{"paper_id":"01f003d9-eef9-48ac-a3b9-c3bd3b2c4dc6","body_text":"Validation and marker development for a chromosome 2 QTL for fire blight resistance in pear | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Validation and marker development for a chromosome 2 QTL for fire blight resistance in pear Shaun Clare, Jason D. Zurn, Mandie Driskill, Sara Montanari, Chris Kirk, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9396360/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Fire blight, caused by the bacterial pathogen Erwinia amylovora , is a persistent problem for pear ( Pyrus spp.) growers across most production regions around the world. Growing resistant varieties is one of the best options for managing fire blight. The resistant P. communis cultivars ‘Potomac’ and ‘Old Home’, and the hybrid selection NJA2R59T69 were used in a previous study to identify quantitative trait loci (QTLs) linked to the resistance. The major chromosome 2 QTLs identified in the ‘Potomac’ and ‘Old Home’ sources overlap with QTLs that were previously identified in ‘Harrow Sweet’ and ‘Moonglow’, while that of NJA2R59T69 (through P. ussuriensis ‘Pai Li’) mapped to a nearby location on chromosome 2. In the current study, genes associated with disease resistance in the two chromosome 2 QTL regions were cloned in 23 accessions representing resistant and susceptible cultivars, and progeny from the three sources. Alleles unique to resistant cultivars in these genic regions were targeted by SeqSNP and used to genotype a diversity set of 382 pear accessions with known fire blight disease responses from multiple sources. Association mapping was conducted across subsets of the 382 accessions based on the shared fire blight resistance source. In the 48 accessions with the ‘Potomac’ source of resistance, association mapping identified a marker (Chr2_3601869) that was predictive with 80.8% and 96.0% accuracy in ‘Potomac’ and ‘Pai Li’, respectively. However, Chr2_3601869 was only 57.0% accurate in ‘Old Home’ derived accessions. This marker was converted to a PACE assay that was predictive in both ‘Pai Li’ and ‘Potomac’ -derived accessions but not ‘Old Home’-sourced resistance. Resistance appears to be dominant, with the majority of accessions being heterozygous at the Chr2_3601869 locus. A large proportion of resistant Asian accessions are homozygous for the resistance allele at Chr2_3601869, suggesting these may be excellent sources to introgress resistance. Future work should look to elucidate the critical recombination points within the three mapping populations to further refine the chromosome 2 resistance loci and determine which allele/haplotype corresponds to each unique source of fire blight resistance. Pyrus fire blight Marker assisted selection DNA-informed breeding PCR allele competitive extension single nucleotide polymorphism SNP Figures Figure 1 Figure 2 Figure 3 Introduction The bacterial disease fire blight is one of the major diseases affecting pears ( Pyrus spp.). Originating in North America, the pathogen Erwinia amylovora has proliferated across North America, Europe, parts of Asia, and northern Africa (van der Zwet, 2002 ; Bell, 2019 ). During winter, the pathogen resides in cankers, and as temperatures rise, emerges through a polysaccharide ooze (Malnoy et al., 2012 ). This ooze acts as both a safeguard against abiotic stresses and an attractant for insects, which disseminate the bacteria to other hosts (Griffith et al., 2003). E. amylovora is also spread through wind and rain. Flowers are the primary concern, as infection typically occurs via floral nectaries (Malnoy et al., 2012 ). Moreover, infection can also take place through wounded shoots, with hail damage sites often playing a significant role in disease development. In environments conducive to disease development, management has proved challenging. Current strategies involve eliminating infected plants and tissue, coupled with the application of antibiotics and copper-based compounds (Psallidas and Tsiantos, 2000 ; McManus et al., 2002 ; Norelli et al., 2003 ; Duffy et al., 2005 ). Resistance to the antibiotics streptomycin and oxolinic acid has been reported in some E. amylovora populations (McManus et al., 2002 ; Manulis et al., 2003 ; Rezzonico et al., 2009 ; Sundin and Wang, 2018 ; Laforest et al., 2019 ). The potential for horizontal gene transfer of antibiotic resistance between plant and human pathogens, as well as concerns regarding the environmental impacts of off-target antibiotic applications, have raised questions about the long-term sustainability of antibiotics as a disease management strategy for bacterial plant diseases (Chiou and Jones, 1995 ; Sundin and Bender, 1996 ; McManus, 2014 ). Genetic resistance is an appealing strategy for disease control, as it mitigates the perceived risks associated with off-target antibiotic use. Currently, the most widely cultivated pear varieties in the U.S.A. are susceptible to fire blight, and a concerted effort by the pear industry would be needed to shift towards resistant varieties (Bell, 2019 ). The development of fire blight-resistant pear cultivars has been a goal for U.S. breeding programs for more than 100 years. For example, the USDA pear scion breeding program was established in the early 1900s with the aim of breeding for improved resistance (Waite, 1895 ; Magness, 1937 ; van der Zwet and Keil, H. L., 1979). It continues to this day, and it has released numerous cultivars with fire blight resistance and/or tolerance (van der Zwet and Keil, H. L., 1979; Gottschalk et al., 2024 ). Outside the U.S.A., other global breeding programs have also focused on improving fire blight resistance, such as the Harrow Pear Breeding Program in Canada (Hunter, 2016 ). Many of these breeding programs have relied on resistances from interspecific hybridizations (van der Zwet et al., 1974 ; van der Zwet and Keil, H. L., 1979), which is a tedious and slow process owing to the need for subsequent backcrossing to restore the desirable characteristics of commercial pears. In addition, breeding programs have historically relied on phenotypic screening of populations using labor-intensive inoculation methods or field-based observations (Oitto et al., 1970 ; van der Zwet and Keil, 1970 ; van der Zwet and Oitto, 1973 ). DNA-informed breeding makes the transition towards resistant cultivars more tenable from a breeding perspective, using either marker-assisted selection (MAS), genomic selection, or a combination of both. The application of DNA-based genetic markers to assist plant breeding decisions began in the 1980s but has only recently started to be used more routinely in the Rosaceae (Xu and Crouch, 2008 ; Iezzoni et al., 2020 ). DNA-informed breeding is particularly effective for traits that are difficult or costly to phenotype or that depend on specific environmental conditions (Peace, 2017; Xu and Crouch, 2008 ), such as disease resistances and qualitative traits such as blackberry thornlessness (Johns et al., 2025 ), hop sex (Clare et al., 2024 ), or peach ripening (da Silva Linge et al., 2024 ). Breeding new pear varieties is a slow and expensive process, where the time from cross to cultivar release can be as long as 20 to 30 years. Typically, resistance to diseases such as fire blight is assessed between years 1 to 10, depending on the breeding program, and seedlings developed each year require maintenance costs. A predictive DNA test would allow breeders to select resistant progeny in the first year of the breeding cycle, redirecting funds which would otherwise be spent maintaining susceptible seedlings over the first 10 years towards other aspects of the breeding program. In the University of Minnesota apple breeding program, applying MAS to achieve a seedling cull rate of at least 13.2% in the first year of a breeding process has provided economic benefits (Wannemuehler et al., 2019 ). QTLs for fire blight resistance have been consistently detected on pear chromosome 2 from multiple parental sources (Dondini et al., 2004 ; Le Roux et al., 2012 ; Montanari et al., 2016 ; Zurn et al., 2020 ; Gabay et al., 2021 ). Zurn et al. ( 2020 ) resolved this QTL in three different bi-parental F 1 populations to two physical positions on chromosome 2 of the ‘Bartlett’ double-haploid (DH) genome assembly (Linsmith et al., 2019 ). The first QTL was detected in both the P. communis ‘Old Home’ × ‘Bartlett’ and ‘Potomac’ × ‘El Dorado’ populations, and was narrowed to a 1.86 Mb region with 205 reported genes. The second QTL was found to be slightly distal to the first in a hybrid NJA2R59T69 × ‘Bartlett’ population, and was mapped to a 1.71 Mb region with 204 reported genes. The objective of this current study was to develop robust and economic DNA markers for fire blight resistance from the P. communis ‘Old Home’, ‘Potomac’, and P. ussuriensis ‘Pai Li’ (NJA2R59T69) sources. We amplified and sequenced genes known to be associated with disease resistance in 23 resistant and susceptible individuals from these three resistance sources. Alleles uniquely associated with resistance were subsequently genotyped using targeted sequencing to identify novel polymorphic SNPs in the two chromosome 2 QTL regions on a diversity set of 467 individuals with multiple sources of resistance including the three mentioned above. Associated polymorphisms identified via association mapping were converted to a PCR Allele Competitive Extension (PACE) and quantitative PCR (qPCR) TaqMan assays to facilitate pear fire blight resistance breeding efforts via MAS. Materials and Methods Identification of Polymorphisms in the two QTL Regions via Amplicon Sequencing Plant material and DNA Extraction Young, actively growing pear leaf tissue was collected from 23 samples with known resistance or susceptibility to pear fire blight, of which 18 samples were from the three segregating bi-parental populations evaluated by Zurn et al. ( 2020 ; Table S1 ). The remaining five samples were ‘Harrow Sweet’, identified as resistant (Dondini et al., 2004 ; Le Roux et al., 2012 ); the resistant cultivars ‘Moonglow’ and ‘Potomac’, which are known to have the chromosome 2 QTL; and the susceptible cultivars ‘Bartlett’ and ‘El Dorado’. Approximately 30–50 mg of tissue from each sample was collected into a 96-well plate format, flash-frozen in liquid nitrogen and stored at − 80°C until DNA extraction. Samples were ground using a mixer mill (MM 301; Retsch International, Hann, Germany) and DNA was extracted using a modified Sbeadex Plant Magnetic Bead Kit (LGC Biosearch Technologies, Hoddesdon, UK). DNA was quantified with a Tecan Infinite M Plex multimode plate reader (Tecan Group Ltd, Zürich, Switzerland) and diluted to 5 ng/µL. Primer Design for Amplicon Sequencing Zurn et al. ( 2020 ) identified two regions for fire blight resistance on chromosome 2. The first region, identified in the ‘Old Home’ × ‘Bartlett’ and ‘Potomac’ × ‘El Dorado’ populations, was narrowed to the physical region of Chromosome 2 from 3,390,229 to 4,332,428 base pairs (bp) (region 1) in the ‘Bartlett’ DH genome assembly (Linsmith et al., 2019 ). The second region, identified in the NJA2R59T69 × ‘Bartlett’ population, corresponds to the region of Chromosome 2 from 5,962,689 to 6,425,332 bp (region 2). FASTA sequences were extracted for the 205 and 204 genes in regions 1 and 2, respectively, and primers were designed using Primer3 v0.4.0 to amplify genic regions (Table S2; (Untergasser et al., 2012 ). For each gene, three or more overlapping tiling primer pairs of approximately 1,000 base pairs (bp) were designed (Table S2). If a gene was under 1,500 bp in length, three primer pairs were designed, to cover as much gene space as possible. To reduce the number of reactions, primers were grouped into 101 optimal multiplex groups using the Multiplex v2.1 software (Kaplinski et al., 2005 ). All but 11 genes’ primer pairs could be multiplexed (Table S2). PCR reactions consisted of 2 µL 5X PrimeSTAR GXL Buffer, 0.8 µL dNTPs, 1.75 µL DNA, 0.2 µL PrimeSTAR GXL DNA Polymerase. Each primer in the reaction had a final concentration of 0.2 µM according to the manufacturer’s recommendations, and water was used to adjust the final reaction volume to 10 µL (TAKARA, San Jose, California, USA). Reactions were amplified in an Eppendorf Gradient thermocycler (Eppendorf Inc., Westbury, NY, USA) using a program consisting of 35 cycles of denaturing at 98°C for 30 s, annealing at 62°C for 30 s, and extension at 68°C for 15 min. Amplicon Sequencing Library Preparation and Sequencing Successful reactions were pooled across each of the 23 genotypes and were cleaned using the Mag-Bind TotalPure NGS kit according to the manufacturer’s recommendations (Omega Bio-tek, Norcross, Georgia, USA). Sixty to 100 µL of purified sample was sonicated using a Bioruptor Pico Sonication Device to produce amplicons approximately 300 bp (Diagenode, Denville, New Jersey, USA). Each sheared sample went through end repair and purification using the Ion Plus Fragment Library Kit according to the manufacturer’s recommendations (Thermo Fisher Scientific, Waltham, MA, USA) and the Mag-Bind TotalPure NGS kit according to manufacturer’s recommendations (Omega Bio-tek, Norcross, Georgia, USA). The Ion Plus Fragment Library Kit (Thermo Fisher Scientific, Waltham, MA, USA) was used to ligate Xpress™ Barcode Adapters (Thermo Fisher Scientific). The prepared libraries were quantified with the Ion Torrent Universal Quantification Library Kit (Thermo Fisher Scientific, Waltham, MA, USA) using a Bio-Rad CFX96 thermocycler (Hercules, California, USA). Libraries were diluted to 100 pM and 20 µL of each library was pooled into a centrifuge tube and homogenized. The Ion Chef Instrument was used to load the libraries onto two Ion 540 chips and single-end sequencing was performed using an Ion GeneStudio S5 Plus System (Thermo Fisher Scientific). Read Cleaning, Alignment, and Variant Identification for Amplicon Sequencing Sequences were demultiplexed on the Ion GeneStudio S5 System (Thermo Fisher Scientific), and 23 FASTQ files were downloaded. FASTQ files were evaluated with FastQC v0.11.9 using default setting (Andrews, 2010 ) to identify sequence contamination such as sequence adapters, poly-tail SNP repeats, and over-represented fungal, bacterial, and plasmid sequences. Contamination was removed with BBDuk v03.28.2018 (Bushnell, 2014 ) using the following parameters: ktrim right, mink 11, hdist 2, tpe, tbo, maq 25, minlen 25. Files were re-evaluated with FastQC to determine the quality of curated data. Curated genomic FASTA files were indexed and aligned to the Pyrus communis ‘Bartlett’ DH Genome v2.0 with bwa-mem v0.7.12 using default settings (Li and Durbin, 2009 ; Linsmith et al., 2019 ). The resulting bam files were sorted by genomic coordinates with samtools v1.18 sort (Li et al., 2009 ) and read groups were added for traceability with GATK v4.2.0 AddorReplaceReadGroups (McKenna et al., 2010 ; Van der Auwera et al., 2013 ). Alignments were checked for mapping location with a custom python script. Only sequences aligning to chromosome 2 were used for subsequent variant calling. Variant calling was performed on each bam file for the 23 individuals using Freebayes v1.3.2 (Garrison et al., 2022 ) and variants were filtered for quality scores above 20 and sequencing depth exceeding ten reads per sample using vcflib vcffilter (Garrison et al., 2022 ). A custom python script was used to identify alleles present only in resistant accessions. Polymorphisms evenly distributed across the two fire blight QTL regions were identified and submitted to LGC Bioresearch Technologies (Wisconsin, U.S.A.) for further genotyping using a targeted genotyping by sequencing (targeted GBS) technique referred to by LGC as the SeqSNP genotyping platform. Genotyping of Historic Individuals and Populations via SeqSNP Plant Materials and Phenotype Compilation A total of 382 lyophilized (10–15 mg each) samples were submitted to LGC for SeqSNP (Table S3). This consisted of a diversity panel of 289 accessions across seven Pyrus species from the NCGR, 21, 18, and 14 progeny of the families ‘Old Home × 'Bartlett', NJA2R59T69 × 'Bartlett', and 'Potomac' × 'El Dorado', respectively, as well as 40 accessions from New Zealand consisting of mostly of breeding selections (Table S3). Disease responses of the accessions in this diversity set were obtained from the literature (Table S3), while the New Zealand accessions and LBJ×OH population were phenotyped as described by (Montanari et al., 2016 ). Sample Preparation and Sequencing Data Curation Raw sequencing reads were assessed using FastQC 0.11.9 and MultiQC 1.12 and subsequently trimmed using fastp 0.22.0 (Andrews, 2010 ; Ewels et al., 2016 ; Chen et al., 2018 ). Trimmed sequenced reads were re-assessed with FastQC and MultiQC to determine if the reads were of sufficient quality. Trimmed reads were aligned and indexed to the ‘Bartlett’ DH Genome v2 using bwa-mem 0.7.17 and samtools 1.16, respectively. Variant calling was performed using GATK 4.2.1.6 HaplotypeCaller, combined with CombineGVCFs, and genotyped using GenotypeGVCFs for a raw vcf file. The raw vcf file was filtered for biallelic SNPs within a minimum depth of three reads, a quality score of 30, and a minor allelic frequency of 0.05, and thinned to 100 bp using vcftools 0.1.16. The remaining missing data were imputed using beagle 5.2 using a burn-in of 10 and iterations of 50 for the final vcf. Association Mapping For association mapping, four datasets were used. The first one, termed the Diversity, set included all susceptible accessions, accessions with unknown resistance sources, and all accessions that shared ancestry with the different known sources of fire blight resistance: ‘Potomac’, ‘Old Home’, and ‘Pai Li’ (NJA2R59T69) (n = 416). The second dataset contained only accessions with known ancestry to the three sources of chromosome 2 resistance (Ancestry set, n = 286). Finally, the third and fourth datasets included samples that shared ancestry with the ‘Potomac’ and the ‘Old Home’ sources of resistance, respectively (n = 122 and n = 143) (Table S3). A dataset containing only the ‘Pai Li’ source of resistance was not included because only 19 accessions could be selected. Association mapping was performed with GAPIT version 3 (Wang and Zhang, 2021 ) using the FarmCPU (Liu et al., 2016 ) and BLINK algorithms (Huang et al., 2019 ), with zero, two, three and five principal components. Principal components were selected based on the standard practices (three), accounting for 50% (two) of the total genotypic variation, and the elbow of the scree plot (five). The best association mapping model for each accession set was determined using a two-step process. Firstly, the mean square difference was calculated (Mamidi et al., 2011 ) to determine the top five models. However, since models with the lowest scores can result in no significant markers, a visual inspection of the quantile-quantile plot was used to confirm that markers followed the expected uniform p -value distribution. A Bonferroni correction was applied to calculate the LOD threshold at the 0.05 α-level of 4.28. The predictive ability of significant markers was assessed by calculating the percentage of calls that correlated with the known phenotype. PACE and TaqMan Assay Development After determining the most predictive marker, sense and antisense PCR Allele Competitive Extension (PACE) assays were designed following guidelines (3CR Biosciences, Harlow, United Kingdom). In brief, an approximate 16–20 bp sequence was selected to terminate on the target SNP, with a common reverse primer of 16–20 bp positioned approximately 6 bp up or downstream, depending on assay orientation, to form allele-specific primers and a common reverse primer, respectively (Table 1 ) . The lengths of the forward and reverse primers were adjusted to achieve annealing temperatures within 1 o C of each other. PACE assay primers were ordered from Integrated DNA Technologies (IDT, Coralville, IA, United States) and optimized for cycle length by using a modified touchdown PCR of 26–36 cycles, reading every two cycles on a known fire blight-resistant (‘Potomac’ source) and susceptible accessions. PCR reactions were conducted in 15-µL reactions on CFX96 (Bio-Rad Laboratories, Hercules CA, United States) and analyzed using CFX Manager. Table 1 Nucleotide sequence for PACE assay primers. Primer sequence includes the tail to bind to mastermix fluorophores. All sense or antisense primers should be complexed together. Name Primer PFB_Sense_F1 GAAGGTGACCAAGTTCATGCTGGTGGAGGAGCTCCG PFB_Sense_F2 GAAGGTCGGAGTCAACGGATTGGTGGAGGAGCTCCT PFB_Sense_CR CGTAAAATAGTCGCAAGATGGAG PFB_AntiSense_F1 GAAGGTGACCAAGTTCATGCTAGTCGCAAGATGGAGGATC PFB_AntiSense_F2 GAAGGTCGGAGTCAACGGATTAGTCGCAAGATGGAGGATA PFB_AntiSense_CR CTGCTAGTTGGTGGAGGA The PACE assay design was also used as a template to design a TaqMan assay to test fire blight-resistant and susceptible accessions at Plant & Food Research in New Zealand (Table S3). A total of 135 progeny from the (LBJ×OH) family and six reference cultivars were screened with the TaqMan assay. The TaqMan assay was designed on the LGC Biosearch Technologies tool RealTimeDesign for SNP genotyping on the ‘least restrictive’ parameter setting (RealTimeDesign qPCR Assay Design Software, | LGC Biosearch Technologies). The oligonucleotide sequences for Allele probe 1 (FAM-BHQplus), Allele probe 2 (CAL Fluor Orange 560-BHQplus), forward primer and reverse primer were CAAGATGGAGGATAGGAGCT, TGGAGGATCGGAGCTC, CCCACTTGAAGGTAGTTTGTCCATA and GAGGTGGTTGGGTTCTGCTA, respectively. The assay was run on the LightCycler480 platform using AccuStart Genotyping Toughmix polymerase (Quanta BioSciences). Cycling parameters: Initial denature 95°C for 5 min, 40 2-step cycles of 95°C for 5 s and 60°C for 45 s with signal acquisition each cycle and a final cooling step 40°C for 30 s. The results were visualized with the Endpoint Genotyping analysis module of the LightCycler software. Results Identification of Polymorphisms in the two QTL regions via amplicon sequencing An average of 175,987.57 ± 26,270.12 reads were generated per sample, ranging from 118,295 reads for the NJA2R59T69 × 'Bartlett' line US200537_043 to 220,607 reads for ‘Harrow Sweet’ (Table S1 ). After filtering, an average of 115,405.26 ± 17,337.38 reads per sample were retained for further analysis. An average of 115,079.70 ± 17,293.75 reads per sample were mapped to the P. communis ‘Bartlett’ DH Genome v2.0. An average of 99.7% of the filtered reads were successfully mapped. Of the mapped reads, 81–94% of the reads with an average of 88.5% mapped to chromosome 2, indicating the regions were successfully targeted. After allele calling and filtering, a total of 13,041 polymorphisms were identified, with 8113 and 4928 in region 1 and region 2, respectively. A python script was used to filter the polymorphisms and identify alleles that were unique to the resistant sources. In region 1, a total of 1100 alleles were identified that were unique to resistant individuals carrying the ‘Old Home’/’Potomac’ source of resistance and 1085 alleles in region 2 that appeared unique to resistant NJA2R59T69 × ‘Bartlett’ individuals. A total of 531 polymorphisms, 364 from region 1 and 167 from region 2, were submitted to LGC for SeqSNP assay design. Genotyping of Historic Individuals and Populations via SeqSNP and Association Analysis A total of 264 million raw sequencing reads were obtained for all samples, ranging from 18,748 to 1.6 million reads per sample. After trimming and quality control, 238 million sequencing reads remained and were mapped to the ‘Bartlett’ DH Genome v2.0, ranging from 16,820 to 1.5 million reads per sample. Before filtering, 60,267 variants were identified. After filtering, 972 high-quality biallelic SNP markers on chromosome 2 were retained for association mapping. When considering only the best models from Diversity, Ancestry and Potomac, a total of four marker trait associations (MTAs) were identified (Fig. 1 , Tables 2 ) . The best Diversity association mapping model (PC0_BLINK) identified two significant markers (Chr2_6146265, LOD 6.52; Chr2_6087809, LOD 6.00), with one each within the previously delimited ‘Pai Li’ region. The best Ancestry model (PC0_BLINK) contained two significant markers (Chr2_3572341, LOD 7.68; Chr2_3601869, LOD 17.63), both within the previously delimited ‘Potomac’/’Old Home’ region. The best ‘Potomac’ association map (PC0_BLINK) identified two significant markers. Two markers (Chr2_3572063, LOD 5.11 and Chr2_3601869, LOD 7.85) were identified in the ‘Potomac’/’Old Home’ delimited region. The best ‘Old Home’ model (PC0_FarmCPU) identified one significant marker (Chr2_6370014, LOD 5.93) in the ‘Pai Li’ region. However, this marker was only 74.1% predictive of resistance in accessions containing ‘Old Home’ in the pedigree and was therefore not pursued for assay development. The Chr2_6146265 marker had a predictive accuracy that varied from 70.4%, 70.4%, 96.0% to 58.4% in the ‘Potomac’-, ‘Old Home’-, ‘Pai Li’-sourced resistances, respectively. Owing to the low numbers of ‘Pai Li’-sourced accessions and availability of more predictive markers, Chr2_6146265 was not pursued. Table 2 Significant markers identified via association mapping against pear fire blight. Bold indicates the best model for the respective data set determined using mean squared deviations (MSD) and visual inspection of the quantile-quantile (QQ) plot. A Bonferroni logarithm of odds (LOD) threshold of 4.28 at the α-level of 0.05. Alleles and phenotypic variation explained is displayed as an R 2 value where negative values indicates the alternative allele increased resistance. Significant Marker Chr Pos Model Identified LOD Score Alleles R 2 Chr2_3572063 Chr 2 3572063 Potomac 5.11 A/T 0.35 Chr2_3572341 Chr 2 3572341 Ancestry 7.68 A/C 0.25 Chr2_3601869 Chr 2 3601869 Ancestry Potomac 17.63 7.85 G/T -0.39 -0.43 Chr2_6087809 Chr 2 6087809 Diversity 6.00 C/A -0.23 Chr2_6146265 Chr 2 6146265 Diversity 6.52 A/G -0.19 Chr2_6370014 Chr 2 6370014 Old Home 5.93 T/A -0.39 The Chr2_3601869 marker had the highest LOD score and was the most predictive of resistance in accessions with ‘Potomac’- and ‘Pai Li’-sourced resistance, at 80.8% and 96.0%, respectively. However, its accuracy was only 57.0% in ‘Old Home’. This SNP is a synonymous mutation within exon 8 (the last exon) of gene pycom02g05390, annotated as a Derlin-1-like gene. PACE and TaqMan Assay Development The sense PACE design failed to separate resistance and susceptible accessions, whereas the antisense design successfully formed compact clusters after 26 cycles. However, it was over-cycled by 34 cycles, as evidenced by the migration of non-template controls (Fig. 2 ). The PACE assay was tested on LGC plate 3, which included accessions with ‘Pai Li’-, ‘Potomac’- and ‘Old Home’-sourced resistance. The PACE assay successfully predicted resistance in accessions with ‘Pai Li’ and ‘Potomac’ resistance, but was not predictive in accessions with ‘Old Home’-sourced resistance (Fig. 3). The TaqMan probes were designed on the Chr2_3601869 marker using the antisense PACE primers. One individual from the LBJ×OH family was removed because it could not be phenotyped. The assay grouped the remaining 140 samples tested into three clusters: 18 samples were homozygous for the T allele (associated to resistance in the PACE assay); 37 samples were homozygous for the G allele (associated with susceptibility); and 85 samples were heterozygous. The average fire blight severity was 44.1% necrosis for samples homozygous for the T allele, 63.8% for samples homozygous for the G allele, and 42.5% for the heterozygotes. All three clusters included samples showing necrosis from 0 to 100%, thus including both resistant and susceptible plants. Discussion The Ancestry set identified markers within both the ‘Potomac’/’Old Home’ and ‘Pai Li’ regions of chromosome 2. Owing to the low number of ‘Pai Li’-pedigreed accessions, a dedicated ‘Pai Li’ association mapping model could not be conducted. Nevertheless, the significant Ancestry marker was located in the ‘Pai Li’ region. However, this marker was not predictive of any specific resistance source and, therefore, could not be pursued further. Given the challenges posed by historical record-keeping, required outcrossing, and the complex genetic architecture of resistance, it would be beneficial to initiate controlled crosses to generate populations with known pedigrees, allowing a more precise resolution of these loci. Despite the identification of a significant marker, the Diversity set failed to identify markers that could be predictive of pear fire blight resistance in any set of accessions with known resistance sources. This is probably because only chromosome 2 markers that saturate the previously delimited resistance loci were used. Therefore, any novel resistance within the population could have confounded the results and not been identified. The ‘Old Home’ resistance source mapped to the same region on chromosome 2 as the ‘Potomac’ resistance; however, the developed diagnostic marker was not predictive of ‘Old Home’ resistance in the LBJxOH family. This may be attributed to imperfect phenotyping, as fire blight development following artificial inoculation can be variable. Alternatively, since the SNP targeted for the ‘Potomac’ source is probably not causal, this suggests either ‘Old Home’ resistance is no longer in linkage with the marker or that a different gene, allele, or haplotype is responsible within these two accessions. Interestingly, the diagnostic marker is predictive of ‘Pai Li’ resistance, despite this resistance mapping downstream to a different region of chromosome 2. Future research should focus on identifying key recombination points within the three mapping populations, to further refine the chromosome 2 resistance loci and determine which allele/haplotype corresponds to each unique source of fire blight resistance. Resistance appears to be dominant, with most accessions being heterozygous at the Chr2_3601869 locus. However, a large proportion of resistant Asian accessions are homozygous for the resistance allele at Chr2_3601869, suggesting they may serve as excellent sources for introgressing resistance if needed. The Chr2_3601869 marker also appears to be predictive of other accessions with unknown resistance sources, including ‘Ayers’, ‘Bantam’, ‘Harrow Delight’, ‘Manning-Miller’, ‘Miney’ and ‘Moe’, but further validation is required. The Chr2_3601869 marker is in a Der-1-like gene, is similar to Derlin-1 gene, which is essential for the degradation of misfolded proteins in yeast and preventing toxicity from such proteins (Badawi et al., 2023 ). The accumulation of misfolded proteins causes cellular stress and death, forming endoplasmic reticulum (ER)-localized aggregates observed in many diseases. However, such aggregates have not been reported in fire blight-infected cells. This observation, along with the fact the Chr2_3601869 marker is a synonymous mutation, probably indicates this is not the causal gene or mutation and is only strongly associated with fire blight resistance for utilization in MAS. Conclusions High-saturation sequencing covering two previously identified QTL regions on chromosome 2 and association mapping of various sources of fire blight resistance accessions identified mutations that could be targeted for diagnostic marker(s). A diagnostic marker developed using PACE technology currently achieves an 88.0% and 96.0% prediction accuracy in identifying fire blight resistance and susceptibility in ‘Potomac’ and ‘Pai Li’ derivatives. Despite ‘Old Home’ resistance mapping to the same location as the ‘Potomac’ resistance in previous studies, the diagnostic marker has a lower predictive accuracy of 69.8%, and therefore further investigation is required for ‘Old Home’-sourced material. This diagnostic marker offers a rapid and cost-effective tool for breeding new fire blight-resistant varieties from ‘Potomac’ and ‘Pai Li’ derivatives via MAS. Declarations Funding We acknowledge funding from CRIS Project 2072-21000-059-000D and partial funding from the USDA National Institute of Food and Agriculture Specialty Crop Research Initiative project “RosBREED: Combining Disease Resistance and Horticultural Quality in New Rosaceous Cultivars” (2014-51181-22378). Data Archiving Statement Raw demultiplex sequencing files have been deposited to NCBI under BioProject accession number PRJNA1395619 with BioSample accessions SAMN54367572-SAMN54367594 used for SeqSNP primer design and SAMN54367595-SAMN54368060 used for SeqSNP genotyping. Supplementary files S1 lists the 23 primers used for SeqSNP primer design; S2 lists the primer sequences for the SeqSNP design; S3 lists the 382 samples submitted for SeqSNP genotyping. S4-S8 will be uploaded to Figshare upon manuscript acceptance; S4 contains the variant calling script; S5, the association mapping script; S6, the genotyping file; S7, the genetic map file; and S8, the phenotype file. Author Contribution S.C. conducted the association mapping and marker development; J.D.Z. conducted the sequencing, data analyses and SeqSNP design; S.C., J.D.Z., and N.B. conceived the research idea, and designed the methodology, and contributed to manuscript drafting; N.B. supervised the project; M.D. designed the primers and conducted the PCR amplification; S.M., C.K. and E.L.-G. provided genetic resources for the project, converted the marker and tested it; V.B. tested genetic resources for fireblight resistance; D.C. and C. G. provided guidance and data interpretation; All authors reviewed and edited the manuscript for intellectual content and clarity and approved the manuscript. Acknowledgement We thank Plant & Food Research staff for helping with some of the experiments: Adrian Grande, for contributing to the design of the TaqMan assay; Asleigh Mosen, for helping with the fire blight phenotyping; Adam Friend for sampling the LBJ×OH population; and Lester Brewer for providing information on the fire blight resistance source in Plant & Food Research selections. Data Availability Data is available in the manuscript, the supplementary files and in figshare as described in the Data Archiving Statement. References Andrews, S. 2010. FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc. Badawi, S., F.E. Mohamed, D.S. Varghese, and B.R. Ali. 2023. Genetic disruption of mammalian endoplasmic reticulum-associated protein degradation: Human phenotypes and animal and cellular disease models. Traffic 24(8): 312–333. doi: 10.1111/tra.12902. Bell, R.L. 2019. 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Bender. 1996. Dissemination of the strA-strB streptomycin-resistance genes among commensal and pathogenic bacteria from humans, animals, and plants. Molecular Ecology 5(1): 133–143. doi: 10.1111/j.1365-294X.1996.tb00299.x. Sundin, G.W., and N. Wang. 2018. Antibiotic Resistance in Plant-Pathogenic Bacteria. Annu Rev Phytopathol 56: 161–180. doi: 10.1146/annurev-phyto-080417-045946. Untergasser, A., I. Cutcutache, T. Koressaar, J. Ye, B.C. Faircloth, et al. 2012. Primer3--new capabilities and interfaces. Nucleic acids research 40(15): e115–e115. doi: 10.1093/nar/gks596. Van der Auwera, G.A., M.O. Carneiro, C. Hartl, R. Poplin, G. del Angel, et al. 2013. From FastQ Data to High-Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline. Current Protocols in Bioinformatics 43(1): 11.10.1-11.10.33. doi: 10.1002/0471250953.bi1110s43. Waite, M.B. 1895. Pollination of pear flowers. U. S. Dept. of Agriculture, Division of Vegetable Pathology, Washington, D.C. Wang, J., and Z. Zhang. 2021. GAPIT version 3: boosting power and accuracy for genomic association and prediction. Genomics, Proteomics & Bioinformatics 19(4): 629–640. doi: 10.1016/j.gpb.2021.08.005. Wannemuehler, S.D., J.J. Luby, C. Yue, D.S. Bedford, R.K. Gallardo, et al. 2019. A Cost–Benefit Analysis of DNA Informed Apple Breeding. HortScience 54(11): 1998–2004. doi: 10.21273/HORTSCI14173-19. Xu, Y., and J.H. Crouch. 2008. Marker-Assisted Selection in Plant Breeding: From Publications to Practice. Crop Science 48(2): 391–407. doi: 10.2135/cropsci2007.04.0191. Zurn, J.D., J.L. Norelli, S. Montanari, R. Bell, and N.V. Bassil. 2020. Dissecting Genetic Resistance to Fire Blight in Three Pear Populations. Phytopathology® 110(7): 1305–1311. doi: 10.1094/PHYTO-02-20-0051-R. van der Zwet, T. 2002. Present worldwide distribution of fire blight. Acta horticulturae (590): 33–34. van der Zwet, T., and H.L. Keil. 1970. Relative Susceptibility of Succulent and Woody Tissue of Magness Pear to Infection by Erwinia amylovora . Phytopathology 60(4): 593. doi: 10.1094/Phyto-60-593. van der Zwet, T., and Keil, H. L. 1979. Fire blight a bacterial disease of rosaceous plants. van der Zwet, T., and W.A. Oitto. 1973. Efficient methods of screening pear seedlings in the greenhouse for resistance to fire blight. Plant Disease Report 57: 20–24. van der Zwet, T., W.A. Oitto, and R.C. Blake. 1974. Fire blight resistance in pear cultivars. HortScience 9: 340–342. doi: 10.21273/HORTSCI.9.4.340. Additional Declarations No competing interests reported. Supplementary Files Clareetal2026SupplementaryFiles.zip Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-9396360\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":634303361,\"identity\":\"6e063a94-c9bb-4e34-a408-d76b1a18dd7f\",\"order_by\":0,\"name\":\"Shaun Clare\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Washington State University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Shaun\",\"middleName\":\"\",\"lastName\":\"Clare\",\"suffix\":\"\"},{\"id\":634303362,\"identity\":\"814dac93-2c14-442f-9bda-75ea26c7cd5c\",\"order_by\":1,\"name\":\"Jason D. 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The left panel is the Manhattan plot for the BLINK model using the Diversity, Ancestry, Potomac and Old Home datasets. Marker position and density are indicated on the \\u003cem\\u003ex\\u003c/em\\u003e-axis, where marker densities are plotted as the numbers of markers per 10 Mb window, with low and high marker density as represented by heat map on the right. The logarithm of odds (LOD) score is on the \\u003cem\\u003ey\\u003c/em\\u003e-axis, with Bonferroni thresholds indicated by the solid (\\u003cem\\u003eα\\u003c/em\\u003e-level = 0.05) and dashed (\\u003cem\\u003eα\\u003c/em\\u003e-level = 0.01) lines. The right panel is the quantile–quantile plot of all datasets, with the expected LOD score on the \\u003cem\\u003ex\\u003c/em\\u003e-axis and observed LOD scores on the \\u003cem\\u003ey\\u003c/em\\u003e-axis. The solid line indicates where all data points did not deviate from the expected LOD score.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1ManhattanQQSC3.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9396360/v1/d2e0178794817044af8ca0cf.jpg\"},{\"id\":108531048,\"identity\":\"448e5fa1-007a-431f-bd6f-32c80240d439\",\"added_by\":\"auto\",\"created_at\":\"2026-05-05 15:56:07\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":175100,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePCR Allele Competitive Extension (PACE) marker optimization. Sense (top) and antisense (bottom) primer pools were tested on a subsample of resistant (red) and susceptible (blue) accessions with two non-template controls (NTC, black). Reactions were every two cycles from 26x to 36x to determine optimal cycle number.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2PFBPACETest.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9396360/v1/ac6ba471de74cf050ed513d4.png\"},{\"id\":108531010,\"identity\":\"63905f13-82f8-4158-9cbd-b8da084d95c9\",\"added_by\":\"auto\",\"created_at\":\"2026-05-05 15:55:58\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":114768,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eScatter plot of the diagnostic assay developed for pear fire blight resistance across resistance sources. The sense diagnostic marker was tested on the ‘Pai Li’, ‘Potomac’, and ‘Old Home’ resistance sources in the left, central and right panels, respectively. Non-template controls (water), resistant controls and susceptible controls are indicated in black, red, and blue, respectively. The HEX and FAM fluorescence signals are displayed on the \\u003cem\\u003ex\\u003c/em\\u003eand \\u003cem\\u003ey\\u003c/em\\u003e axis, respectively.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3ResistanceSource.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9396360/v1/5c11bffd29b295cf9edff03a.png\"},{\"id\":108531147,\"identity\":\"da314776-b83e-4781-b0dc-a1403dae0515\",\"added_by\":\"auto\",\"created_at\":\"2026-05-05 15:56:35\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":875642,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9396360/v1/79b3ffea-60c0-4a9c-9a12-6086045fc2cd.pdf\"},{\"id\":108530961,\"identity\":\"6729d7cf-deee-4909-9c88-fcc7715060a1\",\"added_by\":\"auto\",\"created_at\":\"2026-05-05 15:55:47\",\"extension\":\"zip\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":91819,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Clareetal2026SupplementaryFiles.zip\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9396360/v1/2a40b4cd31004ba72436dcb9.zip\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Validation and marker development for a chromosome 2 QTL for fire blight resistance in pear\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eThe bacterial disease fire blight is one of the major diseases affecting pears (\\u003cem\\u003ePyrus\\u003c/em\\u003e spp.). Originating in North America, the pathogen \\u003cem\\u003eErwinia amylovora\\u003c/em\\u003e has proliferated across North America, Europe, parts of Asia, and northern Africa (van der Zwet, \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e; Bell, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). During winter, the pathogen resides in cankers, and as temperatures rise, emerges through a polysaccharide ooze (Malnoy et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). This ooze acts as both a safeguard against abiotic stresses and an attractant for insects, which disseminate the bacteria to other hosts (Griffith et al., 2003). \\u003cem\\u003eE. amylovora\\u003c/em\\u003e is also spread through wind and rain. Flowers are the primary concern, as infection typically occurs via floral nectaries (Malnoy et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). Moreover, infection can also take place through wounded shoots, with hail damage sites often playing a significant role in disease development.\\u003c/p\\u003e \\u003cp\\u003eIn environments conducive to disease development, management has proved challenging. Current strategies involve eliminating infected plants and tissue, coupled with the application of antibiotics and copper-based compounds (Psallidas and Tsiantos, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e; McManus et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e; Norelli et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e; Duffy et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e). Resistance to the antibiotics streptomycin and oxolinic acid has been reported in some \\u003cem\\u003eE. amylovora\\u003c/em\\u003e populations (McManus et al., \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e; Manulis et al., \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e; Rezzonico et al., \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Sundin and Wang, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Laforest et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). The potential for horizontal gene transfer of antibiotic resistance between plant and human pathogens, as well as concerns regarding the environmental impacts of off-target antibiotic applications, have raised questions about the long-term sustainability of antibiotics as a disease management strategy for bacterial plant diseases (Chiou and Jones, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e1995\\u003c/span\\u003e; Sundin and Bender, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e; McManus, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Genetic resistance is an appealing strategy for disease control, as it mitigates the perceived risks associated with off-target antibiotic use. Currently, the most widely cultivated pear varieties in the U.S.A. are susceptible to fire blight, and a concerted effort by the pear industry would be needed to shift towards resistant varieties (Bell, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe development of fire blight-resistant pear cultivars has been a goal for U.S. breeding programs for more than 100 years. For example, the USDA pear scion breeding program was established in the early 1900s with the aim of breeding for improved resistance (Waite, \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e1895\\u003c/span\\u003e; Magness, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e1937\\u003c/span\\u003e; van der Zwet and Keil, H. L., 1979). It continues to this day, and it has released numerous cultivars with fire blight resistance and/or tolerance (van der Zwet and Keil, H. L., 1979; Gottschalk et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Outside the U.S.A., other global breeding programs have also focused on improving fire blight resistance, such as the Harrow Pear Breeding Program in Canada (Hunter, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Many of these breeding programs have relied on resistances from interspecific hybridizations (van der Zwet et al., \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e1974\\u003c/span\\u003e; van der Zwet and Keil, H. L., 1979), which is a tedious and slow process owing to the need for subsequent backcrossing to restore the desirable characteristics of commercial pears. In addition, breeding programs have historically relied on phenotypic screening of populations using labor-intensive inoculation methods or field-based observations (Oitto et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e1970\\u003c/span\\u003e; van der Zwet and Keil, \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e1970\\u003c/span\\u003e; van der Zwet and Oitto, \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e1973\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eDNA-informed breeding makes the transition towards resistant cultivars more tenable from a breeding perspective, using either marker-assisted selection (MAS), genomic selection, or a combination of both. The application of DNA-based genetic markers to assist plant breeding decisions began in the 1980s but has only recently started to be used more routinely in the Rosaceae (Xu and Crouch, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e; Iezzoni et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). DNA-informed breeding is particularly effective for traits that are difficult or costly to phenotype or that depend on specific environmental conditions (Peace, 2017; Xu and Crouch, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e), such as disease resistances and qualitative traits such as blackberry thornlessness (Johns et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e), hop sex (Clare et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), or peach ripening (da Silva Linge et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Breeding new pear varieties is a slow and expensive process, where the time from cross to cultivar release can be as long as 20 to 30 years. Typically, resistance to diseases such as fire blight is assessed between years 1 to 10, depending on the breeding program, and seedlings developed each year require maintenance costs. A predictive DNA test would allow breeders to select resistant progeny in the first year of the breeding cycle, redirecting funds which would otherwise be spent maintaining susceptible seedlings over the first 10 years towards other aspects of the breeding program. In the University of Minnesota apple breeding program, applying MAS to achieve a seedling cull rate of at least 13.2% in the first year of a breeding process has provided economic benefits (Wannemuehler et al., \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eQTLs for fire blight resistance have been consistently detected on pear chromosome 2 from multiple parental sources (Dondini et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Le Roux et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Montanari et al., \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Zurn et al., \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Gabay et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Zurn et al. (\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) resolved this QTL in three different bi-parental F\\u003csub\\u003e1\\u003c/sub\\u003e populations to two physical positions on chromosome 2 of the \\u0026lsquo;Bartlett\\u0026rsquo; double-haploid (DH) genome assembly (Linsmith et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). The first QTL was detected in both the \\u003cem\\u003eP. communis\\u003c/em\\u003e \\u0026lsquo;Old Home\\u0026rsquo; \\u0026times; \\u0026lsquo;Bartlett\\u0026rsquo; and \\u0026lsquo;Potomac\\u0026rsquo; \\u0026times; \\u0026lsquo;El Dorado\\u0026rsquo; populations, and was narrowed to a 1.86 Mb region with 205 reported genes. The second QTL was found to be slightly distal to the first in a hybrid NJA2R59T69 \\u0026times; \\u0026lsquo;Bartlett\\u0026rsquo; population, and was mapped to a 1.71 Mb region with 204 reported genes.\\u003c/p\\u003e \\u003cp\\u003eThe objective of this current study was to develop robust and economic DNA markers for fire blight resistance from the \\u003cem\\u003eP. communis\\u003c/em\\u003e \\u0026lsquo;Old Home\\u0026rsquo;, \\u0026lsquo;Potomac\\u0026rsquo;, and \\u003cem\\u003eP. ussuriensis\\u003c/em\\u003e \\u0026lsquo;Pai Li\\u0026rsquo; (NJA2R59T69) sources. We amplified and sequenced genes known to be associated with disease resistance in 23 resistant and susceptible individuals from these three resistance sources. Alleles uniquely associated with resistance were subsequently genotyped using targeted sequencing to identify novel polymorphic SNPs in the two chromosome 2 QTL regions on a diversity set of 467 individuals with multiple sources of resistance including the three mentioned above. Associated polymorphisms identified via association mapping were converted to a PCR Allele Competitive Extension (PACE) and quantitative PCR (qPCR) TaqMan assays to facilitate pear fire blight resistance breeding efforts via MAS.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eIdentification of Polymorphisms in the two QTL Regions via Amplicon Sequencing\\u003c/h2\\u003e \\u003cp\\u003ePlant material and DNA Extraction\\u003c/p\\u003e \\u003cp\\u003eYoung, actively growing pear leaf tissue was collected from 23 samples with known resistance or susceptibility to pear fire blight, of which 18 samples were from the three segregating bi-parental populations evaluated by Zurn et al. (\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). The remaining five samples were \\u0026lsquo;Harrow Sweet\\u0026rsquo;, identified as resistant (Dondini et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Le Roux et al., \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e); the resistant cultivars \\u0026lsquo;Moonglow\\u0026rsquo; and \\u0026lsquo;Potomac\\u0026rsquo;, which are known to have the chromosome 2 QTL; and the susceptible cultivars \\u0026lsquo;Bartlett\\u0026rsquo; and \\u0026lsquo;El Dorado\\u0026rsquo;. Approximately 30\\u0026ndash;50 mg of tissue from each sample was collected into a 96-well plate format, flash-frozen in liquid nitrogen and stored at \\u0026minus;\\u0026thinsp;80\\u0026deg;C until DNA extraction. Samples were ground using a mixer mill (MM 301; Retsch International, Hann, Germany) and DNA was extracted using a modified Sbeadex Plant Magnetic Bead Kit (LGC Biosearch Technologies, Hoddesdon, UK). DNA was quantified with a Tecan Infinite M Plex multimode plate reader (Tecan Group Ltd, Z\\u0026uuml;rich, Switzerland) and diluted to 5 ng/\\u0026micro;L.\\u003c/p\\u003e \\u003cp\\u003ePrimer Design for Amplicon Sequencing\\u003c/p\\u003e \\u003cp\\u003eZurn et al. (\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) identified two regions for fire blight resistance on chromosome 2. The first region, identified in the \\u0026lsquo;Old Home\\u0026rsquo; \\u0026times; \\u0026lsquo;Bartlett\\u0026rsquo; and \\u0026lsquo;Potomac\\u0026rsquo; \\u0026times; \\u0026lsquo;El Dorado\\u0026rsquo; populations, was narrowed to the physical region of Chromosome 2 from 3,390,229 to 4,332,428 base pairs (bp) (region 1) in the \\u0026lsquo;Bartlett\\u0026rsquo; DH genome assembly (Linsmith et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). The second region, identified in the NJA2R59T69 \\u0026times; \\u0026lsquo;Bartlett\\u0026rsquo; population, corresponds to the region of Chromosome 2 from 5,962,689 to 6,425,332 bp (region 2). FASTA sequences were extracted for the 205 and 204 genes in regions 1 and 2, respectively, and primers were designed using Primer3 v0.4.0 to amplify genic regions (Table S2; (Untergasser et al., \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). For each gene, three or more overlapping tiling primer pairs of approximately 1,000 base pairs (bp) were designed (Table S2). If a gene was under 1,500 bp in length, three primer pairs were designed, to cover as much gene space as possible.\\u003c/p\\u003e \\u003cp\\u003eTo reduce the number of reactions, primers were grouped into 101 optimal multiplex groups using the Multiplex v2.1 software (Kaplinski et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e). All but 11 genes\\u0026rsquo; primer pairs could be multiplexed (Table S2). PCR reactions consisted of 2 \\u0026micro;L 5X PrimeSTAR GXL Buffer, 0.8 \\u0026micro;L dNTPs, 1.75 \\u0026micro;L DNA, 0.2 \\u0026micro;L PrimeSTAR GXL DNA Polymerase. Each primer in the reaction had a final concentration of 0.2 \\u0026micro;M according to the manufacturer\\u0026rsquo;s recommendations, and water was used to adjust the final reaction volume to 10 \\u0026micro;L (TAKARA, San Jose, California, USA). Reactions were amplified in an Eppendorf Gradient thermocycler (Eppendorf Inc., Westbury, NY, USA) using a program consisting of 35 cycles of denaturing at 98\\u0026deg;C for 30 s, annealing at 62\\u0026deg;C for 30 s, and extension at 68\\u0026deg;C for 15 min.\\u003c/p\\u003e \\u003cp\\u003eAmplicon Sequencing Library Preparation and Sequencing\\u003c/p\\u003e \\u003cp\\u003eSuccessful reactions were pooled across each of the 23 genotypes and were cleaned using the Mag-Bind TotalPure NGS kit according to the manufacturer\\u0026rsquo;s recommendations (Omega Bio-tek, Norcross, Georgia, USA). Sixty to 100 \\u0026micro;L of purified sample was sonicated using a Bioruptor Pico Sonication Device to produce amplicons approximately 300 bp (Diagenode, Denville, New Jersey, USA). Each sheared sample went through end repair and purification using the Ion Plus Fragment Library Kit according to the manufacturer\\u0026rsquo;s recommendations (Thermo Fisher Scientific, Waltham, MA, USA) and the Mag-Bind TotalPure NGS kit according to manufacturer\\u0026rsquo;s recommendations (Omega Bio-tek, Norcross, Georgia, USA). The Ion Plus Fragment Library Kit (Thermo Fisher Scientific, Waltham, MA, USA) was used to ligate Xpress\\u0026trade; Barcode Adapters (Thermo Fisher Scientific).\\u003c/p\\u003e \\u003cp\\u003eThe prepared libraries were quantified with the Ion Torrent Universal Quantification Library Kit (Thermo Fisher Scientific, Waltham, MA, USA) using a Bio-Rad CFX96 thermocycler (Hercules, California, USA). Libraries were diluted to 100 pM and 20 \\u0026micro;L of each library was pooled into a centrifuge tube and homogenized. The Ion Chef Instrument was used to load the libraries onto two Ion 540 chips and single-end sequencing was performed using an Ion GeneStudio S5 Plus System (Thermo Fisher Scientific).\\u003c/p\\u003e \\u003cp\\u003eRead Cleaning, Alignment, and Variant Identification for Amplicon Sequencing\\u003c/p\\u003e \\u003cp\\u003eSequences were demultiplexed on the Ion GeneStudio S5 System (Thermo Fisher Scientific), and 23 FASTQ files were downloaded. FASTQ files were evaluated with FastQC v0.11.9 using default setting (Andrews, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e) to identify sequence contamination such as sequence adapters, poly-tail SNP repeats, and over-represented fungal, bacterial, and plasmid sequences. Contamination was removed with BBDuk v03.28.2018 (Bushnell, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) using the following parameters: ktrim right, mink 11, hdist 2, tpe, tbo, maq 25, minlen 25. Files were re-evaluated with FastQC to determine the quality of curated data. Curated genomic FASTA files were indexed and aligned to the \\u003cem\\u003ePyrus communis\\u003c/em\\u003e \\u0026lsquo;Bartlett\\u0026rsquo; DH Genome v2.0 with bwa-mem v0.7.12 using default settings (Li and Durbin, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Linsmith et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). The resulting bam files were sorted by genomic coordinates with samtools v1.18 sort (Li et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e) and read groups were added for traceability with GATK v4.2.0 AddorReplaceReadGroups (McKenna et al., \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Van der Auwera et al., \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Alignments were checked for mapping location with a custom python script. Only sequences aligning to chromosome 2 were used for subsequent variant calling. Variant calling was performed on each bam file for the 23 individuals using Freebayes v1.3.2 (Garrison et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) and variants were filtered for quality scores above 20 and sequencing depth exceeding ten reads per sample using vcflib vcffilter (Garrison et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). A custom python script was used to identify alleles present only in resistant accessions. Polymorphisms evenly distributed across the two fire blight QTL regions were identified and submitted to LGC Bioresearch Technologies (Wisconsin, U.S.A.) for further genotyping using a targeted genotyping by sequencing (targeted GBS) technique referred to by LGC as the SeqSNP genotyping platform.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eGenotyping of Historic Individuals and Populations via SeqSNP\\u003c/h3\\u003e\\n\\u003cp\\u003ePlant Materials and Phenotype Compilation\\u003c/p\\u003e \\u003cp\\u003eA total of 382 lyophilized (10\\u0026ndash;15 mg each) samples were submitted to LGC for SeqSNP (Table S3). This consisted of a diversity panel of 289 accessions across seven \\u003cem\\u003ePyrus\\u003c/em\\u003e species from the NCGR, 21, 18, and 14 progeny of the families \\u0026lsquo;Old Home \\u0026times; 'Bartlett', NJA2R59T69 \\u0026times; 'Bartlett', and 'Potomac' \\u0026times; 'El Dorado', respectively, as well as 40 accessions from New Zealand consisting of mostly of breeding selections (Table S3). Disease responses of the accessions in this diversity set were obtained from the literature (Table S3), while the New Zealand accessions and LBJ\\u0026times;OH population were phenotyped as described by (Montanari et al., \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSample Preparation and Sequencing Data Curation\\u003c/p\\u003e \\u003cp\\u003eRaw sequencing reads were assessed using FastQC 0.11.9 and MultiQC 1.12 and subsequently trimmed using fastp 0.22.0 (Andrews, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Ewels et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Chen et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Trimmed sequenced reads were re-assessed with FastQC and MultiQC to determine if the reads were of sufficient quality. Trimmed reads were aligned and indexed to the \\u0026lsquo;Bartlett\\u0026rsquo; DH Genome v2 using bwa-mem 0.7.17 and samtools 1.16, respectively. Variant calling was performed using GATK 4.2.1.6 HaplotypeCaller, combined with CombineGVCFs, and genotyped using GenotypeGVCFs for a raw vcf file. The raw vcf file was filtered for biallelic SNPs within a minimum depth of three reads, a quality score of 30, and a minor allelic frequency of 0.05, and thinned to 100 bp using vcftools 0.1.16. The remaining missing data were imputed using beagle 5.2 using a burn-in of 10 and iterations of 50 for the final vcf.\\u003c/p\\u003e \\u003cp\\u003eAssociation Mapping\\u003c/p\\u003e \\u003cp\\u003eFor association mapping, four datasets were used. The first one, termed the Diversity, set included all susceptible accessions, accessions with unknown resistance sources, and all accessions that shared ancestry with the different known sources of fire blight resistance: \\u0026lsquo;Potomac\\u0026rsquo;, \\u0026lsquo;Old Home\\u0026rsquo;, and \\u0026lsquo;Pai Li\\u0026rsquo; (NJA2R59T69) (n\\u0026thinsp;=\\u0026thinsp;416). The second dataset contained only accessions with known ancestry to the three sources of chromosome 2 resistance (Ancestry set, n\\u0026thinsp;=\\u0026thinsp;286). Finally, the third and fourth datasets included samples that shared ancestry with the \\u0026lsquo;Potomac\\u0026rsquo; and the \\u0026lsquo;Old Home\\u0026rsquo; sources of resistance, respectively (n\\u0026thinsp;=\\u0026thinsp;122 and n\\u0026thinsp;=\\u0026thinsp;143) (Table S3). A dataset containing only the \\u0026lsquo;Pai Li\\u0026rsquo; source of resistance was not included because only 19 accessions could be selected. Association mapping was performed with GAPIT version 3 (Wang and Zhang, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) using the FarmCPU (Liu et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) and BLINK algorithms (Huang et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), with zero, two, three and five principal components. Principal components were selected based on the standard practices (three), accounting for 50% (two) of the total genotypic variation, and the elbow of the scree plot (five). The best association mapping model for each accession set was determined using a two-step process. Firstly, the mean square difference was calculated (Mamidi et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e) to determine the top five models. However, since models with the lowest scores can result in no significant markers, a visual inspection of the quantile-quantile plot was used to confirm that markers followed the expected uniform \\u003cem\\u003ep\\u003c/em\\u003e-value distribution. A Bonferroni correction was applied to calculate the LOD threshold at the 0.05 α-level of 4.28. The predictive ability of significant markers was assessed by calculating the percentage of calls that correlated with the known phenotype.\\u003c/p\\u003e \\u003cp\\u003ePACE and TaqMan Assay Development\\u003c/p\\u003e \\u003cp\\u003eAfter determining the most predictive marker, sense and antisense PCR Allele Competitive Extension (PACE) assays were designed following guidelines (3CR Biosciences, Harlow, United Kingdom). In brief, an approximate 16\\u0026ndash;20 bp sequence was selected to terminate on the target SNP, with a common reverse primer of 16\\u0026ndash;20 bp positioned approximately 6 bp up or downstream, depending on assay orientation, to form allele-specific primers and a common reverse primer, respectively (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e. The lengths of the forward and reverse primers were adjusted to achieve annealing temperatures within 1\\u003csup\\u003eo\\u003c/sup\\u003eC of each other. PACE assay primers were ordered from Integrated DNA Technologies (IDT, Coralville, IA, United States) and optimized for cycle length by using a modified touchdown PCR of 26\\u0026ndash;36 cycles, reading every two cycles on a known fire blight-resistant (\\u0026lsquo;Potomac\\u0026rsquo; source) and susceptible accessions. PCR reactions were conducted in 15-\\u0026micro;L reactions on CFX96 (Bio-Rad Laboratories, Hercules CA, United States) and analyzed using CFX Manager.\\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\\u003eNucleotide sequence for PACE assay primers. Primer sequence includes the tail to bind to mastermix fluorophores. All sense or antisense primers should be complexed together.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\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 \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eName\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePrimer\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePFB_Sense_F1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGAAGGTGACCAAGTTCATGCTGGTGGAGGAGCTCCG\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePFB_Sense_F2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGAAGGTCGGAGTCAACGGATTGGTGGAGGAGCTCCT\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePFB_Sense_CR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCGTAAAATAGTCGCAAGATGGAG\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePFB_AntiSense_F1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGAAGGTGACCAAGTTCATGCTAGTCGCAAGATGGAGGATC\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePFB_AntiSense_F2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGAAGGTCGGAGTCAACGGATTAGTCGCAAGATGGAGGATA\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePFB_AntiSense_CR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCTGCTAGTTGGTGGAGGA\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe PACE assay design was also used as a template to design a TaqMan assay to test fire blight-resistant and susceptible accessions at Plant \\u0026amp; Food Research in New Zealand (Table S3). A total of 135 progeny from the (LBJ\\u0026times;OH) family and six reference cultivars were screened with the TaqMan assay.\\u003c/p\\u003e \\u003cp\\u003eThe TaqMan assay was designed on the LGC Biosearch Technologies tool RealTimeDesign for SNP genotyping on the \\u0026lsquo;least restrictive\\u0026rsquo; parameter setting (RealTimeDesign qPCR Assay Design Software, | LGC Biosearch Technologies). The oligonucleotide sequences for Allele probe 1 (FAM-BHQplus), Allele probe 2 (CAL Fluor Orange 560-BHQplus), forward primer and reverse primer were CAAGATGGAGGATAGGAGCT, TGGAGGATCGGAGCTC, CCCACTTGAAGGTAGTTTGTCCATA and GAGGTGGTTGGGTTCTGCTA, respectively.\\u003c/p\\u003e \\u003cp\\u003eThe assay was run on the LightCycler480 platform using AccuStart Genotyping Toughmix polymerase (Quanta BioSciences). Cycling parameters: Initial denature 95\\u0026deg;C for 5 min, 40 2-step cycles of 95\\u0026deg;C for 5 s and 60\\u0026deg;C for 45 s with signal acquisition each cycle and a final cooling step 40\\u0026deg;C for 30 s. The results were visualized with the Endpoint Genotyping analysis module of the LightCycler software.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eIdentification of Polymorphisms in the two QTL regions via amplicon sequencing\\u003c/h2\\u003e \\u003cp\\u003eAn average of 175,987.57\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;26,270.12 reads were generated per sample, ranging from 118,295 reads for the NJA2R59T69 \\u0026times; 'Bartlett' line US200537_043 to 220,607 reads for \\u0026lsquo;Harrow Sweet\\u0026rsquo; (Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). After filtering, an average of 115,405.26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17,337.38 reads per sample were retained for further analysis. An average of 115,079.70\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;17,293.75 reads per sample were mapped to the \\u003cem\\u003eP. communis\\u003c/em\\u003e \\u0026lsquo;Bartlett\\u0026rsquo; DH Genome v2.0. An average of 99.7% of the filtered reads were successfully mapped. Of the mapped reads, 81\\u0026ndash;94% of the reads with an average of 88.5% mapped to chromosome 2, indicating the regions were successfully targeted.\\u003c/p\\u003e \\u003cp\\u003eAfter allele calling and filtering, a total of 13,041 polymorphisms were identified, with 8113 and 4928 in region 1 and region 2, respectively. A python script was used to filter the polymorphisms and identify alleles that were unique to the resistant sources. In region 1, a total of 1100 alleles were identified that were unique to resistant individuals carrying the \\u0026lsquo;Old Home\\u0026rsquo;/\\u0026rsquo;Potomac\\u0026rsquo; source of resistance and 1085 alleles in region 2 that appeared unique to resistant NJA2R59T69 \\u0026times; \\u0026lsquo;Bartlett\\u0026rsquo; individuals. A total of 531 polymorphisms, 364 from region 1 and 167 from region 2, were submitted to LGC for SeqSNP assay design.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eGenotyping of Historic Individuals and Populations via SeqSNP and Association Analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eA total of 264\\u0026nbsp;million raw sequencing reads were obtained for all samples, ranging from 18,748 to 1.6\\u0026nbsp;million reads per sample. After trimming and quality control, 238\\u0026nbsp;million sequencing reads remained and were mapped to the \\u0026lsquo;Bartlett\\u0026rsquo; DH Genome v2.0, ranging from 16,820 to 1.5\\u0026nbsp;million reads per sample. Before filtering, 60,267 variants were identified. After filtering, 972 high-quality biallelic SNP markers on chromosome 2 were retained for association mapping. When considering only the best models from Diversity, Ancestry and Potomac, a total of four marker trait associations (MTAs) were identified (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e. The best Diversity association mapping model (PC0_BLINK) identified two significant markers (Chr2_6146265, LOD 6.52; Chr2_6087809, LOD 6.00), with one each within the previously delimited \\u0026lsquo;Pai Li\\u0026rsquo; region. The best Ancestry model (PC0_BLINK) contained two significant markers (Chr2_3572341, LOD 7.68; Chr2_3601869, LOD 17.63), both within the previously delimited \\u0026lsquo;Potomac\\u0026rsquo;/\\u0026rsquo;Old Home\\u0026rsquo; region. The best \\u0026lsquo;Potomac\\u0026rsquo; association map (PC0_BLINK) identified two significant markers. Two markers (Chr2_3572063, LOD 5.11 and Chr2_3601869, LOD 7.85) were identified in the \\u0026lsquo;Potomac\\u0026rsquo;/\\u0026rsquo;Old Home\\u0026rsquo; delimited region. The best \\u0026lsquo;Old Home\\u0026rsquo; model (PC0_FarmCPU) identified one significant marker (Chr2_6370014, LOD 5.93) in the \\u0026lsquo;Pai Li\\u0026rsquo; region. However, this marker was only 74.1% predictive of resistance in accessions containing \\u0026lsquo;Old Home\\u0026rsquo; in the pedigree and was therefore not pursued for assay development. The Chr2_6146265 marker had a predictive accuracy that varied from 70.4%, 70.4%, 96.0% to 58.4% in the \\u0026lsquo;Potomac\\u0026rsquo;-, \\u0026lsquo;Old Home\\u0026rsquo;-, \\u0026lsquo;Pai Li\\u0026rsquo;-sourced resistances, respectively. Owing to the low numbers of \\u0026lsquo;Pai Li\\u0026rsquo;-sourced accessions and availability of more predictive markers, Chr2_6146265 was not pursued.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSignificant markers identified via association mapping against pear fire blight. Bold indicates the best model for the respective data set determined using mean squared deviations (MSD) and visual inspection of the quantile-quantile (QQ) plot. A Bonferroni logarithm of odds (LOD) threshold of 4.28 at the α-level of 0.05. Alleles and phenotypic variation explained is displayed as an R\\u003csup\\u003e2\\u003c/sup\\u003e value where negative values indicates the alternative allele increased resistance.\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" 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 Marker\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eChr\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePos\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eModel Identified\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eLOD Score\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eAlleles\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eR\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChr2_3572063\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eChr 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3572063\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePotomac\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eA/T\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChr2_3572341\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eChr 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3572341\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eAncestry\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.68\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eA/C\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChr2_3601869\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eChr 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3601869\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eAncestry\\u003c/p\\u003e \\u003cp\\u003ePotomac\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e17.63\\u003c/p\\u003e \\u003cp\\u003e7.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eG/T\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.39\\u003c/p\\u003e \\u003cp\\u003e-0.43\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChr2_6087809\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eChr 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6087809\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDiversity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eC/A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChr2_6146265\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eChr 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6146265\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDiversity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eA/G\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eChr2_6370014\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eChr 2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6370014\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eOld Home\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.93\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eT/A\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e-0.39\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe Chr2_3601869 marker had the highest LOD score and was the most predictive of resistance in accessions with \\u0026lsquo;Potomac\\u0026rsquo;- and \\u0026lsquo;Pai Li\\u0026rsquo;-sourced resistance, at 80.8% and 96.0%, respectively. However, its accuracy was only 57.0% in \\u0026lsquo;Old Home\\u0026rsquo;. This SNP is a synonymous mutation within exon 8 (the last exon) of gene pycom02g05390, annotated as a \\u003cem\\u003eDerlin-1-like\\u003c/em\\u003e gene.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePACE and TaqMan Assay Development\\u003c/h2\\u003e \\u003cp\\u003eThe sense PACE design failed to separate resistance and susceptible accessions, whereas the antisense design successfully formed compact clusters after 26 cycles. However, it was over-cycled by 34 cycles, as evidenced by the migration of non-template controls (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The PACE assay was tested on LGC plate 3, which included accessions with \\u0026lsquo;Pai Li\\u0026rsquo;-, \\u0026lsquo;Potomac\\u0026rsquo;- and \\u0026lsquo;Old Home\\u0026rsquo;-sourced resistance. The PACE assay successfully predicted resistance in accessions with \\u0026lsquo;Pai Li\\u0026rsquo; and \\u0026lsquo;Potomac\\u0026rsquo; resistance, but was not predictive in accessions with \\u0026lsquo;Old Home\\u0026rsquo;-sourced resistance (Fig.\\u0026nbsp;3).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe TaqMan probes were designed on the Chr2_3601869 marker using the antisense PACE primers. One individual from the LBJ\\u0026times;OH family was removed because it could not be phenotyped. The assay grouped the remaining 140 samples tested into three clusters: 18 samples were homozygous for the T allele (associated to resistance in the PACE assay); 37 samples were homozygous for the G allele (associated with susceptibility); and 85 samples were heterozygous. The average fire blight severity was 44.1% necrosis for samples homozygous for the T allele, 63.8% for samples homozygous for the G allele, and 42.5% for the heterozygotes. All three clusters included samples showing necrosis from 0 to 100%, thus including both resistant and susceptible plants.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe Ancestry set identified markers within both the \\u0026lsquo;Potomac\\u0026rsquo;/\\u0026rsquo;Old Home\\u0026rsquo; and \\u0026lsquo;Pai Li\\u0026rsquo; regions of chromosome 2. Owing to the low number of \\u0026lsquo;Pai Li\\u0026rsquo;-pedigreed accessions, a dedicated \\u0026lsquo;Pai Li\\u0026rsquo; association mapping model could not be conducted. Nevertheless, the significant Ancestry marker was located in the \\u0026lsquo;Pai Li\\u0026rsquo; region. However, this marker was not predictive of any specific resistance source and, therefore, could not be pursued further. Given the challenges posed by historical record-keeping, required outcrossing, and the complex genetic architecture of resistance, it would be beneficial to initiate controlled crosses to generate populations with known pedigrees, allowing a more precise resolution of these loci. Despite the identification of a significant marker, the Diversity set failed to identify markers that could be predictive of pear fire blight resistance in any set of accessions with known resistance sources. This is probably because only chromosome 2 markers that saturate the previously delimited resistance loci were used. Therefore, any novel resistance within the population could have confounded the results and not been identified.\\u003c/p\\u003e \\u003cp\\u003eThe \\u0026lsquo;Old Home\\u0026rsquo; resistance source mapped to the same region on chromosome 2 as the \\u0026lsquo;Potomac\\u0026rsquo; resistance; however, the developed diagnostic marker was not predictive of \\u0026lsquo;Old Home\\u0026rsquo; resistance in the LBJxOH family. This may be attributed to imperfect phenotyping, as fire blight development following artificial inoculation can be variable. Alternatively, since the SNP targeted for the \\u0026lsquo;Potomac\\u0026rsquo; source is probably not causal, this suggests either \\u0026lsquo;Old Home\\u0026rsquo; resistance is no longer in linkage with the marker or that a different gene, allele, or haplotype is responsible within these two accessions. Interestingly, the diagnostic marker is predictive of \\u0026lsquo;Pai Li\\u0026rsquo; resistance, despite this resistance mapping downstream to a different region of chromosome 2. Future research should focus on identifying key recombination points within the three mapping populations, to further refine the chromosome 2 resistance loci and determine which allele/haplotype corresponds to each unique source of fire blight resistance. Resistance appears to be dominant, with most accessions being heterozygous at the Chr2_3601869 locus. However, a large proportion of resistant Asian accessions are homozygous for the resistance allele at Chr2_3601869, suggesting they may serve as excellent sources for introgressing resistance if needed. The Chr2_3601869 marker also appears to be predictive of other accessions with unknown resistance sources, including \\u0026lsquo;Ayers\\u0026rsquo;, \\u0026lsquo;Bantam\\u0026rsquo;, \\u0026lsquo;Harrow Delight\\u0026rsquo;, \\u0026lsquo;Manning-Miller\\u0026rsquo;, \\u0026lsquo;Miney\\u0026rsquo; and \\u0026lsquo;Moe\\u0026rsquo;, but further validation is required.\\u003c/p\\u003e \\u003cp\\u003eThe Chr2_3601869 marker is in a Der-1-like gene, is similar to Derlin-1 gene, which is essential for the degradation of misfolded proteins in yeast and preventing toxicity from such proteins (Badawi et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The accumulation of misfolded proteins causes cellular stress and death, forming endoplasmic reticulum (ER)-localized aggregates observed in many diseases. However, such aggregates have not been reported in fire blight-infected cells. This observation, along with the fact the Chr2_3601869 marker is a synonymous mutation, probably indicates this is not the causal gene or mutation and is only strongly associated with fire blight resistance for utilization in MAS.\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eHigh-saturation sequencing covering two previously identified QTL regions on chromosome 2 and association mapping of various sources of fire blight resistance accessions identified mutations that could be targeted for diagnostic marker(s). A diagnostic marker developed using PACE technology currently achieves an 88.0% and 96.0% prediction accuracy in identifying fire blight resistance and susceptibility in \\u0026lsquo;Potomac\\u0026rsquo; and \\u0026lsquo;Pai Li\\u0026rsquo; derivatives. Despite \\u0026lsquo;Old Home\\u0026rsquo; resistance mapping to the same location as the \\u0026lsquo;Potomac\\u0026rsquo; resistance in previous studies, the diagnostic marker has a lower predictive accuracy of 69.8%, and therefore further investigation is required for \\u0026lsquo;Old Home\\u0026rsquo;-sourced material. This diagnostic marker offers a rapid and cost-effective tool for breeding new fire blight-resistant varieties from \\u0026lsquo;Potomac\\u0026rsquo; and \\u0026lsquo;Pai Li\\u0026rsquo; derivatives via MAS.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eFunding\\u003c/h2\\u003e \\u003cp\\u003eWe acknowledge funding from CRIS Project 2072-21000-059-000D and partial funding from the USDA National Institute of Food and Agriculture Specialty Crop Research Initiative project \\u0026ldquo;RosBREED: Combining Disease Resistance and Horticultural Quality in New Rosaceous Cultivars\\u0026rdquo; (2014-51181-22378).\\u003c/p\\u003e \\u003cp\\u003eData Archiving Statement\\u003c/p\\u003e \\u003cp\\u003eRaw demultiplex sequencing files have been deposited to NCBI under BioProject accession number PRJNA1395619 with BioSample accessions SAMN54367572-SAMN54367594 used for SeqSNP primer design and SAMN54367595-SAMN54368060 used for SeqSNP genotyping. Supplementary files S1 lists the 23 primers used for SeqSNP primer design; S2 lists the primer sequences for the SeqSNP design; S3 lists the 382 samples submitted for SeqSNP genotyping. S4-S8 will be uploaded to Figshare upon manuscript acceptance; S4 contains the variant calling script; S5, the association mapping script; S6, the genotyping file; S7, the genetic map file; and S8, the phenotype file.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eS.C. conducted the association mapping and marker development; J.D.Z. conducted the sequencing, data analyses and SeqSNP design; S.C., J.D.Z., and N.B. conceived the research idea, and designed the methodology, and contributed to manuscript drafting; N.B. supervised the project; M.D. designed the primers and conducted the PCR amplification; S.M., C.K. and E.L.-G. provided genetic resources for the project, converted the marker and tested it; V.B. tested genetic resources for fireblight resistance; D.C. and C. G. provided guidance and data interpretation; All authors reviewed and edited the manuscript for intellectual content and clarity and approved the manuscript.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eWe thank Plant \\u0026amp; Food Research staff for helping with some of the experiments: Adrian Grande, for contributing to the design of the TaqMan assay; Asleigh Mosen, for helping with the fire blight phenotyping; Adam Friend for sampling the LBJ\\u0026times;OH population; and Lester Brewer for providing information on the fire blight resistance source in Plant \\u0026amp; Food Research selections.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eData is available in the manuscript, the supplementary files and in figshare as described in the Data Archiving Statement.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAndrews, S. 2010. 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Present worldwide distribution of fire blight. Acta horticulturae (590): 33\\u0026ndash;34.\\u003c/li\\u003e\\n\\u003cli\\u003evan der Zwet, T., and H.L. Keil. 1970. Relative Susceptibility of Succulent and Woody Tissue of Magness Pear to Infection by \\u003cem\\u003eErwinia amylovora\\u003c/em\\u003e. Phytopathology 60(4): 593. doi: 10.1094/Phyto-60-593.\\u003c/li\\u003e\\n\\u003cli\\u003evan der Zwet, T., and Keil, H. L. 1979. Fire blight a bacterial disease of rosaceous plants.\\u003c/li\\u003e\\n\\u003cli\\u003evan der Zwet, T., and W.A. Oitto. 1973. Efficient methods of screening pear seedlings in the greenhouse for resistance to fire blight. Plant Disease Report 57: 20\\u0026ndash;24.\\u003c/li\\u003e\\n\\u003cli\\u003evan der Zwet, T., W.A. Oitto, and R.C. Blake. 1974. Fire blight resistance in pear cultivars. HortScience 9: 340\\u0026ndash;342. doi: 10.21273/HORTSCI.9.4.340.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Pyrus, fire blight, Marker assisted selection, DNA-informed breeding, PCR allele competitive extension, single nucleotide polymorphism, SNP\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9396360/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9396360/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eFire blight, caused by the bacterial pathogen \\u003cem\\u003eErwinia amylovora\\u003c/em\\u003e, is a persistent problem for pear (\\u003cem\\u003ePyrus\\u003c/em\\u003e spp.) growers across most production regions around the world. Growing resistant varieties is one of the best options for managing fire blight. The resistant \\u003cem\\u003eP. communis\\u003c/em\\u003e cultivars \\u0026lsquo;Potomac\\u0026rsquo; and \\u0026lsquo;Old Home\\u0026rsquo;, and the hybrid selection NJA2R59T69 were used in a previous study to identify quantitative trait loci (QTLs) linked to the resistance. The major chromosome 2 QTLs identified in the \\u0026lsquo;Potomac\\u0026rsquo; and \\u0026lsquo;Old Home\\u0026rsquo; sources overlap with QTLs that were previously identified in \\u0026lsquo;Harrow Sweet\\u0026rsquo; and \\u0026lsquo;Moonglow\\u0026rsquo;, while that of NJA2R59T69 (through \\u003cem\\u003eP. ussuriensis\\u003c/em\\u003e \\u0026lsquo;Pai Li\\u0026rsquo;) mapped to a nearby location on chromosome 2. In the current study, genes associated with disease resistance in the two chromosome 2 QTL regions were cloned in 23 accessions representing resistant and susceptible cultivars, and progeny from the three sources. Alleles unique to resistant cultivars in these genic regions were targeted by SeqSNP and used to genotype a diversity set of 382 pear accessions with known fire blight disease responses from multiple sources. Association mapping was conducted across subsets of the 382 accessions based on the shared fire blight resistance source. In the 48 accessions with the \\u0026lsquo;Potomac\\u0026rsquo; source of resistance, association mapping identified a marker (Chr2_3601869) that was predictive with 80.8% and 96.0% accuracy in \\u0026lsquo;Potomac\\u0026rsquo; and \\u0026lsquo;Pai Li\\u0026rsquo;, respectively. However, Chr2_3601869 was only 57.0% accurate in \\u0026lsquo;Old Home\\u0026rsquo; derived accessions. This marker was converted to a PACE assay that was predictive in both \\u0026lsquo;Pai Li\\u0026rsquo; and \\u0026lsquo;Potomac\\u0026rsquo; -derived accessions but not \\u0026lsquo;Old Home\\u0026rsquo;-sourced resistance. Resistance appears to be dominant, with the majority of accessions being heterozygous at the Chr2_3601869 locus. A large proportion of resistant Asian accessions are homozygous for the resistance allele at Chr2_3601869, suggesting these may be excellent sources to introgress resistance. Future work should look to elucidate the critical recombination points within the three mapping populations to further refine the chromosome 2 resistance loci and determine which allele/haplotype corresponds to each unique source of fire blight resistance.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Validation and marker development for a chromosome 2 QTL for fire blight resistance in pear\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-05-05 15:54:28\",\"doi\":\"10.21203/rs.3.rs-9396360/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"9559033d-b0fb-4985-867c-02a5084f8f4f\",\"owner\":[],\"postedDate\":\"May 5th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-05-18T11:01:08+00:00\",\"index\":21,\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-05T15:54:29+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-05-05 15:54:28\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9396360\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9396360\",\"identity\":\"rs-9396360\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}