{"paper_id":"29f5047c-3f8d-426f-9a75-764df479a2b4","body_text":"1 Reduced confidence intervals and novel candidate genes for quantitative trait \n2 loci associated with apple scab resistance in Malus domestica\n3\n4 Short title: Fine mapping of apple scab resistance quantitative trait loci\n5\n6 Romane Lapous 1¶*, Camille Haquet 1¶#a, Caroline Denancé 1, Juliette Bénéjam 1#b, Laure \n7 Perchepied 1, Kaat Hellyn1, Hélène Muranty1, Charles-Eric Durel1, Julie Ferreira de Carvalho1*\n8\n9 1 Univ Angers, Institut Agro, INRAE, IRHS, SFR QUASAV, F-49000 Angers, France\n10 #a Current address: INRAE, UMR 1095 INRAE–UCA Genetics, Diversity & Ecophysiology of \n11 Cereals, Clermont-Ferrand, France.\n12 #b Current address: Univ. Bordeaux, INRAE, UMR BFP, F-33140, Villenave d’Ornon, France.\n13\n14 *Corresponding authors\n15 E-mail: romane.lapous.pro@outlook.com (RL), julie.ferreira-de-carvalho@inrae.fr (JF)\n16\n17 ¶ These authors contributed equally to this work.\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n18 Abstract \n19 Apple scab, caused by Venturia inaequalis, remains one of the most damaging diseases in \n20 apple orchards, driving intensive pesticide use worldwide. Reducing this dependence requires \n21 the deployment of durable resistance, ideally through the combination of major resistance \n22 genes (R genes) with quantitative trait loci (QTL) that confer partial and potentially \n23 complementary protection. Yet, few apple scab QTLs have been functionally validated, and \n24 their underlying mechanisms remain largely unresolved.\n25 Here, we refined and functionally described, with transcriptomic data, five resistance QTLs in \n26 a biparental population of 1,970 individuals derived from the cross ‘TN 10-8’ × ‘Fiesta’. Using \n27 43 newly developed KASP markers, QTL locations were substantially precised through high-\n28 resolution genotyping and phenotyping with two V. inaequalis isolates exhibiting contrasting \n29 virulence. Four QTL (qT1, qF11, qF17, qT13) were validated, while qF3 was not confirmed. \n30 Transcriptomic data comparison revealed the expression of candidate genes within the \n31 narrowed intervals, including receptor-like proteins in qT1, and RNAi- and signaling-related \n32 genes in qF11 and qF17, suggesting a diversified and complementary defense network. \n33 These findings refine the genetic architecture of apple scab resistance and suppose the \n34 existence of shared molecular pathways between major R gene, such as the well-described \n35 Rvi6 gene, and quantitative resistance, with for instance the QTL qT1. The identified loci and \n36 markers provide robust tools for marker-assisted and genomic breeding aimed at developing \n37 apple cultivars with complementary and potentially durable resistance pathways.\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n38 Introduction\n39 Venturia inaequalis, the causal agent of apple scab, remains the most economically damaging \n40 fungal disease of apple worldwide, causing substantial yield losses and requiring intensive \n41 fungicide applications in temperate regions 1. The pathogen overwinters in fallen leaves, \n42 producing ascospores that initiate primary infections on young leaves and fruits during spring. \n43 Subsequent cycles of conidial dissemination lead to secondary infections, resulting in \n44 defoliation, fruit deformation, and, in severe cases, tree weakening 2. To limit the \n45 environmental and health impacts of repeated fungicide use, the development of genetically \n46 resistant cultivars has become an essential strategy for sustainable apple production 3.\n47 Historically, breeding for scab resistance has relied on monogenic resistance conferred by \n48 resistance genes or R genes. Up to now, at least 18 such loci (Rvi1 –Rvi18) have been identified \n49 from cultivated and wild Malus accessions 4–7. The first and most widely used, Rvi6 (previously \n50 named Vf), originates from Malus floribunda and encodes a leucine-rich repeat receptor-like \n51 protein (LRR-RLP) 8–10. Approximately 90% of resistant cultivars released since the 1980s carry \n52 this Rvi6 locus 11. However, some V. inaequalis populations are able to overcome this \n53 resistance, as firstly reported in the cultivar ‘Prima’ in Germany in 1988 12. The origin of such \n54 populations could result from the migration of virulent isolates from non-European apples, \n55 and their maintenance in environmental disease reservoirs, like ornamental crabapples 13. \n56 This breakdown underscores the limitations of monogenic resistance, which imposes strong \n57 selection pressure on pathogen populations.\n58 In contrast, quantitative resistance, controlled by quantitative trait loci (QTL), typically \n59 provides partial but presumably more durable protection, covering functions that might \n60 complement R genes 14. Each locus contributes modestly to resistance, and the combined \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n61 effect of multiple QTLs may slow pathogen adaptation and enhance resistance durability if \n62 they mobilize different mechanisms. To date, at least 14 apple scab QTLs have been reported \n63 7, yet only a few have been precisely mapped or functionally characterized due to broad \n64 confidence intervals (CIs) and limited marker density in earlier studies. Among these, three \n65 QTL—qT1, qF11, and qF17—have been repeatedly detected and partially characterized in \n66 biparental populations derived from the cultivars ‘TN10-8’ and ‘Fiesta’ 15–18. The locus qT1, \n67 located on chromosome 1 and derived from ‘TN10-8’, co-localizes with Rvi6, which has been \n68 shown to be a member of an extracellular leucine-rich repeat receptor family 8,9. In contrast, \n69 qF11 and qF17, both originating from ‘Fiesta’, reside on chromosomes 11 and 17, respectively, \n70 and exhibit a strong synergistic interaction, reducing disease severity only when both \n71 resistance alleles are combined 18,19. Interestingly, these two QTLs were effective against a \n72 broader range of isolates than qT1 16,18. Moreover, the pyramiding of qT1, qF11, and qF17 in \n73 a ‘TN10-8 × Fiesta’ progeny has been shown to block V. inaequalis development at multiple \n74 infection stages—from penetration to sporulation 20—although subsequent pathogen \n75 adaptation has eroded the protection conferred by qF11 and qF17 19. More recently, two \n76 additional QTLs—qT13 on chromosome 13 (from ‘TN10-8’) and qF3 on chromosome 3 (from \n77 ‘Fiesta’)—were reported by Bénéjam et al. (2021) 18 but remained poorly resolved due to wide \n78 CIs and low SNP density. \n79 To elucidate their genetic basis and exploit them for breeding, it is essential to increase \n80 mapping precision and identify putative causal genes. Fine-mapping approaches rely on larger \n81 offspring populations (~500 to >10,000 individuals when available), thereby increasing the \n82 number of recombination events 21. Coupled with SNP genotyping—the most common source \n83 of DNA sequence variation—these approaches help reduce QTL CIs, provide new markers for \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n84 marker-assisted selection (MAS), limit the co-introgression of undesirable genes, and decrease \n85 the number of candidate genes for functional validation 22–25. In addition to these approaches, \n86 integrating ‘-omics’ data—such as genomics, transcriptomics, proteomics, and \n87 metabolomics—can help prioritize candidate genes for functional validation. Such integration \n88 may involve sequence alignment 21, gene expression analyses 25, or the identification of QTL \n89 co-localizations 26–28.\n90 In this study, we analyzed an extended ‘TN10-8 × Fiesta’ progeny of 1,970 individuals to refine \n91 the genetic localization of five QTL (qT1, qF11, qF17, qT13, and qF3) involved in quantitative \n92 resistance to V. inaequalis. Using newly developed KASP markers and phenotyping with two \n93 V. inaequalis isolates of contrasting virulence, we improved the mapping resolution of the \n94 QTLs. Then, the integration of transcriptomic data from Bénéjam et al. (2024) 29 enable us to \n95 propose candidate genes potentially involved in apple scab resistance. These findings improve \n96 our understanding of the genetic basis of quantitative resistance in apple and provide \n97 molecular resources for marker-assisted and genomics-assisted breeding strategies aimed at \n98 combining complementary resistance mechanisms for durable scab resistance.\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n99 Materials and methods\n100 Plant material\n101 Seeds of the cross between apple genotype ‘TN10-8’ and cultivar ‘Fiesta’ (referred to as the \n102 ‘TxF' progeny) were produced in the framework of the DARE European project (Durable Apple \n103 Resistance in Europe, 1998-2002). A part of these seeds were firstly elevated in greenhouse \n104 until their implantation in orchards, then allowing their phenotyping through grafting to \n105 decipher the genetic control of scab resistance segregating in this material 16. This initial \n106 population, hereafter referred as base population, is composed of 267 individuals and allowed \n107 for the detection of three QTLs of ‘Fiesta’ and two of ‘TN10-8’ 15,16,18,30,31. ‘Fiesta’ is an offspring \n108 from a cross between the cultivars ‘Cox’s Orange Pippin’ (resistant to apple scab) and ‘Idared’ \n109 (susceptible to apple scab) and is carrying resistance and susceptibility alleles on each of \n110 chromosomes 3, 11 and 17 that are subsequently referred as qF3, qF11 and qF17, respectively. \n111 The hybrid ‘TN10-8’, is coming from a cross between the French heirloom cultivar ‘Reinette \n112 Clochard’ and a descendent from the Russian cultivar ‘Antonovka’ (fairly resistant to apple \n113 scab) 32. This genotype is carrying resistance and susceptibility alleles on each of chromosome \n114 1 (qT1) and 13 (qT13). The MUNQ (Malus UNiQue genotype code), as described by Muranty \n115 et al. (2020) 33 and Durel et al. (2023) 34, was 2396 for ‘TN10-8’, 763 for ‘Fiesta, 163 for ‘Cox’s \n116 Orange Pippin', 717 for ‘Idared’, 118 for ‘Reinette Clochard’ and 4 for Antonovka.\n117 The remaining seeds from the ‘TxF' progeny, stored at -20°C, were used in the present study \n118 and each seed still corresponds to a unique genotype. This population, hereafter referred as \n119 extended population, is composed of 1,970 seeds. In the fall 2022, seeds were germinated, \n120 transplanted into soil, and subsequently cultivated in a greenhouse on their own roots. Young \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n121 leaf discs of each individual were sampled using the “BioArkTM Leaf collection kit” from LGC \n122 Biosearch Technologies TM company (United Kingdom, UK) for subsequent genotyping. \n123 Identification of QTL boundaries, development of SNP markers and \n124 qPCR-based assays\n125 In previous studies, QTL locations were assessed using 267 individuals 18, genotyped with SNPs \n126 from the 20K SNP arrays 35,36. Context sequences of 43 SNPs localized upstream, within, and \n127 downstream of the five QTL CIs, and heterozygous in ‘Fiesta’ or ‘TN10-8’, were retrieved from \n128 both the 20K and 480K SNP arrays and blasted against the reference genome of ‘Golden \n129 Delicious’ doubled haploid GDDH13 37 to then be visualized using JBrowse 38. This step allowed \n130 the assignment of broader context sequence surrounding each candidate SNP (mean length \n131 150 bp; range 105-216 bp). Additional SNPs, identified by aligning the sequencing data used \n132 to develop the 480K array 36 to the GDDH13 genome 37, were also considered in order to \n133 develop adapted primers for KASP genotyping.\n134 Leaf material was sent to LGC Biosearch Technologies TM (Herts, UK) for DNA extraction and \n135 SNP genotyping using KASP technology. For each selected SNP, two allele specific forward \n136 primers and one common reverse primer were designed and validated by LGC Biosearch \n137 Technologies TM (Herts, UK). The assay mix preparation and PCR amplifications were \n138 performed according to the user’s guide and manual (LGC Biosearch Technologies TM, \n139 Hoddesdon, Herts, UK). The concentration of DNA samples was measured using \n140 NanoDrop2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). DNA \n141 samples were diluted for genotyping, as required. KASP genotyping was carried out on a \n142 StepOnePlusTM real-time PCR system (Thermo Fisher Scientific, Waltham, MA, USA). \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n143 Genotyping data were cleaned and analyzed using SNPviewer™ (LGC Biosearch Technologies, \n144 UK) at INRAE (Angers), which visualizes KASP results as cluster plots. Marker quality was \n145 evaluated based on clustering performance and genotype discrimination among seedlings \n146 derived from two genotyped parents. Out of the 43 markers tested, 36 were validated. The \n147 seven non validated markers exhibited poor clustering, likely resulting from suboptimal primer \n148 hybridization due to imperfect primer design or additional polymorphisms near the target \n149 SNP.\n150 Using the same DNA extracts, additional SNP markers, were developed at INRAE to further \n151 densify the CIs and peak of four QTLs (qT1, qF11, qF17, and qT13). A second set of seven SNP \n152 markers was successfully designed and incorporated. KASP primers were designed at Eurofins \n153 Genomics. These markers were analyzed on the ANAN genotyping platform (Univ. Angers, \n154 France). Genotyping was then done using the PACE® mix (3crbio, Harlow, UK) following \n155 manufacturer protocol. Thus, a total of 43 SNPs distributed along the five QTLs of interest \n156 were used. Each SNP is presented with sequence context, SNP of interest position and \n157 additional SNPs in S1 File. This file also contains virtual markers, added with computational \n158 analyses described below. \n159 Apple scab phenotypic scoring and analyses\n160 Venturia inaequalis isolate inoculum and apple scab scoring\n161 The seedlings described above were own-rooted and non-replicated; therefore, scab \n162 inoculations were performed sequentially on the same plants, which were pruned between \n163 each inoculation. Seedlings were randomly distributed into four blocks in the greenhouse.\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n164 Two V. inaequalis isolates were used: the reference isolate ‘EU-B04’ (Origin: Belgium, host: \n165 ‘Golden Delicious’) previously described in Caffier et al. (2015) 39 and Le Cam et al. (2019) 40 \n166 and the isolate ‘09BCZ014’ (Origin: France, host: ‘TN10-8’ × ‘Prima' progeny), referred to as \n167 isolate ‘2557’ in Laloi et al. (2017) 20. The isolate ‘09BCZ014’ partially overcomes the resistance \n168 conferred by the qT1 resistance QTL segregating in the ‘TxF’ progeny. The use of this isolate \n169 allows to better study the effect of other QTLs segregating in the progeny.\n170 Monoconidial suspensions were prepared from diseased dry leaves at a concentration of 2.5 \n171 × 10 5 conidia.ml−1 and sprayed on seedlings. Inoculated plants were then incubated for two \n172 days at 17°C under a plastic sheet to maintain high humidity, according to the conditions \n173 described by Caffier et al. (2010) 41. The percentage of leaf surface exhibiting sporulating \n174 lesions was scored at 14 and 21 days post-inoculation (dpi) using the ordinal scale (0 to 7) \n175 described in Calenge et al. (2004) 16. Three experiments were conducted from January to May \n176 2022 (Table 1). One experiment was performed with isolate ‘EU-B04’ (coded ‘Vi-B04’) for \n177 which additional scoring of resistance symptoms (leaf chlorosis and crispation) was recorded \n178 at 14 dpi using the same ordinal scale (0 to 7 scores, reflecting the percentage of foliar surface \n179 covered with chlorosis or crisped). Two other experiments were then conducted using the \n180 isolate ‘09BCZ014’ (coded ’Vi-Z14_1’ and ’Vi-Z14_2’). ’Vi-Z14_1’ was performed on all the ‘TxF' \n181 progeny seedlings (1,970) whereas ’Vi-Z14_2’ was restricted to individuals recombining in CIs \n182 of QTLs of interest (1,101). For ‘Vi-Z14’ experiments, only leaf sporulation was recorded \n183 (susceptibility symptoms).\n184 Table 1. Summary of experiment characteristics.\nExperiment 1 Experiment 2 Experiment 3\nName Vi-B04 Vi-Z14_1 Vi-Z14_2\nMonth January March May\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\nSeedling number 1,970 1,970 1,101\nVi isolate EU-B04 09BCZ014 09BCZ014\nPhenotyping\nSporulation at 14 \nand 21 dpi\nChlorosis at 14 dpi\nCrispation at 14 dpi\nSporulation at 14 \nand 21 dpi\nSporulation at 14 \nand 21 dpi\n185 Dpi, days post-inoculation.\n186 Using sporulation scores at both dates i.e 14 dpi and 21 dpi, the Area Under Disease \n187 Progression Curve (AUDPC) was calculated as a quantitative summary of disease severity over \n188 the course of the infection using the following equation:\n189 𝐴𝑘 =\n𝑁𝑖―1\n𝑖=1\n(𝑦𝑖 + 𝑦𝑖+1)\n2 (𝑡𝑖+1 ― 𝑡𝑖)\n190 Where yi is the disease score at the ith day of observation and t is the number of days after V. \n191 inaequalis inoculation at the ith observation. For AUDPC calculation, the initial time point (t0) \n192 was set to 0.\n193 All statistical analyses were performed using the software R V2022.07.2+576 (R Core Team, \n194 2022). ANOVAs (function  aov from the stats package) were conducted to test for assessor and \n195 block effects and, when necessary, were followed by Tuckey post hoc tests (function \n196 tukey_hsd from the rstatix package). Although each block consisted of genetically distinct \n197 individuals, their mean genetic value was assumed to be equivalent due to the random \n198 distribution of individuals among blocks, allowing adjustment of phenotypic variables for \n199 block, assessor, and experiment effects, with the experiment effect included only for the Vi-\n200 Z14 experiments. For the ‘Vi-B04’ experiment, QTL mapping was performed using the adjusted \n201 AUDPC, chlorosis and crispation scores (hereafter ‘AUDPC_B04’, ‘CHLOROSIS_B04’ and \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n202 ‘CRISPATION_B04’). For the ‘Vi-Z14’ experiments, adjusted AUDPC additionally accounted for \n203 variations between the two experiments (‘AUDPC Vi-Z14_adj’). Phenotyping data used for QTL \n204 mapping are available in S2 File.\n205 Distributions and correlations between studied variables were examined with ggplot function \n206 (ggplot2 package) and ggpairs function (GGally  package), respectively. In the ‘Vi-B04’ \n207 experiment, the strong effect of the major QTL qT1, detected on LG1 of ‘TN10-8’ with the \n208 reference isolate ‘EU-B04’ 16,18, is hiding the more moderate effects of other QTLs. Therefore, \n209 the 'TxF' progeny was further subdivided into two subsets of individuals according to the \n210 presence or absence of the resistance allele at qT1 predicted by SNP data at the QTL peak. \n211 AUDPC distributions were then observed on two distinct subpopulations: one including the \n212 entire extended progeny (1,970 individuals) and a second, named ‘qT1-’ including 721 \n213 individuals carrying the susceptible allele at qT1.\n214 Mapping and genetic analyses of the targeted QTLs\n215 Genetic maps: Genotyping data were analyzed using SNPviewer software provided by LGC \n216 Biosearch Technologies TM with manual correction whenever necessary to improve dataset \n217 quality. The order of SNP physical positions was checked against the latest apple reference \n218 genetic map 42. A linkage map was constructed for each parent with JoinMap 4.1 software 43. \n219 In total, over the five targeted regions, 43 SNP markers were used and the associated genetic \n220 map is available in S3 File. \n221 QTL mapping: \n222 QTL analyses were conducted using the R/qtl package 44. In addition to SNP markers, ‘virtual’ \n223 markers were added with calc.genoprob and sim.geno functions using 1,000 simulations in \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n224 order to have one marker per centiMorgan (cM). Simple interval mapping and composite \n225 interval mapping were then performed using a multiple imputation method and a normal \n226 distribution model (using cim function). The logarithm of the odd (LOD) score threshold to \n227 identify the statistically significant QTLs was determined using 1,000 permutations (α = 0.05). \n228 LOD thresholds were approximately 1.80 for all variables. \n229 The LOD score, the 2-LOD support CI, and the contribution of each QTL to the overall \n230 phenotypic variance (individual R 2) were extracted from R/qtl analyses, together with the \n231 global QTL contribution (global R 2). Individual and global R² were calculated with the fitqtl \n232 function (used for fitting a defined multiple-QTL model). Interactions between QTLs were \n233 studied by variance analysis using the genotyping data of each SNP closest to the peak of each \n234 QTL, and were detailed by the effectplot function. These results were used to define the model \n235 for the calculation of the global R 2 with the fitqtl function. Graphics with LOD score and \n236 positions of markers were constructed using MapChart 2.32 45. \n237 Identification of candidate genes \n238 Sequence contexts of SNPs localized at both extremities of QTL CIs were blasted against \n239 reference genome GDDH13 v1.1 (using INRAE JBrowse tool) to retrieve QTL physical borders. \n240 Physical positions of virtual markers were estimated according to the physical positions of \n241 flanking SNP markers. The CI representing the shortest genetic region for each QTL was used \n242 to look for candidate genes involved in plant-pathogen interaction. For all QTLs, lists of genes \n243 along with their functional annotations and orthologs in  Arabidopsis thaliana were extracted \n244 from GDDH13 v1.1 gene annotation (https://iris.angers.inra.fr/gddh13/the-apple-genome-\n245 downloads.html).\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n246 Transcriptomic datasets were retrieved from a previous experiment performed on offspring \n247 of the base population of the ‘TxF' progeny 29. Four individuals for each of four classes were \n248 pooled according to the presence/absence of favorable alleles at three QTLs: qT1, qF11 and \n249 qF17. The four classes were constructed depending on the combination of QTLs present with \n250 either ‘NoScabQTL’, ‘qT1’, both ‘qF11qF17’ and all three ‘qT1qF11qF17’. This design allowed \n251 to test for the effects of QTLs on gene transcription before and after inoculation of V. \n252 inaequalis with the ‘EU-B04’ isolate (see Bénéjam et al. (2024) 29 for additional information).\n253 For all candidate genes underlying the three QTLs qT1, qF11 and qF17, we thus retrieved fold \n254 changes and corrected p-values for genes showing differential transcription before and after \n255 V. inaequalis inoculation according to the genotypic classes. For instance, for genes within the \n256 qT1 range, we analyzed differential transcription between both qT1 and qT1qF11qF17 classes \n257 against the 'NoScabQTL' class, but also between qT1qF11qF17 class against the qF11qF17 \n258 class, at 0 dpi (before V. inaequalis inoculation) and 5 dpi (after V. inaequalis inoculation). \n259 Candidate genes with an absolute fold change (|FC|) > 1.5 and a Benjamini–Hochberg \n260 adjusted p-value (BH method) < 0.05 were selected for further analysis 46.\n261 For the qT13 and qF3 QTLs, no transcriptomic data were available to study the expression of \n262 candidate genes contained in the CIs. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n263 Results\n264 1. Broadening the mapping population and recombinant detection\n265 Among the 1,970 genotyped seedlings, 1,101 displayed at least one recombination event \n266 within the five target QTL regions, thereby increasing mapping precision relative to Bénéjam \n267 et al. (2021) 18. Recombinant counts were notably higher for qT1 and qF11 (188 and 432 \n268 individuals, respectively; Table 2), substantially refining the genetic resolution. \n269 Table 2. Summary of number of individuals presenting recombination events in confidence \n270 intervals of resistance quantitative trait loci.\nRecombinant number from \nBénéjam et al. (2021)\nRecombinant number in this \nstudy\nqT1 7 2% 188 9%\nqT13 16 6% 168 8%\nqF3 47 17% 364 18%\nqF11 43 16% 432 21%\nqF17 30 11% 229 11%\nProgeny size 266 1,970\n271 2. Phenotypic variation and symptom characterization\n272 Phenotypes revealed a clear segregation pattern within the extended ‘TxF’ progeny. In the ‘Vi-\n273 B04’ experiment, out of the 1,970 seedlings, AUDPC values showed a distribution with a \n274 bimodal tendency (Fig 1A), with a large subset of ~300 seedlings displaying near-zero values, \n275 indicative of strong resistance. The distribution of AUDPC showed a maximum value of 109 \n276 and a median of 44.6. When individuals carrying the favourable qT1 allele (n = 1,249) were \n277 removed, the remaining distribution became unimodal and continuous (Fig 1B). Median \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n278 AUDPC value rose to 63.5, demonstrating the effect of qT1 face to this isolate. In contrast, \n279 adjusted AUDPC values obtained with ‘Vi-Z14’ experiments, induced a unimodal distribution \n280 across the 1,101 recombinants ranging from 0 to 112 AUDPC values, with a median of 55.8 \n281 (Fig 1C). The increase of median AUDPC in ‘Vi-Z14’ experiments illustrates the ability of the \n282 ‘09BCZ014’ to partially overcome the QTL qT1.\n283 Fig 1. Distribution of susceptibility symptoms of the extended 'TxF' progeny. Adjusted Area \n284 Under the Disease Progress Curves (AUDPC) variation for the experiment with ‘EU-B04’ isolate \n285 (A) and experiment with the ’09BCZ014’ isolate (C) are shown. Distribution of AUDPC \n286 estimated with ‘EU-B04’ isolate (B) is also represented in the subpopulation qT1- (i.e., \n287 individuals selected as not carrying the resistance allele of the major quantitative trait locus \n288 qT1).\n289 Additionally, leaf resistance responses were scored in the ‘Vi-B04’ experiment through the \n290 assessment of chlorosis and crispation, each ranging from 0 to 7. Over 80% of genotypes \n291 exhibited both reactions (scores > 1). These two symptoms were strongly correlated (r = 0.649, \n292 p < 0.001) and inversely associated with disease severity (AUDPC; Fig 2). However, a \n293 substantial amount of genotypes was resistant (low AUDPC values) without presenting \n294 resistance symptoms, as illustrated by the low correlation scores between AUDPC and \n295 resistance phenotypes (r = -0.405 and -0.368).\n296 Fig 2. Relationship between resistance and susceptibility symptoms of the extended 'TxF' \n297 progeny after inoculation with isolate ‘EU-B04’. Pearson coefficient of correlation (Corr) are \n298 indicated for each couple of variables. Three variables measured in ‘Vi-B04’ experiment are \n299 presented: Area Under the Disease Progress Curve (AUDPC), chlorosis and crispation scoring.\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n300 3. QTL validation and fine mapping\n301 QTL mapping analyses with ‘Vi-B04’ experiment variables were conducted on the entire \n302 progeny (1,970 seedlings) while only recombinants (1,101 seedlings) were considered for the \n303 analysis of AUDPC from ‘Vi-Z14’ experiment. Four of the five targeted QTLs were validated \n304 across experiments and phenotypic variables (qT1, qF11, qF17,  and qT13) (Fig 3, S1 Table and \n305 S1 Fig). For the QTL qF3, crispation variable reached the detection threshold (LOD > 1.8) with \n306 a maximum value of 3.3, anchored by the virtual marker c3.loc15 (15 cM). Another signal was \n307 detected at the border of the studied interval (20 cM) with a value of 1.88 obtained with \n308 AUDPC from ‘Vi-Z14’ experiments. Despite dense marker coverage, this variable did not lead \n309 to the refinement of qF3 region and other phenotypic variables (chlorosis and AUDPC from \n310 ‘Vi-B04’ experiment) failed to reach the detection threshold (S1 Table and S1 Fig). \n311 Fig 3. Genetic maps and LOD curves of the fine-mapped scab resistance quantitative trait \n312 loci of the extended 'TxF' progeny. Genetic position (in centiMorgans) and marker name used \n313 in this study are respectively indicated on the left and right of linkage groups (LG). Marker \n314 name associated to maximum LOD value for each QTL are bolded. 2-LOD and 1-LOD support \n315 QTL confidence interval are represented by vertical lines and solid rectangles, respectively.\n316 The QTL qT1  was detected in ‘Vi-B04’ experiment for all phenotypic traits, displayed the \n317 highest significance (LOD = 202) when using chlorosis as the trait. Its 2-LOD CI was narrowed \n318 to 1.1 cM (~600 kb on the GDDH13 genome), anchored by marker AX-115385376. Similar \n319 intervals were estimated with AUDPC and crispation variables from this experiment.\n320 For qF11 and qF17, the shortest 2-LOD CIs were obtained with AUDPC from ‘Vi-Z14’ \n321 experiments. The qF11 region was delimited to a 6 cM region (~2.5 Mb), while qF17 was \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n322 refined to 3.5 cM (~1.2 Mb). The most probable position of these QTLs (i.e. the positions with \n323 highest LOD scores) were anchored by markers AX-115185079 and AX-115185087 for qF11 \n324 (5.2 cM), and by the virtual marker c17.loc12 (12 cM) for qF17. These two QTLs were also \n325 identified in the ‘Vi-B04’ experiment using AUDPC and exhibited longer intervals.\n326 Finally, qT13 was detected exclusively under ‘Vi-Z14’ inoculation, within a 7 cM (~2 Mb) region \n327 on chromosome 13, anchored by the AX-115187347 marker. Although it did not reach the LOD \n328 threshold, the AUDPC from the ‘Vi-B04’ experiment yielded a LOD score of 1.78 on this marker.\n329 4. Epistatic interaction between qF11 and qF17\n330 Consistent with prior findings, seedlings carrying favorable alleles at both qF11 and qF17 \n331 exhibited significantly lower AUDPC values than all other genotypic classes (p < 0.001; S2 Fig). \n332 Under ‘Vi-B04’, median AUDPC dropped from 50–60 in single-allele carriers to ~15 in double \n333 carriers; under ‘Vi-Z14’, values decreased from ~60 to ~30. Also, single-allele exhibited \n334 susceptibility levels similar to those of non-carriers. These patterns confirm a synergistic effect \n335 between qF11 and qF17.\n336 5. Identification of candidate genes and defense mechanisms\n337 To pinpoint plausible resistance determinants, we focused on genes located within the refined \n338 CIs and showing differential expression in relevant genotypic classes (before and five days \n339 after inoculation).\n340 qT1 region\n341 The narrow 590 kb qT1 interval encompassed 89 annotated genes, 10 of which were \n342 differentially expressed (Table 2). Most of these were upregulated following inoculation in \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n343 resistant (qT1+) genotypes. Notably, five clustered genes (MD01G1178700, MD01G1179300, \n344 MD01G1179700, MD01G1179800,  and MD01G1180500) encode Leucine-Rich Repeat \n345 Receptor-Like Proteins (LRR-RLPs), classical sensors of fungal or oomycete invasion. This \n346 transcriptional pattern supports the hypothesis that qT1 could represent either a functional \n347 variant or a paralog of Rvi6  with partial quantitative activity.\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n348 Table 2. Annotation and relative expression of genes underlying the QTL qT1 confidence interval before (0 dpi) or five days (5 dpi) after \n349 infection with Vi in different genetic background of pooled-samples from the extended ‘TxF’ progeny.\n0 dpi 5 dpi 0 dpi versus 5 dpi\nQTL Gene ID Annotation \nSwissprot TAIR Name Biological process NoScabQTL/\nqT1\nNoScabQTL/ \nqT1qF11qF17\nqF11qF17/ \nqT1qF11qF17\nNoScabQTL/\nqT1\nNoScabQTL/ \nqT1qF11qF17\nqF11qF17/ \nqT1qF11qF17 NoScabQTL qT1 qF11qF17 qT1qF11qF17\nMD01G1173000 Phytosulfokine \nreceptor 1 AT1G74190.1 Signal transduction 2,4 - - - - - - 2,2 - -\nMD01G1177000\nEthylene-\nresponsive \ntranscription \nfactor 2 \nAT4G17500.1 Cellular process / \nresponse to nematode - - - - - - - 1,6 - -\nMD01G1177100\nEthylene-\nresponsive \ntranscription \nfactor 5 \nAT5G51190.1 Response to heat - - - 1,67 - - - - - -\nMD01G1178700\nLRR receptor-like \nserine/threonine\n-protein kinase \nFLS2 \nAT2G34930.1 Defense response to \nfungus and oomycetes - - - - - - - 2,7 - 2,6\nMD01G1178900 Receptor-like \nprotein 12 AT1G07390.3 Signal transduction - - - - - - - 2,7 - -\nMD01G1179300\nLRR receptor-like \nserine/threonine\n-protein kinase \nGSO2 \nAT2G34930.1 Defense response to \nfungus and oomycetes - - - - - - 1,7 1,5 - -\nMD01G1179700\nProbable LRR \nreceptor-like \nserine/threonine\n-protein kinase \nIRK \nAT2G34930.1 Defense response to \nfungus and oomycetes - - - - - - - 2,8 - 5,4\nMD01G1179800 Receptor-like \nprotein 12 AT2G34930.1 Defense response to \nfungus and oomycetes - - - - - - - 2,3 - -\nMD01G1180500 Receptor-like \nprotein 12 AT2G34930.1 Defense response to \nfungus and oomycetes - - -1,5 - - - - 2,3 - 2,4\nqT1\nMD01G1181100\nProbable inactive \nleucine-rich \nrepeat receptor \nkinase XIAO \nAT2G34930.1 Defense response to \nfungus and oomycetes - 2,3 - 3,19 3 3,3 - - - -\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n351 qF11 and qF17 regions\n352 The refined qF11 (2.6 Mb) and qF17 (1 Mb) intervals contained 304 and 130 genes, \n353 respectively. Fourteen were differentially expressed in each region (Table 3). In qF11 interval, \n354 several DEGs (MD11G1025200–MD11G1033300) were constitutively upregulated in resistant \n355 genotypes at both time points, suggesting baseline activation of signaling pathways rather \n356 than induced defense. Their functions, which are linked to RNA interference and signal \n357 transduction, suggest the existence of a regulatory layer that modulates defense gene \n358 networks by inducing kinases, ligases, and helicases. \n359 Conversely, two DEGs in qF17 interval ( MD17G110420, MD17G1105000) were constitutively \n360 downregulated in resistant individuals. Although their annotations remain uncertain (one \n361 glucosyltransferase and one uncharacterized protein), predicted enzymatic roles could cover \n362 various functions. Interestingly, several DEGs in both regions were induced by inoculation, but \n363 none of them was specific to the qF11qF17 genotypic class. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n364 Table 3. Annotation and relative expression of genes underlying the QTL qF11 and qF17 confidence intervals before (0 dpi) or five days (5 dpi) \n365 infection with Vi in different genetic background of pooled-samples from the extended ‘TxF’ progeny.\n0 dpi 5 dpi 0 dpi versus 5 dpi\nQTL Gene ID Annotation \nSwissprot TAIR Name Biological process NoScabQTL/ \nqF11qF17\nNoScabQTL/ \nqT1qF11qF17\nqT1/ \nqT1qF11qF17\nNoScabQTL/ \nqF11qF17\nNoScabQTL/ \nqT1qF11qF17\nqT1/ \nqT1qF11qF17 NoScabQTL qT1 qF11qF17 qT1qF11qF17\nMD11G1022100 - - - 2,4 2,2 - - - - - - - -\nMD11G1025200\nG-type lectin S-\nreceptor-like \nserine/threonine-\nprotein kinase \nLECRK3 \nAT5G60900.1 - 3,4 3,3 2,2 3,4 4,2 2,1 - - - -\nMD11G1025300\nDEAD-box ATP-\ndependent RNA \nhelicase 20 \nAT1G55150.1 RNAi-mediated \nimmune response 8,7 8,7 8,8 8,5 8,5 7,7 - - - -\nMD11G1025500\nG-type lectin S-\nreceptor-like \nserine/threonine-\nprotein kinase \nLECRK3 \nAT5G60900.1 - 2,7 2,4 - 2,4 3,3 - - 1,8 - 1,5\nMD11G1026100\nDEAD-box ATP-\ndependent RNA \nhelicase 20 \nAT1G55150.1 RNAi-mediated \nimmune response 10,3 10,2 10,4 10,8 10,9 12,4 - - - -\nMD11G1027500 Beta-glucosidase 12 AT2G44480.1 Carbohydrates \nmetabolic process - - - - - - 1,8 2,5 - -\nMD11G1027800\nTIR domain-\ncontaining adapter \nmolecule 1 \nAT4G12070.1 - - - - - - - - 2,2 - 2,7\nMD11G1031100 F-box protein \nCPR30 AT4G12560.1 Regulation of defense \nresponse - - - - 2,6 - - - - -\nMD11G1031200 #N/A #N/A - - - - -2,0 - - - - - -\nMD11G1033300\nSerine/threonine-\nprotein kinase \nSTY17 \nAT3G22750.1 Signal transduction 6,0 6,0 3,4 5,3 4,5 2,9 - - - -\nMD11G1038900\nFructose-\nbisphosphate \naldolase 5, cytosolic \nAT4G26530.1 Carbohydrates \nmetabolic process - - - -1,6 - - - - - -\nqF11\nMD11G1039300\nHeavy metal-\nassociated \nisoprenylated plant \nprotein 16 \nAT3G07600.1 - - -3,5 -3,8 - - - - - - -\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\nMD11G1043900\nC6 finger domain \ntranscription factor \ntcpZ \nAT1G07745.1 Cellular process: DNA \nrepair - - -3,1 - - - - - - -\nMD11G1049600 Cytochrome P450 \n714C2 AT5G24910.1 - - - - - - - - - - 2,8\nMD17G1098400 Trophinin AT5G07570.1 - 1,5 - - - - - - - - -\nMD17G1099000 WRKY transcription \nfactor 42 AT4G04450.1 Leaf senescence \nregulation - - - - - - - - - 1,7\nMD17G1099500 Cellulose synthase-\nlike protein B3 AT2G32530.1 Cellulose biosynthetic \nprocess - - - - - - - 2,0 - -\nMD17G1099600 Cellulose synthase-\nlike protein H1 AT2G32530.1 Cellulose biosynthetic \nprocess - - - - - - - 1,7 - -\nMD17G1100100\nFatty acid oxidation \ncomplex subunit \nalpha \nAT4G20720.1 - - 1,6 - - - - - - - -\nMD17G1101300 Condensin complex \nsubunit 2 AT2G32590.1 Mitotic chromosome \ncondensation - 2,1 3,7 - - - - - - -\nMD17G1101700 UPF0753 protein \nYbcC AT1G77130.1 Plat secondary cell \nwall biogenesis - - - - - - - 1,8 - -\nMD17G1102000 Ribonuclease 3-like \nprotein 3 AT4G15417.1 RNA processing - - - - - - - 2,5 - -\nMD17G1104200\nUDP-\nglucose:glycoprotei\nn \nglucosyltransferase \nAT5G04700.1 - -12,2 -12,0 -11,8 -5,9 -8,4 -8,5 - - - -\nMD17G1104700\nDEAD-box ATP-\ndependent RNA \nhelicase 45 \nAT3G09620.1 mRNA splicing - - - - - -5,0 - - - -\nMD17G1105000\nUncharacterized \nprotein \nDDB_G0275933 \nAT5G16060.1 - -2,0 -2,1 -1,8 -2,1 -2,2 -2,2 - - - -\nMD17G1105800\nPalmitoyl-\nmonogalactosyldiac\nylglycerol delta-7 \ndesaturase, \nchloroplastic \nAT3G15850.1 Fatty acid \nbiosynthetic process - - - - - - - - - 1,6\nMD17G1106300\n1-\naminocyclopropane\n-1-carboxylate \noxidase \nAT1G05010.1\nEthylene biosynthetic \nprocess / response to \nfungus\n- - - - - - - 2,0 - 2,1\nqF17\nMD17G1109600\nChlorophyll a-b \nbinding protein 40, \nchloroplastic \nAT1G29930.1\nPhotosynthesis / \nresponse to light \nstimulus\n- - 4,3 - - - - 3,6 - -\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n367 qT13 region\n368 qT13 spanned a 2 Mb interval containing 289 genes, 22 of which lacked functional annotation \n369 (S2 Table).\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n370 Discussion\n371 The broadening of the biparental population and the analysis of nearly 2,000 seedlings \n372 allowed the refinement and validation of several resistance QTLs previously reported in the \n373 ‘TxF' progeny. Among the five QTLs studied, qT1, qF11, qF17, and qT13 were validated under \n374 at least one experimental condition, while qF3 could not be confirmed despite an increased \n375 number of individuals. The ‘Vi-B04’ experiment revealed a somewhat bimodal distribution of \n376 resistance phenotypes mainly driven by the presence of the qT1 QTL. The joint analysis of \n377 differential gene expression within refined CIs identified several candidate genes, including \n378 RLPs in qT1 and constitutively expressed genes in qF11 and qF17. Epistatic interaction \n379 between qF11 and qF17 was confirmed, supporting the functional association of their \n380 underlying genes in resistance expression. \n381 Experimental validation limits and interpretation of QTL stability\n382 The absence of qF3 detection and the variability observed in QTL expression across \n383 experiments underline the context-dependence of QTL detection. For qF3, QTL detection was \n384 successful only for the crispation variable, whereas no QTL were identified for chlorosis, in \n385 contrast to other detections (Fig 3) and despite their correlation (Fig 2). In addition, given the \n386 low LOD score, this result should be interpreted with caution and confirmed in independent \n387 experiments with crispation variable. Although the overall results are mixed, we observed an \n388 increase in LOD values towards the end of the targeted genomic region, which was even more \n389 pronounced when using AUDPC from the ‘Vi-Z14’ experiment (S1 Fig). This pattern may \n390 suggest that the QTL is located slightly downstream of the initially targeted region. Such an \n391 interpretation would be consistent with previous findings, in which the position of the highest \n392 LOD scores varied among QTL detections performed with two different Vi isolates 18. Although, \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n393 Bénéjam et al. (2021) 18 studied 267 individuals, out of them 17% presented recombination \n394 events in qF3 interval, which is similar to our study (Table 2). However, without biological \n395 replicates, it is possible that phenotypic responses were less precise in this study, leading to \n396 an absence of detection of low effect QTLs, such as qF3. Another possible explanation is that \n397 qF3 may have been a false positive in the previous study, in which it was detected for the first \n398 time 18. Further analyses using a higher number of markers and replicates would be required \n399 to clarify this point.\n400 The non-validation of qT13 in ‘Vi-B04’ experiment contrasts with its previous identification by \n401 Bénéjam et al. (2021) 18 in both isolate contexts. However, the improved precision of its \n402 mapping interval supports the robustness of the locus itself. In addition, QTL detection with \n403 AUDPC from ‘Vi-B04' experiment almost reach the LOD threshold (Fig 3). Increasing the level \n404 of replication in future experiments, notably with the isolate ‘EU-B04’ could help refine the \n405 localization of the QTL by estimating more reliable AUDPC values. The current detection of \n406 qT13 only in ‘Vi-Z14’ experiments may also indicate isolate-specific activation or \n407 environmental modulation of gene expression. Despite the reduction of the confidence \n408 interval, 283 genes still fall within the boundaries of qT13 (S2 Table). With only seven markers \n409 on this QTL in this study, it would be relevant to further increase marker density in this region, \n410 particularly around the marker AX-115187347 (5.89 cM), since the peak of LOD score was \n411 located near it (S1 Table). Moreover, no transcriptomic data are available for this QTL, \n412 preventing the initiation of gene number reduction and the investigation of potential gene \n413 functions. Due to its efficacy against at least isolate ‘09BCZ014’, ‘TN10-8’ remains an attractive \n414 parent for breeding. \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n415 In addition, several non-genetic factors may have contributed to this instability. Differences in \n416 environmental conditions between experiments, and particularly the use of rootstocks for \n417 individual replication in earlier studies, could have modified physiological parameters and \n418 biased disease symptom expression. Ontogenic effects and developmental stage of seedlings \n419 at inoculation are known to strongly influence the expression of resistance loci, as reported in \n420 apple and other perennial hosts 17,47,48. Here, scab inoculations were carried out sequentially \n421 on the same plants, which were pruned between each inoculation. Similarly, Soufflet-Freslon \n422 et al. (2008) 17 reported differences in AUDPC distributions of a progeny when screened \n423 successively in two scab experiments using the same Vi isolate, highlighting differential \n424 resistance expression depending on the physiological state of the plants. Collectively, these \n425 results suggest that the variability in QTL validation partly reflects the ontogenic and polygenic \n426 nature of apple scab resistance.\n427 Functional interpretation and convergence of molecular mechanisms\n428 The co-localization of differentially expressed genes (DEGs) with refined QTL intervals can \n429 reveal potential functions of these loci. The working hypothesis underlying this analysis is that \n430 the QTL effect results from allele-specific differences in gene expression, caused by sequence \n431 variation in regulatory regions of the underlying gene. Under this model, differential \n432 transcription between alleles would drive the observed phenotypic variation. Alternative \n433 mechanisms cannot be excluded, including coding sequence variation leading to structural \n434 differences in the encoded protein without expression differences 49, or epigenetic regulation \n435 generating allele-specific expression in the absence of sequence polymorphism 50. In this \n436 context, RNA-seq data provide a relevant framework to investigate the expression-based \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n437 hypothesis and to prioritize genes within the QTL CI, notably by excluding genes not expressed \n438 under our experimental conditions.\n439 For qT1, due to its specificity, large-effect and physical localization on the genome, it has \n440 already been hypothesized to be an allelic variant or a paralog of the major/R gene Rvi6 16,18. \n441 Moreover, qT1 often leads to a hypersensitive response 20 and transcriptomic datasets \n442 highlighted a large amount of DEGs (~1,500) when comparing genotypes carrying \n443 susceptibility and resistance alleles of qT1 infected with ‘EU-B04’ isolate 29. Here, we refined \n444 the CI of qT1 to a ~600 kb region (Fig 3, S1 Table) and analyzed DEGs within this interval. More \n445 precisely, several RLPs were upregulated upon Vi inoculation (Table 2), a pattern consistent \n446 with canonical R-gene–mediated perception 51. This supports the hypothesis that qT1 might \n447 be an allele or a paralog of the well-known Rvi6 locus, with different specificities 52. A \n448 comparative genomic analysis of the potential allelic series in Rvi6 region, comprising Rvi6 \n449 cloned in 'Florina' 8,9,53 and also present in recently sequenced ‘Prima’ and ‘Priscilla’ 54, Rvi17 \n450 in 'Antonovka' 55, Vhc1 in 'Honeycrisp' 56 and qT1 in 'TN 10-8', would help confirm this co-\n451 localization and clarify their possible redundancy or evolutionary divergence. A definitive test \n452 would examine segregation in progeny from crosses between parents carrying different \n453 putative alleles of these R genes/QTLs. Detection of both “alleles” in offspring would indicate \n454 recombination, showing they are distinct loci rather than alternative alleles. In a perennial \n455 species, such crosses are labor- and time-intensive, and testing all pairwise allele combinations \n456 is practically difficult.\n457 For qF11 and qF17, CIs were narrowed to 2.6 Mb and 1 Mb (Fig 3, S1 Table), respectively. Their \n458 epistatic interaction was also confirmed (S2 Fig), consistent with other studies  18–20. \n459 Integration of RNA-seq data provided additional insight into the biological mechanisms \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n460 potentially underlying the interaction between these QTLs, possibly through coordinated \n461 transcriptional regulation. Within the qF11 interval, genes putatively involved in RNAi \n462 signaling were expressed, whereas the qF17 interval showed repression of genes with \n463 currently unknown functions (Table 3). In both regions, expression differences were primarily \n464 associated with genotypic classes rather than with Vi inoculation, supporting the hypothesis \n465 that these QTLs are constitutively expressed and may contribute to basal resistance 20. \n466 Nevertheless, improved gene annotation will be necessary to better characterize the \n467 molecular basis linking these two loci. The use of haplotype-resolved parental genomes would \n468 provide an ideal framework for identifying genes underlying QTLs. The causal gene may be \n469 absent from one haplotype in the case of hemizygous genes, or even missing from the \n470 reference genome if the sequenced genotype does not carry the QTL allele.\n471 Conclusions\n472 The development of new markers and the screening of nearly 2,000 seedlings helped \n473 consolidate our understanding of the genetic architecture of scab resistance in the ‘TxF' \n474 progeny, validating four QTLs with a promising breadth of action. The improved precision of \n475 QTL intervals, comprised between 2.6 Mb and 600 kb, and integration of transcriptomic data \n476 strengthen the genetic framework for downstream applications. Future efforts should focus \n477 on (i) confirming the co-localization between qT1 and Rvi6, (ii) functionally characterizing \n478 qT13, and (iii) elucidating the biological basis of the epistatic interaction between qF11 and \n479 qF17 integrating other phenotypic data. In breeding terms, the combination of these loci \n480 through marker-assisted selection, supported by the development of new SNP markers, \n481 represents a promising path toward durable resistance in apple.\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n482 Author contributions\n483 Romane Lapous: Investigation, Data Curation, Validation and Visualization of results, Writing \n484 – Original Draft Preparation, Review & Editing. Camille Haquet: Investigation, Data Curation, \n485 Formal analysis, Writing – Review & Editing. Caroline Denancé: Investigation, Data Curation. \n486 Juliette Bénéjam: Validation and Visualization of results, Writing – Review & Editing. Laure \n487 Perchepied: Validation and Visualization of results, Writing – Review & Editing. Kaat Hellyn: \n488 Resources. Hélène Muranty: Conceptualization, Funding Acquisition, Writing – Review & \n489 Editing. Charles-Eric Durel: Conceptualization, Funding Acquisition, Writing – Review & \n490 Editing. Julie Ferreira de Carvalho: Investigation, Conceptualization, Funding Acquisition, \n491 Project Administration, Supervision, Writing – Original Draft Preparation, Review & Editing. \n492 Acknowledgements\n493 This work was supported by (i) a grant to the METAdiVERSE project from the BAP (Plant biology \n494 and breeding) division of INRAE and by (ii) a grant from the French government managed by \n495 the Agence Nationale de la Recherche (ANR) as part of the Programme Prioritaire de \n496 Recherche “Cultiver et Protéger Autrement” under the reference ANR-20-PCPA-0003 \n497 (CapZeroPhyto project). Romane Lapous was supported by a Ph.D. fellowship from the BAP \n498 division of INRAE and the ‘Pays de la Loire’ region (France). Camille Haquet was supported by \n499 a GIS Fruit grant. The authors greatly thank the PHENOTIC platform \n500 (https://doi.org/10.17180/YKBZ-2V85) for carefully taking care of the plant material. The \n501 authors would like to thank the Biological Resource Center “RosePom - Pome Fruits and \n502 Roses” (https://eng-irhs.angers-nantes.hub.inrae.fr/shared-facilities/genetic-resources/crb-\n503 pome-fruit-and-rose) and associated staff for maintaining the plant material and associated \n504 datasets used in the present article. We are also grateful to the Horticole Experimental Unit \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n505 (https://doi.org/10.15454/1.5573931618268674E12) for maintaining the young trees derived \n506 from seedlings. For generating the seeds used in this study, we thank present and former staff \n507 of the VaDiPom team (IRHS). We thank Dr. Diego Micheletti for sharing the alignment of \n508 sequencing data to the GGDH13 genome. For selecting and developing SNP markers, we thank \n509 Aurélien Petiteau and the ANAN platform (SFR QuaSaV, Univ Angers). Finally, the authors wish \n510 to thank M. Tiret for thorough reviewing of the original draft as well as all the people who \n511 helped in the sampling of leaf material and apple scab symptom scoring: L. Vitteaut, A. \n512 Daligault, A. Petiteau, B. Petit, R. Leclair, F. Lebreton.\n513 References\n514 1. Bowen JK, Mesarich CH, Bus VGM, Beresford RM, Plummer KM, Templeton MD. \n515 Venturia inaequalis: the causal agent of apple scab. Molecular Plant Pathology. \n516 2011;12(2):105-122. doi:10.1111/j.1364-3703.2010.00656.x\n517 2. Jha G, Thakur K, Thakur P. The Venturia Apple Pathosystem: Pathogenicity \n518 Mechanisms and Plant Defense Responses. BioMed Research International. \n519 2009;2009(1):680160. doi:10.1155/2009/680160\n520 3. Didelot F, Caffier V, Orain G, Lemarquand A, Parisi L. Sustainable management of scab \n521 control through the integration of apple resistant cultivars in a low-fungicide input \n522 system. Agriculture, Ecosystems & Environment. 2016;217:41-48. \n523 doi:10.1016/j.agee.2015.10.023\n524 4. Gessler C, Patocchi A, Sansavini S, Tartarini S, Gianfranceschi L. Venturia inaequalis  \n525 Resistance in Apple. Critical Reviews in Plant Sciences. 2006;25(6):473-503. \n526 doi:10.1080/07352680601015975\n527 5. Bus VGM, Rikkerink EHA, Caffier V, Durel CE, Plummer KM. Revision of the \n528 Nomenclature of the Differential Host-Pathogen Interactions of Venturia inaequalis \n529 and Malus. Annu Rev Phytopathol . 2011;49(1):391-413. doi:10.1146/annurev-phyto-\n530 072910-095339\n531 6. Khajuria YP, Kaul S, Wani AA, Dhar MK. Genetics of resistance in apple against Venturia \n532 inaequalis (Wint.) Cke. Tree Genetics & Genomes. 2018;14(2):16. doi:10.1007/s11295-\n533 018-1226-4\n534 7. Švara A, De Storme N, Carpentier S, Keulemans W, De Coninck B. Phenotyping, genetics \n535 and ‘-omics’ approaches to unravel and introgress enhanced resistance against apple \n536 scab (Venturia inaequalis) in apple cultivars (Malus × domestica). Horticulture \n537 Research. Published online January 10, 2024:uhae002. doi:10.1093/hr/uhae002\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n538 8. Vinatzer BA, Patocchi A, Gianfranceschi L, et al. Apple Contains Receptor-like Genes \n539 Homologous to the Cladosporium fulvum Resistance Gene Family of Tomato with a \n540 Cluster of Genes Cosegregating with Vf Apple Scab Resistance. MPMI. 2001;14(4):508-\n541 515. doi:10.1094/MPMI.2001.14.4.508\n542 9. Belfanti E, Silfverberg-Dilworth E, Tartarini S, et al. The HcrVf2 gene from a wild apple \n543 confers scab resistance to a transgenic cultivated variety. Proc Natl Acad Sci USA. \n544 2004;101(3):886-890. doi:10.1073/pnas.0304808101\n545 10. Vinatzer BA, Patocchi A, Tartarini S, Gianfranceschi L, Sansavini S, Gessler C. Isolation \n546 of two microsatellite markers from BAC clones of the Vf scab resistance region and \n547 molecular characterization of scab-resistant accessions in Malus germplasm. Plant \n548 Breeding. 2004;123(4):321-326. doi:10.1111/j.1439-0523.2004.00973.x\n549 11. Brown SK, Maloney KE. An Update on Apple Cultivars, Brands and Club-Marketing. . \n550 NUMBER. 2013;21(1).\n551 12. Parisi L, Durel CE, Laurens F. A New Race of Venturia inaequalis Virulent to Apples with \n552 Resistance due to the Vf Gene. Phytopathology. 1993;83(5):533. doi:10.1094/Phyto-\n553 83-533\n554 13. Lemaire C, De Gracia M, Leroy T, et al. Emergence of new virulent populations of apple \n555 scab from nonagricultural disease reservoirs. New Phytologist. 2016;209(3):1220-\n556 1229. doi:10.1111/nph.13658\n557 14. Pilet-Nayel ML, Moury B, Caffier V, et al. Quantitative Resistance to Plant Pathogens in \n558 Pyramiding Strategies for Durable Crop Protection. Front Plant Sci . 2017;8(1838). \n559 doi:10.3389/fpls.2017.01838\n560 15. Durel CE, Parisi L, Laurens F, Van De Weg WE, Liebhard R, Jourjon MF. Genetic \n561 dissection of partial resistance to race 6 of Venturia inaequalis  in apple. Genome. \n562 2003;46(2):224-234. doi:10.1139/g02-127\n563 16. Calenge F, Faure A, Goerre M, et al. Quantitative Trait Loci (QTL) Analysis Reveals Both \n564 Broad-Spectrum and Isolate-Specific QTL for Scab Resistance in an Apple Progeny \n565 Challenged with Eight Isolates of Venturia inaequalis . Phytopathology®. \n566 2004;94(4):370-379. doi:10.1094/PHYTO.2004.94.4.370\n567 17. Soufflet-Freslon V, Gianfranceschi L, Patocchi A, Durel CE. Inheritance studies of apple \n568 scab resistance and identification of Rvi14 , a new major gene that acts together with \n569 other broad-spectrum QTL. Francki M, ed. Genome. 2008;51(8):657-667. \n570 doi:10.1139/G08-046\n571 18. Bénéjam J, Ravon E, Gaucher M, Brisset MN, Durel CE, Perchepied L. Acibenzolar- S -\n572 Methyl and Resistance Quantitative Trait Loci Complement Each Other to Control \n573 Apple Scab and Fire Blight. Plant Disease. 2021;105(6):1702-1710. doi:10.1094/PDIS-\n574 07-20-1439-RE\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n575 19. Caffier V, Lasserre-Zuber P, Giraud M, et al. Erosion of quantitative host resistance in \n576 the apple ×Venturia inaequalis pathosystem. Infection, Genetics and Evolution. \n577 2014;27:481-489. doi:10.1016/j.meegid.2014.02.003\n578 20. Laloi G, Vergne E, Durel CE, Le Cam B, Caffier V. Efficiency of pyramiding of three \n579 quantitative resistance loci to apple scab. Plant Pathology. 2017;66(3):412-422. \n580 doi:10.1111/ppa.12581\n581 21. Jaganathan D, Bohra A, Thudi M, Varshney RK. Fine mapping and gene cloning in the \n582 post-NGS era: advances and prospects. Theor Appl Genet. 2020;133(5):1791-1810. \n583 doi:10.1007/s00122-020-03560-w\n584 22. Soriano JM, Madduri M, Schaart JG, et al. Fine mapping of the gene Rvi18 (V25) for \n585 broad-spectrum resistance to apple scab, and development of a linked SSR marker \n586 suitable for marker-assisted breeding. Mol Breeding. 2014;34(4):2021-2032. \n587 doi:10.1007/s11032-014-0159-3\n588 23. Pagliarani G, Dapena E, Miñarro M, et al. Fine mapping of the rosy apple aphid \n589 resistance locus Dp-fl on linkage group 8 of the apple cultivar ‘Florina.’ Tree Genetics \n590 & Genomes. 2016;12(3):56. doi:10.1007/s11295-016-1015-x\n591 24. Emeriewen OF, Richter K, Flachowsky H, Malnoy M, Peil A. Genetic Analysis and Fine \n592 Mapping of the Fire Blight Resistance Locus of Malus ×arnoldiana on Linkage Group 12 \n593 Reveal First Candidate Genes. Front Plant Sci . 2021;12. doi:10.3389/fpls.2021.667133\n594 25. Švara A, Feulner H, Sakina A, et al. Fine-Mapping of the Vhc1 QTL for Apple Scab \n595 Resistance on Linkage Group 1 of ‘Honeycrisp.’ Plant Breeding . Published online \n596 September 28, 2025:pbr.70035. doi:10.1111/pbr.70035\n597 26. Cubillos FA, Coustham V, Loudet O. Lessons from eQTL mapping studies: non-coding \n598 regions and their role behind natural phenotypic variation in plants. Current Opinion \n599 in Plant Biology. 2012;15(2):192-198. doi:10.1016/j.pbi.2012.01.005\n600 27. Zhou Q, Fu Z, Liu H, et al. Mining novel kernel size-related genes by pQTL mapping and \n601 multi-omics integrative analysis in developing maize kernels. Plant Biotechnol J. \n602 2021;19(8):1489-1491. doi:10.1111/pbi.13634\n603 28. Lapous R, Amegan KE, Caromel B, et al. When Metabolomics Meets Quantitative \n604 Genetics: An Integrative Strategy to Elucidate Plant Resistance Mechanisms. Plant, Cell \n605 & Environment. 2026;49(3):1712-1727. doi:10.1111/pce.70328\n606 29. Bénéjam J, Ferreira De Carvalho J, Ravon E, et al. Phenotyping data coupled with RNA \n607 sequencing of apple genotypes exhibiting contrasted quantitative trait loci \n608 architecture for apple scab (Venturia inaequalis) resistance. Data in Brief. \n609 2024;56:110778. doi:10.1016/j.dib.2024.110778\n610 30. Durel CE, Calenge F, Parisi L, et al. AN OVERVIEW OF THE POSITION AND ROBUSTNESS \n611 OF SCAB RESISTANCE QTLS AND MAJOR GENES BY ALIGNING GENETIC MAPS OF FIVE \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n612 APPLE PROGENIES. Acta Hortic. 2004;(663):135-140. \n613 doi:10.17660/ActaHortic.2004.663.17\n614 31. Lê Van A, Gladieux P, Lemaire C, et al. Evolution of pathogenicity traits in the apple \n615 scab fungal pathogen in response to the domestication of its host: Pathogen evolution \n616 under host domestication. Evolutionary Applications. 2012;5(7):694-704. \n617 doi:10.1111/j.1752-4571.2012.00246.x\n618 32. Bus VGM, van de Weg WE, Peil A, et al. The role of Schmidt ‘Antonovka’ in apple scab \n619 resistance breeding. Tree Genetics & Genomes . 2012;8(4):627-642. \n620 doi:10.1007/s11295-012-0470-2\n621 33. Muranty H, Denancé C, Feugey L, et al. Using whole-genome SNP data to reconstruct \n622 a large multi-generation pedigree in apple germplasm. BMC Plant Biol. 2020;20(1):2. \n623 doi:10.1186/s12870-019-2171-6\n624 34. Durel CE, Denancé C, Muranty H, Lateur M, Ordidge M. MUNQ and PUNQ – a European \n625 and international apple and pear germplasm coding system. Acta Hortic. \n626 2023;(1384):471-476. doi:10.17660/ActaHortic.2023.1384.59\n627 35. Bianco L, Cestaro A, Sargent DJ, et al. Development and Validation of a 20K Single \n628 Nucleotide Polymorphism (SNP) Whole Genome Genotyping Array for Apple (Malus × \n629 domestica Borkh). PLOS ONE. 2014;9(10):e110377. \n630 doi:10.1371/journal.pone.0110377\n631 36. Bianco L, Cestaro A, Linsmith G, et al. Development and validation of the \n632 Axiom®Apple480K SNP genotyping array. The Plant Journal. 2016;86(1):62-74. \n633 doi:10.1111/tpj.13145\n634 37. Daccord N, Celton JM, Linsmith G, et al. High-quality de novo assembly of the apple \n635 genome and methylome dynamics of early fruit development. Nat Genet. \n636 2017;49(7):1099-1106. doi:10.1038/ng.3886\n637 38. Diesh C, Stevens GJ, Xie P, et al. JBrowse 2: a modular genome browser with views of \n638 synteny and structural variation. Genome Biol. 2023;24(1):74. doi:10.1186/s13059-\n639 023-02914-z\n640 39. Caffier V, Patocchi A, Expert P, et al. Virulence Characterization of Venturia inaequalis \n641 Reference Isolates on the Differential Set of Malus Hosts. Plant Disease. \n642 2015;99(3):370-375. doi:10.1094/PDIS-07-14-0708-RE\n643 40. Le Cam B, Sargent D, Gouzy J, et al. Population Genome Sequencing of the Scab Fungal \n644 Species Venturia inaequalis, Venturia pirina, Venturia aucupariae and Venturia \n645 asperata. G3 Genes|Genomes|Genetics. 2019;9(8):2405-2414. \n646 doi:10.1534/g3.119.400047\n647 41. Caffier V, Didelot F, Pumo B, Causeur D, Durel CE, Parisi L. Aggressiveness of eight \n648 Venturia inaequalis isolates virulent or avirulent to the major resistance gene Rvi6 on \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n649 a non-Rvi6 apple cultivar: Aggressiveness of Venturia inaequalis. Plant Pathology . \n650 2010;59(6):1072-1080. doi:10.1111/j.1365-3059.2010.02345.x\n651 42. Howard NP, Troggio M, Durel CE, et al. Integration of Infinium and Axiom SNP array \n652 data in the outcrossing species Malus × domestica and causes for seemingly \n653 incompatible calls. BMC Genomics. 2021;22(1):246. doi:10.1186/s12864-021-07565-7\n654 43. Van Ooijen JW. JoinMap® 4, Software for the Calculation of Genetic Linkage Maps in \n655 Experimental Populations. Kyazma BV, Wageningen, Netherlands . Published online \n656 2006. Accessed May 15, 2025. https://cir.nii.ac.jp/crid/1370287939162096384\n657 44. Broman KW, Wu H, Sen S, Churchill GA. R/qtl: QTL mapping in experimental crosses. \n658 Bioinformatics. 2003;19(7):889-890. doi:10.1093/bioinformatics/btg112\n659 45. Voorrips RE. MapChart: Software for the Graphical Presentation of Linkage Maps and \n660 QTLs. Journal of Heredity. 2002;93(1):77-78. doi:10.1093/jhered/93.1.77\n661 46. Pelletier S. AnaDiff: A tool for differential analysis of microarrays and RNAseq. \n662 Published online April 22, 2022. doi:10.5281/ZENODO.6477917\n663 47. Develey-Rivière MP, Galiana E. Resistance to pathogens and host developmental stage: \n664 a multifaceted relationship within the plant kingdom. New Phytologist. \n665 2007;175(3):405-416. doi:10.1111/j.1469-8137.2007.02130.x\n666 48. Gusberti M, Gessler C, Broggini GAL. RNA-Seq Analysis Reveals Candidate Genes for \n667 Ontogenic Resistance in Malus-Venturia Pathosystem. PLOS ONE. 2013;8(11):e78457. \n668 doi:10.1371/journal.pone.0078457\n669 49. Cloutier S, Reimer E, Khadka B, McCallum BD. Variations in exons 11 and 12 of the \n670 multi-pest resistance wheat gene Lr34 are independently additive for leaf rust \n671 resistance. Front Plant Sci . 2023;13. doi:10.3389/fpls.2022.1061490\n672 50. Gravot A, Liégard B, Quadrana L, et al. Two adjacent NLR genes conferring quantitative \n673 resistance to clubroot disease in Arabidopsis are regulated by a stably inherited \n674 epiallelic variation. Plant Comm. 2024;5(5). doi:10.1016/j.xplc.2024.100824\n675 51. Kourelis J, van der Hoorn RAL. Defended to the Nines: 25 Years of Resistance Gene \n676 Cloning Identifies Nine Mechanisms for R Protein Function. The Plant Cell . \n677 2018;30(2):285-299. doi:10.1105/tpc.17.00579\n678 52. Poland JA, Balint-Kurti PJ, Wisser RJ, Pratt RC, Nelson RJ. Shades of gray: the world of \n679 quantitative disease resistance. Trends in Plant Science. 2009;14(1):21-29. \n680 doi:10.1016/j.tplants.2008.10.006\n681 53. Vinatzer BA, Zhang HB, Sansavini S. Construction and characterization of a bacterial \n682 artificial chromosome library of apple. Theor Appl Genet. 1998;97(7):1183-1190. \n683 doi:10.1007/s001220051008\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n684 54. Watts S, Yates S, Vanderzande S, et al. Haplotype-resolved chromosome-level genome \n685 assemblies of nineteen apple (Malus domestica Borkh.) cultivars. Sci Data. \n686 2026;13(1):258. doi:10.1038/s41597-026-06583-y\n687 55. Švara A, Sun H, Fei Z, Khan A. Chromosome-level phased genome assembly of \n688 “Antonovka” identified candidate apple scab-resistance genes highly homologous to \n689 HcrVf2 and HcrVf1 on linkage group 1. Akhunov E, ed. G3: Genes, Genomes, Genetics. \n690 2024;14(1):jkad253. doi:10.1093/g3journal/jkad253\n691 56. Khan A, Carey SB, Serrano A, et al. A phased, chromosome-scale genome of \n692 ‘Honeycrisp’ apple (Malus domestica). GigaByte. 2022;2022:gigabyte69. \n693 doi:10.46471/gigabyte.69\n694\n695 Supporting information\n696 S1 Fig. Genetic maps and LOD curves of minor variables used for scab resistance quantitative \n697 trait loci mapping of the extended TxF progeny. Genetic position (in centiMorgans) and \n698 marker name used in this study are respectively indicated on the left and right of linkage \n699 groups (LG). Marker name associated to maximum LOD value for each QTL are bolded. 2-LOD \n700 and 1-LOD support QTL confidence interval are represented by vertical lines and solid \n701 rectangles, respectively.\n702 S2 Fig. Distribution of AUDPC scores after scab infection with two isolates according to \n703 favorable allele of two epistatic QTL segregating in the extended TxF progeny. Genotypic \n704 classes are attributed according to allelic variant of marker associated to maximum LOD score \n705 of each QTL. Two variables are presented (adjusted AUDPC from ‘Vi-B04’ and ‘Vi-Z14’ \n706 experiments). Significant differences between classes have been evaluated through ANOVA \n707 test.\n708 S1 Table. Parameters associated with the fine-mapped quantitative trait loci (QTL) identified \n709 for scab resistance of the extended TxF progeny. Virtual markers, for which physical positions \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n710 were estimated, are also included (e.g., ‘c11.loc9’, which is placed on chromosome 11 at 9 \n711 centiMorgans). | Chr,  chromosome, LOD, Logarithm of the odds; R², variance; CI, confidence \n712 interval.\n713 S2 Table. Gene list included in the refined confidence interval of qT13. Gene names (‘Gene \n714 ID’) are extracted from the GDDH13 reference genome. | QTL, Quantitative Trait Loci.\n715 S1 File. Markers comprising the genetic map of the extended TxF progeny. Virtual markers, \n716 for which physical positions were estimated, are included (e.g., ‘c11.loc9’, which is placed on \n717 chromosome 11 at 9 centiMorgans). Physical positions are extracted from the GDDH13 \n718 reference genome.\n719 S2 File. Phenotyping data used for QTL mapping in the extended TxF progeny. Data were \n720 acquired across three experiments. One includes the inoculation with the Vi isolate ‘EU-B04’ \n721 and three phenotypic variables were scored. The two others experiments were conducted \n722 with the inoculation of isolate ‘09BCZ14’ and only sporulation symptoms were measured. \n723 Missing values are indicated with an asterisk (*).\n724 S3 File. Genotyping data used for QTL mapping in the extended TxF progeny. For each SNP, \n725 alleles are coded as 1 or 2.  Virtual markers, further imputed in genetic analyses, are not \n726 included. Missing values are indicated with an asterisk (*). \n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint \n\n.CC-BY 4.0 International licensemade available under a \n(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is \nThe copyright holder for this preprintthis version posted April 10, 2026. ; https://doi.org/10.64898/2026.04.08.717319doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}