Received: 23 January 2025 Accepted: 14 May 2025 DOI: 10.1002/tpg2.70059 The Plant Genome O R I G I N A L A R T I C L E S p e c i a l S e c t i o n : Tr i b u t e t o R o n P h i l l i p s : C r o p G e n e t i c s , G e n o m i c s a n d B i o t e c h n o l o g y Genetic dissection of crown rust resistance in oat and the identification of key adult plant resistance genes Nikwan Shariatipour1 Mahboobeh Yazdani1 Anders Carlsson1 Therése Bengtsson1 Shahryar F. Kianian2 Marja Jalli3 Mahbubjon Rahmatov1 the PPP RobOat Consortium 1Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden 2USDA-ARS Cereal Disease Laboratory, Saint Paul, Minnesota, USA 3Natural Resources Institute Finland, Jokioinen, Finland Correspondence Mahbubjon Rahmatov and Nikwan Shariatipour, Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden. Email: mahbubjon.rahmatov@slu.se and nikwan.shariatipour@slu.se Assigned to Associate Editor Roberto Tuberosa. Funding information Nordic Council of Ministers, Grant/Award Number: RobOat Abstract Crown rust (Puccinia coronata f. sp. Avenae Erikss.) poses a significant threat to oat production worldwide. The most effective strategy for managing this disease involves identifying, mapping, and deploying resistance genes to develop cultivars with enhanced resistance. In this study, we conducted a meta-analysis of quantitative trait loci (QTLs) linked to crown rust resistance across diverse oat populations and environments. From 11 studies conducted between 2003 and 2024, we selected 167 QTLs, of which 127 were successfully mapped onto an oat consensus linkage map. These QTLs were mainly located on chromosomes of the D and C sub-genomes, showing considerable variation in genetic distances and marker associations. Based on the integration of these QTLs in a meta-QTL (MQTL) analysis, 23 MQTLs were identified for crown rust resistance in the oat genome. Gene mining within the MQTL intervals identified 1526 candidate genes, most of which were located in the D sub-genome. Functional analysis revealed that these genes play key roles in stress response, hormonal regulation, and polyamine metabolism, which are cru- cial for plant defense. Conserved regulatory elements (cis-acting regulatory element [CAREs]) were also identified in the promoter regions of key resistance genes, indi- cating their involvement in light response, stress regulation, and hormone signaling. This study represents a significant advancement in understanding the genetic archi- tecture of crown rust resistance in oat and provides a valuable resource for breeding programs focused on improving disease resistance. Abbreviations: ABA, abscisic acid; AFLP, amplified fragment length polymorphism; AIC, Akaike information criterion; APR, adult plant resistance; CAREs, cis-acting regulatory element; CI, confidence interval; CRs, crown rust severity; DS, disease severity; GA, gibberellic acid; GO, gene ontology; JA, jasmonic acid; LOD, logarithm of odds; MAS, marker-assisted selection; MeJA, methyl jasmonate; MQTL, meta quantitative trait locus; PAs, polyamines; Pc, Puccinia coronata; Pca, Puccinia coronata f.sp. avenae Erikss.; PVE, phenotypic variation explained; QTL, quantitative trait locus; RFLP, restriction fragment length polymorphism. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2025 The Author(s). The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America. Plant Genome. 2025;18:e70059. wileyonlinelibrary.com/journal/tpg2 1 of 21 https://doi.org/10.1002/tpg2.70059 https://orcid.org/0000-0003-4174-4375 https://orcid.org/0000-0002-9523-5368 https://orcid.org/0000-0002-8525-9753 https://orcid.org/0000-0003-4784-1723 https://orcid.org/0000-0003-4968-3140 https://orcid.org/0000-0003-3574-9639 https://orcid.org/0000-0001-7491-2836 mailto:mahbubjon.rahmatov@slu.se mailto:nikwan.shariatipour@slu.se http://creativecommons.org/licenses/by/4.0/ https://wileyonlinelibrary.com/journal/tpg2 https://doi.org/10.1002/tpg2.70059 http://crossmark.crossref.org/dialog/?doi=10.1002%2Ftpg2.70059&domain=pdf&date_stamp=2025-06-13 2 of 21 SHARIATIPOUR ET AL.The Plant Genome Plain Language Summary Crown rust is a major disease threatening global oat (Avena sativa L.) production. This study compiled data from multiple sources to identify 167 genetic loci linked to crown rust resistance. Through a detailed analysis, researchers pinpointed 23 key genomic regions associated with resistance. This study identified several important genes involved in stress response, plant defense, and hormone regulation. These find- ings provide valuable insights for breeders to develop more resistant oat varieties, enhancing protection against crown rust and securing improved crop stability. 1 INTRODUCTION Crown rust, caused by the fungus Puccinia coronata f.sp. avenae Erikss. (Pca), is a major fungal pathogen of oat, threatening global oat production by significantly reducing yield and quality. This disease affects various oat species, such as Avena sativa L. (AACCDD, 2n = 6x = 42), Avena sterilis (AACCDD, 2n = 6x = 42), Avena strigosa (AsAs, 2n = 2x = 14), Avena barbata (AABB, 2n = 4x = 28), and so on (Chong et al., 2011). Yield losses in severe outbreaks can exceed 50%, creating a substantial economic burden for oat producers (McNish et al., 2020; Nazareno et al., 2018). The increasing cultivation of oat in Europe and the Nordic countries has corresponded with a rise in crown rust sever- ity (CRs) (Marshall et al., 2013; Vilvert et al., 2021), largely due to the expansion of oat-growing areas and the presence of alternate hosts, such as buckthorn, which create favorable conditions for pathogen survival and evolution (Berlin et al., 2018). Additionally, changes in environmental conditions are projected to increase the severity and frequency of crown rust outbreaks in more geographical locations, further threatening oat production (Kim et al., 2024). The complex interaction between host availability and pathogen genetic diversity is central to developing new viru- lent pathogen races capable of overcoming existing resistance in oat cultivars (Nazareno et al., 2018). Sexual reproduc- tion facilitated by alternate hosts like buckthorn increases pathogen genetic diversity, which allows for the emergence of novel combinations of pathogenicity genes (Simons, 1985). In response to the threat posed by crown rust, oat breed- ing programs have focused on developing resistant varieties through the identification and deployment of resistance genes, particularly race-specific genes known as R genes. Over 100 such resistance genes (Puccinia coronata [Pc] genes) have been identified in species like A. sativa, A. strigosa, Avena byzantina, and A. sterilis (Klos et al., 2017; McMullen et al., 2005; Park et al., 2022). These genes follow a gene-for-gene interaction model, where resistance is conferred by specific interactions between host resistance genes and correspond- ing virulent genes in the pathogen (Flor, 1955). However, the high genetic variability of the Pc (Park et al., 2022) under- mines the durability of this race-specific resistance, as new pathogen races can rapidly evolve, rendering many resistance genes ineffective (Miller et al., 2020). This dynamic has been observed by many widely used Pc genes, which lose effec- tiveness as new pathogen races evolve. For example, virulent races have rendered Pc36, Pc38, Pc39, Pc50, Pc68, Pc70, and Pc71 ineffective in certain regions (Moreau et al., 2024). Recent studies have demonstrated the importance of par- tial or quantitative resistance as a more durable resistance to crown rust. Multiple minor-effect genes largely control quantitative resistance and are often expressed as adult plant resistance (APR), which helps limit pathogen spread dur- ing the later stages of plant development (Díaz-Lago et al., 2003; Leonard, 2002; Ohm & Shaner, 1992; Sunstrum et al., 2019; Winkler et al., 2016). The APR genes Pc27, Pc28, Pc69, Pc72, Pc73, and Pc74 have only been identified in oat (Park et al., 2022). Unlike race-specific resistance conferred by single major genes, which evolving pathogen popula- tions can quickly overcome, additive resistance provides broad-spectrum protection and reduces the risk of resistance breakdown. To enhance crown rust resistance in oat, breeders have increasingly focused on identifying quantitative trait loci (QTLs) associated with APR, a strategy that holds promise for durable disease resistance (Chowdhury et al., 2024; Lin et al., 2014; Nazareno et al., 2022; Rines et al., 2018). These QTLs often exhibit additive effects, with lines homozygous for resistant alleles showing significantly lower disease severity (DS) (Lin et al., 2014; Nazareno et al., 2023). Integrating these QTLs and APR genes into breed- ing programs is critical for developing durable oat cultivars. Additive effects enhance resistance durability and effective- ness by enabling the synergistic contribution of multiple loci, leading to a more robust and stable defense that is less likely to be overcome by evolving pathogen popula- tions (Nazareno et al., 2023). At the same time, this strategy reduces selection pressure on the pathogen, further supporting resistance breeding’s long-term potential. In addition to QTL mapping, gene mining has become valuable for identifying candidate genes associated with disease resistance. Analyzing 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense SHARIATIPOUR ET AL. 3 of 21The Plant Genome genomic resources alongside disease-resistance loci enables researchers to discover key metabolic pathways and biologi- cal processes contributing to plant defense mechanisms. This approach facilitates the identification of positional candidate genes, which can be further investigated to determine their functional roles in pathogen resistance. These advances have laid the basis for meta-QTL (MQTL) analysis, which provides an even more refined approach to improving crown rust resistance. Unlike conventional QTL mapping, which often relies on individual studies, MQTL analysis integrates data from multiple independent studies. This integration enhances the reliability of identifying QTLs by making them more stable across different genetic back- grounds and environmental conditions (Goffinet & Gerber, 2000; X. L. Wu & Hu, 2012). MQTL analysis also reduces the 95% confidence intervals (CIs) for QTLs, improving genomic positioning accuracy and increasing QTL utility in breeding programs (Martinez et al., 2016; Zhang et al., 2017). Narrow- ing the CI is a critical goal in genetic mapping, as it allows for more precise localization of the genes and underlying traits of interest (Kearsey & Farquhar, 1998). This increased accuracy allows for the development of molecular markers tightly linked to QTLs, thereby improving the efficiency of marker-assisted selection (MAS) and enabling more tar- geted and effective breeding for crown rust resistance (Aloryi et al., 2022). Although MQTL analysis has been successfully applied to various crop species, such as wheat (S. Kumar et al., 2023; N. Pal et al., 2022; Saini et al., 2022; Vasistha et al., 2024), rice (Devanna et al., 2024; Goyal et al., 2024; I. S. Kumar & Nadarajah, 2020), and maize (M. Gupta et al., 2023; Sunitha et al., 2024), its application to oat has been limited by the complexity of its genome (Zhu & Kaeppler, 2003). These studies have demonstrated that MQTLs, also referred to as “MAS-friendly MQTLs” (M. Gupta et al., 2023), pro- vide more robust QTLs for MAS by addressing the problem of heterogeneity among QTL studies, thus refining QTL loca- tion and the magnitude of genetic effects (Saini et al., 2022). Such MAS-friendly MQTLs can be effectively used for selec- tion through associated molecular markers, enabling efficient selection for resistance traits and accelerating the develop- ment of improved oat cultivars. Progress has already been made in applying associated markers for disease resistance breeding in wheat (P. K. Gupta et al., 2022; S. Kaur et al., 2020; Sharma et al., 2021). This study represents the first MQTL mapping effort to elucidate the genetic architecture of oat crown rust resistance. This study will combine infor- mation from previously reported QTL with derived genomic data to identify candidate genes associated with resistance. Incorporating insights from oat genomics and regulatory ele- ments associated with resistance mechanisms strengthens our understanding of the underlying genetic crown rust resistance pathway. Ultimately, these findings provide valuable tools for oat breeders and support the development of cultivars with durable crown rust resistance. Core Ideas ∙ For genome-wide meta-analysis, 167 QTLs for crown rust resistance in oat were included. ∙ The study identified 23 meta quantitative trait loci (MQTL) regions, which provide the basis for breeding programs to improve oat resistance to crown rust. ∙ Gene mining within MQTL intervals revealed 1526 candidate genes associated with stress response and defense mechanisms. ∙ Functional analysis identified 12 key genes involved in plant defense, hormonal regulation, and stress adaptation. ∙ Findings provide valuable insights into developing oat cultivars with enhanced and durable crown rust resistance. 2 MATERIALS AND METHODS 2.1 Collection of data on QTL associated with crown rust resistance Research articles on crown rust resistance QTLs in the oat genome were systematically collected from reputable repos- itories and databases, such as Web of Knowledge, PubMed, Google Scholar, and other relevant, accessible data sources. The collected data consisted of detailed information on (i) the markers flanking the individual QTL, (ii) the peak posi- tions and CIs of the identified QTL, (iii) the type and size of the mapping population used in the respective studies, and (iv) logarithm of odds (LOD) scores and phenotypic variation explained (PVE) or R2 values (Table 1; Figure 1A–D). For cases where QTL peak positions were not explicitly reported in the source study, the midpoints of the two flanking markers were calculated and used as estimated peak positions. Simi- larly, when information on LOD scores was missing, a default threshold LOD score of 3.0 was applied to ensure uniformity in the analysis. During subsequent analyses, each QTL was assigned a unique identifier for clarity and consistency. The mapping study employed 19 mapping populations (comprising 17 recombinant inbred lines and one F2 popula- tion, and one F6 population), with population sizes ranging from 80 to 191 individuals evaluated across various envi- ronments and years (Table 1). These diverse populations provided a robust foundation for identifying QTL associ- ated with crown rust resistance in oat. These linkage-based mapping studies utilized a wide array of molecular mark- ers to achieve high-resolution genetic mapping, such as restriction fragment length polymorphism (RFLP), amplified fragment length polymorphism (AFLP), random amplified 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 4 of 21 SHARIATIPOUR ET AL.The Plant Genome T A B L E 1 Information about the oat (Avena sativa) populations used for the meta quantitative trait locus (MQTL) analysis. Study Population parents Type of population Population size Marker system Traits Reference 1 Ogle (CI9401) × MAM17-5 RIL 152 AFLP, RFLP, SSR CRs, RT Zhu and Kaeppler (2003) 2 MN841801-1 × Noble-2′ RIL 158 AFLP, RFLP, SCAR, SSR PCRr, CRr Portyanko et al. (2005) 3 UFRGS7 × UFRGS-910906 F2 86 AFLP PCRr Barbosa et al. (2006) F6 90 4 Ogle × TAM O-301 (OT) RIL 136 RFLP, AFLP, RAPD, STS, SSR CRr Jackson et al. (2008) 5 MN841801-1 × Noble-2′ RIL 150 AFLP, RFLP, SCAR, SSR PCRr Acevedo et al. (2010) 6 AC Assiniboia × MN841801 RIL 163 SNP CInf, DS, RC Lin et al. (2014) AC Medallion × MN841801 RIL 156 Makuru × MN841801 RIL 160 7 Provena × CDC-Boyer RIL 148 SNP CRr Babiker et al. (2015) Provena × 94197A1-9-2-2-2-5 RIL 145 CDC Boyer × 94197A1-9-2-2-2-5 RIL 80 8 TX07CS-1948 × SA04213 RIL 178 SNP CRr Sunstrum et al. (2019) 9 Otana × CI9416-2 RIL 130 SNP CInf, DS, RC Nazareno et al. (2022)Otana × PI189733 RIL 185 10 OtanaA × CI1712-5 RIL 191 SNP CInf, DS, RC Nazareno et al. (2023)OtanaA × CI17035-1 RIL 172 OtanaI × PI263412 RIL 173 11 PI 258731 × PI 573582 RIL 168 SNP CInf, DS, RC Chowdhury et al. (2024) Abbreviations: AFLP, amplified fragment length polymorphism; CInf, coefficient of infection; CRr, crown rust resistance; CRs, crown rust severity; DS, disease severity; PCRr, partial crown rust resistance; RAPD, random amplified polymorphic DNA; RC, reaction class; RFLP, restriction fragment length polymorphism; RIL, recombinant inbred line; RT, reaction type; SCAR, sequence characterized amplified regions; SNP, single nucleotide polymorphism; SSR, simple sequence repeats; STS, sequence tagged sites. polymorphic DNA, sequence tagged sites, simple sequence repeats, sequence characterized amplified regions, and sin- gle nucleotide polymorphism markers. Using such a broad spectrum of marker systems reflects the evolution of genetic mapping technologies and their integration into oat research. These mapping studies identified 167 QTLs associated with crown rust resistance across various oat populations (Figure 1A). Among them, 62 QTLs were linked to partial crown rust resistance, 56 QTLs were linked to crown rust resistance, 18 QTLs were linked to DS, 12 QTLs were linked to coefficient of infection and reaction class, 4 QTLs were linked to CRs, and 3 QTLs were linked to reaction type. Of the 167 QTLs, 127 (76%) were successfully mapped onto the QTL consensus map. The remaining 40 QTLs could not be mapped due to the absence of common markers between the original linkage maps and the consensus map, which limited their alignment. 2.2 Evaluation of phenotypic data for crown rust QTL identification Phenotypic data from 11 studies were analyzed to identify crown rust QTL, with most studies focusing on APR screen- ing in the field, while some also included seedling resistance tests. Four studies conducted phenotyping at the Matt Moore Buckthorn Plots at the Minnesota Agricultural Experiment Station in Saint Paul, MN, where natural inoculation was facilitated using a buckthorn nursery (Babiker et al., 2015; Chowdhury et al., 2024; Nazareno et al., 2022, 2023). Five studies employed artificial field inoculations for APR pheno- typing (Acevedo et al., 2010; Jackson et al., 2008; Lin et al., 2014; Portyanko et al., 2005; Zhu & Kaeppler, 2003), and one study used artificial field inoculations at another loca- tion (Chowdhury et al., 2024). Two studies conducted natural infection-based evaluations of APR (Barbosa et al., 2006; 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense SHARIATIPOUR ET AL. 5 of 21The Plant Genome F I G U R E 1 Quantitative trait locus (QTL) associated with crown rust resistance in oat were analyzed across chromosomes, focusing on (a) their chromosome-wise distribution, (b) confidence intervals (CIs), (c) logarithm of odds (LOD) scores, and (d) phenotypic variation explained (PVE) values. Key traits studied include crown rust severity (CRs), reaction type (RT), partial crown rust resistance (PCRr), crown rust resistance (CRr), coefficient of infection (CInf), disease severity (DS), and reaction class (RC). Portyanko et al., 2005), and one study conducted additional evaluations under natural infection conditions (Babiker et al., 2015). One study only tested seedling resistance (Sunstrum et al., 2019), while another evaluated seedling resistance with two races in addition to APR testing (Chowdhury et al., 2024). These phenotyping approaches collectively provided a robust dataset for QTL analysis, encompassing diverse environmental conditions and experimental methodologies. 2.3 Initial QTL projection and MQTL prediction After collecting the initial QTL data, all individual QTL were projected onto a reference map using BioMercator v4.2.3 software (Sosnowski et al., 2012). For this analysis, the high- density genetic map (OatConsensusMap-2018) was employed (Bekele et al., 2018). This map contains a dense set of 99,878 mapped molecular markers, spanning a total length of 2973.1 cM with a marker density of 33.6 markers per cM (Figure 2). QTLs that could not be aligned to the consensus map were excluded from further analysis. The linkage group nomen- clature used in this study (e.g., Mrg01) follows the system established in the OatConsensusMap-2018 (Bekele et al., 2018), while the alignment of these linkage groups with the AACCDD chromosome nomenclature was based on Jellen et al. (2024). This approach enables consistency with cur- rent standards in oat genetics research and facilitates reliable interpretation across studies. The projection of QTL onto the consensus map was performed using a homothetic function, assuming a linear relationship between the original QTL map and the reference map. This approach allowed for the estima- tion of most likely position of each QTL, along with its left and right flanking ends of the CI, based on common mark- ers shared between QTL and reference maps (Chardon et al., 2004). The nearest common marker was used where flank- ing markers were unavailable, as long as they were located within a reasonable distance (≤10 cM) from the original peak. When no common markers were identified between the original QTL maps and the consensus map, an intermediary consensus map was used to bridge the original QTL maps to the OatConsensusMap-2018 (Chaffin et al., 2016). This 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 6 of 21 SHARIATIPOUR ET AL.The Plant Genome F I G U R E 2 The distribution of the markers on the oat consensus map (OatConsensusMap-2018; Bekele et al., 2018) for meta quantitative trait locus (MQTL) analysis in the present study (color intensity from green to red indicates low to high marker density, respectively). approach enabled reliable projection of QTLs onto the refer- ence map, even without directly shared markers between the original and consensus maps. QTLs that could not be aligned due to missing shared markers, uncertain intervals, or other inconsistencies were excluded from further analysis. The MQTL analysis was conducted using the Veyrieras two-step algorithm (Veyrieras et al., 2007), which was applied individually to each chromosome. In the first step, the opti- mal QTL model was selected based on achieving the lowest criterion values in at least three of the five model selec- tion criteria: Akaike information criterion (AIC), Corrected AIC, AIC Model-3, Bayesian information criterion, and aver- age weight of evidence criterion (Sosnowski et al., 2012). This approach ensured the selection of a robust and statisti- cally supported model for MQTL analysis. In the second step, the selected model was utilized to determine the number of MQTL on each chromosome. The consensus locations of the MQTL were calculated using the variances of the initial QTL positions, while their 95% CIs were determined based on the variances of the QTL intervals (Sosnowski et al., 2012). The 23 MQTLs detected in this study were used to nominate posi- tional candidate genes for gene mining analyses. For breeding implications, we selected MQTL based on the following strin- gent criteria: (i) a CI of less than 1 cM and (ii) the inclusion of at least five initial QTLs. 2.4 Determination of MQTL physical positions Flanking marker positions on the PepsiCo OT3098 v2 hexaploid oat genome sequence were determined using tools in the T3/Oat (https://oat.triticeaetoolbox.org), Grain- Genes (https://wheat.pw.usda.gov/GG3), and EnsemblPlants (https://plants.ensembl.org/index.html) databases, depending on marker type. The distribution and positions of MQTL within each linkage group were visualized as a heatmap using the RIdeogram R package (Hao et al., 2020), pro- ducing a visual graphical representation of their genomic locations. 2.5 Candidate genes and gene ontology (GO) enrichment analysis Genes located in the MQTL intervals were identified through high-confidence gene annotations from the oat reference genome sequence (Oat_OT3098_v2), available in the Ensem- blPlants database (https://plants.ensembl.org/index.html). To gain functional insights into the identified genes, gene ontol- ogy (GO) enrichment analysis was conducted using the g:Profiler web tool (https://biit.cs.ut.ee/gprofiler/gost) (Kol- berg et al., 2023). The analysis used an over-representation model that assumes no GO term enrichment in the gene list (i.e., the null hypothesis). This model evaluates whether cer- tain GO terms are over-represented in the query gene list compared to a random sampling of terms associated with a positional candidate gene list of the same size. The p-values for each GO term were adjusted for multiple testing using the g:SCS threshold method to control the false discovery rate. GO terms were considered significantly over-represented compared to a random sampling of terms associated with a positional candidate gene list of the same size when the adjusted p-value (Padj) was ≤0.05. This approach identified key biological processes, molecular functions, and cellu- lar components associated with the candidate genes in the 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense https://oat.triticeaetoolbox.org https://wheat.pw.usda.gov/GG3 https://plants.ensembl.org/index.html https://plants.ensembl.org/index.html https://biit.cs.ut.ee/gprofiler/gost SHARIATIPOUR ET AL. 7 of 21The Plant Genome MQTL regions. Functional evidence for the GO domains of molecular function, cellular component, and biological pro- cesses was derived from the OT3098 reference genome, accessed through the EnsemblPlants database (https://plants. ensembl.org/index.html). 2.6 Cis-acting regulatory element (CAREs) analysis For the identification of cis-acting regulatory element (CAREs), 1.5 kb upstream sequences from the 5′ untrans- lated regions of selected genes were extracted from the OT3098 v2 reference genome using the EnsemblPlants database (https://plants.ensembl.org/index.html). Candidate genes were selected by intersecting those associated with significantly enriched GO terms (identified via g:Profiler analysis) with genes located within the MQTL regions iden- tified in this study. This filtering process resulted in a subset of 12 genes for further analysis. The upstream sequences of these genes were then analyzed using the PlantCARE database (http://bioinformatics.psb.ugent.be/webtools/ plantcare/html/), which is a curated tool for identifying plant- specific cis-regulatory elements. The detected CAREs were annotated and categorized based on their known associations with biological functions such as hormone signaling, biotic stress responses, and transcription factor binding. 3 RESULTS 3.1 Crown rust resistance QTL and their distribution on the oat genome The 127 mapped QTLs were distributed across 12 chromo- somes with varying densities. The majority of QTLs were located on chromosomes 1D (31 QTLs), 4A (16 QTLs), 5C (15 QTLs), 5D (13 QTLs), and 7A (11 QTLs), while fewer QTLs were found on chromosomes 4D (8 QTLs), 4C (7 QTLs), 7C (7 QTLs), 2C (6 QTLs), 2D (6 QTLs), 3C (4 QTLs), and 6C (3 QTLs). The analysis revealed that crown rust resistance QTLs were predominantly distributed on the D sub-genome (58/127, 45.7%) and the C sub-genome (42/127, 33.1%), while fewer QTLs were mapped to the A sub-genome (27/127, 21.3%) (Figure 1a, Table 2). Of the 127 mapped QTLs, 52 (40.9%) had CIs of less than 15 cM, whereas eight QTLs (6.3%) had CIs exceeding 75 cM (Figure 1b). The LOD scores of individual QTLs ranged from ≤3.0 to a maximum of 31.8. Among these, 42 QTLs had LOD scores between 4 and 10 (Figure 1c). The PVE by the QTL ranged from 2.9% to 75.8%, with an average PVE of 23.7%. Of the identified QTL, 38 exhibited PVE values of ≤10%, while 14 demonstrated PVE values exceeding 50% (Figure 1d). 3.2 Consensus map and MQTL for crown rust resistance Twenty-three MQTLs were identified for crown rust resistance based on 127 mapped QTLs on the consensus map (Figure 3; Table 2). Among the sub-genomes, seven MQTLs were determined on sub-genome A, with chromosome 4A con- taining the highest number (five MQTLs) and chromosome 7A containing two MQTLs. Similarly, seven MQTLs were predicted on sub-genome D, with chromosome 1D harboring the maximum (three MQTLs), followed by two MQTLs on chromosome 5D, while chromosomes 2D and 4D each displayed a single MQTL. Sub-genome C had the highest number of MQTL, with nine in total. Chromosome 5C harbored the maximum (three MQTLs) of these, followed by two MQTLs on chromosome 4C and one MQTL each on chromosomes 2C, 3C, 6C, and 7C (Figure 3; Table 2). The number of QTL contributing to individu- al MQTL varied significantly, ranging from ≤3 QTL in 6 MQTLs to ≥6 QTLs in 10 MQTLs, including MQTL(Pc)1D.1, MQTL(Pc)1D.2, MQTL(Pc)1D.3, MQTL(Pc) 5C.1, MQTL(Pc)5D.2: MQTL(Pc)2D.1, MQTL(Pc)7C.1, MQTL(Pc)7A.2, MQTL(Pc)2C.1, and MQTL(Pc)4D.1 (Figure 3, Table 2). The CIs of the reported MQTL ranged from 0.02 to 5.69 cM, with an average of 1.37 cM, repre- senting a 5.55-fold reduction compared to the CIs of the original QTL (Table 2). All 23 MQTLs were physically anchored to the oat reference genome. The physical CIs of the MQTL ranged from 0.01 (MQTL(Pc)4A.1) to 25.73 Mb (MQTL(Pc)4D.1), with a mean physical CI of 7.29 Mb (Table 2). 3.3 Gene mining and GO analysis within MQTL regions Table 2 presents the number of candidate genes iden- tified in the intervals of the detected MQTL, while detailed annotations for all genes in each MQTL inter- val are available in Table S1. Gene mining in crown rust resistance MQTL revealed 1526 unique genes. Collectively, the MQTL detected on the chromosomes in the D sub-genome contained the largest number of genes (642), followed by the C sub-genome with 477 genes and the A sub-genome with 407 genes (Figure 4; Table 2; Table S1). The MQTL with the highest number of candidate genes was MQTL(Pc)5C.1, containing 257 genes, followed by MQTL(Pc)4D.1 with 236 genes (Table 2). Interestingly, the number of predicted genes largely cor- responds to the size of the MQTL 95% CIs, with larger MQTL CIs encompassing more genes. On the other hand, MQTL(Pc)4A.1, which had the smallest size, contained 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense https://plants.ensembl.org/index.html https://plants.ensembl.org/index.html https://plants.ensembl.org/index.html http://bioinformatics.psb.ugent.be/webtools/plantcare/html/ http://bioinformatics.psb.ugent.be/webtools/plantcare/html/ 8 of 21 SHARIATIPOUR ET AL.The Plant Genome T A B L E 2 Id en tif ie d m et a qu an tit at iv e tr ai tl oc us (M Q T L )a ss oc ia te d w ith cr ow n ru st re si st an ce in th e oa t( Av en a sa tiv a) ge no m e (O at C on se ns us M ap -2 01 8 an d th e O at _O T 30 98 _v 2 re fe re nc e ge no m e) . M Q TL C hr . (g en et ic m ap ) C hr . (p hy sic al m ap ) Fl an ki ng m ar ke rs Po sit io n (c M ) C I( cM ) Ph ys ic al po sit io n (M b) Q TL N o. N um be r of ge ne s w ith in th e M Q TL in te rv al M Q T L (P c) L G 1. 1 M rg 01 1D G M I_ E S0 3_ c1 72 72 _2 13 – G M I_ E S0 2_ c2 37 03 _2 43 2. 8 1. 01 48 3. 01 –4 85 .3 3 9 22 M Q T L (P c) L G 1. 2 M rg 01 1D av gb s_ 74 97 7. 1. 55 –a vg bs _1 03 69 5. 1. 23 13 .4 1. 63 47 5. 04 –4 78 .3 8 6 37 M Q T L (P c) L G 1. 3 M rg 01 1D av gb s_ cl us te r_ 19 98 .1 .4 8– av gb s_ 22 50 27 .1 .5 3 73 .6 2 0. 06 35 2. 58 –3 59 .1 7 16 12 8 M Q T L (P c) L G 3. 1 M rg 03 5C av gb s_ 21 38 50 .1 .6 2– av gb s_ 11 53 55 .1 .5 2 40 .9 5 2. 93 54 1. 65 –5 61 .9 5 6 25 7 M Q T L (P c) L G 3. 2 M rg 03 5C av gb s2 _9 97 40 .1 .1 1– av gb s_ 90 17 0. 1. 39 53 .2 4 2. 74 51 7. 71 –5 24 .0 0 4 44 M Q T L (P c) L G 3. 3 M rg 03 5C av gb s_ 10 89 83 .1 .6 4– G M I_ G B S_ 96 20 65 .9 5 0. 78 47 7. 98 –4 85 .8 2 5 43 M Q T L (P c) L G 6. 1 M rg 06 5D av gb s_ 11 15 62 .1 .6 3– av gb s_ 49 59 4. 1. 48 38 .2 2 2. 81 46 4. 40 –4 71 .6 5 4 60 M Q T L (P c) L G 6. 2 M rg 06 5D av gb s_ 22 13 41 .1 .3 9– av gb s_ cl us te r_ 23 66 5. 1. 10 96 .8 2 0. 36 41 .5 1– 57 .7 5 9 12 5 M Q T L (P c) L G 8. 1 M rg 08 2D av gb s_ cl us te r_ 28 30 6. 1. 46 –G M I_ D S_ oP t- 17 69 4_ 37 4 25 .5 8 0. 02 5. 50 –9 .4 4 6 34 M Q T L (P c) L G 9. 1 M rg 09 4C G M I_ E S0 2_ lr c3 70 77 _7 35 –a vg bs _4 15 27 .1 .4 5 3. 67 1. 78 5. 57 –5 .7 4 3 5 M Q T L (P c) L G 9. 2 M rg 09 4C av gb s_ cl us te r_ 30 12 9. 1. 15 – av gb s_ cl us te r_ 25 91 3. 1. 32 11 1. 5 4. 06 52 8. 07 –5 28 .8 2 4 7 M Q T L (P c) L G 11 .1 M rg 11 7C av gb s_ 20 52 34 .1 .4 9– av gb s_ 11 73 85 .1 .3 8 19 .5 3 1. 24 64 5. 41 –6 51 .2 6 7 0 M Q T L (P c) L G 12 .1 M rg 12 7A U M N 29 5A –a vg bs _c lu st er _6 82 5. 1. 9 0. 64 0. 27 0. 37 –0 .5 4 5 4 M Q T L (P c) L G 12 .2 M rg 12 7A av gb s_ 96 00 7. 1. 25 –a vg bs _c lu st er _2 65 22 .1 .1 7 11 .8 9 0. 02 48 3. 42 –4 91 .7 3 6 70 M Q T L (P c) L G 13 .1 M rg 13 2C av gb s_ cl us te r_ 52 34 .1 .1 8– av gb s2 _1 64 42 8. 1. 63 12 .9 3 0. 26 1. 64 –8 .7 9 6 81 M Q T L (P c) L G 15 .1 M rg 15 3C av gb s_ 11 59 50 .1 .4 2– av gb s_ cl us te r_ 44 25 5. 1. 62 58 .6 4 0. 54 19 .7 1– 23 .1 2 4 29 M Q T L (P c) L G 17 .1 M rg 17 6C G M I_ E S1 7_ c1 74 42 _3 34 –a vg bs 2_ 20 00 54 .1 .1 8 8. 05 2. 12 0. 32 –1 .4 9 3 11 M Q T L (P c) L G 20 .1 M rg 20 4A C SU 25 –U M N 36 3B 2. 87 0. 58 0. 53 –0 .5 4 3 1 M Q T L (P c) L G 20 .2 M rg 20 4A G M I_ E S1 5_ c7 63 2_ 38 4– av gb s_ 11 19 3. 1. 11 17 .3 0. 58 17 8. 05 –1 94 .8 1 3 89 M Q T L (P c) L G 20 .3 M rg 20 4A av gb s_ 10 90 50 .1 .5 1– G M I_ E S0 5_ c2 06 6_ 50 3 28 .0 5 0. 71 22 8. 87 –2 29 .7 8 2 7 M Q T L (P c) L G 20 .4 M rg 20 4A G M I_ E S2 2_ c4 46 3_ 27 5– av gb s_ 16 20 1. 1. 59 33 .2 2 0. 45 22 9. 78 –2 36 .5 6 5 44 M Q T L (P c) L G 20 .5 M rg 20 4A av gb s_ cl us te r_ 32 78 1. 1. 30 –a vg bs _1 20 85 7. 1. 15 72 .4 5. 69 25 2. 01 –2 68 .4 2 3 19 2 M Q T L (P c) L G 21 .1 M rg 21 4D av gb s2 _3 03 00 .1 .5 6– av gb s_ cl us te r_ 23 33 6. 1. 30 96 .3 0. 04 23 6. 20 –2 61 .9 3 8 23 6 A bb re vi at io ns :C I, co nf id en ce in te rv al ;C hr ., ch ro m os om e. 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense SHARIATIPOUR ET AL. 9 of 21The Plant Genome F I G U R E 3 Position of detected meta quantitative trait locus (MQTL) on the oat genome (OatConsensusMap-2018; Bekele et al., 2018) associated with crown rust resistance with a 95% confidence interval. Each color in a different linkage group indicates the number of initial quantitative trait locus (QTL) involved in each MQTL. The flanking markers for each MQTL are presented on the left side of the linkage groups.CInf, coefficient of infection; CRr, CRs, crown rust resistance; crown rust severity; DS, disease severity; PCRr, partial crown rust resistance; RC, reaction class. only one gene, while MQTL(Pc)7A.1 and MQTL(Pc)4C.1 contained four and five genes, respectively (Figure 4; Table 2). Among the candidate genes identified within the MQTL regions, 12 genes were determined for their potential signifi- cance based on functional annotations, which were arginine decarboxylase (ADC) activity, glutamine metabolism, spermidine biosynthesis, polyamine metabolism, and the regulation of cytokinin-activated signaling path- ways. These include AVESA.00001b.r3.5Dg0003146, 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 10 of 21 SHARIATIPOUR ET AL.The Plant Genome F I G U R E 4 A heat map illustrates the gene density of Avena sativa chromosomes (Oat_OT3098_v2). The positions of the genes detected at each meta quantitative trait locus (MQTL) interval are displayed on the right side of the chromosomes. Pink triangles indicate significant functional candidate genes associated with crown rust resistance. AVESA.00001b.r3.5Dg0003147, AVESA.00001b.r3.5Dg000 3148, AVESA.00001b.r3.5Dg0003149, AVESA.00001b.r3. 5Dg0003150, AVESA.00001b.r3.5Dg0003151, AVESA.000 01b.r3.5Cg0002554, AVESA.00001b.r3.4Ag0000856, AVE SA.00001b.r3.4Ag0000857, AVESA.00001b.r3.4Ag0000 858, AVESA.00001b.r3.4Dg0001208, and AVESA.00001b.r3 .4Dg0001209 (Table 3). GO analysis revealed diverse and significant functions associated with these genes, includ- ing (i) ADC activity and arginine metabolic processes, (ii) glutamine family amino acid catabolic processes, (iii) spermidine metabolism and biosynthesis, (iv) polyamine metabolism and biosynthesis, and (v) the regulation of cytokinin-activated signaling pathways (Figure 5). 3.4 Identification of CAREs in the promoter site of functional genes The identified CAREs in the promoter regions of the 12 genes located within the crown rust resistance MQTL regions in oat were presented in Figure 6 and Table 3. These CAREs were predominantly linked to functions related to light response, hormonal regulation, and stress response, which are criti- cal for the regulation of crown rust resistance genes in oat (Table 3). Key elements identified included the CAAT-box (25.34%) and the TATA-box (14.12%), which are essential sequences in the promoter regions of many genes and play crucial roles in transcriptional regulation. Hormonal regu- latory elements identified in the promoter regions included those responsive to methyl jasmonate (MeJA), gibberellic acid (GA), and abscisic acid (ABA), such as ABA responsive element (ABRE; 4.45%), CGTCA-motif (2.03%), TGACG- motif (2.03%), P-box (0.1%), and TATC-box (0.1%) (Table 3). Light-responsive CAREs were also abundant in the pro- moter regions of the detected resistance genes. These included G-box (4.84%), G-Box (1.93%), TCCC-motif (0.77%), TCT- motif (0.68%), GT1-motif (0.48%), I-box (0.39%), GATA- motif (0.29%), GA-motif (0.19%), AAAC-motif (0.1%), and ATCT-motif (0.1%) (Table 3). These elements highlight the role of light-regulated transcriptional processes in crown rust resistance gene expression. Aside from this, stress-responsive elements were present in the promoter regions, such as MYB (7.25%), MYC (2.51%), TC-rich repeats (0.68%), and the circadian element (0.1%) (Table 3). 4 DISCUSSION This study provides valuable insights into the genetic archi- tecture of crown rust resistance in oat, achieved through the comprehensive dissection of QTL and MQTL associated with this globally significant trait. Identifying key candidate 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense SHARIATIPOUR ET AL. 11 of 21The Plant Genome T A B L E 3 The most prevalent cis-regulatory elements in the promoter of crown rust resistance responsive genes in oat. Cis-regulatory element Function Cis-regulatory element Function A-box Cis-acting regulatory element HD-Zip 1 Element involved in differentiation of the palisade mesophyll cells AAAC-motif Light responsive element I-box Part of a light responsive element AAGAA-motif – LTR Cis-acting element involved in low-temperature responsiveness ABRE Cis-acting element involved in the abscisic acid responsiveness MBS MYB binding site involved in drought-inducibility ABRE2 MBSI MYB binding site involved in flavonoid biosynthetic genes regulation ABRE3a – MRE MYB binding site involved in light responsiveness ABRE4 – MYB – AC-I – MYB recognition site – ACTCATCCT sequence – MYB-like sequence – AE-box Part of a module for light response MYC – ARE Cis-acting regulatory element essential for the anaerobic induction Myb – AT-rich element Binding site of AT-rich DNA binding protein (ATBP-1) Myb-binding site – ATCT-motif Part of a conserved DNA module involved in light responsiveness Myc – AT1-motif Part of a light responsive module NON-box – AT∼TATA-box – O2-site Cis-acting regulatory element involved in zein metabolism regulation Box 4 Part of a conserved DNA module involved in light responsiveness RY-element Cis-acting regulatory element involved in seed-specific regulation Box II Part of a light responsive element P-box Gibberellin-responsive element Box II -like sequence Cis-acting regulatory element STRE – CAAT-box Common cis-acting element in promoter and enhancer regions Sp1 Light responsive element CAT-box Cis-acting regulatory element related to meristem expression TATA – CCAAT-box Mybhv1 binding site TATA-box Core promoter element around -30 of transcription start CCGTCC motif – TATC-box Cis-acting element involved in gibberellin-responsiveness CGTCA-motif Cis-acting regulatory element involved in the MeJA-responsiveness TC-rich repeats Cis-acting element involved in defense and stress responsiveness CTAG-motif – TCA – DRE core – TCA-element Cis-acting element involved in salicylic acid responsiveness ERE – TCCC-motif Part of a light responsive element DRE1 – TCT-motif Part of a light responsive element G-Box Cis-acting regulatory element involved in light responsiveness TGA-element Auxin-responsive element G-box Cis-acting regulatory element involved in light responsiveness TGACG-motif Cis-acting regulatory element involved in the MeJA-responsiveness (Continues) 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 12 of 21 SHARIATIPOUR ET AL.The Plant Genome T A B L E 3 (Continued) Cis-regulatory element Function Cis-regulatory element Function GA-motif Part of a light responsive element W box – GATA-motif Part of a light responsive element WRE3 – GC-motif Enhancer-like element involved in anoxic specific inducibility WUN-motif – GCN4_motif Cis-regulatory element involved in endosperm expression as-1 – GT1-motif Light responsive element box S – circadian Cis-acting regulatory element involved in circadian control Abbreviation: MeJA, methyl jasmonate. F I G U R E 5 Gene ontology (GO) and functional enrichment analysis of candidate genes in meta quantitative trait locus (MQTL) regions of the oat genome (Oat_OT3098_v2) in response to crown rust revealed important insights into their functional roles. Each circle represents a significant GO term, with significance determined by an adjusted p-value (padj ≤ 0.05) using G:Profiler. The y-axis represents −log10(padj), and circles are color-coded to indicate their respective GO domains: molecular function (MF), cellular component (CC), and biological process (BP), highlighting the diverse roles of the identified genes in crown rust resistance. genes and refined genomic regions contributing to crown rust resistance in Avena species has substantial relevance for resistance breeding and the sustainable management of crown rust, which remains a major constraint to oat produc- tion worldwide. The MQTL analysis conducted in this study consolidated 127 QTLs into 23 robust MQTLs across 12 oat chromosomes, significantly improving the precision and sta- bility for mapping crown rust resistance. Chromosomes 4A, 1D, and 5C contained the highest number of MQTL, with five, three, and three MQTLs, respectively, indicating their critical roles in harboring resistance loci. Among the detected MQTL, MQTL(Pc)1D.3, MQTL(Pc)1D.1, MQTL(Pc)5D.2, and MQTL(Pc)4D.1 emerged as the most promising candi- dates for breeding implications, as they exhibited the highest number of initial QTL identified across independent popu- lations. These MQTLs represent viable, stable, and robust loci effective under diverse experimental conditions, mak- ing them key targets for further genetic exploration. The stability of these MQTLs across different populations and conditions reinforces their utility in breeding programs to improve resistance, durability, and adaptability. The improved resolution achieved through this MQTL analysis enhances 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense SHARIATIPOUR ET AL. 13 of 21The Plant Genome F I G U R E 6 Distribution of major cis-acting regulatory elements at the oat’s promoter site of crown rust resistance genes. our understanding of the genomic landscape associated with crown rust resistance. With this approach, CIs have been reduced and data from multiple studies have been incorpo- rated, thereby increasing the accuracy of mapping resistance loci and enabling the identification of candidate genes. This will facilitate future fine-mapping efforts, the development of high-resolution markers, and the functional validation of resistance-associated genes (Jackson et al., 2008; Löffler et al., 2009). In this study, MQTL analysis was conducted using QTL for crown rust resistance previously reported in independent experiments. This approach aimed to enhance our understanding of genetic regulation of crown rust resis- tance in oat. The process began with projecting the original QTL onto a consensus map, a critical first step in identify- ing consensus regions through meta-analysis. This projection refines QTL positions, reducing their CIs and increasing their reliability for breeding applications. The genetics of quantitative resistance to crown rust has been extensively studied in various oat species using QTL mapping methodologies (Acevedo et al., 2010; Barbosa et al., 2006; Chowdhury et al., 2024; Jackson et al., 2008; Lin et al., 2014; Nazareno et al., 2018; Portyanko et al., 2005; Sun- strum et al., 2019; Zhu & Kaeppler, 2003). These studies have identified numerous QTLs associated with crown rust resistance in oat. However, QTL identified in one mapped population often fail to perform well in breeding programs involving different populations or parental lines (S. Kumar et al., 2023; Yang et al., 2021). This reflects the inherent chal- lenges in dissecting the genetic architecture of complex traits, which remains a significant obstacle for plant breeders and geneticists. While traditional QTL mapping approaches, such as linkage mapping, have been instrumental in identifying marker-trait associations for complex traits like partial resis- tance to crown rust, their limitations are well-documented. These include confounding effects from genetic background, environmental variation, limited marker density, and low mapping resolution. MQTL analysis addresses these chal- lenges by integrating QTL from multiple studies, refining their positions, and identifying those that are stable and robust across diverse genetic and environmental contexts (S. Kaur et al., 2023; Pascual et al., 2016; Y. Xu et al., 2017). Unlike traditional methods, MQTL analysis reduces heterogeneity between studies and narrows CIs, improving the utility of QTL in breeding programs (Arcade et al., 2004; S. Kaur et al., 2023; S. Kumar et al., 2023; Saini et al., 2022). The distribution of crown rust resistance genes, catego- rized into seedling resistance and APR genes, was analyzed across seven Avena species using previously published data (Park et al., 2022). The MQTL and candidate genes iden- tified in this study provide new insights into the genetic architecture of crown rust resistance in oat. In A. sativa, 13 seedling resistance genes and two APR genes were previ- ously reported (Park et al., 2022), and our analysis identified 22 MQTLs linked to crown rust resistance in this species, encompassing key genomic regions associated with durable resistance. Eleven of these MQTLs contain nine candi- 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 14 of 21 SHARIATIPOUR ET AL.The Plant Genome date genes associated with APR, which demonstrates their potential importance in developing improved resistance in breeding programs (Figure 4). Avena strigosa has been recog- nized as an important source of resistance, with 22 resistance genes identified (Park et al., 2022), and our study detected one MQTL in this species that harbors three functional resistance genes linked to crown rust resistance (Figure 4). Although A. byzantina has 13 R genes and A. sterilis contains 41 seedling resistance genes and four APR genes (Park et al., 2022), but no MQTLs were detected in these species. Other species, such as Avena glabrata, Avena trichophylla, and Avena longiglumis, exhibit limited resistance sources, with only one or two R genes and no reported APR genes (Park et al., 2022). Accordingly, no MQTLs were identified in these species, underscoring their minimal contribution to crown rust resistance. 4.1 Candidate gene mining in the MQTL and their association with crown rust resistance Candidate gene mining from the 23 identified MQTLs revealed several genes linked to key biological pro- cesses, providing insights into crown rust resistance mechanisms. Polyamines (PAs) play a crucial role in plant-pathogen interactions by amplifying pattern-triggered immunity through the production of reactive oxygen species (ROS) (Gerlin et al., 2021). Among the iden- tified candidate genes, AVESA.00001b.r3.5Dg0003146, AVESA.00001b.r3.5Dg0003147, AVESA.00001b.r3.5Dg000 3148, AVESA.00001b.r3.5Dg0003149, AVESA.00001b .r3.5Dg0003150, and AVESA.00001b.r3.5Dg0003151 were closely linked to polyamine metabolism, namely, ADC activ- ity and spermidine biosynthesis. As part of the metabolism of PAs, ADC converts arginine to agmatine, which serves as a precursor for PAs such as putrescine, spermidine, and spermine (Blázquez, 2024; Gerlin et al., 2021; M. Pal & Janda, 2017; Zeier, 2013). These PAs enhance plant defense by strengthening cell walls, scavenging ROS, and mitigating oxidative damage during pathogen infection. In oat, increased ADC activity has been associated with improved resistance to Pca, particularly during the critical pre-penetration and penetration stages of infection (Montilla-Bascón et al., 2016; Winter et al., 2015; Zeier, 2013). These findings demonstrate the importance of polyamine metabolism in increasing plant defenses against crown rust. Amino acids like arginine and glutamine play dual roles as essential protein building blocks and regulators of stress responses (J. Cai & Aharoni, 2022; Hildebrandt et al., 2015). The gene AVESA.00001b.r3.5Cg0002554, associated with arginine metabolism, underscores the importance of arginine as a precursor for polyamine synthesis, which supports plant development and enhances resilience to stress (Bagni & Tas- soni, 2001; Winter et al., 2015). Similarly, glutamine is critical for nitrogen metabolism, incorporating ammonium into key metabolic pathways essential for plant growth and stress toler- ance. Overexpression of glutamine synthetase genes has been shown to improve drought and salt tolerance by maintaining photosystem function, mitigating oxidative stress, and sup- porting photorespiration (Ganie, 2021; Hoshida et al., 2000; James et al., 2018; Y. Liu et al., 2017). The genes linked to glutamine metabolism identified in this study further empha- size their role in enhancing plant defenses against biotic stress. This is achieved through regulating ROS levels and enhancing antioxidant capacity, highlighting glutamine’s importance in strengthening plant immunity. Cytokinin signaling balances plant growth and defense, enabling plants to allocate resources effectively based on environmental conditions (Akhtar et al., 2020; Pieterse et al., 2014). In this study, several identi- fied genes, including AVESA.00001b.r3.4Ag0000856, AVESA.00001b.r3.4Ag0000857, AVESA.00001b.r3.4Ag0000 858, AVESA.00001b.r3.4Dg0001208, and AVESA .00001b.r3.4Dg0001209, were associated with the reg- ulation and negative regulation of cytokinin-activated signaling pathways. Cytokinin influences various processes related to plant development and pathogen resistance (Kieber & Schaller, 2018). It also interacts with other hormonal pathways, such as jasmonic acid (JA), to enhance plant defense mechanisms (Akhtar et al., 2020; Prasad, 2022). These findings underline cytokinin’s critical role in mod- ulating nutrient responses and strengthening resistance to pathogens, particularly crown rust (Cortleven et al., 2019). The identification of candidate genes involved in cytokinin signaling suggests their potential role in enhancing resistance mechanisms. The study also demonstrated the importance of polyamine metabolism, amino acid pathways, and cytokinin signaling in crown rust resistance, revealing diverse biochem- ical and regulatory pathways that enhance plant defenses. Functional annotations of these candidate genes demonstrate their potential to strengthen resistance mechanisms, making them valuable targets for further validation. These insights can be utilized in MAS strategies for integrating these genes into breeding programs. This approach may enhance the resilience of oat cultivars to crown rust, ensuring sustainable production in biotic stress conditions. 4.2 Functional implications of cis-acting regulatory elements in oat crown rust resistance Identifying CAREs in the promoter regions of functional genes associated with crown rust resistance in oat reveals their significant role in transcriptional regulation and plant defense (Cui et al., 2023). The promoter types selected for this analysis were chosen based on two primary crite- ria: (i) their established relevance to biotic stress responses, particularly in relation to crown rust resistance, and (ii) their 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense SHARIATIPOUR ET AL. 15 of 21The Plant Genome frequency within the promoter regions of the 12 candidate genes under investigation. Elements such as ABRE, MYB, MYC, TGACG-motif, and TC-rich repeats have been doc- umented in previous reports for their functional association with plant defense mechanisms, hormonal signaling, and stress adaptation (Hou et al., 2016; Huda et al., 2013; Li et al., 2020). Transcriptional regulation involves the interac- tion between transcription factors and specific CAREs in the promoter regions, making these elements essential for orches- trating plant immune responses (Kaur & Pati, 2016; A. Kaur et al., 2017). CAREs are key regulatory units in plant genomes that control gene expression in response to biotic and abiotic stresses, as highlighted by their role in regulating resistance mechanisms to crown rust in oat (Cui et al., 2023; A. Kaur et al., 2017). The CAREs identified in this study were pri- marily associated with hormonal regulation, light response, and stress response, emphasizing the complexity of the plant immune system. These categories demonstrate how diverse regulatory elements collaborate to enable resistance against pathogens like crown rust. Hormonal regulation is central in shaping plant defense mechanisms against biotic stresses. This study identified sev- eral hormone-related CAREs, particularly ABRE, CGTCA- motif, TGACG-motif, P-box, and TATC-box, which are involved in the regulation of hormones such as ABA and JA (Shariatipour & Heidari, 2018; Shariatipour & Heidari, 2020). ABA, a key phytohormone, mediates plant responses to stress by regulating defense pathways. Its role in resis- tance to both abiotic and biotic stresses is well-documented, with the ABRE element acting as a crucial mediator in ABA- induced gene expression (Hewage et al., 2020; Rai et al., 2024). Jasmonates, including JA and MeJA, are lipid-derived hormones that regulate defense responses under stress. The identified TGACG and CGTCA motifs, associated with MeJA response, are critical for activating jasmonate signaling path- ways, which help plants combat pathogen attacks (Rehman et al., 2023; Siddiqi & Husen, 2019). GA is also involved in plant growth and defense regulation. The presence of GA- related CAREs in crown rust resistance genes suggests that GA signaling modulates immune responses, further illustrat- ing the interconnected nature of plant hormone pathways (Hedden, 2020; K. Wu et al., 2021; H. Xu et al., 2014). These elements suggest hormonal signaling pathways actively modulate crown rust resistance in oat. Light-responsive CAREs such as G-box, TCCC-motif, GT1-motif, and GATA-motif were identified in the pro- moter regions of crown rust resistance genes. These elements regulate gene expression in response to light, influencing plant growth, development, and immune responses. As a critical environmental factor, light modulates defense mech- anisms by coordinating immune-related pathway activation (A. Kaur et al., 2017). The presence of light-responsive motifs highlights their role in enhancing plant adaptabil- ity to environmental conditions that may affect pathogen growth and infection. Stress-responsive CAREs, like MYB, MYC, TC-rich repeats, and TCA elements are crucial in regulating plant responses to biotic stress. MYB transcrip- tion factors regulate secondary metabolites like phenolics and flavonoids, which are essential for plant defense against pathogens (Biswas et al., 2023; Song et al., 2022). Simi- larly, MYC transcription factors control jasmonate-responsive genes, linking them to biotic stress tolerance (Biswas et al., 2023). The TC-rich repeats and TCA elements activate genes that respond to pathogen attack, further strengthening the plant’s defense mechanisms (Diévart & Clark, 2004; Mer- lot et al., 2001). These elements highlight the crucial role of transcriptional regulation in mediating crown rust resistance genes’ responses to biotic and abiotic stresses. The findings provide critical insights into the transcriptional regulatory mechanisms underlying crown rust resistance in oat, identi- fying potential targets for genetic improvement in breeding programs. The identified CAREs point to a complex network of regulatory pathways that govern plant immunity. Hormonal regulation (via ABA, JA, and GA), light responsiveness, and stress-responsive elements (such as MYB, MYC, TC-rich repeats, and TCA elements) collectively activate and fine- tune defense genes, enabling effective responses to crown rust infection. Understanding these regulatory mechanisms offers valuable insights into the genetic basis of disease resistance, creating opportunities for the development of resistant oat cul- tivars through molecular breeding and genetic engineering. Integrating these CAREs into functional analyses and breed- ing strategies could significantly enhance the development of resistant oat cultivars. 4.3 MQTL‑assisted breeding for crown rust resistance MQTL-assisted breeding primarily focuses on developing improved cultivars with increased disease resistance, with a focus on long-term durability. MQTL with reduced CIs and those incorporating multiple QTLs have been widely recommended in previous studies for breeding disease- resistant crops such as wheat (S. Kumar et al., 2023; N. Pal et al., 2022; Saini et al., 2022; Vasistha et al., 2024), rice (Devanna et al., 2024; Goyal et al., 2024; I. S. Kumar & Nadarajah, 2020), maize (M. Gupta et al., 2023; Sunitha et al., 2024), and cotton (Huo et al., 2023). This study selected specific MQTL for potential utilization in breeding programs based on stringent criteria: (i) a CI of less than 1 cM and (ii) the inclusion of at least five initial QTLs. These MQTLs include MQTL(Pc)1D.3, MQTL(Pc)2C.1, MQTL(Pc)2D.1, MQTL(Pc)4A.4, MQTL(Pc)4D.1, MQTL(Pc) 5C.3, MQTL(Pc)5D.2, MQTL(Pc)7A.1, and MQTL(Pc)7A.2. The number of initial QTL contributing to these MQTLs ranges from 5 to 16, reflecting their robustness and integration of valuable resistance-associated loci. Strategic 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense 16 of 21 SHARIATIPOUR ET AL.The Plant Genome selection of MQTL regions is highly valuable for breeding programs, particularly when they coincide with genes already present in mapped populations. In cases where these genes are absent, MQTL can guide pre-breeding efforts by iden- tifying resistance-associated loci for targeted introgression. By consolidating information from multiple QTLs, MQTLs highlight stable genomic regions linked to resistance traits. Molecular markers, such as Kompetitive allele specific PCR markers, developed from these regions, serve as efficient tools for MAS, facilitating the detection and incorporation of favorable alleles while simplifying breeding workflows and reducing costs. Incorporating well-characterized MQTL into breeding pipelines enhances genetic gains and promotes stable resis- tance traits across diverse environments. This strategy under- scores the value of MQTL-assisted breeding in accelerating the development of oat cultivars with durable crown rust resis- tance. To maximize practical application, it is essential to link MQTL regions to germplasm sources by identifying specific oat lines or populations carrying favorable alleles. This will enable breeding programs to efficiently introduce these resis- tance loci into new cultivars. The integration of robust genetic information through breeder-targeted MQTL lays a strong foundation for effective MAS, contributing to reliable trait improvement and long-term disease management. The com- parative analysis of crown rust resistance across Avena species reveals significant genetic diversity, indicating opportunities for targeted breeding strategies. Combining previously iden- tified R and APR genes with MQTL and candidate genes identified in this study, we have gained a better understanding of the genomic architecture underlying crown rust resistance. Species such as A. sterilis and A. sativa are valuable resources due to their multiple resistance genes and associated MQTL. Refined MQTLs, characterized by narrow CIs and enriched with candidate genes, provide precise genomic targets for resistance breeding. These results emphasize the substantial genetic diversity across Avena species, positioning A. sterilis and A. sativa as key contributors to breeding programs. While seedling resistance genes dominate, APR genes are essen- tial for durable resistance, as their allowance of low levels of disease exerts weaker selection pressure on pathogens, reduc- ing the likelihood of adaptation to resistance (Rimbaud et al., 2023). Identifying MQTL linked to these resistance genes refines genomic regions for targeted breeding efforts. This study underscores the importance of utilizing diverse Avena germplasms to enhance crown rust resistance in oat varieties and advance sustainable breeding strategies. 5 CONCLUSION This study represents the first report of MQTL analysis for crown rust resistance in oat, identifying several major genomic regions associated with this critical trait. By com- bining data from multiple QTL mapping studies, the study gained a better understanding of the genetic architecture of crown rust resistance in the oat genome. A total of 23 reli- able MQTLs were identified across 12 chromosomes, with notable examples like MQTL(Pc)1D.3, MQTL(Pc)1D.1, and MQTL(Pc)5D.2, which consolidate a large number of ini- tial QTL, demonstrating their stability and importance for crown rust resistance. The analysis of candidate genes in these MQTLs revealed their involvement in key metabolic pathways, such as polyamine metabolism and amino acid breakdown, which play crucial roles in enhancing plant defense mechanisms. Crown rust resistance appears to be gov- erned by complex regulatory mechanisms, as demonstrated by identifying CAREs associated with hormone signaling, light response, and stress response. These findings have signifi- cant implications for oat breeding programs. MQTLs with narrow CIs and many integrated QTL can greatly improve MAS and genomic prediction models. This targeted approach accelerates the breeding process and ensures the development of oat varieties with durable and stable resistance to crown rust across diverse environmental conditions. MQTL and their associated candidate genes for crown rust resistance provide oat breeders with valuable genetic resources for developing cultivars with enhanced crown rust resistance. AU T H O R C O N T R I B U T I O N S Nikwan Shariatipour: Conceptualization; data curation; for- mal analysis; investigation; methodology; writing—original draft; writing—review and editing. Mahboobeh Yazdani: Data curation; investigation; methodology; writing—original draft; writing—review and editing. Anders Carlsson: Writing—review and editing. Therése Bengtsson: Funding acquisition; writing—review and editing. Shahryar F. Kianian: Investigation; writing—original draft; writing— review and editing. Marja Jalli: Funding acquisition; writing—review and editing. Mahbubjon Rahmatov: Conceptualization; funding acquisition; investigation; methodology; project administration; writing—original draft; writing—review and editing. The manuscript has been shared with all the PPP RobOat Consortium members, who have reviewed it and endorsed it for submission. A C K N O W L E D G M E N T S The authors acknowledge the support and resources the Swedish University of Agricultural Sciences (SLU) provided. The authors also acknowledge the Nordic countries through the Nordic Council of Ministers for supporting the PPP project RobOat (Robustness of Oat for the Nordic Region). The authors also thank Nick Sirijovski, Jihad Orabi, Anja Karine Ruud, Alf Ceplitis, and Morten Lillemo for their valuable feedback and suggestions for the manuscript. The name of the PPP RobOat Consortium members are as fol- 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense SHARIATIPOUR ET AL. 17 of 21The Plant Genome lows: Mahbubjon Rahmatov, Therése Bengtsson, Sajeevan Radha Sivarajan, and Anders Carlsson (Swedish Univer- sity of Agricultural Sciences, Sweden); Marja Jalli, Teija Tenhola-Roininen, Lidija Bitz, Lauri Jauhiainen, and Juho Hautsalo (Natural Resources Institute Finland, Finland); Muath Alsheikh, Espen Sørensen, and Susanne Windju (Graminor AS, Norway); Ahmed Jahoor, Jihad Orabi, and Lukas Oertelt (Nordic Seed, Denmark); Alf Ceplitis, Johanna Holmblad, and Pernilla Vallenback (Plant Breeding, Lant- männen, Sweden); Morten Lillemo and Anja Karine Ruud (Norwegian University of Life Sciences, Norway); Hrannar Smári Hilmarsson (Agricultural University of Iceland, Ice- land); Jan Svensson (NordGen, Sweden); Anders Borgen (Agrologica, Denmark); Hanna Haikka, Outi Manninen, and Merja Veteläinen (Boreal Plant Breeding Ltd., Finland); Nick Sirijovski (Food Science Organisation, Oatly AB, Sweden). C O N F L I C T O F I N T E R E S T S T AT E M E N T The authors declare no conflicts of interest. D AT A AVA I L A B I L I T Y S T AT E M E N T All the data generated or analyzed during the current study are included in this published article and its Supporting Information files. O R C I D Nikwan Shariatipour https://orcid.org/0000-0003-4174- 4375 Mahboobeh Yazdani https://orcid.org/0000-0002-9523- 5368 Anders Carlsson https://orcid.org/0000-0002-8525-9753 Therése Bengtsson https://orcid.org/0000-0003-4784- 1723 Shahryar F. Kianian https://orcid.org/0000-0003-4968- 3140 Marja Jalli https://orcid.org/0000-0003-3574-9639 Mahbubjon Rahmatov https://orcid.org/0000-0001-7491- 2836 R E F E R E N C E S Acevedo, M., Jackson, E. W., Chong, J., Rines, H. W., Harrison, S., & Bonman, J. M. (2010). Identification and validation of quantitative trait loci for partial resistance to crown rust in oat. Phytopathology, 100(5), 511–521. https://doi.org/10.1094/PHYTO-100-5-0511 Akhtar, S. S., Mekureyaw, M. F., Pandey, C., & Roitsch, T. (2020). Role of cytokinins for interactions of plants with microbial pathogens and pest insects. Frontiers in Plant Science, 10, 1777. https://doi.org/10. 3389/fpls.2019.01777 Aloryi, K. D., Okpala, N. E., Amo, A., Bello, S. F., Akaba, S., & Tian, X. (2022). A meta-quantitative trait loci analysis identified consensus genomic regions and candidate genes associated with grain yield in rice. Frontiers in Plant Science, 13, 1035851. https://doi.org/10.3389/ fpls.2022.1035851 Arcade, A., Labourdette, A., Falque, M., Mangin, B., Chardon, F., Charcosset, A., & Joets, J. (2004). BioMercator: Integrating genetic maps and QTL towards discovery of candidate genes. Bioinformatics, 20(14), 2324–2326. https://doi.org/10.1093/bioinformatics/bth230 Babiker, E. M., Gordon, T. C., Jackson, E. W., Chao, S., Harrison, S. A., Carson, M. L., Obert, D. E., & Bonman, J. M. (2015). Quantitative trait loci from two genotypes of oat (Avena sativa) conditioning resis- tance to Puccinia coronata. Phytopathology, 105, 239–245. https:// doi.org/10.1094/PHYTO-04-14-0114-R Bagni, N., & Tassoni, A. (2001). Biosynthesis, oxidation and conjugation of aliphatic polyamines in higher plants. Amino Acids, 20, 301–317. https://doi.org/10.1007/s007260170046 Barbosa, M. M., Federizzi, L. C., Milach, S. C., Martinelli, J. A., & Thomé, G. C. (2006). Molecular mapping and identification of QTL’s associated to oat crown rust partial resistance. Euphytica, 150, 257–269. https://doi.org/10.1007/s10681-006-9117-4 Bekele, W. A., Wight, C. P., Chao, S., Howarth, C. J., & Tinker, N. A. (2018). Haplotype-based genotyping-by-sequencing in oat genome research. Plant Biotechnology Journal, 16(8), 1452–1463. https://doi. org/10.1111/pbi.12888 Berlin, A., Wallenhammar, A., & Andersson, B. (2018). Population dif- ferentiation of puccinia coronata between hosts—Implications for the epidemiology of oat crown rust. European Journal of Plant Pathol- ogy, 152(4), 901–907. https://doi.org/10.1007/s10658-018-01605- x Biswas, D., Gain, H., & Mandal, A. (2023). MYB transcription factor: A new weapon for biotic stress tolerance in plants. Plant Stress, 10, 100252. https://doi.org/10.1016/j.stress.2023.100252 Blázquez, M. A. (2024). Polyamines: Their role in plant development and stress. Annual Review of Plant Biology, 75(1), 95–117. https:// doi.org/10.1146/annurev-arplant-070623-110056 Cai, J., & Aharoni, A. (2022). Amino acids and their derivatives medi- ating defense priming and growth tradeoff. Current Opinion in Plant Biology, 69, 102288. https://doi.org/10.1016/j.pbi.2022.102288 Chaffin, A. S., Huang, Y. F., Smith, S., Bekele, W. A., Babiker, E., Gnanesh, B. N., Foresman, B. J., Blanchard, S. G., Jay, J. J., Reid, R. W., & Wight, C. P. (2016). A consensus map in cultivated hexaploid oat reveals conserved grass synteny with substantial subgenome rear- rangement. The Plant Genome, 9(2), 2015–2010. https://doi.org/10. 3835/plantgenome2015.10.0102 Chardon, F., Virlon, B., Moreau, L., Falque, M., Joets, J., Decousset, L., Murigneux, A., & Charcosset, A. (2004). Genetic architecture of flowering time in maize as inferred from quantitative trait loci meta- analysis and synteny conservation with the rice genome. Genetics, 168(4), 2169–2185. https://doi.org/10.1534/genetics.104.032375 Chong, J., Gruenke, J., Dueck, R., Mayert, W., Fetch, J. M., & Mccartney, C. (2011). Virulence of Puccinia coronata f. sp. avenae in the East- ern Prairie Region of Canada during 2007–2009. Canadian Journal of Plant Pathology, 33(1), 77–87. https://doi.org/10.1080/07060661. 2010.546957 Chowdhury, R. N., Gordon, T., Babar, M. A., Harrison, S. A., Kianian, S. F., & Klos, K. E. (2024). Mapping crown rust resistance in the oat diploid accession PI 258731 (Avena strigosa). PLoS One, 19(2), e0295006. https://doi.org/10.1371/journal.pone.0295006 Cortleven, A., Leuendorf, J. E., Frank, M., Pezzetta, D., Bolt, S., & Schmülling, T. (2019). Cytokinin action in response to abiotic and biotic stresses in plants. Plant, Cell & Environment, 42(3), 998–1018. https://doi.org/10.1111/pce.13494 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense https://orcid.org/0000-0003-4174-4375 https://orcid.org/0000-0003-4174-4375 https://orcid.org/0000-0003-4174-4375 https://orcid.org/0000-0002-9523-5368 https://orcid.org/0000-0002-9523-5368 https://orcid.org/0000-0002-9523-5368 https://orcid.org/0000-0002-8525-9753 https://orcid.org/0000-0002-8525-9753 https://orcid.org/0000-0003-4784-1723 https://orcid.org/0000-0003-4784-1723 https://orcid.org/0000-0003-4784-1723 https://orcid.org/0000-0003-4968-3140 https://orcid.org/0000-0003-4968-3140 https://orcid.org/0000-0003-4968-3140 https://orcid.org/0000-0003-3574-9639 https://orcid.org/0000-0003-3574-9639 https://orcid.org/0000-0001-7491-2836 https://orcid.org/0000-0001-7491-2836 https://orcid.org/0000-0001-7491-2836 https://doi.org/10.1094/PHYTO-100-5-0511 https://doi.org/10.3389/fpls.2019.01777 https://doi.org/10.3389/fpls.2019.01777 https://doi.org/10.3389/fpls.2022.1035851 https://doi.org/10.3389/fpls.2022.1035851 https://doi.org/10.1093/bioinformatics/bth230 https://doi.org/10.1094/PHYTO-04-14-0114-R https://doi.org/10.1094/PHYTO-04-14-0114-R https://doi.org/10.1007/s007260170046 https://doi.org/10.1007/s10681-006-9117-4 https://doi.org/10.1111/pbi.12888 https://doi.org/10.1111/pbi.12888 https://doi.org/10.1007/s10658-018-01605-x https://doi.org/10.1007/s10658-018-01605-x https://doi.org/10.1016/j.stress.2023.100252 https://doi.org/10.1146/annurev-arplant-070623-110056 https://doi.org/10.1146/annurev-arplant-070623-110056 https://doi.org/10.1016/j.pbi.2022.102288 https://doi.org/10.3835/plantgenome2015.10.0102 https://doi.org/10.3835/plantgenome2015.10.0102 https://doi.org/10.1534/genetics.104.032375 https://doi.org/10.1080/07060661.2010.546957 https://doi.org/10.1080/07060661.2010.546957 https://doi.org/10.1371/journal.pone.0295006 https://doi.org/10.1111/pce.13494 18 of 21 SHARIATIPOUR ET AL.The Plant Genome Cui, Y., Cao, Q., Li, Y., He, M., & Liu, X. (2023). Advances in cis- element-and natural variation-mediated transcriptional regulation and applications in gene editing of major crops. Journal of Experimental Botany, 74(18), 5441–5457. https://doi.org/10.1093/jxb/erad248 Devanna, B. N., Sucharita, S., Sunitha, N. C., Anilkumar, C., Singh, P. K., Pramesh, D., Samantaray, S., Behera, L., Katara, J. L., Parameswaran, C., & Rout, P. (2024). Refinement of rice blast disease resistance QTLs and gene networks through meta-QTL analysis. Sci- entific Reports, 14(1), Article 16458. https://doi.org/10.1038/s41598- 024-64142-0 Díaz-Lago, J. E., Stuthman, D. D., & Leonard, K. J. (2003). Evaluation of components of partial resistance to oat crown rust using digital image analysis. Plant Disease, 87, 667–674. https://doi.org/10.1094/PDIS. 2003.87.6.667 Diévart, A., & Clark, S. E. (2004). LRR-containing receptors regulat- ing plant development and defense. Development, 131(2), 251–261. https://doi.org/10.1242/dev.00998 Flor, H. H. (1955). Host-parasite interaction in flax rust: Its genetics and other implications. Phytopathology, 45, 680–685. Ganie, S. A. (2021). Amino acids other than proline and their participa- tion in abiotic stress tolerance. In S. H. Wani, M. P. Gangola, & B. R. Ramadoss (Eds.), Compatible solutes engineering for crop plants facing climate change (pp. 47–96). Springer. https://doi.org/10.1007/ 978-3-030-80674-3_3 Gerlin, L., Baroukh, C., & Genin, S. (2021). Polyamines: Double agents in disease and plant immunity. Trends in Plant Science, 26(10), 1061– 1071. https://doi.org/10.1016/j.tplants.2021.05.007 Goffinet, B., & Gerber, S. (2000). Quantitative trait loci: A meta- analysis. Genetics, 155(1), 463–473. https://doi.org/10.1093/ genetics/155.1.463 Goyal, S., Saini, D. K., Kumar, P., Kaur, G., Praba, U. P., Karnatam, K. S., Chhabra, G., Singh, R., & Vikal, Y. (2024). Defining genomic landscape for identification of potential candidate resistance genes associated with major rice diseases through MetaQTL analysis. Jour- nal of Biosciences, 49(3), Article 76. https://doi.org/10.1007/s12038- 024-00460-9 Gupta, M., Choudhary, M., Singh, A., Sheoran, S., Singla, D., & Rakshit, S. (2023). Meta-QTL analysis for mining of candidate genes and con- stitutive gene network development for fungal disease resistance in maize (Zea mays L.). The Crop Journal, 11(2), 511–522. https://doi. org/10.1016/j.cj.2022.07.020 Gupta, P. K., Balyan, H. S., Chhuneja, P., Jaiswal, J. P., Tamhankar, S., Mishra, V. K., Bains, N. S., Chand, R., Joshi, A. K., Kaur, S., & Kaur, H. (2022). Pyramiding of genes for grain protein content, grain quality, and rust resistance in eleven Indian bread wheat culti- vars: A multi-institutional effort. Molecular Breeding, 42(4), Article 21. https://doi.org/10.1007/s11032-022-01277-w Hao, Z., Lv, D., Ge, Y., Shi, J., Weijers, D., Yu, G., & Chen, J. (2020). RIdeogram: Drawing SVG graphics to visualize and map genome- wide data on the idiograms. PeerJ Computer Science, 6, e251. https:// doi.org/10.7717/peerj-cs.251 Hedden, P. (2020). The current status of research on gibberellin biosyn- thesis. Plant and Cell Physiology, 61(11), 1832–1849. https://doi.org/ 10.1093/pcp/pcaa092 Hewage, K. A. H., Yang, J. F., Wang, D., Hao, G. F., Yang, G. F., & Zhu, J. K. (2020). Chemical manipulation of abscisic acid signaling: A new approach to abiotic and biotic stress management in agri- culture. Advanced Science, 7(18), 2001265. https://doi.org/10.1002/ advs.202001265 Hildebrandt, T. M., Nesi, A. N., Araújo, W. L., & Braun, H. P. (2015). Amino acid catabolism in plants. Molecular plant, 8(11), 1563–1579. https://doi.org/10.1016/j.molp.2015.09.005 Hoshida, H., Tanaka, Y., Hibino, T., Hayashi, Y., Tanaka, A., Takabe, T., & Takabe, T. (2000). Enhanced tolerance to salt stress in transgenic rice that overexpresses chloroplast glutamine synthetase. Plant Molecular Biology, 43, 103–111. https://doi.org/10.1023/ A:1006408712416 Hou, J., Jiang, P., Qi, S., Zhang, K., He, Q., Xu, C., Ding, Z., Zhang, K., & Li, K. (2016). Isolation and functional validation of salin- ity and osmotic stress inducible promoter from the maize type-II H+-pyrophosphatase gene by deletion analysis in transgenic tobacco plants. PLoS One, 11(4), e0154041. https://doi.org/10.1371/journal. pone.0154041 Huda, K. M. K., Banu, M. S. A., Pathi, K. M., & Tuteja, N. (2013). Reproductive organ and vascular specific promoter of the rice plasma membrane Ca2+ ATPase mediates environmental stress responses in plants. PLoS One, 8(3), e57803. https://doi.org/10.1371/journal.pone. 0057803 Huo, W. Q., Zhang, Z. Q., Ren, Z. Y., Zhao, J. J., Song, C. X., Wang, X. X., Pei, X. Y., Liu, Y. G., He, K. L., Zhang, F., & Li, X. Y. (2023). Unraveling genomic regions and candidate genes for multiple disease resistance in upland cotton using meta-QTL analysis. Heliyon, 9(8), e18731. https://doi.org/10.1016/j.heliyon.2023.e18731 Jackson, E. W., Obert, D. E., Menz, M., Hu, G., & Bonman, J. M. (2008). Qualitative and quantitative trait loci conditioning resistance to Puc- cinia coronata pathotypes NQMG and LGCG in the oat (Avena sativa L.) cultivars Ogle and TAM O-301. Theoretical and Applied Genetics, 116, 517–527. https://doi.org/10.1007/s00122-007-0687-x James, D., Borphukan, B., Fartyal, D., Achary, V. M. M., & Reddy, M. K. (2018). Transgenic manipulation of glutamine synthetase: A target with untapped potential in various aspects of crop improvement. In S. S. Gosal & S. H. Wani (Eds.), Biotechnology of crop improvement (pp. 367–416). Springer International Publishing AG. Jellen, E. N., Wight, C. P., Spannagl, M., Blake, V. C., Chong, J., Herrmann, M. H., Howarth, C. J., Huang, Y. F., Juqing, J., Katsiotis, A., Langdon, T., Li, C., Park, R., Tinker, N. A., & Sen, T. Z. (2024). A uniform gene and chromosome nomenclature system for oat (Avena spp.). Crop and Pasture Science, 75, CP23247. https://doi.org/10. 1071/CP23247 Kaur, A., Pati, P. K., Pati, A. M., & Nagpal, A. K. (2017). In-silico analysis of cis-acting regulatory elements of pathogenesis-related pro- teins of Arabidopsis thaliana and Oryza sativa. PLoS One, 12(9), e0184523. https://doi.org/10.1371/journal.pone.0184523 Kaur, G., & Pati, P. K. (2016). Analysis of cis-acting regulatory ele- ments of respiratory burst oxidase homolog (Rboh) gene families in Arabidopsis and rice provides clues for their diverse functions. Com- putational Biology and Chemistry, 62, 104–118. https://doi.org/10. 1016/j.compbiolchem.2016.04.002 Kaur, S., Das, A., Sheoran, S., & Rakshit, S. (2023). QTL meta- analysis: An Approach to detect robust and precise QTL. Tropical Plant Biology, 16(4), 225–243. https://doi.org/10.1007/s12042-023- 09335-z Kaur, S., Kaur, J., Mavi, G. S., Dhillon, G. S., Sharma, A., Singh, R., Devi, U., & Chhuneja, P. (2020). Pyramiding of high grain weight with stripe rust and leaf rust resistance in elite Indian wheat cultivar using a combination of marker assisted and phenotypic selec- tion. Frontiers in Genetics, 11, 593426. https://doi.org/10.3389/fgene. 2020.593426 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense https://doi.org/10.1093/jxb/erad248 https://doi.org/10.1038/s41598-024-64142-0 https://doi.org/10.1038/s41598-024-64142-0 https://doi.org/10.1094/PDIS.2003.87.6.667 https://doi.org/10.1094/PDIS.2003.87.6.667 https://doi.org/10.1242/dev.00998 https://doi.org/10.1007/978-3-030-80674-3_3 https://doi.org/10.1007/978-3-030-80674-3_3 https://doi.org/10.1016/j.tplants.2021.05.007 https://doi.org/10.1093/genetics/155.1.463 https://doi.org/10.1093/genetics/155.1.463 https://doi.org/10.1007/s12038-024-00460-9 https://doi.org/10.1007/s12038-024-00460-9 https://doi.org/10.1016/j.cj.2022.07.020 https://doi.org/10.1016/j.cj.2022.07.020 https://doi.org/10.1007/s11032-022-01277-w https://doi.org/10.7717/peerj-cs.251 https://doi.org/10.7717/peerj-cs.251 https://doi.org/10.1093/pcp/pcaa092 https://doi.org/10.1093/pcp/pcaa092 https://doi.org/10.1002/advs.202001265 https://doi.org/10.1002/advs.202001265 https://doi.org/10.1016/j.molp.2015.09.005 https://doi.org/10.1023/A:1006408712416 https://doi.org/10.1023/A:1006408712416 https://doi.org/10.1371/journal.pone.0154041 https://doi.org/10.1371/journal.pone.0154041 https://doi.org/10.1371/journal.pone.0057803 https://doi.org/10.1371/journal.pone.0057803 https://doi.org/10.1016/j.heliyon.2023.e18731 https://doi.org/10.1007/s00122-007-0687-x https://doi.org/10.1071/CP23247 https://doi.org/10.1071/CP23247 https://doi.org/10.1371/journal.pone.0184523 https://doi.org/10.1016/j.compbiolchem.2016.04.002 https://doi.org/10.1016/j.compbiolchem.2016.04.002 https://doi.org/10.1007/s12042-023-09335-z https://doi.org/10.1007/s12042-023-09335-z https://doi.org/10.3389/fgene.2020.593426 https://doi.org/10.3389/fgene.2020.593426 SHARIATIPOUR ET AL. 19 of 21The Plant Genome Kearsey, M. J., & Farquhar, A. G. L. (1998). QTL analysis in plants; where are we now? Heredity, 80(2), 137–142. https://doi.org/10.1046/ j.1365-2540.1998.00500.x Kieber, J. J., & Schaller, G. E. (2018). Cytokinin signaling in plant devel- opment. Development, 145(4), dev149344. https://doi.org/10.1242/ dev.149344 Kim, Y. N., Lee, J. H., & Kim, Y. C. (2024). First report of oat crown rust caused by Puccinia coronata f. sp. avenae in South Korea. Journal of Phytopathology, 172, e13295. https://doi.org/10.1111/jph. 13295 Klos, K. E., Yimer, B. A., Babiker, E. M., Beattie, A. D., Bonman, J. M., Carson, M. L., Chong, J., Harrison, S. A., Ibrahim, A. M. H., Kolb, F. L., McCartney, C. A., McMullen, M., Fetch, J. M., Mohammadi, M., Murphy, J. P., & Tinker, N. A. (2017). Genome-wide associa- tion mapping of crown rust resistance in oat elite germplasm. The Plant Genome, 10(2), plantgenome2016.10.0107. https://doi.org/10. 3835/plantgenome2016.10.0107 Kolberg, L., Raudvere, U., Kuzmin, I., Adler, P., Vilo, J., & Peterson, H. (2023). g:Profiler—Interoperable web service for functional enrich- ment analysis and gene identifier mapping (2023 update). Nucleic Acids Research, 51(W1), W207–W212. https://doi.org/10.1093/nar/ gkad347 Kumar, I. S., & Nadarajah, K. (2020). A meta-analysis of quantitative trait loci associated with multiple disease resistance in rice (Oryza sativa L.). Plants, 9(11), 1491. https://doi.org/10.3390/plants9111491 Kumar, S., Saini, D. K., Jan, F., Jan, S., Tahir, M., Djalovic, I., Latkovic, D., Khan, M. A., Kumar, S., Vikas, V. K., & Kumar, U. (2023). Com- prehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat. BMC Genomics, 24(1), Article 259. https://doi.org/10.1186/s12864-023-09336-y Leonard, K. J. (2002). Oat lines with effective adult plant resistance to crown rust. Plant Disease, 86, 593–598. https://doi.org/10.1094/ PDIS.2002.86.6.593 Li, R., Zhu, F., & Duan, D. (2020). Function analysis and stress-mediated cis-element identification in the promoter region of VqMYB15. Plant Signaling and Behavior, 15(7), 1773664. https://doi.org/10.1080/ 15592324.2020.1773664 Lin, Y., Gnanesh, B. N., Chong, J., Chen, G., Beattie, A. D., Fetch, J. W. M., Kutcher, H. R., Eckstein, P. E., Menzies, J. G., Jackson, E. W., & McCartney, C. A. (2014). A major quantitative trait locus conferring adult plant partial resistance to crown rust in oat. BMC Plant Biology, 14, Article 250. https://doi.org/10.1186/s12870-014-0250-2 Liu, G., Ji, Y., Bhuiyan, N. H., Pilot, G., Selvaraj, G., Zou, J., & Wei, Y. (2010). Amino acid homeostasis modulates salicylic acid–associated redox status and defense responses in Arabidopsis. The Plant Cell, 22(11), 3845–3863. https://doi.org/10.1105/tpc.110.079392 Liu, Y., Kou, J., Takano, T., Liu, S., & Bu, Y. (2017). Overexpression of AtGS1. 5 gene improves salt stress tolerance during seed germination in Arabidopsis thaliana. Molecular Soil Biology, 8(1), 1–16. https:// doi.org/10.5376/MSB.2017.08.0001 Löffler, M., Schön, C. C., & Miedaner, T. (2009). Revealing the genetic architecture of FHB resistance in hexaploid wheat (Triticum aestivum L.) by QTL meta-analysis. Molecular Breeding, 23(3), 473–488. https://doi.org/10.1007/s11032-008-9250-y Marshall, A., Cowan, S., Edwards, S., Griffiths, I., Howarth, C., Langdon, T., & White, E. (2013). Crops that feed the world 9. Oats—A cereal crop for human and livestock feed with indus- trial applications. Food Security, 5, 13–33. https://doi.org/10.1007/ s12571-012-0232-x Martinez, A. K., Soriano, J. M., Tuberosa, R., Koumproglou, R., Jahrmann, T., & Salvi, S. (2016). Yield QTLome distribution cor- relates with gene density in maize. Plant Science, 242, 300–309. https://doi.org/10.1016/j.plantsci.2015.09.022 McMullen, M. S., Doehlert, D. C., & Miller, J. D. (2005). Registration of ‘HiFi’ oat. Crop Science, 45, 1664–1664. https://doi.org/10.2135/ cropsci2005.003 McNish, I. G., Zimmer, C. M., Susko, A. Q., Heuschele, D. J., Tiede, T., Case, A. J., & Smith, K. P. (2020). Mapping crown rust resistance at multiple time points in elite oat germplasm. The Plant Genome, 13(1), e20007. https://doi.org/10.1002/tpg2.20007 Merlot, S., Gosti, F., Guerrier, D., Vavasseur, A., & Giraudat, J. (2001). The ABI1 and ABI2 protein phosphatases 2C act in a negative feed- back regulatory loop of the abscisic acid signalling pathway. The Plant Journal, 25(3), 295–303. https://doi.org/10.1046/j.1365-313x.2001. 00965.x Miller, M. E., Nazareno, E. S., Rottschaefer, S. M., Riddle, J., Pereira, D. A., Li, F., Nguyen, H., Henningsen, E., Persoons, A., Saunders, D., Stukenbrock, E., Dodds, P., Kianian, S., & Figueroa, M. (2020). Increased virulence of Puccinia coronata f. sp. Avenae popula- tions through allele frequency changes at multiple putative avr loci. PLoS Genetics, 16(12), e1009291. https://doi.org/10.1371/journal. pgen.1009291 Montilla-Bascón, G., Rubiales, D., Altabella, T., & Prats, E. (2016). Free polyamine and polyamine regulation during pre-penetration and pene- tration resistance events in oat against crown rust (Puccinia coronata f. sp. avenae). Plant Pathology, 65(3), 392–401. https://doi.org/10. 1111/ppa.12423 Moreau, E. L., Riddle, J. M., Nazareno, E. S., & Kianian, S. F. (2024). Three decades of rust surveys in the United States reveal drastic vir- ulence changes in oat crown rust. Plant Disease, 108(5), 1298–1307. https://doi.org/10.1094/PDIS-09-23-1956-RE Nazareno, E. S., Fiedler, J., Ardayfio, N. K., Miller, M. E., Figueroa, M., & Kianian, S. (2023). Genetic analysis and physical mapping of oat adult plant resistance loci against Puccinia coronata f. sp. avenae. Phytopathology, 113(7), 1307–1316. https://doi.org/10.1094/ PHYTO-10-22-0395-R Nazareno, E. S., Fiedler, J., Miller, M. E., Figueroa, M., & Kianian, S. F. (2022). A reference-anchored oat linkage map reveals quantita- tive trait loci conferring adult plant resistance to crown rust (Puccinia coronata f. sp. avenae). Theoretical and Applied Genetics, 135(10), 3307–3321. https://doi.org/10.1007/s00122-022-04128-6 Nazareno, E. S., Li, F., Smith, M., Park, R. F., Kianian, S. F., & Figueroa, M. (2018). Puccinia coronata f. sp. avenae: A threat to global oat production. Molecular Plant Pathology, 19, 1047–1060. https://doi. org/10.1111/mpp.12608 Ohm, H., & Shaner, G. (1992). Breeding oat for resistance to diseases. In H. Marshall & M. Sorells (Eds.), Oat science and technology (pp. 657–698). ASA/CSA. https://doi.org/10.2134/agronmonogr33.c18 Pal, M., & Janda, T. (2017). Role of polyamine metabolism in plant pathogen interactions. Journal of Plant Science and Phytopathology, 1(2), 095–100. 10.29328/journal.jpsp.1001012 Pal, N., Jan, I., Saini, D. K., Kumar, K., Kumar, A., Sharma, P. K., Kumar, S., Balyan, H. S., & Gupta, P. K. (2022). Meta-QTLs for multiple disease resistance involving three rusts in common wheat (Triticum aestivum L.). Theoretical and Applied Genetics, 135(7), 2385–2405. https://doi.org/10.1007/s00122-022-04119-7 Park, R. F., Boshoff, W. H. P., Cabral, A. L., Chong, J., Martinelli, J. A., McMullen, M. S., Fetch, J. W. M., Paczos-Grzeda, E., Prats, E., 19403372, 2025, 2, D ow nloaded from https://acsess.onlinelibrary.w iley.com /doi/10.1002/tpg2.70059 by L uonnonvarakeskus, W iley O nline L ibrary on [14/08/2025]. See the T erm s and C onditions (https://onlinelibrary.w iley.com /term s-and-conditions) on W iley O nline L ibrary for rules of use; O A articles are governed by the applicable C reative C om m ons L icense https://doi.org/10.1046/j.1365-2540.1998.00500.x https://doi.org/10.1046/j.1365-2540.1998.00500.x https://doi.org/10.1242/dev.149344 https://doi.org/10.1242/dev.149344 https://doi.org/10.1111/jph.13295 https://doi.org/10.1111/jph.13295 https://doi.org/10.3835/plantgenome2016.10.0107 https://doi.org/10.3835/plantgenome2016.10.0107 https://doi.org/10.1093/nar/gkad347 https://doi.org/10.1093/nar/gkad347 https://doi.org/10.3390/plants9111491 https://doi.org/10.1186/s12864-023-09336-y https://doi.org/10.1094/PDIS.2002.86.6.593 https://doi.org/10.1094/PDIS.2002.86.6.593 https://doi.org/10.1080/15592324.2020.1773664 https://doi.org/10.1080/15592324.2020.1773664 https://doi.org/10.1186/s12870-014-0250-2 https://doi.org/10.1105/tpc.110.079392 https://doi.org/10.5376/MSB.2017.08.0001 https://doi.org/10.5376/MSB.2017.08.0001 https://doi.org/10.1007/s11032-008-9250-y https://doi.org/10.1007/s12571-012-0232-x https://doi.org/10.1007/s12571-012-0232-x https://doi.org/10.1016/j.plantsci.2015.09.022 https://doi.org/10.2135/cropsci2005.003 https://doi.org/10.2135/cropsci2005.003 https://doi.org/10.1002/tpg2.20007 https://doi.org/10.1046/j.1365-313x.2001.00965.x https://doi.org/10.1046/j.1365-313x.2001.00965.x https://doi.org/10.1371/journal.pgen.1009291 https://doi.org/10.1371/journal.pgen.1009291 https://doi.org/10.1111/ppa.12423 https://doi.org/10.1111/ppa.12423 https://doi.org/10.1094/PDIS-09-23-1956-RE https://doi.org/10.1094/PHYTO-10-22-0395-R https://doi.org/10.1094/PHYTO-10-22-0395-R https://doi.org/10.1007/s00122-022-04128-6 https://doi.org/10.1111/mpp.12608 https://doi.org/10.1111/mpp.12608 https://doi.org/10.2134/agronmonogr33.c18 https://doi.org/10.29328/journal.jpsp.1001012 https://doi.org/10.1007/s00122-022-04119-7 20 of 21 SHARIATIPOUR ET AL.The Plant Genome Roake, J., Sowa, S., Ziems, L., & Singh, D. (2022). Breeding oat for resistance to the crown rust pathogen Puccinia coronata f. sp. avenae: Achievements and prospects. Theoretical and Applied Genetics, 135, 3709–3734. https://doi.org/10.1007/s00122-022-04121-z Pascual, L., Albert, E., Sauvage, C., Duangjit, J., Bouchet, J. P., Bitton, F., Desplat, N., Brunel, D., Le, M. C., Ranc, N., & Bruguier, L. (2016). Dissecting quantitative trait variation in the resequencing era: Complementarity of bi-parental, multi-parental and association pan- els. Plant Science, 242, 120–130. https://doi.org/10.1016/j.plantsci. 2015.06.017 Pieterse, C. M., Zamioudis, C., Berendsen, R. L., Weller, D. M., Van Wees, S. C., & Bakker, P. A. (2014). Induced systemic resistance by beneficial microbes. Annual Review of Phytopathology, 52(1), 347– 375. https://doi.org/10.1146/annurev-phyto-082712-102340 Portyanko, V. A., Chen, G., Rines, H. W., Phillips, R. L., Leonard, K. J., Ochocki, G. E., & Stuthman, D. D. (2005). Quantitative trait loci for partial resistance to crown rust, Puccinia coronata, in cultivated oat, Avena sativa L. Theoretical and Applied Genetics, 111, 313–324. https://doi.org/10.1007/s00122-005-2024-6 Prasad, R. (2022). Cytokinin and its key role to enrich the plant nutrients and growth under adverse conditions-an update. Frontiers in Genetics, 13, 883924. https://doi.org/10.3389/fgene.2022.883924 Rai, G. K., Khanday, D. M., Choudhary, S. M., Kumar, P., Kumari, S., Martínez-Andújar, C., Martínez-Melgarejo, P. A., Rai, P. K., & Pérez- Alfocea, F. (2024). Unlocking nature’s stress buster: Abscisic acid’s crucial role in defending plants against abiotic stress. Plant Stress, 11, 100359. https://doi.org/10.1016/j.stress.2024.100359 Rehman, M., Saeed, M. S., Fan, X., Salam, A., Munir, R., Yasin, M. U., Khan, A. R., Muhammad, S., Ali, B., Ali, I., & Khan, J. (2023). The multifaceted role of jasmonic acid in plant stress miti- gation: An overview. Plants, 12(23), 3982. https://doi.org/10.3390/ plants12233982 Rimbaud, L., Papaïx, J., Rey, J. F., Moury, B., Barrett, L. G., & Thrall, P. H. (2023). Durable resista