Unlocking Hidden Sources of Resistance to Ascochyta Blight in Moderately Resistant Chickpea Genotypes

Unlocking Hidden Sources of Resistance to Ascochyta Blight in Moderately Resistant Chickpea Genotypes | 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 Unlocking Hidden Sources of Resistance to Ascochyta Blight in Moderately Resistant Chickpea Genotypes Clara Crociara, Lucio Valetti, Patricia Castro, Teresa Millán, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6106863/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jun, 2025 Read the published version in Euphytica → Version 1 posted 12 You are reading this latest preprint version Abstract Ascochyta blight, caused by Ascochyta rabiei , is a major threat to global chickpea ( Cicer arietinum L.) production, significantly reducing yield under favorable conditions. This study aimed to characterize the resistance responses of nine chickpea genotypes, previously classified as moderately resistant, by subjecting them to enhanced disease pressure. Phenotypic evaluation, including the area under the disease progress curve (AUDPC) and severity scoring was carried out. To explore the genetic basis of resistance, molecular markers associated with quantitative trait loci (QTLs) for resistance were analyzed. The results revealed significant variability among the MR genotypes, with three genotypes FLIP06-86C, FLIP07-35C, and FLIP03-100C outperforming the resistant control. The results from hierarchical clustering (UPGMA), principal component analysis (PCA), and principal coordinate analysis (PCoA) highlighted genetic substructures consistent with observed phenotypic behaviors. However, unexpected marker-phenotype associations were detected, questioning the utility of specific markers such as SCY17 and CAETR in marker-assisted selection. These findings underline the complexity of polygenic resistance to A. rabiei and emphasize the importance of integrating phenotypic screening with genetic analyses to improve the reliability of chickpea breeding programs. This work also contributes to identifying superior MR genotypes, providing valuable resources for the development of resistant cultivars. Ascochyta rabiei Molecular markers markers pulse breeding Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Chickpea ( Cicer arietinum L.) is one of the most widely cultivated legume crops in the world, representing approximately 15% of the total area and production of legume globally (Foresto et al. 2023). Argentina ranks among the top 10 largest exporters of chickpeas (BCR, 2021; USA Dry Pea & Lentil Council, n.d.) and around 90% of chickpea production is intended for export. Ascochyta blight (AB), caused by the fungal pathogen Ascochyta rabiei (Pass.) Labr. (teleomorph: Didymella rabiei (Kovatsch.) Arx) , is one of the most devastating diseases affecting chickpea cultivation worldwide. It poses a significant threat to crop yields, reaching up to 100% loss under favorable environmental conditions (Pande et al. 2005; Viotti et al. 2012 , Crociara et al. 2022 ). Effective management of AB requires an integrated approach that combines the use of chemical fungicides, cultural practices, and the implementation of genetic resistance to minimize its impact (Harveson et al. 2011; Pastor et al. 2022 ). The pursuit of genetic resistance to A. rabiei is one of the main objectives of chickpea breeding programs (Sharma et al. 2016; Pastor et al. 2022 ; Zangene et al. 2022 ) as it is the most effective, efficient, and environmentally sustainable method for controlling this disease. Evaluating genetic resistance to AB is challenging due to its complex nature, which exhibits low to moderate heritability, being significantly influenced by environmental and whose inheritance resistance to Ascochyta rabiei , a necrotrophic pathogen, is quantitative in nature (Deokar et al. 2019a). Such complexity arises from the presence of multiple regions in the genome, known as Quantitative Trait Loci (QTLs), that are associated with this trait (Collard et al. 2005 ; Deokar et al. 2019 a and b). Traditional breeding methods that rely on phenotypic selection face several challenges, particularly because many quantitative traits are significantly influenced by the environment, expressed later in development, and/or exhibit continuous variation. The severity of the disease depends on the inoculum concentration (Trapero-Casas and Kaiser (1992b); Dólar et al . (1994); Chen et al. ( 2005 ); Tivoli et al . (2006); Harijati y Keane (2012). Furthermore, there is a tendency for increased susceptibility in reproductive stages (Hafiz (1952); Reddy and Singh (1984); Nene y Reddy (1987); Chongo and B.D. Gossen 2001) indicating that fungicidal application may still be required at flowering and pod formation stages (Gayacharan et al. 2020). Selection assisted by molecular markers (MAS) utilizes genotypic data to enhance breeding efforts. Although several markers associated with resistance to AB have been developed, their application remains limited (Deokar et al. 2019a). In their study, Iruela et al . (2006) developed the SCAR marker SCY17 associated with resistance to AB and mapped it on linkage group four, flanked by SSR markers: TA146 and TA72. Additionally, Tar’an et al . (2007) reported three SSRs (TA64, TS54, and TA176) that were strongly associated with AB resistance. On the other hand, Millán et al . (2010) and Iruela et al . (2006) developed the sequence-tagged microsatellite sites (STMS) TA130, TA72, TA146, TA194, TR19, and TR58 which were linked to resistance against both AB and Fusarium spp . Furthermore, Madrid et al. ( 2013 ) developed an ASAP marker (allele-specific associated primer) CAETR based on the sequence of an ethylene receptor-like gene . The authors demonstrated that the combined use of SCY17 and CAETR markers can be effective to detect sources of resistance to AB in breeding programs. In previous research, we screened 109 chickpea inbred lines provided by International Center for Agricultural Research in the Dry Areas (ICARDA) to assess their response to Ascochyta rabiei (Pastor et al. 2022 ). One of the most notable findings was that 32% of these lines were classified as Moderately Resistant (MR). Remarkably, approximately half of these MR genotypes displayed disease scores that were not significantly different from those of resistant lines (R), suggesting that additional genotypes with robust performance against AB may have been overlooked. In this sense, the use of MR genotypes to develop genotypes with improved resistance has already been reported (Deokar et al. 2019b). The current study aimed to differentiate levels of resistance among nine selected genotypes previously classified as MR by Pastor et al. ( 2022 ). To achieve this, these genotypes were exposed to increased disease pressure to comprehensively evaluate their resistance. Subsequently, they were analyzed using a set of molecular markers linked to Quantitative Trait Loci (QTLs) associated with Ascochyta blight (AB) resistance. Materials and methods Pot trials under controlled conditions Nine chickpea genotypes belonging to ICARDA were chosen for their classification as moderately resistant (MR) according to Pastor et al. ( 2022 ). Additionally, both susceptible and resistant control genotypes were included in the experiment (Table 1 ). Table 1 Genotypes tested in pots assay for resistance against Ascochyta rabiei Genotype designation Origin/Source Response to A.rabiei . Reference FLIP03-27C ICARDA MR Pastor et al. ( 2022 ) FLIP03-100C ICARDA MR FLIP05-67C ICARDA MR FLIP06-52C ICARDA MR FLIP06-86C ICARDA MR FLIP07-35C ICARDA MR FLIP88-85C ICARDA MR X08TH77 ICARDA MR FLIP08-35C ICARDA MR FLIP07-25C ICARDA R Chañaritos S-156 UNC S R : resistant control; MR : moderate resistant; S : susceptible control. UNC : Universidad Nacional de Córdoba. Argentina. The seeds of each inbred line were surface disinfected through sequential immersion in a 70% alcohol solution for 30 secs, followed by 4 minutes in a 0.5% active chlorine sodium hypochlorite solution, and a triple rinse with sterile distilled water. To promote germination, the seeds were placed in Petri dishes containing sterile water agar and incubated in the dark at 25ºC until germination. Healthy, fungus-free seeds were selected and transferred to seedbeds containing a 3:1 mixture of pasteurized soil and perlite, where they were acclimatized in a greenhouse. Once the seedlings reached the stage of three unfolded leaves, they were transplanted into 10 L pots. Five pots of every genotype with one plant each were relocated to an experimental plot surrounded by anti-aphid netting and equipped with irrigation sprinklers. Once plants reached the flowering stage, they were artificially inoculated and maintained under these conditions throughout the crop cycle. Disease assessment was carried out two times with n = 5. Inoculum preparation To maximize disease severity, A. rabiei RCB isolate registered as LJC N° 10673 (WDCM904 Colección de fitopatógenos de cultivos hortícolas, EEA La Consulta INTA, Argentina) was used (Crociara et al. 2022 ). Spore suspension was obtained according to Valetti et al. (2021). The inoculum concentration was adjusted to 1x10 7 conidia/ml (Chen, Mc Phee, & Muehlbauer 2005) to increase disease pressure on the inbred lines. Inoculation was carried out by using a hand-held pressure sprayer. For the 14 days following inoculation, environmental humidity was maintained by a sprinkler system programmed to operate for one hour every two hours. Disease evaluation Disease severity was assessed fourteen days after inoculation marking the first assessment. Subsequent evaluations were conducted every seven days until the first dead plant appeared. Disease severity was measured using a 0–9 rating scale (Singh et al. 1981) adapted by Nasir et al . (2000), as follows: 1 = no visible lesions, 3 = only flecks on leaves, 5 = lesions on stems and flecks on leaves, 7 = stem breakage in injured areas, and 9 = plant death. Severity data were recorded for each plant of every genotype at every evaluation. In addition, average severity for each genotype in each assessment was translated into a resistance scale according to Pande et al . (2012) as follows: 1–3: Resistant; 3–5: Moderately Resistant; 5–7: Susceptible; 7–9: Highly Susceptible. The area under the disease progress curve (AUDPC) was calculated for each genotype using the following formula described by Sánchez et al. (2017). $$\:AUDPC=\:\sum\:_{i=1}^{a}=\:\left[\left\{\frac{\left({Y}_{i}+{Y}_{\left(i+1\right)}\:\right)}{2}\right\}x({t}_{\left(i+1\right)}-{t}_{i}\right]$$ Where Y i is the blight score of the i th evaluation, Y i +1 is the blight score of the i + 1th evaluation and ( t i +1 − t i ) is the number of days between two evaluations (Campbell and Madden 1990 ). The data were subjected to analysis of variance (ANOVA) following by the comparison of treatment means by Di Rienzo, Guzmán and Casanoves (DGC) test (p < 0,05) using software Infostat 2020 (Di Rienzo et al. 2020). Molecular markers assay DNA isolation For DNA extraction, 10 mg of apical shoots from each genotype listed in Table 1 were collected. The harvested shoots were dehydrated in kraft paper bags, kept in airtight boxes with silica gel until completely dried. They were then stored until DNA extraction. For DNA extraction, E-Z 96 Plant DNA Kit (Omega Bio-Tek, Norcross, GA, USA) was used following the manufacturer instructions. The DNA concentrations obtained were adjusted to 10 ng/ml and stored at -20ºC until use. For the molecular markers assay, the DNA of an additional set of genotypes was provided by the Department of Genetics UCO (Cordoba, Spain) (Table 2 ) and included as susceptible and resistant controls. Table 2 Genotypes included in molecular marker assay as susceptible and resistant controls according to international literature Genotype designation Origin/Source Response to A. rabiei . Reference ILC3279 ICARDA R Alo et al. (2022), Sharma & Ghosh (2016), Madrid et al. ( 2013 ), Iruela et al. (2006) ILC182 ICARDA R Sharma & Ghosh (2016); Benzohra et al. ( 2015 ), Labdi et al. (2013), Madrid et al. ( 2013 ) ILC5921 ICARDA R Labdi et al. (2013) ILC187 ICARDA R Sharma & Ghosh (2016); Benzohra et al. ( 2015 ), Labdi et al. (2013) EBT3 UCO S - WR315 ICRISAT S Bouhadida et al. (2013), Madrid et al. ( 2013 ), Iruela et al. (2006) CUAIZ ITACyL S Morcuende Herrero, (2024) Ca2139 UCO S Cobos et al. (2005) R : resistant; S : susceptible. ICRISAT : International Crops Research Institute for the Semi-Arid Tropics. Instituto Tecnológico Agrario de Castilla y Léon. PCR conditions The primers used in this study are described in Table 3 . These markers were selected based on international literature and their availability at the Department of Genetics of the University of Córdoba (Spain). Table 3 Primers sequences of the molecular markers used for grouping the genotypes under study Primer name Primer sequence Marker Type Reference LG QTL SCY17 590 F1: GACGTGGTGACTATCTAGC R1: GACGTGGTGAAATAGATACC SCAR Iruela et al. (2006) 4 QTLAR2 CaETR 4 F: 6CAGGAAGTT CAATGGCCCTA R1: TAAGTTGTGACAAAAGACTCAATCG R2: TGTGGCACAGTGGACCCCATCT ASAP Madrid et al. ( 2013 ) 4 QTL AR1 TA64 F: ATATATCGTAACTCATTAATCATCCGC R: AAATTGTTGTCATCAAATGGAAAATA STMS Winter et al. (1999) ; Taran et al. (2007); Anbessa et al. (2009) 3 QTL1 QTL2 TA72 F:8GAAAGATTTAAAAGATTTTCCACGTTA R: TTAGAAGCATATTGTTGGGATAAGAGT STMS Winter et al. (1999) ; Ramakuri, P. (2005) ; Iruela et al. (2006) 4 QTL2 TA130 F*:6TCTTTCTTTGCTTCCAATGT R: FGTAAATCCCACGAGAAATCAA STMS Winter et al. (1999) ; Flandez-Galvez et al. (2003) 4 QTL5 TA146 F: CTAAGTTTAATATGTTAGTCCTTAAATTAT R: ACGAACGCAACATTAATTTTATATT STMS Winter et al. (1999) ; Iruela et al. (2006) ; Flandez-Galvez ; et al . (2003); Kottapalli et al . (2009) 4 QTL 6; QTL5; QTLAR2; QTL2 TA176 F: ATTTGGCTTAAACCCTCTTC R: TTTATGCTTCCTCTTCTTCG STMS Winter et al. (1999) ; Taran et al. (2007) ; Anbessa et al. (2009) 6 QTL3; QTL4 TR19 F: TCAGTATCACGTGTAATTCGT R: CATGAACATCAAGTTCTCCA STMS Winter et al. (1999); Anbessa et al. (2009) 2 QTL1 TA194 F: TTTTTGGCTTATTAGACTGACTT R: TTGCCATAAAATACAAAATCC STMS Winter et al. (1999) ; Iruela et al. (2007) 7 QTLAR3 TR58 F*:6CTCTATATTTGTTTGTTTTTCGTTTTG R: TAAAATGTGTAGGGTGCAGAATAAATA STMS Winter et al. (1999) 3 QTLAR3; QTL 1 LG : Linkage Group * Fluorochrome-labeled primer. All STMS markers were amplified in a final mix volume of 10 µl, 1X buffer (50 mM KCl, 10 mM Tris–HCl, 0.1% Triton X-100), 1.5 mM MgCl 2 , 0.25 mM dNTPs, 0.2 µM of each primer, and 0.25 U of Taq DNA polymerase (GoTaq Flexi, Promega). For amplification, the thermocycler was programmed for 35 cycles of: 94 ºC for 30 sec, 58 ºC for 50 sec, and 60 ºC for 50 sec, followed by a final extension at 72 ºC for 10 min. The PCR product from all primer pairs labeled with the fluorochrome 6FAM was analyzed using fragment analysis. Amplified products were analyzed in an automated capillary sequencer (CBI 3130 Genetic Analyzer, Biosystems Applied/HITACHI). The PCR product from unlabeled primers was visualized on 1% polyacrylamide gel. For the SCY17 marker amplification, the reaction was prepared in a final volume of 10 µl, 1X buffer (50 mM KCl, 10 mM Tris–HCl, 0.1% Triton X-100), 2 mM MgCl 2 , 0.25 mM dNTPs, 0.2 µM of each primer, and 0.4 U of Taq DNA polymerase (GoTaq Flexi, Promega). For amplification, the thermocycler was programmed for 30 cycles of: 95 ºC for 1 min, 50 ºC for 30 sec, and 72 ºC for 40 sec, followed by a final extension at 72 ºC for 8 min. The PCR product was observed on a 2.5% agarose gel stained with GelRed™ (Biotium, CA, USA). The ASAP CaETR4 marker was amplified in a final volume of 10 µl, 1X buffer (50 mM KCl, 10 mM Tris–HCl, 0.1% Triton X-100), 2.5 mM MgCl 2 , 0.4 mM dNTPs, 0.4 µM of each primer, and 0.25 U of Taq DNA polymerase (GoTaq Flexi, Promega). For amplification, the thermocycler was programmed for 30 cycles of: 95 ºC for 30 sec, 62 ºC for 30 sec, and 72 ºC for 50 sec, followed by a final extension at 72 ºC for 7 min. The PCR product was observed on a 2% agarose gel stained with GelRed™ (Biotium, CA, USA). Molecular markers data analysis The allelic data set was coded as binary data where 0 = Absence of band) and 1 = presence of band). Each allele was assigned to a separate column in the dataset. The generated matrix was used for clustering analysis using the UPGMA algorithm as the grouping criterion, with the square root transformation of the complement of one of the Jaccard similarity index serving as a genetic distance measure (Rajput et al. 2023; Sahu et al. (2020); Choudhary et al. 2013 ; Bruno & Balzarini 2010 ). Results were visualized through a dendrogram. To reduce the dimensionality of the data and to identify the main axes of variation that explain most of the diversity in the resistance response a Principal Component Analysis (PCA) and Principal Coordinates Analysis (PCoA) (Choudhary, et al 2013 ) was also performed. The genetic distance measure was Jaccard similarity index too. The ordering visualization was conducted using a Biplot. All analyses were performed using INFOSTAT software (Di Rienzo et al. 2020) Results Pot assay under controlled condition The assay allowed the longitudinal observation of the disease progression over time. Consequently, AUDPC was calculated to compare the phenotypic responses of the genotypes under evaluation. Notably discernible differentiation among the moderately resistant (MR) genotypes was achievable. As illustrated in Fig. 1 , significant divergence in AUDPC values was observed, enabling clear delineation between the two distinct groups. The first group encompasses the susceptible control Chañaritos-S 156, together with FLIP06-52C, FLIP88-85C, FLIP05-67C, X08TH77 and FLIP03-27C. Conversely, the second group comprises the resistant control (RC) represented by FLIP06-86C, FLIP07-35C, FLIP03-100C, and FLIP08-35C. While AUDPC is a valuable metric for comprehensively assessing the overall performance of each genotype, it is equally interesting to analyze the average disease severity attained by each genotype at various scoring intervals and interpret these values in the context of resistance levels. Figure 2 illustrates the initial susceptibility of both the susceptible control Chañaritos-S116 and the MR genotypes FLIP06-52C, FLIP88-85C from the first evaluation. Similarly, FLIP05-67C, X08TH77, and FLIP03-27C reached the upper threshold of the MR range, indicating stem damage across all evaluated plants. By the second assessment (21 dip), all three of them exhibited stem breakage, in concordance with their respective susceptibility categories. In contrast, the resistant control (RC) as well as MRs: FLIP06-86C, FLIP07-35C, FLIP03-100C and FLIP08-35C showed severity values within the MR range, some of them persisting in this range for an extended period. Notably, FLIP06-86C demonstrated the most resilient behavior, reaching the susceptibility range only during the final scoring (42 dpi). Molecular markers assay The PCR amplification of the molecular markers used in this study revealed several allelic variants for each microsatellite marker. Additionally, two distinct alleles, one resistant and one susceptible were identified in relation to CAETR and SCY17 markers (Table 4 ). Table 4 Association of resistance and susceptible alleles of two DNA markers, SCY17 and CAETR, with the resistant, moderate resistant and susceptible phenotypes of chickpea accessions. Germoplasm line Ascochyta blight phenotype a Genotype b SCY17 CAETR FLIP88-85C MR SS RR FLIP05-67C MR RR RR FLIP03-27C MR SS RR FLIP07-35C MR RS RR FLIP08-35C MR RR SS FLIP03-100C MR RR RR FLIP06-86C MR RS RR FLIP06-52C MR RR SS X08TH77 MR RR RR FLIP07-25C (RC) R SS RR ILC3279 R RR RR ILC182 R RR SS ILC5921 R RR RR ILC187 R RR RR Chañaritos S-156 (CH) S RR SS EBT3 S SS SS WR315 S SS SS CUAIZ S RR SS CA2139 S RR SS RC : resistant control CH : susceptible control. a : resistant (R), moderate resistant (MR) and susceptible (S); b : R and S indicate resistant and susceptible alleles, respectively. Allelic variants of all markers analyzed were converted into binary data. Hierarchical clustering by UPGMA was carried out to generate a dendrogram (Fig. 3 ) illustrating the relationship among the genotypes. The analysis revealed five different clusters. Cluster I includes three susceptible controls EBT 3, WR315, and CH along the most sensible genotype FLIP06-52C. The genotype FLIP08-35C stands apart from the rest of the clusters (cluster II). Then two smaller clusters (III and V) include the resistant control R together with FLIP88-85C and FLIP03-27C genotypes; and FLIP05-67C and the susceptible control CUAIZ respectively. Probably the most interesting cluster comprises four of the resistant controls (ILC 5921, ILC 187, ILC 182, ILC 3279) along with the three genotypes exhibiting the best performance against A. rabiei : FLIP06-86C, FLIP07-35C, FLIP03-100C (cluster IV). However, this group also includes the susceptible control Ca2139. Additionally, PCA and PCoA Biplot are presented in Fig. 4 . The PCA allowed for the representation of genetic variability in a two-dimensional space, explaining 32.8% of the total variance across the first two principal components (19.7% and 13.1%, respectively) (Fig. 4 A). This analysis revealed a prominent dispersion of genotypes within the spatial representation, highlighting discernible patterns of genetic substructure that corroborate the observed grouping in the dendrogram (Fig. 3 ). The analysis highlighted the intern relationship among resistant genotypes (Group I) comprising ILC 5921, ILC 187, ILC 182, ILC 3279 and best-behaved genotype FLIP08-35C best behaved genotype as they clustered together within two CP. Furthermore, at the opposite end, susceptible genotypes: WR315, CH, EBT3 and Cuaiz clustered closely together, with the most susceptible MR, FLIP06-52C (Group II). Finally, certain association is also apparent among the highest performing genotypes for AB resistance: FLIP06-86C, FLIP07-35C, FLIP03-100C, and FLIP05-67C genotypes (Group III). The principal coordinate analysis (PCoA), calculated with the same genetic distances (Jaccard, Fig. 4 B), exhibited a dispersion pattern like the observed in the PCA (Fig. 4 ), though offering a more nuanced representation of the genetic distances between accessions. This method explained 28.7% of the total variability across the first two axes (15.4% and 13.3%, respectively). Once again, distinctive associations emerged showing a clustering of resistant genotypes along with FLIP08-35C (Group I), a second grouping of susceptible controls and FLIP06-52C (Group II) and a consistent third group including genotypes FLIP06-86C, FLIP07-35C, FLIP03-100C and FLIP05-67C (Group III). Discussion The search for genetic resistance to AB is one of the main challenges worldwide. This complexity arises from the polygenic nature of the trait, influenced by various genomic loci and environmental factors (Deokar et al. 2019 a; Carmona et al. 2023 ). Indeed, most of the observed resistance to AB is because of quantitative genes. Despite of moderately resistance has been reported (Deokar et al. 2019 a y b; Pastor et al. 2022 ; Aydoğan 2024 ) the exact genetic and molecular mechanism governing resistance against A. rabiei infection remains elusive (Deokar et al. 2019a). This last fact caught particularly our attention. For instance, categorizing a genotype with minimal leaf lesions alongside another genotype with stem damage but without stem breakage (see the scale in Materials and Methods section), underscores the need for discerning such distinctions. These observations drove us to look for a way to discriminate these differences, since the quest for novel sources of resistance is imperative to sustain chickpea cultivation and productivity (Gayacharan et al. 2020). To unravel the inherent resistance mechanism within each genotype we subjected them to a highly concentrated spore dosage (Chen, Mc Phee, & Muehlbauer 2005), of a very aggressive isolate (Crociara et al. 2022 ) targeting a critical stage in chickpea, such as the flowering stage (Gayacharan et al. 2020). Interestingly, the pot trial facilitated the discrimination between the best and worst performances among a set of MR lines, with genotypes FLIP06-86C, FLIP07-35C, and FLIP03-100C exhibiting robust responses against the pathogen, even outperforming the resistant control (RC) (Fig. 4 ). As tools to address these adverse conditions are logically at the genome level, we attempted to explain the contrasting behaviors using molecular markers known to be associated with QTLs related to AB resistance. The dendrogram allowed the identification of clear hierarchical groupings among the evaluated accessions. PCA analysis revealed a notable dispersion of genotypes in space, highlighting patterns of genetic substructure that align with the groupings observed in the dendrogram. The principal coordinate analysis (PCoA), based on the same genetic distance (Jaccard), showed a dispersion pattern like that of the PCA, although with a more accurate representation of the distances between accessions. These results support the conclusions drawn from the PCA, highlighting the consistency between both methods. It was interesting to confirm the aggrupation of FLIP06-52C, the most susceptible genotype together with susceptible controls. Something similar occurred with more resistant FLIP06-86C and FLIP07-35C which are tightly grouped by the three methods, alongside FLIP03-100C and FLIP08-35C (Fig. 4 B). Those were also grouped with resistant controls (Fig. 3 ). However, some unexpected associations were observed, for instance all methods consistently grouped the X08TH77 genotype with resistant controls, despite its high susceptibility to A. rabiei . Another contradictory aggrupation was observed regarding CA2139 susceptible control which was grouped with the best-performed MRs, FLIP06-86C, FLIP07-35C, FLIP03-100C y FLIP08-35C. Additionally, FLIP05-67C genotype which exhibited a poor resistance response against A. rabiei , was grouped together with those four genotypes through PCA y PCoA (Fig. 4 ). However, in the dendrogram, it clustered with the susceptible control CUAIZ, which aligns more closely with the response observed in infected plants. Multivariate analysis includes all markers assessed. Given the greater challenge in predicting resistance alleles with STMS markers compared to specific markers (Madrid et al. 2013 ; Collard et al. 2005 ), we analyzed the amplification of CAETR and SCY17 markers specifically. Madrid et al. ( 2013 ) noted the absence of resistance alleles of the SCY17 and CAETR markers in all susceptible genotypes and its presence in 68–75% of the resistant ones; although they clarify that there were four resistant genotypes that did not have the resistance. Noteworthy discrepancies were observed with the presence of resistant alleles in susceptible controls and susceptible alleles in resistant controls for these markers (refer to Table 3 ). Furthermore, the identification of resistance alleles in FLIP06-52C (one of the most susceptible ones) and susceptible alleles in the top-performing genotypes (FLIP06-86C, FLIP03-100C, FLIP08-35C, FLIP07-35C) highlights the limitations of relying solely on CAETR and SCY17 markers in Marker-Assisted Selection (MAS). Those observations lead to note the inconvenience of using just CAETR and SCY17 in MAS. Since Madrid et al. ( 2013 ) and Imtiaz et al. (2008) have succeeded in selecting resistant cultivars using SCY17 marker, a recent work (Aydoğan, 2024 ) reported SCY17 marker overestimated resistance by amplifying the resistance allele in susceptible controls. Low effectiveness of SCY17 and TA72 markers has been reported too by Castro et al. ( 2015 ). The scenario where phenotypic resistance is evident in genotypes lacking the R alleles can be rationalized by the polygenic and additive nature of AB resistance (Saxena and Singh, 1984; Ahmad et al. 2010; Aryamanesh et al. 2010; Houasli et al. 2020). Genotypes may harbor distinct resistance genes or QTLs not captured by the SCY17 and CAETR markers (Madrid et al. 2013 ). The application of markers for enhancing polygenic quantitative traits remains challenging, with limited success stories attributed to the intricate interplay of numerous QTLs and their complex effects on quantitative traits, making their prediction arduous (Castro et al. 2015 ; Ilyas et al. 2022). Although MAS reduces the time taken for selecting lines it is also more expensive than phenotypic selection. We agree with Aydoğan ( 2024 ) genotypes might be inaccurately classified using markers for disease resistance, suggesting that controlled environment artificial inoculations could be a more pragmatic screening approach for chickpea germplasm selection. Declarations Fundings: This study was funded by Instituto Nacional de Tecnología Agropecuaria (INTA) /Argentina; Fundación Argeninta/Argentina; Centro de transferencia de Bioinsumos (CETBIO) - Universidad Nacional de Córdoba (UNC) / Argentina; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) / Argentina. Asociación Universitaria Iberoamericana de Postgrado (AUIP) financed the movility to Spain. Universidad de Córdoba (UCO)/ Spain funded Molecular Markers assay. Author Contribution C.C. conducted all the experiments, wrote the first draft of the manuscript, and contributed to its revision and editing.L.V. participated in the pot trials, contributed to the first draft of the manuscript, and was involved in the revision and final version.P.C. and T.M. supervised the molecular marker analysis, provided funding for this part of the study, and reviewed the final version of the manuscript.J.I. contributed to the interpretation of molecular marker data and participated in the writing of the final version of the manuscript.S.P. provided funding for the pot trials, critically revised, and edited the manuscript. Acknowledgement The first author sincerely acknowledges the support of the Asociación Universitaria Iberoamericana de Postgrado (AUIP) through the mobility grant that facilitated this research. The authors also extend their gratitude to the Universidad de Córdoba (UCO) for providing the necessary resources to conduct this study. Special thanks are given to María José Allende and Ana Fekete for generously supplying the seeds for the pot trials. Finally, the authors wish to express their deep appreciation to Dr. Mariela Acuña for her invaluable contribution to data interpretation. Data Availability Primary data that support the fidings of this study are available in the followin liks:https://repositorio.inta.gob.ar/xmlui/handle/20.500.12123/21430https://repositorio.inta.gob.ar/xmlui/handle/20.500.12123/21433 References Aydoğan, A. (2024). Comparison of different screening methods for selection of chickpea blight disease on chickpea ( Cicer arietinum L.) genotypes. Frontiers in Plant Science , 15 , 1347884. https://doi.org/… Benzohra, I. E., Bendahmane, B. S., Benkada, M. Y., & Labdi, M. (2015). Screening of 15 chickpea germplasm accessions for resistance to Ascochyta rabiei in North West of Algeria. Am.-Eurasian J. Agric. Environ. Sci., 15 , 109–114. Bruno, C., & Balzarini, M. (2010). Distancias genéticas entre perfiles moleculares obtenidos desde marcadores multilocus multialélicos. Revista de la Facultad de Ciencias Agrarias UNCuyo, 41 (3), 11. Campbell, C. L., & Madden, L. V. (1990). Temporal analysis of epidemics. I. Description and comparison of disease progress curves. In: Introduction to plant disease epidemiology . John Wiley and Sons, New York, pp. 161–202. Carmona, A., Rubio, J., Millán, T., Gil, J., Die, J. V., & Castro, P. (2023). Four haplotype blocks linked to chickpea blight disease resistance in chickpea under Mediterranean conditions. Frontiers in Plant Science , 14 , 1183287. https://doi.org/… Castro, P., Rubio, J., Madrid, E., Fernández-Romero, M. D., Millán, T., & Gil, J. (2015). Efficiency of marker-assisted selection for chickpea blight in chickpea. The Journal of Agricultural Science, 153 (1), 56–67. Chen, W., McPhee, K. E., & Muehlbauer, F. J. (2005). Use of a mini-dome bioassay and grafting to study resistance of chickpea to Ascochyta blight. Journal of Phytopathology, 153 (10), 579–587. Chen, W., Sharma, H. C., & Muehlbauer, F. J. (2011). Compendium of chickpea and lentil diseases and pests (pp. ix-165). Chongo, G., & Gossen, B. D. (2001). Effect of plant age on resistance to Ascochyta rabiei in chickpea. Canadian Journal of Plant Pathology, 23 (4), 358–363. Choudhary, P., Khanna, S. M., Jain, P. K., et al. (2013). Molecular characterization of primary gene pool of chickpea based on ISSR markers. Biochemical Genetics, 51 , 306–322. https://doi.org/10.1007/s10528-012-9564-7 Collard, B. C., Jahufer, M. Z. Z., Brouwer, J. B., & Pang, E. C. K. (2005). An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica, 142 , 169–196. Crociara, C., Valetti, L., Bernardi Lima, N., Iglesias, J., & Pastor, S. (2022). Morphological and molecular characterization, pathogenicity and sexual reproduction of Ascochyta rabiei isolates of chickpea fields in Argentina. Journal of Phytopathology, 170 (4), 221–232. Madrid, E., Chen, W., Rajesh, P. N., Castro, P., Millán, T., & Gil, J. (2013). Allele-specific amplification for the detection of chickpea blight resistance in chickpea. Euphytica, 189 , 183–190. Millan, T., Winter, P., Jüngling, R., Gil, J., Rubio, J., Cho, S., et al. (2010). A consensus genetic map of chickpea ( Cicer arietinum L.) based on 10 mapping populations. Euphytica, 175 , 175–189. Pastor, S., Crociara, C., Valetti, L., Peña Malavera, A., Fekete, A., Allende, M. J., & Carreras, J. (2022). Screening of chickpea germplasm for chickpea blight resistance under controlled environment. Euphytica, 218 (2), 12. Singh, R., Kumar, K., Purayannur, S., Chen, W., & Verma, P. K. (2022). Ascochyta rabiei : A threat to global chickpea production. Molecular Plant Pathology, 23 , 1241–1261. https://doi.org/10.1111/mpp.13235 . Varshney, R. K., Song, C., Saxena, R. K., Azam, S., Yu, S., Sharpe, A. G., Cannon, S., Baek, J., Rosen, B. D., & Tar'an, B. (2013). Draft genome sequence of chickpea ( Cicer arietinum ) provides a resource for trait improvement. Nature Biotechnology, 31 , 240–246. Viotti, G., Carmona, M. A., Scandiani, M., Formento, A. N., & Luque, A. (2012). First report of Ascochyta rabiei causing chickpea blight in Argentina. Plant Disease, 96 (9), 1375–1375. Zangene, K., Emamjomeh, A., Shokouhifar, F., Mamar Badi, M., & Mehdinezhad, N. (2022). Differentiation of an Iranian resistance chickpea line to chickpea blight from a susceptible line using a functional SNP. AMB Express, 12 (1), 45. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Jun, 2025 Read the published version in Euphytica → Version 1 posted Editorial decision: Revision requested 08 May, 2025 Reviews received at journal 07 May, 2025 Reviewers agreed at journal 07 May, 2025 Reviewers agreed at journal 21 Apr, 2025 Reviews received at journal 17 Apr, 2025 Reviewers agreed at journal 12 Apr, 2025 Reviews received at journal 22 Mar, 2025 Reviewers agreed at journal 17 Mar, 2025 Reviewers invited by journal 08 Mar, 2025 Editor assigned by journal 25 Feb, 2025 Submission checks completed at journal 25 Feb, 2025 First submitted to journal 25 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6106863","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434749161,"identity":"c6dce8e4-e480-4854-8eb1-d51cecbbcae8","order_by":0,"name":"Clara Crociara","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYLCCBDDJ3AAkbHigYgeI0cII0pLGw8BGjBYGhJbDDAS18M8+fPDDg5p78gzsBxs/V9SclzGXb37A+KXiDlAKuzaJc2nJEgnHig0beBKbJc8cu81j2cZmwCxz5hlQKgGrFgMeHjOGBLYExgYJxgbJBrbbPAbHGAyYJduALjyD3WEQLf8S7IFamn82/DsH1ML+AaxFHp+WxLaERKCWNsnGtgNALTwGjB+BWgxwaJE4w5YskdiXkNzGk9hm2diXDNSSU3CY4cwzHkMcWvh7mA9+/PEtwbaf/fDhmw3f7OwNDh/f+PBHxR05ORxa4IANmXMYmAZ4cKnEDhh/kKZ+FIyCUTAKhjcAANzFWnqzJ8IFAAAAAElFTkSuQmCC","orcid":"","institution":"Consejo Nacional de Investigaciones Científicas y Técnicas. Unidad de Fitopatología y Modelización Agrícola (UFYMA). Córdoba","correspondingAuthor":true,"prefix":"","firstName":"Clara","middleName":"","lastName":"Crociara","suffix":""},{"id":434749162,"identity":"96ca9b62-487d-4990-b325-c312fba4424b","order_by":1,"name":"Lucio Valetti","email":"","orcid":"","institution":"Instituto Nacional de Tecnología Agropecuaria, Instituto de Patología Vegetal (IPAVE). Córdoba","correspondingAuthor":false,"prefix":"","firstName":"Lucio","middleName":"","lastName":"Valetti","suffix":""},{"id":434749163,"identity":"995a74f3-ba71-4722-890d-6d1c909defa0","order_by":2,"name":"Patricia Castro","email":"","orcid":"","institution":"Universidad de Córdoba (UCO)","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Castro","suffix":""},{"id":434749164,"identity":"9c063528-4a3c-442d-a9d1-d3b73bbb9639","order_by":3,"name":"Teresa Millán","email":"","orcid":"","institution":"Universidad de Córdoba (UCO)","correspondingAuthor":false,"prefix":"","firstName":"Teresa","middleName":"","lastName":"Millán","suffix":""},{"id":434749165,"identity":"206761ee-e756-4e43-9d1d-6a69e38dcd7a","order_by":4,"name":"Juliana Iglesias","email":"","orcid":"","institution":"Instituto Nacional de Tecnología Agropecuaria, Estación Experimental Pergamino","correspondingAuthor":false,"prefix":"","firstName":"Juliana","middleName":"","lastName":"Iglesias","suffix":""},{"id":434749166,"identity":"2857055a-ba88-4441-a4fc-5785009a184f","order_by":5,"name":"Silvina Pastor","email":"","orcid":"","institution":"Instituto Nacional de Tecnología Agropecuaria, Instituto de Patología Vegetal (IPAVE). Córdoba","correspondingAuthor":false,"prefix":"","firstName":"Silvina","middleName":"","lastName":"Pastor","suffix":""}],"badges":[],"createdAt":"2025-02-25 16:08:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6106863/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6106863/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10681-025-03557-w","type":"published","date":"2025-06-14T15:57:21+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79777545,"identity":"13cd801e-e8ce-4659-b179-8a3f90c18a9c","added_by":"auto","created_at":"2025-04-02 14:29:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35715,"visible":true,"origin":"","legend":"\u003cp\u003eAUDPC of moderately resistant genotypes and resistant and susceptible controls. Plants were inoculated with a spore suspension (10\u003csup\u003e7 \u003c/sup\u003econidia/ml) at the flowering stage. Data represents the mean ± SE of two independent replicates (n=5). Different letters indicate significant differences using DGC test at p \u0026lt; 0.05. RC: resistant control (FLIP07-25C). CH: susceptible control (Chañaritos S-156\u003cem\u003e).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6106863/v1/cac4d91851028d89f299394c.png"},{"id":79776670,"identity":"9b4b771c-555e-4294-948c-467342a694e8","added_by":"auto","created_at":"2025-04-02 14:21:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34986,"visible":true,"origin":"","legend":"\u003cp\u003eSeverity values of genotypes at different days post-infection (dpi). RC: resistant control (FLIP07-25C). CH: susceptible control (Chañaritos S-156). Plants were inoculated with a spore suspension (10\u003csup\u003e7 \u003c/sup\u003econidia/ml) at the flowering stage R: Resistant, MR: Moderately Resistant, S: Susceptible, HS: Highly Susceptible (Pande et al. 2012).\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6106863/v1/dc68b4e83a1418ffb1bcbac3.png"},{"id":79776675,"identity":"815d5ff7-59b2-4f6a-8041-a1cde3f4962a","added_by":"auto","created_at":"2025-04-02 14:21:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70198,"visible":true,"origin":"","legend":"\u003cp\u003eUPGMA dendrogram from cluster analysis of 19 chickpea genotypes based on Jaccard’s coefficient of similarity using eight STMS markers, one SCAR, and one ASAP. Resistant control (RC): FLIP07-25C. Susceptible control (CH): Chañaritos S-156. Resistant genotypes (white circles); susceptible genotypes (black circles); moderately resistant genotypes (grey circles). Different clusters are indicated in Roman numerals.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6106863/v1/fd5c9a9c2019b6c900b960d7.png"},{"id":79776671,"identity":"7d428e67-9850-4b6a-b771-d9c446ba3068","added_by":"auto","created_at":"2025-04-02 14:21:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":27455,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal Component Analysis (PCA) (A) and Principal Coordinates Analysis (PCoA) based on Jaccard's similarity coefficient (B) of 19 chickpea genotypes using eight STMS markers, one SCAR marker, and one ASAP marker. Resistant control (RC): FLIP07-25C. Susceptible control (CH): Chañaritos S-156. Resistant genotypes are represented by white circles, susceptible genotypes by black circles, and moderately resistant genotypes by grey circles. Grouping I (dashed line), Grouping II (solid line), and Grouping III (dotted line) are highlighted.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6106863/v1/11fb6dc6eb8b2b6560e80d0a.png"},{"id":84726569,"identity":"9b56bea4-4261-46ee-ad1c-e21c8a8d2c85","added_by":"auto","created_at":"2025-06-16 16:07:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1095088,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6106863/v1/127ec59a-6735-490d-84cc-5178a5d0c67d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unlocking Hidden Sources of Resistance to Ascochyta Blight in Moderately Resistant Chickpea Genotypes","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.) is one of the most widely cultivated legume crops in the world, representing approximately 15% of the total area and production of legume globally (Foresto \u003cem\u003eet al.\u003c/em\u003e 2023). Argentina ranks among the top 10 largest exporters of chickpeas (BCR, 2021; USA Dry Pea \u0026amp; Lentil Council, n.d.) and around 90% of chickpea production is intended for export.\u003c/p\u003e \u003cp\u003eAscochyta blight (AB), caused by the fungal pathogen \u003cem\u003eAscochyta rabiei (Pass.) Labr. (teleomorph: Didymella rabiei (Kovatsch.) Arx)\u003c/em\u003e, is one of the most devastating diseases affecting chickpea cultivation worldwide. It poses a significant threat to crop yields, reaching up to 100% loss under favorable environmental conditions (Pande \u003cem\u003eet al.\u003c/em\u003e 2005; Viotti et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Crociara et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Effective management of AB requires an integrated approach that combines the use of chemical fungicides, cultural practices, and the implementation of genetic resistance to minimize its impact (Harveson \u003cem\u003eet al.\u003c/em\u003e 2011; Pastor et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The pursuit of genetic resistance to \u003cem\u003eA. rabiei\u003c/em\u003e is one of the main objectives of chickpea breeding programs (Sharma \u003cem\u003eet al.\u003c/em\u003e 2016; Pastor et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zangene et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) as it is the most effective, efficient, and environmentally sustainable method for controlling this disease.\u003c/p\u003e \u003cp\u003eEvaluating genetic resistance to AB is challenging due to its complex nature, which exhibits low to moderate heritability, being significantly influenced by environmental and whose inheritance resistance to \u003cem\u003eAscochyta rabiei\u003c/em\u003e, a necrotrophic pathogen, is quantitative in nature (Deokar \u003cem\u003eet al.\u003c/em\u003e 2019a). Such complexity arises from the presence of multiple regions in the genome, known as Quantitative Trait Loci (QTLs), that are associated with this trait (Collard et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Deokar \u003cem\u003eet al.\u003c/em\u003e 2019 a and b).\u003c/p\u003e \u003cp\u003eTraditional breeding methods that rely on phenotypic selection face several challenges, particularly because many quantitative traits are significantly influenced by the environment, expressed later in development, and/or exhibit continuous variation. The severity of the disease depends on the inoculum concentration (Trapero-Casas and Kaiser (1992b); D\u0026oacute;lar \u003cem\u003eet al\u003c/em\u003e. (1994); Chen et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); Tivoli \u003cem\u003eet al\u003c/em\u003e. (2006); Harijati y Keane (2012). Furthermore, there is a tendency for increased susceptibility in reproductive stages (Hafiz (1952); Reddy and Singh (1984); Nene y Reddy (1987); Chongo and B.D. Gossen 2001) indicating that fungicidal application may still be required at flowering and pod formation stages (Gayacharan \u003cem\u003eet al.\u003c/em\u003e 2020).\u003c/p\u003e \u003cp\u003eSelection assisted by molecular markers (MAS) utilizes genotypic data to enhance breeding efforts. Although several markers associated with resistance to AB have been developed, their application remains limited (Deokar \u003cem\u003eet al.\u003c/em\u003e 2019a). In their study, Iruela \u003cem\u003eet al\u003c/em\u003e. (2006) developed the SCAR marker SCY17 associated with resistance to AB and mapped it on linkage group four, flanked by SSR markers: TA146 and TA72. Additionally, Tar\u0026rsquo;an \u003cem\u003eet al\u003c/em\u003e. (2007) reported three SSRs (TA64, TS54, and TA176) that were strongly associated with AB resistance. On the other hand, Mill\u0026aacute;n \u003cem\u003eet al\u003c/em\u003e. (2010) and Iruela \u003cem\u003eet al\u003c/em\u003e. (2006) developed the sequence-tagged microsatellite sites (STMS) TA130, TA72, TA146, TA194, TR19, and TR58 which were linked to resistance against both AB and \u003cem\u003eFusarium spp\u003c/em\u003e. Furthermore, Madrid et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) developed an ASAP marker (allele-specific associated primer) CAETR based on the sequence of an \u003cem\u003eethylene receptor-like gene\u003c/em\u003e. The authors demonstrated that the combined use of SCY17 and CAETR markers can be effective to detect sources of resistance to AB in breeding programs.\u003c/p\u003e \u003cp\u003eIn previous research, we screened 109 chickpea inbred lines provided by International Center for Agricultural Research in the Dry Areas (ICARDA) to assess their response to \u003cem\u003eAscochyta rabiei\u003c/em\u003e (Pastor et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). One of the most notable findings was that 32% of these lines were classified as Moderately Resistant (MR). Remarkably, approximately half of these MR genotypes displayed disease scores that were not significantly different from those of resistant lines (R), suggesting that additional genotypes with robust performance against AB may have been overlooked. In this sense, the use of MR genotypes to develop genotypes with improved resistance has already been reported (Deokar et al. 2019b).\u003c/p\u003e \u003cp\u003eThe current study aimed to differentiate levels of resistance among nine selected genotypes previously classified as MR by Pastor et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To achieve this, these genotypes were exposed to increased disease pressure to comprehensively evaluate their resistance. Subsequently, they were analyzed using a set of molecular markers linked to Quantitative Trait Loci (QTLs) associated with Ascochyta blight (AB) resistance.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePot trials under controlled conditions\u003c/h2\u003e \u003cp\u003eNine chickpea genotypes belonging to ICARDA were chosen for their classification as moderately resistant (MR) according to Pastor et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, both susceptible and resistant control genotypes were included in the experiment (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenotypes tested in pots assay for resistance against Ascochyta rabiei\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype designation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrigin/Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResponse to \u003cem\u003eA.rabiei\u003c/em\u003e.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP03-27C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICARDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003ePastor et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP03-100C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICARDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP05-67C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICARDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP06-52C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICARDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP06-86C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICARDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP07-35C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICARDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP88-85C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICARDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX08TH77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICARDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP08-35C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICARDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP07-25C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICARDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCha\u0026ntilde;aritos S-156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUNC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e: resistant control; \u003cb\u003eMR\u003c/b\u003e: moderate resistant; \u003cb\u003eS\u003c/b\u003e: susceptible control. \u003cb\u003eUNC\u003c/b\u003e: Universidad Nacional de C\u0026oacute;rdoba. Argentina.\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 seeds of each inbred line were surface disinfected through sequential immersion in a 70% alcohol solution for 30 secs, followed by 4 minutes in a 0.5% active chlorine sodium hypochlorite solution, and a triple rinse with sterile distilled water. To promote germination, the seeds were placed in Petri dishes containing sterile water agar and incubated in the dark at 25\u0026ordm;C until germination. Healthy, fungus-free seeds were selected and transferred to seedbeds containing a 3:1 mixture of pasteurized soil and perlite, where they were acclimatized in a greenhouse. Once the seedlings reached the stage of three unfolded leaves, they were transplanted into 10 L pots. Five pots of every genotype with one plant each were relocated to an experimental plot surrounded by anti-aphid netting and equipped with irrigation sprinklers. Once plants reached the flowering stage, they were artificially inoculated and maintained under these conditions throughout the crop cycle. Disease assessment was carried out two times with n\u0026thinsp;=\u0026thinsp;5.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eInoculum preparation\u003c/h3\u003e\n\u003cp\u003eTo maximize disease severity, \u003cem\u003eA. rabiei\u003c/em\u003e RCB isolate registered as LJC N\u0026deg; 10673 (WDCM904 Colecci\u0026oacute;n de fitopat\u0026oacute;genos de cultivos hort\u0026iacute;colas, EEA La Consulta INTA, Argentina) was used (Crociara et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Spore suspension was obtained according to Valetti \u003cem\u003eet al.\u003c/em\u003e (2021). The inoculum concentration was adjusted to 1x10\u003csup\u003e7\u003c/sup\u003e conidia/ml (Chen, Mc Phee, \u0026amp; Muehlbauer 2005) to increase disease pressure on the inbred lines. Inoculation was carried out by using a hand-held pressure sprayer. For the 14 days following inoculation, environmental humidity was maintained by a sprinkler system programmed to operate for one hour every two hours.\u003c/p\u003e\n\u003ch3\u003eDisease evaluation\u003c/h3\u003e\n\u003cp\u003eDisease severity was assessed fourteen days after inoculation marking the first assessment. Subsequent evaluations were conducted every seven days until the first dead plant appeared. Disease severity was measured using a 0\u0026ndash;9 rating scale (Singh \u003cem\u003eet al.\u003c/em\u003e 1981) adapted by Nasir \u003cem\u003eet al\u003c/em\u003e. (2000), as follows: 1\u0026thinsp;=\u0026thinsp;no visible lesions, 3\u0026thinsp;=\u0026thinsp;only flecks on leaves, 5\u0026thinsp;=\u0026thinsp;lesions on stems and flecks on leaves, 7\u0026thinsp;=\u0026thinsp;stem breakage in injured areas, and 9\u0026thinsp;=\u0026thinsp;plant death. Severity data were recorded for each plant of every genotype at every evaluation. In addition, average severity for each genotype in each assessment was translated into a resistance scale according to Pande \u003cem\u003eet al\u003c/em\u003e. (2012) as follows: 1\u0026ndash;3: Resistant; 3\u0026ndash;5: Moderately Resistant; 5\u0026ndash;7: Susceptible; 7\u0026ndash;9: Highly Susceptible.\u003c/p\u003e \u003cp\u003eThe area under the disease progress curve (AUDPC) was calculated for each genotype using the following formula described by S\u0026aacute;nchez \u003cem\u003eet al.\u003c/em\u003e (2017).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:AUDPC=\\:\\sum\\:_{i=1}^{a}=\\:\\left[\\left\\{\\frac{\\left({Y}_{i}+{Y}_{\\left(i+1\\right)}\\:\\right)}{2}\\right\\}x({t}_{\\left(i+1\\right)}-{t}_{i}\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eY\u003c/em\u003e \u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the blight score of the \u003cem\u003ei\u003c/em\u003eth evaluation, \u003cem\u003eY\u003c/em\u003e \u003csub\u003e\u003cem\u003ei\u003c/em\u003e+1\u003c/sub\u003e is the blight score of the \u003cem\u003ei\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1th evaluation and (\u003cem\u003et\u003c/em\u003e \u003csub\u003e\u003cem\u003ei\u003c/em\u003e+1\u003c/sub\u003e\u0026minus;\u003cem\u003et\u003c/em\u003e \u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e ) is the number of days between two evaluations (Campbell and Madden \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1990\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe data were subjected to analysis of variance (ANOVA) following by the comparison of treatment means by Di Rienzo, Guzm\u0026aacute;n and Casanoves (DGC) test (p\u0026thinsp;\u0026lt;\u0026thinsp;0,05) using software Infostat 2020 (Di Rienzo \u003cem\u003eet al.\u003c/em\u003e 2020).\u003c/p\u003e\n\u003ch3\u003eMolecular markers assay\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDNA isolation\u003c/h2\u003e \u003cp\u003eFor DNA extraction, 10 mg of apical shoots from each genotype listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e were collected. The harvested shoots were dehydrated in kraft paper bags, kept in airtight boxes with silica gel until completely dried. They were then stored until DNA extraction. For DNA extraction, E-Z 96 Plant DNA Kit (Omega Bio-Tek, Norcross, GA, USA) was used following the manufacturer instructions. The DNA concentrations obtained were adjusted to 10 ng/ml and stored at -20\u0026ordm;C until use.\u003c/p\u003e \u003cp\u003eFor the molecular markers assay, the DNA of an additional set of genotypes was provided by the Department of Genetics UCO (Cordoba, Spain) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and included as susceptible and resistant controls.\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\u003eGenotypes included in molecular marker assay as susceptible and resistant controls according to international literature\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotype designation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrigin/Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResponse to \u003cem\u003eA. rabiei\u003c/em\u003e.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILC3279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICARDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlo \u003cem\u003eet al.\u003c/em\u003e (2022), Sharma \u0026amp; Ghosh (2016), Madrid et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Iruela \u003cem\u003eet al.\u003c/em\u003e (2006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILC182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICARDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSharma \u0026amp; Ghosh (2016); Benzohra et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Labdi \u003cem\u003eet al.\u003c/em\u003e (2013), Madrid et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILC5921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICARDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLabdi \u003cem\u003eet al.\u003c/em\u003e (2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILC187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICARDA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSharma \u0026amp; Ghosh (2016); Benzohra et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Labdi \u003cem\u003eet al.\u003c/em\u003e (2013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEBT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWR315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRISAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBouhadida \u003cem\u003eet al.\u003c/em\u003e (2013), Madrid et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), Iruela \u003cem\u003eet al.\u003c/em\u003e (2006)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCUAIZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eITACyL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMorcuende Herrero, (2024)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCa2139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCobos \u003cem\u003eet al.\u003c/em\u003e (2005)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u003c/b\u003e: resistant; \u003cb\u003eS\u003c/b\u003e: susceptible. \u003cb\u003eICRISAT\u003c/b\u003e: International Crops Research Institute for the Semi-Arid Tropics. Instituto Tecnol\u0026oacute;gico Agrario de Castilla y L\u0026eacute;on.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePCR conditions\u003c/h2\u003e \u003cp\u003eThe primers used in this study are described in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. These markers were selected based on international literature and their availability at the Department of Genetics of the University of C\u0026oacute;rdoba (Spain).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimers sequences of the molecular markers used for grouping the genotypes under study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimer name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer sequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarker Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQTL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCY17\u003csub\u003e590\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF1: GACGTGGTGACTATCTAGC\u003c/p\u003e \u003cp\u003eR1: GACGTGGTGAAATAGATACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIruela \u003cem\u003eet al.\u003c/em\u003e (2006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQTLAR2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaETR\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: 6CAGGAAGTT CAATGGCCCTA\u003c/p\u003e \u003cp\u003eR1: TAAGTTGTGACAAAAGACTCAATCG\u003c/p\u003e \u003cp\u003eR2: TGTGGCACAGTGGACCCCATCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMadrid et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQTL\u003csub\u003eAR1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: ATATATCGTAACTCATTAATCATCCGC\u003c/p\u003e \u003cp\u003eR: AAATTGTTGTCATCAAATGGAAAATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter \u003cem\u003eet al.\u003c/em\u003e (1999) ; Taran \u003cem\u003eet al.\u003c/em\u003e (2007); Anbessa \u003cem\u003eet al.\u003c/em\u003e (2009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQTL1\u003c/p\u003e \u003cp\u003eQTL2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF:8GAAAGATTTAAAAGATTTTCCACGTTA\u003c/p\u003e \u003cp\u003eR: TTAGAAGCATATTGTTGGGATAAGAGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter \u003cem\u003eet al.\u003c/em\u003e (1999) ; Ramakuri, P. (2005) ;\u0026nbsp;Iruela \u003cem\u003eet al.\u003c/em\u003e (2006)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQTL2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF*:6TCTTTCTTTGCTTCCAATGT\u003c/p\u003e \u003cp\u003eR: FGTAAATCCCACGAGAAATCAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter \u003cem\u003eet al.\u003c/em\u003e (1999) ; Flandez-Galvez \u003cem\u003eet al.\u003c/em\u003e (2003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQTL5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: CTAAGTTTAATATGTTAGTCCTTAAATTAT\u003c/p\u003e \u003cp\u003eR: ACGAACGCAACATTAATTTTATATT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter \u003cem\u003eet al.\u003c/em\u003e (1999) ; Iruela \u003cem\u003eet al.\u003c/em\u003e (2006) ; Flandez-Galvez ; \u003cem\u003eet al\u003c/em\u003e. (2003); Kottapalli \u003cem\u003eet al\u003c/em\u003e. (2009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQTL 6; QTL5; QTLAR2; QTL2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: ATTTGGCTTAAACCCTCTTC\u003c/p\u003e \u003cp\u003eR: TTTATGCTTCCTCTTCTTCG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter \u003cem\u003eet al.\u003c/em\u003e (1999) ; Taran \u003cem\u003eet al.\u003c/em\u003e (2007) ; Anbessa \u003cem\u003eet al.\u003c/em\u003e (2009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQTL3; QTL4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: TCAGTATCACGTGTAATTCGT\u003c/p\u003e \u003cp\u003eR: CATGAACATCAAGTTCTCCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter \u003cem\u003eet al.\u003c/em\u003e (1999);\u003c/p\u003e \u003cp\u003eAnbessa \u003cem\u003eet al.\u003c/em\u003e (2009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQTL1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTA194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: TTTTTGGCTTATTAGACTGACTT\u003c/p\u003e \u003cp\u003eR: TTGCCATAAAATACAAAATCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter \u003cem\u003eet al.\u003c/em\u003e (1999) ; Iruela \u003cem\u003eet al.\u003c/em\u003e (2007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQTLAR3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTR58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF*:6CTCTATATTTGTTTGTTTTTCGTTTTG\u003c/p\u003e \u003cp\u003eR: TAAAATGTGTAGGGTGCAGAATAAATA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter \u003cem\u003eet al.\u003c/em\u003e (1999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQTLAR3; QTL 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLG\u003c/b\u003e: Linkage Group\u003c/p\u003e \u003cp\u003e\u003cb\u003e*\u003c/b\u003eFluorochrome-labeled primer.\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\u003eAll STMS markers were amplified in a final mix volume of 10 \u0026micro;l, 1X buffer (50 mM KCl, 10 mM Tris\u0026ndash;HCl, 0.1% Triton X-100), 1.5 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 0.25 mM dNTPs, 0.2 \u0026micro;M of each primer, and 0.25 U of Taq DNA polymerase (GoTaq Flexi, Promega). For amplification, the thermocycler was programmed for 35 cycles of: 94 \u0026ordm;C for 30 sec, 58 \u0026ordm;C for 50 sec, and 60 \u0026ordm;C for 50 sec, followed by a final extension at 72 \u0026ordm;C for 10 min. The PCR product from all primer pairs labeled with the fluorochrome 6FAM was analyzed using fragment analysis. Amplified products were analyzed in an automated capillary sequencer (CBI 3130 Genetic Analyzer, Biosystems Applied/HITACHI). The PCR product from unlabeled primers was visualized on 1% polyacrylamide gel.\u003c/p\u003e \u003cp\u003eFor the SCY17 marker amplification, the reaction was prepared in a final volume of 10 \u0026micro;l, 1X buffer (50 mM KCl, 10 mM Tris\u0026ndash;HCl, 0.1% Triton X-100), 2 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 0.25 mM dNTPs, 0.2 \u0026micro;M of each primer, and 0.4 U of Taq DNA polymerase (GoTaq Flexi, Promega). For amplification, the thermocycler was programmed for 30 cycles of: 95 \u0026ordm;C for 1 min, 50 \u0026ordm;C for 30 sec, and 72 \u0026ordm;C for 40 sec, followed by a final extension at 72 \u0026ordm;C for 8 min. The PCR product was observed on a 2.5% agarose gel stained with GelRed\u0026trade; (Biotium, CA, USA).\u003c/p\u003e \u003cp\u003eThe ASAP CaETR4 marker was amplified in a final volume of 10 \u0026micro;l, 1X buffer (50 mM KCl, 10 mM Tris\u0026ndash;HCl, 0.1% Triton X-100), 2.5 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 0.4 mM dNTPs, 0.4 \u0026micro;M of each primer, and 0.25 U of Taq DNA polymerase (GoTaq Flexi, Promega). For amplification, the thermocycler was programmed for 30 cycles of: 95 \u0026ordm;C for 30 sec, 62 \u0026ordm;C for 30 sec, and 72 \u0026ordm;C for 50 sec, followed by a final extension at 72 \u0026ordm;C for 7 min. The PCR product was observed on a 2% agarose gel stained with GelRed\u0026trade; (Biotium, CA, USA).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMolecular markers data analysis\u003c/h3\u003e\n\u003cp\u003eThe allelic data set was coded as binary data where 0\u0026thinsp;=\u0026thinsp;Absence of band) and 1\u0026thinsp;=\u0026thinsp;presence of band). Each allele was assigned to a separate column in the dataset. The generated matrix was used for clustering analysis using the UPGMA algorithm as the grouping criterion, with the square root transformation of the complement of one of the Jaccard similarity index serving as a genetic distance measure (Rajput \u003cem\u003eet al.\u003c/em\u003e 2023; Sahu \u003cem\u003eet al.\u003c/em\u003e (2020); Choudhary et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bruno \u0026amp; Balzarini \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Results were visualized through a dendrogram. To reduce the dimensionality of the data and to identify the main axes of variation that explain most of the diversity in the resistance response a Principal Component Analysis (PCA) and Principal Coordinates Analysis (PCoA) (Choudhary, et al \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) was also performed. The genetic distance measure was Jaccard similarity index too. The ordering visualization was conducted using a Biplot. All analyses were performed using INFOSTAT software (Di Rienzo et al. 2020)\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePot assay under controlled condition\u003c/h2\u003e \u003cp\u003eThe assay allowed the longitudinal observation of the disease progression over time. Consequently, AUDPC was calculated to compare the phenotypic responses of the genotypes under evaluation. Notably discernible differentiation among the moderately resistant (MR) genotypes was achievable. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, significant divergence in AUDPC values was observed, enabling clear delineation between the two distinct groups. The first group encompasses the susceptible control Cha\u0026ntilde;aritos-S 156, together with FLIP06-52C, FLIP88-85C, FLIP05-67C, X08TH77 and FLIP03-27C. Conversely, the second group comprises the resistant control (RC) represented by FLIP06-86C, FLIP07-35C, FLIP03-100C, and FLIP08-35C.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhile AUDPC is a valuable metric for comprehensively assessing the overall performance of each genotype, it is equally interesting to analyze the average disease severity attained by each genotype at various scoring intervals and interpret these values in the context of resistance levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the initial susceptibility of both the susceptible control Cha\u0026ntilde;aritos-S116 and the MR genotypes FLIP06-52C, FLIP88-85C from the first evaluation. Similarly, FLIP05-67C, X08TH77, and FLIP03-27C reached the upper threshold of the MR range, indicating stem damage across all evaluated plants. By the second assessment (21 dip), all three of them exhibited stem breakage, in concordance with their respective susceptibility categories.\u003c/p\u003e \u003cp\u003eIn contrast, the resistant control (RC) as well as MRs: FLIP06-86C, FLIP07-35C, FLIP03-100C and FLIP08-35C showed severity values within the MR range, some of them persisting in this range for an extended period. Notably, FLIP06-86C demonstrated the most resilient behavior, reaching the susceptibility range only during the final scoring (42 dpi).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMolecular markers assay\u003c/h2\u003e \u003cp\u003eThe PCR amplification of the molecular markers used in this study revealed several allelic variants for each microsatellite marker. Additionally, two distinct alleles, one resistant and one susceptible were identified in relation to CAETR and SCY17 markers (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of resistance and susceptible alleles of two DNA markers, SCY17 and CAETR, with the resistant, moderate resistant and susceptible phenotypes of chickpea accessions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGermoplasm line\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAscochyta blight phenotype\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGenotype\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCY17\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCAETR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP88-85C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP05-67C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP03-27C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP07-35C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP08-35C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP03-100C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP06-86C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP06-52C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX08TH77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFLIP07-25C (RC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILC3279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILC182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILC5921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eILC187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCha\u0026ntilde;aritos S-156 (CH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEBT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWR315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCUAIZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCA2139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRC\u003c/b\u003e: resistant control \u003cb\u003eCH\u003c/b\u003e: susceptible control. \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e: resistant (R), moderate resistant (MR) and susceptible (S); \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e: R and S indicate resistant and susceptible alleles, respectively.\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\u003eAllelic variants of all markers analyzed were converted into binary data. Hierarchical clustering by UPGMA was carried out to generate a dendrogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) illustrating the relationship among the genotypes. The analysis revealed five different clusters. Cluster I includes three susceptible controls EBT 3, WR315, and CH along the most sensible genotype FLIP06-52C. The genotype FLIP08-35C stands apart from the rest of the clusters (cluster II). Then two smaller clusters (III and V) include the resistant control R together with FLIP88-85C and FLIP03-27C genotypes; and FLIP05-67C and the susceptible control CUAIZ respectively. Probably the most interesting cluster comprises four of the resistant controls (ILC 5921, ILC 187, ILC 182, ILC 3279) along with the three genotypes exhibiting the best performance against \u003cem\u003eA. rabiei\u003c/em\u003e: FLIP06-86C, FLIP07-35C, FLIP03-100C (cluster IV). However, this group also includes the susceptible control Ca2139.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, PCA and PCoA Biplot are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The PCA allowed for the representation of genetic variability in a two-dimensional space, explaining 32.8% of the total variance across the first two principal components (19.7% and 13.1%, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This analysis revealed a prominent dispersion of genotypes within the spatial representation, highlighting discernible patterns of genetic substructure that corroborate the observed grouping in the dendrogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The analysis highlighted the intern relationship among resistant genotypes (Group I) comprising ILC 5921, ILC 187, ILC 182, ILC 3279 and best-behaved genotype FLIP08-35C best behaved genotype as they clustered together within two CP. Furthermore, at the opposite end, susceptible genotypes: WR315, CH, EBT3 and Cuaiz clustered closely together, with the most susceptible MR, FLIP06-52C (Group II). Finally, certain association is also apparent among the highest performing genotypes for AB resistance: FLIP06-86C, FLIP07-35C, FLIP03-100C, and FLIP05-67C genotypes (Group III).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe principal coordinate analysis (PCoA), calculated with the same genetic distances (Jaccard, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), exhibited a dispersion pattern like the observed in the PCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), though offering a more nuanced representation of the genetic distances between accessions. This method explained 28.7% of the total variability across the first two axes (15.4% and 13.3%, respectively). Once again, distinctive associations emerged showing a clustering of resistant genotypes along with FLIP08-35C (Group I), a second grouping of susceptible controls and FLIP06-52C (Group II) and a consistent third group including genotypes FLIP06-86C, FLIP07-35C, FLIP03-100C and FLIP05-67C (Group III).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe search for genetic resistance to AB is one of the main challenges worldwide. This complexity arises from the polygenic nature of the trait, influenced by various genomic loci and environmental factors (Deokar \u003cem\u003eet al.\u003c/em\u003e 2019 a; Carmona et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Indeed, most of the observed resistance to AB is because of quantitative genes. Despite of moderately resistance has been reported (Deokar \u003cem\u003eet al.\u003c/em\u003e 2019 a y b; Pastor et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Aydoğan \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) the exact genetic and molecular mechanism governing resistance against \u003cem\u003eA. rabiei\u003c/em\u003e infection remains elusive (Deokar \u003cem\u003eet al.\u003c/em\u003e 2019a). This last fact caught particularly our attention. For instance, categorizing a genotype with minimal leaf lesions alongside another genotype with stem damage but without stem breakage (see the scale in Materials and Methods section), underscores the need for discerning such distinctions.\u003c/p\u003e \u003cp\u003eThese observations drove us to look for a way to discriminate these differences, since the quest for novel sources of resistance is imperative to sustain chickpea cultivation and productivity (Gayacharan \u003cem\u003eet al.\u003c/em\u003e 2020).\u003c/p\u003e \u003cp\u003eTo unravel the inherent resistance mechanism within each genotype we subjected them to a highly concentrated spore dosage (Chen, Mc Phee, \u0026amp; Muehlbauer 2005), of a very aggressive isolate (Crociara et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) targeting a critical stage in chickpea, such as the flowering stage (Gayacharan \u003cem\u003eet al.\u003c/em\u003e 2020).\u003c/p\u003e \u003cp\u003eInterestingly, the pot trial facilitated the discrimination between the best and worst performances among a set of MR lines, with genotypes FLIP06-86C, FLIP07-35C, and FLIP03-100C exhibiting robust responses against the pathogen, even outperforming the resistant control (RC) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs tools to address these adverse conditions are logically at the genome level, we attempted to explain the contrasting behaviors using molecular markers known to be associated with QTLs related to AB resistance. The dendrogram allowed the identification of clear hierarchical groupings among the evaluated accessions. PCA analysis revealed a notable dispersion of genotypes in space, highlighting patterns of genetic substructure that align with the groupings observed in the dendrogram. The principal coordinate analysis (PCoA), based on the same genetic distance (Jaccard), showed a dispersion pattern like that of the PCA, although with a more accurate representation of the distances between accessions. These results support the conclusions drawn from the PCA, highlighting the consistency between both methods.\u003c/p\u003e \u003cp\u003eIt was interesting to confirm the aggrupation of FLIP06-52C, the most susceptible genotype together with susceptible controls. Something similar occurred with more resistant FLIP06-86C and FLIP07-35C which are tightly grouped by the three methods, alongside FLIP03-100C and FLIP08-35C (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Those were also grouped with resistant controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, some unexpected associations were observed, for instance all methods consistently grouped the X08TH77 genotype with resistant controls, despite its high susceptibility to \u003cem\u003eA. rabiei\u003c/em\u003e. Another contradictory aggrupation was observed regarding CA2139 susceptible control which was grouped with the best-performed MRs, FLIP06-86C, FLIP07-35C, FLIP03-100C y FLIP08-35C. Additionally, FLIP05-67C genotype which exhibited a poor resistance response against \u003cem\u003eA. rabiei\u003c/em\u003e, was grouped together with those four genotypes through PCA y PCoA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, in the dendrogram, it clustered with the susceptible control CUAIZ, which aligns more closely with the response observed in infected plants.\u003c/p\u003e \u003cp\u003eMultivariate analysis includes all markers assessed. Given the greater challenge in predicting resistance alleles with STMS markers compared to specific markers (Madrid et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Collard et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), we analyzed the amplification of CAETR and SCY17 markers specifically. Madrid et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) noted the absence of resistance alleles of the SCY17 and CAETR markers in all susceptible genotypes and its presence in 68\u0026ndash;75% of the resistant ones; although they clarify that there were four resistant genotypes that did not have the resistance. Noteworthy discrepancies were observed with the presence of resistant alleles in susceptible controls and susceptible alleles in resistant controls for these markers (refer to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, the identification of resistance alleles in FLIP06-52C (one of the most susceptible ones) and susceptible alleles in the top-performing genotypes (FLIP06-86C, FLIP03-100C, FLIP08-35C, FLIP07-35C) highlights the limitations of relying solely on CAETR and SCY17 markers in Marker-Assisted Selection (MAS). Those observations lead to note the inconvenience of using just CAETR and SCY17 in MAS. Since Madrid et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Imtiaz \u003cem\u003eet al.\u003c/em\u003e (2008) have succeeded in selecting resistant cultivars using SCY17 marker, a recent work (Aydoğan, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported SCY17 marker overestimated resistance by amplifying the resistance allele in susceptible controls. Low effectiveness of SCY17 and TA72 markers has been reported too by Castro et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe scenario where phenotypic resistance is evident in genotypes lacking the R alleles can be rationalized by the polygenic and additive nature of AB resistance (Saxena and Singh, 1984; Ahmad et al. 2010; Aryamanesh et al. 2010; Houasli et al. 2020). Genotypes may harbor distinct resistance genes or QTLs not captured by the SCY17 and CAETR markers (Madrid et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The application of markers for enhancing polygenic quantitative traits remains challenging, with limited success stories attributed to the intricate interplay of numerous QTLs and their complex effects on quantitative traits, making their prediction arduous (Castro et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ilyas et al. 2022). Although MAS reduces the time taken for selecting lines it is also more expensive than phenotypic selection. We agree with Aydoğan (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) genotypes might be inaccurately classified using markers for disease resistance, suggesting that controlled environment artificial inoculations could be a more pragmatic screening approach for chickpea germplasm selection.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFundings:\u003c/h2\u003e \u003cp\u003eThis study was funded by Instituto Nacional de Tecnolog\u0026iacute;a Agropecuaria (INTA) /Argentina; Fundaci\u0026oacute;n Argeninta/Argentina; Centro de transferencia de Bioinsumos (CETBIO) - Universidad Nacional de C\u0026oacute;rdoba (UNC) / Argentina; Consejo Nacional de Investigaciones Cient\u0026iacute;ficas y T\u0026eacute;cnicas (CONICET) / Argentina. Asociaci\u0026oacute;n Universitaria Iberoamericana de Postgrado (AUIP) financed the movility to Spain. Universidad de C\u0026oacute;rdoba (UCO)/ Spain funded Molecular Markers assay.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eC.C. conducted all the experiments, wrote the first draft of the manuscript, and contributed to its revision and editing.L.V. participated in the pot trials, contributed to the first draft of the manuscript, and was involved in the revision and final version.P.C. and T.M. supervised the molecular marker analysis, provided funding for this part of the study, and reviewed the final version of the manuscript.J.I. contributed to the interpretation of molecular marker data and participated in the writing of the final version of the manuscript.S.P. provided funding for the pot trials, critically revised, and edited the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe first author sincerely acknowledges the support of the Asociaci\u0026oacute;n Universitaria Iberoamericana de Postgrado (AUIP) through the mobility grant that facilitated this research. The authors also extend their gratitude to the Universidad de C\u0026oacute;rdoba (UCO) for providing the necessary resources to conduct this study. Special thanks are given to Mar\u0026iacute;a Jos\u0026eacute; Allende and Ana Fekete for generously supplying the seeds for the pot trials. Finally, the authors wish to express their deep appreciation to Dr. Mariela Acu\u0026ntilde;a for her invaluable contribution to data interpretation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003ePrimary data that support the fidings of this study are available in the followin liks:https://repositorio.inta.gob.ar/xmlui/handle/20.500.12123/21430https://repositorio.inta.gob.ar/xmlui/handle/20.500.12123/21433\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAydoğan, A. (2024). Comparison of different screening methods for selection of chickpea blight disease on chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.) genotypes. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e, 1347884. https://doi.org/\u0026hellip;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenzohra, I. E., Bendahmane, B. S., Benkada, M. Y., \u0026amp; Labdi, M. (2015). Screening of 15 chickpea germplasm accessions for resistance to \u003cem\u003eAscochyta rabiei\u003c/em\u003e in North West of Algeria. Am.-Eurasian J. Agric. Environ. Sci., \u003cem\u003e15\u003c/em\u003e, 109\u0026ndash;114.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBruno, C., \u0026amp; Balzarini, M. (2010). Distancias gen\u0026eacute;ticas entre perfiles moleculares obtenidos desde marcadores multilocus multial\u0026eacute;licos. Revista de la Facultad de Ciencias Agrarias UNCuyo, \u003cem\u003e41\u003c/em\u003e(3), 11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell, C. L., \u0026amp; Madden, L. V. (1990). Temporal analysis of epidemics. I. Description and comparison of disease progress curves. In: \u003cem\u003eIntroduction to plant disease epidemiology\u003c/em\u003e. John Wiley and Sons, New York, pp. 161\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarmona, A., Rubio, J., Mill\u0026aacute;n, T., Gil, J., Die, J. V., \u0026amp; Castro, P. (2023). Four haplotype blocks linked to chickpea blight disease resistance in chickpea under Mediterranean conditions. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 1183287. https://doi.org/\u0026hellip;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastro, P., Rubio, J., Madrid, E., Fern\u0026aacute;ndez-Romero, M. D., Mill\u0026aacute;n, T., \u0026amp; Gil, J. (2015). Efficiency of marker-assisted selection for chickpea blight in chickpea. The Journal of Agricultural Science, \u003cem\u003e153\u003c/em\u003e(1), 56\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, W., McPhee, K. E., \u0026amp; Muehlbauer, F. J. (2005). Use of a mini-dome bioassay and grafting to study resistance of chickpea to \u003cem\u003eAscochyta\u003c/em\u003e blight. Journal of Phytopathology, \u003cem\u003e153\u003c/em\u003e(10), 579\u0026ndash;587.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, W., Sharma, H. C., \u0026amp; Muehlbauer, F. J. (2011). \u003cem\u003eCompendium of chickpea and lentil diseases and pests\u003c/em\u003e (pp. ix-165).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChongo, G., \u0026amp; Gossen, B. D. (2001). Effect of plant age on resistance to \u003cem\u003eAscochyta rabiei\u003c/em\u003e in chickpea. Canadian Journal of Plant Pathology, \u003cem\u003e23\u003c/em\u003e(4), 358\u0026ndash;363.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoudhary, P., Khanna, S. M., Jain, P. K., et al. (2013). Molecular characterization of primary gene pool of chickpea based on ISSR markers. Biochemical Genetics, \u003cem\u003e51\u003c/em\u003e, 306\u0026ndash;322. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10528-012-9564-7\u003c/span\u003e\u003cspan address=\"10.1007/s10528-012-9564-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollard, B. C., Jahufer, M. Z. Z., Brouwer, J. B., \u0026amp; Pang, E. C. K. (2005). An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica, \u003cem\u003e142\u003c/em\u003e, 169\u0026ndash;196.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrociara, C., Valetti, L., Bernardi Lima, N., Iglesias, J., \u0026amp; Pastor, S. (2022). Morphological and molecular characterization, pathogenicity and sexual reproduction of \u003cem\u003eAscochyta rabiei\u003c/em\u003e isolates of chickpea fields in Argentina. Journal of Phytopathology, \u003cem\u003e170\u003c/em\u003e(4), 221\u0026ndash;232.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMadrid, E., Chen, W., Rajesh, P. N., Castro, P., Mill\u0026aacute;n, T., \u0026amp; Gil, J. (2013). Allele-specific amplification for the detection of chickpea blight resistance in chickpea. Euphytica, \u003cem\u003e189\u003c/em\u003e, 183\u0026ndash;190.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMillan, T., Winter, P., J\u0026uuml;ngling, R., Gil, J., Rubio, J., Cho, S., et al. (2010). A consensus genetic map of chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.) based on 10 mapping populations. Euphytica, \u003cem\u003e175\u003c/em\u003e, 175\u0026ndash;189.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePastor, S., Crociara, C., Valetti, L., Pe\u0026ntilde;a Malavera, A., Fekete, A., Allende, M. J., \u0026amp; Carreras, J. (2022). Screening of chickpea germplasm for chickpea blight resistance under controlled environment. Euphytica, \u003cem\u003e218\u003c/em\u003e(2), 12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh, R., Kumar, K., Purayannur, S., Chen, W., \u0026amp; Verma, P. K. (2022). \u003cem\u003eAscochyta rabiei\u003c/em\u003e: A threat to global chickpea production. Molecular Plant Pathology, \u003cem\u003e23\u003c/em\u003e, 1241\u0026ndash;1261. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mpp.13235\u003c/span\u003e\u003cspan address=\"10.1111/mpp.13235\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarshney, R. K., Song, C., Saxena, R. K., Azam, S., Yu, S., Sharpe, A. G., Cannon, S., Baek, J., Rosen, B. D., \u0026amp; Tar'an, B. (2013). Draft genome sequence of chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e) provides a resource for trait improvement. Nature Biotechnology, \u003cem\u003e31\u003c/em\u003e, 240\u0026ndash;246.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViotti, G., Carmona, M. A., Scandiani, M., Formento, A. N., \u0026amp; Luque, A. (2012). First report of \u003cem\u003eAscochyta rabiei\u003c/em\u003e causing chickpea blight in Argentina. Plant Disease, \u003cem\u003e96\u003c/em\u003e(9), 1375\u0026ndash;1375.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZangene, K., Emamjomeh, A., Shokouhifar, F., Mamar Badi, M., \u0026amp; Mehdinezhad, N. (2022). Differentiation of an Iranian resistance chickpea line to chickpea blight from a susceptible line using a functional SNP. AMB Express, \u003cem\u003e12\u003c/em\u003e(1), 45.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Ascochyta rabiei, Molecular markers markers, pulse breeding","lastPublishedDoi":"10.21203/rs.3.rs-6106863/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6106863/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAscochyta blight, caused by \u003cem\u003eAscochyta rabiei\u003c/em\u003e, is a major threat to global chickpea (\u003cem\u003eCicer arietinum\u003c/em\u003e L.) production, significantly reducing yield under favorable conditions. This study aimed to characterize the resistance responses of nine chickpea genotypes, previously classified as moderately resistant, by subjecting them to enhanced disease pressure. Phenotypic evaluation, including the area under the disease progress curve (AUDPC) and severity scoring was carried out. To explore the genetic basis of resistance, molecular markers associated with quantitative trait loci (QTLs) for resistance were analyzed. The results revealed significant variability among the MR genotypes, with three genotypes FLIP06-86C, FLIP07-35C, and FLIP03-100C outperforming the resistant control. The results from hierarchical clustering (UPGMA), principal component analysis (PCA), and principal coordinate analysis (PCoA) highlighted genetic substructures consistent with observed phenotypic behaviors. However, unexpected marker-phenotype associations were detected, questioning the utility of specific markers such as SCY17 and CAETR in marker-assisted selection. These findings underline the complexity of polygenic resistance \u003cem\u003eto A. rabiei\u003c/em\u003e and emphasize the importance of integrating phenotypic screening with genetic analyses to improve the reliability of chickpea breeding programs. This work also contributes to identifying superior MR genotypes, providing valuable resources for the development of resistant cultivars.\u003c/p\u003e","manuscriptTitle":"Unlocking Hidden Sources of Resistance to Ascochyta Blight in Moderately Resistant Chickpea Genotypes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 14:20:55","doi":"10.21203/rs.3.rs-6106863/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-08T14:14:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-07T13:51:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202760050671080030957689274960768353221","date":"2025-05-07T12:28:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"237539969129415360444617918010328429916","date":"2025-04-21T05:29:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-17T22:55:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279108466529137637813717355786791307095","date":"2025-04-12T20:31:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-22T16:16:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"52671137946388433673830252412922646778","date":"2025-03-17T10:17:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-08T06:59:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-26T04:48:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-26T04:46:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"Euphytica","date":"2025-02-25T16:05:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"26a28d6b-faf4-4bff-8f6d-c43e31239fc3","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-16T16:03:02+00:00","versionOfRecord":{"articleIdentity":"rs-6106863","link":"https://doi.org/10.1007/s10681-025-03557-w","journal":{"identity":"euphytica","isVorOnly":false,"title":"Euphytica"},"publishedOn":"2025-06-14 15:57:21","publishedOnDateReadable":"June 14th, 2025"},"versionCreatedAt":"2025-04-02 14:20:55","video":"","vorDoi":"10.1007/s10681-025-03557-w","vorDoiUrl":"https://doi.org/10.1007/s10681-025-03557-w","workflowStages":[]},"version":"v1","identity":"rs-6106863","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6106863","identity":"rs-6106863","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}