Genomic Regions Influencing the Hyperspectral Phenome of Deoxynivalenol Infected Wheat

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Concepcion, Amanda D. Noble, Addie M. Thompson, Yanhong Dong, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3954059/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract The quantitative nature of Fusarium Head Blight (FHB) resistance requires further exploration of the wheat genome to identify regions conferring resistance. In this study, we explored the application of hyperspectral imaging of Fusarium-infected wheat kernels and identify regions of the wheat genome contributing significantly to the accumulation of Deoxynivalenol (DON) mycotoxin. Strong correlations were identified between hyperspectral reflectance values for 204 wavebands in the 397 nm to 673 nm range and DON mycotoxin. Dimensionality reduction using principal components was performed for all 204 wavebands and 38 sliding windows across the range of wavebands. PC1 of all 204 wavebands explained 70% of the total variation in waveband reflectance values and was highly correlated with DON mycotoxin. PC1 was used as a phenotype in GWAS and a large effect QTL on chromosome 2D was identified for PC1 of all wavebands as well as nearly all 38 sliding windows. The allele contributing variation in PC1 values also led to a substantial reduction in DON. The 2D polymorphism affecting DON levels localized to the exon of TraesCS2D02G524600 which is upregulated in wheat spike and rachis tissues during FHB infection. This work demonstrates the value of hyperspectral imaging as a correlated trait for investigating the genetic basis of resistance and developing wheat varieties with enhanced resistance to FHB. Biological sciences/Plant sciences/Plant stress responses/Biotic Biological sciences/Genetics/Genetic association study/Genome wide association studies Biological sciences/Genetics/Agricultural genetics Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Fusarium head blight (FHB) results in significant grain quality and yield reductions that limit profits for wheat farmers and presents challenges in managing mycotoxins and affects wheat production worldwide. During FHB infection by the ascomycete fungus Fusarium graminearum Schwabe, Deoxynivalenol (DON) mycotoxin accumulates in wheat kernels (Mirocha et al., 1994 ). DON is harmful to both humans and animals when ingested (Foroud et al., 2019 ) and is tightly regulated by testing grain at the point of sale prior to entering the marketplace. Increasing the level of genetic resistance to FHB through breeding is a highly effective mechanism to minimize DON levels on individual farms and limit the amount of mycotoxin entering the grain marketplace (Mesterhazy 2014). Type III resistance to FHB, resistance to DON accumulation, remains a challenge in FHB-improvement programs. As a quantitative trait, Type III resistance is controlled by multiple QTLs (Bai et al., 2018 ). Several QTLs for lower DON accumulation have been reported with da Silva et al. ( 2019 ) reporting a large effect QTL in 5A accounting for 13% phenotypic variation for DON. He et al. ( 2019 ) identified two major QTLs in 3B and 3D and Larkin et al. ( 2020 ) identified ten significant marker trait associations across the genome. Recently, Haile et al. ( 2023 ) identified nine QTLs associated with DON accumulation using a multi-locus GWAS model. Phenotyping DON mycotoxin in grain samples relies on GC/MS (Gas Chromatography/Mass Spectrometry) (Tacke and Casper, 1996 ) methods that require extensive logistics that are time consuming and labor intensive. Obtaining samples for DON analysis requires a FHB nursery with disease pressure, extensive sampling in the field followed by threshing and milling of grain samples. High throughput imaging technologies have been explored and exploited to improve the overall process and accuracy in phenotyping DON levels in wheat kernels (Ropelewska, 2019 ; Jaillais et al., 2015 ; Cambaza et al., 2019 ; Shi et al., 2020 ). However, DON phenotyping remains a bottleneck in elucidating the genetic basis of resistance to DON accumulation during FHB infection. Recently, hyperspectral imaging has been explored to further increase accuracy and intensity in evaluating DON content in barley (Su et al., 2021 ), oats (Tekle et al., 2015 ; Teixido-Orries et al., 2023 ), and wheat (Femenias et al, 2022 ). At a single kernel resolution, Shen et al. ( 2022 ) and Femenias et al. ( 2022 ) imaged grain samples using wavebands at the NIR range to quantify DON. Mobile handheld hyperspectral cameras like the Specim IQ (Specim, Oulo, Finland) detect reflectance values at wavebands from the visible to near infrared (VIS/NIRS) regions (Behman et al., 2018) and have been used for disease detection of root rot in grapevine (Calamita et al., 2021 ), powdery mildew in wild rocket (Pane et al., 2021 ), and root and crown rot in sugar beet (Barreto et al., 2020 ). Phenomics and imaging technologies have been integrated with genome wide association studies (GWAS) to elucidate the genetic architecture of quantitative traits (Xiao et al., 2022 ). Several studies have reported the integration of phenomics and high-throughput phenotyping for GWAS in wheat (Jiang et al., 2019 ; Rasheed et al., 2014 ; Yates et al., 2019 ), rice (Barnaby et al. 2020 ; Feng et al., 2017 ; Sun et al.,2019), soybean (Herritt et al., 2016 ; Dhanapal et al., 2016 ; Xavier et al., 2017 ), and maize (Muraya et al., 2017 ; Gage et al., 2018 ; Wang et al., 2019 ). While the genetic basis of hyperspectral imaging-derived phenotypes has been investigated in rice (Feng et al., 2017 ; Barnaby et al., 2020 ), and soybean (Wang et al., 2021 ; Yoosefzadeh-Najafabadi et al., 2021 ), great potential exists to leverage imaging technologies to investigate the genetic basis of quantitative traits. This study leverages hyperspectral imaging in the identification of genomic regions associated with DON accumulation in soft winter wheat adapted to the Eastern United States. DON-infected wheat kernels of diverse wheat varieties and elite breeding lines were imaged using a mobile handheld hyperspectral imaging system. The hyperspectral reflectance values generated were used to 1) determine the relationship of the hyperspectral phenome with DON mycotoxin levels in wheat kernels and 2) identify genomic regions associated with mycotoxin levels and variation in the hyperspectral phenome of DON-infected kernels. Results Deoxynivalenol concentration Wheat genotypes show variation for DON concentration (p-value: 3.016x10 − 7 ) based on non-parametric Kruskal-Wallis Rank Test (Fig. 1 a; Supplementary Table 2). Pairwise comparison of means revealed 227 genotypes (72.3%) having significantly lower DON content in comparison with susceptible check Ambassador. In comparison 248 genotypes (79.3%) were not significantly different from the resistant check, MI14W0190 and 21 genotypes (6.7%) have significantly lower DON content than MI14W0190. Hyperspectral reflectance values and dimensionality reduction of the hyperspectral phenome Significant variation among the genotypes based on Kruskal-Wallis Rank Test and ANOVA F-Test for wavebands meeting normality and variance homogeneity assumptions, were observed in each of the 204 wavebands generated (Supplementary Table 2; Supplementary Fig. 1). Of the 204 wavebands evaluated, only 15 wavebands (7.35%) met the normality and variance homogeneity assumptions (Supplementary Table 2). Significant positive correlations were found between the 204 wavebands generated and DON content, with 97 wavebands in the 455 nm to 739 nm range demonstrating a correlation of greater than 0.5 with DON content (Supplementary Table 3). The 584 nm to 673 nm waveband range demonstrated the highest correlations with DON greater than 0.6 (Supplementary Table 3). Principal component analysis was performed for all 204 wavebands and the first two principal components accounted for 74.0% and 6.0% of variation in hyperspectral reflectance values (Supplementary Table 6), respectively. Genotypes having lower PC1 values demonstrated lower DON content and PC1 of all wavebands was correlated with DON at 0.57. The waveband at 502 nm demonstrated the highest component loading (0.101) and was also found to be the most discriminatory among the wavebands, followed by 499 nm, 505 nm, 508 nm, and 511 nm (Supplementary Table 4). Wavebands in the 397 nm to 780 nm demonstrated higher component loading than most wavebands beyond 780 nm (Supplementary Table 4). Principal component analysis was also carried out for binned wavebands using a sliding window approach. The first principal component for each of the 38 binned wavebands (windows) explained 51–99% of the variation in hyperspectral reflectance values within each bin (Supplementary Table 6). PC1 of each waveband bin was found to be significantly correlated with DON content (Fig. 2 , Supplementary Table 7). Windows 1 to 10 spanning wavebands 397 nm to 584 nm demonstrated significant positive correlations of 0.50 to 0.58 with GC/MS-derived DON content, while Windows 11 to 25 spanning wavebands 542 nm to 810 nm demonstrated a significant negative correlation from − 0.40 to -0.64 (Fig. 2 ; Supplementary Table 7). The sign of the correlation between waveband window PC1 values and DON inverted six times across the waveband spectrum (Fig. 2 ). The correlation of PC1 with DON inverted from positive in Window 1 to 10 to negative from Windows 11 to 25. Correlation with DON inverted again from Windows 25 to 26, 27 to 28, 29 to 30 and 33 to 34. Genome wide association study for deoxynivalenol accumulation GWAS was performed to identify marker trait associations (MTAs) associated with DON content (Supplementary Table 8). Five significant MTAs were identified in chromosomes 2A, 2B, 3A, and 5A explaining 1.96–4.03% of the variation in DON (Table 1 , Supplementary Table 8, Supplementary File 1). Favorable alleles at all five loci demonstrated a reduction in DON content (Fig. 3 ) Table 1 Marker-trait associations identified using GC/MS-derived DON content as phenotypic input influencing DON accumulation. Significant SNP (MTA) Chromosome Position (mb) Allele* p.value Alleles Effect PVE (%) S2A_PART2_18053 2A 642.9 A /G 6.11 x 10 − 8 3.77 3.85 S2B_PART2_24719 2B 700.4 T /C 6.68 x 10 − 7 2.47 1.39 S3A_PART2_25234 3A 706.4 C /T 6.32 x 10 − 7 3.64 2.65 S3A_PART2_25425 3A 708.4 C /T 9.82 x 10 − 7 4.24 4.03 S5A_PART1_18813 5A 18.81 C /T 4.65 x 10 − 7 2.88 1.96 *Alleles in bold reduce DON accumulation PVE = Phenotypic Variance Explained GWAS using PC1 of all 204 wavebands identifies a single locus on chromosome 2D (S2D_PART2_15090) at 613.12 Mb on chromosome 2D was identified using PC1 explaining 26.4% of the phenotypic variation in the hyperspectral phenome of DON-infected wheat kernels (Fig. 3 ). Genotypes carrying the A allele demonstrate a 6.3 ppm reduction in DON compared with genotypes bearing the G allele (Fig. 4 c, Supplementary Table 8). The A allele at the 2D locus is the minor allele with a high frequency of 0.47. GWAS using the PC1 value for all 38 waveband bins consistently identifies the 2D locus in 35 of the 38 waveband bins, explaining 6.4–28.3% of the phenotypic variation in PC1. The S2D_PART2_15090 SNP demonstrates a negative allele effect from Windows 1 to 10 (397 nm to 584 nm) and a positive allele effect from Windows 11 to 25 (543 nm to 811 nm). The inversion of allele effect is consistent with the observed change in sign in the correlation between waveband bin PC1 values and DON content. An additional 18 MTAs were identified across the hyperspectral phenome of DON infected wheat kernels on chromosomes 1A, 1B, 1D, 2B, 2D, 3A, 3B, 4A, 4B, 7A, 7B and 7D (Supplementary Table 8, Supplementary File 1) across different waveband ranges. A locus on 1B was identified from 396 nm to 467 nm explaining 18.8% and 17.6% of variation in PC1 of waveband reflectance values and reducing DON by 2.0 ppm and 2.2 ppm at waveband bins 1 and 2, respectively (Supplementary Table 8). Other MTAs explained 0.2–7.6% of variation in PC1 of reflectance values within individual waveband bins. Several SNPs significantly associated with waveband bin PC1 values were found to be in LD (Supplementary Table 9). On chromosome 2D, the SNPs S2D_PART2_13700 and S2D_PART2_15090, were found to be in high LD with r2 = 0.88 at substantial distance of 13.9 mb (Supplementary Table 9). Weaker LD was detected between SNPs S2B_PART2_28124 and S2B_PART2_27654 on chromosome 2B at a distance of 4.69 mb (r 2 = 0.72), and between SNPs S1B_PART2_24837 and S1B_PART2_24652 on chromosome 1B (distance: 1.85 mb) (r 2 = 0.25). Putative candidate gene identified on chromosome 2D The SNP identified on 2D associated with PC1 of the entire hyperspectral phenome of DON infected wheat kernels and PC1 of nearly all sliding windows, S2D_PART2_15090, is located in the single exon of a 1,104 kb gene, TraesCS2D02G524600 , coding for a protein with an F-box domain. TraesCS2D02G524600 is upregulated in the spikelets and rachis in response to inoculation with F. graminearum in near-isogenic lines (NILs) bearing a 2DL introgression conferring FHB resistance (Biselli et al. 2018 ) (Supplementary Fig. 5) and has been implicated in Fhb1 resistance with higher expression in NILs carrying Fhb1 (Ma et al., 2021 ) (Supplementary Fig. 6). The expression of TraesCS2D02G524600 and 17 adjacent genes within a 2Mb region, 1Mb upstream and downstream, was investigated across tissues and developmental stages under F. graminearum infection (Wheat Expression Browser, Ramirez-Gonzalez et al., 2021) (Supplementary Fig. 7). TraesCS2D02G524600 is inducible by infection with F. graminearum and highly expressed in the spike at the reproductive stage, which is consistent across gene expression data sets. Only one gene in the 2Mb interval, TraesCS2D02G524400 , located upstream of TraesCS2D02G524600 , demonstrates the exact same expression profile. TraesCS2D02G524600 is a likely candidate gene for the large effect locus on 2D that reduces DON accumulation in wheat kernels during infection by F. graminearum . Discussion Breeding for resistance to FHB requires evaluation of the multiple components of resistance (Steiner et al., 2017 ). Each FHB resistance component has a different relationship to DON (Buerstmayr and Lemmens, 2015 , Mesterhazy et al., 2015, Paul et al., 2005 ) and evaluating multiple traits can lead to better selection decisions in breeding. Visual observations of FHB severity and incidence are used to develop an overall visual FHB index (Steiner et al., 2017 ). The proportion of Fusarium damaged kernels can be estimated on samples of infected grain and a high correlation with DON has been demonstrated for this resistance component (Mesterhazy et al., 2015). In this study, we generate multiple visual FHB resistance phenotypes using the hyperspectral phenome of DON infected wheat kernels. Reflectance values at individual wavebands can be considered unique phenotypes and high correlations were found between DON and reflectance values at individual wavebands, especially at the visible light spectrum range. PC1 of the reflectance values from all wavebands compresses the entire hyperspectral phenome into a single phenotype that incorporates the information from all wavebands. The hyperspectral phenome was dissected further into 38 sliding windows and PC1 of each window was used as a separate phenotype that is correlated with DON. In this study, we identified five MTAs on chromosomes 2A, 2B, 3A, and 5A that co-localize with previously reported genomic regions conferring resistance to DON (Supplementary Table 10). Individually, each of the MTAs identified for DON content explain only 1–4% of the variation in DON and reduce DON by 2.5 ppm to 4.2 ppm. FHB resistance traits can differ in their genetic architecture. Developing the hyperspectral phenome into a novel FHB resistance phenotype using PC1 of all wavebands, which is correlated to DON, led to identification of a comparably large effect locus on 2D explaining 26% of the variation in PC1 and reducing DON by 6.8 ppm. While several genomic regions were identified across waveband ranges, the 2D locus was identified using PC1 across waveband ranges, further establishing its association with the hyperspectral phenome of DON infected whet kernels. By leveraging the hyperspectral phenome as a correlated trait, we were able to identify a locus influencing the target trait, DON mycotoxin content. The large effect locus on 2D localizes to the exon of an F-box protein encoding gene and captures a large proportion of variation in the hyperspectral phenome of DON infected wheat kernels. Two SNPs were identified in the exon of this gene; however, the BLINK algorithm removes SNPs that are in LD. Multiple gene expression studies demonstrate the gene is inducible upon infection with F. graminearum and is expressed exclusively in tissues of the spike and rachis as a component of the defense response. It may be possible to select for the DON-reducing allele at 2D locus in a breeding context using a Kompetitive Allele Specific PCR (KASP) marker assay (He et al., 2014 ). Evaluation of DON production during infection by F. graminearum is an integral part in developing wheat varieties with resistance to Fusarium Head Blight (FHB), which has long been done using GC/MS (Tacke and Casper, 1996 ). Preparation and phenotyping of DON infected wheat kernel samples is time consuming, tedious, and labor intensive (Steiner et al., 2017 ). This study demonstrates that hyperspectral imaging can reduce the amount of time and physical resources necessary to make selection decisions in breeding for lower DON. Methods Plant materials A set of 200 soft red and 114 soft white winter wheat genotypes (n = 314), comprised of advanced breeding lines and commercial varieties (Supplementary Table 1) were used in this study. Genotypes MI14W0190 and Ambassador were considered checks FHB-resistant, low DON and FHB-susceptible, high DON checks, respectively. Wheat genotypes were planted in a misted and inoculated Fusarium screening nursery in East Lasing, MI (º42.69 N, º84.48W, Elevation: 264 m) in one-meter rows using a completely randomized designed with two to four replicates per genotype. Fusarium inoculum Fusarium graminearum cultures were collected in 2020 from Huron, Ingham, Monroe, Tuscola and Sanilac counties in Michigan, USA. Initial cultures were grown by placing infected seed in Nash-Synder Media for 5 to 7 days at room temperature. Isolates for field inoculation were cultured in spawn bags with 0.2-micron filter patch (Unicorn Bags, TX, USA) containing 1.5 kg corn kernels. Corn was soaked in deionized (DI) water for 24 to 48 hours and autoclaved three times for 90 minutes. One culture plate of a four to six days-old culture and 100 ml autoclaved deionized water were added to each spawn bag. Cultures developed over two to three weeks and were dried in biohazard hood for 48 hours at ambient temperature. Isolates from different locations were cultured separately. After drying, F. graminearum grain spawn cultures from the five locations were pooled in equal proportions by weight prior to inoculation. Field inoculation was carried out five times beginning at approximately 5 weeks prior to flowering. A misting system was run throughout the nursery 10 minutes every hour for 12 hours, 6 am to 6 pm, to promote infection and disease development. Deoxynivalenol evaluation Wheat heads from the middle 0.3 meter of each row were sampled separately and harvested by hand. The heads from each row were threshed together and all seeds were retained. A subsample of 10 grams from each row was ball-milled using Restch MM 400 miller (Retsch, PA, USA) to generate flour meeting the guidelines set by the US Wheat and Barley Scab Initiative (USWBI) ( https://scabusa.org/don_labs_umn_testinglab_protocol ). Deoxynivalenol concentration of flour samples was determined using Gas Chromatography / Mass Spectrometry (GC/MS) at the Department of Plant Pathology, University of Minnesota. Hyperspectral image acquisition FHB-infected wheat kernels from each genotype sent for DON content measurement were imaged using, a handheld, push broom hyperspectral camera, Specim IQ (Specim, Oulo, Finland). A sample of 50 to 80 wheat kernels from each replicate of each genotype were imaged. Seeds were placed against a black background side-by-side with the white reference panel. Imaging was done inside a 51 x 51 x 51-centimeter light box (Finnhomy, USA) using the attached LED light source. The hyperspectral camera was mounted on a tripod and angled 45º facing downward over the kernels. Default Recording Mode was used to capture reflectance values from 204 wavebands from 397 to 1004 nm with an integration time of 30 to 40 seconds and focus set at automatic. Image processing and reflectance value extraction Hyperspectral images were processed using QGIS 3.10.2 (QGIS, 2020). Image files (.dat) were imported as raster layer. Rendering was carried out using multiband color with Band 088 (651.92 nm) as Red Band, Band 057 (560.30 nm) as Green Band, and Band 037 (501.72) as Blue Band. Color enhancements were set at Stretch to MinMax and normal blending mode. Raster calculation was carried out at 0.3 to 0.8 threshold. Raster calculated images were saved as GeoTIFF (.tif) file and converted to vector image (Polygonize) using default settings. To determine region of interest (wheat kernels) and remove unnecessary features, toggle editing by selecting features was used. Vectorized images with region of interest determined were saved as ESRI Shape File (.shp). Spectral reflectance values were extracted from each ESRI shape file using “raster” package (Hijmans et al., 2023 ) in R v4.2.2 (R Core Team, 2021) by calculating mean reflectance values in each waveband. Statistical analysis The normality of hyperspectral reflectance data was assessed using Shapiro-Wilk Test and variance homogeneity assumption was carried out using Levene’s Test. Wavebands with p-values < 0.05 failed to meet normality and homogeneity assumption. To test variation among the wheat genotypes for DON content and spectral reflectance values in all 204 wavebands, ANOVA was carried out for reflectance values at wavebands meeting normality and homogeneity assumptions, otherwise non-parametric Kruskal-Wallis Rank Test was employed following the model: y = G + e Where y is the DON content or spectral reflectance value of each waveband, G is the fixed effect of genotype, and e is the residual. Shapiro-Wilk Test, Levene’s Test, ANOVA F-Test, and Kruska-Wallis Rank Test were carried out in R v4.2.2 (R Core Team, 2021). Means of DON content and spectral reflectance values for two to four replicates per genotype were calculated using the “emmeans” package (Lenth et al., 2018). Pearson’s Correlation Coefficient was computed between DON means and spectral reflectance values at all individual wavebands. Principal Component Analysis of wavebands Principal Component Analysis was carried out using reflectance values for all wavebands to dimensionally reduce the spectral data and identify wavebands potentially associated with DON. To evaluate the contribution of waveband ranges across the hyperspectral phenome of DON infected wheat kernels, we employed a “Sliding Window” approach where the first twenty wavebands were binned and subjected to Principal Component Analysis. The bin was then “slid” at five-waveband intervals and PCs were generated for the next twenty wavebands to 1004 nm for a total of 38 windows (binned wavebands). The resulting PC1 waveband reflectance values from the 38 windows were then correlated to the GC/MS-derived DON content and used as predictors of DON. PCA and correlation was carried out in R v4.2.2 (R Core Team, 2021). DNA isolation and genotyping Tissue was collected from all genotypes evaluated and DNA was isolated according to Wiersma et al. ( 2016 ). Genotyping-by-sequencing libraries were prepared according to Poland et al. ( 2012 ) scaled to a 24uL volume in 384-well format. Libraries were sequenced at 384-plex on an Illumina HiSeq 4000 instrument. Single nucleotide polymorphisms (SNPs) were called using the TASSEL 5 GBS pipeline (Glaubitz et al., 2014 ). Reads were aligned to the RefSeq v1.0 wheat reference genome assembly (International Wheat Genome Sequencing Consortium) using default parameters. For the GBSSeqToTagDBPlugin and ProductionSNPCallerPluginV2 steps, the k-mer length was set to 64 base pairs and a minimum coverage of five reads was required for each k-mer. Default settings were used for all other steps. SNPs were initially called using all families and parents. SNPs were subsequently filtered for 0.85 call rate and 0.05 MAF. Genome wide association mapping Phenotypes for Genome Wide Association (GWAS) included: 1) GC/MS-derived DON content, 2) PC1 of all 204 wavebands and 3) PC1 of 38 waveband bins from the “Sliding Window” approach. GWAS was carried out using the Bayesian-information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) (Huang et al., 2019 ) model in GAPIT v3 (Genomic Association and Prediction Integrated Tool) (Lipka et al., 2012 ; latest version: March 12, 2022). A total of 9,961 SNPs across all 21 chromosomes remained after filtering at minor allele frequency (MAF) < 0.05 and 0.85 call rate. To address potential population structure, three principal components were used in GWAS models with the exception of two principal components for one phenotypic input, and four principal components for four phenotypic inputs (Supplementary Table 7). Linkage disequilibrium between Marker-Trait Associations (MTAs) was investigated using TASSEL 5 (Glaubitz et al., 2014 ). Candidate gene identification Significant SNPs identified in GWAS were assigned to high confidence gene models in IWGSC RefSeq Annotation V1.0 ( www.wheatgenome.org ). Descriptions of putative candidate genes were derived from the public wheat expression database Triticeae Multi-omics center ( http://202.194.139.32 ) (Ma et al., 2021 ) and Wheat Expression Browser (Ramirez-Gonzalez et al., 2021). Declarations Author Contribution EO and JC conceptualized the study, AN designed and established FHB nursery, YD analyzed DON concentration, JC acquired hyperspectral images, AT supported the hyperspectral image processing, JC processed and analyzed hyperspectral images, JC and EO conducted statistical analyses, JC wrote the first draft of the manuscript, EO wrote portions and revised the manuscript. All authors have read and contributed to the manuscript. The authors declare no conflict of interest. Data Availability All data used in this study, including the raw and processed hyperspectral images, phenotypic (DON) data, and genotypic data are available as per request. Acknowledgements The authors would like to acknowledge Amelia Orr, Samantha Mitchell, Dennis Pennington, Elizabeth Ross, Sadie Finegan, Maddie Pennington, and Jordan Parish for their assistance in field establishment and maintenance, and sample harvesting and preparation. The authors would also like to acknowledge Dr. Katherine Frels (University of Nebraska), Dr. Francisco Gomez (Syngenta) and Dr. Leonardo Volpato (Corteva) for their insights. This project was supported by AFRI Competitive Grant 2022-68013-36439 (WheatCAP) from the USDA-NIFA, The US Wheat and Barley Scab Initiative under USDA-ARS agreement 59-0206-2-135 and The Michigan Wheat Program agreement: 15-08-03-ES. References Mirocha, C. J. et al. Production of trichothecene mycotoxins by Fusarium graminearum and Fusarium culmorum on barley and wheat. 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Fusarium Head Blight Resistance QTL in the Spring Wheat Cross Kenyon/86ISMN 2137. Frontiers in Microbiology 7, (2016). Miedaner, T., Kalih, R., Großmann, M. S. & Maurer, H. P. Correlation between Fusarium head blight severity and DON content in triticale as revealed by phenotypic and molecular data. Plant Breeding 135, 31–37 (2016). Miedaner, T. et al. Broad-spectrum resistance loci for three quantitatively inherited diseases in two winter wheat populations. Mol Breeding 29, 731–742 (2012). Otto, C. D., Kianian, S. F., Elias, E. M., Stack, R. W. & Joppa, L. R. Genetic dissection of a major Fusarium head blight QTL in tetraploid wheat. Plant Mol Biol 48, 625–632 (2002). Paillard, S. et al. QTL analysis of resistance to Fusarium head blight in Swiss winter wheat (Triticum aestivum L.). Theor Appl Genet 109, 323–332 (2004). Petersen, S. et al. Mapping of Fusarium Head Blight Resistance Quantitative Trait Loci in Winter Wheat Cultivar NC-Neuse. Crop Science 56, 1473–1483 (2016). Petersen, S. et al. Validation of Fusarium Head Blight Resistance QTL in US Winter Wheat. Crop Science 57, 1–12 (2017). Ren, J. et al. Detection and validation of a novel major QTL for resistance to Fusarium head blight from Triticum aestivum in the terminal region of chromosome 7DL. Theor Appl Genet 132, 241–255 (2019). Szavo-Herver, A. et al. Differential influence of QTL linked to Fusarium head blight, Fusarium-damaged kernel, deoxynivalenol contents and associated morphological traits in a Frontana-derived wheat population. Euphytica 200, 9–26 (2014). Ruan, Y. et al. Identification of novel QTL for resistance to Fusarium head blight in a tetraploid wheat population. Genome 55, 853–864 (2012). Sari, E. et al. High density genetic mapping of Fusarium head blight resistance QTL in tetraploid wheat. PLoS ONE 13, e0204362 (2018). Shah, L. et al. Quantitative trait loci associated to Fusarium head blight resistance in wheat populations containing Annong–1124 and Zhoumai–27. Physiological and Molecular Plant Pathology 100, 67–74 (2017). Srinivasachary, S. et al. Mapping of QTL associated with Fusarium head blight in spring wheat RL4137. Czech J. Genet. Plant Breed. 44, 147–159 (2008). Tacke, B. K. & Casper, H. H. Determination of deoxynivalenol in wheat, barley, and malt by column cleanup and gas chromatography with electron capture detection. J AOAC Int 79, 472–475 (1996). He, X., Dreisigacker, S., Singh, R. P. & Singh, P. K. Genetics for low correlation between Fusarium head blight disease and deoxynivalenol (DON) content in a bread wheat mapping population. Theor Appl Genet 132, 2401–2411 (2019). Zhang, Q. et al. Identification and molecular mapping of quantitative trait loci for Fusarium head blight resistance in emmer and durum wheat using a single nucleotide polymorphism-based linkage map. Mol Breeding 34, 1677–1687 (2014). Zhang, W. et al. Genetic characterization of type II Fusarium head blight resistance derived from transgressive segregation in a cross between Eastern and Western Canadian spring wheat. Mol Breeding 38, 13 (2018). Zhang, G. & Mergoum, M. Molecular mapping of kernel shattering and its association with Fusarium head blight resistance in a Sumai3 derived population. Theor Appl Genet 115, 757–766 (2007). Zhou, W. et al. Molecular characterization of Fusarium head blight resistance in Wangshuibai with simple sequence repeat and amplified fragment length polymorphism markers. Genome 47, 1137–1143 (2004). Zhu, X. et al. Toward a better understanding of the genomic region harboring Fusarium head blight resistance QTL Qfhs.ndsu-3AS in durum wheat. Theor Appl Genet 129, 31–43 (2016). Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures.docx SupplementaryFile1.docx SupplementaryFile2.docx SupplementaryTables.xlsx Cite Share Download PDF Status: Published Journal Publication published 20 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 27 May, 2024 Reviews received at journal 06 May, 2024 Reviews received at journal 18 Apr, 2024 Reviews received at journal 18 Apr, 2024 Reviewers agreed at journal 08 Apr, 2024 Reviewers agreed at journal 08 Apr, 2024 Reviewers agreed at journal 28 Mar, 2024 Reviewers invited by journal 28 Mar, 2024 Editor assigned by journal 26 Mar, 2024 Editor invited by journal 27 Feb, 2024 Submission checks completed at journal 27 Feb, 2024 First submitted to journal 13 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3954059","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":275249710,"identity":"33f4b400-80c0-407d-9543-77a047065c1a","order_by":0,"name":"Jonathan S. Concepcion","email":"","orcid":"","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"S.","lastName":"Concepcion","suffix":""},{"id":275249711,"identity":"563378ab-dfe7-4fe1-9f4f-d62f086d4510","order_by":1,"name":"Amanda D. Noble","email":"","orcid":"","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"Amanda","middleName":"D.","lastName":"Noble","suffix":""},{"id":275249712,"identity":"67d5d97a-9d4a-4232-9afd-6ef9701245e5","order_by":2,"name":"Addie M. Thompson","email":"","orcid":"","institution":"Michigan State University","correspondingAuthor":false,"prefix":"","firstName":"Addie","middleName":"M.","lastName":"Thompson","suffix":""},{"id":275249713,"identity":"35354e59-ac93-4988-ad05-c7efd4d456b9","order_by":3,"name":"Yanhong Dong","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Yanhong","middleName":"","lastName":"Dong","suffix":""},{"id":275249714,"identity":"b3fb9a48-a11e-4d07-a313-215d555b6fcd","order_by":4,"name":"Eric L. 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Frequency distribution of GC/MS-derived DON content of the 314 soft winter wheat genotypes (red line represents the population mean DON content).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3954059/v1/49d98728118e7d7aaabd27f2.png"},{"id":51820395,"identity":"d47925aa-df06-4752-b13f-5a428f6a6cac","added_by":"auto","created_at":"2024-02-29 16:00:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":428668,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of correlations among PC1 between 38 sliding windows (binned wavebands) and DON content.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3954059/v1/1d3fd85f1f3688f80dc9d849.png"},{"id":51820389,"identity":"7043589b-7375-4d5c-a69f-66a2c0b36760","added_by":"auto","created_at":"2024-02-29 16:00:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":554998,"visible":true,"origin":"","legend":"\u003cp\u003eAlleles reducing GC/MS-derived DON identified by GWAS.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3954059/v1/d02e12820bb6d55d7aa7467a.png"},{"id":51820396,"identity":"ad7d9447-a914-4414-a6d2-c60bd12464d5","added_by":"auto","created_at":"2024-02-29 16:00:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2980443,"visible":true,"origin":"","legend":"\u003cp\u003eGenome wide association using PC1 of the 204 wavebands generated using hyperspectral imaging. (a) Manhattan plot identifying the MTA on chromosome 2DL and (b) its corresponding quantile-quantile plot, (c) variation in actual DON content of genotypes carrying the A and G allele in SNP S2D_PART2_15090.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3954059/v1/585c3b024c37c768b031738d.png"},{"id":63998560,"identity":"b69c242d-b91b-4c37-a8d9-81d2dc696c03","added_by":"auto","created_at":"2024-09-04 18:22:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4090260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3954059/v1/33019829-8580-450d-967c-1b993bd4fb2e.pdf"},{"id":51821143,"identity":"7d2dc3a0-2580-44e9-8c3e-22fec3e5b755","added_by":"auto","created_at":"2024-02-29 16:08:45","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3028454,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-3954059/v1/7359ec8a8148724c51bcfc42.docx"},{"id":51820391,"identity":"5f664cc3-2e9f-4613-9bfd-912fc8dd15ee","added_by":"auto","created_at":"2024-02-29 16:00:46","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2719159,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3954059/v1/4ec3bf439c924089ba9a03b2.docx"},{"id":51820394,"identity":"5c7c1c8c-b2af-4874-bbe5-c840023a861c","added_by":"auto","created_at":"2024-02-29 16:00:46","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3143448,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3954059/v1/6b65349440c39e85b46eb276.docx"},{"id":51820393,"identity":"7531fa08-e18c-4cd2-8eff-a606aafcd3d8","added_by":"auto","created_at":"2024-02-29 16:00:46","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":854266,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3954059/v1/de916513c08e9ec9ca870fa1.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genomic Regions Influencing the Hyperspectral Phenome of Deoxynivalenol Infected Wheat","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFusarium head blight (FHB) results in significant grain quality and yield reductions that limit profits for wheat farmers and presents challenges in managing mycotoxins and affects wheat production worldwide. During FHB infection by the ascomycete fungus \u003cem\u003eFusarium graminearum\u003c/em\u003e Schwabe, Deoxynivalenol (DON) mycotoxin accumulates in wheat kernels (Mirocha et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). DON is harmful to both humans and animals when ingested (Foroud et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and is tightly regulated by testing grain at the point of sale prior to entering the marketplace. Increasing the level of genetic resistance to FHB through breeding is a highly effective mechanism to minimize DON levels on individual farms and limit the amount of mycotoxin entering the grain marketplace (Mesterhazy 2014).\u003c/p\u003e \u003cp\u003eType III resistance to FHB, resistance to DON accumulation, remains a challenge in FHB-improvement programs. As a quantitative trait, Type III resistance is controlled by multiple QTLs (Bai et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Several QTLs for lower DON accumulation have been reported with da Silva et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reporting a large effect QTL in 5A accounting for 13% phenotypic variation for DON. He et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) identified two major QTLs in 3B and 3D and Larkin et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) identified ten significant marker trait associations across the genome. Recently, Haile et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) identified nine QTLs associated with DON accumulation using a multi-locus GWAS model.\u003c/p\u003e \u003cp\u003ePhenotyping DON mycotoxin in grain samples relies on GC/MS (Gas Chromatography/Mass Spectrometry) (Tacke and Casper, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) methods that require extensive logistics that are time consuming and labor intensive. Obtaining samples for DON analysis requires a FHB nursery with disease pressure, extensive sampling in the field followed by threshing and milling of grain samples. High throughput imaging technologies have been explored and exploited to improve the overall process and accuracy in phenotyping DON levels in wheat kernels (Ropelewska, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Jaillais et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Cambaza et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shi et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, DON phenotyping remains a bottleneck in elucidating the genetic basis of resistance to DON accumulation during FHB infection.\u003c/p\u003e \u003cp\u003eRecently, hyperspectral imaging has been explored to further increase accuracy and intensity in evaluating DON content in barley (Su et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), oats (Tekle et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Teixido-Orries et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and wheat (Femenias et al, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). At a single kernel resolution, Shen et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Femenias et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) imaged grain samples using wavebands at the NIR range to quantify DON. Mobile handheld hyperspectral cameras like the Specim IQ (Specim, Oulo, Finland) detect reflectance values at wavebands from the visible to near infrared (VIS/NIRS) regions (Behman et al., 2018) and have been used for disease detection of root rot in grapevine (Calamita et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), powdery mildew in wild rocket (Pane et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and root and crown rot in sugar beet (Barreto et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePhenomics and imaging technologies have been integrated with genome wide association studies (GWAS) to elucidate the genetic architecture of quantitative traits (Xiao et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Several studies have reported the integration of phenomics and high-throughput phenotyping for GWAS in wheat (Jiang et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rasheed et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yates et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), rice (Barnaby et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Feng et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Sun et al.,2019), soybean (Herritt et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Dhanapal et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Xavier et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and maize (Muraya et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gage et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). While the genetic basis of hyperspectral imaging-derived phenotypes has been investigated in rice (Feng et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Barnaby et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and soybean (Wang et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yoosefzadeh-Najafabadi et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), great potential exists to leverage imaging technologies to investigate the genetic basis of quantitative traits.\u003c/p\u003e \u003cp\u003eThis study leverages hyperspectral imaging in the identification of genomic regions associated with DON accumulation in soft winter wheat adapted to the Eastern United States. DON-infected wheat kernels of diverse wheat varieties and elite breeding lines were imaged using a mobile handheld hyperspectral imaging system. The hyperspectral reflectance values generated were used to 1) determine the relationship of the hyperspectral phenome with DON mycotoxin levels in wheat kernels and 2) identify genomic regions associated with mycotoxin levels and variation in the hyperspectral phenome of DON-infected kernels.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDeoxynivalenol concentration\u003c/h2\u003e \u003cp\u003eWheat genotypes show variation for DON concentration (p-value: 3.016x10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e) based on non-parametric Kruskal-Wallis Rank Test (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea; Supplementary Table\u0026nbsp;2). Pairwise comparison of means revealed 227 genotypes (72.3%) having significantly lower DON content in comparison with susceptible check Ambassador. In comparison 248 genotypes (79.3%) were not significantly different from the resistant check, MI14W0190 and 21 genotypes (6.7%) have significantly lower DON content than MI14W0190.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eHyperspectral reflectance values and dimensionality reduction of the hyperspectral phenome\u003c/h2\u003e \u003cp\u003eSignificant variation among the genotypes based on Kruskal-Wallis Rank Test and ANOVA F-Test for wavebands meeting normality and variance homogeneity assumptions, were observed in each of the 204 wavebands generated (Supplementary Table\u0026nbsp;2; Supplementary Fig.\u0026nbsp;1). Of the 204 wavebands evaluated, only 15 wavebands (7.35%) met the normality and variance homogeneity assumptions (Supplementary Table\u0026nbsp;2). Significant positive correlations were found between the 204 wavebands generated and DON content, with 97 wavebands in the 455 nm to 739 nm range demonstrating a correlation of greater than 0.5 with DON content (Supplementary Table\u0026nbsp;3). The 584 nm to 673 nm waveband range demonstrated the highest correlations with DON greater than 0.6 (Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003ePrincipal component analysis was performed for all 204 wavebands and the first two principal components accounted for 74.0% and 6.0% of variation in hyperspectral reflectance values (Supplementary Table\u0026nbsp;6), respectively. Genotypes having lower PC1 values demonstrated lower DON content and PC1 of all wavebands was correlated with DON at 0.57. The waveband at 502 nm demonstrated the highest component loading (0.101) and was also found to be the most discriminatory among the wavebands, followed by 499 nm, 505 nm, 508 nm, and 511 nm (Supplementary Table\u0026nbsp;4). Wavebands in the 397 nm to 780 nm demonstrated higher component loading than most wavebands beyond 780 nm (Supplementary Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003ePrincipal component analysis was also carried out for binned wavebands using a sliding window approach. The first principal component for each of the 38 binned wavebands (windows) explained 51\u0026ndash;99% of the variation in hyperspectral reflectance values within each bin (Supplementary Table\u0026nbsp;6). PC1 of each waveband bin was found to be significantly correlated with DON content (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplementary Table\u0026nbsp;7). Windows 1 to 10 spanning wavebands 397 nm to 584 nm demonstrated significant positive correlations of 0.50 to 0.58 with GC/MS-derived DON content, while Windows 11 to 25 spanning wavebands 542 nm to 810 nm demonstrated a significant negative correlation from \u0026minus;\u0026thinsp;0.40 to -0.64 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Table\u0026nbsp;7). The sign of the correlation between waveband window PC1 values and DON inverted six times across the waveband spectrum (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The correlation of PC1 with DON inverted from positive in Window 1 to 10 to negative from Windows 11 to 25. Correlation with DON inverted again from Windows 25 to 26, 27 to 28, 29 to 30 and 33 to 34.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenome wide association study for deoxynivalenol accumulation\u003c/h2\u003e \u003cp\u003eGWAS was performed to identify marker trait associations (MTAs) associated with DON content (Supplementary Table\u0026nbsp;8). Five significant MTAs were identified in chromosomes 2A, 2B, 3A, and 5A explaining 1.96\u0026ndash;4.03% of the variation in DON (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Supplementary Table\u0026nbsp;8, Supplementary File 1). Favorable alleles at all five loci demonstrated a reduction in DON content (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMarker-trait associations identified using GC/MS-derived DON content as phenotypic input influencing DON accumulation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificant SNP (MTA)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePosition (mb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAllele*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep.value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAlleles Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePVE (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS2A_PART2_18053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e642.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eA\u003c/b\u003e/G\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.11 x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;8\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS2B_PART2_24719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e700.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eT\u003c/b\u003e/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.68 x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;7\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS3A_PART2_25234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e706.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e6.32 x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;7\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS3A_PART2_25425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e708.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e9.82 x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;7\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS5A_PART1_18813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eC\u003c/b\u003e/T\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4.65 x 10\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;7\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Alleles in bold reduce DON accumulation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003ePVE\u0026thinsp;=\u0026thinsp;Phenotypic Variance Explained\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGWAS using PC1 of all 204 wavebands identifies a single locus on chromosome 2D (S2D_PART2_15090) at 613.12 Mb on chromosome 2D was identified using PC1 explaining 26.4% of the phenotypic variation in the hyperspectral phenome of DON-infected wheat kernels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Genotypes carrying the A allele demonstrate a 6.3 ppm reduction in DON compared with genotypes bearing the G allele (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, Supplementary Table\u0026nbsp;8). The A allele at the 2D locus is the minor allele with a high frequency of 0.47.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGWAS using the PC1 value for all 38 waveband bins consistently identifies the 2D locus in 35 of the 38 waveband bins, explaining 6.4\u0026ndash;28.3% of the phenotypic variation in PC1. The S2D_PART2_15090 SNP demonstrates a negative allele effect from Windows 1 to 10 (397 nm to 584 nm) and a positive allele effect from Windows 11 to 25 (543 nm to 811 nm). The inversion of allele effect is consistent with the observed change in sign in the correlation between waveband bin PC1 values and DON content.\u003c/p\u003e \u003cp\u003eAn additional 18 MTAs were identified across the hyperspectral phenome of DON infected wheat kernels on chromosomes 1A, 1B, 1D, 2B, 2D, 3A, 3B, 4A, 4B, 7A, 7B and 7D (Supplementary Table\u0026nbsp;8, Supplementary File 1) across different waveband ranges. A locus on 1B was identified from 396 nm to 467 nm explaining 18.8% and 17.6% of variation in PC1 of waveband reflectance values and reducing DON by 2.0 ppm and 2.2 ppm at waveband bins 1 and 2, respectively (Supplementary Table\u0026nbsp;8). Other MTAs explained 0.2\u0026ndash;7.6% of variation in PC1 of reflectance values within individual waveband bins.\u003c/p\u003e \u003cp\u003eSeveral SNPs significantly associated with waveband bin PC1 values were found to be in LD (Supplementary Table\u0026nbsp;9). On chromosome 2D, the SNPs S2D_PART2_13700 and S2D_PART2_15090, were found to be in high LD with r2\u0026thinsp;=\u0026thinsp;0.88 at substantial distance of 13.9 mb (Supplementary Table\u0026nbsp;9). Weaker LD was detected between SNPs S2B_PART2_28124 and S2B_PART2_27654 on chromosome 2B at a distance of 4.69 mb (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.72), and between SNPs S1B_PART2_24837 and S1B_PART2_24652 on chromosome 1B (distance: 1.85 mb) (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.25).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePutative candidate gene identified on chromosome 2D\u003c/h2\u003e \u003cp\u003eThe SNP identified on 2D associated with PC1 of the entire hyperspectral phenome of DON infected wheat kernels and PC1 of nearly all sliding windows, S2D_PART2_15090, is located in the single exon of a 1,104 kb gene, \u003cem\u003eTraesCS2D02G524600\u003c/em\u003e, coding for a protein with an F-box domain. \u003cem\u003eTraesCS2D02G524600\u003c/em\u003e is upregulated in the spikelets and rachis in response to inoculation with \u003cem\u003eF. graminearum\u003c/em\u003e in near-isogenic lines (NILs) bearing a 2DL introgression conferring FHB resistance (Biselli et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) (Supplementary Fig.\u0026nbsp;5) and has been implicated in \u003cem\u003eFhb1\u003c/em\u003e resistance with higher expression in NILs carrying \u003cem\u003eFhb1\u003c/em\u003e (Ma et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (Supplementary Fig.\u0026nbsp;6). The expression of \u003cem\u003eTraesCS2D02G524600\u003c/em\u003e and 17 adjacent genes within a 2Mb region, 1Mb upstream and downstream, was investigated across tissues and developmental stages under \u003cem\u003eF. graminearum\u003c/em\u003e infection (Wheat Expression Browser, Ramirez-Gonzalez et al., 2021) (Supplementary Fig.\u0026nbsp;7). \u003cem\u003eTraesCS2D02G524600\u003c/em\u003e is inducible by infection with \u003cem\u003eF. graminearum\u003c/em\u003e and highly expressed in the spike at the reproductive stage, which is consistent across gene expression data sets. Only one gene in the 2Mb interval, \u003cem\u003eTraesCS2D02G524400\u003c/em\u003e, located upstream of \u003cem\u003eTraesCS2D02G524600\u003c/em\u003e, demonstrates the exact same expression profile. TraesCS2D02G524600 is a likely candidate gene for the large effect locus on 2D that reduces DON accumulation in wheat kernels during infection by \u003cem\u003eF. graminearum\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBreeding for resistance to FHB requires evaluation of the multiple components of resistance (Steiner et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Each FHB resistance component has a different relationship to DON (Buerstmayr and Lemmens, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Mesterhazy et al., 2015, Paul et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and evaluating multiple traits can lead to better selection decisions in breeding. Visual observations of FHB severity and incidence are used to develop an overall visual FHB index (Steiner et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The proportion of Fusarium damaged kernels can be estimated on samples of infected grain and a high correlation with DON has been demonstrated for this resistance component (Mesterhazy et al., 2015).\u003c/p\u003e \u003cp\u003eIn this study, we generate multiple visual FHB resistance phenotypes using the hyperspectral phenome of DON infected wheat kernels. Reflectance values at individual wavebands can be considered unique phenotypes and high correlations were found between DON and reflectance values at individual wavebands, especially at the visible light spectrum range. PC1 of the reflectance values from all wavebands compresses the entire hyperspectral phenome into a single phenotype that incorporates the information from all wavebands. The hyperspectral phenome was dissected further into 38 sliding windows and PC1 of each window was used as a separate phenotype that is correlated with DON.\u003c/p\u003e \u003cp\u003eIn this study, we identified five MTAs on chromosomes 2A, 2B, 3A, and 5A that co-localize with previously reported genomic regions conferring resistance to DON (Supplementary Table\u0026nbsp;10). Individually, each of the MTAs identified for DON content explain only 1\u0026ndash;4% of the variation in DON and reduce DON by 2.5 ppm to 4.2 ppm. FHB resistance traits can differ in their genetic architecture. Developing the hyperspectral phenome into a novel FHB resistance phenotype using PC1 of all wavebands, which is correlated to DON, led to identification of a comparably large effect locus on 2D explaining 26% of the variation in PC1 and reducing DON by 6.8 ppm. While several genomic regions were identified across waveband ranges, the 2D locus was identified using PC1 across waveband ranges, further establishing its association with the hyperspectral phenome of DON infected whet kernels. By leveraging the hyperspectral phenome as a correlated trait, we were able to identify a locus influencing the target trait, DON mycotoxin content.\u003c/p\u003e \u003cp\u003eThe large effect locus on 2D localizes to the exon of an F-box protein encoding gene and captures a large proportion of variation in the hyperspectral phenome of DON infected wheat kernels. Two SNPs were identified in the exon of this gene; however, the BLINK algorithm removes SNPs that are in LD. Multiple gene expression studies demonstrate the gene is inducible upon infection with \u003cem\u003eF. graminearum\u003c/em\u003e and is expressed exclusively in tissues of the spike and rachis as a component of the defense response. It may be possible to select for the DON-reducing allele at 2D locus in a breeding context using a Kompetitive Allele Specific PCR (KASP) marker assay (He et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEvaluation of DON production during infection by \u003cem\u003eF. graminearum\u003c/em\u003e is an integral part in developing wheat varieties with resistance to Fusarium Head Blight (FHB), which has long been done using GC/MS (Tacke and Casper, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Preparation and phenotyping of DON infected wheat kernel samples is time consuming, tedious, and labor intensive (Steiner et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This study demonstrates that hyperspectral imaging can reduce the amount of time and physical resources necessary to make selection decisions in breeding for lower DON.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials\u003c/h2\u003e \u003cp\u003eA set of 200 soft red and 114 soft white winter wheat genotypes (n\u0026thinsp;=\u0026thinsp;314), comprised of advanced breeding lines and commercial varieties (Supplementary Table\u0026nbsp;1) were used in this study. Genotypes MI14W0190 and Ambassador were considered checks FHB-resistant, low DON and FHB-susceptible, high DON checks, respectively. Wheat genotypes were planted in a misted and inoculated Fusarium screening nursery in East Lasing, MI (\u0026ordm;42.69 N, \u0026ordm;84.48W, Elevation: 264 m) in one-meter rows using a completely randomized designed with two to four replicates per genotype.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFusarium\u003c/b\u003e \u003cb\u003einoculum\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eFusarium graminearum\u003c/em\u003e cultures were collected in 2020 from Huron, Ingham, Monroe, Tuscola and Sanilac counties in Michigan, USA. Initial cultures were grown by placing infected seed in Nash-Synder Media for 5 to 7 days at room temperature. Isolates for field inoculation were cultured in spawn bags with 0.2-micron filter patch (Unicorn Bags, TX, USA) containing 1.5 kg corn kernels. Corn was soaked in deionized (DI) water for 24 to 48 hours and autoclaved three times for 90 minutes. One culture plate of a four to six days-old culture and 100 ml autoclaved deionized water were added to each spawn bag. Cultures developed over two to three weeks and were dried in biohazard hood for 48 hours at ambient temperature. Isolates from different locations were cultured separately. After drying, \u003cem\u003eF. graminearum\u003c/em\u003e grain spawn cultures from the five locations were pooled in equal proportions by weight prior to inoculation. Field inoculation was carried out five times beginning at approximately 5 weeks prior to flowering. A misting system was run throughout the nursery 10 minutes every hour for 12 hours, 6 am to 6 pm, to promote infection and disease development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDeoxynivalenol evaluation\u003c/h2\u003e \u003cp\u003eWheat heads from the middle 0.3 meter of each row were sampled separately and harvested by hand. The heads from each row were threshed together and all seeds were retained. A subsample of 10 grams from each row was ball-milled using Restch MM 400 miller (Retsch, PA, USA) to generate flour meeting the guidelines set by the US Wheat and Barley Scab Initiative (USWBI) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://scabusa.org/don_labs_umn_testinglab_protocol\u003c/span\u003e\u003cspan address=\"https://scabusa.org/don_labs_umn_testinglab_protocol\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Deoxynivalenol concentration of flour samples was determined using Gas Chromatography / Mass Spectrometry (GC/MS) at the Department of Plant Pathology, University of Minnesota.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHyperspectral image acquisition\u003c/h2\u003e \u003cp\u003eFHB-infected wheat kernels from each genotype sent for DON content measurement were imaged using, a handheld, push broom hyperspectral camera, Specim IQ (Specim, Oulo, Finland). A sample of 50 to 80 wheat kernels from each replicate of each genotype were imaged. Seeds were placed against a black background side-by-side with the white reference panel. Imaging was done inside a 51 x 51 x 51-centimeter light box (Finnhomy, USA) using the attached LED light source. The hyperspectral camera was mounted on a tripod and angled 45\u0026ordm; facing downward over the kernels. Default Recording Mode was used to capture reflectance values from 204 wavebands from 397 to 1004 nm with an integration time of 30 to 40 seconds and focus set at automatic.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eImage processing and reflectance value extraction\u003c/h2\u003e \u003cp\u003eHyperspectral images were processed using QGIS 3.10.2 (QGIS, 2020). Image files (.dat) were imported as raster layer. Rendering was carried out using multiband color with Band 088 (651.92 nm) as Red Band, Band 057 (560.30 nm) as Green Band, and Band 037 (501.72) as Blue Band. Color enhancements were set at Stretch to MinMax and normal blending mode. Raster calculation was carried out at 0.3 to 0.8 threshold. Raster calculated images were saved as GeoTIFF (.tif) file and converted to vector image (Polygonize) using default settings. To determine region of interest (wheat kernels) and remove unnecessary features, toggle editing by selecting features was used. Vectorized images with region of interest determined were saved as ESRI Shape File (.shp). Spectral reflectance values were extracted from each ESRI shape file using \u0026ldquo;raster\u0026rdquo; package (Hijmans et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) in R v4.2.2 (R Core Team, 2021) by calculating mean reflectance values in each waveband.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe normality of hyperspectral reflectance data was assessed using Shapiro-Wilk Test and variance homogeneity assumption was carried out using Levene\u0026rsquo;s Test. Wavebands with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 failed to meet normality and homogeneity assumption. To test variation among the wheat genotypes for DON content and spectral reflectance values in all 204 wavebands, ANOVA was carried out for reflectance values at wavebands meeting normality and homogeneity assumptions, otherwise non-parametric Kruskal-Wallis Rank Test was employed following the model:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ey\u0026thinsp;=\u0026thinsp;G\u0026thinsp;+\u0026thinsp;e\u003c/h2\u003e \u003cp\u003eWhere y is the DON content or spectral reflectance value of each waveband, G is the fixed effect of genotype, and e is the residual. Shapiro-Wilk Test, Levene\u0026rsquo;s Test, ANOVA F-Test, and Kruska-Wallis Rank Test were carried out in R v4.2.2 (R Core Team, 2021).\u003c/p\u003e \u003cp\u003eMeans of DON content and spectral reflectance values for two to four replicates per genotype were calculated using the \u0026ldquo;emmeans\u0026rdquo; package (Lenth et al., 2018). Pearson\u0026rsquo;s Correlation Coefficient was computed between DON means and spectral reflectance values at all individual wavebands.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal Component Analysis of wavebands\u003c/h2\u003e \u003cp\u003ePrincipal Component Analysis was carried out using reflectance values for all wavebands to dimensionally reduce the spectral data and identify wavebands potentially associated with DON. To evaluate the contribution of waveband ranges across the hyperspectral phenome of DON infected wheat kernels, we employed a \u0026ldquo;Sliding Window\u0026rdquo; approach where the first twenty wavebands were binned and subjected to Principal Component Analysis. The bin was then \u0026ldquo;slid\u0026rdquo; at five-waveband intervals and PCs were generated for the next twenty wavebands to 1004 nm for a total of 38 windows (binned wavebands). The resulting PC1 waveband reflectance values from the 38 windows were then correlated to the GC/MS-derived DON content and used as predictors of DON. PCA and correlation was carried out in R v4.2.2 (R Core Team, 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDNA isolation and genotyping\u003c/h2\u003e \u003cp\u003eTissue was collected from all genotypes evaluated and DNA was isolated according to Wiersma et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Genotyping-by-sequencing libraries were prepared according to Poland et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) scaled to a 24uL volume in 384-well format. Libraries were sequenced at 384-plex on an Illumina HiSeq 4000 instrument. Single nucleotide polymorphisms (SNPs) were called using the TASSEL 5 GBS pipeline (Glaubitz et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Reads were aligned to the RefSeq v1.0 wheat reference genome assembly (International Wheat Genome Sequencing Consortium) using default parameters. For the GBSSeqToTagDBPlugin and ProductionSNPCallerPluginV2 steps, the k-mer length was set to 64 base pairs and a minimum coverage of five reads was required for each k-mer. Default settings were used for all other steps. SNPs were initially called using all families and parents. SNPs were subsequently filtered for 0.85 call rate and 0.05 MAF.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eGenome wide association mapping\u003c/h2\u003e \u003cp\u003ePhenotypes for Genome Wide Association (GWAS) included: 1) GC/MS-derived DON content, 2) PC1 of all 204 wavebands and 3) PC1 of 38 waveband bins from the \u0026ldquo;Sliding Window\u0026rdquo; approach. GWAS was carried out using the Bayesian-information and Linkage Disequilibrium Iteratively Nested Keyway (BLINK) (Huang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) model in GAPIT v3 (Genomic Association and Prediction Integrated Tool) (Lipka et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; latest version: March 12, 2022). A total of 9,961 SNPs across all 21 chromosomes remained after filtering at minor allele frequency (MAF)\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and 0.85 call rate. To address potential population structure, three principal components were used in GWAS models with the exception of two principal components for one phenotypic input, and four principal components for four phenotypic inputs (Supplementary Table\u0026nbsp;7). Linkage disequilibrium between Marker-Trait Associations (MTAs) was investigated using TASSEL 5 (Glaubitz et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eCandidate gene identification\u003c/h2\u003e \u003cp\u003eSignificant SNPs identified in GWAS were assigned to high confidence gene models in IWGSC RefSeq Annotation V1.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.wheatgenome.org\u003c/span\u003e\u003cspan address=\"http://www.wheatgenome.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Descriptions of putative candidate genes were derived from the public wheat expression database Triticeae Multi-omics center (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://202.194.139.32\u003c/span\u003e\u003cspan address=\"http://202.194.139.32\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Ma et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Wheat Expression Browser (Ramirez-Gonzalez et al., 2021).\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eEO and JC conceptualized the study, AN designed and established FHB nursery, YD analyzed DON concentration, JC acquired hyperspectral images, AT supported the hyperspectral image processing, JC processed and analyzed hyperspectral images, JC and EO conducted statistical analyses, JC wrote the first draft of the manuscript, EO wrote portions and revised the manuscript. All authors have read and contributed to the manuscript. The authors declare no conflict of interest.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eAll data used in this study, including the raw and processed hyperspectral images, phenotypic (DON) data, and genotypic data are available as per request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge Amelia Orr, Samantha Mitchell, Dennis Pennington, Elizabeth Ross, Sadie Finegan, Maddie Pennington, and Jordan Parish for their assistance in field establishment and maintenance, and sample harvesting and preparation. The authors would also like to acknowledge Dr. Katherine Frels (University of Nebraska), Dr. Francisco Gomez (Syngenta) and Dr. Leonardo Volpato (Corteva) for their insights. This project was supported by AFRI Competitive Grant 2022-68013-36439 (WheatCAP) from the USDA-NIFA, The US Wheat and Barley Scab Initiative under USDA-ARS agreement 59-0206-2-135 and The Michigan Wheat Program agreement: 15-08-03-ES.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMirocha, C. J. \u003cem\u003eet al.\u003c/em\u003e Production of trichothecene mycotoxins by \u003cem\u003eFusarium graminearum\u003c/em\u003e and \u003cem\u003eFusarium culmorum\u003c/em\u003e on barley and wheat. Mycopathologia 128, 19\u0026ndash;23 (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForoud, N. A. \u003cem\u003eet al.\u003c/em\u003e Trichothecenes in Cereal Grains \u0026ndash; An Update. Toxins 11, 634 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMesterh\u0026aacute;zy, \u0026Aacute;. 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Theor Appl Genet 129, 31\u0026ndash;43 (2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-3954059/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3954059/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe quantitative nature of Fusarium Head Blight (FHB) resistance requires further exploration of the wheat genome to identify regions conferring resistance. In this study, we explored the application of hyperspectral imaging of Fusarium-infected wheat kernels and identify regions of the wheat genome contributing significantly to the accumulation of Deoxynivalenol (DON) mycotoxin. Strong correlations were identified between hyperspectral reflectance values for 204 wavebands in the 397 nm to 673 nm range and DON mycotoxin. Dimensionality reduction using principal components was performed for all 204 wavebands and 38 sliding windows across the range of wavebands. PC1 of all 204 wavebands explained 70% of the total variation in waveband reflectance values and was highly correlated with DON mycotoxin. PC1 was used as a phenotype in GWAS and a large effect QTL on chromosome 2D was identified for PC1 of all wavebands as well as nearly all 38 sliding windows. The allele contributing variation in PC1 values also led to a substantial reduction in DON. The 2D polymorphism affecting DON levels localized to the exon of TraesCS2D02G524600 which is upregulated in wheat spike and rachis tissues during FHB infection. This work demonstrates the value of hyperspectral imaging as a correlated trait for investigating the genetic basis of resistance and developing wheat varieties with enhanced resistance to FHB.\u003c/p\u003e","manuscriptTitle":"Genomic Regions Influencing the Hyperspectral Phenome of Deoxynivalenol Infected Wheat","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-29 16:00:28","doi":"10.21203/rs.3.rs-3954059/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-27T05:41:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-06T08:17:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-18T07:27:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-04-18T06:17:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4643b84a-e183-4e03-8694-4b8bfb4cc822","date":"2024-04-08T13:20:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"a7fbf339-837c-4ea9-9cd7-69b10ec49741","date":"2024-04-08T07:57:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6a58a7b0-1b05-47b8-a14f-9afd3af5e047","date":"2024-03-28T15:11:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-28T13:11:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-26T08:28:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-02-27T16:45:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-27T06:05:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-02-13T17:23:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ac6bf1b7-3774-4c51-83a9-f541ad978388","owner":[],"postedDate":"February 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":29013133,"name":"Biological sciences/Plant sciences/Plant stress responses/Biotic"},{"id":29013134,"name":"Biological sciences/Genetics/Genetic association study/Genome wide association studies"},{"id":29013135,"name":"Biological sciences/Genetics/Agricultural genetics"}],"tags":[],"updatedAt":"2024-09-04T17:23:39+00:00","versionOfRecord":{"articleIdentity":"rs-3954059","link":"https://doi.org/10.1038/s41598-024-69830-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-08-20 15:57:12","publishedOnDateReadable":"August 20th, 2024"},"versionCreatedAt":"2024-02-29 16:00:28","video":"","vorDoi":"10.1038/s41598-024-69830-5","vorDoiUrl":"https://doi.org/10.1038/s41598-024-69830-5","workflowStages":[]},"version":"v1","identity":"rs-3954059","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3954059","identity":"rs-3954059","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}