Image-based leaf disc assay for the rapid evaluation of genetic resistance to fire blight in apples

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To conduct QTL (Quantitative Trait Locus) studies for breeding resistant apple cultivars, phenotyping of large genetic mapping populations of apples for fire blight resistance is essential. This, however, necessitates precise, quantitative data spanning multiple years, locations, and pathogen strains. It can be time-consuming and resource-intensive to keep QTL mapping populations for apples in the field and greenhouse. This creates a bottleneck for identifying novel QTL for fire blight resistance or developing resistant cultivars. To address this challenge, we present an image-based method for rapid and accurate phenotyping fire blight resistance using apple leaf discs. This leaf disc assay demonstrates significant ( p < 0.05) percent disease area (PDA) differences in fire blight inoculations among eight apple genotypes with well-known resistance levels. Furthermore, the image-based leaf disc assay consistently shows a 40–70% difference in PDA between resistant and susceptible checks. We also report high within and across trial broad sense heritability values ranging from 0.86–0.97. We demonstrate the use of K-means clustering and best linear unbiased estimators (BLUEs) to combine multiple trials. This assay offers an efficient alternative to traditional fire blight screening methods, potentially improving our understanding of the host response and accelerating the development of resistant apple cultivars. leaf disc assay quantitative trait locus image analysis disease resistance phenotyping Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Modern crop breeding and genetic mapping studies rely on precise phenotyping of complex traits, including disease resistance. However, accurate measures of disease severity require skilled evaluators and replicated experiments across multiple years, locations, and pathogen strains/isolates (Chiang et al. 2016 ). Visual rating scales are the most used method for evaluating plant disease severity (Bock et al. 2022 ). These discrete rating scales are limited by a low throughput, high labor cost, rater biases and represent an imperfect measurement of the continuous underlying phenotypic variation (Bock et al. 2010 ; Chiang et al. 2016 ; Bock et al. 2022 ). As a result, there has been a growing interest in image-based phenotyping methods that use sensors and cameras to accurately evaluate plant traits (Li et al. 2014 ). Image-based analysis of disease symptoms can improve the standardization of rating scales, increase throughput, and lower the cost of data acquisition (Bock et al. 2008 a, Mukta and Bart 2015). Additionally, wavelengths outside the visible spectrum, such as ultra-violet, near infrared, and short-wave infrared, can measure traits beyond human capabilities (Bock et al. 2010 ; Li et al. 2014 ; Thomas et al. 2018 ). These methods are particularly valuable for the decomposition of complex traits and can have a positive impact on crop genetics research (Mukta and Bart 2015; Mir et al. 2019 ). Disease resistance is often a complex trait controlled by many additive minor effect loci with a complicated genetic architecture (Brachi et al. 2011). Therefore, discrete disease rating scales can lead to a loss of variation that makes it difficult to detect minor and moderate effect resistance QTL (Mir et al. 2019 ). Though major effect QTL can be easily observed visually, loci with a minor to moderate effect size require precise quantitative data (Desnoues et al. 2018 ). The loci often have low heritability as their effects are highly influenced by the environment, pathogen strain, and experimental factors (Desnoues et al. 2018 ). Biased and inaccurate phenotype data further compound these challenges and can result in the advancement of inferior breeding lines, insufficient experimental power, or the identification of ghost QTL (Wallin et al. 2021 ). By increasing the number of samples screened, image-based phenotyping can increase the statistical power needed to identify novel QTL for breeding (Bock et al. 2010 ). Moreover, the evaluation of more time points and pathogen strains can lead to the selective advancement of lines with more robust disease resistance (Cowger et al. 2018). This can be particularly impactful for crops where generating large population sizes is challenging. Breeding for disease resistance in long-cycle woody perennial crops like apples is a slow and difficult process. The priority of many apple breeding programs is host resistance to the most destructive bacterial disease, fire blight, caused by Erwinia amylovora (Peil et al. 2021 , Khan and Korban 2022 ). However, the development of apple populations for breeding or QTL mapping is time-consuming and requires considerable resources (Peil et al. 2021 , Khan and Korban 2022 ). This is partly due to the high number of replicates needed to accurately measure fire blight resistance (Kostick et al. 2021 ). The screening of fire blight resistance in apples is performed via controlled inoculations of either grafted plants in the greenhouse or established orchard trees (Desnoues et al. 2018 ). Manual quantitative measurements of black or brown necrotic tissue are used to estimate disease severity (Desnoues et al. 2018 ; Kostick et al. 2019 ). The inoculation of young shoots of greenhouse-grown plants is the most cost-effective and space-efficient method. This method has been effective in identifying several important fire blight resistance QTL (Khan et al. 2006 , Khan et al. 2007 , Peil et al. 2008 , Durel et al. 2009 ; Emeriewen et al. 2014 ; Emeriewen et al. 2017 ; Desnouses et al. 2018; Emeriewen et al. 2021 ). However, it is limited to single E. amylovora strain infections of vegetative tissue and may not accurately reflect the resistance level expected in the field (Peil et al. 2019 ). Field inoculations, performed by either shoot inoculation or blossom spray, provide the most biologically relevant results (Peil et al. 2019 ; Zeng et al. 2021 ). However, they are expensive, require considerable maintenance of mapping populations, and are subject to variable environmental conditions (Kostick et al. 2021 ). In both cases, inoculations take several weeks to conduct and can only be performed once or twice a year on the same plants (Emeriewen et al. 2021 ). Any more would risk long-term damage to important germplasm. These current fire blight inoculation methods are laborious and slow down identification of novel QTLs. Leaf disc assays have been proposed to screen large populations of crops for genetic mapping of disease resistance, and to overcome the challenges of phenotyping. An image-based leaf disc assay offers a highly standardized method to efficiently evaluate many genotypes for genetic mapping studies (Divilov et al. 2019; Zendler et al. 2021 ). These rapid and non-destructive disease screening methods have been successfully applied to many crops, including grape, cucumber, and potato (Leonards-Schippers et al. 1994 ; Liu Longzhou et al. 2008 ; Divilov et al. 2019; Zendler et al. 2021 ). Previous studies explored alternative fire blight assays using whole detached apple leaves (Donovan 1991 , Martinez-Bilbao et al. 2009). Martinez-Bilbao et al. (2009) found a moderate correlation (0.56) between greenhouse-inoculated plants and inoculated detached leaves of the same genotypes. These inoculations on whole leaves had limitations. These include an arbitrary visual rating system and maintaining detached leaves for 5–6 days, which can introduce confounding abiotic factors. Additionally, both studies found disease severity scores were affected by the age of the leaf, indicating the influence of ontogenic resistance (Panter and Jones 2002 ; Whalen et al. 2005; Develey-Riviere and Galiana 2007). Recently, Zendler et al. 2021 demonstrated the potential of coupling a grape downy mildew leaf disc assay with an automated image analysis pipeline to screen a genetic mapping population. In this study, we developed a non-destructive and high-throughput image-based leaf disc assay for rapidly phenotyping apple genotypes for fire blight resistance. In three replicated trials, this method consistently demonstrated a 40–70% difference in the percent disease area (PDA) between highly resistant and highly susceptible checks, indicating strong discriminatory power. We screened additional check genotypes known to have varying degrees of fire blight resistance, and the results showed significant ( p < 0.05) mean differences between high, moderate, and low levels of resistance. Materials and Methods Plant Material and Leaf Collection We selected a panel of apple genotypes comprising both wild Malus accessions and cultivars with known fire blight resistance levels. The panel included eight genotypes representing a range of fire blight resistance classes, namely highly susceptible (HS), moderately resistant (M), resistant (R) and highly resistant (HR). The HS class comprised M. domestica cv. 'Gala' (PI392303) (Harshman et al. 2017 ; Kostick et al 2019 ; Dougherty et al. 2021 ). The M class included M. domestica cv. 'Cox's Orange Pippin' (PI588853) and M. domestica cv. 'Enterprise' (PI590210), while the R class consisted of M. sieversii 'KAZ 95 18 − 14' (PI657054) and M. hybrid 'KAZ 96 01–03' (PI657085) (Khan et al. 2006 ; Khan et al. 2007 ; Harshman et al. 2017 ; van de Weg et al. 2018 ; Kostick et al. 2019 ; Dougherty et al. 2021 ). The HR class comprised M. hybrid 'KAZ 96 08-01P-37' (PI657115), M. floribunda 'Floribunda 821' (PI589827), and M. × robusta 'Robusta 5' (PI588825) (Peil et al. 2007; Durel et al. 2009 ; Harshman et al. 2017 ; Dougherty et al. 2021 ). Leaves were collected from healthy, actively growing apple trees from the McCarthy Research Farm, at the USDA-PGRU (Plant Genetic Resources Unit) located at 2865 Co Rd 6, Geneva, NY 14456. Leaf collection was conducted between June and July 2023. Only young leaves with a length of 3-5cm and a slightly glossy cuticle were selected for the assay. The leaves were detached at the petiole, placed in labeled plastic Ziploc bags, and transported in a cooler with an ice pack. The total collection and transport time was approximately one hour. Prior to use, the leaves were cleaned with a 1% bleach solution by submerging for about 20 seconds and rinsed with deionized water. They were then left to dry on a clean paper towel for five minutes. All equipment and surfaces were sterilized using 70% ethanol. Using an 8mm diameter leather punch, 1–2 leaf discs were cut from each leaf (Fig. 1 A). Leaf discs were immediately plated in columns grouped by genotype on the imaging tray after cutting using a grid template to separate each disc into 2cm 2 cells (Fig. 1 A). Image Data Collection Custom acrylic scanner trays (TAP Plastics; San Leano, CA) specifically designed for the Epson 12000XL flatbed scanner were utilized for the assay and imaging. The dimensions of the tray are 40cm × 30cm × 2cm. Each tray was thoroughly sterilized with a 70% ethanol solution before use. A 300ml 1% water agar solution was prepared in advance and poured evenly into the tray. Any bubbles formed during the pouring process were removed with a pipette tip to ensure that the leaf discs were not obscured during imaging. Erwinia amylovora cultures were obtained from glycerol stock stored at -80°C. The plates were streaked 16 hours prior to inoculation on LB media and incubated at 28°C. The bacterial colonies were washed from the plates and prepared as a liquid bacterial suspension in sterile type I water at a concentration of 1×10 9 CFU/ml. The fire blight inoculum was prepared as a liquid suspension using the aggressive Canadian strain of E. amlyovora , Ea4001. 25ml of the fire blight inoculum was evenly sprayed across all samples using a 3 oz. manual spray bottle (Good to Go, Navajo Inc; Denver, CO) over the entire tray from approximately 5 inches away. The tray was then covered with plastic cling wrap to maintain high humidity and incubated in the dark for 48 hours at 25°C. The trays were imaged at 48 hours post inoculation (hpi) using an Epson 12000XL flatbed scanner and Epson Scan 2 app (Fig. 1 B). The main settings were adjusted to document source as transparency unit, document type as color positive film, image type as 48-bit color, resolution at 700dpi and scanning quality as high. The color was corrected by setting the brightness to 100%, contrast to 50%, and saturation to 100%, while all other parameters were set to default. Image Analysis To preprocess the images, we used Fiji version 2.3.1 (Shindelin et al. 2012). We cropped the image edges to remove the tray and used the paint tool to eliminate any extraneous objects (dirt, leaf debris, double-stacked discs). The resulting images were then cropped to isolate each column containing a single genotype, and the .jpeg file names were annotated with the date, experiment, and genotype. For image analysis, a custom python script was developed using the OpenCV and PlantCV python libraries as the foundation (Gehan et al. 2017). The code is available on GitHub at the following link: github-link . Additional documentation can be found at https://plantcv.readthedocs.io/en/latest/naive_bayes_classifier/ . The script applies a threshold filter to the grayscale images of the leaf disc matrix, creates binary masks, segments the leaf discs, and crops each disc to create a single image (Fig. 1 C). Pixels in each single-disc image were then classified using a naïve Bayes classifier, which was trained with a text file containing R, G, B pixel data using Fiji Pixel Inspector. The file consists of 20 pixels of training data for each of the four classes: healthy (H), chlorotic (C), necrotic (N), and white background (WB). The classifier uses a probability density function to bin pixels into the appropriate category. The resulting image mask recolors the leaf disc image so that H pixels appear green, C pixels appear yellow, N pixels appear red, and WB pixels appear white. The trait analyzed in our study is the percent disease area (PDA), which is calculated as the percentage of N pixels to the total area of the leaf disc (N + C + D). Before generating a .csv dataset with the image names, replicate number, and disease severity scores, the output images were checked to ensure that no unrelated objects in the tray were analyzed. Statistical Analysis All statistics were performed in R (version 4.0.1; R Core Team, 2021 ). One-way analysis of variance (ANOVA) followed by Turkey-Kramer multiple comparison test was performed to compare the PDA of multiple groups (Fig. 2 ). To check the normality of residuals QQ plots and Shapiro-Wilks tests were used. To test the homogeneity of variances across genotypes and trials, Levene’s Test was applied with the leveneTest() function from the R package ‘Car’ (Fox et al. 2019). To satisfy the assumptions of normality of residuals and homogeneity of variances, a BoxCox transformation was applied to the PDA data with a lambda value of 0.3. Broad sense heritability (H 2 ) was calculated using Eq. 1 (Calenge et al. 2005 ; Kruijer et al. 2014 ). The terms are defined as genetic variance ( \({{\sigma }}_{G}^{2}\) ), environmental variance ( \({{\sigma }}_{E}^{2}\) ), and average number of replicates ( \({n}_{rep}\) ). Variance components were estimated by calculating the mean square (MS) error of genotype and residual error from ANOVA, where \({{\sigma }}_{G}^{2}\) = MS(genotype) – MS(residual error) / \({n}_{rep}\) and \({{\sigma }}_{E}^{2}\) = MS(residual error) (Kruijer et al. 2014 ). \({H}^{2}= \frac{{{\sigma }}_{G}^{2}}{{{\sigma }}_{G}^{2}+{{\sigma }}_{E}^{2} }\) [1] Multi-trial PDA BLUEs of response variable ( \({Y}_{ijk}\) ) were estimated using Eq. 2 with a mixed linear model fit by restricted maximum likelihood (REML) using the lme4 package (Bates et al. 2015). The response variable, \({Y}_{ijk}\) , is the PDA value for the i th genotype, j th trial, and k th replicate. \({Y}_{ijk}= \mu + {G}_{i}+ {T}_{j}+ {GT}_{ij}+ {\epsilon }_{ijk}\) [2] The model terms include grand mean ( \(\mu\) ), trial treatment effect ( \({T}_{j}\) ), genotype ( \({G}_{i}\) ) effect, genotype by trial interaction ( \({GT}_{ij}\) ), and residual error ( \({\epsilon }_{ijk}\) ). The model term \({G}_{i}\) was fitted as a fixed effect, while all other terms were fitted as random effects. Trial was defined as an independent replicated experiment (A-C) each performed on a single tray. The BLUE values were used to rank genotypes and compare resistance levels among checks. For K-mean clustering, the data was formatted to have each genotype as a row name and the PDA value of each trial as a separate column. The randomized seed was set to 1 and the PDA values were divided by 100 to get scaled values from 0–1. The Kmeans() function from the R package ‘Stats’ was used to calculate the K centers and cluster values (R Core Team, 2022). A custom R script was used to loop through center values 1–6 to find the best fitting number of K by calculating the total within-cluster sum of squares (WSS) values. The best fitting K value was determined by plotting K by WSS and finding the value K associated with the inflection point in the curve. We used the Kmeans() function to calculate the cluster positions for the leaf disc assay data with the best fitting K value and a random starting set of centroids (nstart) as 25. We plotted the clusters with the fviz_cluster() function and ellipses from the factoextra R package. We assessed the disease rating of each individual by examining the cluster membership in relation to the susceptible and resistant checks. Results Phenotypic Quality and Repeatability There was a wide variation in percent disease area (PDA) values across three trials ranging from 0.5–95%. Tukey Kramer multiple comparison test analysis showed that across trials there were significant ( p < 0.05) mean differences between HS, M, R, and HR classes, forming four distinct groups. Within trials these groups were less consistent with only the HS and HR classes having consistent mean differences. The groups with the greatest difference in PDA mean across all trials were between the highly susceptible check ‘Gala’ in the ‘a’ group and the highly resistant check ‘Robusta 5’ in the ‘d’ group (Fig. 2 ). The difference in PDA values between ‘Gala’ and ‘Robusta 5’ within trials A, B, and C were 38%, 76%, 43%, respectively (Fig. 2 ). Across all trials the PDA difference between ‘Gala’ and ‘Robusta 5’ was 53% (Fig. 2 ). The ‘b’ group was comprised of all genotypes in the M and R classes (Fig. 2 ). These included ‘Enterprise’, ‘Cox’s Orange Pippin’, M. sieversii PI657054, and M. hybrid PI657085. The ‘c’ group was formed from the HR genotypes ‘Floribunda 821’ and M. hybrid PI657115 (Fig. 2 ). As a measure of repeatability, the broad sense heritability (H 2 ) was calculated within and across trials. The within-trial broad-sense heritability of trials A, B, and C were 0.93, 0.95, and 0.86, respectively. The across-trial broad-sense heritability was 0.97. This shows the environmental/experimental effects are minimal, and the data is useful for predicting genetic effects. The Pearson correlation coefficient for trial A × B was 0.7, A × C was 0.8, and B × C was 0.86. The pairwise Pearson correlations showed each trial was significantly ( p < 0.05) correlated to each other. Across an average replicate number of 14 discs per genotype, the standard deviation in PDA values was on average 17 with a maximum of 31 (Supp. Table 1). The various metrics of repeatability and data quality indicate that the phenotype data generated from the leaf disc assay method is reliable. Multi-Trial Genotype Classification The K-mean cluster analysis combining trials A-C was able to capture 96.9% of the phenotypic variation across three replicated trials. The first two dimensions captured 86.4% and 10.5% of the variation in the data, respectively (Fig. 3 ). Three clusters formed based on the mean PDA differences (Fig. 3 ). Cluster 1 was formed with only the genotype ‘Gala’ representing a highly susceptible group with a mean PDA of 57.9%. Cluster 2 contained each genotype that was classified as M or R including M. sieversii PI657054, M. hybrid PI657085, Cox’s Orange Pippin, and Enterprise. The mean PDA for that cluster was 32.6%. Cluster 3 was formed with all HR genotypes, M. hybrid PI657115, Floribunda 821, and Robusta 5. The mean PDA for cluster 3 is 13.5%. These classes follow closely with the Tukey Kramer results where four significantly ( p < 0.05) distinct groups were identified that followed the rank orders of the resistance classes (Fig. 2 ). The only difference is that with the K-mean clustering the M and R classes were combined into a single group. Each genotype was ranked based on the scaled PDA BLUE value (Fig. 4 ). The ranked values largely matched the results from the prior analyses. The pre-assigned classes were ordered correctly based on expected level of fire blight resistance (Fig. 4 ). The genotypes in the HS group and the HR group were ranked on both extreme ends of the distribution ranging from approximately − 4 to + 1. Genotypes from the M and R class have highly similar values around − 1 ranked accurately in the middle of the distribution. ‘Floribunda 821’ and M. hybrid PI657115 are both in the HR class and have similar PDA BLUE values between − 2 and − 3. Discussion We have developed an image-based leaf disc assay that can greatly reduce the time and labor required for fire blight resistance phenotyping in apples for genetic research and breeding. The analytical pipeline of the assay utilizes standardized, high-resolution RGB images to estimate the percentage of fire blight lesions. Leaf disc assays have been popular in studying host-pathogen interactions but have not been widely used for genetic mapping and breeding (Kortemkamp 2006; Gomes et al. 2019 ; Buscaill et al. 2020), except for fungal diseases (Leonards-Schippers et al. 1994 ; Liu Longzhou et al. 2008 ; Divilov et al. 2019; Zendler et al. 2021 ; Hernandez et al. 2022). Detached leaf assays have been developed for evaluating resistance to bacterial diseases in apple, pear, cherry, and citrus (Donovan 1991 ; Bedford et al. 2002; Moragrega et al. 2003 ; Martinez-Bilbao et al. 2009; Francis et al. 2010 ). However, all these studies primarily used subjective visual rating scales to quantify disease severity (Bock et al. 2022 ). Martinez-Bilbao et al. (2009) found a moderate correlation (0.56) between fire blight inoculations of shoots and detached leaves of the same cultivars when using a visual rating scale, likely due to the bias and subjectivity in severity scoring. We have shown that this newly developed assay had a high accuracy and requires minimal technical expertise to evaluate fire blight response in the 48-hours. We were able to correctly classify ( P < 0.05) eight genotypes with previously described resistance to their established classes, based on percent fire blight infection estimated using this assay. The long turn-around of detached leaf assays for disease response evaluation can potentially introduce confounding abiotic factors (Donovan 1991 , Martinez-Bilbao et al. 2009). Detached leaf assays are time-sensitive, since the leaf rapidly starts degrading upon detachment and reactive oxygen species (ROS) begin accumulating (Wang et al. 2011). Given that ROS production is common to both apple leaf senescence and the Erwinia amylovora host defense response, cross-tolerance plays a role in the assay results (Perez and Brown 2014 ; Campa et al. 2019 ; Kharadi et al. 2021 ). Wang et al. (2011) found significant ( P < 0.05) chlorophyll loss and H 2 O 2 accumulation in detached apple leaves beginning after 4 days for the cultivar M. domestica cv. ‘Hanfu’. Although we did not observe abiotic stress on the leaf discs, we cannot rule out the impact of stress from detaching and injuring leaves, as well as physiological differences of the leaves used, on the assay. The PDA value is based only on pixels classified as necrotic, directly capturing the hypersensitive response caused by effector-triggered immunity of the host, while excluding chlorotic pixels that are not specific to E. amylovora symptoms (Yuan et al. 2021). The necrotic symptoms observed include rapidly growing orange-brown necrotic lesions that initially colonize in the midrib and produce ooze (Kharadi et al. 2021 ). The high broad-sense heritability (0.86–0.97) indicates that the experimental/environmental factors ( i.e. , field collection of leaves) contribute minimal variation to the PDA. These broad-sense heritability estimates are comparable to greenhouse screenings of fire blight QTL mapping populations (0.71–0.96) and higher than fire blight field inoculations (0.44–0.75) (Calenge et al. 2005 ; Khan et al. 2006 ; Durel et al. 2009 ; Desnoues et al. 2018 ; Kostick et al. 2021 ). Our results also showed that non-hierarchical cluster analysis using K-means clustering was successful in capturing 96.9% of the phenotypic variance in the first two dimensions, with clusters largely corresponding to anticipated resistance categories, including HS, M, and R classes combined, and HR. Kostick et al. ( 2019 ) utilized a similar K-means clustering approach to classify fire blight resistance of 94 apple cultivars, resulting in three clusters that categorized cultivars into susceptible, intermediate, and resistant classes. ‘Gala’ and ‘Enterprise’ were categorized as HS and M-HR, respectively, matching our classifications from the leaf disc assay. However, Kostick et al. ( 2019 ) classified 'Cox's Orange Pippin' as moderately susceptible, while it was clustered in the M-R group in our study. This may be due to environmental differences between these studies that directly impact this resistance locus. It is also important to note that PDA values are not absolute, but rather relative metrics meant to be ranked with HS and HR checks in every experiment. Our results and conclusions were based on only eight genotypes, and screening more genotypes would help to better understand the accuracy and sensitivity of this assay. For genotypes with unknown resistance levels, it is recommended to include checks and control genotypes similar to those used by Zendler et al. ( 2021 ). Best linear unbiased estimators (BLUEs) of the genotype effects were able to correctly rank genotypes based on their expected levels of fire blight resistance across multiple trials. A mixed model approach is useful for combining data from multiple trials to estimate genotype effects while accounting for environmental/experimental variation between trials (Bernardo et al. 2020). BLUEs are widely used for estimating breeding values of diverse populations screened for disease resistance (Steiner et al. 2019; Abdelraheem et al. 2020 ). This method is also well-suited for unbalanced data, which is a likely case for leaf disc assays where the leaves available for each genotype may vary. For studies that plan to incorporate multiple strains, severity metrics, and time-points, using a mixed model approach will improve the integration of this data to estimate genotypic effects more accurately (Juliana et al. 2019; Bernardo et al. 2020). Conclusion The image-based leaf disc assay is an effective method to phenotype fire blight resistance in multiple genetic mapping populations with reduced time and labor. Additionally, it requires minimal technical expertise and familiarity with fire blight, allowing for precise quantitative data to be obtained through the 48-hour inoculation and image analysis protocols. Our research further demonstrates that combining trial data from multiple experiments using multi-trial best linear unbiased estimators (BLUEs) and K-means clustering can account for random experimental variation and improve classification of fire blight resistant apple genotypes. Therefore, this assay presents a valuable alternative for phenotype fire blight in apples for breeding and mapping of fire blight resistance QTL. Declarations Acknowledgements This research was funded by the New York State Department of Agriculture & Markets, Apple Research & Development Program (ARDP), grant number: CM04068AQ, Khan. We would like to acknowledge the USDA (United States Department of Agriculture) Plant Genetic Resources Unit (PGRU) in Geneva, New York, for maintaining the US national Malus collection, where all plant material was collected. Competing interests The authors declare that they have no competing interests. Author Contribution A.K., conceptualized, designed, and managed the project. R.T., and D.H., done the experiments, analysis and interpretation. R.T., drafted the manuscript. R.T., and D.H., K.R., and A.K., revised, and finalized the manuscript. All authors read and approved the final version. References Abdelraheem, A., Elassbli, H., Zhu, Y., Kuraparthy, V., Hinze, L., Stelly, D., Wedegaertner, T., & Zhang, J. (2020). A genome-wide association study uncovers consistent quantitative trait loci for resistance to Verticillium wilt and Fusarium wilt race 4 in the US Upland cotton. 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Plant Pathol. 103:13–24 Available at: https://link.springer.com/article/10.1007/s42161-020-00675-3 Supplementary Files SupplementaryTable.docx Cite Share Download PDF Status: Published Journal Publication published 24 Aug, 2023 Read the published version in European Journal of Plant Pathology → Version 1 posted Editorial decision: Major revisions 28 Jun, 2023 Reviewers agreed at journal 28 Apr, 2023 Reviewers invited by journal 28 Apr, 2023 Editor invited by journal 26 Apr, 2023 Editor assigned by journal 19 Apr, 2023 First submitted to journal 17 Apr, 2023 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. 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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-2829015","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":195904442,"identity":"10d2d515-3763-49b1-a1d9-4fac5c524c1b","order_by":0,"name":"Richard Tegtmeier","email":"","orcid":"","institution":"Cornell University College of Agriculture and Life Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Richard","middleName":"","lastName":"Tegtmeier","suffix":""},{"id":195904443,"identity":"4e18ffdd-054f-45ae-b864-faf589afc495","order_by":1,"name":"David Hickok","email":"","orcid":"","institution":"Cornell University College of Agriculture and Life Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Hickok","suffix":""},{"id":195904444,"identity":"f31f36d6-15c0-47d1-b4ab-3388d14c7201","order_by":2,"name":"Kelly Robins","email":"","orcid":"","institution":"Cornell University College of Agriculture and Life Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kelly","middleName":"","lastName":"Robins","suffix":""},{"id":195904445,"identity":"498b9abc-4c50-4e4f-a668-7efa6c0ad597","order_by":3,"name":"Awais Khan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYJCCAwwMCQkMDMwHwDw+diDBQ5wWtgQwj42ZCC0MEC08BsRp4Z+R/PDgD4a0PP7ZPR8/fNxzRw6ohfHB2zbcWiRupBkc5mHIKZa4c3az5Ixnz4yBWpgN5+LRwnA7weAwA0NFYsON3G3MPAcOJ7YxM7BJ8+LRIn87/QPQYRWJ82/kPANpqQdqYf+NT4vB7RyDA0CHJW64kcMG0pIAdBgbMz4thvffFBzmMUhL3HgjzVhyxoHDhm3MjM2Sc87h1iJ35vjmjz8qkhPn3Uh++OHDgcPy/OzNBz+8KcPjfYjzUHiMDYTUj4JRMApGwSggAACi8lOFA0j3MQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-0424-7727","institution":"Cornell University College of Agriculture and Life Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Awais","middleName":"","lastName":"Khan","suffix":""}],"badges":[],"createdAt":"2023-04-17 22:42:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2829015/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2829015/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10658-023-02750-8","type":"published","date":"2023-08-24T15:02:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":36590552,"identity":"96588d8a-c97f-4dd2-9c0d-0aca467e19bb","added_by":"auto","created_at":"2023-05-03 21:13:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":437377,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical representation of the major steps of the leaf disc assay to phenotype fire blight resistance in apples. A) Apple leaves on the upper portion of the panel demonstrate the age of the leaf and number of leaf discs taken from each sample. The bottom portion shows the leaf discs plated on a water agar tray to be sprayed with a \u003cem\u003eErwinia amylovora\u003c/em\u003e liquid inoculum. B) This panel shows the flatbed scanner used for image data collection and an image of the leaf disc matrix that is used for image analysis. C) The steps of segmenting each leaf disc in the matrix is shown in the upper portion of the panel. Two representative leaf discs with a high and low amount of disease severity are shown on the bottom portion of the panel. To the right of each disc is the pseudo-colored classified mask (red = necrosis, yellow = chlorosis, green = healthy).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-2829015/v1/dd57c6f3091110e9d6a8cc76.png"},{"id":36590551,"identity":"8cf70698-aaca-4f64-a5e7-69ee7a3eab56","added_by":"auto","created_at":"2023-05-03 21:13:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26109,"visible":true,"origin":"","legend":"\u003cp\u003eHorizontal barplots with the mean percent disease area (PDA) values for fire blight infection in eight apple genotypes with standard error bars on the x-axis and the genotype on the y-axis. Each color represents a different replicated trial (A-orange; B-blue; C-green; All Trials-grey). Each genotype name is labeled with the abbreviated fire blight resistance class in parentheses (HS = highly susceptible; M = Moderate; R = resistant; HR = highly resistant). Tukey Kramer groups are shown with letters at the end of each bar representing statistical significance (p \u0026lt; 0.05) within a group. PI number for \u003cem\u003eMalus sieversii\u003c/em\u003eand \u003cem\u003eMalus hybrid\u003c/em\u003e accessions is the plant introduction (unique identifier) from USDA-GRIN database.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-2829015/v1/0c8f6f934473708eb9ebf244.png"},{"id":36590555,"identity":"2d4d690a-ebb5-4b26-8ed0-cc4b7121293d","added_by":"auto","created_at":"2023-05-03 21:13:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":19887,"visible":true,"origin":"","legend":"\u003cp\u003eApple genotypes screened for fire blight resistance with the leaf disc assay grouped into three clusters via K-mean clustering. The K centers and grouping of genotypes is a based on the percent disease area (PDA) values of the three replicated trials. The data was plotted on a biplot where the x and y axis are the first and second dimension, respectively. Next to each axis name, in parentheses, is the percent variance explained by that dimension. Each genotype name is labeled with the resistance class in parentheses (HS = highly susceptible; M = Moderate; R = resistant; HR = highly resistant). Cluster 1 (n=1) is red with circle points, cluster 2 (n=4) is blue with triangle points, and cluster 3 (n=3) is green with square points. PI number for \u003cem\u003eMalus sieversii\u003c/em\u003e and \u003cem\u003eMalus hybrid\u003c/em\u003e accessions is the plant introduction (unique identifier) from USDA-GRIN database.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-2829015/v1/f96e773834f7357788c79328.png"},{"id":36590554,"identity":"c43d0297-b533-4b32-bb89-1d49a4f45310","added_by":"auto","created_at":"2023-05-03 21:13:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18853,"visible":true,"origin":"","legend":"\u003cp\u003eHorizontal barplots with the percent disease area (PDA) BLUE value for fire blight infection in eight apple genotypes on the x-axis and the genotype on the y-axis. Next to each genotype name is the abbreviated fire blight resistance class in parentheses (HS = highly susceptible; M = Moderate; R = resistant; HR = highly resistant). PI number for \u003cem\u003eMalus sieversii\u003c/em\u003e and \u003cem\u003eMalus hybrid\u003c/em\u003eaccessions is the plant introduction (unique identifier) from USDA-GRIN database.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-2829015/v1/ec506d13f7b72c8580d55f84.png"},{"id":42781139,"identity":"310ece14-049a-4253-9ee0-f444beb69638","added_by":"auto","created_at":"2023-09-07 15:08:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":918687,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2829015/v1/b2ad602d-b142-49d7-990a-faa189e0f2f2.pdf"},{"id":36590885,"identity":"17709639-852c-4376-9cf3-d6a2a86096cf","added_by":"auto","created_at":"2023-05-03 21:21:12","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17897,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-2829015/v1/7ebf421df130eb8d12965060.docx"}],"financialInterests":"","formattedTitle":"Image-based leaf disc assay for the rapid evaluation of genetic resistance to fire blight in apples","fulltext":[{"header":"Introduction","content":"\u003cp\u003eModern crop breeding and genetic mapping studies rely on precise phenotyping of complex traits, including disease resistance. However, accurate measures of disease severity require skilled evaluators and replicated experiments across multiple years, locations, and pathogen strains/isolates (Chiang et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Visual rating scales are the most used method for evaluating plant disease severity (Bock et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These discrete rating scales are limited by a low throughput, high labor cost, rater biases and represent an imperfect measurement of the continuous underlying phenotypic variation (Bock et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chiang et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bock et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As a result, there has been a growing interest in image-based phenotyping methods that use sensors and cameras to accurately evaluate plant traits (Li et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Image-based analysis of disease symptoms can improve the standardization of rating scales, increase throughput, and lower the cost of data acquisition (Bock et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2008\u003c/span\u003ea, Mukta and Bart 2015). Additionally, wavelengths outside the visible spectrum, such as ultra-violet, near infrared, and short-wave infrared, can measure traits beyond human capabilities (Bock et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Thomas et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These methods are particularly valuable for the decomposition of complex traits and can have a positive impact on crop genetics research (Mukta and Bart 2015; Mir et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDisease resistance is often a complex trait controlled by many additive minor effect loci with a complicated genetic architecture (Brachi et al. 2011). Therefore, discrete disease rating scales can lead to a loss of variation that makes it difficult to detect minor and moderate effect resistance QTL (Mir et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Though major effect QTL can be easily observed visually, loci with a minor to moderate effect size require precise quantitative data (Desnoues et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The loci often have low heritability as their effects are highly influenced by the environment, pathogen strain, and experimental factors (Desnoues et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Biased and inaccurate phenotype data further compound these challenges and can result in the advancement of inferior breeding lines, insufficient experimental power, or the identification of ghost QTL (Wallin et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). By increasing the number of samples screened, image-based phenotyping can increase the statistical power needed to identify novel QTL for breeding (Bock et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Moreover, the evaluation of more time points and pathogen strains can lead to the selective advancement of lines with more robust disease resistance (Cowger et al. 2018). This can be particularly impactful for crops where generating large population sizes is challenging.\u003c/p\u003e \u003cp\u003eBreeding for disease resistance in long-cycle woody perennial crops like apples is a slow and difficult process. The priority of many apple breeding programs is host resistance to the most destructive bacterial disease, fire blight, caused by \u003cem\u003eErwinia amylovora\u003c/em\u003e (Peil et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Khan and Korban \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the development of apple populations for breeding or QTL mapping is time-consuming and requires considerable resources (Peil et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Khan and Korban \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This is partly due to the high number of replicates needed to accurately measure fire blight resistance (Kostick et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The screening of fire blight resistance in apples is performed \u003cem\u003evia\u003c/em\u003e controlled inoculations of either grafted plants in the greenhouse or established orchard trees (Desnoues et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Manual quantitative measurements of black or brown necrotic tissue are used to estimate disease severity (Desnoues et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kostick et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The inoculation of young shoots of greenhouse-grown plants is the most cost-effective and space-efficient method. This method has been effective in identifying several important fire blight resistance QTL (Khan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Khan et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Peil et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Durel et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Emeriewen et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Emeriewen et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Desnouses et al. 2018; Emeriewen et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, it is limited to single \u003cem\u003eE. amylovora\u003c/em\u003e strain infections of vegetative tissue and may not accurately reflect the resistance level expected in the field (Peil et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Field inoculations, performed by either shoot inoculation or blossom spray, provide the most biologically relevant results (Peil et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zeng et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, they are expensive, require considerable maintenance of mapping populations, and are subject to variable environmental conditions (Kostick et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In both cases, inoculations take several weeks to conduct and can only be performed once or twice a year on the same plants (Emeriewen et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Any more would risk long-term damage to important germplasm. These current fire blight inoculation methods are laborious and slow down identification of novel QTLs.\u003c/p\u003e \u003cp\u003eLeaf disc assays have been proposed to screen large populations of crops for genetic mapping of disease resistance, and to overcome the challenges of phenotyping. An image-based leaf disc assay offers a highly standardized method to efficiently evaluate many genotypes for genetic mapping studies (Divilov et al. 2019; Zendler et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These rapid and non-destructive disease screening methods have been successfully applied to many crops, including grape, cucumber, and potato (Leonards-Schippers et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Liu Longzhou et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Divilov et al. 2019; Zendler et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Previous studies explored alternative fire blight assays using whole detached apple leaves (Donovan \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1991\u003c/span\u003e, Martinez-Bilbao et al. 2009). Martinez-Bilbao et al. (2009) found a moderate correlation (0.56) between greenhouse-inoculated plants and inoculated detached leaves of the same genotypes. These inoculations on whole leaves had limitations. These include an arbitrary visual rating system and maintaining detached leaves for 5\u0026ndash;6 days, which can introduce confounding abiotic factors. Additionally, both studies found disease severity scores were affected by the age of the leaf, indicating the influence of ontogenic resistance (Panter and Jones \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Whalen et al. 2005; Develey-Riviere and Galiana 2007). Recently, Zendler et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e demonstrated the potential of coupling a grape downy mildew leaf disc assay with an automated image analysis pipeline to screen a genetic mapping population.\u003c/p\u003e \u003cp\u003eIn this study, we developed a non-destructive and high-throughput image-based leaf disc assay for rapidly phenotyping apple genotypes for fire blight resistance. In three replicated trials, this method consistently demonstrated a 40\u0026ndash;70% difference in the percent disease area (PDA) between highly resistant and highly susceptible checks, indicating strong discriminatory power. We screened additional check genotypes known to have varying degrees of fire blight resistance, and the results showed significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) mean differences between high, moderate, and low levels of resistance.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant Material and Leaf Collection\u003c/h2\u003e \u003cp\u003eWe selected a panel of apple genotypes comprising both wild \u003cem\u003eMalus\u003c/em\u003e accessions and cultivars with known fire blight resistance levels. The panel included eight genotypes representing a range of fire blight resistance classes, namely highly susceptible (HS), moderately resistant (M), resistant (R) and highly resistant (HR). The HS class comprised \u003cem\u003eM. domestica\u003c/em\u003e cv. 'Gala' (PI392303) (Harshman et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kostick et al \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dougherty et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The M class included \u003cem\u003eM. domestica\u003c/em\u003e cv. 'Cox's Orange Pippin' (PI588853) and \u003cem\u003eM. domestica\u003c/em\u003e cv. 'Enterprise' (PI590210), while the R class consisted of \u003cem\u003eM. sieversii\u003c/em\u003e 'KAZ 95 18\u0026thinsp;\u0026minus;\u0026thinsp;14' (PI657054) and \u003cem\u003eM. hybrid\u003c/em\u003e 'KAZ 96 01\u0026ndash;03' (PI657085) (Khan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Harshman et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; van de Weg et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kostick et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dougherty et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The HR class comprised \u003cem\u003eM. hybrid\u003c/em\u003e 'KAZ 96 08-01P-37' (PI657115), \u003cem\u003eM. floribunda\u003c/em\u003e 'Floribunda 821' (PI589827), and \u003cem\u003eM. \u0026times; robusta\u003c/em\u003e 'Robusta 5' (PI588825) (Peil et al. 2007; Durel et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Harshman et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dougherty et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLeaves were collected from healthy, actively growing apple trees from the McCarthy Research Farm, at the USDA-PGRU (Plant Genetic Resources Unit) located at 2865 Co Rd 6, Geneva, NY 14456. Leaf collection was conducted between June and July 2023. Only young leaves with a length of 3-5cm and a slightly glossy cuticle were selected for the assay. The leaves were detached at the petiole, placed in labeled plastic Ziploc bags, and transported in a cooler with an ice pack. The total collection and transport time was approximately one hour. Prior to use, the leaves were cleaned with a 1% bleach solution by submerging for about 20 seconds and rinsed with deionized water. They were then left to dry on a clean paper towel for five minutes. All equipment and surfaces were sterilized using 70% ethanol. Using an 8mm diameter leather punch, 1\u0026ndash;2 leaf discs were cut from each leaf (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Leaf discs were immediately plated in columns grouped by genotype on the imaging tray after cutting using a grid template to separate each disc into 2cm\u003csup\u003e2\u003c/sup\u003e cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eImage Data Collection\u003c/h2\u003e \u003cp\u003eCustom acrylic scanner trays (TAP Plastics; San Leano, CA) specifically designed for the Epson 12000XL flatbed scanner were utilized for the assay and imaging. The dimensions of the tray are 40cm \u0026times; 30cm \u0026times; 2cm. Each tray was thoroughly sterilized with a 70% ethanol solution before use. A 300ml 1% water agar solution was prepared in advance and poured evenly into the tray. Any bubbles formed during the pouring process were removed with a pipette tip to ensure that the leaf discs were not obscured during imaging.\u003c/p\u003e \u003cp\u003e \u003cem\u003eErwinia amylovora\u003c/em\u003e cultures were obtained from glycerol stock stored at -80\u0026deg;C. The plates were streaked 16 hours prior to inoculation on LB media and incubated at 28\u0026deg;C. The bacterial colonies were washed from the plates and prepared as a liquid bacterial suspension in sterile type I water at a concentration of 1\u0026times;10\u003csup\u003e9\u003c/sup\u003e CFU/ml. The fire blight inoculum was prepared as a liquid suspension using the aggressive Canadian strain of \u003cem\u003eE. amlyovora\u003c/em\u003e, Ea4001. 25ml of the fire blight inoculum was evenly sprayed across all samples using a 3 oz. manual spray bottle (Good to Go, Navajo Inc; Denver, CO) over the entire tray from approximately 5 inches away. The tray was then covered with plastic cling wrap to maintain high humidity and incubated in the dark for 48 hours at 25\u0026deg;C.\u003c/p\u003e \u003cp\u003eThe trays were imaged at 48 hours post inoculation (hpi) using an Epson 12000XL flatbed scanner and Epson Scan 2 app (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The main settings were adjusted to document source as transparency unit, document type as color positive film, image type as 48-bit color, resolution at 700dpi and scanning quality as high. The color was corrected by setting the brightness to 100%, contrast to 50%, and saturation to 100%, while all other parameters were set to default.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eImage Analysis\u003c/h2\u003e \u003cp\u003eTo preprocess the images, we used Fiji version 2.3.1 (Shindelin et al. 2012). We cropped the image edges to remove the tray and used the paint tool to eliminate any extraneous objects (dirt, leaf debris, double-stacked discs). The resulting images were then cropped to isolate each column containing a single genotype, and the .jpeg file names were annotated with the date, experiment, and genotype.\u003c/p\u003e \u003cp\u003eFor image analysis, a custom python script was developed using the OpenCV and PlantCV python libraries as the foundation (Gehan et al. 2017). The code is available on GitHub at the following link: \u003cem\u003egithub-link\u003c/em\u003e. Additional documentation can be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://plantcv.readthedocs.io/en/latest/naive_bayes_classifier/\u003c/span\u003e\u003cspan address=\"https://plantcv.readthedocs.io/en/latest/naive_bayes_classifier/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The script applies a threshold filter to the grayscale images of the leaf disc matrix, creates binary masks, segments the leaf discs, and crops each disc to create a single image (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Pixels in each single-disc image were then classified using a na\u0026iuml;ve Bayes classifier, which was trained with a text file containing R, G, B pixel data using Fiji Pixel Inspector. The file consists of 20 pixels of training data for each of the four classes: healthy (H), chlorotic (C), necrotic (N), and white background (WB). The classifier uses a probability density function to bin pixels into the appropriate category. The resulting image mask recolors the leaf disc image so that H pixels appear green, C pixels appear yellow, N pixels appear red, and WB pixels appear white. The trait analyzed in our study is the percent disease area (PDA), which is calculated as the percentage of N pixels to the total area of the leaf disc (N\u0026thinsp;+\u0026thinsp;C\u0026thinsp;+\u0026thinsp;D). Before generating a .csv dataset with the image names, replicate number, and disease severity scores, the output images were checked to ensure that no unrelated objects in the tray were analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistics were performed in R (version 4.0.1; R Core Team, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). One-way analysis of variance (ANOVA) followed by Turkey-Kramer multiple comparison test was performed to compare the PDA of multiple groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To check the normality of residuals QQ plots and Shapiro-Wilks tests were used. To test the homogeneity of variances across genotypes and trials, Levene\u0026rsquo;s Test was applied with the leveneTest() function from the R package \u0026lsquo;Car\u0026rsquo; (Fox et al. 2019). To satisfy the assumptions of normality of residuals and homogeneity of variances, a BoxCox transformation was applied to the PDA data with a lambda value of 0.3. Broad sense heritability (H\u003csup\u003e2\u003c/sup\u003e) was calculated using Eq.\u0026nbsp;1 (Calenge et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Kruijer et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The terms are defined as genetic variance (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\sigma }}_{G}^{2}\\)\u003c/span\u003e\u003c/span\u003e), environmental variance (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\sigma }}_{E}^{2}\\)\u003c/span\u003e\u003c/span\u003e), and average number of replicates (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({n}_{rep}\\)\u003c/span\u003e\u003c/span\u003e). Variance components were estimated by calculating the mean square (MS) error of genotype and residual error from ANOVA, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\sigma }}_{G}^{2}\\)\u003c/span\u003e\u003c/span\u003e = MS(genotype) \u0026ndash; MS(residual error) / \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({n}_{rep}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\sigma }}_{E}^{2}\\)\u003c/span\u003e\u003c/span\u003e = MS(residual error) (Kruijer et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({H}^{2}= \\frac{{{\\sigma }}_{G}^{2}}{{{\\sigma }}_{G}^{2}+{{\\sigma }}_{E}^{2} }\\)\u003c/span\u003e \u003c/span\u003e [1]\u003c/p\u003e \u003cp\u003eMulti-trial PDA BLUEs of response variable (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e) were estimated using Eq.\u0026nbsp;2 with a mixed linear model fit by restricted maximum likelihood (REML) using the lme4 package (Bates et al. 2015). The response variable, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e, is the PDA value for the i\u003csup\u003eth\u003c/sup\u003e genotype, j\u003csup\u003eth\u003c/sup\u003e trial, and k\u003csup\u003eth\u003c/sup\u003e replicate.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({Y}_{ijk}= \\mu + {G}_{i}+ {T}_{j}+ {GT}_{ij}+ {\\epsilon }_{ijk}\\)\u003c/span\u003e \u003c/span\u003e [2]\u003c/p\u003e \u003cp\u003eThe model terms include grand mean (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mu\\)\u003c/span\u003e\u003c/span\u003e), trial treatment effect (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({T}_{j}\\)\u003c/span\u003e\u003c/span\u003e), genotype (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({G}_{i}\\)\u003c/span\u003e\u003c/span\u003e) effect, genotype by trial interaction (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({GT}_{ij}\\)\u003c/span\u003e\u003c/span\u003e), and residual error (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{ijk}\\)\u003c/span\u003e\u003c/span\u003e). The model term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({G}_{i}\\)\u003c/span\u003e\u003c/span\u003e was fitted as a fixed effect, while all other terms were fitted as random effects. Trial was defined as an independent replicated experiment (A-C) each performed on a single tray. The BLUE values were used to rank genotypes and compare resistance levels among checks.\u003c/p\u003e \u003cp\u003eFor K-mean clustering, the data was formatted to have each genotype as a row name and the PDA value of each trial as a separate column. The randomized seed was set to 1 and the PDA values were divided by 100 to get scaled values from 0\u0026ndash;1. The Kmeans() function from the R package \u0026lsquo;Stats\u0026rsquo; was used to calculate the K centers and cluster values (R Core Team, 2022). A custom R script was used to loop through center values 1\u0026ndash;6 to find the best fitting number of K by calculating the total within-cluster sum of squares (WSS) values. The best fitting K value was determined by plotting K by WSS and finding the value K associated with the inflection point in the curve. We used the Kmeans() function to calculate the cluster positions for the leaf disc assay data with the best fitting K value and a random starting set of centroids (nstart) as 25. We plotted the clusters with the fviz_cluster() function and ellipses from the factoextra R package. We assessed the disease rating of each individual by examining the cluster membership in relation to the susceptible and resistant checks.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic Quality and Repeatability\u003c/h2\u003e \u003cp\u003eThere was a wide variation in percent disease area (PDA) values across three trials ranging from 0.5\u0026ndash;95%. Tukey Kramer multiple comparison test analysis showed that across trials there were significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) mean differences between HS, M, R, and HR classes, forming four distinct groups. Within trials these groups were less consistent with only the HS and HR classes having consistent mean differences. The groups with the greatest difference in PDA mean across all trials were between the highly susceptible check \u0026lsquo;Gala\u0026rsquo; in the \u0026lsquo;a\u0026rsquo; group and the highly resistant check \u0026lsquo;Robusta 5\u0026rsquo; in the \u0026lsquo;d\u0026rsquo; group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The difference in PDA values between \u0026lsquo;Gala\u0026rsquo; and \u0026lsquo;Robusta 5\u0026rsquo; within trials A, B, and C were 38%, 76%, 43%, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Across all trials the PDA difference between \u0026lsquo;Gala\u0026rsquo; and \u0026lsquo;Robusta 5\u0026rsquo; was 53% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The \u0026lsquo;b\u0026rsquo; group was comprised of all genotypes in the M and R classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These included \u0026lsquo;Enterprise\u0026rsquo;, \u0026lsquo;Cox\u0026rsquo;s Orange Pippin\u0026rsquo;, \u003cem\u003eM. sieversii\u003c/em\u003e PI657054, and \u003cem\u003eM. hybrid\u003c/em\u003e PI657085. The \u0026lsquo;c\u0026rsquo; group was formed from the HR genotypes \u0026lsquo;Floribunda 821\u0026rsquo; and \u003cem\u003eM. hybrid\u003c/em\u003e PI657115 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs a measure of repeatability, the broad sense heritability (H\u003csup\u003e2\u003c/sup\u003e) was calculated within and across trials. The within-trial broad-sense heritability of trials A, B, and C were 0.93, 0.95, and 0.86, respectively. The across-trial broad-sense heritability was 0.97. This shows the environmental/experimental effects are minimal, and the data is useful for predicting genetic effects. The Pearson correlation coefficient for trial A \u0026times; B was 0.7, A \u0026times; C was 0.8, and B \u0026times; C was 0.86. The pairwise Pearson correlations showed each trial was significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) correlated to each other. Across an average replicate number of 14 discs per genotype, the standard deviation in PDA values was on average 17 with a maximum of 31 (Supp. Table\u0026nbsp;1). The various metrics of repeatability and data quality indicate that the phenotype data generated from the leaf disc assay method is reliable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMulti-Trial Genotype Classification\u003c/h2\u003e \u003cp\u003eThe K-mean cluster analysis combining trials A-C was able to capture 96.9% of the phenotypic variation across three replicated trials. The first two dimensions captured 86.4% and 10.5% of the variation in the data, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Three clusters formed based on the mean PDA differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Cluster 1 was formed with only the genotype \u0026lsquo;Gala\u0026rsquo; representing a highly susceptible group with a mean PDA of 57.9%. Cluster 2 contained each genotype that was classified as M or R including \u003cem\u003eM. sieversii\u003c/em\u003e PI657054, \u003cem\u003eM. hybrid\u003c/em\u003e PI657085, Cox\u0026rsquo;s Orange Pippin, and Enterprise. The mean PDA for that cluster was 32.6%. Cluster 3 was formed with all HR genotypes, \u003cem\u003eM. hybrid\u003c/em\u003e PI657115, Floribunda 821, and Robusta 5. The mean PDA for cluster 3 is 13.5%. These classes follow closely with the Tukey Kramer results where four significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) distinct groups were identified that followed the rank orders of the resistance classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The only difference is that with the K-mean clustering the M and R classes were combined into a single group. Each genotype was ranked based on the scaled PDA BLUE value (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The ranked values largely matched the results from the prior analyses. The pre-assigned classes were ordered correctly based on expected level of fire blight resistance (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The genotypes in the HS group and the HR group were ranked on both extreme ends of the distribution ranging from approximately \u0026minus;\u0026thinsp;4 to +\u0026thinsp;1. Genotypes from the M and R class have highly similar values around \u0026minus;\u0026thinsp;1 ranked accurately in the middle of the distribution. \u0026lsquo;Floribunda 821\u0026rsquo; and \u003cem\u003eM. hybrid\u003c/em\u003e PI657115 are both in the HR class and have similar PDA BLUE values between \u0026minus;\u0026thinsp;2 and \u0026minus;\u0026thinsp;3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe have developed an image-based leaf disc assay that can greatly reduce the time and labor required for fire blight resistance phenotyping in apples for genetic research and breeding. The analytical pipeline of the assay utilizes standardized, high-resolution RGB images to estimate the percentage of fire blight lesions. Leaf disc assays have been popular in studying host-pathogen interactions but have not been widely used for genetic mapping and breeding (Kortemkamp 2006; Gomes et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Buscaill et al. 2020), except for fungal diseases (Leonards-Schippers et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Liu Longzhou et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Divilov et al. 2019; Zendler et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hernandez et al. 2022). Detached leaf assays have been developed for evaluating resistance to bacterial diseases in apple, pear, cherry, and citrus (Donovan \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Bedford et al. 2002; Moragrega et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Martinez-Bilbao et al. 2009; Francis et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, all these studies primarily used subjective visual rating scales to quantify disease severity (Bock et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Martinez-Bilbao et al. (2009) found a moderate correlation (0.56) between fire blight inoculations of shoots and detached leaves of the same cultivars when using a visual rating scale, likely due to the bias and subjectivity in severity scoring.\u003c/p\u003e \u003cp\u003eWe have shown that this newly developed assay had a high accuracy and requires minimal technical expertise to evaluate fire blight response in the 48-hours. We were able to correctly classify (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) eight genotypes with previously described resistance to their established classes, based on percent fire blight infection estimated using this assay. The long turn-around of detached leaf assays for disease response evaluation can potentially introduce confounding abiotic factors (Donovan \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1991\u003c/span\u003e, Martinez-Bilbao et al. 2009). Detached leaf assays are time-sensitive, since the leaf rapidly starts degrading upon detachment and reactive oxygen species (ROS) begin accumulating (Wang et al. 2011). Given that ROS production is common to both apple leaf senescence and the \u003cem\u003eErwinia amylovora\u003c/em\u003e host defense response, cross-tolerance plays a role in the assay results (Perez and Brown \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Campa et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kharadi et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Wang et al. (2011) found significant (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05) chlorophyll loss and H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e accumulation in detached apple leaves beginning after 4 days for the cultivar \u003cem\u003eM. domestica\u003c/em\u003e cv. \u0026lsquo;Hanfu\u0026rsquo;. Although we did not observe abiotic stress on the leaf discs, we cannot rule out the impact of stress from detaching and injuring leaves, as well as physiological differences of the leaves used, on the assay. The PDA value is based only on pixels classified as necrotic, directly capturing the hypersensitive response caused by effector-triggered immunity of the host, while excluding chlorotic pixels that are not specific to \u003cem\u003eE. amylovora\u003c/em\u003e symptoms (Yuan et al. 2021). The necrotic symptoms observed include rapidly growing orange-brown necrotic lesions that initially colonize in the midrib and produce ooze (Kharadi et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The high broad-sense heritability (0.86\u0026ndash;0.97) indicates that the experimental/environmental factors (\u003cem\u003ei.e.\u003c/em\u003e, field collection of leaves) contribute minimal variation to the PDA. These broad-sense heritability estimates are comparable to greenhouse screenings of fire blight QTL mapping populations (0.71\u0026ndash;0.96) and higher than fire blight field inoculations (0.44\u0026ndash;0.75) (Calenge et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Khan et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Durel et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Desnoues et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kostick et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results also showed that non-hierarchical cluster analysis using K-means clustering was successful in capturing 96.9% of the phenotypic variance in the first two dimensions, with clusters largely corresponding to anticipated resistance categories, including HS, M, and R classes combined, and HR. Kostick et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) utilized a similar K-means clustering approach to classify fire blight resistance of 94 apple cultivars, resulting in three clusters that categorized cultivars into susceptible, intermediate, and resistant classes. \u0026lsquo;Gala\u0026rsquo; and \u0026lsquo;Enterprise\u0026rsquo; were categorized as HS and M-HR, respectively, matching our classifications from the leaf disc assay. However, Kostick et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) classified 'Cox's Orange Pippin' as moderately susceptible, while it was clustered in the M-R group in our study. This may be due to environmental differences between these studies that directly impact this resistance locus. It is also important to note that PDA values are not absolute, but rather relative metrics meant to be ranked with HS and HR checks in every experiment. Our results and conclusions were based on only eight genotypes, and screening more genotypes would help to better understand the accuracy and sensitivity of this assay. For genotypes with unknown resistance levels, it is recommended to include checks and control genotypes similar to those used by Zendler et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Best linear unbiased estimators (BLUEs) of the genotype effects were able to correctly rank genotypes based on their expected levels of fire blight resistance across multiple trials. A mixed model approach is useful for combining data from multiple trials to estimate genotype effects while accounting for environmental/experimental variation between trials (Bernardo et al. 2020). BLUEs are widely used for estimating breeding values of diverse populations screened for disease resistance (Steiner et al. 2019; Abdelraheem et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This method is also well-suited for unbalanced data, which is a likely case for leaf disc assays where the leaves available for each genotype may vary. For studies that plan to incorporate multiple strains, severity metrics, and time-points, using a mixed model approach will improve the integration of this data to estimate genotypic effects more accurately (Juliana et al. 2019; Bernardo et al. 2020).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe image-based leaf disc assay is an effective method to phenotype fire blight resistance in multiple genetic mapping populations with reduced time and labor. Additionally, it requires minimal technical expertise and familiarity with fire blight, allowing for precise quantitative data to be obtained through the 48-hour inoculation and image analysis protocols. Our research further demonstrates that combining trial data from multiple experiments using multi-trial best linear unbiased estimators (BLUEs) and K-means clustering can account for random experimental variation and improve classification of fire blight resistant apple genotypes. Therefore, this assay presents a valuable alternative for phenotype fire blight in apples for breeding and mapping of fire blight resistance QTL.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the New York State Department of Agriculture \u0026amp; Markets, Apple Research \u0026amp; Development Program (ARDP), grant number: CM04068AQ, Khan. We would like to acknowledge the USDA (United States Department of Agriculture) Plant Genetic Resources Unit (PGRU) in Geneva, New York, for maintaining the US national \u003cem\u003eMalus\u003c/em\u003e collection, where all plant material was collected. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.K., conceptualized, designed, and managed the project. R.T., and D.H., done the experiments, analysis and interpretation. R.T., drafted the manuscript. R.T., and D.H., K.R., and A.K., revised, and finalized the manuscript. All authors read and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdelraheem, A., Elassbli, H., Zhu, Y., Kuraparthy, V., Hinze, L., Stelly, D., Wedegaertner, T., \u0026amp; Zhang, J. (2020). A genome-wide association study uncovers consistent quantitative trait loci for resistance to Verticillium wilt and Fusarium wilt race 4 in the US Upland cotton. \u003cem\u003eTheoretical and Applied Genetics\u003c/em\u003e, \u003cem\u003e133\u003c/em\u003e(2), 563\u0026ndash;577. https://doi.org/10.1007/S00122-019-03487-X/FIGURES/3\u003c/li\u003e\n\u003cli\u003eBedford, K. E., Sholberg, P. L., \u0026amp; Kappel, F. (2003). Use of a detached leaf bioassay for screening sweet cherry cultivars for bacterial canker resistance. \u003cem\u003eActa Horticulturae\u003c/em\u003e, \u003cem\u003e622\u003c/em\u003e, 365\u0026ndash;368. https://doi.org/10.17660/ACTAHORTIC.2003.622.37\u003c/li\u003e\n\u003cli\u003eBernardo, R. 2020. Reinventing quantitative genetics for plant breeding: something old, something new, something borrowed, something BLUE. Hered. 2020 1256. 125:375\u0026ndash;385 Available at: https://www.nature.com/articles/s41437-020-0312-1\u003c/li\u003e\n\u003cli\u003eBock, C. H., Poole, G. H., Parker, P. E., and Gottwald, T. R. 2010. Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging. https://doi.org/10.1080/07352681003617285. 29:59\u0026ndash;107 Available at: https://www.tandfonline.com/doi/abs/10.1080/07352681003617285\u003c/li\u003e\n\u003cli\u003eBock, C. H., Chiang, K. S., and Del Ponte, E. M. 2022. Plant disease severity estimated visually: a century of research, best practices, and opportunities for improving methods and practices to maximize accuracy. Trop. Plant Pathol. 47:25\u0026ndash;42 Available at: https://link.springer.com/article/10.1007/s40858-021-00439-z \u003c/li\u003e\n\u003cli\u003eBock, C. H., Parker, P. E., Cook, A. Z., and Gottwald, T. R. 2008. Visual rating and the use of image analysis for assessing different symptoms of citrus canker on grapefruit leaves. Plant Dis. 92:530\u0026ndash;541 Available at: https://pubmed.ncbi.nlm.nih.gov/30769647/ \u003c/li\u003e\n\u003cli\u003eBuscaill, P., Sanguankiattichai, N., Lee, Y. J., Kourelis, J., Preston, G., \u0026amp; van der Hoorn, R. A. L. (2021). Agromonas: a rapid disease assay for \u003cem\u003ePseudomonas syringae\u003c/em\u003e growth in agroinfiltrated leaves. \u003cem\u003eThe Plant Journal\u003c/em\u003e, \u003cem\u003e105\u003c/em\u003e(3), 831\u0026ndash;840. https://doi.org/10.1111/TPJ.15056\u003c/li\u003e\n\u003cli\u003eCalenge, F., Drouet, D., Denanc\u0026eacute;, C., Van De Weg, W. E., Brisset, M. N., Paulin, J. P., et al. 2005. Identification of a major QTL together with several minor additive or epistatic QTLs for resistance to fire blight in apple in two related progenies. Theor. Appl. 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CHAPTER 2: Spread and Current Distribution of Fire Blight. \u003cem\u003eFire Blight: History, Biology, and Management\u003c/em\u003e, 15\u0026ndash;36. https://doi.org/10.1094/9780890544839.003 \u003c/li\u003e\n\u003cli\u003eWallin, J., Bogdan, M., Szulc, P. A., Doerge, R. W., and Siegmund, D. O. 2021. Ghost QTL and hotspots in experimental crosses: novel approach for modeling polygenic effects. Genetics. 217 Available at: https://academic.oup.com/genetics/article/217/3/iyaa041/6067404 \u003c/li\u003e\n\u003cli\u003eWang, P., Yin, L., Liang, D., Li, C., Ma, F., \u0026amp; Yue, Z. (2012). Delayed senescence of apple leaves by exogenous melatonin treatment: toward regulating the ascorbate\u0026ndash;glutathione cycle. \u003cem\u003eJournal of Pineal Research\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(1), 11\u0026ndash;20. https://doi.org/10.1111/J.1600-079X.2011.00966.X \u003c/li\u003e\n\u003cli\u003eWhalen, M. C. 2005. Host defense in a developmental context. Mol. 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Effectors, chaperones, and harpins of the Type III secretion system in the fire blight pathogen Erwinia amylovora: a review. \u003cem\u003eJournal of Plant Pathology 2020 103:1\u003c/em\u003e, \u003cem\u003e103\u003c/em\u003e(1), 25\u0026ndash;39. https://doi.org/10.1007/S42161-020-00623-1 \u003c/li\u003e\n\u003cli\u003eZendler, D., Malagol, N., Schwandner, A., T\u0026ouml;pfer, R., Hausmann, L., and Zyprian, E. 2021. High-Throughput Phenotyping of Leaf Discs Infected with Grapevine Downy Mildew Using Shallow Convolutional Neural Networks. Agron. 2021, Vol. 11, Page 1768. 11:1768 Available at: https://www.mdpi.com/2073-4395/11/9/1768/htm \u003c/li\u003e\n\u003cli\u003eZeng, Q., Puławska, J., and Schachterle, J. 2021. Early events in fire blight infection and pathogenesis of \u003cem\u003eErwinia amylovora\u003c/em\u003e. J. Plant Pathol. 103:13\u0026ndash;24 Available at: https://link.springer.com/article/10.1007/s42161-020-00675-3\u003c/li\u003e\n\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-plant-pathology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejpp","sideBox":"Learn more about [European Journal of Plant Pathology](http://link.springer.com/journal/10658)","snPcode":"10658","submissionUrl":"https://www.editorialmanager.com/ejpp/default2.aspx","title":"European Journal of Plant Pathology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"leaf disc assay, quantitative trait locus, image analysis, disease resistance phenotyping","lastPublishedDoi":"10.21203/rs.3.rs-2829015/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2829015/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFire blight, caused by \u003cem\u003eErwinia amylovora\u003c/em\u003e, is a destructive bacterial disease that severely hampers apple production. To conduct QTL (Quantitative Trait Locus) studies for breeding resistant apple cultivars, phenotyping of large genetic mapping populations of apples for fire blight resistance is essential. This, however, necessitates precise, quantitative data spanning multiple years, locations, and pathogen strains. It can be time-consuming and resource-intensive to keep QTL mapping populations for apples in the field and greenhouse. This creates a bottleneck for identifying novel QTL for fire blight resistance or developing resistant cultivars. To address this challenge, we present an image-based method for rapid and accurate phenotyping fire blight resistance using apple leaf discs. This leaf disc assay demonstrates significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) percent disease area (PDA) differences in fire blight inoculations among eight apple genotypes with well-known resistance levels. Furthermore, the image-based leaf disc assay consistently shows a 40\u0026ndash;70% difference in PDA between resistant and susceptible checks. We also report high within and across trial broad sense heritability values ranging from 0.86\u0026ndash;0.97. We demonstrate the use of K-means clustering and best linear unbiased estimators (BLUEs) to combine multiple trials. This assay offers an efficient alternative to traditional fire blight screening methods, potentially improving our understanding of the host response and accelerating the development of resistant apple cultivars.\u003c/p\u003e","manuscriptTitle":"Image-based leaf disc assay for the rapid evaluation of genetic resistance to fire blight in apples","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-03 21:13:07","doi":"10.21203/rs.3.rs-2829015/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2023-06-28T04:06:30+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2023-04-28T11:13:02+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-04-28T08:01:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"European Journal of Plant Pathology","date":"2023-04-26T04:49:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-04-19T07:00:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Plant Pathology","date":"2023-04-17T18:42:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-plant-pathology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejpp","sideBox":"Learn more about [European Journal of Plant Pathology](http://link.springer.com/journal/10658)","snPcode":"10658","submissionUrl":"https://www.editorialmanager.com/ejpp/default2.aspx","title":"European Journal of Plant Pathology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9c8c4ae5-f399-4cde-b43c-5f0a0cead07e","owner":[],"postedDate":"May 3rd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-09-07T15:05:31+00:00","versionOfRecord":{"articleIdentity":"rs-2829015","link":"https://doi.org/10.1007/s10658-023-02750-8","journal":{"identity":"european-journal-of-plant-pathology","isVorOnly":false,"title":"European Journal of Plant Pathology"},"publishedOn":"2023-08-24 15:02:11","publishedOnDateReadable":"August 24th, 2023"},"versionCreatedAt":"2023-05-03 21:13:07","video":"","vorDoi":"10.1007/s10658-023-02750-8","vorDoiUrl":"https://doi.org/10.1007/s10658-023-02750-8","workflowStages":[]},"version":"v1","identity":"rs-2829015","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2829015","identity":"rs-2829015","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}