Next Article in Journal
Sida L.: Ethnobotany, Pharmacology, and Phytochemistry: A Review
Previous Article in Journal
Identification and Functional Analysis of Two UGT84 Glycosyltransferases in Flavonoid Biosynthesis of Carthamus tinctorius
Previous Article in Special Issue
Genetic Architecture and Meta-QTL Identification of Yield Traits in Maize (Zea mays L.)
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Plants
Volume 14
Issue 19
10.3390/plants14193113
plants-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

QTL Mapping for Leaf Rust Resistance in a Common Wheat Recombinant Inbred Line Population of Doumai/Shi4185

by
Yamei Wang
1,†,
Wenjing Li
1,†,
Rui Wang
1,
Nannan Zhao
1,
Xinye Zhang
1,
Shu Zhu
1,* and
Jindong Liu
2,*
1
College of Life Sciences, Langfang Normal University, Langfang 065000, China
2
Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Plants 2025, 14(19), 3113; https://doi.org/10.3390/plants14193113
Submission received: 20 August 2025 / Revised: 28 September 2025 / Accepted: 6 October 2025 / Published: 9 October 2025
(This article belongs to the Special Issue Recent Advances in the Genetics, Genomics and Breeding of Cereal Crops)
Browse Figures
Review Reports Versions Notes

Abstract

Leaf rust, a devastating fungal disease caused by Puccinia triticina (Pt), severely impacts wheat quality and yield. Identifying genetic loci for wheat leaf rust resistance, developing molecular markers, and breeding resistant varieties is the most environmentally friendly and economical strategy for disease control. This study utilized a recombinant inbred line (RIL) population of Doumai and Shi4185, combined with the wheat 90 K single nucleotide polymorphisms (SNPs) chip data and maximum disease severity (MDS) of leaf rust from four environments, to identify adult plant resistance (APR) loci through linkage mapping. Additionally, kompetitive allele-specific PCR (KASP) markers suitable for breeding were developed, and genetic effects were validated in a natural population. In this study, 5 quantitative trait loci (QTL) on chromosomes 1B (2), 2A and 7B (2) were identified through inclusive composite interval mapping, and named as QLr.lfnu-1BL1, QLr.lfnu-1BL2, QLr.lfnu-2AL, QLr.lfnu-7BL1 and QLr.lfnu-7BL2, respectively, explaining 4.54–8.91% of the phenotypic variances. The resistance alleles of QLr.lfnu-1BL1 and QLr.lfnu-1BL2 originated from Doumai, while the resistance alleles of QLr.lfnu-2AL, QLr.lfnu-7BL1 and QLr.lfnu-7BL2 came from Shi4185. Among these, QLr.lfnu-1BL2, QLr.lfnu-7BL1 and QLr.lfnu-7BL2 overlapped with previously reported loci, whereas QLr.lfnu-1BL1 and QLr.lfnu-2AL are likely to be novel. Two KASP markers, QLr.lfnu-2AL and QLr.lfnu-7BL, were significantly associated with leaf rust resistance in a diverse panel of 150 wheat varieties mainly from China. Totally, 34 potential candidate genes encoded the NLR proteins, receptor-like kinases, signaling kinases and transcription factors were selected as candidate genes for the resistance loci. These findings will provide stable QTL, available breeding KASP markers and candidate genes, and will accelerate the progresses of wheat leaf rust resistance improvement through marker-assisted selection breeding.

1. Introduction

Wheat production is severely threatened by leaf rust, a devastating fungal disease caused by Puccinia triticina (Pt). This pathogen can bring yield losses exceeding 50% [1]. More seriously, global warming has driven an increasing trend in leaf rust epidemics across major wheat-growing areas worldwide, including Southern United States, South America, Canada, Eastern Europe, Egypt, and notably throughout China [2,3]. Severe yield losses due to leaf rust have occurred in 2008, 2009, 2012, 2015, and 2023 in China [4,5,6]. The annual wheat planting area in China covers approximately 23.7 million hectares, with leaf rust affecting around 15 million hectares each year, leading to an estimated yield loss of 3 million tons annual, particularly in the major wheat-producing Yellow and Huai River Valleys Wheat Zone. Hence, effective control of leaf rust is crucial for ensuring grain security.
Developing resistant varieties is the most economical and effective strategy to control leaf rust. Wheat relies on two main types of resistance: race-specific and race non-specific. Race-specific resistance, often called all-stage resistance (ASR), is usually controlled by one or a few major genes. It functions based on the gene-for-gene principle. This means resistance requires a specific interaction between a host resistance (R) gene and a corresponding pathogen avirulence (Avr) gene [7,8]. ASR is only effective against pathogen races that carry those specific Avr genes. However, ASR can be overcome as Pt races evolve new virulent forms. Major R genes, such as Lr1, Lr10, and Lr21, typically encode NLR (Nucleotide-binding, Leucine-rich repeat) proteins and trigger a rapid hypersensitive response [9]. In contrast, race non-specific resistance is known as adult-plant resistance (APR) or slow-rusting resistance. It involves multiple minor genes that work together [7,8]. APR reduces disease severity by prolonging the time before symptoms appear (latent period), lowering the number of infections, producing smaller spore masses (pustules), and reducing spore production [10]. This type of resistance works against all pathogen races, slowing down pathogen evolution and providing longer-lasting protection [3,10,11]. Examples include Lr34 (encodes an ABC transporter) and Lr67 (encodes a hexose transporter) with multiple effects and provide broad-spectrum, durable resistance against multiple rust diseases [12].
Significant progress has been made in understanding the genetic basis of leaf rust resistance in wheat. To date, over 100 leaf rust resistance genes (Lr genes) have been identified in bread wheat and its relatives, with 91 formally cataloged [13,14,15,16,17,18,19]. These genes are distributed on all 21 wheat chromosomes [20,21,22]. Most Lr genes provide ASR while others confer durable, race-non-specific APR. Examples of APR genes include the Lr34/Yr18/Sr57 [23], Lr46/Yr29/Sr58 [24], Lr67/Yr46/Sr55 [25], Lr68, Lr74, Lr75, Lr77, and Lr78 [6]. In China, ASR genes such as Lr9, Lr19, Lr24, Lr28, Lr29, Lr38, Lr47, Lr51, and Lr53 remain effective [3]. However, their utilization is limited because they are sometimes linked to undesirable traits [3,5]. Genes like Lr1, Lr2c, Lr3, Lr16, Lr17, Lr26, LrB, Lr3bg, Lr14b, Lr23, and Lr39 have largely lost effectiveness against current Pt races [3]. Cloning Lr genes is crucial to understand their function and enable precise breeding. Currently, at least 10 Lr genes have been cloned: including the Lr1 (NLR), Lr9/Lr58 (kinase/VWA domains), Lr10 (NLR), Lr13, Lr14a (membrane protein), Lr21 (NLR), Lr22a (NLR), Lr42 (NLR), along with the pleiotropic APR genes Lr34 and Lr67. Additionally, over 200 quantitative trait loci (QTL) for leaf rust resistance have been mapped across various wheat populations, which further increased the pool of potential resistance sources [3].
Given the growing threat of leaf rust under climate change and limitations of existing resistance genes, the need for more durable solutions, continuous discovery and employing new resistance sources is an urgent global priority. Therefore, identifying and cloning novel genes, uncovering their genetic mechanisms, and developing molecular tools for marker-assisted selection (MAS) breeding are crucial challenges. In this study, we employed a bi-parental population of 212 recombinant inbred lines (RILs) derived from a cross between Doumai (a landrace with moderated APR to leaf rust) and Shi 4185 (a high-yield variety with highly leaf rust APR). The objectives of this study are to: (1) identify novel resistance loci and tightly linked SNP markers through genetic analysis; and (2) develop robust, breeder-friendly kompetitive allele specific PCR (KASP) markers for wheat efficient MAS breeding for leaf rust resistance.

2. Results

2.1. Phenotypic Analysis

Statistical analysis of leaf rust maximum disease severity (MDS) was conducted for Doumai, Shi4185, and the RIL population at adult plant stages. Seedling inoculation results revealed that Doumai and Shi4185 exhibited moderate to high susceptibility (IT = 3–4) to the leaf rust races THTT, PHTT, and THTS. Adult plant stage evaluations showed that Shi4185 displayed higher resistance (MDS 18–32% across all 4 environments) to the leaf rust races, while Doumai exhibited moderate leaf rust resistance (MDS ranged from 22% to 52%). MDS of 212 lines in the RIL population across four environments ranged from 0% to 98%. The correlation coefficients of leaf rust resistance in the Doumai/Shi4185 RIL population across four environments ranged from 0.50 to 0.61 with significant correlations (p < 0.01). Under multiple environmental conditions, MDS showed a continuous distribution, indicating that the leaf rust resistance in Doumai and Shi4185 is controlled by multiple minor-effect genes. ANOVA indicated that the leaf rust resistance of Doumai/Shi4185 RIL population was significantly influenced by genotype, environment, and genotype-by-environment interactions (p < 0.01) with the leaf rust resistance broad-sense heritability (Hb2) was 0.63, indicating a high level of genetic stability (Table 1).

2.2. QTL for Leaf Rust Resistance

We have identified five QTL associated with leaf rust resistance in Doumai / Shi4185 RIL population across four environments. Two QTL, QLr.lfnu-1BL1 and QLr.lfnu-1BL2, on chromosome 1B were identified, and they were both detected in Xinxiang2021 and Zhengzhou2022. QLr.lfnu-1BL1 was linked to tplb0033h08_101 (484.2 Mb) and D_contig03023_692 (487.6 Mb), and explained 6.24–7.15% of the phenotypic variance (PVE). QLr.lfnu-1BL2 was linked to Tdurum_contig84791_198 (674.0 Mb) and Ex_c1058_1537 (678.3 Mb), explaining 6.85–8.67% of the PVE. On chromosome 2A, QLr.lfnu-2AL (772.9–779.9 Mb) identified in Zhengzhou2021 and Zhengzhou2022, flanked by wsnp_JD_c289_450995 and Excalibur_c40335_198, and explained 6.75–6.98% of the PVE. Two QTLs were identified on chromosome 7B. QLr.lfnu-7BL1 (675.7–681.8 Mb) effective in Xinxiang2021 and Zhengzhou2021, was close to BobWhite_c7208_88 and IACX8294, with the PVE of 4.54–8.91%. QLr.lfnu-7BL2 (701.3–708.0 Mb) detected in Xinxiang2022 and Zhengzhou2022, was linked to tplb0058p02_2806 and BobWhite_rep_c53128_119, and could explain 4.97–7.41% of the PVE. In addition, the resistance alleles of QLr.lfnu-1BL1 and QLr.lfnu-1BL2 originated from Doumai, while the resistance alleles of QLr.lfnu-2AL, QLr.lfnu-7BL1 and QLr.lfnu-7BL2 were derived from Shi4185 (Table 2; Figure 1).

2.3. Additive Effect of QTL for Leaf Rust Resistance

To further investigate the comprehensive genetic effects of different leaf rust resistance QTL, the number of favorable alleles in the Doumai/Shi4185 RIL population across four environments was counted. The RILs were grouped based on the number of resistant alleles they carried (from one to four). Notably, no lines in the population carried all five resistant alleles or lacked any resistant alleles. The 212 lines were primarily divided into four groups, carrying 1, 2, 3, and 4 favorable alleles, respectively. Overall, with the number of resistance alleles increased, the MDS gradually decreased. Specifically, lines with one, two, three, and four resistance alleles had mean MDS of 49.0% (standard errors: 17.2 with 95% confidence intervals: 42.3–54.2%), 50.7% (standard errors: 14.9 with 95% confidence intervals: 43.1–56.0%), 45.2% (standard errors: 15.3 with 95% confidence intervals: 38.6–49.9%), and 31.5% (standard errors: 12.9 with 95% confidence intervals: 26.9–40.3%), respectively (Figure 2). This indicate that the five QTL exhibit additive effects, and wheat leaf rust resistance at adult plant stage can be strengthened by pyramiding multiple favorable alleles in breeding practice.

2.4. Validation of KASP Markers

To effectively utilize the QTL with stable and consistent effects on leaf rust resistance via MAS, we endeavored to transform the tightly linked SNP markers into KASP-LR-1BL1 (QLr.lfnu-1BL1); KASP-LR-2AL (QLr.lfnu-2AL); KASP-LR-7BL1 (QLr.lfnu-7BL1) and KASP-LR-7BL2 (QLr.lfnu-7BL2). Among these, KASP-LR-2AL and KASP-LR-7BL2 could distinguish two genotypes in both the Doumai/Shi4185 RIL population and natural population (Table S1), while KASP-LR-1BL1 and KASP-LR-7BL1 could not be validated by natural population. In addition, we utilized KASP-LR-2AL and KASP-LR-7BL2 to classify the 212 lines of Doumai/Shi4185 into four types, that is 2AS7BS, 2AR7BS, 2AS7BR and 2AR7BR. The genotypes carrying resistance alleles (2AR7BR) at both QLr.lfnu-2AL and QLr.lfnu-7BL2 exhibited significantly lower MDS across all environments, and their mean MDS was also lower than those 2AR7BS, 2AS7BR, and 2AS7BS groups. In contrast, genotypes with both susceptibility alleles (2AS7BS) showed significantly higher MDS compared to the 2AR7BR, 2AS7BR, and 2AR7BS groups. Thus, KASP-LR-2AL and KASP-LR-7BL2 can be effectively used in detecting resistant accessions in Doumai/Shi4185 RIL population. Thus, combining the resistance alleles of QLr.lfnu-2AL and QLr.lfnu-7BL2 can significantly enhance resistance to leaf rust (Figure 3).
The natural population was genotyped using the KASP-LR-2AL and KASP-LR-7BL2. For KASP-LR-2AL, accessions possessing resistance alleles at QLr.lfnu-2AL exhibited significantly greater leaf rust resistance (99 lines with an average MDS of 47.9, standard errors: 18.7 with 95% confidence intervals: 44.2–51.6) compared to 43 lines with susceptible alleles (average MDS of 54.1, standard errors: 12.8 with 95% confidence intervals: 50.2–58.0%) (p < 0.05). Likewise, marked difference in MDS were observed between lines harboring the resistance allele (82 lines with an average MDS of 47.6, standard errors: 19.2 with 95% confidence intervals: 42.4–51.0%) and those with the susceptibility allele (68 lines with an average MDS of 53.1, standard errors: 13.9 with 95% confidence intervals: 50.3–57.1%) by KASP-LR-7BL2. Furthermore, the resistance alleles at QLr.lfnu-2AL and QLr.lfnu-7BL2 were widespread, showing high frequencies of 66.0% and 54.6%, respectively, in the natural population (Table 3; Figure 4).

2.5. Prediction of Candidate Genes for Leaf Rust Resistance

To identify potential candidate genes underlying the identified QTL, we analyzed the genomic intervals of the five stable QTLs using the Chinese Spring reference genome (IWGSC RefSeq v2.1). A total of 53 high-confidence genes were located within these regions. Based on functional annotation, these genes encode proteins known to be involved in plant disease resistance, including NLR proteins, receptor-like kinases, signaling kinases (e.g., CDPK), transcription factors (e.g., WRKY, NAC, MYB, bZIP), pathogenesis-related proteins (e.g., β-1,3-glucanase), and ABC transporters. We further refined the candidate gene list by examining their expression patterns in public databases (http://www.wheat-expression.com/, accessed on 18 July 2025), prioritizing those with high expression in leaves. This filtering step yielded a final set of 34 candidate genes with higher confidence. The distribution of these potential candidate genes across the QTLs is as follows: QLr.lfnu-1BL1 (6 genes), QLr.lfnu-1BL2 (10 genes), QLr.lfnu-2AL (6 genes), QLr.lfnu-7BL1 (3 genes), and QLr.lfnu-7BL2 (9 genes) (Figure 5; Table S2).

3. Discussion

Since the 1970s, the International Maize and Wheat Improvement Center (CIMMYT) has made significant progress in research on APR to wheat rusts, and have successfully developed wheat varieties with durable resistance to rust diseases. The resistance of these varieties has remained effective over 50 years [2]. Recent advances in genomics and molecular breeding have provided powerful tools to dissect rust resistance [26]. By integrating QTL mapping, meta-analysis, and transcriptomic studies, researchers can identify stable and effective loci and candidate genes associated with durable resistance [27]. Excavating resistance genes from important/superior germplasms, developing molecular markers, and creating new germplasms are of great significance to promote wheat breeding for leaf rust resistance.
In this study, most of the QTLs identified were detected in two environments, and no QTLs were found to be present across all environments. The inconsistent detection of QTLs across environments is a common phenomenon in QTL mapping, often due to the interaction between genetic loci and the environment (G × E interaction). The variation in detection could be attributed to several factors, including: (1) For quantitative genetic traits in wheat (such as rust resistance, etc.), it is generally believed that QTLs detected in more than two environments exhibit a certain level of stability and can be utilized in wheat genetic breeding. Therefore, we typically select and report QTL that are present in two or more environments for further research and application. (2) Differences in disease pressure and environmental conditions (e.g., temperature, humidity) across the four trial environments, which can influence the expression of APR genes. (3) The possibility that some QTLs have minor effects that are statistically detectable only under specific environmental stresses. (4) The loci consistently detected in multiple environments are likely to be more stable and thus of higher priority for breeding applications, while those detected in only one or two environments may be conditionally effective.

3.1. Comparison with Previously Reported QTL

3.1.1. QLr.lfnu-1BL1 and QLr.lfnu-1BL2

Six known leaf rust resistance genes, Lr26 [28], Lr33 [29], Lr44 [30], Lr46 [30], Lr51 [31], and Lr75 [32], and five stable QTL, QLr.sfr-1BS [33], QLr.pser-1BL [34], QLr.pbi-1B [13], QLr.caas-1BL, and QLr.hebau-1BL [4], were detected on chromosome 1B. Among these genes, Lr26, Lr33, Lr44, and Lr51 confer ASR, while Lr46 and Lr75 confer APR. Lr55 distributed on chromosome 1BS was identified using SSR and DArT markers [35]. Dyck and Samborski [29] reported that Lr33 provides resistance at both seedling and adult stages. Herrera-Foessel et al. [36] revealed that Lr44 shows partial dominance to Lr33. Kuraparthy et al. [37] reported that Lr71 was mapped near the centromere of chromosome 1B. Additionally, QLr.sfr-1BS originate from Forno was later designated as Lr75 [32]. QLr.pser-1BL controls leaf rust resistance in Ning7840 [34]. LrZH84 is another leaf rust resistance gene distributed on chromosome 1BL from Zhou8425B. Lr46 (also known as Yr29/Sr58/Pm39) has been widely used in breeding, and several linked markers have been developed [30]. In addition, QTL detected in Bainong 64 were predicted to be Lr46 [38,39]. Lr51, derived from Ae. speltoides, was mapped to chromosome 1BL [31]. Lr26, located on the 1BL/1RS translocation, may benefit from new rye genome resources. The QTL QLr.pbi-1B in Beaver was associated with the 1BL/1RS translocation [13]. Furthermore, Amo et al. [18] performed a meta-analysis on 320 leaf rust resistance QTL and identified a meta-locus, MQTL1B.5 (flanked by BS00000010_51 and 1103838), on chromosome 1B at position 674.0–678.6 Mb. MQTL1B.5 overlaps with QLr.lfnu-1BL2 (flanked by Tdurum_contig84791_198 and Ex_c1058_1537) at position 674.0–678.3 Mb identified in this study. However, no overlap was observed between QLr.lfnu-1BL1 (flanked by tplb0033h08_101-D_contig03023_692, position 484.2–487.6 Mb) and the loci mentioned above. In addition, we compared our results with recently published studies on the genetics of leaf rust resistance in wheat. Based on reference genome alignment, no previously reported loci colocalizing with or closely linked to QLr.lfnu-1BL1 were identified. [40,41,42,43]. Therefore, QLr.lfnu-1BL1 may be novel.

3.1.2. QLr.lfnu-2AL

Multiple leaf rust resistance genes and QTL distributed on chromosome 2A, including the Lr17a [44], Lr37 [45], Lr45 [46], Lr65 [15], Lr81 [16], and LrM [19], as well as the QTL QLr.ifa-2AL [47], QLr.hebau-2AL [48], and QLr.spa-2A [49]. Among these, Lr17a, Lr37, Lr65, Lr81, and LrM are located on the 2AS, while Lr45 and QLr.ifa-2AL are mapped to 2AL. Lr65 was fine-mapped to a 60.11 Kb region on 2AS [15]. Lr37, introgressed from Aegilops ventricosa, was initially mapped using the traditional RFLP and CAPS markers [45]. Lr17a, widely deployed in North American wheat varieties, has been overcome by emerging Pt pathotypes [44]. Lr81 was localized to a 100 Kb region on 2AS [16]. LrM, derived from Ae. markgrafii, provides broad-spectrum resistance [19]. Lr45, introgressed from Secale cereale, was assigned to 2AS [46]. A GWAS analysis of 496 durum wheat accessions excavated Lr-2AL [50]. APR QTL QLr.ifa-2AL, QLr.ifa-2BL, and QLr.ifa-3BS were detected in the Australian cultivar Capo [47]. In Zhou 8425B, QLr.hebau-2AL and QLr.hebau-4AL were identified [48], whereas the Canadian cultivars AC Cadillac and Carberry contributed QLr.spa-2A, QLr.spa-2B and QLr.spa-4B [49]. Additionally, Amo et al. [18] performed a meta-QTL analysis and identified the meta-locus MQTL2A.3 on 2A (729.9–752.3 Mb), which differs from QLr.lfnu-2AL (772.9–779.9 Mb) detected in this study. Additionally, we compared our findings with recently published genetic studies on leaf rust resistance in wheat. Based on comparisons using the reference genome, no genetic loci identical or closely linked to QLr.lfnu-2AL were identified [41,42,50,51]. Therefore, QLr.lfnu-2AL represents a novel locus for leaf rust resistance on 2AL.

3.1.3. QLr.lfnu-7BL1 and QLr.lfnu-7BL2

As reported, over 10 loci and genes for leaf rust resistance distributed on chromosome 7B, including Lr14a [17], Lr68 [14], Lr72 [52], and LrFun [53], QLr.sfrs-7B.2 [39], QLr.cimmyt-7BL1 [54], QLr.osu-7BL [55], QLr.ubo-7B.2 [46], and QLr.ksu-7BL [56]. These loci are distributed across 7BS (Lr72) [47] and 7BL (Lr14a, Lr68, LrFun). Lr14a encodes a membrane-localized protein with ankyrin repeats and Ca2+-permeable ion channel domains [17]. Lr68, a slow-rusting gene from Parula, was mapped to the distal 7BL region [14]. QLr.osu-7BL showed consistent detection in CI13227 [55]. QLr.ubo-7B.2 conferred APR for leaf rust in durum wheat Colosseo [51]. LrFun from Fundulea900 resides in the 7BL distal region [53]. Amo et al. [18] performed meta-QTL analysis and identified two meta loci on chromosome 7B, e.g., MQTL7B.1 and MQTL7B.2. Among these, QLr.lfnu-7BL1 (675.7–681.8 Mb), was overlapped with MQTL7B.1 (675.1–679.3 Mb). In addition, the MQTL7B.2 (1114524-BobWhite_c2892_167) (700.7–721.2 Mb) overlapped with QLr.lfnu-7BL2 (701.3–708.0 Mb) identified in this study.

3.2. Prediction of Potential Candidate Genes for Leaf Rust Resistance

Wheat leaf rust has a complex genetic resistance mechanism. The cloned leaf rust resistance genes primarily encode key components of the plant immune system, whose core function is to recognize pathogens and activate defense responses. The core mechanisms include four categories: NLR proteins, acting as intracellular receptors to recognize effector proteins of P. triticina and form resistance bodies, initiating hypersensitive response that leads to cell death at infection sites, thereby limiting pathogen spread (e.g., Lr1, Lr10, Lr21) [3,20]. Membrane receptor proteins (RLKs/RLPs) perceiving pathogen-associated molecular patterns (PAMPs) or extracellular effector proteins, activating early defenses such as reactive oxygen species (ROS) burst and callose deposition through phosphorylation cascades. Signaling kinases (e.g., MAPK, CDPK) amplify and transmit defense signals, while transcription factors (WRKY, NAC, MYB, bZIP) coordinate the expression of defense-related genes [6,23,25]. Defense execution proteins (PR proteins, phytoalexin synthases), such as chitinases and antimicrobial proteins, directly inhibit pathogens. Additionally, candidate genes for resistance loci involve three functional categories: oxidative redox pathway genes (e.g., POD, PPO, LOX), which participate in phenolic metabolism, ROS regulation, and the synthesis of antimicrobial compounds; hormone signaling genes (e.g., F-box, serine/threonine kinases), which regulate defense hormone pathways such as SA, JA, and ET; and defense regulatory genes [3,6,20,21,23,25].
This study identified 53 potential candidate genes across five leaf rust resistance loci. These genes encode proteins including: NLR proteins, receptor-like kinases, signaling kinases (e.g., CDPK), transcription factors (e.g., WRKY, NAC, MYB, bZIP), pathogenesis-related proteins (e.g., β-1,3-glucanase), and ABC transporters. Using expression data from public databases, we further filtered and identified 34 potential candidate genes. The number of potential candidate genes corresponding to each resistance locus is as follows: QLr.lfnu-1BL1 (6 potential candidate genes), QLr.lfnu-1BL2 (10 potential candidate genes), QLr.lfnu-2AL (6 potential candidate genes), QLr.lfnu-7BL1 (3 potential candidate genes), and QLr.lfnu-7BL2 (9 potential candidate genes) (Figure 5; Table S2).
Map-based cloning in wheat is hindered by its large (16 Gb), repetitive (>85%) allohexaploid (AABBDD) genome [27]. The existence of three homologous sub-genomes generates many homologous sequences near the target gene, which increases the difficulty of specific marker development and positional cloning [27]. The low recombination frequency also necessitates extremely large segregating populations (thousands to tens of thousands of individuals) for mapping. In this study, the identified loci spanned 3.4–7.0 Mb, which is too broad for direct gene identification. The list of resistance genes identified here provides a foundation for future research. It is important to note that these are only potential candidates, selected through computational analyses like annotation and expression profiling. While this approach is effective for generating hypotheses, it cannot prove the genes’ actual functions or roles in disease resistance. Future work on these loci will focus on: (1) constructing derivative populations from residual heterozygous lines; (2) developing KASP markers for fine mapping; (3) narrowing regions to <0.5 Mb and preliminarily identifying targets using annotation, variation, and expression data; and (4) functional validation via transgenic and gene editing techniques [19,57,58]. Final confirmation of their role requires direct experimental validation through genetic or transgenic studies. This distinction between prediction and confirmation is essential for rigorous scientific reporting.

3.3. Application in Wheat Breeding

Traditional breeding has enhanced wheat resistance to leaf rust. However, identifying this resistance in the field involves pathogen inoculation and maintaining specific disease conditions, which is challenging, costly, and time-consuming. This process leads to low efficiency in breeding efforts [59,60]. In recent years, advances in wheat genomics and molecular genetics have significantly accelerated wheat molecular breeding progress [27,61]. Particularly, the emergence of KASP markers has provided a high-throughput, cost-effective, and flexible tool for MAS breeding. In this study, five stable QTL were identified, and their aggregation demonstrated a favorable additive effect on leaf rust resistance. In the current study, we have successfully developed and validated two KASP markers, KASP-LR-2AL (QLr.lfnu-2AL) and KASP-LR-7BL2 (QLr.lfnu-7BL2), which could serve as a molecular tool for wheat MAS breeding programs. However, it is also important to note that, due to the limitations imposed by the diversity of genetic backgrounds and environmental influences, these two KASP markers may also have limitations in wheat breeding. Additionally, several varieties with resistance alleles at QLr.lfnu-2AL and QLr.lfnu-7BL2 loci perform excellent leaf rust resistance and agronomic traits, such as Huaimai21, Linkang 12, Shan512, Shannong78-59, Taishan5, Xinmai19, Xinmai9408, Qinnong731, Wanmai29, Xiaoyan54, Zhoumai18, Gaoyou503 and Jimai20, can also be used as excellent parental lines for wheat breeding.

4. Methods

4.1. Plant Materials and Phenotypic Evaluation

Doumai exhibits moderate susceptibility to moderate resistance against leaf rust at the adult plant stage, while Shi4185 shows moderate to high resistance. The Doumai/Shi4185 RIL population was cultivated in Xinxiang, Henan (34°53′ N, 113°23′ E) and Zhengzhou, Henan (34°74′ N, 113°62′ E) during the 2020–2021 and 2021–2022 growing seasons. The experimental design consisted of 2 m long rows with a randomized complete block design and three replicates. Zhengzhou5389 was used as the susceptible control and spreader row. Susceptible controls were planted every 10 rows in the field. A natural population comprising 150 accessions was employed to validate the genetic effects of leaf rust resistance QTL. This natural population was planted in Baoding of Hebei (38°86’ N, 115°48′ E) and Zhengzhou of Henan (34°74′ N, 113°62′ E) during the 2020–2021 and 2021–2022 growing seasons. The experiment was a completely randomized block design with three replicates, 2 m long rows, and 20 cm row spacing.
To ensure consistent pathogen pressure across different environments, inoculation was performed using a mixture of leaf rust spores. Xinxiang and Zhengzhou in Henan are regions with high incidence of wheat leaf rust, where the prevalent physiological races include THTT, PHTT, and THTS. At the jointing stage of wheat, urediniospores of THTT, PHTT, and THTS were mixed in equal proportions, and a few drops of 0.03% Tween 20 solution were added before spraying. We assessed disease severity (DS), defined as the percentage of leaf area covered by leaf rust urediniospore pustules, using a single-row plot design with 30 plants per row to evaluate the overall disease response of the group. Approximately four weeks after inoculation, when the DS of the control variety Zhengzhou5389 approached 100% with nearly the entire leaf surface covered, disease evaluations were initiated. Field assessments were conducted weekly for the population and its parents, with the process repeated 2–3 times. The MDS observed across these assessments in each environment was selected as the phenotypic data for subsequent linkage analysis. Biological replicates were averaged prior to analysis.

4.2. Statistical Analyses and Linkage Mapping

Analysis of variance (ANOVA) was performed using IciMapping v4.2 (http://www.isbreeding.net/, accessed on 18 July 2025) [62]. The Pearson correlation coefficient was used to evaluate the correlation of MDS. The broad-sense heritability (Hb2) of leaf rust resistance was calculated using the formula: Hb2 = σg2/(σg2 + σge2/e + σε2/re), where σg2 represents genotypic variance, σge2 represents genotype-by-environment interaction variance, and σε2 represents error variance. Here, e denotes the number of environments, and r denotes the number of replicates.
The wheat 90K SNP chip was used to genotype Doumai, Shi4185, and all lines within the RIL population. A total of 80,587 SNPs were detected, and the genetic map was constructed by Wen et al. [63]. This map contains 9354 high-quality polymorphic markers. Inclusive composite interval mapping (ICIM) in IciMapping v2.1 was employed for QTL mapping of MDS in the Doumai/Shi4185 RIL population across four environments. This method combines interval mapping and multiple regression analysis. A significance level (p = 0.05) was determined through 1000 permutation tests, with an LOD threshold set at 2.5 and a step size of 1.0 cM. LOD value curves and QTL position information was generated. In this study, QTL detected in two or more environments were defined as stable QTL.

4.3. Development of KASP Markers

KASP primers were developed for the flanking and intra-interval markers of the identified leaf rust resistance QTL. The primers were designed using the PolyMarker website (http://www.polymarker.info/, accessed on 18 July 2025) (Table A1). KASP primers consist of two competitive primers and one common primer, where the two competitive primers differ by only one base at the 3’ end. Specific or semi-specific KASP primers for the locus were selected, and FAM and HEX tag sequences were added to the 5′ ends of the competitive primers, respectively. The primer sequences were submitted to Sangon Biotech (Shanghai, China) Co., Ltd. (https://store.sangon.com/, accessed on 18 July 2025) for synthesis. The KASP marker PCR amplification system was as follows: 2.0 µL KASP 2× Master Mix, 0.0336 µL KASP primers, and 2 µL template DNA (50 ng/µL). The amplification reaction was performed using a water bath PCR instrument from LGC (Laboratory of the Government Chemist, London, England). The specific reaction program was as follows: initial denaturation at 94 °C for 15 min (1 cycle), followed by 10 cycles of denaturation at 94 °C for 20 s and annealing at a temperature gradient from 65 °C to 57 °C for 1 min. Finally, 35 cycles of denaturation at 94 °C for 20 s and annealing at 57 °C for 1 min were performed. The temperature and time were gradually adjusted to ensure effective DNA amplification. After the amplification program, the sample plate was scanned using the 384 KASP program on the PHEstar instrument (BMG Labtech GmbH, Ortenberg, Germany) to read fluorescence values. The genotyping results were visualized using KLUSTER V1.0 (LGC, Hoddesdon, UK). Typically, if FAM fluorescence alone was detected, the amplification product emitted blue fluorescence; if HEX fluorescence alone was detected, the product emitted red fluorescence; and if FAM and HEX fluorescence were both detected, the product emitted green fluorescence.

4.4. Candidate Gene Prediction

Candidate genes were selected based on gene annotation information from the Chinese Spring reference genome (IWGSC RefSeq v2.1). The wheat gene expression database (http://www.wheat-expression.com/, accessed on 18 July 2025) was used to analyze the expression patterns of candidate genes and identify those specifically and highly expressed in leaves. The specific steps were as follows: First, high-confidence candidate genes within the identified QTL regions were screened using the annotation information provided by IWGSC RefSeq v2.1. Second, the candidate gene list was further simplified by including proteins encoded by reported cloned genes related to wheat fungal diseases, such as lectin receptor kinase proteins and ABC transporter proteins. Third, existing public databases were used to determine the expression patterns and expression tissues of the candidate genes. The above three criteria provide a preliminary candidate gene.

5. Conclusions

This study identified five APR QTLs for leaf rust resistance in wheat, each explaining 4.54–8.91% of the PVE. QLr.lfnu-1BL1 and QLr.lfnu-2AL are likely novel loci, enriching the genetic resources available for breeding. We developed two breeder-friendly KASP markers, validated in a natural population, demonstrating their potential for MAS breeding. Furthermore, 34 genes within the QTL intervals were prioritized as candidate genes. While the individual QTL effects are moderate, their pyramiding could enhance resistance durability. Future work should focus on fine-mapping the novel QTLs, validating these markers in diverse genetic backgrounds, and functionally characterizing the leading candidate genes. This study provides foundational resources for developing wheat cultivars with durable leaf rust resistance.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants14193113/s1, Table S1: The diverse panel used for KASP markers validation; Table S2: The candidate genes identified for leaf rust resistance.

Author Contributions

Conceptualization, Y.W. and J.L.; methodology, Y.W. and R.W.; software, Y.W. and W.L.; validation, Y.W. and W.L.; formal analysis, W.L., R.W., N.Z. and X.Z.; investigation, S.Z. and J.L.; resources, J.L.; data curation, J.L.; writing—review and editing, Y.W. and J.L.; visualization, Y.W. and J.L.; supervision, S.Z. and J.L.; project administration, S.Z. and J.L.; funding acquisition, S.Z., J.L., Y.W., R.W. and N.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Hebei Provincial Natural Science Foundation, Youth Science Foundation Project (C2021408014); Shenzhen Key Science and Technology Program (KCXFZ20240903092403005); National Natural Science Foundation of China Youth Science Fund Project (Class C) (32401902); Langfang Normal University Research Foundation Project—Doctoral (Postdoctoral) Research Startup Fund Project (XBQ202301); the Science and Technology Research Project for Colleges and Universities in Hebei Province (QN2023004); Langfang Normal University Doctoral Research Initiation Fund (XBQ202307).

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

AbbreviationFull name
ABCATP-binding cassette
ANOVAAnalysis of variance
APRAdult-plant resistance
ASRAll-stage resistance
BLUEBest linear unbiased estimation
Hb2Broad-sense heritability
KASPKompetitive allele-specific PCR
LODLog of odds
MASMarker-assisted selection
MDSMaximum disease severity
PtPuccinia triticina
PVEPhenotypic variances explained
QTLQuantitative trait loci
RILsRecombinant inbred lines
SNPSingle-nucleotide polymorphism

Appendix A

Table A1. The KASP primers for two KASP markers developed in this study.
Table A1. The KASP primers for two KASP markers developed in this study.
Kasp MarkerPrimeSequence
KASP-LR-2ALFAMGAAGGTGACCAAGTTCATGCTAGTCACAACCCCGCCTCA
HEXGAAGGTCGGAGTCAACGGATTAGTCACAACCCCGCCTCG
CommonACATGTGCCTCCAGCTGC
KASP-LR-7BL2FAMGAAGGTGACCAAGTTCATGCTGCACGTCTCCTTCATTGGAT
HEXGAAGGTCGGAGTCAACGGATTGCACGTCTCCTTCATTGGAC
CommonCTCATCTTCTTTCAGGCCGG
The sequence of the FAM and HEX are underlined.

References

  1. Huerta-Espino, J.; Singh, R.P.; Germán, S.; McCallum, B.D.; Park, R.F.; Chen, W.Q.; Bhardwaj, S.C.; Goyeau, H. Global status of wheat leaf rust caused by Puccinia triticina. Euphytica 2011, 179, 143–160. [Google Scholar] [CrossRef]
  2. Li, Z.; Lan, C.; He, Z.; Singh, R.P.; Rosewarne, G.M.; Chen, X.; Xia, X. Overview and application of QTL for adult plant resistance to leaf rust and powdery mildew in wheat. Crop Sci. 2014, 54, 1907–1925. [Google Scholar] [CrossRef]
  3. Rehman, S.U.; Qiao, L.; Shen, T.; Hua, L.; Li, H.; Ahmad, Z.; Chen, S. Exploring the frontier of wheat rust resistance: Latest approaches, mechanisms, and novel insights. Plants 2024, 13, 2502. [Google Scholar] [CrossRef]
  4. Wang, N.; Tang, C.; Fan, X.; He, M.; Gan, P.; Zhang, S.; Hu, Z.; Wang, X.; Yan, T.; Shu, W.; et al. Inactivation of a wheat protein kinase gene confers broad-spectrum resistance to rust fungi. Cell 2022, 185, 2961–2974. [Google Scholar] [CrossRef] [PubMed]
  5. Singh, R.P.; William, H.M.; Huerta-Espino, J.; Rosewarne, G. Wheat rust in Asia: Meeting the challenges with old and new technologies. In Proceedings of the 4th International Crop Science Congress, Brisbane, Australia, 26 September–1 October 2004; Volume 26, pp. 1–13. [Google Scholar]
  6. Zhao, J.; Kang, Z. Fighting wheat rusts in China: A look back and into the future. Phytopathol. Res. 2023, 5, 6. [Google Scholar] [CrossRef]
  7. Basnet, B.; Juliana, P.; Bhattarai, K.; Upreti, U. A review on major rust resistance gene and amino acid changes on wheat (Triticum aestivum L). Adv. Agric. 2022, 2022, 7419326. [Google Scholar] [CrossRef]
  8. Panneer, S.; Sivasamy, M.; Vikas, V.K.; Bhardwaj, S.C.; Gangwar, O.P.; Peter, J.; Sivasamy, V. Stacking effective ASR and APR rust genes for multiple disease resistance in bread wheat cultivars. Crop Breed. Appl. Biotechnol. 2023, 23, e44082317. [Google Scholar]
  9. Sharma, D.; Avni, R.; Gutierrez-Gonzalez, J.; Kumar, R.; Sela, H.; Prusty, M.R.; Shatil-Cohen, A.; Molnár, I.; Holušová, K.; Said, M.; et al. A single NLR gene confers resistance to leaf and stripe rust in wheat. Nat. Commun. 2024, 15, 9925. [Google Scholar] [CrossRef]
  10. Anguelova-Merhar, V.S.; VanDer Westhuizen, A.J.; Pretorius, Z.A. β-1,3-glucanase and chitinase activities and the resistance response of wheat to leaf rust. J. Phytopathol. 2001, 149, 381–384. [Google Scholar]
  11. Yang, W.; Liu, D.; Li, J.; Zhang, L.; Wei, H.; Hu, X.; Zheng, Y.; He, Z.; Xia, X. Synthetic hexaploid wheat and its utilization for wheat genetic improvement in China. J. Genet. Genom. 2015, 42, 539–546. [Google Scholar] [CrossRef] [PubMed]
  12. Das, M.K.; Rajaram, S.; Kronstad, W.E.; Mundt, C.C.; Singh, R.P. Associations and genetics of three components of slow rusting in leaf rust of wheat. Euphytica 1993, 68, 99–109. [Google Scholar] [CrossRef]
  13. Bansal, U.K.; Forrest, K.L.; Hayden, M.J.; Miah, H.; Singh, D.; Bariana, H.S. Characterisation of a new stripe rust resistance gene Yr47 and its genetic association with the leaf rust resistance gene Lr52. Theor. Appl. Genet. 2011, 122, 1461–1466. [Google Scholar] [CrossRef]
  14. Herrera-Foessel, S.A.; Singh, R.P.; Huerta-Espino, J.; Rosewarne, G.M.; Periyannan, S.K.; Viccars, L.; Calvo-Salazar, V.; Lan, C.; Lagudah, E.S. Lr68: A new gene conferring slow rusting resistance to leaf rust in wheat. Theor. Appl. Genet. 2012, 124, 1475–1486. [Google Scholar] [CrossRef] [PubMed]
  15. Klymiuk, V.; Yaniv, E.; Huang, L.; Raats, D.; Fatiukha, A.; Chen, S.; Feng, L.; Frenkel, Z.; Krugman, T.; Lidzbarsky, G.; et al. Cloning of the wheat Yr15 resistance gene sheds light on the plant tandem kinase-pseudokinase family. Nat. Commun. 2018, 9, 3735. [Google Scholar] [CrossRef]
  16. Kolmer, J.A.; Fajolu, O.L.; Bai, B.; Chen, X.; Jin, Y. Genetics of leaf rust resistance in the soft red winter wheat cultivar AGS 2000. Plant Dis. 2021, 105, 1593–1598. [Google Scholar]
  17. Kolodziej, M.C.; Singla, J.; Sánchez-Martín, J.; Zbinden, H.; Šimková, H.; Karafiátová, M.; Doležel, J.; Gronnier, J.; Poretti, M.; Glauser, G.; et al. A membrane-bound ankyrin repeat protein confers race-specific leaf rust disease resistance in wheat. Nat. Commun. 2021, 12, 956. [Google Scholar] [CrossRef]
  18. Amo, A.; Soriano, J.M. Unravelling consensus genomic regions conferring leaf rust resistance in wheat via meta-QTL analysis. Plant Genome 2022, 15, e20185. [Google Scholar] [CrossRef]
  19. Li, C.; Liu, H.; Wang, J.; Pan, Q.; Wang, Y.; Wu, K.; Jia, P.; Mu, Y.; Tang, H.; Xu, Q.; et al. Characterization and fine mapping of a lesion mimic mutant (Lm5) with enhanced stripe rust and powdery mildew resistance in bread wheat (Triticum aestivum L.). Theor. Appl. Genet. 2022, 135, 421–438. [Google Scholar] [CrossRef]
  20. Pal, N.; Jan, I.; Saini, D.K.; Kumar, K.; Kumar, A.; Sharma, P.K.; Kumar, S.; Balyan, H.S.; Gupta, P.K. Meta-QTLs for multiple disease resistance involving three rusts in common wheat (Triticum aestivum L.). Theor. Appl. Genet. 2022, 135, 2385–2405. [Google Scholar] [CrossRef]
  21. Soriano, J.M.; Royo, C. Dissecting the genetic architecture of leaf rust resistance in wheat by QTL meta-analysis. Phytopathology 2015, 105, 1585–1593. [Google Scholar] [CrossRef]
  22. Bhardwaj, S.C.; Singh, G.P.; Gangwar, O.P.; Prasad, P.; Kumar, S. Status of wheat rust research and progress in rust management-Indian context. Agronomy 2019, 9, 892. [Google Scholar] [CrossRef]
  23. Suenaga, K.; Singh, R.P.; Huerta-Espino, J.; William, H.M. Microsatellite markers for genes Lr34/Yr18 and other quantitative trait loci for leaf rust and stripe rust resistance in bread wheat. Phytopathology 2003, 93, 881–890. [Google Scholar] [CrossRef]
  24. Bariana, H.; Forrest, K.; Qureshi, N.; Miah, H.; Hayden, M.; Bansal, U. Adult plant stripe rust resistance gene Yr71 maps close to Lr24 in chromosome 3D of common wheat. Mol. Breed. 2016, 36, 98. [Google Scholar] [CrossRef]
  25. Chugunkova, T.V.; Pastukhova, N.L.; Pirko, Y.V.; Blume, Y.B. Genetic Basis of Resistance to Wheat Yellow Rust. Cytol. Genet. 2025, 59, 186–196. [Google Scholar] [CrossRef]
  26. Majeed, K.; Sufyan, M.; Abbasi, K.; Ahsan, R.; Bux, H.; Zakria, M.; Fayyaz, M.; Mirza, J.I.; Ali, M.; Rasheed, A.; et al. Stripe rust resistance in historical wheat cultivars at seedling and adult plant stages and tagging effective resistance genes using molecular markers. J. Plant Pathol. 2025, 107, 1–12. [Google Scholar] [CrossRef]
  27. Rasheed, A.; Liu, J.; Appels, R.; He, Z. Mobilizing Triticeae diversity from gene banks to farmer’s field. Mol. Plant 2025, 18, 566–569. [Google Scholar] [CrossRef]
  28. Mago, R.; Bariana, H.S.; Dundas, I.S.; Spielmeyer, W.; Lawrence, G.J.; Pryor, A.J.; Ellis, J.G. Development of PCR markers for the selection of wheat stem rust resistance genes Sr24 and Sr26 in diverse wheat germplasm. Theor. Appl. Genet. 2005, 111, 496–504. [Google Scholar] [CrossRef]
  29. Dyck, P.L.; Samborski, D.J. Inheritance of resistance to leaf rust and stem rust in the wheat cultivar Glenlea. Can. J. Genet. Cytol. 1974, 16, 323–332. [Google Scholar] [CrossRef]
  30. Singh, R.P.; Huerta-Espino, J.; Rajaram, S.; Crossa, J. Agronomic effects from chromosome translocations 7DL.7Ag and 1BL1RS in spring wheat. Crop Sci. 1998, 38, 27–33. [Google Scholar] [CrossRef]
  31. Marais, G.F.; Pretorius, Z.A.; Wellings, C.R.; McCallum, B.; Marais, A.S. Lr51: A new gene for leaf rust resistance in wheat. Theor. Appl. Genet. 2005, 110, 971–976. [Google Scholar]
  32. Koller, T.; Brunner, S.; Herren, G.; Hurni, S.; Keller, B. Pyramiding of transgenic Pm3 alleles in wheat results in improved powdery mildew resistance in the field. Theor. Appl. Genet. 2018, 131, 861–871. [Google Scholar] [CrossRef]
  33. Schnurbusch, T.; Paillard, S.; Schori, A.; Messmer, M.; Schachermayr, G.; Winzeler, M.; Keller, B. Dissection of quantitative and durable leaf rust resistance in Swiss winter wheat reveals a major resistance QTL in the Lr34 chromosomal region. Theor. Appl. Genet. 2004, 108, 477–484. [Google Scholar] [CrossRef]
  34. Li, Z.F.; Xia, X.C.; He, Z.H.; Li, X.; Zhang, L.J.; Wang, H.Y.; Meng, Q.F.; Yang, W.X.; Li, G.Q.; Liu, D.Q. Seedling and slow rusting resistance to leaf rust in Chinese wheat cultivars. Plant Dis. 2010, 94, 45–53. [Google Scholar] [CrossRef]
  35. Hiebert, C.W.; Thomas, J.B.; McCallum, B.D.; Humphreys, D.G.; DePauw, R.M.; Hayden, M.J.; Mago, R.; Schnippenkoetter, W.; Spielmeyer, W. An introgression on wheat chromosome 4DL in RL6077 (Thatcher6/PI 250413) confers adult plant resistance to stripe rust and leaf rust (Lr67). Theor. Appl. Genet. 2010, 121, 1083–1091. [Google Scholar] [CrossRef]
  36. Herrera-Foessel, S.A.; Singh, R.P.; Huerta-Espino, J.; Crossa, J.; Yuen, J.; Djurle, A. Genetic analysis of slow-rusting resistance to leaf rust in spring wheat. Theor. Appl. Genet. 2008, 117, 1029–1039. [Google Scholar]
  37. Kuraparthy, V.; Chhuneja, P.; Dhaliwal, H.S.; Kaur, S.; Bowden, R.L.; Gill, B.S. Characterization and mapping of cryptic alien introgression from Aegilops geniculata with new leaf rust and stripe rust resistance genes Lr57 and Yr40 in wheat. Theor. Appl. Genet. 2007, 114, 1379–1389. [Google Scholar] [CrossRef]
  38. William, H.M.; Singh, R.P.; Huerta-Espino, J.; Palacios, G.; Suenaga, K. Characterization of genetic loci conferring adult plant resistance to leaf rust and stripe rust in spring wheat. Genome 2006, 49, 977–990. [Google Scholar] [CrossRef]
  39. Lillemo, M.; Asalf, B.; Singh, R.P.; Huerta-Espino, J.; Chen, X.M.; He, Z.H.; Bjørnstad, Å. The adult plant rust resistance loci Lr34/Yr18 and Lr46/Yr29 are important determinants of partial resistance to powdery mildew in bread wheat line Saar. Theor. Appl. Genet. 2008, 116, 1155–1166. [Google Scholar] [CrossRef]
  40. Zhang, J.; Kang, Z.; Li, X.; Li, M.; Xue, L.; Li, X. QTL Mapping of Adult Plant Resistance to Wheat Leaf Rust in the Xinong1163-4× Thatcher RIL Population. Agronomy 2025, 15, 1717. [Google Scholar] [CrossRef]
  41. Liu, S.; Zhao, L.; Hao, C.; Pan, Y.; Guo, M.; Huang, Y.; Zhang, X. TaRLK-1B: A novel wheat gene conferring resistance to leaf rust revealed by a genome-wide association study. J. Integr. Agric. 2025, in press. [Google Scholar] [CrossRef]
  42. Gao, P.; Zhou, Y.; Gebrewahid, T.W.; Zhang, P.; Wang, S.; Liu, D.; Li, Z. QTL mapping for adult-plant resistance to leaf rust in Italian wheat cultivar Libellula. Plant Dis. 2024, 108, 13–19. [Google Scholar] [CrossRef]
  43. Li, X.; Tan, W.; Feng, J.; Yan, Q.; Tian, R.; Chen, Q.; Zhou, X. Mapping QTLs for Stripe Rust Resistance and Agronomic Traits in Chinese Winter Wheat Lantian 31 Using 15K SNP Array. Agriculture 2025, 15, 1444. [Google Scholar] [CrossRef]
  44. Dyck, P.L. The transfer of leaf rust resistance from Triticum turgidum ssp. dicoccoides to hexaploid wheat. Can. J. Plant Sci. 1994, 74, 671–673. [Google Scholar] [CrossRef]
  45. Seah, S.; Bariana, H.; Jahier, J.; Sivasithamparam, K.; Lagudah, E.S. The introgressed segment carrying rust resistance genes Yr17, Lr37 and Sr38 in wheat can be assayed by a cloned disease resistance gene-like sequence. Theor. Appl. Genet. 2001, 102, 600–605. [Google Scholar] [CrossRef]
  46. Hysing, S.C.; Merker, A.; Liljeroth, E.; Koebner, R.M.; Zeller, F.J.; Hsam, S.L.K. Powdery mildew resistance in 155 Nordic bread wheat cultivars and landraces. Hereditas 2007, 144, 102–119. [Google Scholar] [CrossRef]
  47. Boukhatem, N.; Baret, P.V.; Mingeot, D.; Jacquemin, J.M. Quantitative trait loci for resistance against yellow rust in two wheat-derived recombinant inbred line populations. Theor. Appl. Genet. 2002, 104, 111–118. [Google Scholar] [CrossRef]
  48. Wang, W.; Pan, Q.; Tian, B.; He, F.; Chen, Y.; Bai, G.; Akhunova, A.; Trick, H.N.; Akhunov, E. Gene editing of the wheat homologs of TONNEAU 1-recruiting motif encoding gene affects grain shape and weight in wheat. Plant J. 2019, 100, 251–264. [Google Scholar] [CrossRef]
  49. McCallum, B.D.; Hiebert, C.W.; Cloutier, S.; Bakkeren, G.; Rosa, S.B.; Humphreys, D.G.; Marais, G.F.; McCartney, C.A.; Panwar, V.; Rampitsch, C.; et al. A review of wheat leaf rust research and the development of resistant cultivars in Canada. Can. J. Plant Pathol. 2016, 38, 1–18. [Google Scholar] [CrossRef]
  50. Li, C.; Xu, X.T.; Zhang, Y.; Liu, S.; Wu, J.; Han, D.; Bai, G. Mapping QTLs for adult-plant resistance to yellow rust in a hard winter wheat population Heyne× Lakin. Theor. Appl. Genet. 2025, 138, 192. [Google Scholar] [CrossRef]
  51. Maccaferri, M.; Zhang, J.; Bulli, P.; Abate, Z.; Chao, S.; Cantu, D.; Bossolini, E.; Chen, X.; Pumphrey, M.; Dubcovsky, J. A genome-wide association study of resistance to stripe rust (Puccinia striiformis f. sp. tritici) in a worldwide collection of hexaploid spring wheat (Triticum aestivum L.). G3 Genes Genomes Genet. 2015, 5, 449–465. [Google Scholar] [CrossRef]
  52. Patpour, M.; Hovmøller, M.S.; Rodriguez-Algaba, J.; Randazzo, B.; Villegas, D.; Shamanin, V.P.; Berlin, A.; Flath, K.; Czembor, P.; Hanzalova, A.; et al. Wheat stem rust back in Europe: Diversity, prevalence and impact on host resistance. Front. Plant Sci. 2022, 13, 882440. [Google Scholar] [CrossRef]
  53. Singh, R.P.; Singh, P.K.; Rutkoski, J.; Hodson, D.P.; He, X.; Jørgensen, L.N.; Hovmøller, M.S.; Huerta-Espino, J. Disease impact on wheat yield potential and prospects of genetic control. Annu. Rev. Phytopathol. 2016, 54, 303–322. [Google Scholar] [CrossRef]
  54. Rosewarne, G.M.; Herrera-Foessel, S.A.; Singh, R.P.; Huerta-Espino, J.; Lan, C.X.; He, Z. Quantitative trait loci of stripe rust resistance in wheat. Theor. Appl. Genet. 2013, 126, 2427–2449. [Google Scholar] [CrossRef] [PubMed]
  55. Lin, X.; N’Diaye, A.; Walkowiak, S.; Nilsen, K.T.; Cory, A.T.; Haile, J.; Kutcher, H.R.; Ammar, K.; Loladze, A.; Huerta-Espino, J.; et al. Genetic analysis of resistance to stripe rust in durum wheat (Triticum turgidum L. var. durum). PLoS ONE 2018, 13, e0203283. [Google Scholar] [CrossRef]
  56. Lagudah, E.S.; McFadden, H.; Singh, R.P.; Huerta-Espino, J.; Bariana, H.S.; Spielmeyer, W. Molecular genetic characterization of the Lr34/Yr18 slow rusting resistance gene region in wheat. Theor. Appl. Genet. 2006, 114, 21–30. [Google Scholar] [CrossRef]
  57. Zhao, D.; Yang, L.; Liu, D.; Zeng, J.; Cao, S.; Xia, X.; Yan, J.; Song, X.; He, Z.; Zhang, Y. Fine mapping and validation of a major QTL for grain weight on chromosome 5B in bread wheat. Theor. Appl. Genet. 2021, 134, 3731–3741. [Google Scholar] [CrossRef]
  58. Dong, Y.; Xu, D.; Xu, X.; Ren, Y.; Gao, F.; Song, J.; Jia, A.; Hao, Y.; He, Z.; Xia, X. Fine mapping of QPm.caas-3BS, a stable QTL for adult-plant resistance to powdery mildew in wheat (Triticum aestivum L.). Theor. Appl. Genet. 2022, 135, 1083–1099. [Google Scholar] [CrossRef]
  59. Rasheed, A.; Wen, W.; Gao, F.; Zhai, S.; Jin, H.; Liu, J.; Guo, Q.; Zhang, Y.; Dreisigacker, S.; Xia, X.; et al. Development and validation of KASP assays for genes underpinning key economic traits in bread wheat. Theor. Appl. Genet. 2016, 129, 1843–1860. [Google Scholar] [CrossRef] [PubMed]
  60. Rasheed, A.; Hao, Y.; Xia, X.; Khan, A.; Xu, Y.; Varshney, R.K.; He, Z. Crop breeding chips and genotyping platforms: Progress, challenges, and perspectives. Mol. Plant 2017, 10, 1047–1064. [Google Scholar] [CrossRef]
  61. Guo, H.; Liu, Y.; Yuan, S.; Yue, J.; Li, Y.; Liao, X.; Ying, S.; Liu, Z.; Bai, J.; Zhang, L. The new function of superoxide dismutase (SOD) in Triticum aestivum L.: Promote early flowering. Agric. Commun. 2023, 1, 100007. [Google Scholar]
  62. Meng, L.; Li, H.; Zhang, L.; Wang, J. QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J. 2015, 3, 269–283. [Google Scholar] [CrossRef]
  63. Wen, W.; He, Z.; Gao, F.; Liu, J.; Jin, H.; Zhai, S.; Qu, Y.; Xia, X. A high-density consensus map of common wheat integrating four mapping populations sffcanned by the 90K SNP array. Front. Plant Sci. 2017, 8, 1389. [Google Scholar] [CrossRef] [PubMed]
Plants 14 03113 g001
Figure 1. The QTL for leaf rust resistance identified in Doumai/Shi4185 RIL population.
Figure 1. The QTL for leaf rust resistance identified in Doumai/Shi4185 RIL population.
Plants 14 03113 g001
Plants 14 03113 g002
Figure 2. The effects of favorable allele combinations on maximum disease severity (MDS) of leaf rust among different classes in the Doumai/Shi4185 RIL population. The number of favorable alleles combined in each subset of RIL. Different alphabets indicate significant difference at p < 0.05.
Figure 2. The effects of favorable allele combinations on maximum disease severity (MDS) of leaf rust among different classes in the Doumai/Shi4185 RIL population. The number of favorable alleles combined in each subset of RIL. Different alphabets indicate significant difference at p < 0.05.
Plants 14 03113 g002
Plants 14 03113 g003
Figure 3. Validation of KASP markers QLr.lfnu-2AL and QLr.lfnu-7BL in the Doumai/Shi4185 RIL population. n means the number of lines. Different alphabets indicate significant difference at p < 0.05. MDS: means the maximum disease severity; 2AS or 2AR means the combination of the resistance or susperiatble allelel of QLr.lfnu-2AL; 7BS or 7BR means the combination of the resistance or susperiatble allelel of QLr.lfnu-7BL.
Figure 3. Validation of KASP markers QLr.lfnu-2AL and QLr.lfnu-7BL in the Doumai/Shi4185 RIL population. n means the number of lines. Different alphabets indicate significant difference at p < 0.05. MDS: means the maximum disease severity; 2AS or 2AR means the combination of the resistance or susperiatble allelel of QLr.lfnu-2AL; 7BS or 7BR means the combination of the resistance or susperiatble allelel of QLr.lfnu-7BL.
Plants 14 03113 g003
Plants 14 03113 g004
Figure 4. Validation of QTL QLr.lfnu-2AL and QLr.lfnu-7BL2 in the panel of 150 wheat cultivars from the Huang-Huai River Valleys region. Different alphabets indicate significance at p < 0.05. MDS: maximum disease severity for leaf rust.
Figure 4. Validation of QTL QLr.lfnu-2AL and QLr.lfnu-7BL2 in the panel of 150 wheat cultivars from the Huang-Huai River Valleys region. Different alphabets indicate significance at p < 0.05. MDS: maximum disease severity for leaf rust.
Plants 14 03113 g004
Plants 14 03113 g005
Figure 5. Expression pattern of the candidate genes for leaf rust resistance (https://www.wheat-expression.com/).
Figure 5. Expression pattern of the candidate genes for leaf rust resistance (https://www.wheat-expression.com/).
Plants 14 03113 g005
Table 1. Summary of the MDS for leaf rust resistance in Doumai/Shi4185 RIL population.
Table 1. Summary of the MDS for leaf rust resistance in Doumai/Shi4185 RIL population.
EnvironmentDoumaiShi4185Mean
MDS (%)		Range of
MDS (%)		Standard
Deviation		Coefficient of
Variation
Xinxiang202148.038.043.3 0–9833.2 0.8
Xinxiang202252.032.053.8 0–9528.8 0.5
Zhengzhou202150.035.052.2 0–9633.8 0.6
Zhengzhou202222.018.034.9 1–9828.3 0.8
Table 2. The QTL for leaf rust resistance identified in Doumai/Shi4185 RIL population.
Table 2. The QTL for leaf rust resistance identified in Doumai/Shi4185 RIL population.
QTLChromosomeMarker
Interval		Physical
Interval (Mb)		LODPVE (%) aAdditiveEnvironment b
QLr.lfnu-1BL11Btplb0033h08_101~
D_contig03023_692		484.2–487.62.92–3.566.24–7.15−7.82–9.04E1, E3
QLr.lfnu-1BL21BTdurum_contig84791_198~
Ex_c1058_1537		674.0–678.33.38–3.986.85–8.67−8.58–9.97E1, E4
QLr.lfnu-2AL2Awsnp_JD_c289_450995~
Excalibur_c40335_198		772.9–779.91.84–1.966.75–6.987.57–8.56E3, E4
QLr.lfnu-7BL17BBobWhite_c7208_88~
IACX8294		675.7–681.82.71–3.544.54–8.916.75–8.57E1, E3
QLr.lfnu-7BL27Btplb0058p02_2806~
BobWhite_rep_c53128_119		701.3–708.02.41–3.554.97–7.417.53–9.20E2, E4
a PVE: Phenotypic variance explained (%); b E1: Xinxiang at 2021–2022 cropping season; E2: Xinxiang at 2022–2023 cropping season; E3: Zhengzhou at 2021–2022 cropping season; E4: Zhengzhou at 2022–2023 cropping season.
Table 3. KASP-LR-2AL and KASP-LR-7BL2 validated in the diverse panel.
Table 3. KASP-LR-2AL and KASP-LR-7BL2 validated in the diverse panel.
QTLKASP Marker NameGenotypeNo. of LinesMaximum Disease Severity (%)p-Value
QLr.lfnu-2ALKASP-LR-2ALAA4354.10.049
GG9947.9
QLr.lfnu-7BL2KASP-LR-7BL2TT6853.10.007
CC8247.6
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, Y.; Li, W.; Wang, R.; Zhao, N.; Zhang, X.; Zhu, S.; Liu, J. QTL Mapping for Leaf Rust Resistance in a Common Wheat Recombinant Inbred Line Population of Doumai/Shi4185. Plants 2025, 14, 3113. https://doi.org/10.3390/plants14193113

AMA Style

Wang Y, Li W, Wang R, Zhao N, Zhang X, Zhu S, Liu J. QTL Mapping for Leaf Rust Resistance in a Common Wheat Recombinant Inbred Line Population of Doumai/Shi4185. Plants. 2025; 14(19):3113. https://doi.org/10.3390/plants14193113

Chicago/Turabian Style

Wang, Yamei, Wenjing Li, Rui Wang, Nannan Zhao, Xinye Zhang, Shu Zhu, and Jindong Liu. 2025. "QTL Mapping for Leaf Rust Resistance in a Common Wheat Recombinant Inbred Line Population of Doumai/Shi4185" Plants 14, no. 19: 3113. https://doi.org/10.3390/plants14193113

APA Style

Wang, Y., Li, W., Wang, R., Zhao, N., Zhang, X., Zhu, S., & Liu, J. (2025). QTL Mapping for Leaf Rust Resistance in a Common Wheat Recombinant Inbred Line Population of Doumai/Shi4185. Plants, 14(19), 3113. https://doi.org/10.3390/plants14193113

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop