for the pathogen where it can grow saprophytically depending upon factors like temperature, water activity and other physico-chemical factors. Therefore, intensive tillage practices can decrease FHB inoculum by increasing residue exposure to sunlight, which expedites its decomposition (Pereyra et al., 2004). Employing a four-year crop rotation strategy involving non-host crops has also been shown to have a notable impact on reducing FHB intensity (Shah et al., 2017). Experimental evidence indicates that FHB intensity is highest when wheat follows corn, compared to when it follows soybean (Dill-Macky and Jones, 2000). Moreover, irrigation practices can significantly affect FHB infection. Moisture levels surrounding the spikelet region have a significant influence on disease progression with increased humidity increasing disease severity (Cowger et al., 2009). Both FHB intensity and the 5 occurrence of FDKs exhibit a linear response to increased moisture within the crop microclimate. This consistent response to varying moisture conditions underscores the importance of avoiding post-anthesis irrigation, particularly during the grain-filling stage, as a strategic approach to disease management. In conjunction with cultural practices, there exists a substantial reliance on fungicides for FHB management due to their availability and efficacy. Fungicides belonging to the triazole group, also known as the demethylation inhibitor group (DMI), have demonstrated higher efficacy compared to other classes of fungicides in FHB management (Gilbert and Haber, 2013). Specifically, Tebuconazole, Prothioconazole, and Metconazole represent prominent members within this group. Notably, their effectiveness is significantly enhanced when applied in combination, such as Prothioconazole in conjunction with Tebuconazole or Metconazole, as contrasted with individual applications of Tebuconazole and Metconazole alone, as reviewed by Gilbert and Haber in 2013. The timing of fungicide application plays an important role in the extent to which the disease can be effectively managed. In areas of high disease pressure, optimal control measures are attained when fungicides are applied during the most susceptible stage of wheat to FHB, approximately one week after anthesis. Applying fungicides to control FHB (although still prevalent) remains challenging because of the short application window, potential environmental contamination and cost. For example, a single fungicide application on 20 million acres of wheat in Canada would lead to an annual cost of at least $400 million CAD. However, areas with high disease pressure (e.g., southern Manitoba) often require two applications and doubling the cost. 6 1.3.2 Host resistance Genetic resistance is the most sustainable way to effectively reduce FHB impacts on Canadian wheat industry. A few well characterized resistance sources (i.e., the nearly resistant AAC Tenacious) have been successfully identified but they often come with poor agronomic and grain quality traits, which limit their large-scale integration. Identifying FHB resistance sources and their subsequent introgression into elite breeding lines is challenging due to both the quantitative (oligogenic/polygenic) inheritance of resistance and significant genotype-environment interactions. Miedaner and Korzun (2012) defines quantitative resistance as ‘resistance inherited by several genes known as quantitative trait loci (QTL) with minor effects, usually non-race-specific and prone to nongenetic interactions, also named horizontal resistance’. Moreover, plant morphological and developmental traits including plant height, awn character, spikelet density, flowering date, degree of floret opening during flowering, and grain filling rate also influence disease intensity (Bai et al., 2018). The contribution of these traits towards low or high FHB incidence could simply be passive (non-genetic) or active (genetic). Passive plant defense includes range of morphological and physiological traits, which constitutes the first layer of defense against FHB. It limits or escape the infection through physical/morphological barriers or developmental traits such as early or delayed flowering times (Gorash et al., 2021). Active plant resistance resist pathogen invasion by pathogen detection, signaling, accumulation of resistance – related proteins. Therefore, evaluating and correlating the morphological/developmental traits with FHB resistance is critical to understanding the disease complex and how best to breed host resistant cultivars. 7 The resistance mechanisms against FHB in wheat have been broadly classified as Type I, Type II, Type III, Type IV and Type V, based on the host response to the pathogen (Sax 1923). Type I is the resistance to initial infection and is evaluated by spray inoculation method. It is measured as disease incidence level in the field. Type II resistance measures the disease severity and is the resistance against the spread of infection. It is determined by point inoculation method in a controlled environment (greenhouse) or by measuring severity in the field. Type III resistance is characterized by resistance to DON accumulation and is measured by quantifying the DON concentration. Usually, there are three mechanisms underlying DON resistance. Resistance can be due to active mycotoxin degradation (DON to non-toxic DON-3-glucoside), insensitivity to the mycotoxin, or due to the limited accumulation of the mycotoxin in head tissues. Type IV resistance is the resistance against kernel infection. It is measured as the number of FDKs per spike at harvest. Type V resistance is a tolerance mechanism through which the plant is able to maintain sufficient yield despite the presence of disease (Mesterhazy, 2020). 1.3.3 Molecular markers and genetic improvement Molecular markers represent an important tool in genetic research, particularly in assessing the genetic architecture of a trait and identifying associations among agriculturally significant traits. For example, molecular markers enable the exploration of covariation between QTL responsible for disease resistance and those governing characteristics like heading date or plant height (as in FHB) (Miedaner and Korzun, 2012). Marker assisted selection (MAS) utilizes molecular markers to indirectly select for a desirable trait through linkage to the marker(s) (Fernando et al., 2021). In an article by Varshney, Graner, and Sorrells (2005), they remind their readers of how 8 modern genetic markers were once called ‘factors for qualitative traits’ and QTL were ‘size factors’ as originally described in 1923 (Sax). It took almost a century for marker-aided selection to become a routine part of plant breeding. The evolution of marker assisted selection began with linkage maps based on morphological traits (i.e., Mendel’s wrinkled seed) that then were then augmented with DNA based markers. These DNA markers have also evolved from RFLP to microsatellites, which now have been replaced by single nucleotide polymorphisms (SNPs), diversity array technology (DArT), and genotyping by sequencing (GBS). The development of dense linkage maps, mainly through the use of SNPs, has enabled comprehensive genome scans, revealing regions distributed across the genome that control crucial adaptive traits in almost all major crops, particularly wheat. Over the past decade, various genotyping platforms have emerged, with SNP-based maps containing thousands of markers for QTL mapping. In 2014, Wang et al. introduced a high-throughput SNP array, known as the Wheat 90K iSelect SNP array, comprising 81,587 markers for precise mapping of genomic regions associated with economically significant traits in wheat. Nevertheless, it's worth noting that the Wheat 90K iSelect SNP array proves less effective in identifying variations in landraces and germplasms derived from secondary and tertiary gene pools. Winfield et al. (2018) developed the Wheat 820K array through the exome capture of 43 wheat and wild species accessions. This array was designed to encompass a broad spectrum of global wheat lines, including elite cultivars, landraces, synthetic hexaploids, and wheat relatives. The Wheat Breeder’s 35K Axiom® array, derived from the Wheat 820K Axiom® SNP array, is a low-throughput option that offers markers tailored to the needs of breeders. Additionally, the Chinese Academy of Agricultural Sciences (CAAS) designed the Wheat 660K SNP array, which includes hexaploid and tetraploid wheat, 9 emmer wheat, and Aegilops tauschii markers. It is genome-specific, high-density, cost-effective, and versatile, featuring a wide range of applications. 1.3.4 Skim sequencing Skim-sequencing, a high-throughput sequencing method, is generally employed for 1) exploring genetically diverse genotypes, particularly for disease resistance, 2) mapping alien introgressions, resulting from crosses between an elite cultivar and landrace/wild relatives/distant relative, and 3) mapping internal translocations. Alien introgressions are indeed valuable assets for crop improvement, but marker development poses a significant challenge (Adhikari et al., 2022). Several options for exploring next-generation sequencing (NGS) exist, such as cytogenic methods like GISH and FISH, and whole-genome sequencing. However, low throughput in cytogenic approaches and high costs in whole-genome sequencing can be limiting factors. Skim sequencing and GBS is often used to address these constraints. Time is of utmost importance in research. In this regard, simplified library preparation methods have been developed, such as Nextera. Unlike GBS, where restriction digestion and adapter ligations are two-step processes, Nextera employs a transposome complex to perform random double-stranded breaks and ligate adapters in genomic DNA in a single step, a process known as tagmentation. This significantly reduces the time needed for library preparation. For instance, library prep in Nextera is completed in just 4 hours compared to GBS, which takes 3 days. These libraries can then be sequenced to varying levels of whole genome coverage for genomic analysis. Ensuring the accuracy of identified SNPs is crucial for reliable data interpretation. Normally, a sequencing depth of 5X to 10X is preferred to minimize errors. In Skim sequencing a sequencing depth of 10 0.1X is used in order to bring down the cost as it relies on known SNPs between two robust reference genomes. Despite the low depth, skim sequencing can still identify over a million genetic markers. This makes genetic analysis affordable while maintaining accuracy. 1.3.5 Breeding for resistance against FHB in Canada More than 300 QTL have been successfully mapped to FHB resistance, but only a few explain a large proportion of the phenotypic variation when introgressed into different genetic backgrounds. Seven genes (Fhb1 to Fhb7) for FHB resistance in wheat are formally designated as single Mendelized genes: Fhb1 (Type II resistance) derived from Chinese spring cultivar Sumai-3 has been extensively used in Canadian wheat breeding programs and is the most stable locus in multiple genetic backgrounds. There are abundant molecular markers available for Fhb1 (UNM10 is the most common) which are being utilized for its introgression into elite wheat breeding lines. Although cloning of Fhb1 has been accomplished, there remains a lack of agreement within the breeding community regarding the specific genes involved. Some studies have suggested that the PFT (pore-forming toxin-like protein) and His (histidine-rich calcium-binding protein) genes, either individually or as a combined haplotype, play a crucial role in the Fhb1 region (Dhariwal et al., 2020). Fhb2 and Fhb5 were also derived from ‘Sumai-3’ and confers both Type I and II resistance. The gene Fhb4 confers Type I and II resistance and is derived from Chinese landrace Wangshuibai (Zhang et al., 2010). Other mapped genes with major effects are Fhb3 which is derived from Leymus racemosus (Tein.) Tzvelev (syn Elymus giganteus Vahl) and Fhb6, derived from Elymus tsukushiensis Honda (2n = 6x = 42, StsStsHtsHtsYtsYts, syn. Roegneria kamoji C. Koch). The tall wheatgrass species Thinopyrum 11 elongatum (Podp.) Barkworth and D.R. Dewey (2n = 10X = 70) chromosome 7el2, also carries a strong FHB resistance locus located on the long arm of chromosome 7 (7EL), formally designated as Fhb-7EL and is one of the primary contributor of FHB resistance in wheat cultivars grown worldwide (Konkin et al., 2022). This gene encodes a glutathione S-transferase that detoxifies DON, conferring semi-dominant resistance (Fernando et al., 2021). The first effort to breed spring wheat for FHB resistance started by utilizing a Brazilian cultivar Frontana, resulting in the moderately resistant CWRS cultivar Neepawa and is a common parent in Canadian wheat cultivars. However, some of the most successful cultivars released by Canadian breeding programs relied on Chinese germplasm Sumai 3 as a source of Fhb1 and include ‘AAC Brandon’, ‘AAC Elie’, and ‘Cardale’. AC Barrie a CWRS (hard red) cultivar carries native resistance (intermediate) to FHB infection and is a parent to many newly released Canadian wheat cultivars (Thambugala et al., 2020). Interestingly, the resistance in ‘AC Barrie’ was contributed by the Canadian heritage wheat cultivar Neepawa, but is significantly higher yielding, shorter in stature, less prone to lodging, with a higher protein content than ‘Neepawa’. Although AC Barrie was the leading cultivar from 1998 to 2005, other cultivars were grown on a significant proportion of the seeded area including ‘AC Domain’, ‘CDC Teal’, ‘McKenzie’, ‘AC Splendor’, ‘AC Intrepid’, ‘Prodigy’, and ‘Superb’. Some of these more recently registered cultivars are significantly higher yielding than previous CWRS cultivars. The only Canadian wheat cultivar assigned a resistant rating is AAC Tenacious with its QTL imparting resistance having recently been mapped (results presented at Tri-Society Meeting 2023, Ottawa). This QTL is believed to carry major resistance component genes from one of its progenitors, the moderately resistant cultivar Neepawa (Dhariwal et al., 2020). 12 The current situation is such that even with the availability of moderately resistant wheat cultivars suitable for Canadian cereal-growing regions, FHB continues to cause severe economic losses during years of high disease pressure. This persistent issue is primarily attributed to the scarcity of diverse breeding resources that encompass both exceptional quality and agronomic traits, coupled with effective FHB resistance. 1.3.6 Landrace cultivars: importance for genetic diversity Lately, many breeding programs have taken a notable shift towards utilizing landrace cultivars (a highly diverse germplasm source) to hunt for disease-related loci. As a part of “green revolution” selections for favourable traits is known to have introduced induced population bottlenecks in cultivated crop plants (Doebley et al., 2006). In general, LCs exhibit a substantially greater degree of genetic diversity than elite cultivars. There is a well-established and acknowledged necessity for breeding initiatives to harness this genetic diversity, especially for FHB resistance, by integrating landrace cultivars into their breeding programs (Moore, 2015). This is despite the challenges of using this germplasm that include altered meiosis, sexual incompatibility, and reduced fertility in subsequent segregating populations. During the 1930s, A. E. Watkins obtained landrace cultivars of bread wheat through official channels of the Board of Trade in London. These landrace cultivars, which now constitute the Watkins collection comprising 826 accessions, were sourced from various local markets in 32 countries. This collection represents a broad geographical area, encompassing multiple countries 13 in Asia and Europe, along with some contributions from Africa (Wingen et al., 2014). The Watkins collection has proven valuable in the discovery of novel alleles or genes associated with resistance to leaf rust, stripe rust, and stem rust. Notably, these traits are governed by Mendelian inheritance, making it relatively easy to identify their chromosomal locations. However, for a better understanding of LC collections, there's an essential need to enhance our genetic insight into these collections to dissect the genetic architecture of complex traits like FHB. 1.4 Research objectives The current study presents the results of marker-based mapping of FHB resistance in Watkins landrace Wat.1190580 (origin: Iran) entitled ‘Genetic mapping of resistance to Fusarium head blight and DON accumulation in Watkins landrace Wat.1190580’. The specific objective of the current study was: to map regions for FHB and DON resistance from Watkins landrace Wat.1190580. 14 Chapter 2: Genetic mapping of resistance to Fusarium head blight and DON accumulation in Watkins landrace Wat.1190580 2.1 Introduction Bread wheat (Triticum aestivum. L.), also known as common wheat, is one of the ‘big three’ cereals in the world following maize and rice in terms of production and consumption (Shiferaw et al., 2013). In terms of acreage under cultivation, it is a leading commercial crop and a predominant food grain source for humans (FAOSTAT 2021). Unrivalled in its geographic range, it’s the first crop to be domesticated more than 10,000 years ago (Shewry, 2009). Its range of diversity is also unmatched, as the extent to which it has been ingrained in the culture and religions of many societies. Newman (1928) described wheat as ‘‘the economic fairy to the industrial and commercial life of Canada, having built practically the whole economic structure of the Prairie Provinces’’. Canada alone contributes close to 4% of total wheat production of the world (Statistics Canada) and is the third largest exporter (~22 million tons) after Australia and the United States. The majority of the wheat cropping area is across the southern latitudes, with the Prairie regions (Alberta, Saskatchewan, Manitoba) being the leading provincial producers. Canada classifies wheat into market classes based on quality factors such as kernel hardiness (hard or soft), seed coat color (amber), grain protein content, gluten strength, and spring or winter based on growth habit (Dexter et al. 2006). In addition, wheat classes are also based on regions of cultivation and include the eastern Canadian wheat classes and the western Canadian wheat classes, respectively. 15 Within eastern Canada, the wheat classes are Canadian Eastern Red Spring (CERS), Canadian Eastern Hard Red Winter (CEHRW), Canadian Eastern Soft Red Winter (CESRW), Canadian Eastern Amber Durum (CEAD), Canadian Eastern White Winter (CEWW), Canadian Eastern Feed (CE Feed). Cultivar classes grown in western Canada include Canadian Northern Hard Red (CNHR), Canadian Prairie Spring Red (CPSR), Canadian Western Special Purpose (CWSP), Canadian Prairie Spring White (CPSW), Canadian Western Amber Durum (CWAD), Canadian Western Extra Strong (CWES), Canadian Western Hard White Spring (CWHWS), Canadian Western Red Spring (CWRS), Canadian Western Red Winter (CWRW), and Canadian Western Soft White Spring (CWSWS) (Canadian Grain Commission, 2020). Fungal pathogens are one of the most important biotic stressors to wheat production in Canada. Of the fungal pathogens, F. graminearum, the main cause of Fusarium head blight (FHB) is the most devastating fungal disease in Canada, infecting spikes and causing yield and quality losses. The disease was first identified in Canada in the early 1900s and has since become a major concern for wheat producers across the country. The disease is particularly problematic in wheat-growing regions with humid and wet conditions, particularly the eastern Prairie region. FHB emerged as the most economically impactful wheat disease in Canada following the 2016 epidemic, which resulted in an estimated loss of approximately $1 Billion to the Canadian economy. In addition to the direct economic impacts, the disease also has implications for human and animal health. The fungus can produce mycotoxins, mainly deoxynivalenol (DON), which can contaminate grain and pose a risk to human and animal consumption. This has led to 16 increased scrutiny of wheat imports and exports, as well as stricter regulations on grain quality and safety affecting trade with other countries. There are different strategies to manage FHB with the most efficacious and economical being breeding for resistance. To date, more than 100 QTL of various types of resistance have been mapped and only 50 were identified to have unique regions (Liu et al., 2009). Combining strong FHB resistance with important phenological traits i.e., plant height (PHT) and days to anthesis (DTA), and strong genotype by environment interactions complicate the development of resistant cultivars. High-density genetic maps are an important resource to identify and resolve QTL and at lowering the chances of co-segregation of unwanted traits with disease resistance. High-density maps using SNP markers are preferred over other genetic markers due to their abundance, high throughput, and low error rates. To enhance FHB resistance in wheat germplasm, a panel of 336 Watkins landraces had previously been screened for resistance to FHB, with a focus on incidence (INC), severity (SEV), Fusarium-damaged kernels (FDKs), and DON levels. Through the screening process, a Watkins landrace, namely 'Wat.1190580' was identified as having significant resistance (Type I, II, III, and IV) to FHB. The specific objective of this study was: to map QTL for FHB and DON resistance from Watkins landrace Wat.1190580. 17 2.2 Materials and methods 2.2.1 Plant material The landrace Wat.1190580 served as the pollen parent, facilitating the development of a recombinant inbred lines (RIL) population through hybridization with the FHB-susceptible Paragon. Following the initial hybridization, the RIL population (n=75) was advanced to the F6 generation using the single seed descent (SSD) method at John Innes Centre (JIC), UK. After receiving the RILs from JIC, the subsequent advancement (using SSD) to F8 generation was performed at UBC, Vancouver. The population, namely Paragon X Wat.1190580 (PXW580) was then evaluated for FHB resistance across six site years with pathogen-inoculated trials: Greenhouse, UBC, 2021 (GH_UBC21); Carman, Manitoba, 2021 (CR21); Carman, Manitoba, 2022 (CR22); Morden, Manitoba, 2021 (MR21); Morden, Manitoba, 2022 (MR22); Morden, Manitoba, 2023 (MR23). 2.2.2 Greenhouse FHB evaluation The GH_UBC21 evaluation consisted of 75 PXW580 RILs, Paragon (the female parent), and number of check cultivars (CDC Teal as susceptible (S) check, AC Barrie as intermediate (I) check, and AAC Tenacious as resistant (R) check) were evaluated for SEV. The RILs were evaluated in South Campus Greenhouse at UBC, under supplemental LED lighting with 16 h of light at 25°C, followed by 8 h of dark at 18°C (lights come off at 10 pm and on at 6 am) with regular watering. The plants were grown in medium-sized pots (6 in diameter x 4.5 in height), filled with 1 L of Seed Starter Mix soil. One seed was placed at the center of each pot. One week after germination, 5 g of slow-release classic fertilizer (Osmocote 14-14-14) was added to each 18 pot. Two experimental replicates (Rep 1 performed from May-August and Rep 2 from September onwards) were arranged in a completely randomized design. Each replicate consisted of one pot for each genotype in each experimental replicate. A total of eight to 10 spikes per pot/genotype in each experimental replication were evaluated and averaged for disease severity. F. graminearum isolate ‘109b’ (Sandha 2021) was collected from Saskatchewan and kindly provided by Dr. Gursahib Singh (University of Saskatchewan). It was sub-cultured following the procedures of Leslie and Summerell (2008). The inoculation suspension was set at a concentration of 50,000 spores mL−1. FHB inoculations were performed using the single-spikelet inoculation method described by Stack et al. (2002) by injecting 10μL of the suspension into one floret of a single central spikelet per spike at anthesis. Approximately, eight to 10 spikes per RIL in each replicate were inoculated. The inoculated spike was then covered with a clear plastic bag and lightly misted for 24 h to maintain high humidity. In Rep 1, SEV was scored as a percentage of diseased spikelets per spike through visually counting the number of diseased and total spikelets per whole spike. However, in Rep 2, the SEV was recorded only below the point of inoculation. SEV data in each replication was recorded at three time points: 14, 21, and 28 days post inoculation and the area under disease progress curve (AUDPC) was calculated according to Buerstmayr et al. (2000). 2.2.3 Field FHB evaluations Field evaluations of PXW580 (n=75) were performed across five site years as follows: CR21 (F7), MR21 (F7), CR22 (F8), MR22 (F8), MR23 (F8). Plots at all the site years consisted of single 19 1.5 m rows and ‘AAC Tenacious’ (R), ‘FHB 37’ (MR), ‘AC Cora’ (I), and ‘CDC Teal’ (S) were included as checks in each trial. At CR21 and CR22, a macroconidial suspension of four different F. graminearum isolates was artificially sprayed over plants at the 50% anthesis stage (Zadoks GS65). The spray concentration was adjusted to 5 × 104 conidia mL-1 with distilled water and Tween 20 (10 ml per 100 ml) added as a wetting agent. For the first application, the inoculum was applied at a rate of 50 ml per plot, using a backpack-mounted sprayer pressurized to 30 PSI with CO2 gas. A second application (another 50 ml) was applied after two or three days of the first inoculation specifically on late-maturing lines. After each inoculation, plots were mist-irrigated for five minutes every hour for 8 hours using an overhead misting system to maintain a humid environment conducive to disease development. At MR21, MR22, and MR23, a corn-spawn method of inoculation was employed. Corn spawn consists of grains inoculated with F. graminearum isolate, after which the colonized kernels are air-dried. The air-dried kernels are spread twice at weekly intervals at a rate of 8 g per single meter row approximately 2-3 weeks prior to heading. To create favorable conditions for F. graminearum, plots were irrigated three times per week using Cadman irrigation travelers with Briggs booms. FHB traits - INC (percentage of spikes showing symptoms) and SEV (percentage of spikelets infected in a spike) were recorded 21 days post inoculation (for each row) at all the site-years. Since there is a large variation for maturity within the RILs, an additional round of data collection was performed for late-maturing genotypes on 5-6th day after the first data collection. 20 FHB index was calculated from the INC and SEV rating data using the formula (INC × SEV)/100 and it is also called combined Type I and II resistance and field resistance. The tallest plant in a plot was measured for PHT using a cm scale from the soil surface to the tip of spikes excluding the awns. For each plot, days between the seeding date and anthesis date were counted for data on DTA. 2.2.4 FDK assessment Up to 30 spikes of each RIL were hand-harvested and retained for FDKs and DON quantification from MR21, MR22, and CR22. To ascertain the percentage of FDKs within each population, a systematic approach was employed. 100 kernels were selected randomly from each RIL, and the number of FDKs present were counted. The visual observation served as the foundation for assessing FDKs where healthy kernels appeared hard in texture and red colored, whereas FDKs appeared white, soft and shrunken. The resulting count was then converted into a percentage. The sampling process was repeated twice for each line to ensure reliability and consistency. The two sets of results were then averaged to obtain a representative value. The following equation was used to calculate FDKs: (N1 + N2)/200*100 % = FDKs (%) N = the number of FDKs in a single sampling trial. To efficiently count FDKs, 100 kernels were randomly selected from a given line, and their surface areas were encircled on a flat surface. The subsequent sampling process involved 21 counting the FDKs within this enclosed area. This method consistently yielded accurate measurements, ranging from 95 to 105 kernels each time, due to the approximate uniformity in volume among the kernels. Consequently, this technique significantly reduced the sampling time while ensuring valid results through repeated counting and averaging. 2.2.5 DON quantification The inoculated spikes of each line from MR21, MR22, and CR22 were hand harvested at maturity and threshed carefully (to avoid mixing) for the DON test. The threshed kernels were shipped to the Core Mass Spectrometry Facility from the College of Pharmacy and Nutrition at the University of Saskatchewan where a protocol by Wang et al. (2022) was followed. Briefly, samples were milled in a Laboratory Mill 3610 grinder (2015 Perten Instruments® AB) to whole wheat flour (particle size<800 μm) and extracted by shaking with acetonitrile-water (84/16, v/v) using 8 ml of solvent per 2 g of sample for 2 h at room temperature. The extracts were diluted 10-fold in ddH2O with 5 mM of ammonium acetate. All diluted extracts were filtered through an AcroPrep Advance 96-well filter plate. Thirty microliters of this filtrate were injected into the Liquid chromatography tandem mass spectrometry (LC-MS/MS) without any further cleanup. The DON concentration in the original grain sample is calculated as follows: concentration calculated in MS/MS (ng/ml) × 8 ml of extraction buffer×10 dilution rate/2 g of ground grain=DON concentration (ng/g). 22 The LC-MS/MS conditions were optimized using an Agilent Series 1260 Infinity HPLC system coupled with an AB Sciex API 4000 hybrid triple quadrupole linear ion trap (QTRAP) mass spectrometer, featuring a Turboionspray interface. System control and quantification were managed using Applied Biosystems/MDS Sciex Analyst software (Version 1.7). The injection of a 30-μl sample volume occurred through a guard column (Eclipse XDB C18, 4.6×5 mm, 1.8 μm) from Agilent Technologies. An isocratic mobile phase of water/methanol (80:20, v/v) with 5 mM ammonium acetate at a flow rate of 300 μl/min was employed for a 2-minute run. The multiple reaction monitoring (MRM) mode of the hybrid triple quadrupole-linear ion trap system was utilized for the specific monitoring and quantification of DON. Electro-spray ionization (ESI) was explored in both positive and negative ion modes, with superior ion counts achieved in negative ionization. Instrument parameters included a source temperature of 650°C, ion spray voltage of –4,500 V, curtain gas at 40 psi, declustering potential at –50 V, nebulizer gas at 40 psi, heater gas at 40 psi, and collision gas at 6 psi. For the quantifier transition of [M+CH3COO−]−m/z 355.0→m/z 295.1, collision energy (CE) was set at –16 V, exit potential (CXP) at –7 V, while the qualifier transition of m/z 355.0→m/z 265.2 had a CE value of –22 V and CXP value of –7 V. Nitrogen gas was the sole gas used in the mass spectrometer. In comparison with the standard method, a conventional analytic column (Agilent ZORBAX Eclipse XDB C18, 4.6×100 mm, 1.8 μm) was employed. Extraction and MS parameters mirrored those in the FC-MS/MS assay. A linear binary gradient system with solvent A (water with 5 mM 23 ammonium acetate) and solvent B (methanol with 5 mM ammonium acetate) was applied. The chromatographic gradient initiated at 20% of solvent B for 0.5 min, followed by a linear increase to 90% over 10 min, a 2-minute hold at 90%, and a return to initial conditions for an 8-minute equilibration. The flow rate was maintained at 300 μl/min. 2.2.6 Statistical analyses Statistical analysis of the phenotypic data from GH_UBC21, MR21, CR21, MR22, and MR23 was performed in R 4.3.1 environment (The R Core Team 2020). The density plots were created using the ggplot2 package (Tollefson, 2021) for visual representation of distribution of RILs in response to INC, SEV, FDKs, DON, PHT, and DTA from all site-years (only SEV in GH_UBC21). The data was averaged for each trait from two biological replicates and was plotted separately from each site-year (except for GH_UBC21 were two experimental replicates are plotted separately on a graph). For testing if the observed distribution of RILs in the density plots was normal, the Shapiro-Wilk’s normality test was performed using R 4.3.1 (The R Core Team 2020). For normality test the phenotypic data for each trait (averaged for two biological replications) was evaluated separately for each site-year. Levene’s Test was used to test the homogeneity of variance for all the traits (i.e., class variables) across site-years using the package ‘car’. As data for PHT and DTA was only collected from MR21, MR22, and MR23 each trait was averaged across these site-years to evaluate correlations between disease traits and developmental traits using package GGally (Schloerke et al., 2021). To evaluate if GXE interactions (for all the traits) are significant, analysis of variance (ANOVA) was performed for each trait separately by combining data (not averaging) from multiple site-years and significance 24 was declared at alpha P = 0.05. Furthermore, broad-sense heritability (H2) was estimated using the variance components calculated using restricted maximum-likelihood method in SAS (Statistical Analysis Software) ver. 9.4 (SAS Institute, Inc., Cary, NC). Heritability coefficients for multiple seasons were estimated using the equation H2 = σ2G/(σ2G + σ2GXE/E + σ2Er/Er), where σ2G is the genotypic variance (calculated by treating genotype as random), σ2GXE = genotype by environment interaction variance, σ2Er is the error variance, E = number of environments, and r = number of replicates. 2.2.7 Genotyping and linkage map construction DNA extraction from freshly harvested leaf samples of immature seedlings, including parents and F8 RILs derived from PXW580 (n=75), was carried out using a standard cetyltrimethylammonium bromide (CTAB)-based method. The isolated DNA was then quantified and adjusted to a concentration of 50 ng/L by measuring it on a NanoDropTM One/OneC Microvolume UV-Vis Spectrophotometer (Thermo Fisher Scientific Inc., Bartlesville, OK, United States). Subsequent genotyping of the DNA was executed using the Wheat 90K iSelect SNP array, following the methodology described by Wang et al. (2014). To determine genotype clusters for each SNP, the manual option of Genome Studio (version 2011.1) with the polyploid clustering module v1.0 (Illumina) was employed. Chi-squared (χ2) analysis was applied to the data obtained from FHB screening tests to validate the observed ratios compared to theoretical expectations. Linkage maps were constructed using 25 the software QTL IciMapping (Meng et al., 2015) and the Kosambi mapping function (Kosambi, 1943) was employed to estimate map distances. Markers were grouped based on the logarithm of odds (LOD) threshold value of 3 followed by the ordering using the algorithm ‘nnTwoOpt’. The markers were then rippled using the function ‘sum of adjacent recombination frequencies’ (SARF). 2.2.8 Skim-sequencing Wheat plants were raised from seeds in pots (6 × 6 × 10 cm) in a growth chamber at 20°C under a 16 h/8 h daylight/night with high-output white fluorescent tubes. Leaf samples from the parents and the RILs of PXW580 (F8) were collected at the three-leaf stage and stored at -80°C after dipping in liquid nitrogen (-190°C) for 5-10 minutes and grounded using the bead rupture QIAGEN TissueLyser®. Genomic DNA from leaf samples was extracted utilizing the GenJet™ Plant Genomic DNA Extraction kit (Thermo Fisher Scientific, Waltham, MA, USA), following the manufacturer's protocol. The DNA was eluted using sterile nuclease-free water. The concentration and quality of the obtained DNA were assessed using a NanoDrop 2000C UV-VIS spectrophotometer (Thermo Fisher Scientific, Waltham, USA) as that A260/A280 is >1.8 and A260/A230 is >2.0. The DNA samples were diluted to ~20 ng/µl and quantified using Qubit HS assay kit on a Qubit Fluorometer (Thermo Fisher Scientific, Waltham, MA, USA). Normalizing the quantified DNA to 1ng/ul was the next step which is crucial to match the concentration ratio of input DNA and 26 enzyme for the efficient and consistent tagmentation reactions. The tagmentation reaction (refer Appendix G for protocol) will cut genomic DNA and ligate DNA fragments to Illumina adapters by using Illumina Tagment DNA TDE1 Enzyme and Buffer Kits (Illumina catalog no. 20034197, USA). The 5 ul reaction system for a single sample involves reagents dH2O (3.3964 ul), TD buffer (0.504 ul), TDE1 (0.0996 ul), and genomic DNA (1 ul) that mixed and incubated at 55°C for 15 minutes. Indexes located on PCR primers were dual indexes of Illumina i7 (forward) and i5 (reverse) indexing reads which correspond to the multiplexed libraries. The combination of two indexes was a unique identity to demultiplex into individual library sample after sequencing. To incorporate dual indexes into the tagmented DNA, PCR amplification of the libraries was performed. Each sample was assigned either a unique i5 index or a distinct i7 index for distinguishment for the single sample, a reaction mixture of 25 μl was prepared, comprising 5.0 μl of previously tagmented product, 12.5 μl of Taq 2X Master Mix from New England Biolabs Inc. (Ipswich, of combined i7 and i5 index adapters at a concentration of 2.5 μM each, and 5.5 μl of water. The PCR amplification was performed as follows: an initial denaturation at 95°C for 1 minute, followed by 18 cycles of denaturation at 95°C for 10 seconds, annealing at 55°C for 20 seconds, extension at 72°C for 3 minutes, and a final extension cycle at 72°C for 5 minutes. After PCR amplification, the consistency of the size range and distribution of a single library was determined on an agarose gel. For sample multiplexing, the barcoded and amplified samples underwent quantification using using Qubit HS assay kit. After normalization to 6 ng/μl with a final volume of 15 μl, the samples were pooled into a single tube. Subsequent library purification following the New 27 England Biolabs’ protocol (Size Selection using AMPure XP Beads | NEB) utilized the QIAquick PCR Purification Kit (QIAGEN, Hilden, Germany), followed by two-end size selection from 300 to 800 bp using the Ampure beads (Beckman Coulter, Inc.). The library underwent additional purification, and fragment size distribution was confirmed on agarose gel through electrophoresis. Before paired-end sequencing, the size ranges and concentrations of multiplexed libraries were quantified using a Bioanalyzer provided by Nonogene (USA). Paired-end sequencing was conducted by Novogene (USA) utilizing the Illumina NovaSeq 6000 n platforms. 2.2.9 Sequence alignment and linkage map construction PacBio HiFi ccs reads from ‘Wat.1190580’ were aligned to the reference ‘CDC Landmark’ genome (Walkowiak et al., 2020) using minimap2 in -map-hifi mode. Aligned reads were converted to sorted.bam and called for SNPs using samtools and bcftools (Danecek et al., 2021)respectively. SNPs were required to be supported by between 10 and 200 reads (average expected coverage 20X), with a QUAL score of >200, to be on a chromosome and not an unmapped contig, and to be homozygous and biallelic. SNP positions were extracted for further use. PE150 gDNA reads from each member of the populations were aligned to the reference Landmark genome using hisat2 with the settings: --rdg 10,5 --no-spliced-alignment --no-mixed --no-discordant to minimise off-target alignment and prevent splicing of gDNA reads. 28 Mapped reads were then converted to sorted.bam and into pileup format, using samtools and bcftools respectively. Pileups were filtered to only include positions previously identified as containing a high-quality SNP. A several million potential SNPs were obtained after the filtering. To remove noise and simplify the QTL mapping process, SNPs were ‘binned’ into groups based on their physical position. The ‘CDC Landmark’ genome was indexed into 5 Mbp bins using bedtools makewindows (Quinlan et al., 2014) , and the number of forward and reverse reads supporting either reference or alternate calls at SNP locations within each bin was aggregated into a single number per bin using bedtools map. Within each bin, on a per-line basis, the percentage of alternate reads was used to call the bin as either reference (<40% alternate reads), alternate (>60% alternate reads), missing (<10 reads) or heterozygous/ambiguous (any other result). Heterozygous calls are expected if a recombination break-point occurs within a bin and reads are evenly distribute around the break-point. Finally, within each bin, on a per-population basis, bins were retained if they did not contain more than 50% missing/ambiguous calls or more than 90% identical calls across the entire population. Following this, we performed imputation on the PXW580 using R package: ABHgenotype to correct the heterozygous or incorrect sequence calls and fill in the missing information (Appendix. E). Linkage maps were constructed using QTL IciMapping (Meng et al., 2015) and the ‘Kosambi’ mapping function (Kosambi, 1943) was employed to estimate map distances. Markers were grouped based on the anchor information and followed by the ordering using the algorithm ‘nnTwoOpt’. The markers were then rippled using the function ‘sum of adjacent recombination frequencies’ (SARF). 29 Based on the assumption of the additivity of QTL effects on the phenotype of trait in interest, we used the statistical method of inclusive composite interval mapping (ICIM) in IciMapping software (Meng et al., 2015) for QTL mapping. The QTL analysis was performed using phenotypic data from individual site-year since significant GXE interactions were observed for the response of RILs to FHB infection. The determination of the significance of a QTL threshold was established through 1,000 permutations (Doerge et al., 1996) with a significance set at 0.05. QTL exceeding the LOD score threshold (2.5, LOD for QTL) was considered significant. Results were validated in R/qtl (Arends et al., 2010) as well. 2.3 Results 2.3.1 Phenotypic results For GH_UBC21, RILs in Rep 1 displayed a variable response to point inoculation (F. graminearum), measured by AUDPC, with a nearly continuous distribution skewed towards the susceptible parent Paragon (Appendix A). AAC Tenacious (R check) and Paragon (susceptible parent) had AUDPC values of 5.25 and 19.4, respectively. RILs exhibited AUDPC values ranging from 1.31 to 21 with a mean of 13.72. For Rep 2, the lowest was 0.5, and 19 was the highest AUDPC point with a mean AUDPC of 12.75. However, the distribution in Rep1 was more balanced between resistance and susceptible ranges than Rep 1. 30 The average INC for PXW580 was 61%, 64%, 19%, 43%, and 52% in the field experiments CR21, CR22, MR21, MR22 and MR23, respectively (Appendix B). Checks AAC Tenacious (R), AC Cora (I), and CDC Teal (S) exhibited distinct INC ranges. SEV varied among trials, with the highest mean at CR22 (57%) and the lowest at MR21 (21.9%). Over 60% of RILs had SEV below 50%, except in MR21 where all lines remained below this threshold. DON and FDK tests were conducted on PXW580 samples from MR21, MR22, and CR22, revealing varying distribution patterns of RILs across environment. Mean DON values were 17 ppm (MR21), 51.3 ppm (MR22), and 89.6 ppm (CR22), while FDK values were 10% (MR21), 4% (MR22), and 12% (CR22). DON samples ranged from 0 ppm to 196 ppm, and FDKs varied from 0% to 40% across all environments. Density plots indicated consistent slopes for PHT across environments (Appendix Bf). In the case of DTA, MR22 data revealed a shorter maturity period compared to longer maturing RILs observed in other environments. The Shapiro-Wilk’s normality test indicated significant deviation of residuals from normal distribution in PXW580 INC for most site-years, while SEV showed a normal distribution in three trials (Appendix Ca). Normal distribution was not observed DON and FDKs in any site-year except for DON in CR22 (P = 0.25). Normal distribution was also observed for PHT in MR21 (P = 0.62) and MR23 (P = 0.19). For DTA, significant deviations from normality were detected in most site-years, except MR21 (P = 0.21). Levene’s test revealed heterogeneous variances among all environments for both INC and SEV (P < 0.01, df = 4) as summarized in Appendix C (b). Variance was heterogenous between site-years for both DON (P < 0.01, df = 2) and FDKs (P < 0.01, df = 2) as well. Notably, among all recorded traits for PXW580, only PHT 31 exhibited homogenous variance across environments (P = 0.2, df = 2). Correlation analysis demonstrated positive relationships among INC, SEV, FDKs and DON, and between DTA and PHT (r = 0.6), but a negative relationship between FHB-related traits and DTA and PHT (Appendix D) indicating usefulness of PHT and DTA for determining FHB resistance and DON accumulation. INC, SEV, FDKs, and DON correlated well with each other (r = 0.3–0.7) The ANOVA of INC, SEV, VRI, FDKs, DON, PHT, and DTA over the five site-years showed that genotype differed significantly at P < 0.001 for all the traits (PHT, at P < 0.01), genotype x environment (GXE) interaction was significant at P < 0.001 (FDKs, at P < 0.05) (Table 1). The heritability (H2) for these traits was calculated. Heritability was recorded highest for PHT (0.92), followed by DTA (0.91), FDKs (0.77), VRI (0.75), SEV (0.73), DON (0.68), and INC (0.63). 2.3.2 Genotyping results 2.3.2.1 Linkage map From the wheat 90K iSelect SNP array, of a total of 81,580 SNPs, 65,890 (80.3%) were monomorphic between the parental lines, while 7,752 (8.7%) had poor genotype calls. There were 7,938 (9%) polymorphic SNPs, but 6,293 of them did not fit the expected χ2 goodness of fit ratio of 1:1 and were excluded. Eventually, 1,645 SNPs were used to create genetic maps with 75 RILs from the population. 32 The information on the 90K genetic map is summarized in Appendix F. The map contained 424 skeleton/unique SNP markers on the A genome, covering 5,878 cM, 571 markers on the B genome, covering 7,502 cM, and 650 markers on the D genome, covering 8,327 cM. Each genome had approximately a similar number of markers. The entire map covered an unusual genetic distance of 21,707.18 cM. The lengths of individual chromosomes varied, with chromosome 4A being 563.6 cM and chromosome 2B being 1,652 cM. This difference was because of the varying number of markers found on each chromosome. The number of loci per chromosome ranged from 42 on chromosome 4A to 121 on chromosome 2B. The average distance between two loci varied from 12.03 cM on chromosome 5D to 15.1 cM on chromosome 2A, with an average of 13.8 cM per locus for the A genome, 13.1 cM per locus for the B genome, and 12.8 cM per locus for the D genome. Due to the poor representation of Wat.1190580 haplotypes by 90K markers, the resulting large linkage maps (>1000 cM) were deemed unsuitable for genetic mapping. 2.3.3 Skim-sequencing results 2.3.3.1 Linkage map Skim sequencing enhanced our ability to map the variations between the two parents, which resulted in realistic genetic maps for PXW580, except for 5D, 6D, and 7A (Table 2). Initially, several million SNPs were detected in the RILs at 0.1x coverage, but through refinement and binning, the number was narrowed down to 1,276 informative markers (markers with 33 approximate 1:1 A/B calls). Notably, the average length of linkage groups ranged from 43 to 189, with 5D, 6D, and 7A exhibiting exceptionally large intervals due to missing marker information in their telomeric regions. The genetic distances between loci varied between 1.36 on 4A to 37.13 on 5D. Overall, the skim-sequencing approach proved better in representation of genetic variation in both Paragon and Wat.1190580 (improving the resolution and accuracy of the genetic maps) compared to the 90K genotyping method. 2.3.3.2 QTL mapping QTL regions associated with INC, SEV, DON, FDKs resistance, and PHT, DTA were successfully mapped using the phenotypic data and skim-sequencing data of PXW580. A total of four QTL were identified for SEV on 2B (2), 3B (1), and 5B (1) (Table 3 and Figure 1). Paragon contributed the resistance alleles for QTL on chromosomes 2B, 3B, and 5A, and Wat.1190580 contributed the resistance alleles for QTL on chromosomes 2B, 5A, and 5B. The phenotypic data from GH_UBC21, CR21, MR21, CR22, MR22, and MR23 was used to map QTL regions for FHB traits from each site year separately. At least one QTL was detected in all the site years except GH_UBC21 where no QTL was detected. The INC QTL on chromosome 5A detected in MR21, MR22, and MR23 explained up to 18% variation and was significant in three of the five environments, and is flanked by the markers L21_560 and L21_595. In the same region, a QTL for PHT and one for DTA were also detected in MR21, MR22, and MR23. Another INC resistance related QTL explaining 18% variation on 5A chromosome between markers L21_660 and L21_665 was coincident with QTL for DON 34 explaining 16% variation and FDKs (explaining 21% variation) resistance, all three contributed by Wat.1190580 (Table 3). Wat.1190580 contributed a PHT QTL explaining 13% on 3A and was detected only in MR21. Since VRI is a product of INC and SEV, its QTL coincided the same region within or close to QTL for INC and SEV. The other two INC QTL were identified on chromosome 2B, one near the centromere between markers L13_85 and L13_105 and the other between L13_740 and L13_750. The alleles for two of these two INC QTL were contributed by Paragon and each of them were only detected in a single environment. SEV resistance related regions were mapped on 2B, 3B, and 5B. Unlike 5A INC region, no SEV QTL was consistent over environments. Two regions were observed on chromosome 2B (in CR22), one with allele from Wat.1190580 and other with allele from Paragon. The single QTL in CR21 at 3B was contributed by Paragon and another single region mapped on 5B (in MR23) was contributed by Wat.1190580. 2.4 Discussion 2.4.1 Premature ripening above the point of inoculation Visual symptoms of FHB infection in GH_UBC21 were evident early in development and were generally more severe in spikes of susceptible RILs compared with resistant RILs. Consistent inoculum amounts and greenhouse conditions were maintained across experimental replications, however, AUDPC values of the RIL population varied. This may be attributed to the rapid change in daylight duration in Vancouver during the month of July which could influence the temperature, potentially explaining the increased disease severity of the Rep 1 (experimental 35 replication) in summer than that of the Rep 2 (experimental replication) in autumn (Appendix A). It is also confirmed by previous studies that FHB severity in wheat infected by F. graminearum significantly increases from 25°C to 30°C (Rossi et al., 2001). Two experimental replications were conducted to evaluate Type II resistance in the PXW580 population. However, during the first experimental replicate, data was recorded from the whole spike leading to more susceptible count of RILs for FHB. In an article by (Schroeder and Christensen, 1963), it was proposed that the clogging of vascular tissue in the rachis above the point of inoculation of infected spikes caused the head to ripen prematurely, so that even grains not directly infected were shriveled because of a shortage of water and nutrients. The second experimental replication, the disease was scored only below the point of inoculation, resulting in better distribution of RILs for Type II resistance. Variations in disease severity across experimental replications highlight the importance of precise assessment methods to avoid overestimation. 2.4.2 Wheat 90K iSelect SNP genotyping may not represent the genetic diversity in Watkins landraces SNP arrays have proven useful in mapping studies, providing high-density scanning and robust call rates (Sun et al., 2020). For instance, the Illumina Wheat 90K iSelect SNP genotyping array (Wang et al., 2014) has been effectively utilized in mapping FHB resistance-related QTL in RIL populations derived from both modern cultivars (MCs) and landraces (LCs). Jiang et al. (2020) 36 employed this array to map a novel FHB resistance QTL on 5AL in RILs derived from Ningmai 9 × Yangmai 158, while Chen et al. (2021) utilized an optimized version, the '15K wheat SNP' array, to identify FHB resistance QTL in RILs derived from N553 (LC) X Yungmai 3 (MC). However, studies also suggest limitations of the Wheat 90K iSelect SNP array in mapping regions in LCs and germplasm from secondary and tertiary gene pools (Rasheed and Xia, 2019). Rasheed and Xia (2019) concluded their study that SNPs identified by the 90K arrays are biased in having more representation from cultivated wheat (or MCs) and are ineffective in detecting polymorphisms in landraces. In other words, polymorphisms were detected among the elite cultivars and was used to design the SNP array. This confusion of why Wheat 90K iSelect SNP array works in certain landraces and does not in others can be explained by the study conducted by (Raman et al., 2010). He writes that within landraces, there exists a notable genetic diversity and this diversity arises from factors such as domestication history, evolutionary processes, selective breeding, and environmental influences. Distinct genetic groups among LCs exist based on their genetic similarity and distinctness from MCs. Therefore, the applicability of modern molecular markers, such as the Wheat 90K iSelect SNP array, in mapping studies varies depending on unique haplotypes retained by LCs from MCs versus LCs in close relation to the immediate ancestors of MCs, therefore, share some level of genetic similarity to MCs. In our study, we employed the Wheat 90K iSelect SNP array to map FHB resistance regions in the PXW580 population, and large introgressions were observed, potentially because of the underrepresentation of haplotype blocks of Wat.1190580 by the fixed set of markers in the 90K array. 37 The other but important reason behind large genetic distances could be the population size and density of linkage maps. Although the population size remained same over time and skim sequencing improved the genetic distances, but the resolution can be further improved with a larger population size. Population size and density of markers are correlated as the genetic density of the markers in the map is limited not only by the number of markers but also by the number of recombination events occurring in meiosis that are represented in the mapped population (Ferreira et al. 2006). Ideally, the majority of the QTL mapping experiments have used 100-200 individuals primarily due to excessive costs associated with large populations. Results from Ferreira et al. (2006) showed that the effect of population size on the size of linkage groups varies in different mapping populations. However, in comparison with F2 dominant, backcrossing (BC), and double haploid population (DH) they observed that RILs produced the most accurate genetic maps when population size was increased from 50 to 1000. This is because compared to other types of mapping populations, the RIL population undergoes successive meiosis cycles, increasing the recombination probability of strongly linked markers. The study after comparing different sizes of mapping populations (50, 100, 200, 500, 1000) concluded that in RIL population size of around 200 individuals is enough when the goal is construction of reasonably accurate linkage and save cost (on development of large populations). Since the population size of PXW580 in our study was 75, it explains a part of the story of why the linkage groups spanned to distance of >1000 cM. Therefore, the type of mapping population, population size, type of germplasm (MCs, LCs, wild relatives), genotyping/sequencing techniques, and informative markers are some of the key determinants that decide the accuracy of linkage maps. 38 2.4.3 QTL mapping for FHB resistance in PXW580 The utilization of skim-sequencing successfully addressed anomalies within linkage maps, facilitating the QTL mapping associated with FHB and phenological traits. However, we observed the number of markers (1,276) from skim-sequencing (after binning and further filtering) used to construct linkage maps were even less compared to Wheat 90K iSelect SNP array (1,645). The term informative markers in the previous sections have been used and it means the markers that have nearly 50:50 A vs B genotype calls at each locus in the whole population. A reasonable number of informative markers can potentially help improve the linkage group compared to a large number of bad and redundant markers. Although the population size remains the same, it does not change the fact a larger size of the mapping population will further improve the genetic maps and the resolution of QTL mapping. Improved genetic distances with skim sequencing approach, underscores the very fact that one reason behind the unusual linkage maps in PXW580 is because markers in the Wheat 90K iSelect SNP array failed to capture the allelic diversity in Wat.1190580 haplotypes in the RILs. Nonetheless, persistent challenges were encountered in some linkage groups: 5D, 6D, and 7A, characterized by linkage maps extending over distances >500 cM (large gaps observed near the telomeres). These regions exhibited a clear absence of markers, raising concerns regarding the underlying causes. A plausible explanation for such maps is the occurrence of sequencing artifacts within these genomic segments, thereby contributing to the observed gaps in linkage maps. For example, the wheat genome is over 80% repetitive and a region of poor SNP density 39 and high similarity to paralogous regions in the same genome may produce errors in the alignment process. Skim-sequencing facilitated the identification of allelic diversity between the two parents, resulting in ~7 million SNPs. However, due to the random nature of sequencing, a substantial portion of the genome lacked SNP information. Imputation, a crucial step after alignment is possible only when the presence of SNP information in the genome exceeds the extent of missing data. To enable imputation, the genome was partitioned into 5 Mb bins, resulting in a limited number of informative markers (1,276) where each bin (averaged for A vs B) represented a marker. In instances where a recombination event occurred within a bin, it resulted in a heterozygous or missing genotype call which was further improved by imputation using the R package ABHgenotype. This approach enhanced the genotype information for QTL mapping analyses. The main purpose of binning the markers was to simplify analysis with the mapping software by selecting a set of informative markers. Dividing the vast wheat genome of 15 Gb into 5 Mb bins provided a sufficient number of markers for mapping, effectively covering a significant portion of the genome. Mapping for disease traits including INC, SEV, VRI, FDKs, DON, and phenological traits such as PHT and DTA was conducted using the IciMapping software, employing the Kosambi function. Notably, no significant QTL were detected in GH_UBC21. This observation could potentially be attributed to non-uniformity in inoculum dose or variations in GH conditions, 40 resulting in reduced variation. However, utilizing data from field trials conducted at CR21, CR22, MR21, MR22, and MR23, significant regions associated with INC, SEV, DON, FDKs resistance, PHT, and DTA were successfully mapped. These regions encompassed both unique loci and shared QTL regions across different trials. The INC resistance related QTL on chromosome 5A detected in MR21, MR22, and MR23 explained 14.75-17.95% variation and was significant in three out of the five environments, and is located between markers L21_560 and L21_595. At the very similar region a QTL for PHT and one for DTA were also detected in MR21, MR22, and MR23. The allele at INC resistance related QTL was contributed by Wat.1190580, whereas for PHT and DTA, Paragon was the contributing alleles. As the alleles for the INC resistance and PHT or DTA are not contributed by same parent, it rules out the possibility of a single gene with pleiotropic effects. The possible outcomes are: 1) These three regions for INC resistance, PHT, and DTA are tightly linked 2) due to the effect of DTA and/or PHT, less FHB infection was observed (as these regions are only observed in MR21, MR22, and MR23, method of inoculation seems to have an effect in this interaction) and we observed a peak for INC resistance sharing same region with PHT and DTA. The observed reduction in INC levels contributed by the 5A region appears to be influenced by the effects of PHT and DTA. Notably, this 5A INC resistance related region has been already mapped in the study by Jiang et al., (2020), situated between the markers BS00060445_51 (504414021 bp) and IAAV7827 (646352523 bp). Although the markers do not share identical physical locations, their proximity suggests the potential identity of these loci. The PHT region on chromosome 5A was mapped in close proximity to dwarfism-induced genes by DELLA 41 mutant (hypothetical protein CFC21_063994) Rht dp (Chai et al., 2021). The Rht dp in this study by Chai et al., (2021) was mapped in a polish wheat (Triticum polonicum) and is the same gene as Rht-1Bb in bread wheat (Triticum aestivum). However, a QTL for PHT contributed by Wat.1190580 on chromosome 3A was not identified in the relevant literature. The DTA region mapped on chromosome 5A coincided with the vernalization gene VrnA1 (Díaz et al., 2012), suggesting potential regulatory interactions. Two INC resistance related loci at 2B flanked by markers L13_100 and L13_105 could not be traced to any mapping study and seems putatively novel. Furthermore, at 5A, a region flanked by L21_650 and L21_665 was observed to be significant for INC, FDKs, and DON resistance. Since these three regions are contributed by Wat.1190580, the possibilities are either a single gene has pleiotropic effects or these three regions/genes are tightly linked. Despite thorough examination and extensive literature review, no prior data or research findings were identified for these particular genomic regions. Specifically, a unique QTL associated with SEV resistance was identified on chromosome 3B, although exclusively observed in CR21, explained 16.58% variation. A similar region for FHB resistance had been previously mapped in a study by (Jiang et al., 2020), flanked by the marker BS00069175_51, sharing the same physical location on chromosome 3B at position 486369408 Mb in CDC Landmark. Furthermore, two additional unique QTL associated with resistance to SEV were mapped on chromosome 2B (contributed by Paragon and Wat.1190580) and one on 5B (contributed by Wat.1190580). However, these QTL were only detected in CR22 and MR23 but seems putatively novel. Similarly, two INC loci at 2B was detected in MR23 and was contributed by the cultivar Paragon. We investigated these four SEV-resistance QTL mapped in 42 our study and found no prior data for them. This led us to believe that these regions are also putatively novel. Further study regarding the INC, SEV, FDKs, DON-related QTL identified specifically in Wat.1190580, particularly those localized at 2B and 5B is required, as they appear potentially novel. Subsequent experiments should focus on validating these QTL across multiple environments, employing larger population sizes, and conducting fine mapping to elucidate their precise genomic locations and effects. 2.5 Future work and conclusions The project primarily aimed to map FHB-resistant loci from Wat.1190580 using a Wheat 90K iSelect SNP array. However, it encountered challenges with the array's failure to generate linkage maps. This led to exploring alternative genotyping/sequencing technologies, with skim sequencing emerging as a cost-effective method for detecting polymorphisms in RILs derived from crosses involving Watkins landrace as a parent. The study's results indicate that Wat.1190580 contributes to FHB resistance, but further validation across multiple environments is needed. It was also evident that phenological traits: PHT and DTA had pronounced effects on FHB resistance, but whether these loci exhibit pleiotropic effects or help in escape mechanism is not clear. Future work will involve increasing population size, collecting phenotypic data across multiple environments, and creating high-resolution QTL maps to validate the effects of mapped QTL. 43 2.6 Tables and figures Table 1. Analysis of variance (ANOVA) for phenotypic field variables. Summary of the recombinant inbred line (RIL) population PXW580, phenotyped for disease incidence (INC), disease severity (SEV), visual rating index (VRI), Fusarium-damaged kernels (FDKs), deoxynivalenol content (DON), plant height (PHT), and days to anthesis (DTA) from multiple field locations over 3 years (2021, 2022, and 2023). σ2G is the genotypic variance, σ2GxE is the genotype by environment interaction, σ2Er is the variance due to error an H2 represents the broad-sense heritability. *Significant at P < 0.05, **Significant at P < 0.01, ***Significant at P < 0.001, nsnon-significant Variance components Trait Min Mean Max σ2G σ2GxE σ2Er H2 PXW580 INC 0.9 48.7 95 66.2*** 98.9*** 180.6*** 0.63 SEV 1 43.9 100 75.7*** 52*** 33.3*** 0.73 VRI 0.5 24.9 90 67.1*** 44.4*** 123.2*** 0.75 FDKs 0 11.4 93 29.4*** 10.2* 63.37*** 0.77 DON 0 47.6 200 182.8*** 172*** 482.2*** 0.68 PHT 54 101 142 89.7** 12.5*** 45.6*** 0.92 DTA 46 58.9 80 15.8*** 3.3*** 7.2*** 0.91 44 Table 2. Summary of the genetic map constructed with 75 RILs from ‘Paragon X Wat.1190580’ (PXW580) cross using a skim sequencing approach. Chromosome All markers Length (cM) cM/Locus 1A 71 133 1.87 1B 103 153 1.49 1D 31 66.7 2.15 2A 79 153 1.94 2B 79 161 2.04 2D 37 139 3.76 3A 51 131 2.57 3B 78 146.7 1.88 3D 13 100 7.69 4A 117 159 1.36 4B 68 169.5 2.49 4D 23 99.1 4.31 5A 105 167.2 1.59 5B 100 148.9 1.49 5D 15 556.9 37.13 6A 95 189.2 1.99 6B 88 131.2 1.49 6D 17 582.5 34.26 7A 72 626.1 8.70 7B 31 127.6 4.12 7D 3 43 14.33 Group 1 205 352.7 1.72 Group 2 195 453 2.32 Group 3 142 377.7 2.66 Group 4 208 427.6 2.06 Group 5 220 873 3.97 Group 6 200 902.9 4.51 Group 7 106 796.7 7.52 A genome 590 1558.5 2.64 B genome 547 1037.9 1.90 D genome 139 1587.2 11.42 Whole genome 1276 4183.6 3.28 45 Table 3. Quantitative trait loci (QTL) associated with FHB incidence (INC), severity (SEV), visual rating index (VRI), Fusarium damaged kernels (FDKs), DON accumulation (DON), plant height (PHT), and days to anthesis (DTA), in the Paragon X Wat.1190580 (PXW580) recombinant inbred line (RIL) population. Carman2021 (CR21), Carman2022 (CR22), Morden2021 (MR21), Morden2022 (MR22), and Morden2023 (MR23) represents the environments for screening of RILs. LOD and PVE stand for the Logarithm of odds and phenotypic variance explained, respectively. Trait Chr Position (cM) Left marker Right marker LOD PVE (%) Contributing Parent Left CI Right CI CR21 SEV 3B 37 L16_15 L16_20 2.8 16.5 Paragon 34.5 40.5 CR22 INC 5A 142 L21_660 L21_665 3.1 17.8 Wat.1190580 140.5 144.5 SEV 2B 0 L12_790 L13_5 3.9 19.5 Wat.1190580 0.0 13.5 SEV 2B 58 L13_155 L13_160 2.5 12.2 Paragon 55.5 58.5 VRI 5A 142 L21_660 L21_665 4.6 26.3 Wat.1190580 140.5 144.5 FDK 5A 140 L21_650 L21_655 3.8 20.9 Wat.1190580 136.5 140.5 DON 5A 142 L21_660 L21_665 2.9 16.4 Wat.1190580 140.5 145.5 MR21 INC 2B 54 L13_100 L13_105 3.4 16.0 Paragon 53.5 55.5 INC 5A 114 L21_595 L21_600 3.5 16.9 Wat.1190580 112.5 119.5 VRI 2B 54 L13_100 L13_105 3.3 16.6 Paragon 53.5 55.5 VRI 5A 114 L21_595 L21_600 2.7 13.5 Wat.1190580 112.5 119.5 PHT 3A 100 L15_685 L15_690 2.9 13.4 Wat.1190580 96.5 101.5 PHT 5A 111 L21_570 L21_585 4.3 22.3 Paragon 104.5 112.5 DTA 5A 111 L21_570 L21_585 4.2 25.1 Paragon 104.5 112.5 MR22 INC 5A 103 L21_560 L21_565 2.5 14.7 Wat.1190580 100.5 109.5 PHT 5A 112 L21_570 L21_585 5.3 28.8 Paragon 108.5 112.5 DTA 5A 111 L21_570 L21_585 4.3 26.6 Paragon 104.5 112.5 MR23 INC 2B 113 L13_740 L13_750 2.7 11.4 Paragon 108.5 116.5 INC 5A 112 L21_570 L21_585 4.0 17.9 Wat.1190580 107.5 112.5 SEV 5B 111 L22_600 L22_605 3.0 7.6 Wat.1190580 109.5 111.5 PHT 5A 112 L21_570 L21_585 5.0 26.9 Paragon 108.5 112.5 DTA 5A 1 L21_570 L21_585 4.9 26.4 Paragon 107.5 112.5 46 Figure 1. Chromosomal location of QTL (regions overlapping are shown in separate chromosomes, for example 5A) detected for FHB incidence (INC, in red), severity (SEV, in green), fusarium damaged kernels (FDKs, in pink), and agro-morphological trait Plant height (PHT, in blue) in the Paragon X Wat.1190580 (PXW580) recombinant inbred line (RIL) population. 47 Figure 2. Chromosomal location of QTL (regions overlapping are shown in separate chromosomes, for example 5A) detected for visual rating index (VRI, in light blue), deoxynivalenol accumulation (DON, in dark red), and days to anthesis (DTA, in light green) in the Paragon X Wat.1190580 (PXW580) recombinant inbred line (RIL) population. 48 References Aboukhaddour, R., Fetch, T., McCallum, B. D., Harding, M. W., Beres, B. L., and Graf, R. J. (2020). Wheat diseases on the prairies: A Canadian story. Plant Pathology, 69(3), 418–432. https://doi.org/10.1111/ppa.13147 Adhikari, L., Shrestha, S., Wu, S., Crain, J., Gao, L., Evers, B., Wilson, D., Ju, Y., Koo, D.-H., Hucl, P., Pozniak, C., Walkowiak, S., Wang, X., Wu, J., Glaubitz, J. C., DeHaan, L., Friebe, B., and Poland, J. (2022). A high-throughput skim-sequencing approach for genotyping, dosage estimation and identifying translocations. Scientific Reports, 12(1), Article 1. https://doi.org/10.1038/s41598-022-19858-2 Arends, D., Prins, P., Jansen, R. C., and Broman, K. W. (2010). R/qtl: High-throughput multiple QTL mapping. Bioinformatics, 26(23), 2990–2992. https://doi.org/10.1093/bioinformatics/btq565 Bai, G., Su, Z., and Cai, J. (2018). Wheat resistance to Fusarium head blight. Canadian Journal of Plant Pathology, 40(3), 336–346. https://doi.org/10.1080/07060661.2018.1476411 Brar, G. S., Brûlé-Babel, A. L., Ruan, Y., Henriquez, M. A., Pozniak, C. J., Kutcher, H. R., and Hucl, P. J. (2019). Genetic factors affecting Fusarium head blight resistance improvement from introgression of exotic Sumai 3 alleles (including Fhb1, Fhb2, and Fhb5) in hard red spring wheat. BMC Plant Biology, 19(1), 179. https://doi.org/10.1186/s12870-019-1782-2 Buerstmayr, H., Steiner, B., Lemmens, M., and Ruckenbauer, P. (2000). Resistance to Fusarium Head Blight in Winter Wheat: Heritability and Trait Associations. Crop Science, 40(4), 1012–1018. https://doi.org/10.2135/cropsci2000.4041012x 49 Chai, S., Yao, Q., Zhang, X., Xiao, X., Fan, X., Zeng, J., Sha, L., Kang, H., Zhang, H., Li, J., Zhou, Y., and Wang, Y. (2021). The semi-dwarfing gene Rht-dp from dwarf polish wheat (Triticum polonicum L.) is the "Green Revolution” gene Rht-B1b. BMC Genomics, 22(1), 63. https://doi.org/10.1186/s12864-021-07367-x Charmet, G. (2011). Wheat domestication: Lessons for the future. Comptes Rendus Biologies, 334(3), 212–220. https://doi.org/10.1016/j.crvi.2010.12.013 Chen, S., Zhang, Z., Sun, Y., Li, D., Gao, D., Zhan, K., and Cheng, S. (2021). Identification of quantitative trait loci for Fusarium head blight (FHB) resistance in the cross between wheat landrace N553 and elite cultivar Yangmai 13. Molecular Breeding, 41(3), 24. https://doi.org/10.1007/s11032-021-01220-5 Cowger, C., Patton-Özkurt, J., Brown-Guedira, G., and Perugini, L. (2009). Post-Anthesis Moisture Increased Fusarium Head Blight and Deoxynivalenol Levels in North Carolina Winter Wheat. Phytopathology®, 99(4), 320–327. https://doi.org/10.1094/PHYTO-99-4-0320 Danecek, P., Bonfield, J. K., Liddle, J., Marshall, J., Ohan, V., Pollard, M. O., Whitwham, A., Keane, T., McCarthy, S. A., Davies, R. M., and Li, H. (2021). Twelve years of SAMtools and BCFtools. GigaScience, 10(2), giab008. https://doi.org/10.1093/gigascience/giab008 Dhariwal, R., Henriquez, M. A., Hiebert, C., McCartney, C. A., and Randhawa, H. S. (2020). Mapping of Major Fusarium Head Blight Resistance from Canadian Wheat cv. AAC Tenacious. International Journal of Molecular Sciences, 21(12), Article 12. https://doi.org/10.3390/ijms21124497 50 Díaz, A., Zikhali, M., Turner, A. S., Isaac, P., and Laurie, D. A. (2012). Copy Number Variation Affecting the Photoperiod-B1 and Vernalization-A1 Genes Is Associated with Altered Flowering Time in Wheat (Triticum aestivum). PLOS ONE, 7(3), e33234. https://doi.org/10.1371/journal.pone.0033234 Dill-Macky, R., and Jones, R. K. (2000). The Effect of Previous Crop Residues and Tillage on Fusarium Head Blight of Wheat. Plant Disease, 84(1), 71–76. https://doi.org/10.1094/PDIS.2000.84.1.71 Doebley, J. F., Gaut, B. S., and Smith, B. D. (2006). The Molecular Genetics of Crop Domestication. Cell, 127(7), 1309–1321. https://doi.org/10.1016/j.cell.2006.12.006 Faris, J. D. (2014). Wheat Domestication: Key to Agricultural Revolutions Past and Future. In R. Tuberosa, A. Graner, and E. Frison (Eds.), Genomics of Plant Genetic Resources: Volume 1. Managing, sequencing and mining genetic resources (pp. 439–464). Springer Netherlands. https://doi.org/10.1007/978-94-007-7572-5_18 Fernando, W. G. D., Oghenekaro, A. O., Tucker, J. R., and Badea, A. (2021). Building on a foundation: Advances in epidemiology, resistance breeding, and forecasting research for reducing the impact of fusarium head blight in wheat and barley. Canadian Journal of Plant Pathology, 43(4), 495–526. https://doi.org/10.1080/07060661.2020.1861102 Ferreira, A., Silva, M. F. da, Silva, L. da C. e, and Cruz, C. D. (2006). Estimating the effects of population size and type on the accuracy of genetic maps. Genetics and Molecular Biology, 29, 187–192. https://doi.org/10.1590/S1415-47572006000100033 51 Gilbert, J., and Haber, S. (2013). Overview of some recent research developments in fusarium head blight of wheat. Canadian Journal of Plant Pathology, 35(2), 149–174. https://doi.org/10.1080/07060661.2013.772921 Gorash, A., Armonienė, R., and Kazan, K. (2021). Can effectoromics and loss-of-susceptibility be exploited for improving Fusarium head blight resistance in wheat? The Crop Journal, 9(1), 1–16. https://doi.org/10.1016/j.cj.2020.06.012 Gunupuru, L. R., Perochon, A., and Doohan, F. M. (2017). Deoxynivalenol resistance as a component of FHB resistance. Tropical Plant Pathology, 42(3), 175–183. https://doi.org/10.1007/s40858-017-0147-3 Harlan, J. R., de Wet, J. M. J., and Price, E. G. (1973). Comparative Evolution of Cereals. Evolution, 27(2), 311–325. https://doi.org/10.2307/2406971 Jiang, P., Zhang, X., Wu, L., He, Y., Zhuang, W., Cheng, X., Ge, W., Ma, H., and Kong, L. (2020). A novel QTL on chromosome 5AL of Yangmai 158 increases resistance to Fusarium head blight in wheat. Plant Pathology, 69(2), 249–258. https://doi.org/10.1111/ppa.13130 Konkin, D., Hsueh, Y.-C., Kirzinger, M., Kubaláková, M., Haldar, A., Balcerzak, M., Han, F., Fedak, G., Doležel, J., Sharpe, A., and Ouellet, T. (2022). Genomic sequencing of Thinopyrum elongatum chromosome arm 7EL, carrying fusarium head blight resistance, and characterization of its impact on the transcriptome of the introgressed line CS-7EL. BMC Genomics, 23(1), 228. https://doi.org/10.1186/s12864-022-08433-8 Kosambi, D. D. (1943). The Estimation of Map Distances from Recombination Values. Annals of Eugenics, 12(1), 172–175. https://doi.org/10.1111/j.1469-1809.1943.tb02321.x 52 Leslie, J. F., and Summerell, B. A. (2008). The Fusarium Laboratory Manual. John Wiley and Sons. Liu, S., Hall, M. D., Griffey, C. A., and McKendry, A. L. (2009). Meta-Analysis of QTL Associated with Fusarium Head Blight Resistance in Wheat. Crop Science, 49(6), 1955–1968. https://doi.org/10.2135/cropsci2009.03.0115 Meng, L., Li, H., Zhang, L., and Wang, J. (2015). QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. The Crop Journal, 3(3), 269–283. https://doi.org/10.1016/j.cj.2015.01.001 Miedaner, T., and Korzun, V. (2012). Marker-Assisted Selection for Disease Resistance in Wheat and Barley Breeding. Phytopathology®, 102(6), 560–566. https://doi.org/10.1094/PHYTO-05-11-0157 Moore, G. (2015). Strategic pre-breeding for wheat improvement. Nature Plants, 1(3), Article 3. https://doi.org/10.1038/nplants.2015.18 Pereyra, S. A., Dill-Macky, R., and Sims, A. L. (2004). Survival and Inoculum Production of Gibberella zeae in Wheat Residue. Plant Disease, 88(7), 724–730. https://doi.org/10.1094/PDIS.2004.88.7.724 Quinlan, A. R. (2014). BEDTools: The Swiss‐Army Tool for Genome Feature Analysis. Current Protocols in Bioinformatics, 47(1). https://doi.org/10.1002/0471250953.bi1112s47 Raman, H., Stodart, B. J., Cavanagh, C., Mackay, M., Morell, M., Milgate, A., and Martin, P. (2010). Molecular diversity and genetic structure of modern and traditional landrace cultivars of wheat (Triticum aestivum L.). Crop and Pasture Science, 61(3), 222–229. https://doi.org/10.1071/CP09093 53 Rasheed, A., and Xia, X. (2019). From markers to genome-based breeding in wheat. Theoretical and Applied Genetics, 132(3), 767–784. https://doi.org/10.1007/s00122-019-03286-4 Rossi, V., Ravanetti, A., Pattori, E., and Giosuè, S. (2001). Influence of Temperature and Humidity on the Infection of Wheat Spikes by Some Fungi Causing Fusarium Head Blight. Journal of Plant Pathology, 83(3), 189–198. Sandha, G. S. (2021). Improving Fusarium Head Blight Management in Durum Wheat in Saskatchewan (Doctoral dissertation, University of Saskatchewan). Sax, K. (1923). THE ASSOCIATION OF SIZE DIFFERENCES WITH SEED-COAT PATTERN AND PIGMENTATION IN PHASEOLUS VULGARIS. Genetics, 8(6), 552–560. https://doi.org/10.1093/genetics/8.6.552 Schloerke, B., Cook, D., Larmarange, J., Briatte, F., Marbach, M., Thoen, E., Elberg, A., Toomet, O., Crowley, J., Hofmann, H., and Wickham, H. (2021). GGally: Extension to “ggplot2.” https://doi.org/10.5281/zenodo.5009047 Schroeder, H. W., and Christensen, J. J. (1963). Factors affecting resistance of wheat to scab caused by Gibberella zeae. Phytopathology, 53(7, 1), 831–838. Shewry, P. R. (2009). Wheat. Journal of Experimental Botany, 60(6), 1537–1553. https://doi.org/10.1093/jxb/erp058 Shiferaw, B., Smale, M., Braun, H.-J., Duveiller, E., Reynolds, M., and Muricho, G. (2013). Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Security, 5(3), 291–317. https://doi.org/10.1007/s12571-013-0263-y 54 Stack, R. W., Elias, E. M., Fetch, J. M., Miller, J. D., and Joppa, L. R. (2002). Fusarium Head Blight Reaction of Langdon Durum-Triticum dicoccoides Chromosome Substitution Lines. Crop Science, 42(2), 637–642. https://doi.org/10.2135/cropsci2002.6370 Sun, C., Dong, Z., Zhao, L., Ren, Y., Zhang, N., and Chen, F. (2020). The Wheat 660K SNP array demonstrates great potential for marker-assisted selection in polyploid wheat. Plant Biotechnology Journal, 18(6), 1354–1360. https://doi.org/10.1111/pbi.13361 Thambugala, D., Brûlé-Babel, A. L., Blackwell, B. A., Fedak, G., Foster, A. J., MacEachern, D., Gilbert, J., Henriquez, M. A., Martin, R. A., McCallum, B. D., Spaner, D., Iqbal, M., Pozniak, C. J., N’Diaye, A., and McCartney, C. A. (2020). Genetic analyses of native Fusarium head blight resistance in two spring wheat populations identifies QTL near the B1, Ppd-D1, Rht-1, Vrn-1, Fhb1, Fhb2, and Fhb5 loci. Theoretical and Applied Genetics, 133(10), 2775–2796. https://doi.org/10.1007/s00122-020-03631-y Tollefson, M. (2021). Graphics with the ggplot2 Package: An Introduction. In M. Tollefson (Ed.), Visualizing Data in R 4: Graphics Using the base, graphics, stats, and ggplot2 Packages (pp. 281–293). Apress. https://doi.org/10.1007/978-1-4842-6831-5_7 Varshney, R. K., Graner, A., and Sorrells, M. E. (2005). Genomics-assisted breeding for crop improvement. Trends in Plant Science, 10(12), 621–630. Walkowiak, S., Gao, L., Monat, C., Haberer, G., Kassa, M. T., Brinton, J., Ramirez-Gonzalez, R. H., Kolodziej, M. C., Delorean, E., Thambugala, D., Klymiuk, V., Byrns, B., Gundlach, H., Bandi, V., Siri, J. N., Nilsen, K., Aquino, C., Himmelbach, A., Copetti, D., … Pozniak, C. J. (2020). Multiple wheat genomes reveal global variation in modern breeding. Nature, 588(7837), 277–283. https://doi.org/10.1038/s41586-020-2961-x 55 Wang, L., Michel, D., Zhang, W., El-Aneed, A., Fobert, P. R., Ruan, Y., Berraies, S., Cuthbert, R., and Kutcher, H. R. (2022). A High-Throughput Fast Chromatography-Tandem Mass Spectrometry-Based Method for Deoxynivalenol Quantification in Wheat Grain. PhytoFrontiersTM, 2(4), 322–330. https://doi.org/10.1094/PHYTOFR-03-22-0024-TA Wang, S., Wong, D., Forrest, K., Allen, A., Chao, S., Huang, B. E., Maccaferri, M., Salvi, S., Milner, S. G., Cattivelli, L., Mastrangelo, A. M., Whan, A., Stephen, S., Barker, G., Wieseke, R., Plieske, J., Consortium, I. W. G. S., Lillemo, M., Mather, D., … Akhunov, E. (2014). Characterization of polyploid wheat genomic diversity using a high-density 90 000 single nucleotide polymorphism array. Plant Biotechnology Journal, 12(6), 787–796. https://doi.org/10.1111/pbi.12183 Winfield, M. O., Allen, A. M., Wilkinson, P. A., Burridge, A. J., Barker, G. L. A., Coghill, J., Waterfall, C., Wingen, L. U., Griffiths, S., and Edwards, K. J. (2018). High-density genotyping of the A.E. Watkins Collection of hexaploid landraces identifies a large molecular diversity compared to elite bread wheat. Plant Biotechnology Journal, 16(1), 165–175. https://doi.org/10.1111/pbi.12757 Wingen, L. U., Orford, S., Goram, R., Leverington-Waite, M., Bilham, L., Patsiou, T. S., Ambrose, M., Dicks, J., and Griffiths, S. (2014). Establishing the A. E. Watkins landrace cultivar collection as a resource for systematic gene discovery in bread wheat. Theoretical and Applied Genetics, 127(8), 1831–1842. https://doi.org/10.1007/s00122-014-2344-5 56 Zhang, M., Zhang, R., Yang, J., and Luo, P. (2010). Identification of a new QTL for Fusarium head blight resistance in the wheat genotype “Wang shui-bai.” Molecular Biology Reports, 37(2), 1031–1035. https://doi.org/10.1007/s11033-009-9809-7 57 Appendices Appendix A Area under the disease progress curve (AUDPC) calculated for recombinant inbred lines (RILs) resulting from ‘Paragon x Wat.1190580’ (PXW580) cross. RILs evaluated for disease severity (SEV) in two experimental replications (Rep 1 and Rep 2) over three time-points for each rep: 14 days post-inoculation (14 dpi), 21 dpi, and 28 dpi at UBC greenhouse in the year 2021 (GH_UBC21). 58 Appendix B Distribution of phenotypic traits of ‘Paragon X Wat.1190580’ (PXW580) population from field trials in different years and locations. Environment (Env) represents the site years for Fusarium head blight (FHB) screening of recombinant inbred lines (RILs). (a) Incidence (INC; on a scale of 1-100%), (b) severity (SEV; on a scale of 1-100%), (c) visual rating index (VRI; INC x SEV) at Carman 2021 (CR21), Morden 2021 (MR21), Carman 2022 (CR22), Morden 2022 (MR22), Morden 2023 (MR23). (d) Fusarium damaged kernels (FDKs; on a scale of 0-100%), (e) Deoxynivalenol (DON; measured in ppm) at CR22, MR21, and MR22. (f) Plant height (PHT; measured in cm) at MR21, MR22, MR23 and (g) days to anthesis (DTA; measured in days) at CR21, MR21, MR22, MR23. 59 Appendix C (a) Shapiro-Wilk’s and (b) Levene’s test of phenotypic traits from multiple site years. The Recombinant inbred line (RIL) population: PXW580, tested in Carman 2021, 2022 (CR21, CR22), Morden 2021, 2022, 2023 (MR21, MR22, MR23. INC is Incidence, SEV is severity, FDKs stands for Fusarium damaged kernels, DON is deoxynivalenol, PHT is plant height, and DTA represents days to anthesis. Shapiro-Wilk’s Normality Test Env INC SEV FDKs DON PHT DTA GH_UBC21 n/a 0.91*** n/a n/a n/a n/a CR21 0.94** 0.90 *** n/a n/a n/a 0.95** CR22 0.96* 0.98ns 0.93*** 0.98ns n/a n/a MR21 0.92*** 0.94* 0.80*** 0.83*** 0.98ns 0.97ns MR22 0.97ns 0.96ns 0.82*** 0.91*** 0.96* 0.93*** MR23 0.95* 0.97ns n/a n/a 0.97ns 0.93*** *Significant at P < 0.05, **Significant at P < 0.01, ***Significant at P < 0.001, nsnon-significant, n/a ‘not applicable’ Levene’s Test for Homogeneity of Variance Population INC SEV FDKs DON PHT DTA PXW580 8.4*** 16.1*** 15*** 23.2*** 1.47ns 14.1*** *Significant at P < 0.05, **Significant at P < 0.01, ***Significant at P < 0.001, nsnon-significant, n/a ‘not applicable’ a b 60 Appendix D Pearson correlation test of recombinant inbred lines (RIL) population from cross Paragon X Wat.1190580 (PXW580). A combined correlogram of RILs drawn using pooled phenotypic data (average of all environments) of five Fusarium head blight (FHB) traits disease incidence (INC; measured on a scale of 0-100), severity (SEV; measured on a scale of 0-100), visual rating index (VRI; INC x SEV), Fusarium damaged kernels (FDKs; measured on a scale of 0-100) deoxynivalenol (DON; measured in ppm) content, and days to anthesis (DTA; days) and plant height (PHT; measured in cm) evaluated at Carman (CR) and Morden (MR) in Canada. For each pair of traits, scatterplots with linear and exponential regression lines are drawn on the lower-left part of the plot matrix. Dots in scatterplots represent RILs. Pearson correlations are displayed on the upper-right part of the plot matrix. Frequency distribution plots for each trait are shown on the diagonal 61 Appendix E Imputation of the marker data using R package: ABH genotype where a) is before imputation and b) is after imputation. A represents reference ‘A’ (Landmark), ‘B’ represents reference B (Wat.1190580), and ‘Hetero’ represents the missing marker information. a b a b 62 Appendix F Summary of the genetic map constructed with 75 RILs from Paragon X Wat.1190580 (PXW580) cross using Wheat 90K iSelect SNP array. Chromosome All markers Length (cM) cM/Locus 1A 60 857.04 14.28 1B 93 1130.50 12.16 1D 105 1356.48 12.92 2A 71 1073.90 15.13 2B 121 1652.31 13.66 2D 113 1404.70 12.43 3A 55 744.24 13.53 3B 71 938.74 13.22 3D 89 1103.66 12.40 4A 42 563.67 13.42 4B 51 664.80 13.04 4D 76 1000.94 13.17 5A 59 811.98 13.76 5B 85 1048.17 12.33 5D 107 1287.64 12.03 6A 57 751.18 13.18 6B 78 1081.71 13.87 6D 65 865.16 13.31 7A 80 1076.21 13.45 7B 72 985.85 13.69 7D 95 1308.30 13.77 Group 1 258 3344.02 12.96 Group 2 305 4130.91 13.54 Group 3 215 2786.64 12.96 Group 4 169 2229.41 13.19 Group 5 251 3147.79 12.54 Group 6 200 2698.05 13.49 Group 7 247 3370.36 13.65 A genome 424 5878.22 13.86 B genome 571 7502.08 13.14 D genome 650 8326.88 12.81 Whole genome 1645 21707.18 13.20 63 Appendix G Illumina Nextera library preparation workflow. (A) Bead like transposomes mediate the simultaneous fragmentation of gDNA and the addition of Illumina sequencing primers. (B) Reduced cycle PCR amplification amplifies sequencing ready DNA fragments and add indexes and adapters. (C) Sequencing-ready fragments are washed and pooled.
Thesis/Dissertation
eng
Plant Science
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Riseman, Andrew
Genetic mapping of resistance to Fusarium head blight and DON accumulation in Watkins landrace Wat.1190580
Text