Next Article in Journal
Effects of Temperature Fluctuations on Cabernet Sauvignon Branches and Wine Grape Appellation Yields: An Analysis Based on the Standardized Temperature Adaptation Index
Previous Article in Journal
Comprehensive Genome-Wide Characterization of L-Type Lectin Receptor-like Kinase (L-LecRLK) Genes in Wheat (Triticum aestivum L.) and Their Response to Abiotic Stress
Previous Article in Special Issue
Yield Determination in Major Small Grain Crops in Response to Nitrogen Fertilization
 
 
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 12
10.3390/plants14121883
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

Genome-Wide Identification of Wheat Gene Resources Conferring Resistance to Stripe Rust

by
Qiaoyun Ma
1,†,
Dong Yan
2,†,
Binshuang Pang
1,
Jianfang Bai
1,
Weibing Yang
1,
Jiangang Gao
1,
Xianchao Chen
1,
Qiling Hou
1,
Honghong Zhang
1,
Li Tian
1,
Yahui Li
1,
Jizeng Jia
2,
Lei Zhang
3,
Zhaobo Chen
1,*,
Lifeng Gao
2 and
Xiangzheng Liao
1,*
1
Institute of Hybrid Wheat, Beijing Academy of Agricultural and Forestry Sciences (BAAFS), Beijing 100097, China
2
Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
3
Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Plants 2025, 14(12), 1883; https://doi.org/10.3390/plants14121883
Submission received: 19 May 2025 / Revised: 13 June 2025 / Accepted: 16 June 2025 / Published: 19 June 2025
(This article belongs to the Special Issue Improvement of Agronomic Traits and Nutritional Quality of Wheat)
Browse Figures
Review Reports Versions Notes

Abstract

:
Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), threatens global wheat production. Breeding resistant varieties is a key to disease control. In this study, 198 modern wheat varieties were phenotyped with the prevalent Pst races CYR33 and CYR34 at the seedling stage and with mixed Pst races at the adult-plant stage. Seven stable resistance varieties with infection type (IT) ≤ 2 and disease severity (DS) ≤ 20% were found, including five Chinese accessions (Zhengpinmai8, Zhengmai1860, Zhoumai36, Lantian36, and Chuanmai32), one USA accession (GA081628-13E16), and one Pakistani accession (Pa12). The genotyping applied a 55K wheat single-nucleotide polymorphism (SNP) array. A genome-wide association study (GWAS) identified 14 QTL using a significance threshold of p ≤ 0.001, which distributed on chromosomes 1B (4), 1D (2), 2B (4), 6B, 6D, 7B, and 7D (4 for CYR33, 7 for CYR34, 3 for mixed Pst races), explaining 6.04% to 18.32% of the phenotypic variance. Nine of these QTL were potentially novel, as they did not overlap with the previously reported Yr or QTL loci within a ±5.0 Mb interval (consistent with genome-wide LD decay). The haplotypes and resistance effects were evaluated to identify the favorable haplotype for each QTL. Candidate genes within the QTL regions were inferred based on their transcription levels following the stripe rust inoculation. These resistant varieties, QTL haplotypes, and favorable alleles will aid in wheat breeding for stripe rust resistance.

1. Introduction

Bread wheat (Triticum aestivum L.) is one of the most crucial food crops globally and the second most widely grown food crop in China. Ensuring an adequate grain supply for the growing population is of utmost importance for food security. However, abiotic and biotic stresses present major challenges to wheat production. Stripe rust (Yr), caused by the fungus Puccinia striiformis f. sp. tritici Erikss. (Pst), seriously threatens global wheat production. It generally leads to 5–25% yield losses, and, in some cases, the losses can reach 70% [1]. In China, devastating stripe rust epidemics occurred in 1950, 1964, 1990, and 2002 [2]. In 2017, the disease affected 1.65 million hectares of wheat planting area across 12 provinces [3].
Wheat resistance to stripe rust can be classified as all-stage resistance (ASR) or adult-plant resistance (APR). ASR is detectable at the seedling stage and remains effective throughout the wheat development. It is usually controlled by a single major gene, which is often race-specific and qualitatively inherited. However, due to its specific nature and the strong selection pressure exerted by evolving pathogen populations, ASR is often overcome [4]. Consequently, ASR usually becomes ineffective within 3–5 years. In contrast, APR is typically controlled by multiple genes, each with a minor or partial effect, and it is highly expressed at the adult-plant stage. These genes do not confer immunity or high resistance and are known as horizontal, partial, race-nonspecific, slow-rusting, or durable resistances [5,6].
To date, more than 80 Yr genes for stripe rust resistance have been formally designated [7], and more than 100 temporarily named Yr genes and numerous QTL have been reported in wheat [8,9]. Most of these genes and QTL are valuable for breeding programs, and many, especially those identified in the past decade, have been used in marker-assisted selection. Nevertheless, most of the identified genes are race-specific. They interact with the pathogen following the gene-for-gene model and trigger hypersensitive reactions, rendering them ineffective against newly prevalent Pst races [1,10]. Pyramiding effective and race-nonspecific resistance genes into wheat varieties is an essential strategy to confer resistance against emerging Pst races [11,12].
China is regarded as a unique epidemiological stripe rust zone and the world’s largest independent epidemic region in terms of the wheat area affected by the disease [8,13]. By 2016, 34 Pst races, designated CYR1 to CYR34, had been identified in China using diverse sets of stripe rust differentials. Newly evolved Pst races (e.g., CYR34) exhibit broader virulence spectra, overcoming most known resistance genes [14,15,16]. Only genes Yr5, Yr15, and Yr61, along with several tentatively named genes, are effective against CYR34 [17]. Thus, new genes that are resistant to CYR34 are needed for wheat breeding.
The genome-wide association study (GWAS), an effective method of investigating the genetic variation underlying complex traits through a correlation analysis, has been successfully applied to explore the genes related to stripe rust resistance in wheat [18,19,20,21,22,23,24]. In this study, 198 wheat cultivars were evaluated for resistance to Pst at the seedling stage in a greenhouse and at the adult-plant stage in the field. We conducted a GWAS using the wheat 55K SNP array. The objectives of this study were to identify new resistance loci to Pst races and tightly linked SNPs that can be used to improve wheat stripe rust resistance in future breeding programs.

2. Results

2.1. Stripe Rust Response at the Seedling Stage

The IT and DS of 198 cultivars to CYR33 and CYR34 at the seedling stage were recorded in two replications (Table S1). The phenotypic performance ranged from 0 to the maximum of 4 in infection types (ITs) and from 1 to 80% in disease severity (DS) (Figure 1a). A significant correlation was detected between the pairwise IT and DS values for both CYR34 and CYR33 infections (Figure 1b). The correlation coefficient for CYR33 (r = 0.37~0.60, p < 0.001) was lower than that for CYR34 (r = 0.72~0.91, p < 0.001), indicating different responses of accessions to CYR34 and CYR33.
Of the 198 accessions, 29 (14.6%) and 85 (42.9%) were resistant to CYR33 and CYR34 (DS ≤ 20%, IT 0–2), respectively (Table S1). Nineteen accessions, including 17 Chinese modern cultivars, one from the USA (GA081628-13E16), and one from Pakistan (Pa 12), exhibited resistance to both Pst races. Among the 17 resistant cultivars widely grown in China, Yumai13, Zhengpinmai8, Fengdecun1, and Xiaoyan54 showed immune or nearly immune responses to both CYR33 and CYR34 (IT 0–0, DS < 1%) based on the classification standard of GB/T 1443.1-2007 [25].

2.2. Stripe Rust Response at the Adult-Plant Stage

The stripe rust responses of the 198 accessions were also evaluated in the field in three environments (CD18, CD19, and CD20) using a mixture of seven Pst races prevalent in China. The phenotypic performance ranged from 0 to the maximum of 4 in IT, and from 0 to 100% in DS (Figure 1a). Significant correlations (r = 0.67~0.94, p < 0.001) were observed for the IT and DS values among the three environments at the adult-plant stage (Figure 1b). In total, 41 accessions exhibited resistance to the mixed Pst races (Table S1). Three accessions from the USA (GA081628-13E16, LA05032D-10, and LA06067C-P20) showed nearly immune responses to stripe rust with DS ≤ 1% and IT 0. Fifteen accessions had high resistance (DS ≤ 10% and IT ≤ 1), with three from the USA, three from Pakistan, and nine from China. If the DS range was set from 10% to 20%, and the IT range was set from 1 to 2 as moderately resistant (MR), an additional 23 accessions were identified, including six from Pakistan. Two accessions (St2422/464 and St1472/506) from Italy that are widely used in breeding in China showed moderate resistance to the mixed Pst races.
The panel had a higher frequency of resistance at the adult-plant stage (41 accessions) than at the seedling stage (19 accessions). Throughout the entire development, five wheat accessions from China (Chuanmai32, Lantian36, Zhoumai36, Zhengpinmai8, Zhengmai1860), one USA accession (GA081628-13E16), and one Pakistani accession (Pa12) demonstrated stable resistance to Pst races prevalent in China.

2.3. Population Structure, Genetic Diversity, and LD

After filtering, 44,759 SNP markers were used for the genetic analysis. The 198 accessions were grouped into two major clusters (G1 and G2) based on the SNP data, with G1 further divided into two subgroups and G2 further divided into eleven subgroups (Figure 2a). Most of the accessions in G1 were derivatives of Zhoumai9 and Aikang58 (Table S1). In G1, subgroups G1-1 and G1-2 each contained 14 accessions. In the tests against the mixed races, CYR34, and CYR33, the number of resistant cultivars detected in G1-1 was 5, 11, and 5, respectively, while in G1-2, it was 3, 8, and 2. Therefore, at both the adult-plant and seedling stages, the frequency of resistant accessions in G1-1 was slightly higher than that in G1-2. Subgroup G2-2-1 mainly consisted of cultivars from Pakistan, which showed resistance to stripe rust at the adult-plant stage. Five accessions clustered in G2-6 showed high resistance to CYR34 at the seedling stage but were susceptible to stripe rust at the adult-plant stage, and three of them are pedigrees from Lovrin10. All seven American accessions were clustered in G2-7, and six of them showed high-resistance responses (IT 0~1) at the adult-plant stage (Figure 2a, Table S1).
Based on the squared correlation coefficients (r2s) of all marker pairs among the 44,759 SNP markers, the extent of the LD decay in the 198 accessions was estimated, and the average LD decay rate was plotted against the physical distance. At the genome-wide level, when r2 ≥ 0.30, the LD decay distance was greater than 5.3 Mb, which was subsequently used as the confidence interval for significantly associated loci (Figure 2b).

2.4. GWAS and Haplotype Analysis for Stripe Rust Resistance

A GWAS using a mixed linear model identified 101 SNP markers that were significantly associated with stripe rust resistance. Specifically, 24 SNP markers were associated with resistance to CYR33 at the seedling stage, 50 were associated with resistance to CYR34 at the seedling stage, and 27 were associated with resistance to the mixed Pst races at the adult-plant stage. Considering the LD decay distance (±5.3 Mb), the MTA SNPs were clustered into 14 QTL, of which 11 were detected at the seedling stage and 3 at the adult-plant stage (Table 1). These 14 QTL mapped to chromosomes 1B, 1D, 2B, 6B, 6D, 7B, and 7D, explaining phenotypic variances (R2s) ranging from 6.04% to 18.32%. When compared with the previously reported Yr genes and QTL based on the CS reference genome, nine QTL, including Yr.baafs-1B.1, Yr.baafs-1B.2, Yr.baafs-1B.3, Yr.baafs-1B.4, Yr.baafs-1D.1, Yr.baafs-1D.2, Yr.baafs-2B.1, Yr.baafs-2B.2, and Yr.baafs-7B, were potentially novel loci. The remaining five QTL might be identical to the previously reported stripe rust resistance QTL or genes.

2.4.1. QTL Resistant to CYR33 at the Seedling Stage

Four QTL significantly associated with resistance to CYR33 were detected based on the DS and/or IT records. These QTL were located on chromosome 2B (3) and chromosome 7B (1), explaining 6.1–17.2% of the phenotypic variance (Table 1).
QYr.baafs-2B.1 was located within an 8 Mb interval (643.69–655.52 Mb, Figure 3a). A previously reported adult-plant resistance QTL, QYrSM155.1, was also located in this interval [26]. In QYr.baafs-2B.1, seven SNP markers significantly associated with CYR33 resistance were identified, forming 12 major haplotypes (Table S1). Approximately 85.9% of the accessions carried haplotypes QYr.baafs-2B.1-Hap1 to Hap5, with mean DS and IT values of 32.9% and 2.8, respectively. These results indicated that most of the Chinese commercial wheat varieties used in this study were moderately susceptible to the CYR33 race.
Among the identified haplotypes, QYr.baafs-2B.1-Hap6 conferred the strongest resistance to CYR33 (Figure 3b). Six accessions carrying this haplotype exhibited strong resistance, including four immune (Zhengpinmai8, Fengdecunmai1, Shi4185, and Yumai13) and two moderately resistant cultivars (Aikang58 and Guomai9) to CYR33. Aikang58, Zhengpinmai8, Fengdecunmai1, and Guomai9 are derivatives of Zhou8425B, which was previously reported to have high resistance to stripe rust [31]. Zhou8425B carries multiple stripe rust resistance genes, including YrZH84, YrZH84.2, YrZH22, and Yr30. Additionally, Shi4185 and Yumai13 are derivatives of Lovrin10 and Funo, respectively. These parental varieties have been widely used in Chinese wheat breeding programs and are well-known for their resistance to stripe rust [32].
Among the seven SNP markers associated with a CYR33 response in QYr.baafs-2B.1, three peak SNPs (AX-110393982, AX-108773165, and AX-109974803) were identified within a 683 Kb region. These SNPs formed a haplotype block (Figure 3c), which encompasses eight high-confidence genes (TraesCS2B02G452200~452900). Five genes of these (TraesCS2B02G452200 to TraesCS2B02G452500, and TraesCS2B02G452900) were expressed in the root, leaf, young spike, and developing grain tissues in Chinese Spring (Table S2, data derived from WheatOmics) [33].
Upon further annotation, it was discovered that TraesCS2B02G452300 contains a plastid lipid-associated protein PAP/fibrillin domain, while TraesCS2B02G452400 encodes a receptor protein kinase with a leucine-rich repeat N-terminal domain. These protein families have been previously linked to disease resistance, especially the leucine-rich repeat kinase family. For instance, TaFBN4 (TraesCS4A02G272000), encoding a plastid lipid-associated protein PAP/fibrillin domain, has been reported to be essential for wheat resistance against Pst race CYR33 [34]. The gene TraesCS2B02G452300 was named TaPAP8 and showed a high sequence similarity to its homologs in wheat (>90%) and other monocots (>80%). However, its sequence differed distinctly from TraesCS4A02G272000 (Figure S1). Moreover, transcriptional data from WheatOmics [33,35] indicated that the expression patterns of TaFBN4 and TaPAP8 were similar in their responses to infection by Pst race CYR31 (Figure S2). These results suggest that TraesCS2B02G452300 is a promising candidate gene underlying QYr.baafs-2B.1.

2.4.2. QTL for Resistance to CYR34 at the Seedling Stage

Seven QTL conferring resistance to CYR34 were identified in both the IT and DS trials. These QTL were distributed across chromosomes 1BS (3), 1DS (2), 6DL(1), and 7DS(1). Six of these loci were regarded as major QTL, each accounting for over 10% of the phenotypic variance (Table 1).

QTL on Chromosome 1B

Three QTL located on chromosome 1B conferred resistance to CYR34. QYr.baafs-1B.1, QYr.baafs-1B.2, and QYr.baafs-1B.3 were positioned on the short arm, within the intervals of 11.4 Mb, 6.3 Mb, and 6.7 Mb, respectively (Table 1). These QTL explained 8.23–18.32% of the phenotypic variance. Based on CS v1.0, Yr15 (Wtk1) was mapped to 6BS, and its homolog TraesCS1B01G079900 was located between 62,409,160 bp and 62,419,621 bp, 5 Mb proximal to QYr.baafs-1B.1. For QYr.baafs-1B.1, -1B.2, and -1B.3, seven, eight, and seven haplotypes were identified, respectively. The accessions carrying QYr.baafs-1B.1-Hap2, QYr.baafs-1B.2-Hap2, and QYr.baafs-1B.3-Hap1 showed high resistance to CYR34. These resistance haplotypes were present in 38 accessions, all demonstrating resistance to CYR34 (IT 0 or 0 and DS 1%, Table S1).
The QYr.baafs-1B.2 locus, which explained 18.32% of the phenotypic variance, was located within the 90.6–96.9 Mb interval. According to CS v1.0, the interval contains 50 high-confidence genes and 6 low-confidence genes. Based on the transcriptome data [33,35], we found that three genes (TraesCS1B02G090500, TraesCS1B02G091500, and TraesCS1B02G091900) had their expression levels increased by more than double following the inoculation with Pst race CYR31 (Table S3). TraesCS1B02G090500 encodes a glutamate–cysteine ligase (GSH1) associated with a defense response against bacterial and fungal infections [36]. TraesCS1B02G091500 encodes a bZIP transcription factor, which acts as a positive regulator in resistance to CYR32 [37]. TraesCS1B02G091900 encodes cysteine-rich receptor-like protein kinase 2 (TaCRK2), involved in wheat rust resistance [38,39]. These genes are potential candidates for QYr.baafs-1B.2.

QTL on Chromosome 1D

Two QTL conferring resistance to CYR34 were mapped to chromosome 1D in both the IT and DS trials (Table S1). The haplotypes Yr.baafs-1D.1-Hap3 and Yr.baafs-1D.2-Hap2 demonstrated significantly greater resistance to CYR34 infection compared to the other haplotypes. Specifically, these haplotypes exhibited IT values below 1 and DS values less than 10% (Figure S3). Among the four SNP markers strongly associated with CYR34 resistance in the QYr.baafs-1D.2 region, AX-94555465 is located within the gene TraesCS1D02G112300.1. This gene encodes 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase, a crucial regulatory enzyme involved in the primary carbohydrate metabolism of photosynthetic plant tissues. Based on the transcriptome data [33,35], the expression level of TraesCS1D02G112300.1 was found to be upregulated after the inoculation with stripe rust and powdery mildew (Figure S4).

QTL on Chromosome 6DL

QYr.baafs-6D was mapped to a 7.8 Mb interval on chromosome 6DL (465.05–472.82 Mb) (Table 1). Two SNP markers (AX-109362174 and AX-110999889), positioned at 467.5 Mb, explained up to 15% of the phenotypic variance at both the DS and IT trials. This locus overlapped with QYrCL.sicau-6DL, a previously identified adult-plant resistance QTL [29]. Three haplotypes formed for QYr.baafs-6D, and QYr.baafs-6D-Hap3 was present in 32 accessions showing immunity or near immunity to CYR34 (Table S1).

QTL on Chromosome 7DS

QYr.baafs-7D was mapped to a 100.7–104.7 Mb interval on the short arm of chromosome 7D. This locus explained 7.49–9.03% of the phenotypic variance for stripe rust resistance (Table 1). Notably, QYr.baafs-7D co-located with a previously reported adult-plant resistance QTL, QYr.rcrrc-7D [30]. Six SNP markers, namely AX-108860147, AX-109369183, AX-110392085, AX-111517979, AX-111855949, and AX-111687163, were significantly associated with stripe rust resistance. These markers defined three major haplotypes (Table S1). Sixty-three accessions with QYr.baafs-7D.1-Hap1 displayed immunity or near immunity to CYR34 (DS < 1% and IT 0 or 0). However, across all of the trials, the differences in the resistance effects among the haplotypes were not statistically significant. According to the WheatOmics data [33,35], within the QYr.baafs-7D region, the gene TraesCS7D02G155100, which encodes a protein kinase, showed upregulated expression levels at 24 h post inoculation (hpi) when challenged with the CYR31 and CYR32 races (Figure S5).

2.4.3. QTL Resistant to Stripe Rust at the Adult-Plant Stage

Three QTL associated with resistance to the mixed Pst races at the adult-plant stage were identified on chromosomes 1BL, 2BL, and 6BS, with one locus located on each chromosome. These three APR QTL had relatively minor effects on stripe rust resistance, accounting for 6.04–9.27% of the phenotypic variations. Notably, QYr.baafs-2B.2 and QYr.baafs-6B overlapped previously reported loci (Table 1).
Based on the CS v1.0 reference genome, 29 high-confidence genes are annotated in the QYr.baafs-1B.4 region (487.94–491.08 Mb). Among these genes, TraesCS1B01G281200.1 exhibited a positive response to both the stripe rust and powdery mildew infections (Figure S5). TraesCS1B01G281200.1 encodes a transmembrane protein, and a previous study [40] has reported that such proteins are involved in plant disease resistance. Four SNP markers in the QYr.baafs-1B.4 region (AX-110930185, AX-109885265, AX-108802815, and AX-108900982) were significantly associated with stripe rust resistance at the adult-plant stage. These four SNPs formed four haplotypes. QYr.baafs-1B.4-Hap4 was present in five accessions that showed the highest susceptibility to mixed Pst infections at the adult-plant stage (Table S1, Figure S6).
QYr.baafs-2B.4 was positioned within a 7.2 Mb interval (ranging from 780.02 to 787.23 Mb) and overlapped with qNV.Yr-2B.3, a locus previously reported in Vavilov wheat [6]. QYr.baafs-6B was mapped between the SNP markers AX-110198445 (73.54 Mb) and AX-110360466 (91.01 Mb), overlapping with two APR QTL, QYr.sicau-6BS [27] and QYr.nwafu-6BS.3 [28]. For QYr.sicau-6BS, 14 SNP markers associated with resistance formed nine haplotypes. Among these, QYr.sicau-6BS-Hap5 exhibited the highest resistance to the mixed Pst infections (Figure S7). Four of the eight accessions carrying QYr.sicau-6BS-Hap5 showed remarkable resistance phenotypes: LA06067C-P20 exhibited near immunity, with an IT of 0 and a DS of less than 1%, while Chuannong 19, Chuanmai 55, and Zhengmai 1860 showed resistance, with IT values below 1 and DS values under 20%. On chromosome 6BS, APR gene Yr78 was finely mapped between CDM88 (101.735 Mb) and CDM103 (112.898 Mb) [41]. This mapping position is approximately 10 Mb proximal to QYr.sicau-6BS, suggesting that QYr.sicau-6BS likely represents a different APR gene.

2.4.4. Distributions and Frequency of Favorable Haplotypes for Stripe Rust Resistance

The frequency of accessions carrying FHs was evaluated to provide valuable guidance for breeding programs. Among the 14 QTL identified, three QTL detected at the adult-plant stage (Yr.baafs-1B.4, Yr.baafs-2B.4, and Yr.baafs-6B) exhibited weak associations with dominant resistance haplotypes. This phenomenon can likely be attributed to their relatively small contributions to the phenotypic variance (Table S1). Only four accessions, LA06067C-P20, Chuannong 19, Chuanmai 55, and Zhengmai 1860, were found to carry a FH of Yr.baafs-6B. Among them, Chuannong 19 and Chuanmai 55 demonstrated high resistance to the mixed Pst races that are prevalent in China. Across three different field environments at the adult-plant stage, these two varieties exhibited a DS of less than 10% and an IT of less than 1.
For the seven QTL associated with resistance to the CYR34 race at the seedling stage, 26 accessions possessed only one FH, while 39 accessions carried three to six FHs. In total, 49 accessions, all of which carried FHs, exhibited immunity to near immunity against the CYR34 race. To assess the relationship between the number of FHs and the disease response, we plotted the DS and IT values against the number of FHs (Figure S8). The analysis revealed a highly significant inverse correlation (p < 0.001), indicating that as the number of FHs increased, both the DS and the IT values decreased. These findings strongly suggest a cumulative effect of pyramiding favorable alleles, which significantly enhance the resistance of wheat to stripe rust.
Zhou8425B has been used as a founder parent in wheat breeding in China since its release in 1988 and exhibits durable adult-plant resistance to yellow rust [42]. In the present study, 18 accessions are the derivative varieties of Zhou8425B. With the exception of Zhengmai 158, the remaining 17 accessions carried at least one FH associated with resistance to the CYR34 race. Among them, Zhengpinmai 8, Guomai 9, and Fengdecunmai 1 also possessed FHs conferring resistance to the CYR33 race. Additionally, Zhengmai 1860 has an extra APR favorable haplotype related to adult-plant resistance (Table S1).

3. Discussion

Due to the rapid breakdown of race-specific resistance, there is a growing consensus on the importance of breeding for durable resistance in crops. Pyramiding different resistance genes is an efficient strategy to develop varieties with durable resistance. In the past, this was nearly unfeasible because of the inability to detect different genes. However, molecular markers tightly linked to the target genes have now become powerful tools for this purpose. In the current study, we identified 14 QTL associated with stripe rust resistance, and their corresponding SNP markers will be invaluable for pyramiding resistance genes and breeding varieties with durable resistance. Additionally, we found that some varieties already carry multiple favorable alleles. For example, Zhengpinmai 8 carries seven FHs; these varieties may exhibit more durable resistance and could be selected as excellent resistant parents.
The SNP markers used for genotyping in this study are based on the 55K SNP array, which has been extensively used for the detection of agronomically important genes/QTL, with hundreds of studies published. Utilizing the same SNP markers facilitates the integration of the results, enabling the generation of a high-density map for agronomic traits and the identification of linked loci. For instance, the QYr.baafs-6D region was associated not only with yellow rust resistance but also with yield-related traits [43,44].
In this study, the R2 values of most QTL explaining phenotypic variances were less than 10%, lower than those detected in bi-parental populations. This phenomenon might be due to the relatively greater number of QTL identified here (a total of 14 QTL). Generally, the more QTL detected, the lower the R2 values tend to be. This is consistent with previous studies, where the percentage of the explained phenotypic variance (R2) of the associated markers was reported to range from 5 to 6% [45] and from 5 to 11% [46]. We observed some varieties carrying QTL with FHs but showing susceptibility to stripe rust, which might be due to background effects. Conversely, some accessions without FHs showed high resistance. For example, although Mazhamai showed immunity to CYR34, it did not possess any of the identified FHs, suggesting the presence of undetected resistance loci. To detect these missing QTL, the development of bi-parental populations is necessary. In addition, epistatic networks may suppress or enhance the effects of identified FHs. In some cases, recessive alleles at modifying loci could dampen the resistance expression in FH-carrying accessions, while synergistic interactions between minor-effect QTL might drive resistance in lines lacking major FHs.
It is noteworthy that we identified several accessions with FHs conferring resistance against stripe rust. Among them, Zhengmai 1860 showed resistance to both mixed Pst races and the CYR34 race. Additionally, seven accessions, including Guomai 9, Zhengpinmai 8, Shi 4185, Fengdecunmai 1, Yumai 13, Aikang 58, and Chuanmai 32, were resistant to both the CYR33 and CYR34 races. However, no accession was found to carry FHs against the CYR33, CYR34, and mixed Pst races simultaneously. Aikang 58 harbored the FHs of QYr.baafs-2B.1 and QYr.baafs-1D.1, which conferred moderate resistance to the CYR33 race and immunity to the CYR34 race. These accessions can serve as valuable parental lines for future wheat breeding programs to enhance stripe rust resistance.

4. Materials and Methods

4.1. Plant Materials

A panel of 198 wheat accessions conserved in the Beijing Academy of Agricultural and Forest Sciences was used in this study. The panel comprised 173 accessions from China, 5 from Italy, 7 from the USA, 12 from Pakistan, and one from CIMMYT (Table S1). The 173 Chinese wheat cultivars have been (or were) widely grown in the main wheat production zones since the early 20th century. The wheat cultivar Mingxian 169 served as a susceptible control.

4.2. Evaluation for Stripe Rust Resistance at the Seedling Stage

The 198 wheat cultivars were inoculated with the prevalent Chinese Pst races CYR33 and CYR34 at the seedling stage under controlled greenhouse conditions at the Plant Protection Institute of the Sichuan Academy of Agricultural Sciences, China. Five to six seeds of each cultivar were sown in a plastic pot filled with nutrient soil, with two replicates.
At the two-leaf stage (15 days after sowing), the seedlings were inoculated with urediniospores mixed with talc at an approximate ratio of 1:20. The inoculated plants were placed in a dew chamber at 10 ± 2 °C in darkness for 24 h and then transferred to a greenhouse at 18 ± 1 °C, with light supplementation of 10,000 lux and a photoperiod of 16 h light and 8 h darkness. The stripe rust reactions were recorded when the susceptible control, Mingxian 169, was fully infected, approximately 18 d post inoculation (dpi). The infection types (ITs) were scored using the 0–4 scale (0, 0;, 1, 2, 3, and 4), with the resistant (0–2) and susceptible (3–4) classifications as described previously [8]. The disease severity (DS) was scored as the percentage of the infected leaf area (0, 5, 10, 20, 40, 60, 80, or 100%) according to the rules for monitoring and forecasting wheat stripe rust according to GB/T 15795–2011 [47]. Accessions with a DS ≤ 20% were classified as resistance genotypes. Accessions with ITs of 0 to 0 and a DS < 1% were defined as immune or near immune according to GB/T 1443.1-2007 [25].

4.3. Evaluation of Stripe Rust Resistance at the Adult-Plant Stage

The APR of the 198 cultivars was evaluated in the field trials after artificial inoculation in three consecutive crop seasons (2018 to 2020) in Chengdu, Sichuan province (30°33′ N, 103°39′ E), hereafter abbreviated as CD18, CD19, and CD20, respectively. Each cultivar was sown in a 2.0 m row with 0.3 m between rows, and the sowing density was 30 seeds per row. The susceptible control, Mingxian 169, was planted every 20 rows, and spreader rows were planted to enhance the uniformity of the stripe rust inoculum across the trials. The field management followed local practices. At the tillering stage, the plants were artificially inoculated with a mixture of Pst races prevalent in China, including CYR32, CYR33, CYR34, Sull-4, Sull-5, Sull-7, and G22-14, as described previously [2]. The stripe rust scoring commenced when the disease severity on the flag leaves of Mingxian 169 reached 80%. The stripe rust symptoms were recorded three times at 7–10 day intervals. For each accession, the most severe disease response among the three scores was used for the analysis. The IT was estimated and classified, as at the seedling stage. Plants with an IT ≤ 2 and a DS ≤ 20% were classified as resistance genotypes, as described previously [8].

4.4. Genotyping and Genetic Analysis

The genomic DNA of the 198 cultivars was extracted from the leaves of two-week-old seedlings using a plant DNA kit (Biofit Co., Ltd., Beijing, China). A total of 53,063 probes from the wheat 55K SNP array (Affymetrix Axiom Wheat55K, Thermo Fisher Scientific Inc., Beijing, China) were used for the genotyping. Markers with missing values less than 10% and minor allele frequency (MAF) values greater than 5% were selected for subsequent analysis. The chromosomal positions of these SNPs were referred to the Chinese Spring reference genome (CS v1.0, http://202.194.139.32/, accessed on 1 January 2021).
The population structure was characterized using the Bayesian clustering algorithm implemented in STRUCTURE v2.3.4 [48] based on 44,759 polymorphic SNPs distributed on 21 wheat chromosomes. The number of presumed subpopulations (K) ranged from 2 to 10, with an admixture model and correlated allelic frequencies assumed. Ten independent STRUCTURE runs were performed with 50,000 replications for a burn-in and 10,000 replications for the Markov chain Monte Carlo (MCMC) analysis. The number of subpopulations and the best output were determined following the delta K method [49]. A neighbor-joining tree was constructed using software Tassel v3.0 and MEGA7 and visualized using the iTOL website (https://itol.embl.de/, accessed on 1 March 2021).
The genome-wide linkage disequilibrium (LD) was estimated as the squared allele frequency correlation (r2) between pairs of 44,759 SNP markers with known physical positions using TASSEL 3.0 [50]. The extent of the LD between pairs of loci was depicted with the r2 values against the inter-marker physical distance (Mb). Locally weighted polynomial regression curves were then fitted into the scatter plot. A marker–trait association analysis (MTA) was determined based on a mixed linear model (MLM) with the structure (Q) matrix as the fixed factor and the kinship (K) matrix as the random factor (Q+K, MLM) implemented in TASSEL 3.0 (https://www.thefreedictionary.com/tassel, accessed on 1 March 2021). The significant MTAs were selected using the criteria p ≤ 0.001 for all chromosomes at both the seedling and adult-plant stages. The high-confidence associated loci (p ≤ 0.01) flanking the significant MTA markers within the LD ≥ 0.3 decay distance region (about 5 Mb) on the same chromosome were considered as the QTL intervals. The MTAs detected in at least two environments at the adult-plant stage and in the two replications in the seedling trials, or detected at both the DS and IT trials, were considered as stable and reported in this study. Manhattan plots were generated using the CMplot package in the R program (https://github.com/YinLiLin/CMplot, accessed on 1 March 2021).
The QTL in this study were compared with previously reported Yr genes and QTL based on the physical positions of the significant markers using the Chinese Spring reference genome (CS v1.0, http://202.194.139.32/, accessed on 1 October 2021). Novel QTL were determined if they were not overlapping with previous Yr or QTL within a ±5.0 Mb interval.

5. Conclusions

This study identified seven elite wheat cultivars (five Chinese, one American, one Pakistani) exhibiting stable, high-level resistance (IT ≤ 2, DS ≤ 20%) against prevalent Pst races across the seedling and adult-plant stages. A GWAS revealed 14 robust QTL conferring resistance (11 ASR, 3 APR), including nine potentially novel loci. Within these QTL regions, favorable resistance haplotypes were defined, and key candidate genes (such as TaPAP8 and TaCRK2) implicated in defense responses were identified. The integration of these validated resistant accessions, novel QTL, haplotype-specific markers, and functional gene candidates provides powerful molecular tools for efficiently pyramiding resistance genes, accelerating the development of durable wheat cultivars against stripe rust.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/plants14121883/s1: Figure S1. Sequence similarity of PAP8 orthologs and TaFBN4 across plant species. Figure S2. Expression patterns of TaFBN4 and TaPAP8 genes in response to the Puccinia striiformis f. sp. tritici race CYR31 infection. Figure S3. Phenotypic effects of QYr.baafs-1D.2 haplotypes on the disease severity (DS) and infection type (IT) against the CYR34 race. Figure S4. Expression of candidate gene TraesCS1D02G112300.1 in QYr.baafs-1D.2 region during biotic stress. Figure S5. Expression of candidate gene TraesCS1B01G281200.1 in QYr.baafs-1B.4 region during biotic stress. Figure S6. Phenotypic effects of QYr.baafs.1B.4 haplotypes on disease severity (DS) and infection type (IT) against mixed stripe rust races at adult-plant stage. Figure S7. Phenotypic effects of QYr.sicau-6BS haplotypes on disease severity (DS) and infection type (IT) against mixed stripe rust races at adult-plant stage. Figure S8. Correlation between the number of favorable haplotypes (FHs) and the stripe rust resistance to the CYR34 race. Table S1. Phenotype of accessions against stripe rust and QTL haplotypes. (See Refs. [51,52,53,54]). Table S2. Expression levels (TPMs) of predicted genes in the QYr.baafs-2B.1 region. Table S3. Expression variation of genes in the QYr.baafs-1B.4 region after Pst inoculation.

Author Contributions

Data curation, D.Y.; Investigation, J.B., J.G. and X.C.; Resources, B.P., Y.L. and Z.C.; Validation, W.Y., Q.H., H.Z. and L.T.; Writing—original draft, Q.M., L.G. and X.L.; Writing—review and editing, J.J., L.Z. and L.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Special Project of Science and Technology Innovation Ability Construction of Beijing Academy of Agriculture and Forestry Sciences (KJCX20230402 and KJCX20240103), the National Key Research and Development Program of China (2023YFD1902601-03), and the Science and Technology Innovation Project of BAAFS, China (KJCX20230307).

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Chen, X.M. Pathogens which threaten food security: Puccinia striiformis, the wheat stripe rust pathogen. Food Secur. 2020, 12, 239–251. [Google Scholar] [CrossRef]
  2. Chen, W.Q.; Wu, L.R.; Liu, T.G.; Xu, S.C.; Jin, S.L.; Peng, Y.L.; Wang, B.T. Race dynamics, diversity, and virulence evolution in Puccinia striiformis f. sp. tritici, the causal agent of wheat stripe rust in China from 2003 to 2007. Plant Dis. 2009, 93, 1093–1101. [Google Scholar] [CrossRef] [PubMed]
  3. Wang, L.; Zheng, D.; Zuo, S.X.; Chen, X.M.; Zhuang, H.; Huang, L.L.; Kang, Z.S.; Zhao, J. Inheritance and linkage of virulence genes in Chinese predominant race CYR32 of the wheat stripe rust pathogen Puccinia striiformis f. sp. tritici. Front. Plant Sci. 2018, 9, 120. [Google Scholar] [CrossRef] [PubMed]
  4. Ellis, J.G.; Lagudah, E.S.; Spielmeyer, W.; Dodds, P.N. The past, present and future of breeding rust resistant wheat. Front. Plant Sci. 2014, 5, 641. [Google Scholar] [CrossRef]
  5. Schwessinger, B. Fundamental wheat stripe rust research in the 21(st) century. New Phytol. 2017, 213, 1625–1631. [Google Scholar] [CrossRef]
  6. Jambuthenne, D.T.; Riaz, A.; Athiyannan, N.; Alahmad, S.; Ng, W.L.; Ziems, L.; Afanasenko, O.; Periyannan, S.K.; Aitken, E.; Platz, G.; et al. Mining the Vavilov wheat diversity panel for new sources of adult plant resistance to stripe rust. Theor. Appl. Genet. 2022, 135, 1355–1373. [Google Scholar] [CrossRef]
  7. Boden, S.A.; McIntosh, R.A.; Uauy, C.; Krattinger, S.G.; Dubcovsky, J.; Rogers, W.J.; Xia, X.C.; Badaeva, E.D.; Bentley, A.R.; Brown-Guedira, G.; et al. Updated guidelines for gene nomenclature in wheat. Theor. Appl. Genet. 2023, 136, 72. [Google Scholar] [CrossRef]
  8. Wang, Y.Q.; Yu, C.; Cheng, Y.K.; Yao, F.J.; Long, L.; Wu, Y.; Li, J.; Li, H.; Wang, J.R.; Jiang, Q.T.; et al. Genome-wide association mapping reveals potential novel loci controlling stripe rust resistance in a Chinese wheat landrace diversity panel from the southern autumn-sown spring wheat zone. BMC Genom. 2021, 22, 34. [Google Scholar] [CrossRef]
  9. Klymiuk, V.; Chawla, H.S.; Wiebe, K.; Ens, J.; Fatiukha, A.; Govta, L.; Fahima, T.; Pozniak, C.J. Discovery of stripe rust resistance with incomplete dominance in wild emmer wheat using bulked segregant analysis sequencing. Commun. Biol. 2022, 5, 826. [Google Scholar] [CrossRef]
  10. Juliana, P.; Singh, R.P.; Huerta-Espino, J.; Bhavani, S.; Randhawa, M.S.; Kumar, U.; Joshi, A.K.; Bhati, P.K.; Mir, H.E.V.; Mishra, C.N.; et al. Genome-wide mapping and allelic fingerprinting provide insights into the genetics of resistance to wheat stripe rust in India, Kenya and Mexico. Sci. Rep. 2020, 10, 10908. [Google Scholar] [CrossRef]
  11. Abou-Zeid, M.A.; Mourad, A.M.I. Genomic regions associated with stripe rust resistance against the Egyptian race revealed by genome-wide association study. BMC Plant Biol. 2021, 21, 42. [Google Scholar] [CrossRef]
  12. Bouvet, L.; Holdgate, S.; James, L.; Thomas, J.; Mackay, I.J.; Cockram, J. The evolving battle between yellow rust and wheat: Implications for global food security. Theor. Appl. Genet. 2022, 135, 741–753. [Google Scholar] [CrossRef] [PubMed]
  13. Chen, W.Q.; Kang, Z.S.; Man, Z.H.; Xu, S.C.; Jin, S.L. Integrated management of wheat stripe rust caused by Puccinia striiformis f. sp. tritici in China. Sci. Agric. Sin. 2013, 20, 4254–4262. [Google Scholar]
  14. Bai, B.B.; Liu, T.G.; Liu, B.; Gao, L.; Chen, W.Q. High relative parasitic fitness of G22 derivatives is associated with the epidemic potential of wheat stripe rust in China. Plant Dis. 2018, 102, 483–487. [Google Scholar] [CrossRef]
  15. Liu, B.; Liu, T.G.; Zhang, Z.Y.; Jia, Q.Z.; Wang, B.T.; Gao, L.; Peng, Y.L.; Jin, S.L.; Chen, W.Q. Discovery and pathogenicity of CYR34, a new race of Puccinia striiformis f. sp. tritici in China. Acta Phytopathol. Sin. 2017, 47, 681–687. [Google Scholar]
  16. Han, D.J.; Wang, Q.L.; Chen, X.M.; Zeng, Q.D.; Wu, J.H.; Xue, W.B.; Zhan, G.M.; Huang, L.L.; Kang, Z.S. Emerging Yr26-virulent races of Puccinia striiformis f. tritici are threatening wheat production in the Sichuan basin, China. Plant Dis. 2015, 99, 754–760. [Google Scholar] [CrossRef] [PubMed]
  17. Zeng, Q.D.; Shen, C.; Yuan, F.P.; Wang, Q.L.; Wu, J.H.; Xue, W.B.; Zhan, G.M.; Yao, S.; Chen, W. The resistance evaluation of the Yr genes to the main prevalent pathotypes of Puccinia striiformis f. sp. tritici in China. Acta Phytopathol. Sin. 2015, 45, 641–650. [Google Scholar]
  18. Zegeye, H.; Rasheed, A.; Makdis, F.; Badebo, A.; Ogbonnaya, F.C. Genome-wide association mapping for seedling and adult plant resistance to stripe rust in synthetic hexaploid wheat. PLoS ONE 2014, 9, e105593. [Google Scholar] [CrossRef]
  19. Maccaferri, M.; Zhang, J.; Bulli, P.; Abate, Z.; Chao, S.; Cantu, D.; Bossolini, E.; Chen, X.M.; 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 (Bethesda) 2015, 5, 449–465. [Google Scholar] [CrossRef]
  20. Long, L.; Yao, F.J.; Yu, C.; Ye, X.L.; Cheng, Y.K.; Wang, Y.Q.; Wu, Y.; Li, J.; Wang, J.R.; Jiang, Q.T.; et al. Genome-wide association study for adult-plant resistance to stripe rust in Chinese wheat landraces (Triticum aestivum L.) from the Yellow and Huai river valleys. Front. Plant Sci. 2019, 10, 596. [Google Scholar] [CrossRef]
  21. Cheng, B.; Gao, X.; Cao, N.; Ding, Y.Q.; Gao, Y.; Chen, T.Q.; Xin, Z.H.; Zhang, L.Y. Genome-wide association analysis of stripe rust resistance loci in wheat accessions from southwestern China. J. Appl. Genet. 2020, 61, 37–50. [Google Scholar] [CrossRef]
  22. Vikram, P.; Sehgal, D.; Sharma, A.; Bhavani, S.; Gupta, P.; Randhawa, M.; Pardo, N.; Basandra, D.; Srivastava, P.; Singh, S.; et al. Genome-wide association analysis of Mexican bread wheat landraces for resistance to yellow and stem rust. PLoS ONE 2021, 16, e0246015. [Google Scholar] [CrossRef] [PubMed]
  23. Mahmood, Z.; Ali, M.; Mirza, J.I.; Fayyaz, M.; Majeed, K.; Naeem, M.K.; Aziz, A.; Trethowan, R.; Ogbonnaya, F.C.; Poland, J.; et al. Genome-wide association and genomic prediction for stripe rust resistance in synthetic-derived wheats. Front. Plant Sci. 2022, 13, 788593. [Google Scholar] [CrossRef]
  24. Marone, D.; Laidò, G.; Saccomanno, A.; Petruzzino, G.; Giaretta Azevedo, C.V.; de Vita, P.; Mastrangelo, A.M.; Gadaleta, A.; Ammar, K.; Bassi, F.M.; et al. Genome-wide association study of common resistance to rust species in tetraploid wheat. Front. Plant Sci. 2024, 14, 1290643. [Google Scholar] [CrossRef]
  25. GB/T 1443.1-2007; Rules for Resistance Evaluation of Wheat to Diseases and Insect Pests Part 1: Rule for Resistance Evaluation of Wheat to Yellow Rust (Puccinia striiformis West. f. sp. tritici Eriks. et Henn.). Ministry of Agriculture: Beijing, China, 2007.
  26. Zhou, C.E.; Liu, D.; Zhang, X.; Wu, Q.M.; Liu, S.J.; Zeng, Q.D.; Wang, Q.L.; Wang, C.F.; Li, C.L.; Singh, R.P.; et al. Combined linkage and association mapping reveals two major QTL for stripe rust adult plant resistance in Shaanmai 155 and their haplotype variance in common wheat germplasm. Crop J. 2022, 10, 783–792. [Google Scholar] [CrossRef]
  27. Wang, Y.F.; Hu, Y.L.; Gong, F.Y.; Jin, Y.R.; Xia, Y.J.; He, Y.; Jiang, Y.; Zhou, Q.; He, J.S.; Feng, L.H.; et al. Identification and mapping of QTL for stripe rust resistance in the Chinese wheat cultivar Shumai126. Plant Dis. 2022, 106, 1278–1285. [Google Scholar] [CrossRef]
  28. Xiang, M.J.; Tian, B.; Cao, J.H.; Liu, S.J.; Zhou, C.E.; Wang, X.T.; Zhang, Y.B.; Li, J.L.; Yuan, X.Y.; Wan, J.F.; et al. Yr29 combined with QYr.nwafu-4BL.3 confers durable resistance to stripe rust in wheat cultivar Jing 411. Theor. Appl. Genet. 2024, 137, 252. [Google Scholar] [CrossRef] [PubMed]
  29. Yao, F.J.; Guan, F.N.; Duan, L.Y.; Long, L.; Tang, H.; Jiang, Y.F.; Li, H.; Jiang, Q.T.; Wang, J.R.; Qi, P.F.; et al. Genome-wide association analysis of stable stripe rust resistance loci in a Chinese wheat landrace panel using the 660K SNP array. Front. Plant Sci. 2021, 12, 783830. [Google Scholar] [CrossRef]
  30. Tehseen, M.M.; Tonk, F.A.; Tosun, M.; Randhawa, H.S.; Kurtulus, E.; Ozseven, I.; Akin, B.; Nur Zulfuagaoglu, O.; Nazari, K. QTL mapping of adult plant resistance to stripe rust in a doubled haploid wheat population. Front. Genet. 2022, 13, 900558. [Google Scholar] [CrossRef]
  31. Li, Z.F.; Zheng, T.C.; He, Z.H.; Li, G.Q.; Xu, S.C.; Li, X.P.; Yang, G.Y.; Singh, R.P.; Xia, X.C. Molecular tagging of stripe rust resistance gene YrZH84 in Chinese wheat line Zhou 8425B. Theor. Appl. Genet. 2006, 112, 1098–1103. [Google Scholar] [CrossRef]
  32. Wang, B.Y.; Gan, A.H.; Feng, J.; Wang, F.T.; Lin, R.M.; Chen, W.Q.; Xu, S.C. Genetic analysis of resistance to Puccinia striiformis f. sp. tritici of Chinese differential hosts Lovrin 10 and Lovrin 13. Acta Phytopathol. 2015, 45, 401–409. [Google Scholar]
  33. Ma, S.W.; Wang, M.; Wu, J.H.; Guo, W.L.; Chen, Y.M.; Li, G.W.; Wang, Y.P.; Shi, W.M.; Xia, G.M.; Fu, D.L.; et al. WheatOmics: A platform combining multiple omics data to accelerate functional genomics studies in wheat. Mol. Plant 2021, 14, 1965–1968. [Google Scholar] [CrossRef]
  34. Jin, P.F.; Chao, K.X.; Li, J.; Wang, Z.H.; Cheng, P.; Li, Q.; Wang, B.T. Functional verification of two genes related to stripe rust resistance in the wheat-Leymus mollis introgression line M8664-3. Front. Plant Sci. 2021, 12, 754823. [Google Scholar]
  35. Zhang, H.; Yang, Y.Z.; Wang, C.Y.; Liu, M.; Li, H.; Fu, Y.; Wang, Y.J.; Nie, Y.B.; Liu, X.L.; Ji, W.Q. Large-scale transcriptome comparison reveals distinct gene activations in wheat responding to stripe rust and powdery mildew. BMC Genom. 2014, 15, 898. [Google Scholar] [CrossRef] [PubMed]
  36. McKown, A.D.; Guy, R.D.; Quamme, L.; Klápště, J.; La Mantia, J.; Constabel, C.P.; El-Kassaby, Y.A.; Hamelin, R.C.; Zifkin, M.; Azam, M.S. Association genetics, geography and ecophysiology link stomatal patterning in Populus trichocarpa with carbon gain and disease resistance trade-offs. Mol. Ecol. 2014, 23, 5771–5790. [Google Scholar] [CrossRef] [PubMed]
  37. Wang, F.; Lin, R.; Li, Y.; Wang, P.; Feng, J.; Chen, W.; Xu, S. TabZIP74 acts as a positive regulator in wheat stripe rust resistance and involves root development by mRNA splicing. Front. Plant Sci. 2019, 10, 1551. [Google Scholar] [CrossRef]
  38. Zhou, H.B.; Li, S.F.; Deng, Z.Y.; Wang, X.P.; Chen, T.; Zhang, J.S.; Chen, S.Y.; Ling, H.Q.; Zhang, A.M.; Wang, D.W.; et al. Molecular analysis of three new receptor-like kinase genes from hexaploid wheat and evidence for their participation in the wheat hypersensitive response to stripe rust fungus infection. Plant J. 2007, 52, 420–434. [Google Scholar] [CrossRef]
  39. Gu, J.; Sun, J.W.; Liu, N.; Sun, X.Z.; Liu, C.J.; Wu, L.Z.; Liu, G.; Zeng, F.L.; Hou, C.Y.; Han, S.F.; et al. A novel cysteine-rich receptor-like kinase gene, TaCRK2, contributes to leaf rust resistance in wheat. Mol. Plant Pathol. 2020, 21, 732–746. [Google Scholar] [CrossRef]
  40. Reveguk, T.; Fatiukha, A.; Potapenko, E.; Reveguk, I.; Sela, H.; Klymiuk, V.; Li, Y.; Pozniak, C.; Wicker, T.; Coaker, G.; et al. Tandem kinase proteins across the plant kingdom. Nat. Genet. 2025, 57, 254–262. [Google Scholar] [CrossRef]
  41. Dang, C.; Zhang, J.; Dubcovsky, J. High-resolution mapping of Yr78, an adult plant resistance gene to wheat stripe rust. Plant Genome 2022, 15, e20212. [Google Scholar] [CrossRef]
  42. Li, G.; Ren, Y.; Yang, Y.; Chen, S.; Zheng, J.; Zhang, X.; Li, J.; Chen, M.; Sun, X.; Lv, C.; et al. Genomic analysis of Zhou8425B, a key founder parent, reveals its genetic contributions to elite agronomic traits in wheat breeding. Plant Commun. 2025, 6, 101222. [Google Scholar] [CrossRef]
  43. Li, Y.; Tao, F.; Hao, Y.; Tong, J.; Xiao, Y.; He, Z.; Reynolds, M. Variations in phenological, physiological, plant architectural and yield-related traits, their associations with grain yield and genetic basis. Ann. Bot. 2023, 19, mcad003. [Google Scholar] [CrossRef] [PubMed]
  44. Ma, J.; Ye, M.; Liu, Q.; Yuan, M.; Zhang, D.; Li, C.; Zeng, Q.; Wu, J.; Han, D.; Jiang, L. Genome-wide association study for grain zinc concentration in bread wheat (Triticum aestivum L.). Front. Plant Sci. 2023, 14, 1169858. [Google Scholar] [CrossRef] [PubMed]
  45. Shahinnia, F.; Mohler, V.; Hartl, L. Genetic basis of resistance to Warrior (-) yellow rust race at the seedling stage in current Central and Northern European winter wheat germplasm. Plants 2023, 12, 420. [Google Scholar] [CrossRef]
  46. Shahinnia, F.; Geyer, M.; Schürmann, F.; Rudolphi, S.; Holzapfel, J.; Kempf, H.; Stadlmeier, M.; Löschenberger, F.; Morales, L.; Buerstmayr, H.; et al. Genome-wide association study and genomic prediction of resistance to stripe rust in current Central and Northern European winter wheat germplasm. Theor. Appl. Genet. 2022, 135, 3583–3595. [Google Scholar] [CrossRef]
  47. GB/T 15795–2011; Rules for Monitoring and Forecast of the Wheat Stripe Rust (Puccinia striiformis West). Ministry of Agriculture and Rural Affairs: Beijing, China, 2011.
  48. Pritchard, J.K.; Stephens, M.; Rosenberg, N.A.; Donnelly, P. Association mapping in structured populations. Am. J. Hum. Genet. 2000, 67, 170–181. [Google Scholar] [CrossRef]
  49. Evanno, G.; Regnaut, S.; Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 2005, 14, 2611–2620. [Google Scholar] [CrossRef] [PubMed]
  50. Bradbury, P.J.; Zhang, Z.; Kroon, D.E.; Casstevens, T.M.; Ramdoss, Y.; Buckler, E.S. TASSEL: Software for association mapping of complex traits in diverse samples. Bioinformatics 2007, 23, 2633–2635. [Google Scholar] [CrossRef]
  51. Kong, X.X. Quality Characteristics and Molecular Marker Analysis of 58 Wheat Cultivars (Lines). Master’s Thesis, Henan University of Science and Technology, Luoyang, China, 2016. (In Chinese). [Google Scholar]
  52. Tang, H.J. Molecular Characterization of 1B/1R Translocation Lines and Stem Rust Resistance Genes in Common Wheat. Master’s Thesis, XinJiang Agricultural Unviersity, Ürümqi, China, 2010. (In Chinese). [Google Scholar]
  53. Tang, J.W.; Yin, G.H.; Gao, Y.; Wang, L.N.; Han, Y.L.; Huang, F.; Yu, H.F.; Yang, G.Y.; Li, X.P.; Xiao, Y.G.; et al. Comprehensive analysis on agronomic traits and processing quality of core parent Zhou8425B and its derivatives. J. Triticeae Crops 2016, 35, 777–784. (In Chinese) [Google Scholar]
  54. Yang, E.N. A study on Allelic Variation for High Molecular Weight Glutenin Subunit (HMW-GS) and 1B/1R Translocation from Resent Released Wheat Varieties in Sichuan Province. Master’s Thesis, SiChuan Agricultural University, Ya’an, China, 2008. (In Chinese). [Google Scholar]
Plants 14 01883 g001
Figure 1. The distribution and correlation of the stripe rust responses at the seedling and adult-plant stages. (a) Violin plots illustrating the distribution of stripe rust resistance phenotypes for the CYR33 and CYR34 races at the seedling stage, and mixed Pst races at the adult-plant stage across three field environments (Pst18/19/20 represent the DS values scored at the CD18, CD19, and CD20 environments, respectively). (b) Heatmap of Pearson correlation coefficients quantifying the relationships among the stripe rust responses across the different races and developmental stages. The color intensity reflects the strength of the correlation, with positive (blue) and negative (red) values indicating direct or inverse associations, respectively. The phenotypic data consist of infection type (IT) and/or disease severity (DS) values, as defined in Section 4. “CYR33/34–DS/IT–I/II” represents the DS or IT values obtained after the inoculation with CYR33 or CYR34, which were scored during the first or second replicate in the greenhouse at the seedling stage.
Figure 1. The distribution and correlation of the stripe rust responses at the seedling and adult-plant stages. (a) Violin plots illustrating the distribution of stripe rust resistance phenotypes for the CYR33 and CYR34 races at the seedling stage, and mixed Pst races at the adult-plant stage across three field environments (Pst18/19/20 represent the DS values scored at the CD18, CD19, and CD20 environments, respectively). (b) Heatmap of Pearson correlation coefficients quantifying the relationships among the stripe rust responses across the different races and developmental stages. The color intensity reflects the strength of the correlation, with positive (blue) and negative (red) values indicating direct or inverse associations, respectively. The phenotypic data consist of infection type (IT) and/or disease severity (DS) values, as defined in Section 4. “CYR33/34–DS/IT–I/II” represents the DS or IT values obtained after the inoculation with CYR33 or CYR34, which were scored during the first or second replicate in the greenhouse at the seedling stage.
Plants 14 01883 g001
Plants 14 01883 g002
Figure 2. The phylogenetic relationships and linkage disequilibrium (LD) decay in 198 wheat accessions. (a) The neighbor-joining phylogenetic tree, constructed from the genome-wide single-nucleotide polymorphism (SNP) data, depicting the genetic relationships among the accessions. The branch colors correspond to the predefined groups (Table S1). (b) A linkage disequilibrium decay plot based on 44,759 high-quality SNP markers. The LD is measured as the squared correlation coefficient (r²) between pairs of SNPs, plotted against the genomic distance (kilobase, Kb).
Figure 2. The phylogenetic relationships and linkage disequilibrium (LD) decay in 198 wheat accessions. (a) The neighbor-joining phylogenetic tree, constructed from the genome-wide single-nucleotide polymorphism (SNP) data, depicting the genetic relationships among the accessions. The branch colors correspond to the predefined groups (Table S1). (b) A linkage disequilibrium decay plot based on 44,759 high-quality SNP markers. The LD is measured as the squared correlation coefficient (r²) between pairs of SNPs, plotted against the genomic distance (kilobase, Kb).
Plants 14 01883 g002
Plants 14 01883 g003
Figure 3. The effects of the QYr.baafs-2B.1 haplotypes and candidate genes on the stripe rust resistance to the CYR33 race. (a) A Manhattan plot from a GWAS analyzing the seedling-stage responses to the CYR33 race. The genome-wide significance threshold is set at a –log10 (p) value of 3.0, with each dot representing an SNP marker. The red dots represent SNP markers significantly associated with Pst resistance. (b) The phenotypic effects of the QYr.baafs-2B.1 haplotypes in the seedling-stage trials with the CYR33 race. The disease severity (DS) and infection type (IT) are shown as means ± standard deviation (SD). (c) A local Manhattan plot and haplotype block around QYr.baafs-2B.1. The zoomed region highlights the peak-associated SNPs (–log10 (p) ≥ 4.0) and their LD structure.
Figure 3. The effects of the QYr.baafs-2B.1 haplotypes and candidate genes on the stripe rust resistance to the CYR33 race. (a) A Manhattan plot from a GWAS analyzing the seedling-stage responses to the CYR33 race. The genome-wide significance threshold is set at a –log10 (p) value of 3.0, with each dot representing an SNP marker. The red dots represent SNP markers significantly associated with Pst resistance. (b) The phenotypic effects of the QYr.baafs-2B.1 haplotypes in the seedling-stage trials with the CYR33 race. The disease severity (DS) and infection type (IT) are shown as means ± standard deviation (SD). (c) A local Manhattan plot and haplotype block around QYr.baafs-2B.1. The zoomed region highlights the peak-associated SNPs (–log10 (p) ≥ 4.0) and their LD structure.
Plants 14 01883 g003
Table 1. Stripe rust resistance QTL identified at both seedling and adult-plant stages.
Table 1. Stripe rust resistance QTL identified at both seedling and adult-plant stages.
QTLTraitStageChr.Position Peak Marker−log(P)Marker R2RegionFHFH
Frequency		QTL/GeneReference
(Mb)(%)(Mb)
Yr.baafs-1B.1CYR34DS-I/II, CYR34IT-I/IISeedling1BS56.89AX-1111707243.52–6.428.23–16.6746.03–57.38Hap20.202
Yr.baafs-1B.2CYR34DS-I/II, CYR34IT-I/IISeedling1BS95.67AX-944325353.23–6.938.84–18.3290.55–96.88Hap20.209
Yr.baafs-1B.3CYR34DS-I/II, CYR34IT-I/IISeedling1BS229.31AX-1100841263.68–6.0810.41–15.80224.21–230.95Hap10.213
Yr.baafs-1B.420DS,19/20ITAdult-plant1BL488.6AX-1087910493.00–3.727.44–9.25487.94–491.08
Yr.baafs-1D.1CYR34DS-I/II, CYR34IT-I/IISeedling1DS73.73AX-948022453.04–6.247.68–17.5459.53–84.62Hap30.358
Yr.baafs-1D.2CYR34DS-I/II, CYR34IT-I/IISeedling1DS109.82AX-947473573.46–6.548.56–17.08102.78–109.82Hap20.189
Yr.baafs-2B.1CYR33IT-I/II, CYR33DS-ISeedling2BL646.9AX-1087731653.05–6.607.61–17.16643.69–655.52Hap60.032QYrSM155.1[26]
Yr.baafs-2B.2CYR33IT-I/IISeedling2BL675.36AX-1110266743.01~4.057.54–10.33673.05–675.79Hap70.016
Yr.baafs-2B.3CYR33IT-I/IISeedling2BL706.85AX-1110776153.26–5.708.65–14.82706.72–707.18Hap40.063
Yr.baafs-2B.418DS,18ITAdult-plant2BL785.83AX-1089009823.06–3.156.04–7.79780.02–787.23 qNV.Yr-2B.3[6]
Yr.baafs-6B18/20DS,20ITAdult-plant6BS90.4AX-1099122483.08–3.616.50–9.2773.54–91.01Hap50.042QYr.sicau-6BS[27]
QYr.nwafu-6BS.3[28]
Yr.baafs-6DCYR34DS-I/II, CYR34IT-I/IISeedling6DL467.52AX-1093621744.69–5.8812.55–15.15465.05–472.82Hap30.202QYrCL.sicau-6DL[29]
Yr.baafs-7BCYR33IT-I, CYR33DS-ISeedling7BS64.33AX-1111437113.00–3.506.06–8.7064.32–66.51
Yr.baafs-7DCYR34DS-II, CYR34IT-I/IISeedling7DS104.49AX-1118559493.01–3.607.49–9.03100.65–104.74 QYr.rcrrc-7D[30]
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

Ma, Q.; Yan, D.; Pang, B.; Bai, J.; Yang, W.; Gao, J.; Chen, X.; Hou, Q.; Zhang, H.; Tian, L.; et al. Genome-Wide Identification of Wheat Gene Resources Conferring Resistance to Stripe Rust. Plants 2025, 14, 1883. https://doi.org/10.3390/plants14121883

AMA Style

Ma Q, Yan D, Pang B, Bai J, Yang W, Gao J, Chen X, Hou Q, Zhang H, Tian L, et al. Genome-Wide Identification of Wheat Gene Resources Conferring Resistance to Stripe Rust. Plants. 2025; 14(12):1883. https://doi.org/10.3390/plants14121883

Chicago/Turabian Style

Ma, Qiaoyun, Dong Yan, Binshuang Pang, Jianfang Bai, Weibing Yang, Jiangang Gao, Xianchao Chen, Qiling Hou, Honghong Zhang, Li Tian, and et al. 2025. "Genome-Wide Identification of Wheat Gene Resources Conferring Resistance to Stripe Rust" Plants 14, no. 12: 1883. https://doi.org/10.3390/plants14121883

APA Style

Ma, Q., Yan, D., Pang, B., Bai, J., Yang, W., Gao, J., Chen, X., Hou, Q., Zhang, H., Tian, L., Li, Y., Jia, J., Zhang, L., Chen, Z., Gao, L., & Liao, X. (2025). Genome-Wide Identification of Wheat Gene Resources Conferring Resistance to Stripe Rust. Plants, 14(12), 1883. https://doi.org/10.3390/plants14121883

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