QTL mapping of oat crown rust resistance in Australian fields and identification of a seedling resistance locus in oat line GS7

QTL mapping of oat crown rust resistance in Australian fields and identification of a seedling resistance locus in oat line GS7 | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results QTL mapping of oat crown rust resistance in Australian fields and identification of a seedling resistance locus in oat line GS7 Duong T. Nguyen , David Lewis , View ORCID Profile Eva C. Henningsen , Zhouyang Su , Rohit Mago , Jana Sperschneider , Peter N. Dodds , Allan Rattey , Belayneh A. Yimer , Kathy Esvelt Klos , View ORCID Profile Melania Figueroa doi: https://doi.org/10.1101/2025.08.24.671654 Duong T. Nguyen 1 Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food , Canberra, ACT 2600, Australia 2 Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food , Adelaide, SA 5064, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site David Lewis 1 Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food , Canberra, ACT 2600, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Eva C. Henningsen 1 Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food , Canberra, ACT 2600, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Eva C. Henningsen Zhouyang Su 1 Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food , Canberra, ACT 2600, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rohit Mago 1 Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food , Canberra, ACT 2600, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jana Sperschneider 1 Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food , Canberra, ACT 2600, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Peter N. Dodds 1 Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food , Canberra, ACT 2600, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Allan Rattey 3 Intergrain , 19 Ambitious Link, Bibra Lake, WA 6163, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site Belayneh A. Yimer 4 USDA-ARS, Small Grains and Potato Germplasm Research Unit , 1691 South 2700 West, Aberdeen, ID 83210, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Kathy Esvelt Klos 4 USDA-ARS, Small Grains and Potato Germplasm Research Unit , 1691 South 2700 West, Aberdeen, ID 83210, USA Find this author on Google Scholar Find this author on PubMed Search for this author on this site Melania Figueroa 1 Commonwealth Scientific and Industrial Research Organisation, Agriculture and Food , Canberra, ACT 2600, Australia Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Melania Figueroa For correspondence: melania.figueroa{at}csiro.au Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract The development of oat cultivars with resistance to crown rust caused by Puccinia coronata f. sp. A venae ( Pca ) is key for sustainable disease control. This study examined two recombinant inbred line populations, Provena x GS7 and Boyer x GS7, to identify adult plant resistance QTL in Australian fields. Seven distinct QTL associated with rust resistance were identified, with KASP markers developed for single nucleotide polymorphisms (SNPs) tightly linked to the four most significant QTL on chromosomes 4A and 7A. A major QTL named QPc_GS7_4A.2 with a resistance allele derived from line GS7 was mapped to chromosome 4A, overlapping with genomic regions previously associated with both resistance gene Pc61 and adult plant resistance. Genetic mapping for rust resistance at seedling stage using a subset of Provena x GS7 lines with contrasting alleles at QPc_GS7_4A.2 confirmed the role of this locus on seedling resistance, likely by Pc61 . Furthermore, we found similar resistance profiles between GS7 and the Pc61 differential line against 20 Pca isolates at the seedling stage. Haplotype analysis of QPc_GS7_4A.2 in the oat crown rust differential set and an oat collection revealed the resistance haplotype in lines previously postulated to carry resistance gene Pc61 . These results suggest that the QTL QPc_GS7_4A.2 is closely linked to the Pc61 locus on chromosome 4A. The KASP markers associated with Pc61 and QTL identified in this study will be valuable tools, allowing breeders to efficiently integrate the resistance allele for gene combinations in new cultivars, particularly in regions where Pc61 remains effective. Introduction Crown rust, a fungal disease caused by Puccinia coronata f. sp. avenae ( Pca ) is a significant threat to oat production worldwide ( Nazareno et al. 2018 ; Simons 1985 ). Major resistance ( R ) genes conferring race-specific resistance against Pca have been widely used in oat breeding to safeguard crops from this disease. Such R genes usually encode specific receptors in the host that recognise effector molecules, which can vary between pathogen races, explaining the race-specificity ( Dodds et al. 2024 ; Jones et al. 2024 ). This type of rust resistance is usually expressed from the seedling stage and lasts throughout the plant life cycle, so it is called all-stage resistance (ASR) ( Periyannan et al. 2017 ). However, the effectiveness of ASR genes often diminishes over time due to the emergence of variants in the pathogen population to overcome resistance ( Figueroa et al. 2020 ). Oat cultivars with ASR genes for crown rust typically lose resistance within five years of release, a pattern likely to persist with continued reliance on race-specific resistance genes ( Carson 2008 ). To combat this challenge, researchers and breeders have switched their focus to identifying and harnessing sources of more durable resistance. Another class of rust R genes defined in cereals confer non-race specific resistance associated with a partial resistance phenotype. Partial resistance is considered highly effective in managing the disease because it slows down the evolution of pathogen virulence. Unlike race-specific ASR, partial resistance is not limited to specific pathogen races and operates at the adult plant stages, therefore it is called adult plant resistance (APR) ( Ellis et al. 2014 ; Periyannan et al. 2017 ). While APR does not entirely inhibit fungal sporulation, it curtails pustule size, and spore production, and prolongs the latent period ( Portyanko et al. 2005 ). However, incorporating APR into breeding programs presents challenges due to its quantitative nature, and multiple loci must be combined to achieve high levels of resistance ( Nazareno et al. 2022 ). Therefore, introgression of these novel alleles into elite germplasm is a lengthy process. Nevertheless, several major loci governing APR have been identified offering potential shortcuts in breeding for durable disease resistance. For instance, the genes Lr34 and Lr67 in wheat encode membrane transporters that suppress rust growth independently of specific recognition ( Krattinger et al. 2009 , 2016 ; Milne et al. 2019 ; Moore et al. 2015 ). In oat, of approximately 100 loci conferring resistance to Pca that have been catalogued to date, six are associated with APR ( Pc27 , Pc28 , Pc69 , Pc72 , Pc73 , Pc74 ). However, the chromosomal locations for all these genes are unknown ( Carson 2017 ; Harder et al. 1984 ). Additional sources of APR have been postulated in various cultivars ( Cabral et al. 2011 ; Heagle and Moore 1970 ; Luke et al. 1972 ; Upadhyaya and Baker 1962 ; Welsh et al. 1953 ). Although most of these APR sources remain uncharacterised, and their underlying genetic mechanisms are unknown. So far, few molecular markers associated with APR in oat have been developed for use in breeding and selection ( Lin et al. 2014 ; Nazareno et al. 2022 ; Rines et al. 2018 ). Previously, Babiker et al. (2015) identified three APR QTL in three mapping populations of recombinant inbred lines (RILs) derived from crossing the partially resistant sources, CDC Boyer (referred to as Boyer hereafter) and a breeding line 94197A1-9-2-2-2-5 (referred to as GS7 hereafter) with the susceptible cultivar Provena, and with each other (Boyer x GS7). In their study, Babiker et al. (2015) found that Boyer contributed the resistance alleles of two QTL located on chromosomes 12D (intervals of approximately 15.8 cM) and 19A (9.7 cM) (based on the cytology-based nomenclature by Sanz et al. 2010), while GS7 contributed one QTL on chromosome 13A (15.4 cM). These correspond to positions on chromosomes 2Ds, 4As and 7As, respectively, under the uniform nomenclature system of Jellen et al. (2024) . This study aimed to evaluate the effectiveness of APR resistance loci from Boyer and GS7 under Australian field conditions using the same RIL populations. DArTSeq genotyping was employed to enhance marker density to resolve QTL regions. A total of seven distinct QTL associated with oat crown rust resistance were detected, and KASP markers were developed for single nucleotide polymorphisms (SNPs) tightly linked to the four most significant QTL on chromosomes 4A and 7A. KASP assays were implemented to assess the presence of the resistance alleles in an oat collection of 182 lines, including 150 lines from Nguyen et al. (2023) and 32 lines postulated to carry APR. A strong QTL from GS7 linked to crown rust resistance was identified on homoeologous regions of chromosomes 4A and 4D, overlapping with genomic regions previously linked to the all-stage resistance gene Pc61 and adult plant resistance. Genetic mapping for rust resistance at the seedling stage using a subset of Provena x GS7 RILs with contrasting alleles at QPc_GS7_4A.2 confirmed that this locus contains a seedling resistance gene. Haplotype analysis using SNPs linked to this QTL in the differential set ( Henningsen et al. 2024 ) and designed KASP markers in the oat collection identified the QPc_GS7_4A.2 resistance haplotype as highly specific to Pc61 carriers. The results suggest a strong linkage or potential identity between the QPc_GS7_4A.2 on chromosome 4A and the Pc61 locus. Overall, these findings underscore the potential of GS7 and Boyer as a valuable sources of crown rust resistance, with the identified QTL and associated KASP markers providing insights to uncover underlying mechanisms and support marker-assisted selection in breeding programs. Materials and methods Plant material The recombinant inbred lines (RILs) from the two mapping populations, ‘Provena x 94197A1-9-2-2-2-5’ and ‘CDC Boyer x 94197A1-9-2-2-2-5’, referred to as Provena x GS7 (n=91) and Boyer x GS7 (n=98), respectively, were developed and described by Babiker et al. (2015) through eight generations of selfing after F2 generation. These RILs were sourced from the USDA Agricultural Research Service, Aberdeen, ID, USA. This study also includes an oat collection of 182 lines, comprising 32 lines postulated to carry APR and 150 lines recently compiled by Nguyen et al. (2023). These lines were sourced from USDA-ARS (St. Paul, MN, USA), the Australian Grain Genebank (AGG), and the CSIRO Avena seed stock. The list of plant materials is included in Supplementary File 1, Table S1 and Table S2 . Disease resistance phenotyping Field infection data for Provena x GS7 and Boyer x GS7 RILs was collected from field trials in Australia at Manjimup, WA (33.24° S, 116.16° E), in 2023 and 2024, and Cobbitty, NSW (33.99° S, 150.69° E), in 2024. Data for the oat collection was recorded in Manjimup (2023) and Cobbitty (2024). In all trials, lines were planted 1-row wide x ∼0.5m long. In the Manjimup trials in 2023 and 2024 (MJ23 and MJ24), rust races (triplet codes as described by Park 2000 ) 0001-2 and 0005-0 were used, while in the Cobbitty trial 2024 (CB24), the pathotypes “4473-4,6,10, Bett, Barc”, “0767-3,4,5,6,10,Wa,Vo”, and “3707-1,4,5,6,7,10,12,Wa,Nu,Gw,Ge,Dr,Al” were utilised as they are highly frequently found on those regions. Around the flowering between Zadoks growth stage GS59 - GS80 ( Zadoks et al. 1974 ), rust infection severity was scored using the 0-to-100 modified Cobb scale ( Peterson et al. 1948 ) in Manjimup, whereas in Cobbitty, severity was rated on a 1–9 scale. To enable comparison between the two locations, the 1-9 scale scores from CB24 were converted into percent severities following the method of Bariana et al. (2007) . Rust infection assays at the seedling stage were conducted in growth cabinets on a subset of 30 RILs from the Provena x GS7 population, which were selected for having contrasting alleles at the QTL QPc_GS7_4A.2 identified through field-based QTL mapping. These lines were tested against Pca isolate 22WA54, which was virulent Provena but avirulent to GS7 to evaluate the effect of the locus QPc_GS7_4A.2 at the seedling stage. In addition, similar seedling assays were also performed on GS7, Provena, Pc60, Pc61, and Swan oat lines against 20 Pca isolates to compare their resistance profiles ( Henningsen et al. 2024 ; Nguyen et al. 2025 ). Plants were grown under controlled conditions (23/18°C, 16/8hrs, light/dark) and Pca spore samples were applied to plants 10 days after sowing, and infection scores were recorded 10 days post-inoculation. Seedling infection and scoring methods were described by Miller et al. (2020) , with infection type scores (“0”, “0;”, “;”, “;C”, “1;”, “1”, “2”, “3”, “3+”, “4”) converted to a 0-9 numeric scale (0, 1, 2, 3, 4, 5, 6, 7, 8, 9) respectively for plotting as heatmaps using ComplexHeatmap v2.20.0 in R v4.4.0 ( Gu 2022 ). Genotyping Five seeds from each oat line were sent to Diversity Arrays Technology Pty Ltd. (Canberra, Australia; https://www.diversityarrays.com/ ) for DNA extraction and genotyping using their proprietary genome complexity reduction-based sequencing technology. To identify SNP markers, the sequences obtained from DArTSeq were aligned against the reference genome sequence Avena sativa OT3098 v2 (PepsiCo, https://wheat.pw.usda.gov/jb?data=/ggds/oat-ot3098v2-pepsico ), deposited in the GrainGenes database ( Yao et al. 2022 ), using BLAST ( Altschul et al. 1990 ) with an expected value (E) threshold of less than 5e-7 and sequence identity greater than 70%. Only markers that matched a genomic location were kept. The SNP dataset was filtered in dartR (v2.9.7) by first removing loci with all missing data, then excluding monomorphic loci to retain only polymorphic markers ( Gruber et al. 2018 ). A call rate filter removed loci with over 50% missing data and markers with a minor allele frequency below 0.01 were discarded. The genotypic data of Provena x GS7 and Boyer x GS7 RILs were transformed to a parent-based format (ABH) by using the GenosToABH plugin from TASSEL (v.5.2.64), using the codes A: male parent, B: female parent, H: heterozygous. Data imputation was conducted using ABHgenotypeR v.1.0.1 R package ( Furuta et al. 2017 ). Linkage map construction for Provena x GS7 and Boyer x GS7 RILs Linkage group construction was carried out using the “mstmap” algorithm implemented in the R package ASMap v.1.0.7 R package ( Taylor and Butler 2017 ). The initial group assignment was based on p -value thresholds of 1e-19 for Provena x GS7 and 1e-21 for Boyer x GS7, with the “Kosambi” function used for genetic distances. Markers not assigned to linkage groups were removed. Subsequently, chromosome names were assigned to linkage groups by identifying the most frequent chromosome location of DArTSeq markers mapped to the OT3098 v2 reference genome. Linkage groups with the same chromosome name were then merged using the “mergeCross” function with a gap threshold of 10 cM, and marker order was refined through a second round of “mstmap” using a p-value threshold of 1e-6. Linkage groups with zero length or fewer than seven markers were filtered out. Unmerged linkages were merged with other already-merged linkage groups sharing the same chromosome name using a permissive p-value threshold of 1e-2, considering orientation and correlation with the physical map. Genetic distances were recalculated using the “Kosambi” function after merging. The correlation between genetic map marker order and the reference genome was calculated using a Pearson’s correlation test and visualized with the “ggplot2” R package ( Wickham 2016 ). QTL mapping QTL mapping was performed for the Provena x GS7 and Boyer x GS7 RILs using the genetic maps and field phenotypic data. For the subset of RILs from Provena x GS7 (n=30), mapping was carried out using the genetic map and seedling resistance data. All analyses were conducted using composite interval mapping (CIM) with the R/qtl package (v1.66) (Haley-Knott with forward selection to three markers and a window size of 10 cM) ( Broman and Sen 2009 ). The threshold for the logarithm of odds (LOD) for significant QTL declaration was defined by 1,000 permutations at p ≤0.05. The custom interactive R script for QTL mapping, developed in RStudio 2024.04.2, is based on the R/qtl manual by Broman and Sen (2009) . The percentage of genotypic variance explained (GVE) was estimated using the formula 1 – 10 −2 LOD/ n , where n is the sample size and LOD is the LOD score of the QTL (Broman et al. 2003). Comparative mapping analysis The flanking sequences of significant markers associated with QTL from previous studies ( Babiker et al. 2015 ; Klos et al. 2017 ; Nazareno et al. 2022 ; Rines et al. 2018 ; Wight et al. 2004) were searched against the reference genome OT3098 v2 by BLAST using Geneious Prime® 2022.2.2. The flanking sequences of markers were taken from NCBI GenBank ( https://www.ncbi.nlm.nih.gov/nuccore/ ) and T3/Oat ( https://oat.triticeaetoolbox.org/ ). Syntenic relationships between chr4A and 4D were established and visualised through a Comparative Genomics Platform (CoGe; https://genomevolution.org/coge/SynMap.pl ) and the SynMap2 tool ( Haug-Baltzell et al. 2017 ). KASP assays We designed KASP markers for the SNPs closely linked to identified QTL: SNP-350765064_4A , SNP-351486359_4A , and SNP-352361337_4A for QPc_GS7_4A.1, SNP-459779133_4A, SNP-459972095_4A, and SNP-460562184_4A for QPc_GS7_4A.2, SNP-4039736_7A and SNP-411798_7A for QPc_GS7_7A, and SNP-67318388_7A and SNP-69787185_7A for QPc_Provena_7A/QPc_Boyer_7A . The flanking sequences of these SNPs were imported into the Kraken™ software system for marker design using the default parameters (LGC Biosearch Technologies, UK; https://www.biosearchtech.com/ ). KASP assays were performed in the SNPline PCR Genotyping System (LGC, Middlesex, United Kingdom), following the methods described by Shi et al. (2023) . KASP primer sequences are included in Supplementary File 1 Table S3. Haplotype visualisation Haplotype of QTL QPc_GS7_4A.2 identified in chr4A from mapping analysis was visualised in the oat collection and the differential set using Flapjack ( Milne et al. 2010 ). The QPc_GS7_4A.2 haplotype in the oat collection is visualised based on the result of KASP genotyping assays of KASP_4A_459779133, KASP_4A_459972095, and KASP_4A_460562184; and the QPc_GS7_4A.2 haplotype in the differential set ( Henningsen et al. 2024 ) was observed based on the genotype of SNP markers SNP-459779133_4A, SNP-459972095_4A , and SNP-460562184_4A taken from DArTSeq genotypic data (Nguyen et al. 2023). Pedigree evaluations The pedigree records of oat lines of interest were obtained from Fitzsimmons et al. (1983) , and T3/Oat database ( Morales et al. 2022 ), with information extracted from the “Pedigrees of Oat Lines” POOL database ( https://triticeaetoolbox.org/POOL/index_db.php ; Tinker and Deyl 2005 ). Results Field assessment of rust infection severity The Pca race used in Manjimup fields 2023 and 2024 (MJ23 and MJ24), was 0001-2 and 0005-0, respectively, while the Cobbitty 2024 (CB24) nursery included a mix of races: 0767-3,4,5,6,10,Wa, Vo; 3707-1,4,5,6,7,10,12,Wa,Nu,Gw,Ge,Dr,Al; and 4473-4,6,10, Bett, Barc. The virulence profiles of these races were determined by nursery managers, following the methods described by Park (2000) and presented in Supplementary File 1 Table S4 . The rust races in CB24 were broadly virulent, exhibiting virulence against 29 differential lines. In contrast, the races in MJ23 and MJ24 are virulent to only Swan, Pc46, and Pc38 in MJ23, and Swan and Pc71 in MJ24. The RILs from Provena x GS7 and Boyer x GS7 populations and the parental lines were scored for crown rust infection severity (CRS) at the flowering stage in MJ23 and MJ24, and CB24. The data showed rust resistance trait segregation ( Fig. 1 ), with a high correlation between crown rust severity percentage in MJ23 and MJ24 for both populations, Provena x GS7 (r = 0.72) and Boyer x GS7 (0.58) ( Supplementary File 1 Table S5). In contrast, lower correlations were observed between the Manjimup trials (MJ23 and MJ24) and the Cobbitty trial (CB24) (r ≤3.5) ( Supplementary File 1 Table S5 ). Rust severity was higher in Cobbitty than in Manjimup, as a greater number of susceptible lines were observed in Cobbitty ( Fig. 1 ). Among the parental lines, Provena remained consistently susceptible across trials (CRS = 80–100%), Boyer showed high resistance in all trials (CRS = 0–15%), and GS7 exhibited strong resistance in MJ23 and MJ24 trials (CRS = 0–5%) but displayed a moderate resistance-to-moderate susceptibility in CB24 (CRS = 40%). Across all trials, a greater number of lines with low crown rust severity (CRS = 0 – 40%) were observed in the Boyer x GS7 population, compared to Provena x GS7 ( Fig. 1 ), likely reflecting the previously reported combined resistance contribution from both parental lines, Boyer and GS7 ( Babiker et al. 2015 ). Download figure Open in new tab Fig. 1 Histogram of field infection severity data from Manjimup 2023 (MJ23), 2024 (MJ24) and Cobbitty 2024 (CB24) of (A) Provena (P) x GS7 (G) RILs (n=91); (B) Boyer (B) x GS7 (G) RILs (n = 98). The x-axis represents crown rust severity = CRS, CRS < 50 % = resistance and CRS ≥ 50 % = susceptibility whereas the y-axis represents number of lines. The dash lines separate resistance (left) and susceptibility (right). Construction of genetic maps of Provena x GS7 and Boyer x GS7 RILs The RILs from Provena x GS7 and Boyer x GS7 mapping populations and the parental lines were genotyped using DArTSeq, generating 31,101 and 26,385 segregating SNPs, respectively. After filtering and converting to a parent-based format (ABH), 5,931 and 6,386 markers were used to construct a genetic map for each family. The final genetic maps included 4,493 and 5,048 SNPs for Provena x GS7 and Boyer x GS7, respectively, with 2,323 common markers between the two populations ( Fig. 2 and Supplementary File 2 ). The genetic maps spanned 2191.5 cM and 2568.2 cM, encompassing 43 and 40 linkage groups with an average marker spacing of 0.5 cM in both maps ( Supplementary File 2 and Supplementary File 3 Fig. S1 ). The DArTSeq markers were also assigned to genomic locations in the Avena sativa OT3098 v2 reference genome sequence based on sequence alignment. A high proportion of markers within the same linkage group aligned to the same chromosome on the physical map (OT3098 v2): 96.75% for Provena x GS7 ( Supplementary File 3 Fig. S2 ) and 95% for Boyer x GS7 ( Supplementary File 3 Fig. S3 ). The average correlation coefficient between genetic and physical map orders for 21 chromosomes was r = 0.86 in both populations. This allowed the genetic maps to be anchored to the 21 oat chromosomes using ASMap R package ( Taylor and Butler 2017 ) and each map provided close to complete coverage of the genome ( Fig. 1 ). Chr1D showed the longest linkage group in Provena x GS7 (262.3 cM), while chr2D was the longest in Boyer x GS7 (269.8 cM) ( Fig. 1 ). Download figure Open in new tab Fig. 2 A Distribution of markers (4493 SNPs) across 21 oat chromosomes in the genetic map of the Provena x GS7 RIL population (n =91); B Distribution of markers (5048 SNPs) across 21 oat chromosomes in the genetic map of the Boyer x GS7 RIL population (n =98). The x-axis represents the 21 oat chromosomes anchored to the genetic maps, while the y-axis shows the genetic positions of markers in centimorgans (cM). Identification of QTL associated with oat crown rust severity Provena x GS7 population QTL analysis identified significant peaks on chr2A ( QPc_Provena_2A ), 4A ( QPc_GS7_4A.1 and QPc_GS7_4A.2 ), 5C ( QPc_GS7_5C ), and 7A ( QPc_GS7_7A and QPc_Provena_7A ), with the majority of them carrying resistance alleles from GS7. The exceptions were QPc_Provena_2A and QPc_Provena_7A , which has the resistance allele from Provena ( Fig. 3 and Table 1 ). QPc_GS7_4A.2 , located between 158.00 – 162.16 cM, was the only QTL detected in two trials (MJ23 and MJ24) but showed the strongest significance ( Fig. 3A ), accounting for approximately 45% of the genotypic variance explained (GVE) in MJ23 and 35% in MJ24 ( Table 1 ). The second most significant detected QTL, QPc_GS7_4A.1 (LOD = 4.38; GVE = 19.86), which appeared only in the CB24 trial, was located on chr4A between 85.36–104.09 cM, approximately 54 cM away from QPc_GS7_4A.2 . Low linkage disequilibrium (LD) was found between QPc_GS7_4A.1 and QPc_GS7_4A.2 ( Supplementary File 3 Fig. S4A ). The two minor QTL on chr7A, QPc_GS7_7A and QPc_Provena_7A , were identified in different trials and have low LD to each other ( Supplementary File 3 Fig. S4A ). The QPc_GS7_7A , carrying a favourable allele from GS7, was detected in MJ24, whereas QPc_Provena_7A , derived from the susceptible parent Provena, was identified in CB24. The two other QTL, QPc_Provena_2A and QPc_GS7_5C , were both significant in MJ23. QPc_Provena_2A was located between 2.89 - 26.4 cM on ch2A, while QPc_GS7_5C was located on chr5C, with GVE values of 14.33% and 16.59%, respectively. Download figure Open in new tab Fig. 3 QTL mapping in RIL populations according to field trial. Plots for Provena x GS7 (left) and Boyer x GS7 (right) mapping populations at Manjmup in 2023 (MJ23), 2024 (MJ24), and Cobbitty 2024 (CB24) are shown with chromosome numbers in the x axis and LOD score in the y-axis. LOD threshold (dash lines) = 3. The colours indicate co-located QTL. View this table: View inline View popup Download powerpoint Table 1 Summary of composite interval mapping in Provena x GS7 and Boyer x GS7 RILs for crown rust severity in Manjmup 2023 (MJ23) - 2024 (MJ24), and Cobbitty 2024 (CB24). Number of * indicates co- located QTL. GVE: Genotypic variance explained Boyer x GS7 populatio Three significant QTL were identified in the Boyer x GS7 RILs on ch1D ( QPc_Boyer_1D with GVE = 16.69%) and 7A ( QPc_GS7_7A with GVE = 37.28% and QPc_Boyer_7A with GVE = 15.46%) ( Fig. 3 ). All were detected in CB24, while none were identified in MJ23 or MJ24, likely due to the high resistance of most lines, with only a few RILs exhibiting susceptibility in Manjimup trials ( Fig. 1 ). The QPc_GS7_7A identified in this population, located at 0.00 - 1.92 cM and carrying a favourable allele from GS7, corresponds to the same QTL identified in the Provena x GS7 population ( Table 1 ). Similarly, QPc_Boyer_7A mapped between 48.3 - 50.14 cM, with a resistance allele from Boyer, co-located with QPc_Provena_7A , previously identified in the Provena x GS7 population. KASP marker development and screening of the oat collection Several SNPs that were significantly associated with QPc_GS7_4A.1 , QPc_GS7_4A.2 , QPc_GS7_7A , and QPc_Provena_7A / QPc_Boyer_7A were successfully converted into KASP markers, including KASP_SNP-350765064_4A, KASP_SNP-351486359_4A, and KASP_SNP-352361337_4A for QPc_GS7_4A.1 , KASP_SNP-459779133_4A , KASP_SNP-459972095_4A , and KASP_SNP-460562184_4A for QPc_GS7_4A.2 , KASP_SNP-4039736_7A and KASP_SNP-411798_7A for QPc_GS7_7A , and KASP_SNP-67318388_7A and KASP_SNP-69787185_7A for QPc_Provena_7A / QPc_Boyer_7A ( Supplementary File 1 Table S3 ). KASP assays were performed to assess the presence of these QTL in an oat collection of 182 oat lines ( Supplementary File 1 Table S6 ). The results were integrated with rust severity scores of the oat collection from MJ23 and CB24, and a T-test was conducted to evaluate allelic effects on the phenotype ( Fig. 4 ). Download figure Open in new tab Fig. 4. Phenotypic effect of KASPs associated with QTL A QPc_GS7_4A.1; B QPc_GS7_4A.2; C QPc_GS7_7A; D QPc_Provena_7A/QPc_Boyer_7A in the oat collection (n=182). Pairwise t-tests were used to compare the allelic effect on rust severity scores in Manjimup 2023 (MJ23) and Cobbitty 2024 (CB24); Significant levels are based on *p < 0.05, **p < 0.01, ***p < 0.001. The teal colour indicates the resistant alleles, the rose indicates the susceptible alleles and the green indicates the heterozygous allele. The boxes’ solid and dash lines indicate median and mean, respectively). The markers KASP_SNP-351486359_4A ( QPc_GS7_4A.1 ), KASP_SNP-411798_7A ( QPc_GS7_7A ), and KASP_SNP-67318388_7A ( QPc_Provena_7A / QPc_Boyer_7A ) showed consistent associations with crown rust severity in the oat collection across both MJ23 and CB24 trials ( Fig. 4 ). In contrast, the markers for QPc_GS7_4A.2 , KASP_SNP-459972095_4A and KASP_SNP-460562184_4A were only associated with resistance scores in MJ23 but not CB24, aligning with QTL mapping results that identified this QTL only in MJ23 and MJ24. QTL QPc_GS7_4A.2 is colocalised with genomic regions associated with Adult Plant Resistance and other race-specific resistance To further explore the most significant QTL QPc_GS7_4A.2 derived from GS7 on chr4A, we examined its colocalization with known resistance loci through comparative mapping analysis. A BLAST search of the flanking sequences of markers significantly associated with rust resistance from previous studies ( Babiker et al. 2015 ; Klos et al. 2017 ; Nazareno et al. 2022 ) identified significant matches (% identity > 90.1 and e-value < 2.55E-18) on both chr4A and 4D in reference genome OT3098 v2 ( Supplementary File 1 Table S7 and Supplementary File 3 Fig. S5 ). Additionally, some markers physically mapped to chr4D in our QTL QPc_GS7_4A.2 overlap with the syntenic APR locus QPc.APR4D.2 identified by Nazareno et al. (2022) on chr4D ( Supplementary File 3 Fig. S5A ). Synteny analysis revealed significant homology across most regions of chr4A and 4D ( Supplementary File 3 Fig. S5B ). The QPc_GS7_4A.2 is overlapped with the genomic region associated with the seedling resistance gene Pc61 and APR loci identified by Klos et al. (2017) and Nazareno et al. (2022), respectively ( Supplementary File 3 Fig. S5A ). Pedigree connections were found between GS7 and both Coker234 ( Pc61 ) and Coker227 ( Pc60 ) ( Fig 5A ). Download figure Open in new tab Fig. 5 A Pedigree relationship of GS7 and Coker234 (Pc61) and Coker227 (Pc60). Pedigrees were obtained from “Pedigrees of Oat Lines” POOL database ( https://triticeaetoolbox.org/POOL ; Tinker and Deyl, 2005 ) and Fitzsimmons et al. (1983) . Red lines indicate a maternal relationship, and blue indicates a paternal relationship. Name of oat lines in bold represent lines that carry the resistance haplotype at QPc_GS7_4A.2. B Heatmap comparing virulence profiles of 20 Pca isolates on GS7, Provena, Pc61, and Pc60. Swan is a widely susceptible cultivar used as the susceptible control. The colour range indicates the infection type of isolate on host: resistant (0 to 2) to susceptible (3 to 4). C Presence of haplotype 4A_GS7 in the oat differential set (n=49) and D the oat collection (n=182). The markers on top are the most significant SNP at the QTL region on chr4A taken from DArTSeq in the differential set, converted to KASP, and tested in the oat collection. Colour indicates the allele of GS7 (green), heterozygous (grey), and the contrasting allele (rose). Given that QPc_GS7_4A.2 overlapped with the known Pc61 locus and manifested only in the Manjimup field across both the Provena x GS7 mapping population and the oat collection, along with GS7’s pedigree connection to both Coker234 ( Pc61 ) and Coker227 ( Pc60 ), we proposed that this QTL may associate with a seedling resistance gene. A subset of 30 RILs from the Provena x GS7 population, carrying contrasting alleles at the QPc_GS7_4A.2 locus (15 with the resistance allele and 15 with the susceptible allele), was selected for seedling resistance testing against the rust isolate 22WA54. The results showed that GS7 and the 15 RILs carrying the QPc_GS7_4A.2 resistance allele exhibited resistance, whereas Provena and the 15 RILs carrying the QPc_GS7_4A.2 susceptible allele were all susceptible ( Supplementary File 2 ). QTL analysis using the seedling resistance data from this subset of samples identified only a single peak that co-mapped with the QPc_GS7_4A.2 locus ( Supplementary File 3 Fig. S6 ), confirming its role as an ASR locus. Another rust seedling resistance phenotyping experiment at the seedling stage was conducted to compare the resistance profiles of Provena, GS7, Pc60, and Pc61. The experiment used 20 contemporary Pca isolates collected from WA in 2022 and 2023 ( Henningsen et al. 2024 ; Nguyen et al. 2025 ). The result indicated that Provena was susceptible to all tested isolates, while GS7 exhibited seedling resistance to nine Pca isolates, confirming it carries at least one ASR gene. Additionally, GS7 displayed a highly similar resistance profile to Coker234 ( Pc61 differential line), differing only for one isolate ( Fig. 5B ). To confirm the specificity of QPc_GS7_4A.2 in the oat crown rust differential set (n = 49; Supplementary File 1 Table S8 ), previously used in Pca virulence surveillance ( Henningsen et al. 2024 ; Nguyen et al. 2025 ), we examined the presence of QPc_GS7_4A.2 resistance haplotype in these lines ( Fig. 5C ). The resistance haplotype of QPc_GS7_4A.2 are defined by three SNPs previously used to develop KASP assays for QPc_GS7_4A.2 , SNP-459779133_4A , SNP-459972095_4A , and SNP-460562184_4A that were also found in the DArTSeq data of the oat crown rust differential set ( Nguyen et al. 2024 ). In the differential set, the resistance haplotype of QPc_GS7_4A.2 was present in six lines, for which the presence of certain race-specific genes has been postulated based on pathogenicity assays ( Fig. 5C ). These lines include Coker234 ( Pc61 differential line), Coker227 ( Pc60 differential line), Warrego ( Pc61 +), Barcoo ( Pc61 , Pc39 , PcBett ), Belle ( Pc58 , Pc59 , Pc62 , Pc? ), and TAM-O-405 (Unknown resistance) ( Carson 2017 ; Forsberg et al. 1999 ; Park et al. 2009 ). Belle also has a pedigree connection to Coker24 ( Pc61 ) ( Fig. 5A ). In the oat collection, the resistance haplotype of QPc_GS7_4A.2 was found in 12 lines through genotyping with KASP markers KASP_SNP-459779133_4A , KASP_SNP-459972095_4A , and KASP_SNP-460562184_4A ( Fig. 5D and Supplementary File 1 Table S6 ). In addition to GS7, eleven other oat lines carried the 4A_GS7 resistance haplotype, including Quoll ( Pc61 ), Warrego ( Pc61 +), Barcoo ( Pc61 , Pc39 , PcBett ), Wizard ( Pc61 ) ( Park et al. 2009 ; Park 2013 ; Cuddy et al. 2016 ), Volta-2 ( Pc50 , Pc91 ), Volta-3 ( Pc50 ), Aladdin-1 ( Pc91 ), Aladdin-2 (unknown Pc gene(s)) (Park et al. 2013; Nguyen et al. 2023), Mesquite, Drummond ( Pc39 ) ( Nguyen et al. 2024 ), and Mannus. Among these, Mannus was found to have a pedigree connection with Coker234 ( Pc61 ) ( Fig. 5A ). Notably, Mesquite is the only postulated APR line in the oat collection that was found to carry the resistance haplotype of QPc_GS7_4A.2 ( Supplementary File 1 Table S6 ). Previously, a QTL in Mesquite was mapped to chr4A by Nazareno et al. (2022) , which is 50 Mb from QPc_GS7_4A.2 ( Supplementary File 3 Fig. S5A ). Two other lines, Amarela and NMO 877, which also had APR QTL mapped to the same region of QPc_GS7_4A.2 on chr4A ( Nazareno et al., 2022 ( Supplementary File 3 Fig. S5A ), differed from the QPc_GS7_4A.2 resistance haplotype by one marker ( Supplementary File 1 Table S6 ). The QPc_GS7_4A.2 interval (∼5 Mbp on chr4A, 456,300,687–461,488,017 bp) in the Avena sativa OT3098 v2 genome contains 69 annotated genes, including 25 related to disease resistance ( Supplementary File 1 Table S9) . Notably, a cluster of 10 Disease Resistance Protein RGA5 genes (458,178,696–459,022,508 bp) and a tandem of three ACCELERATED CELL DEATH 6 (ACD6) genes were identified. Other potential candidates include Ankyrin repeat-containing protein NPR4, WRKY transcription factor 49, Receptor-like cytoplasmic kinase 176, and Disease resistance protein Pik-2. Discussion This study employed two established RIL mapping populations (Babika et al. 2015) derived from APR carrying lines, GS7 and Boyer, and a susceptible cultivar (Provena) to assess the effectiveness of the postulated APR loci present in these families under Australian field conditions. Both Boyer and GS7 were confirmed to exhibit high levels of resistance to oat crown rust, while Provena was highly susceptible. QTL analysis using Provena x GS7 and Boyer x GS7 RILs identified multiple loci of interest. Those derived from GS7 were located on chr4A, 5C, and 7A, while loci from Boyer were found on chr1D and 7A, along with two Provena-derived QTL ( QPc_Provena_2A and QPc_Provena_7A ). Notably, QPc_Provena_7A and QPc_Boyer_7A both co-locate with QCr.cdl11-13A , a GS7-derived QTL reported by Babiker et al. (2015) , which was previously identified in both Provena x GS7 and Boyer x GS7 mapping populations across three trials in the U.S. environment. Another difference between this study and the findings from Babiker et al. (2015) is the detection of QPc_GS7_4A.1 at 352 Mb in the Provena x GS7 population, a locus previously detected in the Provena × Boyer population in the US, where the resistance allele originated from Boyer. The discrepancy in QTL identification between our study and Babiker et al. (2015) could be attributed to factors such as environmental variation, genotype-environment interactions, QTL epistasis, or differences in pathogen mixtures used at the nurseries during evaluation ( Lindhout 2002 ). The strongest loci in this study are QPc_GS7_4A.2 , detected in the trials MJ23 and MJ24, and QPc_Boyer_7A , identified in the CB24 trial. The QTL QPc_GS7_4A.2 overlapped with an APR QTL on chr4A reported by Nazareno et al. (2022) and a QTL region previously associated with the ASR genes Pc61 and Pc64 ( Klos et al. 2017 ). Analysis of the QPc_GS7_4A.2 haplotype at the QTL across the differential set identified the Pc61 differential line (Coker234) as one of the carriers, but not the Pc64 differential line. In the oat collection, the QPc_GS7_4A.2 resistance haplotype was also present in other lines postulated to carry Pc61 , such as Quoll, Wizard, Warrego, and Barcoo. The rust phenotyping experiment at the seedling stage using 20 Pca isolates showed similar resistance profiles between GS7 and the Pc61 differential line. The seedling resistance assay and subsequent QTL mapping analysis using 30 RILs carrying contrasting alleles at QPc_GS7_4A.2 further confirmed the association of this locus with seedling resistance. All these findings, along with Klos et al. (2017) identifying the same genomic region linked to Pc61 , strongly suggest that the mapped QTL in GS7 is closely linked to the Pc61 locus or potentially represents Pc61 itself. Furthermore, based on the distribution of virulent isolates across trials, QPc_GS7_4A.2 was effective in MJ23 and MJ24, but not in CB24, likely due to the presence of Pc61 -virulent pathotypes in CB24, which were absent in the MJ23 trial. In CB24, the resistance in the Provena x GS7 RILs was attributed to the QTL QPc_GS7_4A.1 and QPc_Provena_7A . In the oat collection, the QPc_GS7_4A.1 locus was significantly associated with resistance in both MJ23 and CB24, suggesting it may be a potential APR locus effective across multiple environments and diverse Pca isolates. This locus is collocated with previously identified APR loci identified by Babiker et al. (2015) and Nazareno et al. (2022). The findings QPc_GS7_4A.1 and QPc_GS7_4A.2 in GS7 and their difference in effectiveness across environments indicate that GS7 carries both ASR ( Pc61 ) and APR. The co-presence of ASR gene with APR loci has been previously reported. For example, the oat line Garry carries multiple ASR genes ( Pc24 , Pc25 , Pc26 ) alongside APR genes ( Pc27 and Pc28 ), and the oat line TAM O-301 harbors Pc58 and additional APR-contributing genes ( Upadhyaya and Baker 1960 , 1962 ; Cason 2017; Hoffman et al. 2006 ). Co-location of APR and seedling resistance genes has also been documented in wheat. For example, Lr12 (APR) and Lr31 (ASR) both map to chromosome 4B, and their complementary interaction contributes to resistance ( Singh et al. 1999 ). Similarly, a major field stem rust resistance gene co-locates with Sr12 in ‘Thatcher’ wheat ( Hiebert et al. 2016 ). The oat line Coker227 ( Pc60 ), which shares a high genetic similarity with Coker234 ( Pc61 ) ( Nguyen et al. 2024 ) but showed a different seedling resistance profile, also carries the QPc_GS7_4A.2 resistance haplotype. This is likely a result of GS7’s descent from Pc60 in the pedigree, with both Coker227 ( Pc60 ) and Coker234 ( Pc61) originating from a common source A . sterilis PI 287211 ( Carson 2017 ). Moreover, the QPc_GS7_4A.2 resistance haplotype was also found in several crown rust-resistant lines not yet catalogued to carry any known Pc gene TAM-O-405, Drummond, and Mannus. Of these three lines, Mannus has pedigree connection with Coker234 ( Pc61) . These lines may potentially harbor the QPc_GS7_4A.2 as part of their resistance mechanism, though further studies are needed to confirm this. Oat cultivars Volta-2 and Aladdin-1, previously postulated to carry Pc91 (Nguyen et al. 2023), were also found to harbor the QPc_GS7_4A.2 resistance haplotype. Interestingly, a recent genome-wide association study identified a shared genomic interval in the Pca genome significantly associated with virulence to both Pc61 and Pc91 ( Hewitt et al. 2024 ), suggesting a possible mechanistic link between these resistance loci in oats. Our findings align with this observation, supporting a connection between Pc61 and Pc91 . A candidate gene search identified 25 of 69 annotated genes in the 4A QTL interval that are putatively linked to disease resistance mechanisms. Notably, a cluster of 10 Disease Resistance RGA5 and three ACD6 genes on chromosome 4A were found in this region. These RGA5 belong to the gene family that encodes NB-LRR proteins in rice, known to mediate resistance to the fungal pathogen Magnaporthe oryzae, and exhibit diverse functions in Avr recognition ( Césari et al. 2014 ). On the other hand, in Arabidopsis, an ACD6 was recently identified as the causal gene for leaf senescence ( Jasinski et al. 2021 ). Notably, a gene annotated as Pik2 was identified approximately 2 Mb from the RGA5 cluster within the QTL interval. Pik2, like RGA5, is an NLR protein, and they are functionally related. In rice, RGA5 acts as the sensor in the sensor-helper pair RGA5-RGA4, while Pik2 serves as the helper in the Pik1-Pik2 pair ( Zdrzałek et al. 2020 ). These genetically linked NLR pairs typically operate as sensor-helper systems, where one component recognises pathogen effectors and the other activates immune responses ( Zhai et al. 2011 ; Zdrzałek et al. 2020 ). In addition, a putative protein WRK49 was also found. In wheat, WRK49 was claimed to confer differential high-temperature seedling-plant resistance to Puccinia striiformis f. sp. tritici ( Pst) ( Wang et al. 2017 ). Furthermore, there is a putative protein Ankyrin repeat-containing protein NPR4 in the QLT region, which was orthologous to TaANKTM1B-4, TaANKTM2B-1, TaANKTM1D-6, TaANKTM3B-9, TaANKTM4B-5 in wheat. The ankyrin-transmembrane (ANKTM) subfamily is the most abundant subgroup of the ANK superfamily, with roles in pathogen defense ( Hu et al. 2022 ). A homolog of these genes, TaANKTM2A-5 was found to regulate powdery mildew resistance in wheat ( Hu et al. 2022 ). The candidate genes listed here could be potential targets for cloning from the resistant parent to support functional characterisation. In conclusion, this study emphasises the potential of GS7 and Boyer as a useful source of crown rust resistance in Australia. The QTL QPc_GS7_4A. 2 is a seedling resistance locus that is closely linked to Pc61 and potentially Pc61 itself. The KASP markers were developed for QPc_GS7_4A.1, QPc_GS7_4A.2, QPc_GS7_7A, and QPc_Provena_7A/QPc_Boyer_7A will be valuable for marker-assisted breeding in oat improvement programs. Future research should focus on stacking these QTL to evaluate their interaction in a common background, while fine mapping is needed to pinpoint their genetic mechanism. Statements & Declarations Conflict of interest The authors declare that this study received funding from the GRDC. The funder was not involved in the study design, collection, analysis interpretation of data, the writing of this article, or the decision to submit it for publication. Author contributions This study was planned and designed by MF and PND. AR conducted rust infection phenotyping in the field. DTN performed the genotypic analysis. DL and ECH implemented virulence assessments for the plants at the seedling stage in the growth cabinets. RM assisted with marker design. JS supported computational analyses. DTN, PND and MF wrote the first draft of the manuscript. All authors contributed to the interpretation of results, reviewed the manuscript, and approved the final version. Data availability The DArTSeq genotypes of the oat lines included in this study are deposited in the CSIRO Data Access Portal repository https://doi.org/10.25919/x7sk-qp24 . The scripts for genotypic data analysis and QTL mapping are available on GitHub at https://github.com/duongnguyen1987/QTL_mapping . Acknowledgments We acknowledge the Australian Grains Genebank, Shahryar F. Kianian and Tyler Gordon at US Department of Agriculture-Agricultural Research Service for research discussions, and the GRDC-funded oat phenology project team (grant CSP2007) for contributing oat germplasm. Funder Information Declared GRDC , CSP2204-007RTX Footnotes Funding: Grains Research and Development Corporation (GRDC) project grant CSP2204-007RTX and CSIRO. https://doi.org/10.25919/x7sk-qp24 Literature Cited ↵ Altschul SF , Gish W , Miller W , Myers EW , Lipman DJ ( 1990 ) Basic local alignment search tool . J. Mol. Biol . 215 : 403 – 410 . doi: 10.1016/S0022-2836(05)80360-2 OpenUrl CrossRef PubMed Web of Science Ao Y , Li Z , Feng D , et al. ( 2014 ) OsCERK1 and OsRLCK176 play important roles in peptidoglycan and chitin signaling in rice innate immunity . 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